[wrh]
609 lines
68 KiB
Plaintext
609 lines
68 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [],
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"source": [
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"import pandas as pd\n",
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"import numpy as np\n",
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"import matplotlib.pyplot as plt\n",
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"from matplotlib.colors import ListedColormap\n",
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"import seaborn as sns\n"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## 第二组大作业过程\n",
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"\n",
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"我们对幸福感的数据进行了分析。其过程如下：\n",
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"\n",
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"### 〇：数据集概览"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"(8000, 42)\n",
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"Index(['id', 'happiness', 'survey_type', 'province', 'city', 'county',\n",
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" 'survey_time', 'gender', 'birth', 'nationality', 'religion',\n",
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" 'religion_freq', 'edu', 'income', 'political', 'floor_area',\n",
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" 'height_cm', 'weight_jin', 'health', 'health_problem', 'depression',\n",
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" 'hukou', 'socialize', 'relax', 'learn', 'equity', 'class', 'work_exper',\n",
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" 'work_status', 'work_yr', 'work_type', 'work_manage', 'family_income',\n",
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" 'family_m', 'family_status', 'house', 'car', 'marital', 'status_peer',\n",
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" 'status_3_before', 'view', 'inc_ability'],\n",
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" dtype='object')\n"
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]
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}
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],
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"source": [
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"df = pd.read_csv('happiness_train_abbr.csv')\n",
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"print(df.shape)\n",
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"print(df.columns)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"该幸福感的数据集共有8000条，包含42个因素。\n",
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"\n",
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"其中，happiness就是幸福的等级，也就是因变量。分为1-5也就是不幸福到幸福。\n",
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"\n",
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"自变量含有很多，我们小组了解到的自变量的说明如下\n",
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"- survey type中，1是城镇，2是农村。\n",
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"- province表示省份，每个数字代表一个省。city和county都是用代码表示城市和乡县\n",
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"- gender中，1为男，2为女\n",
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"- nationality表示民族，religion表示宗教信仰\n",
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"- edu为教育程度，有1-14。从1-13学历逐次递增。14表示其他。\n",
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"- politicial是政治面貌，有4个等级\n",
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"- floor_area是住房面积\n",
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"- health表示健康程度，depression表示抑郁程度。都是1-5。越高表示越健康，越不抑郁\n",
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"- socialize，relax，learn表示你的社交、休闲、学习频率。都是1-5，越高表示越频繁。\n",
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"- equity表示你认为社会是否公平。从1-5是不公平到公平\n",
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"- class表示你社会的层级，从1-10表示底层到顶层\n",
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"- work的很多因素中，有工作经历、在工作中角色地位、工作年数、工作性质、管理情况\n",
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"- 家庭的因素中个，包含家庭收入、家庭成员人数、家庭地位\n",
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"- 房产、车产、婚姻状况\n",
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"- 与同龄人相比的地位"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"此外，我们注意到很多列中存在-1，-2，-3，-8。经查询我们发现这些都表示异常值：\n",
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"\n",
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"-1表示不适用，-2表示不知道，-3表示拒绝回答，-8表示无法回答。"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### 一：数据处理\n",
