dataset_clean.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "6d2da891",
   "metadata": {
    "direction": "ltr"
   },
   "source": [
    "# Cleaning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "id": "9efb69f6",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "pd.set_option('display.max_columns', None)\n",
    "%config Completer.use_jedi = True\n",
    "# sns.set(rc={'figure.figsize':(10,10)})\n",
    "from sklearn.impute import SimpleImputer\n",
    "from matplotlib.pyplot import figure"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "id": "222e1976",
   "metadata": {},
   "outputs": [],
   "source": [
    "untouched_df = pd.read_excel('data.xlsx')\n",
    "metro_untouched = pd.read_excel('adressesV1.xlsx')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "id": "9beda1e2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INITIAL LENGTH 42 366\n"
     ]
    }
   ],
   "source": [
    "df = untouched_df \n",
    "metro = metro_untouched\n",
    "print('INITIAL LENGTH', len(df.columns), len(metro))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "id": "0b15c116",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Месяц и год даты договора</th>\n",
       "      <th>Год договора</th>\n",
       "      <th>Год регистрации</th>\n",
       "      <th>Секция</th>\n",
       "      <th>Этаж</th>\n",
       "      <th>Площадь согласно ПД</th>\n",
       "      <th>Цена за кв. метр</th>\n",
       "      <th>Расчетный бюджет объекта</th>\n",
       "      <th>Кол-во покупателей</th>\n",
       "      <th>Кол-во месяцев обременения</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>64678.000000</td>\n",
       "      <td>64678.000000</td>\n",
       "      <td>64678.000000</td>\n",
       "      <td>60322.000000</td>\n",
       "      <td>64678.000000</td>\n",
       "      <td>64678.000000</td>\n",
       "      <td>63771.000000</td>\n",
       "      <td>6.377100e+04</td>\n",
       "      <td>64678.000000</td>\n",
       "      <td>15588.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>6.434001</td>\n",
       "      <td>2019.904558</td>\n",
       "      <td>2019.929621</td>\n",
       "      <td>4.554590</td>\n",
       "      <td>8.156715</td>\n",
       "      <td>49.392188</td>\n",
       "      <td>147025.782105</td>\n",
       "      <td>6.991997e+06</td>\n",
       "      <td>1.250379</td>\n",
       "      <td>236.412304</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.357450</td>\n",
       "      <td>0.795120</td>\n",
       "      <td>0.830034</td>\n",
       "      <td>3.799397</td>\n",
       "      <td>4.842335</td>\n",
       "      <td>18.438301</td>\n",
       "      <td>37655.933685</td>\n",
       "      <td>2.512630e+06</td>\n",
       "      <td>0.600305</td>\n",
       "      <td>149.899187</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.201900</td>\n",
       "      <td>2019.000000</td>\n",
       "      <td>2014.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>16.390000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>3.202100</td>\n",
       "      <td>2019.000000</td>\n",
       "      <td>2019.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>36.000000</td>\n",
       "      <td>121000.000000</td>\n",
       "      <td>5.182402e+06</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>180.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>6.202000</td>\n",
       "      <td>2020.000000</td>\n",
       "      <td>2020.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>45.300000</td>\n",
       "      <td>140576.120000</td>\n",
       "      <td>6.693371e+06</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>240.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>9.202000</td>\n",
       "      <td>2021.000000</td>\n",
       "      <td>2021.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>60.800000</td>\n",
       "      <td>169323.750000</td>\n",
       "      <td>8.398981e+06</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>300.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>12.202000</td>\n",
       "      <td>2021.000000</td>\n",
       "      <td>2021.000000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>165.700000</td>\n",
       "      <td>356900.000000</td>\n",
       "      <td>3.824300e+07</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>15882.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Месяц и год даты договора  Год договора  Год регистрации        Секция  \\\n",
       "count               64678.000000  64678.000000     64678.000000  60322.000000   \n",
       "mean                    6.434001   2019.904558      2019.929621      4.554590   \n",
       "std                     3.357450      0.795120         0.830034      3.799397   \n",
       "min                     1.201900   2019.000000      2014.000000      1.000000   \n",
       "25%                     3.202100   2019.000000      2019.000000      2.000000   \n",
       "50%                     6.202000   2020.000000      2020.000000      4.000000   \n",
       "75%                     9.202000   2021.000000      2021.000000      6.000000   \n",
       "max                    12.202000   2021.000000      2021.000000     21.000000   \n",
       "\n",
       "               Этаж  Площадь согласно ПД  Цена за кв. метр  \\\n",
       "count  64678.000000         64678.000000      63771.000000   \n",
       "mean       8.156715            49.392188     147025.782105   \n",
       "std        4.842335            18.438301      37655.933685   \n",
       "min        1.000000            16.390000          0.000000   \n",
       "25%        4.000000            36.000000     121000.000000   \n",
       "50%        7.000000            45.300000     140576.120000   \n",
       "75%       11.000000            60.800000     169323.750000   \n",
       "max       25.000000           165.700000     356900.000000   \n",
       "\n",
       "       Расчетный бюджет объекта  Кол-во покупателей  \\\n",
       "count              6.377100e+04        64678.000000   \n",
       "mean               6.991997e+06            1.250379   \n",
       "std                2.512630e+06            0.600305   \n",
       "min                0.000000e+00            1.000000   \n",
       "25%                5.182402e+06            1.000000   \n",
       "50%                6.693371e+06            1.000000   \n",
       "75%                8.398981e+06            1.000000   \n",
       "max                3.824300e+07           18.000000   \n",
       "\n",
       "       Кол-во месяцев обременения  \n",
       "count                15588.000000  \n",
       "mean                   236.412304  \n",
       "std                    149.899187  \n",
       "min                      0.000000  \n",
       "25%                    180.000000  \n",
       "50%                    240.000000  \n",
       "75%                    300.000000  \n",
       "max                  15882.000000  "
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "id": "a71f5d16",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "42"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "id": "17911c71",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID проекта</th>\n",
       "      <th>ID корпуса</th>\n",
       "      <th>Проект</th>\n",
       "      <th>Локация</th>\n",
       "      <th>Округ</th>\n",
       "      <th>Район</th>\n",
       "      <th>Адрес корпуса</th>\n",
       "      <th>Стадия строительной готовности на дату договора</th>\n",
       "      <th>Заявленный срок ввода в эксплуатацию</th>\n",
       "      <th>Старт продаж</th>\n",
       "      <th>Класс</th>\n",
       "      <th>Девелопер</th>\n",
       "      <th>Дата договора</th>\n",
       "      <th>Месяц и год даты договора</th>\n",
       "      <th>Квартал и год договора</th>\n",
       "      <th>Год договора</th>\n",
       "      <th>Дата регистрации</th>\n",
       "      <th>Месяц и год даты регистрации</th>\n",
       "      <th>Квартал и год регистрации</th>\n",
       "      <th>Год регистрации</th>\n",
       "      <th>Тип сделки</th>\n",
       "      <th>ID лота</th>\n",
       "      <th>Тип объекта</th>\n",
       "      <th>Секция</th>\n",
       "      <th>Этаж</th>\n",
       "      <th>Количество комнат в прайс-листе, типология bnmap.pro</th>\n",
       "      <th>Площадь согласно ПД</th>\n",
       "      <th>Цена за кв. метр</th>\n",
       "      <th>Метод определения цены</th>\n",
       "      <th>Расчетный бюджет объекта</th>\n",
       "      <th>Включение скидок/акций в ценообразование</th>\n",
       "      <th>Скидки по дате договора</th>\n",
       "      <th>Отделка по корпусу</th>\n",
       "      <th>Тип продавца</th>\n",
       "      <th>Тип покупателя</th>\n",
       "      <th>Кол-во покупателей</th>\n",
       "      <th>Тип ипотеки</th>\n",
       "      <th>Кол-во месяцев обременения</th>\n",
       "      <th>Участие объекта в оптовой сделке</th>\n",
       "      <th>Срок в экспозиции до момента сделки, дней</th>\n",
       "      <th>Рост цены за 1 кв.м за период экспонирования</th>\n",
       "      <th>Рост бюджета покупки за период экспонирования</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>9a80461dd54fbcef2ac42a6b78af0c3a</td>\n",
       "      <td>2a3a5563a920d27ca7d1acbf4afa87bd</td>\n",
       "      <td>Баркли Медовая Долина</td>\n",
       "      <td>Новая Москва</td>\n",
       "      <td>НАО</td>\n",
       "      <td>НАО</td>\n",
       "      <td>Крекшино, д. 9 (3 оч.)</td>\n",
       "      <td>Строительство не начато</td>\n",
       "      <td>2020-06-30</td>\n",
       "      <td>2017-05-01</td>\n",
       "      <td>Комфорт</td>\n",
       "      <td>Barkli</td>\n",
       "      <td>2019-01-01</td>\n",
       "      <td>1.2019</td>\n",
       "      <td>1кв. 2019</td>\n",
       "      <td>2019</td>\n",
       "      <td>2019-02-18</td>\n",
       "      <td>2019-02-01</td>\n",
       "      <td>1кв. 2019</td>\n",
       "      <td>2019</td>\n",
       "      <td>Договор участия</td>\n",
       "      <td>efad7e1a12462258b1111dc006cb76e5</td>\n",
       "      <td>квартира</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "      <td>ст</td>\n",
       "      <td>27.43</td>\n",
       "      <td>95325.34</td>\n",
       "      <td>Прайс совпадение площади</td>\n",
       "      <td>2.614774e+06</td>\n",
       "      <td>без скидки</td>\n",
       "      <td>Скидка 12% на ограниченный пул квартир. Скидка...</td>\n",
       "      <td>С отделкой</td>\n",
       "      <td>ЮЛ</td>\n",
       "      <td>ФЛ</td>\n",
       "      <td>1</td>\n",
       "      <td>Нет</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Нет</td>\n",
       "      <td>н/д</td>\n",
       "      <td>н/д</td>\n",
       "      <td>н/д</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>c995291bb13726dbaf1e579cea7680c3</td>\n",
       "      <td>ac46a131b160a231a148cda32fe3cdee</td>\n",
       "      <td>Румянцево-Парк</td>\n",
       "      <td>Новая Москва</td>\n",
       "      <td>НАО</td>\n",
       "      <td>НАО</td>\n",
       "      <td>Московский п., в районе Румянцево дер., уч. 17...</td>\n",
