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machine_learning_projects/购物评论 情感分类/病马/Untitled2.ipynb
T
吴沂钊 e4bbe5593a 平时作业 
Signed-off-by: 吴沂钊 <13190667+Yizhao_Wu4926@user.noreply.gitee.com>
2023-07-17 06:20:37 +00:00

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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"id": "2ce6608d-60e0-49ac-895f-223a699d4c06",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\73247\\AppData\\Local\\Temp\\ipykernel_20444\\419372476.py:26: DeprecationWarning: `import pandas_profiling` is going to be deprecated by April 1st. Please use `import ydata_profiling` instead.\n",
" from pandas_profiling import ProfileReport\n"
]
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "0f4deab612a54e40a6d22b16ba7352aa",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Summarize dataset: 0%| | 0/5 [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"D:\\Anacond3\\lib\\site-packages\\pandas_profiling\\visualisation\\utils.py:73: UserWarning: Glyph 30452 (\\N{CJK UNIFIED IDEOGRAPH-76F4}) missing from current font.\n",
" plt.savefig(\n",
"D:\\Anacond3\\lib\\site-packages\\pandas_profiling\\visualisation\\utils.py:73: UserWarning: Glyph 32928 (\\N{CJK UNIFIED IDEOGRAPH-80A0}) missing from current font.\n",
" plt.savefig(\n",
"D:\\Anacond3\\lib\\site-packages\\pandas_profiling\\visualisation\\utils.py:73: UserWarning: Glyph 28201 (\\N{CJK UNIFIED IDEOGRAPH-6E29}) missing from current font.\n",
" plt.savefig(\n",
"D:\\Anacond3\\lib\\site-packages\\pandas_profiling\\visualisation\\utils.py:73: UserWarning: Glyph 24230 (\\N{CJK UNIFIED IDEOGRAPH-5EA6}) missing from current font.\n",
" plt.savefig(\n",
"D:\\Anacond3\\lib\\site-packages\\pandas_profiling\\visualisation\\utils.py:73: UserWarning: Glyph 33033 (\\N{CJK UNIFIED IDEOGRAPH-8109}) missing from current font.\n",
" plt.savefig(\n",
"D:\\Anacond3\\lib\\site-packages\\pandas_profiling\\visualisation\\utils.py:73: UserWarning: Glyph 25615 (\\N{CJK UNIFIED IDEOGRAPH-640F}) missing from current font.\n",
" plt.savefig(\n",
"D:\\Anacond3\\lib\\site-packages\\pandas_profiling\\visualisation\\utils.py:73: UserWarning: Glyph 30452 (\\N{CJK UNIFIED IDEOGRAPH-76F4}) missing from current font.\n",
" plt.savefig(\n",
"D:\\Anacond3\\lib\\site-packages\\pandas_profiling\\visualisation\\utils.py:73: UserWarning: Glyph 32928 (\\N{CJK UNIFIED IDEOGRAPH-80A0}) missing from current font.\n",
" plt.savefig(\n",
"D:\\Anacond3\\lib\\site-packages\\pandas_profiling\\visualisation\\utils.py:73: UserWarning: Glyph 28201 (\\N{CJK UNIFIED IDEOGRAPH-6E29}) missing from current font.\n",
" plt.savefig(\n",
"D:\\Anacond3\\lib\\site-packages\\pandas_profiling\\visualisation\\utils.py:73: UserWarning: Glyph 24230 (\\N{CJK UNIFIED IDEOGRAPH-5EA6}) missing from current font.\n",
" plt.savefig(\n",
"D:\\Anacond3\\lib\\site-packages\\pandas_profiling\\visualisation\\utils.py:73: UserWarning: Glyph 21628 (\\N{CJK UNIFIED IDEOGRAPH-547C}) missing from current font.\n",
" plt.savefig(\n",
"D:\\Anacond3\\lib\\site-packages\\pandas_profiling\\visualisation\\utils.py:73: UserWarning: Glyph 21560 (\\N{CJK UNIFIED IDEOGRAPH-5438}) missing from current font.\n",
" plt.savefig(\n",
"D:\\Anacond3\\lib\\site-packages\\pandas_profiling\\visualisation\\utils.py:73: UserWarning: Glyph 39057 (\\N{CJK UNIFIED IDEOGRAPH-9891}) missing from current font.\n",
" plt.savefig(\n",
"D:\\Anacond3\\lib\\site-packages\\pandas_profiling\\visualisation\\utils.py:73: UserWarning: Glyph 29575 (\\N{CJK UNIFIED IDEOGRAPH-7387}) missing from current font.\n",
" plt.savefig(\n",
"D:\\Anacond3\\lib\\site-packages\\pandas_profiling\\visualisation\\utils.py:73: UserWarning: Glyph 30452 (\\N{CJK UNIFIED IDEOGRAPH-76F4}) missing from current font.\n",
" plt.savefig(\n",
"D:\\Anacond3\\lib\\site-packages\\pandas_profiling\\visualisation\\utils.py:73: UserWarning: Glyph 32928 (\\N{CJK UNIFIED IDEOGRAPH-80A0}) missing from current font.\n",
" plt.savefig(\n",
"D:\\Anacond3\\lib\\site-packages\\pandas_profiling\\visualisation\\utils.py:73: UserWarning: Glyph 28201 (\\N{CJK UNIFIED IDEOGRAPH-6E29}) missing from current font.\n",
" plt.savefig(\n",
"D:\\Anacond3\\lib\\site-packages\\pandas_profiling\\visualisation\\utils.py:73: UserWarning: Glyph 24230 (\\N{CJK UNIFIED IDEOGRAPH-5EA6}) missing from current font.\n",
" plt.savefig(\n",
"D:\\Anacond3\\lib\\site-packages\\pandas_profiling\\visualisation\\utils.py:73: UserWarning: Glyph 32418 (\\N{CJK UNIFIED IDEOGRAPH-7EA2}) missing from current font.\n",
" plt.savefig(\n",
"D:\\Anacond3\\lib\\site-packages\\pandas_profiling\\visualisation\\utils.py:73: UserWarning: Glyph 32454 (\\N{CJK UNIFIED IDEOGRAPH-7EC6}) missing from current font.\n",
" plt.savefig(\n",
"D:\\Anacond3\\lib\\site-packages\\pandas_profiling\\visualisation\\utils.py:73: UserWarning: Glyph 32990 (\\N{CJK UNIFIED IDEOGRAPH-80DE}) missing from current font.\n",
" plt.savefig(\n",
"D:\\Anacond3\\lib\\site-packages\\pandas_profiling\\visualisation\\utils.py:73: UserWarning: Glyph 20307 (\\N{CJK UNIFIED IDEOGRAPH-4F53}) missing from current font.\n",
" plt.savefig(\n",