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"\n",
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"#### 1：缺失值与异常值"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"我们首先观察一下缺失值的情况。因为列数较多，直接`df.info()`较为麻烦。所以手写了返回isnull和notnull的函数。"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"{'work_status': 5049,\n",
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" 'work_yr': 5049,\n",
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" 'work_type': 5049,\n",
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" 'work_manage': 5049,\n",
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" 'family_income': 1}"
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]
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},
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"execution_count": 3,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"def isnotnull(df):\n",
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" isnull_dict={}\n",
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" notnull_list=[]\n",
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" for name in df.columns:\n",
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" if df[name].isnull().sum()>0:\n",
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" isnull_dict[name]=df[name].isnull().sum()\n",
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" else:\n",
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" notnull_list.append(name)\n",
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" return isnull_dict,notnull_list\n",
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"a,b=isnotnull(df)\n",
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"a"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"我们发现work_status,yr,type,manage 这些列缺失值太多甚至超过60%缺失，因而这些列不能用于分析，故删除。\n",
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"\n",
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"同时自然把第一个id这个编号这一列也删除。"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"(8000, 37)"
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]
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},
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"execution_count": 4,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"df=df.drop(labels=[\"id\",\"work_status\",\"work_yr\",\"work_type\",\"work_manage\"],axis=1)\n",
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"df.shape"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"考虑到-1，-2，-3，-8都是异常值，为此，我们把所有这些异常值全部视为缺失值nan"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"{'happiness': 12,\n",
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" 'nationality': 18,\n",
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" 'religion': 108,\n",
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" 'religion_freq': 15,\n",
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" 'edu': 11,\n",
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" 'income': 441,\n",
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" 'political': 41,\n",
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" 'health': 5,\n",
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" 'health_problem': 43,\n",
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" 'depression': 16,\n",
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" 'socialize': 6,\n",
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" 'relax': 17,\n",
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" 'learn': 21,\n",
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" 'equity': 42,\n",
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" 'class': 81,\n",
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" 'family_income': 667,\n",
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" 'family_m': 22,\n",
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" 'family_status': 46,\n",
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" 'house': 118,\n",
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" 'car': 10,\n",
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" 'status_peer': 49,\n",
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" 'status_3_before': 48,\n",
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" 'view': 208,\n",
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" 'inc_ability': 965}"
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]
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},
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"execution_count": 5,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"ErrValue=[-8,-3,-2,-1]\n",
