data_cleaning_example.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "10914fcd",
   "metadata": {},
   "source": [
    "# Очистка данных на примере датасета о квартирах в Москве"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "d736459f-4c1e-4e3c-a6b2-e41147b3fd60",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "35a13914-88e3-4f8e-8083-d5d5ca24fe4b",
   "metadata": {},
   "source": [
    "### Знакомство с данными"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5d0f67f4",
   "metadata": {},
   "source": [
    "Подробнее о данных можно узнать [здесь](./README.md)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "a335aae4-20f0-496f-a3ca-477990729d23",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of data: (30471, 61)\n"
     ]
    },
    {
     "data": {
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       "<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>id</th>\n",
       "      <th>full_sq</th>\n",
       "      <th>life_sq</th>\n",
       "      <th>floor</th>\n",
       "      <th>sub_area</th>\n",
       "      <th>preschool_quota</th>\n",
       "      <th>preschool_education_centers_raion</th>\n",
       "      <th>school_quota</th>\n",
       "      <th>school_education_centers_raion</th>\n",
       "      <th>school_education_centers_top_20_raion</th>\n",
       "      <th>...</th>\n",
       "      <th>office_km</th>\n",
       "      <th>additional_education_km</th>\n",
       "      <th>preschool_km</th>\n",
       "      <th>big_church_km</th>\n",
       "      <th>church_synagogue_km</th>\n",
       "      <th>theater_km</th>\n",
       "      <th>museum_km</th>\n",
       "      <th>ecology</th>\n",
       "      <th>mosque_count_1000</th>\n",
       "      <th>price_doc</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>43</td>\n",
       "      <td>27.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Bibirevo</td>\n",
       "      <td>5001.0</td>\n",
       "      <td>5</td>\n",
       "      <td>11065.0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.637189</td>\n",
       "      <td>0.947962</td>\n",
       "      <td>0.177975</td>\n",
       "      <td>0.625783</td>\n",
       "      <td>0.628187</td>\n",
       "      <td>14.053047</td>\n",
       "      <td>7.389498</td>\n",
       "      <td>good</td>\n",
       "      <td>0</td>\n",
       "      <td>5850000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>34</td>\n",
       "      <td>19.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Nagatinskij Zaton</td>\n",
       "      <td>3119.0</td>\n",
       "      <td>5</td>\n",
       "      <td>6237.0</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.688796</td>\n",
       "      <td>1.072315</td>\n",
       "      <td>0.273345</td>\n",
       "      <td>0.967821</td>\n",
       "      <td>0.471447</td>\n",
       "      <td>6.829889</td>\n",
       "      <td>0.709260</td>\n",
       "      <td>excellent</td>\n",
       "      <td>0</td>\n",
       "      <td>6000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>43</td>\n",
       "      <td>29.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Tekstil'shhiki</td>\n",
       "      <td>1463.0</td>\n",
       "      <td>4</td>\n",
       "      <td>5580.0</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.543049</td>\n",
       "      <td>0.391957</td>\n",
       "      <td>0.158072</td>\n",
       "      <td>3.178751</td>\n",
       "      <td>0.755946</td>\n",
       "      <td>4.273200</td>\n",
       "      <td>3.156423</td>\n",
       "      <td>poor</td>\n",
       "      <td>0</td>\n",
       "      <td>5700000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>89</td>\n",
       "      <td>50.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>Mitino</td>\n",
       "      <td>6839.0</td>\n",
       "      <td>9</td>\n",
       "      <td>17063.0</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.934273</td>\n",
       "      <td>0.892674</td>\n",
       "      <td>0.236455</td>\n",
       "      <td>1.031777</td>\n",
       "      <td>1.561505</td>\n",
       "      <td>16.990677</td>\n",
       "      <td>16.041521</td>\n",
       "      <td>good</td>\n",
       "      <td>0</td>\n",
       "      <td>13100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>77</td>\n",
       "      <td>77.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Basmannoe</td>\n",
       "      <td>3240.0</td>\n",
       "      <td>7</td>\n",
       "      <td>7770.0</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.077901</td>\n",
       "      <td>0.810801</td>\n",
       "      <td>0.376838</td>\n",
       "      <td>0.378756</td>\n",
       "      <td>0.121681</td>\n",
       "      <td>1.112486</td>\n",
       "      <td>1.800125</td>\n",
       "      <td>excellent</td>\n",
       "      <td>0</td>\n",
       "      <td>16331452</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 61 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   id  full_sq  life_sq  floor           sub_area  preschool_quota  \\\n",
       "0   1       43     27.0    4.0           Bibirevo           5001.0   \n",
       "1   2       34     19.0    3.0  Nagatinskij Zaton           3119.0   \n",
       "2   3       43     29.0    2.0     Tekstil'shhiki           1463.0   \n",
       "3   4       89     50.0    9.0             Mitino           6839.0   \n",
       "4   5       77     77.0    4.0          Basmannoe           3240.0   \n",
       "\n",
       "   preschool_education_centers_raion  school_quota  \\\n",
       "0                                  5       11065.0   \n",
       "1                                  5        6237.0   \n",
       "2                                  4        5580.0   \n",
       "3                                  9       17063.0   \n",
       "4                                  7        7770.0   \n",
       "\n",
       "   school_education_centers_raion  school_education_centers_top_20_raion  ...  \\\n",
       "0                               5                                      0  ...   \n",
       "1                               8                                      0  ...   \n",
       "2                               7                                      0  ...   \n",
       "3                              10                                      0  ...   \n",
       "4                               9                                      0  ...   \n",
       "\n",
       "   office_km  additional_education_km  preschool_km  big_church_km  \\\n",
       "0   0.637189                 0.947962      0.177975       0.625783   \n",
       "1   0.688796                 1.072315      0.273345       0.967821   \n",
       "2   1.543049                 0.391957      0.158072       3.178751   \n",
       "3   0.934273                 0.892674      0.236455       1.031777   \n",
       "4   0.077901                 0.810801      0.376838       0.378756   \n",
       "\n",
       "   church_synagogue_km  theater_km  museum_km    ecology mosque_count_1000  \\\n",
       "0             0.628187   14.053047   7.389498       good                 0   \n",
       "1             0.471447    6.829889   0.709260  excellent                 0   \n",
       "2             0.755946    4.273200   3.156423       poor                 0   \n",
       "3             1.561505   16.990677  16.041521       good                 0   \n",
       "4             0.121681    1.112486   1.800125  excellent                 0   \n",
       "\n",
       "  price_doc  \n",
       "0   5850000  \n",
       "1   6000000  \n",
       "2   5700000  \n",
       "3  13100000  \n",
       "4  16331452  \n",
       "\n",
       "[5 rows x 61 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sber_data = pd.read_csv('data/sber_data.csv')\n",
    "print('Shape of data: {}'.format(sber_data.shape))\n",
    "sber_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "ee513d42",
   "metadata": {},
   "outputs": [],
   "source": [
    "#display(sber_data.info());"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c02b8f38-2b46-4099-9de6-9b64a41e3e85",
   "metadata": {},
   "source": [
    "### Обнаружение пропусков"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "af8e2cb6-2d34-4cd1-972c-df5007f22bed",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th>id</th>\n",
       "      <th>full_sq</th>\n",
       "      <th>life_sq</th>\n",