       "      <td>Монтажные и отделочные работы</td>\n",
       "      <td>2020-06-30</td>\n",
       "      <td>2018-04-01</td>\n",
       "      <td>Комфорт</td>\n",
       "      <td>Lexion Development</td>\n",
       "      <td>2019-01-01</td>\n",
       "      <td>1.2019</td>\n",
       "      <td>1кв. 2019</td>\n",
       "      <td>2019</td>\n",
       "      <td>2019-05-08</td>\n",
       "      <td>2019-05-01</td>\n",
       "      <td>2кв. 2019</td>\n",
       "      <td>2019</td>\n",
       "      <td>Договор участия</td>\n",
       "      <td>8049f8214898fdf4ba8991e54d880a18</td>\n",
       "      <td>квартира</td>\n",
       "      <td>4.0</td>\n",
       "      <td>11</td>\n",
       "      <td>2</td>\n",
       "      <td>62.60</td>\n",
       "      <td>114000.00</td>\n",
       "      <td>Прайс совпадение площади</td>\n",
       "      <td>7.136400e+06</td>\n",
       "      <td>без скидки</td>\n",
       "      <td>Скидка 2% при 100% оплате или ипотеке. При еди...</td>\n",
       "      <td>Без отделки</td>\n",
       "      <td>ЮЛ</td>\n",
       "      <td>ФЛ</td>\n",
       "      <td>1</td>\n",
       "      <td>Нет</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Нет</td>\n",
       "      <td>211</td>\n",
       "      <td>7,5%</td>\n",
       "      <td>7,5%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>c995291bb13726dbaf1e579cea7680c3</td>\n",
       "      <td>ac46a131b160a231a148cda32fe3cdee</td>\n",
       "      <td>Румянцево-Парк</td>\n",
       "      <td>Новая Москва</td>\n",
       "      <td>НАО</td>\n",
       "      <td>НАО</td>\n",
       "      <td>Московский п., в районе Румянцево дер., уч. 17...</td>\n",
       "      <td>Монтажные и отделочные работы</td>\n",
       "      <td>2020-06-30</td>\n",
       "      <td>2018-04-01</td>\n",
       "      <td>Комфорт</td>\n",
       "      <td>Lexion Development</td>\n",
       "      <td>2019-01-01</td>\n",
       "      <td>1.2019</td>\n",
       "      <td>1кв. 2019</td>\n",
       "      <td>2019</td>\n",
       "      <td>2019-05-08</td>\n",
       "      <td>2019-05-01</td>\n",
       "      <td>2кв. 2019</td>\n",
       "      <td>2019</td>\n",
       "      <td>Договор участия</td>\n",
       "      <td>b4aaa625763725a4ce03aa28b607df2f</td>\n",
       "      <td>квартира</td>\n",
       "      <td>13.0</td>\n",
       "      <td>22</td>\n",
       "      <td>ст</td>\n",
       "      <td>24.10</td>\n",
       "      <td>143000.00</td>\n",
       "      <td>Прайс совпадение площади</td>\n",
       "      <td>3.446300e+06</td>\n",
       "      <td>без скидки</td>\n",
       "      <td>Скидка 2% при 100% оплате или ипотеке. При еди...</td>\n",
       "      <td>Без отделки</td>\n",
       "      <td>ЮЛ</td>\n",
       "      <td>ФЛ</td>\n",
       "      <td>3</td>\n",
       "      <td>Нет</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Нет</td>\n",
       "      <td>211</td>\n",
       "      <td>16,3%</td>\n",
       "      <td>16,3%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         ID проекта                        ID корпуса  \\\n",
       "0  9a80461dd54fbcef2ac42a6b78af0c3a  2a3a5563a920d27ca7d1acbf4afa87bd   \n",
       "1  c995291bb13726dbaf1e579cea7680c3  ac46a131b160a231a148cda32fe3cdee   \n",
       "2  c995291bb13726dbaf1e579cea7680c3  ac46a131b160a231a148cda32fe3cdee   \n",
       "\n",
       "                  Проект       Локация Округ Район  \\\n",
       "0  Баркли Медовая Долина  Новая Москва   НАО   НАО   \n",
       "1         Румянцево-Парк  Новая Москва   НАО   НАО   \n",
       "2         Румянцево-Парк  Новая Москва   НАО   НАО   \n",
       "\n",
       "                                       Адрес корпуса  \\\n",
       "0                             Крекшино, д. 9 (3 оч.)   \n",
       "1  Московский п., в районе Румянцево дер., уч. 17...   \n",
       "2  Московский п., в районе Румянцево дер., уч. 17...   \n",
       "\n",
       "  Стадия строительной готовности на дату договора  \\\n",
       "0                         Строительство не начато   \n",
       "1                   Монтажные и отделочные работы   \n",
       "2                   Монтажные и отделочные работы   \n",
       "\n",
       "  Заявленный срок ввода в эксплуатацию Старт продаж    Класс  \\\n",
       "0                           2020-06-30   2017-05-01  Комфорт   \n",
       "1                           2020-06-30   2018-04-01  Комфорт   \n",
       "2                           2020-06-30   2018-04-01  Комфорт   \n",
       "\n",
       "            Девелопер Дата договора  Месяц и год даты договора  \\\n",
       "0              Barkli    2019-01-01                     1.2019   \n",
       "1  Lexion Development    2019-01-01                     1.2019   \n",
       "2  Lexion Development    2019-01-01                     1.2019   \n",
       "\n",
       "  Квартал и год договора  Год договора Дата регистрации  \\\n",
       "0              1кв. 2019          2019       2019-02-18   \n",
       "1              1кв. 2019          2019       2019-05-08   \n",
       "2              1кв. 2019          2019       2019-05-08   \n",
       "\n",
       "  Месяц и год даты регистрации Квартал и год регистрации  Год регистрации  \\\n",
       "0                   2019-02-01                 1кв. 2019             2019   \n",
       "1                   2019-05-01                 2кв. 2019             2019   \n",
       "2                   2019-05-01                 2кв. 2019             2019   \n",
       "\n",
       "        Тип сделки                           ID лота Тип объекта  Секция  \\\n",
       "0  Договор участия  efad7e1a12462258b1111dc006cb76e5    квартира     NaN   \n",
       "1  Договор участия  8049f8214898fdf4ba8991e54d880a18    квартира     4.0   \n",
       "2  Договор участия  b4aaa625763725a4ce03aa28b607df2f    квартира    13.0   \n",
       "\n",
       "   Этаж Количество комнат в прайс-листе, типология bnmap.pro  \\\n",
       "0     5                                                 ст     \n",
       "1    11                                                  2     \n",
       "2    22                                                 ст     \n",
       "\n",
       "   Площадь согласно ПД  Цена за кв. метр    Метод определения цены  \\\n",
       "0                27.43          95325.34  Прайс совпадение площади   \n",
       "1                62.60         114000.00  Прайс совпадение площади   \n",
       "2                24.10         143000.00  Прайс совпадение площади   \n",
       "\n",
       "   Расчетный бюджет объекта Включение скидок/акций в ценообразование  \\\n",
       "0              2.614774e+06                               без скидки   \n",
       "1              7.136400e+06                               без скидки   \n",
       "2              3.446300e+06                               без скидки   \n",
       "\n",
       "                             Скидки по дате договора Отделка по корпусу  \\\n",
       "0  Скидка 12% на ограниченный пул квартир. Скидка...         С отделкой   \n",
       "1  Скидка 2% при 100% оплате или ипотеке. При еди...        Без отделки   \n",
       "2  Скидка 2% при 100% оплате или ипотеке. При еди...        Без отделки   \n",
       "\n",
       "  Тип продавца Тип покупателя  Кол-во покупателей Тип ипотеки  \\\n",
       "0           ЮЛ             ФЛ                   1         Нет   \n",
       "1           ЮЛ             ФЛ                   1         Нет   \n",
       "2           ЮЛ             ФЛ                   3         Нет   \n",
       "\n",
       "   Кол-во месяцев обременения Участие объекта в оптовой сделке  \\\n",
       "0                         NaN                              Нет   \n",
       "1                         NaN                              Нет   \n",
       "2                         NaN                              Нет   \n",
       "\n",
       "  Срок в экспозиции до момента сделки, дней  \\\n",
       "0                                       н/д   \n",
       "1                                       211   \n",
       "2                                       211   \n",
       "\n",
       "  Рост цены за 1 кв.м за период экспонирования  \\\n",
       "0                                          н/д   \n",
       "1                                         7,5%   \n",
       "2                                        16,3%   \n",
       "\n",
       "  Рост бюджета покупки за период экспонирования  \n",
       "0                                           н/д  \n",
       "1                                          7,5%  \n",
       "2                                         16,3%  "
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "id": "4e32acf1",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "НАО                 63927\n",
       "ТАО                   748\n",
       "Одинцовский г.о.        3\n",
       "Name: Район, dtype: int64"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Район'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "id": "e162dafb",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "df = df.drop(['ID проекта', 'ID корпуса', 'Локация', 'ID лота', 'Расчетный бюджет объекта', 'Кол-во месяцев обременения', 'Рост бюджета покупки за период экспонирования', 'Округ', 'Месяц и год даты договора','Месяц и год даты регистрации', 'Проект'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "id": "cdf24892",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "df = df.rename(columns={'Количество комнат в прайс-листе, типология bnmap.pro': 'Количество комнат'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "id": "9202f8eb",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Район</th>\n",
       "      <th>Адрес корпуса</th>\n",
       "      <th>Стадия строительной готовности на дату договора</th>\n",
       "      <th>Заявленный срок ввода в эксплуатацию</th>\n",
       "      <th>Старт продаж</th>\n",
       "      <th>Класс</th>\n",
       "      <th>Девелопер</th>\n",
       "      <th>Дата договора</th>\n",
       "      <th>Квартал и год договора</th>\n",
       "      <th>Год договора</th>\n",
       "      <th>Дата регистрации</th>\n",
       "      <th>Квартал и год регистрации</th>\n",
       "      <th>Год регистрации</th>\n",
       "      <th>Тип сделки</th>\n",
       "      <th>Тип объекта</th>\n",
       "      <th>Секция</th>\n",
       "      <th>Этаж</th>\n",
       "      <th>Количество комнат</th>\n",
       "      <th>Площадь согласно ПД</th>\n",
       "      <th>Цена за кв. метр</th>\n",
       "      <th>Метод определения цены</th>\n",
       "      <th>Включение скидок/акций в ценообразование</th>\n",
       "      <th>Скидки по дате договора</th>\n",
       "      <th>Отделка по корпусу</th>\n",
       "      <th>Тип продавца</th>\n",
       "      <th>Тип покупателя</th>\n",
       "      <th>Кол-во покупателей</th>\n",
       "      <th>Тип ипотеки</th>\n",
       "      <th>Участие объекта в оптовой сделке</th>\n",
       "      <th>Срок в экспозиции до момента сделки, дней</th>\n",
       "      <th>Рост цены за 1 кв.м за период экспонирования</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>НАО</td>\n",
