Сравнение выручки.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "915eaac3",
   "metadata": {},
   "source": [
    "# Сравнение выручки"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "id": "ab4c4ea5",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from datetime import datetime\n",
    "from scipy import stats as st"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "id": "0f3a6f70",
   "metadata": {},
   "outputs": [],
   "source": [
    "font = {'family': 'sans-serif',\n",
    "        'color':  '#a72693',\n",
    "        'weight': 'normal',\n",
    "        'size': 20,\n",
    "        }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "id": "deff1359",
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.set_option('display.max_columns', None)\n",
    "pd.options.display.float_format = '{:,.2f}'.format"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "id": "b7c31246",
   "metadata": {},
   "outputs": [],
   "source": [
    "#открывем файлы с данными, смотрит структуру таблиц\n",
    "users = pd.read_csv('users.csv')\n",
    "calls = pd.read_csv('calls.csv')\n",
    "messages = pd.read_csv('messages.csv')\n",
    "internet = pd.read_csv('internet.csv')\n",
    "tariffs = pd.read_csv('tariffs.csv')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "id": "1c1cab91",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>age</th>\n",
       "      <th>churn_date</th>\n",
       "      <th>city</th>\n",
       "      <th>first_name</th>\n",
       "      <th>last_name</th>\n",
       "      <th>reg_date</th>\n",
       "      <th>tariff</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1000</td>\n",
       "      <td>52</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Краснодар</td>\n",
       "      <td>Рафаил</td>\n",
       "      <td>Верещагин</td>\n",
       "      <td>2018-05-25</td>\n",
       "      <td>ultra</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1001</td>\n",
       "      <td>41</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Москва</td>\n",
       "      <td>Иван</td>\n",
       "      <td>Ежов</td>\n",
       "      <td>2018-11-01</td>\n",
       "      <td>smart</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1002</td>\n",
       "      <td>59</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Стерлитамак</td>\n",
       "      <td>Евгений</td>\n",
       "      <td>Абрамович</td>\n",
       "      <td>2018-06-17</td>\n",
       "      <td>smart</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1003</td>\n",
       "      <td>23</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Москва</td>\n",
       "      <td>Белла</td>\n",
       "      <td>Белякова</td>\n",
       "      <td>2018-08-17</td>\n",
       "      <td>ultra</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1004</td>\n",
       "      <td>68</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Новокузнецк</td>\n",
       "      <td>Татьяна</td>\n",
       "      <td>Авдеенко</td>\n",
       "      <td>2018-05-14</td>\n",
       "      <td>ultra</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   user_id  age churn_date         city first_name  last_name    reg_date  \\\n",
       "0     1000   52        NaN    Краснодар     Рафаил  Верещагин  2018-05-25   \n",
       "1     1001   41        NaN       Москва       Иван       Ежов  2018-11-01   \n",
       "2     1002   59        NaN  Стерлитамак    Евгений  Абрамович  2018-06-17   \n",
       "3     1003   23        NaN       Москва      Белла   Белякова  2018-08-17   \n",
       "4     1004   68        NaN  Новокузнецк    Татьяна   Авдеенко  2018-05-14   \n",
       "\n",
       "  tariff  \n",
       "0  ultra  \n",
       "1  smart  \n",
       "2  smart  \n",
       "3  ultra  \n",
       "4  ultra  "
      ]
     },
     "execution_count": 216,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "users.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "id": "0e2e83d8",
   "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>user_id</th>\n",
       "      <th>age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>500.00</td>\n",
       "      <td>500.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1,249.50</td>\n",
       "      <td>46.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>144.48</td>\n",
       "      <td>16.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1,000.00</td>\n",
       "      <td>18.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1,124.75</td>\n",
       "      <td>32.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1,249.50</td>\n",
       "      <td>46.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1,374.25</td>\n",
       "      <td>62.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1,499.00</td>\n",
       "      <td>75.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "       user_id    age\n",
       "count   500.00 500.00\n",
       "mean  1,249.50  46.59\n",
       "std     144.48  16.67\n",
       "min   1,000.00  18.00\n",
       "25%   1,124.75  32.00\n",
       "50%   1,249.50  46.00\n",
       "75%   1,374.25  62.00\n",
       "max   1,499.00  75.00"
      ]
     },
     "execution_count": 217,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#статистика таблицы\n",
    "users.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "id": "08882e9e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# приводим даты к нужному формату\n",
    "users['churn_date'] = pd.to_datetime(users['churn_date'], format=\"%Y-%m-%d\")\n",
    "users['reg_date'] = pd.to_datetime(users['reg_date'], format=\"%Y-%m-%d\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "id": "0b207e34",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 500 entries, 0 to 499\n",
      "Data columns (total 8 columns):\n",
      " #   Column      Non-Null Count  Dtype         \n",
      "---  ------      --------------  -----         \n",
      " 0   user_id     500 non-null    int64         \n",
      " 1   age         500 non-null    int64         \n",
      " 2   churn_date  38 non-null     datetime64[ns]\n",
      " 3   city        500 non-null    object        \n",
      " 4   first_name  500 non-null    object        \n",
      " 5   last_name   500 non-null    object        \n",
      " 6   reg_date    500 non-null    datetime64[ns]\n",
      " 7   tariff      500 non-null    object        \n",
      "dtypes: datetime64[ns](2), int64(2), object(4)\n",
      "memory usage: 31.4+ KB\n"
     ]
    }
   ],
   "source": [
    "users.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "id": "3e859d55",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1000_0</td>\n",
       "      <td>2018-07-25</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1000_1</td>\n",
       "      <td>2018-08-17</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1000_2</td>\n",
       "      <td>2018-06-11</td>\n",
       "      <td>2.85</td>\n",
       "      <td>1000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1000_3</td>\n",
       "      <td>2018-09-21</td>\n",
       "      <td>13.80</td>\n",
       "      <td>1000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1000_4</td>\n",
       "      <td>2018-12-15</td>\n",
       "      <td>5.18</td>\n",
       "      <td>1000</td>\n",
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      "text/plain": [
       "       id   call_date  duration  user_id\n",
       "0  1000_0  2018-07-25      0.00     1000\n",
       "1  1000_1  2018-08-17      0.00     1000\n",
       "2  1000_2  2018-06-11      2.85     1000\n",
       "3  1000_3  2018-09-21     13.80     1000\n",
       "4  1000_4  2018-12-15      5.18     1000"
      ]
     },
     "execution_count": 220,
     "metadata": {},
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    }
   ],
   "source": [
    "calls.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "id": "8aa681cd",
   "metadata": {},
   "outputs": [
    {
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       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>202,607.00</td>\n",
       "      <td>202,607.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>6.76</td>\n",
       "      <td>1,253.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>5.84</td>\n",
       "      <td>144.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.00</td>\n",
       "      <td>1,000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.30</td>\n",
       "      <td>1,126.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>6.00</td>\n",
       "      <td>1,260.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>10.70</td>\n",
       "      <td>1,379.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>38.00</td>\n",
       "      <td>1,499.00</td>\n",
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      ],
      "text/plain": [
       "        duration    user_id\n",
       "count 202,607.00 202,607.00\n",
       "mean        6.76   1,253.94\n",
       "std         5.84     144.72\n",
       "min         0.00   1,000.00\n",
       "25%         1.30   1,126.00\n",
       "50%         6.00   1,260.00\n",
       "75%        10.70   1,379.00\n",
       "max        38.00   1,499.00"
      ]
     },
     "execution_count": 221,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "calls.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "id": "cf990c1e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 202607 entries, 0 to 202606\n",
      "Data columns (total 4 columns):\n",
      " #   Column     Non-Null Count   Dtype  \n",
      "---  ------     --------------   -----  \n",
      " 0   id         202607 non-null  object \n",
      " 1   call_date  202607 non-null  object \n",
      " 2   duration   202607 non-null  float64\n",
      " 3   user_id    202607 non-null  int64  \n",
      "dtypes: float64(1), int64(1), object(2)\n",
      "memory usage: 6.2+ MB\n"
     ]
    }
   ],
   "source": [
    "calls.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "id": "9a445f97",
   "metadata": {},
   "outputs": [],
   "source": [
    "calls['call_date'] = pd.to_datetime(calls['call_date'], format=\"%Y-%m-%d\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "id": "32a89595",
   "metadata": {},
   "outputs": [],
   "source": [
    "#определяем целочисленный формат\n",
    "calls['id'] = calls['id'].astype('int')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "id": "cbbaddad",
   "metadata": {},
   "outputs": [],
   "source": [
    "calls['duration'] = calls['duration'].apply(np.ceil).astype('int')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 226,
   "id": "e71f70c8",
   "metadata": {},
   "outputs": [
    {
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       "      <th>0</th>\n",
       "      <td>10000</td>\n",
       "      <td>2018-07-25</td>\n",
       "      <td>0</td>\n",
