diff --git a/共享单车数据分析/十三组大作业/.keep b/1、人才招聘数据分析/第3组-夏添/homework/.keep
similarity index 100%
rename from 共享单车数据分析/十三组大作业/.keep
rename to 1、人才招聘数据分析/第3组-夏添/homework/.keep
diff --git a/共享单车数据分析/十三组平时作业（实战）/.keep b/共享民宿平台担保交易房子评分的影响研究/homeworks/.keep
similarity index 100%
rename from 共享单车数据分析/十三组平时作业（实战）/.keep
rename to 共享民宿平台担保交易房子评分的影响研究/homeworks/.keep
diff --git a/共享民宿平台担保交易房子评分的影响研究/homeworks/horse_analysis.ipynb b/共享民宿平台担保交易房子评分的影响研究/homeworks/horse_analysis.ipynb
new file mode 100644
index 0000000..905cd95
--- /dev/null
+++ b/共享民宿平台担保交易房子评分的影响研究/homeworks/horse_analysis.ipynb
@@ -0,0 +1,2205 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {
+    "collapsed": true,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "from sklearn import preprocessing\n",
+    "from sklearn.preprocessing import StandardScaler\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.metrics import accuracy_score,confusion_matrix,roc_curve,roc_auc_score\n",
+    "from sklearn.ensemble import RandomForestClassifier,BaggingClassifier,AdaBoostClassifier\n",
+    "from sklearn.naive_bayes import GaussianNB\n",
+    "from sklearn.tree import DecisionTreeClassifier\n",
+    "from sklearn.svm import SVC\n",
+    "from sklearn.linear_model import LogisticRegression\n",
+    "from sklearn.neighbors import KNeighborsClassifier\n",
+    "# 中文正常显示\n",
+    "plt.rcParams['font.sans-serif'] = ['SimHei']\n",
+    "plt.rcParams['axes.unicode_minus'] = False"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "   直肠温度   脉搏  呼吸频率  红细胞体积  总蛋白值    y\n0  38.5   66    28   45.0   8.4  0.0\n1  39.2   88    20   50.0  85.0  0.0\n2  38.3   40    24   33.0   6.7  1.0\n3  39.1  164    84   48.0   7.2  0.0\n4  37.3  104    35   74.0   7.4  0.0",
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>直肠温度</th>\n      <th>脉搏</th>\n      <th>呼吸频率</th>\n      <th>红细胞体积</th>\n      <th>总蛋白值</th>\n      <th>y</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>38.5</td>\n      <td>66</td>\n      <td>28</td>\n      <td>45.0</td>\n      <td>8.4</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>39.2</td>\n      <td>88</td>\n      <td>20</td>\n      <td>50.0</td>\n      <td>85.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>38.3</td>\n      <td>40</td>\n      <td>24</td>\n      <td>33.0</td>\n      <td>6.7</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>39.1</td>\n      <td>164</td>\n      <td>84</td>\n      <td>48.0</td>\n      <td>7.2</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>37.3</td>\n      <td>104</td>\n      <td>35</td>\n      <td>74.0</td>\n      <td>7.4</td>\n      <td>0.0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df = pd.read_csv(\"data_horse.csv\",encoding=\"gbk\")\n",
+    "df.head()"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "Summarize dataset:   0%|          | 0/19 [00:00<?, ?it/s]",
+      "application/vnd.jupyter.widget-view+json": {
+       "version_major": 2,
+       "version_minor": 0,
+       "model_id": "e34168b30cea418e82fffe8c57f268f3"
+      }
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 30452 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 32928 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 28201 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 24230 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 33033 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 25615 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 30452 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 32928 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 28201 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 24230 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 30452 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 32928 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 28201 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 24230 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 33033 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 25615 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 33033 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 25615 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 30452 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 32928 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 28201 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 24230 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 21628 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 21560 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 39057 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 29575 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 30452 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 32928 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 28201 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 24230 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 30452 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 32928 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 28201 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 24230 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 21628 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 21560 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 39057 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 29575 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 21628 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 21560 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 39057 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 29575 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 30452 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 32928 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 28201 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 24230 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 32418 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 32454 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 32990 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 20307 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 31215 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 30452 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 32928 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 28201 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 24230 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 30452 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 32928 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 28201 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 24230 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 32418 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 32454 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 32990 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 20307 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 31215 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 32418 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 32454 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 32990 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 20307 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 31215 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 30452 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 32928 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 28201 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 24230 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 24635 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 34507 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 30333 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 20540 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 30452 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 32928 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 28201 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 24230 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 30452 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 32928 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 28201 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 24230 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 24635 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 34507 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 30333 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 20540 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 24635 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 34507 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 30333 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 20540 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 33033 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 25615 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 30452 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 32928 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 28201 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 24230 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 33033 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 25615 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 33033 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 25615 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 30452 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 32928 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 28201 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 24230 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 30452 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 32928 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 28201 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 24230 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 33033 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 25615 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 21628 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 21560 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 39057 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 29575 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 33033 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 25615 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 33033 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 25615 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 21628 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 21560 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 39057 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 29575 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 21628 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 21560 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 39057 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 29575 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 33033 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 25615 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 32418 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 32454 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 32990 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 20307 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 31215 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 33033 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 25615 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 33033 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 25615 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 32418 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 32454 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 32990 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 20307 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 31215 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 32418 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 32454 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 32990 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 20307 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 31215 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 33033 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 25615 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 24635 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 34507 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 30333 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 20540 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 33033 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 25615 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 33033 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 25615 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 24635 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 34507 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 30333 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 20540 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 24635 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 34507 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 30333 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 20540 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 21628 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 21560 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 39057 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 29575 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 30452 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 32928 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 28201 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 24230 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 21628 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 21560 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 39057 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 29575 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 21628 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 21560 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 39057 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 29575 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 30452 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 32928 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 28201 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 24230 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 30452 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 32928 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 28201 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 24230 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 21628 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 21560 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 39057 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 29575 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 33033 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 25615 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 21628 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 21560 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 39057 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 29575 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 21628 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 21560 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 39057 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 29575 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 33033 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 25615 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 33033 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 25615 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 21628 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 21560 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 39057 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 29575 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 32418 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 32454 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 32990 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 20307 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 31215 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 21628 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 21560 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 39057 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 29575 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 21628 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 21560 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 39057 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 29575 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 32418 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 32454 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 32990 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 20307 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 31215 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 32418 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 32454 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 32990 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 20307 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 31215 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 21628 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 21560 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 39057 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 29575 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 24635 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 34507 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 30333 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 20540 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 21628 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 21560 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 39057 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 29575 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 21628 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 21560 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 39057 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 29575 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 24635 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 34507 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 30333 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 20540 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 24635 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 34507 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 30333 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 20540 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 32418 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 32454 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 32990 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 20307 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 31215 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 30452 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 32928 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 28201 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 24230 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 32418 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 32454 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 32990 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 20307 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 31215 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 32418 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 32454 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 32990 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 20307 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 31215 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 30452 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 32928 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 28201 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 24230 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 30452 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 32928 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 28201 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 24230 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 32418 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 32454 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 32990 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 20307 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 31215 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 33033 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 25615 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 32418 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 32454 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 32990 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 20307 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 31215 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 32418 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 32454 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 32990 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 20307 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 31215 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 33033 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 25615 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 33033 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 25615 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 32418 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 32454 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 32990 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 20307 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 31215 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 21628 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 21560 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 39057 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 29575 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 32418 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 32454 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 32990 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 20307 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 31215 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 32418 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 32454 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 32990 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 20307 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 31215 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 21628 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 21560 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 39057 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 29575 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 21628 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 21560 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 39057 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 29575 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 32418 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 32454 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 32990 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 20307 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 31215 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 24635 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 34507 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 30333 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 20540 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 32418 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 32454 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 32990 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 20307 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 31215 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 32418 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 32454 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 32990 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 20307 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 31215 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 24635 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 34507 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 30333 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 20540 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 24635 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 34507 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 30333 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 20540 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 24635 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 34507 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 30333 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 20540 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 30452 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 32928 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 28201 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 24230 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 24635 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 34507 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 30333 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 20540 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 24635 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 34507 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 30333 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 20540 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 30452 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 32928 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 28201 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 24230 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 30452 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 32928 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 28201 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 24230 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 24635 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 34507 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 30333 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 20540 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 33033 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 25615 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 24635 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 34507 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 30333 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 20540 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 24635 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 34507 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 30333 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 20540 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 33033 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 25615 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 33033 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 25615 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 24635 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 34507 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 30333 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 20540 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 21628 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 21560 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 39057 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 29575 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 24635 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 34507 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 30333 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 20540 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 24635 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 34507 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 30333 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 20540 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 21628 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 21560 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 39057 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 29575 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 21628 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 21560 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 39057 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 29575 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 24635 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 34507 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 30333 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 20540 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 32418 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 32454 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 32990 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 20307 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 31215 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 24635 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 34507 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 30333 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 20540 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 24635 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 34507 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 30333 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 20540 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 32418 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 32454 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 32990 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 20307 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 31215 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 32418 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 32454 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 32990 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 20307 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 31215 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 30452 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 32928 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 28201 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 24230 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 33033 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 25615 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 21628 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 21560 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 39057 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 29575 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 32418 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 32454 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 32990 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 20307 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 31215 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 24635 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 34507 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 30333 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 20540 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 30452 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 32928 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 28201 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 24230 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 33033 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 25615 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 21628 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 21560 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 39057 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 29575 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 32418 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 32454 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 32990 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 20307 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 31215 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 24635 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 34507 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 30333 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 20540 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 30452 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 32928 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 28201 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 24230 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 33033 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 25615 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 21628 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 21560 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 39057 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 29575 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 32418 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 32454 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 32990 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 20307 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 31215 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 24635 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 34507 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 30333 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 20540 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 30452 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 32928 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 28201 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 24230 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 33033 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 25615 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 21628 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 21560 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 39057 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 29575 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 32418 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 32454 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 32990 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 20307 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 31215 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 24635 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 34507 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 30333 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 20540 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 30452 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 32928 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 28201 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 24230 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 33033 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 25615 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 21628 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 21560 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 39057 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 29575 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 32418 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 32454 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 32990 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 20307 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 31215 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 24635 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 34507 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 30333 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 20540 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 30452 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 32928 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 28201 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 24230 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 33033 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 25615 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 21628 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 21560 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 39057 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 29575 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 32418 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 32454 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 32990 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 20307 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 31215 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 24635 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 34507 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 30333 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 20540 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 24635 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 34507 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 30333 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 20540 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 32418 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 32454 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 32990 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 20307 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 31215 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 21628 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 21560 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 39057 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 29575 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 30452 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 32928 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 28201 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 24230 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 33033 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 25615 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 24635 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 34507 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 30333 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 20540 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 32418 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 32454 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 32990 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 20307 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 31215 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 21628 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 21560 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 39057 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 29575 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 30452 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 32928 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 28201 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 24230 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 33033 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 25615 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 24635 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 34507 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 30333 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 20540 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 32418 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 32454 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 32990 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 20307 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 31215 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 21628 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 21560 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 39057 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 29575 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 30452 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 32928 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 28201 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 24230 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 33033 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 25615 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": "Generate report structure:   0%|          | 0/1 [00:00<?, ?it/s]",
+      "application/vnd.jupyter.widget-view+json": {
+       "version_major": 2,
+       "version_minor": 0,
+       "model_id": "545c132fec8e48fc88f7bdea87791054"
+      }
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 30452 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 32928 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 28201 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 24230 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 33033 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 25615 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 21628 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 21560 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 39057 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 29575 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 32418 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 32454 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 32990 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 20307 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 31215 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 24635 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 34507 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 30333 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 20540 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 30452 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 32928 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 28201 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 24230 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 33033 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 25615 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 21628 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 21560 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 39057 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 29575 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 32418 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 32454 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 32990 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 20307 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 31215 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 24635 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 34507 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 30333 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 20540 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 30452 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 32928 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 28201 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 24230 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 33033 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 25615 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 21628 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 21560 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 39057 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 29575 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 32418 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 32454 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 32990 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 20307 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 31215 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 24635 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 34507 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 30333 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 20540 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 30452 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 32928 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 28201 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 24230 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 33033 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 25615 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 21628 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 21560 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 39057 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 29575 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 32418 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 32454 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 32990 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 20307 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 31215 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 24635 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 34507 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 30333 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 20540 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 30452 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 32928 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 28201 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 24230 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 33033 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 25615 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 21628 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 21560 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 39057 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 29575 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 32418 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 32454 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 32990 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 20307 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 31215 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 24635 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 34507 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 30333 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 20540 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 30452 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 32928 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 28201 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 24230 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 33033 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 25615 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 21628 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 21560 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 39057 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 29575 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 32418 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 32454 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 32990 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 20307 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 31215 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 24635 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 34507 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 30333 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 20540 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 30452 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 32928 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 28201 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 24230 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 33033 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 25615 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 21628 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 21560 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 39057 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 29575 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 32418 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 32454 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 32990 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 20307 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 31215 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 24635 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 34507 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 30333 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 20540 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 30452 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 32928 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 28201 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 24230 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 33033 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 25615 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 21628 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 21560 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 39057 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 29575 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 32418 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 32454 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 32990 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 20307 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 31215 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 24635 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 34507 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 30333 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 20540 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 30452 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 32928 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 28201 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 24230 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 33033 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 25615 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 21628 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 21560 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 39057 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 29575 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 32418 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 32454 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 32990 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 20307 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 31215 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 24635 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 34507 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 30333 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 20540 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 24635 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 34507 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 30333 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 20540 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 30452 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 32928 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 28201 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 24230 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 32418 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 32454 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 32990 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 20307 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 31215 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 21628 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 21560 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 39057 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 29575 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 33033 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 25615 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 24635 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 34507 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 30333 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 20540 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 30452 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 32928 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 28201 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 24230 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 32418 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 32454 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 32990 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 20307 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 31215 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 21628 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 21560 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 39057 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 29575 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 33033 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 25615 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 24635 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 34507 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 30333 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 20540 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 30452 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 32928 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 28201 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 24230 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 32418 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 32454 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 32990 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 20307 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 31215 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 21628 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 21560 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 39057 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 29575 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 33033 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 25615 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": "Render HTML:   0%|          | 0/1 [00:00<?, ?it/s]",
