rent_price_prediction.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 23699 entries, 0 to 23698\n",
      "Data columns (total 22 columns):\n",
      " #   Column                Non-Null Count  Dtype  \n",
      "---  ------                --------------  -----  \n",
      " 0   ,total_images         23699 non-null  object \n",
      " 1   last_price            23699 non-null  float64\n",
      " 2   total_area            23699 non-null  float64\n",
      " 3   first_day_exposition  23699 non-null  object \n",
      " 4   rooms                 23699 non-null  int64  \n",
      " 5   ceiling_height        14504 non-null  float64\n",
      " 6   floors_total          23613 non-null  float64\n",
      " 7   living_area           21796 non-null  float64\n",
      " 8   floor                 23699 non-null  int64  \n",
      " 9   is_apartment          2775 non-null   object \n",
      " 10  studio                23699 non-null  bool   \n",
      " 11  open_plan             23699 non-null  bool   \n",
      " 12  kitchen_area          21421 non-null  float64\n",
      " 13  balcony               12180 non-null  float64\n",
      " 14  locality_name         23650 non-null  object \n",
      " 15  airports_nearest      18157 non-null  float64\n",
      " 16  cityCenters_nearest   18180 non-null  float64\n",
      " 17  parks_around3000      18181 non-null  float64\n",
      " 18  parks_nearest         8079 non-null   float64\n",
      " 19  ponds_around3000      18181 non-null  float64\n",
      " 20  ponds_nearest         9110 non-null   float64\n",
      " 21  days_exposition       20518 non-null  float64\n",
      "dtypes: bool(2), float64(14), int64(2), object(4)\n",
      "memory usage: 3.7+ MB\n",
      "None\n"
     ]
    },
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>,total_images</th>\n",
       "      <th>last_price</th>\n",
       "      <th>total_area</th>\n",
       "      <th>first_day_exposition</th>\n",
       "      <th>rooms</th>\n",
       "      <th>ceiling_height</th>\n",
       "      <th>floors_total</th>\n",
       "      <th>living_area</th>\n",
       "      <th>floor</th>\n",
       "      <th>is_apartment</th>\n",
       "      <th>studio</th>\n",
       "      <th>open_plan</th>\n",
       "      <th>kitchen_area</th>\n",
       "      <th>balcony</th>\n",
       "      <th>locality_name</th>\n",
       "      <th>airports_nearest</th>\n",
       "      <th>cityCenters_nearest</th>\n",
       "      <th>parks_around3000</th>\n",
       "      <th>parks_nearest</th>\n",
       "      <th>ponds_around3000</th>\n",
       "      <th>ponds_nearest</th>\n",
       "      <th>days_exposition</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>144</th>\n",
       "      <td>144,1</td>\n",
       "      <td>2450000.0</td>\n",
       "      <td>27.00</td>\n",
       "      <td>2017-03-30T00:00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>24.0</td>\n",
       "      <td>15.50</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>посёлок Мурино</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>181.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>349</th>\n",
       "      <td>349,4</td>\n",
       "      <td>2320000.0</td>\n",
       "      <td>25.00</td>\n",
       "      <td>2017-09-27T00:00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14.0</td>\n",
       "      <td>17.00</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>деревня Кудрово</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>45.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>440</th>\n",
       "      <td>440,8</td>\n",
       "      <td>2480000.0</td>\n",
       "      <td>27.11</td>\n",
       "      <td>2018-03-12T00:00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17.0</td>\n",
       "      <td>24.75</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Санкт-Петербург</td>\n",
       "      <td>38171.0</td>\n",
       "      <td>15015.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>982.0</td>\n",
       "      <td>64.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>508</th>\n",
       "      <td>508,0</td>\n",
       "      <td>3375000.0</td>\n",
       "      <td>34.40</td>\n",
       "      <td>2017-03-28T00:00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>26.0</td>\n",
       "      <td>24.30</td>\n",
       "      <td>19</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>посёлок Парголово</td>\n",
       "      <td>53115.0</td>\n",
       "      <td>19292.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>593.0</td>\n",
       "      <td>430.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>608</th>\n",
       "      <td>608,2</td>\n",
       "      <td>1850000.0</td>\n",
       "      <td>25.00</td>\n",
       "      <td>2019-02-20T00:00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>посёлок Шушары</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>29.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "    ,total_images  last_price  total_area first_day_exposition  rooms  \\\n",
       "144         144,1   2450000.0       27.00  2017-03-30T00:00:00      0   \n",
       "349         349,4   2320000.0       25.00  2017-09-27T00:00:00      0   \n",
       "440         440,8   2480000.0       27.11  2018-03-12T00:00:00      0   \n",
       "508         508,0   3375000.0       34.40  2017-03-28T00:00:00      0   \n",
       "608         608,2   1850000.0       25.00  2019-02-20T00:00:00      0   \n",
       "\n",
       "     ceiling_height  floors_total  living_area  floor is_apartment  studio  \\\n",
       "144             NaN          24.0        15.50      2          NaN    True   \n",
       "349             NaN          14.0        17.00      1          NaN   False   \n",
       "440             NaN          17.0        24.75      4          NaN    True   \n",
       "508             NaN          26.0        24.30     19          NaN   False   \n",
       "608             NaN          10.0          NaN      7          NaN    True   \n",
       "\n",
       "     open_plan  kitchen_area  balcony      locality_name  airports_nearest  \\\n",
       "144      False           NaN      2.0     посёлок Мурино               NaN   \n",
       "349       True           NaN      0.0    деревня Кудрово               NaN   \n",
       "440      False           NaN      2.0    Санкт-Петербург           38171.0   \n",
       "508       True           NaN      1.0  посёлок Парголово           53115.0   \n",
       "608      False           NaN      NaN     посёлок Шушары               NaN   \n",
       "\n",
       "     cityCenters_nearest  parks_around3000  parks_nearest  ponds_around3000  \\\n",
       "144                  NaN               NaN            NaN               NaN   \n",
       "349                  NaN               NaN            NaN               NaN   \n",
       "440              15015.0               0.0            NaN               2.0   \n",
       "508              19292.0               0.0            NaN               1.0   \n",
       "608                  NaN               NaN            NaN               NaN   \n",
       "\n",
       "     ponds_nearest  days_exposition  \n",
       "144            NaN            181.0  \n",
       "349            NaN             45.0  \n",
