763 lines
24 KiB
Plaintext
763 lines
24 KiB
Plaintext
{
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"cells": [
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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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"Загрузка данных в DataFrame"
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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": 1,
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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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"<class 'pandas.core.frame.DataFrame'>\n",
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"Index: 19237 entries, 45654403 to 45813273\n",
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"Data columns (total 17 columns):\n",
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" # Column Non-Null Count Dtype \n",
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"--- ------ -------------- ----- \n",
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" 0 Price 19237 non-null int64 \n",
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" 1 Levy 19237 non-null object \n",
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" 2 Manufacturer 19237 non-null object \n",
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" 3 Model 19237 non-null object \n",
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" 4 Prodyear 19237 non-null int64 \n",
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" 5 Category 19237 non-null object \n",
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" 6 Leatherinterior 19237 non-null object \n",
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" 7 Fueltype 19237 non-null object \n",
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" 8 Engine volume 19237 non-null object \n",
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" 9 Mileage 19237 non-null object \n",
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" 10 Cylinders 19237 non-null float64\n",
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" 11 Gear box type 19237 non-null object \n",
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" 12 Drive wheels 19237 non-null object \n",
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" 13 Doors 19237 non-null object \n",
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" 14 Wheel 19237 non-null object \n",
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" 15 Color 19237 non-null object \n",
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" 16 Airbags 19237 non-null int64 \n",
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"dtypes: float64(1), int64(3), object(13)\n",
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"memory usage: 2.6+ MB\n",
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"(19237, 17)\n"
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]
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},
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{
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"data": {
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"text/html": [
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"<div>\n",
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"<style scoped>\n",
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" .dataframe tbody tr th:only-of-type {\n",
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" vertical-align: middle;\n",
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" }\n",
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"\n",
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" .dataframe tbody tr th {\n",
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" vertical-align: top;\n",
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" }\n",
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"\n",
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" .dataframe thead th {\n",
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" text-align: right;\n",
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" }\n",
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"</style>\n",
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"<table border=\"1\" class=\"dataframe\">\n",
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" <thead>\n",
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" <tr style=\"text-align: right;\">\n",
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" <th></th>\n",
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" <th>Price</th>\n",
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" <th>Levy</th>\n",
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" <th>Manufacturer</th>\n",
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" <th>Model</th>\n",
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" <th>Prodyear</th>\n",
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" <th>Category</th>\n",
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" <th>Leatherinterior</th>\n",
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" <th>Fueltype</th>\n",
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" <th>Engine volume</th>\n",
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" <th>Mileage</th>\n",
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" <th>Cylinders</th>\n",
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" <th>Gear box type</th>\n",
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" <th>Drive wheels</th>\n",
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" <th>Doors</th>\n",
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" <th>Wheel</th>\n",
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" <th>Color</th>\n",
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" <th>Airbags</th>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>ID</th>\n",
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" <th></th>\n",
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" <th></th>\n",
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" <th></th>\n",
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" <th></th>\n",
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" <th></th>\n",
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" <th></th>\n",
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" <th></th>\n",
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" <th></th>\n",
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" <th></th>\n",
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" <th></th>\n",
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" <th></th>\n",
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" <th></th>\n",
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" <th></th>\n",
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" <th></th>\n",
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" <th></th>\n",
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" <th></th>\n",
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" <th></th>\n",
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" </tr>\n",
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" </thead>\n",
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" <tbody>\n",
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" <tr>\n",
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" <th>45654403</th>\n",
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" <td>13328</td>\n",
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" <td>1399</td>\n",