       "      <td>Крекшино, д. 9 (3 оч.)</td>\n",
       "      <td>Строительство не начато</td>\n",
       "      <td>2020-06-30</td>\n",
       "      <td>2017-05-01</td>\n",
       "      <td>Комфорт</td>\n",
       "      <td>Barkli</td>\n",
       "      <td>2019-01-01</td>\n",
       "      <td>1кв. 2019</td>\n",
       "      <td>2019</td>\n",
       "      <td>2019-02-18</td>\n",
       "      <td>1кв. 2019</td>\n",
       "      <td>2019</td>\n",
       "      <td>Договор участия</td>\n",
       "      <td>квартира</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "      <td>ст</td>\n",
       "      <td>27.43</td>\n",
       "      <td>95325.34</td>\n",
       "      <td>Прайс совпадение площади</td>\n",
       "      <td>без скидки</td>\n",
       "      <td>Скидка 12% на ограниченный пул квартир. Скидка...</td>\n",
       "      <td>С отделкой</td>\n",
       "      <td>ЮЛ</td>\n",
       "      <td>ФЛ</td>\n",
       "      <td>1</td>\n",
       "      <td>Нет</td>\n",
       "      <td>Нет</td>\n",
       "      <td>н/д</td>\n",
       "      <td>н/д</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>НАО</td>\n",
       "      <td>Московский п., в районе Румянцево дер., уч. 17...</td>\n",
       "      <td>Монтажные и отделочные работы</td>\n",
       "      <td>2020-06-30</td>\n",
       "      <td>2018-04-01</td>\n",
       "      <td>Комфорт</td>\n",
       "      <td>Lexion Development</td>\n",
       "      <td>2019-01-01</td>\n",
       "      <td>1кв. 2019</td>\n",
       "      <td>2019</td>\n",
       "      <td>2019-05-08</td>\n",
       "      <td>2кв. 2019</td>\n",
       "      <td>2019</td>\n",
       "      <td>Договор участия</td>\n",
       "      <td>квартира</td>\n",
       "      <td>4.0</td>\n",
       "      <td>11</td>\n",
       "      <td>2</td>\n",
       "      <td>62.60</td>\n",
       "      <td>114000.00</td>\n",
       "      <td>Прайс совпадение площади</td>\n",
       "      <td>без скидки</td>\n",
       "      <td>Скидка 2% при 100% оплате или ипотеке. При еди...</td>\n",
       "      <td>Без отделки</td>\n",
       "      <td>ЮЛ</td>\n",
       "      <td>ФЛ</td>\n",
       "      <td>1</td>\n",
       "      <td>Нет</td>\n",
       "      <td>Нет</td>\n",
       "      <td>211</td>\n",
       "      <td>7,5%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>НАО</td>\n",
       "      <td>Московский п., в районе Румянцево дер., уч. 17...</td>\n",
       "      <td>Монтажные и отделочные работы</td>\n",
       "      <td>2020-06-30</td>\n",
       "      <td>2018-04-01</td>\n",
       "      <td>Комфорт</td>\n",
       "      <td>Lexion Development</td>\n",
       "      <td>2019-01-01</td>\n",
       "      <td>1кв. 2019</td>\n",
       "      <td>2019</td>\n",
       "      <td>2019-05-08</td>\n",
       "      <td>2кв. 2019</td>\n",
       "      <td>2019</td>\n",
       "      <td>Договор участия</td>\n",
       "      <td>квартира</td>\n",
       "      <td>13.0</td>\n",
       "      <td>22</td>\n",
       "      <td>ст</td>\n",
       "      <td>24.10</td>\n",
       "      <td>143000.00</td>\n",
       "      <td>Прайс совпадение площади</td>\n",
       "      <td>без скидки</td>\n",
       "      <td>Скидка 2% при 100% оплате или ипотеке. При еди...</td>\n",
       "      <td>Без отделки</td>\n",
       "      <td>ЮЛ</td>\n",
       "      <td>ФЛ</td>\n",
       "      <td>3</td>\n",
       "      <td>Нет</td>\n",
       "      <td>Нет</td>\n",
       "      <td>211</td>\n",
       "      <td>16,3%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Район                                      Адрес корпуса  \\\n",
       "0   НАО                             Крекшино, д. 9 (3 оч.)   \n",
       "1   НАО  Московский п., в районе Румянцево дер., уч. 17...   \n",
       "2   НАО  Московский п., в районе Румянцево дер., уч. 17...   \n",
       "\n",
       "  Стадия строительной готовности на дату договора  \\\n",
       "0                         Строительство не начато   \n",
       "1                   Монтажные и отделочные работы   \n",
       "2                   Монтажные и отделочные работы   \n",
       "\n",
       "  Заявленный срок ввода в эксплуатацию Старт продаж    Класс  \\\n",
       "0                           2020-06-30   2017-05-01  Комфорт   \n",
       "1                           2020-06-30   2018-04-01  Комфорт   \n",
       "2                           2020-06-30   2018-04-01  Комфорт   \n",
       "\n",
       "            Девелопер Дата договора Квартал и год договора  Год договора  \\\n",
       "0              Barkli    2019-01-01              1кв. 2019          2019   \n",
       "1  Lexion Development    2019-01-01              1кв. 2019          2019   \n",
       "2  Lexion Development    2019-01-01              1кв. 2019          2019   \n",
       "\n",
       "  Дата регистрации Квартал и год регистрации  Год регистрации  \\\n",
       "0       2019-02-18                 1кв. 2019             2019   \n",
       "1       2019-05-08                 2кв. 2019             2019   \n",
       "2       2019-05-08                 2кв. 2019             2019   \n",
       "\n",
       "        Тип сделки Тип объекта  Секция  Этаж Количество комнат  \\\n",
       "0  Договор участия    квартира     NaN     5                ст   \n",
       "1  Договор участия    квартира     4.0    11                 2   \n",
       "2  Договор участия    квартира    13.0    22                ст   \n",
       "\n",
       "   Площадь согласно ПД  Цена за кв. метр    Метод определения цены  \\\n",
       "0                27.43          95325.34  Прайс совпадение площади   \n",
       "1                62.60         114000.00  Прайс совпадение площади   \n",
       "2                24.10         143000.00  Прайс совпадение площади   \n",
       "\n",
       "  Включение скидок/акций в ценообразование  \\\n",
       "0                               без скидки   \n",
       "1                               без скидки   \n",
       "2                               без скидки   \n",
       "\n",
       "                             Скидки по дате договора Отделка по корпусу  \\\n",
       "0  Скидка 12% на ограниченный пул квартир. Скидка...         С отделкой   \n",
       "1  Скидка 2% при 100% оплате или ипотеке. При еди...        Без отделки   \n",
       "2  Скидка 2% при 100% оплате или ипотеке. При еди...        Без отделки   \n",
       "\n",
       "  Тип продавца Тип покупателя  Кол-во покупателей Тип ипотеки  \\\n",
       "0           ЮЛ             ФЛ                   1         Нет   \n",
       "1           ЮЛ             ФЛ                   1         Нет   \n",
       "2           ЮЛ             ФЛ                   3         Нет   \n",
       "\n",
       "  Участие объекта в оптовой сделке Срок в экспозиции до момента сделки, дней  \\\n",
       "0                              Нет                                       н/д   \n",
       "1                              Нет                                       211   \n",
       "2                              Нет                                       211   \n",
       "\n",
       "  Рост цены за 1 кв.м за период экспонирования  \n",
       "0                                          н/д  \n",
       "1                                         7,5%  \n",
       "2                                        16,3%  "
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "id": "58b03b06",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count                             64678\n",
       "unique                                7\n",
       "top       Монтажные и отделочные работы\n",
       "freq                              35493\n",
       "Name: Стадия строительной готовности на дату договора, dtype: object"
      ]
     },
     "execution_count": 142,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Стадия строительной готовности на дату договора'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "id": "de1b1dbf",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "df['Заявленный срок ввода в эксплуатацию'] = pd.to_datetime(df['Заявленный срок ввода в эксплуатацию'], errors='coerce')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "id": "21f080fb",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "df = df.drop('Год договора', axis=1)\n",
    "df = df.drop('Год регистрации', axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "id": "165ceb41",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def split_quater(name):\n",
    "    quater,year = [],[]\n",
    "    for i in df[name]:\n",
    "        quater.append(i.split(' ')[0][0])\n",
    "        year.append(i.split(' ')[-1])\n",
    "    return quater, year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "id": "2b0980cd",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "df.insert(9, \"Квартал договора\", split_quater('Квартал и год договора')[0])\n",
    "df = df.drop('Квартал и год договора', axis=1)\n",
    "df['Квартал договора'] = df['Квартал договора'].astype(str).astype(int)\n",
    "df['Дата договора'] = pd.to_datetime(df['Дата договора'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "id": "c3cc82b3",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "df.insert(12, \"Квартал регистрации\", split_quater('Квартал и год регистрации')[0])\n",
    "df = df.drop('Квартал и год регистрации', axis=1)\n",
    "df['Квартал регистрации'] = df['Квартал регистрации'].astype(str).astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "id": "7bb960b0",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4356\n"
     ]
    }
   ],
   "source": [
    "print(df['Секция'].isnull().sum())\n",
    "df['Секция']=df['Секция'].replace(float('nan'),0)\n",
    "imputer = SimpleImputer(missing_values=0, strategy='mean')\n",
    "df['Секция'] = imputer.fit_transform(df['Секция'].values.reshape(-1, 1))\n",
    "df['Секция'] = df['Секция'].astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "id": "fa02e613",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "907\n"
     ]
    }
   ],
   "source": [
    "print(df['Цена за кв. метр'].isnull().sum())\n",
    "df = df.dropna(subset = ['Цена за кв. метр'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "id": "9d1e4372",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1     23297\n",
       "2     22203\n",
       "ст     8924\n",
       "3      8035\n",
       "4       863\n",
       "-         4\n",
       "Name: Количество комнат, dtype: int64"
      ]
     },
     "execution_count": 150,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Количество комнат'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "id": "02fc5077",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "445\n",
      "['студия' '2' '1' '3' 0 '4' '-']\n",
      "['студия' '2' '1' '3' '4']\n"
     ]
    }
   ],
   "source": [
    "df['Количество комнат'] = df['Количество комнат'].replace('ст','студия')\n",
    "print(df['Количество комнат'].isnull().sum())\n",
    "df['Количество комнат'] = df['Количество комнат'].replace(float('nan'),0)\n",
    "print(df['Количество комнат'].unique())\n",
    "df = df[df['Количество комнат'] != 0]\n",