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"for name in df.columns :\n",
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" for val in ErrValue:\n",
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" df[name]=df[name].replace(val,np.nan)\n",
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"a,b=isnotnull(df)\n",
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"a"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"这也就是所有在视异常值微缺失值之后的结果了。"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"我们注意到对happiness因变量因素，绝大多数是4或者5，所以我们把-8按照众数替换为4"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"4.0 4818\n",
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"5.0 1410\n",
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"3.0 1159\n",
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"2.0 497\n",
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"1.0 104\n",
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"Name: happiness, dtype: int64"
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]
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},
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"execution_count": 6,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"df['happiness'] = df['happiness'].map(lambda x:4 if x == -8 else x)\n",
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"df['happiness'].value_counts()"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"由此可见，绝大多数的人都感觉到至少是比较幸福的。"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"#### 2：年龄信息的补充\n",
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"\n",
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"我们注意到有2列分别是调查时间与出生日期。其中调查时间包括年份。\n",
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"\n",
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"因此我们提取调查时间的年份减去出生日期，便可得到年龄的信息。\n",
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"\n",
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"我们新得到Age一列，同时删去survey time和birth这两列"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"metadata": {},
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"outputs": [],
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"source": [
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"df['survey_time'] = pd.to_datetime(df['survey_time'],format='%Y-%m-%d %H:%M:%S')\n",
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"df['survey_time'] = df['survey_time'].dt.year\n",
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"df['Age'] = df['survey_time']-df['birth']\n"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"下一步，我们将其他列中的缺失值填充\n",
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"\n",
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"我们取出无nan的列作为notnull_list，在isnull的list也就是含有nan的列中，我们每次取一个含有nan的列，与那些无nan的列合并\n",
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"\n",
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"通过随机森林把这一列的nan填充。"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 8,
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"metadata": {},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"c:\\Users\\王瑞恒\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\base.py:409: UserWarning: X does not have valid feature names, but RandomForestRegressor was fitted with feature names\n",
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" warnings.warn(\n",
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"c:\\Users\\王瑞恒\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\base.py:409: UserWarning: X does not have valid feature names, but RandomForestRegressor was fitted with feature names\n",
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" warnings.warn(\n",
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"c:\\Users\\王瑞恒\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\base.py:409: UserWarning: X does not have valid feature names, but RandomForestRegressor was fitted with feature names\n",
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" warnings.warn(\n",
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"c:\\Users\\王瑞恒\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\base.py:409: UserWarning: X does not have valid feature names, but RandomForestRegressor was fitted with feature names\n",
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" warnings.warn(\n",
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"c:\\Users\\王瑞恒\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\base.py:409: UserWarning: X does not have valid feature names, but RandomForestRegressor was fitted with feature names\n",