       "      <th>floor</th>\n",
       "      <th>sub_area</th>\n",
       "      <th>preschool_quota</th>\n",
       "      <th>preschool_education_centers_raion</th>\n",
       "      <th>school_quota</th>\n",
       "      <th>school_education_centers_raion</th>\n",
       "      <th>school_education_centers_top_20_raion</th>\n",
       "      <th>...</th>\n",
       "      <th>office_km</th>\n",
       "      <th>additional_education_km</th>\n",
       "      <th>preschool_km</th>\n",
       "      <th>big_church_km</th>\n",
       "      <th>church_synagogue_km</th>\n",
       "      <th>theater_km</th>\n",
       "      <th>museum_km</th>\n",
       "      <th>ecology</th>\n",
       "      <th>mosque_count_1000</th>\n",
       "      <th>price_doc</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>30466</th>\n",
       "      <td>False</td>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30469</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <td>False</td>\n",
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      ],
      "text/plain": [
       "          id  full_sq  life_sq  floor  sub_area  preschool_quota  \\\n",
       "30466  False    False    False  False     False            False   \n",
       "30467  False    False    False  False     False            False   \n",
       "30468  False    False     True  False     False             True   \n",
       "30469  False    False    False  False     False            False   \n",
       "30470  False    False    False  False     False            False   \n",
       "\n",
       "       preschool_education_centers_raion  school_quota  \\\n",
       "30466                              False         False   \n",
       "30467                              False         False   \n",
       "30468                              False          True   \n",
       "30469                              False         False   \n",
       "30470                              False         False   \n",
       "\n",
       "       school_education_centers_raion  school_education_centers_top_20_raion  \\\n",
       "30466                           False                                  False   \n",
       "30467                           False                                  False   \n",
       "30468                           False                                  False   \n",
       "30469                           False                                  False   \n",
       "30470                           False                                  False   \n",
       "\n",
       "       ...  office_km  additional_education_km  preschool_km  big_church_km  \\\n",
       "30466  ...      False                    False         False          False   \n",
       "30467  ...      False                    False         False          False   \n",
       "30468  ...      False                    False         False          False   \n",
       "30469  ...      False                    False         False          False   \n",
       "30470  ...      False                    False         False          False   \n",
       "\n",
       "       church_synagogue_km  theater_km  museum_km  ecology  mosque_count_1000  \\\n",
       "30466                False       False      False    False              False   \n",
       "30467                False       False      False    False              False   \n",
       "30468                False       False      False    False              False   \n",
       "30469                False       False      False    False              False   \n",
       "30470                False       False      False    False              False   \n",
       "\n",
       "       price_doc  \n",
       "30466      False  \n",
       "30467      False  \n",
       "30468      False  \n",
       "30469      False  \n",
       "30470      False  \n",
       "\n",
       "[5 rows x 61 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(sber_data.isnull().tail())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b86413b9-805f-4261-8dbd-f7bf61767d18",
   "metadata": {},
   "source": [
    "**1. Список столбцов с пропусками**\n",
    "\n",
    "Первый способ — это вывести на экран названия столбцов, где число пропусков больше 0. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "1fa35a47-5008-4230-8883-78339b8f6677",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "hospital_beds_raion          47.392603\n",
       "preschool_quota              21.948738\n",
       "school_quota                 21.938893\n",
       "life_sq                      20.947786\n",
       "floor                         0.548062\n",
       "metro_min_walk                0.082045\n",
       "metro_km_walk                 0.082045\n",
       "railroad_station_walk_km      0.082045\n",
       "railroad_station_walk_min     0.082045\n",
       "dtype: float64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cols_null_persent = sber_data.isnull().mean() * 100\n",
    "cols_with_null = cols_null_persent[cols_null_persent>0].sort_values(ascending=False)\n",
    "display(cols_with_null)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "54fdb86e",
   "metadata": {},
   "source": [
    "Итак, можно увидеть, что у нас большое число пропусков (более 47 %) в столбце hospital_beds_raion (количество больничных коек в округе). \n",
    "\n",
    "Далее у нас идут столбцы с числом пропусков чуть больше 20 %: \n",
    "\n",
    "* preschool_quota (число мест в детском саду в районе);\n",
    "* school_quota (число мест в школах в районе);\n",
    "* life_sq (жилая площадь здания в квадратных метрах). \n",
    "\n",
    "Менее одного процента пропусков содержат признаки:\n",
    "\n",
    "* floor (число этажей в доме);\n",
    "* metro_min_walk (время от дома до ближайшего метро пешком в минутах);\n",
    "* metro_km_walk (расстояние до ближайшего метро в километрах);\n",
    "* railroad_station_walk_km (расстояние до ближайшей ж. д. станции в километрах);\n",
    "* railroad_station_walk_min (время до ближайшей ж. д. станции пешком в минутах). "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b5012889-66b1-44d8-be05-b8573d89862b",
   "metadata": {},
   "source": [
    "**2. Столбчатая диаграмма пропусков**\n",
    "\n",
    "Иногда столбцов с пропусками становится слишком много и прочитать информацию о них из списка признаков с цифрами становится слишком затруднительно — цифры начинают сливаться воедино. \n",
    "\n",
    "Можно воспользоваться столбчатой диаграммой, чтобы визуально оценить соотношение числа пропусков к числу записей."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "e255b245-f764-4c49-935a-362e562ba61c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cols_with_null.plot(\n",
    "    kind='bar',\n",
    "    figsize=(10, 4),\n",
    "    title='Распределение пропусков в данных'\n",
    ");"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f9ffdb03-2062-4934-9824-bf22e217cf14",
   "metadata": {},
   "source": [
    "**3. Тепловая карта пропусков**\n",
    "\n",
    "Ещё один распространённый способ визуализации пропусков — тепловая карта. \n",
    "\n",
    "Её часто используют, когда столбцов с пропусками не так много (меньше 10). Она позволяет понять не только соотношение пропусков в данных, но и их характерное местоположение в таблице. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "0d5f9438-c3e5-48c5-9a16-7b3ce7c402a2",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "colors = ['blue', 'yellow'] \n",
    "fig = plt.figure(figsize=(10, 4))\n",
    "cols = cols_with_null.index\n",
    "ax = sns.heatmap(\n",
    "    sber_data[cols].isnull(),\n",
    "    cmap=sns.color_palette(colors),\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "133b2d9a",
   "metadata": {},
   "source": [
    "На полученной тепловой карте мы не видим чётких процентных соотношений для числа пропусков в данных, однако мы можем увидеть места их концентрации в таблице. Например, видно, что признаки preschool_quota и school_quota очень сильно связаны друг с другом по части пропусков: во всех записях, где хотя бы один не определён, не указан и второй (жёлтые линии для двух этих признаков полностью совпадают друг с другом)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "df4c91db",
   "metadata": {},
   "source": [
    "### Методы обработки пропусков"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "00b74c2b",
   "metadata": {},
   "source": [