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" <td>LEXUS</td>\n",
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" <td>RX 450</td>\n",
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" <td>2010</td>\n",
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" <td>Jeep</td>\n",
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" <td>1</td>\n",
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" <td>Hybrid</td>\n",
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" <td>3.5</td>\n",
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" <td>186005 km</td>\n",
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" <td>6.0</td>\n",
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" <td>Automatic</td>\n",
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" <td>4x4</td>\n",
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" <td>04-May</td>\n",
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" <td>Left wheel</td>\n",
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" <td>Silver</td>\n",
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" <td>12</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>44731507</th>\n",
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" <td>16621</td>\n",
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" <td>1018</td>\n",
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" <td>CHEVROLET</td>\n",
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" <td>Equinox</td>\n",
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" <td>2011</td>\n",
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" <td>Jeep</td>\n",
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" <td>0</td>\n",
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" <td>Petrol</td>\n",
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" <td>3</td>\n",
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" <td>192000 km</td>\n",
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" <td>6.0</td>\n",
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" <td>Tiptronic</td>\n",
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" <td>4x4</td>\n",
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" <td>04-May</td>\n",
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" <td>Left wheel</td>\n",
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" <td>Black</td>\n",
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" <td>8</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>45774419</th>\n",
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" <td>8467</td>\n",
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" <td>-</td>\n",
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" <td>HONDA</td>\n",
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" <td>FIT</td>\n",
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" <td>2006</td>\n",
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" <td>Hatchback</td>\n",
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" <td>0</td>\n",
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" <td>Petrol</td>\n",
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" <td>1.3</td>\n",
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" <td>200000 km</td>\n",
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" <td>4.0</td>\n",
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" <td>Variator</td>\n",
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" <td>Front</td>\n",
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" <td>04-May</td>\n",
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" <td>Right-hand drive</td>\n",
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" <td>Black</td>\n",
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" <td>2</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>45769185</th>\n",
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" <td>3607</td>\n",
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" <td>862</td>\n",
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" <td>FORD</td>\n",
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" <td>Escape</td>\n",
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" <td>2011</td>\n",
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" <td>Jeep</td>\n",
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" <td>1</td>\n",
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" <td>Hybrid</td>\n",
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" <td>2.5</td>\n",
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" <td>168966 km</td>\n",
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" <td>4.0</td>\n",
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" <td>Automatic</td>\n",
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" <td>4x4</td>\n",
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" <td>04-May</td>\n",
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" <td>Left wheel</td>\n",
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" <td>White</td>\n",
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" <td>0</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>45809263</th>\n",
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" <td>11726</td>\n",
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" <td>446</td>\n",
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" <td>HONDA</td>\n",
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" <td>FIT</td>\n",
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" <td>2014</td>\n",
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" <td>Hatchback</td>\n",
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" <td>1</td>\n",
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" <td>Petrol</td>\n",
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" <td>1.3</td>\n",
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" <td>91901 km</td>\n",
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" <td>4.0</td>\n",
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" <td>Automatic</td>\n",
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" <td>Front</td>\n",
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" <td>04-May</td>\n",
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" <td>Left wheel</td>\n",
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" <td>Silver</td>\n",
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" <td>4</td>\n",
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" </tr>\n",
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" </tbody>\n",
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"</table>\n",
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"</div>"
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],
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"text/plain": [
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" Price Levy Manufacturer Model Prodyear Category \\\n",
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"ID \n",
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"45654403 13328 1399 LEXUS RX 450 2010 Jeep \n",
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"44731507 16621 1018 CHEVROLET Equinox 2011 Jeep \n",
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"45774419 8467 - HONDA FIT 2006 Hatchback \n",
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"45769185 3607 862 FORD Escape 2011 Jeep \n",
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"45809263 11726 446 HONDA FIT 2014 Hatchback \n",