+      "application/vnd.jupyter.widget-view+json": {
+       "version_major": 2,
+       "version_minor": 0,
+       "model_id": "942c0a25ca7347018839697a63d1b3d2"
+      }
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": "Export report to file:   0%|          | 0/1 [00:00<?, ?it/s]",
+      "application/vnd.jupyter.widget-view+json": {
+       "version_major": 2,
+       "version_minor": 0,
+       "model_id": "98b179f718c94b6ab109fe1532aacbcc"
+      }
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from pandas_profiling import ProfileReport\n",
+    "profile = ProfileReport(df, title='Pandas Profiling Report', html={'style':{'full_width':True}})\n",
+    "profile.to_file(output_file=\"your_report.html\")"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "y        1.000000\n直肠温度     0.172629\n总蛋白值     0.114651\n呼吸频率    -0.036938\n红细胞体积   -0.152119\n脉搏      -0.276255\nName: y, dtype: float64"
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "## 可视化列的缺失值、唯一值、类型\n",
+    "df.corr()\n",
+    "# 可视化变量y与其他的相关性曲线\n",
+    "df.corr()['y'].sort_values(ascending=False)"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "   直肠温度   脉搏  红细胞体积  总蛋白值    y\n0  38.5   66   45.0   8.4  0.0\n1  39.2   88   50.0  85.0  0.0\n2  38.3   40   33.0   6.7  1.0\n3  39.1  164   48.0   7.2  0.0\n4  37.3  104   74.0   7.4  0.0",
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>直肠温度</th>\n      <th>脉搏</th>\n      <th>红细胞体积</th>\n      <th>总蛋白值</th>\n      <th>y</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>38.5</td>\n      <td>66</td>\n      <td>45.0</td>\n      <td>8.4</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>39.2</td>\n      <td>88</td>\n      <td>50.0</td>\n      <td>85.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>38.3</td>\n      <td>40</td>\n      <td>33.0</td>\n      <td>6.7</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>39.1</td>\n      <td>164</td>\n      <td>48.0</td>\n      <td>7.2</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>37.3</td>\n      <td>104</td>\n      <td>74.0</td>\n      <td>7.4</td>\n      <td>0.0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# 剔除相关性绝对值小于0.1的变量\n",
+    "df = df[df.columns[df.corr()['y'].abs()>0.1]]\n",
+    "df.head()"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "直肠温度     0\n脉搏       0\n红细胞体积    0\n总蛋白值     0\ny        9\ndtype: int64"
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.isnull().sum()"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "<Figure size 720x360 with 1 Axes>",
+      "image/png": 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\n"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": "<Figure size 720x360 with 1 Axes>",
+      "image/png": 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\n"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": "<Figure size 720x360 with 1 Axes>",
+      "image/png": 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\n"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": "<Figure size 720x360 with 1 Axes>",
+      "image/png": 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\n"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": "<Figure size 720x360 with 1 Axes>",
+      "image/png": "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\n"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#可视化每一个变量\n",
+    "import warnings\n",
+    "warnings.filterwarnings(\"ignore\")\n",
+    "for i in df.columns:\n",
+    "    plt.figure(figsize=(10,5))\n",
+    "    plt.title(i)\n",
+    "    # 设置字体旋转角度\n",
+    "    plt.xticks(rotation=90)\n",
+    "    sns.countplot(df[i])\n",
+    "    plt.show()\n"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "直肠温度     68\n脉搏       26\n红细胞体积    36\n总蛋白值     42\ny         0\ndtype: int64"
+     },
+     "execution_count": 39,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# 我们认为这些变量中的0值都是不合理的，所以我们将除了y(最后一列)以外的变量中的0值都替换为np.nan\n",
+    "df[df.columns[:-1]] = df[df.columns[:-1]].replace(0,np.nan)\n",
+    "df.isnull().sum()"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "直肠温度     0\n脉搏       0\n红细胞体积    0\n总蛋白值     0\ny        0\ndtype: int64"
+     },
+     "execution_count": 40,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# 用众数填充缺失值\n",
+    "# 观察发现直肠温度列是正太分布的，因此可以用均值填充缺失值，其他使用中位数\n",
+    "df['直肠温度'] = df['直肠温度'].fillna(df['直肠温度'].mean())\n",
+    "df[['脉搏','红细胞体积','总蛋白值']]  = df[['脉搏','红细胞体积','总蛋白值']].fillna(df[['脉搏','红细胞体积','总蛋白值']].median())\n",
+    "df.isnull().sum()"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "outputs": [],
+   "source": [
+    "## 删除缺失值\n",
+    "df = df.dropna()"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "<Figure size 720x360 with 1 Axes>",
+      "image/png": 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\n"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": "<Figure size 720x360 with 1 Axes>",
+      "image/png": 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\n"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": "<Figure size 720x360 with 1 Axes>",
+      "image/png": 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\n"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": "<Figure size 720x360 with 1 Axes>",
+      "image/png": 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\n"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": "<Figure size 720x360 with 1 Axes>",
+      "image/png": "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\n"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# 再查看一下每个变量的分布\n",
+    "for i in df.columns:\n",
+    "    plt.figure(figsize=(10,5))\n",
+    "    plt.title(i)\n",
+    "    # 设置字体旋转角度\n",
+    "    plt.xticks(rotation=90)\n",
+    "    sns.countplot(df[i])\n",
+    "    plt.show()\n"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "outputs": [],
+   "source": [
+    "# 由于变量的取值范围不一样，因此需要对数据进行标准化\n",
+    "import time\n",
+    "X_train,X_test,y_train,y_test = train_test_split(df[df.columns[:-1]],df['y'],test_size=0.3,random_state=0)\n",
+    "X_train = StandardScaler().fit_transform(X_train)\n",
+    "X_test = StandardScaler().fit_transform(X_test)\n",
+    "\n",
+    "model_result = {}\n",
+    "\n",
+    "models = [RandomForestClassifier(),BaggingClassifier(),AdaBoostClassifier(),GaussianNB(),LogisticRegression(),DecisionTreeClassifier(),SVC(),KNeighborsClassifier()]\n",
+    "for model in models:\n",
+    "    try:\n",
+    "        model_name = str(model).split('(')[0]\n",
+    "        start = time.time()\n",
+    "        model.fit(X_train,y_train)\n",
+    "        y_pred = model.predict(X_test)\n",
+    "        end = time.time()\n",
+    "        # 存储准确率和混淆矩阵\n",
+    "        model_result[model_name] = [accuracy_score(y_test,y_pred),confusion_matrix(y_test,y_pred),end-start]\n",
+    "    except Exception as e:\n",
+    "        print(model_name,e)\n",
+    "\n",
+    "## 使用df保存模型的结果\n",
+    "df_result = pd.DataFrame(model_result).T\n",
+    "df_result.columns = ['accuracy_score','confusion_matrix','time']\n",
+    "\n",
+    "df_result.sort_values(by='accuracy_score',ascending=False,inplace=True)\n"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "<Figure size 720x360 with 1 Axes>",
+      "image/png": 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\n"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# 绘制各个模型的准确率柱状图，并在柱状图上标注值\n",
+    "plt.figure(figsize=(10,5))\n",
+    "plt.title('各个模型的准确率')\n",
+    "sns.barplot(x=df_result.index,y=df_result['accuracy_score'])\n",
+    "for i in range(df_result.shape[0]):\n",
+    "    plt.text(i,df_result['accuracy_score'][i],round(df_result['accuracy_score'][i],3),ha='center')\n",
+    "# 设置字体选择\n",
+    "plt.xticks(rotation=90)\n",
+    "plt.show()\n"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
\ No newline at end of file
diff --git a/共享民宿平台担保交易房子评分的影响研究/homeworks/人脸识别.ipynb b/共享民宿平台担保交易房子评分的影响研究/homeworks/人脸识别.ipynb
new file mode 100644
index 0000000..05baa33
--- /dev/null
+++ b/共享民宿平台担保交易房子评分的影响研究/homeworks/人脸识别.ipynb
@@ -0,0 +1,267 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "4b3ff29d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "D:\\anaconda\\lib\\site-packages\\scipy\\__init__.py:146: UserWarning: A NumPy version >=1.16.5 and <1.23.0 is required for this version of SciPy (detected version 1.24.3\n",
+      "  warnings.warn(f\"A NumPy version >={np_minversion} and <{np_maxversion}\"\n"
+     ]
+    }
+   ],
+   "source": [
+    "import tensorflow as tf\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "keras = tf.keras\n",
+    "layers = keras.layers"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "f6e155f3",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Found 1611 files belonging to 6 classes.\n",
+      "Using 1289 files for training.\n",
+      "Found 1611 files belonging to 6 classes.\n",
+      "Using 322 files for validation.\n"
+     ]
+    }
+   ],
+   "source": [
+    "data_train = keras.utils.image_dataset_from_directory(\n",
+    "    r\"D:\\data\\gray_face_images\",\n",
+    "    color_mode=\"grayscale\",\n",
+    "    validation_split=0.2,\n",
+    "    subset=\"training\",\n",
+    "    seed=42,\n",
+    ")\n",
+    "data_val = keras.utils.image_dataset_from_directory(\n",
+    "    r\"D:\\data\\gray_face_images\",\n",
+    "    color_mode=\"grayscale\",\n",
+    "    validation_split=0.2,\n",
+    "    subset=\"validation\",\n",
+    "    seed=42,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "ceac26df",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def data_convert(x, y):\n",
+    "    return x / 255., tf.one_hot(y, depth=6)\n",
+    "data_train = data_train.map(data_convert)\n",
+    "data_val = data_val.map(data_convert)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "d6c8a2d3",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(32, 256, 256, 1) (32, 6)\n"
+     ]
+    }
+   ],
+   "source": [
+    "for x, y in data_train:\n",
+    "    print(x.shape, y.shape)\n",
+    "    break"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "d9026c53",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<IPython.core.display.Image object>"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "inputs = layers.Input(shape=(256,256,1), name='Input')\n",
+    "x = layers.Conv2D(filters=32, kernel_size=3,  activation='relu', name='Cov1')(inputs)\n",
+    "x = layers.MaxPool2D(pool_size=2, name='Pool1')(x)\n",
+    "x = layers.Dropout(rate=0.5, name='Dropout1')(x)\n",
+    "\n",
+    "x = layers.Conv2D(filters=64, kernel_size=3, activation='relu', name='Cov2')(x)\n",
+    "x = layers.MaxPool2D(pool_size=2, name='Pool2')(x)\n",
+    "x = layers.Dropout(rate=0.5, name='Dropout2')(x)\n",
+    "\n",
+    "x = layers.Conv2D(filters=64, kernel_size=3, activation='relu', name='Cov3')(x)\n",
+    "x = layers.MaxPool2D(pool_size=2, name='Pool3')(x)\n",
+    "x = layers.Dropout(rate=0.5, name='Dropout3')(x)\n",
+    "\n",
+    "x = layers.Flatten(name=\"Flatten\")(x)\n",
+    "x = layers.Dropout(rate=0.75, name='Dropout4')(x)\n",
+    "x = layers.Dense(units=512, activation='relu', name=\"Dense1\")(x)\n",
+    "outputs = layers.Dense(units=6, activation='softmax', name=\"Output\")(x)\n",
+    "model = keras.Model(inputs, outputs)\n",
+    "keras.utils.plot_model(model, show_shapes=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "ddf4e697",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Model: \"model\"\n",
+      "_________________________________________________________________\n",
+      " Layer (type)                Output Shape              Param #   \n",
+      "=================================================================\n",
+      " Input (InputLayer)          [(None, 256, 256, 1)]     0         \n",
+      "                                                                 \n",
+      " Cov1 (Conv2D)               (None, 254, 254, 32)      320       \n",
+      "                                                                 \n",
+      " Pool1 (MaxPooling2D)        (None, 127, 127, 32)      0         \n",
+      "                                                                 \n",
+      " Dropout1 (Dropout)          (None, 127, 127, 32)      0         \n",
+      "                                                                 \n",
+      " Cov2 (Conv2D)               (None, 125, 125, 64)      18496     \n",
+      "                                                                 \n",
+      " Pool2 (MaxPooling2D)        (None, 62, 62, 64)        0         \n",
+      "                                                                 \n",
+      " Dropout2 (Dropout)          (None, 62, 62, 64)        0         \n",
+      "                                                                 \n",
+      " Cov3 (Conv2D)               (None, 60, 60, 64)        36928     \n",
+      "                                                                 \n",
+      " Pool3 (MaxPooling2D)        (None, 30, 30, 64)        0         \n",
+      "                                                                 \n",
+      " Dropout3 (Dropout)          (None, 30, 30, 64)        0         \n",
+      "                                                                 \n",
+      " Flatten (Flatten)           (None, 57600)             0         \n",
+      "                                                                 \n",
+      " Dropout4 (Dropout)          (None, 57600)             0         \n",
+      "                                                                 \n",
+      " Dense1 (Dense)              (None, 512)               29491712  \n",
+      "                                                                 \n",
+      " Output (Dense)              (None, 6)                 3078      \n",
+      "                                                                 \n",
+      "=================================================================\n",
+      "Total params: 29,550,534\n",
+      "Trainable params: 29,550,534\n",
+      "Non-trainable params: 0\n",
+      "_________________________________________________________________\n"
+     ]
+    }
+   ],
+   "source": [
+    "model.summary()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "4af04e8f",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1/10\n",
+      "41/41 [==============================] - 8s 75ms/step - loss: 2.1733 - categorical_accuracy: 0.2234 - val_loss: 1.7901 - val_categorical_accuracy: 0.1460\n",
+      "Epoch 2/10\n",
+      "41/41 [==============================] - 3s 63ms/step - loss: 1.3984 - categorical_accuracy: 0.4259 - val_loss: 0.7413 - val_categorical_accuracy: 0.8851\n",
+      "Epoch 3/10\n",
+      "41/41 [==============================] - 3s 63ms/step - loss: 0.5248 - categorical_accuracy: 0.8348 - val_loss: 0.2736 - val_categorical_accuracy: 0.9658\n",
+      "Epoch 4/10\n",
+      "41/41 [==============================] - 3s 63ms/step - loss: 0.2337 - categorical_accuracy: 0.9348 - val_loss: 0.1263 - val_categorical_accuracy: 0.9814\n",
+      "Epoch 5/10\n",
+      "41/41 [==============================] - 3s 63ms/step - loss: 0.1423 - categorical_accuracy: 0.9558 - val_loss: 0.0671 - val_categorical_accuracy: 0.9907\n",
+      "Epoch 6/10\n",
+      "41/41 [==============================] - 3s 63ms/step - loss: 0.0916 - categorical_accuracy: 0.9744 - val_loss: 0.0493 - val_categorical_accuracy: 0.9907\n",
+      "Epoch 7/10\n",
+      "41/41 [==============================] - 3s 64ms/step - loss: 0.0835 - categorical_accuracy: 0.9744 - val_loss: 0.0723 - val_categorical_accuracy: 0.9752\n",
+      "Epoch 8/10\n",
+      "41/41 [==============================] - 3s 63ms/step - loss: 0.0830 - categorical_accuracy: 0.9752 - val_loss: 0.0447 - val_categorical_accuracy: 0.9938\n",
+      "Epoch 9/10\n",
+      "41/41 [==============================] - 3s 63ms/step - loss: 0.0490 - categorical_accuracy: 0.9829 - val_loss: 0.1132 - val_categorical_accuracy: 0.9627\n",
+      "Epoch 10/10\n",
+      "41/41 [==============================] - 3s 64ms/step - loss: 0.0558 - categorical_accuracy: 0.9829 - val_loss: 0.0259 - val_categorical_accuracy: 0.9907\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<keras.callbacks.History at 0x17bc55b5430>"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model.compile(optimizer=keras.optimizers.Adam(learning_rate=0.001),\n",
+    "              loss=keras.losses.CategoricalCrossentropy(from_logits=False),\n",
+    "              metrics=[keras.metrics.CategoricalAccuracy()])\n",
+    "model.fit(data_train, validation_data=data_val, epochs=10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6933885f",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/共享民宿平台担保交易房子评分的影响研究/homeworks/国民经济核算数据分析.ipynb b/共享民宿平台担保交易房子评分的影响研究/homeworks/国民经济核算数据分析.ipynb
new file mode 100644
index 0000000..55900fe
--- /dev/null
+++ b/共享民宿平台担保交易房子评分的影响研究/homeworks/国民经济核算数据分析.ipynb
@@ -0,0 +1,304 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "375dc0f1",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['columns', 'values']"
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "data=np.load('./国民经济核算季度数据.npz',allow_pickle=True)\n",
+    "data.files"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "6a0b74f6",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['序号', '时间', '国内生产总值_当季值(亿元)', '第一产业增加值_当季值(亿元)', '第二产业增加值_当季值(亿元)',\n",
+       "       '第三产业增加值_当季值(亿元)', '农林牧渔业增加值_当季值(亿元)', '工业增加值_当季值(亿元)',\n",
+       "       '建筑业增加值_当季值(亿元)', '批发和零售业增加值_当季值(亿元)', '交通运输、仓储和邮政业增加值_当季值(亿元)',\n",
+       "       '住宿和餐饮业增加值_当季值(亿元)', '金融业增加值_当季值(亿元)', '房地产业增加值_当季值(亿元)',\n",
+       "       '其他行业增加值_当季值(亿元)'], dtype=object)"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data['columns']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "24dfbbdf",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[1, '2000年第一季度', 21329.9, ..., 1235.9, 933.7, 3586.1],\n",
+       "       [2, '2000年第二季度', 24043.4, ..., 1124.0, 904.7, 3464.9],\n",
+       "       [3, '2000年第三季度', 25712.5, ..., 1170.4, 1070.9, 3518.2],\n",
+       "       ...,\n",
+       "       [67, '2016年第三季度', 190529.5, ..., 15472.5, 12164.1, 37964.1],\n",
+       "       [68, '2016年第四季度', 211281.3, ..., 15548.7, 13214.9, 39848.4],\n",
+       "       [69, '2017年第一季度', 180682.7, ..., 17213.5, 12393.4, 42443.1]],\n",
+       "      dtype=object)"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data['values']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "858e1482",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#第一、二、三产业增长值\n",
+    "for i in [3,4,5]:\n",
+    "    plt.scatter(range(69),data['values'][:,i])\n",
+    "plt.legend(['1','2','3'])\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "34358f54",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#生产总值\n",
+    "plt.plot(range(69),data['values'][:,2])\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "c560b8bc",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#c为点的颜色，marker为点的类型，alpha为点的透明度，值为0-1，越小点越透明\n",
+    "l=['r','g','b']  #点的颜色\n",
+    "m=['o','*','D']  #点的类型\n",
+    "#j代表位置，i代表值，比如j=0时，i=3；j=1时，i=4；j=2时，i=5\n",
+    "for j,i in enumerate([3,4,5]):\n",
+    "    plt.plot(range(69),data['values'][:,i],c=l[j],marker=m[j],alpha=0.3)\n",
+    "plt.legend(['1','2','3'])\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "957f7f36",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 288x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(4,4))\n",
+    "num=data['values'][68,3:6]\n",
+    "plt.pie(num,autopct='%2.f %%',explode=[0.1,0,0],labels=['1','2','3'],labeldistance=1.2)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "48b0ea5b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYoAAAD4CAYAAADy46FuAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/YYfK9AAAACXBIWXMAAAsTAAALEwEAmpwYAAAQM0lEQVR4nO3df6ie5X3H8fdnSWttO60/jpIlYcdh6KbC1hlSt0IZTanZHIt/KJxCaxiBgLjNjsGI+0e2EVAYdROmEKozulINacHQ4roQW0rBxR6tm42peKhOz8zM6WKtDrSL++6Pc53x5PTkSnKeJE9i3i94uO/7e1/Xle/D88cn930/55xUFZIkHckvjLoBSdLpzaCQJHUZFJKkLoNCktRlUEiSupaOuoET7eKLL67x8fFRtyFJZ5Snnnrqx1U1ttC591xQjI+PMzk5Oeo2JOmMkuTfj3TOW0+SpC6DQpLUZVBIkroMCklSl0EhSeoyKCRJXQaFJKnLoJAkdRkUkqSu99xPZks6vY1v/saoW3jPeumO607Kuke9okhyf5IDSX4wULswya4kL7TtBQPnbksyleT5JNcO1K9O8mw7d3eStPo5SR5p9T1JxgfmbGj/xgtJNpywdy1JOmbHcuvpAWDdvNpmYHdVrQJ2t2OSXAFMAFe2OfckWdLm3AtsAla119yaG4HXq+py4C7gzrbWhcDtwMeBNcDtg4EkSTo1jhoUVfUd4OC88npgW9vfBlw/UH+4qt6pqheBKWBNkmXAeVX1RM3+ke4H582ZW2sHsLZdbVwL7Kqqg1X1OrCLnw8sSdJJttiH2ZdW1X6Atr2k1ZcDrwyMm2615W1/fv2wOVV1CHgDuKiz1s9JsinJZJLJmZmZRb4lSdJCTvS3nrJArTr1xc45vFi1tapWV9XqsbEFf526JGmRFhsUr7XbSbTtgVafBlYOjFsBvNrqKxaoHzYnyVLgfGZvdR1pLUnSKbTYoNgJzH0LaQPw6EB9on2T6TJmH1o/2W5PvZnkmvb84aZ5c+bWugF4vD3H+CbwmSQXtIfYn2k1SdIpdNSfo0jyFeB3gIuTTDP7TaQ7gO1JNgIvAzcCVNXeJNuB54BDwC1V9W5b6mZmv0F1LvBYewHcBzyUZIrZK4mJttbBJH8NfK+N+6uqmv9QXZJ0kh01KKrqs0c4tfYI47cAWxaoTwJXLVB/mxY0C5y7H7j/aD1Kkk4ef4WHJKnLoJAkdRkUkqQug0KS1GVQSJK6DApJUpdBIUnqMigkSV0GhSSpy6CQJHUZFJKkLoNCktRlUEiSugwKSVKXQSFJ6jIoJEldBoUkqcugkCR1GRSSpC6DQpLUZVBIkroMCklSl0EhSeoyKCRJXQaFJKnLoJAkdRkUkqQug0KS1GVQSJK6DApJUpdBIUnqGiookvxpkr1JfpDkK0k+kOTCJLuSvNC2FwyMvy3JVJLnk1w7UL86ybPt3N1J0urnJHmk1fckGR+mX0nS8Vt0UCRZDvwJsLqqrgKWABPAZmB3Va0CdrdjklzRzl8JrAPuSbKkLXcvsAlY1V7rWn0j8HpVXQ7cBdy52H4lSYsz7K2npcC5SZYCHwReBdYD29r5bcD1bX898HBVvVNVLwJTwJoky4DzquqJqirgwXlz5tbaAaydu9qQJJ0aiw6KqvoP4G+Al4H9wBtV9c/ApVW1v43ZD1zSpiwHXhlYYrrVlrf9+fXD5lTVIeAN4KL5vSTZlGQyyeTMzMxi35IkaQHD3Hq6gNn/8V8G/BLwoSSf601ZoFadem/O4YWqrVW1uqpWj42N9RuXJB2XYW49fRp4sapmqup/gK8Bvw281m4n0bYH2vhpYOXA/BXM3qqabvvz64fNabe3zgcODtGzJOk4DRMULwPXJPlge26wFtgH7AQ2tDEbgEfb/k5gon2T6TJmH1o/2W5PvZnkmrbOTfPmzK11A/B4e44hSTpFli52YlXtSbIDeBo4BHwf2Ap8GNieZCOzYXJjG783yXbguTb+lqp6ty13M/AAcC7wWHsB3Ac8lGSK2SuJicX2K0lanEUHBUBV3Q7cPq/8DrNXFwuN3wJsWaA+CVy1QP1tWtBIkkbDn8yWJHUZFJKkLoNCktRlUEiSugwKSVKXQSFJ6jIoJEldBoUkqcugkCR1GRSSpC6DQpLUZVBIkroMCklSl0EhSeoyKCRJXUP9PQpp1MY3f2PULbxnvXTHdaNuQacJrygkSV0GhSSpy6CQJHUZFJKkLoNCktRlUEiSugwKSVKXQSFJ6jIoJEldBoUkqcugkCR1GRSSpC6DQpLUZVBIkroMCklSl0EhSeoaKiiSfCTJjiQ/TLIvyW8luTDJriQvtO0FA+NvSzKV5Pkk1w7Ur07ybDt3d5K0+jlJHmn1PUnGh+lXknT8hr2i+Dvgn6rqV4FfB/YBm4HdVbUK2N2OSXIFMAFcCawD7kmypK1zL7AJWNVe61p9I/B6VV0O3AXcOWS/kqTjtOigSHIe8EngPoCq+llV/QRYD2xrw7YB17f99cDDVfVOVb0ITAFrkiwDzquqJ6qqgAfnzZlbawewdu5qQ5J0agxzRfErwAzwD0m+n+RLST4EXFpV+wHa9pI2fjnwysD86VZb3vbn1w+bU1WHgDeAi+Y3kmRTkskkkzMzM0O8JUnSfMMExVLgN4F7q+pjwH/TbjMdwUJXAtWp9+YcXqjaWlWrq2r12NhYv2tJ0nEZJiimgemq2tOOdzAbHK+120m07YGB8SsH5q8AXm31FQvUD5uTZClwPnBwiJ4lScdp0UFRVf8JvJLko620FngO2AlsaLUNwKNtfycw0b7JdBmzD62fbLen3kxyTXv+cNO8OXNr3QA83p5jSJJOkaVDzv9j4MtJ3g/8CPhDZsNne5KNwMvAjQBVtTfJdmbD5BBwS1W929a5GXgAOBd4rL1g9kH5Q0mmmL2SmBiyX0nScRoqKKrqGWD1AqfWHmH8FmDLAvVJ4KoF6m/TgkaSNBr+ZLYkqcugkCR1GRSSpC6DQpLUZVBIkroMCklSl0EhSeoyKCRJXQaFJKnLoJAkdRkUkqQug0KS1GVQSJK6DApJUpdBIUnqMigkSV0GhSSpy6CQJHUZFJKkLoNCktRlUEiSugwKSVKXQSFJ6jIoJEldBoUkqcugkCR1GRSSpC6DQpLUZVBIkroMCklSl0EhSeoaOiiSLEny/SRfb8cXJtmV5IW2vWBg7G1JppI8n+TagfrVSZ5t5+5OklY/J8kjrb4nyfiw/UqSjs+JuKK4Fdg3cLwZ2F1Vq4Dd7ZgkVwATwJXAOuCeJEvanHuBTcCq9lrX6huB16vqcuAu4M4T0K8k6TgMFRRJVgDXAV8aKK8HtrX9bcD1A/WHq+qdqnoRmALWJFkGnFdVT1RVAQ/OmzO31g5g7dzVhiTp1Bj2iuJvgT8H/negdmlV7Qdo20tafTnwysC46VZb3vbn1w+bU1WHgDeAi4bsWZJ0HBYdFEl+HzhQVU8d65QFatWp9+bM72VTkskkkzMzM8fYjiTpWAxzRfEJ4A+SvAQ8DHwqyT8Cr7XbSbTtgTZ+Glg5MH8F8Gqrr1igfticJEuB84GD8xupqq1VtbqqVo+NjQ3xliRJ8y06KKrqtqpaUVXjzD6kfryqPgfsBDa0YRuAR9v+TmCifZPpMmYfWj/Zbk+9meSa9vzhpnlz5ta6of0bP3dFIUk6eZaehDXvALYn2Qi8DNwIUFV7k2wHngMOAbdU1bttzs3AA8C5wGPtBXAf8FCSKWavJCZOQr+SpI4TEhRV9W3g223/v4C1Rxi3BdiyQH0SuGqB+tu0oJEkjYY/mS1J6jIoJEldBoUkqcugkCR1GRSSpC6DQpLUZVBIkroMCklSl0EhSeoyKCRJXQaFJKnLoJAkdRkUkqQug0KS1GVQSJK6DApJUpdBIUnqMigkSV0GhSSpy6CQJHUZFJKkLoNCktRlUEiSugwKSVKXQSFJ6jIoJEldBoUkqcugkCR1GRSSpC6DQpLUZVBIkroMCklS16KDIsnKJN9Ksi/J3iS3tvqFSXYleaFtLxiYc1uSqSTPJ7l2oH51kmfbubuTpNXPSfJIq+9JMj7Ee5UkLcIwVxSHgD+rql8DrgFuSXIFsBnYXVWrgN3tmHZuArgSWAfck2RJW+teYBOwqr3WtfpG4PWquhy4C7hziH4lSYuw6KCoqv1V9XTbfxPYBywH1gPb2rBtwPVtfz3wcFW9U1UvAlPAmiTLgPOq6omqKuDBeXPm1toBrJ272pAknRon5BlFuyX0MWAPcGlV7YfZMAEuacOWA68MTJtuteVtf379sDlVdQh4A7hogX9/U5LJJJMzMzMn4i1JkpqhgyLJh4GvAl+oqp/2hi5Qq069N+fwQtXWqlpdVavHxsaO1rIk6TgMFRRJ3sdsSHy5qr7Wyq+120m07YFWnwZWDkxfAbza6isWqB82J8lS4Hzg4DA9S5KOzzDfegpwH7Cvqr44cGonsKHtbwAeHahPtG8yXcbsQ+sn2+2pN5Nc09a8ad6cubVuAB5vzzEkSafI0iHmfgL4PPBskmda7S+AO4DtSTYCLwM3AlTV3iTbgeeY/cbULVX1bpt3M/AAcC7wWHvBbBA9lGSK2SuJiSH6lSQtwqKDoqq+y8LPEADWHmHOFmDLAvVJ4KoF6m/TgkaSNBr+ZLYkqcugkCR1GRSSpC6DQpLUZVBIkroMCklSl0EhSeoyKCRJXQaFJKnLoJAkdRkUkqQug0KS1GVQSJK6DApJUpdBIUnqMigkSV0GhSSpa5g/hfqeNL75G6Nu4T3rpTuuG3ULkhbBKwpJUpdBIUnqMigkSV0GhSSpy6CQJHUZFJKkLoNCktRlUEiSugwKSVKXQSFJ6jIoJEldBoUkqcugkCR1GRSSpK4zIiiSrEvyfJKpJJtH3Y8knU1O+6BIsgT4e+B3gSuAzya5YrRdSdLZ47QPCmANMFVVP6qqnwEPA+tH3JMknTXOhL9wtxx4ZeB4Gvj44IAkm4BN7fCtJM+fot5G7WLgx6Nu4ljlzlF3cFo4Yz4zP6//d7Z8Zr98pBNnQlBkgVoddlC1Fdh6ato5fSSZrKrVo+5Dx87P7MzjZ3Zm3HqaBlYOHK8AXh1RL5J01jkTguJ7wKoklyV5PzAB7BxxT5J01jjtbz1V1aEkfwR8E1gC3F9Ve0fc1unirLvd9h7gZ3bmOes/s1TV0UdJks5aZ8KtJ0nSCBkUkqQug+IMlOT+JAeS/GDUvejokqxM8q0k+5LsTXLrqHtSX5IPJHkyyb+2z+wvR93TKPmM4gyU5JPAW8CDVXXVqPtRX5JlwLKqejrJLwJPAddX1XMjbk1HkCTAh6rqrSTvA74L3FpV/zLi1kbCK4ozUFV9Bzg46j50bKpqf1U93fbfBPYx+xsHdJqqWW+1w/e111n7v2qDQjqFkowDHwP2jLgVHUWSJUmeAQ4Au6rqrP3MDArpFEnyYeCrwBeq6qej7kd9VfVuVf0Gs78NYk2Ss/Y2r0EhnQLtPvdXgS9X1ddG3Y+OXVX9BPg2sG60nYyOQSGdZO3B6H3Avqr64qj70dElGUvykbZ/LvBp4IcjbWqEDIozUJKvAE8AH00ynWTjqHtS1yeAzwOfSvJMe/3eqJtS1zLgW0n+jdnfN7erqr4+4p5Gxq/HSpK6vKKQJHUZFJKkLoNCktRlUEiSugwKSVKXQSFJ6jIoJEld/wfbnVbspFlQ7QAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.bar(range(len(num)),num)\n",
+    "plt.xticks(range(len(num)),['1','2','3'])\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "a37fa88b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#图上的横线表示上边缘，下边缘，上4分位数，下4分位数，中位数，边缘之外的为异常值\n",
+    "num=(list(data['values'][:,3]),list(data['values'][:,4]),list(data['values'][:,5]))\n",
+    "plt.boxplot(num)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "9f351764",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "D:\\anaconda\\lib\\site-packages\\scipy\\__init__.py:146: UserWarning: A NumPy version >=1.16.5 and <1.23.0 is required for this version of SciPy (detected version 1.24.3\n",
+      "  warnings.warn(f\"A NumPy version >={np_minversion} and <{np_maxversion}\"\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from scipy import stats\n",
+    "#产生100个正态分布的随机数\n",
+    "x=stats.uniform.rvs(size=100)\n",
+    "#直方图\n",
+    "plt.hist(x,bins=20)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0b8bee42",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/共享民宿平台担保交易房子评分的影响研究/homeworks/房价分析.ipynb b/共享民宿平台担保交易房子评分的影响研究/homeworks/房价分析.ipynb
new file mode 100644
index 0000000..f6dfe88
--- /dev/null
+++ b/共享民宿平台担保交易房子评分的影响研究/homeworks/房价分析.ipynb
@@ -0,0 +1,883 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "metadata": {
+    "collapsed": true,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "from matplotlib.colors import ListedColormap\n",
+    "import pandas as pd\n",
+    "import seaborn as sns\n",
+    "from sklearn.svm import SVC,SVR\n",
+    "from sklearn.datasets import load_iris\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.preprocessing import StandardScaler, MinMaxScaler\n",
+    "from sklearn.metrics import confusion_matrix, accuracy_score, classification_report, mean_squared_error, r2_score\n",
+    "from sklearn.tree import DecisionTreeClassifier, DecisionTreeRegressor,plot_tree\n",
+    "from sklearn.neighbors import KNeighborsClassifier\n",
+    "# 设置中文字体\n",
+    "plt.rcParams['font.sans-serif'] = ['SimHei']\n",
+    "plt.rcParams['axes.unicode_minus'] = False\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "outputs": [],
+   "source": [
+    "iris = load_iris()\n",
+    "X = iris.data\n",
+    "y = iris.target"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "outputs": [],
+   "source": [
+    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3)"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "outputs": [],
+   "source": [
+    "scaler = StandardScaler()\n",
+    "scaler.fit(X_train)\n",
+    "X_train_std = scaler.transform(X_train)\n",
+    "X_test_std = scaler.transform(X_test)"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "outputs": [],
+   "source": [
+    "def plot_decision_regions(X, y, classifier, test_idx=None, resolution=0.02):\n",
+    "\n",
+    "    # setup marker generator and color map\n",
+    "    markers = ('s', 'x', 'o', '^', 'v')\n",
+    "    colors = ('red', 'blue', 'lightgreen', 'gray', 'cyan')\n",
+    "    cmap = ListedColormap(colors[:len(np.unique(y))])\n",
+    "\n",
+    "    # plot the decision surface\n",
+    "    x1_min, x1_max = X[:, 0].min() - 1, X[:, 0].max() + 1\n",
+    "    x2_min, x2_max = X[:, 1].min() - 1, X[:, 1].max() + 1\n",
+    "    xx1, xx2 = np.meshgrid(np.arange(x1_min, x1_max, resolution),\n",
+    "                           np.arange(x2_min, x2_max, resolution))\n",
+    "    Z = classifier.predict(np.array([xx1.ravel(), xx2.ravel()]).T)\n",
+    "    Z = Z.reshape(xx1.shape)\n",
+    "    plt.contourf(xx1, xx2, Z, alpha=0.3, cmap=cmap)\n",
+    "    plt.xlim(xx1.min(), xx1.max())\n",
+    "    plt.ylim(xx2.min(), xx2.max())\n",
+    "\n",
+    "    for idx, cl in enumerate(np.unique(y)):\n",
+    "        plt.scatter(x=X[y == cl, 0],\n",
+    "                    y=X[y == cl, 1],\n",
+    "                    alpha=0.8,\n",
+    "                    c=colors[idx],\n",
+    "                    marker=markers[idx],\n",
+    "                    label=cl,\n",
+    "                    edgecolor='black')\n",
+    "\n",
+    "    # highlight test samples\n",
+    "    if test_idx:\n",
+    "        # plot all samples\n",
+    "        X_test, y_test = X[test_idx, :], y[test_idx]\n",
+    "        plt.scatter(X_test[:, 0],\n",
+    "                    X_test[:, 1],\n",
+    "                    c='',\n",
+    "                    edgecolor='black',\n",
+    "                    alpha=1.0,\n",
+    "                    linewidth=1,\n",
+    "                    marker='o',\n",
+    "                    s=100,\n",
+    "                    label='test set')\n"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "E:\\anaconda3\\lib\\site-packages\\ipykernel_launcher.py:40: MatplotlibDeprecationWarning: Using a string of single character colors as a color sequence is deprecated since 3.2 and will be removed two minor releases later. Use an explicit list instead.\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": "<Figure size 432x288 with 1 Axes>",
+      "image/png": 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\n"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# X只选取第3，4列特征\n",
+    "X_train_std = X_train_std[:, 2:]\n",
+    "X_test_std = X_test_std[:, 2:]\n",
+    "\n",
+    "X_combined_std = np.vstack((X_train_std, X_test_std))\n",
+    "y_combined = np.hstack((y_train, y_test))\n",
+    "svm = SVC(kernel='linear', C=1.0, random_state=1)\n",
+    "svm.fit(X_train_std, y_train)\n",
+    "plot_decision_regions(X=X_combined_std, y=y_combined,\n",
+    "                      classifier=svm, test_idx=range(105, 150))\n",
+    "plt.xlabel('petal length [standardized]')\n",
+    "plt.ylabel('petal width [standardized]')\n",
+    "plt.legend(loc='upper left')\n",
+    "plt.tight_layout()\n",
+    "#plt.savefig('images/03_01.png', dpi=300)\n",
+    "plt.show()\n"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "<Figure size 432x288 with 1 Axes>",
+      "image/png": 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\n"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "np.random.seed(1)\n",
+    "X_xor = np.random.randn(200, 2)\n",
+    "y_xor = np.logical_xor(X_xor[:, 0] > 0,\n",
+    "                       X_xor[:, 1] > 0)\n",
+    "y_xor = np.where(y_xor, 1, -1)\n",
+    "plt.scatter(X_xor[y_xor == 1, 0],\n",
+    "            X_xor[y_xor == 1, 1],\n",
+    "            c='b',\n",
+    "            marker='x',\n",
+    "            label='1')\n",
+    "plt.scatter(X_xor[y_xor == -1, 0],\n",
+    "            X_xor[y_xor == -1, 1],\n",
+    "            c='r',\n",
+    "            marker='s',\n",
+    "            label='-1')\n",
+    "plt.xlim([-3, 3])\n",
+    "plt.ylim([-3, 3])\n",
+    "plt.legend(loc='best')\n",
+    "plt.tight_layout()\n",
+    "plt.show()\n"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "<matplotlib.legend.Legend at 0x1f67ac87518>"
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "text/plain": "<Figure size 432x288 with 1 Axes>",
+      "image/png": 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\n"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "svm = SVC(kernel='rbf', random_state=1, gamma=0.20, C=1.0)\n",
+    "svm.fit(X_xor, y_xor)\n",
+    "plot_decision_regions(X_xor, y_xor,classifier=svm)\n",
+    "plt.legend(loc='upper left')"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## SVR回归\n",
+    "对于回归问题，SVR的主要思想是找到一个函数，它在给定的容忍度ε内尽可能地适应训练数据，同时保持函数的平滑性。也就是说，SVR试图找到一个满足大部分数据点（在ε容忍度内）的函数，但仍然让函数尽可能的平滑。SVR通过使用类似于SVM的损失函数实现这个目标，该损失函数在ε范围内为零，超出这个范围则与超出的程度成正比。\n",
+    "\n",
+    "SVR试图找到一个函数f(x)，它满足对于每个训练样本(xi, yi)，|f(xi) - yi| ≤ ε，并且f(x)的复杂度最小。SVR的目标是使模型尽可能地平坦，同时限制模型复杂度，从而使模型的泛化能力更强。\n"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MSE: 39.03\n"
+     ]
+    }
+   ],
+   "source": [
+    "## 导入波士顿房价数据集\n",
+    "from sklearn.datasets import load_boston\n",
+    "boston = load_boston()\n",
+    "X = boston.data\n",
+    "y = boston.target\n",
+    "\n",
+    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2,random_state=0)\n",
+    "scaler = StandardScaler()\n",
+    "scaler.fit(X_train)\n",
+    "X_train_std = scaler.transform(X_train)\n",
+    "X_test_std = scaler.transform(X_test)\n",
+    "\n",
+    "svr = SVR(kernel='linear', C=1.0, epsilon=0.2)\n",
+    "svr.fit(X_train_std, y_train)\n",
+    "y_test_pred = svr.predict(X_test_std)\n",
+    "mse = mean_squared_error(y_test, y_test_pred)\n",
+    "print('MSE: %.2f' % mse)"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "<Figure size 720x432 with 1 Axes>",
+      "image/png": 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\n"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(10, 6))\n",
+    "plt.scatter(y_test, y_test_pred, color='blue')\n",
+    "plt.plot([y_test.min(), y_test.max()], [y_test.min(), y_test.max()], 'k--', lw=3)\n",
+    "plt.xlabel('True Values')\n",
+    "plt.ylabel('Predicted Values')\n",
+    "plt.title('SVR Predicted vs Actual Values')\n",
+    "plt.show()\n"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "<Figure size 720x432 with 1 Axes>",
+      "image/png": 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\n"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "\n",
+    "# 生成一个表示数据点顺序的数组\n",
+    "x = np.arange(len(y_test))\n",
+    "\n",
+    "# 绘制真实值和预测值\n",
+    "plt.figure(figsize=(10, 6))\n",
+    "plt.plot(x, y_test, label='True Values', color='blue')\n",
+    "plt.plot(x, y_test_pred, label='Predicted Values', color='red')\n",
+    "\n",
+    "plt.xlabel('Order of Data Points')\n",
+    "plt.ylabel('Values')\n",
+    "plt.title('SVR Predicted Values vs Actual Values')\n",
+    "plt.legend()\n",
+    "plt.show()\n"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "{0, 1, 2, 3, 4, 5, 6, 7, 8, 9}"
+     },
+     "execution_count": 44,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.datasets import load_digits\n",
+    "digits = load_digits()\n",
+    "X = digits.data\n",
+    "y = digits.target\n",
+    "set(y)"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "<matplotlib.image.AxesImage at 0x1f67b7b98d0>"
+     },
+     "execution_count": 48,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "text/plain": "<Figure size 432x288 with 1 Axes>",
+      "image/png": "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\n"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "image = digits.images[2]\n",
+    "plt.imshow(image, cmap=plt.cm.gray_r, interpolation='nearest')"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Accuracy: 0.99\n"
+     ]
+    }
+   ],
+   "source": [
+    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2,random_state=0)\n",
+    "clf = SVC(kernel='rbf', C=1.0, random_state=0)\n",
+    "clf.fit(X_train, y_train)\n",
+    "y_test_pred = clf.predict(X_test)\n",
+    "print('Accuracy: %.2f' % accuracy_score(y_test, y_test_pred))"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "<Figure size 432x288 with 2 Axes>",
+      "image/png": "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\n"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "cm = confusion_matrix(y_test, y_test_pred)\n",
+    "sns.heatmap(cm, annot=True, fmt='d')\n",
+    "plt.xlabel('Predicted Label')\n",
+    "plt.ylabel('True Label')\n",
+    "plt.title('Confusion Matrix')\n",
+    "plt.show()\n"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "<Figure size 720x432 with 1 Axes>",
+      "image/png": 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\n"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "## 决策树模型\n",
+    "X = iris.data\n",
+    "y = iris.target\n",
+    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3,random_state=0)\n",
+    "## gini, entropy\n",
+    "tree = DecisionTreeClassifier(criterion='entropy', max_depth=3, random_state=0)\n",
+    "tree.fit(X_train, y_train)\n",
+    "# 绘制决策树\n",
+    "plt.figure(figsize=(10, 6))\n",
+    "plot_tree(tree, filled=True)\n",
+    "plt.show()"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Accuracy: 0.98\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": "<Figure size 720x432 with 1 Axes>",
+      "image/png": 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\n"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "## 使用gini指数划分,不设置最大深度（预剪枝），使用ccp_alpha（后剪枝）\n",
+    "tree_cart = DecisionTreeClassifier(criterion='gini', ccp_alpha=0.02, random_state=0)\n",
+    "tree_cart.fit(X_train, y_train)\n",
+    "\n",
+    "y_test_pred = tree_cart.predict(X_test)\n",
+    "print('Accuracy: %.2f' % accuracy_score(y_test, y_test_pred))\n",
+    "# 绘制决策树\n",
+    "plt.figure(figsize=(10, 6))\n",
+    "plot_tree(tree, filled=True)\n",
+    "plt.show()\n"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 80,
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "E:\\anaconda3\\lib\\site-packages\\ipykernel_launcher.py:40: MatplotlibDeprecationWarning: Using a string of single character colors as a color sequence is deprecated since 3.2 and will be removed two minor releases later. Use an explicit list instead.\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": "<Figure size 432x288 with 1 Axes>",
+      "image/png": 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\n"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "## 使用knn分类\n",
+    "# X只选取第3，4列特征\n",
+    "\n",
+    "scaler = StandardScaler()\n",
+    "scaler.fit(X_train)\n",
+    "X_train_std = scaler.transform(X_train)\n",
+    "X_test_std = scaler.transform(X_test)\n",
+    "X_train_std = X_train_std[:, [2, 3]]\n",
+    "X_test_std = X_test_std[:, [2, 3]]\n",
+    "\n",
+    "X_combined_std = np.vstack((X_train_std, X_test_std))\n",
+    "y_combined = np.hstack((y_train, y_test))\n",
+    "knn = KNeighborsClassifier(n_neighbors=5, p=2, metric='minkowski')\n",
+    "knn.fit(X_train_std, y_train)\n",
+    "plot_decision_regions(X=X_combined_std, y=y_combined,\n",
+    "                      classifier=knn, test_idx=range(105, 150))\n",
+    "plt.xlabel('petal length [standardized]')\n",
+    "plt.ylabel('petal width [standardized]')\n",
+    "plt.legend(loc='upper left')\n",
+    "plt.tight_layout()\n",
+    "#plt.savefig('images/03_01.png', dpi=300)\n",
+    "plt.show()\n"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 73,
+   "outputs": [],
+   "source": [
+    "from collections import Counter\n",
+    "\n",
+    "## 不使用sklearn实现knn算法\n",
+    "class knnCls:\n",
+    "    def __init__(self, k=5):\n",
+    "        self.k = k\n",
+    "    def fit(self, X, y):\n",
+    "        self.X_train = X\n",
+    "        self.y_train = y\n",
+    "\n",
+    "    def predict(self, X):\n",
+    "        predicted_labels = [self._predict(X) for x in X]\n",
+    "        return np.array(predicted_labels)\n",
+    "    def _predict(self, x):\n",
+    "        \"\"\"计算X中每个样本点的最近邻并选择预测值\"\"\"\n",
+    "        # 计算距离\n",
+    "        distances = [np.linalg.norm(x-x_train) for x_train in self.X_train]\n",
+    "        # 选择最近的k个点的索引值\n",
+    "        k_idx = np.argsort(distances)[:self.k]\n",
+    "        # 获取k个点的标签\n",
+    "        k_neighbor_labels = [self.y_train[i] for i in k_idx]\n",
+    "        # 选择出现次数最多的标签\n",
+    "        most_common = Counter(k_neighbor_labels).most_common(1)\n",
+    "        return most_common[0][0]"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 82,
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Accuracy: 0.40\n"
+     ]
+    }
+   ],
+   "source": [
+    "my_knn = knnCls(5)\n",
+    "my_knn.fit(X_train_std, y_train)\n",
+    "y_test_pred = my_knn.predict(X_test_std)\n",
+    "print('Accuracy: %.2f' % accuracy_score(y_test, y_test_pred))\n"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 88,
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "E:\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
+      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
+      "\n",
+      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
+      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
+      "Please also refer to the documentation for alternative solver options:\n",
+      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