    "df = df[df['Количество комнат'] != 'bez-tipa']\n",
    "df = df[df['Количество комнат'] != '-']\n",
    "print(df['Количество комнат'].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "id": "9f1aa675",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.loc[df['Цена за кв. метр']>0]\n",
    "df = df.loc[df['Площадь согласно ПД']>0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "id": "02cf8f53",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Комфорт    57529\n",
      "Эконом      4386\n",
      "Бизнес-      833\n",
      "Бизнес       292\n",
      "Name: Класс, dtype: int64\n",
      "Комфорт    57529\n",
      "Эконом      4386\n",
      "Бизнес      1125\n",
      "Name: Класс, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(df['Класс'].value_counts())\n",
    "df['Класс'].replace('Бизнес-', 'Бизнес', inplace=True)\n",
    "print(df['Класс'].value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "id": "5358ab0a",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.insert(2,'Расстояние до метро', 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "id": "635404ed",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "366"
      ]
     },
     "execution_count": 155,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(metro['Расстояние до метро'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "id": "0a8b0762",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "398"
      ]
     },
     "execution_count": 156,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(set(df['Адрес корпуса']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "id": "cd22cf13",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "29.7761005816732\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "metro['Расстояние до метро'].hist()\n",
    "print(metro['Расстояние до метро'].max())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "id": "519f1901",
   "metadata": {},
   "outputs": [],
   "source": [
    "d = {}\n",
    "for i in range(len(metro)):\n",
    "    d[metro.loc[i, 'Адрес']] = metro.loc[i, 'Расстояние до метро']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "id": "769e482e",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Район</th>\n",
       "      <th>Адрес корпуса</th>\n",
       "      <th>Расстояние до метро</th>\n",
       "      <th>Стадия строительной готовности на дату договора</th>\n",
       "      <th>Заявленный срок ввода в эксплуатацию</th>\n",
       "      <th>Старт продаж</th>\n",
       "      <th>Класс</th>\n",
       "      <th>Девелопер</th>\n",
       "      <th>Дата договора</th>\n",
       "      <th>Квартал договора</th>\n",
       "      <th>Дата регистрации</th>\n",
       "      <th>Тип сделки</th>\n",
       "      <th>Квартал регистрации</th>\n",
       "      <th>Тип объекта</th>\n",
       "      <th>Секция</th>\n",
       "      <th>Этаж</th>\n",
       "      <th>Количество комнат</th>\n",
       "      <th>Площадь согласно ПД</th>\n",
       "      <th>Цена за кв. метр</th>\n",
       "      <th>Метод определения цены</th>\n",
       "      <th>Включение скидок/акций в ценообразование</th>\n",
       "      <th>Скидки по дате договора</th>\n",
       "      <th>Отделка по корпусу</th>\n",
       "      <th>Тип продавца</th>\n",
       "      <th>Тип покупателя</th>\n",
       "      <th>Кол-во покупателей</th>\n",
       "      <th>Тип ипотеки</th>\n",
       "      <th>Участие объекта в оптовой сделке</th>\n",
       "      <th>Срок в экспозиции до момента сделки, дней</th>\n",
       "      <th>Рост цены за 1 кв.м за период экспонирования</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>НАО</td>\n",
       "      <td>Крекшино, д. 9 (3 оч.)</td>\n",
       "      <td>0</td>\n",
       "      <td>Строительство не начато</td>\n",
       "      <td>2020-06-30</td>\n",
       "      <td>2017-05-01</td>\n",
       "      <td>Комфорт</td>\n",
       "      <td>Barkli</td>\n",
       "      <td>2019-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>2019-02-18</td>\n",
       "      <td>Договор участия</td>\n",
       "      <td>1</td>\n",
       "      <td>квартира</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>студия</td>\n",
       "      <td>27.43</td>\n",
       "      <td>95325.34</td>\n",
       "      <td>Прайс совпадение площади</td>\n",
       "      <td>без скидки</td>\n",
       "      <td>Скидка 12% на ограниченный пул квартир. Скидка...</td>\n",
       "      <td>С отделкой</td>\n",
       "      <td>ЮЛ</td>\n",
       "      <td>ФЛ</td>\n",
       "      <td>1</td>\n",
       "      <td>Нет</td>\n",
       "      <td>Нет</td>\n",
       "      <td>н/д</td>\n",
       "      <td>н/д</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>НАО</td>\n",
       "      <td>Московский п., в районе Румянцево дер., уч. 17...</td>\n",
       "      <td>0</td>\n",
       "      <td>Монтажные и отделочные работы</td>\n",
       "      <td>2020-06-30</td>\n",
       "      <td>2018-04-01</td>\n",
       "      <td>Комфорт</td>\n",
       "      <td>Lexion Development</td>\n",
       "      <td>2019-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>2019-05-08</td>\n",
       "      <td>Договор участия</td>\n",
       "      <td>2</td>\n",
       "      <td>квартира</td>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "      <td>2</td>\n",
       "      <td>62.60</td>\n",
       "      <td>114000.00</td>\n",
       "      <td>Прайс совпадение площади</td>\n",
       "      <td>без скидки</td>\n",
       "      <td>Скидка 2% при 100% оплате или ипотеке. При еди...</td>\n",
       "      <td>Без отделки</td>\n",
       "      <td>ЮЛ</td>\n",
       "      <td>ФЛ</td>\n",
       "      <td>1</td>\n",
       "      <td>Нет</td>\n",
       "      <td>Нет</td>\n",
       "      <td>211</td>\n",
       "      <td>7,5%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>НАО</td>\n",
       "      <td>Московский п., в районе Румянцево дер., уч. 17...</td>\n",
       "      <td>0</td>\n",
       "      <td>Монтажные и отделочные работы</td>\n",
       "      <td>2020-06-30</td>\n",
       "      <td>2018-04-01</td>\n",
       "      <td>Комфорт</td>\n",
       "      <td>Lexion Development</td>\n",
       "      <td>2019-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>2019-05-08</td>\n",
       "      <td>Договор участия</td>\n",
       "      <td>2</td>\n",
       "      <td>квартира</td>\n",
       "      <td>13</td>\n",
       "      <td>22</td>\n",
       "      <td>студия</td>\n",
       "      <td>24.10</td>\n",
       "      <td>143000.00</td>\n",
       "      <td>Прайс совпадение площади</td>\n",
       "      <td>без скидки</td>\n",
       "      <td>Скидка 2% при 100% оплате или ипотеке. При еди...</td>\n",
       "      <td>Без отделки</td>\n",
       "      <td>ЮЛ</td>\n",
       "      <td>ФЛ</td>\n",
       "      <td>3</td>\n",
       "      <td>Нет</td>\n",
       "      <td>Нет</td>\n",
       "      <td>211</td>\n",
       "      <td>16,3%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>НАО</td>\n",
       "      <td>Крекшино, д. 5 (2 оч.)</td>\n",
       "      <td>0</td>\n",
       "      <td>Монтажные и отделочные работы</td>\n",
       "      <td>2019-09-30</td>\n",
       "      <td>2016-08-01</td>\n",
       "      <td>Комфорт</td>\n",
       "      <td>Barkli</td>\n",
       "      <td>2019-01-02</td>\n",
       "      <td>1</td>\n",
       "      <td>2019-10-14</td>\n",
       "      <td>Договор уступки</td>\n",
       "      <td>4</td>\n",
       "      <td>квартира</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>студия</td>\n",
       "      <td>28.22</td>\n",
       "      <td>102625.78</td>\n",
       "      <td>Прайс совпадение площади</td>\n",
       "      <td>без скидки</td>\n",
       "      <td>Скидка 12% на ограниченный пул квартир. Скидка...</td>\n",
       "      <td>Без отделки</td>\n",
       "      <td>ЮЛ</td>\n",
       "      <td>ФЛ</td>\n",
       "      <td>1</td>\n",
       "      <td>Ипотека</td>\n",
       "      <td>Нет</td>\n",
       "      <td>н/д</td>\n",
       "      <td>н/д</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>НАО</td>\n",
       "      <td>Московский п., в районе дер. Саларьево, к. 15</td>\n",
       "      <td>0</td>\n",
       "      <td>Монтажные и отделочные работы</td>\n",
       "      <td>2020-03-31</td>\n",
       "      <td>2018-02-01</td>\n",
       "      <td>Комфорт</td>\n",
       "      <td>ПИК</td>\n",
       "      <td>2019-01-03</td>\n",
       "      <td>1</td>\n",
       "      <td>2019-02-13</td>\n",
       "      <td>Договор участия</td>\n",
       "      <td>1</td>\n",
       "      <td>квартира</td>\n",
       "      <td>1</td>\n",
       "      <td>21</td>\n",
       "      <td>2</td>\n",
       "      <td>51.20</td>\n",
       "      <td>131700.00</td>\n",
       "      <td>Прайс совпадение площади</td>\n",
       "      <td>без скидки</td>\n",
       "      <td>Скидка до 2% при 100% оплате или ипотеке (дейс...</td>\n",
       "      <td>С отделкой</td>\n",
       "      <td>ЮЛ</td>\n",
       "      <td>ФЛ</td>\n",
       "      <td>1</td>\n",
       "      <td>Нет</td>\n",
       "      <td>Нет</td>\n",
       "      <td>275</td>\n",
       "      <td>15,0%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Район                                      Адрес корпуса  \\\n",
       "0   НАО                             Крекшино, д. 9 (3 оч.)   \n",
       "1   НАО  Московский п., в районе Румянцево дер., уч. 17...   \n",
       "2   НАО  Московский п., в районе Румянцево дер., уч. 17...   \n",
       "3   НАО                             Крекшино, д. 5 (2 оч.)   \n",
       "4   НАО      Московский п., в районе дер. Саларьево, к. 15   \n",
       "\n",
       "   Расстояние до метро Стадия строительной готовности на дату договора  \\\n",
       "0                    0                         Строительство не начато   \n",
       "1                    0                   Монтажные и отделочные работы   \n",
       "2                    0                   Монтажные и отделочные работы   \n",
       "3                    0                   Монтажные и отделочные работы   \n",
       "4                    0                   Монтажные и отделочные работы   \n",
       "\n",
       "  Заявленный срок ввода в эксплуатацию Старт продаж    Класс  \\\n",
       "0                           2020-06-30   2017-05-01  Комфорт   \n",
       "1                           2020-06-30   2018-04-01  Комфорт   \n",
       "2                           2020-06-30   2018-04-01  Комфорт   \n",
       "3                           2019-09-30   2016-08-01  Комфорт   \n",
       "4                           2020-03-31   2018-02-01  Комфорт   \n",
       "\n",
       "            Девелопер Дата договора  Квартал договора Дата регистрации  \\\n",
       "0              Barkli    2019-01-01                 1       2019-02-18   \n",
       "1  Lexion Development    2019-01-01                 1       2019-05-08   \n",
       "2  Lexion Development    2019-01-01                 1       2019-05-08   \n",
       "3              Barkli    2019-01-02                 1       2019-10-14   \n",
       "4                 ПИК    2019-01-03                 1       2019-02-13   \n",