"D:\\Anacond3\\lib\\site-packages\\pandas_profiling\\visualisation\\utils.py:73: UserWarning: Glyph 31215 (\\N{CJK UNIFIED IDEOGRAPH-79EF}) missing from current font.\n",
" plt.savefig(\n",
"D:\\Anacond3\\lib\\site-packages\\pandas_profiling\\visualisation\\utils.py:73: UserWarning: Glyph 30452 (\\N{CJK UNIFIED IDEOGRAPH-76F4}) missing from current font.\n",
" plt.savefig(\n",
"D:\\Anacond3\\lib\\site-packages\\pandas_profiling\\visualisation\\utils.py:73: UserWarning: Glyph 32928 (\\N{CJK UNIFIED IDEOGRAPH-80A0}) missing from current font.\n",
" plt.savefig(\n",
"D:\\Anacond3\\lib\\site-packages\\pandas_profiling\\visualisation\\utils.py:73: UserWarning: Glyph 28201 (\\N{CJK UNIFIED IDEOGRAPH-6E29}) missing from current font.\n",
" plt.savefig(\n",
"D:\\Anacond3\\lib\\site-packages\\pandas_profiling\\visualisation\\utils.py:73: UserWarning: Glyph 24230 (\\N{CJK UNIFIED IDEOGRAPH-5EA6}) missing from current font.\n",
" plt.savefig(\n",
"D:\\Anacond3\\lib\\site-packages\\pandas_profiling\\visualisation\\utils.py:73: UserWarning: Glyph 24635 (\\N{CJK UNIFIED IDEOGRAPH-603B}) missing from current font.\n",
" plt.savefig(\n",
"D:\\Anacond3\\lib\\site-packages\\pandas_profiling\\visualisation\\utils.py:73: UserWarning: Glyph 34507 (\\N{CJK UNIFIED IDEOGRAPH-86CB}) missing from current font.\n",
" plt.savefig(\n",
"D:\\Anacond3\\lib\\site-packages\\pandas_profiling\\visualisation\\utils.py:73: UserWarning: Glyph 30333 (\\N{CJK UNIFIED IDEOGRAPH-767D}) missing from current font.\n",
" plt.savefig(\n",
"D:\\Anacond3\\lib\\site-packages\\pandas_profiling\\visualisation\\utils.py:73: UserWarning: Glyph 20540 (\\N{CJK UNIFIED IDEOGRAPH-503C}) missing from current font.\n",
" plt.savefig(\n",
"D:\\Anacond3\\lib\\site-packages\\pandas_profiling\\visualisation\\utils.py:73: UserWarning: Glyph 30452 (\\N{CJK UNIFIED IDEOGRAPH-76F4}) missing from current font.\n",
" plt.savefig(\n",
"D:\\Anacond3\\lib\\site-packages\\pandas_profiling\\visualisation\\utils.py:73: UserWarning: Glyph 32928 (\\N{CJK UNIFIED IDEOGRAPH-80A0}) missing from current font.\n",
" plt.savefig(\n",
"D:\\Anacond3\\lib\\site-packages\\pandas_profiling\\visualisation\\utils.py:73: UserWarning: Glyph 28201 (\\N{CJK UNIFIED IDEOGRAPH-6E29}) missing from current font.\n",
" plt.savefig(\n",
"D:\\Anacond3\\lib\\site-packages\\pandas_profiling\\visualisation\\utils.py:73: UserWarning: Glyph 24230 (\\N{CJK UNIFIED IDEOGRAPH-5EA6}) missing from current font.\n",
" plt.savefig(\n",
"D:\\Anacond3\\lib\\site-packages\\pandas_profiling\\visualisation\\utils.py:73: UserWarning: Glyph 30452 (\\N{CJK UNIFIED IDEOGRAPH-76F4}) missing from current font.\n",
" plt.savefig(\n",
"D:\\Anacond3\\lib\\site-packages\\pandas_profiling\\visualisation\\utils.py:73: UserWarning: Glyph 32928 (\\N{CJK UNIFIED IDEOGRAPH-80A0}) missing from current font.\n",
" plt.savefig(\n",
"D:\\Anacond3\\lib\\site-packages\\pandas_profiling\\visualisation\\utils.py:73: UserWarning: Glyph 28201 (\\N{CJK UNIFIED IDEOGRAPH-6E29}) missing from current font.\n",
" plt.savefig(\n",
"D:\\Anacond3\\lib\\site-packages\\pandas_profiling\\visualisation\\utils.py:73: UserWarning: Glyph 24230 (\\N{CJK UNIFIED IDEOGRAPH-5EA6}) missing from current font.\n",
" plt.savefig(\n",
"D:\\Anacond3\\lib\\site-packages\\pandas_profiling\\visualisation\\utils.py:73: UserWarning: Glyph 33033 (\\N{CJK UNIFIED IDEOGRAPH-8109}) missing from current font.\n",
" plt.savefig(\n",
"D:\\Anacond3\\lib\\site-packages\\pandas_profiling\\visualisation\\utils.py:73: UserWarning: Glyph 25615 (\\N{CJK UNIFIED IDEOGRAPH-640F}) missing from current font.\n",
" plt.savefig(\n",
"D:\\Anacond3\\lib\\site-packages\\pandas_profiling\\visualisation\\utils.py:73: UserWarning: Glyph 33033 (\\N{CJK UNIFIED IDEOGRAPH-8109}) missing from current font.\n",
" plt.savefig(\n",
"D:\\Anacond3\\lib\\site-packages\\pandas_profiling\\visualisation\\utils.py:73: UserWarning: Glyph 25615 (\\N{CJK UNIFIED IDEOGRAPH-640F}) missing from current font.\n",
" plt.savefig(\n",
"D:\\Anacond3\\lib\\site-packages\\pandas_profiling\\visualisation\\utils.py:73: UserWarning: Glyph 21628 (\\N{CJK UNIFIED IDEOGRAPH-547C}) missing from current font.\n",
" plt.savefig(\n",
"D:\\Anacond3\\lib\\site-packages\\pandas_profiling\\visualisation\\utils.py:73: UserWarning: Glyph 21560 (\\N{CJK UNIFIED IDEOGRAPH-5438}) missing from current font.\n",
" plt.savefig(\n",
"D:\\Anacond3\\lib\\site-packages\\pandas_profiling\\visualisation\\utils.py:73: UserWarning: Glyph 39057 (\\N{CJK UNIFIED IDEOGRAPH-9891}) missing from current font.\n",
" plt.savefig(\n",
"D:\\Anacond3\\lib\\site-packages\\pandas_profiling\\visualisation\\utils.py:73: UserWarning: Glyph 29575 (\\N{CJK UNIFIED IDEOGRAPH-7387}) missing from current font.\n",
" plt.savefig(\n",
"D:\\Anacond3\\lib\\site-packages\\pandas_profiling\\visualisation\\utils.py:73: UserWarning: Glyph 33033 (\\N{CJK UNIFIED IDEOGRAPH-8109}) missing from current font.\n",
" plt.savefig(\n",
"D:\\Anacond3\\lib\\site-packages\\pandas_profiling\\visualisation\\utils.py:73: UserWarning: Glyph 25615 (\\N{CJK UNIFIED IDEOGRAPH-640F}) missing from current font.\n",
" plt.savefig(\n",
"D:\\Anacond3\\lib\\site-packages\\pandas_profiling\\visualisation\\utils.py:73: UserWarning: Glyph 32418 (\\N{CJK UNIFIED IDEOGRAPH-7EA2}) missing from current font.\n",