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" warnings.warn(\n",
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"c:\\Users\\王瑞恒\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\base.py:409: UserWarning: X does not have valid feature names, but RandomForestRegressor was fitted with feature names\n",
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" warnings.warn(\n",
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"c:\\Users\\王瑞恒\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\base.py:409: UserWarning: X does not have valid feature names, but RandomForestRegressor was fitted with feature names\n",
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" warnings.warn(\n",
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"c:\\Users\\王瑞恒\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\base.py:409: UserWarning: X does not have valid feature names, but RandomForestRegressor was fitted with feature names\n",
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" warnings.warn(\n",
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"c:\\Users\\王瑞恒\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\base.py:409: UserWarning: X does not have valid feature names, but RandomForestRegressor was fitted with feature names\n",
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" warnings.warn(\n",
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"c:\\Users\\王瑞恒\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\base.py:409: UserWarning: X does not have valid feature names, but RandomForestRegressor was fitted with feature names\n",
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" warnings.warn(\n",
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"c:\\Users\\王瑞恒\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\base.py:409: UserWarning: X does not have valid feature names, but RandomForestRegressor was fitted with feature names\n",
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" warnings.warn(\n",
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"c:\\Users\\王瑞恒\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\base.py:409: UserWarning: X does not have valid feature names, but RandomForestRegressor was fitted with feature names\n",
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" warnings.warn(\n",
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"c:\\Users\\王瑞恒\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\base.py:409: UserWarning: X does not have valid feature names, but RandomForestRegressor was fitted with feature names\n",
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" warnings.warn(\n",
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"c:\\Users\\王瑞恒\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\base.py:409: UserWarning: X does not have valid feature names, but RandomForestRegressor was fitted with feature names\n",
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" warnings.warn(\n",
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"c:\\Users\\王瑞恒\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\base.py:409: UserWarning: X does not have valid feature names, but RandomForestRegressor was fitted with feature names\n",
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" warnings.warn(\n",
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"c:\\Users\\王瑞恒\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\base.py:409: UserWarning: X does not have valid feature names, but RandomForestRegressor was fitted with feature names\n",
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" warnings.warn(\n",
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"c:\\Users\\王瑞恒\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\base.py:409: UserWarning: X does not have valid feature names, but RandomForestRegressor was fitted with feature names\n",
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" warnings.warn(\n",
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"c:\\Users\\王瑞恒\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\base.py:409: UserWarning: X does not have valid feature names, but RandomForestRegressor was fitted with feature names\n",
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" warnings.warn(\n",
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"c:\\Users\\王瑞恒\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\base.py:409: UserWarning: X does not have valid feature names, but RandomForestRegressor was fitted with feature names\n",