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"\n",
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" Leatherinterior Fueltype Engine volume Mileage Cylinders \\\n",
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"ID \n",
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"45654403 1 Hybrid 3.5 186005 km 6.0 \n",
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"44731507 0 Petrol 3 192000 km 6.0 \n",
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"45774419 0 Petrol 1.3 200000 km 4.0 \n",
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"45769185 1 Hybrid 2.5 168966 km 4.0 \n",
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"45809263 1 Petrol 1.3 91901 km 4.0 \n",
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"\n",
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" Gear box type Drive wheels Doors Wheel Color Airbags \n",
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"ID \n",
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"45654403 Automatic 4x4 04-May Left wheel Silver 12 \n",
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"44731507 Tiptronic 4x4 04-May Left wheel Black 8 \n",
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"45774419 Variator Front 04-May Right-hand drive Black 2 \n",
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"45769185 Automatic 4x4 04-May Left wheel White 0 \n",
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"45809263 Automatic Front 04-May Left wheel Silver 4 "
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]
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},
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"execution_count": 1,
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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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"import pandas as pd\n",
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"\n",
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"df = pd.read_csv(\"data/car_price_prediction.csv\", index_col=\"ID\")\n",
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"\n",
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"df.info()\n",
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"\n",
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"df[\"Leatherinterior\"] = df[\"Leatherinterior\"].apply(\n",
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" lambda x: 1 if x == 'Yes' else 0,\n",
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")\n",
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"\n",
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"print(df.shape)\n",
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"\n",
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"df.head()"
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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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"Получение сведений о пропущенных данных"
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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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"Типы пропущенных данных:\n",
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"- None - представление пустых данных в Python\n",
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"- NaN - представление пустых данных в Pandas\n",
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"- '' - пустая строка"
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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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"Price 0\n",
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"Levy 0\n",
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"Manufacturer 0\n",
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"Model 0\n",
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"Prodyear 0\n",
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"Category 0\n",
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"Leatherinterior 0\n",
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"Fueltype 0\n",
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"Engine volume 0\n",
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"Mileage 0\n",
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"Cylinders 0\n",
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"Gear box type 0\n",
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"Drive wheels 0\n",
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"Doors 0\n",
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"Wheel 0\n",
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"Color 0\n",
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"Airbags 0\n",
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"dtype: int64\n",
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"\n",
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"Price False\n",
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"Levy False\n",
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"Manufacturer False\n",
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"Model False\n",
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"Prodyear False\n",
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"Category False\n",
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"Leatherinterior False\n",
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"Fueltype False\n",
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"Engine volume False\n",
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"Mileage False\n",
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"Cylinders False\n",
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"Gear box type False\n",
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"Drive wheels False\n",
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"Doors False\n",
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"Wheel False\n",
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"Color False\n",
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"Airbags False\n",
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"dtype: bool\n",
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"\n"
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]
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}
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],
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"source": [
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"# Количество пустых значений признаков\n",
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"print(df.isnull().sum())\n",
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"\n",
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"print()\n",
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"\n",
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"# Есть ли пустые значения признаков\n",
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"print(df.isnull().any())\n",
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"\n",
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"print()\n",
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"\n",
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"# Процент пустых значений признаков\n",
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"for i in df.columns:\n",
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" null_rate = df[i].isnull().sum() / len(df) * 100\n",
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" if null_rate > 0:\n",