    "**1. Отбрасывание записей и признаков**\n",
    "\n",
    "Первая техника самая простая из всех, она предполагает простое удаление записей или признаков, в которых содержатся пропуски. Здесь важно правильно выбрать ось удаления: если мы избавимся от большого числа строк, то мы рискуем потерять важные данные, а если мы удалим столбцы, то мы можем потерять важные признаки.\n",
    "\n",
    "<img src= https://raw.githubusercontent.com/AndreyRysistov/DatasetsForPandas/main/drop_na.jpg>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "ac65348b-b0bc-43a7-86cd-5eae3dff4319",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id                                       0.0\n",
       "full_sq                                  0.0\n",
       "life_sq                                  0.0\n",
       "floor                                    0.0\n",
       "sub_area                                 0.0\n",
       "preschool_quota                          0.0\n",
       "preschool_education_centers_raion        0.0\n",
       "school_quota                             0.0\n",
       "school_education_centers_raion           0.0\n",
       "school_education_centers_top_20_raion    0.0\n",
       "healthcare_centers_raion                 0.0\n",
       "university_top_20_raion                  0.0\n",
       "sport_objects_raion                      0.0\n",
       "additional_education_raion               0.0\n",
       "culture_objects_top_25_raion             0.0\n",
       "shopping_centers_raion                   0.0\n",
       "office_raion                             0.0\n",
       "oil_chemistry_raion                      0.0\n",
       "railroad_terminal_raion                  0.0\n",
       "nuclear_reactor_raion                    0.0\n",
       "metro_min_avto                           0.0\n",
       "metro_km_avto                            0.0\n",
       "metro_min_walk                           0.0\n",
       "metro_km_walk                            0.0\n",
       "kindergarten_km                          0.0\n",
       "school_km                                0.0\n",
       "park_km                                  0.0\n",
       "green_zone_km                            0.0\n",
       "industrial_km                            0.0\n",
       "railroad_station_walk_km                 0.0\n",
       "railroad_station_walk_min                0.0\n",
       "public_transport_station_km              0.0\n",
       "public_transport_station_min_walk        0.0\n",
       "mkad_km                                  0.0\n",
       "ttk_km                                   0.0\n",
       "sadovoe_km                               0.0\n",
       "bulvar_ring_km                           0.0\n",
       "kremlin_km                               0.0\n",
       "big_road1_1line                          0.0\n",
       "big_market_km                            0.0\n",
       "market_shop_km                           0.0\n",
       "fitness_km                               0.0\n",
       "swim_pool_km                             0.0\n",
       "ice_rink_km                              0.0\n",
       "stadium_km                               0.0\n",
       "basketball_km                            0.0\n",
       "hospice_morgue_km                        0.0\n",
       "university_km                            0.0\n",
       "workplaces_km                            0.0\n",
       "shopping_centers_km                      0.0\n",
       "office_km                                0.0\n",
       "additional_education_km                  0.0\n",
       "preschool_km                             0.0\n",
       "big_church_km                            0.0\n",
       "church_synagogue_km                      0.0\n",
       "theater_km                               0.0\n",
       "museum_km                                0.0\n",
       "ecology                                  0.0\n",
       "mosque_count_1000                        0.0\n",
       "price_doc                                0.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#создаем копию исходной таблицы\n",
    "drop_data = sber_data.copy()\n",
    "#задаем минимальный порог: вычисляем 70% от числа строк\n",
    "thresh = drop_data.shape[0]*0.7\n",
    "#удаляем столбцы, в которых более 30% (100-70) пропусков\n",
    "drop_data = drop_data.dropna(thresh=thresh, axis=1)\n",
    "#удаляем записи, в которых есть хотя бы 1 пропуск\n",
    "drop_data = drop_data.dropna(axis=0)\n",
    "#отображаем результирующую долю пропусков\n",
    "drop_data.isnull().mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "7e985b96-8d97-405b-87a4-cbb12432c712",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(20888, 60)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "drop_data.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "802d85ba",
   "metadata": {},
   "source": [
    "Итак, мы удалили один столбец и ⅓ всех записей для того, чтобы избавиться от пропусков. Не слишком ли это много и что с этим можно сделать?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "08dbdc43-5647-4866-92d0-fa9f664a99d5",
   "metadata": {},
   "source": [
    "**2. Заполнение недостающих значений**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ddb9f33b",
   "metadata": {},
   "source": [
    "Другой способ бороться с пропусками - заполнить их константами. Чаще всего пустые места заполняют средним/медианой/модой для числовых признаков и модальным значением для категориальных признаков.\n",
    "\n",
    "Недостаток данного метода - искажение истинных распределений признаков\n",
    "<img src = https://raw.githubusercontent.com/AndreyRysistov/DatasetsForPandas/main/fill_na.jpg>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c63d8e59",
   "metadata": {},
   "source": [
    "Рассмотрим распределения признаков до заполнения"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "e777f146-9f29-40be-ba06-93b997c0523c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 2000x800 with 9 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cols = cols_with_null.index\n",
    "sber_data[cols].hist(figsize=(20, 8));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "0c4c6493-5c3c-47f5-8616-a0fba5eea942",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id                   0.0\n",
       "full_sq              0.0\n",
       "life_sq              0.0\n",
       "floor                0.0\n",
       "sub_area             0.0\n",
       "                    ... \n",
       "theater_km           0.0\n",
       "museum_km            0.0\n",
       "ecology              0.0\n",
       "mosque_count_1000    0.0\n",
       "price_doc            0.0\n",
       "Length: 61, dtype: float64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#создаем копию исходной таблицы\n",
    "fill_data = sber_data.copy()\n",
    "#создаем словарь имя столбца: число(признак) на который надо заменить пропуски\n",
    "values = {\n",
    "    'life_sq': fill_data['full_sq'],\n",
    "    'metro_min_walk': fill_data['metro_min_walk'].median(),\n",
    "    'metro_km_walk': fill_data['metro_km_walk'].median(),\n",
    "    'railroad_station_walk_km': fill_data['railroad_station_walk_km'].median(),\n",
    "    'railroad_station_walk_min': fill_data['railroad_station_walk_min'].median(),\n",
    "    'hospital_beds_raion': fill_data['hospital_beds_raion'].mode()[0],\n",
    "    'preschool_quota': fill_data['preschool_quota'].mode()[0],\n",
    "    'school_quota': fill_data['school_quota'].mode()[0],\n",
    "    'floor': fill_data['floor'].mode()[0]\n",
    "}\n",
    "#заполняем пропуски в соответствии с заявленным словарем\n",
    "fill_data = fill_data.fillna(values)\n",
    "#выводим результирующую долю пропусков\n",
    "fill_data.isnull().mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6b29f83a",
   "metadata": {},
   "source": [
    "Рассмотрим распределения признаков после заполнения пропусков константами"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "947f1523-1258-454f-a9f4-13e2d02a6847",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2000x800 with 9 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cols = cols_with_null.index\n",
    "fill_data[cols].hist(figsize=(20, 8));"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c82340a2",
   "metadata": {},
   "source": [