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" warnings.warn(\n",
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"c:\\Users\\王瑞恒\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\base.py:409: UserWarning: X does not have valid feature names, but RandomForestRegressor was fitted with feature names\n",
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" warnings.warn(\n",
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"c:\\Users\\王瑞恒\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\base.py:409: UserWarning: X does not have valid feature names, but RandomForestRegressor was fitted with feature names\n",
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" warnings.warn(\n",
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"c:\\Users\\王瑞恒\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\base.py:409: UserWarning: X does not have valid feature names, but RandomForestRegressor was fitted with feature names\n",
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" warnings.warn(\n",
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"c:\\Users\\王瑞恒\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\base.py:409: UserWarning: X does not have valid feature names, but RandomForestRegressor was fitted with feature names\n",
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" warnings.warn(\n",
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"c:\\Users\\王瑞恒\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\base.py:409: UserWarning: X does not have valid feature names, but RandomForestRegressor was fitted with feature names\n",
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" warnings.warn(\n"
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]
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}
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],
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"source": [
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"from sklearn.ensemble import RandomForestRegressor\n",
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"isnull_dict,notnull_list=isnotnull(df)\n",
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"df_copy = df[notnull_list].copy()\n",
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"for col in list(isnull_dict.keys()):\n",
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" df_copy = df[notnull_list].copy()\n",
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" df_copy=pd.concat([df[col],df_copy],axis=1)\n",
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" df_copy_notnull = df_copy.loc[df_copy[col].notnull()]\n",
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" df_copy_isnull = df_copy.loc[df_copy[col].isnull()]\n",
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" x = df_copy_notnull.iloc[:, 1:]\n",
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" y = df_copy_notnull.values[:, 0]\n",
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" x_test = df_copy_isnull.values[:, 1:]\n",
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" rfr = RandomForestRegressor(n_estimators=1000, n_jobs=-1)\n",
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" rfr.fit(x, y)\n",
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" y_pred = rfr.predict(x_test).astype(int)\n",
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" # 为保证填入数值格式的一样性，转换为整数\n",
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" df.loc[df[col].isnull(), col] = y_pred\n",
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" "
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]
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},
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{
|
||
"cell_type": "code",
|
||
"execution_count": 9,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"{}"
|
||
]
|
||
},
|
||
"execution_count": 9,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"a,b=isnotnull(df)\n",
|
||
"a"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"此时我们发现不再有含有nan的列"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 10,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Counter({4.0: 4819, 5.0: 1410, 3.0: 1169, 2.0: 498, 1.0: 104})\n",
|
||
"Counter({1: 4756, 2: 3244})\n",
|
||
"Counter({21: 434, 18: 429, 31: 424, 6: 414, 10: 409, 1: 396, 12: 394, 4: 385, 15: 378, 16: 352, 22: 352, 19: 349, 5: 349, 27: 296, 2: 280, 29: 278, 13: 277, 9: 262, 17: 224, 7: 213, 24: 204, 11: 195, 26: 191, 28: 172, 23: 136, 8: 70, 3: 70, 30: 67})\n",
|
||