       "\n",
       "        Тип сделки  Квартал регистрации Тип объекта  Секция  Этаж  \\\n",
       "0  Договор участия                    1    квартира       4     5   \n",
       "1  Договор участия                    2    квартира       4    11   \n",
       "2  Договор участия                    2    квартира      13    22   \n",
       "3  Договор уступки                    4    квартира       4     6   \n",
       "4  Договор участия                    1    квартира       1    21   \n",
       "\n",
       "  Количество комнат  Площадь согласно ПД  Цена за кв. метр  \\\n",
       "0            студия                27.43          95325.34   \n",
       "1                 2                62.60         114000.00   \n",
       "2            студия                24.10         143000.00   \n",
       "3            студия                28.22         102625.78   \n",
       "4                 2                51.20         131700.00   \n",
       "\n",
       "     Метод определения цены Включение скидок/акций в ценообразование  \\\n",
       "0  Прайс совпадение площади                               без скидки   \n",
       "1  Прайс совпадение площади                               без скидки   \n",
       "2  Прайс совпадение площади                               без скидки   \n",
       "3  Прайс совпадение площади                               без скидки   \n",
       "4  Прайс совпадение площади                               без скидки   \n",
       "\n",
       "                             Скидки по дате договора Отделка по корпусу  \\\n",
       "0  Скидка 12% на ограниченный пул квартир. Скидка...         С отделкой   \n",
       "1  Скидка 2% при 100% оплате или ипотеке. При еди...        Без отделки   \n",
       "2  Скидка 2% при 100% оплате или ипотеке. При еди...        Без отделки   \n",
       "3  Скидка 12% на ограниченный пул квартир. Скидка...        Без отделки   \n",
       "4  Скидка до 2% при 100% оплате или ипотеке (дейс...         С отделкой   \n",
       "\n",
       "  Тип продавца Тип покупателя  Кол-во покупателей Тип ипотеки  \\\n",
       "0           ЮЛ             ФЛ                   1         Нет   \n",
       "1           ЮЛ             ФЛ                   1         Нет   \n",
       "2           ЮЛ             ФЛ                   3         Нет   \n",
       "3           ЮЛ             ФЛ                   1     Ипотека   \n",
       "4           ЮЛ             ФЛ                   1         Нет   \n",
       "\n",
       "  Участие объекта в оптовой сделке Срок в экспозиции до момента сделки, дней  \\\n",
       "0                              Нет                                       н/д   \n",
       "1                              Нет                                       211   \n",
       "2                              Нет                                       211   \n",
       "3                              Нет                                       н/д   \n",
       "4                              Нет                                       275   \n",
       "\n",
       "  Рост цены за 1 кв.м за период экспонирования  \n",
       "0                                          н/д  \n",
       "1                                         7,5%  \n",
       "2                                        16,3%  \n",
       "3                                          н/д  \n",
       "4                                        15,0%  "
      ]
     },
     "execution_count": 159,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "id": "49df81ec",
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in range(len(df)):\n",
    "    try:\n",
    "        df.loc[i, 'Расстояние до метро'] = round(d[df.loc[i, 'Адрес корпуса']],2)\n",
    "    except:\n",
    "        pass\n",
    "df['Расстояние до метро'].replace(0, np.nan, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "id": "59a2e657",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "FINAL LENGTH 59346\n"
     ]
    }
   ],
   "source": [
    "df.dropna(inplace=True)\n",
    "print('FINAL LENGTH', len(df))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e061f371",
   "metadata": {},
   "source": [
    "# Anaaaaaalyze"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "id": "09c30d64",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Район</th>\n",
       "      <th>Адрес корпуса</th>\n",
       "      <th>Расстояние до метро</th>\n",
       "      <th>Стадия строительной готовности на дату договора</th>\n",
       "      <th>Заявленный срок ввода в эксплуатацию</th>\n",
       "      <th>Старт продаж</th>\n",
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       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>НАО</td>\n",
       "      <td>Крекшино, д. 9 (3 оч.)</td>\n",
       "      <td>15.10</td>\n",
       "      <td>Строительство не начато</td>\n",
       "      <td>2020-06-30</td>\n",
       "      <td>2017-05-01</td>\n",
       "      <td>Комфорт</td>\n",
       "      <td>Barkli</td>\n",
       "      <td>2019-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>2019-02-18</td>\n",
       "      <td>Договор участия</td>\n",
       "      <td>1</td>\n",
       "      <td>квартира</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>студия</td>\n",
       "      <td>27.43</td>\n",
       "      <td>95325.34</td>\n",
       "      <td>Прайс совпадение площади</td>\n",
       "      <td>без скидки</td>\n",
       "      <td>Скидка 12% на ограниченный пул квартир. Скидка...</td>\n",
       "      <td>С отделкой</td>\n",
       "      <td>ЮЛ</td>\n",
       "      <td>ФЛ</td>\n",
       "      <td>1</td>\n",
       "      <td>Нет</td>\n",
       "      <td>Нет</td>\n",
       "      <td>н/д</td>\n",
       "      <td>н/д</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>НАО</td>\n",
       "      <td>Московский п., в районе Румянцево дер., уч. 17...</td>\n",
       "      <td>0.13</td>\n",
       "      <td>Монтажные и отделочные работы</td>\n",
       "      <td>2020-06-30</td>\n",
       "      <td>2018-04-01</td>\n",
       "      <td>Комфорт</td>\n",
       "      <td>Lexion Development</td>\n",
       "      <td>2019-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>2019-05-08</td>\n",
       "      <td>Договор участия</td>\n",
       "      <td>2</td>\n",
       "      <td>квартира</td>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "      <td>2</td>\n",
       "      <td>62.60</td>\n",
       "      <td>114000.00</td>\n",
       "      <td>Прайс совпадение площади</td>\n",
       "      <td>без скидки</td>\n",
       "      <td>Скидка 2% при 100% оплате или ипотеке. При еди...</td>\n",
       "      <td>Без отделки</td>\n",
       "      <td>ЮЛ</td>\n",
       "      <td>ФЛ</td>\n",
       "      <td>1</td>\n",
       "      <td>Нет</td>\n",
       "      <td>Нет</td>\n",
       "      <td>211</td>\n",
       "      <td>7,5%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>НАО</td>\n",
       "      <td>Московский п., в районе Румянцево дер., уч. 17...</td>\n",
       "      <td>0.13</td>\n",
       "      <td>Монтажные и отделочные работы</td>\n",
       "      <td>2020-06-30</td>\n",
       "      <td>2018-04-01</td>\n",
       "      <td>Комфорт</td>\n",
       "      <td>Lexion Development</td>\n",
       "      <td>2019-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>2019-05-08</td>\n",
       "      <td>Договор участия</td>\n",
       "      <td>2</td>\n",
       "      <td>квартира</td>\n",
       "      <td>13</td>\n",
       "      <td>22</td>\n",
       "      <td>студия</td>\n",
       "      <td>24.10</td>\n",
       "      <td>143000.00</td>\n",
       "      <td>Прайс совпадение площади</td>\n",
       "      <td>без скидки</td>\n",
       "      <td>Скидка 2% при 100% оплате или ипотеке. При еди...</td>\n",
       "      <td>Без отделки</td>\n",
       "      <td>ЮЛ</td>\n",
       "      <td>ФЛ</td>\n",
       "      <td>3</td>\n",
       "      <td>Нет</td>\n",
       "      <td>Нет</td>\n",
       "      <td>211</td>\n",
       "      <td>16,3%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Район                                      Адрес корпуса  \\\n",
       "0   НАО                             Крекшино, д. 9 (3 оч.)   \n",
       "1   НАО  Московский п., в районе Румянцево дер., уч. 17...   \n",
       "2   НАО  Московский п., в районе Румянцево дер., уч. 17...   \n",
       "\n",
       "   Расстояние до метро Стадия строительной готовности на дату договора  \\\n",
       "0                15.10                         Строительство не начато   \n",
       "1                 0.13                   Монтажные и отделочные работы   \n",
       "2                 0.13                   Монтажные и отделочные работы   \n",
       "\n",
       "  Заявленный срок ввода в эксплуатацию Старт продаж    Класс  \\\n",
       "0                           2020-06-30   2017-05-01  Комфорт   \n",
       "1                           2020-06-30   2018-04-01  Комфорт   \n",
       "2                           2020-06-30   2018-04-01  Комфорт   \n",
       "\n",
       "            Девелопер Дата договора  Квартал договора Дата регистрации  \\\n",
       "0              Barkli    2019-01-01                 1       2019-02-18   \n",
       "1  Lexion Development    2019-01-01                 1       2019-05-08   \n",
       "2  Lexion Development    2019-01-01                 1       2019-05-08   \n",
       "\n",
       "        Тип сделки  Квартал регистрации Тип объекта  Секция  Этаж  \\\n",
       "0  Договор участия                    1    квартира       4     5   \n",
       "1  Договор участия                    2    квартира       4    11   \n",
       "2  Договор участия                    2    квартира      13    22   \n",
       "\n",
       "  Количество комнат  Площадь согласно ПД  Цена за кв. метр  \\\n",
       "0            студия                27.43          95325.34   \n",
       "1                 2                62.60         114000.00   \n",
       "2            студия                24.10         143000.00   \n",
       "\n",
       "     Метод определения цены Включение скидок/акций в ценообразование  \\\n",
       "0  Прайс совпадение площади                               без скидки   \n",
       "1  Прайс совпадение площади                               без скидки   \n",
       "2  Прайс совпадение площади                               без скидки   \n",
       "\n",
       "                             Скидки по дате договора Отделка по корпусу  \\\n",
       "0  Скидка 12% на ограниченный пул квартир. Скидка...         С отделкой   \n",
       "1  Скидка 2% при 100% оплате или ипотеке. При еди...        Без отделки   \n",
       "2  Скидка 2% при 100% оплате или ипотеке. При еди...        Без отделки   \n",
       "\n",
       "  Тип продавца Тип покупателя  Кол-во покупателей Тип ипотеки  \\\n",
       "0           ЮЛ             ФЛ                   1         Нет   \n",
       "1           ЮЛ             ФЛ                   1         Нет   \n",
       "2           ЮЛ             ФЛ                   3         Нет   \n",
       "\n",