       "440          982.0             64.0  \n",
       "508          593.0            430.0  \n",
       "608            NaN             29.0  "
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "# для отображения большего количества колонок \n",
    "pd.set_option('display.max_columns', 30)\n",
    "# прячем предупреждение SettingWithCopyWarning\n",
    "pd.options.mode.chained_assignment = None  \n",
    "\n",
    "df = pd.read_csv(\"./real_estate_data.csv\", delimiter  = \"\\t\")\n",
    "print(df.info())\n",
    "df[df[\"rooms\"] == 0].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Вывод"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### В полученной выгрузке видим частичное отсутствие данных в некоторых колонках и несовпадение типов между реальным и предполагаемым по смыслу колонки. Так например в колонке first_day_exposition при парсинге данных неправильно распознался тип колонки, а отсутствующие данные в колонке balcony можно заполнить по смыслу.  \n",
    "#### В выгрузке множество колонок которые можно обработать и добавить в них данных, например заполнить ponds_nearest на основе ponds_around3000, однако эти колонки не участвуют в финальных расчетах, поэтому их можно пропустить."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Шаг 2. Предобработка данных"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 23699 entries, 0 to 23698\n",
      "Data columns (total 22 columns):\n",
      "total_images            23699 non-null int64\n",
      "last_price              23699 non-null int64\n",
      "total_area              23699 non-null float64\n",
      "first_day_exposition    23699 non-null datetime64[ns]\n",
      "rooms                   23699 non-null int64\n",
      "ceiling_height          14504 non-null float64\n",
      "floors_total            23699 non-null int64\n",
      "living_area             21796 non-null float64\n",
      "floor                   23699 non-null int64\n",
      "is_apartment            23699 non-null bool\n",
      "studio                  23699 non-null bool\n",
      "open_plan               23699 non-null bool\n",
      "kitchen_area            21421 non-null float64\n",
      "balcony                 23699 non-null float64\n",
      "locality_name           23650 non-null object\n",
      "airports_nearest        18157 non-null float64\n",
      "cityCenters_nearest     18180 non-null float64\n",
      "parks_around3000        18181 non-null float64\n",
      "parks_nearest           8079 non-null float64\n",
      "ponds_around3000        18181 non-null float64\n",
      "ponds_nearest           9110 non-null float64\n",
      "days_exposition         20518 non-null float64\n",
      "dtypes: bool(3), datetime64[ns](1), float64(12), int64(5), object(1)\n",
      "memory usage: 3.5+ MB\n",
      "None\n"
     ]
    },
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>total_images</th>\n",
       "      <th>last_price</th>\n",
       "      <th>total_area</th>\n",
       "      <th>first_day_exposition</th>\n",
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       "      <th>floors_total</th>\n",
       "      <th>living_area</th>\n",
       "      <th>floor</th>\n",
       "      <th>is_apartment</th>\n",
       "      <th>studio</th>\n",
       "      <th>open_plan</th>\n",
       "      <th>kitchen_area</th>\n",
       "      <th>balcony</th>\n",
       "      <th>locality_name</th>\n",
       "      <th>airports_nearest</th>\n",
       "      <th>cityCenters_nearest</th>\n",
       "      <th>parks_around3000</th>\n",
       "      <th>parks_nearest</th>\n",
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       "      <th>0</th>\n",
       "      <td>20</td>\n",
       "      <td>13000000</td>\n",
       "      <td>108.0</td>\n",
       "      <td>2019-03-07</td>\n",
       "      <td>3</td>\n",
       "      <td>2.70</td>\n",
       "      <td>16</td>\n",
       "      <td>51.0</td>\n",
       "      <td>8</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Санкт-Петербург</td>\n",
       "      <td>18863.0</td>\n",
       "      <td>16028.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>482.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>755.0</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7</td>\n",
       "      <td>3350000</td>\n",
       "      <td>40.4</td>\n",
       "      <td>2018-12-04</td>\n",
       "      <td>1</td>\n",
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       "      <td>11</td>\n",
       "      <td>18.6</td>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>11.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>посёлок Шушары</td>\n",
       "      <td>12817.0</td>\n",
       "      <td>18603.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>81.0</td>\n",
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       "      <th>2</th>\n",
       "      <td>10</td>\n",
       "      <td>5196000</td>\n",
       "      <td>56.0</td>\n",
       "      <td>2015-08-20</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "      <td>34.3</td>\n",
       "      <td>4</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>8.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Санкт-Петербург</td>\n",
       "      <td>21741.0</td>\n",
       "      <td>13933.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>574.0</td>\n",
       "      <td>558.0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>64900000</td>\n",
       "      <td>159.0</td>\n",
       "      <td>2015-07-24</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14</td>\n",
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       "      <td>False</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>Санкт-Петербург</td>\n",
       "      <td>28098.0</td>\n",
       "      <td>6800.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>84.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>234.0</td>\n",
       "      <td>424.0</td>\n",
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       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>10000000</td>\n",
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       "      <td>2018-06-19</td>\n",
       "      <td>2</td>\n",
       "      <td>3.03</td>\n",
       "      <td>14</td>\n",
       "      <td>32.0</td>\n",
       "      <td>13</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>41.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Санкт-Петербург</td>\n",
       "      <td>31856.0</td>\n",
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       "      <td>112.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>121.0</td>\n",
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       "</div>"
      ],
      "text/plain": [
       "   total_images  last_price  total_area first_day_exposition  rooms  \\\n",
       "0            20    13000000       108.0           2019-03-07      3   \n",
       "1             7     3350000        40.4           2018-12-04      1   \n",
       "2            10     5196000        56.0           2015-08-20      2   \n",
       "3             0    64900000       159.0           2015-07-24      3   \n",
       "4             2    10000000       100.0           2018-06-19      2   \n",
       "\n",