" plt.savefig(\n",
"D:\\Anacond3\\lib\\site-packages\\pandas_profiling\\visualisation\\utils.py:73: UserWarning: Glyph 32454 (\\N{CJK UNIFIED IDEOGRAPH-7EC6}) missing from current font.\n",
" plt.savefig(\n",
"D:\\Anacond3\\lib\\site-packages\\pandas_profiling\\visualisation\\utils.py:73: UserWarning: Glyph 32990 (\\N{CJK UNIFIED IDEOGRAPH-80DE}) missing from current font.\n",
" plt.savefig(\n",
"D:\\Anacond3\\lib\\site-packages\\pandas_profiling\\visualisation\\utils.py:73: UserWarning: Glyph 20307 (\\N{CJK UNIFIED IDEOGRAPH-4F53}) missing from current font.\n",
" plt.savefig(\n",
"D:\\Anacond3\\lib\\site-packages\\pandas_profiling\\visualisation\\utils.py:73: UserWarning: Glyph 31215 (\\N{CJK UNIFIED IDEOGRAPH-79EF}) missing from current font.\n",
" plt.savefig(\n",
"D:\\Anacond3\\lib\\site-packages\\pandas_profiling\\visualisation\\utils.py:73: UserWarning: Glyph 33033 (\\N{CJK UNIFIED IDEOGRAPH-8109}) missing from current font.\n",
" plt.savefig(\n",
"D:\\Anacond3\\lib\\site-packages\\pandas_profiling\\visualisation\\utils.py:73: UserWarning: Glyph 25615 (\\N{CJK UNIFIED IDEOGRAPH-640F}) missing from current font.\n",
" plt.savefig(\n",
"D:\\Anacond3\\lib\\site-packages\\pandas_profiling\\visualisation\\utils.py:73: UserWarning: Glyph 24635 (\\N{CJK UNIFIED IDEOGRAPH-603B}) missing from current font.\n",
" plt.savefig(\n",
"D:\\Anacond3\\lib\\site-packages\\pandas_profiling\\visualisation\\utils.py:73: UserWarning: Glyph 34507 (\\N{CJK UNIFIED IDEOGRAPH-86CB}) missing from current font.\n",
" plt.savefig(\n",
"D:\\Anacond3\\lib\\site-packages\\pandas_profiling\\visualisation\\utils.py:73: UserWarning: Glyph 30333 (\\N{CJK UNIFIED IDEOGRAPH-767D}) missing from current font.\n",
" plt.savefig(\n",
"D:\\Anacond3\\lib\\site-packages\\pandas_profiling\\visualisation\\utils.py:73: UserWarning: Glyph 20540 (\\N{CJK UNIFIED IDEOGRAPH-503C}) missing from current font.\n",
" plt.savefig(\n",
"D:\\Anacond3\\lib\\site-packages\\pandas_profiling\\visualisation\\utils.py:73: UserWarning: Glyph 33033 (\\N{CJK UNIFIED IDEOGRAPH-8109}) missing from current font.\n",
" plt.savefig(\n",
"D:\\Anacond3\\lib\\site-packages\\pandas_profiling\\visualisation\\utils.py:73: UserWarning: Glyph 25615 (\\N{CJK UNIFIED IDEOGRAPH-640F}) missing from current font.\n",
" plt.savefig(\n",
"D:\\Anacond3\\lib\\site-packages\\pandas_profiling\\visualisation\\utils.py:73: UserWarning: Glyph 30452 (\\N{CJK UNIFIED IDEOGRAPH-76F4}) missing from current font.\n",
" plt.savefig(\n",
"D:\\Anacond3\\lib\\site-packages\\pandas_profiling\\visualisation\\utils.py:73: UserWarning: Glyph 32928 (\\N{CJK UNIFIED IDEOGRAPH-80A0}) missing from current font.\n",
" plt.savefig(\n",
"D:\\Anacond3\\lib\\site-packages\\pandas_profiling\\visualisation\\utils.py:73: UserWarning: Glyph 28201 (\\N{CJK UNIFIED IDEOGRAPH-6E29}) missing from current font.\n",
" plt.savefig(\n",
"D:\\Anacond3\\lib\\site-packages\\pandas_profiling\\visualisation\\utils.py:73: UserWarning: Glyph 24230 (\\N{CJK UNIFIED IDEOGRAPH-5EA6}) missing from current font.\n",
" plt.savefig(\n",
"D:\\Anacond3\\lib\\site-packages\\pandas_profiling\\visualisation\\utils.py:73: UserWarning: Glyph 21628 (\\N{CJK UNIFIED IDEOGRAPH-547C}) missing from current font.\n",
" plt.savefig(\n",
"D:\\Anacond3\\lib\\site-packages\\pandas_profiling\\visualisation\\utils.py:73: UserWarning: Glyph 21560 (\\N{CJK UNIFIED IDEOGRAPH-5438}) missing from current font.\n",
" plt.savefig(\n",
"D:\\Anacond3\\lib\\site-packages\\pandas_profiling\\visualisation\\utils.py:73: UserWarning: Glyph 39057 (\\N{CJK UNIFIED IDEOGRAPH-9891}) missing from current font.\n",
" plt.savefig(\n",
"D:\\Anacond3\\lib\\site-packages\\pandas_profiling\\visualisation\\utils.py:73: UserWarning: Glyph 29575 (\\N{CJK UNIFIED IDEOGRAPH-7387}) missing from current font.\n",
" plt.savefig(\n",
"D:\\Anacond3\\lib\\site-packages\\pandas_profiling\\visualisation\\utils.py:73: UserWarning: Glyph 33033 (\\N{CJK UNIFIED IDEOGRAPH-8109}) missing from current font.\n",
" plt.savefig(\n",
"D:\\Anacond3\\lib\\site-packages\\pandas_profiling\\visualisation\\utils.py:73: UserWarning: Glyph 25615 (\\N{CJK UNIFIED IDEOGRAPH-640F}) missing from current font.\n",
" plt.savefig(\n",
"D:\\Anacond3\\lib\\site-packages\\pandas_profiling\\visualisation\\utils.py:73: UserWarning: Glyph 21628 (\\N{CJK UNIFIED IDEOGRAPH-547C}) missing from current font.\n",
" plt.savefig(\n",
"D:\\Anacond3\\lib\\site-packages\\pandas_profiling\\visualisation\\utils.py:73: UserWarning: Glyph 21560 (\\N{CJK UNIFIED IDEOGRAPH-5438}) missing from current font.\n",
" plt.savefig(\n",
"D:\\Anacond3\\lib\\site-packages\\pandas_profiling\\visualisation\\utils.py:73: UserWarning: Glyph 39057 (\\N{CJK UNIFIED IDEOGRAPH-9891}) missing from current font.\n",
" plt.savefig(\n",
"D:\\Anacond3\\lib\\site-packages\\pandas_profiling\\visualisation\\utils.py:73: UserWarning: Glyph 29575 (\\N{CJK UNIFIED IDEOGRAPH-7387}) missing from current font.\n",
" plt.savefig(\n",
"D:\\Anacond3\\lib\\site-packages\\pandas_profiling\\visualisation\\utils.py:73: UserWarning: Glyph 21628 (\\N{CJK UNIFIED IDEOGRAPH-547C}) missing from current font.\n",