    "Обратим внимание на то, как сильно изменилось распределение для признака hospital_beds_raion. Это связано с тем, что мы заполнили модальным значением почти 47 % общих данных. В результате мы кардинально исказили исходное распределение признака, что может плохо сказаться на модели."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "25d21025-bfe2-40bc-9a3d-f1a0a39393ed",
   "metadata": {},
   "source": [
    "**3. Заполнение недостающих значений с добавлением признака-индикатора**\n",
    "\n",
    "Если мы используем заполнение пропусков константами, то может имеет смысл сказать модели о том, что на этом месте был пропуск? Давайте добавим к нашим данным признаки-индикаторы, которые будут сигнализировать о том, что в столбце на определенном месте в таблице был пропуск, это место в столбце- индикаторе будем помечать как True. Эта эвристика пытается снизить влияние искажения признака, указав модели на места, где мы \"нафантазировали\" данные.\n",
    "\n",
    "<img src=https://raw.githubusercontent.com/AndreyRysistov/DatasetsForPandas/main/indicator_na.jpg>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "9fa32fff-abc5-47cb-9f65-c9bc02df8eb4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id                                    0.0\n",
       "full_sq                               0.0\n",
       "life_sq                               0.0\n",
       "floor                                 0.0\n",
       "sub_area                              0.0\n",
       "                                     ... \n",
       "floor_was_null                        0.0\n",
       "metro_min_walk_was_null               0.0\n",
       "metro_km_walk_was_null                0.0\n",
       "railroad_station_walk_km_was_null     0.0\n",
       "railroad_station_walk_min_was_null    0.0\n",
       "Length: 70, dtype: float64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#создаем копию исходной таблицы\n",
    "indicator_data = sber_data.copy()\n",
    "#в цикле пробегаемся по названиям столбцов с пропусками\n",
    "for col in cols_with_null.index:\n",
    "    #создаем новый признак-индикатор как col_was_null\n",
    "    indicator_data[col + '_was_null'] = indicator_data[col].isnull()\n",
    "#создаем словарь имя столбца: число(признак) на который надо заменить пропуски   \n",
    "values = {\n",
    "    'life_sq': indicator_data['full_sq'],\n",
    "    'metro_min_walk': indicator_data['metro_min_walk'].median(),\n",
    "    'metro_km_walk': indicator_data['metro_km_walk'].median(),\n",
    "    'railroad_station_walk_km': indicator_data['railroad_station_walk_km'].median(),\n",
    "    'railroad_station_walk_min': indicator_data['railroad_station_walk_min'].median(),\n",
    "    'hospital_beds_raion': indicator_data['hospital_beds_raion'].mode()[0],\n",
    "    'preschool_quota': indicator_data['preschool_quota'].mode()[0],\n",
    "    'school_quota': indicator_data['school_quota'].mode()[0],\n",
    "    'floor': indicator_data['floor'].mode()[0]\n",
    "}\n",
    "#заполняем пропуски в соответствии с заявленным словарем\n",
    "indicator_data = indicator_data.fillna(values)\n",
    "#выводим результирующую долю пропусков\n",
    "indicator_data.isnull().mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "46c4ccae",
   "metadata": {},
   "source": [
    "Метод исходит из предположения, что, если дать модели информацию о том, что в ячейке ранее была пустота, то она будет меньше доверять таким записям и меньше учитывать её в процессе обучения. Иногда такие фишки действительно работают, иногда не дают эффекта, а иногда и вовсе могут ухудшить результат обучения и затруднить процесс обучения."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "489ba777-cebe-44a9-b26e-9174a204029e",
   "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>id</th>\n",
       "      <th>full_sq</th>\n",
       "      <th>life_sq</th>\n",
       "      <th>floor</th>\n",
       "      <th>sub_area</th>\n",
       "      <th>preschool_quota</th>\n",
       "      <th>preschool_education_centers_raion</th>\n",
       "      <th>school_quota</th>\n",
       "      <th>school_education_centers_raion</th>\n",
       "      <th>school_education_centers_top_20_raion</th>\n",
       "      <th>...</th>\n",
       "      <th>price_doc</th>\n",
       "      <th>hospital_beds_raion_was_null</th>\n",
       "      <th>preschool_quota_was_null</th>\n",
       "      <th>school_quota_was_null</th>\n",
       "      <th>life_sq_was_null</th>\n",
       "      <th>floor_was_null</th>\n",
       "      <th>metro_min_walk_was_null</th>\n",
       "      <th>metro_km_walk_was_null</th>\n",
       "      <th>railroad_station_walk_km_was_null</th>\n",
       "      <th>railroad_station_walk_min_was_null</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>43</td>\n",
       "      <td>27.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Bibirevo</td>\n",
       "      <td>5001.0</td>\n",
       "      <td>5</td>\n",
       "      <td>11065.0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>5850000</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>34</td>\n",
       "      <td>19.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Nagatinskij Zaton</td>\n",
       "      <td>3119.0</td>\n",
       "      <td>5</td>\n",
       "      <td>6237.0</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>6000000</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>43</td>\n",
       "      <td>29.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Tekstil'shhiki</td>\n",
       "      <td>1463.0</td>\n",
       "      <td>4</td>\n",
       "      <td>5580.0</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>5700000</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>89</td>\n",
       "      <td>50.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>Mitino</td>\n",
       "      <td>6839.0</td>\n",
       "      <td>9</td>\n",
       "      <td>17063.0</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>13100000</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>77</td>\n",
       "      <td>77.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Basmannoe</td>\n",
       "      <td>3240.0</td>\n",
       "      <td>7</td>\n",
       "      <td>7770.0</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>16331452</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 70 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   id  full_sq  life_sq  floor           sub_area  preschool_quota  \\\n",
       "0   1       43     27.0    4.0           Bibirevo           5001.0   \n",
       "1   2       34     19.0    3.0  Nagatinskij Zaton           3119.0   \n",
       "2   3       43     29.0    2.0     Tekstil'shhiki           1463.0   \n",
       "3   4       89     50.0    9.0             Mitino           6839.0   \n",
       "4   5       77     77.0    4.0          Basmannoe           3240.0   \n",
       "\n",
       "   preschool_education_centers_raion  school_quota  \\\n",
       "0                                  5       11065.0   \n",
       "1                                  5        6237.0   \n",
       "2                                  4        5580.0   \n",
       "3                                  9       17063.0   \n",
       "4                                  7        7770.0   \n",
       "\n",
       "   school_education_centers_raion  school_education_centers_top_20_raion  ...  \\\n",
       "0                               5                                      0  ...   \n",
       "1                               8                                      0  ...   \n",
       "2                               7                                      0  ...   \n",
       "3                              10                                      0  ...   \n",
       "4                               9                                      0  ...   \n",
       "\n",
       "   price_doc  hospital_beds_raion_was_null  preschool_quota_was_null  \\\n",
       "0    5850000                         False                     False   \n",
       "1    6000000                         False                     False   \n",
       "2    5700000                         False                     False   \n",
       "3   13100000                          True                     False   \n",
       "4   16331452                         False                     False   \n",
       "\n",
       "   school_quota_was_null  life_sq_was_null  floor_was_null  \\\n",