       "  Участие объекта в оптовой сделке Срок в экспозиции до момента сделки, дней  \\\n",
       "0                              Нет                                       н/д   \n",
       "1                              Нет                                       211   \n",
       "2                              Нет                                       211   \n",
       "\n",
       "  Рост цены за 1 кв.м за период экспонирования  \n",
       "0                                          н/д  \n",
       "1                                         7,5%  \n",
       "2                                        16,3%  "
      ]
     },
     "execution_count": 162,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "id": "d9133605",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 200x300 with 0 Axes>"
      ]
     },
     "execution_count": 163,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 200x300 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "figure(figsize=(2, 3), dpi=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "id": "e3926923",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 164,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# figure(figsize=(10, 5), dpi=300)\n",
    "import matplotlib.pyplot as plt\n",
    "plt.xticks(rotation=0)\n",
    "df['Девелопер'].value_counts(sort=True, ascending=True).plot.barh()\n",
    "# plt.savefig(f'developer.jpg', bbox_inches = 'tight', dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "id": "d3893b3c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 165,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# figure(figsize=(10, 5), dpi=300)\n",
    "\n",
    "plt.xticks(rotation=0)\n",
    "df['Метод определения цены'].value_counts(sort=True, ascending=True).plot.barh()\n",
    "# plt.savefig(f'Метод определения цены.jpg', bbox_inches = 'tight', dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "id": "84dd2e9f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 166,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# figure(figsize=(10, 5), dpi=300)\n",
    "\n",
    "plt.xticks(rotation=0)\n",
    "df['Стадия строительной готовности на дату договора'].value_counts(sort=True, ascending=True).plot.barh()\n",
    "# plt.savefig(f'Стадия строительной готовности на дату договора.jpg', bbox_inches = 'tight', dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "id": "c6a89d80",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 400x200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "figure(figsize=(4, 2), dpi=100)\n",
    "for i in ['Район',\n",
    "        'Класс', \n",
    "         'Тип сделки', 'Тип объекта', 'Тип продавца',\n",
    "       'Тип покупателя', 'Тип ипотеки', 'Участие объекта в оптовой сделке']:\n",
    "    plt.title(i)\n",
    "    plot = df[i].value_counts(sort=True, ascending=True).plot.bar()\n",
    "    \n",
    "    \n",
    "#     for bar in plot.patches:\n",
    "#         plot.annotate(format(bar.get_height(), '.2f'),\n",
    "#                        (bar.get_x() + bar.get_width() / 2,\n",
    "#                         bar.get_height()), ha='center', va='center',\n",
    "#                        size=5, xytext=(0, 3),\n",
    "#                        textcoords='offset points')\n",
    "    \n",
    "    plt.xticks(rotation=0)\n",
    "#     plt.savefig(f'{i}.jpg', bbox_inches = 'tight', dpi=300)\n",
    "#     print(plt.show())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7992d60b",
   "metadata": {},
   "source": [
    "## Распределения"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "id": "2cfb091c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 168,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['Цена за кв. метр'].hist()  \n",
    "# plt.savefig(f'Цена за кв. метр.jpg', bbox_inches = 'tight', dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "id": "0b4813d6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 169,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['Площадь согласно ПД'].hist()\n",
    "# plt.savefig(f'Площадь.jpg', bbox_inches = 'tight', dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "id": "95279aa6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 170,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['Расстояние до метро'].hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "id": "b439951a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "29.78"
      ]
     },
     "execution_count": 171,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "max(df['Расстояние до метро'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "acce4a4f",
   "metadata": {},
   "source": [
    "## Transforming for ML"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "id": "1909783a",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import preprocessing\n",
    "\n",
    "label_dict = {}\n",
    "\n",
    "def label_encode(name):\n",
    "    le = preprocessing.LabelEncoder()\n",
    "    df[name] = le.fit_transform(df[name])\n",
    "    keys = le.classes_\n",
    "    values = le.transform(le.classes_)\n",
    "    dictionary = dict(zip(keys, values))\n",
    "    label_dict.update(dictionary)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "id": "4bcf136d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Район</th>\n",
       "      <th>Адрес корпуса</th>\n",
       "      <th>Расстояние до метро</th>\n",
       "      <th>Стадия строительной готовности на дату договора</th>\n",
       "      <th>Заявленный срок ввода в эксплуатацию</th>\n",
       "      <th>Старт продаж</th>\n",
       "      <th>Класс</th>\n",
       "      <th>Девелопер</th>\n",
       "      <th>Дата договора</th>\n",
       "      <th>Квартал договора</th>\n",
       "      <th>Дата регистрации</th>\n",
       "      <th>Тип сделки</th>\n",
       "      <th>Квартал регистрации</th>\n",
       "      <th>Тип объекта</th>\n",
       "      <th>Секция</th>\n",
       "      <th>Этаж</th>\n",
       "      <th>Количество комнат</th>\n",
       "      <th>Площадь согласно ПД</th>\n",
       "      <th>Цена за кв. метр</th>\n",
       "      <th>Метод определения цены</th>\n",
       "      <th>Включение скидок/акций в ценообразование</th>\n",
       "      <th>Скидки по дате договора</th>\n",
       "      <th>Отделка по корпусу</th>\n",
       "      <th>Тип продавца</th>\n",
       "      <th>Тип покупателя</th>\n",
       "      <th>Кол-во покупателей</th>\n",
       "      <th>Тип ипотеки</th>\n",
       "      <th>Участие объекта в оптовой сделке</th>\n",
       "      <th>Срок в экспозиции до момента сделки, дней</th>\n",
       "      <th>Рост цены за 1 кв.м за период экспонирования</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>НАО</td>\n",
       "      <td>Крекшино, д. 9 (3 оч.)</td>\n",
       "      <td>15.10</td>\n",
       "      <td>Строительство не начато</td>\n",
       "      <td>2020-06-30</td>\n",
       "      <td>2017-05-01</td>\n",
       "      <td>Комфорт</td>\n",
       "      <td>Barkli</td>\n",
       "      <td>2019-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>2019-02-18</td>\n",
       "      <td>Договор участия</td>\n",
       "      <td>1</td>\n",
       "      <td>квартира</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>студия</td>\n",
       "      <td>27.43</td>\n",
       "      <td>95325.34</td>\n",
       "      <td>Прайс совпадение площади</td>\n",
       "      <td>без скидки</td>\n",
       "      <td>Скидка 12% на ограниченный пул квартир. Скидка...</td>\n",
       "      <td>С отделкой</td>\n",
       "      <td>ЮЛ</td>\n",
       "      <td>ФЛ</td>\n",
       "      <td>1</td>\n",
       "      <td>Нет</td>\n",
       "      <td>Нет</td>\n",
       "      <td>н/д</td>\n",
       "      <td>н/д</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>НАО</td>\n",
       "      <td>Московский п., в районе Румянцево дер., уч. 17...</td>\n",
       "      <td>0.13</td>\n",
       "      <td>Монтажные и отделочные работы</td>\n",
       "      <td>2020-06-30</td>\n",
       "      <td>2018-04-01</td>\n",
       "      <td>Комфорт</td>\n",
       "      <td>Lexion Development</td>\n",
       "      <td>2019-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>2019-05-08</td>\n",
       "      <td>Договор участия</td>\n",
       "      <td>2</td>\n",
       "      <td>квартира</td>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "      <td>2</td>\n",
       "      <td>62.60</td>\n",
       "      <td>114000.00</td>\n",
       "      <td>Прайс совпадение площади</td>\n",
       "      <td>без скидки</td>\n",
       "      <td>Скидка 2% при 100% оплате или ипотеке. При еди...</td>\n",
       "      <td>Без отделки</td>\n",
       "      <td>ЮЛ</td>\n",
       "      <td>ФЛ</td>\n",
       "      <td>1</td>\n",
       "      <td>Нет</td>\n",
       "      <td>Нет</td>\n",
       "      <td>211</td>\n",
       "      <td>7,5%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>НАО</td>\n",
       "      <td>Московский п., в районе Румянцево дер., уч. 17...</td>\n",
       "      <td>0.13</td>\n",
       "      <td>Монтажные и отделочные работы</td>\n",
       "      <td>2020-06-30</td>\n",
       "      <td>2018-04-01</td>\n",
       "      <td>Комфорт</td>\n",
       "      <td>Lexion Development</td>\n",
       "      <td>2019-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>2019-05-08</td>\n",
       "      <td>Договор участия</td>\n",
       "      <td>2</td>\n",
       "      <td>квартира</td>\n",
       "      <td>13</td>\n",
       "      <td>22</td>\n",
       "      <td>студия</td>\n",
       "      <td>24.10</td>\n",
       "      <td>143000.00</td>\n",
       "      <td>Прайс совпадение площади</td>\n",
       "      <td>без скидки</td>\n",
       "      <td>Скидка 2% при 100% оплате или ипотеке. При еди...</td>\n",
       "      <td>Без отделки</td>\n",
       "      <td>ЮЛ</td>\n",
       "      <td>ФЛ</td>\n",
       "      <td>3</td>\n",
       "      <td>Нет</td>\n",
       "      <td>Нет</td>\n",
       "      <td>211</td>\n",
       "      <td>16,3%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Район                                      Адрес корпуса  \\\n",
       "0   НАО                             Крекшино, д. 9 (3 оч.)   \n",
       "1   НАО  Московский п., в районе Румянцево дер., уч. 17...   \n",
       "2   НАО  Московский п., в районе Румянцево дер., уч. 17...   \n",
       "\n",
       "   Расстояние до метро Стадия строительной готовности на дату договора  \\\n",
       "0                15.10                         Строительство не начато   \n",
       "1                 0.13                   Монтажные и отделочные работы   \n",
       "2                 0.13                   Монтажные и отделочные работы   \n",
       "\n",
       "  Заявленный срок ввода в эксплуатацию Старт продаж    Класс  \\\n",
       "0                           2020-06-30   2017-05-01  Комфорт   \n",
       "1                           2020-06-30   2018-04-01  Комфорт   \n",
       "2                           2020-06-30   2018-04-01  Комфорт   \n",
       "\n",
       "            Девелопер Дата договора  Квартал договора Дата регистрации  \\\n",
       "0              Barkli    2019-01-01                 1       2019-02-18   \n",