" plt.savefig(\n",
"D:\\Anacond3\\lib\\site-packages\\pandas_profiling\\visualisation\\utils.py:73: UserWarning: Glyph 21560 (\\N{CJK UNIFIED IDEOGRAPH-5438}) missing from current font.\n",
" plt.savefig(\n",
"D:\\Anacond3\\lib\\site-packages\\pandas_profiling\\visualisation\\utils.py:73: UserWarning: Glyph 39057 (\\N{CJK UNIFIED IDEOGRAPH-9891}) missing from current font.\n",
" plt.savefig(\n",
"D:\\Anacond3\\lib\\site-packages\\pandas_profiling\\visualisation\\utils.py:73: UserWarning: Glyph 29575 (\\N{CJK UNIFIED IDEOGRAPH-7387}) missing from current font.\n",
" plt.savefig(\n",
"D:\\Anacond3\\lib\\site-packages\\pandas_profiling\\visualisation\\utils.py:73: UserWarning: Glyph 32418 (\\N{CJK UNIFIED IDEOGRAPH-7EA2}) missing from current font.\n",
" plt.savefig(\n",
"D:\\Anacond3\\lib\\site-packages\\pandas_profiling\\visualisation\\utils.py:73: UserWarning: Glyph 32454 (\\N{CJK UNIFIED IDEOGRAPH-7EC6}) missing from current font.\n",
" plt.savefig(\n",
"D:\\Anacond3\\lib\\site-packages\\pandas_profiling\\visualisation\\utils.py:73: UserWarning: Glyph 32990 (\\N{CJK UNIFIED IDEOGRAPH-80DE}) missing from current font.\n",
" plt.savefig(\n",
"D:\\Anacond3\\lib\\site-packages\\pandas_profiling\\visualisation\\utils.py:73: UserWarning: Glyph 20307 (\\N{CJK UNIFIED IDEOGRAPH-4F53}) missing from current font.\n",
" plt.savefig(\n",
"D:\\Anacond3\\lib\\site-packages\\pandas_profiling\\visualisation\\utils.py:73: UserWarning: Glyph 31215 (\\N{CJK UNIFIED IDEOGRAPH-79EF}) missing from current font.\n",
" plt.savefig(\n",
"D:\\Anacond3\\lib\\site-packages\\pandas_profiling\\visualisation\\utils.py:73: UserWarning: Glyph 21628 (\\N{CJK UNIFIED IDEOGRAPH-547C}) missing from current font.\n",
" plt.savefig(\n",
"D:\\Anacond3\\lib\\site-packages\\pandas_profiling\\visualisation\\utils.py:73: UserWarning: Glyph 21560 (\\N{CJK UNIFIED IDEOGRAPH-5438}) missing from current font.\n",
" plt.savefig(\n",
"D:\\Anacond3\\lib\\site-packages\\pandas_profiling\\visualisation\\utils.py:73: UserWarning: Glyph 39057 (\\N{CJK UNIFIED IDEOGRAPH-9891}) missing from current font.\n",
" plt.savefig(\n",
"D:\\Anacond3\\lib\\site-packages\\pandas_profiling\\visualisation\\utils.py:73: UserWarning: Glyph 29575 (\\N{CJK UNIFIED IDEOGRAPH-7387}) missing from current font.\n",
" plt.savefig(\n",
"D:\\Anacond3\\lib\\site-packages\\pandas_profiling\\visualisation\\utils.py:73: UserWarning: Glyph 24635 (\\N{CJK UNIFIED IDEOGRAPH-603B}) missing from current font.\n",
" plt.savefig(\n",
"D:\\Anacond3\\lib\\site-packages\\pandas_profiling\\visualisation\\utils.py:73: UserWarning: Glyph 34507 (\\N{CJK UNIFIED IDEOGRAPH-86CB}) missing from current font.\n",
" plt.savefig(\n",
"D:\\Anacond3\\lib\\site-packages\\pandas_profiling\\visualisation\\utils.py:73: UserWarning: Glyph 30333 (\\N{CJK UNIFIED IDEOGRAPH-767D}) missing from current font.\n",
" plt.savefig(\n",
"D:\\Anacond3\\lib\\site-packages\\pandas_profiling\\visualisation\\utils.py:73: UserWarning: Glyph 20540 (\\N{CJK UNIFIED IDEOGRAPH-503C}) missing from current font.\n",
" plt.savefig(\n",
"D:\\Anacond3\\lib\\site-packages\\pandas_profiling\\visualisation\\utils.py:73: UserWarning: Glyph 21628 (\\N{CJK UNIFIED IDEOGRAPH-547C}) missing from current font.\n",
" plt.savefig(\n",
"D:\\Anacond3\\lib\\site-packages\\pandas_profiling\\visualisation\\utils.py:73: UserWarning: Glyph 21560 (\\N{CJK UNIFIED IDEOGRAPH-5438}) missing from current font.\n",
" plt.savefig(\n",
"D:\\Anacond3\\lib\\site-packages\\pandas_profiling\\visualisation\\utils.py:73: UserWarning: Glyph 39057 (\\N{CJK UNIFIED IDEOGRAPH-9891}) missing from current font.\n",
" plt.savefig(\n",
"D:\\Anacond3\\lib\\site-packages\\pandas_profiling\\visualisation\\utils.py:73: UserWarning: Glyph 29575 (\\N{CJK UNIFIED IDEOGRAPH-7387}) missing from current font.\n",
" plt.savefig(\n",
"D:\\Anacond3\\lib\\site-packages\\pandas_profiling\\visualisation\\utils.py:73: UserWarning: Glyph 30452 (\\N{CJK UNIFIED IDEOGRAPH-76F4}) missing from current font.\n",
" plt.savefig(\n",
"D:\\Anacond3\\lib\\site-packages\\pandas_profiling\\visualisation\\utils.py:73: UserWarning: Glyph 32928 (\\N{CJK UNIFIED IDEOGRAPH-80A0}) missing from current font.\n",
" plt.savefig(\n",
"D:\\Anacond3\\lib\\site-packages\\pandas_profiling\\visualisation\\utils.py:73: UserWarning: Glyph 28201 (\\N{CJK UNIFIED IDEOGRAPH-6E29}) missing from current font.\n",
" plt.savefig(\n",
"D:\\Anacond3\\lib\\site-packages\\pandas_profiling\\visualisation\\utils.py:73: UserWarning: Glyph 24230 (\\N{CJK UNIFIED IDEOGRAPH-5EA6}) missing from current font.\n",
" plt.savefig(\n",
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" plt.savefig(\n",
"D:\\Anacond3\\lib\\site-packages\\pandas_profiling\\visualisation\\utils.py:73: UserWarning: Glyph 32454 (\\N{CJK UNIFIED IDEOGRAPH-7EC6}) missing from current font.\n",
" plt.savefig(\n",
"D:\\Anacond3\\lib\\site-packages\\pandas_profiling\\visualisation\\utils.py:73: UserWarning: Glyph 32990 (\\N{CJK UNIFIED IDEOGRAPH-80DE}) missing from current font.\n",
" plt.savefig(\n",