+      "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "LogisticRegression()\n",
+      "Classification Report:\n",
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "           0       1.00      1.00      1.00        10\n",
+      "           1       1.00      1.00      1.00         9\n",
+      "           2       1.00      1.00      1.00        11\n",
+      "\n",
+      "    accuracy                           1.00        30\n",
+      "   macro avg       1.00      1.00      1.00        30\n",
+      "weighted avg       1.00      1.00      1.00        30\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": "<Figure size 720x504 with 2 Axes>",
+      "image/png": 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\n"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "====================================\n",
+      "DecisionTreeClassifier()\n",
+      "Classification Report:\n",
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "           0       1.00      1.00      1.00        10\n",
+      "           1       1.00      1.00      1.00         9\n",
+      "           2       1.00      1.00      1.00        11\n",
+      "\n",
+      "    accuracy                           1.00        30\n",
+      "   macro avg       1.00      1.00      1.00        30\n",
+      "weighted avg       1.00      1.00      1.00        30\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": "<Figure size 720x504 with 2 Axes>",
+      "image/png": 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\n"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "====================================\n",
+      "SVC()\n",
+      "Classification Report:\n",
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "           0       1.00      1.00      1.00        10\n",
+      "           1       1.00      1.00      1.00         9\n",
+      "           2       1.00      1.00      1.00        11\n",
+      "\n",
+      "    accuracy                           1.00        30\n",
+      "   macro avg       1.00      1.00      1.00        30\n",
+      "weighted avg       1.00      1.00      1.00        30\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": "<Figure size 720x504 with 2 Axes>",
+      "image/png": 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\n"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "====================================\n",
+      "KNeighborsClassifier()\n",
+      "Classification Report:\n",
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "           0       1.00      1.00      1.00        10\n",
+      "           1       1.00      1.00      1.00         9\n",
+      "           2       1.00      1.00      1.00        11\n",
+      "\n",
+      "    accuracy                           1.00        30\n",
+      "   macro avg       1.00      1.00      1.00        30\n",
+      "weighted avg       1.00      1.00      1.00        30\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": "<Figure size 720x504 with 2 Axes>",
+      "image/png": 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\n"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "====================================\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.metrics import accuracy_score, confusion_matrix, classification_report\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "import numpy as np\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "\n",
+    "class BaseEstimator:\n",
+    "    def __init__(self, X, y, test_size=0.2, random_state=42):\n",
+    "        self.accuracy = None\n",
+    "        self.pred = None\n",
+    "        self.model = None\n",
+    "        self.X_train, self.X_test, self.y_train, self.y_test = train_test_split(X, y, test_size=test_size, random_state=random_state)\n",
+    "\n",
+    "    def train_and_predict(self, model):\n",
+    "        self.model = model\n",
+    "        self.model.fit(self.X_train, self.y_train)\n",
+    "        self.pred = self.model.predict(self.X_test)\n",
+    "        self.accuracy = self.model.score(self.X_test, self.y_test)\n",
+    "        return self.accuracy\n",
+    "\n",
+    "    def evaluate(self):\n",
+    "        if self.model is None:\n",
+    "            print('Please train the model first!')\n",
+    "        else:\n",
+    "            model_name = type(self.model).__name__\n",
+    "            cm = confusion_matrix(self.y_test, self.pred)\n",
+    "            print('Classification Report:')\n",
+    "            print(classification_report(self.y_test, self.pred))\n",
+    "            # Create a heatmap for the confusion matrix for better visualization\n",
+    "            plt.figure(figsize=(10,7))\n",
+    "            sns.heatmap(cm, annot=True, cmap='Blues')\n",
+    "            plt.xlabel('Predicted')\n",
+    "            plt.ylabel('Actual')\n",
+    "            plt.title(f'Confusion Matrix for {model_name}: Accuracy of {self.accuracy:.3f}')\n",
+    "            plt.show()\n",
+    "\n",
+    "# Example of usage\n",
+    "from sklearn.datasets import load_iris\n",
+    "from sklearn.linear_model import LogisticRegression\n",
+    "from sklearn.tree import DecisionTreeClassifier\n",
+    "from sklearn.svm import SVC\n",
+    "from sklearn.neighbors import KNeighborsClassifier\n",
+    "\n",
+    "X, y = load_iris(return_X_y=True)\n",
+    "estimator = BaseEstimator(X, y)\n",
+    "\n",
+    "models = [LogisticRegression(), DecisionTreeClassifier(), SVC(), KNeighborsClassifier()]\n",
+    "for model in models:\n",
+    "    estimator.train_and_predict(model)\n",
+    "    estimator.evaluate()\n",
+    "    print(\"====================================\")\n"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
\ No newline at end of file
diff --git a/共享民宿平台担保交易房子评分的影响研究/homeworks/泰坦尼克号.ipynb b/共享民宿平台担保交易房子评分的影响研究/homeworks/泰坦尼克号.ipynb
new file mode 100644
index 0000000..f119138
--- /dev/null
+++ b/共享民宿平台担保交易房子评分的影响研究/homeworks/泰坦尼克号.ipynb
@@ -0,0 +1,2275 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "import itertools\n",
+    "from scipy import stats,integrate\n",
+    "plt.rcParams['font.sans-serif']=['SimHei']  #用来正常显示中文标签\n",
+    "plt.rcParams['axes.unicode_minus']=False   #用来正常显示负号"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": [
+    "## 1 数据预处理"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "   PassengerId  Survived  Pclass  \\\n0            1         0       3   \n1            2         1       1   \n2            3         1       3   \n3            4         1       1   \n4            5         0       3   \n\n                                                Name     Sex   Age  SibSp  \\\n0                            Braund, Mr. Owen Harris    male  22.0      1   \n1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n2                             Heikkinen, Miss. Laina  female  26.0      0   \n3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n4                           Allen, Mr. William Henry    male  35.0      0   \n\n   Parch            Ticket     Fare Cabin Embarked  \n0      0         A/5 21171   7.2500   NaN        S  \n1      0          PC 17599  71.2833   C85        C  \n2      0  STON/O2. 3101282   7.9250   NaN        S  \n3      0            113803  53.1000  C123        S  \n4      0            373450   8.0500   NaN        S  ",
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>PassengerId</th>\n      <th>Survived</th>\n      <th>Pclass</th>\n      <th>Name</th>\n      <th>Sex</th>\n      <th>Age</th>\n      <th>SibSp</th>\n      <th>Parch</th>\n      <th>Ticket</th>\n      <th>Fare</th>\n      <th>Cabin</th>\n      <th>Embarked</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1</td>\n      <td>0</td>\n      <td>3</td>\n      <td>Braund, Mr. Owen Harris</td>\n      <td>male</td>\n      <td>22.0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>A/5 21171</td>\n      <td>7.2500</td>\n      <td>NaN</td>\n      <td>S</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2</td>\n      <td>1</td>\n      <td>1</td>\n      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n      <td>female</td>\n      <td>38.0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>PC 17599</td>\n      <td>71.2833</td>\n      <td>C85</td>\n      <td>C</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>3</td>\n      <td>1</td>\n      <td>3</td>\n      <td>Heikkinen, Miss. Laina</td>\n      <td>female</td>\n      <td>26.0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>STON/O2. 3101282</td>\n      <td>7.9250</td>\n      <td>NaN</td>\n      <td>S</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>4</td>\n      <td>1</td>\n      <td>1</td>\n      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n      <td>female</td>\n      <td>35.0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>113803</td>\n      <td>53.1000</td>\n      <td>C123</td>\n      <td>S</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>5</td>\n      <td>0</td>\n      <td>3</td>\n      <td>Allen, Mr. William Henry</td>\n      <td>male</td>\n      <td>35.0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>373450</td>\n      <td>8.0500</td>\n      <td>NaN</td>\n      <td>S</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# 导入，读取数据\n",
+    "titanic_df = pd.read_csv('train.csv')\n",
+    "titanic_df.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": [
+    "数据集的属性列表：\n",
+    "- PassengerId – 乘客编号，每个ID对应与唯一的乘客\n",
+    "- Pclass – 船舱等级，1 一等舱，2 二等舱，3 三等舱\n",
+    "- Sex – 乘客性别，0 男性，1 女性\n",
+    "- Age – 乘客年龄\n",
+    "- SibSp - 同辈亲属数量（比如兄弟姐妹或者配偶）\n",
+    "- Parch – 非同辈亲属数量（比如父母和子女）\n",
+    "- Fare – 船票价格\n",
+    "- Embarked – 上船的港口，0S (South Ampton), 1C (Cherbourg), 2Q (Queenstown)\n",
+    "\n",
+    "数据集的标签：\n",
+    "- Survived – 该乘客是否幸存， 0 遇难，1幸存"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 891 entries, 0 to 890\n",
+      "Data columns (total 12 columns):\n",
+      " #   Column       Non-Null Count  Dtype  \n",
+      "---  ------       --------------  -----  \n",
+      " 0   PassengerId  891 non-null    int64  \n",
+      " 1   Survived     891 non-null    int64  \n",
+      " 2   Pclass       891 non-null    int64  \n",
+      " 3   Name         891 non-null    object \n",
+      " 4   Sex          891 non-null    object \n",
+      " 5   Age          714 non-null    float64\n",
+      " 6   SibSp        891 non-null    int64  \n",
+      " 7   Parch        891 non-null    int64  \n",
+      " 8   Ticket       891 non-null    object \n",
+      " 9   Fare         891 non-null    float64\n",
+      " 10  Cabin        204 non-null    object \n",
+      " 11  Embarked     889 non-null    object \n",
+      "dtypes: float64(2), int64(5), object(5)\n",
+      "memory usage: 83.7+ KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "# 观察数据完整性\n",
+    "titanic_df.info()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": [
+    "有三个属性存在数据缺失：**Age，Cabin 和 Embarked\n",
+    "**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "       PassengerId    Survived      Pclass         Age       SibSp  \\\ncount   891.000000  891.000000  891.000000  714.000000  891.000000   \nmean    446.000000    0.383838    2.308642   29.699118    0.523008   \nstd     257.353842    0.486592    0.836071   14.526497    1.102743   \nmin       1.000000    0.000000    1.000000    0.420000    0.000000   \n25%     223.500000    0.000000    2.000000   20.125000    0.000000   \n50%     446.000000    0.000000    3.000000   28.000000    0.000000   \n75%     668.500000    1.000000    3.000000   38.000000    1.000000   \nmax     891.000000    1.000000    3.000000   80.000000    8.000000   \n\n            Parch        Fare  \ncount  891.000000  891.000000  \nmean     0.381594   32.204208  \nstd      0.806057   49.693429  \nmin      0.000000    0.000000  \n25%      0.000000    7.910400  \n50%      0.000000   14.454200  \n75%      0.000000   31.000000  \nmax      6.000000  512.329200  ",
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>PassengerId</th>\n      <th>Survived</th>\n      <th>Pclass</th>\n      <th>Age</th>\n      <th>SibSp</th>\n      <th>Parch</th>\n      <th>Fare</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>count</th>\n      <td>891.000000</td>\n      <td>891.000000</td>\n      <td>891.000000</td>\n      <td>714.000000</td>\n      <td>891.000000</td>\n      <td>891.000000</td>\n      <td>891.000000</td>\n    </tr>\n    <tr>\n      <th>mean</th>\n      <td>446.000000</td>\n      <td>0.383838</td>\n      <td>2.308642</td>\n      <td>29.699118</td>\n      <td>0.523008</td>\n      <td>0.381594</td>\n      <td>32.204208</td>\n    </tr>\n    <tr>\n      <th>std</th>\n      <td>257.353842</td>\n      <td>0.486592</td>\n      <td>0.836071</td>\n      <td>14.526497</td>\n      <td>1.102743</td>\n      <td>0.806057</td>\n      <td>49.693429</td>\n    </tr>\n    <tr>\n      <th>min</th>\n      <td>1.000000</td>\n      <td>0.000000</td>\n      <td>1.000000</td>\n      <td>0.420000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>25%</th>\n      <td>223.500000</td>\n      <td>0.000000</td>\n      <td>2.000000</td>\n      <td>20.125000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>7.910400</td>\n    </tr>\n    <tr>\n      <th>50%</th>\n      <td>446.000000</td>\n      <td>0.000000</td>\n      <td>3.000000</td>\n      <td>28.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>14.454200</td>\n    </tr>\n    <tr>\n      <th>75%</th>\n      <td>668.500000</td>\n      <td>1.000000</td>\n      <td>3.000000</td>\n      <td>38.000000</td>\n      <td>1.000000</td>\n      <td>0.000000</td>\n      <td>31.000000</td>\n    </tr>\n    <tr>\n      <th>max</th>\n      <td>891.000000</td>\n      <td>1.000000</td>\n      <td>3.000000</td>\n      <td>80.000000</td>\n      <td>8.000000</td>\n      <td>6.000000</td>\n      <td>512.329200</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# 观察数据基本统计信息\n",
+    "titanic_df.describe()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": [
+    "<hr>\n",
+    "\n",
+    "## 1.1 属性分析\n",
+    "\n",
+    "- PassengerId – 随机赋予的乘客编号；理论上与该乘客是否存活不存在任何关联关系，所以**删除PassengerId属性**\n",
+    "- Name - 乘客姓名，同上，无关属性\n",
+    "- Pclass – 船舱等级；与是否存活下来有关\n",
+    "- Sex – 乘客性别；与是否存活下来有关\n",
+    "- Age – 乘客年龄；与是否存活下来有关\n",
+    "- SibSp - 同辈亲属数量；与是否存活下来有关\n",
+    "- Parch – 非同辈亲属数量；与是否存活下来有关\n",
+    "- Fare – 船票价格；与是否存活下来有关\n",
+    "- Embarked – 上船的港口，0S (South Ampton), 1C (Cherbourg), 2Q (Queenstown)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "   Survived  Pclass     Sex   Age  SibSp  Parch            Ticket     Fare  \\\n0         0       3    male  22.0      1      0         A/5 21171   7.2500   \n1         1       1  female  38.0      1      0          PC 17599  71.2833   \n2         1       3  female  26.0      0      0  STON/O2. 3101282   7.9250   \n3         1       1  female  35.0      1      0            113803  53.1000   \n4         0       3    male  35.0      0      0            373450   8.0500   \n\n  Cabin Embarked  \n0   NaN        S  \n1   C85        C  \n2   NaN        S  \n3  C123        S  \n4   NaN        S  ",
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Survived</th>\n      <th>Pclass</th>\n      <th>Sex</th>\n      <th>Age</th>\n      <th>SibSp</th>\n      <th>Parch</th>\n      <th>Ticket</th>\n      <th>Fare</th>\n      <th>Cabin</th>\n      <th>Embarked</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0</td>\n      <td>3</td>\n      <td>male</td>\n      <td>22.0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>A/5 21171</td>\n      <td>7.2500</td>\n      <td>NaN</td>\n      <td>S</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1</td>\n      <td>1</td>\n      <td>female</td>\n      <td>38.0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>PC 17599</td>\n      <td>71.2833</td>\n      <td>C85</td>\n      <td>C</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>1</td>\n      <td>3</td>\n      <td>female</td>\n      <td>26.0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>STON/O2. 3101282</td>\n      <td>7.9250</td>\n      <td>NaN</td>\n      <td>S</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>1</td>\n      <td>1</td>\n      <td>female</td>\n      <td>35.0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>113803</td>\n      <td>53.1000</td>\n      <td>C123</td>\n      <td>S</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>0</td>\n      <td>3</td>\n      <td>male</td>\n      <td>35.0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>373450</td>\n      <td>8.0500</td>\n      <td>NaN</td>\n      <td>S</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# 从数据集中删除PassengerId，Name变量\n",
+    "titanic_df.drop(['PassengerId','Name'], axis=1, inplace=True)\n",
+    "# 观察更新后的数据\n",
+    "titanic_df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "<Figure size 576x432 with 1 Axes>",
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAe4AAAFvCAYAAACM+ygrAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8QVMy6AAAACXBIWXMAAAsTAAALEwEAmpwYAAAd8klEQVR4nO3dfZhdZXnv8e90kkAyE0hoRiCeola5blF5sViKECEiFNFyWqjF1la0Fm2FcHlqS4GCFBDFSrVSoIItitAjNgfRgtCqKBgQiRpehFrugpiAvGiAhDHklcmcP9Yakg6zZyaTvWfn2fl+rmuu2fvZ6+WemZX89vOstZ/VNTg4iCRJKsMvtbsASZI0fga3JEkFMbglSSqIwS1JUkEMbkmSCmJwS5JUkCntLmA8Nm7cODgw4MfWJEnbj6lTu58E+oa3FxHcAwODrFy5ut1lSJI0afr6Zi4bqd2hckmSCmJwS5JUEINbkqSCFHGOW5Kk0QwMPMeKFct57rn17S5li02ZMo3Zs/vo7h5fJBvckqTirVixnB13nEFPz250dXW1u5xxGxwc5Nln+1mxYjlz5uw+rnUcKpckFe+559bT07NTUaEN0NXVRU/PTls0UmBwS5I6QmmhPWRL6za4JUkdZ82aNZx++l+yYMH7+PCHP8Tg4NZP4nXhhZ/YqvUvv/wy7rzzB1tdh8EtSeo4X/vaDbzmNXtz8cWfYerUadx//4+2epsf+MBfNKGyrWdwS5I6zpw5L2LRolt45JGHOe20D3H77bc939u98cbrufHG6wFYsOB9XHLJhXzwgwsAuPLKz7Jo0S0AXHXVFXzzm994fpsLFrzv+ccjLbd27VrOPPOvOOmk9/KJT/wtAP39/XzgAydy8sl/yl13LWnKz2ZwS5I6zrx5h3Dcce/gjDNO4VOfuoCNGzeOuNyPfnQfr3nN3nzykxcDMH/+m7jjju8AcPfdd3LQQfNGXG+k5a677lpe9rKXc8kl/8RTTz3Jgw8+wHXXXcvBB8/joosuY8qU5nyQy+CWJHWcRx55mAMPfD2f+9wXWLFiBV//+r8//9q6deuef/yyl72cQw897Pnne+zxEp58cjnPPruK3t4epk+fPuL2R1ru4YeXsWjRLSxY8D4ee+xRli//OY8//hgvf/meALzyla9qys9mcEuSOs7113+FRYtuobu7m1/91Zdz1FG/xerV1c2qFi++/fnlRgrmvfZ6NQsXXs3BBx866j6GL7fHHi/huOP+gIsv/gzvfe/72XXX3dhtt91ZuvQhAB544L+b8rN1/AQsvTvtyPQdpra7jEmzZt0GVvWvbXcZktRWxx33B5xzzpnccMN19Pb2csIJ7+fv//7jfPe7t7HTTjuPuu78+W/ixBNP4Etfun6Lljv66GP46EfP4cYbr6enp4e/+ZvzOProY/jQh07l5pu/ycDAc0352bqacYl8q23YMDA40dt69vXNZP9TrmxyRduuJRccz/Llv2h3GZI0qZ54Yhm77faSdpcxYSPV39c3cwnwuuHLOlQuSVJBDG5JkgpicEuSVBCDW5KkghjckiQVpOM/DiZJ2r41+2PB7f7YrcEtSepo03eY2tSPBS+54HhWMXZwn3/+uSxbtpQDDzyId7/7hKbt36FySZKa7Nvf/hYbN27k0ks/y5NPPskjjzzctG0b3JIkNdlddy3hsMMOB2D//V/HD394d9O2bXBLktRka9asYc6cFwHQ09PL008/3bRtG9ySJDXZ9Okznr8L2Zo1qxkcHPm2ohNhcEuS1GQRr3x+ePzBBx9gt93mNm3bXlUuSepoa9ZtYMkFxzd1e2M55JD5nHjie3nqqeXcccftXHbZFU3bv8EtSepoq/rXjuvjW83U09PLRRddxve/v5h3vON4ent7m7Ztg1uSpBbYaaedeNObjmj6dlsS3BGxK/AfmfnaiLgc2Au4MTPPq19/QZskSRpbqy5O+ztgekQcC3Rn5kHA3IjYc6S2FtUgSVLHaXpwR8RhwLPAE8B8YGH90reAeQ3aJEnSODR1qDwipgFnAb8DfAXoAR6tX+4HXtGgbVTd3V3MmjWjmaV2NH9XkrY3P/tZF93dI/dFd+7tpnvajk3b18D6tTyzamDM5Z5++in++q9P4dJLPzvmsl1d48+5Zp/jPg24JDNXRgTAKmB6/VovVQ9/pLZRDQwMsnLl6gkV1Nc3c0LrlWyivytJKtXg4CADAyNPctI9rYeHz927afva46x7GRj4xajL9Pf3c845Z7FmzZqGdW1ucPCFOdcov5o9VH44cFJE3ALsBxzNpqHwfYGlwJIR2iRJ6hjd3b/Eueeez4wZPU3fdlN73Jl5yNDjOrz/N3BrRMwFjgIOBAZHaJMkqWP09DTvc9vDtWzK08ycn5n9VBej3QG8MTOfGamtVTVIktRpWj4BS2auYNNV5A3bJEnS2LzJiCRJBXHKU0lSRxtYv5Y9zrq3qdsbr4sv/kzT9jvE4JYkdbSnn9kAjH1Hr1I4VC5JUkEMbklSRxgcHGx3CROypXUb3JKk4k2ZMo1nn+0vLrwHBwd59tl+pkyZNu51PMctSSre7Nl9rFixnFWrVra7lC02Zco0Zs/uG//yLaxFkqRJ0d09hTlzdm93GZPCoXJJkgpicEuSVBCDW5KkghjckiQVxOCWJKkgBrckSQUxuCVJKojBLUlSQQxuSZIKYnBLklQQg1uSpIIY3JIkFcTgliSpIAa3JEkFMbglSSqIwS1JUkEMbkmSCmJwS5JUEINbkqSCTGnFRiNiF2B/4K7MfLIV+5AkaXvU9OCOiN2Ba4GvAp+MiMOAJcBD9SInZ+a9EXEO8BZgcWYuaHYdkiR1olb0uF8N/Hlm3hERs4H3AFdn5qlDC0TE64B5wAHAqRFxeGbe1IJaJEnqKE0P7qEAjohDqIL5GuCYiDgYWAa8CzgE+FJmDkbETcDRQMPg7u7uYtasGc0utWP5u5KkztWqc9xdwNuBDcA9wKGZ+XhEXEI1PN4D/LhevB/YdbTtDQwMsnLl6gnV0tc3c0LrlWyivytJ0rajUX615KryzBzMzJOA24HdMvPx+qX7gT2BVcD0uq23VXVIktRpmh6YEXFqRBxfP50FXBoR+0ZEN3AMVQ98CdU5boB9gaXNrkOSpE7Uip7uZ4B3RsQioJvqfPZVwN3Ad+tz4LcBr42IC4HTgKtbUIckSR2nFRenrQCOGNa8z7BlNkbE4cBbgQsz8yfNrkOSpE7UkovTxiMz11BdcS5JksbJi8IkSSqIwS1JUkEMbkmSCmJwS5JUEINbkqSCGNySJBXE4JYkqSAGtyRJBTG4JUkqiMEtSVJBDG5JkgpicEuSVBCDW5KkghjckiQVxOCWJKkgBrckSQUxuCVJKojBLUlSQQxuSZIKYnBLklQQg1uSpIIY3JIkFcTgliSpIAa3JEkFMbglSSqIwS1JUkGmtGKjEbELsD9wV2Y+2Yp9SJK0PWp6cEfE7sC1wFeBT0bEYcDHgL2AGzPzvHq5y4e3SZKk0bViqPzVwJ9n5keArwGHAd2ZeRAwNyL2jIhjh7e1oA5JkjpO03vcmXkTQEQcAhwA7AIsrF/+FjAPeO0IbQ80uxZJkjpNq85xdwFvBzYAXcCj9Uv9wCuAnhHaGuru7mLWrBmtKLUj+buSpM7VkuDOzEHgpIj4MPA2YHr9Ui/V8PyqEdoaGhgYZOXK1ROqpa9v5oTWK9lEf1eSpG1Ho/xq+jnuiDg1Io6vn86iujBtXv18X2ApsGSENkmSNIZW9Lg/AyyMiBOA+4CvAIsiYi5wFHAgMAjcOqxNkiSNoRUXp60Ajti8LSLm120fz8xnGrVJkqTRteQc93B1mC8cq02SJI3OKU8lSSqIwS1JUkEMbkmSCmJwS5JUEINbkqSCGNySJBXE4JYkqSAGtyRJBTG4JUkqiMEtSVJBDG5JkgpicEuSVBCDW5KkghjckiQVxOCWJKkgBrckSQUxuCVJKojBLUlSQQxuSZIKYnBLklQQg1uSpIIY3JIkFcTgliSpIAa3JEkFMbglSSqIwS1JUkEMbkmSCmJwS5JUkCnN3mBE7Ax8sd72KuDtwIPAQ/UiJ2fmvRFxDvAWYHFmLmh2HZIkdaJW9Lj/EPhkZh4BPAGcBlydmfPrr3sj4nXAPOAA4KcRcXgL6pAkqeM0vcedmf+42dM+4BHgmIg4GFgGvAs4BPhSZg5GxE3A0cBNjbbZ3d3FrFkzml1qx/J3JUmdq+nBPSQiXg/MBr4BfC4zH4+IS6iGx3uAH9eL9gO7jratgYFBVq5cPaE6+vpmTmi9kk30dyVJ2nY0yq+WBHdE7AJcBPwu8ERmrqtfuh/Yk+rc9/S6rRcvkpMkaVyaHpgRMQ1YCJyemcuAqyJi34joBo4B7gGWUJ3jBtgXWNrsOiRJ6kSt6On+CbA/cEZE3AL8J3AVcDfw3cy8CbgNeG1EXEh98VoL6pAkqeO04uK0TwOfHtZ8zrBlNtZXkr8VuDAzf9LsOiRJ6kQtuzhtLJm5BrimXfuXJKlEXhQmSVJBDG5JkgoyoeCOiHljLyVJkpptXMEdEd8Y1nR+C2qRJEljGPXitIjYB3gt8OKIOL5u7gHWtrowSZL0QmP1uLtG+P4UcFzLKpIkSQ2N2uPOzHuAeyIiMvPKSapJkiQ1MN7PcX8qIn4fmDbUYJBLkjT5xntV+X8A/4tqqHzoS5IkTbLx9rj7M/PvWlqJJEka03iD+7aIuBq4EngWIDMXtawqSZI0ovEG9waqe2n/OtUw+SBgcEuSNMnGG9xLqcJ6KLQlSVIbbMmUp13AdOBY4JDWlCNJkkYzrh53Zn5+s6eXRsQ/tqgeSZI0inEFd0Rs3sPeCXh1a8qRJEmjGe857jey6dz2euDE1pQjSZJGM97g/ijwHmAv4D4gW1aRtsrgc+vo65vZ7jImxcD6tTz9zIZ2lyFJk2q8wf1Z4L+BfwcOBD4HvLNVRWniuqbswMPn7t3uMibFHmfdS/VJRUnafow3uH8lM4eC+msR8e1WFSRJkhobb3A/FhGnA4uB1wOPtq4kSZLUyHg/x/1nVCH/NqAf+NOWVSRJkhoab3D/C/BwZp4IzKQ65y1JkibZeIN79tAkLJn5UWBO60qSJEmNjPcc908j4lTge1Q3Gvl560qSJEmNjLfH/W5gNdU57jXA8a0qSJIkNTbeucrXARe1uBZJkjSG8Q6Vj1tE7Ax8sd72KuDtwKepZl27MTPPq5e7fHibJEka3Zbc1nO8/hD4ZGYeATwB/D7QnZkHAXMjYs+IOHZ4WwvqkCSp4zS9x52Zm9/ysw/4I+BT9fNvAfOA1wILh7U90Gib3d1dzJo1o9mlqgN4XEja3jQ9uIdExOuB2cBSNs201g+8AugZoa2hgYFBVq5cPaE6tpcbbmyvJnpcSNK2rlF+tWKonIjYhepitvdQneeeXr/UW+9zpDZJkjSGpgdmREyjGgY/PTOXAUuohsIB9qXqgY/UJkmSxtCKofI/AfYHzoiIM6hvARoRc4GjqG4LOgjcOqxNkiSNoRUXp32a6uNfz4uI64AjgI9n5jN12/zhbZIkaXQtuzhtc5m5gk1XkTdskyRJo/OiMEmSCmJwS5JUEINbkqSCGNySJBXE4JYkqSAGtyRJBTG4JUkqiMEtSVJBDG5JkgpicEuSVBCDW5KkghjckiQVxOCWJKkgBrckSQUxuCVJKojBLUlSQQxuSZIKYnBLklQQg1uSpIIY3JIkFcTgliSpIAa3JEkFMbglSSqIwS1JUkEMbkmSCmJwS5JUEINbkqSCTGnFRiNiV+CazHxDRLwYWAw8WL/8e5m5PCIuB/YCbszM81pRhyRJnabpPe6ImA18Huipm34D+Ehmzq+/lkfEsUB3Zh4EzI2IPZtdhyRJnagVQ+UDwNuB/vr5gcCJEfHdiPj7um0+sLB+/C1gXgvqkCSp4zR9qDwz+wEiYqjp34EPZ+YvIuKGiNiHqjf+aP16P/CK0bbZ3d3FrFkzml2qOoDHhaTtTUvOcQ9ze2auqx/fD+wJrAKm1229jNHzHxgYZOXK1RPaeV/fzAmtpzJM9LiQpG1do/yajKvKvxYRu0fEDOBI4D5gCZuGx/cFlk5CHZIkFW8yetznADcD64FLMzMj4nHg1oiYCxxFdR5ckiSNoWXBnZnz6+83A68c9lp/RMwHjgA+npnPtKoOSZI6yWT0uEeUmSvYdGW5JEkaB2dOkySpIAa3JEkFMbglSSqIwS1JUkEMbkmSCmJwS5JUEINbkqSCGNySJBXE4JYkqSAGtyRJBTG4JUkqiMEtSVJBDG5JkgpicEuSVBCDW5KkghjckiQVxOCWJKkgBrckSQUxuCVJKojBLUlSQQxuSZIKYnBLklQQg1uSpIIY3JIkFcTgliSpIAa3JEkFMbglSSqIwS1JUkGmtGKjEbErcE1mviEipgJfBnYB/jkzPztSWyvqkCSp0zS9xx0Rs4HPAz1108nADzLzIOC3ImJmgzZJkjSGVvS4B4C3A/9WP58PnFY/vh14XYO2mxttsLu7i1mzZrSgVJXO40LS9qbpwZ2Z/QARMdTUAzxaP+4Hdm3Q1tDAwCArV66eUD19fXbmO9lEjwtJ2tY1yq/JuDhtFTC9ftxb73OkNkmSNIbJCMwlwLz68b7A0gZtkiRpDC25qnyYzwM3RsQbgFcBi6mGyYe3SZKkMbQsuDNzfv19WUQcQdXDPiszB4CR2iRtg3bZeSrd03ZsdxmTYmD9Wp5+ZkO7y5BGNRk9bjLzMWDhWG2Stj3d03bk4XP3bncZk2KPs+4FDG5t27woTJKkghjckiQVxOCWJKkgBrckSQUxuCVJKojBLUlSQQxuSZIKYnBLklQQg1uSpIIY3JIkFcTgliSpIJMyV7nUSXp32pHpO0xtdxmStlMGt7SFpu8wlf1PubLdZUyaJRcc3+4SJG3GoXJJkgpicEuSVBCDW5KkghjckiQVxOCWJKkgBrckSQUxuCVJKojBLUlSQQxuSZIKYnBLklQQg1uSpIIY3JIkFcTgliSpIAa3JEkFafltPSNiCvBQ/QVwMvA24C3A4sxc0OoaJEnqFJPR494HuDoz52fmfGAHYB5wAPDTiDh8EmqQJKkjtLzHDRwIHBMRBwPLgHuAL2XmYETcBBwN3DTaBrq7u5g1a0brK1VxPC7UbB5T2tZNRnB/Hzg0Mx+PiEuA6UDWr/UDu461gYGBQVauXD2hnff1zZzQeirDRI+LreEx1dnacUxJI2n0f81kBPcPM3Nd/fh+YBpVeAP04gVykiSN22QE91UR8RHgPuAY4Baqc9xfBPYFlk5CDZK0zerdaUem7zC13WVMijXrNrCqf227yyjaZAT3ucAXgC7gOuA84NaIuBB4c/0lSdut6TtMZf9Trmx3GZNiyQXHswqDe2u0PLgz8z6qK8ufV19J/lbgwsz8SatrkCSpU0xGj/sFMnMNcE079i1JUsm8MEySpIIY3JIkFcTgliSpIAa3JEkFMbglSSqIwS1JUkEMbkmSCmJwS5JUkLZMwCJJ2j4NPrduu7rD3sD6tTz9zIambtPgliRNmq4pO/DwuXu3u4xJs8dZ9wLNDW6HyiVJKojBLUlSQQxuSZIKYnBLklQQg1uSpIIY3JIkFcTgliSpIAa3JEkFMbglSSqIwS1JUkEMbkmSCmJwS5JUEINbkqSCGNySJBXE4JYkqSAGtyRJBTG4JUkqyJR27jwiLgf2Am7MzPPaWYskSSVoW487Io4FujPzIGBuROzZrlokSSpFO4fK5wML68ffAua1rxRJksrQNTg42JYd18Pk/5CZ90TEbwK/lpkfa7D4cmDZ5FUnSVLbvQToG97YznPcq4Dp9eNeRu/9v6BwSZK2R+0cKl/CpuHxfYGl7StFkqQytLPH/RXg1oiYCxwFHNjGWiRJKkLbznEDRMRs4AhgUWY+0bZCJEkqRFuDW5IkbRlnTpMkqSBtnTmtk0VED/AvwC7Aw8DxmTnu4Y2I2A8gM++un58N3JKZt4xj3aF9zsnMtVtY9//Yr8oRER8G3gT8DPgx8NXxHC8jbOdsxnmsSZp89rhb553AdzPzUGAd8LotXH+/+msijgB2AA6ZwLpbs1+1SUQcBLwBOBj4OvC+9lakUkVEb0R8OSK+ExGfj4hJ6eBFxH5DHQeNzh536zwKvCsivpyZJwBExA7AFcBc4KfAHwN/Td27iYh31+sGcEy9zjsz8011+xERcQ6wM/DmUS7oezNwSf396xGxBPg5sB7YDbiaKpx3AnYF7srMBRFx/vD9jlLzVKqP841ViybHkVRz/g9GxNeAExh2vAD9wJXAi4B7M/MkgIi4Bfg+sE9mHllv74P1uo8Df5iZAxFxEdVxsxI4HvhtgMy8IiLmA/Mz8+x6e/8G/HFm7hMRXVSjTy+n+nexLDM/2MpfhrbKycADmXlMRHwROA74wiTsd7/6+92TsK+iGdwtkpnXR8R04NqIuBn4c+C9wH2Z+QcR8TfAexqse3pEZP34is1eekVmHhoRfwUcRuN/TK+nCtVv1s9nAL8H3AscDpxZt1+TmV+IiBsiYv8G+21U83hr0eTYFfgBQGY+FBHXA3sN+xu9iOpveXZEXBsR+2TmD6k+ivkPmXnKZtv7QWaeGxGXAUdHxHPAjpn5hoh4F3AqcH+DWnYHBjNzn/r5bOBFmXlgRNyZmb/b7B9eTfUbwD/Xj28Dfj0iHhvWuVjIsDeBETFjhLazGfYmH3gG+H9UHYcnqd4YfJjxdRq+A7yV6v+y/YDLge8N30endyQcKm+R+qYp/0F1cPUBfwS8ClhcL7KY6s5om5vO6K6sv/8cmNZgv/sAc4BrgJdGxK8AP8vMVVTTxg4AXfXiS+rvPwRe2mCfjWoesxZNqn6qGQiJiAOAU3jh3yiAY+oe8a8CL65fvy8zrx22vaG/+Z1UPeUtOXafAf5hs+ergR0iYjHwf7f0B9Okmwk8Wz9+lipgh3sf1XFzCLB7/f/OSG1Qv8mnenN/GNWxtLFe7jNAb2aeDnwM+NhmI4xDnYZDgf+m6jT8hGp06XvAb1IdnyPto6MZ3K1zAnBMZg4A9wE7Av/JpolmDqyfr6f6hwLVu9Eha6h6ytRDjbDpH9NojgQ+mpnzqf7zPHKUZQ+ov+8HPNhgvyPVPN5aNHm+Q3VtA8ChVH/H4X+jBD5VHxtnUl3ACNX0w8PtX3/fh2pWw7GO3aM2W3d1Zm7c7PkBwJcz8zcy8xPj/5HUJr8AeiPit4Ffo3ojNmToDdpIbwIbvTEc/gbyTuC+iPg6cDTVG7uRjPRm8U6qHvoNVKOIQ52P7aojYXC3zoXAu+uD+ADgKqrhp1dHxCJgT6phoOuAUyLiUuCpzdb/BnBsRHyH6qKj8TqS6m5r1N/fPMqyv1Vv//7MvKfBfkeqWdue64CHIuJ2qr/b50ZY5p+Ao+q/5Z8Bj4yyvTdExLephuD/LTNvANZExK3A7wIXUB1fvxcRlwDdo2zrfqpz5jdHxJciYkuOZ02+71HdvXEl8P76+/DOxUhvAhu9MRz+BnJf4DuZ+ZtUp1GGjofxdBruAt5I9f/UkWzqcW9XHQknYNlORcQVwNmZubTNpajDRcRbqM6Jr6f6z/kLmfnF9lalRiJiJ6qLCecAr6T6dMo/Aj+i6s0uojpH/Tmqi137gXdQnYYb3vZBXnjx7VeAf6V6M7CWamTymfpjrAupevWnU/Wyr6DquT9CdY57Z+DbwGuARzLzxZt/fHFoH8OuDeo4BrckaUQR8Qrgx1syB4Vaz+CWJKkgnuOWJKkgBrckSQUxuKUOExFnR8R/RcQt9dd+Iyxzy1Zuf/4WrnNFRLx0ovuUtIkzp0md6SOZ+S/tLkJS8xncUoeLiIeAx6jmCQ/g3Lr9CqpJLv41Mz8REXOpZp7qAm7LzDPqXvJHqD7KRWb+8WbbfSPwf4C3Ud0F7wqqj+tcn5nnR8TLqGZKW83Is29JmgCHyqXOdMbQUDlVEL+LajKVvwD2rpf5LHAQ8M6ImEP1edkzqeaCPnqzbR0NXLZ5aAOvBj4OvCMzN1B97vZfM/Mg4Hci4peBv6qXeTObJvCQtJUMbqkzfSQz59ezWA3NUb+U/zlX/eLMfA74L+AlwHPAaVSz5W0etF/PzDuGbf8kqqkwf7l+HsD76zcKPVQ3hngZ8MN6H3c38WeTtmsGt7T92j8ifolqdqxHqGa5Op9qnv3NJ3gYaS7zDwJnUN3VCarpLk+r3yh8DHia6g3DqyKim029fElbyeCWOtPmQ+W7NljmJOAOqiHunwNfBS6lmvd8dUS8uMF6AGszczGwc33V+seAv6znuH8z8DOq+czPpJpXev3W/0iSwJnTJEkqij1uSZIKYnBLklQQg1uSpIIY3JIkFcTgliSpIAa3JEkFMbglSSrI/wfYciczVgrxDwAAAABJRU5ErkJggg==\n"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "titanic_df[\"Embarked\"] = titanic_df[\"Embarked\"].dropna(0)\n",
+    "\n",
+    "f, ax = plt.subplots(figsize = (8, 6))\n",
+    "sns.countplot(x=\"Embarked\", data=titanic_df,hue=\"Survived\")  # hue=\"Survived\"\n",
+    "ax.set_xticklabels([\"South Ampton\",\"Cherbourg\",\"Queenstown\"])\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "  Embarked  Survived\n0        C  0.553571\n1        Q  0.389610\n2        S  0.336957",
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Embarked</th>\n      <th>Survived</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>C</td>\n      <td>0.553571</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Q</td>\n      <td>0.389610</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>S</td>\n      <td>0.336957</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# 分析不同码头上船的乘客们的生存几率\n",
+    "titanic_df[['Embarked', 'Survived']].groupby(['Embarked'], as_index=False).mean().sort_values(by='Embarked', ascending=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": [
+    "#### Age | 存在多项缺失值，由于年龄是数值型数据，运用随机森林进行预测填充"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "from sklearn.ensemble import RandomForestRegressor"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "E:\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
+      "  warnings.warn(msg, FutureWarning)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": "<Figure size 432x288 with 1 Axes>",
+      "image/png": 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\n"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "age_df = titanic_df[[\"Age\", \"Fare\", \"Parch\", \"SibSp\", \"Pclass\"]].copy()  # .copy()用于复制原始数据 挑选数值型字段\n",
+    "\n",
+    "# 分割有缺失值的数据集\n",
+    "age_df_notnull = age_df.loc[age_df.Age.notnull()]\n",
+    "age_df_isnull = age_df.loc[age_df.Age.isnull()]\n",
+    "\n",
+    "# 利用非缺失数据建模\n",
+    "x = age_df_notnull.iloc[:, 1:]\n",
+    "y = age_df_notnull.values[:, 0]\n",
+    "x_test = age_df_isnull.values[:, 1:]\n",
+    "\n",
+    "rfr = RandomForestRegressor(n_estimators=1000, n_jobs=-1)\n",
+    "rfr.fit(x, y)\n",
+    "y_pred = rfr.predict(x_test)\n",
+    "\n",
+    "titanic_df.loc[titanic_df[\"Age\"].isnull(), \"Age\"] = y_pred\n",
+    "sns.distplot(titanic_df[\"Age\"].dropna(), kde=True, bins=50, fit=stats.gamma)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": [
+    "### 对Age属性的进一步处理尝试：划分为多个年龄段"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>AgeLevel</th>\n",
+       "      <th>Survived</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>(0.34, 20.315]</td>\n",
+       "      <td>0.428571</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>(20.315, 40.21]</td>\n",
+       "      <td>0.369732</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>(40.21, 60.105]</td>\n",
+       "      <td>0.398601</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>(60.105, 80.0]</td>\n",
+       "      <td>0.217391</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "          AgeLevel  Survived\n",
+       "0   (0.34, 20.315]  0.428571\n",
+       "1  (20.315, 40.21]  0.369732\n",
+       "2  (40.21, 60.105]  0.398601\n",
+       "3   (60.105, 80.0]  0.217391"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "titanic_df['AgeLevel'] = pd.cut(titanic_df['Age'], 4)\n",
+    "titanic_df[['AgeLevel', 'Survived']].groupby(['AgeLevel'], as_index=False).mean().sort_values(by='AgeLevel', ascending=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Survived</th>\n",
+       "      <th>Pclass</th>\n",
+       "      <th>Sex</th>\n",
+       "      <th>Age</th>\n",
+       "      <th>SibSp</th>\n",
+       "      <th>Parch</th>\n",
+       "      <th>Ticket</th>\n",
+       "      <th>Fare</th>\n",
+       "      <th>Cabin</th>\n",
+       "      <th>Embarked</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>male</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>A/5 21171</td>\n",
+       "      <td>7.2500</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>female</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>PC 17599</td>\n",
+       "      <td>71.2833</td>\n",
+       "      <td>C85</td>\n",
+       "      <td>C</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "      <td>female</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>STON/O2. 3101282</td>\n",
+       "      <td>7.9250</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>female</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>113803</td>\n",
+       "      <td>53.1000</td>\n",
+       "      <td>C123</td>\n",
+       "      <td>S</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>male</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>373450</td>\n",
+       "      <td>8.0500</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Survived  Pclass     Sex  Age  SibSp  Parch            Ticket     Fare  \\\n",
+       "0         0       3    male    1      1      0         A/5 21171   7.2500   \n",
+       "1         1       1  female    1      1      0          PC 17599  71.2833   \n",
+       "2         1       3  female    1      0      0  STON/O2. 3101282   7.9250   \n",
+       "3         1       1  female    1      1      0            113803  53.1000   \n",
+       "4         0       3    male    1      0      0            373450   8.0500   \n",
+       "\n",
+       "  Cabin Embarked  \n",
+       "0   NaN        S  \n",
+       "1   C85        C  \n",
+       "2   NaN        S  \n",
+       "3  C123        S  \n",
+       "4   NaN        S  "
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "titanic_df.loc[titanic_df['Age'] <= 20.315, 'Age'] = 0\n",
+    "titanic_df.loc[(titanic_df['Age'] > 20.315) & (titanic_df['Age'] <= 40.21), 'Age'] = 1\n",
+    "titanic_df.loc[(titanic_df['Age'] > 40.21) & (titanic_df['Age'] <= 60.105), 'Age'] = 2\n",
+    "titanic_df.loc[titanic_df['Age'] > 60.105, 'Age'] = 3\n",
+    "titanic_df['Age'] = titanic_df['Age'].astype(int)\n",
+    "titanic_df.drop(['AgeLevel'], axis=1, inplace=True)\n",
+    "titanic_df.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": [
+    "### Pclass | 船舱等级，可以看出等级越高，存活率越高，成正相关"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x432 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "titanic_df[['Pclass', 'Survived']].groupby(['Pclass'], as_index=False).mean()\n",
+    "\n",
+    "f, ax = plt.subplots(figsize = (8, 6))\n",
+    "sns.countplot(x=\"Pclass\", hue=\"Survived\", data=titanic_df)\n",
+    "ax.set_xticklabels([\"一等舱\",\"二等舱\",\"三等舱\"])\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": [
+    "### Sex | 性别，可以看出女性存活率比男性高"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "<Figure size 576x432 with 1 Axes>",
+      "image/png": "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\n"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "titanic_df[[\"Sex\", \"Survived\"]].groupby(['Sex'], as_index=False).mean()\n",
+    "\n",
+    "f, ax = plt.subplots(figsize = (8, 6))\n",
+    "sns.countplot(x=\"Sex\", hue=\"Survived\", data=titanic_df)\n",
+    "ax.set_xticklabels([\"男性\",\"女性\"])\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": [
+    "### SibSp + Parch | 家属们"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Survived</th>\n",
+       "      <th>Pclass</th>\n",
+       "      <th>Sex</th>\n",
+       "      <th>Age</th>\n",
+       "      <th>Ticket</th>\n",
+       "      <th>Fare</th>\n",
+       "      <th>Cabin</th>\n",
+       "      <th>Embarked</th>\n",
+       "      <th>IsAlone</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>male</td>\n",
+       "      <td>1</td>\n",
+       "      <td>A/5 21171</td>\n",
+       "      <td>7.2500</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>female</td>\n",
+       "      <td>1</td>\n",
+       "      <td>PC 17599</td>\n",
+       "      <td>71.2833</td>\n",
+       "      <td>C85</td>\n",
+       "      <td>C</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "      <td>female</td>\n",
+       "      <td>1</td>\n",
+       "      <td>STON/O2. 3101282</td>\n",
+       "      <td>7.9250</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>female</td>\n",
+       "      <td>1</td>\n",
+       "      <td>113803</td>\n",
+       "      <td>53.1000</td>\n",
+       "      <td>C123</td>\n",
+       "      <td>S</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>male</td>\n",
+       "      <td>1</td>\n",
+       "      <td>373450</td>\n",
+       "      <td>8.0500</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Survived  Pclass     Sex  Age            Ticket     Fare Cabin Embarked  \\\n",
+       "0         0       3    male    1         A/5 21171   7.2500   NaN        S   \n",
+       "1         1       1  female    1          PC 17599  71.2833   C85        C   \n",
+       "2         1       3  female    1  STON/O2. 3101282   7.9250   NaN        S   \n",
+       "3         1       1  female    1            113803  53.1000  C123        S   \n",
+       "4         0       3    male    1            373450   8.0500   NaN        S   \n",
+       "\n",
+       "   IsAlone  \n",
+       "0        1  \n",
+       "1        1  \n",
+       "2        0  \n",
+       "3        1  \n",
+       "4        0  "
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Family = SibSp + Parch\n",
+    "titanic_df['FamilySize'] = titanic_df['SibSp'] + titanic_df['Parch']\n",
+    "titanic_df['IsAlone'] = 0\n",
+    "titanic_df.loc[titanic_df['FamilySize'] == 1, 'IsAlone'] = 1\n",
+    "\n",
+    "# 删除原有的列 Parch & SibSp\n",
+    "titanic_df.drop(['SibSp'], axis=1, inplace=True)\n",
+    "titanic_df.drop(['Parch'], axis=1, inplace=True)\n",
+    "titanic_df.drop(['FamilySize'], axis=1, inplace=True)\n",
+    "titanic_df.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": [
+    "### Fare | 使用分位数对数据进行划分，可以得到大小基本相等的bin，转换为数值型数据"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "         FareLevel  Survived\n0   (-0.001, 7.91]  0.197309\n1   (7.91, 14.454]  0.303571\n2   (14.454, 31.0]  0.454955\n3  (31.0, 512.329]  0.581081",
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>FareLevel</th>\n      <th>Survived</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>(-0.001, 7.91]</td>\n      <td>0.197309</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>(7.91, 14.454]</td>\n      <td>0.303571</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>(14.454, 31.0]</td>\n      <td>0.454955</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>(31.0, 512.329]</td>\n      <td>0.581081</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "titanic_df['FareLevel'] = pd.qcut(titanic_df['Fare'], 4)\n",
+    "titanic_df[['FareLevel', 'Survived']].groupby(['FareLevel'], as_index=False).mean().sort_values(by='FareLevel', ascending=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "   Survived  Pclass     Sex   Age  SibSp  Parch            Ticket  Fare Cabin  \\\n0         0       3    male  22.0      1      0         A/5 21171     0   NaN   \n1         1       1  female  38.0      1      0          PC 17599     3   C85   \n2         1       3  female  26.0      0      0  STON/O2. 3101282     1   NaN   \n3         1       1  female  35.0      1      0            113803     3  C123   \n4         0       3    male  35.0      0      0            373450     1   NaN   \n\n  Embarked  男性  女性  港口S  港口C  港口Q  一等舱  二等舱  三等舱  \n0        S   0   1    0    0    1    0    0    1  \n1        C   1   0    1    0    0    1    0    0  \n2        S   1   0    0    0    1    0    0    1  \n3        S   1   0    0    0    1    1    0    0  \n4        S   0   1    0    0    1    0    0    1  ",
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Survived</th>\n      <th>Pclass</th>\n      <th>Sex</th>\n      <th>Age</th>\n      <th>SibSp</th>\n      <th>Parch</th>\n      <th>Ticket</th>\n      <th>Fare</th>\n      <th>Cabin</th>\n      <th>Embarked</th>\n      <th>男性</th>\n      <th>女性</th>\n      <th>港口S</th>\n      <th>港口C</th>\n      <th>港口Q</th>\n      <th>一等舱</th>\n      <th>二等舱</th>\n      <th>三等舱</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0</td>\n      <td>3</td>\n      <td>male</td>\n      <td>22.0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>A/5 21171</td>\n      <td>0</td>\n      <td>NaN</td>\n      <td>S</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1</td>\n      <td>1</td>\n      <td>female</td>\n      <td>38.0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>PC 17599</td>\n      <td>3</td>\n      <td>C85</td>\n      <td>C</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>1</td>\n      <td>3</td>\n      <td>female</td>\n      <td>26.0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>STON/O2. 3101282</td>\n      <td>1</td>\n      <td>NaN</td>\n      <td>S</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>1</td>\n      <td>1</td>\n      <td>female</td>\n      <td>35.0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>113803</td>\n      <td>3</td>\n      <td>C123</td>\n      <td>S</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>0</td>\n      <td>3</td>\n      <td>male</td>\n      <td>35.0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>373450</td>\n      <td>1</td>\n      <td>NaN</td>\n      <td>S</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "titanic_df.loc[titanic_df['Fare'] <= 7.91, 'Fare'] = 0\n",
+    "titanic_df.loc[(titanic_df['Fare'] > 7.91) & (titanic_df['Fare'] <= 14.454), 'Fare'] = 1\n",
+    "titanic_df.loc[(titanic_df['Fare'] > 14.454) & (titanic_df['Fare'] <= 31.0), 'Fare'] = 2\n",
+    "titanic_df.loc[titanic_df['Fare'] > 31.0, 'Fare'] = 3\n",
+    "titanic_df['Fare'] = titanic_df['Fare'].astype(int)\n",
+    "titanic_df.drop(['FareLevel'], axis=1, inplace=True)\n",
+    "titanic_df.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": [
+    "### OneHotEncoder | 将类别型变量全部onehot"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [
+    {
+     "ename": "ValueError",
+     "evalue": "columns overlap but no suffix specified: Index(['男性', '女性'], dtype='object')",
+     "output_type": "error",
+     "traceback": [
+      "\u001B[1;31m---------------------------------------------------------------------------\u001B[0m",
+      "\u001B[1;31mValueError\u001B[0m                                Traceback (most recent call last)",
+      "\u001B[1;32m<ipython-input-18-9ea17318b5e1>\u001B[0m in \u001B[0;36m<module>\u001B[1;34m()\u001B[0m\n\u001B[0;32m      2\u001B[0m \u001B[0msex_dummies_titanic\u001B[0m  \u001B[1;33m=\u001B[0m \u001B[0mpd\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mget_dummies\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mtitanic_df\u001B[0m\u001B[1;33m[\u001B[0m\u001B[1;34m'Sex'\u001B[0m\u001B[1;33m]\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m      3\u001B[0m \u001B[0msex_dummies_titanic\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mcolumns\u001B[0m \u001B[1;33m=\u001B[0m \u001B[1;33m[\u001B[0m\u001B[1;34m'男性'\u001B[0m\u001B[1;33m,\u001B[0m \u001B[1;34m'女性'\u001B[0m\u001B[1;33m]\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[1;32m----> 4\u001B[1;33m \u001B[0mtitanic_df\u001B[0m \u001B[1;33m=\u001B[0m \u001B[0mtitanic_df\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mjoin\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0msex_dummies_titanic\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0m\u001B[0;32m      5\u001B[0m \u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m      6\u001B[0m \u001B[0membark_dummies_titanic\u001B[0m  \u001B[1;33m=\u001B[0m \u001B[0mpd\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mget_dummies\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mtitanic_df\u001B[0m\u001B[1;33m[\u001B[0m\u001B[1;34m'Embarked'\u001B[0m\u001B[1;33m]\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n",
+      "\u001B[1;32mE:\\anaconda3\\lib\\site-packages\\pandas\\core\\frame.py\u001B[0m in \u001B[0;36mjoin\u001B[1;34m(self, other, on, how, lsuffix, rsuffix, sort)\u001B[0m\n\u001B[0;32m   7873\u001B[0m         \"\"\"\n\u001B[0;32m   7874\u001B[0m         return self._join_compat(\n\u001B[1;32m-> 7875\u001B[1;33m             \u001B[0mother\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0mon\u001B[0m\u001B[1;33m=\u001B[0m\u001B[0mon\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0mhow\u001B[0m\u001B[1;33m=\u001B[0m\u001B[0mhow\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0mlsuffix\u001B[0m\u001B[1;33m=\u001B[0m\u001B[0mlsuffix\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0mrsuffix\u001B[0m\u001B[1;33m=\u001B[0m\u001B[0mrsuffix\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0msort\u001B[0m\u001B[1;33m=\u001B[0m\u001B[0msort\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0m\u001B[0;32m   7876\u001B[0m         )\n\u001B[0;32m   7877\u001B[0m \u001B[1;33m\u001B[0m\u001B[0m\n",
+      "\u001B[1;32mE:\\anaconda3\\lib\\site-packages\\pandas\\core\\frame.py\u001B[0m in \u001B[0;36m_join_compat\u001B[1;34m(self, other, on, how, lsuffix, rsuffix, sort)\u001B[0m\n\u001B[0;32m   7896\u001B[0m                 \u001B[0mright_index\u001B[0m\u001B[1;33m=\u001B[0m\u001B[1;32mTrue\u001B[0m\u001B[1;33m,\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m   7897\u001B[0m                 \u001B[0msuffixes\u001B[0m\u001B[1;33m=\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mlsuffix\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0mrsuffix\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m,\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[1;32m-> 7898\u001B[1;33m                 \u001B[0msort\u001B[0m\u001B[1;33m=\u001B[0m\u001B[0msort\u001B[0m\u001B[1;33m,\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0m\u001B[0;32m   7899\u001B[0m             )\n\u001B[0;32m   7900\u001B[0m         \u001B[1;32melse\u001B[0m\u001B[1;33m:\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n",
+      "\u001B[1;32mE:\\anaconda3\\lib\\site-packages\\pandas\\core\\reshape\\merge.py\u001B[0m in \u001B[0;36mmerge\u001B[1;34m(left, right, how, on, left_on, right_on, left_index, right_index, sort, suffixes, copy, indicator, validate)\u001B[0m\n\u001B[0;32m     87\u001B[0m         \u001B[0mvalidate\u001B[0m\u001B[1;33m=\u001B[0m\u001B[0mvalidate\u001B[0m\u001B[1;33m,\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m     88\u001B[0m     )\n\u001B[1;32m---> 89\u001B[1;33m     \u001B[1;32mreturn\u001B[0m \u001B[0mop\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mget_result\u001B[0m\u001B[1;33m(\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0m\u001B[0;32m     90\u001B[0m \u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m     91\u001B[0m \u001B[1;33m\u001B[0m\u001B[0m\n",
+      "\u001B[1;32mE:\\anaconda3\\lib\\site-packages\\pandas\\core\\reshape\\merge.py\u001B[0m in \u001B[0;36mget_result\u001B[1;34m(self)\u001B[0m\n\u001B[0;32m    669\u001B[0m \u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m    670\u001B[0m         llabels, rlabels = _items_overlap_with_suffix(\n\u001B[1;32m--> 671\u001B[1;33m             \u001B[0mself\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mleft\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0m_info_axis\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0mself\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mright\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0m_info_axis\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0mself\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0msuffixes\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0m\u001B[0;32m    672\u001B[0m         )\n\u001B[0;32m    673\u001B[0m \u001B[1;33m\u001B[0m\u001B[0m\n",
+      "\u001B[1;32mE:\\anaconda3\\lib\\site-packages\\pandas\\core\\reshape\\merge.py\u001B[0m in \u001B[0;36m_items_overlap_with_suffix\u001B[1;34m(left, right, suffixes)\u001B[0m\n\u001B[0;32m   2094\u001B[0m \u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m   2095\u001B[0m     \u001B[1;32mif\u001B[0m \u001B[1;32mnot\u001B[0m \u001B[0mlsuffix\u001B[0m \u001B[1;32mand\u001B[0m \u001B[1;32mnot\u001B[0m \u001B[0mrsuffix\u001B[0m\u001B[1;33m:\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[1;32m-> 2096\u001B[1;33m         \u001B[1;32mraise\u001B[0m \u001B[0mValueError\u001B[0m\u001B[1;33m(\u001B[0m\u001B[1;34mf\"columns overlap but no suffix specified: {to_rename}\"\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0m\u001B[0;32m   2097\u001B[0m \u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m   2098\u001B[0m     \u001B[1;32mdef\u001B[0m \u001B[0mrenamer\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mx\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0msuffix\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m:\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n",
+      "\u001B[1;31mValueError\u001B[0m: columns overlap but no suffix specified: Index(['男性', '女性'], dtype='object')"