       "1  Lexion Development    2019-01-01                 1       2019-05-08   \n",
       "2  Lexion Development    2019-01-01                 1       2019-05-08   \n",
       "\n",
       "        Тип сделки  Квартал регистрации Тип объекта  Секция  Этаж  \\\n",
       "0  Договор участия                    1    квартира       4     5   \n",
       "1  Договор участия                    2    квартира       4    11   \n",
       "2  Договор участия                    2    квартира      13    22   \n",
       "\n",
       "  Количество комнат  Площадь согласно ПД  Цена за кв. метр  \\\n",
       "0            студия                27.43          95325.34   \n",
       "1                 2                62.60         114000.00   \n",
       "2            студия                24.10         143000.00   \n",
       "\n",
       "     Метод определения цены Включение скидок/акций в ценообразование  \\\n",
       "0  Прайс совпадение площади                               без скидки   \n",
       "1  Прайс совпадение площади                               без скидки   \n",
       "2  Прайс совпадение площади                               без скидки   \n",
       "\n",
       "                             Скидки по дате договора Отделка по корпусу  \\\n",
       "0  Скидка 12% на ограниченный пул квартир. Скидка...         С отделкой   \n",
       "1  Скидка 2% при 100% оплате или ипотеке. При еди...        Без отделки   \n",
       "2  Скидка 2% при 100% оплате или ипотеке. При еди...        Без отделки   \n",
       "\n",
       "  Тип продавца Тип покупателя  Кол-во покупателей Тип ипотеки  \\\n",
       "0           ЮЛ             ФЛ                   1         Нет   \n",
       "1           ЮЛ             ФЛ                   1         Нет   \n",
       "2           ЮЛ             ФЛ                   3         Нет   \n",
       "\n",
       "  Участие объекта в оптовой сделке Срок в экспозиции до момента сделки, дней  \\\n",
       "0                              Нет                                       н/д   \n",
       "1                              Нет                                       211   \n",
       "2                              Нет                                       211   \n",
       "\n",
       "  Рост цены за 1 кв.м за период экспонирования  \n",
       "0                                          н/д  \n",
       "1                                         7,5%  \n",
       "2                                        16,3%  "
      ]
     },
     "execution_count": 173,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "id": "d47c0888",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 174,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Тип ипотеки'].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "id": "0f882bed",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 175,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Район'].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "id": "14c42306",
   "metadata": {},
   "outputs": [],
   "source": [
    "label_encode('Район')\n",
    "label_encode('Тип сделки')\n",
    "label_encode('Тип объекта')\n",
    "label_encode('Метод определения цены')\n",
    "label_encode('Включение скидок/акций в ценообразование')\n",
    "label_encode('Тип продавца')\n",
    "label_encode('Тип покупателя')\n",
    "label_encode('Тип ипотеки')\n",
    "label_encode('Участие объекта в оптовой сделке')\n",
    "label_encode('Стадия строительной готовности на дату договора')\n",
    "label_encode('Класс')\n",
    "label_encode('Девелопер')\n",
    "label_encode('Отделка по корпусу')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "id": "d988fa29",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'НАО': 0,\n",
       " 'ТАО': 1,\n",
       " 'Договор уступки': 0,\n",
       " 'Договор участия': 1,\n",
       " 'апартаменты': 0,\n",
       " 'квартира': 1,\n",
       " 'Аналитика по корпусу': 0,\n",
       " 'Аналитика по типу комнатности': 1,\n",
       " 'Крайние значения обновлений': 2,\n",
       " 'Прайс совпадение площади': 3,\n",
       " 'Прайс точное совпадение': 4,\n",
       " 'Цена по последнему историческому слою': 5,\n",
       " 'Цена позже даты сделки': 6,\n",
       " 'без скидки': 0,\n",
       " 'со скидкой': 1,\n",
       " 'со скидкой и без скидки': 2,\n",
       " 'ФЛ': 0,\n",
       " 'ЮЛ': 1,\n",
       " 'Ипотека': 0,\n",
       " 'Нет': 1,\n",
       " 'ДА': 0,\n",
       " 'Введен в эксплуатацию': 0,\n",
       " 'Монтажные и отделочные работы': 1,\n",
       " 'Начало монтажных работ': 2,\n",
       " 'Получение РВЭ, благоустройство территории': 3,\n",
       " 'Работы нулевого цикла': 4,\n",
       " 'Строительство не начато': 5,\n",
       " 'Бизнес': 0,\n",
       " 'Комфорт': 1,\n",
       " 'Эконом': 2,\n",
       " 'Barkli': 0,\n",
       " 'INGRAD': 1,\n",
       " 'Lexion Development': 2,\n",
       " 'PPF Real Estate': 3,\n",
       " 'Sezar Group': 4,\n",
       " 'А101': 5,\n",
       " 'Абсолют Недвижимость': 6,\n",
       " 'Аквилон Инвест': 7,\n",
       " 'ГК Родина': 8,\n",
       " 'Гранель': 9,\n",
       " 'Инвесттраст': 10,\n",
       " 'Крост': 11,\n",
       " 'МИЦ': 12,\n",
       " 'Миэль Загородная Недвижимость': 13,\n",
       " 'ПИК': 14,\n",
       " 'СЗ СК Ключ': 15,\n",
       " 'Самолет': 16,\n",
       " 'СтройИнвестТопаз': 17,\n",
       " 'ТБ-Инвест': 18,\n",
       " 'УЭЗ': 19,\n",
       " 'Центрстрой': 20,\n",
       " 'Без отделки': 0,\n",
       " 'Без отделки, под чистовую и с отделкой': 1,\n",
       " 'Без отделки, с отделкой и мебель как доп. опция': 2,\n",
       " 'Отделка как доп. опция': 3,\n",
       " 'Под чистовую': 4,\n",
       " 'Под чистовую и без отделки': 5,\n",
       " 'Под чистовую и отделка как доп. опция': 6,\n",
       " 'Под чистовую и с отделкой': 7,\n",
       " 'С отделкой': 8,\n",
       " 'С отделкой и без': 9,\n",
       " 'С отделкой и мебелью': 10,\n",
       " 'С отделкой и отделка как доп. опция': 11,\n",
       " 'С отделкой, мебель как доп. опция': 12}"
      ]
     },
     "execution_count": 178,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#LABELS\n",
    "# label_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "id": "50e8b3ff",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Количество комнат'] = df['Количество комнат'].replace('студия',0)\n",
    "df['Количество комнат'] = df['Количество комнат'].astype(str).astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "id": "b4359bec",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.drop('Адрес корпуса', axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "id": "d976413d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# max может влиять на модель, проверить с min\n",
    "import re\n",
    "c = 0\n",
    "discounts = []\n",
    "df['Скидки по дате договора'] = df['Скидки по дате договора'].replace(float('nan'),'')\n",
    "for i in df['Скидки по дате договора']:\n",
    "    found = \"\".join(re.findall(r'Скидка \\d+%|Скидка до \\d+%',i))\n",
    "    if len(found) != 0:\n",
    "        max_discount = max([int(i) for i in re.findall('[0-9]+',found)])\n",
    "        discounts.append(max_discount)\n",
    "    else:\n",
    "        discounts.append(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "id": "5b3389a9",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Скидки по дате договора'] = discounts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "id": "ff014064",
   "metadata": {},
   "outputs": [],
   "source": [
    "# н/д в 0 может повлиять на модель!\n",
    "\n",
    "df['Срок в экспозиции до момента сделки, дней'] = df['Срок в экспозиции до момента сделки, дней'].replace('н/д', 0)\n",
    "df['Срок в экспозиции до момента сделки, дней'] = df['Срок в экспозиции до момента сделки, дней'].astype(str).astype(int)\n",
    "df['Срок в экспозиции до момента сделки, дней'] = df['Срок в экспозиции до момента сделки, дней'].abs()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "id": "651f7946",
   "metadata": {},
   "outputs": [],
   "source": [
    "price = df['Рост цены за 1 кв.м за период экспонирования']\n",
    "price = [float(i.split('%')[0].replace(',','.')) if '%' in i else i for i in price]\n",
    "df['Рост цены за 1 кв.м за период экспонирования'] = price\n",
    "df['Рост цены за 1 кв.м за период экспонирования'] = df['Рост цены за 1 кв.м за период экспонирования'].replace('н/д', 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "id": "56d7483c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2017-05-01 00:00:00')"
      ]
     },
     "execution_count": 185,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Старт продаж'][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "id": "632d1c84",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2020-06-30 00:00:00')"
      ]
     },
     "execution_count": 186,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Заявленный срок ввода в эксплуатацию'][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "id": "ad0bd910",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "df.insert(4, 'Срок ввода в эксплуатацию - Старт продаж' ,(df['Заявленный срок ввода в эксплуатацию'] - df['Старт продаж']).dt.days)\n",
    "\n",
    "\n",
    "df.insert(8, 'Срок ввода в эксплуатацию - Договор' ,(df['Заявленный срок ввода в эксплуатацию']-df['Дата договора']).dt.days)\n",
    "\n",
    "\n",
    "df = df.drop(['Заявленный срок ввода в эксплуатацию','Дата договора', 'Старт продаж', 'Дата регистрации'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 269,
   "id": "b61a3dfe",
   "metadata": {},
   "outputs": [],
   "source": [
    "# BOXPLOTS\n",
    "# for i in df.columns:\n",
    "#     print(i)\n",
    "#     figure(figsize=(5, 2), dpi=50)\n",
    "# #     sns.boxplot(x=i,data=df)\n",
    "#     try:\n",
    "#         df[i].plot.box()\n",
    "#         print(plt.show())\n",
    "#     except:\n",
    "#         pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "id": "552ef0fa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "59346"
      ]
     },
     "execution_count": 189,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# df = df[df['Кол-во покупателей'] < 5]\n",
    "len(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "id": "ec135b80",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Район</th>\n",
       "      <th>Расстояние до метро</th>\n",
       "      <th>Стадия строительной готовности на дату договора</th>\n",
       "      <th>Срок ввода в эксплуатацию - Старт продаж</th>\n",
       "      <th>Класс</th>\n",
       "      <th>Девелопер</th>\n",
       "      <th>Срок ввода в эксплуатацию - Договор</th>\n",
       "      <th>Квартал договора</th>\n",
       "      <th>Тип сделки</th>\n",
       "      <th>Квартал регистрации</th>\n",
       "      <th>Тип объекта</th>\n",
       "      <th>Секция</th>\n",
       "      <th>Этаж</th>\n",
       "      <th>Количество комнат</th>\n",
       "      <th>Площадь согласно ПД</th>\n",
       "      <th>Цена за кв. метр</th>\n",
       "      <th>Метод определения цены</th>\n",
       "      <th>Включение скидок/акций в ценообразование</th>\n",