       "      <td>1000</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10001</td>\n",
       "      <td>2018-08-17</td>\n",
       "      <td>0</td>\n",
       "      <td>1000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10002</td>\n",
       "      <td>2018-06-11</td>\n",
       "      <td>3</td>\n",
       "      <td>1000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10003</td>\n",
       "      <td>2018-09-21</td>\n",
       "      <td>14</td>\n",
       "      <td>1000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10004</td>\n",
       "      <td>2018-12-15</td>\n",
       "      <td>6</td>\n",
       "      <td>1000</td>\n",
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      ],
      "text/plain": [
       "      id  call_date  duration  user_id\n",
       "0  10000 2018-07-25         0     1000\n",
       "1  10001 2018-08-17         0     1000\n",
       "2  10002 2018-06-11         3     1000\n",
       "3  10003 2018-09-21        14     1000\n",
       "4  10004 2018-12-15         6     1000"
      ]
     },
     "execution_count": 226,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "calls.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 227,
   "id": "1eb6fee6",
   "metadata": {},
   "outputs": [],
   "source": [
    "calls['duration'] = calls['duration'].replace(0, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 228,
   "id": "f4e9fa16",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "\n",
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       "    }\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>duration</th>\n",
       "      <th>user_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>202,607.00</td>\n",
       "      <td>202,607.00</td>\n",
       "      <td>202,607.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1,212,254.17</td>\n",
       "      <td>7.35</td>\n",
       "      <td>1,253.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1,725,756.38</td>\n",
       "      <td>5.73</td>\n",
       "      <td>144.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>10,000.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1,000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1,012,286.50</td>\n",
       "      <td>2.00</td>\n",
       "      <td>1,126.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1,179,376.00</td>\n",
       "      <td>6.00</td>\n",
       "      <td>1,260.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1,358,125.50</td>\n",
       "      <td>11.00</td>\n",
       "      <td>1,379.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>14,581,154.00</td>\n",
       "      <td>38.00</td>\n",
       "      <td>1,499.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 id   duration    user_id\n",
       "count    202,607.00 202,607.00 202,607.00\n",
       "mean   1,212,254.17       7.35   1,253.94\n",
       "std    1,725,756.38       5.73     144.72\n",
       "min       10,000.00       1.00   1,000.00\n",
       "25%    1,012,286.50       2.00   1,126.00\n",
       "50%    1,179,376.00       6.00   1,260.00\n",
       "75%    1,358,125.50      11.00   1,379.00\n",
       "max   14,581,154.00      38.00   1,499.00"
      ]
     },
     "execution_count": 228,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "calls.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 229,
   "id": "0e589814",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 202607 entries, 0 to 202606\n",
      "Data columns (total 4 columns):\n",
      " #   Column     Non-Null Count   Dtype         \n",
      "---  ------     --------------   -----         \n",
      " 0   id         202607 non-null  int32         \n",
      " 1   call_date  202607 non-null  datetime64[ns]\n",
      " 2   duration   202607 non-null  int32         \n",
      " 3   user_id    202607 non-null  int64         \n",
      "dtypes: datetime64[ns](1), int32(2), int64(1)\n",
      "memory usage: 4.6 MB\n"
     ]
    }
   ],
   "source": [
    "calls.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 230,
   "id": "a00bd427",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>message_date</th>\n",
       "      <th>user_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1000_0</td>\n",
       "      <td>2018-06-27</td>\n",
       "      <td>1000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1000_1</td>\n",
       "      <td>2018-10-08</td>\n",
       "      <td>1000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1000_2</td>\n",
       "      <td>2018-08-04</td>\n",
       "      <td>1000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1000_3</td>\n",
       "      <td>2018-06-16</td>\n",
       "      <td>1000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1000_4</td>\n",
       "      <td>2018-12-05</td>\n",
       "      <td>1000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       id message_date  user_id\n",
       "0  1000_0   2018-06-27     1000\n",
       "1  1000_1   2018-10-08     1000\n",
       "2  1000_2   2018-08-04     1000\n",
       "3  1000_3   2018-06-16     1000\n",
       "4  1000_4   2018-12-05     1000"
      ]
     },
     "execution_count": 230,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "messages.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 231,
   "id": "ce41c916",
   "metadata": {},
   "outputs": [],
   "source": [
    "messages['id'] = messages['id'].astype('int')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 232,
   "id": "13dce255",
   "metadata": {},
   "outputs": [],
   "source": [
    "messages['message_date'] = pd.to_datetime(messages['message_date'], format=\"%Y-%m-%d\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 233,
   "id": "81c5cc56",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 123036 entries, 0 to 123035\n",
      "Data columns (total 3 columns):\n",
      " #   Column        Non-Null Count   Dtype         \n",
      "---  ------        --------------   -----         \n",
      " 0   id            123036 non-null  int32         \n",
      " 1   message_date  123036 non-null  datetime64[ns]\n",
      " 2   user_id       123036 non-null  int64         \n",
      "dtypes: datetime64[ns](1), int32(1), int64(1)\n",
      "memory usage: 2.3 MB\n"
     ]
    }
   ],
   "source": [
    "messages.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 234,
   "id": "10e8d6c3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>mb_used</th>\n",
       "      <th>session_date</th>\n",
       "      <th>user_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1000_0</td>\n",
       "      <td>112.95</td>\n",
       "      <td>2018-11-25</td>\n",
       "      <td>1000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1000_1</td>\n",
       "      <td>1,052.81</td>\n",
       "      <td>2018-09-07</td>\n",
       "      <td>1000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1000_2</td>\n",
       "      <td>1,197.26</td>\n",
       "      <td>2018-06-25</td>\n",
       "      <td>1000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1000_3</td>\n",
       "      <td>550.27</td>\n",
       "      <td>2018-08-22</td>\n",
       "      <td>1000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1000_4</td>\n",
       "      <td>302.56</td>\n",
       "      <td>2018-09-24</td>\n",
       "      <td>1000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       id  mb_used session_date  user_id\n",
       "0  1000_0   112.95   2018-11-25     1000\n",
       "1  1000_1 1,052.81   2018-09-07     1000\n",
       "2  1000_2 1,197.26   2018-06-25     1000\n",
       "3  1000_3   550.27   2018-08-22     1000\n",
       "4  1000_4   302.56   2018-09-24     1000"
      ]
     },
     "execution_count": 234,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "internet.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 235,
   "id": "a79c7ae0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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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>mb_used</th>\n",
       "      <th>user_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>149,396.00</td>\n",
       "      <td>149,396.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>370.19</td>\n",
       "      <td>1,252.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>278.30</td>\n",
       "      <td>144.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.00</td>\n",
       "      <td>1,000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>138.19</td>\n",
       "      <td>1,130.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>348.01</td>\n",
       "      <td>1,251.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>559.55</td>\n",
       "      <td>1,380.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1,724.83</td>\n",
       "      <td>1,499.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         mb_used    user_id\n",
       "count 149,396.00 149,396.00\n",
       "mean      370.19   1,252.10\n",
       "std       278.30     144.05\n",
       "min         0.00   1,000.00\n",
       "25%       138.19   1,130.00\n",
       "50%       348.01   1,251.00\n",
       "75%       559.55   1,380.00\n",
       "max     1,724.83   1,499.00"
      ]
     },
     "execution_count": 235,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "internet.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 236,
   "id": "f3bb582c",
   "metadata": {},
   "outputs": [],
   "source": [
    "internet['id'] = internet['id'].astype('int')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 237,
   "id": "052aa6e8",
   "metadata": {},
   "outputs": [],
   "source": [
    "internet['session_date'] = pd.to_datetime(internet['session_date'], format=\"%Y-%m-%d\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 238,
   "id": "fa640388",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 149396 entries, 0 to 149395\n",
      "Data columns (total 4 columns):\n",
      " #   Column        Non-Null Count   Dtype         \n",
      "---  ------        --------------   -----         \n",
      " 0   id            149396 non-null  int32         \n",
      " 1   mb_used       149396 non-null  float64       \n",
      " 2   session_date  149396 non-null  datetime64[ns]\n",
      " 3   user_id       149396 non-null  int64         \n",
      "dtypes: datetime64[ns](1), float64(1), int32(1), int64(1)\n",
      "memory usage: 4.0 MB\n"
     ]
    }
   ],
   "source": [
    "internet.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 239,
   "id": "c5039008",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>messages_included</th>\n",
       "      <th>mg_per_month_included</th>\n",
       "      <th>minutes_included</th>\n",
       "      <th>rub_monthly_fee</th>\n",
       "      <th>rub_per_gb</th>\n",
       "      <th>rub_per_message</th>\n",
       "      <th>rub_per_minute</th>\n",