"D:\\Anacond3\\lib\\site-packages\\pandas_profiling\\visualisation\\utils.py:73: UserWarning: Glyph 20307 (\\N{CJK UNIFIED IDEOGRAPH-4F53}) missing from current font.\n",
" plt.savefig(\n",
"D:\\Anacond3\\lib\\site-packages\\pandas_profiling\\visualisation\\utils.py:73: UserWarning: Glyph 31215 (\\N{CJK UNIFIED IDEOGRAPH-79EF}) missing from current font.\n",
" plt.savefig(\n",
"D:\\Anacond3\\lib\\site-packages\\pandas_profiling\\visualisation\\utils.py:73: UserWarning: Glyph 33033 (\\N{CJK UNIFIED IDEOGRAPH-8109}) missing from current font.\n",
" plt.savefig(\n",
"D:\\Anacond3\\lib\\site-packages\\pandas_profiling\\visualisation\\utils.py:73: UserWarning: Glyph 25615 (\\N{CJK UNIFIED IDEOGRAPH-640F}) missing from current font.\n",
" plt.savefig(\n",
"D:\\Anacond3\\lib\\site-packages\\pandas_profiling\\visualisation\\utils.py:73: UserWarning: Glyph 32418 (\\N{CJK UNIFIED IDEOGRAPH-7EA2}) missing from current font.\n",
" plt.savefig(\n",
"D:\\Anacond3\\lib\\site-packages\\pandas_profiling\\visualisation\\utils.py:73: UserWarning: Glyph 32454 (\\N{CJK UNIFIED IDEOGRAPH-7EC6}) missing from current font.\n",
" plt.savefig(\n",
"D:\\Anacond3\\lib\\site-packages\\pandas_profiling\\visualisation\\utils.py:73: UserWarning: Glyph 32990 (\\N{CJK UNIFIED IDEOGRAPH-80DE}) missing from current font.\n",
" plt.savefig(\n",
"D:\\Anacond3\\lib\\site-packages\\pandas_profiling\\visualisation\\utils.py:73: UserWarning: Glyph 20307 (\\N{CJK UNIFIED IDEOGRAPH-4F53}) missing from current font.\n",
" plt.savefig(\n",
"D:\\Anacond3\\lib\\site-packages\\pandas_profiling\\visualisation\\utils.py:73: UserWarning: Glyph 31215 (\\N{CJK UNIFIED IDEOGRAPH-79EF}) missing from current font.\n",
" plt.savefig(\n",
"D:\\Anacond3\\lib\\site-packages\\pandas_profiling\\visualisation\\utils.py:73: UserWarning: Glyph 21628 (\\N{CJK UNIFIED IDEOGRAPH-547C}) missing from current font.\n",
" plt.savefig(\n",
"D:\\Anacond3\\lib\\site-packages\\pandas_profiling\\visualisation\\utils.py:73: UserWarning: Glyph 21560 (\\N{CJK UNIFIED IDEOGRAPH-5438}) missing from current font.\n",
" plt.savefig(\n",
"D:\\Anacond3\\lib\\site-packages\\pandas_profiling\\visualisation\\utils.py:73: UserWarning: Glyph 39057 (\\N{CJK UNIFIED IDEOGRAPH-9891}) missing from current font.\n",
" plt.savefig(\n",
"D:\\Anacond3\\lib\\site-packages\\pandas_profiling\\visualisation\\utils.py:73: UserWarning: Glyph 29575 (\\N{CJK UNIFIED IDEOGRAPH-7387}) missing from current font.\n",
" plt.savefig(\n",
"D:\\Anacond3\\lib\\site-packages\\pandas_profiling\\visualisation\\utils.py:73: UserWarning: Glyph 32418 (\\N{CJK UNIFIED IDEOGRAPH-7EA2}) missing from current font.\n",
" plt.savefig(\n",
"D:\\Anacond3\\lib\\site-packages\\pandas_profiling\\visualisation\\utils.py:73: UserWarning: Glyph 32454 (\\N{CJK UNIFIED IDEOGRAPH-7EC6}) missing from current font.\n",
" plt.savefig(\n",
"D:\\Anacond3\\lib\\site-packages\\pandas_profiling\\visualisation\\utils.py:73: UserWarning: Glyph 32990 (\\N{CJK UNIFIED IDEOGRAPH-80DE}) missing from current font.\n",
" plt.savefig(\n",
"D:\\Anacond3\\lib\\site-packages\\pandas_profiling\\visualisation\\utils.py:73: UserWarning: Glyph 20307 (\\N{CJK UNIFIED IDEOGRAPH-4F53}) missing from current font.\n",
" plt.savefig(\n",
"D:\\Anacond3\\lib\\site-packages\\pandas_profiling\\visualisation\\utils.py:73: UserWarning: Glyph 31215 (\\N{CJK UNIFIED IDEOGRAPH-79EF}) missing from current font.\n",
" plt.savefig(\n",
"D:\\Anacond3\\lib\\site-packages\\pandas_profiling\\visualisation\\utils.py:73: UserWarning: Glyph 32418 (\\N{CJK UNIFIED IDEOGRAPH-7EA2}) missing from current font.\n",
" plt.savefig(\n",
"D:\\Anacond3\\lib\\site-packages\\pandas_profiling\\visualisation\\utils.py:73: UserWarning: Glyph 32454 (\\N{CJK UNIFIED IDEOGRAPH-7EC6}) missing from current font.\n",
" plt.savefig(\n",
"D:\\Anacond3\\lib\\site-packages\\pandas_profiling\\visualisation\\utils.py:73: UserWarning: Glyph 32990 (\\N{CJK UNIFIED IDEOGRAPH-80DE}) missing from current font.\n",
" plt.savefig(\n",
"D:\\Anacond3\\lib\\site-packages\\pandas_profiling\\visualisation\\utils.py:73: UserWarning: Glyph 20307 (\\N{CJK UNIFIED IDEOGRAPH-4F53}) missing from current font.\n",
" plt.savefig(\n",
"D:\\Anacond3\\lib\\site-packages\\pandas_profiling\\visualisation\\utils.py:73: UserWarning: Glyph 31215 (\\N{CJK UNIFIED IDEOGRAPH-79EF}) missing from current font.\n",
" plt.savefig(\n",
"D:\\Anacond3\\lib\\site-packages\\pandas_profiling\\visualisation\\utils.py:73: UserWarning: Glyph 24635 (\\N{CJK UNIFIED IDEOGRAPH-603B}) missing from current font.\n",
" plt.savefig(\n",
"D:\\Anacond3\\lib\\site-packages\\pandas_profiling\\visualisation\\utils.py:73: UserWarning: Glyph 34507 (\\N{CJK UNIFIED IDEOGRAPH-86CB}) missing from current font.\n",
" plt.savefig(\n",
"D:\\Anacond3\\lib\\site-packages\\pandas_profiling\\visualisation\\utils.py:73: UserWarning: Glyph 30333 (\\N{CJK UNIFIED IDEOGRAPH-767D}) missing from current font.\n",
" plt.savefig(\n",
"D:\\Anacond3\\lib\\site-packages\\pandas_profiling\\visualisation\\utils.py:73: UserWarning: Glyph 20540 (\\N{CJK UNIFIED IDEOGRAPH-503C}) missing from current font.\n",
" plt.savefig(\n",
"D:\\Anacond3\\lib\\site-packages\\pandas_profiling\\visualisation\\utils.py:73: UserWarning: Glyph 32418 (\\N{CJK UNIFIED IDEOGRAPH-7EA2}) missing from current font.\n",