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" print(f\"{i} процент пустых значений: %{null_rate:.2f}\")"
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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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"Заполнение пропущенных данных\n",
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"\n",
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"https://pythonmldaily.com/posts/pandas-dataframes-search-drop-empty-values\n",
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"\n",
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"https://scales.arabpsychology.com/stats/how-to-fill-nan-values-with-median-in-pandas/"
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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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"source": [
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"# fillna_df = df.fillna(0)\n",
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"\n",
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"# print(fillna_df.shape)\n",
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"\n",
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"# print(fillna_df.isnull().any())\n",
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"\n",
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"# # Замена пустых данных на 0\n",
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"# df[\"AgeFillNA\"] = df[\"Age\"].fillna(0)\n",
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"\n",
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"# # Замена пустых данных на медиану\n",
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"# df[\"AgeFillMedian\"] = df[\"Age\"].fillna(df[\"Age\"].median())\n",
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"\n",
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"# df.tail()"
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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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"source": [
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"# df[\"AgeCopy\"] = df[\"Age\"]\n",
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"\n",
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"# # Замена данных сразу в DataFrame без копирования\n",
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"# df.fillna({\"AgeCopy\": 0}, inplace=True)\n",
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"\n",
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"# df.tail()"
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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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||
"Удаление наблюдений с пропусками"
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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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"source": [
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"# dropna_df = df.dropna()\n",
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"\n",
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"# print(dropna_df.shape)\n",
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"\n",
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"# print(fillna_df.isnull().any())"
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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": [
|
||
"Создание выборок данных\n",
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"\n",
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||
"Библиотека scikit-learn\n",
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"\n",
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||
"https://scikit-learn.org/stable/index.html"
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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": [
|
||
"<img src=\"assets/lec2-split.png\" width=\"600\" style=\"background-color: white\">"
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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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||
"source": [
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"# Функция для создания выборок\n",
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"from sklearn.model_selection import train_test_split\n",
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"\n",
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"\n",
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"def split_stratified_into_train_val_test(\n",
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" df_input,\n",
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" stratify_colname=\"y\",\n",
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" frac_train=0.6,\n",
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" frac_val=0.15,\n",
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" frac_test=0.25,\n",
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" random_state=None,\n",
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"):\n",
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" \"\"\"\n",
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" Splits a Pandas dataframe into three subsets (train, val, and test)\n",
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" following fractional ratios provided by the user, where each subset is\n",
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" stratified by the values in a specific column (that is, each subset has\n",
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" the same relative frequency of the values in the column). It performs this\n",
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" splitting by running train_test_split() twice.\n",
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"\n",
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" Parameters\n",
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" ----------\n",
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" df_input : Pandas dataframe\n",
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" Input dataframe to be split.\n",
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" stratify_colname : str\n",
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" The name of the column that will be used for stratification. Usually\n",
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" this column would be for the label.\n",
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" frac_train : float\n",
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" frac_val : float\n",
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" frac_test : float\n",
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" The ratios with which the dataframe will be split into train, val, and\n",
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" test data. The values should be expressed as float fractions and should\n",
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" sum to 1.0.\n",
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" random_state : int, None, or RandomStateInstance\n",
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" Value to be passed to train_test_split().\n",
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"\n",
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" Returns\n",
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" -------\n",
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" df_train, df_val, df_test :\n",