       "      <th>tariff_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>50</td>\n",
       "      <td>15360</td>\n",
       "      <td>500</td>\n",
       "      <td>550</td>\n",
       "      <td>200</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>smart</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1000</td>\n",
       "      <td>30720</td>\n",
       "      <td>3000</td>\n",
       "      <td>1950</td>\n",
       "      <td>150</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>ultra</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   messages_included  mg_per_month_included  minutes_included  \\\n",
       "0                 50                  15360               500   \n",
       "1               1000                  30720              3000   \n",
       "\n",
       "   rub_monthly_fee  rub_per_gb  rub_per_message  rub_per_minute tariff_name  \n",
       "0              550         200                3               3       smart  \n",
       "1             1950         150                1               1       ultra  "
      ]
     },
     "execution_count": 239,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tariffs.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 240,
   "id": "1a022527",
   "metadata": {},
   "outputs": [],
   "source": [
    "#переименовываем столбец\n",
    "tariffs = tariffs.rename(columns={'tariff_name':'tariff'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 241,
   "id": "c351a200",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2 entries, 0 to 1\n",
      "Data columns (total 8 columns):\n",
      " #   Column                 Non-Null Count  Dtype \n",
      "---  ------                 --------------  ----- \n",
      " 0   messages_included      2 non-null      int64 \n",
      " 1   mg_per_month_included  2 non-null      int64 \n",
      " 2   minutes_included       2 non-null      int64 \n",
      " 3   rub_monthly_fee        2 non-null      int64 \n",
      " 4   rub_per_gb             2 non-null      int64 \n",
      " 5   rub_per_message        2 non-null      int64 \n",
      " 6   rub_per_minute         2 non-null      int64 \n",
      " 7   tariff                 2 non-null      object\n",
      "dtypes: int64(7), object(1)\n",
      "memory usage: 256.0+ bytes\n"
     ]
    }
   ],
   "source": [
    "tariffs.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 242,
   "id": "9188e8c8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# выводим по дате название месяца\n",
    "calls['month'] = calls['call_date'].dt.month_name()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 243,
   "id": "e5024a83",
   "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</th>\n",
       "      <th>call_date</th>\n",
       "      <th>duration</th>\n",
       "      <th>user_id</th>\n",
       "      <th>month</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10000</td>\n",
       "      <td>2018-07-25</td>\n",
       "      <td>1</td>\n",
       "      <td>1000</td>\n",
       "      <td>July</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10001</td>\n",
       "      <td>2018-08-17</td>\n",
       "      <td>1</td>\n",
       "      <td>1000</td>\n",
       "      <td>August</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10002</td>\n",
       "      <td>2018-06-11</td>\n",
       "      <td>3</td>\n",
       "      <td>1000</td>\n",
       "      <td>June</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10003</td>\n",
       "      <td>2018-09-21</td>\n",
       "      <td>14</td>\n",
       "      <td>1000</td>\n",
       "      <td>September</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10004</td>\n",
       "      <td>2018-12-15</td>\n",
       "      <td>6</td>\n",
       "      <td>1000</td>\n",
       "      <td>December</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      id  call_date  duration  user_id      month\n",
       "0  10000 2018-07-25         1     1000       July\n",
       "1  10001 2018-08-17         1     1000     August\n",
       "2  10002 2018-06-11         3     1000       June\n",
       "3  10003 2018-09-21        14     1000  September\n",
       "4  10004 2018-12-15         6     1000   December"
      ]
     },
     "execution_count": 243,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "calls.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 244,
   "id": "3b703b6c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# группируем данные в таблице по номеру пользователя и месяцу, суммируя минуты и находя количество звонков\n",
    "user_calls = calls.groupby(['user_id', 'month'])\\\n",
    "                  .agg({'duration':'sum', 'id':'count'})\\\n",
    "                  .reset_index()\\\n",
    "                  .rename(columns={'duration':'month_calls_duration','id':'calls_total_in_month'})\\\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 245,
   "id": "8b304bec",
   "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>user_id</th>\n",
       "      <th>month</th>\n",
       "      <th>month_calls_duration</th>\n",
       "      <th>calls_total_in_month</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1000</td>\n",
       "      <td>August</td>\n",
       "      <td>418</td>\n",
       "      <td>52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1000</td>\n",
       "      <td>December</td>\n",
       "      <td>341</td>\n",
       "      <td>46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1000</td>\n",
       "      <td>July</td>\n",
       "      <td>346</td>\n",
       "      <td>47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1000</td>\n",
       "      <td>June</td>\n",
       "      <td>187</td>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1000</td>\n",
       "      <td>May</td>\n",
       "      <td>164</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   user_id     month  month_calls_duration  calls_total_in_month\n",
       "0     1000    August                   418                    52\n",
       "1     1000  December                   341                    46\n",
       "2     1000      July                   346                    47\n",
       "3     1000      June                   187                    43\n",
       "4     1000       May                   164                    22"
      ]
     },
     "execution_count": 245,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Количество сделанных звонков и израсходованных минут разговора по месяцам;\n",
    "user_calls.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 246,
   "id": "a7d1a219",
   "metadata": {},
   "outputs": [],
   "source": [
    "# вводим названия месяцев\n",
    "messages['month'] = messages['message_date'].dt.month_name()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 247,
   "id": "7032483c",
   "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>message_date</th>\n",
       "      <th>user_id</th>\n",
       "      <th>month</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10000</td>\n",
       "      <td>2018-06-27</td>\n",
       "      <td>1000</td>\n",
       "      <td>June</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10001</td>\n",
       "      <td>2018-10-08</td>\n",
       "      <td>1000</td>\n",
       "      <td>October</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10002</td>\n",
       "      <td>2018-08-04</td>\n",
       "      <td>1000</td>\n",
       "      <td>August</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10003</td>\n",
       "      <td>2018-06-16</td>\n",
       "      <td>1000</td>\n",
       "      <td>June</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10004</td>\n",
       "      <td>2018-12-05</td>\n",
       "      <td>1000</td>\n",
       "      <td>December</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      id message_date  user_id     month\n",
       "0  10000   2018-06-27     1000      June\n",
       "1  10001   2018-10-08     1000   October\n",
       "2  10002   2018-08-04     1000    August\n",
       "3  10003   2018-06-16     1000      June\n",
       "4  10004   2018-12-05     1000  December"
      ]
     },
     "execution_count": 247,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "messages.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 248,
   "id": "cc6c91bc",
   "metadata": {},
   "outputs": [],
   "source": [
    "# группируем, находим количество сообщений по месяцам для каждого пользователя\n",
    "user_messages = messages.groupby(['user_id', 'month'])\\\n",
    "                        .agg({'id':'count'})\\\n",
    "                        .reset_index()\\\n",
    "                        .rename(columns={'id':'sms_total_in_month'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 249,
   "id": "708e2633",
   "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>user_id</th>\n",
       "      <th>month</th>\n",
       "      <th>sms_total_in_month</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1000</td>\n",
       "      <td>August</td>\n",
       "      <td>81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1000</td>\n",
       "      <td>December</td>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1000</td>\n",
       "      <td>July</td>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1000</td>\n",
       "      <td>June</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1000</td>\n",
       "      <td>May</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   user_id     month  sms_total_in_month\n",
       "0     1000    August                  81\n",
       "1     1000  December                  70\n",
       "2     1000      July                  75\n",
       "3     1000      June                  60\n",
       "4     1000       May                  22"
      ]
     },
     "execution_count": 249,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Kоличество отправленных сообщений по месяцам\n",
    "user_messages.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 250,
   "id": "058785c4",
   "metadata": {},
   "outputs": [],
   "source": [
    "internet['month'] = internet['session_date'].dt.month_name()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 251,
   "id": "2c20b412",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\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>mb_used</th>\n",
       "      <th>session_date</th>\n",
       "      <th>user_id</th>\n",
       "      <th>month</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10000</td>\n",
       "      <td>112.95</td>\n",
       "      <td>2018-11-25</td>\n",
       "      <td>1000</td>\n",
       "      <td>November</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10001</td>\n",
       "      <td>1,052.81</td>\n",
       "      <td>2018-09-07</td>\n",
       "      <td>1000</td>\n",
       "      <td>September</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10002</td>\n",