"Counter({1: 396, 7: 385, 32: 254, 64: 219, 18: 213, 87: 204, 57: 203, 81: 172, 36: 148, 46: 147, 52: 145, 8: 145, 65: 142, 82: 141, 27: 138, 66: 136, 80: 81, 39: 81, 41: 79, 62: 77, 72: 77, 76: 76, 24: 76, 89: 76, 49: 76, 56: 76, 77: 75, 86: 75, 4: 75, 22: 75, 48: 74, 50: 74, 42: 74, 55: 74, 67: 74, 26: 74, 51: 74, 12: 73, 40: 73, 63: 73, 34: 73, 2: 72, 9: 72, 10: 72, 16: 71, 14: 71, 54: 71, 43: 71, 5: 71, 79: 70, 19: 70, 74: 70, 47: 70, 78: 70, 6: 70, 59: 70, 83: 69, 69: 69, 88: 69, 35: 69, 45: 69, 13: 68, 84: 68, 30: 68, 28: 67, 17: 67, 33: 67, 85: 67, 68: 67, 44: 66, 29: 66, 53: 65, 61: 65, 23: 65, 70: 64, 15: 64, 71: 63, 3: 62, 31: 61, 21: 61, 20: 61, 11: 60, 37: 60, 25: 54, 75: 45})\n",
|
||
"Counter({120: 81, 70: 81, 4: 80, 72: 79, 98: 77, 112: 77, 116: 76, 46: 76, 134: 76, 82: 76, 101: 76, 90: 76, 77: 75, 117: 75, 129: 75, 11: 75, 44: 75, 66: 75, 85: 74, 83: 74, 73: 74, 89: 74, 107: 74, 48: 74, 84: 74, 124: 73, 24: 73, 102: 73, 67: 73, 28: 73, 71: 73, 103: 73, 130: 73, 99: 73, 23: 72, 9: 72, 78: 72, 50: 72, 25: 72, 26: 72, 86: 71, 32: 71, 30: 71, 88: 71, 74: 71, 12: 71, 100: 70, 119: 70, 92: 70, 41: 70, 114: 70, 79: 70, 118: 70, 13: 70, 95: 70, 126: 69, 109: 69, 133: 69, 21: 69, 65: 69, 105: 69, 76: 69, 104: 69, 125: 68, 29: 68, 91: 68, 127: 68, 3: 68, 53: 68, 51: 67, 33: 67, 106: 67, 62: 67, 128: 67, 108: 67, 49: 66, 131: 66, 75: 66, 52: 66, 87: 65, 132: 65, 93: 65, 8: 65, 97: 65, 45: 65, 110: 64, 31: 64, 121: 64, 111: 63, 10: 62, 54: 61, 43: 61, 42: 61, 27: 60, 68: 60, 122: 59, 80: 55, 47: 54, 123: 49, 20: 48, 14: 46, 115: 45, 22: 44, 2: 41, 55: 41, 39: 39, 60: 38, 63: 38, 1: 38, 7: 38, 17: 38, 61: 37, 57: 36, 18: 36, 15: 36, 59: 35, 19: 35, 56: 35, 6: 35, 64: 35, 16: 33, 58: 32, 38: 32, 34: 32, 36: 31, 5: 31, 35: 31, 40: 25, 37: 23, 81: 19})\n",
|
||
"Counter({2015: 8000})\n",
|
||
"Counter({2: 4240, 1: 3760})\n",
|
||
"Counter({1965: 199, 1963: 197, 1968: 196, 1970: 195, 1955: 187, 1964: 181, 1969: 175, 1967: 174, 1957: 168, 1975: 168, 1966: 167, 1953: 167, 1954: 166, 1973: 162, 1952: 161, 1950: 159, 1949: 153, 1962: 152, 1958: 146, 1956: 145, 1960: 139, 1972: 139, 1951: 137, 1945: 133, 1990: 131, 1948: 129, 1974: 128, 1971: 127, 1978: 124, 1985: 122, 1982: 122, 1980: 121, 1947: 119, 1976: 118, 1977: 116, 1987: 115, 1986: 112, 1959: 111, 1988: 109, 1979: 108, 1989: 106, 1961: 105, 1946: 102, 1944: 99, 1981: 97, 1993: 90, 1992: 89, 1984: 88, 1983: 86, 1942: 86, 1991: 86, 1994: 82, 1940: 80, 1941: 76, 1943: 75, 1997: 74, 1995: 73, 1937: 66, 1938: 64, 1996: 61, 1939: 58, 1935: 57, 1936: 56, 1933: 53, 1934: 44, 1930: 35, 1932: 26, 1931: 25, 1928: 21, 1929: 18, 1927: 11, 1926: 10, 1925: 9, 1922: 6, 1924: 4, 1923: 2, 1921: 2})\n",
|
||
"Counter({1.0: 7361, 8.0: 289, 4.0: 157, 6.0: 97, 3.0: 70, 2.0: 20, 5.0: 5, 7.0: 1})\n",
|
||
"Counter({1.0: 7042, 0.0: 958})\n",
|
||
"Counter({1.0: 6901, 3.0: 355, 4.0: 230, 2.0: 188, 6.0: 97, 8.0: 91, 9.0: 53, 5.0: 45, 7.0: 40})\n",
|
||
"Counter({4.0: 2268, 3.0: 1854, 1.0: 1054, 6.0: 960, 12.0: 493, 10.0: 390, 7.0: 350, 9.0: 188, 11.0: 155, 5.0: 90, 13.0: 79, 2.0: 67, 8.0: 46, 14.0: 6})\n",
|
||
"Counter({0.0: 1199, 20000.0: 604, 30000.0: 586, 10000.0: 536, 50000.0: 362, 40000.0: 331, 36000.0: 225, 5000.0: 219, 60000.0: 216, 24000.0: 200, 100000.0: 183, 2000.0: 173, 15000.0: 172, 3000.0: 163, 12000.0: 146, 25000.0: 140, 1000.0: 106, 6000.0: 99, 4000.0: 91, 80000.0: 88, 8000.0: 84, 35000.0: 76, 18000.0: 71, 48000.0: 70, 70000.0: 65, 200000.0: 46, 7000.0: 40, 120000.0: 39, 45000.0: 38, 1500.0: 35, 150000.0: 31, 14000.0: 27, 90000.0: 26, 1200.0: 24, 13000.0: 24, 42000.0: 21, 2500.0: 20, 720.0: 20, 600.0: 19, 660.0: 18, 500.0: 18, 800.0: 17, 9000.0: 17, 28000.0: 17, 22000.0: 16, 19200.0: 15, 900.0: 14, 11000.0: 14, 26400.0: 14, 26000.0: 14, 55000.0: 13, 32000.0: 13, 2400.0: 13, 3600.0: 12, 300000.0: 12, 21600.0: 12, 9600.0: 12, 14400.0: 12, 37200.0: 11, 400000.0: 11, 28800.0: 11, 23000.0: 11, 16000.0: 11, 38000.0: 10, 1000000.0: 10, 6500.0: 9, 17000.0: 9, 75000.0: 9, 110000.0: 9, 960.0: 9, 3500.0: 9, 840.0: 9, 39600.0: 8, 44400.0: 8, 700.0: 8, 84000.0: 8, 16800.0: 8, 72000.0: 8, 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|
||
"Counter({1.0: 6754, 4.0: 829, 2.0: 406, 3.0: 11})\n",