" plt.savefig(\n",
"D:\\Anacond3\\lib\\site-packages\\pandas_profiling\\visualisation\\utils.py:73: UserWarning: Glyph 32454 (\\N{CJK UNIFIED IDEOGRAPH-7EC6}) missing from current font.\n",
" plt.savefig(\n",
"D:\\Anacond3\\lib\\site-packages\\pandas_profiling\\visualisation\\utils.py:73: UserWarning: Glyph 32990 (\\N{CJK UNIFIED IDEOGRAPH-80DE}) missing from current font.\n",
" plt.savefig(\n",
"D:\\Anacond3\\lib\\site-packages\\pandas_profiling\\visualisation\\utils.py:73: UserWarning: Glyph 20307 (\\N{CJK UNIFIED IDEOGRAPH-4F53}) missing from current font.\n",
" plt.savefig(\n",
"D:\\Anacond3\\lib\\site-packages\\pandas_profiling\\visualisation\\utils.py:73: UserWarning: Glyph 31215 (\\N{CJK UNIFIED IDEOGRAPH-79EF}) missing from current font.\n",
" plt.savefig(\n",
"D:\\Anacond3\\lib\\site-packages\\pandas_profiling\\visualisation\\utils.py:73: UserWarning: Glyph 30452 (\\N{CJK UNIFIED IDEOGRAPH-76F4}) missing from current font.\n",
" plt.savefig(\n",
"D:\\Anacond3\\lib\\site-packages\\pandas_profiling\\visualisation\\utils.py:73: UserWarning: Glyph 32928 (\\N{CJK UNIFIED IDEOGRAPH-80A0}) missing from current font.\n",
" plt.savefig(\n",
"D:\\Anacond3\\lib\\site-packages\\pandas_profiling\\visualisation\\utils.py:73: UserWarning: Glyph 28201 (\\N{CJK UNIFIED IDEOGRAPH-6E29}) missing from current font.\n",
" plt.savefig(\n",
"D:\\Anacond3\\lib\\site-packages\\pandas_profiling\\visualisation\\utils.py:73: UserWarning: Glyph 24230 (\\N{CJK UNIFIED IDEOGRAPH-5EA6}) missing from current font.\n",
" plt.savefig(\n",
"D:\\Anacond3\\lib\\site-packages\\pandas_profiling\\visualisation\\utils.py:73: UserWarning: Glyph 24635 (\\N{CJK UNIFIED IDEOGRAPH-603B}) missing from current font.\n",
" plt.savefig(\n",
"D:\\Anacond3\\lib\\site-packages\\pandas_profiling\\visualisation\\utils.py:73: UserWarning: Glyph 34507 (\\N{CJK UNIFIED IDEOGRAPH-86CB}) missing from current font.\n",
" plt.savefig(\n",
"D:\\Anacond3\\lib\\site-packages\\pandas_profiling\\visualisation\\utils.py:73: UserWarning: Glyph 30333 (\\N{CJK UNIFIED IDEOGRAPH-767D}) missing from current font.\n",
" plt.savefig(\n",
"D:\\Anacond3\\lib\\site-packages\\pandas_profiling\\visualisation\\utils.py:73: UserWarning: Glyph 20540 (\\N{CJK UNIFIED IDEOGRAPH-503C}) missing from current font.\n",
" plt.savefig(\n",
"D:\\Anacond3\\lib\\site-packages\\pandas_profiling\\visualisation\\utils.py:73: UserWarning: Glyph 33033 (\\N{CJK UNIFIED IDEOGRAPH-8109}) missing from current font.\n",
" plt.savefig(\n",
"D:\\Anacond3\\lib\\site-packages\\pandas_profiling\\visualisation\\utils.py:73: UserWarning: Glyph 25615 (\\N{CJK UNIFIED IDEOGRAPH-640F}) missing from current font.\n",
" plt.savefig(\n",
"D:\\Anacond3\\lib\\site-packages\\pandas_profiling\\visualisation\\utils.py:73: UserWarning: Glyph 24635 (\\N{CJK UNIFIED IDEOGRAPH-603B}) missing from current font.\n",
" plt.savefig(\n",
"D:\\Anacond3\\lib\\site-packages\\pandas_profiling\\visualisation\\utils.py:73: UserWarning: Glyph 34507 (\\N{CJK UNIFIED IDEOGRAPH-86CB}) missing from current font.\n",
" plt.savefig(\n",
"D:\\Anacond3\\lib\\site-packages\\pandas_profiling\\visualisation\\utils.py:73: UserWarning: Glyph 30333 (\\N{CJK UNIFIED IDEOGRAPH-767D}) missing from current font.\n",
" plt.savefig(\n",
"D:\\Anacond3\\lib\\site-packages\\pandas_profiling\\visualisation\\utils.py:73: UserWarning: Glyph 20540 (\\N{CJK UNIFIED IDEOGRAPH-503C}) missing from current font.\n",
" plt.savefig(\n",
"D:\\Anacond3\\lib\\site-packages\\pandas_profiling\\visualisation\\utils.py:73: UserWarning: Glyph 21628 (\\N{CJK UNIFIED IDEOGRAPH-547C}) missing from current font.\n",
" plt.savefig(\n",
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" plt.savefig(\n",
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" plt.savefig(\n",
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" plt.savefig(\n",
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" plt.savefig(\n",
"D:\\Anacond3\\lib\\site-packages\\pandas_profiling\\visualisation\\utils.py:73: UserWarning: Glyph 34507 (\\N{CJK UNIFIED IDEOGRAPH-86CB}) missing from current font.\n",
" plt.savefig(\n",
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" plt.savefig(\n",
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" plt.savefig(\n",
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" plt.savefig(\n",
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" plt.savefig(\n",
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" plt.savefig(\n",
"D:\\Anacond3\\lib\\site-packages\\pandas_profiling\\visualisation\\utils.py:73: UserWarning: Glyph 20307 (\\N{CJK UNIFIED IDEOGRAPH-4F53}) missing from current font.\n",
" plt.savefig(\n",
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" plt.savefig(\n",
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" plt.savefig(\n",
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" plt.savefig(\n",
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" plt.savefig(\n",
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" plt.savefig(\n",
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" plt.savefig(\n",