       "      <td>1,197.26</td>\n",
       "      <td>2018-06-25</td>\n",
       "      <td>1000</td>\n",
       "      <td>June</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10003</td>\n",
       "      <td>550.27</td>\n",
       "      <td>2018-08-22</td>\n",
       "      <td>1000</td>\n",
       "      <td>August</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10004</td>\n",
       "      <td>302.56</td>\n",
       "      <td>2018-09-24</td>\n",
       "      <td>1000</td>\n",
       "      <td>September</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      id  mb_used session_date  user_id      month\n",
       "0  10000   112.95   2018-11-25     1000   November\n",
       "1  10001 1,052.81   2018-09-07     1000  September\n",
       "2  10002 1,197.26   2018-06-25     1000       June\n",
       "3  10003   550.27   2018-08-22     1000     August\n",
       "4  10004   302.56   2018-09-24     1000  September"
      ]
     },
     "execution_count": 251,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "internet.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 252,
   "id": "7725a2a0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# группируем, находим суммарный трафик по месяцам для каждого пользователя\n",
    "user_internet = internet.groupby(['user_id', 'month'])\\\n",
    "                        .agg({'mb_used':'sum'})\\\n",
    "                        .reset_index()\\\n",
    "                        .rename(columns={'mb_used':'mb_total_in_month'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 253,
   "id": "472ac3cd",
   "metadata": {
    "scrolled": true
   },
   "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>user_id</th>\n",
       "      <th>month</th>\n",
       "      <th>mb_total_in_month</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1000</td>\n",
       "      <td>August</td>\n",
       "      <td>14,055.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1000</td>\n",
       "      <td>December</td>\n",
       "      <td>9,817.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1000</td>\n",
       "      <td>July</td>\n",
       "      <td>14,003.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1000</td>\n",
       "      <td>June</td>\n",
       "      <td>23,233.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1000</td>\n",
       "      <td>May</td>\n",
       "      <td>2,253.49</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   user_id     month  mb_total_in_month\n",
       "0     1000    August          14,055.93\n",
       "1     1000  December           9,817.61\n",
       "2     1000      July          14,003.64\n",
       "3     1000      June          23,233.77\n",
       "4     1000       May           2,253.49"
      ]
     },
     "execution_count": 253,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Объем израсходованного интернет-трафика по месяцам;\n",
    "user_internet.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 254,
   "id": "57709811",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 3203 entries, 0 to 3202\n",
      "Data columns (total 3 columns):\n",
      " #   Column             Non-Null Count  Dtype  \n",
      "---  ------             --------------  -----  \n",
      " 0   user_id            3203 non-null   int64  \n",
      " 1   month              3203 non-null   object \n",
      " 2   mb_total_in_month  3203 non-null   float64\n",
      "dtypes: float64(1), int64(1), object(1)\n",
      "memory usage: 75.2+ KB\n"
     ]
    }
   ],
   "source": [
    "user_internet.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 255,
   "id": "434225ec",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>age</th>\n",
       "      <th>churn_date</th>\n",
       "      <th>city</th>\n",
       "      <th>first_name</th>\n",
       "      <th>last_name</th>\n",
       "      <th>reg_date</th>\n",
       "      <th>tariff</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1000</td>\n",
       "      <td>52</td>\n",
       "      <td>NaT</td>\n",
       "      <td>Краснодар</td>\n",
       "      <td>Рафаил</td>\n",
       "      <td>Верещагин</td>\n",
       "      <td>2018-05-25</td>\n",
       "      <td>ultra</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1001</td>\n",
       "      <td>41</td>\n",
       "      <td>NaT</td>\n",
       "      <td>Москва</td>\n",
       "      <td>Иван</td>\n",
       "      <td>Ежов</td>\n",
       "      <td>2018-11-01</td>\n",
       "      <td>smart</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1002</td>\n",
       "      <td>59</td>\n",
       "      <td>NaT</td>\n",
       "      <td>Стерлитамак</td>\n",
       "      <td>Евгений</td>\n",
       "      <td>Абрамович</td>\n",
       "      <td>2018-06-17</td>\n",
       "      <td>smart</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1003</td>\n",
       "      <td>23</td>\n",
       "      <td>NaT</td>\n",
       "      <td>Москва</td>\n",
       "      <td>Белла</td>\n",
       "      <td>Белякова</td>\n",
       "      <td>2018-08-17</td>\n",
       "      <td>ultra</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1004</td>\n",
       "      <td>68</td>\n",
       "      <td>NaT</td>\n",
       "      <td>Новокузнецк</td>\n",
       "      <td>Татьяна</td>\n",
       "      <td>Авдеенко</td>\n",
       "      <td>2018-05-14</td>\n",
       "      <td>ultra</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   user_id  age churn_date         city first_name  last_name   reg_date  \\\n",
       "0     1000   52        NaT    Краснодар     Рафаил  Верещагин 2018-05-25   \n",
       "1     1001   41        NaT       Москва       Иван       Ежов 2018-11-01   \n",
       "2     1002   59        NaT  Стерлитамак    Евгений  Абрамович 2018-06-17   \n",
       "3     1003   23        NaT       Москва      Белла   Белякова 2018-08-17   \n",
       "4     1004   68        NaT  Новокузнецк    Татьяна   Авдеенко 2018-05-14   \n",
       "\n",
       "  tariff  \n",
       "0  ultra  \n",
       "1  smart  \n",
       "2  smart  \n",
       "3  ultra  \n",
       "4  ultra  "
      ]
     },
     "execution_count": 255,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "users.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 256,
   "id": "8ce84214",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Склеим добро в одно целое добро\n",
    "df = user_calls.merge(user_messages, on=['user_id','month'], how='outer')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 257,
   "id": "28a80b14",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "        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>user_id</th>\n",
       "      <th>month</th>\n",
       "      <th>month_calls_duration</th>\n",
       "      <th>calls_total_in_month</th>\n",
       "      <th>sms_total_in_month</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1000</td>\n",
       "      <td>August</td>\n",
       "      <td>418.00</td>\n",
       "      <td>52.00</td>\n",
       "      <td>81.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1000</td>\n",
       "      <td>December</td>\n",
       "      <td>341.00</td>\n",
       "      <td>46.00</td>\n",
       "      <td>70.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1000</td>\n",
       "      <td>July</td>\n",
       "      <td>346.00</td>\n",
       "      <td>47.00</td>\n",
       "      <td>75.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1000</td>\n",
       "      <td>June</td>\n",
       "      <td>187.00</td>\n",
       "      <td>43.00</td>\n",
       "      <td>60.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1000</td>\n",
       "      <td>May</td>\n",
       "      <td>164.00</td>\n",
       "      <td>22.00</td>\n",
       "      <td>22.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   user_id     month  month_calls_duration  calls_total_in_month  \\\n",
       "0     1000    August                418.00                 52.00   \n",
       "1     1000  December                341.00                 46.00   \n",
       "2     1000      July                346.00                 47.00   \n",
       "3     1000      June                187.00                 43.00   \n",
       "4     1000       May                164.00                 22.00   \n",
       "\n",
       "   sms_total_in_month  \n",
       "0               81.00  \n",
       "1               70.00  \n",
       "2               75.00  \n",
       "3               60.00  \n",
       "4               22.00  "
      ]
     },
     "execution_count": 257,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 258,
   "id": "b5726c44",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>month</th>\n",
       "      <th>month_calls_duration</th>\n",
       "      <th>calls_total_in_month</th>\n",
       "      <th>sms_total_in_month</th>\n",
       "      <th>mb_total_in_month</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3209</th>\n",
       "      <td>1489</td>\n",
       "      <td>December</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>35.00</td>\n",
       "      <td>17,322.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3210</th>\n",
       "      <td>1489</td>\n",
       "      <td>November</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20.00</td>\n",
       "      <td>17,491.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3211</th>\n",
       "      <td>1489</td>\n",
       "      <td>October</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>21.00</td>\n",
       "      <td>19,559.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3212</th>\n",
       "      <td>1489</td>\n",
       "      <td>September</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>32.00</td>\n",
       "      <td>17,206.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3213</th>\n",
       "      <td>1476</td>\n",
       "      <td>April</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>530.78</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      user_id      month  month_calls_duration  calls_total_in_month  \\\n",
       "3209     1489   December                   NaN                   NaN   \n",
       "3210     1489   November                   NaN                   NaN   \n",
       "3211     1489    October                   NaN                   NaN   \n",
       "3212     1489  September                   NaN                   NaN   \n",
       "3213     1476      April                   NaN                   NaN   \n",
       "\n",
       "      sms_total_in_month  mb_total_in_month  \n",
       "3209               35.00          17,322.51  \n",
       "3210               20.00          17,491.56  \n",
       "3211               21.00          19,559.44  \n",
       "3212               32.00          17,206.30  \n",