+     ]
+    }
+   ],
+   "source": [
+    "# one-hot编码\n",
+    "sex_dummies_titanic  = pd.get_dummies(titanic_df['Sex'])\n",
+    "sex_dummies_titanic.columns = ['男性', '女性']\n",
+    "titanic_df = titanic_df.join(sex_dummies_titanic)\n",
+    "\n",
+    "embark_dummies_titanic  = pd.get_dummies(titanic_df['Embarked'])\n",
+    "embark_dummies_titanic.columns = ['港口S', '港口C','港口Q']\n",
+    "titanic_df = titanic_df.join(embark_dummies_titanic)\n",
+    "\n",
+    "class_dummies_titanic  = pd.get_dummies(titanic_df['Pclass'])\n",
+    "class_dummies_titanic.columns = ['一等舱', '二等舱', '三等舱']\n",
+    "titanic_df = titanic_df.join(class_dummies_titanic)\n",
+    "\n",
+    "age_dummies_titanic  = pd.get_dummies(titanic_df['Age'])\n",
+    "age_dummies_titanic.columns = ['孩子', '少年', '中年','老人']\n",
+    "titanic_df = titanic_df.join(age_dummies_titanic)\n",
+    "\n",
+    "fare_dummies_titanic  = pd.get_dummies(titanic_df['Fare'])\n",
+    "fare_dummies_titanic.columns = ['便宜票价', '普通票价', '高级票价','豪华票价']\n",
+    "titanic_df = titanic_df.join(fare_dummies_titanic)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "del titanic_df['Sex']\n",
+    "del titanic_df['Embarked']\n",
+    "del titanic_df['IsAlone']\n",
+    "del titanic_df['Pclass']\n",
+    "del titanic_df['Age']\n",
+    "del titanic_df['Fare']\n",
+    "del titanic_df['Cabin']\n",
+    "del titanic_df['Ticket']"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": [
+    "## 2 使用清理后的数据"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.metrics import precision_recall_curve, roc_auc_score, roc_curve\n",
+    "from sklearn.metrics import accuracy_score, mean_squared_error, r2_score, confusion_matrix\n",
+    "from sklearn.model_selection import GridSearchCV  # 参数调优"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Survived</th>\n",
+       "      <th>男性</th>\n",
+       "      <th>女性</th>\n",
+       "      <th>港口S</th>\n",
+       "      <th>港口C</th>\n",
+       "      <th>港口Q</th>\n",
+       "      <th>一等舱</th>\n",
+       "      <th>二等舱</th>\n",
+       "      <th>三等舱</th>\n",
+       "      <th>孩子</th>\n",
+       "      <th>少年</th>\n",
+       "      <th>中年</th>\n",
+       "      <th>老人</th>\n",
+       "      <th>便宜票价</th>\n",
+       "      <th>普通票价</th>\n",
+       "      <th>高级票价</th>\n",
+       "      <th>豪华票价</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Survived  男性  女性  港口S  港口C  港口Q  一等舱  二等舱  三等舱  孩子  少年  中年  老人  便宜票价  普通票价  \\\n",
+       "0         0   0   1    0    0    1    0    0    1   0   1   0   0     1     0   \n",
+       "1         1   1   0    1    0    0    1    0    0   0   1   0   0     0     0   \n",
+       "2         1   1   0    0    0    1    0    0    1   0   1   0   0     0     1   \n",
+       "3         1   1   0    0    0    1    1    0    0   0   1   0   0     0     0   \n",
+       "4         0   0   1    0    0    1    0    0    1   0   1   0   0     0     1   \n",
+       "\n",
+       "   高级票价  豪华票价  \n",
+       "0     0     0  \n",
+       "1     0     1  \n",
+       "2     0     0  \n",
+       "3     0     1  \n",
+       "4     0     0  "
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dataset = titanic_df\n",
+    "# 观察数据\n",
+    "dataset.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Survived</th>\n",
+       "      <th>男性</th>\n",
+       "      <th>女性</th>\n",
+       "      <th>港口S</th>\n",
+       "      <th>港口C</th>\n",
+       "      <th>港口Q</th>\n",
+       "      <th>一等舱</th>\n",
+       "      <th>二等舱</th>\n",
+       "      <th>三等舱</th>\n",
+       "      <th>孩子</th>\n",
+       "      <th>少年</th>\n",
+       "      <th>中年</th>\n",
+       "      <th>老人</th>\n",
+       "      <th>便宜票价</th>\n",
+       "      <th>普通票价</th>\n",
+       "      <th>高级票价</th>\n",
+       "      <th>豪华票价</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>891.000000</td>\n",
+       "      <td>891.000000</td>\n",
+       "      <td>891.000000</td>\n",
+       "      <td>891.000000</td>\n",
+       "      <td>891.000000</td>\n",
+       "      <td>891.000000</td>\n",
+       "      <td>891.000000</td>\n",
+       "      <td>891.000000</td>\n",
+       "      <td>891.000000</td>\n",
+       "      <td>891.000000</td>\n",
+       "      <td>891.000000</td>\n",
+       "      <td>891.000000</td>\n",
+       "      <td>891.000000</td>\n",
+       "      <td>891.000000</td>\n",
+       "      <td>891.000000</td>\n",
+       "      <td>891.000000</td>\n",
+       "      <td>891.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>0.383838</td>\n",
+       "      <td>0.352413</td>\n",
+       "      <td>0.647587</td>\n",
+       "      <td>0.188552</td>\n",
+       "      <td>0.086420</td>\n",
+       "      <td>0.722783</td>\n",
+       "      <td>0.242424</td>\n",
+       "      <td>0.206510</td>\n",
+       "      <td>0.551066</td>\n",
+       "      <td>0.227834</td>\n",
+       "      <td>0.585859</td>\n",
+       "      <td>0.160494</td>\n",
+       "      <td>0.025814</td>\n",
+       "      <td>0.250281</td>\n",
+       "      <td>0.243547</td>\n",
+       "      <td>0.257015</td>\n",
+       "      <td>0.249158</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>0.486592</td>\n",
+       "      <td>0.477990</td>\n",
+       "      <td>0.477990</td>\n",
+       "      <td>0.391372</td>\n",
+       "      <td>0.281141</td>\n",
+       "      <td>0.447876</td>\n",
+       "      <td>0.428790</td>\n",
+       "      <td>0.405028</td>\n",
+       "      <td>0.497665</td>\n",
+       "      <td>0.419670</td>\n",
+       "      <td>0.492850</td>\n",
+       "      <td>0.367270</td>\n",
+       "      <td>0.158668</td>\n",
+       "      <td>0.433418</td>\n",
+       "      <td>0.429463</td>\n",
+       "      <td>0.437233</td>\n",
+       "      <td>0.432769</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.500000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         Survived          男性          女性         港口S         港口C         港口Q  \\\n",
+       "count  891.000000  891.000000  891.000000  891.000000  891.000000  891.000000   \n",
+       "mean     0.383838    0.352413    0.647587    0.188552    0.086420    0.722783   \n",
+       "std      0.486592    0.477990    0.477990    0.391372    0.281141    0.447876   \n",
+       "min      0.000000    0.000000    0.000000    0.000000    0.000000    0.000000   \n",
+       "25%      0.000000    0.000000    0.000000    0.000000    0.000000    0.000000   \n",
+       "50%      0.000000    0.000000    1.000000    0.000000    0.000000    1.000000   \n",
+       "75%      1.000000    1.000000    1.000000    0.000000    0.000000    1.000000   \n",
+       "max      1.000000    1.000000    1.000000    1.000000    1.000000    1.000000   \n",
+       "\n",
+       "              一等舱         二等舱         三等舱          孩子          少年          中年  \\\n",
+       "count  891.000000  891.000000  891.000000  891.000000  891.000000  891.000000   \n",
+       "mean     0.242424    0.206510    0.551066    0.227834    0.585859    0.160494   \n",
+       "std      0.428790    0.405028    0.497665    0.419670    0.492850    0.367270   \n",
+       "min      0.000000    0.000000    0.000000    0.000000    0.000000    0.000000   \n",
+       "25%      0.000000    0.000000    0.000000    0.000000    0.000000    0.000000   \n",
+       "50%      0.000000    0.000000    1.000000    0.000000    1.000000    0.000000   \n",
+       "75%      0.000000    0.000000    1.000000    0.000000    1.000000    0.000000   \n",
+       "max      1.000000    1.000000    1.000000    1.000000    1.000000    1.000000   \n",
+       "\n",
+       "               老人        便宜票价        普通票价        高级票价        豪华票价  \n",
+       "count  891.000000  891.000000  891.000000  891.000000  891.000000  \n",
+       "mean     0.025814    0.250281    0.243547    0.257015    0.249158  \n",
+       "std      0.158668    0.433418    0.429463    0.437233    0.432769  \n",
+       "min      0.000000    0.000000    0.000000    0.000000    0.000000  \n",
+       "25%      0.000000    0.000000    0.000000    0.000000    0.000000  \n",
+       "50%      0.000000    0.000000    0.000000    0.000000    0.000000  \n",
+       "75%      0.000000    0.500000    0.000000    1.000000    0.000000  \n",
+       "max      1.000000    1.000000    1.000000    1.000000    1.000000  "
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# 继续观察数据基本统计信息\n",
+    "dataset.describe()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# 获得数据集的特征（输入变量）和输出\n",
+    "x = np.array(dataset.iloc[:, 1:])\n",
+    "y = np.array(dataset.iloc[:, 0])\n",
+    "# 分割数据集\n",
+    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.3, random_state=33)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# 绘制混淆矩阵函数\n",
+    "def plot_confusion_matrix(cm, classes,\n",
+    "                          normalize=False,\n",
+    "                          title='Confusion matrix',\n",
+    "                          cmap=plt.cm.Blues):\n",
+    "    plt.figure()\n",
+    "    plt.imshow(cm, interpolation='nearest', cmap=cmap)\n",
+    "    plt.title(title)\n",
+    "    plt.colorbar()\n",
+    "    tick_marks = np.arange(len(classes))\n",
+    "    plt.xticks(tick_marks, classes, rotation=45)\n",
+    "    plt.yticks(tick_marks, classes)\n",
+    "\n",
+    "    fmt = '.2f' if normalize else 'd'\n",
+    "    thresh = cm.max() / 2.\n",
+    "    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n",
+    "        plt.text(j, i, format(cm[i, j], fmt),\n",
+    "                 horizontalalignment=\"center\",\n",
+    "                 color=\"white\" if cm[i, j] > thresh else \"black\")\n",
+    "\n",
+    "    plt.tight_layout()\n",
+    "    plt.ylabel('True label')\n",
+    "    plt.xlabel('Predicted label')\n",
+    "    plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# 模型性能评估\n",
+    "def model_performance_evaluation(model_name, test, pred):\n",
+    "    print(model_name,  '| 准确率: %.4f' % accuracy_score(test, pred))\n",
+    "    print(model_name,'| 均方误差: %.4f' % mean_squared_error(test, pred))\n",
+    "    print(model_name, '| R2-score: %.4f' % r2_score(test, pred))\n",
+    "    print(model_name, '| 混淆矩阵：\\n', confusion_matrix(test, pred))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": [
+    "## 3 对数几率回归模型"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "from sklearn.linear_model import LogisticRegression"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# 模型参数组合\n",
+    "param_lr = {'penalty':['l1','l2'], 'C':[1,1e+2,1e+4], 'max_iter':[1e+2,1e+3,1e+4]}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "LogisticRegression(C=1, class_weight=None, dual=False, fit_intercept=True,\n",
+       "          intercept_scaling=1, max_iter=100.0, multi_class='ovr', n_jobs=1,\n",
+       "          penalty='l1', random_state=None, solver='liblinear', tol=0.0001,\n",
+       "          verbose=0, warm_start=False)"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# 建模、训练和预测\n",
+    "base_line_model = LogisticRegression() # 默认 penalty='l2'\n",
+    "lr = GridSearchCV(estimator=base_line_model, param_grid=param_lr, cv=5, n_jobs=3)\n",
+    "lr.fit(x_train, y_train)\n",
+    "y_pred_lr = lr.predict(x_test)\n",
+    "lr.best_estimator_"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "对数几率回归模型在训练集上的性能 -- \n",
+      "LogisticRegression | 准确率: 0.7865\n",
+      "LogisticRegression | 均方误差: 0.2135\n",
+      "LogisticRegression | R2-score: 0.0957\n",
+      "LogisticRegression | 混淆矩阵：\n",
+      " [[323  62]\n",
+      " [ 71 167]]\n",
+      "\n",
+      "\n",
+      "\n",
+      "对数几率回归模型在测试集上的性能 -- \n",
+      "LogisticRegression | 准确率: 0.8134\n",
+      "LogisticRegression | 均方误差: 0.1866\n",
+      "LogisticRegression | R2-score: 0.2144\n",
+      "LogisticRegression | 混淆矩阵：\n",
+      " [[140  24]\n",
+      " [ 26  78]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# 模型性能评估\n",
+    "print(\"对数几率回归模型在训练集上的性能 -- \")\n",
+    "model_performance_evaluation('LogisticRegression', y_train, lr.predict(x_train))\n",
+    "print(\"\\n\"*2)\n",
+    "print(\"对数几率回归模型在测试集上的性能 -- \")\n",
+    "model_performance_evaluation('LogisticRegression', y_test, y_pred_lr)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# 绘制混淆矩阵\n",
+    "cnf_matrix = confusion_matrix(y_test, y_pred_lr)\n",
+    "np.set_printoptions(precision=2)  # 设置打印数量的阈值\n",
+    "class_names = [0, 1]\n",
+    "plot_confusion_matrix(cnf_matrix, classes=class_names, title='Confusion matrix')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "该支持向量机模型的AUC值为 0.8018292682926829\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# 绘制ROC曲线并计算AUC值\n",
+    "auc_lr = roc_auc_score(y_test, y_pred_lr)\n",
+    "print(f\"该支持向量机模型的AUC值为 {auc_lr}\")\n",
+    "fpr_lr, tpr_lr, thresholds = roc_curve(y_test, y_pred_lr)\n",
+    "plt.plot(fpr_lr, tpr_lr, label=\"对数几率模型: \"+str(round(auc_lr, 3)))\n",
+    "plt.xlabel('False Positive Rate')\n",
+    "plt.ylabel('True Positive Rate')\n",
+    "plt.title('ROC')\n",
+    "plt.xlim([0,1])\n",
+    "plt.ylim([0,1.1])\n",
+    "plt.grid()\n",
+    "plt.legend(loc='lower right')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": [
+    "## 4 决策树"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "from sklearn.tree import DecisionTreeClassifier, export_graphviz\n",
+    "params_dt = {'criterion':['entropy','gini'], 'splitter':['best', 'random']}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "DecisionTreeClassifier(class_weight=None, criterion='entropy', max_depth=None,\n",
+       "            max_features=None, max_leaf_nodes=None,\n",
+       "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
+       "            min_samples_leaf=1, min_samples_split=2,\n",
+       "            min_weight_fraction_leaf=0.0, presort=False, random_state=None,\n",
+       "            splitter='best')"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "base_line_model = DecisionTreeClassifier()\n",
+    "dtc = GridSearchCV(estimator=base_line_model, param_grid=params_dt, cv=5, n_jobs=3)\n",
+    "dtc.fit(x_train, y_train)\n",
+    "y_pred_dt = dtc.predict(x_test)\n",
+    "dtc.best_estimator_"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "决策树模型在 训练集 上的性能 -- \n",
+      "DecisionTree | 准确率: 0.8411\n",
+      "DecisionTree | 均方误差: 0.1589\n",
+      "DecisionTree | R2-score: 0.3269\n",
+      "DecisionTree | 混淆矩阵：\n",
+      " [[350  35]\n",
+      " [ 64 174]]\n",
+      "\n",
+      "\n",
+      "\n",
+      "决策树模型在 测试集 上的性能 -- \n",
+      "DecisionTree | 准确率: 0.8470\n",
+      "DecisionTree | 均方误差: 0.1530\n",
+      "DecisionTree | R2-score: 0.3558\n",
+      "DecisionTree | 混淆矩阵：\n",
+      " [[151  13]\n",
+      " [ 28  76]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# 模型性能评估\n",
+    "print(\"决策树模型在 训练集 上的性能 -- \")\n",
+    "model_performance_evaluation('DecisionTree', y_train, dtc.predict(x_train))\n",
+    "print(\"\\n\"*2)\n",
+    "\n",
+    "print(\"决策树模型在 测试集 上的性能 -- \")\n",
+    "model_performance_evaluation('DecisionTree', y_test, y_pred_dt)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# 计算混淆矩阵\n",
+    "cnf_matrix = confusion_matrix(y_test, y_pred_dt)\n",
+    "np.set_printoptions(precision=2)  # 设置打印数量的阈值\n",
+    "class_names = [0, 1]\n",
+    "# 绘制混淆矩阵\n",
+    "plot_confusion_matrix(cnf_matrix, classes=class_names, title='Confusion matrix')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "该支持向量机模型的AUC值为 0.825750469043152\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# 绘制ROC曲线并计算AUC值\n",
+    "auc_dt = roc_auc_score(y_test, y_pred_dt)\n",
+    "print(f\"该支持向量机模型的AUC值为 {auc_dt}\")\n",
+    "fpr_dt, tpr_dt, thresholds = roc_curve(y_test, y_pred_dt)\n",
+    "plt.plot(fpr_dt, tpr_dt, label=\"决策树: \"+str(round(auc_dt, 3)))\n",
+    "plt.plot(fpr_lr, tpr_lr, label=\"对数几率模型: \"+str(round(auc_lr, 3)))\n",
+    "plt.xlabel('False Positive Rate')\n",
+    "plt.ylabel('True Positive Rate')\n",
+    "plt.title('ROC')\n",
+    "plt.xlim([0,1])\n",
+    "plt.ylim([0,1.1])\n",
+    "plt.grid()\n",
+    "plt.legend(loc='lower right')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": [
+    "## 5 神经网络"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "from sklearn.neural_network import MLPClassifier\n",
+    "params_mlp = {'solver':['sgd', 'lbfgs'], 'alpha': [1e-3, 1e-4], 'learning_rate_init':[1e-3, 1e-4] }"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "MLPClassifier(activation='relu', alpha=0.001, batch_size='auto', beta_1=0.9,\n",
+       "       beta_2=0.999, early_stopping=False, epsilon=1e-08,\n",
+       "       hidden_layer_sizes=(6, 6, 6), learning_rate='constant',\n",
+       "       learning_rate_init=0.001, max_iter=1000, momentum=0.9,\n",
+       "       nesterovs_momentum=True, power_t=0.5, random_state=None,\n",
+       "       shuffle=True, solver='lbfgs', tol=0.0001, validation_fraction=0.1,\n",
+       "       verbose=False, warm_start=False)"
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# 神经网络训练模型\n",
+    "base_line_model = MLPClassifier(solver='lbfgs', alpha=1e-5, hidden_layer_sizes=(6, 6, 6), max_iter=1000)\n",
+    "mlp = GridSearchCV(estimator=base_line_model, param_grid=params_mlp, cv=5, n_jobs=3)\n",
+    "mlp.fit(x_train, y_train)\n",
+    "y_pred_nn = mlp.predict(x_test)\n",
+    "mlp.best_estimator_"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "神经网络模型在 训练集 上的性能 -- \n",
+      "Neural Network | 准确率: 0.8411\n",
+      "Neural Network | 均方误差: 0.1589\n",
+      "Neural Network | R2-score: 0.3269\n",
+      "Neural Network | 混淆矩阵：\n",
+      " [[340  45]\n",
+      " [ 54 184]]\n",
+      "\n",
+      "\n",
+      "\n",
+      "神经网络模型在 测试集 上的性能 -- \n",
+      "Neural Network | 准确率: 0.8209\n",
+      "Neural Network | 均方误差: 0.1791\n",
+      "Neural Network | R2-score: 0.2458\n",
+      "Neural Network | 混淆矩阵：\n",
+      " [[145  19]\n",
+      " [ 29  75]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# 模型性能评估\n",
+    "print(\"神经网络模型在 训练集 上的性能 -- \")\n",
+    "model_performance_evaluation('Neural Network', y_train, mlp.predict(x_train))\n",
+    "print(\"\\n\"*2)\n",
+    "\n",
+    "print(\"神经网络模型在 测试集 上的性能 -- \")\n",
+    "model_performance_evaluation('Neural Network', y_test, y_pred_nn)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# 计算混淆矩阵\n",
+    "cnf_matrix = confusion_matrix(y_test, y_pred_nn)\n",
+    "np.set_printoptions(precision=2)  # 设置打印数量的阈值\n",
+    "class_names = [0, 1]\n",
+    "# 绘制混淆矩阵\n",
+    "plot_confusion_matrix(cnf_matrix, classes=class_names, title='Confusion matrix')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "神经网络的AUC值为 0.8026500938086304\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# 绘制ROC曲线并计算AUC值\n",
+    "auc_nn = roc_auc_score(y_test, y_pred_nn)\n",
+    "print(f\"神经网络的AUC值为 {auc_nn}\")\n",
+    "fpr_nn, tpr_nn, thresholds = roc_curve(y_test, y_pred_nn)\n",
+    "plt.plot(fpr_nn, tpr_nn, label=\"神经网络: \"+str(round(auc_nn, 3)))\n",
+    "plt.plot(fpr_dt, tpr_dt, label=\"决策树: \"+str(round(auc_dt, 3)))\n",
+    "plt.plot(fpr_lr, tpr_lr, label=\"对数几率模型: \"+str(round(auc_lr, 3)))\n",
+    "plt.xlabel('False Positive Rate')\n",
+    "plt.ylabel('True Positive Rate')\n",
+    "plt.title('ROC')\n",
+    "plt.xlim([0,1])\n",
+    "plt.ylim([0,1.1])\n",
+    "plt.grid()\n",
+    "plt.legend(loc='lower right')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": [
+    "# 6、支持向量机\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "SVC(C=100, cache_size=200, class_weight=None, coef0=0.0,\n",
+       "  decision_function_shape='ovr', degree=3, gamma=1, kernel='rbf',\n",
+       "  max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
+       "  tol=0.001, verbose=False)"
+      ]
+     },
+     "execution_count": 43,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn import svm\n",
+    "params_svm = {'kernel':['rbf','linear'], 'gamma':[10,1,0.1,1e-2], 'C':[1,100,1e4]}\n",
+    "# 支持向量机训练模型\n",
+    "base_line_model = svm.SVC() # 默认 kernel='rbf'\n",
+    "svc = GridSearchCV(estimator=base_line_model, param_grid=params_svm, cv=5, n_jobs=3)\n",
+    "svc.fit(x_train, y_train)\n",
+    "y_pred_svm = svc.predict(x_test)\n",
+    "svc.best_estimator_"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "支持向量机模型在 训练集 上的性能 -- \n",
+      "Support Vector Machinen | 准确率: 0.8411\n",
+      "Support Vector Machinen | 均方误差: 0.1589\n",
+      "Support Vector Machinen | R2-score: 0.3269\n",
+      "Support Vector Machinen | 混淆矩阵：\n",
+      " [[348  37]\n",
+      " [ 62 176]]\n",
+      "\n",
+      "\n",
+      "\n",
+      "支持向量机模型在 测试集 上的性能 -- \n",
+      "Support Vector Machine | 准确率: 0.8507\n",
+      "Support Vector Machine | 均方误差: 0.1493\n",
+      "Support Vector Machine | R2-score: 0.3715\n",
+      "Support Vector Machine | 混淆矩阵：\n",
+      " [[151  13]\n",
+      " [ 27  77]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# 模型性能评估\n",
+    "print(\"支持向量机模型在 训练集 上的性能 -- \")\n",
+    "model_performance_evaluation('Support Vector Machinen', y_train, svc.predict(x_train))\n",
+    "print(\"\\n\"*2)\n",
+    "print(\"支持向量机模型在 测试集 上的性能 -- \")\n",
+    "model_performance_evaluation('Support Vector Machine', y_test, y_pred_svm)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "该支持向量机模型的AUC值为 0.8305581613508443\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# 绘制ROC曲线并计算AUC值\n",
+    "auc_svm = roc_auc_score(y_test, y_pred_svm)\n",
+    "print(f\"该支持向量机模型的AUC值为 {auc_svm}\")\n",
+    "fpr_svm, tpr_svm, thresholds = roc_curve(y_test, y_pred_svm)\n",
+    "plt.plot(fpr_svm, tpr_svm, label=\"支持向量机: \"+str(round(auc_svm, 3)))\n",
+    "plt.plot(fpr_nn, tpr_nn, label=\"神经网络: \"+str(round(auc_nn, 3)))\n",
+    "plt.plot(fpr_dt, tpr_dt, label=\"决策树: \"+str(round(auc_dt, 3)))\n",
+    "plt.plot(fpr_lr, tpr_lr, label=\"对数几率模型: \"+str(round(auc_lr, 3)))\n",
+    "plt.xlabel('False Positive Rate')\n",
+    "plt.ylabel('True Positive Rate')\n",
+    "plt.title('ROC')\n",
+    "plt.xlim([0,1])\n",
+    "plt.ylim([0,1.1])\n",
+    "plt.grid()\n",
+    "plt.legend(loc='lower right')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": [
+    "## 由上述结果可以看到，决支持向量机的分类准确率最高，所以接下来我们使用决策树模型将结果填入submission"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": [
+    "## 七、使用SVM对测试集进行训练，并写入submission文件"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 96,
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "写入完成！\n"
+     ]
+    }
+   ],
+   "source": [
+    "test_df=pd.read_csv(\"test.csv\")\n",
+    "test_df.drop(['PassengerId','Name','Cabin'], axis=1, inplace=True)\n",
+    "test_df.Fare=test_df.Fare.fillna(test_df.Fare.mean())\n",
+    "age_df = test_df[[\"Age\", \"Fare\", \"Parch\", \"SibSp\", \"Pclass\"]].copy()  # .copy()用于复制原始数据 挑选数值型字段\n",
+    "# 分割有缺失值的数据集\n",
+    "age_df_notnull = age_df.loc[age_df.Age.notnull()]\n",
+    "age_df_isnull = age_df.loc[age_df.Age.isnull()]\n",
+    "# 利用非缺失数据建模\n",
+    "x = age_df_notnull.iloc[:, 1:]\n",
+    "y = age_df_notnull.values[:, 0]\n",
+    "x_test = age_df_isnull.values[:, 1:]\n",
+    "\n",
+    "rfr = RandomForestRegressor(n_estimators=1000, n_jobs=-1)\n",
+    "rfr.fit(x, y)\n",
+    "y_pred = rfr.predict(x_test)\n",
+    "\n",
+    "test_df.loc[test_df[\"Age\"].isnull(), \"Age\"] = y_pred\n",
+    "test_df.loc[test_df['Age'] <= 20.315, 'Age'] = 0\n",
+    "test_df.loc[(test_df['Age'] > 20.315) & (test_df['Age'] <= 40.21), 'Age'] = 1\n",
+    "test_df.loc[(test_df['Age'] > 40.21) & (test_df['Age'] <= 60.105), 'Age'] = 2\n",
+    "test_df.loc[test_df['Age'] > 60.105, 'Age'] = 3\n",
+    "test_df['Age'] = test_df['Age'].astype(int)\n",
+    "\n",
+    "test_df[\"Embarked\"] = test_df[\"Embarked\"].dropna(0)\n",
+    "test_df.loc[test_df['Fare'] <= 7.91, 'Fare'] = 0\n",
+    "test_df.loc[(test_df['Fare'] > 7.91) & (test_df['Fare'] <= 14.454), 'Fare'] = 1\n",
+    "test_df.loc[(test_df['Fare'] > 14.454) & (test_df['Fare'] <= 31.0), 'Fare'] = 2\n",
+    "test_df.loc[test_df['Fare'] > 31.0, 'Fare'] = 3\n",
+    "test_df['Fare'] = test_df['Fare'].astype(int)\n",
+    "\n",
+    "test_df['FamilySize'] = test_df['SibSp'] + test_df['Parch']\n",
+    "test_df['IsAlone'] = 0\n",
+    "test_df.loc[test_df['FamilySize'] == 1, 'IsAlone'] = 1\n",
+    "# 删除原有的列 Parch & SibSp\n",
+    "test_df.drop(['SibSp'], axis=1, inplace=True)\n",
+    "test_df.drop(['Parch'], axis=1, inplace=True)\n",
+    "test_df.drop(['FamilySize'], axis=1, inplace=True)\n",
+    "\n",
+    "# one-hot编码\n",
+    "sex_dummies_titanic  = pd.get_dummies(test_df['Sex'])\n",
+    "sex_dummies_titanic.columns = ['男性', '女性']\n",
+    "test_df = test_df.join(sex_dummies_titanic)\n",
+    "\n",
+    "embark_dummies_titanic  = pd.get_dummies(test_df['Embarked'])\n",
+    "embark_dummies_titanic.columns = ['港口S', '港口C','港口Q']\n",
+    "test_df = test_df.join(embark_dummies_titanic)\n",
+    "\n",
+    "class_dummies_titanic  = pd.get_dummies(test_df['Pclass'])\n",
+    "class_dummies_titanic.columns = ['一等舱', '二等舱', '三等舱']\n",
+    "test_df = test_df.join(class_dummies_titanic)\n",
+    "\n",
+    "age_dummies_titanic  = pd.get_dummies(test_df['Age'])\n",
+    "age_dummies_titanic.columns = ['孩子', '少年', '中年','老人']\n",
+    "test_df = test_df.join(age_dummies_titanic)\n",
+    "\n",
+    "fare_dummies_titanic  = pd.get_dummies(test_df['Fare'])\n",
+    "fare_dummies_titanic.columns = ['便宜票价', '普通票价', '高级票价','豪华票价']\n",
+    "\n",
+    "drop_items=['Sex','Embarked','IsAlone','Pclass','Age','Fare','Ticket']\n",
+    "test_df.drop(drop_items, axis=1, inplace=True)\n",
+    "test_df = test_df.join(fare_dummies_titanic)\n",
+    "result=svc.predict(test_df)\n",
+    "submission= pd.read_csv(\"gender_submission.csv\")\n",
+    "submission['Survived']=result\n",
+    "submission.to_csv(\"gender_submission.csv\",index=None)\n",
+    "print(\"写入完成！\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 91,
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
\ No newline at end of file
diff --git a/共享单车数据分析/十三组平时作业（实战）/离职分析.ipynb b/共享民宿平台担保交易房子评分的影响研究/homeworks/离职分析.ipynb
similarity index 100%
rename from 共享单车数据分析/十三组平时作业（实战）/离职分析.ipynb
rename to 共享民宿平台担保交易房子评分的影响研究/homeworks/离职分析.ipynb
diff --git a/共享民宿平台担保交易房子评分的影响研究/homeworks/鸢尾花数据集的回归与聚类.ipynb b/共享民宿平台担保交易房子评分的影响研究/homeworks/鸢尾花数据集的回归与聚类.ipynb
new file mode 100644
index 0000000..c99d446
--- /dev/null
+++ b/共享民宿平台担保交易房子评分的影响研究/homeworks/鸢尾花数据集的回归与聚类.ipynb
@@ -0,0 +1,727 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "c5c256bf",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "D:\\anaconda\\lib\\site-packages\\scipy\\__init__.py:146: UserWarning: A NumPy version >=1.16.5 and <1.23.0 is required for this version of SciPy (detected version 1.24.3\n",
+      "  warnings.warn(f\"A NumPy version >={np_minversion} and <{np_maxversion}\"\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "(array([[5.1, 3.5, 1.4, 0.2],\n",
+       "        [4.9, 3. , 1.4, 0.2],\n",
+       "        [4.7, 3.2, 1.3, 0.2],\n",
+       "        [4.6, 3.1, 1.5, 0.2],\n",
+       "        [5. , 3.6, 1.4, 0.2],\n",
+       "        [5.4, 3.9, 1.7, 0.4],\n",
+       "        [4.6, 3.4, 1.4, 0.3],\n",
+       "        [5. , 3.4, 1.5, 0.2],\n",
+       "        [4.4, 2.9, 1.4, 0.2],\n",
+       "        [4.9, 3.1, 1.5, 0.1],\n",
+       "        [5.4, 3.7, 1.5, 0.2],\n",
+       "        [4.8, 3.4, 1.6, 0.2],\n",
+       "        [4.8, 3. , 1.4, 0.1],\n",
+       "        [4.3, 3. , 1.1, 0.1],\n",
+       "        [5.8, 4. , 1.2, 0.2],\n",
+       "        [5.7, 4.4, 1.5, 0.4],\n",
+       "        [5.4, 3.9, 1.3, 0.4],\n",
+       "        [5.1, 3.5, 1.4, 0.3],\n",
+       "        [5.7, 3.8, 1.7, 0.3],\n",
+       "        [5.1, 3.8, 1.5, 0.3],\n",
+       "        [5.4, 3.4, 1.7, 0.2],\n",
+       "        [5.1, 3.7, 1.5, 0.4],\n",
+       "        [4.6, 3.6, 1. , 0.2],\n",
+       "        [5.1, 3.3, 1.7, 0.5],\n",
+       "        [4.8, 3.4, 1.9, 0.2],\n",
+       "        [5. , 3. , 1.6, 0.2],\n",
+       "        [5. , 3.4, 1.6, 0.4],\n",
+       "        [5.2, 3.5, 1.5, 0.2],\n",
+       "        [5.2, 3.4, 1.4, 0.2],\n",
+       "        [4.7, 3.2, 1.6, 0.2],\n",
+       "        [4.8, 3.1, 1.6, 0.2],\n",
+       "        [5.4, 3.4, 1.5, 0.4],\n",
+       "        [5.2, 4.1, 1.5, 0.1],\n",
+       "        [5.5, 4.2, 1.4, 0.2],\n",
+       "        [4.9, 3.1, 1.5, 0.2],\n",
+       "        [5. , 3.2, 1.2, 0.2],\n",
+       "        [5.5, 3.5, 1.3, 0.2],\n",
+       "        [4.9, 3.6, 1.4, 0.1],\n",
+       "        [4.4, 3. , 1.3, 0.2],\n",
+       "        [5.1, 3.4, 1.5, 0.2],\n",
+       "        [5. , 3.5, 1.3, 0.3],\n",
+       "        [4.5, 2.3, 1.3, 0.3],\n",
+       "        [4.4, 3.2, 1.3, 0.2],\n",
+       "        [5. , 3.5, 1.6, 0.6],\n",
+       "        [5.1, 3.8, 1.9, 0.4],\n",
+       "        [4.8, 3. , 1.4, 0.3],\n",
+       "        [5.1, 3.8, 1.6, 0.2],\n",
+       "        [4.6, 3.2, 1.4, 0.2],\n",
+       "        [5.3, 3.7, 1.5, 0.2],\n",
+       "        [5. , 3.3, 1.4, 0.2],\n",
+       "        [7. , 3.2, 4.7, 1.4],\n",
+       "        [6.4, 3.2, 4.5, 1.5],\n",
+       "        [6.9, 3.1, 4.9, 1.5],\n",
+       "        [5.5, 2.3, 4. , 1.3],\n",
+       "        [6.5, 2.8, 4.6, 1.5],\n",
+       "        [5.7, 2.8, 4.5, 1.3],\n",
+       "        [6.3, 3.3, 4.7, 1.6],\n",
+       "        [4.9, 2.4, 3.3, 1. ],\n",
+       "        [6.6, 2.9, 4.6, 1.3],\n",
+       "        [5.2, 2.7, 3.9, 1.4],\n",
+       "        [5. , 2. , 3.5, 1. ],\n",
+       "        [5.9, 3. , 4.2, 1.5],\n",
+       "        [6. , 2.2, 4. , 1. ],\n",
+       "        [6.1, 2.9, 4.7, 1.4],\n",
+       "        [5.6, 2.9, 3.6, 1.3],\n",
+       "        [6.7, 3.1, 4.4, 1.4],\n",
+       "        [5.6, 3. , 4.5, 1.5],\n",
+       "        [5.8, 2.7, 4.1, 1. ],\n",
+       "        [6.2, 2.2, 4.5, 1.5],\n",
+       "        [5.6, 2.5, 3.9, 1.1],\n",
+       "        [5.9, 3.2, 4.8, 1.8],\n",
+       "        [6.1, 2.8, 4. , 1.3],\n",
+       "        [6.3, 2.5, 4.9, 1.5],\n",
+       "        [6.1, 2.8, 4.7, 1.2],\n",
+       "        [6.4, 2.9, 4.3, 1.3],\n",
+       "        [6.6, 3. , 4.4, 1.4],\n",
+       "        [6.8, 2.8, 4.8, 1.4],\n",
+       "        [6.7, 3. , 5. , 1.7],\n",
+       "        [6. , 2.9, 4.5, 1.5],\n",
+       "        [5.7, 2.6, 3.5, 1. ],\n",
+       "        [5.5, 2.4, 3.8, 1.1],\n",
+       "        [5.5, 2.4, 3.7, 1. ],\n",
+       "        [5.8, 2.7, 3.9, 1.2],\n",
+       "        [6. , 2.7, 5.1, 1.6],\n",
+       "        [5.4, 3. , 4.5, 1.5],\n",
+       "        [6. , 3.4, 4.5, 1.6],\n",
+       "        [6.7, 3.1, 4.7, 1.5],\n",
+       "        [6.3, 2.3, 4.4, 1.3],\n",
+       "        [5.6, 3. , 4.1, 1.3],\n",
+       "        [5.5, 2.5, 4. , 1.3],\n",
+       "        [5.5, 2.6, 4.4, 1.2],\n",
+       "        [6.1, 3. , 4.6, 1.4],\n",
+       "        [5.8, 2.6, 4. , 1.2],\n",
+       "        [5. , 2.3, 3.3, 1. ],\n",
+       "        [5.6, 2.7, 4.2, 1.3],\n",
+       "        [5.7, 3. , 4.2, 1.2],\n",
+       "        [5.7, 2.9, 4.2, 1.3],\n",
+       "        [6.2, 2.9, 4.3, 1.3],\n",
+       "        [5.1, 2.5, 3. , 1.1],\n",
+       "        [5.7, 2.8, 4.1, 1.3],\n",
+       "        [6.3, 3.3, 6. , 2.5],\n",
+       "        [5.8, 2.7, 5.1, 1.9],\n",
+       "        [7.1, 3. , 5.9, 2.1],\n",
+       "        [6.3, 2.9, 5.6, 1.8],\n",
+       "        [6.5, 3. , 5.8, 2.2],\n",
+       "        [7.6, 3. , 6.6, 2.1],\n",
+       "        [4.9, 2.5, 4.5, 1.7],\n",
+       "        [7.3, 2.9, 6.3, 1.8],\n",
+       "        [6.7, 2.5, 5.8, 1.8],\n",
+       "        [7.2, 3.6, 6.1, 2.5],\n",
+       "        [6.5, 3.2, 5.1, 2. ],\n",
+       "        [6.4, 2.7, 5.3, 1.9],\n",
+       "        [6.8, 3. , 5.5, 2.1],\n",
+       "        [5.7, 2.5, 5. , 2. ],\n",
+       "        [5.8, 2.8, 5.1, 2.4],\n",
+       "        [6.4, 3.2, 5.3, 2.3],\n",
+       "        [6.5, 3. , 5.5, 1.8],\n",
+       "        [7.7, 3.8, 6.7, 2.2],\n",
+       "        [7.7, 2.6, 6.9, 2.3],\n",
+       "        [6. , 2.2, 5. , 1.5],\n",
+       "        [6.9, 3.2, 5.7, 2.3],\n",
+       "        [5.6, 2.8, 4.9, 2. ],\n",
+       "        [7.7, 2.8, 6.7, 2. ],\n",
+       "        [6.3, 2.7, 4.9, 1.8],\n",
+       "        [6.7, 3.3, 5.7, 2.1],\n",
+       "        [7.2, 3.2, 6. , 1.8],\n",
+       "        [6.2, 2.8, 4.8, 1.8],\n",
+       "        [6.1, 3. , 4.9, 1.8],\n",
+       "        [6.4, 2.8, 5.6, 2.1],\n",
+       "        [7.2, 3. , 5.8, 1.6],\n",
+       "        [7.4, 2.8, 6.1, 1.9],\n",
+       "        [7.9, 3.8, 6.4, 2. ],\n",
+       "        [6.4, 2.8, 5.6, 2.2],\n",
+       "        [6.3, 2.8, 5.1, 1.5],\n",
+       "        [6.1, 2.6, 5.6, 1.4],\n",
+       "        [7.7, 3. , 6.1, 2.3],\n",
+       "        [6.3, 3.4, 5.6, 2.4],\n",
+       "        [6.4, 3.1, 5.5, 1.8],\n",
+       "        [6. , 3. , 4.8, 1.8],\n",
+       "        [6.9, 3.1, 5.4, 2.1],\n",
+       "        [6.7, 3.1, 5.6, 2.4],\n",
+       "        [6.9, 3.1, 5.1, 2.3],\n",
+       "        [5.8, 2.7, 5.1, 1.9],\n",
+       "        [6.8, 3.2, 5.9, 2.3],\n",
+       "        [6.7, 3.3, 5.7, 2.5],\n",
+       "        [6.7, 3. , 5.2, 2.3],\n",
+       "        [6.3, 2.5, 5. , 1.9],\n",
+       "        [6.5, 3. , 5.2, 2. ],\n",
+       "        [6.2, 3.4, 5.4, 2.3],\n",
+       "        [5.9, 3. , 5.1, 1.8]]),\n",
+       " array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
+       "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
+       "        0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
+       "        1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
+       "        1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
+       "        2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
+       "        2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2]))"
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#逻辑回归对鸢尾花数据集\n",
+    "from sklearn import datasets\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.linear_model import LogisticRegression\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import matplotlib\n",
+    "import numpy as np\n",
+    "from sklearn.datasets import load_iris\n",
+    "iris=load_iris()\n",
+    "iris.data,iris.target"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "455a18cd",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#sigmoid函数\n",
+    "def sigmoid(x):\n",
+    "    return 1/(1+np.exp(-x))\n",
+    "\n",
+    "#x为将-10到10分成100份\n",
+    "x=np.linspace(-10,10,100)\n",
+    "y=sigmoid(x)\n",
+    "plt.plot(x,y)\n",
+    "#[0.5]乘len是为了保持和x的数目一致，有这么多个0.5\n",
+    "plt.plot(x,[0.5]*len(y),'r--')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "edfd6016",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#手动实现逻辑回归\n",
+    "class Logistic:\n",
+    "    def __init__(self,lr=0.01,n_iters=101):\n",
+    "        self.lr=lr\n",
+    "        self.n_iters=n_iters\n",
+    "        self.weights=None\n",
+    "        self.bias=None\n",
+    "    \n",
+    "    def fit(self,x,y):\n",
+    "        [m,d]=np.shape(x)\n",
+    "        self.weights=np.zeros(d)\n",
+    "        self.bias=0\n",
+    "        for i in range(self.n_iters):\n",
+    "            linear_model=np.dot(x,self.weights)+self.bias\n",
+    "            y_pred=self.sigmoid(linear_model)\n",
+    "            \n",
+    "            dw=(1/m)*np.dot(x.T,(y_pred-y))\n",
+    "            db=(1/m)*np.sum(y_pred-y)\n",
+    "            \n",
+    "            self.weights-=self.lr*dw\n",
+    "            self.bias-=self.lr*db\n",
+    "            \n",
+    "            if i%100==0:\n",
+    "                loss=self.cross_entropy(y,y_pred)\n",
+    "                print(f'loss at iteration {i} : {loss}')\n",
+    "                \n",
+    "    def predict(self,x):\n",
+    "        linear_model=np.dot(x,self.weights)+self.bias\n",
+    "        y_pred=self.sigmoid(linear_model)\n",
+    "        for i in range(len(y_pred)):\n",
+    "            if y_pred[i]>0.5:\n",
+    "                y_pred[i]=1\n",
+    "            else:\n",
+    "                y_pred[i]=0   \n",
+    "        return y_pred       \n",
+    "    \n",
+    "        \n",
+    "    def sigmoid(self,x):\n",
+    "        return 1/(1+np.exp(-x))\n",
+    "    \n",
+    "    def cross_entropy(self,y_true,y_pred):\n",
+    "        return -np.mean(y_true*np.log(y_pred)+(1-y_true)*np.log(1-y_pred))\n",
+    "        "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "97172fd7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x=iris.data[:,:2]\n",
+    "y=iris.target\n",
+    "\n",
+    "#括号中代表索引下标  你、\n",
+    "\n",
+    "   \n",
+    "x=x[y!=2]\n",
+    "y=y[y!=2]\n",
+    "\n",
+    "x_train,x_test,y_train,y_test=train_test_split(x,y,test_size=0.2,random_state=0)\n",
+    "\n",
+    "clf=LogisticRegression()\n",
+    "clf.fit(x_train,y_train)\n",
+    "\n",
+    "y_pred=clf.predict(x_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "a40fc0f7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#混淆矩阵\n",
+    "import seaborn as sns\n",
+    "from sklearn.metrics import confusion_matrix\n",
+    "from sklearn.metrics import accuracy_score\n",
+    "score=accuracy_score(y_test,y_pred)\n",
+    "plt.title(f\"The accuracy score is {score*100}%\")\n",
+    "cm=confusion_matrix(y_test,y_pred)\n",
+    "sns.heatmap(cm,annot=True)\n",
+    "plt.xlabel('Predicted')\n",
+    "plt.ylabel('True')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "d67e6803",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#ROC曲线\n",
+    "from sklearn.metrics import roc_curve,auc\n",
+    "\n",
+    "fpr,tpr,thresholds=roc_curve(y_test,y_pred)\n",
+    "roc_auc=auc(fpr,tpr)\n",
+    "plt.figure()\n",
+    "plt.plot(fpr,tpr,color='darkorange',lw=2,label='ROC curve (area=%0.2f)' % roc_auc) #伪真率为横坐标，真正率为纵坐标\n",
+    "plt.plot([0,1],[0,1],color='navy',lw=2,linestyle='--') #画对角线\n",
+    "#x和y的范围\n",
+    "plt.xlim=([0,1])\n",
+    "plt.ylim=([0,1.05])\n",
+    "#横纵坐标的含义\n",
+    "plt.xlabel('False Positive Rate')\n",
+    "plt.ylabel('True Positive Rate')\n",
+    "plt.title('Receiver operating characteristic example')\n",
+    "#legend表示标签放在右下角\n",
+    "plt.legend(loc=\"lower right\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "78d77fb7",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\99319\\AppData\\Local\\Temp\\ipykernel_26672\\1895947525.py:14: UserWarning: You passed a edgecolor/edgecolors ('k') for an unfilled marker ('+').  Matplotlib is ignoring the edgecolor in favor of the facecolor.  This behavior may change in the future.\n",
+      "  scatter1=plt.scatter(x[y==1,0],x[y==1,1],c='purple',edgecolors='k',marker='+',label='Class 1')\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#定义网格范围，绘制决策边界\n",
+    "x_min,x_max=x[:,0].min()-1,x[:,0].max()+1\n",
+    "y_min,y_max=x[:,1].min()-1,x[:,1].max()+1\n",
+    "xx,yy=np.meshgrid(np.arange(x_min,x_max,0.02),\n",
+    "                  np.arange(y_min,y_max,0.02))\n",
+    "\n",
+    "#对每个点在模型中预测\n",
+    "Z=clf.predict(np.c_[xx.ravel(),yy.ravel()])\n",
+    "#reshape成网格的形状\n",
+    "Z=Z.reshape(xx.shape)\n",
+    "#绘制等高线，用不同颜色表示分类区域\n",
+    "plt.contourf(xx,yy,Z,1,alpha=0.8)\n",
+    "scatter0=plt.scatter(x[y==0,0],x[y==0,1],c='yellow',edgecolors='k',marker='o',label='Class 0')\n",
+    "scatter1=plt.scatter(x[y==1,0],x[y==1,1],c='purple',edgecolors='k',marker='+',label='Class 1')\n",
+    "\n",
+    "plt.legend(handles=[scatter0,scatter1])\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "268e5dfc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.preprocessing import StandardScaler,MinMaxScaler\n",
+    "from sklearn.metrics import confusion_matrix,accuracy_score,classification_report,mean_squared_error\n",
+    "from sklearn.svm import SVC\n",
+    "plt.rcParams['font.sans-serif']=['SimHei']\n",
+    "plt.rcParams['axes.unicode_minus']=False"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "33757302",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[-0.91520253,  1.05978084],\n",
+       "       [ 0.32315141, -1.02666268],\n",
+       "       [-0.91520253,  0.82795378],\n",
+       "       [-0.04835477, -0.79483563],\n",
+       "       [-0.54369635,  1.52343495],\n",
+       "       [-0.91520253,  1.52343495],\n",
+       "       [ 0.81849298, -0.09935445],\n",
+       "       [-1.53437949,  0.82795378],\n",
+       "       [-1.03903792, -2.41762503],\n",
+       "       [-0.17219017, -0.09935445],\n",
+       "       [ 1.93301152, -0.56300857],\n",
+       "       [ 2.3045177 ,  1.75526201],\n",
+       "       [ 0.19931601, -0.33118151],\n",
+       "       [ 0.81849298, -0.09935445],\n",
+       "       [-0.04835477, -0.79483563],\n",
+       "       [ 0.4469868 , -0.33118151],\n",
+       "       [-0.91520253, -1.25848974],\n",
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+       "       [ 1.06616377,  0.1324726 ],\n",
+       "       [-1.16287331,  0.1324726 ],\n",
+       "       [ 0.81849298, -0.56300857],\n",
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+       "       [-0.79136713,  1.05978084],\n",
+       "       [ 1.06616377,  0.1324726 ],\n",
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+       "       [-0.04835477, -0.56300857],\n",
+       "       [-0.17219017, -1.25848974],\n",
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+       "       [ 0.32315141, -0.33118151],\n",
+       "       [ 1.06616377, -0.09935445],\n",
+       "       [-1.28670871, -0.09935445],\n",
+       "       [ 0.69465759,  0.1324726 ],\n",
+       "       [-0.17219017, -0.33118151],\n",
+       "       [-1.03903792, -1.72214386],\n",
+       "       [-0.79136713,  0.82795378],\n",
+       "       [-0.17219017, -1.02666268],\n",
+       "       [ 1.06616377,  0.1324726 ],\n",
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+       "       [-0.04835477, -0.79483563],\n",
+       "       [-1.03903792,  0.59612672],\n",
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+       "       [-1.16287331, -1.4903168 ],\n",
+       "       [ 0.57082219,  0.59612672],\n",
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+       "       [ 0.32315141, -0.56300857],\n",
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+       "       [ 0.57082219, -1.72214386],\n",
+       "       [-1.16287331,  0.1324726 ],\n",
+       "       [-1.03903792,  0.36429966],\n",
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+       "       [ 1.06616377,  0.59612672],\n",
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+       "       [-0.91520253,  1.75526201],\n",
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+       "       [-1.03903792,  1.05978084],\n",
+       "       [-1.16287331,  1.29160789],\n",
+       "       [ 0.4469868 , -0.56300857],\n",
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+       "       [ 0.81849298, -0.09935445],\n",
+       "       [-0.41986095, -1.4903168 ],\n",
+       "       [-1.16287331, -1.25848974],\n",
+       "       [-0.29602556, -0.79483563],\n",
+       "       [ 2.3045177 , -1.02666268],\n",
+       "       [-1.78205028,  0.36429966],\n",
+       "       [-1.53437949,  0.1324726 ],\n",
+       "       [ 0.4469868 , -1.95397092],\n",
+       "       [-0.29602556, -0.09935445],\n",
+       "       [ 0.19931601, -0.09935445],\n",
+       "       [-0.41986095,  2.68257024],\n",
+       "       [ 2.3045177 , -0.09935445],\n",
+       "       [ 2.55218849,  1.75526201],\n",
+       "       [-0.54369635,  1.98708907],\n",
+       "       [-0.41986095, -1.72214386],\n",
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+       "       [-1.03903792, -0.09935445],\n",
+       "       [-0.29602556, -0.33118151],\n",
+       "       [-0.29602556, -0.56300857],\n",
+       "       [-0.41986095, -1.4903168 ],\n",
+       "       [-0.17219017,  1.75526201],\n",
+       "       [-0.17219017, -0.56300857]])"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.datasets import load_iris\n",
+    "iris=load_iris()\n",
+    "x=iris.data[:,:2]\n",
+    "y=iris.target\n",
+    "x_train,x_test,y_train,y_test=train_test_split(x,y,test_size=0.3)\n",
+    "scaler=StandardScaler()\n",
+    "scaler.fit(x_train)\n",
+    "x_train_std=scaler.transform(x_train)\n",
+    "x_test_std=scaler.transform(x_test)\n",
+    "x_train_std"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "1428bb57",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\99319\\AppData\\Local\\Temp\\ipykernel_26672\\2116216888.py:27: UserWarning: You passed a edgecolor/edgecolors ('black') for an unfilled marker ('x').  Matplotlib is ignoring the edgecolor in favor of the facecolor.  This behavior may change in the future.\n",