       "      <th>Скидки по дате договора</th>\n",
       "      <th>Отделка по корпусу</th>\n",
       "      <th>Тип продавца</th>\n",
       "      <th>Тип покупателя</th>\n",
       "      <th>Кол-во покупателей</th>\n",
       "      <th>Тип ипотеки</th>\n",
       "      <th>Участие объекта в оптовой сделке</th>\n",
       "      <th>Срок в экспозиции до момента сделки, дней</th>\n",
       "      <th>Рост цены за 1 кв.м за период экспонирования</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>15.10</td>\n",
       "      <td>5</td>\n",
       "      <td>1156</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>546</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>27.43</td>\n",
       "      <td>95325.34</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>0.13</td>\n",
       "      <td>1</td>\n",
       "      <td>821</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>546</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "      <td>2</td>\n",
       "      <td>62.60</td>\n",
       "      <td>114000.00</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>211</td>\n",
       "      <td>7.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>0.13</td>\n",
       "      <td>1</td>\n",
       "      <td>821</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>546</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>13</td>\n",
       "      <td>22</td>\n",
       "      <td>0</td>\n",
       "      <td>24.10</td>\n",
       "      <td>143000.00</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>211</td>\n",
       "      <td>16.3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Район  Расстояние до метро  \\\n",
       "0      0                15.10   \n",
       "1      0                 0.13   \n",
       "2      0                 0.13   \n",
       "\n",
       "   Стадия строительной готовности на дату договора  \\\n",
       "0                                                5   \n",
       "1                                                1   \n",
       "2                                                1   \n",
       "\n",
       "   Срок ввода в эксплуатацию - Старт продаж  Класс  Девелопер  \\\n",
       "0                                      1156      1          0   \n",
       "1                                       821      1          2   \n",
       "2                                       821      1          2   \n",
       "\n",
       "   Срок ввода в эксплуатацию - Договор  Квартал договора  Тип сделки  \\\n",
       "0                                  546                 1           1   \n",
       "1                                  546                 1           1   \n",
       "2                                  546                 1           1   \n",
       "\n",
       "   Квартал регистрации  Тип объекта  Секция  Этаж  Количество комнат  \\\n",
       "0                    1            1       4     5                  0   \n",
       "1                    2            1       4    11                  2   \n",
       "2                    2            1      13    22                  0   \n",
       "\n",
       "   Площадь согласно ПД  Цена за кв. метр  Метод определения цены  \\\n",
       "0                27.43          95325.34                       3   \n",
       "1                62.60         114000.00                       3   \n",
       "2                24.10         143000.00                       3   \n",
       "\n",
       "   Включение скидок/акций в ценообразование  Скидки по дате договора  \\\n",
       "0                                         0                       12   \n",
       "1                                         0                        2   \n",
       "2                                         0                        2   \n",
       "\n",
       "   Отделка по корпусу  Тип продавца  Тип покупателя  Кол-во покупателей  \\\n",
       "0                   8             1               0                   1   \n",
       "1                   0             1               0                   1   \n",
       "2                   0             1               0                   3   \n",
       "\n",
       "   Тип ипотеки  Участие объекта в оптовой сделке  \\\n",
       "0            1                                 1   \n",
       "1            1                                 1   \n",
       "2            1                                 1   \n",
       "\n",
       "   Срок в экспозиции до момента сделки, дней  \\\n",
       "0                                          0   \n",
       "1                                        211   \n",
       "2                                        211   \n",
       "\n",
       "   Рост цены за 1 кв.м за период экспонирования  \n",
       "0                                           0.0  \n",
       "1                                           7.5  \n",
       "2                                          16.3  "
      ]
     },
     "execution_count": 190,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "id": "411b1bbe",
   "metadata": {},
   "outputs": [],
   "source": [
    "# df.head(5).to_csv('ml.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "id": "420fe1c7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# data = pd.read_csv('data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "id": "a0ee874f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# data.head(5).to_csv('data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "id": "d33e4bc7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# df.to_excel('ml.xlsx')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "id": "ead4a66f",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x288 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "cols = list(df.columns)\n",
    "cols.remove('Цена за кв. метр')\n",
    "fig, (ax1, ax2, ax3, ax4, ax5, ax6) = plt.subplots(nrows=1, ncols=6)\n",
    "\n",
    "ax1.violinplot(df['Район'], showmedians=True)\n",
    "ax2.violinplot(df['Девелопер'], showmedians=True)\n",
    "ax3.violinplot(df['Площадь согласно ПД'], showmedians=True)\n",
    "ax4.violinplot(df['Отделка по корпусу'], showmedians=True)\n",
    "ax5.violinplot(df['Класс'], showmedians=True)\n",
    "ax6.violinplot(df['Цена за кв. метр'], showmedians=True)\n",
    "\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "id": "30d65d55",
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.set(rc={'figure.figsize':(15,10)})\n",
    "\n",
    "cor = df.corr()\n",
    "cor = cor[abs(cor) >= 0.5]\n",
    "cor = cor[cor != 1.0]\n",
    "cor.replace(np.nan,0,inplace=True)\n",
    "cor = cor.drop_duplicates()\n",
    "for col in cor.columns:\n",
    "    if len(set(list(cor[col].values))) == 1:\n",
    "        cor = cor.drop([col], axis=1)\n",
    "cor.replace(0,np.nan,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "id": "a00f4896",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Район', 'Расстояние до метро', 'Стадия строительной готовности на дату договора', 'Срок ввода в эксплуатацию - Старт продаж', 'Класс', 'Девелопер', 'Срок ввода в эксплуатацию - Договор', 'Квартал договора', 'Тип сделки', 'Квартал регистрации', 'Тип объекта', 'Секция', 'Этаж', 'Количество комнат', 'Площадь согласно ПД', 'Цена за кв. метр', 'Метод определения цены', 'Включение скидок/акций в ценообразование', 'Скидки по дате договора', 'Отделка по корпусу', 'Тип продавца', 'Тип покупателя', 'Кол-во покупателей', 'Тип ипотеки', 'Участие объекта в оптовой сделке', 'Срок в экспозиции до момента сделки, дней', 'Рост цены за 1 кв.м за период экспонирования']\n"
     ]
    }
   ],
   "source": [
    "# sns.set(font_scale=1)\n",
    "# sns.heatmap(cor, annot=True)\n",
    "# plt.savefig('corr.png', bbox_inches='tight')\n",
    "# plt.show()\n",
    "df.head()\n",
    "print(list(df.columns))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "id": "3e0b7810",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "#  ЦЕНА ОТ ВСЕГО\n",
    "# cols = list(df.columns)\n",
    "# for col1 in cols:\n",
    "#     sns.set(rc={'figure.figsize':(8,4)})\n",
    "#     ax = sns.scatterplot(x=df[col1], y=df['Цена за кв. метр'])\n",
    "#     print(plt.show())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "id": "fd9ab1e1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# # ЦЕНА ОТ ВСЕГО + РАЗНЫЕ КЛАССЫ\n",
    "# cols = list(df.columns)\n",
    "# for col1 in cols:\n",
    "#     for i in range(3):\n",
    "#             print({0: 'Бизнес',\n",
    "#      1: 'Комфорт',\n",
    "#      2: 'Эконом',}[i].upper())\n",
    "#             sns.set(rc={'figure.figsize':(8,4)})\n",
    "#             ax = sns.scatterplot(x=df[df['Класс']==i][col1], y=df[df['Класс']==i]['Цена за кв. метр']*df[df['Класс']==i]['Площадь согласно ПД'])\n",
    "#             print(plt.show())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 272,
   "id": "d8a33659",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Площадь согласно ПД', ylabel='Цена за кв. метр'>"
      ]
     },
     "execution_count": 272,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import math\n",
    "sns.set(rc={'figure.figsize':(8,4)})\n",
    "# sns.scatterplot(x='Площадь согласно ПД', y='Цена за кв. метр', data=df)\n",
    "\n",
    "sns.regplot(x=(df[\"Площадь согласно ПД\"]), y=np.log(df[\"Цена за кв. метр\"]),\n",
    "             scatter_kws={\"s\": 2,},\n",
    "                line_kws={'color':'red'},\n",
    "            order=3.9, ci=100, fit_reg=True, seed=42, truncate=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "id": "f90d2d11",
   "metadata": {},
   "outputs": [],
   "source": [
    "# cols = list(df.columns)\n",
    "# for col1 in cols:\n",
    "#     for i in range(2):\n",
    "#             print({0: 'НАО',\n",
    "#      1: 'ТАО'}[i].upper())\n",
    "#             sns.set(rc={'figure.figsize':(8,4)})\n",
    "#             ax = sns.scatterplot(x=df[df['Район']==i][col1], y=df[df['Район']==i]['Цена за кв. метр'])\n",
    "#             print(plt.show())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "id": "faefd576",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import Ridge\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.model_selection import validation_curve"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "id": "994e857b",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df[['Этаж', 'Площадь согласно ПД', 'Район', 'Класс', 'Расстояние до метро', 'Девелопер']]\n",
    "y = df['Цена за кв. метр']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "id": "6fd42146",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 262,
   "id": "d606bae1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ridge(alpha=1)"
      ]
     },
     "execution_count": 262,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf = Ridge(alpha=1)\n",
    "clf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 263,
   "id": "7f52a9d6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.2941154590083067"
      ]
     },
     "execution_count": 263,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e866f796",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "direction": "ltr",
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}