       "3213                 NaN             530.78  "
      ]
     },
     "execution_count": 258,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#добавляем интернет  за счет склеивания\n",
    "df = df.merge(user_internet, on=['user_id','month'], how = 'outer')\n",
    "df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 259,
   "id": "9c843a7a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>month</th>\n",
       "      <th>month_calls_duration</th>\n",
       "      <th>calls_total_in_month</th>\n",
       "      <th>sms_total_in_month</th>\n",
       "      <th>mb_total_in_month</th>\n",
       "      <th>age</th>\n",
       "      <th>churn_date</th>\n",
       "      <th>city</th>\n",
       "      <th>first_name</th>\n",
       "      <th>last_name</th>\n",
       "      <th>reg_date</th>\n",
       "      <th>tariff</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1000</td>\n",
       "      <td>August</td>\n",
       "      <td>418.00</td>\n",
       "      <td>52.00</td>\n",
       "      <td>81.00</td>\n",
       "      <td>14,055.93</td>\n",
       "      <td>52</td>\n",
       "      <td>NaT</td>\n",
       "      <td>Краснодар</td>\n",
       "      <td>Рафаил</td>\n",
       "      <td>Верещагин</td>\n",
       "      <td>2018-05-25</td>\n",
       "      <td>ultra</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1000</td>\n",
       "      <td>December</td>\n",
       "      <td>341.00</td>\n",
       "      <td>46.00</td>\n",
       "      <td>70.00</td>\n",
       "      <td>9,817.61</td>\n",
       "      <td>52</td>\n",
       "      <td>NaT</td>\n",
       "      <td>Краснодар</td>\n",
       "      <td>Рафаил</td>\n",
       "      <td>Верещагин</td>\n",
       "      <td>2018-05-25</td>\n",
       "      <td>ultra</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1000</td>\n",
       "      <td>July</td>\n",
       "      <td>346.00</td>\n",
       "      <td>47.00</td>\n",
       "      <td>75.00</td>\n",
       "      <td>14,003.64</td>\n",
       "      <td>52</td>\n",
       "      <td>NaT</td>\n",
       "      <td>Краснодар</td>\n",
       "      <td>Рафаил</td>\n",
       "      <td>Верещагин</td>\n",
       "      <td>2018-05-25</td>\n",
       "      <td>ultra</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1000</td>\n",
       "      <td>June</td>\n",
       "      <td>187.00</td>\n",
       "      <td>43.00</td>\n",
       "      <td>60.00</td>\n",
       "      <td>23,233.77</td>\n",
       "      <td>52</td>\n",
       "      <td>NaT</td>\n",
       "      <td>Краснодар</td>\n",
       "      <td>Рафаил</td>\n",
       "      <td>Верещагин</td>\n",
       "      <td>2018-05-25</td>\n",
       "      <td>ultra</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1000</td>\n",
       "      <td>May</td>\n",
       "      <td>164.00</td>\n",
       "      <td>22.00</td>\n",
       "      <td>22.00</td>\n",
       "      <td>2,253.49</td>\n",
       "      <td>52</td>\n",
       "      <td>NaT</td>\n",
       "      <td>Краснодар</td>\n",
       "      <td>Рафаил</td>\n",
       "      <td>Верещагин</td>\n",
       "      <td>2018-05-25</td>\n",
       "      <td>ultra</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   user_id     month  month_calls_duration  calls_total_in_month  \\\n",
       "0     1000    August                418.00                 52.00   \n",
       "1     1000  December                341.00                 46.00   \n",
       "2     1000      July                346.00                 47.00   \n",
       "3     1000      June                187.00                 43.00   \n",
       "4     1000       May                164.00                 22.00   \n",
       "\n",
       "   sms_total_in_month  mb_total_in_month  age churn_date       city  \\\n",
       "0               81.00          14,055.93   52        NaT  Краснодар   \n",
       "1               70.00           9,817.61   52        NaT  Краснодар   \n",
       "2               75.00          14,003.64   52        NaT  Краснодар   \n",
       "3               60.00          23,233.77   52        NaT  Краснодар   \n",
       "4               22.00           2,253.49   52        NaT  Краснодар   \n",
       "\n",
       "  first_name  last_name   reg_date tariff  \n",
       "0     Рафаил  Верещагин 2018-05-25  ultra  \n",
       "1     Рафаил  Верещагин 2018-05-25  ultra  \n",
       "2     Рафаил  Верещагин 2018-05-25  ultra  \n",
       "3     Рафаил  Верещагин 2018-05-25  ultra  \n",
       "4     Рафаил  Верещагин 2018-05-25  ultra  "
      ]
     },
     "execution_count": 259,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#добавляем тариф в таблицу за счет склеивания\n",
    "df = df.merge(users, on='user_id', how='left')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 260,
   "id": "cbde9de2",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>1</th>\n",
       "      <td>1000</td>\n",
       "      <td>December</td>\n",
       "      <td>341.00</td>\n",
       "      <td>46.00</td>\n",
       "      <td>70.00</td>\n",
       "      <td>9,817.61</td>\n",
       "      <td>52</td>\n",
       "      <td>NaT</td>\n",
       "      <td>Краснодар</td>\n",
       "      <td>Рафаил</td>\n",
       "      <td>Верещагин</td>\n",
       "      <td>2018-05-25</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1000</td>\n",
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       "      <td>47.00</td>\n",
       "      <td>75.00</td>\n",
       "      <td>14,003.64</td>\n",
       "      <td>52</td>\n",
       "      <td>NaT</td>\n",
       "      <td>Краснодар</td>\n",
       "      <td>Рафаил</td>\n",
       "      <td>Верещагин</td>\n",
       "      <td>2018-05-25</td>\n",
       "      <td>ultra</td>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1000</td>\n",
       "      <td>June</td>\n",
       "      <td>187.00</td>\n",
       "      <td>43.00</td>\n",
       "      <td>60.00</td>\n",
       "      <td>23,233.77</td>\n",
       "      <td>52</td>\n",
       "      <td>NaT</td>\n",
       "      <td>Краснодар</td>\n",
       "      <td>Рафаил</td>\n",
       "      <td>Верещагин</td>\n",
       "      <td>2018-05-25</td>\n",
       "      <td>ultra</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1000</td>\n",
       "      <td>May</td>\n",
       "      <td>164.00</td>\n",
       "      <td>22.00</td>\n",
       "      <td>22.00</td>\n",
       "      <td>2,253.49</td>\n",
       "      <td>52</td>\n",
       "      <td>NaT</td>\n",
       "      <td>Краснодар</td>\n",
       "      <td>Рафаил</td>\n",
       "      <td>Верещагин</td>\n",
       "      <td>2018-05-25</td>\n",
       "      <td>ultra</td>\n",
       "      <td>1000</td>\n",
       "      <td>30720</td>\n",
       "      <td>3000</td>\n",
       "      <td>1950</td>\n",
       "      <td>150</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   user_id     month  month_calls_duration  calls_total_in_month  \\\n",
       "0     1000    August                418.00                 52.00   \n",
       "1     1000  December                341.00                 46.00   \n",
       "2     1000      July                346.00                 47.00   \n",
       "3     1000      June                187.00                 43.00   \n",
       "4     1000       May                164.00                 22.00   \n",
       "\n",
       "   sms_total_in_month  mb_total_in_month  age churn_date       city  \\\n",
       "0               81.00          14,055.93   52        NaT  Краснодар   \n",
       "1               70.00           9,817.61   52        NaT  Краснодар   \n",
       "2               75.00          14,003.64   52        NaT  Краснодар   \n",
       "3               60.00          23,233.77   52        NaT  Краснодар   \n",
       "4               22.00           2,253.49   52        NaT  Краснодар   \n",
       "\n",
       "  first_name  last_name   reg_date tariff  messages_included  \\\n",
       "0     Рафаил  Верещагин 2018-05-25  ultra               1000   \n",
       "1     Рафаил  Верещагин 2018-05-25  ultra               1000   \n",
       "2     Рафаил  Верещагин 2018-05-25  ultra               1000   \n",
       "3     Рафаил  Верещагин 2018-05-25  ultra               1000   \n",
       "4     Рафаил  Верещагин 2018-05-25  ultra               1000   \n",
       "\n",
       "   mg_per_month_included  minutes_included  rub_monthly_fee  rub_per_gb  \\\n",
       "0                  30720              3000             1950         150   \n",
       "1                  30720              3000             1950         150   \n",
       "2                  30720              3000             1950         150   \n",
       "3                  30720              3000             1950         150   \n",
       "4                  30720              3000             1950         150   \n",
       "\n",
       "   rub_per_message  rub_per_minute  \n",
       "0                1               1  \n",
       "1                1               1  \n",
       "2                1               1  \n",
       "3                1               1  \n",
       "4                1               1  "
      ]
     },
     "execution_count": 260,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#добавляем информацию о тарифе за счет склеивания\n",
    "df = df.merge(tariffs, on='tariff', how='left')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 261,
   "id": "c073fc2a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "user_id                     0\n",
       "month                       0\n",
       "month_calls_duration       40\n",
       "calls_total_in_month       40\n",
       "sms_total_in_month        497\n",
       "mb_total_in_month          11\n",
       "age                         0\n",
       "churn_date               3027\n",
       "city                        0\n",
       "first_name                  0\n",
       "last_name                   0\n",
       "reg_date                    0\n",
       "tariff                      0\n",
       "messages_included           0\n",
       "mg_per_month_included       0\n",
       "minutes_included            0\n",
       "rub_monthly_fee             0\n",
       "rub_per_gb                  0\n",
       "rub_per_message             0\n",
       "rub_per_minute              0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 261,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 262,
   "id": "df2f73b8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Поставим нули людям-героям: тем, которые не тратят интернет и не пишут смс\n",
    "df['sms_total_in_month'] = df['sms_total_in_month'].fillna(0)\n",
    "df['mb_total_in_month'] = df['mb_total_in_month'].fillna(0)\n",
    "df['month_calls_duration'] = df['month_calls_duration'].fillna(0)\n",