+      "  plt.scatter(x=X[y == cl, 0],\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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x48pQ24rIH9zN+lXt+bhgNW8veZdzJ09xQccOnDhczbmzYptIdeuZ/3mGlatWctOim0jLSKMov4iVc1cCaEBXtsSEYZFtxoABJj+StkyNYxl33GG/z/nevZSkpzduf/y4q61xXQlQorMpDYOtMfDBB9Bx0jrGDO/k126KM26dydemj6Fjr7q1QCdLdvHp8ndY8soLLe7j2GFjuWnRTfS/oi4Jvfu93fxl9l/YuH1ji++volO/pH5bjTEZdp/pnLmKCIFOdPrScNQsUlcZumN/mV/3KCnez7Bx3vPjw8alUhKgBOipE6dIy0jzupaWkcapE6cCcn8VezSYq7ALRqITGld2NvVL6JBK78pQX3r1TWX7Bu/AvX3Dfnr1TXX9XDsdkjtQlF/kda0ov4gOyR1c3ysQ/VGRT+fMQ8xtRadT+y8OH+YCu4lZh0pPJ/86dowdx441uh7NiU6oq/T0zI17kp9t20JWlvPfKy+0KkPHDMdxusUY6NrpLlY99hRT/ssakW/fsJ9Vj62nW8cHePgH+Rw5/htKiq3g3rXTXQy9fDwX9/mr3xWj06ZPY+XclUyZP6V2znzV3FVMmz7NVUXqgX3Xc/pkK+740fHan8PSp1KaVfGqIpsG8xBzW9Hp1L5naSklbdo0uu5U6emkAvtEZ4VDnwJ2dqdnfjwIgdy70rNu1YpnFYuvBOWlXdMpKDtIWVWZYzAXgaGXW2eHfrr8N7zxhBW0R2X8iD2fwftbn2LKf13HrbVB/im+LPmIs63+wUQ/K0Y9Sc5XZ3uvZhk6YqjfFanPZy8m/aL2fPrRZADu+NFxlj6VwtoV7itenfqpIocmQEPMKeHolFh0at9z924OJiU1ut6rooKS+gupA9yfgAhBorP+MkSP+qtYfMnL8y8ZapdInTndPjG6cs6LXhWjAF98+AXv/vpdXnCRMJ1560xG3zO60X0aVqR6rm/+9btkZv6RtSvqpmdu+Pap2pG6v/d3208VHJoAVREjVInO+pWeHv4EcrC6VV44sMlkqF0i1SkxGqiKUbcVqSXF+7njR96/YTkFcl/318rWyKfBXIVMsBKddhpWeoL13t9fRC/tms7RI1BY7m7fc6fEaHMqRhumLaqr3Vek9uqbytKnvH/zWvpUiuPPwen+Wtka+XTOPMI5JSidnKuuJuOOOxpdd0qYNmsrWreCmOi041TpWX8O3Z8RennhQAooZODl/j+3a6e7eOWB/6ZL71a1xURH/1nNoK/cxuvZuV5z5r7O1pz9H90o2vMPuvZ6lpJ9++nVJ5XDJbNo1+4H5Gb/wrYi9bn/eIlWrc9TfqacpHZJVFe2oW/q/9bOkdefMwf7EbqeARq9NJiHmNuKTqcEZTn2yc5EcJUwdb0VrVshKgSqz7nS07rub3WmJxn6ZpF/Z4aKwAVtDcld23DVDO8y/0v6D2ds1gC/ztasroaiPf/g0MlfknljFrdOvJgtrx9gxxO/pDsPcO+P7+aVBhWp//fsLpKSWzF53jT6XNabfR/+k9WPrOV42TZu/u5VtYHbM+XSvqP9LpF6Bmj00gRohIu0hKkrYQjk9dklKN2W2fubDPUIVAJxxr/PJP3Gq0jpXVcBevyfu9mb8zZL/tj4PmOHjeXGX95En4y6xOu+/F3kPPAXNny8scU/BxUZNAGqQi6UFZ1O7BKUbvmbDPUIVAKxZN9+Rk682OvayIkXU7LP/j6nTpwifWSa17X0kVbFaCB+Diry+XugcxcRuUZEuga7Qyr6NUx0uq1AtEv8Nec+gWrvJhnanASi3XN79Ully+sHvK5vef0AvfrY36dDcgf2binyurZ3S/MqRlV08ucM0M7AX4HXgEUiMsEYU2rTbikwGHjNGDM/4D2NMk6Vm1+eOMFFycmNrjtVgLr1RWkpw0sb/ePhPHDWZqvbSrBNmDarPzaJTreVmAsXwpkzMGcOtGplBfLHH4eyMhgzxv/75ObC7t25nCh/lkOlRXTvlkZy0iz698+iW/dc1r9Vd/268bMoPZTl2D4rqy4Z2mn4QZ/TLb4SiPffeT8b39pIxfkKEtskMnb8WC4f9UKjCs3f/CqFrW/fx7b8n3JhnwQqzpwksV1Hjuyron3CL9iQu5FXflevQvM/ppOedhs5c5dz4/wppI9MY++WInLmriI97TbHaZVwnW2qZ6oGhz8J0GHAbGPM+zWBfQSwvn4DEZkKJBhjrhCRl0RkgDFmVxD6GzWcKjd7HTniqgLUbcI0EXjF5vp4oPEhcFaC1dUZo05s5sfdVmJWV1uBfPt2K4DPmWP9+fHH0KMHvP22f/cxxgrkOw/M44aHJzFo5C3s3FLM2gXzOHwkn+oOr3Pd/ZNIHXwL+3cUs/rJebQ6lU9p+euN2lv3y/KrMhScE4hrV6zlg48/4DvPf4e+X+9L8QfF5MzJYdeOe8D8Dqir0PxrzWqTCzokceWdmfQY0IV/7TpKbnYe58u2snjx640qSQf1nw1AzgMv11aMpqfdxrXf+jEijcv2w3W2qZ6pGjx+J0BF5CpgPnC9MeZEg8/+F8g1xqwTkX8H2hpjfut0r3hIgIZra9leU6Y4nul50OZMz4D0x0ei020lpmckvn173bVhw+Dhh2HNGv/vs2DhDYyYOYpOfeu+t7LiveTMXcL3np5B32F114u37+X39y3hxvkzGrXf9sL7PPyTtYD7ZGh9X+/3db7z/Hfo941+tdf2bN7DH37wB2b+5+eNKjTztvw7g78zhuR6laQnSnaR46OSdPHyF/xOdLqtJA1UBWi4nhsrWpwAFREBbgGOYQ3mGmoPlNS8Pgr0sLnHDBHJF5H8UrejPhWxmkp0uq3EbNXKCtz1PfwwJCTA5Mne1ydPdr7PodIiBo3s63Vt0Mi+nDlzktTB3tdTB1vX7dofKi2qndP2JEM//WeZ/UNr2M2BV5yvoO/Xve/f9+t9qThfwe33ef/3cPt9xynZt5/hDSpJhzdRSeom0Rmus031TNXg8SuYG8ssYDvwLZsmp4C2Na872N3XGLPEGJNhjMnoFuwilQhWVV3NjqKiRl//clEYFBHy8vyq6HRbibluHdx7r/e1e++FX/7SGrF7/p4x1vvcXPv7dO+Wxs4txV7Xdm4pJjGxo+31Vgkd+ejtYq/7f/R2MeXn0rz6O/zCdFY/cwm/WnyajW9sZOatM/nm6G8y89aZbHxjI6+82NGrwtKzS2FC60SKP/B+bvEHxSS0TmTmTd5jn5k39aB1Yt+AVZLaac7Zpnbfr1t6pmrwNBnMReRBEfHUrXQCymyabQXG1LweDhQFoG8xa3BCQqOv6lBsORso9ROdfgRyz9z2woXWn5s22Qf0qip47TXYvx+6d4c//tH685//tE4C2r4dunSBJ5+0/ty+3bre8EdnDCQnzWLtgnWUFe/lop5VlBXvZc2CdSSZu1izYB0Fm/dSVVlFwWbretWpu1iXvY5Du6z2h3bt5bVfrKP85Czefruuv6tWwYdr08n7x3s88+wzjL5nNPevv5/R94zm+ecW88nWt1i7okNtQPdUXKZedA05c3LYs3kPlecr2bN5DzlzckhqlcW7G9rSvWclOZtL6N6zks1vteVIySxWPbaekyW7uGTAWU6W7GLVY+tJT7MqSb/48AuqKqv44sMvyM3OZfr3p7v6Rzj9+9PJtbnPtOnTbK8PHW7t1lj/+1383GLXAd3tc91+X/GsyTnzmqTnCiAJKACeBW41xsyt1yYZ2AT8A5gIjDLGOM6lxMOcudNqlr2lpaS3bpx33g8cXLmyxc/tNW0a2FSGnjOGS7o2XlnqenWNy0Kg5qxm2bvXOiLUs5qlogKSkqBzZzh6tO4+XbpARgZMnGj/XLvVKf36ZbFnTy7bPn2WysoiWrdOY8TXrOv/+EcuR0/XXe/Sfhb/9m9ZnDvXeK4+v+gqrnvoG3xt5Fdrrze1S+EDd9msZhn5Am+ua8e/vmxd+331uKiSjsnVXNDudQ6XtWxfdF/crCpZ/tvlAdtNUVezNJ+vOfOAVYDWBP1rgLeNMT6PaomHYO4kLFvOBkhzN8pyW4lZVQUPPFD3/pe/tObMq6vh/vvrrj/5pBXw3T7XGPjJT+quL1xoXXe6v137H84eyo/evJuU5DYkipVwrqqs4snrnuSvm19jyuhete1XvVvi8/utroYbv1HXPmdzSe1zI6Vy85ujv8n96+8noXVC7TXP9/vau6+Fp1NxKCQVoMaYY8aYFU0FchWdWrLjoZvEnDHWqpX61qyxAp7bXRDtnus0h19dDTk53tdzcpyf2zs1jZJthzh3vqr2elO7FNolRo2Bl572bv/S0ym2gdvzPhzHwOluipFP92YJsagbmYdwx0NPoM1dn0u7zs9iTBEiaZw5Oovycmu649vfhhtvtALtihXQsyfcdlsu6zd4FwFdPqLxHI7Tbopvvw3HjlnTQd/+NkydCitXWvdv2xaSknJp16Vef47NYugQ2H1wNm0uhDNlp+nYpSMVJyq4bOg83srdgUl8iYrzJ0ls0xGpuJ0RI+/n0sxy7vxxXXHQi79K4aO8JPYXJzba1dDpAIlXXuzo+hi4QLBbH15/N8VgTvuoOr5G5rprYoi5LQIKqxBvXSsCpYdz6dBrHlP/q66oZ+Wj8zj5GZSXZ1FQYAXhggIoL4dTp3NZvWGedxHQQqvYp2FAd9pN0fPbwPnzsGOH9T+LHTus9wmtc7kwfR5Tf16vP4/No+zERJLaJzHqtpF069+JY/uO8v6L7/P5jr+Q1H0LUx+fWnt258o5/8fuz6vYXzy3dufCpU+l8NdXO5Dat4Lr6wVuX7saGgOnT7by2sK2qWPgAsWpGAoIyDFzelxdy+nIXNkL046HCxbewNj7RzUq6tn45PtUnl7LJ5/UtR06FFq3d27vKfZpyG4u2rPUsWGxUkI7+/v//r664qOzcorEpCoO7/iSZ257htteuI3+V9QVaO1+bzd/mf0Xbvv+R7aJUfB/brz+CpmG9wnHfHqgdonU4+r8o7smKlfCuePhodIi26KeQ6VFzJ3r3XbuXN/tndjNRbdqZW0fUN+cOc73r198VH2qA5WVcNGQi6g4X0FaRppX+7QMa/dCp+Pb3OQU6o/eG94nHAK1S6QeV9dyGsyVF38LgXy9b4nu3dLYv8O7uGb/jmK6d0vj8ce92z7+uO/2Tuz679lGwN/7t2vXsfZ6+/Zgzl3Anm3FJLZJpCi/yKt9Ub61e6Gb49t89T0Q9wmUQCVGNcHachrMlcVT0dlEIVBurvcqktqkpUMlpltdk2ex8rF1FG+3inqKt+9l5aPr2Fs4i+3bramPP/3J+nP7dijYNoul977Eouk/Z/43H2DR9J+z9N6XOH1slt/9z8mB++7D9v5Hv5zF+ie9+7N+4TomjL6LNQv+zLa3NlJc/Ak7N73P6v9ey2VXXkbOnBwKNxVy+uRpCjcVkjMnh/S022rntle9W8IN3z7lVVzkj/pTLC25TyA5FQEFqohJi4b8pwlQVTc/3kSi0+0uiG4ZA926ZrF1K/z5oWcx5o81q0ceoUP7LPr0rtsad84cmD8fPt2RT5v2FVx511i69etO6Z5DrH/y7xw9mk91dZbXOnSn/r/zjrWOfehQ7/s//ji0a5fFuPGw/slnOVT6R7p3S2Py+EcAeG/7cjb/32ZOHztF+84dOHeygoSOF1NxZgevzn619izOxIRELuk/nG+M9y/R6UTEan+DnwnTUAjUMXN6XF3LaQI03rlMdLrdBdEtX/c3xrtIqLoafjC7LzcumkpaRl3CsSh/NzmzV/LCU97TI825v1NRklOi9rf3Pcf3nv9eo8pQt7sa+hJJxUQqtDQBqmw1J9HpdhdEt3zdv2FgbdUKzpw5Sb/MNK/r/TLTOHPGfs212/s7cUqMnjt9lh4DUzlXfa72enN2NfRFj4FTdjSYx6mWlOa7rcT0da+G733d3659u3Yd2ZNX5HV9T14R7dp1dHxmIPrvlBht374jJdsOeW2Po4k8FQo6Zx5vWlAI5FRBWX8O2t9Rot0GXDk58PnncOhQ4/t/9hl85StWQU/99mUH72L1Iy8yed6N9MtMY09eEasfyeHMsbsaTZM01f8+ffyrJAW4bvwsVi+cx3U/mUTq4L7s31Fcmxjd8PTrSNJV9MtI5dCnh7wqJZUzrQBtGQ3m8aSFhUBOFZRgXfc3kPtKRHbvbn//zz6zPvdc8wThthfM5VAhrLjnN1RUnCQxsSPHD95F795zbacjnPpfejiXD7/wr5K0/rWGidHLR2SxdVsGr859lpPnV5A2oJcm8vygFaAtpwnQeBHAis5AJOB8JSKh8f3Bvv3kyXX/I/AYM6ZuBO9v/59Y5L6StCkFrd+lW78yJqQNbNbfjydaAeofTYDGuUBXdAYiAecrEWl3f6f2rVpZgbs+X4Hcrr8izaskbcqQytEcPQIHK3Uj0aZoBWjLaTCPcS3ZujaYlZ5uE52+tq4NZkLTbSVpQ+WFA3nn4zJ3nYlDWgHacjpnHqtsEp1bt+Wy/i3/EnxuTwhywxj4xS+sefBvfatuDnzNGti4Ea66qnGi8/PP4Ysi761xc3NnUVCQxdGj1t9pSULWKaHpKRCy+/nYnWTUv3+W18/n0q7pFJQdpLC8kIFJ7qZb4ikhOP3701mc7bzFrmpak8FcRFKAPwIJwGngFmPM+QZtWgN7a74A7jHGfIIKD5v58a3b/N8qNtiVng2fVf/PqirrOfUTne+8A4ltbLbGfWwe58rhqquyWpSQBd8JTbs+796dy84D87jh4UkMGnkLO7cUs3bBvJrPs7yeXXmwJwUFZXQafpCerXv61Z94SwhqBWjL+XMG6A+BXcaYv4nI88Drxpg1DdqMwAryfv0urwnQ4KmdVmmQ6PS1taxdgi8UlZ45OY0Tl1OmwOrVjZ+7c69z/382e23IKyIXLLyBETNH0alvXX/Kivey7QX7n+dHh/dy0TcK/U6GakJQ2WlRAtQY85wx5m81b7sBh2yajQKuF5E8EVlaM1L3IiIzRCRfRPJLbQ5nUC3nNT/eINHpNsEXikpPu8Rlq1b2z/XV/3BURB4qLWLQSO/+DBrp/PM8vzedo0egsLzQr/trQlC55XcCVESuADobY963+fgD4GpjTCaQCExq2MAYs8QYk2GMyegWiafqRDM/djx0m+ALZKWn0/3dnLnZnARlMHXvlsbOLd792bnFuT+ZmXBy3SQOfOnf/TUhqNzyK5iLSBfg18DtDk22G2M8/5rmAwMC0DflDz8rOm23ln1sHV2TG28V27BScuFC689NmwIT0I2BH//YOmNzzBjr/mPGWO9vv906k7Phc5OT7LeivW68/Va3wWSM1Z+1C9ZRVryXi3pWUVa8l7UL1pGcNMvx55OZCUePwJtFTY/OdUtY5ZY/CdA2wKvAz4wxjbehs7wsIo8DBcAUYEHAeqic+VkI5Gtr2W5DshrNMQeq0tNXf6qqrDM8G57pecEFdXPn3s/N4oru/iUog00E+ve3nrvthWfJnW/1Z9DFj9C/f5bPn8/JdZNg0joOpvpOhmpCULnlTwL0B1jB+eOaS28BicaYufXaDAGWAwKsMcbMaXSjejQB2nJu1483J6EZzK1Wq6ut/cgbnuk5d27jwqFI3eK1uT8frQxVzeUrAarl/FGoJTse/uQnde8XLgxvkKyuhltuqXv/pz/53nY2lhS0fpexY/F7qaJSoOX8scPPo93suK24DDanMzerq4P/7EBpyc+t8mBP3vm4TEv9VcBoBWiEmDB7Nidslmwmp6Tw5qJFQdu69rPP4KtfDU6lp5PqauvMzf37YcSIuiPatm2zrj/9dOSP0FtaIeupDC2rKtPRuQoIDeYR4sTx4+TbLNnMOH48aFvXGmOVyYei0rNhfxISICkJBg+23g8eDJ9+al2PxPnx+gJVIducylClnGgwj3BnT1TU7XjYAllZ3kGmftGOJxB5glMgKz3tiMCvfgUrV8LmzdYXwLRpMHVq5Afz+qtsWvJzu7RrOh8Vwo4LC+mZpsFctUyE/zIb38oOnrVetDCQe7jZWjbYAVXECtz1RUMg9wjUz81tZahSTjSYR6LTp61AntgGkpOD+qhgV3pG2nM9z/b13p/2geq/28pQpZzoNEukOXKEsop2ViC/sAscLw3aowJ5pmc0PBf837q2fns3Z5U2p/+ZmbDzCLyJ/xtxKdWQBvMIkZySQsaBA5ytagNJVdC6Co6XkpTSPWjPDHalZ6Q91+3Wtc05q7S5/R90eBI7aboyVCknWjQUIVpyIlBLBbPSM9Ke63brWrdnlbak/wWt3yWhUxk3X66jc2VPi4YiXDgDOQTmTM9oea7brWvdnlXaEkMqR1NV1kkLiVSzaDAPpxZUdKrmcbt1bagTtVoZqppLg3m4LFtmVXSOG++6olM1j9uta4O9FbCdS7umU144kLKqssDfXMU0TYCGQwsrOlXzuN26NlyJWrC2BGaI+0OgVfzSBGgI1c6Ng06rhJHbxGs4ErVuzwxV8cFXAlRH5qHQgk2yVOC5TVyGI1F7fm86RwcWUniRjs6Vf3TOPNiiYG48HFvgKt+0MlS5pcE8mKJgbjw31zuZ50n65eaGs1cK3J0ZqlSTwVxEUkTkdRF5Q0Ryas4EtWu3VETeE5G5dp/Hm7XZO+p2O4zQQF6/wtET0D2rN86e1RF6JBh0eBJHj6BLFVWT/Jkz/w6wyBjzNxF5HsgC1tRvICJTgQRjzBUi8pKIDDDG7ApCfyNfFM2PB2orVxVcVWWdeOfjMsYM12PmlLMmR+bGmOeMMX+redsNOGTTbBywoub1G8CYhg1EZIaI5ItIfqnNiToxIYoCuUe4tsBV/vNUhirli99z5iJyBdDZGPO+zcftgZKa10eBHg0bGGOWGGMyjDEZ3WxO1Il6UZDotBPOrWiV/7QyVDXFr6WJItIF+DVwk0OTU0DbmtcdiLPEqrV+vOWnAYVaOLeiVe54TiUqG1KoUy3Klj8J0DbAq8DPjDHFDs22Uje1MhwoCkjvokC4N8lqCacKxyuvDH6Fo2qeggI9lUjZ82dkfgcwApgjInOAt4BEY0z9VSurgE0icjEwERgV6I5GnCicH7djdzaojsgj06Vd08lbl86BC9cxMC3cvVGRpslgbox5Hni+iTYnRGQccA2QbYyJ0QxnjRgJ5B7h2gJXNc9RPZVI2QjY3LYx5pgxZoUxJrYzNFGa6FSxwVMZqlRDujeLC9Ga6FSxRc8MVXbiatVJS0RzolPFnkGHJ1G6R08lUnV0ZN6UGJsfV7FFK0OVh47MffHMj2sgVxFIK0NVfRrMndTf8VADuYpQnspQXXuudJrFhiY6VbTwVIYyRIN5vNOReQOa6FTRSCtDlQZzj7w8K5Bf3EsDuYoql3ZN11OJlAZzQAuBVEzQU4nimwbzKDjaTammZGZaa89V/IrrBKgmOlWsOXoE/nykkJsv18rQeBO3I3NNdKpYNOjwJKrKtDI0HsXfyFwrOlUc0MrQ+BNfI3NNdKo4MKRyNOWFOs0Sb+ImmK/N3qGJThVXtDI0vvgVzEWkh4hs8vF5axHZJyIbar6GBq6LLec1P66BXMWBS7um6+g8zjQ5Zy4inYHfAe19NBsGvGKMiaxsos6Pqzh2fm86BQWFHLhQ9z2PB/6MzKuAW4ATPtqMAq4XkTwRWSoi4U+saiBXcU5PJYovTQZzY8wJP870/AC42hiTCSQCjf4NEpEZIpIvIvmlx4N8RKgmOpWqpZWh8SFQI+jtxpjymtf5wICGDYwxS4AlABkDBpgAPbcRLQRSqk5mJnB4Ev+6cF24u6KCLFCrWV4WkeEikgBMAT4O0H1d0UIgpeyV7unEn7cWajFRDHMdzEVksIjMb3D5MeBl4CPgPWPM3wPQN//pjodK+aSnEsU+v6dZjDHjav7cAcxt8FkB1oqW0NNEp1J+08rQ2BXdRUOa6FTKb1oZGtvCv4SwmTTRqVTzvPNxGUOGlDEwSQN7LInKkbkmOpVqHq0MjV3RNTLX+XGlWkwrQ2NT9IzMdX5cqYDQytDYFB3BXI92UyqgMjO1MjTWRPw0iyY6lQqOQYcnsZN1kBbunqhAiOiRuSY6lQquqjKtDI0VkTky10SnUiExpHI0BWXvhrsbKgAib2SuiU6lQqryYE/e+bhMR+dRLrKCuSY6lQo5z9rzsqqycHdFtUDETLNoolOp8CooAIYUamVolIqIkbkmOpUKL60MjX7hHZlrolOpiKGVodEtfCNzTXQqFVG0MjS6hSWYHz94VhOdSikVQOEZmSe2sebHNZArFVEyM60j5rTMP/qEJ5hf2CUsj1VKNW1I5WiOHkHXnUcZv4K5iPQQkU1NtFkqIu+JyFxf7ZRSka+qrJMWEkWZJoO5iHQGfge099FmKpBgjLkCSBeRAYHrolIq1PQA6Ojjz9LEKuAWYLWPNuOAFTWv3wDGALvqNxCRGcCMmrenvvUt+cxVT8OjK3A43J0IIf1+Y5t+v9Gvr9MHTQZzY8wJABHx1aw9UFLz+igwwuY+S4AlTT0vkohIvjEmI9z9CBX9fmObfr+xLVAJ0FNA25rXHQJ4X6WUUn4IVNDdijW1AjAcKArQfZVSSvnBdTm/iAwGphtj6q9aWQVsEpGLgYnAqMB0L+yialooAPT7jW36/cYwMcYE5kbWqpdrgLeNMbqeSSmlQihgwVwppVT4aKJSKaVigAZzH2oqXz8Mdz+CTURai8g+EdlQ8zU03H0KBRF5TkRuCHc/gk1EflDvn+1HIvJCuPsUTCLSWUTWiUh+rH+v9Wkw9+1J6pZcxrJhwCvGmHE1X5+Eu0PBJiJXAj2NMWvD3ZdgM8Y87/lnC2wCfhPmLgXbbcAfataYdxSRuFhrrsHcgYhMAE4D8ZDMHQVcLyJ5NXvsRMxxgsEgIolYAa1IRCaHuz+hIiK9gB7GmPxw9yXIjgBDRKQT0Bv4Z3i7ExoazG2ISBvgEeChcPclRD4ArjbGZAKJQKyfUPA9YAeQDWSKyD1h7k+ozAKeD3cnQuAdrLL3e4GdWFXpMU+Dub2HgOeMMWXh7kiIbDfGfFnzOh+I9Y3SLgOW1CyhXQaMD3N/gk5EWmF9nxvC3JVQ+DlwtzHmMaAQ+H6Y+xMSGsztXQ3MEpENwKUi8mKY+xNsL4vIcBFJAKYAH4e5P8G2G0iveZ0BFIexL6FyJbDFxMda5M7A0Jp/n0cC8fA96zrzpojIhprEUcwSkSHAckCANcaYOWHuUlCJSEfgJaAH1rTSzcaYEt9/K7qJyAIg3xizMtx9CTYRyQR+izXV8h5wozHmVHh7FXwazJVSKgboNItSSsUADeZKKRUDNJgrpVQM0GCulFIxQIO5UkrFAA3mSikVA/4foE2ZH9XkgTkAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from matplotlib.colors import ListedColormap\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "#resolution为区间步长\n",
+    "def plot_decision_regions(X, y, classifier, test_idx=None, resolution=0.02):\n",
+    "\n",
+    "    # setup marker generator and color map\n",
+    "    markers = ('s', 'x', 'o', '^', 'v')\n",
+    "    colors = ('red', 'blue', 'lightgreen', 'gray', 'cyan')\n",
+    "    #按照标签种类数取图像颜色的种类数\n",
+    "    cmap = ListedColormap(colors[:len(np.unique(y))])\n",
+    "\n",
+    "    # 图像的横纵坐标取值范围\n",
+    "    x1_min, x1_max = X[:, 0].min() - 1, X[:, 0].max() + 1\n",
+    "    x2_min, x2_max = X[:, 1].min() - 1, X[:, 1].max() + 1\n",
+    "    #将间距分成网格\n",
+    "    xx1, xx2 = np.meshgrid(np.arange(x1_min, x1_max, resolution),\n",
+    "                           np.arange(x2_min, x2_max, resolution))\n",
+    "    #ravel的是为了降成1维，然后合并成两个特征的矩阵进行预测\n",
+    "    Z = classifier.predict(np.array([xx1.ravel(), xx2.ravel()]).T)\n",
+    "    Z = Z.reshape(xx1.shape)\n",
+    "    plt.contourf(xx1, xx2, Z, alpha=0.3, cmap=cmap)\n",
+    "    #plt.xlim(xx1.min(), xx1.max())\n",
+    "    #plt.ylim(xx2.min(), xx2.max())\n",
+    "\n",
+    "    for idx, cl in enumerate(np.unique(y)):\n",
+    "        plt.scatter(x=X[y == cl, 0], \n",
+    "                    y=X[y == cl, 1],\n",
+    "                    alpha=0.8, \n",
+    "                    c=colors[idx],\n",
+    "                    marker=markers[idx], \n",
+    "                    label=cl, \n",
+    "                    edgecolor='black')\n",
+    "\n",
+    "    # highlight test samples\n",
+    "    if test_idx:\n",
+    "        # plot all samples\n",
+    "        X_test, y_test = X[test_idx, :], y[test_idx]\n",
+    "\n",
+    "        plt.scatter(X_test[:, 0],\n",
+    "                    X_test[:, 1],\n",
+    "                    c='',\n",
+    "                    edgecolor='black',\n",
+    "                    alpha=1.0,\n",
+    "                    linewidth=1,\n",
+    "                    marker='o',\n",
+    "                    s=100, \n",
+    "                    label='test set')\n",
+    "        \n",
+    "svc=SVC(kernel='linear')\n",
+    "svc.fit(x,y)\n",
+    "plot_decision_regions(x,y, classifier=svc, test_idx=None, resolution=0.02)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "a990dd86",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\99319\\AppData\\Local\\Temp\\ipykernel_26672\\2116216888.py:27: UserWarning: You passed a edgecolor/edgecolors ('black') for an unfilled marker ('x').  Matplotlib is ignoring the edgecolor in favor of the facecolor.  This behavior may change in the future.\n",
+      "  plt.scatter(x=X[y == cl, 0],\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#核svm\n",
+    "svm=SVC(kernel='rbf',random_state=1,gamma=0.10,C=10.0)\n",
+    "svm.fit(x,y)\n",
+    "plot_decision_regions(x,y,classifier=svm)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "added54b",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/共享民宿平台担保交易房子评分的影响研究/torch_reg.ipynb b/共享民宿平台担保交易房子评分的影响研究/torch_reg.ipynb
new file mode 100644
index 0000000..f3d96ab
--- /dev/null
+++ b/共享民宿平台担保交易房子评分的影响研究/torch_reg.ipynb
@@ -0,0 +1,863 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "a31d3c40-3593-4ca3-980e-578ee51e171a",
+   "metadata": {},
+   "source": [
+    "# 用神经网络进行回归预测"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "738c03b4-3ca0-4a87-9143-53ddea5179be",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import torch\n",
+    "import torch.nn as nn\n",
+    "import torch.optim as optim\n",
+    "from torch.utils.data import Dataset, DataLoader\n",
+    "from sklearn.preprocessing import StandardScaler\n",
+    "from sklearn.model_selection import train_test_split"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "637063c9-5924-416c-8201-adaae8514ffd",
+   "metadata": {},
+   "source": [
+    "设置神经网络超参数，批大小为500，学习率为0.01，一共分别进行100次正向传播和反向传播"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "b909615c-783d-4bfb-acf6-1bc3bacfbb18",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# torch参数\n",
+    "batch_size = 500\n",
+    "lr = 0.01\n",
+    "max_epochs = 100\n",
+    "num_workers = 0\n",
+    "device = torch.device(\"cuda:1\" if torch.cuda.is_available() else \"cpu\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "904cfade-5667-440a-a13c-34fdf57f7b0f",
+   "metadata": {},
+   "source": [
+    "定义所需数据集类"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "f6e65156-8535-49c2-8d9d-ecb8639855c9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "variables = ['number_of_reviews', 'price', 'review_scores_rating']\n",
+    "# 数据集类\n",
+    "\n",
+    "\n",
+    "class USDataset(Dataset):\n",
+    "    def __init__(self, df):\n",
+    "        '''\n",
+    "        初始化\n",
+    "        df: 处理后的数据集\n",
+    "        '''\n",
+    "        self.df = df\n",
+    "        self.info = df[['number_of_reviews', 'price']].values\n",
+    "        self.target = df['review_scores_rating'].values\n",
+    "\n",
+    "    def __getitem__(self, index):\n",
+    "        '''\n",
+    "        根据编号返回信息\n",
+    "        index: 样本编号\n",
+    "        '''\n",
+    "        info = self.info[index]\n",
+    "        target = self.target[index]\n",
+    "        return info, target\n",
+    "\n",
+    "    def __len__(self):\n",
+    "        '''\n",
+    "        返回数据集样本个数\n",
+    "        '''\n",
+    "        return len(self.df)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fc574f2e-6443-48af-9fee-dcf5fb29af58",
+   "metadata": {},
+   "source": [
+    "读入数据，由于样本量很大，直接删除有缺失值的样本。"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "17f8aedb-abf1-4622-98ec-d525a779274b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>price</th>\n",
+       "      <th>number_of_reviews</th>\n",
+       "      <th>review_scores_rating</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>120.0</td>\n",
+       "      <td>90.0</td>\n",
+       "      <td>4.50</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>90.0</td>\n",
+       "      <td>351.0</td>\n",
+       "      <td>4.58</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>66.0</td>\n",
+       "      <td>67.0</td>\n",
+       "      <td>4.52</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>33.0</td>\n",
+       "      <td>297.0</td>\n",
+       "      <td>4.70</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>125.0</td>\n",
+       "      <td>58.0</td>\n",
+       "      <td>4.96</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>203252</th>\n",
+       "      <td>152.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>4.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>203253</th>\n",
+       "      <td>45.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>3.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>203254</th>\n",
+       "      <td>40.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>203276</th>\n",
+       "      <td>43.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>5.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>203308</th>\n",
+       "      <td>110.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>5.00</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>162429 rows × 3 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        price  number_of_reviews  review_scores_rating\n",
+       "0       120.0               90.0                  4.50\n",
+       "1        90.0              351.0                  4.58\n",
+       "2        66.0               67.0                  4.52\n",
+       "3        33.0              297.0                  4.70\n",
+       "4       125.0               58.0                  4.96\n",
+       "...       ...                ...                   ...\n",
+       "203252  152.0                1.0                  4.00\n",
+       "203253   45.0                1.0                  3.00\n",
+       "203254   40.0                1.0                  1.00\n",
+       "203276   43.0                1.0                  5.00\n",
+       "203308  110.0                1.0                  5.00\n",
+       "\n",
+       "[162429 rows x 3 columns]"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# 读入数据\n",
+    "df = pd.read_csv('./data/2022-01(US_25).csv', usecols=variables)\n",
+    "df['price'] = df['price'].replace('\\$', '', regex=True)\n",
+    "df['price'] = df['price'].replace('\\,', '', regex=True).astype(float)\n",
+    "df[['number_of_reviews']] = df[['number_of_reviews']].astype(float)\n",
+    "for col in variables:\n",
+    "    df[col] = df[col].astype(np.float32)\n",
+    "    df = df[np.isnan(df[col]) != 1]\n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "758d8171-9310-4a51-b9a6-b0fa6274ffe1",
+   "metadata": {},
+   "source": [
+    "将数据标准化"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "2cbe7cab-42cd-4b3b-bf11-080cc0a0f4c4",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "df_train:\n",
+      "            price  number_of_reviews  review_scores_rating\n",
+      "0      -0.331270          -0.218031                  4.71\n",
+      "1      -0.030076           0.450276                  4.99\n",
+      "2       0.425112          -0.603593                  5.00\n",
+      "3      -0.324476          -0.590741                  2.50\n",
+      "4       0.017481          -0.577889                  4.00\n",
+      "...          ...                ...                   ...\n",
+      "113695 -0.381091           0.128974                  4.98\n",
+      "113696 -0.220303          -0.282292                  4.96\n",
+      "113697 -0.165953          -0.603593                  3.00\n",
+      "113698 -0.152365          -0.539333                  4.00\n",
+      "113699  0.162417          -0.590741                  5.00\n",
+      "\n",
+      "[113700 rows x 3 columns]\n",
+      "============================================\n",
+      "df_test:\n",
+      "           price  number_of_reviews  review_scores_rating\n",
+      "0     -0.249743           0.411720                  4.84\n",
+      "1     -0.211245          -0.565037                  5.00\n",
+      "2     -0.376562          -0.603593                  5.00\n",
+      "3     -0.088956          -0.539333                  4.33\n",
+      "4      0.517962           0.013306                  4.90\n",
+      "...         ...                ...                   ...\n",
+      "48724  0.028804          -0.192327                  4.91\n",
+      "48725 -0.367503          -0.500777                  5.00\n",
+      "48726 -0.360710          -0.436517                  4.86\n",
+      "48727 -0.084426          -0.603593                  1.00\n",
+      "48728  0.067303          -0.603593                  5.00\n",
+      "\n",
+      "[48729 rows x 3 columns]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# 固定划分测试集和训练集\n",
+    "col_df = df.columns\n",
+    "info = df.iloc[:, :-1].values\n",
+    "target = df.iloc[:, -1].values\n",
+    "# 标准化\n",
+    "stdscaler = StandardScaler()\n",
+    "info_train, info_test, target_train, target_test = train_test_split(\n",
+    "    info, target, test_size=0.3, random_state=420)\n",
+    "info_train = stdscaler.fit_transform(info_train)\n",
+    "info_test = stdscaler.transform(info_test)\n",
+    "df_train = np.hstack((info_train, target_train.reshape(-1, 1)))\n",
+    "df_test = np.hstack((info_test, target_test.reshape(-1, 1)))\n",
+    "df_train = pd.DataFrame(df_train, columns=col_df)\n",
+    "df_test = pd.DataFrame(df_test, columns=col_df)\n",
+    "print(\"df_train:\\n\", df_train)\n",
+    "print(\"============================================\")\n",
+    "print(\"df_test:\\n\", df_test)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1a9661ee-3dc4-4d5c-8a8d-bab3dc299d41",
+   "metadata": {},
+   "source": [
+    "设置训练集和验证集"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "a10021bb-4e08-49b2-8269-114b8ce37be6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 初始化数据集\n",
+    "train_data = USDataset(df_train)\n",
+    "test_data = USDataset(df_test)\n",
+    "train_loader = DataLoader(train_data, batch_size=batch_size,\n",
+    "                          num_workers=num_workers, shuffle=True, drop_last=True)\n",
+    "test_loader = DataLoader(test_data, batch_size=batch_size,\n",
+    "                         num_workers=num_workers, shuffle=False)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "41a15589-9aaf-4541-a603-30a9f433c385",
+   "metadata": {},
+   "source": [
+    "设计并初始化网络，其中神经网络是有一个输入层，一个输出层，三个隐藏层的前馈神经网络，激活函数是Sigmoid函数"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "764f28bc-81dd-4924-9039-5eceba54d739",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[Parameter containing:\n",
+      "tensor([[-0.3798,  0.4787],\n",
+      "        [-0.6687,  0.3603],\n",
+      "        [-0.4871,  0.1701],\n",
+      "        [ 0.1127, -0.2635],\n",
+      "        [ 0.6846,  0.6578],\n",
+      "        [-0.2820,  0.6887],\n",
+      "        [ 0.6581, -0.6334],\n",
+      "        [ 0.1057, -0.2249],\n",
+      "        [ 0.0650,  0.6174],\n",
+      "        [ 0.1768, -0.5373],\n",
+      "        [-0.5809,  0.6241],\n",
+      "        [ 0.1711,  0.3307],\n",
+      "        [ 0.0546,  0.6606],\n",
+      "        [ 0.0528,  0.1988],\n",
+      "        [ 0.6816, -0.2682]], device='cuda:0', requires_grad=True), Parameter containing:\n",
+      "tensor([-0.0835, -0.5775, -0.2021,  0.6027, -0.6389,  0.3593,  0.1442,  0.0746,\n",
+      "        -0.6356,  0.3029, -0.5204, -0.4724,  0.2451, -0.0103, -0.6133],\n",
+      "       device='cuda:0', requires_grad=True), Parameter containing:\n",
+      "tensor([[-0.2190,  0.1091,  0.1611,  0.0540,  0.2386, -0.0399,  0.0086, -0.2273,\n",
+      "          0.1421,  0.0286,  0.0507, -0.1721,  0.1504, -0.1714,  0.0113],\n",
+      "        [ 0.1312,  0.0581,  0.1805,  0.1385, -0.1394, -0.2539, -0.0047,  0.0753,\n",
+      "          0.2134,  0.1119,  0.0534, -0.1073,  0.2128,  0.1909,  0.2403],\n",
+      "        [ 0.0414,  0.1597, -0.2470, -0.0118,  0.1226, -0.1825,  0.2529,  0.2161,\n",
+      "          0.0246,  0.2333, -0.0733,  0.1474,  0.2101,  0.0246,  0.0514],\n",
+      "        [ 0.1203,  0.0301,  0.1293, -0.0800, -0.1533, -0.1585, -0.0815, -0.1242,\n",
+      "          0.1415, -0.1520,  0.1287, -0.0701, -0.2560, -0.0094, -0.2361],\n",
+      "        [-0.1045,  0.0645,  0.2181, -0.0610, -0.0585, -0.1396,  0.2380,  0.2466,\n",
+      "         -0.2495, -0.0690,  0.1078, -0.1838,  0.0231, -0.0264, -0.1335],\n",
+      "        [ 0.0328,  0.2338,  0.0761,  0.2368, -0.0891, -0.1581,  0.1605, -0.0699,\n",
+      "          0.1658, -0.0683, -0.2559, -0.0122,  0.1695,  0.2399, -0.1445],\n",
+      "        [-0.2489,  0.1559, -0.0083,  0.0980, -0.1653,  0.0678, -0.1833, -0.0217,\n",
+      "         -0.0746,  0.0633, -0.0559,  0.0334,  0.1273, -0.1259, -0.1527],\n",
+      "        [ 0.2253, -0.0182,  0.1613, -0.1602, -0.2205, -0.2047,  0.2046, -0.0824,\n",
+      "          0.1261, -0.1015,  0.2257, -0.1276, -0.0197, -0.0505, -0.2408],\n",
+      "        [ 0.1883, -0.0590, -0.1909, -0.0710, -0.0304, -0.0085, -0.1848,  0.0480,\n",
+      "         -0.0447,  0.1001,  0.2330, -0.1368, -0.1478, -0.1676,  0.1942],\n",
+      "        [ 0.0554, -0.1686, -0.2217, -0.0343,  0.0218,  0.2300,  0.0303,  0.0711,\n",
+      "         -0.0932, -0.1791,  0.0978, -0.1902, -0.2322, -0.1179, -0.1592]],\n",
+      "       device='cuda:0', requires_grad=True), Parameter containing:\n",
+      "tensor([-0.0498, -0.1153,  0.0559,  0.1391, -0.0231,  0.1752,  0.1824, -0.1185,\n",
+      "        -0.1845,  0.1776], device='cuda:0', requires_grad=True), Parameter containing:\n",
+      "tensor([[-0.1260, -0.2593, -0.2229,  0.0065,  0.0831, -0.2702, -0.0523,  0.2718,\n",
+      "          0.2547,  0.0824],\n",
+      "        [ 0.2193, -0.2204,  0.2573,  0.2804,  0.2895, -0.1308,  0.0558, -0.2521,\n",
+      "         -0.2073, -0.2508],\n",
+      "        [ 0.2057, -0.2991,  0.0932, -0.1904,  0.2148, -0.1540,  0.0756, -0.1294,\n",
+      "         -0.2314, -0.3153],\n",
+      "        [ 0.0636, -0.1243,  0.0715,  0.0041, -0.0620, -0.0212,  0.0812,  0.1220,\n",
+      "         -0.1704,  0.1767],\n",
+      "        [ 0.0371, -0.1627,  0.1664,  0.0266,  0.2154, -0.2726, -0.2169, -0.2568,\n",
+      "          0.0378, -0.1306],\n",
+      "        [-0.0030, -0.1174, -0.0169, -0.2378,  0.2668, -0.1097, -0.0550, -0.3043,\n",
+      "          0.1614,  0.2220]], device='cuda:0', requires_grad=True), Parameter containing:\n",
+      "tensor([ 0.2263, -0.1245, -0.0600, -0.1303,  0.2717, -0.2886], device='cuda:0',\n",
+      "       requires_grad=True), Parameter containing:\n",
+      "tensor([[-0.0118, -0.0705, -0.3342, -0.0979, -0.0048, -0.1048]],\n",
+      "       device='cuda:0', requires_grad=True), Parameter containing:\n",
+      "tensor([0.3463], device='cuda:0', requires_grad=True)]\n",
+      "==================================================\n",
+      "[Parameter containing:\n",
+      "tensor([[ 0.1109, -0.5283],\n",
+      "        [-0.3936, -0.6835],\n",
+      "        [ 1.6326, -1.0302],\n",
+      "        [ 1.3571,  1.2765],\n",
+      "        [-2.0980,  1.2285],\n",
+      "        [-0.2051,  0.3710],\n",
+      "        [-0.8627,  0.1593],\n",
+      "        [ 0.2369,  0.0855],\n",
+      "        [ 1.7011, -1.2683],\n",
+      "        [ 0.0239, -0.0731],\n",
+      "        [ 0.3759,  0.6586],\n",
+      "        [ 0.1748,  1.2761],\n",
+      "        [ 0.7805, -0.3940],\n",
+      "        [-1.7492, -0.3534],\n",
+      "        [-0.1294,  2.1254]], device='cuda:0', requires_grad=True), Parameter containing:\n",
+      "tensor([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
+      "       device='cuda:0', requires_grad=True), Parameter containing:\n",
+      "tensor([[-1.0957,  1.0378,  0.3581,  0.6767, -0.0237,  0.6258, -0.6001,  0.4281,\n",
+      "          0.0584, -0.7526, -0.7916,  0.6348,  0.2372, -0.0493, -1.0156],\n",
+      "        [ 0.5839,  0.1032,  1.3735, -0.1767, -1.5280,  0.9311,  1.2752,  0.0721,\n",
+      "         -0.0195, -1.0396, -1.2622, -0.2573,  1.1950,  1.2330, -1.1773],\n",
+      "        [-1.7392, -0.7176,  0.2932, -1.0405,  1.1690, -0.2543, -0.3711, -0.5608,\n",
+      "          0.2679, -0.1302,  1.1744,  0.6578,  0.2809, -0.4317,  0.3950],\n",
+      "        [ 0.4147, -0.5277, -1.3396,  0.6607,  0.3036,  1.3448, -0.6774,  0.2073,\n",
+      "          1.4682,  1.1449,  0.9241,  1.1630,  0.8778, -0.8538,  1.1645],\n",
+      "        [-0.2414, -1.1791, -0.9192, -1.3586,  0.7888, -1.2873, -0.7585,  0.1478,\n",
+      "         -0.2461, -1.7109, -1.3293, -0.1607, -0.0435,  0.7059,  0.8339],\n",
+      "        [ 0.5776, -1.4641, -0.8456,  1.4503, -0.0230,  0.9254, -1.1829, -0.7663,\n",
+      "         -0.5102, -1.7416, -0.2776,  1.8553,  0.0287,  1.3071, -0.0577],\n",
+      "        [-2.0167,  0.5340,  0.2045, -1.0659,  0.1410, -0.2611,  1.8272, -0.3517,\n",
+      "         -1.3150,  0.9499,  1.4403,  2.6355,  1.0081, -0.1915, -1.7888],\n",
+      "        [ 0.5569, -0.7237, -0.2397,  1.7227,  1.0570,  0.1441, -0.0228,  1.0863,\n",
+      "          0.5095, -0.1426, -1.6044, -0.4300,  1.5130,  0.7247, -0.1672],\n",
+      "        [-0.2202,  0.8956, -0.5899,  0.6426,  1.0715,  2.8716,  0.6675, -0.3307,\n",
+      "          0.4560, -1.6545,  0.6043,  1.4544,  0.3106,  2.0657,  0.4080],\n",
+      "        [-1.5387,  0.4301,  1.4037,  1.2928,  0.9105,  0.2546, -1.9276, -0.9019,\n",
+      "         -0.0115,  0.4342, -0.1543,  1.0857,  0.7773,  0.0530,  1.0929]],\n",
+      "       device='cuda:0', requires_grad=True), Parameter containing:\n",
+      "tensor([0., 0., 0., 0., 0., 0., 0., 0., 0., 0.], device='cuda:0',\n",
+      "       requires_grad=True), Parameter containing:\n",
+      "tensor([[ 3.2868,  2.1744, -0.4586,  0.3916,  1.0370,  0.2906, -0.6354, -1.7215,\n",
+      "         -1.3256,  1.4936],\n",
+      "        [ 0.5906, -1.0098, -1.7117,  0.8507,  0.3034,  1.7005, -2.9092, -0.1906,\n",
+      "         -1.4068,  2.3292],\n",
+      "        [ 0.2435, -0.9280, -0.4463, -0.4837, -2.3409, -0.5112,  0.6242,  0.9315,\n",
+      "          1.6558, -1.3278],\n",
+      "        [ 0.4638,  0.5903, -0.7866,  1.3214,  0.6855,  0.7143,  1.2887, -0.2633,\n",
+      "         -0.0572,  1.0767],\n",
+      "        [ 1.8814, -1.2857,  0.4642, -0.2101,  2.0882,  1.3320, -0.3117, -0.7563,\n",
+      "          0.7099,  0.2467],\n",
+      "        [ 0.7781, -1.1896, -0.2684,  1.3948, -0.8702,  0.3317,  0.6835,  0.2728,\n",
+      "          0.3942,  1.5071]], device='cuda:0', requires_grad=True), Parameter containing:\n",
+      "tensor([0., 0., 0., 0., 0., 0.], device='cuda:0', requires_grad=True), Parameter containing:\n",
+      "tensor([[ 1.5669,  0.3820,  1.0288,  1.5494, -0.9633, -0.8551]],\n",
+      "       device='cuda:0', requires_grad=True), Parameter containing:\n",
+      "tensor([0.], device='cuda:0', requires_grad=True)]\n"
+     ]
+    }
+   ],
+   "source": [
+    "def initialize(self):\n",
+    "    '''\n",
+    "    初始化网络参数\n",
+    "    '''\n",
+    "    for m in self.modules():\n",
+    "        if isinstance(m, nn.Linear):\n",
+    "            torch.nn.init.normal_(m.weight.data, 0.1)\n",
+    "            if m.bias is not None:\n",
+    "                torch.nn.init.zeros_(m.bias.data)\n",
+    "\n",
+    "\n",
+    "class Net(nn.Module):\n",
+    "    '''\n",
+    "    网络结构\n",
+    "    '''\n",
+    "\n",
+    "    def __init__(self, **kwargs):\n",
+    "        super(Net, self).__init__()\n",
+    "        self.fc = nn.Sequential(\n",
+    "            nn.Linear(2, 15),\n",
+    "            nn.Sigmoid(),\n",
+    "            nn.Linear(15, 10),\n",
+    "            nn.Sigmoid(),\n",
+    "            nn.Linear(10, 6),\n",
+    "            nn.Sigmoid(),\n",
+    "            nn.Linear(6, 1)\n",
+    "        )\n",
+    "\n",
+    "    def forward(self, x):\n",
+    "        x = x.view(-1, 2)\n",
+    "        x = self.fc(x)\n",
+    "        return x.squeeze(-1)\n",
+    "\n",
+    "\n",
+    "# 初始化网络\n",
+    "model = Net()\n",
+    "model = model.cuda()\n",
+    "print(list(model.parameters()))\n",
+    "initialize(model)\n",
+    "print(\"==================================================\")\n",
+    "print(list(model.parameters()))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "20fcccfe-3f82-44c3-bba2-9c66fb15cecd",
+   "metadata": {},
+   "source": [
+    "定义损失函数为均方误差损失函数，优化算法为随机梯度下降"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "7b530a85-50b1-4d0f-b47e-14831d0ee85d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 损失函数\n",
+    "criterion = nn.MSELoss()\n",
+    "# 优化器\n",
+    "optimizer = optim.SGD(model.parameters(), lr=lr)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "8694737f-afb5-4e58-ad14-3220971b142f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def train(epoch):\n",
+    "    '''\n",
+    "    训练器\n",
+    "\n",
+    "    Parameters\n",
+    "    ----------\n",
+    "    epoch : int\n",
+    "\n",
+    "    Returns\n",
+    "    -------\n",
+    "    None.\n",
+    "\n",
+    "    '''\n",
+    "    model.train()\n",
+    "    train_loss = 0\n",
+    "    for info, target in train_loader:\n",
+    "        info = info.cuda()\n",
+    "        target = target.cuda()\n",
+    "        optimizer.zero_grad()\n",
+    "        output = model(info)\n",
+    "        loss = criterion(output, target)\n",
+    "        loss.backward()\n",
+    "        optimizer.step()\n",
+    "        train_loss += loss.item()*info.size(0)\n",
+    "    train_loss = train_loss/len(train_loader.dataset)\n",
+    "    print(\"epoch:%d, train loss:%.4f\" % (epoch, train_loss))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "bb6be6f8-16e6-4c2b-a56c-232b09faa654",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def validate(epoch):\n",
+    "    '''\n",
+    "    验证\n",
+    "\n",
+    "    Parameters\n",
+    "    ----------\n",
+    "    epoch : int\n",
+    "\n",
+    "    Returns\n",
+    "    -------\n",
+    "    None.\n",
+    "\n",
+    "    '''\n",
+    "    model.eval()\n",
+    "    val_loss = 0\n",
+    "    with torch.no_grad():\n",
+    "        for info, target in test_loader:\n",
+    "            info, target = info.cuda(), target.cuda()\n",
+    "            output = model(info)\n",
+    "            loss = criterion(output, target)\n",
+    "            val_loss += loss.item()*info.size(0)\n",
+    "    val_loss = val_loss/len(test_loader.dataset)\n",
+    "    print(\"epoch:%d, validation loss:%.4f\" %\n",
+    "          (epoch, val_loss))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "40532ea8-acfe-469a-aa9f-14d3bec2619d",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "epoch:1, train loss:0.5139\n",
+      "epoch:1, validation loss:0.3730\n",
+      "epoch:2, train loss:0.3496\n",
+      "epoch:2, validation loss:0.3698\n",
+      "epoch:3, train loss:0.3471\n",
+      "epoch:3, validation loss:0.3677\n",
+      "epoch:4, train loss:0.3450\n",
+      "epoch:4, validation loss:0.3662\n",
+      "epoch:5, train loss:0.3439\n",
+      "epoch:5, validation loss:0.3652\n",
+      "epoch:6, train loss:0.3429\n",
+      "epoch:6, validation loss:0.3644\n",
+      "epoch:7, train loss:0.3425\n",
+      "epoch:7, validation loss:0.3635\n",
+      "epoch:8, train loss:0.3418\n",
+      "epoch:8, validation loss:0.3631\n",
+      "epoch:9, train loss:0.3417\n",
+      "epoch:9, validation loss:0.3625\n",
+      "epoch:10, train loss:0.3411\n",
+      "epoch:10, validation loss:0.3621\n",
+      "epoch:11, train loss:0.3405\n",
+      "epoch:11, validation loss:0.3620\n",
+      "epoch:12, train loss:0.3406\n",
+      "epoch:12, validation loss:0.3617\n",
+      "epoch:13, train loss:0.3393\n",
+      "epoch:13, validation loss:0.3615\n",
+      "epoch:14, train loss:0.3401\n",
+      "epoch:14, validation loss:0.3616\n",
+      "epoch:15, train loss:0.3396\n",
+      "epoch:15, validation loss:0.3610\n",
+      "epoch:16, train loss:0.3400\n",
+      "epoch:16, validation loss:0.3608\n",
+      "epoch:17, train loss:0.3390\n",
+      "epoch:17, validation loss:0.3607\n",
+      "epoch:18, train loss:0.3392\n",
+      "epoch:18, validation loss:0.3608\n",
+      "epoch:19, train loss:0.3396\n",
+      "epoch:19, validation loss:0.3606\n",
+      "epoch:20, train loss:0.3396\n",
+      "epoch:20, validation loss:0.3604\n",
+      "epoch:21, train loss:0.3396\n",
+      "epoch:21, validation loss:0.3604\n",
+      "epoch:22, train loss:0.3389\n",
+      "epoch:22, validation loss:0.3603\n",
+      "epoch:23, train loss:0.3389\n",
+      "epoch:23, validation loss:0.3601\n",
+      "epoch:24, train loss:0.3387\n",
+      "epoch:24, validation loss:0.3602\n",
+      "epoch:25, train loss:0.3390\n",
+      "epoch:25, validation loss:0.3600\n",
+      "epoch:26, train loss:0.3393\n",
+      "epoch:26, validation loss:0.3600\n",
+      "epoch:27, train loss:0.3385\n",
+      "epoch:27, validation loss:0.3599\n",
+      "epoch:28, train loss:0.3388\n",
+      "epoch:28, validation loss:0.3598\n",
+      "epoch:29, train loss:0.3388\n",
+      "epoch:29, validation loss:0.3598\n",
+      "epoch:30, train loss:0.3388\n",
+      "epoch:30, validation loss:0.3597\n",
+      "epoch:31, train loss:0.3387\n",
+      "epoch:31, validation loss:0.3596\n",
+      "epoch:32, train loss:0.3390\n",
+      "epoch:32, validation loss:0.3596\n",
+      "epoch:33, train loss:0.3384\n",
+      "epoch:33, validation loss:0.3596\n",
+      "epoch:34, train loss:0.3389\n",
+      "epoch:34, validation loss:0.3595\n",
+      "epoch:35, train loss:0.3385\n",
+      "epoch:35, validation loss:0.3594\n",
+      "epoch:36, train loss:0.3381\n",
+      "epoch:36, validation loss:0.3594\n",
+      "epoch:37, train loss:0.3380\n",
+      "epoch:37, validation loss:0.3593\n",
+      "epoch:38, train loss:0.3387\n",
+      "epoch:38, validation loss:0.3593\n",
+      "epoch:39, train loss:0.3388\n",
+      "epoch:39, validation loss:0.3592\n",
+      "epoch:40, train loss:0.3385\n",
+      "epoch:40, validation loss:0.3592\n",
+      "epoch:41, train loss:0.3386\n",
+      "epoch:41, validation loss:0.3593\n",
+      "epoch:42, train loss:0.3384\n",
+      "epoch:42, validation loss:0.3595\n",
+      "epoch:43, train loss:0.3373\n",
+      "epoch:43, validation loss:0.3590\n",
+      "epoch:44, train loss:0.3376\n",
+      "epoch:44, validation loss:0.3591\n",
+      "epoch:45, train loss:0.3385\n",
+      "epoch:45, validation loss:0.3590\n",
+      "epoch:46, train loss:0.3381\n",
+      "epoch:46, validation loss:0.3589\n",
+      "epoch:47, train loss:0.3382\n",
+      "epoch:47, validation loss:0.3591\n",
+      "epoch:48, train loss:0.3381\n",
+      "epoch:48, validation loss:0.3589\n",
+      "epoch:49, train loss:0.3381\n",
+      "epoch:49, validation loss:0.3588\n",
+      "epoch:50, train loss:0.3379\n",
+      "epoch:50, validation loss:0.3587\n",
+      "epoch:51, train loss:0.3383\n",
+      "epoch:51, validation loss:0.3587\n",
+      "epoch:52, train loss:0.3379\n",
+      "epoch:52, validation loss:0.3587\n",
+      "epoch:53, train loss:0.3378\n",
+      "epoch:53, validation loss:0.3587\n",
+      "epoch:54, train loss:0.3379\n",
+      "epoch:54, validation loss:0.3585\n",
+      "epoch:55, train loss:0.3380\n",
+      "epoch:55, validation loss:0.3586\n",
+      "epoch:56, train loss:0.3378\n",
+      "epoch:56, validation loss:0.3585\n",
+      "epoch:57, train loss:0.3378\n",
+      "epoch:57, validation loss:0.3586\n",
+      "epoch:58, train loss:0.3379\n",
+      "epoch:58, validation loss:0.3587\n",
+      "epoch:59, train loss:0.3374\n",
+      "epoch:59, validation loss:0.3583\n",
+      "epoch:60, train loss:0.3380\n",
+      "epoch:60, validation loss:0.3583\n",
+      "epoch:61, train loss:0.3375\n",
+      "epoch:61, validation loss:0.3587\n",
+      "epoch:62, train loss:0.3375\n",
+      "epoch:62, validation loss:0.3583\n",
+      "epoch:63, train loss:0.3377\n",
+      "epoch:63, validation loss:0.3582\n",
+      "epoch:64, train loss:0.3373\n",
+      "epoch:64, validation loss:0.3584\n",
+      "epoch:65, train loss:0.3374\n",
+      "epoch:65, validation loss:0.3581\n",
+      "epoch:66, train loss:0.3373\n",
+      "epoch:66, validation loss:0.3581\n",
+      "epoch:67, train loss:0.3378\n",
+      "epoch:67, validation loss:0.3581\n",
+      "epoch:68, train loss:0.3370\n",
+      "epoch:68, validation loss:0.3581\n",
+      "epoch:69, train loss:0.3370\n",
+      "epoch:69, validation loss:0.3582\n",
+      "epoch:70, train loss:0.3374\n",
+      "epoch:70, validation loss:0.3580\n",
+      "epoch:71, train loss:0.3374\n",
+      "epoch:71, validation loss:0.3580\n",
+      "epoch:72, train loss:0.3375\n",
+      "epoch:72, validation loss:0.3579\n",
+      "epoch:73, train loss:0.3373\n",
+      "epoch:73, validation loss:0.3580\n",
+      "epoch:74, train loss:0.3371\n",
+      "epoch:74, validation loss:0.3583\n",
+      "epoch:75, train loss:0.3376\n",
+      "epoch:75, validation loss:0.3578\n",
+      "epoch:76, train loss:0.3369\n",
+      "epoch:76, validation loss:0.3577\n",
+      "epoch:77, train loss:0.3371\n",
+      "epoch:77, validation loss:0.3577\n",
+      "epoch:78, train loss:0.3373\n",
+      "epoch:78, validation loss:0.3577\n",
+      "epoch:79, train loss:0.3373\n",
+      "epoch:79, validation loss:0.3578\n",
+      "epoch:80, train loss:0.3374\n",
+      "epoch:80, validation loss:0.3577\n",
+      "epoch:81, train loss:0.3368\n",
+      "epoch:81, validation loss:0.3576\n",
+      "epoch:82, train loss:0.3365\n",
+      "epoch:82, validation loss:0.3577\n",
+      "epoch:83, train loss:0.3371\n",
+      "epoch:83, validation loss:0.3576\n",
+      "epoch:84, train loss:0.3371\n",
+      "epoch:84, validation loss:0.3575\n",
+      "epoch:85, train loss:0.3371\n",
+      "epoch:85, validation loss:0.3574\n",
+      "epoch:86, train loss:0.3370\n",
+      "epoch:86, validation loss:0.3574\n",
+      "epoch:87, train loss:0.3371\n",
+      "epoch:87, validation loss:0.3574\n",
+      "epoch:88, train loss:0.3369\n",
+      "epoch:88, validation loss:0.3574\n",
+      "epoch:89, train loss:0.3364\n",
+      "epoch:89, validation loss:0.3573\n",
+      "epoch:90, train loss:0.3367\n",
+      "epoch:90, validation loss:0.3573\n",
+      "epoch:91, train loss:0.3367\n",
+      "epoch:91, validation loss:0.3575\n",
+      "epoch:92, train loss:0.3367\n",
+      "epoch:92, validation loss:0.3573\n",
+      "epoch:93, train loss:0.3372\n",
+      "epoch:93, validation loss:0.3572\n",
+      "epoch:94, train loss:0.3368\n",
+      "epoch:94, validation loss:0.3572\n",
+      "epoch:95, train loss:0.3369\n",
+      "epoch:95, validation loss:0.3571\n",
+      "epoch:96, train loss:0.3366\n",
+      "epoch:96, validation loss:0.3571\n",
+      "epoch:97, train loss:0.3368\n",
+      "epoch:97, validation loss:0.3571\n",
+      "epoch:98, train loss:0.3367\n",
+      "epoch:98, validation loss:0.3571\n",
+      "epoch:99, train loss:0.3367\n",
+      "epoch:99, validation loss:0.3573\n",
+      "epoch:100, train loss:0.3365\n",
+      "epoch:100, validation loss:0.3571\n"
+     ]
+    }
+   ],
+   "source": [
+    "# 训练过程\n",
+    "for epoch in range(1, max_epochs+1):\n",
+    "    train(epoch)\n",
+    "    validate(epoch)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e3f438d3-813f-4db5-b8e9-da71d54730d4",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/大众点评数据集分析/第七组/第七组实践代码/Boston.py b/大众点评数据集分析/第七组/第七组实践代码/Boston.py
new file mode 100644
index 0000000..c7eaf1d
--- /dev/null
+++ b/大众点评数据集分析/第七组/第七组实践代码/Boston.py
@@ -0,0 +1,86 @@
+import tensorflow as tf
+dataset_path = keras.utils.get_file("housing.data",
+"https://archive.ics.uci.edu/ml/machine-learning-databases/housing/housing.data")
+column_names = ['CRIM','ZN','INDUS','CHAS','NOX',
+'RM', 'AGE', 'DIS','RAD','TAX','PTRATION', 'B', 'LSTAT', 'MEDV']
+raw_dataset = pd.read_csv(dataset_path, names=column_names,
+na_values = "?", comment='\t',
+sep=" ", skipinitialspace=True)
+dataset = raw_dataset.copy()
+#下面的函数用以返回最后n行。
+#dataset.tail(n=10)
+# 将数据集分为训练集和测试集
+# p 为训练集所占数据比例
+p=0.8
+trainDataset = dataset.sample(frac=p,random_state=0)
+testDataset = dataset.drop(trainDataset.index)
+
+import matplotlib.pyplot as plt
+fig, ax = plt.subplots()
+x = trainDataset['RM']
+y = trainDataset['MEDV']
+ax.scatter(x, y, edgecolors=(0, 0, 0))
+ax.set_xlabel('RM')
+ax.set_ylabel('MEDV')
+plt.show()
+trainInput = trainDataset['RM']
+trainTarget = trainDataset['MEDV']
+testInput = testDataset['RM']
+testTarget = testDataset['MEDV']
+
+model = keras.Sequential([
+layers.Dense(1, use_bias=True, input_shape=(1,))
+])
+
+optimizer = tf.keras.optimizers.Adam(
+learning_rate=0.01, beta_1=0.9, beta_2=0.99, epsilon=1e-05, amsgrad=False,
+name='Adam')
+model.compile(loss='mse', optimizer=optimizer, metrics=['mae','mse'])
+
+
+n_idle_epochs = 100
+earlyStopping = tf.keras.callbacks.EarlyStopping(monitor='val_loss',
+patience=n_idle_epochs, min_delta=0.01)
+class NEPOCHLogger(tf.keras.callbacks.Callback):
+def __init__(self,per_epoch=100):
+'''3 实例 4
+display: Number of batches to wait before outputting loss
+'''
+self.seen = 0
+self.per_epoch = per_epoch
+def on_epoch_end(self, epoch, logs=None):
+if epoch % self.per_epoch == 0:
+print('Epoch {}, loss {:.2f}, val_loss {:.2f},
+mae {:.2f}, val_mae {:.2f}, mse {:.2f}, val_mse {:.2f}'\
+.format(epoch, logs['loss'], logs['val_loss'],logs['mae'],
+logs['val_mae'],logs['mse'], logs['val_mse']))
+log_display = NEPOCHLogger(per_epoch=100)
+n_epochs = 2000
+history = model.fit(
+trainDataOne, trainLabelOne, batch_size=256,
+epochs=n_epochs, validation_split = 0.1, verbose=0,
+callbacks=[earlyStopping,log_display])
+#打印训练集和验证集MAE
+import numpy as np
+import pandas as pd
+import seaborn as sns
+mae = np.asarray(history.history['mae'])
+val_mae = np.asarray(history.history['val_mae'])
+num_values = (len(mae))
+values = np.zeros((num_values,2), dtype=float)
+values[:,0] = mae
+values[:,1] = val_mae
+steps = pd.RangeIndex(start=0,stop=num_values)
+data = pd.DataFrame(values, steps, columns=["mae", "va-mae"])
+sns.set(style="whitegrid")
+sns.lineplot(data=data, palette="tab10", linewidth=2.5)
+
+predictions = model.predict(testInput).flatten()
+a = plt.axes(aspect='equal')
+plt.scatter(predictions, testTarget, edgecolors=(0, 0, 0))
+plt.xlabel('True Values')
+plt.ylabel('Predictions')
+lims = [0, 50]
+plt.xlim(lims)
+plt.ylim(lims)
+_ = plt.plot(lims, lims)
\ No newline at end of file
diff --git a/大众点评数据集分析/第七组/第七组实践代码/病马分类.ipynb b/大众点评数据集分析/第七组/第七组实践代码/病马分类.ipynb
new file mode 100644
index 0000000..daef5d7
--- /dev/null
+++ b/大众点评数据集分析/第七组/第七组实践代码/病马分类.ipynb
@@ -0,0 +1,822 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "062c9f2b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "from sklearn import preprocessing\n",
+    "from sklearn.preprocessing import StandardScaler\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.metrics import accuracy_score,confusion_matrix,roc_curve,roc_auc_score\n",
+    "from sklearn.ensemble import RandomForestClassifier,BaggingClassifier,AdaBoostClassifier\n",
+    "from sklearn.naive_bayes import GaussianNB\n",
+    "from sklearn.tree import DecisionTreeClassifier\n",
+    "from sklearn.svm import SVC\n",
+    "from sklearn.linear_model import LogisticRegression\n",
+    "from sklearn.neighbors import KNeighborsClassifier\n",
+    "# 中文正常显示\n",
+    "plt.rcParams['font.sans-serif'] = ['SimHei']\n",
+    "plt.rcParams['axes.unicode_minus'] = False"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "37cfbc42",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>直肠温度</th>\n",
+       "      <th>脉搏</th>\n",
+       "      <th>呼吸频率</th>\n",
+       "      <th>红细胞体积</th>\n",
+       "      <th>总蛋白值</th>\n",
+       "      <th>y</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>38.5</td>\n",
+       "      <td>66</td>\n",
+       "      <td>28</td>\n",
+       "      <td>45.0</td>\n",
+       "      <td>8.4</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>39.2</td>\n",
+       "      <td>88</td>\n",
+       "      <td>20</td>\n",
+       "      <td>50.0</td>\n",
+       "      <td>85.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>38.3</td>\n",
+       "      <td>40</td>\n",
+       "      <td>24</td>\n",
+       "      <td>33.0</td>\n",
+       "      <td>6.7</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>39.1</td>\n",
+       "      <td>164</td>\n",
+       "      <td>84</td>\n",
+       "      <td>48.0</td>\n",
+       "      <td>7.2</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>37.3</td>\n",
+       "      <td>104</td>\n",
+       "      <td>35</td>\n",
+       "      <td>74.0</td>\n",
+       "      <td>7.4</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   直肠温度   脉搏  呼吸频率  红细胞体积  总蛋白值    y\n",
+       "0  38.5   66    28   45.0   8.4  0.0\n",
+       "1  39.2   88    20   50.0  85.0  0.0\n",
+       "2  38.3   40    24   33.0   6.7  1.0\n",
+       "3  39.1  164    84   48.0   7.2  0.0\n",
+       "4  37.3  104    35   74.0   7.4  0.0"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df = pd.read_csv(\"C:/Users/skey/Desktop/data/data_horse.csv\",encoding=\"gbk\")\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "0e8a1d6f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "y        1.000000\n",
+       "直肠温度     0.172629\n",
+       "总蛋白值     0.114651\n",
+       "呼吸频率    -0.036938\n",
+       "红细胞体积   -0.152119\n",
+       "脉搏      -0.276255\n",
+       "Name: y, dtype: float64"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "## 可视化列的缺失值、唯一值、类型\n",
+    "df.corr()\n",
+    "# 可视化变量y与其他的相关性曲线\n",
+    "df.corr()['y'].sort_values(ascending=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "27bb1d29",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>直肠温度</th>\n",
+       "      <th>脉搏</th>\n",
+       "      <th>红细胞体积</th>\n",
+       "      <th>总蛋白值</th>\n",