       "0                  False             False           False   \n",
       "1                  False             False           False   \n",
       "2                  False             False           False   \n",
       "3                  False             False           False   \n",
       "4                  False             False           False   \n",
       "\n",
       "   metro_min_walk_was_null  metro_km_walk_was_null  \\\n",
       "0                    False                   False   \n",
       "1                    False                   False   \n",
       "2                    False                   False   \n",
       "3                    False                   False   \n",
       "4                    False                   False   \n",
       "\n",
       "  railroad_station_walk_km_was_null railroad_station_walk_min_was_null  \n",
       "0                             False                              False  \n",
       "1                             False                              False  \n",
       "2                             False                              False  \n",
       "3                             False                              False  \n",
       "4                             False                              False  \n",
       "\n",
       "[5 rows x 70 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(indicator_data.head())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7a87d37b-9863-43ba-880f-36c43077553b",
   "metadata": {},
   "source": [
    "**4. Комбинирование методов**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ec900231",
   "metadata": {},
   "source": [
    "Методы можно комбинировать. Например, мы можем:\n",
    "\n",
    "* удалить столбцы, в которых более 30 % пропусков;\n",
    "* удалить записи, в которых более двух пропусков одновременно;\n",
    "* заполнить оставшиеся ячейки константами."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "a1c8c825-1066-4b9c-b1ec-409fa649aff6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id                                       0.0\n",
       "full_sq                                  0.0\n",
       "life_sq                                  0.0\n",
       "floor                                    0.0\n",
       "sub_area                                 0.0\n",
       "preschool_quota                          0.0\n",
       "preschool_education_centers_raion        0.0\n",
       "school_quota                             0.0\n",
       "school_education_centers_raion           0.0\n",
       "school_education_centers_top_20_raion    0.0\n",
       "healthcare_centers_raion                 0.0\n",
       "university_top_20_raion                  0.0\n",
       "sport_objects_raion                      0.0\n",
       "additional_education_raion               0.0\n",
       "culture_objects_top_25_raion             0.0\n",
       "shopping_centers_raion                   0.0\n",
       "office_raion                             0.0\n",
       "oil_chemistry_raion                      0.0\n",
       "railroad_terminal_raion                  0.0\n",
       "nuclear_reactor_raion                    0.0\n",
       "metro_min_avto                           0.0\n",
       "metro_km_avto                            0.0\n",
       "metro_min_walk                           0.0\n",
       "metro_km_walk                            0.0\n",
       "kindergarten_km                          0.0\n",
       "school_km                                0.0\n",
       "park_km                                  0.0\n",
       "green_zone_km                            0.0\n",
       "industrial_km                            0.0\n",
       "railroad_station_walk_km                 0.0\n",
       "railroad_station_walk_min                0.0\n",
       "public_transport_station_km              0.0\n",
       "public_transport_station_min_walk        0.0\n",
       "mkad_km                                  0.0\n",
       "ttk_km                                   0.0\n",
       "sadovoe_km                               0.0\n",
       "bulvar_ring_km                           0.0\n",
       "kremlin_km                               0.0\n",
       "big_road1_1line                          0.0\n",
       "big_market_km                            0.0\n",
       "market_shop_km                           0.0\n",
       "fitness_km                               0.0\n",
       "swim_pool_km                             0.0\n",
       "ice_rink_km                              0.0\n",
       "stadium_km                               0.0\n",
       "basketball_km                            0.0\n",
       "hospice_morgue_km                        0.0\n",
       "university_km                            0.0\n",
       "workplaces_km                            0.0\n",
       "shopping_centers_km                      0.0\n",
       "office_km                                0.0\n",
       "additional_education_km                  0.0\n",
       "preschool_km                             0.0\n",
       "big_church_km                            0.0\n",
       "church_synagogue_km                      0.0\n",
       "theater_km                               0.0\n",
       "museum_km                                0.0\n",
       "ecology                                  0.0\n",
       "mosque_count_1000                        0.0\n",
       "price_doc                                0.0\n",
       "dtype: float64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#создаем копию исходной таблицы\n",
    "combine_data = sber_data.copy()\n",
    "\n",
    "#отбрасываем столбцы с числом пропусков более 30% (100-70)\n",
    "n = combine_data.shape[0] #число строк в таблице\n",
    "thresh = n*0.7\n",
    "combine_data = combine_data.dropna(thresh=thresh, axis=1)\n",
    "\n",
    "#отбрасываем строки с числом пропусков более 2 в строке\n",
    "m = combine_data.shape[1] #число признаков после удаления столбцов\n",
    "combine_data = combine_data.dropna(thresh=m-2, axis=0)\n",
    "\n",
    "#создаем словарь имя столбца: число(признак) на который надо заменить пропуски \n",
    "values = {\n",
    "    'life_sq': combine_data['full_sq'],\n",
    "    'metro_min_walk': combine_data['metro_min_walk'].median(),\n",
    "    'metro_km_walk': combine_data['metro_km_walk'].median(),\n",
    "    'railroad_station_walk_km': combine_data['railroad_station_walk_km'].median(),\n",
    "    'railroad_station_walk_min': combine_data['railroad_station_walk_min'].median(),\n",
    "    'preschool_quota': combine_data['preschool_quota'].mode()[0],\n",
    "    'school_quota': combine_data['school_quota'].mode()[0],\n",
    "    'floor': combine_data['floor'].mode()[0]\n",
    "}\n",
    "#заполняем оставшиеся записи константами в соответствии со словарем values\n",
    "combine_data = combine_data.fillna(values)\n",
    "#выводим результирующую долю пропусков\n",
    "display(combine_data.isnull().mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "f800ee52-1afb-4c21-bbd5-1f87b17fe2ec",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(26800, 60)\n"
     ]
    }
   ],
   "source": [
    "print(combine_data.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c9ac6435-0980-4457-b159-5e1efd333404",
   "metadata": {},
   "source": [
    "### Методы выявления выбросов"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7fda25a4-248c-4831-ba1b-596fcd059c71",
   "metadata": {},
   "source": [
    "**1. Использование статистики и здравого смысла**\n",
    "\n",
    "Это самый трудоёмкий метод, основанный на житейской логике, методе пристального взгляда и небольшом количестве статистики. Он предполагает поиск невозможных и нелогичных значений в данных.\n",
    "\n",
    "Пусть у нас есть признак, по которому мы будем искать выбросы. Давайте рассчитаем его статистические показатели (минимум, максимум, среднее, квантили) и по ним попробуем определить наличие выбросов."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "5b9528d7-f5f5-4ba7-abde-6294013ebc2e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    24088.000000\n",
       "mean        34.403271\n",
       "std         52.285733\n",
       "min          0.000000\n",
       "25%         20.000000\n",
       "50%         30.000000\n",
       "75%         43.000000\n",
       "max       7478.000000\n",