    "df['calls_total_in_month'] = df['calls_total_in_month'].fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 263,
   "id": "d05f6a9f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "user_id                     0\n",
       "month                       0\n",
       "month_calls_duration        0\n",
       "calls_total_in_month        0\n",
       "sms_total_in_month          0\n",
       "mb_total_in_month           0\n",
       "age                         0\n",
       "churn_date               3027\n",
       "city                        0\n",
       "first_name                  0\n",
       "last_name                   0\n",
       "reg_date                    0\n",
       "tariff                      0\n",
       "messages_included           0\n",
       "mg_per_month_included       0\n",
       "minutes_included            0\n",
       "rub_monthly_fee             0\n",
       "rub_per_gb                  0\n",
       "rub_per_message             0\n",
       "rub_per_minute              0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 263,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 264,
   "id": "494cdca6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 264,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.duplicated().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 265,
   "id": "6d878a51",
   "metadata": {},
   "outputs": [],
   "source": [
    "# функция для нахождения оплаты для каждой строки с учетом тарифа\n",
    "def above_the_tariff(row):\n",
    "    sms = (row['sms_total_in_month'] - row['messages_included']) * row['rub_per_message']\n",
    "    mins = (row['month_calls_duration'] - row['minutes_included']) * row['rub_per_minute']\n",
    "    mbs = np.floor((row['mb_total_in_month'] - row['mg_per_month_included']) * row['rub_per_gb'] / 1024)\n",
    "    total = 0\n",
    "    for i in [sms, mins, mbs]:\n",
    "        if i > 0:\n",
    "            total += i\n",
    "    return total"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 266,
   "id": "206a9c2b",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['above_the_tariff'] = df.apply(lambda x: above_the_tariff(x), axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 267,
   "id": "54534c15",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['total_paid'] = df['rub_monthly_fee'] + df['above_the_tariff']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 268,
   "id": "3cc3631b",
   "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>user_id</th>\n",
       "      <th>month</th>\n",
       "      <th>month_calls_duration</th>\n",
       "      <th>calls_total_in_month</th>\n",
       "      <th>sms_total_in_month</th>\n",
       "      <th>mb_total_in_month</th>\n",
       "      <th>age</th>\n",
       "      <th>churn_date</th>\n",
       "      <th>city</th>\n",
       "      <th>first_name</th>\n",
       "      <th>last_name</th>\n",
       "      <th>reg_date</th>\n",
       "      <th>tariff</th>\n",
       "      <th>messages_included</th>\n",
       "      <th>mg_per_month_included</th>\n",
       "      <th>minutes_included</th>\n",
       "      <th>rub_monthly_fee</th>\n",
       "      <th>rub_per_gb</th>\n",
       "      <th>rub_per_message</th>\n",
       "      <th>rub_per_minute</th>\n",
       "      <th>above_the_tariff</th>\n",
       "      <th>total_paid</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1792</th>\n",
       "      <td>1286</td>\n",
       "      <td>December</td>\n",
       "      <td>751.00</td>\n",
       "      <td>95.00</td>\n",
       "      <td>68.00</td>\n",
       "      <td>35,108.07</td>\n",
       "      <td>19</td>\n",
       "      <td>NaT</td>\n",
       "      <td>Москва</td>\n",
       "      <td>Рашид</td>\n",
       "      <td>Троицкий</td>\n",
       "      <td>2018-11-27</td>\n",
       "      <td>smart</td>\n",
       "      <td>50</td>\n",
       "      <td>15360</td>\n",
       "      <td>500</td>\n",
       "      <td>550</td>\n",
       "      <td>200</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>4,664.00</td>\n",
       "      <td>5,214.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3105</th>\n",
       "      <td>1486</td>\n",
       "      <td>November</td>\n",
       "      <td>788.00</td>\n",
       "      <td>108.00</td>\n",
       "      <td>96.00</td>\n",
       "      <td>36,266.11</td>\n",
       "      <td>35</td>\n",
       "      <td>NaT</td>\n",
       "      <td>Нижний Новгород</td>\n",
       "      <td>Матвей</td>\n",
       "      <td>Моргунов</td>\n",
       "      <td>2018-10-31</td>\n",
       "      <td>smart</td>\n",
       "      <td>50</td>\n",
       "      <td>15360</td>\n",
       "      <td>500</td>\n",
       "      <td>550</td>\n",
       "      <td>200</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>5,085.00</td>\n",
       "      <td>5,635.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>259</th>\n",
       "      <td>1042</td>\n",
       "      <td>December</td>\n",
       "      <td>755.00</td>\n",
       "      <td>121.00</td>\n",
       "      <td>75.00</td>\n",
       "      <td>37,165.69</td>\n",
       "      <td>42</td>\n",
       "      <td>NaT</td>\n",
       "      <td>Хабаровск</td>\n",
       "      <td>Константин</td>\n",
       "      <td>Добрынин</td>\n",
       "      <td>2018-11-25</td>\n",
       "      <td>smart</td>\n",
       "      <td>50</td>\n",
       "      <td>15360</td>\n",
       "      <td>500</td>\n",
       "      <td>550</td>\n",
       "      <td>200</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>5,098.00</td>\n",
       "      <td>5,648.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1620</th>\n",
       "      <td>1261</td>\n",
       "      <td>December</td>\n",
       "      <td>1,102.00</td>\n",
       "      <td>141.00</td>\n",
       "      <td>22.00</td>\n",
       "      <td>32,650.91</td>\n",
       "      <td>46</td>\n",
       "      <td>NaT</td>\n",
       "      <td>Казань</td>\n",
       "      <td>Мадина</td>\n",
       "      <td>Пантелеева</td>\n",
       "      <td>2018-10-30</td>\n",
       "      <td>smart</td>\n",
       "      <td>50</td>\n",
       "      <td>15360</td>\n",
       "      <td>500</td>\n",
       "      <td>550</td>\n",
       "      <td>200</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>5,183.00</td>\n",
       "      <td>5,733.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3104</th>\n",
       "      <td>1486</td>\n",
       "      <td>December</td>\n",
       "      <td>1,025.00</td>\n",
       "      <td>133.00</td>\n",
       "      <td>83.00</td>\n",
       "      <td>38,552.45</td>\n",
       "      <td>35</td>\n",
       "      <td>NaT</td>\n",
       "      <td>Нижний Новгород</td>\n",
       "      <td>Матвей</td>\n",
       "      <td>Моргунов</td>\n",
       "      <td>2018-10-31</td>\n",
       "      <td>smart</td>\n",
       "      <td>50</td>\n",
       "      <td>15360</td>\n",
       "      <td>500</td>\n",
       "      <td>550</td>\n",
       "      <td>200</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>6,203.00</td>\n",
       "      <td>6,753.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      user_id     month  month_calls_duration  calls_total_in_month  \\\n",
       "1792     1286  December                751.00                 95.00   \n",
       "3105     1486  November                788.00                108.00   \n",
       "259      1042  December                755.00                121.00   \n",
       "1620     1261  December              1,102.00                141.00   \n",
       "3104     1486  December              1,025.00                133.00   \n",
       "\n",
       "      sms_total_in_month  mb_total_in_month  age churn_date             city  \\\n",
       "1792               68.00          35,108.07   19        NaT           Москва   \n",
       "3105               96.00          36,266.11   35        NaT  Нижний Новгород   \n",
       "259                75.00          37,165.69   42        NaT        Хабаровск   \n",
       "1620               22.00          32,650.91   46        NaT           Казань   \n",
       "3104               83.00          38,552.45   35        NaT  Нижний Новгород   \n",
       "\n",
       "      first_name   last_name   reg_date tariff  messages_included  \\\n",
       "1792       Рашид    Троицкий 2018-11-27  smart                 50   \n",
       "3105      Матвей    Моргунов 2018-10-31  smart                 50   \n",
       "259   Константин    Добрынин 2018-11-25  smart                 50   \n",
       "1620      Мадина  Пантелеева 2018-10-30  smart                 50   \n",
       "3104      Матвей    Моргунов 2018-10-31  smart                 50   \n",
       "\n",
       "      mg_per_month_included  minutes_included  rub_monthly_fee  rub_per_gb  \\\n",
       "1792                  15360               500              550         200   \n",
       "3105                  15360               500              550         200   \n",
       "259                   15360               500              550         200   \n",
       "1620                  15360               500              550         200   \n",
       "3104                  15360               500              550         200   \n",
       "\n",
       "      rub_per_message  rub_per_minute  above_the_tariff  total_paid  \n",
       "1792                3               3          4,664.00    5,214.00  \n",
       "3105                3               3          5,085.00    5,635.00  \n",
       "259                 3               3          5,098.00    5,648.00  \n",
       "1620                3               3          5,183.00    5,733.00  \n",
       "3104                3               3          6,203.00    6,753.00  "
      ]
     },
     "execution_count": 268,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sort_values(by='total_paid').tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 269,
   "id": "19d222ec",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Сколько минут разговора, сколько сообщений и какой объём интернет-трафика требуется пользователям каждого тарифа\n",
    "# в месяц? "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 270,
   "id": "093864ce",
   "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>above_the_tariff</th>\n",
       "      <th>total_paid</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>3,214.00</td>\n",
       "      <td>3,214.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>517.13</td>\n",
       "      <td>1,496.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>741.66</td>\n",
       "      <td>785.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.00</td>\n",
       "      <td>550.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.00</td>\n",