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1, 71591.0: 1, 64403.0: 1, 40566.0: 1, 94935.0: 1, 23292.0: 1, 21147.0: 1, 4420.0: 1, 109259.0: 1, 197623.0: 1, 117659.0: 1, 46400.0: 1, 12473.0: 1, 31441.0: 1, 185944.0: 1, 38200.0: 1, 92358.0: 1, 54433.0: 1, 48400.0: 1, 44040.0: 1, 8800.0: 1, 148279.0: 1, 59537.0: 1, 22750.0: 1, 43920.0: 1, 8510.0: 1, 76691.0: 1, 18626.0: 1, 20394.0: 1, 43560.0: 1, 45951.0: 1, 105051.0: 1, 15825.0: 1, 42956.0: 1, 33157.0: 1, 6288.0: 1, 93980.0: 1, 1100.0: 1, 49440.0: 1, 35835.0: 1, 69507.0: 1, 93463.0: 1, 130602.0: 1, 176535.0: 1, 18919.0: 1, 61468.0: 1, 108913.0: 1, 118399.0: 1, 33453.0: 1, 99022.0: 1, 108964.0: 1, 38043.0: 1, 15856.0: 1, 68587.0: 1, 39300.0: 1, 33348.0: 1, 56679.0: 1, 113604.0: 1, 22809.0: 1, 6480.0: 1, 110205.0: 1, 108476.0: 1, 47820.0: 1, 164613.0: 1, 145093.0: 1, 42649.0: 1, 23671.0: 1, 55636.0: 1, 5889.0: 1, 55400.0: 1, 142820.0: 1, 42831.0: 1, 94562.0: 1, 69571.0: 1, 62115.0: 1, 52751.0: 1, 62080.0: 1, 26641.0: 1, 25320.0: 1, 115128.0: 1, 77015.0: 1, 25953.0: 1, 101499.0: 1, 72282.0: 1, 53247.0: 1, 156000.0: 1, 41313.0: 1, 83000.0: 1, 121592.0: 1, 128644.0: 1, 88880.0: 1, 950.0: 1, 115498.0: 1, 85310.0: 1, 5600.0: 1, 26070.0: 1, 16644.0: 1, 480000.0: 1, 35094.0: 1, 45766.0: 1, 96085.0: 1, 81598.0: 1, 90192.0: 1, 190260.0: 1, 3400.0: 1, 13320.0: 1, 93600.0: 1, 181200.0: 1, 66219.0: 1, 204000.0: 1, 145900.0: 1, 29130.0: 1, 122857.0: 1, 207370.0: 1, 33253.0: 1, 94462.0: 1, 134000.0: 1, 73134.0: 1, 95583.0: 1, 86149.0: 1, 38591.0: 1, 33403.0: 1, 102479.0: 1, 117240.0: 1, 267667.0: 1, 104658.0: 1, 39269.0: 1, 81600.0: 1, 187205.0: 1, 47962.0: 1, 37739.0: 1, 61000.0: 1, 62051.0: 1, 118966.0: 1, 111592.0: 1, 2760.0: 1, 64867.0: 1, 80928.0: 1, 40920.0: 1, 550000.0: 1, 35099.0: 1, 19753.0: 1, 152000.0: 1, 117076.0: 1, 47080.0: 1, 84688.0: 1, 86097.0: 1, 450000.0: 1, 59195.0: 1, 114081.0: 1, 152179.0: 1, 36028.0: 1, 95458.0: 1, 83470.0: 1, 67353.0: 1, 87771.0: 1, 219544.0: 1, 98417.0: 1, 41956.0: 1, 79985.0: 1, 48805.0: 1, 39400.0: 1, 165000.0: 1, 85681.0: 1, 66041.0: 1, 66853.0: 1, 8500.0: 1, 26134.0: 1, 50061.0: 1, 214341.0: 1, 71678.0: 1, 1040.0: 1, 70050.0: 1, 83504.0: 1, 152056.0: 1, 159736.0: 1, 55645.0: 1, 116430.0: 1, 114764.0: 1, 73434.0: 1, 66188.0: 1, 64664.0: 1, 89407.0: 1, 235824.0: 1, 41325.0: 1, 2440.0: 1, 280000.0: 1, 2100.0: 1, 36831.0: 1, 29481.0: 1, 199731.0: 1, 46784.0: 1, 126866.0: 1, 86155.0: 1, 73825.0: 1, 18783.0: 1, 163200.0: 1, 30591.0: 1, 46800.0: 1, 73058.0: 1, 26075.0: 1, 100357.0: 1, 133200.0: 1, 10200.0: 1, 19126.0: 1, 82000.0: 1, 38411.0: 1, 396862.0: 1, 33393.0: 1, 1120.0: 1, 45609.0: 1, 90240.0: 1, 39000.0: 1, 86046.0: 1, 40073.0: 1, 26339.0: 1, 62490.0: 1, 70106.0: 1, 46070.0: 1, 96720.0: 1, 115644.0: 1, 88191.0: 1, 2160.0: 1, 16200.0: 1, 43488.0: 1, 9999992.0: 1, 9100.0: 1, 208800.0: 1, 79863.0: 1, 58835.0: 1, 64293.0: 1, 2930214.0: 1, 4000000.0: 1, 112400.0: 1, 20710.0: 1, 62794.0: 1, 1680.0: 1, 49144.0: 1, 63685.0: 1, 18295.0: 1, 25124.0: 1, 13423.0: 1, 127200.0: 1, 590000.0: 1, 52813.0: 1, 120933.0: 1, 49524.0: 1, 90738.0: 1, 68497.0: 1, 328185.0: 1, 79724.0: 1, 93312.0: 1, 106458.0: 1, 15778.0: 1, 13440.0: 1, 95519.0: 1, 616383.0: 1, 36403.0: 1, 6240.0: 1, 113195.0: 1, 40119.0: 1, 81740.0: 1, 169292.0: 1, 29948.0: 1, 35460.0: 1, 39600.0: 1, 79387.0: 1, 435258.0: 1, 28590.0: 1, 23141.0: 1, 9910000.0: 1, 23106.0: 1, 20842.0: 1, 78807.0: 1, 65139.0: 1, 58750.0: 1, 62181.0: 1, 66971.0: 1, 45120.0: 1, 60698.0: 1, 57259.0: 1, 26579.0: 1, 82072.0: 1, 209408.0: 1, 198000.0: 1, 9400.0: 1, 94299.0: 1, 61562.0: 1, 52394.0: 1, 29196.0: 1, 38208.0: 1, 277493.0: 1, 53667.0: 1, 382950.0: 1, 154449.0: 1, 19935.0: 1, 426940.0: 1, 15720.0: 1, 450.0: 1, 25983.0: 1, 39557.0: 1, 414046.0: 1, 53125.0: 1, 107093.0: 1, 8774.0: 1, 42157.0: 1, 3800.0: 1, 169200.0: 1, 98892.0: 1, 71000.0: 1, 129230.0: 1, 111978.0: 1, 76710.0: 1, 191185.0: 1, 19516.0: 1, 660.0: 1, 37758.0: 1, 14544.0: 1, 21839.0: 1, 66686.0: 1, 53530.0: 1, 54313.0: 1, 12500.0: 1, 51244.0: 1, 36780.0: 1, 176089.0: 1, 50660.0: 1, 87600.0: 1, 584766.0: 1, 83630.0: 1, 51000.0: 1, 700.0: 1, 57773.0: 1, 38244.0: 1, 59759.0: 1, 76560.0: 1, 35160.0: 1, 91423.0: 1, 76447.0: 1, 31251.0: 1, 60281.0: 1, 64660.0: 1, 79567.0: 1, 74000.0: 1, 50834.0: 1, 54239.0: 1, 55441.0: 1, 19330.0: 1, 13201.0: 1, 3720.0: 1, 12580.0: 1, 69354.0: 1, 455585.0: 1, 136503.0: 1, 54342.0: 1, 89255.0: 1, 92800.0: 1, 172890.0: 1, 26406.0: 1, 38114.0: 1, 87599.0: 1, 65284.0: 1, 47688.0: 1, 150437.0: 1, 79960.0: 1, 80193.0: 1, 93472.0: 1, 56406.0: 1, 36265.0: 1, 26231.0: 1, 74788.0: 1, 85800.0: 1, 69954.0: 1, 77640.0: 1, 98540.0: 1, 265012.0: 1, 166655.0: 1, 63244.0: 1, 76800.0: 1, 96940.0: 1, 154323.0: 1, 6778.0: 1, 458755.0: 1, 109155.0: 1, 9080000.0: 1, 91654.0: 1, 84457.0: 1, 18759.0: 1, 74653.0: 1, 70778.0: 1, 107974.0: 1, 46668.0: 1, 45280.0: 1, 39120.0: 1, 25058.0: 1, 541887.0: 1, 43200.0: 1, 87831.0: 1, 84087.0: 1, 528000.0: 1, 91027.0: 1, 25777.0: 1, 19986.0: 1, 58614.0: 1, 54818.0: 1, 5999.0: 1, 6840.0: 1, 43000.0: 1, 33287.0: 1, 32856.0: 1, 