       "   ceiling_height  floors_total  living_area  floor  is_apartment  studio  \\\n",
       "0            2.70            16         51.0      8         False   False   \n",
       "1             NaN            11         18.6      1         False   False   \n",
       "2             NaN             5         34.3      4         False   False   \n",
       "3             NaN            14          NaN      9         False   False   \n",
       "4            3.03            14         32.0     13         False   False   \n",
       "\n",
       "   open_plan  kitchen_area  balcony    locality_name  airports_nearest  \\\n",
       "0      False          25.0      0.0  Санкт-Петербург           18863.0   \n",
       "1      False          11.0      2.0   посёлок Шушары           12817.0   \n",
       "2      False           8.3      0.0  Санкт-Петербург           21741.0   \n",
       "3      False           NaN      0.0  Санкт-Петербург           28098.0   \n",
       "4      False          41.0      0.0  Санкт-Петербург           31856.0   \n",
       "\n",
       "   cityCenters_nearest  parks_around3000  parks_nearest  ponds_around3000  \\\n",
       "0              16028.0               1.0          482.0               2.0   \n",
       "1              18603.0               0.0            NaN               0.0   \n",
       "2              13933.0               1.0           90.0               2.0   \n",
       "3               6800.0               2.0           84.0               3.0   \n",
       "4               8098.0               2.0          112.0               1.0   \n",
       "\n",
       "   ponds_nearest  days_exposition  \n",
       "0          755.0              NaN  \n",
       "1            NaN             81.0  \n",
       "2          574.0            558.0  \n",
       "3          234.0            424.0  \n",
       "4           48.0            121.0  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Перед началом основной работы необходимо обработать пропущенные данные (к примеру необязательные поля при создании заявки на сайте)\n",
    "# А также привести типы данных в колонке к удобному для дальнейших расчетов\n",
    "\n",
    "\n",
    "# для поля is_appartment приведем значения к булевому типу, \n",
    "# также будем считать пропущенные значения как false, (аппартаменты нечастый тип недвижимости и имеет свои особенности\n",
    "# вероятней всего пользователь укажет подобный нюанс в объявлении)\n",
    "df[\"is_apartment\"] = df[\"is_apartment\"].fillna(False)\n",
    "\n",
    "# в случае если поле с балконом не заполенно - вероятней всего балконов в квартире нет, и можно заполнить отсутствующее\n",
    "# значение нулями\n",
    "df[\"balcony\"] = df[\"balcony\"].fillna(0)\n",
    "\n",
    "# в случае, если общее количество этажей не указано заполняем их значение нулями,\n",
    "# т.к. в финальных вопросах этот параметр не критичный\n",
    "df[\"floors_total\"] = df[\"floors_total\"].fillna(0)\n",
    "\n",
    "# т.к.last_price и floors_total приведем к целочисленному типу, т.к. не бывает дробных этажей,\n",
    "# а цену можно округлить до рублей\n",
    "df[\"last_price\"] = df[\"last_price\"].astype(int)\n",
    "df[\"floors_total\"] = df[\"floors_total\"].astype(int)\n",
    "\n",
    "# is_apartment используется как булевое значение, приведем к булевому типу. С помощью этой операции мы сможем использовать поле в дальнейшей работе.\n",
    "df[\"is_apartment\"] = df[\"is_apartment\"].astype(bool)\n",
    "\n",
    "# приведем из типа object к типу даты first_day_exposition.\n",
    "# Используя правильный тип колонки мы сможем подробно изучить продажи в конкретные месяца и дни недели\n",
    "df[\"first_day_exposition\"] = pd.to_datetime(df[\"first_day_exposition\"])\n",
    "\n",
    "\n",
    "print(df.info())\n",
    "df.head()\n",
    "\n",
    "# избавимся от крайних и подозрительных значений\n",
    "# print(df.shape[0])\n",
    "# df = df.query('ceiling_height < 10 and last_price >= 430000 and rooms > 0')\n",
    "# проверим, что не срезали слишком много\n",
    "# print(df.shape[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Шаг 3. Посчитайте и добавьте в таблицу"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   total_images  last_price  total_area first_day_exposition  rooms  \\\n",
      "0            20    13000000       108.0           2019-03-07      3   \n",
      "1             7     3350000        40.4           2018-12-04      1   \n",
      "2            10     5196000        56.0           2015-08-20      2   \n",
      "3             0    64900000       159.0           2015-07-24      3   \n",
      "4             2    10000000       100.0           2018-06-19      2   \n",
      "\n",
      "   ceiling_height  floors_total  living_area  floor  is_apartment  studio  \\\n",
      "0            2.70            16         51.0      8         False   False   \n",
      "1             NaN            11         18.6      1         False   False   \n",
      "2             NaN             5         34.3      4         False   False   \n",
      "3             NaN            14          NaN      9         False   False   \n",
      "4            3.03            14         32.0     13         False   False   \n",
      "\n",
      "   open_plan  kitchen_area  balcony    locality_name  airports_nearest  \\\n",
      "0      False          25.0      0.0  Санкт-Петербург           18863.0   \n",
      "1      False          11.0      2.0   посёлок Шушары           12817.0   \n",
      "2      False           8.3      0.0  Санкт-Петербург           21741.0   \n",
      "3      False           NaN      0.0  Санкт-Петербург           28098.0   \n",
      "4      False          41.0      0.0  Санкт-Петербург           31856.0   \n",
      "\n",
      "   cityCenters_nearest  parks_around3000  parks_nearest  ponds_around3000  \\\n",
      "0              16028.0               1.0          482.0               2.0   \n",
      "1              18603.0               0.0            NaN               0.0   \n",
      "2              13933.0               1.0           90.0               2.0   \n",
      "3               6800.0               2.0           84.0               3.0   \n",
      "4               8098.0               2.0          112.0               1.0   \n",
      "\n",
      "   ponds_nearest  days_exposition  square_meter_price  weekday  month  year  \\\n",
      "0          755.0              NaN       120370.370370        3      3  2019   \n",
      "1            NaN             81.0        82920.792079        1     12  2018   \n",
      "2          574.0            558.0        92785.714286        3      8  2015   \n",
      "3          234.0            424.0       408176.100629        4      7  2015   \n",
      "4           48.0            121.0       100000.000000        1      6  2018   \n",
      "\n",
      "   living_to_total  kitchen_to_total floor_type  \n",
      "0         0.472222          0.231481     другой  \n",
      "1         0.460396          0.272277     первый  \n",
      "2         0.612500          0.148214     другой  \n",
      "3              NaN               NaN     другой  \n",
      "4         0.320000          0.410000     другой  \n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 23699 entries, 0 to 23698\n",