+       "      <th>y</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>38.5</td>\n",
+       "      <td>66</td>\n",
+       "      <td>45.0</td>\n",
+       "      <td>8.4</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>39.2</td>\n",
+       "      <td>88</td>\n",
+       "      <td>50.0</td>\n",
+       "      <td>85.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>38.3</td>\n",
+       "      <td>40</td>\n",
+       "      <td>33.0</td>\n",
+       "      <td>6.7</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>39.1</td>\n",
+       "      <td>164</td>\n",
+       "      <td>48.0</td>\n",
+       "      <td>7.2</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>37.3</td>\n",
+       "      <td>104</td>\n",
+       "      <td>74.0</td>\n",
+       "      <td>7.4</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   直肠温度   脉搏  红细胞体积  总蛋白值    y\n",
+       "0  38.5   66   45.0   8.4  0.0\n",
+       "1  39.2   88   50.0  85.0  0.0\n",
+       "2  38.3   40   33.0   6.7  1.0\n",
+       "3  39.1  164   48.0   7.2  0.0\n",
+       "4  37.3  104   74.0   7.4  0.0"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# 剔除相关性绝对值小于0.1的变量\n",
+    "df = df[df.columns[df.corr()['y'].abs()>0.1]]\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "263d36c3",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "直肠温度     0\n",
+       "脉搏       0\n",
+       "红细胞体积    0\n",
+       "总蛋白值     0\n",
+       "y        9\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "0d9e3028",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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Sd73rXXn++eeze/fubNq0KVOnTs2IESMyYsSInHDCCUmSVatWpaenJ4sXL864cePS3t6eO+6440iPDgAAHEeOeCw1Nzdn7ty5SZJdu3blpptuysUXX5zHH388vb29mThxYoYMGZLzzz8/Tz/9dJJk/fr1aW1tzdChQ5MkEyZMSGdn537fo1wup1Qq9VsAAAAORtUe8LB+/fqMHTs2P/7xj/P1r389nZ2dOf3003PXXXdlw4YNqauryxVXXJHk1btRLS0tlWNrampSW1u734dDLFq0KI2NjZWlubn5iFwTAABw7KhaLE2YMCE/+clPMn78+MyfPz9z587NmjVr8t73vjfjx4/Prbfemp/+9KcplUqpq6tLfX19v+MHDx6cHTt2FJ574cKF6enpqSxbtmw5EpcEAAAcQ+qq9cY1NTWZNGlSli9fnnHjxmXbtm0ZMWJEZfuJJ56Y3t7ePPvss2lqasrGjRv7Hb99+/YMGjSo8Nz19fX7xBUAAMDBOOJ3lh566KHK0++SZNCgQampqcn111+flStXVtZ3dHRkwIABaW5uzuTJk9PR0VHZtnnz5pTL5TQ1NR3R2QEAgOPHEb+zdNppp+Xb3/52xo8fnwsuuCDXXXddzj333EyaNCnXXXddxowZkz179uSqq67KZZddlqFDh2bq1KkplUpZunRpLr/88rS3t2f69Ompra090uMDAADHiSMeS29+85vzve99L3/zN3+Tv/u7v8t5552XO++8M29605uyadOmXHLJJamtrc28efPS3t7+6pB1dbn99tszZ86ctLW1ZcCAAVm9evWRHh0AADiO1PT19fVVe4gDtXXr1qxduzatra0ZNWrUAR9XKpXS2NiYnp6eNDQ0vI4THnsmtd1Z7REAAHgNa2+8rNojvKEcTBtU7QEPh2Ls2LGZMWNGtccAAACOA1V7dDgAAMDRTCwBAAAUEEsAAAAFxBIAAEABsQQAAFBALAEAABQQSwAAAAXEEgAAQAGxBAAAUEAsAQAAFBBLAAAABcQSAABAAbEEAABQQCwBAAAUEEsAAAAFxBIAAEABsQQAAFBALAEAABQQSwAAAAXEEgAAQAGxBAAAUEAsAQAAFBBLAAAABcQSAABAAbEEAABQQCwBAAAUEEsAAAAFxBIAAEABsQQAAFBALAEAABQQSwAAAAXEEgAAQAGxBAAAUEAsAQAAFBBLAAAABcQSAABAAbEEAABQoGqxtG3btjz22GPp7u6u1ggAAAD7VZVYuvvuu3PKKadk/vz5ectb3pK77747SbJx48ZMnjw5I0eOTFtbW/r6+irHPPTQQznjjDMyevToLF68uBpjAwAAx5EjHks9PT258sor87Of/SyPP/54lixZkra2tpTL5cyaNSuTJk3KmjVr0tnZmWXLliVJurq6Mnv27MyZMycdHR1ZsWJFHnzwwSM9OgAAcBw54rFUKpVy8803Z8KECUmSd73rXXn++eezatWq9PT0ZPHixRk3blza29tzxx13JElWrFiRk046KZ/97Gczfvz4fO5zn6tsAwAAeD0c8Vhqbm7O3LlzkyS7du3KTTfdlIsvvjjr169Pa2trhg4dmiSZMGFCOjs7kyTr16/P+9///tTU1CRJzj777Kxdu3a/71Eul1MqlfotAAAAB6NqD3hYv359xo4dmx//+Mf5+te/nlKplJaWlsr2mpqa1NbWpru7e59tDQ0NeeaZZ/Z77kWLFqWxsbGyNDc3v67XAgAAHHuqFksTJkzIT37yk4wfPz7z589PXV1d6uvr++0zePDg7NixY59te9fvz8KFC9PT01NZtmzZ8rpdBwAAcGyqWizV1NRk0qRJWb58ee655540NTWlq6ur3z7bt2/PoEGD9tm2d/3+1NfXp6Ghod8CAABwMI54LD300ENpa2urvB40aFBqampyxhlnpKOjo7J+8+bNKZfLaWpqyuTJk/ttW7duXU4++eQjOjcAAHB8OeKxdNppp+Xb3/52vv3tb2fLli35+7//+5x77rm58MILUyqVsnTp0iRJe3t7pk+fntra2syePTuPPvpo7r///uzatSs33HBDzjvvvCM9OgAAcBw54rH05je/Od/73vfyta99LWeddVZ27NiRO++8M3V1dbn99tvziU98IqNHj869996bL3/5y0mS0aNH56abbsqFF16YMWPG5Mknn8x11113pEcHAACOIzV9fX191R7iD23dujVr165Na2trRo0a1W/b5s2b88QTT2TKlCkZPnz4AZ+zVCqlsbExPT09vr90kCa13VntEQAAeA1rb7ys2iO8oRxMG9QdoZkO2NixYzNjxozCbS0tLf0eIQ4AAPB6qdrT8AAAAI5mYgkAAKCAWAIAACgglgAAAAqIJQAAgAJiCQAAoIBYAgAAKCCWAAAACoglAACAAmIJAACggFgCAAAoIJYAAAAKiCUAAIACYgkAAKCAWAIAACgglgAAAAqIJQAAgAJiCQAAoIBYAgAAKCCWAAAACoglAACAAmIJAACggFgCAAAoIJYAAAAKiCUAAIACYgkAAKCAWAIAACgglgAAAAqIJQAAgAJiCQAAoIBYAgAAKCCWAAAACoglAACAAmIJAACggFgCAAAoIJYAAAAKiCUAAIACYgkAAKBAVWLp3nvvzdve9rbU1dVl4sSJ+cUvfpEkufrqq1NTU1NZTj311MoxGzduzOTJkzNy5Mi0tbWlr6+vGqMDAADHiSMeS0899VQuv/zyfOlLX8rvfve7nHbaaZk/f36SZM2aNbnvvvvS3d2d7u7urFu3LklSLpcza9asTJo0KWvWrElnZ2eWLVt2pEcHAACOI0c8ln7xi1/kS1/6Uv7iL/4iY8aMycc+9rGsW7cuu3fvzqZNmzJ16tSMGDEiI0aMyAknnJAkWbVqVXp6erJ48eKMGzcu7e3tueOOO4706AAAwHGk7ki/4cyZM/u9fvLJJzN+/Pg8/vjj6e3tzcSJE/O73/0u06ZNy7e//e289a1vzfr169Pa2pqhQ4cmSSZMmJDOzs79vke5XE65XK68LpVKr8/FAAAAx6yqPuBh586d+epXv5oFCxaks7Mzp59+eu66665s2LAhdXV1ueKKK5K8GjstLS2V42pqalJbW5vu7u7C8y5atCiNjY2Vpbm5+YhcDwAAcOw44neW/tDnP//5DBs2LPPnz8/AgQMzd+7cyrZbb701LS0tKZVKqaurS319fb9jBw8enB07dmTkyJH7nHfhwoW59tprK69LpZJgAgAADkrVYumBBx7IkiVL8vOf/zwDBw7cZ/uJJ56Y3t7ePPvss2lqasrGjRv7bd++fXsGDRpUeO76+vp94goAAOBgVOVjeJs3b86cOXOyZMmSnHnmmUmStra2rFy5srJPR0dHBgwYkObm5kyePDkdHR39ji+Xy2lqajriswMAAMeHI35n6eWXX87MmTNz0UUX5eKLL86LL76Y5NWHNlx33XUZM2ZM9uzZk6uuuiqXXXZZhg4dmqlTp6ZUKmXp0qW5/PLL097enunTp6e2tvZIjw8AABwnjngs/eQnP0lnZ2c6Oztz2223VdZv3rw5f/mXf5lLLrkktbW1mTdvXtrb218dsq4ut99+e+bMmZO2trYMGDAgq1evPtKjAwAAx5Gavr6+vmoPcaC2bt2atWvXprW1NaNGjTrg40qlUhobG9PT05OGhobXccJjz6S2O6s9AgAAr2HtjZdVe4Q3lINpg6o+De9gjR07NjNmzKj2GAAAwHGgqr+zBAAAcLQSSwAAAAXEEgAAQAGxBAAAUEAsAQAAFBBLAAAABcQSAABAAbEEAABQQCwBAAAUEEsAAAAFxBIAAEABsQQAAFBALAEAABQQSwAAAAXEEgAAQAGxBAAAUEAsAQAAFBBLAAAABcQSAABAAbEEAABQQCwBAAAUEEsAAAAFxBIAAEABsQQAAFBALAEAABQQSwAAAAXEEgAAQAGxBAAAUEAsAQAAFBBLAAAABcQSAABAAbEEAABQQCwBAAAUEEsAAAAFxBIAAECBwxZLfX192bNnz+E6HQAAQFUdUixdeeWVKZfL/dY98MADOeOMMw7LUAAAANV2SLH0rW99a59YOuuss/Kb3/zmsAwFAABQbQcVS3feeWfuvPPO9PX1ZeXKlZXXy5cvz6c+9am8+93vPqDz3HvvvXnb296Wurq6TJw4Mb/4xS+SJBs3bszkyZMzcuTItLW1pa+vr3LMQw89lDPOOCOjR4/O4sWLD2ZsAACAg3ZQsbR06dIsXbo0NTU1WbFiReX1nXfemfr6+nz3u9/9o+d46qmncvnll+dLX/pSfve73+W0007L/PnzUy6XM2vWrEyaNClr1qxJZ2dnli1bliTp6urK7NmzM2fOnHR0dGTFihV58MEHD+mCAQAADkRN3x/evjlAAwYMyLZt29LQ0HDQb/jP//zPeeaZZ3LFFVckSR588MHMmDEjK1euzF//9V/nt7/9bYYOHZr169fn4x//eB555JHcfPPN+da3vpXOzs7U1NTk3nvvzd13353vfOc7B/SepVIpjY2N6enpOaSZj2eT2u6s9ggAALyGtTdeVu0R3lAOpg3qDuUNPvrRj6a+vv6Qhps5c2a/108++WTGjx+f9evXp7W1NUOHDk2STJgwIZ2dnUmS9evX5/3vf39qamqSJGeffXY+/elP7/c9yuVyv+9UlUqlQ5oVAAA4fh3SAx6+8Y1vpKamJlu2bMnTTz/dbzkYO3fuzFe/+tUsWLAgpVIpLS0tlW01NTWpra1Nd3f3PtsaGhryzDPP7Pe8ixYtSmNjY2Vpbm4++IsEAACOa4cUS7feemsaGxtzyimn9Fv+MGgOxOc///kMGzYs8+fPT11d3T53qwYPHpwdO3bss23v+v1ZuHBhenp6KsuWLVsO7gIBAIDj3iHF0mc/+9l85StfySuvvJLe3t7KcjA/SvvAAw9kyZIlWblyZQYOHJimpqZ0dXX122f79u0ZNGjQPtv2rt+f+vr6NDQ09FsAAAAOxiHF0gknnJAPfOADGThw4CG96ebNmzNnzpwsWbIkZ555ZpJk8uTJ6ejo6LdPuVxOU1PTPtvWrVuXk08++ZDeGwAA4EAcUizdcsstueKKK7Jp06aDPvbll1/OzJkzc9FFF+Xiiy/Oiy++mBdffDFTpkxJqVTK0qVLkyTt7e2ZPn16amtrM3v27Dz66KO5//77s2vXrtxwww0577zzDmV0AACAA3JIjw5vaWnJ888/n5deeikjR47s9zG3//iP/3jNY++999588IMf3Gf95s2bs2HDhsyZMydDhgzJgAEDsnr16sqdp29+85u5+uqrM3z48IwYMSIdHR0ZM2bMAc3r0eGHzqPDAQCObh4dfnBe90eH7/2x2ENx0UUXZX99dsopp+Spp57K2rVr09ramlGjRlW2LViwIOedd16eeOKJTJkyJcOHDz/kGQAAAP6YQ4qlg33q3cEYO3ZsZsyYsd/3fT3fGwAAYK9DiqVTTjklNTU1lTtEe38sNslBPREPAADgaHVID3jY+5jw3t7evPTSS1m9enXOOeec/OAHPzjM4wEAAFTHIcXSHxoyZEimTJmSH/3oR7n++usPx0wAAABV95+Opb2ef/75/P73vz9cpwMAAKiqQ37Awx9+T6m3tzfPPvtsrrnmmsM2GAAAQDUdlkeH19TU5OSTT864ceMOx0wAAABVd0gfw5s2bVqmTZuWIUOGpKurK0OGDBFKAADAMeWQ7iz97ne/y0UXXZRf/vKXOfnkk/PMM8/ktNNOy7333puTTjrpcM8IAABwxB3SnaWPfvSjefe7352urq784he/yP/5P/8n73rXu/KRj3zkcM8HAABQFYd0Z+mRRx7J448/nvr6+iRJfX19PvOZz2TChAmHdTgAAIBqOaQ7S+94xzuyfPnyfuuWL1+et7/97YdlKAAAgGo7pDtL3/jGN3LeeedlxYoVaWlpyX/8x39k+/bt+clPfnK45wMAAKiKQ4qlt7/97fnlL3+ZH/3oR9myZUv+23/7b5kxY0aGDRt2uOcDAACoikP6GF5nZ2emTJmS2tratLW15Qtf+ELe85735Je//OXhng8AAKAqDvlpeNOmTcu5556bJPn5z3+emTNnZsGCBYd1OAAAgGo5pI/h/e///b/zT//0T2lsbEySDBs2LFdddVXOPPPMwzocAABAtRzy0/CWLVvWb913vvOdnHXWWYdjJgAAgKo7pDtLS5YsyQUXXJC77rorLS0t+fWvf50XXnghP/7xjw/3fAAAAFVxSLH0X//rf80vf/nL3HfffdmyZUvmzZuXGTNmpKGh4XDPBwAAUBWHFEtJ0tDQkDlz5hzOWQAAAI4ah/SdJQAAgGOdWAIAACgglgAAAAqIJQAAgAJiCQAAoIBYAgAAKCCWAAAACoglAACAAmIJAACggFgCAAAoIJYAAAAKiCUAAIACYgkAAKCAWAIAACgglgAAAAqIJQAAgAJiCQAAoIBYAgAAKCCWAAAAClQtlp577rm0tLTk17/+dWXd1VdfnZqamspy6qmnVrZt3LgxkydPzsiRI9PW1pa+vr4qTA0AABwvqhJLzz33XGbOnNkvlJJkzZo1ue+++9Ld3Z3u7u6sW7cuSVIulzNr1qxMmjQpa9asSWdnZ5YtW3bkBwcAAI4bVYmlv/qrv8qll17ab93u3buzadOmTJ06NSNGjMiIESNywgknJElWrVqVnp6eLF68OOPGjUt7e3vuuOOO/Z6/XC6nVCr1WwAAAA5GVWLptttuy9VXX91v3eOPP57e3t5MnDgxQ4YMyfnnn5+nn346SbJ+/fq0trZm6NChSZIJEyaks7Nzv+dftGhRGhsbK0tzc/PrdzEAAMAxqSqx1NLSss+6zs7OnH766bnrrruyYcOG1NXV5YorrkiSlEqlfsfU1NSktrY23d3dhedfuHBhenp6KsuWLVtenwsBAACOWXXVHmCvuXPnZu7cuZXXt956a1paWlIqlVJXV5f6+vp++w8ePDg7duzIyJEj9zlXfX39PvsDAAAcjKP20eEnnnhient78+yzz6apqSldXV39tm/fvj2DBg2q0nQAAMCx7qiJpba2tqxcubLyuqOjIwMGDEhzc3MmT56cjo6OyrbNmzenXC6nqampGqMCAADHgaPmY3jvfOc7c91112XMmDHZs2dPrrrqqlx22WUZOnRopk6dmlKplKVLl+byyy9Pe3t7pk+fntra2mqPDQAAHKOOmliaN29eNm3alEsuuSS1tbWZN29e2tvbkyR1dXW5/fbbM2fOnLS1tWXAgAFZvXp1dQcGAACOaTV9fX191R7iQG3dujVr165Na2trRo0adcDHlUqlNDY2pqenJw0NDa/jhMeeSW13VnsEAABew9obL6v2CG8oB9MGR82dpQMxduzYzJgxo9pjAAAAx4Gj5gEPAAAARxOxBAAAUEAsAQAAFBBLAAAABcQSAABAAbEEAABQQCwBAAAUEEsAAAAFxBIAAEABsQQAAFBALAEAABQQSwAAAAXEEgAAQAGxBAAAUEAsAQAAFBBLAAAABcQSAABAAbEEAABQQCwBAAAUEEsAAAAFxBIAAEABsQQAAFBALAEAABQQSwAAAAXEEgAAQAGxBAAAUEAsAQAAFBBLAAAABcQSAABAAbEEAABQQCwBAAAUEEsAAAAFxBIAAEABsQQAAFBALAEAABQQSwAAAAXEEgAAQAGxBAAAUKBqsfTcc8+lpaUlv/71ryvrNm7cmMmTJ2fkyJFpa2tLX19fZdtDDz2UM844I6NHj87ixYurMDEAAHA8qUosPffcc5k5c2a/UCqXy5k1a1YmTZqUNWvWpLOzM8uWLUuSdHV1Zfbs2ZkzZ046OjqyYsWKPPjgg9UYHQAAOE5UJZb+6q/+Kpdeemm/datWrUpPT08WL16ccePGpb29PXfccUeSZMWKFTnppJPy2c9+NuPHj8/nPve5yrYi5XI5pVKp3wIAAHAwqhJLt912W66++up+69avX5/W1tYMHTo0STJhwoR0dnZWtr3//e9PTU1NkuTss8/O2rVr93v+RYsWpbGxsbI0Nze/TlcCAAAcq6oSSy0tLfusK5VK/dbX1NSktrY23d3d+2xraGjIM888s9/zL1y4MD09PZVly5Yth/cCAACAY15dtQfYq66uLvX19f3WDR48ODt27Nhn2971+1NfX7/PuQAAAA7GUfPo8KampnR1dfVbt3379gwaNGifbXvXAwAAvF6OmliaPHlyOjo6Kq83b96ccrmcpqamfbatW7cuJ598cjXGBAAAjhNHTSxNnTo1pVIpS5cuTZK0t7dn+vTpqa2tzezZs/Poo4/m/vvvz65du3LDDTfkvPPOq/LEAADAseyo+s7S7bffnjlz5qStrS0DBgzI6tWrkySjR4/OTTfdlAsvvDDDhw/PiBEjKr/BBAAA8Hqoaiz19fX1ez179uw89dRTWbt2bVpbWzNq1KjKtgULFuS8887LE088kSlTpmT48OFHelwAAOA4ctTcWdpr7NixmTFjRuG2lpaWwseOAwAAHG5HzXeWAAAAjiZiCQAAoIBYAgAAKCCWAAAACoglAACAAmIJAACggFgCAAAoIJYAAAAKiCUAAIACYgkAAKCAWAIAACgglgAAAAqIJQAAgAJiCQAAoIBYAgAAKCCWAAAACoglAACAAmIJAACggFgCAAAoIJYAAAAKiCUAAIACYgkAAKCAWAIAACgglgAAAAqIJQAAgAJiCQAAoIBYAgAAKCCWAAAACoglAACAAmIJAACggFgCAAAoIJYAAAAKiCUAAIACYgkAAKCAWAIAACgglgAAAAqIJQAAgAJiCQAAoMBRF0tXX311ampqKsupp56aJNm4cWMmT56ckSNHpq2tLX19fVWeFAAAOJYddbG0Zs2a3Hfffenu7k53d3fWrVuXcrmcWbNmZdKkSVmzZk06OzuzbNmyao8KAAAcw46qWNq9e3c2bdqUqVOnZsSIERkxYkROOOGErFq1Kj09PVm8eHHGjRuX9vb23HHHHfs9T7lcTqlU6rcAAAAcjKMqlh5//PH09vZm4sSJGTJkSM4///w8/fTTWb9+fVpbWzN06NAkyYQJE9LZ2bnf8yxatCiNjY2Vpbm5+UhdAgAAcIw4qmKps7Mzp59+eu66665s2LAhdXV1ueKKK1IqldLS0lLZr6amJrW1tenu7i48z8KFC9PT01NZtmzZcqQuAQAAOEbUVXuAPzR37tzMnTu38vrWW29NS0tLzjjjjNTX1/fbd/DgwdmxY0dGjhy5z3nq6+v32R8AAOBgHFV3lv5/J554Ynp7ezN27Nh0dXX127Z9+/YMGjSoSpMBAADHuqMqltra2rJy5crK646OjgwYMCDveMc70tHRUVm/efPmlMvlNDU1VWNMAADgOHBUfQzvne98Z6677rqMGTMme/bsyVVXXZXLLrss5557bkqlUpYuXZrLL7887e3tmT59empra6s9MgAAcIw6qmJp3rx52bRpUy655JLU1tZm3rx5aW9vT11dXW6//fbMmTMnbW1tGTBgQFavXl3tcQEAgGNYTV9fX1+1hzhQW7duzdq1a9Pa2ppRo0Yd8HGlUimNjY3p6elJQ0PD6zjhsWdS253VHgEAgNew9sbLqj3CG8rBtMFRdWfpjxk7dmxmzJhR7TEAAIDjwFH1gAcAAICjhVgCAAAoIJYAAAAKiCUAAIACYgkAAKCAWAIAACgglgAAAAqIJQAAgAJiCQAAoIBYAgAAKCCWAAAACoglAACAAmIJAACggFgCAAAoIJYAAAAKiCUAAIACYgkAAKCAWAIAACgglgAAAAqIJQAAgAJiCQAAoIBYAgAAKCCWAAAACoglAACAAmIJAACggFgCAAAoIJYAAAAKiCUAAIACYgkAAKCAWAIAACgglgAAAAqIJQAAgAJiCQAAoIBYAgAAKCCWAAAACoglAACAAmIJAACggFgCAAAo8IaKpY0bN2by5MkZOXJk2tra0tfXV+2RAACAY9QbJpbK5XJmzZqVSZMmZc2aNens7MyyZcuqPRYAAHCMqqv2AAdq1apV6enpyeLFizN06NC0t7fn4x//eC6//PJ99i2XyymXy5XXPT09SZJSqXTE5j1W7Cm/XO0RAAB4Df4/7sHZ+/c6kE+pvWFiaf369Wltbc3QoUOTJBMmTEhnZ2fhvosWLcr111+/z/rm5ubXdUYAADjSGm9ZUO0R3pC2b9+exsbG19znDRNLpVIpLS0tldc1NTWpra1Nd3d3Ro4c2W/fhQsX5tprr6287u3tzQsvvJBRo0alpqbmiM0MwNGlVCqlubk5W7ZsSUNDQ7XHAaAK+vr6sn379px00kl/dN83TCzV1dWlvr6+37rBgwdnx44d+8RSfX39PvuOGDHi9R4RgDeIhoYGsQRwHPtjd5T2esM84KGpqSldXV391m3fvj2DBg2q0kQAAMCx7A0TS5MnT05HR0fl9ebNm1Mul9PU1FTFqQAAgGPVGyaWpk6dmlKplKVLlyZJ2tvbM3369NTW1lZ5MgDeKOrr6/P5z39+n49qA0CRmr430C+7/vCHP8ycOXMyZMiQDBgwIKtXr86ZZ55Z7bEAAIBj0BsqlpJk69atWbt2bVpbWzNq1KhqjwMAAByj3nCxBAAAcCS8Yb6zBAAAcCSJJQAAgAJiCQAAoEBdtQcAgCOhp6cnO3bsyKBBg9LU1JSamppqjwTAUc6dJQCOWcuXL8+UKVMyatSonHbaaXnPe96TU089NcOHD89FF12UJ554otojAnAUE0sAHJM+9alP5Xvf+16WLFmS559/Pr///e/z9NNPp7u7Oxs3bsyJJ56Yc845J93d3dUeFYCjlEeHA3BMGjVqVNasWZOWlpb97vOmN70pd955Zy644IIjOBkAbxTuLAFwTBo3bly+8pWv5JVXXincvnz58rz88suZNGnSEZ4MgDcKd5YAOCZt3Lgxs2fPTqlUSmtra1paWlJfX5+urq78z//5P7N9+/bcdtttmTVrVrVHBeAoJZYAOGbt3Lkz//Iv/5INGzakVCqlrq4uTU1NmTx5cqZOnZra2tpqjwjAUUwsAQAAFPCdJQAAgAJiCQAAoIBYAgAAKCCWAAAACoglAACAAmIJAACggFgCAAAo8H8BcgL5kgxYSkIAAAAASUVORK5CYII=",
+      "text/plain": [
+       "<Figure size 1000x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#可视化每一个变量\n",
+    "import warnings\n",
+    "warnings.filterwarnings(\"ignore\")\n",
+    "for i in df.columns:\n",
+    "    plt.figure(figsize=(10,5))\n",
+    "    plt.title(i)\n",
+    "    # 设置字体旋转角度\n",
+    "    plt.xticks(rotation=90)\n",
+    "    sns.countplot(df[i])\n",
+    "    plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "adef0c7a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "直肠温度     69\n",
+       "脉搏       26\n",
+       "红细胞体积    36\n",
+       "总蛋白值     42\n",
+       "y         9\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# 我们认为这些变量中的0值都是不合理的，所以我们将除了y(最后一列)以外的变量中的0值都替换为np.nan\n",
+    "df[df.columns[:-1]] = df[df.columns[:-1]].replace(0,np.nan)\n",
+    "df.isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "b7c26b58",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "直肠温度     0\n",
+       "脉搏       0\n",
+       "红细胞体积    0\n",
+       "总蛋白值     0\n",
+       "y        9\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# 用众数填充缺失值\n",
+    "# 观察发现直肠温度列是正太分布的，因此可以用均值填充缺失值，其他使用中位数\n",
+    "df['直肠温度'] = df['直肠温度'].fillna(df['直肠温度'].mean())\n",
+    "df[['脉搏','红细胞体积','总蛋白值']]  = df[['脉搏','红细胞体积','总蛋白值']].fillna(df[['脉搏','红细胞体积','总蛋白值']].median())\n",
+    "df.isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "6ac51452",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "## 删除缺失值\n",
+    "df = df.dropna()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "4318135a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# 再查看一下每个变量的分布\n",
+    "for i in df.columns:\n",
+    "    plt.figure(figsize=(10,5))\n",
+    "    plt.title(i)\n",
+    "    # 设置字体旋转角度\n",
+    "    plt.xticks(rotation=90)\n",
+    "    sns.countplot(df[i])\n",
+    "    plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "c6d7e09c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 由于变量的取值范围不一样，因此需要对数据进行标准化\n",
+    "import time\n",
+    "X_train,X_test,y_train,y_test = train_test_split(df[df.columns[:-1]],df['y'],test_size=0.3,random_state=0)\n",
+    "X_train = StandardScaler().fit_transform(X_train)\n",
+    "X_test = StandardScaler().fit_transform(X_test)\n",
+    "\n",
+    "model_result = {}\n",
+    "\n",
+    "models = [RandomForestClassifier(),BaggingClassifier(),AdaBoostClassifier(),GaussianNB(),LogisticRegression(),DecisionTreeClassifier(),SVC(),KNeighborsClassifier()]\n",
+    "for model in models:\n",
+    "    try:\n",
+    "        model_name = str(model).split('(')[0]\n",
+    "        start = time.time()\n",
+    "        model.fit(X_train,y_train)\n",
+    "        y_pred = model.predict(X_test)\n",
+    "        end = time.time()\n",
+    "        # 存储准确率和混淆矩阵\n",
+    "        model_result[model_name] = [accuracy_score(y_test,y_pred),confusion_matrix(y_test,y_pred),end-start]\n",
+    "    except Exception as e:\n",
+    "        print(model_name,e)\n",
+    "\n",
+    "## 使用df保存模型的结果\n",
+    "df_result = pd.DataFrame(model_result).T\n",
+    "df_result.columns = ['accuracy_score','confusion_matrix','time']\n",
+    "\n",
+    "df_result.sort_values(by='accuracy_score',ascending=False,inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "aebc5270",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([1., 0., 0., 0., 1., 0., 1., 1., 0., 0., 0., 0., 0., 0., 1., 1., 0.,\n",
+       "       1., 1., 0., 1., 1., 0., 1., 0., 0., 1., 0., 0., 1., 1., 1., 0., 1.,\n",
+       "       0., 1., 0., 1., 0., 0., 0., 0., 1., 1., 0., 1., 1., 0., 0., 0., 0.,\n",
+       "       0., 0., 0., 1., 0., 1., 1., 1., 1., 0., 0., 1., 1., 1., 1., 0., 1.,\n",
+       "       1., 0., 0., 1., 0., 1., 1., 0., 0., 1., 0., 0., 0., 1., 1., 0., 1.,\n",
+       "       1., 1., 1., 1., 0., 1., 0., 1., 1., 1., 1., 1., 1., 1., 1., 1., 0.,\n",
+       "       0., 0., 1., 1., 1., 1.])"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#模型应用\n",
+    "RFC = RandomForestClassifier()\n",
+    "RFC.fit(X_train, y_train)\n",
+    "RFC.predict(X_test) "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "00e4c3eb",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([0., 0., 0., 0., 1., 0., 1., 0., 0., 0., 1., 0., 0., 0., 0., 1., 0.,\n",
+       "       1., 1., 0., 0., 1., 0., 0., 0., 0., 1., 0., 0., 1., 1., 1., 1., 0.,\n",
+       "       1., 1., 0., 1., 0., 1., 0., 0., 1., 0., 0., 0., 1., 0., 0., 0., 0.,\n",
+       "       0., 0., 0., 1., 1., 0., 0., 1., 0., 0., 0., 1., 1., 1., 0., 1., 0.,\n",
+       "       1., 0., 1., 1., 0., 1., 1., 0., 0., 0., 0., 0., 0., 1., 1., 1., 1.,\n",
+       "       1., 0., 1., 1., 1., 1., 0., 1., 0., 0., 1., 1., 1., 0., 1., 0., 0.,\n",
+       "       1., 0., 0., 0., 1., 1.])"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "BC = BaggingClassifier()\n",
+    "BC.fit(X_train, y_train)\n",
+    "BC.predict(X_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "2d37875f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([1., 0., 1., 1., 1., 1., 1., 1., 1., 0., 1., 1., 0., 0., 1., 1., 1.,\n",
+       "       1., 1., 1., 1., 1., 0., 1., 1., 0., 1., 1., 0., 1., 1., 1., 1., 0.,\n",
+       "       0., 1., 0., 1., 0., 0., 0., 0., 1., 1., 0., 1., 1., 0., 1., 0., 0.,\n",
+       "       1., 0., 1., 1., 0., 1., 1., 1., 1., 0., 0., 1., 1., 1., 1., 1., 0.,\n",
+       "       0., 1., 0., 1., 0., 1., 1., 1., 1., 1., 0., 0., 0., 1., 1., 1., 1.,\n",
+       "       1., 1., 1., 1., 1., 1., 0., 1., 1., 0., 1., 1., 1., 0., 1., 1., 0.,\n",
+       "       1., 0., 0., 1., 1., 1.])"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ABC = AdaBoostClassifier()\n",
+    "ABC.fit(X_train, y_train)\n",
+    "ABC.predict(X_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "3640cc77",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([1., 0., 1., 1., 1., 0., 1., 1., 1., 0., 1., 0., 1., 1., 1., 1., 1.,\n",
+       "       1., 1., 0., 1., 1., 0., 1., 1., 1., 1., 1., 0., 1., 1., 1., 1., 0.,\n",
+       "       0., 1., 0., 1., 0., 0., 0., 0., 1., 1., 0., 1., 1., 1., 1., 1., 1.,\n",
+       "       1., 1., 1., 1., 1., 1., 1., 1., 1., 0., 1., 0., 1., 1., 1., 1., 0.,\n",
+       "       1., 0., 0., 1., 0., 0., 1., 1., 1., 1., 0., 0., 0., 1., 1., 0., 1.,\n",
+       "       1., 1., 1., 1., 1., 1., 1., 1., 1., 0., 1., 1., 1., 1., 1., 1., 0.,\n",
+       "       0., 1., 0., 1., 1., 1.])"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "GNB = GaussianNB()\n",
+    "GNB.fit(X_train, y_train)\n",
+    "GNB.predict(X_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "c86a8c03",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([1., 0., 1., 1., 1., 0., 1., 1., 0., 0., 1., 1., 1., 1., 1., 1., 1.,\n",
+       "       1., 1., 0., 1., 1., 0., 1., 1., 1., 1., 1., 0., 1., 1., 1., 1., 0.,\n",
+       "       0., 1., 0., 1., 0., 1., 1., 1., 1., 1., 0., 1., 1., 1., 1., 1., 0.,\n",
+       "       0., 0., 1., 1., 1., 1., 1., 1., 1., 0., 1., 0., 1., 1., 1., 1., 0.,\n",
+       "       1., 0., 0., 1., 0., 1., 1., 1., 1., 1., 0., 0., 0., 1., 1., 0., 1.,\n",
+       "       1., 1., 1., 1., 1., 1., 0., 1., 1., 1., 1., 1., 1., 1., 1., 1., 0.,\n",
+       "       0., 0., 0., 1., 1., 1.])"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "LR = LogisticRegression()\n",
+    "LR.fit(X_train, y_train)\n",
+    "LR.predict(X_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "234a3a8b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([1., 1., 1., 0., 1., 1., 0., 1., 1., 0., 1., 1., 0., 1., 1., 1., 1.,\n",
+       "       1., 1., 0., 0., 1., 1., 1., 0., 0., 1., 1., 1., 1., 1., 1., 1., 0.,\n",
+       "       1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 0., 1., 1., 0., 1., 1., 0.,\n",
+       "       0., 0., 0., 1., 1., 1., 1., 1., 1., 0., 1., 0., 1., 1., 0., 0., 0.,\n",
+       "       1., 0., 1., 1., 1., 0., 1., 1., 0., 0., 1., 0., 1., 1., 1., 1., 1.,\n",
+       "       1., 1., 0., 1., 0., 1., 1., 1., 0., 0., 1., 1., 1., 0., 0., 1., 0.,\n",
+       "       0., 0., 0., 1., 0., 1.])"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "DTC = DecisionTreeClassifier()\n",
+    "DTC.fit(X_train, y_train)\n",
+    "DTC.predict(X_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "7c8f80e6",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([1., 0., 0., 1., 1., 1., 1., 1., 0., 0., 1., 1., 1., 1., 1., 1., 1.,\n",
+       "       1., 1., 0., 1., 1., 0., 1., 1., 1., 1., 1., 0., 1., 1., 1., 1., 0.,\n",
+       "       0., 1., 1., 1., 1., 0., 0., 1., 1., 1., 0., 1., 1., 0., 1., 1., 0.,\n",
+       "       0., 1., 1., 1., 1., 1., 1., 1., 1., 0., 1., 1., 1., 1., 1., 1., 0.,\n",
+       "       1., 0., 0., 1., 0., 1., 1., 1., 1., 1., 0., 0., 0., 1., 1., 0., 1.,\n",
+       "       1., 1., 1., 1., 1., 1., 0., 1., 1., 1., 1., 1., 1., 1., 1., 1., 0.,\n",
+       "       0., 0., 0., 1., 1., 1.])"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "svc = SVC()\n",
+    "svc.fit(X_train, y_train)\n",
+    "svc.predict(X_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "58efdd86",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([1., 0., 1., 0., 1., 1., 1., 1., 1., 1., 1., 1., 0., 0., 1., 1., 1.,\n",
+       "       1., 1., 1., 1., 1., 0., 1., 1., 0., 1., 1., 0., 1., 1., 1., 0., 0.,\n",
+       "       0., 1., 1., 1., 0., 0., 1., 1., 1., 0., 0., 1., 1., 0., 1., 0., 0.,\n",
+       "       0., 0., 1., 0., 1., 1., 1., 1., 0., 0., 0., 1., 1., 1., 1., 1., 0.,\n",
+       "       1., 0., 1., 1., 1., 1., 1., 1., 1., 1., 1., 0., 1., 0., 1., 0., 1.,\n",
+       "       1., 1., 1., 1., 1., 1., 0., 1., 1., 1., 1., 1., 1., 1., 1., 1., 0.,\n",
+       "       1., 0., 0., 1., 1., 1.])"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "KNC = KNeighborsClassifier()\n",
+    "KNC.fit(X_train, y_train)\n",
+    "KNC.predict(X_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "944ab14c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# 绘制各个模型的准确率柱状图，并在柱状图上标注值\n",
+    "plt.figure(figsize=(10,5))\n",
+    "plt.title('各个模型的准确率')\n",
+    "sns.barplot(x=df_result.index,y=df_result['accuracy_score'])\n",
+    "for i in range(df_result.shape[0]):\n",
+    "    plt.text(i,df_result['accuracy_score'][i],round(df_result['accuracy_score'][i],3),ha='center')\n",
+    "# 设置字体选择\n",
+    "plt.xticks(rotation=90)\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2c6b386b",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/共享单车数据分析/十三组平时作业（实战）/torch实现cnn服饰分类/.keep b/大众点评数据集分析/第七组/第七组项目代码/.keep
similarity index 100%
rename from 共享单车数据分析/十三组平时作业（实战）/torch实现cnn服饰分类/.keep
rename to 大众点评数据集分析/第七组/第七组项目代码/.keep
diff --git a/大众点评数据集分析/第七组/第七组项目代码/第七组大众点评项目代码.zip b/大众点评数据集分析/第七组/第七组项目代码/第七组大众点评项目代码.zip
new file mode 100644
index 0000000..9ddd7b8
Binary files /dev/null and b/大众点评数据集分析/第七组/第七组项目代码/第七组大众点评项目代码.zip differ
diff --git a/共享单车数据分析/src/main.py b/第十三组/src/main.py
similarity index 100%
rename from 共享单车数据分析/src/main.py
rename to 第十三组/src/main.py
diff --git a/第十三组/共享单车数据集/main.ipynb b/第十三组/共享单车数据集/main.ipynb
new file mode 100644
index 0000000..e84a0d2
--- /dev/null
+++ b/第十三组/共享单车数据集/main.ipynb
@@ -0,0 +1,1237 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "outputs": [],
+   "source": [
+    "import lr\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "import plotly.express as px\n",
+    "from sklearn.metrics import mean_squared_error, r2_score\n",
+    "from sklearn.linear_model import LinearRegression, Ridge\n",
+    "from sklearn.tree import DecisionTreeRegressor\n",
+    "from sklearn.preprocessing import StandardScaler\n",
+    "from sklearn.decomposition import PCA\n",
+    "from sklearn.ensemble import AdaBoostRegressor\n",
+    "from sklearn.ensemble import RandomForestRegressor, GradientBoostingRegressor\n",
+    "from sklearn.svm import SVR\n",
+    "from datetime import datetime\n",
+    "\n",
+    "# 设置中文支持\n",
+    "plt.rcParams['font.sans-serif'] = ['SimHei']\n",
+    "plt.rcParams['axes.unicode_minus'] = False"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 数据预处理"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "train_df = pd.read_csv('train.csv')\n",
+    "test_df = pd.read_csv('test.csv')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "#train_df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 拆分时间变量\n",
+    "train_df['date'] = train_df.datetime.apply(lambda c: c.split()[0])\n",
+    "train_df['hour'] = train_df.datetime.apply(lambda c: c.split()[1].split(':')[0]).astype('int')\n",
+    "train_df['year'] = train_df.datetime.apply(lambda c: c.split()[0].split('-')[0]).astype('int')\n",
+    "train_df['month'] = train_df.datetime.apply(lambda c: c.split()[0].split('-')[1]).astype('int')\n",
+    "\n",
+    "test_df['date'] = test_df.datetime.apply(lambda c: c.split()[0])\n",
+    "test_df['hour'] = test_df.datetime.apply(lambda c: c.split()[1].split(':')[0]).astype('int')\n",
+    "test_df['year'] = test_df.datetime.apply(lambda c: c.split()[0].split('-')[0]).astype('int')\n",
+    "test_df['month'] = test_df.datetime.apply(lambda c: c.split()[0].split('-')[1]).astype('int')\n",
+    "\n",
+    "\n",
+    "def get_weekday(x):\n",
+    "    date1 = x.split()[0]\n",
+    "    date2 = datetime.strptime(date1, '%Y-%m-%d')\n",
+    "    week_day = date2.weekday()\n",
+    "    return week_day\n",
+    "\n",
+    "train_df['weekday'] = train_df.datetime.apply(get_weekday)\n",
+    "test_df['weekday'] = test_df.datetime.apply(get_weekday)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#train_df.info()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "得：无整行重复,无缺失值NAN，但可以评估列中某些值为零的数据（例如风速），以确认 0 是否为缺失值。"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#test_df.info()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 异常值分析"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fig, axes = plt.subplots(nrows=3,ncols=2)\n",
+    "fig.set_size_inches(15, 20)\n",
+    "sns.boxplot(data=train_df,y=\"count\",orient=\"v\",ax=axes[0][0])\n",
+    "sns.boxplot(data=train_df,y=\"count\",x=\"hour\",orient=\"v\",ax=axes[0][1])\n",
+    "sns.boxplot(data=train_df,y=\"count\",x=\"season\",orient=\"v\",ax=axes[1][0])\n",
+    "sns.boxplot(data=train_df,y=\"count\",x=\"workingday\",orient=\"v\",ax=axes[1][1])\n",
+    "sns.boxplot(data=train_df,y=\"count\",x=\"holiday\",orient=\"v\",ax=axes[2][0])\n",
+    "sns.boxplot(data=train_df,y=\"count\",x=\"weather\",orient=\"v\",ax=axes[2][1])\n",
+    "\n",
+    "axes[0][0].set(ylabel='Count',title=\"Count\")\n",
+    "axes[0][1].set(xlabel='Hour Of The Day', ylabel='Count',title=\"Count by Hour of the Day\")\n",
+    "axes[1][0].set(xlabel='Season', ylabel='Count',title=\"Count by Season\")\n",
+    "axes[1][1].set(xlabel='Working Day', ylabel='Count',title=\"Count by Working Day\")\n",
+    "axes[2][0].set(xlabel='Holiday', ylabel='Count',title=\"Count by Holiday\")\n",
+    "axes[2][1].set(xlabel='Weather', ylabel='Count',title=\"Count by Weather\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "图1（count）可以看出存在训练集异常值。  \n",
+    "图3（count by season）显示春季的自行车租赁量较低。  \n",
+    "图2（count by hour）显示最繁忙的时间段是早上7点到8点和下午5点到6点，这意味着用户主要租用自行车去上班/上学和下班回家。  \n",
+    "图4（count by working day），可发现大多数异常值都出现在工作日。图5（count by holiday）显示所有异常值都出现在非节假日。\n",
+    "图6（count by weather）显示了大多数用户在天气晴朗和多云时租用自行车（1和2），而在大雨或下雪时几乎没有用户租用自行车（3）。"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 训练集异常值处理"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "使用3σ规则来剔除异常值。如果有一个数据点落在平均值的三个标准差之外，则该数据点被视为离群点，可以从数据集中删除。这种方法假定数据呈正态分布，离群值为罕见事件，将来不太可能出现。"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "## 查找异常值\n",
+    "def detect_outliers(data):\n",
+    "    # mean, standard deviation and 3-sigma of the data\n",
+    "    mean = np.mean(data)\n",
+    "    std = np.std(data)\n",
+    "    threesigma = 3 * std\n",
+    "\n",
+    "    # print upper, lower boundary\n",
+    "    lower, upper = mean-3*std, mean+3*std\n",
+    "    print(f\"Upper and lower boundary is: {lower}/{upper}\")\n",
+    "\n",
+    "    # identify outliers and return the outliers\n",
+    "    outliers = [x for x in data if np.abs(x - mean) > threesigma]\n",
+    "    print(f\"There are {len(outliers)} outliers based on three-sigma rule\")\n",
+    "\n",
+    "## 删除异常值\n",
+    "def delete_outliers(data, df):\n",
+    "    # detecting and dropping outliers\n",
+    "    original_shape = df.shape\n",
+    "    mean = np.mean(data)\n",
+    "    std = np.std(data)\n",
+    "    outliers = np.abs(data-mean) > (3*std)\n",
+    "    outliers_num = len(train_df[outliers])\n",
+    "    df.drop(index=data[outliers].index, inplace=True)\n",
+    "\n",
+    "    # 输出结果对比\n",
+    "    print(\"Number of outliers deleted:\", outliers_num)\n",
+    "    print (\"Shape of dataframe with Ouliers: \",original_shape)\n",
+    "    print (\"Shape of Dataframe After Deleting the Ouliers: \",df.shape)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "train_with_outliers = train_df\n",
+    "# 查找异常值\n",
+    "detect_outliers(train_df['count'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 从训练集删除异常值\n",
+    "delete_outliers(train_df['count'], train_df)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 数值型特征异常值处理"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 查看temp（温度），atemp（体感温度），humidity（湿度）、windspeed（风速）等数值型数据的分布情况\n",
+    "fig, axes = plt.subplots(2, 2)\n",
+    "fig.set_size_inches(12,10)\n",
+    "\n",
+    "sns.distplot(train_df['temp'],ax=axes[0,0])\n",
+    "sns.distplot(train_df['atemp'],ax=axes[0,1])\n",
+    "sns.distplot(train_df['humidity'],ax=axes[1,0])\n",
+    "sns.distplot(train_df['windspeed'],ax=axes[1,1])\n",
+    "\n",
+    "axes[0,0].set(title='Distribution of temp',)\n",
+    "axes[0,1].set(title='Distribution of atemp')\n",
+    "axes[1,0].set(title='Distribution of humidity')\n",
+    "axes[1,1].set(title='Distribution of windspeed')\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "查看上图发现，风速为和温度可能存在异常值。"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### windspeed 风速异常值处理"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "a = train_df[train_df['windspeed'] == 0].shape[0]\n",
+    "print(f'风速为0的天数为：{a}')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "风速为0天数过多，可能存在异常。"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "## 处理缺失值\n",
+    "windspeed_df1 = train_df[train_df['windspeed'] != 0]\n",
+    "windspeed_df2 = train_df[train_df['windspeed'] == 0]\n",
+    "windspeed_df2['windspeed'] = windspeed_df1['windspeed'].interpolate()\n",
+    "train_df = windspeed_df1.append(windspeed_df1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "a = train_df[train_df['windspeed'] == 0].shape[0]\n",
+    "print(f'风速为0的天数为：{a}')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Humidity 湿度异常值处理"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [],
+   "source": [
+    "train_df[train_df['humidity'] == 0].shape[0]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "湿度值为0和100为异常数据，考虑可能有传感器错误情况。"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 可视化分类数据分析"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sns.pairplot(train_df, x_vars=['holiday', 'workingday', 'weather', 'season', 'windspeed', 'humidity', 'temp', 'atemp'],\n",
+    "             y_vars=['casual', 'registered', 'count'], plot_kws={'alpha': 0.1})"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "1.会员在工作日出行多，节假日出行少，临时用户则相反；  \n",
+    "2.一季度出行人数总体偏少；  \n",
+    "3.租赁数量随天气等级上升而减少；  \n",
+    "4.小时数对租赁情况影响明显，会员呈现两个高峰，非会员呈现一个正态分布；  \n",
+    "5.租赁数量随风速增大而减少；  \n",
+    "6.温度、湿度对非会员影响比较大，对会员影响较小。"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## temp 温度对租赁数量的影响"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 按温度取租赁数量平均值\n",
+    "tempr = train_df.groupby(['temp'], as_index=True).agg({'casual': 'mean', 'registered': 'mean', 'count': 'mean'})\n",
+    "tempr.plot(title='Mean Count by Temp per Hour',figsize=(7,3))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "分析：租车数量整体是随着温度的上升而升高的"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## atemp体感温度"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "atempr = train_df.groupby(['atemp'], as_index=True).agg({'casual': 'mean',\n",
+    "                                                     'registered': 'mean',\n",
+    "                                                     'count': 'mean'})\n",
+    "atempr.plot(title='Mean Count by Atemp per Hour',figsize=(7,3))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "分析：租车数量随体感温度的升高而上升，温度太高时又会下降"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 湿度对租赁数量影响"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "humidityr = train_df.groupby(['humidity'], as_index=True).agg({'casual': 'mean',\n",
+    "                                                           'registered': 'mean',\n",
+    "                                                           'count': 'mean'})\n",
+    "\n",
+    "humidityr.plot(title='Mean Count by Humidity per Hour',figsize=(7,3))\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "分析：租车数量在湿度为20的时候达到高峰，之后逐渐递减"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 年份、月份对租赁数量的影响"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "day_df=train_df.groupby('date').agg({'year':'mean','month':'mean',\n",
+    "                                      'casual':'sum', 'registered':'sum'\n",
+    "                                      ,'count':'sum'})\n",
+    "month_df = day_df.groupby(['year','month'], as_index=True).agg({'casual':'mean',\n",
+    "                                                                  'registered':'mean',                                                                 'count':'mean'})\n",
+    "month_df.plot(fontsize=8,figsize=(15,4))\n",
+    "tick=list(range(24))\n",
+    "labels=['2011.1','2011.2','2011.3','2011.4','2011.5','2011.6','2011.7','2011.8','2011.9','2011.10','2011.11','2011.12','2012.1','2012.2','2012.3','2012.4','2012.5','2012.6','2012.7','2012.8','2012.9','2012.10','2012.11','2012.12']\n",
+    "plt.xticks(tick,labels,fontsize=8)\n",
+    "plt.title('Mean Count per month in two years', fontsize=15)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 按天\n",
+    "day_df1=train_df.groupby('date').agg({\n",
+    "                                      'casual':'sum', 'registered':'sum'\n",
+    "                                      ,'count':'sum'})\n",
+    "day_df1.plot(fontsize=8,figsize=(8,4),linewidth=0.8)\n",
+    "plt.title('Mean Count per day in two years', fontsize=15 )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plt.figure(figsize=(15,4))\n",
+    "# 按年\n",
+    "plt.subplot(1,3,1)\n",
+    "sns.barplot(x=train_df['year'],y=train_df['count'])\n",
+    "plt.title('by year')\n",
+    "\n",
+    "# 按季度\n",
+    "plt.subplot(1,3,2)\n",
+    "sns.barplot(x=train_df['season'],y=train_df['count'])\n",
+    "plt.title('by season')\n",
+    "\n",
+    "# 按月\n",
+    "plt.subplot(1,3,3)\n",
+    "sns.barplot(x=train_df['month'],y=train_df['count'])\n",
+    "plt.title('by month')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "由上分析：  \n",
+    "1、2012年整体租车数量高于2011年。  \n",
+    "2、每年租车数量1月份最低，在6月租车数量达到最高。前半年数量上升，后半年从7月份左右开始下降。  \n",
+    "3、2012年整体波动比2011年剧烈。  \n",
+    "4、有很多局部波谷值。"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 时段对租赁数量的影响"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "fig,(ax1,ax2,ax3,ax4)= plt.subplots(nrows=4)\n",
+    "fig.set_size_inches(12,18)\n",
+    "train_df = train_df.reset_index(drop=True)\n",
+    "\n",
+    "sns.pointplot(x=train_df['hour'],y=train_df['count'],hue=train_df['season'],join=True, palette=\"Paired\", ax=ax1)\n",
+    "\n",
+    "sns.pointplot(x=train_df['hour'],y=train_df['count'],hue=train_df['weekday'],join=True, palette=\"Paired\", ax=ax2)\n",
+    "\n",
+    "sns.lineplot(x=train_df['hour'], y=train_df['registered'], color='red',label='Registered', marker='o', ax=ax3, ci=None)\n",
+    "sns.lineplot(x=train_df['hour'], y=train_df['casual'], color='blue', label='Casual', marker='o', ax=ax3, ci=None)\n",
+    "\n",
+    "sns.pointplot(x=train_df['hour'], y=train_df['count'], hue=train_df['weather'], ax=ax4);\n",
+    "\n",
+    "\n",
+    "ax1.set(xlabel='Hour of the Day', ylabel='Mean Count',title=\"Mean Count by Season per Hour\")\n",
+    "ax2.set(xlabel='Hour of the Day', ylabel='Mean Count',title=\"Mean Count by Weekday per Hour\")\n",
+    "ax3.set(xlabel='Hour of the Day', ylabel='Mean Count',title=\"Mean Count by Casual VS Registered per Hour\")\n",
+    "ax4.set(xlabel='Hour of the Day', ylabel='Mean Count',title=\"Mean Count by Weather per Hour\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "图1显示：在所有季节中，春季是大雨/大雪天数最多的季节，这直接影响了租赁需求的减少，秋季和夏季往往是租车人数最多的季节。  \n",
+    "图2显示：周六和周日的租车模式与平日明显不同，大多数用户在上午11点至下午4点租车。这些周末用户租车是为了休闲，而不是为了上班（上午8点和下午6点）。  \n",
+    "图3显示：大多数用户是注册用户，租用时间在上午7-8点和下午4-5点之间，临时用户的租用时间在白天分布较好，但最繁忙的时间在下午1-2点之间。   \n",
+    "图4显示：天气1和天气2情况下，租赁需求较多，天气3和天气4租赁需求较少，均仍集中在上下班高峰期。   "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 天气对租赁情况影响"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "weather_count = train_df.groupby('weather')\n",
+    "weather_count[['casual', 'registered', 'count']].count()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "weather_df = train_df.groupby('weather', as_index=True).agg({'casual': 'mean', 'registered': 'mean', 'count': 'mean'})\n",
+    "weather_df.plot.bar(stacked=True, title='Mean Count by Weather of Casual VS Registered', color=['#c63d40', '#dc8f75', '#ffddb7'])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "分析：  \n",
+    "1、总体租车数量第1种天气最多，之后是第2，第4种，第3种。  \n",
+    "2、会员租车数量与总体租车数量变化相同。  \n",
+    "3、临时租车数量是按照天气从好到坏的顺序依次递减。"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 风速对租借情况影响"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [],
+   "source": [
+    "# 考虑到风速特别大的时候很少，如果取平均值会出现异常，所以按风速对租赁数量取最大值。\n",
+    "windspeedr = train_df.groupby(['windspeed'], as_index=True).agg({'casual': 'max', 'registered': 'max', 'count': 'max'})\n",
+    "windspeedr.plot(title='Max Count by Windspeed per Hour',figsize=(8,4))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "分析：风速在30以后，风速越大，相对于的租赁数就越少。但在分数在43-44附近出现了异常情况。"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df2 = train_df[train_df['windspeed'] > 40]\n",
+    "df2 = df2[df2['count'] > 400]\n",
+    "df2"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "因为是下班高峰期的时间"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 工作日与非工作日的租赁情况"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "day_df=train_df.groupby('date').agg({'year':'mean','season':'mean',\n",
+    "                                      'casual':'sum', 'registered':'sum'\n",
+    "                                      ,'count':'sum','temp':'mean',\n",
+    "                                      'atemp':'mean','workingday':'mean'})\n",
+    "#day_df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 对工作日和非工作日租赁数量取了平均值，对一周中每天的租赁数量求和\n",
+    "workingday_df = day_df.groupby(['workingday'], as_index=True).agg({'casual': 'mean', 'registered': 'mean'})\n",
+    "#workingday_df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "workingday_df1 = workingday_df.loc[0]\n",
+    "workingday_df2 = workingday_df.loc[1]\n",
+    "#workingday_df1.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 绘制非工作日与工作日的临时借车数量和会员借车数量堆积图\n",
+    "width = 0.3\n",
+    "p1 = plt.bar(workingday_df.index, workingday_df['casual'], width,color='#dc8f75')\n",
+    "p2 = plt.bar(workingday_df.index, workingday_df['registered'], width, bottom=workingday_df['casual'],color='#ffddb7')\n",
+    "plt.title('Mean Count per Day')\n",
+    "plt.xticks([0, 1])#, ('non-working day', 'working day'))\n",
+    "plt.legend((p1[0], p2[0]), ('casual', 'registered'))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 绘制非工作日临时借车数量和会员借车数量占比饼图\n",
+    "plt.subplot2grid((2, 2), (0, 0))\n",
+    "plt.pie(workingday_df1, labels=['casual', 'registered'], autopct='%1.1f%%',\n",
+    "        pctdistance=0.6, labeldistance=1.35, radius=1.3,colors=['#c63d40','#ffddb7'])\n",
+    "plt.axis('equal')\n",
+    "plt.title('non-working day')\n",
+    "\n",
+    "# 绘制工作日临时借车数量和会员借车数量占比饼图\n",
+    "plt.subplot2grid((2, 2), (0, 1))\n",
+    "plt.pie(workingday_df2, labels=['casual', 'registered'], autopct='%1.1f%%',\n",
+    "        pctdistance=0.6, labeldistance=1.35, radius=1.3,colors=['#c63d40','#ffddb7'])\n",
+    "plt.title('working day')\n",
+    "plt.axis('equal')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "分析：  \n",
+    "非工作日时，会员租车数量占68.5%，临时租车数量占31.5%。  \n",
+    "工作日时，会员租车数量占87.0%，临时租车数量占13%。  \n",
+    "工作日的总租车数量略高于非工作日，但非工作日时临时租车数量比工作日多。  "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "# 查看工作日与非工作日每小时的临时租赁数量，会员租赁数量和总数量的平均值\n",
+    "workingday_df=train_df[train_df['workingday']==1]\n",
+    "workingday_df = workingday_df.groupby(['hour'], as_index=True).agg({'casual':'mean',\n",
+    "                                                                    'registered':'mean',\n",
+    "                                                                    'count':'mean'})\n",
+    "\n",
+    "nworkingday_df=train_df[train_df['workingday']==0]\n",
+    "nworkingday_df = nworkingday_df.groupby(['hour'], as_index=True).agg({'casual':'mean',\n",
+    "                                                                      'registered':'mean',\n",
+    "                                                                      'count':'mean'})\n",
+    "fig, axes = plt.subplots(1, 2,sharey = True)\n",
+    "\n",
+    "workingday_df.plot(figsize=(15,5),title = 'Mean Count per Hour in Working Day',ax=axes[0])\n",
+    "nworkingday_df.plot(figsize=(15,5),title = 'Mean Count per Hour in non-Working Day',ax=axes[1])\n",
+    "\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "分析：  \n",
+    "1、周末会员与非会员用户的情况相似。这可能表明，注册用户是在工作日使用自行车的人，而临时用户在周末租车，在工作日租车时，不需要作为上班的交通工具。   \n",
+    "2、周末会员用户租赁数量降低，临时用户租赁数量增加。\n",
+    "3、工作日会员用户出行数量较多，临时用户出行数量较少"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 节假日的租赁情况"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "day_df=train_df.groupby('date').agg({'year':'mean',\n",
+    "                                      'casual':'sum', 'registered':'sum'\n",
+    "                                      ,'count':'sum','holiday':'mean'})\n",
+    "holiday_count=day_df.groupby('year', as_index=True).agg({'holiday':'sum'})\n",
+    "#holiday_count"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "holiday_df = day_df.groupby('holiday', as_index=True).agg({'casual':'mean', 'registered':'mean'})\n",
+    "holiday_df.plot.bar(stacked=True , title = 'Mean Count by Holiday or not per DAY',cmap='Set3')\n",
+    "plt.xticks(rotation=360)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 会员与非会员"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "number_pei=day_df[['casual','registered']].mean()\n",
+    "#number_pei"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "plt.axes(aspect='equal')\n",
+    "plt.pie(number_pei, labels=['casual','registered'], autopct='%1.1f%%',\n",
+    "        pctdistance=0.6 , labeldistance=1.05 , radius=1 ,colors=['#c63d40','#ffddb7'])\n",
+    "\n",
+    "plt.title('Count of Casual VS Registered')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 正态性假设"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "因变量的正态性假设并不总是必要的，多机器学习算法（如决策树、随机森林和神经网络）并不依赖于此假设。\n",
+    "但当输入特征或预测变量近似呈正态分布时，某些算法可能会表现更好。如果因变量表现出极端偏度或异常值，则应用变换或使用对非正态性更稳健的替代模型可能是有益的。\n",
+    "故下面计算不同因变量的偏度。"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 查看temp（温度），atemp（体感温度），humidity（湿度）、windspeed（风速）等数值型数据的分布情况\n",
+    "fig, axes = plt.subplots(2, 2)\n",
+    "fig.set_size_inches(12,10)\n",
+    "\n",
+    "sns.distplot(train_df['temp'],ax=axes[0,0])\n",
+    "sns.distplot(train_df['atemp'],ax=axes[0,1])\n",
+    "sns.distplot(train_df['humidity'],ax=axes[1,0])\n",
+    "sns.distplot(train_df['windspeed'],ax=axes[1,1])\n",
+    "\n",
+    "axes[0,0].set(title='Distribution of temp',)\n",
+    "axes[0,1].set(title='Distribution of atemp')\n",
+    "axes[1,0].set(title='Distribution of humidity')\n",
+    "axes[1,1].set(title='Distribution of windspeed')\n",
+    "\n",
+    "plt.show()\n",
+    "\n",
+    "print(f\"temp偏度为：{train_df['temp'].skew()}\")\n",
+    "print(f\"atemp偏度为：{train_df['atemp'].skew()}\")\n",
+    "print(f\"windspeed偏度为：{train_df['windspeed'].skew()}\")\n",
+    "print(f\"humidity偏度为：{train_df['humidity'].skew()}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 相关性分析"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "train_df.corr()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "plt.figure(figsize=(13, 10))\n",
+    "sns.heatmap(train_df.corr(), annot=True, cmap='RdBu')\n",
+    "plt.title('Heatmap on Correlation', fontsize=20)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 租赁数量与其他变量的相关性\n",
+    "train_df.corr()['count'].sort_values(ascending=False)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "从图中可以看出，temp和atemp之间的相关性较高，为0.99，season和month之间为0.97，同样不低，即存在很强的多重共线性。在进行建立模型预测时，选择保留和count相关性高的变量，剔除atemp和season，以免导致因多重共线性造成的过拟合。"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 模型"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 数据准备"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "train_df.columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X_train_final = train_df.drop(columns=['count','casual', 'registered','datetime','date','atemp','month']).values  # 最终模型训练数据"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#test_df.columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x_date_test = test_df['datetime'].values #提交结果时间栏\n",
+    "X_test_final = test_df.drop(columns=['datetime','date','atemp','month']).values # 预测数据"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 因变量\n",
+    "y = train_df['count'].apply(lambda x: np.log1p(x)).values\n",
+    "y_casual = train_df['casual'].apply(lambda x: np.log1p(x)).values\n",
+    "y_registered = train_df['registered'].apply(lambda x: np.log1p(x)).values\n",
+    "\n",
+    "# 划分训练集与测试集\n",
+    "X_train, X_test, y_train, y_test = train_test_split(X_train_final, y, test_size=0.2, random_state=42)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "## 模型性能评估\n",
+    "def model_performance_evaluation(model_name, test, pred):\n",
+    "    print(model_name,'| 均方误差: %.4f' % mean_squared_error(test, pred))\n",
+    "    print(model_name, '| 决定系数: %.4f' % r2_score(test, pred))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 决策树模型"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "DT = DecisionTreeRegressor()\n",
+    "DT.fit(X_train, y_train)\n",
+    "y_pred = DT.predict(X_test)\n",
+    "\n",
+    "# 可视化y_test与y_pred的差异\n",
+    "plt.figure(figsize=(300,12))\n",
+    "plt.plot(range(len(y_pred)), y_pred, 'b', label=\"predict\")\n",
+    "plt.plot(range(len(y_test)), y_test, 'r', label=\"test\")\n",
+    "plt.legend(loc=\"upper right\")\n",
+    "\n",
+    "# 模型评估\n",
+    "model_performance_evaluation('决策树模型', y_test, y_pred)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "注：在决策树模型中，多次对同一数据进行机器学习，最终预测结果可能会有所不同。"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 线性回归模型"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "LR = LinearRegression()\n",
+    "LR.fit(X_train,y_train)\n",
+    "y_pred = LR.predict(X_test)\n",
+    "## 可视化下y_test与y_pred的差异\n",
+    "plt.figure(figsize=(300,6))\n",
+    "plt.plot(range(len(y_pred)),y_pred,'b',label=\"predict\")\n",
+    "plt.plot(range(len(y_test)),y_test,'r',label=\"test\")\n",
+    "plt.legend(loc=\"upper right\") #显示图中的标签\n",
+    "\n",
+    "## 模型评估\n",