       "      <td>711.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>122.50</td>\n",
       "      <td>1,584.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>853.75</td>\n",
       "      <td>1,950.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>6,203.00</td>\n",
       "      <td>6,753.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       above_the_tariff  total_paid\n",
       "count          3,214.00    3,214.00\n",
       "mean             517.13    1,496.19\n",
       "std              741.66      785.58\n",
       "min                0.00      550.00\n",
       "25%                0.00      711.25\n",
       "50%              122.50    1,584.00\n",
       "75%              853.75    1,950.00\n",
       "max            6,203.00    6,753.00"
      ]
     },
     "execution_count": 270,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[['above_the_tariff', 'total_paid']].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 271,
   "id": "f8eab996",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "smart    2229\n",
       "ultra     985\n",
       "Name: tariff, dtype: int64"
      ]
     },
     "execution_count": 271,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['tariff'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 272,
   "id": "b74588ee",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='Frequency'>"
      ]
     },
     "execution_count": 272,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Тариф ultra, Гистограмма показывает данные (кол-во пользователей) в десяти интервалах от мин-й оплаты, до мак-й  оплаты\n",
    "df[df['tariff'] == 'ultra']['total_paid'].plot(kind='hist', bins=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 273,
   "id": "a48c79b4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='Frequency'>"
      ]
     },
     "execution_count": 273,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Тариф smart\n",
    "df[df['tariff'] == 'smart']['total_paid'].plot(kind='hist', bins=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 274,
   "id": "74098850",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "</style>\n",
       "<table id=\"T_7bcb4\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_7bcb4_level0_col0\" class=\"col_heading level0 col0\" colspan=\"4\">total_paid</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th class=\"blank level1\" >&nbsp;</th>\n",
       "      <th id=\"T_7bcb4_level1_col0\" class=\"col_heading level1 col0\" >median</th>\n",
       "      <th id=\"T_7bcb4_level1_col1\" class=\"col_heading level1 col1\" >mean</th>\n",
       "      <th id=\"T_7bcb4_level1_col2\" class=\"col_heading level1 col2\" >var</th>\n",
       "      <th id=\"T_7bcb4_level1_col3\" class=\"col_heading level1 col3\" >std</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th class=\"index_name level0\" >tariff</th>\n",
       "      <th class=\"blank col0\" >&nbsp;</th>\n",
       "      <th class=\"blank col1\" >&nbsp;</th>\n",
       "      <th class=\"blank col2\" >&nbsp;</th>\n",
       "      <th class=\"blank col3\" >&nbsp;</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_7bcb4_level0_row0\" class=\"row_heading level0 row0\" >smart</th>\n",
       "      <td id=\"T_7bcb4_row0_col0\" class=\"data row0 col0\" >983.00</td>\n",
       "      <td id=\"T_7bcb4_row0_col1\" class=\"data row0 col1\" >1247.13</td>\n",
       "      <td id=\"T_7bcb4_row0_col2\" class=\"data row0 col2\" >631903.74</td>\n",
       "      <td id=\"T_7bcb4_row0_col3\" class=\"data row0 col3\" >794.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7bcb4_level0_row1\" class=\"row_heading level0 row1\" >ultra</th>\n",
       "      <td id=\"T_7bcb4_row1_col0\" class=\"data row1 col0\" >1950.00</td>\n",
       "      <td id=\"T_7bcb4_row1_col1\" class=\"data row1 col1\" >2059.80</td>\n",
       "      <td id=\"T_7bcb4_row1_col2\" class=\"data row1 col2\" >125843.21</td>\n",
       "      <td id=\"T_7bcb4_row1_col3\" class=\"data row1 col3\" >354.74</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x1a1d004d730>"
      ]
     },
     "execution_count": 274,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# данные для гистограмм: кол-во звонков в месяц для каждого тарифа\n",
    "df.groupby('tariff')\\\n",
    "  .agg({'total_paid':['median','mean','var','std']})\\\n",
    "  .style.format(\"{:.2f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 275,
   "id": "28482f65",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Смарт — Звонки в минутах в месяц')"
      ]
     },
     "execution_count": 275,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[df['tariff'] == 'smart']['month_calls_duration'].plot(kind='hist', bins=10)\n",
    "plt.title('Смарт — Звонки в минутах в месяц', fontdict=font)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 276,
   "id": "1ad02e85",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Ультра — Звонки в минутах в месяц')"
      ]
     },
     "execution_count": 276,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[df['tariff'] == 'ultra']['month_calls_duration'].plot(kind='hist', bins=10)\n",
    "plt.title('Ультра — Звонки в минутах в месяц', fontdict=font)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 277,
   "id": "f4df655e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Смарт — Интернет в МБ в месяц')"
      ]
     },
     "execution_count": 277,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# данные для гистограмм: Mb интернета  в месяц для каждого тарифа\n",
    "df[df['tariff'] == 'smart']['mb_total_in_month'].plot(kind='hist', bins=10)\n",
    "plt.title('Смарт — Интернет в МБ в месяц', fontdict=font)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 278,
   "id": "f78b6597",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Ультра — Интернет в МБ в месяц')"
      ]
     },
     "execution_count": 278,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[df['tariff'] == 'ultra']['mb_total_in_month'].plot(kind='hist', bins=10)\n",
    "plt.title('Ультра — Интернет в МБ в месяц', fontdict=font)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 279,
   "id": "f7b02fb4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Смарт — СМС в месяц')"
      ]
     },
     "execution_count": 279,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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zNW3W5e2/HbhI0hbAPcCbSIlogaSTgPuA4wEi4nZJC0iJYxVwakQ82eX4zMysQVeTQkTcAkxvseiwNvXnAfO6GZOZmbXnM5rNzKzgpGBmZgUnBTMzKzgpmJlZwUnBzMwKTgpmZlZwUjAzs4KTgpmZFZwUzMys4KRgZmYFJwUzMys4KZiZWcFJwczMCk4KZmZWcFIwM7OCk4KZmRWcFMzMrOCkYGZmBScFMzMrOCmYmVmhq0lB0lJJt0q6RdLiXDZB0jWS7s7THRrqz5W0RFJd0uHdjM3MzNY0Fi2FQyJi34iYnufnAIsiYhqwKM8jaS+gDOwNzADOk7TpGMRnZmZZL7qPZgLz8+P5wLEN5bWIeDwi7gWWAAeMfXhmZuNXt5NCAFdLuknS7Fy2S0QsB8jTnXP5JOD+hnUHc5mZmY2Rzbq8/ZdExDJJOwPXSLprmLpqURZrVErJZTbArrvuun6iNDMzoMsthYhYlqcrgMtI3UEPSpoIkKcrcvVBYErD6pOBZS22eX5ETI+I6QMDA90M38xs3OlaUpC0jaTthh4D/xu4DVgIzMrVZgFX5McLgbKkLSXtDkwDbuhWfGZmtqZudh/tAlwmaWg/F0fEVZJuBBZIOgm4DzgeICJul7QAuANYBZwaEU92MT4zM2vStaQQEfcA+7Qofxg4rM0684B53YrJzMyG5zOazcys4KRgZmYFJwUzMys4KZiZWcFJwczMCk4KZmZWcFIwM7OCk4KZmRWcFMzMrOCkYGZmhY6SgqTndTsQMzPrvU5bCl+QdIOkt0navpsBmZlZ73SUFCLipcCJpPsdLJZ0saRXdjUyMzMbcx0fU4iIu4EPAO8FXg78h6S7JB3XreDMzGxsdXpM4fmSPgPcCRwKvCoinpsff6aL8ZmZ2Rjq9H4KVeBLwPsi4q9Dhfn+yx/oSmRmZjbmOk0KRwJ/HboTmqRNgK0i4i8RcWHXojMzszHVaVL4EfAK4E95fmvgauDF3QhqYzd1zpU92e/Ss4/qyX7NbMPR6YHmrSJiKCGQH2/dnZDMzKxXOk0Kf5a0/9CMpBcAfx2mvpmZbYA67T46HfiWpGV5fiLwuq5EZGZmPdNRUoiIGyXtCZQAAXdFxN86WVfSpsBi4IGIOFrSBOCbwFRgKfBPEfGHXHcucBLwJPCOiPjh6J6OmZmti9FcEO+FwPOB/YATJL2hw/VOI53fMGQOsCgipgGL8jyS9gLKwN7ADOC8nFDMzGyMdHry2oXAJ4GXkpLDC4HpHaw3GTgK+HJD8Uxgfn48Hzi2obwWEY9HxL3AEuCATuIzM7P1o9NjCtOBvSIiRrn9c4D3ANs1lO0SEcsBImK5pJ1z+STguoZ6g7nsaSTNBmYD7LrrrqMMx8zMhtNp99FtwLNGs2FJRwMrIuKmTldpUbZGEoqI8yNiekRMHxgYGE1IZmY2gk5bCjsBd0i6AXh8qDAijhlmnZcAx0g6EtgKeKakbwAPSpqYWwkTgRW5/iDpKqxDJgPLMDOzMdNpUjhrtBuOiLnAXABJBwPvjojXS/oEMAs4O0+vyKssBC6W9Gng2cA04IbR7tfMzNZep0NSfyJpN2BaRPxI0tbA2o4MOhtYIOkk4D7g+LyP2yUtAO4AVgGnDl1ryczMxkZHSUHSKaSDuxOA55AOAH8BOKyT9SPiWuDa/PjhdutFxDxgXifbNDOz9a/TA82nko4RPArFDXd2HnYNMzPb4HSaFB6PiCeGZiRtRouRQWZmtmHrNCn8RNL7gGfkezN/C/hu98IyM7Ne6DQpzAFWArcCbwG+T7pfs5mZbUQ6HX30FOl2nF/qbjhmZtZLnY4+upfWZxfvsd4jMjOznhnNtY+GbEU6t2DC+g/HzMx6qaNjChHxcMPfAxFxDnBod0MzM7Ox1mn30f4Ns5uQWg7btalufWrqnCt7tu+lZx/Vs32bWec67T76VMPjVeQ7pq33aMzMrKc6HX10SLcDMTOz3uu0++hfh1seEZ9eP+GYmVkvjWb00QtJl7cGeBXwU+D+bgRlZma9MZqb7OwfEY8BSDoL+FZEnNytwMzMbOx1epmLXYEnGuafAKau92jMzKynOm0pXAjcIOky0pnNrwa+3rWozMysJzodfTRP0g+Af8xFb4qIX3YvLDMz64VOu48AtgYejYhzgUFJu3cpJjMz65GOkoKkM4H3AnNz0ebAN7oVlJmZ9UanLYVXA8cAfwaIiGX4MhdmZhudTg80PxERISkAJG0z0gqStiKdy7Bl3s+lEXGmpAnAN0mjl5YC/xQRf8jrzAVOAp4E3hERPxzd07F+1avrLvmaS2aj02lLYYGkLwLbSzoF+BEj33DnceDQiNgH2BeYIelA0l3cFkXENGBRnkfSXkAZ2BuYAZwnadNRPh8zM1sHI7YUJIn0y35P4FGgBHwwIq4Zbr2ICOBPeXbz/BfATODgXD4fuJZ0vGImUIuIx4F7JS0BDgB+MapnZGZma23EpJC7jS6PiBcAwyaCZvmX/k3A3wOfi4jrJe0SEcvztpdL2jlXnwRc17D6YC5r3uZsYDbArrvuOppwzMxsBJ12H10n6YWj3XhEPBkR+wKTgQMkPW+Y6mq1iRbbPD8ipkfE9IGBgdGGZGZmw+j0QPMhwFslLSWNQBKpEfH8TlaOiEckXUs6VvCgpIm5lTARWJGrDQJTGlabDCzrMD4zM1sPhm0pSBrqnzkC2IN0C85XAUfn6XDrDkjaPj9+BvAK4C7SlVZn5WqzgCvy44VAWdKW+cS4acANo3w+Zma2DkZqKVxOujrqbyV9OyJeM4ptTwTm5+MKmwALIuJ7kn5BGs10EnAfcDxARNwuaQFwB+nubqdGxJOjfD5mZrYORkoKjf38e4xmwxHxa2C/FuUPA4e1WWceMG80+zEzs/VnpAPN0eaxmZlthEZqKewj6VFSi+EZ+TGsPtD8zK5GZ2ZmY2rYpBARPqPYzGwcGc2ls83MbCPnpGBmZgUnBTMzKzgpmJlZwUnBzMwKTgpmZlZwUjAzs4KTgpmZFZwUzMys4KRgZmYFJwUzMys4KZiZWcFJwczMCk4KZmZWcFIwM7OCk4KZmRWcFMzMrOCkYGZmha4lBUlTJP1Y0p2Sbpd0Wi6fIOkaSXfn6Q4N68yVtERSXdLh3YrNzMxa62ZLYRXwroh4LnAgcKqkvYA5wKKImAYsyvPkZWVgb2AGcJ4k3yPazGwMdS0pRMTyiLg5P34MuBOYBMwE5udq84Fj8+OZQC0iHo+Ie4ElwAHdis/MzNY0JscUJE0F9gOuB3aJiOWQEgewc642Cbi/YbXBXNa8rdmSFktavHLlyq7GbWY23nQ9KUjaFvg2cHpEPDpc1RZlsUZBxPkRMT0ipg8MDKyvMM3MjC4nBUmbkxLCRRHxnVz8oKSJeflEYEUuHwSmNKw+GVjWzfjMzOzpujn6SMBXgDsj4tMNixYCs/LjWcAVDeVlSVtK2h2YBtzQrfjMzGxNm3Vx2y8B/gW4VdItuex9wNnAAkknAfcBxwNExO2SFgB3kEYunRoRT3YxPhsHps65smf7Xnr2UT3bt9na6lpSiIif0fo4AcBhbdaZB8zrVkxmZjY8n9FsZmYFJwUzMys4KZiZWcFJwczMCk4KZmZWcFIwM7OCk4KZmRWcFMzMrOCkYGZmBScFMzMrOCmYmVnBScHMzApOCmZmVnBSMDOzgpOCmZkVunmTHbNxrVc3+PHNfWxduKVgZmYFJwUzMys4KZiZWcFJwczMCl1LCpK+KmmFpNsayiZIukbS3Xm6Q8OyuZKWSKpLOrxbcZmZWXvdbClcAMxoKpsDLIqIacCiPI+kvYAysHde5zxJm3YxNjMza6FrSSEifgr8vql4JjA/P54PHNtQXouIxyPiXmAJcEC3YjMzs9bG+pjCLhGxHCBPd87lk4D7G+oN5rI1SJotabGkxStXruxqsGZm402/HGhWi7JoVTEizo+I6RExfWBgoMthmZmNL2OdFB6UNBEgT1fk8kFgSkO9ycCyMY7NzGzcG+uksBCYlR/PAq5oKC9L2lLS7sA04IYxjs3MbNzr2rWPJF0CHAzsJGkQOBM4G1gg6STgPuB4gIi4XdIC4A5gFXBqRDzZrdjMzKy1riWFiDihzaLD2tSfB8zrVjxmZjayfjnQbGZmfcBJwczMCr6fgtlGplf3cQDfy2Fj4JaCmZkVnBTMzKzgpGBmZgUnBTMzKzgpmJlZwUnBzMwKTgpmZlZwUjAzs4KTgpmZFZwUzMys4MtcmNl606tLbPjyGuuPWwpmZlZwUjAzs4KTgpmZFZwUzMys4KRgZmYFJwUzMys4KZiZWaHvkoKkGZLqkpZImtPreMzMxpO+OnlN0qbA54BXAoPAjZIWRsQdvY3MzPpZL+9L3SvdOmGv31oKBwBLIuKeiHgCqAEzexyTmdm40VctBWAScH/D/CDwosYKkmYDs/PsnyTV12F/OwEPrcP6Y2VDiRM2nFg3lDhhw4l1Q4kTNoJY9bF12uZu7Rb0W1JQi7J42kzE+cD562Vn0uKImL4+ttVNG0qcsOHEuqHECRtOrBtKnOBYh9Nv3UeDwJSG+cnAsh7FYmY27vRbUrgRmCZpd0lbAGVgYY9jMjMbN/qq+ygiVkmqAD8ENgW+GhG3d3GX66UbagxsKHHChhPrhhInbDixbihxgmNtSxExci0zMxsX+q37yMzMeshJwczMCuMyKfTrpTQkTZH0Y0l3Srpd0mm5fIKkayTdnac79DrWIZI2lfRLSd/L830Zq6TtJV0q6a78+h7Uj7FKemf+398m6RJJW/VLnJK+KmmFpNsaytrGJmlu/ozVJR3eB7F+Iv//fy3pMknb9zrWVnE2LHu3pJC001jGOe6SQsOlNI4A9gJOkLRXb6MqrALeFRHPBQ4ETs2xzQEWRcQ0YFGe7xenAXc2zPdrrOcCV0XEnsA+pJj7KlZJk4B3ANMj4nmkwRZl+ifOC4AZTWUtY8vv2zKwd17nvPzZGysXsGas1wDPi4jnA/8NzIWex9oqTiRNIV3u576GsjGJc9wlBfr4UhoRsTwibs6PHyN9cU0ixTc/V5sPHNuTAJtImgwcBXy5objvYpX0TOBlwFcAIuKJiHiEPoyVNCLwGZI2A7YmnafTF3FGxE+B3zcVt4ttJlCLiMcj4l5gCemzNyZaxRoRV0fEqjx7Hek8qJ7G2uY1BfgM8B6efvLumMQ5HpNCq0tpTOpRLG1JmgrsB1wP7BIRyyElDmDnHobW6BzSG/ephrJ+jHUPYCXwtdzV9WVJ29BnsUbEA8AnSb8OlwN/jIir6bM4m7SLrd8/Z28GfpAf91Wsko4BHoiIXzUtGpM4x2NSGPFSGr0maVvg28DpEfFor+NpRdLRwIqIuKnXsXRgM2B/4PMRsR/wZ/qnW6uQ++NnArsDzwa2kfT63ka11vr2cybp/aSu2ouGilpU60mskrYG3g98sNXiFmXrPc7xmBT6+lIakjYnJYSLIuI7ufhBSRPz8onAil7F1+AlwDGSlpK64A6V9A36M9ZBYDAirs/zl5KSRL/F+grg3ohYGRF/A74DvJj+i7NRu9j68nMmaRZwNHBirD5Jq59ifQ7pR8Gv8mdrMnCzpGcxRnGOx6TQt5fSkCRSv/edEfHphkULgVn58SzgirGOrVlEzI2IyRExlfQa/mdEvJ7+jPV3wP2SSrnoMOAO+i/W+4ADJW2d3wuHkY4r9VucjdrFthAoS9pS0u7ANOCGHsRXkDQDeC9wTET8pWFR38QaEbdGxM4RMTV/tgaB/fN7eGzijIhx9wccSRp98Bvg/b2OpyGul5Kag78Gbsl/RwI7kkZ23J2nE3oda1PcBwPfy4/7MlZgX2Bxfm0vB3box1iBDwF3AbcBFwJb9kucwCWkYx1/I31ZnTRcbKRukN8AdeCIPoh1CalPfuiz9YVex9oqzqblS4GdxjJOX+bCzMwK47H7yMzM2nBSMDOzgpOCmZkVnBTMzKzgpGBmZgUnBTMzKzgpmJlZ4X8A1IJMV6y1ZpsAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# данные для гистограмм: кол-во СМС  в месяц для каждого тарифа\n",