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" Dataframes containing the three splits.\n",
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" \"\"\"\n",
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"\n",
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" if frac_train + frac_val + frac_test != 1.0:\n",
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" raise ValueError(\n",
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" \"fractions %f, %f, %f do not add up to 1.0\"\n",
|
||
" % (frac_train, frac_val, frac_test)\n",
|
||
" )\n",
|
||
"\n",
|
||
" if stratify_colname not in df_input.columns:\n",
|
||
" raise ValueError(\"%s is not a column in the dataframe\" % (stratify_colname))\n",
|
||
"\n",
|
||
" X = df_input # Contains all columns.\n",
|
||
" y = df_input[\n",
|
||
" [stratify_colname]\n",
|
||
" ] # Dataframe of just the column on which to stratify.\n",
|
||
"\n",
|
||
" # Split original dataframe into train and temp dataframes.\n",
|
||
" df_train, df_temp, y_train, y_temp = train_test_split(\n",
|
||
" X, y, stratify=y, test_size=(1.0 - frac_train), random_state=random_state\n",
|
||
" )\n",
|
||
"\n",
|
||
" # Split the temp dataframe into val and test dataframes.\n",
|
||
" relative_frac_test = frac_test / (frac_val + frac_test)\n",
|
||
" df_val, df_test, y_val, y_test = train_test_split(\n",
|
||
" df_temp,\n",
|
||
" y_temp,\n",
|
||
" stratify=y_temp,\n",
|
||
" test_size=relative_frac_test,\n",
|
||
" random_state=random_state,\n",
|
||
" )\n",
|
||
"\n",
|
||
" assert len(df_input) == len(df_train) + len(df_val) + len(df_test)\n",
|
||
"\n",
|
||
" return df_train, df_val, df_test"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 7,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Leatherinterior\n",
|
||
"1 13954\n",
|
||
"0 5283\n",
|
||
"Name: count, dtype: int64\n",
|
||
"Обучающая выборка: (11542, 3)\n",
|
||
"Leatherinterior\n",
|
||
"1 8372\n",
|
||
"0 3170\n",
|
||
"Name: count, dtype: int64\n",
|
||
"Контрольная выборка: (3847, 3)\n",
|
||
"Leatherinterior\n",
|
||
"1 2791\n",
|
||
"0 1056\n",
|
||
"Name: count, dtype: int64\n",
|
||
"Тестовая выборка: (3848, 3)\n",
|
||
"Leatherinterior\n",
|
||
"1 2791\n",
|
||
"0 1057\n",
|
||
"Name: count, dtype: int64\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"# Вывод распределения количества наблюдений по меткам (классам)\n",
|
||
"print(df.Leatherinterior.value_counts())\n",
|
||
"\n",
|
||
"data = df[[\"Leatherinterior\", \"Price\", \"Airbags\"]].copy()\n",
|
||
"\n",
|
||
"df_train, df_val, df_test = split_stratified_into_train_val_test(\n",
|
||
" data,\n",
|
||
" stratify_colname=\"Leatherinterior\",\n",
|
||
" frac_train=0.60,\n",
|
||
" frac_val=0.20,\n",
|
||
" frac_test=0.20,\n",
|
||
")\n",
|
||
"\n",
|
||
"print(\"Обучающая выборка: \", df_train.shape)\n",
|
||
"print(df_train.Leatherinterior.value_counts())\n",
|
||
"\n",
|
||
"print(\"Контрольная выборка: \", df_val.shape)\n",
|
||
"print(df_val.Leatherinterior.value_counts())\n",
|
||
"\n",
|
||
"print(\"Тестовая выборка: \", df_test.shape)\n",
|
||
"print(df_test.Leatherinterior.value_counts())"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"Выборка с избытком (oversampling)\n",
|
||
"\n",
|
||
"https://www.blog.trainindata.com/oversampling-techniques-for-imbalanced-data/\n",
|
||
"\n",
|
||
"https://datacrayon.com/machine-learning/class-imbalance-and-oversampling/\n",
|
||
"\n",
|
||
"Выборка с недостатком (undersampling)\n",
|
||
"\n",
|
||
"https://machinelearningmastery.com/random-oversampling-and-undersampling-for-imbalanced-classification/\n",
|
||
"\n",
|
||
"Библиотека imbalanced-learn\n",
|
||
"\n",
|
||
"https://imbalanced-learn.org/stable/"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 8,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Обучающая выборка: (11542, 3)\n",
|
||
"Leatherinterior\n",
|
||
"1 8372\n",
|
||
"0 3170\n",
|
||
"Name: count, dtype: int64\n",
|
||
"Обучающая выборка после oversampling: (16585, 3)\n",
|
||
"Leatherinterior\n",
|
||
"1 8372\n",
|
||
"0 8213\n",
|
||
"Name: count, dtype: int64\n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
||
"text/html": [
|
||
"<div>\n",
|
||
"<style scoped>\n",
|
||
" .dataframe tbody tr th:only-of-type {\n",
|
||
" vertical-align: middle;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe tbody tr th {\n",
|
||
" vertical-align: top;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe thead th {\n",
|
||
" text-align: right;\n",
|
||
" }\n",
|
||
"</style>\n",
|
||
"<table border=\"1\" class=\"dataframe\">\n",
|
||
" <thead>\n",
|
||
" <tr style=\"text-align: right;\">\n",
|
||
" <th></th>\n",
|
||
" <th>Leatherinterior</th>\n",
|
||
" <th>Price</th>\n",
|
||
" <th>Airbags</th>\n",
|
||
" </tr>\n",
|
||
" </thead>\n",
|
||
" <tbody>\n",
|
||
" <tr>\n",
|
||
" <th>0</th>\n",
|
||
" <td>1</td>\n",
|
||
" <td>12231</td>\n",
|
||
" <td>8</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>1</th>\n",
|
||
" <td>1</td>\n",
|
||
" <td>18817</td>\n",
|
||
" <td>10</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>2</th>\n",
|
||
" <td>1</td>\n",
|
||
" <td>15053</td>\n",
|
||
" <td>4</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>3</th>\n",
|
||
" <td>1</td>\n",
|
||
" <td>470</td>\n",
|
||
" <td>0</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>4</th>\n",
|
||
" <td>1</td>\n",
|
||
" <td>19914</td>\n",
|
||
" <td>6</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>...</th>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>16580</th>\n",
|
||
" <td>0</td>\n",
|
||
" <td>13015</td>\n",
|
||
" <td>4</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>16581</th>\n",
|
||
" <td>0</td>\n",
|
||
" <td>8799</td>\n",
|
||
" <td>2</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>16582</th>\n",
|
||
" <td>0</td>\n",
|
||
" <td>2057</td>\n",
|
||
" <td>2</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>16583</th>\n",
|
||
" <td>0</td>\n",
|
||
" <td>2000</td>\n",
|
||
" <td>2</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>16584</th>\n",
|
||
" <td>0</td>\n",
|
||
" <td>1910</td>\n",
|
||
" <td>1</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"<p>16585 rows × 3 columns</p>\n",
|
||
"</div>"
|
||
],
|
||
"text/plain": [
|
||
" Leatherinterior Price Airbags\n",
|
||
"0 1 12231 8\n",
|
||
"1 1 18817 10\n",
|
||
"2 1 15053 4\n",
|
||
"3 1 470 0\n",
|
||
"4 1 19914 6\n",
|
||
"... ... ... ...\n",
|
||
"16580 0 13015 4\n",
|
||
"16581 0 8799 2\n",
|
||
"16582 0 2057 2\n",
|
||
"16583 0 2000 2\n",
|
||
"16584 0 1910 1\n",
|
||
"\n",
|
||
"[16585 rows x 3 columns]"
|
||
]
|
||
},
|
||
"execution_count": 8,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"from imblearn.over_sampling import ADASYN\n",
|
||
"\n",
|
||
"ada = ADASYN()\n",
|
||
"\n",
|
||
"print(\"Обучающая выборка: \", df_train.shape)\n",
|
||
"print(df_train.Leatherinterior.value_counts())\n",
|
||
"\n",
|
||
"X_resampled, y_resampled = ada.fit_resample(df_train, df_train[\"Leatherinterior\"]) # type: ignore\n",
|
||
"df_train_adasyn = pd.DataFrame(X_resampled)\n",
|
||
"\n",
|
||
"print(\"Обучающая выборка после oversampling: \", df_train_adasyn.shape)\n",
|
||
"print(df_train_adasyn.Leatherinterior.value_counts())\n",
|
||
"\n",
|
||
"df_train_adasyn"
|
||
]
|
||
}
|
||
],
|
||
"metadata": {
|
||
"kernelspec": {
|
||
"display_name": ".venv",
|
||
"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": "3.12.4"
|
||
}
|
||
},
|
||
"nbformat": 4,
|
||
"nbformat_minor": 2
|
||
}
|