+    "model_performance_evaluation('线性回归模型', y_test, y_pred)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 随机森林回归模型"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "RF = RandomForestRegressor(n_estimators=100, random_state=42)\n",
+    "RF.fit(X_train, y_train)\n",
+    "y_pred = RF.predict(X_test)\n",
+    "## 可视化下y_test与y_pred的差异\n",
+    "plt.figure(figsize=(300,6))\n",
+    "plt.plot(range(len(y_pred)),y_pred,'b',label=\"predict\")\n",
+    "plt.plot(range(len(y_test)),y_test,'r',label=\"test\")\n",
+    "plt.legend(loc=\"upper right\") #显示图中的标签\n",
+    "\n",
+    "## 模型评估\n",
+    "model_performance_evaluation('随机森林回归模型', y_test, y_pred)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 逻辑回归模型"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.linear_model import LogisticRegression\n",
+    "LR = LogisticRegression()\n",
+    "LR.fit(X_train,y_train.astype('int'))\n",
+    "y_pred = LR.predict(X_test)\n",
+    "## 可视化下y_test与y_pred的差异\n",
+    "plt.figure(figsize=(300,6))\n",
+    "plt.plot(range(len(y_pred)),y_pred,'b',label=\"predict\")\n",
+    "plt.plot(range(len(y_test)),y_test,'r',label=\"test\")\n",
+    "plt.legend(loc=\"upper right\") #显示图中的标签\n",
+    "\n",
+    "## 模型评估\n",
+    "model_performance_evaluation('逻辑回归模型', y_test, y_pred)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 神经网络回归模型"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.neural_network import MLPClassifier\n",
+    "mlp = MLPClassifier()\n",
+    "mlp.fit(X_train,y_train.astype('int'))\n",
+    "y_pred = mlp.predict(X_test)\n",
+    "## 可视化下y_test与y_pred的差异\n",
+    "plt.figure(figsize=(300,6))\n",
+    "plt.plot(range(len(y_pred)),y_pred,'b',label=\"predict\")\n",
+    "plt.plot(range(len(y_test)),y_test,'r',label=\"test\")\n",
+    "plt.legend(loc=\"upper right\") #显示图中的标签\n",
+    "\n",
+    "## 模型评估\n",
+    "model_performance_evaluation('逻辑回归模型', y_test, y_pred)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 支持向量回归"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "svr = Ridge()\n",
+    "svr.fit(X_train,y_train)\n",
+    "y_pred = svr.predict(X_test)\n",
+    "## 可视化下y_test与y_pred的差异\n",
+    "plt.figure(figsize=(10,6))\n",
+    "plt.plot(range(len(y_pred)),y_pred,'b',label=\"predict\")\n",
+    "plt.plot(range(len(y_test)),y_test,'r',label=\"test\")\n",
+    "plt.legend(loc=\"upper right\") #显示图中的标签\n",
+    "\n",
+    "## 模型评估\n",
+    "model_performance_evaluation('逻辑回归模型', y_test, y_pred)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "根据模型评估结果，随机森林回归模型均方误差最小，决定系数最大，拟合效果最好，因此选择随机森林回归模型为最终模型。"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 结果输出"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "## 输出最好的模型的预测结果，线性回归模型仅为测试代码所用\n",
+    "prediction = []\n",
+    "lr1=lr.fit(X_train_final, y_casual)\n",
+    "lr2=lr.fit(X_train_final, y_registered)\n",
+    "prediction = np.expm1(lr1.predict(X_test_final))+np.expm1(lr2.predict(X_test_final))\n",
+    "submit = pd.DataFrame({'datetime':x_date_test,'count':prediction})\n",
+    "submit.to_csv('submission.csv',index=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "outputs": [],
+   "source": [],
+   "metadata": {
+    "collapsed": false
+   }
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}
diff --git a/共享单车数据分析/readme.md b/第十三组/共享单车数据集/readme.md
similarity index 67%
rename from 共享单车数据分析/readme.md
rename to 第十三组/共享单车数据集/readme.md
index 44cc4ed..6cafb44 100644
--- a/共享单车数据分析/readme.md
+++ b/第十三组/共享单车数据集/readme.md
@@ -1,7 +1,17 @@
-## kaggle共享单车租用数据，通过历史租车数据并结合天气数据来预测租车需求
+#通过历史租车数据并结合天气数据来预测租车需求
 
-通过历史租车数据并结合天气数据来预测租车需求
-### 字段说明： 
+### 目录结构
+```
+|-src
+|  |-main.py
+|-test.csv
+|-train.csv
+|-readme.md
+|-main.ipynb
+```
+src/main.py为前馈型神经网络对结果的预测
+
+### 数据集字段说明： 
 
 datetime：时间 
 
diff --git a/第十三组/共享单车数据集/src/main.py b/第十三组/共享单车数据集/src/main.py
new file mode 100644
index 0000000..abafeb5
--- /dev/null
+++ b/第十三组/共享单车数据集/src/main.py
@@ -0,0 +1,135 @@
+import torch
+import pandas as pd
+import numpy as np
+import os
+import time
+import torch.optim as optim
+import matplotlib.pyplot as plt
+
+train_data = pd.read_csv('../train.csv')
+test_data = pd.read_csv('../test.csv')
+#print(train_data)
+datetime=train_data['datetime'].values
+season=train_data['season'].values
+holiday=train_data['holiday'].values
+workingday=train_data['workingday'].values
+weather=train_data['weather'].values
+temp=train_data['temp'].values
+atemp=train_data['atemp'].values
+humidity=train_data['humidity'].values
+windspeed=train_data['windspeed'].values
+casual=train_data['casual'].values
+registered=train_data['registered'].values
+
+count=train_data['count'].values
+
+'''
+转换datetime,去掉不必要的符号和0
+'''
+def datetime_convert(datetime):
+    for i in range(len(datetime)):
+        datetime[i]=datetime[i].replace("-", "").replace(" ", "").replace(":00:00","")
+        datetime[i]=int(datetime[i])
+    return datetime
+datetime_convert(datetime)
+train_data['datetime']=datetime
+datetime_convert(test_data['datetime'].values)
+test_data['datetime']=test_data['datetime'].values
+'''
+标准化数据
+'''
+def normalize(ndarry):
+    ndarry=(ndarry-ndarry.mean())/ndarry.std()
+    return ndarry
+
+for i in range(12):
+    train_data.iloc[:,i]=normalize(train_data.iloc[:,i])
+
+
+
+
+
+X=torch.tensor(train_data.iloc[:,:9].to_numpy().astype(float),dtype=torch.float32)
+Y=torch.tensor(train_data.iloc[:,9:12].to_numpy().astype(float),dtype=torch.float32)
+X_test=torch.tensor(test_data.iloc[:,:9].to_numpy().astype(float),dtype=torch.float32)
+torch_train_dataset = torch.utils.data.TensorDataset(X, Y)
+batch_size = 10
+torch.manual_seed(seed=2023)
+
+training, vertification = torch.utils.data.random_split(torch_train_dataset, [10000, 886], )
+training_data = torch.utils.data.DataLoader(training,batch_size=batch_size,shuffle=True)
+vertification_data = torch.utils.data.DataLoader(training,batch_size=batch_size,shuffle=True)
+
+feature_number = 9  # 设置特征数目
+out_prediction = 3  # 设置输出数目
+learning_rate = 0.01  # 设置学习率
+epochs = 10 # 设置训练代数
+
+
+class Net(torch.nn.Module):
+    def __init__(self, n_feature, n_output, n_neuron1, n_neuron2,
+                 n_layer):  # n_feature为特征数目，这个数字不能随便取,n_output为特征对应的输出数目，也不能随便取
+        self.n_feature = n_feature
+        self.n_output = n_output
+        self.n_neuron1 = n_neuron1
+        self.n_neuron2 = n_neuron2
+        self.n_layer = n_layer
+        super(Net, self).__init__()
+        self.input_layer = torch.nn.Linear(self.n_feature, self.n_neuron1)  # 输入层
+        self.hidden1 = torch.nn.Linear(self.n_neuron1, self.n_neuron2)  # 1类隐藏层
+        self.hidden2 = torch.nn.Linear(self.n_neuron2, self.n_neuron2)  # 2类隐藏
+        self.predict = torch.nn.Linear(self.n_neuron2, self.n_output)  # 输出层
+
+    def forward(self, x):
+        '''定义前向传递过程'''
+        out = self.input_layer(x)
+        out = torch.relu(out)  # 使用relu函数非线性激活
+        out = self.hidden1(out)
+        out = torch.relu(out)
+        for i in range(self.n_layer):
+            out = self.hidden2(out)
+            out = torch.relu(out)
+        out = self.predict(  # 回归问题最后一层不需要激活函数
+            out
+        )  # 除去feature_number与out_prediction不能随便取，隐藏层数与其他神经元数目均可以适当调整以得到最佳预测效果
+        #print(out.shape)
+        return out
+
+net = Net(n_feature=feature_number,
+          n_output=out_prediction,
+          n_layer=1,
+          n_neuron1=20,
+          n_neuron2=20)  # 这里直接确定了隐藏层数目以及神经元数目，实际操作中需要遍历
+optimizer = optim.Adam(net.parameters(), learning_rate)  # 使用Adam算法更新参数
+criteon = torch.nn.MSELoss()  # 误差计算公式，回归问题采用均方误差
+average_losses=[]
+for epoch in range(epochs):  # 整个数据集迭代次数
+    net.train()  # 启动训练模式
+    for batch_idx, (data, target) in enumerate(training_data):
+        logits = net.forward(data)  # 前向计算结果（预测结果）
+        loss = criteon(logits, target)  # 计算损失
+        optimizer.zero_grad()  # 梯度清零
+        loss.backward()  # 后向传递过程
+        optimizer.step()  # 优化权重与偏差矩阵
+        #print(logits)
+
+    logit = []  # 这个是验证集，可以根据验证集的结果进行调参，这里根据验证集的结果选取最优的神经网络层数与神经元数目
+    target = []
+    net.eval()  # 启动测试模式
+    for data, targets in vertification:  # 输出验证集的平均误差
+        logits = net.forward(data).detach().numpy()
+        targets = targets.detach().numpy()
+        target.append(targets[0])
+        logit.append(logits[0])
+    average_loss = criteon(torch.tensor(logit), torch.tensor(target))
+    average_losses.append(average_loss)
+    print("epoch={},the average loss is {}".format(epoch,average_loss))
+
+Y_test=net.forward(X_test)
+print(Y_test)
+xx=range(epochs)
+yy=average_losses
+plt.xlim(0,epochs)
+plt.ylim(0,10)
+plt.plot(xx,yy)
+plt.show()
\ No newline at end of file
diff --git a/共享单车数据分析/test.csv b/第十三组/共享单车数据集/test.csv
similarity index 100%
rename from 共享单车数据分析/test.csv
rename to 第十三组/共享单车数据集/test.csv
diff --git a/共享单车数据分析/train.csv b/第十三组/共享单车数据集/train.csv
similarity index 100%
rename from 共享单车数据分析/train.csv
rename to 第十三组/共享单车数据集/train.csv
diff --git a/共享单车数据分析/十三组平时作业（实战）/iris线性回归模型.ipynb b/第十三组/十三组平时作业（实战）/iris线性回归模型.ipynb
similarity index 100%
rename from 共享单车数据分析/十三组平时作业（实战）/iris线性回归模型.ipynb
rename to 第十三组/十三组平时作业（实战）/iris线性回归模型.ipynb
diff --git a/第十三组/十三组平时作业（实战）/torch实现cnn服饰分类/.keep b/第十三组/十三组平时作业（实战）/torch实现cnn服饰分类/.keep
new file mode 100644
index 0000000..e69de29
diff --git a/共享单车数据分析/十三组平时作业（实战）/torch实现cnn服饰分类/Model.pkl b/第十三组/十三组平时作业（实战）/torch实现cnn服饰分类/Model.pkl
similarity index 100%
rename from 共享单车数据分析/十三组平时作业（实战）/torch实现cnn服饰分类/Model.pkl
rename to 第十三组/十三组平时作业（实战）/torch实现cnn服饰分类/Model.pkl
diff --git a/共享单车数据分析/十三组平时作业（实战）/torch实现cnn服饰分类/cnn服装分类.ipynb b/第十三组/十三组平时作业（实战）/torch实现cnn服饰分类/cnn服装分类.ipynb
similarity index 100%
rename from 共享单车数据分析/十三组平时作业（实战）/torch实现cnn服饰分类/cnn服装分类.ipynb
rename to 第十三组/十三组平时作业（实战）/torch实现cnn服饰分类/cnn服装分类.ipynb
diff --git a/共享单车数据分析/十三组平时作业（实战）/torch实现cnn服饰分类/cnn网络.ipynb b/第十三组/十三组平时作业（实战）/torch实现cnn服饰分类/cnn网络.ipynb
similarity index 100%
rename from 共享单车数据分析/十三组平时作业（实战）/torch实现cnn服饰分类/cnn网络.ipynb
rename to 第十三组/十三组平时作业（实战）/torch实现cnn服饰分类/cnn网络.ipynb
diff --git a/共享单车数据分析/十三组平时作业（实战）/波士顿房价线性回归模型.ipynb b/第十三组/十三组平时作业（实战）/波士顿房价线性回归模型.ipynb
similarity index 100%
rename from 共享单车数据分析/十三组平时作业（实战）/波士顿房价线性回归模型.ipynb
rename to 第十三组/十三组平时作业（实战）/波士顿房价线性回归模型.ipynb
diff --git a/第十三组/十三组平时作业（实战）/泰坦尼克号.ipynb b/第十三组/十三组平时作业（实战）/泰坦尼克号.ipynb
new file mode 100644
index 0000000..f38602c
--- /dev/null
+++ b/第十三组/十三组平时作业（实战）/泰坦尼克号.ipynb
@@ -0,0 +1,2392 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {
+    "collapsed": true,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "from sklearn import preprocessing\n",
+    "from sklearn.preprocessing import StandardScaler\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.metrics import accuracy_score,confusion_matrix,roc_curve,roc_auc_score\n",
+    "from sklearn.ensemble import RandomForestClassifier,BaggingClassifier,AdaBoostClassifier\n",
+    "from sklearn.naive_bayes import GaussianNB\n",
+    "from sklearn.tree import DecisionTreeClassifier\n",
+    "from sklearn.svm import SVC\n",
+    "from sklearn.linear_model import LogisticRegression\n",
+    "from sklearn.neighbors import KNeighborsClassifier\n",
+    "plt.rcParams['font.sans-serif'] = ['SimHei']#显示中文\n",
+    "plt.rcParams['axes.unicode_minus'] = False"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>直肠温度</th>\n",
+       "      <th>脉搏</th>\n",
+       "      <th>呼吸频率</th>\n",
+       "      <th>红细胞体积</th>\n",
+       "      <th>总蛋白值</th>\n",
+       "      <th>y</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>38.5</td>\n",
+       "      <td>66</td>\n",
+       "      <td>28</td>\n",
+       "      <td>45.0</td>\n",
+       "      <td>8.4</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>39.2</td>\n",
+       "      <td>88</td>\n",
+       "      <td>20</td>\n",
+       "      <td>50.0</td>\n",
+       "      <td>85.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>38.3</td>\n",
+       "      <td>40</td>\n",
+       "      <td>24</td>\n",
+       "      <td>33.0</td>\n",
+       "      <td>6.7</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>39.1</td>\n",
+       "      <td>164</td>\n",
+       "      <td>84</td>\n",
+       "      <td>48.0</td>\n",
+       "      <td>7.2</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>37.3</td>\n",
+       "      <td>104</td>\n",
+       "      <td>35</td>\n",
+       "      <td>74.0</td>\n",
+       "      <td>7.4</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   直肠温度   脉搏  呼吸频率  红细胞体积  总蛋白值    y\n",
+       "0  38.5   66    28   45.0   8.4  0.0\n",
+       "1  39.2   88    20   50.0  85.0  0.0\n",
+       "2  38.3   40    24   33.0   6.7  1.0\n",
+       "3  39.1  164    84   48.0   7.2  0.0\n",
+       "4  37.3  104    35   74.0   7.4  0.0"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df = pd.read_csv(\"data_horse.csv\",encoding=\"gbk\")\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e34168b30cea418e82fffe8c57f268f3",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Summarize dataset:   0%|          | 0/19 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 30452 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 32928 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 28201 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 24230 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 33033 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 25615 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 30452 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 32928 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 28201 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 24230 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 30452 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 32928 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 28201 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 24230 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 33033 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 25615 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 33033 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 25615 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 30452 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 32928 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 28201 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 24230 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 21628 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 21560 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 39057 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 29575 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 30452 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 32928 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 28201 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 24230 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 30452 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 32928 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 28201 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 24230 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 21628 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 21560 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 39057 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 29575 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 21628 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 21560 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 39057 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 29575 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 30452 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 32928 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 28201 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 24230 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 32418 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 32454 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 32990 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 20307 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 31215 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 30452 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 32928 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 28201 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 24230 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 30452 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 32928 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 28201 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 24230 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 32418 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 32454 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 32990 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 20307 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 31215 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 32418 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 32454 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 32990 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 20307 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 31215 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 30452 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 32928 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 28201 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 24230 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 24635 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 34507 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 30333 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 20540 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 30452 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 32928 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 28201 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 24230 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 30452 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 32928 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 28201 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 24230 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 24635 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 34507 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 30333 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 20540 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 24635 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 34507 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 30333 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 20540 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 33033 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 25615 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 30452 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 32928 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 28201 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 24230 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 33033 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 25615 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 33033 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 25615 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 30452 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 32928 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 28201 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 24230 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 30452 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 32928 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 28201 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 24230 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 33033 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 25615 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 21628 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 21560 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 39057 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 29575 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 33033 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 25615 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 33033 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 25615 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 21628 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 21560 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 39057 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 29575 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 21628 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 21560 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 39057 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 29575 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 33033 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 25615 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 32418 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 32454 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 32990 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 20307 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 31215 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 33033 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 25615 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 33033 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 25615 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 32418 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 32454 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 32990 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 20307 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 31215 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 32418 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 32454 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 32990 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 20307 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 31215 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 33033 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 25615 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 24635 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 34507 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 30333 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 20540 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 33033 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 25615 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 33033 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 25615 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 24635 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 34507 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 30333 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 20540 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 24635 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 34507 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 30333 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 20540 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 21628 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 21560 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 39057 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 29575 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 30452 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 32928 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 28201 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 24230 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 21628 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 21560 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 39057 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 29575 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 21628 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 21560 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 39057 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 29575 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 30452 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 32928 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 28201 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 24230 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 30452 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 32928 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 28201 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 24230 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 21628 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 21560 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 39057 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 29575 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 33033 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 25615 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 21628 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 21560 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 39057 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 29575 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 21628 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 21560 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 39057 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 29575 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 33033 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 25615 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 33033 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 25615 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 21628 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 21560 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 39057 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 29575 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 32418 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 32454 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 32990 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 20307 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 31215 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 21628 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 21560 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 39057 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 29575 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 21628 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 21560 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 39057 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 29575 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 32418 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 32454 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 32990 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 20307 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 31215 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 32418 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 32454 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 32990 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 20307 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 31215 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 21628 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 21560 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 39057 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 29575 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 24635 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 34507 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 30333 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 20540 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 21628 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 21560 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 39057 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 29575 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 21628 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 21560 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 39057 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 29575 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 24635 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 34507 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 30333 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 20540 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 24635 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 34507 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 30333 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 20540 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 32418 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 32454 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 32990 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 20307 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 31215 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 30452 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 32928 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 28201 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 24230 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 32418 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 32454 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 32990 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 20307 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 31215 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 32418 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 32454 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 32990 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 20307 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 31215 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 30452 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 32928 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 28201 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 24230 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 30452 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 32928 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 28201 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 24230 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 32418 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 32454 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 32990 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 20307 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 31215 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 33033 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 25615 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 32418 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 32454 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 32990 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 20307 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 31215 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 32418 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 32454 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 32990 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 20307 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 31215 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 33033 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 25615 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 33033 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 25615 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 32418 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 32454 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 32990 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 20307 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 31215 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 21628 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 21560 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 39057 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 29575 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 32418 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 32454 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 32990 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 20307 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 31215 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 32418 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 32454 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 32990 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 20307 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 31215 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 21628 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 21560 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 39057 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 29575 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 21628 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 21560 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 39057 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 29575 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 32418 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 32454 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 32990 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 20307 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 31215 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 24635 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 34507 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 30333 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 20540 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 32418 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 32454 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 32990 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 20307 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 31215 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 32418 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 32454 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 32990 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 20307 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 31215 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 24635 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 34507 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 30333 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 20540 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 24635 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 34507 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 30333 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 20540 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 24635 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 34507 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 30333 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 20540 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 30452 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 32928 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 28201 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 24230 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 24635 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 34507 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 30333 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 20540 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 24635 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 34507 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 30333 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 20540 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 30452 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 32928 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 28201 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 24230 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 30452 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 32928 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 28201 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 24230 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 24635 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 34507 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 30333 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 20540 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 33033 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 25615 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 24635 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 34507 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 30333 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 20540 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 24635 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 34507 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 30333 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 20540 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 33033 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 25615 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 33033 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 25615 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 24635 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 34507 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 30333 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 20540 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 21628 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 21560 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 39057 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 29575 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 24635 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 34507 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 30333 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 20540 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 24635 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 34507 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 30333 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 20540 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 21628 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 21560 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 39057 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 29575 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 21628 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 21560 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 39057 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 29575 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 24635 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 34507 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 30333 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 20540 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 32418 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 32454 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 32990 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 20307 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 31215 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 24635 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 34507 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 30333 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 20540 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 24635 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 34507 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 30333 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 20540 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 32418 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 32454 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 32990 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 20307 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 31215 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 32418 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 32454 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 32990 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 20307 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 31215 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 30452 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 32928 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 28201 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 24230 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 33033 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 25615 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 21628 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 21560 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 39057 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 29575 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 32418 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 32454 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 32990 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 20307 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 31215 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 24635 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 34507 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 30333 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 20540 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 30452 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 32928 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 28201 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 24230 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 33033 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 25615 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 21628 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 21560 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 39057 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 29575 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 32418 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 32454 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 32990 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 20307 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 31215 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 24635 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 34507 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 30333 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 20540 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 30452 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 32928 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 28201 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 24230 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 33033 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 25615 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 21628 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 21560 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 39057 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 29575 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 32418 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 32454 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 32990 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 20307 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 31215 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 24635 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 34507 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 30333 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 20540 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 30452 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 32928 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 28201 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 24230 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 33033 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 25615 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 21628 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 21560 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 39057 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 29575 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 32418 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 32454 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 32990 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 20307 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 31215 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 24635 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 34507 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 30333 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 20540 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 30452 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 32928 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 28201 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 24230 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 33033 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 25615 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 21628 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 21560 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 39057 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 29575 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 32418 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 32454 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 32990 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 20307 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 31215 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 24635 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 34507 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 30333 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 20540 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 30452 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 32928 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 28201 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 24230 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 33033 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 25615 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 21628 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 21560 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 39057 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 29575 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 32418 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 32454 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 32990 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 20307 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 31215 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 24635 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 34507 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 30333 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 20540 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 24635 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 34507 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 30333 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 20540 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 32418 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 32454 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 32990 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 20307 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 31215 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 21628 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 21560 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 39057 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 29575 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 30452 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 32928 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 28201 