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" plt.savefig(\n",
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" plt.savefig(\n",
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" plt.savefig(\n",
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" plt.savefig(\n",
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" plt.savefig(\n",
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" plt.savefig(\n",
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" plt.savefig(\n",
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" plt.savefig(\n",
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" plt.savefig(\n",
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" plt.savefig(\n",
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" plt.savefig(\n",
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" plt.savefig(\n",
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" plt.savefig(\n",
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" plt.savefig(\n",
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" plt.savefig(\n",
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" plt.savefig(\n",
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" plt.savefig(\n",
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" plt.savefig(\n",
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" plt.savefig(\n",
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" plt.savefig(\n",
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" plt.savefig(\n",
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" plt.savefig(\n",
"D:\\Anacond3\\lib\\site-packages\\pandas_profiling\\visualisation\\utils.py:73: UserWarning: Glyph 30452 (\\N{CJK UNIFIED IDEOGRAPH-76F4}) missing from current font.\n",
" plt.savefig(\n",
"D:\\Anacond3\\lib\\site-packages\\pandas_profiling\\visualisation\\utils.py:73: UserWarning: Glyph 32928 (\\N{CJK UNIFIED IDEOGRAPH-80A0}) missing from current font.\n",
" plt.savefig(\n",
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" plt.savefig(\n",
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" plt.savefig(\n",
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" plt.savefig(\n",
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" plt.savefig(\n",
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" plt.savefig(\n",
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" plt.savefig(\n",
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" plt.savefig(\n",
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" plt.savefig(\n",
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" plt.savefig(\n",
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" plt.savefig(\n",
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" plt.savefig(\n",
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" plt.savefig(\n",
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" plt.savefig(\n",
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" plt.savefig(\n",
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" plt.savefig(\n",
"D:\\Anacond3\\lib\\site-packages\\pandas_profiling\\visualisation\\utils.py:73: UserWarning: Glyph 30333 (\\N{CJK UNIFIED IDEOGRAPH-767D}) missing from current font.\n",
" plt.savefig(\n",
"D:\\Anacond3\\lib\\site-packages\\pandas_profiling\\visualisation\\utils.py:73: UserWarning: Glyph 20540 (\\N{CJK UNIFIED IDEOGRAPH-503C}) missing from current font.\n",
" plt.savefig(\n"
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"text/plain": [
"Generate report structure: 0%| | 0/1 [00:00<?, ?it/s]"
]
},
"metadata": {},
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"D:\\Anacond3\\lib\\site-packages\\pandas_profiling\\visualisation\\utils.py:73: UserWarning: Glyph 30452 (\\N{CJK UNIFIED IDEOGRAPH-76F4}) missing from current font.\n",
" plt.savefig(\n",
"D:\\Anacond3\\lib\\site-packages\\pandas_profiling\\visualisation\\utils.py:73: UserWarning: Glyph 32928 (\\N{CJK UNIFIED IDEOGRAPH-80A0}) missing from current font.\n",
" plt.savefig(\n",
"D:\\Anacond3\\lib\\site-packages\\pandas_profiling\\visualisation\\utils.py:73: UserWarning: Glyph 28201 (\\N{CJK UNIFIED IDEOGRAPH-6E29}) missing from current font.\n",
" plt.savefig(\n",
"D:\\Anacond3\\lib\\site-packages\\pandas_profiling\\visualisation\\utils.py:73: UserWarning: Glyph 24230 (\\N{CJK UNIFIED IDEOGRAPH-5EA6}) missing from current font.\n",
" plt.savefig(\n",
"D:\\Anacond3\\lib\\site-packages\\pandas_profiling\\visualisation\\utils.py:73: UserWarning: Glyph 33033 (\\N{CJK UNIFIED IDEOGRAPH-8109}) missing from current font.\n",
" plt.savefig(\n",
"D:\\Anacond3\\lib\\site-packages\\pandas_profiling\\visualisation\\utils.py:73: UserWarning: Glyph 25615 (\\N{CJK UNIFIED IDEOGRAPH-640F}) missing from current font.\n",
" plt.savefig(\n",
"D:\\Anacond3\\lib\\site-packages\\pandas_profiling\\visualisation\\utils.py:73: UserWarning: Glyph 21628 (\\N{CJK UNIFIED IDEOGRAPH-547C}) missing from current font.\n",
" plt.savefig(\n",
"D:\\Anacond3\\lib\\site-packages\\pandas_profiling\\visualisation\\utils.py:73: UserWarning: Glyph 21560 (\\N{CJK UNIFIED IDEOGRAPH-5438}) missing from current font.\n",
" plt.savefig(\n",