115731.0: 1, 86516.0: 1, 276000.0: 1, 13634.0: 1, 161095.0: 1, 28440.0: 1, 59280.0: 1, 31299.0: 1, 9048000.0: 1, 20857.0: 1, 21140.0: 1, 65962.0: 1, 55316.0: 1, 53010.0: 1, 128953.0: 1, 65533.0: 1, 67686.0: 1, 69000.0: 1, 7920.0: 1, 131462.0: 1, 99919.0: 1, 52605.0: 1, 2700.0: 1, 227121.0: 1, 53723.0: 1, 8015.0: 1, 52326.0: 1, 42530.0: 1, 24121.0: 1, 174000.0: 1, 105456.0: 1, 32926.0: 1, 147903.0: 1, 107013.0: 1, 124790.0: 1, 32188.0: 1, 41315.0: 1, 38964.0: 1, 49644.0: 1, 202444.0: 1, 133549.0: 1, 83602.0: 1, 76924.0: 1, 39157.0: 1, 56026.0: 1, 131659.0: 1, 63240.0: 1, 8640.0: 1, 67817.0: 1, 69600.0: 1, 55721.0: 1, 50025.0: 1, 59263.0: 1, 51200.0: 1, 150174.0: 1, 76307.0: 1, 40833.0: 1, 18263.0: 1, 39844.0: 1, 336276.0: 1, 49646.0: 1, 340000.0: 1, 26640.0: 1, 96067.0: 1, 56414.0: 1, 101035.0: 1, 88139.0: 1, 19635.0: 1, 68771.0: 1})\n",
|
||
"Counter({2.0: 2671, 3.0: 2140, 4.0: 1131, 1.0: 1023, 5.0: 640, 6.0: 249, 7.0: 91, 8.0: 36, 9.0: 13, 11.0: 4, 50.0: 1, 13.0: 1})\n",
|
||
"Counter({3.0: 4279, 2.0: 2612, 4.0: 662, 1.0: 428, 5.0: 19})\n",
|
||
"Counter({1.0: 6362, 2.0: 908, 0.0: 546, 3.0: 133, 4.0: 33, 5.0: 11, 10.0: 1, 30.0: 1, 6.0: 1, 7.0: 1, 8.0: 1, 14.0: 1, 11.0: 1})\n",
|
||
"Counter({2.0: 6627, 1.0: 1373})\n",
|
||
"Counter({3: 6059, 1: 828, 7: 718, 6: 171, 4: 146, 2: 52, 5: 26})\n",
|
||
"Counter({2.0: 4936, 3.0: 2679, 1.0: 385})\n",
|
||
"Counter({2.0: 4430, 1.0: 2755, 3.0: 815})\n",
|
||
"Counter({4.0: 3974, 3.0: 2966, 5.0: 613, 2.0: 410, 1.0: 37})\n",
|
||
"Counter({2.0: 5572, 3.0: 2092, 4.0: 217, 1.0: 119})\n",
|
||
"Counter({50: 199, 52: 197, 47: 196, 45: 195, 60: 187, 51: 181, 46: 175, 48: 174, 58: 168, 40: 168, 49: 167, 62: 167, 61: 166, 42: 162, 63: 161, 65: 159, 66: 153, 53: 152, 57: 146, 59: 145, 55: 139, 43: 139, 64: 137, 70: 133, 25: 131, 67: 129, 41: 128, 44: 127, 37: 124, 30: 122, 33: 122, 35: 121, 68: 119, 39: 118, 38: 116, 28: 115, 29: 112, 56: 111, 27: 109, 36: 108, 26: 106, 54: 105, 69: 102, 71: 99, 34: 97, 22: 90, 23: 89, 31: 88, 32: 86, 73: 86, 24: 86, 21: 82, 75: 80, 74: 76, 72: 75, 18: 74, 20: 73, 78: 66, 77: 64, 19: 61, 76: 58, 80: 57, 79: 56, 82: 53, 81: 44, 85: 35, 83: 26, 84: 25, 87: 21, 86: 18, 88: 11, 89: 10, 90: 9, 93: 6, 91: 4, 92: 2, 94: 2})\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"import collections\n",
|
||
"for col in df.columns:\n",
|
||
" print(collections.Counter(df[col]))"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"从counter我们也发现不再有-1，-2，-3，-8这些异常值。"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"#### 3：数据划分\n",
|
||
"\n",
|
||
"我们首先对年龄进行了划分：为此我们先绘出了年龄的直方图"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 11,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"(8000, 38)\n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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",
|
||
"text/plain": [
|
||
"<Figure size 640x480 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"figure, ax = plt.subplots(1,1)\n",
|
||
"df['Age'].plot.hist(ax = ax,color='red',edgecolor='black')\n",
|
||
"print(df.shape)\n",
|
||
"plt.show()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"结合该直方图，我们将年龄划分为5段。16岁开始每16岁是一段。\n",
|
||
"\n",
|
||
"实际上我们发现在样本中年龄最小的是18岁。"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 12,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"df.loc[df['Age']<=16,'Age']=0\n",
|
||
"df.loc[(df['Age'] > 16) & (df['Age'] <= 32), 'Age'] = 1\n",
|
||
"df.loc[(df['Age'] > 32) & (df['Age'] <= 48), 'Age'] = 2\n",
|
||
"df.loc[(df['Age'] > 48) & (df['Age'] <= 64), 'Age'] = 3\n",
|
||
"df.loc[(df['Age'] > 64) & (df['Age'] <= 80), 'Age'] = 4\n",
|
||
"df.loc[ df['Age'] > 80, 'Age'] = 5"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"此外是个人收入的划分。"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 13,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"def income_cut(x):\n",
|
||
" if x<0:\n",
|
||
" return 0\n",
|
||
" elif 0<=x<1200:\n",
|
||
" return 1\n",
|
||
" elif 1200<=x<10000:\n",
|
||
" return 2\n",
|
||
" elif 10000<=x<24000:\n",
|
||
" return 3\n",
|
||
" elif 24000<=x<40000:\n",
|
||
" return 4\n",
|
||
" elif 40000<=x:\n",
|
||
" return 5\n",
|
||
"df[\"income_cut\"]=df[\"income\"].map(income_cut)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 14,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"# df.to_csv(\"Processed.csv\")"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"将数据保存"
|
||
]
|
||
}
|
||
],
|
||
"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": "3.10.7"
|
||
},
|
||
"orig_nbformat": 4
|
||
},
|
||
"nbformat": 4,
|
||
"nbformat_minor": 2
|
||
}
|