       "Name: life_sq, dtype: float64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sber_data['life_sq'].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "34eb11b0",
   "metadata": {},
   "source": [
    "Что нам говорит метод describe()? Во-первых, у нас есть квартиры с нулевой жилой площадью. Во-вторых, в то время как 75-й квантиль равен 43, максимум превышает 7 тысяч квадратных метров (целый дворец, а не квартира!). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "abe6d837-ca18-40ff-a03b-839f54d6d664",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "45\n"
     ]
    }
   ],
   "source": [
    "print(sber_data[sber_data['life_sq'] == 0].shape[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "580ea58d",
   "metadata": {},
   "source": [
    "Таких помещений оказывается 45 штук. Что это за зверь?\n",
    "\n",
    "Это могут быть апартаменты — вид коммерческой недвижимости, которая юридически не является жилой площадью, но законно продаётся по ценам, сопоставимым с квартирными. Оставим их в покое.    "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "978ece7a",
   "metadata": {},
   "source": [
    "А теперь выведем здания с жилой площадью более 1 000 квадратных метров:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "2042b152-4dd9-4b06-91bb-ff5cfe5bb226",
   "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>id</th>\n",
       "      <th>full_sq</th>\n",
       "      <th>life_sq</th>\n",
       "      <th>floor</th>\n",
       "      <th>sub_area</th>\n",
       "      <th>preschool_quota</th>\n",
       "      <th>preschool_education_centers_raion</th>\n",
       "      <th>school_quota</th>\n",
       "      <th>school_education_centers_raion</th>\n",
       "      <th>school_education_centers_top_20_raion</th>\n",
       "      <th>...</th>\n",
       "      <th>office_km</th>\n",
       "      <th>additional_education_km</th>\n",
       "      <th>preschool_km</th>\n",
       "      <th>big_church_km</th>\n",
       "      <th>church_synagogue_km</th>\n",
       "      <th>theater_km</th>\n",
       "      <th>museum_km</th>\n",
       "      <th>ecology</th>\n",
       "      <th>mosque_count_1000</th>\n",
       "      <th>price_doc</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>13546</th>\n",
       "      <td>13549</td>\n",
       "      <td>79</td>\n",
       "      <td>7478.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>Poselenie Voskresenskoe</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>5.072389</td>\n",
       "      <td>1.210613</td>\n",
       "      <td>0.826052</td>\n",
       "      <td>1.582378</td>\n",
       "      <td>0.756119</td>\n",
       "      <td>21.472421</td>\n",
       "      <td>14.917332</td>\n",
       "      <td>no data</td>\n",
       "      <td>0</td>\n",
       "      <td>7705000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 61 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          id  full_sq  life_sq  floor                 sub_area  \\\n",
       "13546  13549       79   7478.0    8.0  Poselenie Voskresenskoe   \n",
       "\n",
       "       preschool_quota  preschool_education_centers_raion  school_quota  \\\n",
       "13546              NaN                                  0           NaN   \n",
       "\n",
       "       school_education_centers_raion  school_education_centers_top_20_raion  \\\n",
       "13546                               0                                      0   \n",
       "\n",
       "       ...  office_km  additional_education_km  preschool_km  big_church_km  \\\n",
       "13546  ...   5.072389                 1.210613      0.826052       1.582378   \n",
       "\n",
       "       church_synagogue_km  theater_km  museum_km  ecology mosque_count_1000  \\\n",
       "13546             0.756119   21.472421  14.917332  no data                 0   \n",
       "\n",
       "      price_doc  \n",
       "13546   7705000  \n",
       "\n",
       "[1 rows x 61 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(sber_data[sber_data['life_sq'] > 1000])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a595a3d5",
   "metadata": {},
   "source": [
    "Выброс налицо: гигантская жилая площадь (life_sq), да ещё почти в 100 раз превышает общую площадь (full_sq).\n",
    "\n",
    "Логичен вопрос: а много ли у нас таких квартир, у которых жилая площадь больше, чем суммарная?\n",
    "\n",
    "Давайте проверим это с помощью фильтрации:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "55a32550-064c-40e4-9c58-0e98e4ade4b2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "37\n"
     ]
    }
   ],
   "source": [
    "outliers = sber_data[sber_data['life_sq'] > sber_data['full_sq']]\n",
    "print(outliers.shape[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cb9e9b20",
   "metadata": {},
   "source": [
    "Таких квартир оказывается 37 штук. Подобные наблюдения уже не поддаются здравому смыслу — они являются ошибочными, и от них стоит избавиться:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "f25fc651-e08e-4b7b-ba54-ecacc195a36a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Результирующее число записей: 30434\n"
     ]
    }
   ],
   "source": [
    "cleaned = sber_data.drop(outliers.index, axis=0)\n",
    "print(f'Результирующее число записей: {cleaned.shape[0]}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c8d36f20",
   "metadata": {},
   "source": [
    "Ещё пример: давайте посмотрим на признак числа этажей (floor)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "2708e8c0-fd8c-40db-8d56-849eb7d3bbac",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    30304.000000\n",
       "mean         7.670803\n",
       "std          5.319989\n",
       "min          0.000000\n",
       "25%          3.000000\n",
       "50%          6.500000\n",
       "75%         11.000000\n",
       "max         77.000000\n",
       "Name: floor, dtype: float64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(sber_data['floor'].describe())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d6c3d874",
   "metadata": {},
   "source": [
    "Снова видим подозрительную максимальную отметку в 77 этажей. Проверим все квартиры, которые находятся выше 50 этажей:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "f978a422-e827-4643-ab5e-5d89643871c7",
   "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>id</th>\n",
       "      <th>full_sq</th>\n",
       "      <th>life_sq</th>\n",
       "      <th>floor</th>\n",
       "      <th>sub_area</th>\n",
       "      <th>preschool_quota</th>\n",
       "      <th>preschool_education_centers_raion</th>\n",
       "      <th>school_quota</th>\n",
       "      <th>school_education_centers_raion</th>\n",
       "      <th>school_education_centers_top_20_raion</th>\n",
       "      <th>...</th>\n",
       "      <th>office_km</th>\n",
       "      <th>additional_education_km</th>\n",
       "      <th>preschool_km</th>\n",
       "      <th>big_church_km</th>\n",
       "      <th>church_synagogue_km</th>\n",
       "      <th>theater_km</th>\n",
       "      <th>museum_km</th>\n",
       "      <th>ecology</th>\n",
       "      <th>mosque_count_1000</th>\n",
       "      <th>price_doc</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>23584</th>\n",
       "      <td>23587</td>\n",
       "      <td>57</td>\n",
       "      <td>33.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>Lomonosovskoe</td>\n",
       "      <td>3091.0</td>\n",
       "      <td>5</td>\n",
       "      <td>8359.0</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0.310557</td>\n",
       "      <td>0.731683</td>\n",
       "      <td>0.489763</td>\n",
       "      <td>0.86354</td>\n",
       "      <td>0.863266</td>\n",
       "      <td>2.920671</td>\n",
       "      <td>2.757058</td>\n",
       "      <td>satisfactory</td>\n",
       "      <td>0</td>\n",
       "      <td>13700000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 61 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          id  full_sq  life_sq  floor       sub_area  preschool_quota  \\\n",
       "23584  23587       57     33.0   77.0  Lomonosovskoe           3091.0   \n",
       "\n",
       "       preschool_education_centers_raion  school_quota  \\\n",
       "23584                                  5        8359.0   \n",
       "\n",
       "       school_education_centers_raion  school_education_centers_top_20_raion  \\\n",
       "23584                               7                                      1   \n",