"D:\\Anacond3\\lib\\site-packages\\pandas_profiling\\visualisation\\utils.py:73: UserWarning: Glyph 39057 (\\N{CJK UNIFIED IDEOGRAPH-9891}) missing from current font.\n",
" plt.savefig(\n",
"D:\\Anacond3\\lib\\site-packages\\pandas_profiling\\visualisation\\utils.py:73: UserWarning: Glyph 29575 (\\N{CJK UNIFIED IDEOGRAPH-7387}) missing from current font.\n",
" plt.savefig(\n",
"D:\\Anacond3\\lib\\site-packages\\pandas_profiling\\visualisation\\utils.py:73: UserWarning: Glyph 32418 (\\N{CJK UNIFIED IDEOGRAPH-7EA2}) missing from current font.\n",
" plt.savefig(\n",
"D:\\Anacond3\\lib\\site-packages\\pandas_profiling\\visualisation\\utils.py:73: UserWarning: Glyph 32454 (\\N{CJK UNIFIED IDEOGRAPH-7EC6}) missing from current font.\n",
" plt.savefig(\n",
"D:\\Anacond3\\lib\\site-packages\\pandas_profiling\\visualisation\\utils.py:73: UserWarning: Glyph 32990 (\\N{CJK UNIFIED IDEOGRAPH-80DE}) missing from current font.\n",
" plt.savefig(\n",
"D:\\Anacond3\\lib\\site-packages\\pandas_profiling\\visualisation\\utils.py:73: UserWarning: Glyph 20307 (\\N{CJK UNIFIED IDEOGRAPH-4F53}) missing from current font.\n",
" plt.savefig(\n",
"D:\\Anacond3\\lib\\site-packages\\pandas_profiling\\visualisation\\utils.py:73: UserWarning: Glyph 31215 (\\N{CJK UNIFIED IDEOGRAPH-79EF}) missing from current font.\n",
" plt.savefig(\n",
"D:\\Anacond3\\lib\\site-packages\\pandas_profiling\\visualisation\\utils.py:73: UserWarning: Glyph 24635 (\\N{CJK UNIFIED IDEOGRAPH-603B}) missing from current font.\n",
" plt.savefig(\n",
"D:\\Anacond3\\lib\\site-packages\\pandas_profiling\\visualisation\\utils.py:73: UserWarning: Glyph 34507 (\\N{CJK UNIFIED IDEOGRAPH-86CB}) missing from current font.\n",
" plt.savefig(\n",
"D:\\Anacond3\\lib\\site-packages\\pandas_profiling\\visualisation\\utils.py:73: UserWarning: Glyph 30333 (\\N{CJK UNIFIED IDEOGRAPH-767D}) missing from current font.\n",
" plt.savefig(\n",
"D:\\Anacond3\\lib\\site-packages\\pandas_profiling\\visualisation\\utils.py:73: UserWarning: Glyph 20540 (\\N{CJK UNIFIED IDEOGRAPH-503C}) missing from current font.\n",
" plt.savefig(\n"
]
},
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"Render HTML: 0%| | 0/1 [00:00<?, ?it/s]"
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"text/plain": [
"Export report to file: 0%| | 0/1 [00:00<?, ?it/s]"
]
},
"metadata": {},
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{
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\n",
"text/plain": [
"<Figure size 1000x500 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 1000x500 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 1000x500 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 1000x500 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 1000x500 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 1000x500 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 1000x500 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 1000x500 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 1000x500 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 1000x500 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"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",
"%matplotlib inline\n",
"\n",
"# 设置中文字体显示正常\n",
"plt.rcParams['font.sans-serif'] = ['SimHei']\n",
"plt.rcParams['axes.unicode_minus'] = False\n",
"\n",
"# 读取数据集\n",
"df = pd.read_csv(\"data_horse.csv\", encoding=\"gbk\")\n",
"df.head()\n",
"\n",
"# 使用Pandas Profiling生成数据报告\n",
"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\")\n",
"\n",
"# 可视化列的缺失值、唯一值、类型\n",
"df.corr()\n",
"\n",
"# 可视化变量y与其他的相关性曲线\n",
"df.corr()['y'].sort_values(ascending=False)\n",
"\n",
"# 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()\n",
"\n",
"# 检查缺失值\n",
"df.isnull().sum()\n",
"\n",
"# 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",
"\n",
"# 将除了y(最后一列)以外的变量中的0值替换为np.nan\n",
"df[df.columns[:-1]] = df[df.columns[:-1]].replace(0, np.nan)\n",
"df.isnull().sum()\n",
"\n",
"# 用众数填充缺失值\n",
"# 观察发现直肠温度列是正态分布的，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()\n",
"\n",
"# 删除缺失值\n",
"df = df.dropna()\n",
"\n",
"# 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": 2,
"id": "c2c25898-1287-4079-982d-8617bed29fd6",
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 1000x500 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"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",
"from sklearn.preprocessing import StandardScaler\n",
"import time\n",
"\n",
"# 划分训练集和测试集\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",
"\n",
"# 标准化数据\n",
"scaler = StandardScaler()\n",
"X_train = scaler.fit_transform(X_train)\n",
"X_test = scaler.transform(X_test)\n",
"\n",
"model_result = {}\n",
"\n",
"models = [RandomForestClassifier(), BaggingClassifier(), AdaBoostClassifier(), GaussianNB(),\n",
" LogisticRegression(), DecisionTreeClassifier(), SVC(), KNeighborsClassifier()]\n",
"\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",
"\n",
"# 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()"
]
}
],
"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": "3.9.13"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
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