      "Data columns (total 29 columns):\n",
      "total_images            23699 non-null int64\n",
      "last_price              23699 non-null int64\n",
      "total_area              23699 non-null float64\n",
      "first_day_exposition    23699 non-null datetime64[ns]\n",
      "rooms                   23699 non-null int64\n",
      "ceiling_height          14504 non-null float64\n",
      "floors_total            23699 non-null int64\n",
      "living_area             21796 non-null float64\n",
      "floor                   23699 non-null int64\n",
      "is_apartment            23699 non-null bool\n",
      "studio                  23699 non-null bool\n",
      "open_plan               23699 non-null bool\n",
      "kitchen_area            21421 non-null float64\n",
      "balcony                 23699 non-null float64\n",
      "locality_name           23650 non-null object\n",
      "airports_nearest        18157 non-null float64\n",
      "cityCenters_nearest     18180 non-null float64\n",
      "parks_around3000        18181 non-null float64\n",
      "parks_nearest           8079 non-null float64\n",
      "ponds_around3000        18181 non-null float64\n",
      "ponds_nearest           9110 non-null float64\n",
      "days_exposition         20518 non-null float64\n",
      "square_meter_price      23699 non-null float64\n",
      "weekday                 23699 non-null int64\n",
      "month                   23699 non-null int64\n",
      "year                    23699 non-null int64\n",
      "living_to_total         21796 non-null float64\n",
      "kitchen_to_total        21421 non-null float64\n",
      "floor_type              23699 non-null object\n",
      "dtypes: bool(3), datetime64[ns](1), float64(15), int64(8), object(2)\n",
      "memory usage: 4.8+ MB\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "# получим цену за квадратный метр\n",
    "df[\"square_meter_price\"] = df[\"last_price\"] / df[\"total_area\"]\n",
    "\n",
    "# получим день недели, месяц и выделим под них колонки\n",
    "# при необходимости можно выделить day_name для дней недели, \n",
    "# однако с численным представлением легче строить графики в дальнейшем\n",
    "df['weekday'] = df['first_day_exposition'].dt.weekday\n",
    "# df['weekday'] = df['first_day_exposition'].dt.day_name()\n",
    "df['month'] = df['first_day_exposition'].dt.month\n",
    "df['year'] = df['first_day_exposition'].dt.year\n",
    "\n",
    "# получим отношение жилой площади и площади кухни к основной \n",
    "df[\"living_to_total\"] = df[\"living_area\"] / df[\"total_area\"]\n",
    "df[\"kitchen_to_total\"] = df[\"kitchen_area\"] / df[\"total_area\"]\n",
    "\n",
    "# функция для получения типа этажа\n",
    "def setFloor(item):\n",
    "    if item[\"floor\"] == 1:\n",
    "        return \"первый\"\n",
    "    elif item[\"floor\"] == item[\"floors_total\"]:\n",
    "        return \"последний\"\n",
    "    else:\n",
    "        return \"другой\"\n",
    "\n",
    "# получим типы этажа\n",
    "df['floor_type'] = df.apply(lambda row: setFloor(row), axis=1)\n",
    "\n",
    "print(df.head())\n",
    "print(df.info())\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Шаг 4. Проведите исследовательский анализ данных и выполните инструкции:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def draw_hist_plot(data, plot_title, max_value):\n",
    "    if max_value == 0: \n",
    "        data.hist(bins=30, figsize=(15,4))\n",
    "    else:\n",
    "        data.hist(bins=30, range=(0, max_value), figsize=(15,4))\n",
    "    plt.suptitle(plot_title, fontsize='large')\n",
    "    plt.show()\n",
    "\n",
    "draw_hist_plot(df[\"total_area\"], \"Total area\", 0)\n",
    "draw_hist_plot(df[\"rooms\"], \"Rooms count\", 0)\n",
    "draw_hist_plot(df[\"ceiling_height\"], \"Ceiling height\", 0)\n",
    "draw_hist_plot(df[\"last_price\"], \"Last price\", 0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Полученные гистограммы сложно назвать информативными, хорошо видно, что часть значений сильно выбивается за пределы нормы, а в части вероятно присутствуют ошибочные данные. \\\n",
    "Например в колонке ceiling_height присутствуют потолки под 100 метров. Вероятно, данные близкие к 100 метрам появились в результате ошибочного заполнения, также как и 20 - 25 метров (по всей видимости, пользователь не добавил точку при заполнении).  Ограничим максимальную высоту в 4 метра, а минимальную - 2 \\\n",
    "Также для гистограммы last_price сложно проанализировать среднее распределение цен из-за слишком высокой стоимостьи жилья для части квартир. В данном случае ограничим для гистограммы цену до 20.000.000 \\\n",
    "В гистограмме rooms заметим, что в небольшой части квартир число комнат равно 0. Избавимся от этих значений. Для гистограммы ограничим максимальное значение комнат до 8 \\\n",
    "В гистограмме total_area ограничим максимальное значение до 300 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Количество строк до обрезки 23699\n",
      "Количество строк после обрезки 23443\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Избавимся от крайних и подозрительных значений\n",
    "print(\"Количество строк до обрезки\", df.shape[0])\n",
    "df = df.query('rooms < 8 and rooms > 0 and total_area < 300 and ((ceiling_height < 4) and (ceiling_height > 2)) or (ceiling_height != ceiling_height)')\n",
    "# проверим, что не срезали слишком много\n",
    "print(\"Количество строк после обрезки\", df.shape[0])\n",
    "\n",
    "# Снова построим гистограммы после редактирования данных, посмотрим как они изменятся\n",
    "draw_hist_plot(df[\"total_area\"], \"Total area\", 300)\n",
    "draw_hist_plot(df[\"rooms\"], \"Rooms count\", 10)\n",
    "draw_hist_plot(df[\"ceiling_height\"], \"Ceiling height\", 0)\n",
    "draw_hist_plot(df[\"last_price\"], \"Last price\", 20000000)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "После преобразований легко проанализировать полученные данные. \\\n",
    "Наиболее часто в объявлениях можно встретить квартиры с площадью 50 метров, одно или двухкомнатные, с высотой потолка в 2,5 метра и ценой от 3 до 5 миллионов рублей."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Среднее значение по колонке days_exposition: 180.42935906271538\n",