       "\n",
       "       ...  office_km  additional_education_km  preschool_km  big_church_km  \\\n",
       "23584  ...   0.310557                 0.731683      0.489763        0.86354   \n",
       "\n",
       "       church_synagogue_km  theater_km  museum_km       ecology  \\\n",
       "23584             0.863266    2.920671   2.757058  satisfactory   \n",
       "\n",
       "      mosque_count_1000 price_doc  \n",
       "23584                 0  13700000  \n",
       "\n",
       "[1 rows x 61 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sber_data[sber_data['floor']> 50]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "89c46668",
   "metadata": {},
   "source": [
    "Всего одна квартира в Ломоносовском районе. Пора идти в интернет в поиске самых высоких зданий в Москве! \n",
    "\n",
    "Убеждаемся в том, что здания выше 70 этажей находятся на территории комплекса Москва-Сити (Пресненский район). В Ломоносовском районе таких жилых высоток нет. Получается, что данное наблюдение — выброс."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "92a98cd2-1e85-4063-92e2-fb9eee803412",
   "metadata": {},
   "source": [
    "**2. Метод межквартильного размаха**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d7351abe",
   "metadata": {},
   "source": [
    "Данный метод относится к категории статистических и реализован в виде отдельной python-функции в дирректории find_outliers в файле find_outliers.py.\n",
    "\n",
    "Подробнее о методе можно в файле [README.md](./find_outliers/README.md)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "d1176bdc",
   "metadata": {},
   "outputs": [],
   "source": [
    "from outliers_lib.find_outliers import find_outliers_iqr"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a81dfd94",
   "metadata": {},
   "source": [
    "На гистограмме мы можем увидеть потенциальные выбросы как низкие далеко отстоящие от основной группы столбцов «пеньки», а на коробчатой диаграмме — точки за пределами усов.\n",
    "\n",
    "Построим гистограмму и коробчатую диаграмму для признака полной площади (full_sq):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "9b8c8709-2ec5-4972-9dd1-9d8abe8d1ba6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(15, 4))\n",
    "histplot = sns.histplot(data=sber_data, x='full_sq', ax=axes[0]);\n",
    "histplot.set_title('Full Square Distribution');\n",
    "boxplot = sns.boxplot(data=sber_data, x='full_sq', ax=axes[1]);\n",
    "boxplot.set_title('Full Square Boxplot');"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0c3b92bd",
   "metadata": {},
   "source": [
    "Основная масса квартир имеет общую площадь менее 250 квадратных метров, и лишь несколько невзрачных наблюдений выбиваются из этого интервала. Максимальная площадь, судя по гистограмме, превышает 5 000 квадратных метров."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "234e5665",
   "metadata": {},
   "source": [
    "Данные не являются нормально-распределенными, распределение даже не является симметричным. Попробуем логарифмировать признак:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "f655c6cb-47c1-435a-a315-02732d1e6573",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(15, 4))\n",
    "histplot = sns.histplot(data=sber_data, x=np.log(sber_data['full_sq']+1), ax=axes[0]);\n",
    "histplot.set_title('Full Square Distribution');\n",
    "boxplot = sns.boxplot(data=sber_data, x=np.log(sber_data['full_sq']+1), ax=axes[1]);\n",
    "boxplot.set_title('Full Square Boxplot');"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "810049d8",
   "metadata": {},
   "source": [
    "С помощью логарифмирования удалось добиться подобия симметрии в данных."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f04b81ef",
   "metadata": {},
   "source": [
    "Воспользуемся методом межквартильного размаха с предварительным логарифмированием и 2-мя размахами в левую и правую сторону распределения. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "471ce824-f066-450f-b11d-8335d4d2d385",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Число выбросов по методу Тьюки: 111\n",
      "Результирующее число записей: 30360\n"
     ]
    }
   ],
   "source": [
    "outliers, cleaned = find_outliers_iqr(sber_data, 'full_sq', left=2, right=2, log_scale=True)\n",
    "print(f'Число выбросов по методу Тьюки: {outliers.shape[0]}')\n",
    "print(f'Результирующее число записей: {cleaned.shape[0]}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6630817b",
   "metadata": {},
   "source": [
    "Посмотрим на распределение после удаления выбросов:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "6038d224-9d63-428b-a051-5f23fd38fabe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(15, 4))\n",
    "histplot = sns.histplot(data=cleaned, x='full_sq', ax=axes[0]);\n",
    "histplot.set_title('Cleaned Full Square Distribution');\n",
    "boxplot = sns.boxplot(data=cleaned, x='full_sq', ax=axes[1]);\n",
    "boxplot.set_title('Cleaned Full Square Boxplot');"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "884b3992-766f-4d40-b0b4-a99fc4c90ec7",
   "metadata": {},
   "source": [
    "**3. Метод z-отклонений**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "829d6b69",
   "metadata": {},
   "source": [
    "Данный метод также относится к категории статистических и реализован в виде отдельной python-функции в дирректории find_outliers в файле find_outliers.py.\n",
    "\n",
    "Подробнее о методе можно в файле [README.md](./find_outliers/README.md)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "521e532f",
   "metadata": {},
   "outputs": [],
   "source": [
    "from outliers_lib.find_outliers import find_outliers_z_score"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9b0b1546",
   "metadata": {},
   "source": [
    "Построим две гистограммы признака расстояния до МКАД (mkad_km): первая — в обычном масштабе, а вторая — в логарифмическом."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "5117abb1-2f79-4d85-9e7d-54c751f164fe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(1, 2, figsize=(15, 4))\n",
    "histplot = sns.histplot(sber_data['mkad_km'], bins=30, ax=axes[0])\n",
    "histplot.set_title('MKAD Km Distribution');\n",
    "\n",
    "log_mkad_km = np.log(sber_data['mkad_km'] + 1)\n",
    "histplot = sns.histplot(log_mkad_km, bins=30, ax=axes[1])\n",
    "histplot.set_title('Log MKAD Km Distribution');"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fdd98f0b",
   "metadata": {},
   "source": [
    "Левое распределение напоминает логнормальное распределение с наличием потенциальных выбросов-«пеньков», далеко отстоящих от основной массы наблюдений.\n",
    "\n",
    "Взяв натуральный логарифм от левого распределения, мы получаем правое, которое напоминает слегка перекошенное нормальное."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3cd389f7",
   "metadata": {},
   "source": [
    "Воспользуемся методом z отклонений с предварительным логарифмированием:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "35e16fbb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Число выбросов по методу z-отклонения: 33\n",
      "Результирующее число записей: 30438\n"
     ]
    }
   ],
   "source": [
    "outliers, cleaned = find_outliers_z_score(sber_data, 'mkad_km', log_scale=True)\n",
    "print(f'Число выбросов по методу z-отклонения: {outliers.shape[0]}')\n",
    "print(f'Результирующее число записей: {cleaned.shape[0]}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b86f28d8",
   "metadata": {},
   "source": [
    "Построим гистограмму очищенных данных:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "422e0a1f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(10, 4))\n",
    "histplot = sns.histplot(cleaned['mkad_km'], bins=30)\n",
    "histplot.set_title('Sqrt Kremlin Km Distribution');"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bd5d81b0",
   "metadata": {},
   "source": [
    "Итак, метод z-отклонения нашел нам 33 потенциальных выброса по признаку расстояния до МКАД. Узнаем, в каких районах (sub_area) представлены эти квартиры:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "024d8575",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Poselenie Rogovskoe' 'Poselenie Kievskij']\n"
     ]
    }
   ],
   "source": [
    "print(outliers['sub_area'].unique())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "294cd3f4",
   "metadata": {},
   "source": [