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 24230 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 33033 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 25615 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 24635 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 34507 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 30333 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 20540 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 32418 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 32454 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 32990 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 20307 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 31215 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 21628 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 21560 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 39057 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 29575 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 30452 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 32928 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 28201 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 24230 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 33033 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 25615 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 24635 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 34507 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 30333 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 20540 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 32418 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 32454 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 32990 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 20307 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 31215 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 21628 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 21560 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 39057 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 29575 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 30452 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 32928 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 28201 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 24230 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 33033 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 25615 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "545c132fec8e48fc88f7bdea87791054",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Generate report structure:   0%|          | 0/1 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 30452 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 32928 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 28201 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 24230 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 33033 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 25615 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 21628 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 21560 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 39057 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 29575 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 32418 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 32454 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 32990 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 20307 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 31215 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 24635 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 34507 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 30333 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 20540 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 30452 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 32928 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 28201 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 24230 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 33033 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 25615 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 21628 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 21560 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 39057 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 29575 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 32418 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 32454 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 32990 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 20307 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 31215 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 24635 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 34507 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 30333 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 20540 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 30452 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 32928 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 28201 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 24230 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 33033 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 25615 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 21628 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 21560 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 39057 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 29575 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 32418 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 32454 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 32990 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 20307 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 31215 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 24635 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 34507 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 30333 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 20540 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 30452 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 32928 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 28201 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 24230 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 33033 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 25615 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 21628 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 21560 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 39057 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 29575 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 32418 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 32454 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 32990 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 20307 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 31215 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 24635 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 34507 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 30333 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 20540 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 30452 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 32928 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 28201 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 24230 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 33033 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 25615 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 21628 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 21560 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 39057 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 29575 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 32418 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 32454 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 32990 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 20307 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 31215 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 24635 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 34507 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 30333 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 20540 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 30452 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 32928 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 28201 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 24230 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 33033 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 25615 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 21628 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 21560 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 39057 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 29575 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 32418 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 32454 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 32990 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 20307 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 31215 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 24635 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 34507 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 30333 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 20540 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 30452 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 32928 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 28201 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 24230 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 33033 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 25615 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 21628 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 21560 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 39057 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 29575 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 32418 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 32454 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 32990 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 20307 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 31215 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 24635 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 34507 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 30333 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 20540 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 30452 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 32928 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 28201 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 24230 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 33033 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 25615 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 21628 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 21560 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 39057 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 29575 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 32418 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 32454 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 32990 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 20307 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 31215 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 24635 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 34507 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 30333 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 20540 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 30452 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 32928 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 28201 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 24230 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 33033 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 25615 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 21628 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 21560 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 39057 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 29575 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 32418 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 32454 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 32990 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 20307 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 31215 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 24635 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 34507 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 30333 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 20540 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 24635 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 34507 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 30333 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 20540 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 30452 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 32928 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 28201 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 24230 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 32418 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 32454 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 32990 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 20307 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 31215 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 21628 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 21560 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 39057 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 29575 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 33033 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\textpath.py:74: RuntimeWarning: Glyph 25615 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=LOAD_NO_HINTING)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 24635 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 34507 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 30333 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 20540 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 30452 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 32928 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 28201 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 24230 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 32418 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 32454 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 32990 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 20307 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 31215 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 21628 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 21560 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 39057 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 29575 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 33033 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 25615 missing from current font.\n",
+      "  font.set_text(s, 0.0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 24635 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 34507 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 30333 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 20540 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 30452 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 32928 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 28201 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 24230 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 32418 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 32454 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 32990 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 20307 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 31215 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 21628 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 21560 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 39057 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 29575 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 33033 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n",
+      "E:\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 25615 missing from current font.\n",
+      "  font.set_text(s, 0, flags=flags)\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "942c0a25ca7347018839697a63d1b3d2",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Render HTML:   0%|          | 0/1 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "98b179f718c94b6ab109fe1532aacbcc",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Export report to file:   0%|          | 0/1 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from pandas_profiling import ProfileReport\n",
+    "profile = ProfileReport(df, title='Pandas Profiling Report', html={'style':{'full_width':True}})\n",
+    "profile.to_file(output_file=\"your_report.html\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "y        1.000000\n",
+       "直肠温度     0.172629\n",
+       "总蛋白值     0.114651\n",
+       "呼吸频率    -0.036938\n",
+       "红细胞体积   -0.152119\n",
+       "脉搏      -0.276255\n",
+       "Name: y, dtype: float64"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.corr()\n",
+    "df.corr()['y'].sort_values(ascending=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>直肠温度</th>\n",
+       "      <th>脉搏</th>\n",
+       "      <th>红细胞体积</th>\n",
+       "      <th>总蛋白值</th>\n",
+       "      <th>y</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>38.5</td>\n",
+       "      <td>66</td>\n",
+       "      <td>45.0</td>\n",
+       "      <td>8.4</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>39.2</td>\n",
+       "      <td>88</td>\n",
+       "      <td>50.0</td>\n",
+       "      <td>85.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>38.3</td>\n",
+       "      <td>40</td>\n",
+       "      <td>33.0</td>\n",
+       "      <td>6.7</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>39.1</td>\n",
+       "      <td>164</td>\n",
+       "      <td>48.0</td>\n",
+       "      <td>7.2</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>37.3</td>\n",
+       "      <td>104</td>\n",
+       "      <td>74.0</td>\n",
+       "      <td>7.4</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   直肠温度   脉搏  红细胞体积  总蛋白值    y\n",
+       "0  38.5   66   45.0   8.4  0.0\n",
+       "1  39.2   88   50.0  85.0  0.0\n",
+       "2  38.3   40   33.0   6.7  1.0\n",
+       "3  39.1  164   48.0   7.2  0.0\n",
+       "4  37.3  104   74.0   7.4  0.0"
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df = df[df.columns[df.corr()['y'].abs()>0.1]]\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "直肠温度     0\n",
+       "脉搏       0\n",
+       "红细胞体积    0\n",
+       "总蛋白值     0\n",
+       "y        9\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAl4AAAFPCAYAAACcQ8AlAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8QVMy6AAAACXBIWXMAAAsTAAALEwEAmpwYAAAPa0lEQVR4nO3db4ylZ1nH8d86u6Xb7cKWODS2iibSXFQiDQG1NC0uTRv+qIk01EKUhqhR08grYwySYMCKBoxCiMALKbSKkFKlIaFIJBBoxVTtC/5Eesd/bUMBAWW7LKUFhvHFOcuWZbedNnOuZ/aczyeZ7Jn7TOZcb+bku/fznOfZtbm5GQAAFu8Hph4AAGBVCC8AgCbCCwCgifACAGgivAAAmggvAIAmwgsAoInwAgBoIrwAAJoIL2DlVNVvVNU7H/L9+6rqyglHAlbELrcMAlZNVT0pyaeTnJNkT5J7kvzYGOP+SQcDlp4dL2DljDG+lOSzSS5JcmmSj4kuoMPuqQcAmMhNSX4xyelJ3jvtKMCqEF7Aqvq7JLcl+XaS35l4FmBFONQIrKQxxueTfCHJp8YYX596HmA12PECVlJV7U7yb0k+PPUswOqw4wWsqi8meWqSmyeeA1ghLicBANDEjhcAQBPhBQDQRHgBADQ5JT7V+J3vfGdzY8O5aADAzrdnz9pXkqyf6LlTIrw2NjZz6JC7eQAAO9/6+v67T/acQ40AAE2EFwBAE+EFANBEeAEANBFeAABNhBcAQBPhBQDQRHgBADQRXgAATYQXAEAT4QUA0OSUuFcjwDJ44hP2ZO2006ceA1bOxjcfyP/d962px0givADarJ12eu557U9OPQasnCe/+tNJdkZ4OdQIANBEeAEANBFeAABNhBcAQBPhBQDQRHgBADQRXgAATYQXAEAT4QUA0ER4AQA0EV4AAE2EFwBAE+EFANBEeAEANBFeAABNhBcAQBPhBQDQRHgBADTZvd2/sKqekOQ98999JMlVSd6a5Pwkt4wxrp3/3NuPXwMAWGaL2PH65SR/Nsa4PMkXk7wkydoY46Ik51TVeVV1xfFrC5gDAGBH2fYdrzHGWx7y7XqSX0nyxvn3H0lycZJnJLnxuLV/P9nvXFvblQMHztjuUQGAFbFTOmLbw+uoqnp2krOS3JXk3vny4SRPSbLvBGsntbGxmUOH7l/MoABN1tf3Tz0CrKzOjni4v/WFnFxfVU9M8uYkv5rZeV5750+dOX/NE60BACy1bQ+eqjots8OIrxxj3J3kjswOJSbJBZntgJ1oDQBgqS3iUOOvJXlmkldV1auSvCPJy6rqnCQvSHJhks0ktx63BgCw1BZxcv1bM7t8xHdV1fuTXJ7k9WOM++ZrB49fAwBYZgs7uf6hxhhfzbFPMZ50DQBgmTmpHQCgifACAGgivAAAmggvAIAmwgsAoInwAgBoIrwAAJoILwCAJsILAKCJ8AIAaCK8AACaCC8AgCbCCwCgifACAGgivAAAmggvAIAmwgsAoInwAgBoIrwAAJoILwCAJsILAKCJ8AIAaCK8AACaCC8AgCbCCwCgifACAGgivAAAmggvAIAmwgsAoInwAgBoIrwAAJoILwCAJsILAKCJ8AIAaCK8AACaCC8AgCbCCwCgifACAGgivAAAmggvAIAmwgsAoInwAgBoIrwAAJoILwCAJsILAKCJ8AIAaCK8AACaCC8AgCbCCwCgifACAGiyexG/tKrOTnLTGOOSqjo3ye1J/mP+9JVjjC9X1duTnJ/kljHGtYuYAwBgJ9n2Ha+qOivJ9Un2zZd+JskfjTEOzr++XFVXJFkbY1yU5JyqOm+75wAA2GkWcahxI8lVSQ7Pv78wyTVV9U9V9efztYNJbpw//kiSixcwBwDAjrLthxrHGIeTpKqOLn0wyR+OMb5WVR+oqqdntht27/z5w0me8nC/c21tVw4cOGO7RwUAVsRO6YiFnON1nE+MMR6cP74zyXlJjiTZO187M4+w87axsZlDh+5f3IQADdbX9089Aqyszo54uL/1jk81fqiqfqiqzkjyvCSfSXJHjh1evCDJXQ1zAABMqmPH6zVJPprkm0neNsYYVfWFJLdW1TlJXpDZeWAAAEttYeE1xjg4//ejSZ563HOHq+pgksuTvH6Mcd+i5gAA2Ck6drxOaIzx1Rz7ZCMAwNJz5XoAgCbCCwCgifACAGgivAAAmggvAIAmwgsAoInwAgBoIrwAAJpMdgHVnezMx5+evY/bM/UYsHK+8eC3cuTwA1OPAbAwwusE9j5uT575uzdMPQasnDvecHWORHgBy8uhRgCAJsILAKCJ8AIAaCK8AACaCC8AgCbCCwCgifACAGgivAAAmggvAIAmwgsAoInwAgBoIrwAAJoILwCAJsILAKCJ8AIAaPKYwquqLt7uQQAAlt2Wwquq/uG4pT9ewCwAAEtt98M9WVVPT/KMJOdW1dXz5X1JHlj0YAAAy+aRdrx2neDf/03ySwubCABgST3sjtcY45NJPllVNca4oWkmAICl9LDh9RBvrKqXJDnt6IIQAwB4dLb6qca/T/LDmR1qPPoFAMCjsNUdr8NjjD9d6CQAAEtuq+F1W1W9O8kNSb6eJGOMjy9sKgCAJbTV8PpWkjuT/FRmhxk3kwgvAIBHYavhdVdmsXU0ugAAeJQezS2DdiXZm+SKJM9ZzDgAAMtrSzteY4zrH/Lt26rqLQuaBwBgaW0pvKrqoTtcj0/ytMWMAwCwvLZ6jtdzc+zcrm8muWYx4wAALK+tnuP1uiT/k+SJSb6SZCxsIgCAJbXV8LouyZOSfDDJuUnesbCJAACW1FYPNf7IGONl88cfqqqPLWogAIBltdXw+nxVvTLJ7UmeneTexY0EALCctnqo8bcyi7QXJzmc5DcXNhEAwJLaanj9dZJ7xhjXJNmf2TlfAAA8ClsNr7OOXkR1jPG6JD+4uJEAAJbTVs/x+lxV/V6Sf87sRtlfWtxIAADLaas7Xi9Pcn9m53h9I8nVixoIAGBZbfVejQ8mefOCZwEAWGpbPdT4qFTV2UluGmNcUlV7krwvs6ve/+UY47oTrS1iDgCAnWSrhxq3rKrOSnJ9kn3zpVck+dcxxkVJfr6q9p9kDQBgqW17eCXZSHJVZtf7SpKDSW6cP/5EkmedZA0AYKlt+6HGMcbhJKmqo0v7cuxK94eTnH2StZNaW9uVAwfO2O5RgR3I3zqwCDvlvWUh53gd50iSvUnuS3Lm/PsTrZ3UxsZmDh26f8FjHrO+7sgnTKXzb72b9xaYzk7piEUcajzeHUkunj++IMldJ1kDAFhqHTte1ye5paouSfITmd1o+94TrAEALLWF7XiNMQ7O/707yeVJ/jHJZWOMjROtLWoOAICdomPHK2OMz+fYpxhPugYAsMw6zvECACDCCwCgjfACAGgivAAAmggvAIAmwgsAoInwAgBoIrwAAJoILwCAJsILAKCJ8AIAaCK8AACaCC8AgCbCCwCgifACAGgivAAAmggvAIAmwgsAoInwAgBoIrwAAJoILwCAJsILAKCJ8AIAaCK8AACaCC8AgCbCCwCgifACAGgivAAAmggvAIAmwgsAoInwAgBoIrwAAJoILwCAJsILAKCJ8AIAaCK8AACaCC8AgCbCCwCgifACAGgivAAAmggvAIAmwgsAoInwAgBoIrwAAJoILwCAJsILAKCJ8AIAaCK8AACaCC8AgCbCCwCgye5Fv0BV7U7yX/OvJHlFkhcneWGS28cYv73oGQAAdoKOHa+nJ3n3GOPgGONgkscluTjJTyf5XFVd1jADAMDkOsLrwiQvqqrbqupdSS5N8rdjjM0kH05yScMMAACTW/ihxiT/kuRnxxhfqKq/SLI3yZg/dzjJ2Y/0C9bWduXAgTMWOCKwU/hbBxZhp7y3dITXp8YYD84f35nktMziK0nOzBZ23TY2NnPo0P0LGu/7ra/vb3st4Ht1/q13894C09kpHdFxqPGvquqCqlpL8qIk+zI7xytJLkhyV8MMAACT69jxem2Sv0myK8n7k1yb5NaqelOS58+/AACW3sLDa4zxmcw+2fhd808y/lySN40x/nvRMwAA7AQdO17fZ4zxjSQ3TfHaAABTceV6AIAmwgsAoInwAgBoIrwAAJoILwCAJsILAKCJ8AIAaCK8AACaCC8AgCbCCwCgifACAGgivAAAmggvAIAmwgsAoInwAgBoIrwAAJoILwCAJsILAKCJ8AIAaCK8AACaCC8AgCbCCwCgifACAGgivAAAmggvAIAmwgsAoInwAgBoIrwAAJoILwCAJsILAKCJ8AIAaCK8AACaCC8AgCbCCwCgifACAGgivAAAmggvAIAmwgsAoInwAgBoIrwAAJoILwCAJsILAKCJ8AIAaCK8AACaCC8AgCbCCwCgifACAGgivAAAmggvAIAmwgsAoMnuKV+8qt6e5Pwkt4wxrp1yFgCARZtsx6uqrkiyNsa4KMk5VXXeVLMAAHSY8lDjwSQ3zh9/JMnF040CALB4Ux5q3Jfk3vnjw0mecrIf3LNn7Svr6/vvbplq7o43XN35csDc+vr+qUdYqCe/+tNTjwArqfm95UdP9sSU4XUkyd754zPz8Ltv64sfBwBgsaY81HhHjh1evCDJXdONAgCweFPueN2c5NaqOifJC5JcOOEsAAALt2tzc3OyF6+qs5JcnuTjY4wvTjYIAECDScMLAGCVuHI9AEAT4QUA0ER4AQA0mfRejbBdqurHM7s8ydmZ/YfiriQfGGN8bcq5gFNbVe1L8qx873vL7WMMJ0jzmDi5nlNeVf1+knMzu/XU4cwuyHtBkiuTPHeM8aUJxwNOUVX18iS/kOQT+d73lvOTXOY/djwWdrxYBi8cYxx/r8/3VdX+JM9JctMEMwGnvl8/wXtLquqNSZ4X7y08BsKLZXBnVV2X2U3X783sVlSXJLk0yWunHAw4pX21ql6d739vuSzJn0w5GKcuhxpZClX1oiQHM7v5+pHMbkl1s0MBwGNVVbuTvCLH3lu+ltl7yw1jjHsmHI1TmPACAGjichIAAE2EFwBAE+HF0qoqJ9YD266qrpl6Bk5dPtXIUjjJBVTfO+VMwNL67NQDcOpycj2nPBdQBeBUYceLZeACqsC2q6qPJzkjs//QHbUryeYY49JppuJUJ7xYBi6gCizClUnemeSqMcbhR/hZ2BKHGlkKLqAKLEJVHUjy7THGkalnYTkILwCAJi4nAQDQRHgBADQRXgAATYQXAEAT4QWsnKp6TVW9dP74D6rqJVPPBKwG4QWsohuSvHT++PlJbp5uFGCVCC9g5Ywx/jPJ/qo6mOQzY4wHJh4JWBHCC1hV70lyXWa7XwAthBewqm5KspnktqkHAVaH8AJWTlU9LckHk7xujOH2HUAbtwwCAGhixwsAoInwAgBoIrwAAJoILwCAJsILAKCJ8AIAaPL/BcvCqIdAqlIAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<Figure size 720x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import warnings\n",
+    "warnings.filterwarnings(\"ignore\")\n",
+    "for i in df.columns:\n",
+    "    plt.figure(figsize=(10,5))\n",
+    "    plt.title(i)\n",
+    "    plt.xticks(rotation=90)#旋转90度\n",
+    "    sns.countplot(df[i])\n",
+    "    plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "直肠温度     68\n",
+       "脉搏       26\n",
+       "红细胞体积    36\n",
+       "总蛋白值     42\n",
+       "y         0\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 39,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df[df.columns[:-1]] = df[df.columns[:-1]].replace(0,np.nan)\n",
+    "df.isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "直肠温度     0\n",
+       "脉搏       0\n",
+       "红细胞体积    0\n",
+       "总蛋白值     0\n",
+       "y        0\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 40,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['直肠温度'] = df['直肠温度'].fillna(df['直肠温度'].mean())\n",
+    "df[['脉搏','红细胞体积','总蛋白值']]  = df[['脉搏','红细胞体积','总蛋白值']].fillna(df[['脉搏','红细胞体积','总蛋白值']].median())\n",
+    "df.isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "df = df.dropna()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 720x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "for i in df.columns:\n",
+    "    plt.figure(figsize=(10,5))\n",
+    "    plt.title(i)\n",
+    "    plt.xticks(rotation=90)\n",
+    "    sns.countplot(df[i])\n",
+    "    plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import time\n",
+    "X_train,X_test,y_train,y_test = train_test_split(df[df.columns[:-1]],df['y'],test_size=0.3,random_state=0)\n",
+    "X_train = StandardScaler().fit_transform(X_train)\n",
+    "X_test = StandardScaler().fit_transform(X_test)\n",
+    "model_result = {}\n",
+    "models = [RandomForestClassifier(),BaggingClassifier(),AdaBoostClassifier(),GaussianNB(),LogisticRegression(),DecisionTreeClassifier(),SVC(),KNeighborsClassifier()]\n",
+    "for model in models:\n",
+    "    try:\n",
+    "        model_name = str(model).split('(')[0]\n",
+    "        start = time.time()\n",
+    "        model.fit(X_train,y_train)\n",
+    "        y_pred = model.predict(X_test)\n",
+    "        end = time.time()\n",
+    "        model_result[model_name] = [accuracy_score(y_test,y_pred),confusion_matrix(y_test,y_pred),end-start]\n",
+    "    except Exception as e:\n",
+    "        print(model_name,e)\n",
+    "\n",
+    "df_result = pd.DataFrame(model_result).T\n",
+    "df_result.columns = ['accuracy_score','confusion_matrix','time']\n",
+    "df_result.sort_values(by='accuracy_score',ascending=False,inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(10,5))\n",
+    "plt.title('各个模型的准确率')\n",
+    "sns.barplot(x=df_result.index,y=df_result['accuracy_score'])\n",
+    "for i in range(df_result.shape[0]):\n",
+    "    plt.text(i,df_result['accuracy_score'][i],round(df_result['accuracy_score'][i],3),ha='center')\n",
+    "plt.xticks(rotation=90)\n",
+    "plt.show()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.8"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}
diff --git a/第十三组/十三组平时作业（实战）/离职分析.ipynb b/第十三组/十三组平时作业（实战）/离职分析.ipynb
new file mode 100644
index 0000000..037605f
--- /dev/null
+++ b/第十三组/十三组平时作业（实战）/离职分析.ipynb
@@ -0,0 +1,510 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": true,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "from sklearn.preprocessing import LabelEncoder"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "   Age Attrition     BusinessTravel              Department  DistanceFromHome  \\\n0   51        No      Travel_Rarely                   Sales                 6   \n1   31       Yes  Travel_Frequently  Research & Development                10   \n2   32        No  Travel_Frequently  Research & Development                17   \n3   38        No         Non-Travel  Research & Development                 2   \n4   32        No      Travel_Rarely  Research & Development                10   \n\n   Education EducationField  EmployeeCount  EmployeeID  Gender  ...  \\\n0          2  Life Sciences              1           1  Female  ...   \n1          1  Life Sciences              1           2  Female  ...   \n2          4          Other              1           3    Male  ...   \n3          5  Life Sciences              1           4    Male  ...   \n4          1        Medical              1           5    Male  ...   \n\n   NumCompaniesWorked Over18 PercentSalaryHike  StandardHours  \\\n0                 1.0      Y                11              8   \n1                 0.0      Y                23              8   \n2                 1.0      Y                15              8   \n3                 3.0      Y                11              8   \n4                 4.0      Y                12              8   \n\n   StockOptionLevel TotalWorkingYears  TrainingTimesLastYear  YearsAtCompany  \\\n0                 0               1.0                      6               1   \n1                 1               6.0                      3               5   \n2                 3               5.0                      2               5   \n3                 3              13.0                      5               8   \n4                 2               9.0                      2               6   \n\n   YearsSinceLastPromotion  YearsWithCurrManager  \n0                        0                     0  \n1                        1                     4  \n2                        0                     3  \n3                        7                     5  \n4                        0                     4  \n\n[5 rows x 24 columns]",
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Age</th>\n      <th>Attrition</th>\n      <th>BusinessTravel</th>\n      <th>Department</th>\n      <th>DistanceFromHome</th>\n      <th>Education</th>\n      <th>EducationField</th>\n      <th>EmployeeCount</th>\n      <th>EmployeeID</th>\n      <th>Gender</th>\n      <th>...</th>\n      <th>NumCompaniesWorked</th>\n      <th>Over18</th>\n      <th>PercentSalaryHike</th>\n      <th>StandardHours</th>\n      <th>StockOptionLevel</th>\n      <th>TotalWorkingYears</th>\n      <th>TrainingTimesLastYear</th>\n      <th>YearsAtCompany</th>\n      <th>YearsSinceLastPromotion</th>\n      <th>YearsWithCurrManager</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>51</td>\n      <td>No</td>\n      <td>Travel_Rarely</td>\n      <td>Sales</td>\n      <td>6</td>\n      <td>2</td>\n      <td>Life Sciences</td>\n      <td>1</td>\n      <td>1</td>\n      <td>Female</td>\n      <td>...</td>\n      <td>1.0</td>\n      <td>Y</td>\n      <td>11</td>\n      <td>8</td>\n      <td>0</td>\n      <td>1.0</td>\n      <td>6</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>31</td>\n      <td>Yes</td>\n      <td>Travel_Frequently</td>\n      <td>Research &amp; Development</td>\n      <td>10</td>\n      <td>1</td>\n      <td>Life Sciences</td>\n      <td>1</td>\n      <td>2</td>\n      <td>Female</td>\n      <td>...</td>\n      <td>0.0</td>\n      <td>Y</td>\n      <td>23</td>\n      <td>8</td>\n      <td>1</td>\n      <td>6.0</td>\n      <td>3</td>\n      <td>5</td>\n      <td>1</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>32</td>\n      <td>No</td>\n      <td>Travel_Frequently</td>\n      <td>Research &amp; Development</td>\n      <td>17</td>\n      <td>4</td>\n      <td>Other</td>\n      <td>1</td>\n      <td>3</td>\n      <td>Male</td>\n      <td>...</td>\n      <td>1.0</td>\n      <td>Y</td>\n      <td>15</td>\n      <td>8</td>\n      <td>3</td>\n      <td>5.0</td>\n      <td>2</td>\n      <td>5</td>\n      <td>0</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>38</td>\n      <td>No</td>\n      <td>Non-Travel</td>\n      <td>Research &amp; Development</td>\n      <td>2</td>\n      <td>5</td>\n      <td>Life Sciences</td>\n      <td>1</td>\n      <td>4</td>\n      <td>Male</td>\n      <td>...</td>\n      <td>3.0</td>\n      <td>Y</td>\n      <td>11</td>\n      <td>8</td>\n      <td>3</td>\n      <td>13.0</td>\n      <td>5</td>\n      <td>8</td>\n      <td>7</td>\n      <td>5</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>32</td>\n      <td>No</td>\n      <td>Travel_Rarely</td>\n      <td>Research &amp; Development</td>\n      <td>10</td>\n      <td>1</td>\n      <td>Medical</td>\n      <td>1</td>\n      <td>5</td>\n      <td>Male</td>\n      <td>...</td>\n      <td>4.0</td>\n      <td>Y</td>\n      <td>12</td>\n      <td>8</td>\n      <td>2</td>\n      <td>9.0</td>\n      <td>2</td>\n      <td>6</td>\n      <td>0</td>\n      <td>4</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows × 24 columns</p>\n</div>"
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df = pd.read_csv('quit_dataset.csv')\n",
+    "df.head()"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "No     3699\nYes     711\nName: Attrition, dtype: int64"
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.Attrition.value_counts()"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "   Age  Attrition  BusinessTravel  Department  DistanceFromHome  Education  \\\n0   51          0               2           2                 6          2   \n1   31          1               1           1                10          1   \n2   32          0               1           1                17          4   \n3   38          0               0           1                 2          5   \n4   32          0               2           1                10          1   \n\n   EducationField  EmployeeCount  EmployeeID  Gender  ...  NumCompaniesWorked  \\\n0               1              1           1       0  ...                 1.0   \n1               1              1           2       0  ...                 0.0   \n2               4              1           3       1  ...                 1.0   \n3               1              1           4       1  ...                 3.0   \n4               3              1           5       1  ...                 4.0   \n\n   Over18  PercentSalaryHike  StandardHours  StockOptionLevel  \\\n0       0                 11              8                 0   \n1       0                 23              8                 1   \n2       0                 15              8                 3   \n3       0                 11              8                 3   \n4       0                 12              8                 2   \n\n   TotalWorkingYears  TrainingTimesLastYear  YearsAtCompany  \\\n0                1.0                      6               1   \n1                6.0                      3               5   \n2                5.0                      2               5   \n3               13.0                      5               8   \n4                9.0                      2               6   \n\n   YearsSinceLastPromotion  YearsWithCurrManager  \n0                        0                     0  \n1                        1                     4  \n2                        0                     3  \n3                        7                     5  \n4                        0                     4  \n\n[5 rows x 24 columns]",
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Age</th>\n      <th>Attrition</th>\n      <th>BusinessTravel</th>\n      <th>Department</th>\n      <th>DistanceFromHome</th>\n      <th>Education</th>\n      <th>EducationField</th>\n      <th>EmployeeCount</th>\n      <th>EmployeeID</th>\n      <th>Gender</th>\n      <th>...</th>\n      <th>NumCompaniesWorked</th>\n      <th>Over18</th>\n      <th>PercentSalaryHike</th>\n      <th>StandardHours</th>\n      <th>StockOptionLevel</th>\n      <th>TotalWorkingYears</th>\n      <th>TrainingTimesLastYear</th>\n      <th>YearsAtCompany</th>\n      <th>YearsSinceLastPromotion</th>\n      <th>YearsWithCurrManager</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>51</td>\n      <td>0</td>\n      <td>2</td>\n      <td>2</td>\n      <td>6</td>\n      <td>2</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>...</td>\n      <td>1.0</td>\n      <td>0</td>\n      <td>11</td>\n      <td>8</td>\n      <td>0</td>\n      <td>1.0</td>\n      <td>6</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>31</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>10</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>2</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0.0</td>\n      <td>0</td>\n      <td>23</td>\n      <td>8</td>\n      <td>1</td>\n      <td>6.0</td>\n      <td>3</td>\n      <td>5</td>\n      <td>1</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>32</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>17</td>\n      <td>4</td>\n      <td>4</td>\n      <td>1</td>\n      <td>3</td>\n      <td>1</td>\n      <td>...</td>\n      <td>1.0</td>\n      <td>0</td>\n      <td>15</td>\n      <td>8</td>\n      <td>3</td>\n      <td>5.0</td>\n      <td>2</td>\n      <td>5</td>\n      <td>0</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>38</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>2</td>\n      <td>5</td>\n      <td>1</td>\n      <td>1</td>\n      <td>4</td>\n      <td>1</td>\n      <td>...</td>\n      <td>3.0</td>\n      <td>0</td>\n      <td>11</td>\n      <td>8</td>\n      <td>3</td>\n      <td>13.0</td>\n      <td>5</td>\n      <td>8</td>\n      <td>7</td>\n      <td>5</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>32</td>\n      <td>0</td>\n      <td>2</td>\n      <td>1</td>\n      <td>10</td>\n      <td>1</td>\n      <td>3</td>\n      <td>1</td>\n      <td>5</td>\n      <td>1</td>\n      <td>...</td>\n      <td>4.0</td>\n      <td>0</td>\n      <td>12</td>\n      <td>8</td>\n      <td>2</td>\n      <td>9.0</td>\n      <td>2</td>\n      <td>6</td>\n      <td>0</td>\n      <td>4</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows × 24 columns</p>\n</div>"
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#将所有字符串列映射成数字\n",
+    "maps={}\n",
+    "for col in df:\n",
+    "    dtype=df[col].dtype\n",
+    "    if dtype==\"object\":\n",
+    "        unique=df[col].unique()\n",
+    "        #训练标签\n",
+    "        le = LabelEncoder()\n",
+    "        le = le.fit(unique)\n",
+    "        #将每列的标签保存进字典\n",
+    "        maps[col]=le\n",
+    "        df[col]=le.transform(df[col])\n",
+    "df.head()"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "0    3699\n1     711\nName: Attrition, dtype: int64"
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.Attrition.value_counts()"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "<AxesSubplot:>"
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "text/plain": "<Figure size 1440x720 with 2 Axes>",
+      "image/png": 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\n"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "## 使用热力图来绘制和离职变量之间的相关性\n",
+    "plt.figure(figsize=(20,10))\n",
+    "sns.heatmap(df.corr(),annot=True)"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "<Figure size 1440x576 with 2 Axes>",
+      "image/png": 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\n"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": "<Figure size 1440x576 with 1 Axes>",
+      "image/png": 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\n"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "corr = df.corr()\n",
+    "corr.head()\n",
+    "corr = corr.fillna(0)\n",
+    "show=corr[[\"Attrition\"]].sort_values(by=\"Attrition\",ascending=False)\n",
+    "# 根据热力图，绘制与Attrition相关性最高的前10个特征\n",
+    "#使用sort_values去求前5和后5的柱状图\n",
+    "# 绘制值最高的前5个\n",
+    "plt.figure(figsize=(20,8))\n",
+    "plt.subplot(1,2,1)\n",
+    "plt.bar(show.index[:5],show.Attrition[:5])\n",
+    "# 绘制值最低的前5个\n",
+    "plt.subplot(1,2,2)\n",
+    "plt.bar(show.index[-5:],show.Attrition[-5:])\n",
+    "plt.title(\"The correlation of attrition with other variables\")\n",
+    "plt.show()\n",
+    "#\n",
+    "last_index=len(show[show.Attrition<0])\n",
+    "show=show.iloc[1:11].append(show.iloc[-last_index:])\n",
+    "show=show.iloc[0:5].append(show.iloc[-5:])\n",
+    "index_name=[\"Over Time\",\"Marita Status\",\"Distance From Home\",\"Job Role\",\"Department\",\"Age\",\"Monthly Income\",\"Years In Current Role\",\"Job Level\",\"Total Working Years\"]\n",
+    "show.index=index_name\n",
+    "plt.figure(figsize=(20,8))\n",
+    "plt.bar(show.iloc[0:6].index,show.iloc[0:6].Attrition)\n",
+    "plt.bar(show.iloc[5:].index,show.iloc[5:].Attrition)\n",
+    "plt.title(\"The correlation of attrition with other variables\")\n",
+    "plt.show()"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "outputs": [],
+   "source": [],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Top Five Reason of Attrition：\n",
+      "MaritalStatus\n",
+      "NumCompaniesWorked\n",
+      "PercentSalaryHike\n",
+      "JobRole\n",
+      "Gender\n"
+     ]
+    }
+   ],
+   "source": [
+    "top5=corr[\"Attrition\"].sort_values(ascending=False)[1:6]\n",
+    "print(\"Top Five Reason of Attrition：\\n%s\"%\"\\n\".join(list(top5.index)))\n"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "outputs": [],
+   "source": [],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "<Figure size 720x576 with 1 Axes>",
+      "image/png": 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\n"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "## 离职与未离职的人Totalworkingyears的占比对比\n",
+    "\n",
+    "quit_df=df[df[\"Attrition\"]==1]#离职\n",
+    "not_quit_df=df[df[\"Attrition\"]==0]#不离职\n",
+    "quit_level=quit_df.TotalWorkingYears.value_counts().sort_index()\n",
+    "not_quit_level=not_quit_df.TotalWorkingYears.value_counts().sort_index()\n",
+    "newdf=pd.DataFrame({\n",
+    "    \"quit\":quit_level,\n",
+    "    \"not_quit\":not_quit_level})\n",
+    "newdf[\"quit\"]/=newdf.quit.sum()\n",
+    "newdf[\"not_quit\"]/=newdf.not_quit.sum()\n",
+    "newdf=newdf.fillna(0)\n",
+    "index=newdf.index\n",
+    "plt.figure(figsize=(10,8))\n",
+    "# 设置柱形图宽度\n",
+    "bar_width = 0.35\n",
+    "# 绘制离职原因柱状图\n",
+    "rects1 = plt.bar(index, newdf.quit.values, bar_width, color='#0072BC', label=\"Attrition\")\n",
+    "# 绘制未离职的柱状图\n",
+    "rects2 = plt.bar(index + bar_width, newdf.not_quit,bar_width, color='#ED1C24', label=\"Non-Attrition\")\n",
+    "# X轴标题\n",
+    "plt.xlabel('years')\n",
+    "plt.ylabel(\"People percentage\")\n",
+    "plt.xticks(index + bar_width, index,rotation=60)\n",
+    "plt.title(\"Attrition and Non-Attrition in TotalWorkingYears\")\n",
+    "plt.legend()\n",
+    "plt.show()\n"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "<Figure size 720x576 with 1 Axes>",
+      "image/png": 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\n"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "quit_level=quit_df.JobLevel.value_counts().sort_index()\n",
+    "not_quit_level=not_quit_df.JobLevel.value_counts().sort_index()\n",
+    "quit_level/=sum(quit_level)\n",
+    "not_quit_level/=sum(not_quit_level)\n",
+    "index=quit_level.index\n",
+    "plt.figure(figsize=(10,8))\n",
+    "# 设置柱形图宽度\n",
+    "bar_width = 0.35\n",
+    "# 绘制离职原因柱状图\n",
+    "rects1 = plt.bar(index, quit_level.values, bar_width, color='#0072BC', label=\"Attrition\")\n",
+    "# 绘制未离职的柱状图\n",
+    "rects2 = plt.bar(index + bar_width, not_quit_level.values, bar_width, color='#ED1C24', label=\"Non-Attrition\")\n",
+    "# X轴标题\n",
+    "plt.xlabel('JobLevel')\n",
+    "plt.ylabel(\"People percentage\")\n",
+    "plt.xticks(index + bar_width, index,rotation=60)\n",
+    "plt.title(\"Attrition and Non-Attrition in JobLevel\")\n",
+    "plt.legend()\n",
+    "plt.show()\n"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## 绘制一个包含两个子图的图表，分别表示离职员工和未离职员工的婚姻状况\n"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "<Figure size 1152x432 with 2 Axes>",
+      "image/png": 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\n"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#离职婚姻\n",
+    "unique=df['MaritalStatus'].unique()\n",
+    "# 根据'MaritalStatus'列还原成字符串\n",
+    "unique = maps['MaritalStatus'].inverse_transform(unique)\n",
+    "marital_dict=dict(zip(unique,[i for i in range(len(unique))]))\n",
+    "plt.figure(figsize=(16,6))\n",
+    "plt.subplot(121)\n",
+    "quit_pie=pd.DataFrame(quit_df[\"MaritalStatus\"].value_counts())\n",
+    "quit_pie.index=quit_pie.index.map(dict(zip(marital_dict.values(),marital_dict.keys())))\n",
+    "quit_pie[\"MaritalStatus\"].plot(kind=\"pie\",\n",
+    "                        autopct='%.1f%%',\n",
+    "                        title=\"Attrition in different Marital Status\"\n",
+    "                        )\n",
+    "plt.subplot(122)\n",
+    "not_quit_pie=pd.DataFrame(not_quit_df[\"MaritalStatus\"].value_counts())\n",
+    "not_quit_pie.index=not_quit_pie.index.map(dict(zip(marital_dict.values(),marital_dict.keys())))\n",
+    "not_quit_pie[\"MaritalStatus\"].plot(kind=\"pie\",\n",
+    "                        autopct='%.1f%%',\n",
+    "                        title=\"Non-attrition in different Marital Status\"\n",
+    "                        )\n",
+    "plt.show()\n"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "<Figure size 1152x432 with 2 Axes>",
+      "image/png": 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\n"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#离职主要部门\n",
+    "unique=df['Department'].unique()\n",
+    "unique = maps['Department'].inverse_transform(unique)\n",
+    "department_dict=dict(zip(unique,[i for i in range(len(unique))]))\n",
+    "plt.figure(figsize=(16,6))\n",
+    "plt.subplot(121)\n",
+    "quit_pie=pd.DataFrame(quit_df[\"Department\"].value_counts())\n",
+    "quit_pie.index=quit_pie.index.map(dict(zip(department_dict.values(),department_dict.keys())))\n",
+    "quit_pie[\"Department\"].plot(kind=\"pie\",\n",
+    "                        autopct='%.1f%%',\n",
+    "                        title=\"Attrition in different departments\"\n",
+    "                        )\n",
+    "plt.subplot(122)\n",
+    "not_quit_pie=pd.DataFrame(not_quit_df[\"Department\"].value_counts())\n",
+    "not_quit_pie.index=not_quit_pie.index.map(dict(zip(department_dict.values(),department_dict.keys())))\n",
+    "not_quit_pie[\"Department\"].plot(kind=\"pie\",\n",
+    "                        autopct='%.1f%%',\n",
+    "                        title=\"Non-attrition in different departments\"\n",
+    "                        )\n",
+    "plt.show()\n"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "<seaborn.axisgrid.PairGrid at 0x1f5b91cb940>"
+     },
+     "execution_count": 42,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "text/plain": "<Figure size 762.375x720 with 20 Axes>",
+      "image/png": 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\n"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# 各个变量的pairplot\n",
+    "sns.pairplot(df, hue='Attrition', vars=['Age', 'DistanceFromHome', 'MonthlyIncome', 'TotalWorkingYears'], palette='husl')\n"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
\ No newline at end of file
diff --git a/共享单车数据分析/十三组平时作业（实战）/苹果公司股价预测.ipynb b/第十三组/十三组平时作业（实战）/苹果公司股价预测.ipynb
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rename from 共享单车数据分析/十三组平时作业（实战）/苹果公司股价预测.ipynb
rename to 第十三组/十三组平时作业（实战）/苹果公司股价预测.ipynb
diff --git a/第十三组/十三组项目大作业/.keep b/第十三组/十三组项目大作业/.keep
new file mode 100644
index 0000000..e69de29
diff --git a/共享单车数据分析/第十三组 刘闻一 组员信息 b/第十三组/第十三组  组员信息
similarity index 100%
rename from 共享单车数据分析/第十三组 刘闻一 组员信息
rename to 第十三组/第十三组  组员信息