      "Среднее значение по колонке days_exposition: 95.0\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA6sAAAGQCAYAAABIwPbCAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjAsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+17YcXAAAegklEQVR4nO3dfbRlZ10f8O/PDC/CYF4anWKSOtgGbCRFYUhofekMaZNA1OBaLoRGCIgrVcGXrizLIEuh+DZWkAoirghpwuuIiCYmUUwjUxbLIiSICS+mGXGATEMCJAQCEYj8+sfZg4fJZGZy5849z9z7+ax11jn72fvs85z7mz33fO/z7H2quwMAAAAj+bpFdwAAAAD2JqwCAAAwHGEVAACA4QirAAAADEdYBQAAYDjCKgAAAMMRVgEAABiOsArAqlZVu6rq7qr6XFV9pqr+sqp+vKqO+N+BVfXiqnrDovsBAIfDEf+LGgAOwvd398OSfEuSbUmen+S1i+0SALA/wioAa0Z339ndlyf54STnV9Wjk6Sqzqmqv66qz1bVx6vqxXueU1VXVtVPze+nqq6vqh+smZdX1W3Tc2/Ys8+9VdWOqvq1qnrPtO1lVXXc3PofqKoPTqO/O6rqX8+te35V7Z5Gh2+sqjOq6uwkP5/kh6vqrqr6m2X9YQHAggmrAKw53f2eJDcn+Z6p6fNJnpnkmCTnJPmJqnrKtO7SJD+y57lV9ZgkJyS5MsmZSb43ySOTHJ3kqUk+vZ+XfmaSH03y8CT3JHnFtM9HJnlzkp9N8o1JrkryJ1X1wKp6VJLnJXn8NDp8VpJd3f1nSX41ye939/rufsySfyAAMCBhFYC16v8lOS5JuntHd9/Q3V/p7uszC47/ftru8iSPrKqTp+VnZBYQv5Tky0keluTbklR3f7i7b9nPa76+uz/Q3Z9P8gtJnlpVR2U20ntld1/d3V9O8tIkX5/k3yX5xyQPSnJKVT2gu3d1998t348BAMYkrAKwVp2Q5PYkqarTq+odVfXJqrozyY8nOT5Juvsfkvx+kh+ZLsr09CSvn9b9RZLfTvKqJLdV1UVV9Q37ec2Pzz3+aJIHTK/zzdNypv1+Zdr2hO7emdmI64un19heVd98qG8eAEYnrAKw5lTV4zMLq++amt6U2QjqSd19dJLfTVJzT7k0yXlJzkjyhe7+P3tWdPcruvtxSU7JbDrwz+3npU+ae/wvMhuZ/VRmo7zfMte/mrbdPb3Gm7r7u6dtOsmv73n5g3/XAHBkEVYBWDOq6huq6vuSbE/yhu6+YVr1sCS3d/c/VNVpSf7T/POmcPqVJC/LNKo67e/x06jsAzI77/Ufpu3uy49U1SlV9ZAkL0ny1u7+xyRvSXLOdOGkByS5MMkXk/xlVT2qqp5YVQ+a9n/33GvcmmTjavgaHgDYm19uAKwFf1JVn8tsau0Lk/xmkmfPrf/JJC+ZtvnFzMLj3l6X5NQk899r+g1Jfi/JHZlN4/10kt/YTz9en+SSJJ9I8uAkP50k3X1jZhdxemVmI63fn9nX7Xwps/NVt03tn0jyTUleMO3vD6b7T1fV+/b3AwCAI011m0EEAAdSVc9McsE0HXcpz9+R2Wjua5a1YwCwShlZBYADmKbt/mSSixbdFwBYK4RVANiPqjorySczOz/0TQvuDgCsGaYBAwAAMBwjqwAAAAxHWAUAAGA4wioAAADDEVYBAAAYjrAKAADAcIRVAAAAhiOsAgAAMBxhFQAAgOEIqwAAAAxHWAUAAGA4wioAAADDEVYBAAAYjrAKAADAcIRVAAAAhiOsAgAAMBxhFQAAgOEIqwAAAAxHWAUAAGA4wioAAADDEVYBAAAYjrAKAADAcIRVAAAAhiOsAgAAMBxhFQAAgOEIqwAAAAxHWAUAAGA4wioAAADDEVYBAAAYjrAKAADAcNYtugP7c/zxx/fGjRsX3Y39+vznP5+HPvShi+4Ge1GXcanNuNRmXGozLrUZk7qMS23GtajaXHfddZ/q7m/c17qhw+rGjRtz7bXXLrob+7Vjx45s3rx50d1gL+oyLrUZl9qMS23GpTZjUpdxqc24FlWbqvrofa0zDRgAAIDhCKsAAAAMR1gFAABgOMIqAAAAwxFWAQAAGI6wCgAAwHCEVQAAAIYjrAIAADAcYRUAAIDhCKsAAAAMR1gFAABgOMIqAAAAwxFWAQAAGM66RXdgrdu49cr9rt+17ZwV6gkAAMA4jKwCAAAwHGEVAACA4QirAAAADEdYBQAAYDjCKgAAAMMRVgEAABiOsAoAAMBwhFUAAACGI6wCAAAwHGEVAACA4QirAAAADEdYBQAAYDjCKgAAAMMRVgEAABiOsAoAAMBwhFUAAACGI6wCAAAwHGEVAACA4QirAAAADEdYBQAAYDjCKgAAAMMRVgEAABiOsAoAAMBwhFUAAACGI6wCAAAwHGEVAACA4QirAAAADEdYBQAAYDjCKgAAAMMRVgEAABiOsAoAAMBwhFUAAACGI6wCAAAwHGEVAACA4QirAAAADEdYBQAAYDjCKgAAAMMRVgEAABjOAcNqVZ1UVe+oqg9V1Qer6mem9uOq6uqqumm6P3Zqr6p6RVXtrKrrq+qxc/s6f9r+pqo6//C9LQAAAI5kBzOyek+SC7v7lCRPSPLcqjolydYk13T3yUmumZaT5ElJTp5uFyR5dTILt0lelOT0JKcledGegAsAAADzDhhWu/uW7n7f9PhzST6c5IQk5ya5dNrs0iRPmR6fm+R1PfPuJMdU1cOTnJXk6u6+vbvvSHJ1krOX9d0AAACwKlR3H/zGVRuTvDPJo5N8rLuPmdoryR3dfUxVXZFkW3e/a1p3TZLnJ9mc5MHd/ctT+y8kubu7X7rXa1yQ2YhsNmzY8Ljt27cfyvs77O66666sX79+yc+/Yfed+11/6glHL3nfa9mh1oXDR23GpTbjUptxqc2Y1GVcajOuRdVmy5Yt13X3pn2tW3ewO6mq9Un+MMnPdvdnZ/l0pru7qg4+9e5Hd1+U5KIk2bRpU2/evHk5dnvY7NixI4fSx2dtvXK/63edt/R9r2WHWhcOH7UZl9qMS23GpTZjUpdxqc24RqzNQV0NuKoekFlQfWN3v21qvnWa3pvp/rapfXeSk+aefuLUdl/tAAAA8DUOOLI6TfF9bZIPd/dvzq26PMn5SbZN95fNtT+vqrZndjGlO7v7lqp6e5Jfnbuo0plJXrA8b2Nxbth9535HR3dtO2cFewMAALA6HMw04O9K8owkN1TV+6e2n88spL6lqp6T5KNJnjqtuyrJk5PsTPKFJM9Oku6+vap+Kcl7p+1e0t23L8u7AAAAYFU5YFidLpRU97H6jH1s30meex/7ujjJxfengwAAAKw9B3XOKgAAAKwkYRUAAIDhCKsAAAAMR1gFAABgOMIqAAAAwxFWAQAAGI6wCgAAwHCEVQAAAIYjrAIAADAcYRUAAIDhCKsAAAAMR1gFAABgOOsW3YHVbuPWKxfdBQAAgCOOkVUAAACGI6wCAAAwHGEVAACA4QirAAAADEdYBQAAYDjCKgAAAMMRVgEAABiOsAoAAMBwhFUAAACGI6wCAAAwHGEVAACA4QirAAAADGfdojvA/m3ceuV+1+/ads4K9QQAAGDlGFkFAABgOMIqAAAAwxFWAQAAGI6wCgAAwHCEVQAAAIYjrAIAADAcYRUAAIDhCKsAAAAMR1gFAABgOMIqAAAAwxFWAQAAGI6wCgAAwHCEVQAAAIYjrAIAADAcYRUAAIDhCKsAAAAMR1gFAABgOMIqAAAAwxFWAQAAGI6wCgAAwHCEVQAAAIYjrAIAADAcYRUAAIDhCKsAAAAMR1gFAABgOMIqAAAAwxFWAQAAGI6wCgAAwHCEVQAAAIYjrAIAADAcYRUAAIDhCKsAAAAM54BhtaourqrbquoDc20vrqrdVfX+6fbkuXUvqKqdVXVjVZ0113721LazqrYu/1sBAABgtTiYkdVLkpy9j/aXd/d3TLerkqSqTknytCTfPj3nd6rqqKo6KsmrkjwpySlJnj5tCwAAAPey7kAbdPc7q2rjQe7v3CTbu/uLSf6+qnYmOW1at7O7P5IkVbV92vZD97vHAAAArHrV3QfeaBZWr+juR0/LL07yrCSfTXJtkgu7+46q+u0k7+7uN0zbvTbJn067Obu7f2xqf0aS07v7eft4rQuSXJAkGzZseNz27dsP4e0dfrfdfmduvXtxr3/qCUcv7sUHdtddd2X9+vWL7gb7oDbjUptxqc241GZM6jIutRnXomqzZcuW67p7077WHXBk9T68OskvJenp/mVJfnSJ+/oa3X1RkouSZNOmTb158+bl2O1h88o3XpaX3bDUH+Oh23Xe5oW99sh27NiR0f/trFVqMy61GZfajEttxqQu41KbcY1YmyWlrO6+dc/jqvq9JFdMi7uTnDS36YlTW/bTDgAAAF9jSV9dU1UPn1v8wSR7rhR8eZKnVdWDquoRSU5O8p4k701yclU9oqoemNlFmC5fercBAABYzQ44slpVb06yOcnxVXVzkhcl2VxV35HZNOBdSf5zknT3B6vqLZldOOmeJM/t7n+c9vO8JG9PclSSi7v7g8v+bgAAAFgVDuZqwE/fR/Nr97P9ryT5lX20X5XkqvvVOwAAANakJU0DBgAAgMNJWAUAAGA4wioAAADDEVYBAAAYjrAKAADAcIRVAAAAhiOsAgAAMBxhFQAAgOEIqwAAAAxHWAUAAGA4wioAAADDEVYBAAAYjrAKAADAcIRVAAAAhiOsAgAAMBxhFQAAgOEIqwAAAAxHWAUAAGA4wioAAADDEVYBAAAYjrAKAADAcIRVAAAAhiOsAgAAMBxhFQAAgOEIqwAAAAxHWAUAAGA4wioAAADDEVYBAAAYjrAKAADAcIRVAAAAhiOsAgAAMBxhFQAAgOEIqwAAAAxHWAUAAGA4wioAAADDWbfoDnD4bdx65X7X79p2zgr1BAAA4OAYWQUAAGA4wioAAADDEVYBAAAYjrAKAADAcIRVAAAAhiOsAgAAMBxhFQAAgOEIqwAAAAxHWAUAAGA4wioAAADDEVYBAAAYjrAKAADAcIRVAAAAhiOsAgAAMBxhFQAAgOEIqwAAAAxHWAUAAGA4wioAAADDWbfoDnBoNm69ctFdAAAAWHZGVgEAABiOsAoAAMBwhFUAAACGc8CwWlUXV9VtVfWBubbjqurqqrppuj92aq+qekVV7ayq66vqsXPPOX/a/qaqOv/wvB0AAABWg4MZWb0kydl7tW1Nck13n5zkmmk5SZ6U5OTpdkGSVyezcJvkRUlOT3JakhftCbgAAACwtwOG1e5+Z5Lb92o+N8ml0+NLkzxlrv11PfPuJMdU1cOTnJXk6u6+vbvvSHJ17h2AAQAAIElS3X3gjao2Jrmiux89LX+mu4+ZHleSO7r7mKq6Ism27n7XtO6aJM9PsjnJg7v7l6f2X0hyd3e/dB+vdUFmo7LZsGHD47Zv336o7/Gwuu32O3Pr3YvuxaE59YSjF92FZXfXXXdl/fr1i+4G+6A241KbcanNuNRmTOoyLrUZ16Jqs2XLluu6e9O+1h3y96x2d1fVgRPvwe/voiQXJcmmTZt68+bNy7Xrw+KVb7wsL7vhyP662l3nbV50F5bdjh07Mvq/nbVKbcalNuNSm3GpzZjUZVxqM64Ra7PUqwHfOk3vzXR/29S+O8lJc9udOLXdVzsAAADcy1LD6uVJ9lzR9/wkl821P3O6KvATktzZ3bckeXuSM6vq2OnCSmdObQAAAHAvB5y/WlVvzuyc0+Or6ubMruq7Lclbquo5ST6a5KnT5lcleXKSnUm+kOTZSdLdt1fVLyV577TdS7p774s2AQAAQJKDCKvd/fT7WHXGPrbtJM+9j/1cnOTi+9U7AAAA1qSlTgMGAACAw0ZYBQAAYDjCKgAAAMMRVgEAABiOsAoAAMBwhFUAAACGI6wCAAAwHGEVAACA4QirAAAADEdYBQAAYDjCKgAAAMMRVgEAABjOukV3gMXbuPXK/a7fte2cFeoJAADAjJFVAAAAhiOsAgAAMBzTgDkg04QBAICVZmQVAACA4QirAAAADEdYBQAAYDjCKgAAAMMRVgEAABiOsAoAAMBwhFUAAACGI6wCAAAwHGEVAACA4QirAAAADEdYBQAAYDjCKgAAAMMRVgEAABiOsAoAAMBwhFUAAACGI6wCAAAwHGEVAACA4QirAAAADEdYBQAAYDjrFt0Bjnwbt1653/W7tp2zQj0BAABWCyOrAAAADEdYBQAAYDjCKgAAAMMRVgEAABiOsAoAAMBwhFUAAACGI6wCAAAwHGEVAACA4QirAAAADEdYBQAAYDjCKgAAAMMRVgEAABiOsAoAAMBwhFUAAACGI6wCAAAwHGEVAACA4axbdAdY/TZuvfKQnr9r2znL1BMAAOBIYWQVAACA4QirAAAADEdYBQAAYDjCKgAAAMMRVgEAABjOIYXVqtpVVTdU1fur6tqp7biqurqqbpruj53aq6peUVU7q+r6qnrscrwBAAAAVp/l+OqaLd39qbnlrUmu6e5tVbV1Wn5+kiclOXm6nZ7k1dM97NeBvvrGV9sAAMDqczimAZ+b5NLp8aVJnjLX/rqeeXeSY6rq4Yfh9QEAADjCHWpY7SR/XlXXVdUFU9uG7r5levyJJBumxyck+fjcc2+e2gAAAOBrVHcv/clVJ3T37qr6piRXJ/mpJJd39zFz29zR3cdW1RVJtnX3u6b2a5I8v7uv3WufFyS5IEk2bNjwuO3bty+5fyvhttvvzK13L7oXa9upJxx9r7a77ror69evX0BvOBC1GZfajEttxqU2Y1KXcanNuBZVmy1btlzX3Zv2te6Qzlnt7t3T/W1V9UdJTktya1U9vLtvmab53jZtvjvJSXNPP3Fq23ufFyW5KEk2bdrUmzdvPpQuHnavfONledkNy3HqL0u167zN92rbsWNHRv+3s1apzbjUZlxqMy61GZO6jEttxjVibZY8DbiqHlpVD9vzOMmZST6Q5PIk50+bnZ/ksunx5UmeOV0V+AlJ7pybLgwAAABfdShDghuS/FFV7dnPm7r7z6rqvUneUlXPSfLRJE+dtr8qyZOT7EzyhSTPPoTXBgAAYBVbcljt7o8kecw+2j+d5Ix9tHeS5y719QAAAFg7DsdX1wAAAMAhEVYBAAAYjrAKAADAcHznCke8jVuvvFfbhafek2fNte/ads5KdgkAADhERlYBAAAYjrAKAADAcIRVAAAAhiOsAgAAMBxhFQAAgOEIqwAAAAzHV9ewJuzr623m+WobAAAYi5FVAAAAhiOsAgAAMBxhFQAAgOEIqwAAAAxHWAUAAGA4wioAAADDEVYBAAAYjrAKAADAcIRVAAAAhrNu0R2AEWzceuV+1+/ads4K9QQAAEiEVTgowiwAAKws04ABAAAYjpFVWAFGZgEA4P4xsgoAAMBwhFUAAACGI6wCAAAwHOeswjI40DmpAADA/WNkFQAAgOEIqwAAAAzHNGAYgK+2AQCAr2VkFQAAgOEYWYVVwMgsAACrjZFVAAAAhmNkFY4AvhoHAIC1RlgFkphKDADAWIRVWANWYmRW2AUAYDkJq8BBMRUZAICV5AJLAAAADMfIKnBEMM0YAGBtEVaBFbEnbF546j15linFAAAcgGnAAAAADEdYBQAAYDjCKgAAAMNxzirAxEWcAADGYWQVAACA4RhZBVaFA42KAgBwZDGyCgAAwHCMrAIcpMN9TqtzZgEA/omwCrBMhE0AgOUjrAKsEsIyALCaCKsARwgXkQIA1hJhFWCNOFDYveTsh65QTwAADkxYBVghq2Fk9FCnGpuqDAAcLGEVgCTJDbvvzLNWQaDeH2EZAI4cwioAy+ZQR48P98gtAHDkEFYBOGKshjBqdBcADo6wCgCT5QjDhxo299WHC0+956tTtI+EMCuQA7AchFUAWEaHe/TXVGlhGGCtWPGwWlVnJ/mtJEcleU13b1vpPgDAarUWwujhfv79Cfzzo94H+/yDseg/Sgj8wAhWNKxW1VFJXpXkPya5Ocl7q+ry7v7QSvYDAFic0QP1osPyKK8x8usnh2fK/XLuHzh0Kz2yelqSnd39kSSpqu1Jzk0irAIADGKEMHog++vjvka8l3P/zCxlhH+0c/B9f/jYqrtX7sWqfijJ2d39Y9PyM5Kc3t3Pm9vmgiQXTIuPSnLjinVwaY5P8qlFd4J7UZdxqc241GZcajMutRmTuoxLbca1qNp8S3d/475WDHeBpe6+KMlFi+7Hwaqqa7t706L7wddSl3GpzbjUZlxqMy61GZO6jEttxjVibb5uhV9vd5KT5pZPnNoAAADgq1Y6rL43yclV9YiqemCSpyW5fIX7AAAAwOBWdBpwd99TVc9L8vbMvrrm4u7+4Er24TA4YqYsrzHqMi61GZfajEttxqU2Y1KXcanNuIarzYpeYAkAAAAOxkpPAwYAAIADElYBAAAYjrC6RFV1dlXdWFU7q2rrovuz1lTVSVX1jqr6UFV9sKp+Zmo/rqqurqqbpvtjp/aqqldM9bq+qh672HewulXVUVX111V1xbT8iKr6q+nn//vTBdZSVQ+alndO6zcust+rXVUdU1Vvraq/raoPV9W/dcyMoar+y/R/2Qeq6s1V9WDHzWJU1cVVdVtVfWCu7X4fJ1V1/rT9TVV1/iLey2pzH7X5jen/tOur6o+q6pi5dS+YanNjVZ011+4z3DLbV23m1l1YVV1Vx0/LjpsVcl91qaqfmo6bD1bVf59rH+6YEVaXoKqOSvKqJE9KckqSp1fVKYvt1ZpzT5ILu/uUJE9I8typBluTXNPdJye5ZlpOZrU6ebpdkOTVK9/lNeVnknx4bvnXk7y8u/9VkjuSPGdqf06SO6b2l0/bcfj8VpI/6+5vS/KYzGrkmFmwqjohyU8n2dTdj87sAoRPi+NmUS5JcvZebffrOKmq45K8KMnpSU5L8qI9AZdDcknuXZurkzy6u/9Nkv+b5AVJMn0meFqSb5+e8zvTH1J9hjs8Lsm9a5OqOinJmUk+NtfsuFk5l2SvulTVliTnJnlMd397kpdO7UMeM8Lq0pyWZGd3f6S7v5Rke2ZFZ4V09y3d/b7p8ecy+9B9QmZ1uHTa7NIkT5ken5vkdT3z7iTHVNXDV7jba0JVnZjknCSvmZYryROTvHXaZO+67KnXW5OcMW3PMquqo5N8b5LXJkl3f6m7PxPHzCjWJfn6qlqX5CFJbonjZiG6+51Jbt+r+f4eJ2clubq7b+/uOzILVPf6IM/9s6/adPefd/c90+K7k5w4PT43yfbu/mJ3/32SnZl9fvMZ7jC4j+Mmmf1B7b8mmb+iq+NmhdxHXX4iybbu/uK0zW1T+5DHjLC6NCck+fjc8s1TGwswTYH7ziR/lWRDd98yrfpEkg3TYzVbOf8js19MX5mW/1mSz8x9mJj/2X+1LtP6O6ftWX6PSPLJJP+zZlO0X1NVD41jZuG6e3dmf9n+WGYh9c4k18VxM5L7e5w4fhbjR5P86fRYbRasqs5Nsru7/2avVWqzWI9M8j3TaST/u6oeP7UPWRdhlSNaVa1P8odJfra7Pzu/rmffy+S7mVZQVX1fktu6+7pF94V7WZfksUle3d3fmeTz+aepjEkcM4syTXM7N7M/KHxzkofGaMKwHCdjqqoXZnaK0BsX3ReSqnpIkp9P8ouL7gv3si7JcZmdRvdzSd4y8uwcYXVpdic5aW75xKmNFVRVD8gsqL6xu982Nd+6Z6ridL9naoOarYzvSvIDVbUrs2kiT8zsPMljpumNydf+7L9al2n90Uk+vZIdXkNuTnJzd//VtPzWzMKrY2bx/kOSv+/uT3b3l5O8LbNjyXEzjvt7nDh+VlBVPSvJ9yU5b/pjQqI2i/YvM/sD3N9MnwlOTPK+qvrnUZtFuznJ26Zp2O/JbCbc8Rm0LsLq0rw3yck1u1LjAzM7GfnyBfdpTZn+AvTaJB/u7t+cW3V5kj1Xjzs/yWVz7c+crkD3hCR3zk3pYpl09wu6+8Tu3pjZcfEX3X1eknck+aFps73rsqdePzRtb8TiMOjuTyT5eFU9amo6I8mH4pgZwceSPKGqHjL937anNo6bcdzf4+TtSc6sqmOnkfMzpzaWWVWdndmpJz/Q3V+YW3V5kqfV7OrZj8jsYj7vic9wK6K7b+jub+rujdNngpuTPHb6XeS4Waw/TrIlSarqkUkemORTGfWY6W63JdySPDmzq879XZIXLro/a+2W5Lszm4Z1fZL3T7cnZ3be1jVJbkryv5IcN21fmV3J7O+S3JDZVTcX/j5W8y3J5iRXTI+/NbP/8HYm+YMkD5raHzwt75zWf+ui+72ab0m+I8m103Hzx0mOdcyMcUvy35L8bZIPJHl9kgc5bhZWizdndu7wlzP7gP2cpRwnmZ0/uXO6PXvR72s13O6jNjszO59uz2eB353b/oVTbW5M8qS5dp/hVqA2e63fleT46bHjZoF1ySycvmH6ffO+JE+c2364Y6amDgAAAMAwTAMGAABgOMIqAAAAwxFWAQAAGI6wCgAAwHCEVQAAAIYjrAIAADAcYRUAAIDh/H+VJfowKlajUgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1152x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "# выведем средние и медианные данные по колонке days_exposition\n",
    "print(\"Среднее значение по колонке days_exposition:\", df[\"days_exposition\"].mean())\n",
    "print(\"Среднее значение по колонке days_exposition:\", df[\"days_exposition\"].median())\n",
    "\n",
    "plt.suptitle('Days post')\n",
    "df[\"days_exposition\"].hist(figsize=(16, 6), bins=100)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Cогласно графику видим - что в среднем объявление снимают по прошествию 200 дней, довольно часты случаи, когда объявление находится на сайте от 200 до 800 дней и совсем уж редко свыше 800 дней"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Корреляция между площадью и ценой: 0.6074752704812997\n",
      "Корреляция между числом комнат и ценой: 0.34090657432723004\n",
      "Корреляция между отдаленностью от центра и ценой: -0.21144313886761978\n",
      "Построим сводную таблицу по каждому из типов этажей:\n",
      "              last_price\n",
      "floor_type              \n",
      "другой      6.546188e+06\n",
      "первый      4.722620e+06\n",
      "последний   6.743169e+06\n"
     ]
    }
   ],
   "source": [
    "# посчитаем корреляции для цены относительно общей площади, количества комнат и удаленности от центра\n",
    "print(\"Корреляция между площадью и ценой:\", df[\"total_area\"].corr(df[\"last_price\"]))\n",
    "print(\"Корреляция между числом комнат и ценой:\", df[\"rooms\"].corr(df[\"last_price\"]))\n",
    "print(\"Корреляция между отдаленностью от центра и ценой:\", df[\"cityCenters_nearest\"].corr(df[\"last_price\"]))\n",
    "print(\"Построим сводную таблицу по каждому из типов этажей:\")\n",
    "print(pd.pivot_table(df, values='last_price', index='floor_type').sort_values(by='floor_type'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Корреляция между площадью и ценой довольно высока, что видится логичным \n",
    "- Корреляция между числом комнат и ценой тоже существует, хоть и не такая значительная. \n",
    "- Корреляция между отдаленностью от центра и ценой отрицательная, однако это ожидаемо. Чем меньше значение в колонке cityCenters_nearest тем цена выше, т.е. цена вырастает по мере приближению к центру \n",
    "\n",
    "Построив сводную таблицу по средней цене в зависимости от этажа заметим значительное снижение стоимости квартиры, в случае если она располагается на первом этаже, и чуть менее значительное, но тоже заметное, если на последнем. \\\n",
    "Однако стоит помнить, что в нашей выборке присутствует жилье, которое состоит из двух-трех этажей. Обычно это старые, недорогие дома, и квартиры в них согласно нашей логике будут либо на первом либо на последнем этаже попробуем получить сводную таблицу за исключением этих типов домов и сравним результаты"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              last_price\n",
      "floor_type              \n",
      "другой      6.557758e+06\n",
      "первый      4.874931e+06\n",
      "последний   7.035720e+06\n"
     ]
    }
   ],
   "source": [
    "df_without_first_3_floors = df.query(\"(floors_total > 3) or (floors_total != floors_total)\")\n",
    "print(pd.pivot_table(df_without_first_3_floors, values='last_price', index='floor_type').sort_values(by='floor_type'))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Получим схожую картину, но с меньшим отрывом в цене"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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5ZmEuT3+0gU0793hdjoiIZwJxDiAH2NTmcZl/Wdi6/tx84sy47w0NFy0isSukJ4HNbLqZFZlZUWVlZSh3/Tm9unTkyjF9mbW4nJKtNZ7VISLipUAEQDnQu83jXP+yL3DOPeKcK3TOFWZkZARg18fvB+MHkJqUoOGiRSRmBSIAXgGu8F8NdAZQ7ZzbEoDtBlXXlA7MOGcAb6zcxqINRzrFISISndpzGeizwAdAgZmVmdnVZjbDzGb4V/k3sBYoBf4E/DBo1QbYd8/qS0ZaEne/VqLhokUk5iQcbQXn3LSjPO+AawNWUQildEjg+nPz+dnLy3m7pJIJg3t6XZKISMjE1J3Ah3Lpqb3p0yOFu2evokXDRYtIDIn5AEiMj+Pm8wexamsN/1y22etyRERCJuYDAOCrI7IZ0qszv567msbmFq/LEREJCQUAEBdn3Da5gI079/C3hRouWkRigwLAb/ygDE7r150H3iylrqHZ63JERIJOAeC3f9KY7bUNPP6+hosWkeinAGjjlD7dOH9oJn98Zy276hq9LkdEJKgUAAeZOamA2sZm/vDOZ16XIiISVAqAgwzKTOOSUbk8sWA9W6r3el2OiEjQKAAO4cbz8sHBA2+s8boUEZGgUQAcQu/uKVx2Rh7PF22itKLW63JERIJCAXAY104YSMfEeH49V8NFi0h0UgAcRnpqEt8b15/Xlm9l6aYqr8sREQk4BcARXHN2f7p36sA9c1Z5XYqISMApAI4gNSmBH00YyPulO3hvzXavyxERCSgFwFFcdkYeOV07cvfsVZo0RkSiigLgKJIS4rnp/EF8Ul7Na8u3el2OiEjAKADaYeqoHAZlpvKrOSU079Nw0SISHRQA7RAfZ8ycNJi12+t4YVGZ1+WIiASEAqCdzhvSk9F5Xbn/jdXUN+3zuhwRkROmAGin/cNFb9vdwF8WrPe6HBGRE6YAOAan9+/B+IIMfv/2Z1TvbfK6HBGRE6IAOEYzJxVQvbeJR+ZruGgRiWwKgGN0UnYXLjo5mz+/t46K3fVelyMictzaFQBmNtnMSsys1Mx+fIjn+5jZm2a2zMzeNrPcwJcaPm4+fxDN+xwPvqXhokUkch01AMwsHngIuAAYCkwzs6EHrfYr4Enn3AjgLuB/A11oOOmb3olpp+Xx3MebWL+9zutyRESOS3taAKcBpc65tc65RuA5YMpB6wwF3vL/Pu8Qz0ed6740kMT4OH7z+mqvSxEROS7tCYAcYFObx2X+ZW0tBS7x/z4VSDOzHideXvjq2TmZq8b25ZWlm1mxudrrckREjlmgTgLfCpxjZouBc4By4At3S5nZdDMrMrOiysrKAO3aO9PHDaBLx0TunaNJY0Qk8rQnAMqB3m0e5/qXtXLObXbOXeKcGwX8p3/ZF2ZRcc494pwrdM4VZmRknEDZ4aFLx0R+OH4Ab5dU8uHaHV6XIyJyTNoTAAuBfDPrZ2YdgEuBV9quYGbpZrZ/Wz8BHgtsmeHrO2P6ktU5WcNFi0jEOWoAOOeagR8Bc4CVwPPOuRVmdpeZXeRfbTxQYmargUzgl0GqN+wkJ8Zz43n5LN5YxeufbvO6HBGRdjOv/motLCx0RUVFnuw70Jr3tTDxvvnExxmzbxxHfJx5XZKIRCkzW+ScKwzEtnQncAAkxMdx66QC1lTUMmtx+dFfICISBhQAAXLBsCyG53ThvtdX09Cs4aJFJPwpAAJk/3DR5VV7efrDjV6XIyJyVAqAABqbn85ZA3vwu3ml1NRruGgRCW8KgAC7bdJgdtY18ui767wuRUTkiBQAAXZy765cODyLR99dy/baBq/LERE5LAVAENwysYD65hYemlfqdSkiIoelAAiCARmpfOOUXJ7+cCObdu7xuhwRkUNSAATJDeflg8H9b2jSGBEJTwqAIOnVpSNXjunLS4vLKNla43U5IiJfoAAIoh+cM4DUDgkaLlpEwpICIIi6derAjPEDeGPlNhZt2Ol1OSIin6MACLLvntWX9NQk7n6tRMNFi0hYUQAEWUqHBG44dyAfr9/J26sjfxY0EYkeCoAQ+NapeeR1T+Ge2SW0tKgVICLhQQEQAh0S4rhl4iBWbtnNP5dt9rocERFAARAyXx2RzZBenfn13NU0Nrd4XY6IiAIgVOLijNsmF7Bx5x7+tlDDRYuI9xQAITR+UAan9evOA2+Wsqex2etyRCTGKQBCyDdpTAHbaxt4/P31XpcjIjFOARBip/TpznlDMnn47c/YVdfodTkiEsMUAB6YOamA2sZmHn7nM69LEZEYpgDwQEFWGlNH5fDEgvVsqd7rdTkiEqMUAB656bxBOAcPaLhoEfFIuwLAzCabWYmZlZrZjw/xfJ6ZzTOzxWa2zMwuDHyp0aV39xQuOyOP54s2UVpR63U5IhKDjhoAZhYPPARcAAwFppnZ0INWuwN43jk3CrgU+H2gC41G104YSMfEeP7nX59S37TP63JEJMa0pwVwGlDqnFvrnGsEngOmHLSOAzr7f+8CaLyDdkhPTeLmiQW8XVLJV3/7Hp+UVXtdkojEkPYEQA6wqc3jMv+ytn4OXG5mZcC/gesCUl0MuHpsP5747qnU1Ddz8e/f577XV9O0T0NFiEjwBeok8DTgCedcLnAh8JSZfWHbZjbdzIrMrKiyUkMj7ze+oCdzbhzHlJOzeeDNNVz80PuaRlJEgq49AVAO9G7zONe/rK2rgecBnHMfAMlA+sEbcs494pwrdM4VZmRkHF/FUapLSiK/+dZIHr78FLZW1/PV377Hw+98xj4NHy0iQdKeAFgI5JtZPzPrgO8k7ysHrbMROBfAzIbgCwD9iX8cJg/LYu5N4/jS4J7832ur+MbDC1i3vc7rskQkCh01AJxzzcCPgDnASnxX+6wws7vM7CL/arcA3zOzpcCzwJVO8x8etx6pSfzh8tE8cOlISitqueCB+Tzx/jpNJiMiAWVefU8XFha6oqIiT/YdSbbtruf2vy/j7ZJKzuzfg3u/MYLcbilelyUiHjGzRc65wkBsS3cCh7nMzsk8fuWp/N8lw1lWVsXk+9/lbws3aoJ5ETlhCoAIYGZceloes28cx7Ccztz+90+46omFbNtd73VpIhLBFAARpHf3FJ655gz++6tD+WDtDibeN59/LClXa0BEjosCIMLExRnfPasf/77+bPpndOKG55Zw7TPF7Kht8Lo0EYkwCoAI1T8jlRdnjOH2yYN549MKJt0/nzkrtnpdlohEEAVABIuPM34wfgCvXHcWPdOS+f5Ti7j5+SVU723yujQRiQAKgCgwOKszL197Ftefm88/lmxm0n3zmb9a9+GJyJEpAKJEh4Q4bj5/ELN+OIbU5ASueOxj/nPWJ9Q1NHtdmoiEKQVAlBmR25VXrxvL9HH9eebjjUx+YD4frd3hdVkiEoYUAFEoOTGen144hOe/fyaGcemfPuQXr2rSGRH5PAVAFDu1b3deu+FsLj+9D39+bx1ffvBdlmyq8rosEQkTCoAo1ykpgV9cPIynrj6NPY37+NofFvCrOSU0NmvSGZFYpwCIEWfnZzDnpnFcMiqH380rZcpD7/Pp5t1elyUiHlIAxJDOyYnc+42T+dMVhVTWNDDlofd4aF4pzZqCUiQmKQBi0PlDM5l70zgmnpTFvXNK+NrDH1BaUet1WSISYgqAGNW9Uwce+vZofjttFBt21PHlB9/lz+9p0hmRWKIAiHFfPTmbuTeNY+zAdH7x6qdM+9OHbNq5x+uyRCQEFABCz7RkHv1OIfd+fQSfbt7NpPvn88xHmnRGJNopAATwTTrzjcLezL5pHKPyuvLTWZ/wnccXsrVak86IRCsFgHxOTteOPHXV6fxiykksXLeTife9w6zFZWoNiEQhBYB8QVyc8R9n9uW1G85mUGYaN/1tKTP+uojtmnRGJKooAOSw+qZ34m/fP5OfXjiYeSWVTLxvPq99ssXrskQkQBQAckTxccb0cQN49bqx5HTtyA+eLuaG5xZTvUeTzohEOgWAtMugzDRe+uEYbjpvEP9atoWJ97/DvJIKr8sSkRPQrgAws8lmVmJmpWb240M8f5+ZLfH/rDYzDTkZhRLj47jhvHxevvYsunRM5LuPL+THf19GTb1aAyKR6KgBYGbxwEPABcBQYJqZDW27jnPuJufcSOfcSOC3wEvBKFbCw7CcLvzzurH8YPwAni/axOT732XBZ9u9LktEjlF7WgCnAaXOubXOuUbgOWDKEdafBjwbiOIkfCUlxHP75MG8MGMMHRLi+PafPuLnr6xgb6MmnRGJFO0JgBxgU5vHZf5lX2BmfYB+wFsnXppEglP6dONf14/lyjF9eWLBei588F0WbdjldVki0g6BPgl8KfCic+6Qfwaa2XQzKzKzosrKygDvWryS0iGBn190Es9cczqNzS184+EF3D17FQ3Nag2IhLP2BEA50LvN41z/skO5lCN0/zjnHnHOFTrnCjMyMtpfpUSEMQPTmX3j2XyzsDd/ePszLvrt+ywvr/a6LBE5jPYEwEIg38z6mVkHfF/yrxy8kpkNBroBHwS2RIkkacmJ/N/XRvD4laeya08jFz/0Pg+8sYYmTTojEnaOGgDOuWbgR8AcYCXwvHNuhZndZWYXtVn1UuA5p0FjBJgwuCdzbxrHl0f04r43VvO1PyxgzbYar8sSkTbMq+/rwsJCV1RU5Mm+JbT+/ckW7nh5ObUNzcycWMBVY/sRH2delyUSkcxskXOuMBDb0p3AEnQXDu/FnBvHMX5QBr/890q+9ccPWL+9zuuyRGKeAkBCIiMtiT/+xyn85psnU7KthgseeJenPlivKShFPKQAkJAxMy4Zncvcm8ZR2LcbP/vHCq547GM2V+31ujSRmKQAkJDr1aUjT151Gr+cOozijbuYdN98XijapElnREJMASCeMDMuO70Ps28Yx5Dszsx8cRnfe7KILdVqDYiEiq4CEs+1tDgeX7Cee2avoqG5hczOSQzKTPP/pJLv/z01KcHrUkU8F8irgPR/lHguLs64emw/vjS4J3NXbGX1tlpWb6vh6Y82UN904AaynK4dGZSZ2iYc0hjYM5WOHeI9rF4kdKr3BnbodQWAhI1+6Z34/jkDWh+3tDjKdu2lZFsNq1t/anm/dAeN/juLzSCvewr5PdMoyPKFQ37PNAb07ERSgoJBIl9jcwtvl1Qwa3E5b64M7CRMCgAJW3FxRl6PFPJ6pHD+0MzW5c37Wtiwcw9rttVQsrWW1RU1rN5aw9slFTT7LyuNjzP69EihIDON/Mw0CvzdSX3TO5EYr1NfEt6ccyzZVMVLxeW8umwzu/Y0kZ7agcvOyOPnAdyPzgFI1GhsbmHd9jpWb6vxhcO2GtZsq2X9jjr2326QGG/0T08lPzO1NRwGZabSp0cn3Z0sntu4Yw+zFpfz8pJy1m2vIykhjoknZXHJqBzG5qeTGB+ncwAih9IhIY6CrDQKstI+t7y+aR+fVda2diGt2VbD0rIqXl22pXWdpIQ4BmSkUpCV1hoOgzLTyOnakTgFgwRR9Z4m/vXJFmYtLmPhet9cGmf0784Pxg/ggmFZpCUnBm3fCgCJesmJ8ZyU3YWTsrt8bnldQzOlFbWfO7/w0dodzFp8YLTzlA7x5PdMbe1Gys/0hURW52TMFAxyfA7u12/c18LAnqnMnFTAxaNyyOnaMSR1KAAkZnVKSuDk3l05uXfXzy3fXd/Emm1tg6GGd1ZX8uKistZ10pISWsPAdwLaFw4ZqUkKBjkk5xzFG6t4efEX+/UvGZXLsJzOIf/sKABEDtI5OZFT+nTjlD7dPrd8V12jLxAqalm91RcMs5dv5dk9B2ZM7ZqS2Hr/woFzDGl079Qh1G9DwsT+fv1Zi8tYv2PP5/r1z85PJ8HDixIUACLt1K1TB07v34PT+/doXeacY3ttY+tJ5/33MPxjyWZq6ptb10tPTfrcPQwFWb5upc5B7N8V71TvaeLVTzYzq7icog27MIMz+vXghxMGBr1f/1goAEROgJmRkZZERloSYwamty53zrFtd4P/SqQaSrb6Wg7PF21iT+OBuZKzOiczKCuNQT1Tff9mppHfM5VOuus54jQ2tzCvpIJZxeW8tcrXr5/fM5XbJhdw8cgcskPUr38s9CkTCQIzI1cXWegAAAuvSURBVKtLMlldkjln0IH5r1taHOVVe1lT4buHYX/L4am1O2hoPnDXc263jgzp1ZnRed0YndeVEblddcdzGNrfrz9rcRmvLttClb9f//Iz+nDJ6BxOyg59v/6xUACIhFBcnNG7ewq9u6fwpcEHbm7b1+LYuHNPm3sYalleXs3rn24DICHO/IHQldF9ujE6rxu53TqG9ZdLNNuwo853vf7ictbv2ENyYhwTh2YxdXQOZw/0tl//WOhGMJEwtrOukcUbd1G8cRfFG6pYWlbV2oWUkZbkC4S8bozu043hOV1ITlQrIViq9jTy6rItzFpcziJ/v/6Z/XswdVQOk0PYrx/IG8EUACIRpHlfC6u21vhDoYrijbvYsGMP4GslnJTdmVH+QBid15WcrmolnIiG5n3MW1XJrMVlzFtVSeO+FgZlpjJ1VC5TRmZ70q+vABCRVttrG1jsD4PiDbtYVlbN3iZfK6FnWpK/heBrKQxTK+GofP36u/zj8Gyhem8T6alJTBmZzdRR3vfraygIEWmVnprE+UMzWwfM299K2B8IxRurmL1iK+AbC2lodpfWrqNT+nQLy6tTvLB+e13rODwb/P36k07KYuqoHMZGUL/+sVALQCQGVNY0+AJh4y4Wb6hiWXlV61wLWZ2TW1sIo/K6MSync8wMpV21p5F/LtvCrOIyijdWYQZjBvRg6qhcJg/LCstJiNQFJCInpGlfCyu37G5tIRRv3EXZLt90nB3i4zgpZ/8lqL7uo15doqeV4OvXr+Cl4nLmlVTQtM9RkJnG1NE5TBmZHfbvNeQBYGaTgQeAeOBR59z/HWKdbwI/Bxyw1Dn37SNtUwEgEl4qauop3lDVetXRsrLq1nsTenVJ9rcQfJehnpQdWa0E5xyLNuzipcXl/KtNv/7FI7OZOjqHob3C+3r9tkIaAGYWD6wGzgfKgIXANOfcp23WyQeeB77knNtlZj2dc0ecukYBIBLeGpv9rYSNu1i0YReLN1ZRXuVvJSTEMTyny+cuQ83snOxxxV+0fnsdL/mv19+4cw8dE+OZdFImF0dwv36oA+BM4OfOuUn+xz8BcM79b5t17gFWO+cebe+OFQAikWfb7np/t5Gv6+iT8moa/a2EnK4dfS0EfyAM7dWZDgmh/4LdVdfIq8s289LichZHSL/+sQj1VUA5wKY2j8uA0w9aZ5C/sPfxdRP93Dk3++ANmdl0YDpAXl7e8dQrIh7K7JzMBcN7ccHwXoCvP/3TzbtbzyMUb9jVOtFO0v5Wgv+ehNF53egZpFZCQ/M+3lpZwUuLy3m7Tb/+jy8YHBH9+l4JVBQmAPnAeCAXmG9mw51zVW1Xcs49AjwCvhZAgPYtIh5JSohnlP/qoavpB8DW6vo2l6Du4on31/PI/AOthNF9unGK/1zCkF6dj3uOZuccRRt81+v/a9lmdtc3k5GWxHfO7Btx/fpeaU8AlAO92zzO9S9rqwz4yDnXBKwzs9X4AmFhQKoUkYiR1SWZC4f34sI2rYTl5btbTy4vXLeTfy7dDEByYhwjcroyyn8Z6ui8bmSkJR1x++u21zGruIxZS8rZtHNva7/+1NG5nDWgR0T263ulPecAEvCdBD4X3xf/QuDbzrkVbdaZjO/E8HfMLB1YDIx0zu043HZ1DkAkdm2u2ts6vlHxxl2s2FxN0z7fd1Hv7h0PXIKa143BvdKoqW/29esXl7Nkk69f/6wB6a3j8MTS8NkhPQfgnGs2sx8Bc/D17z/mnFthZncBRc65V/zPTTSzT4F9wMwjffmLSGzL7tqR7K4d+cqIbADqm/axYnN1ayB8uHYH/1jiayV0TIynaV8LzS2OwVlp/OSCwUwZmUNWl/C76ijS6EYwEQk7zvnmTSje6LsvoUNCHFNOzmFodmevS/OcxgISkahmZuR2SyG3WwoXnZztdTlRS2dLRERilAJARCRGKQBERGKUAkBEJEYpAEREYpQCQEQkRikARERilAJARCRGeXYnsJnVACWe7Dz8pAPbvS4iTOhYHKBjcYCOxQEFzrm0QGzIyzuBSwJ1O3OkM7MiHQsfHYsDdCwO0LE4wMwCNoaOuoBERGKUAkBEJEZ5GQCPeLjvcKNjcYCOxQE6FgfoWBwQsGPh2UlgERHxlrqARERiVMACwMx6m9k8M/vUzFaY2Q3+5d3N7HUzW+P/t5t/+WAz+8DMGszs1kNsL97MFpvZq4GqMVQCeSzMbL2ZfWJmSwJ59j9UAnwsuprZi2a2ysxWmtmZXryn4xWoY2FmBf7Pw/6f3WZ2o1fv63gE+HNxk38by83sWTOLqKnCAnwsbvAfhxXt+kw45wLyA/QCRvt/T8M3j/BQ4B7gx/7lPwbu9v/eEzgV+CVw6yG2dzPwDPBqoGoM1U8gjwWwHkj3+j2FybH4C3CN//cOQFev359Xx6LNNuOBrUAfr9+fF8cCyAHWAR39j58HrvT6/Xl0LIYBy4EUfJf4vwEMPNK+A9YCcM5tcc4V+3+vAVb6/+NMwfc/Lv5/L/avU+GcWwg0HbwtM8sFvgw8Gqj6QimQxyLSBepYmFkXYBzwZ/96jc65qpC8iQAJ0ufiXOAz59yGoBUeBAE+FglARzNLwPfltznI5QdUAI/FEOAj59we51wz8A5wyZH2HZRzAGbWFxgFfARkOue2+J/aCmS2YxP3A7cBLcGoL5QCcCwcMNfMFpnZ9KAUGSIneCz6AZXA4/6uwUfNrFOwag22AHwu9rsUeDagxYXYiRwL51w58CtgI7AFqHbOzQ1asUF2gp+L5cDZZtbDzFKAC4HeR3pBwAPAzFKBvwM3Oud2t33O+dopR7zsyMy+AlQ45xYFurZQO9Fj4TfWOTcauAC41szGBb7S4AvAsUgARgN/cM6NAurwNYsjToA+F5hZB+Ai4IWAFxkiAfi+6IbvL+V+QDbQycwuD1K5QXWix8I5txK4G5gLzAaWAPuO9JqABoCZJeJ7A087517yL95mZr38z/cCKo6ymbOAi8xsPfAc8CUz+2sg6wyFAB2L/X/h4JyrAGYBpwWn4uAJ0LEoA8qccx/5H7+ILxAiSqA+F34XAMXOuW2BrzT4AnQszgPWOecqnXNNwEvAmGDVHCwB/L74s3PuFOfcOGAXvvMJhxXIq4AMX//sSufcb9o89QrwHf/v3wH+caTtOOd+4pzLdc71xde8fcs5F1GJHqhjYWadzCxt/+/ARHzNvIgRwM/FVmCTmRX4F50LfBrgcoMqUMeijWlEaPdPAI/FRuAMM0vxb/NcfH3oESOQnwsz6+n/Nw9f//8zR3xBAM9kj8XXRFmGr+mxBF8fVA/gTWANvrPS3f3rZ+H7q243UOX/vfNB2xxPZF4FFJBjAfQHlvp/VgD/6fV78/JzAYwEivzbehno5vX78/BYdAJ2AF28fl9hcCzuBFbh++PoKSDJ6/fn4bF4F98fRkuBc4+2b90JLCISo3QnsIhIjFIAiIjEKAWAiEiMUgCIiMQoBYCISIxSAIiIxCgFgMgJMLN4r2sQOV4KAIkZZnZX2zHSzeyX/vHTZ5rZQjNbZmZ3tnn+Zf8gfCvaDsRnZrVm9mszWwpE1JwEIm0pACSWPAZcAWBmcfiGGtkK5OMbY2kkcEqbAfeucs6dAhQC15tZD//yTviG3T3ZOfdeKN+ASCAleF2ASKg459ab2Q4zG4VvaN3F+CbWmOj/HSAVXyDMx/elP9W/vLd/+Q58Iyz+PZS1iwSDAkBizaPAlfjGU3kM3+Bh/+uc+2PblcxsPL6RJs90zu0xs7eB/VMN1jvnjjjMrkgkUBeQxJpZwGR8f/nP8f9c5R+LHTPL8Y+o2AXY5f/yHwyc4VXBIsGiFoDEFOdco5nNA6r8f8XPNbMhwAe+UXmpBS7HN6HGDDNbCZQAH3pVs0iwaDRQiSn+k7/FwDecc2u8rkfES+oCkphhZkOBUuBNffmLqAUgIhKz1AIQEYlRCgARkRilABARiVEKABGRGKUAEBGJUQoAEZEY9f8BQo47E39n7PUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# функция построения графика по pivot_table\n",
    "def draw_plot_by_pivot_data(df, values, column, title):\n",
    "    pd.pivot_table(df, values=values, index=column).plot()\n",
    "    plt.suptitle(title, fontsize='large')\n",
    "    plt.show()\n",
    "\n",
    "# получим графики продаж для дней недели, месяцев, годов\n",
    "draw_plot_by_pivot_data(df, 'last_price','weekday', 'Weekday')\n",
    "draw_plot_by_pivot_data(df, 'last_price','month', 'Month')\n",
    "draw_plot_by_pivot_data(df, 'last_price','year', 'Year')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Наиболее часто дорогое жилье выкладывают на сайт в выходные дни \\\n",
    "По графику заметим спад в цене в летние месяца, а в сентябре и конце года стоимость резко подскакивает \\\n",
    "Заметим, что начале запуска системы 2014 - 2015 год, когда сервис был менее популярен на сайте в основном публиковали дорогое жилье, в дальнейшем видим более плавный график цены с небольшим увеличением для 2019 года"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Список 10 самых популярных местностей, отсортированных по цене на квадратный метр\n",
      "                   square_meter_price\n",
      "locality_name                        \n",
      "Выборг                   58250.660654\n",
      "Всеволожск               68563.864824\n",
      "Гатчина                  68811.372828\n",
      "Колпино                  75424.579098\n",
      "посёлок Шушары           78655.592167\n",
      "посёлок Мурино           85696.571504\n",
      "посёлок Парголово        90175.913458\n",
      "деревня Кудрово          92412.495592\n",
      "Пушкин                  102777.293778\n",
      "Санкт-Петербург         114269.391117\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "График резко меняется дважды, на 8 и 3 км.\n",
      "Возьмем точку центра города как 8 км, т.к. это первое резкое изменение цены\n"
     ]
    }
   ],
   "source": [
    "# получим названия 10 местностей с наибольшим числом объявлений \n",
    "top_10_by_locality_name = df[\"locality_name\"].value_counts().sort_values(ascending = False).head(10).index\n",
    "\n",
    "# получим датафрейм, в котором местности входит в 10 самых популярных\n",
    "print('Список 10 самых популярных местностей, отсортированных по цене на квадратный метр')\n",
    "top_10_by_locality_data = df.query('locality_name in @top_10_by_locality_name')\n",
    "print(pd.pivot_table(top_10_by_locality_data, values='square_meter_price', index='locality_name')\n",
    "      .sort_values(by='square_meter_price'))\n",
    "\n",
    "# получим датафрейм с объявленияим из Санкт-Петербурга и выделим в нем отдельную колонку \n",
    "# с расстоянием от центра измеряемого в километрах\n",
    "spb_flats = df[df[\"locality_name\"] == \"Санкт-Петербург\"]\n",
    "spb_flats[\"cityCenters_nearest_km\"] = spb_flats[\"cityCenters_nearest\"] / 1000\n",
    "spb_flats[\"cityCenters_nearest_km\"] = spb_flats[\"cityCenters_nearest_km\"].round()\n",
    "# график зависимости цены от расстояния до центра\n",
    "pd.pivot_table(spb_flats, values='last_price', index='cityCenters_nearest_km').plot(grid = True)\n",
    "plt.suptitle('Ceiling height', fontsize='large')\n",
    "plt.show()\n",
    "print(\"График резко меняется дважды, на 8 и 3 км.\")\n",
    "print(\"Возьмем точку центра города как 8 км, т.к. это первое резкое изменение цены\")\n",
    "# корреляции между площади жилья, расстояния до центра и общей площадью\n",
    "spb_flats_center = spb_flats[spb_flats[\"cityCenters_nearest_km\"] < 8]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "График резко меняется дважды, на 8 и 3 км. \\\n",
    "Будем считать центральной частью зону в 8 км от центра"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Корреляция между площадью и ценой для центра Санкт-Петербурга: 0.565152077705904\n",
      "Корреляция между числом комнат и ценой для центра Санкт-Петербурга: 0.2824325175252883\n",
      "Корреляция между удаленностью от центра и ценой для центра Санкт-Петербурга: -0.050354103589079456\n"
     ]
    }
   ],
   "source": [
    "print(\"Корреляция между площадью и ценой для центра Санкт-Петербурга:\", spb_flats_center[\"total_area\"]\n",
    "      .corr(spb_flats_center[\"last_price\"]))\n",
    "print(\"Корреляция между числом комнат и ценой для центра Санкт-Петербурга:\", spb_flats_center[\"rooms\"]\n",
    "      .corr(spb_flats_center[\"last_price\"]))\n",
    "print(\"Корреляция между удаленностью от центра и ценой для центра Санкт-Петербурга:\", spb_flats_center[\"cityCenters_nearest\"]\n",
    "      .corr(spb_flats_center[\"last_price\"]))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Заметим, что корреляция цены между параметрами количества комнат и площади в целом ниже чем по городу в целом \\\n",
    "И значительно ниже для отношений между параметрами удаленности от центра и ценой. Видимо сам факт нахождения в центральной части уже достаточен для высокой цены"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Шаг 5. Общий вывод"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1) Для квартир находящихся в центре - само расположение является важным элементов ценообразования. Такие квартиры гораздо меньше зависят от площади и числа комнат, чем квартиры в черте города \\\n",
    "2) Самое дешевое жилье как правило располагается на первом этаже, жиль чуть подороже на последнем и самое дорогое между ними \\\n",
    "3) Самое дорогое жилье обычно выставляют в выходные дни, в сентябре и конце года. \\\n",
    "4) Наиболее дорогое жилье Санкт Петербурга находится внутри 8 киломметровой зоны от центра города. \\\n",
    "5) В среднем объявление висит на сайте до 200 дней, в редких случаях объявление может задержаться и более 800 дней"
   ]
  }
 ],
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