    "Наши потенциальные выбросы — это квартиры из поселений «Роговское» и «Киевский». Снова обращаемся к силе интернета и «пробиваем» наших подозреваемых. Эти поселения — самые удалённые районы Московской области; первое из них — это и вовсе граница с Калужской областью. \n",
    "\n",
    "И тут возникает закономерный вопрос: а стоит ли считать такие наблюдения за выбросы? \n",
    "\n",
    "Вопрос в действительности не имеет определенного ответа: с одной стороны, метод прямо-таки говорит нам об этом, а с другой — эти наблюдения имеют право на существование, ведь они являются частью Московской области."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fa12901e",
   "metadata": {},
   "source": [
    "Возможно, мы не учли того факта, что наш логарифм распределения всё-таки не идеально нормален и в нём присутствует некоторая асимметрия. Возможно, стоит дать некоторое «послабление» на границы интервалов? Давайте отдельно построим гистограмму прологарифмированного распределения, а также отобразим на гистограмме вертикальные линии, соответствующие среднему (центру интервала в методе трёх сигм) и границы интервала "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "aeda8d23-474c-4974-a166-af38d42e9b66",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 1, figsize=(8, 4))\n",
    "log_mkad_km = np.log(sber_data['mkad_km'] + 1)\n",
    "histplot = sns.histplot(log_mkad_km, bins=30, ax=ax)\n",
    "histplot.axvline(log_mkad_km.mean(), color='g', lw=2)\n",
    "histplot.axvline(log_mkad_km.mean() + 3 *log_mkad_km.std(), color='r', ls='--', lw=1)\n",
    "histplot.axvline(log_mkad_km.mean() - 3 *log_mkad_km.std(), color='r', ls='--', lw=1)\n",
    "histplot.set_title('Log MKAD Km Distribution');"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6705e6b9",
   "metadata": {},
   "source": [
    "Ассиметрия распределения является левой. В этом можно убедиться, вычислив коэффициент ассиметрии:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "ab90819f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-0.14263612203024953\n"
     ]
    }
   ],
   "source": [
    "print(log_mkad_km.skew())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1e5940b7",
   "metadata": {},
   "source": [
    "Из графика видно, что из-за левой ассиметриии 3ех сигм не достаточно, чтобы покрыть весь интервал. Увеличим значения границ:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "cc4b5c02",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Число выбросов по методу z-отклонения: 0\n",
      "Результирующее число записей: 30471\n"
     ]
    }
   ],
   "source": [
    "outliers, cleaned = find_outliers_z_score(sber_data, 'mkad_km', log_scale=True, right=3.5)\n",
    "print(f'Число выбросов по методу z-отклонения: {outliers.shape[0]}')\n",
    "print(f'Результирующее число записей: {cleaned.shape[0]}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "859b3bea",
   "metadata": {},
   "source": [
    "Стоило нам немного увеличить правую границу метода z-отклонений, как мы получили отсутствие выбросов в признаке."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bc2c54f4",
   "metadata": {},
   "source": [
    "### Поиск и ликвидация дубликатов"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d32f4147",
   "metadata": {},
   "source": [
    "Способ обнаружения дубликатов зависит от того, что именно вы считаете дубликатом. Например, за дубликаты можно посчитать записи, у которых совпадают все признаки или их часть. Если в таблице есть столбец с уникальным идентификатором (id), вы можете попробовать поискать дубликаты по нему: одинаковые записи могут иметь одинаковый id.\n",
    "\n",
    "Проверим, есть у нас такие записи: для этого сравним число уникальных значений в столбце id с числом строк. Число уникальных значений вычислим с помощью метода nunique():"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "01146244-586f-4033-8498-ce7a79d08888",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sber_data['id'].nunique() == sber_data.shape[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9330a309",
   "metadata": {},
   "source": [
    "Вроде бы всё в порядке:  \n",
    "* каждой записи в таблице соответствует свой уникальный идентификатор.   \n",
    "* Но это ещё не означает, что в таблице нет дубликатов!"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dcd32c3c",
   "metadata": {},
   "source": [
    "Столбец с **_id_** задаёт каждой строке свой уникальный номер,  \n",
    "поэтому сама по себе каждая строка является уникальной.    \n",
    "Однако содержимое других столбцов может повторяться.  "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ec762d2c",
   "metadata": {},
   "source": [
    " Найдём число полных дубликатов таблице sber_data.   \n",
    " Предварительно создадим список столбцов dupl_columns,  \n",
    " по которым будем искать совпадения (все столбцы, не включая id). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "81c068e6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Число найденных дубликатов: 562\n"
     ]
    }
   ],
   "source": [
    "dupl_columns = list(sber_data.columns)\n",
    "dupl_columns.remove('id')\n",
    "\n",
    "mask = sber_data.duplicated(subset=dupl_columns)\n",
    "sber_duplicates = sber_data[mask]\n",
    "print(f'Число найденных дубликатов: {sber_duplicates.shape[0]}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "98fd9e1c",
   "metadata": {},
   "source": [
    "Итак, 562 строки в таблице являются полными копиями других записей.  \n",
    "Теперь нам необходимо от них избавиться."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "4710eed0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Результирующее число записей: 29909\n"
     ]
    }
   ],
   "source": [
    "sber_dedupped = sber_data.drop_duplicates(subset=dupl_columns)\n",
    "print(f'Результирующее число записей: {sber_dedupped.shape[0]}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9a2375d0",
   "metadata": {},
   "source": [
    " <strong> Mardown </strong> - простой язык\n",
    " \n",
    " решение **принято**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "65529917",
   "metadata": {},
   "source": [
    "***формула:***\n",
    "$$ a_{j+1} = a_{i} +10^2$$\n",
    "$$x_{1}^2 $$"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "731ca1cc",
   "metadata": {},
   "source": [
    "> fde\n",
    "  1. one\n",
    "      * two\n",
    "          1. dfhs\n",
    "          1. sdd\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f5855291",
   "metadata": {},
   "source": [
    "![](images/boxplot.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "89a35226",
   "metadata": {},
   "source": [
    "# <center> Заголовок </center>\n",
    "----\n",
    "## второй заголовок\n",
    "- авмввфа\n",
    "    * вымывм\n",
    "    \n",
    "- вамуав  \n",
    "```python\n",
    "dupl_columns = list(sber_data.columns)\n",
    "dupl_columns.remove('id')\n",
    "mask = sber_data.duplicated(subset=dupl_columns)\n",
    "sber_duplicates = sber_data[mask]\n",
    "print(f'Число найденных дубликатов: {sber_duplicates.shape[0]}')\n",
    "\n",
    "```\n",
    "> Цитата\n",
    "\n",
    "% такой пример работает корректно:\n",
    "\\\\begin{vmatrix} -3 & 3 & -4 \\\\\\\\ 0 & 3 & 2 \\\\\\\\ -3 & 2 & -4 \\\\end{vmatrix}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0ae82ed4",
   "metadata": {},
   "source": [
    "$R=\\frac{I}{U}$\n",
    "\n",
    "\n",
    "$$\\forall x \\in X, \\quad \\exists y \\leq \\epsilon$$\n",
    "$$\\pi=3.14$$\n",
    "$^3/_7$\n",
    "\n",
    "$\\frac{3}{7}$  \n",
    "$\\sqrt{\\frac{a}{b}}$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "70dff3c7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Число выбросов: 598\n",
      "Результирующее число записей: 29851\n"
     ]
    }
   ],
   "source": [
    "from outliers_lib.find_outliers import find_outliers_quantile\n",
    "\n",
    "outliers, cleaned = find_outliers_quantile(sber_data, 'mkad_km')\n",
    "print(f'Число выбросов: {outliers.shape[0]}')\n",
    "print(f'Результирующее число записей: {cleaned.shape[0]}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dc4191ba",
   "metadata": {},
   "outputs": [],
   "source": []
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   "source": []
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   "id": "4877e1aa",
   "metadata": {},
   "source": []
  }
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