    "df[df['tariff'] == 'smart']['sms_total_in_month'].plot(kind='hist', bins=10)\n",
    "plt.title('Смарт — СМС в месяц', fontdict=font)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 280,
   "id": "b2ee1e37",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Ультра — СМС в месяц')"
      ]
     },
     "execution_count": 280,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[df['tariff'] == 'ultra']['sms_total_in_month'].plot(kind='hist', bins=10)\n",
    "plt.title('Ультра — СМС в месяц', fontdict=font)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 281,
   "id": "f0fba2fe",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Проверка гипотез: средняя выручка пользователей тарифов «Ультра» и «Смарт» различаются\n",
    "#Будем использовать t-критерий Стьюдента"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 282,
   "id": "dc42bdb9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Напишем функцию для удаления выбросов\n",
    "def delete_outliers(df, column, graphic=0, x = 10, y = 10):\n",
    "    q1 = df[column].quantile(0.25)                 \n",
    "    q3 = df[column].quantile(0.75)\n",
    "    iqr = q3 - q1\n",
    "    iqr_test = (df[column] >= (q1 - 1.5 * iqr)) & (df[column] <= (q3 + 1.5 * iqr))\n",
    "    if graphic == 1:\n",
    "        return df.loc[iqr_test].boxplot([column], figsize=(x,y))\n",
    "    else:\n",
    "        return df.loc[iqr_test]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 283,
   "id": "138c82f5",
   "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>user_id</th>\n",
       "      <th>month</th>\n",
       "      <th>month_calls_duration</th>\n",
       "      <th>calls_total_in_month</th>\n",
       "      <th>sms_total_in_month</th>\n",
       "      <th>mb_total_in_month</th>\n",
       "      <th>age</th>\n",
       "      <th>churn_date</th>\n",
       "      <th>city</th>\n",
       "      <th>first_name</th>\n",
       "      <th>last_name</th>\n",
       "      <th>reg_date</th>\n",
       "      <th>tariff</th>\n",
       "      <th>messages_included</th>\n",
       "      <th>mg_per_month_included</th>\n",
       "      <th>minutes_included</th>\n",
       "      <th>rub_monthly_fee</th>\n",
       "      <th>rub_per_gb</th>\n",
       "      <th>rub_per_message</th>\n",
       "      <th>rub_per_minute</th>\n",
       "      <th>above_the_tariff</th>\n",
       "      <th>total_paid</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1000</td>\n",
       "      <td>August</td>\n",
       "      <td>418.00</td>\n",
       "      <td>52.00</td>\n",
       "      <td>81.00</td>\n",
       "      <td>14,055.93</td>\n",
       "      <td>52</td>\n",
       "      <td>NaT</td>\n",
       "      <td>Краснодар</td>\n",
       "      <td>Рафаил</td>\n",
       "      <td>Верещагин</td>\n",
       "      <td>2018-05-25</td>\n",
       "      <td>ultra</td>\n",
       "      <td>1000</td>\n",
       "      <td>30720</td>\n",
       "      <td>3000</td>\n",
       "      <td>1950</td>\n",
       "      <td>150</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1,950.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1000</td>\n",
       "      <td>December</td>\n",
       "      <td>341.00</td>\n",
       "      <td>46.00</td>\n",
       "      <td>70.00</td>\n",
       "      <td>9,817.61</td>\n",
       "      <td>52</td>\n",
       "      <td>NaT</td>\n",
       "      <td>Краснодар</td>\n",
       "      <td>Рафаил</td>\n",
       "      <td>Верещагин</td>\n",
       "      <td>2018-05-25</td>\n",
       "      <td>ultra</td>\n",
       "      <td>1000</td>\n",
       "      <td>30720</td>\n",
       "      <td>3000</td>\n",
       "      <td>1950</td>\n",
       "      <td>150</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1,950.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1000</td>\n",
       "      <td>July</td>\n",
       "      <td>346.00</td>\n",
       "      <td>47.00</td>\n",
       "      <td>75.00</td>\n",
       "      <td>14,003.64</td>\n",
       "      <td>52</td>\n",
       "      <td>NaT</td>\n",
       "      <td>Краснодар</td>\n",
       "      <td>Рафаил</td>\n",
       "      <td>Верещагин</td>\n",
       "      <td>2018-05-25</td>\n",
       "      <td>ultra</td>\n",
       "      <td>1000</td>\n",
       "      <td>30720</td>\n",
       "      <td>3000</td>\n",
       "      <td>1950</td>\n",
       "      <td>150</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1,950.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1000</td>\n",
       "      <td>June</td>\n",
       "      <td>187.00</td>\n",
       "      <td>43.00</td>\n",
       "      <td>60.00</td>\n",
       "      <td>23,233.77</td>\n",
       "      <td>52</td>\n",
       "      <td>NaT</td>\n",
       "      <td>Краснодар</td>\n",
       "      <td>Рафаил</td>\n",
       "      <td>Верещагин</td>\n",
       "      <td>2018-05-25</td>\n",
       "      <td>ultra</td>\n",
       "      <td>1000</td>\n",
       "      <td>30720</td>\n",
       "      <td>3000</td>\n",
       "      <td>1950</td>\n",
       "      <td>150</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1,950.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1000</td>\n",
       "      <td>May</td>\n",
       "      <td>164.00</td>\n",
       "      <td>22.00</td>\n",
       "      <td>22.00</td>\n",
       "      <td>2,253.49</td>\n",
       "      <td>52</td>\n",
       "      <td>NaT</td>\n",
       "      <td>Краснодар</td>\n",
       "      <td>Рафаил</td>\n",
       "      <td>Верещагин</td>\n",
       "      <td>2018-05-25</td>\n",
       "      <td>ultra</td>\n",
       "      <td>1000</td>\n",
       "      <td>30720</td>\n",
       "      <td>3000</td>\n",
       "      <td>1950</td>\n",
       "      <td>150</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1,950.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   user_id     month  month_calls_duration  calls_total_in_month  \\\n",
       "0     1000    August                418.00                 52.00   \n",
       "1     1000  December                341.00                 46.00   \n",
       "2     1000      July                346.00                 47.00   \n",
       "3     1000      June                187.00                 43.00   \n",
       "4     1000       May                164.00                 22.00   \n",
       "\n",
       "   sms_total_in_month  mb_total_in_month  age churn_date       city  \\\n",
       "0               81.00          14,055.93   52        NaT  Краснодар   \n",
       "1               70.00           9,817.61   52        NaT  Краснодар   \n",
       "2               75.00          14,003.64   52        NaT  Краснодар   \n",
       "3               60.00          23,233.77   52        NaT  Краснодар   \n",
       "4               22.00           2,253.49   52        NaT  Краснодар   \n",
       "\n",
       "  first_name  last_name   reg_date tariff  messages_included  \\\n",
       "0     Рафаил  Верещагин 2018-05-25  ultra               1000   \n",
       "1     Рафаил  Верещагин 2018-05-25  ultra               1000   \n",
       "2     Рафаил  Верещагин 2018-05-25  ultra               1000   \n",
       "3     Рафаил  Верещагин 2018-05-25  ultra               1000   \n",
       "4     Рафаил  Верещагин 2018-05-25  ultra               1000   \n",
       "\n",
       "   mg_per_month_included  minutes_included  rub_monthly_fee  rub_per_gb  \\\n",
       "0                  30720              3000             1950         150   \n",
       "1                  30720              3000             1950         150   \n",
       "2                  30720              3000             1950         150   \n",
       "3                  30720              3000             1950         150   \n",
       "4                  30720              3000             1950         150   \n",
       "\n",
       "   rub_per_message  rub_per_minute  above_the_tariff  total_paid  \n",
       "0                1               1              0.00    1,950.00  \n",
       "1                1               1              0.00    1,950.00  \n",
       "2                1               1              0.00    1,950.00  \n",
       "3                1               1              0.00    1,950.00  \n",
       "4                1               1              0.00    1,950.00  "
      ]
     },
     "execution_count": 283,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 284,
   "id": "d6583a85",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Выделяем из общей таблицы выручку пользователей для каждого тарифа \n",
    "ultra = df[df['tariff'] == 'ultra']\n",
    "smart = df[df['tariff'] == 'smart']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 285,
   "id": "8077300c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# удаляем строки с нулевой оплатой, исключаем их из выборки\n",
    "ultra = delete_outliers(ultra, 'total_paid', 0)\n",
    "ultra = ultra['total_paid']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 286,
   "id": "4e1a3639",
   "metadata": {},
   "outputs": [],
   "source": [
    "smart = delete_outliers(smart, 'total_paid', 0)\n",
    "smart = smart['total_paid']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 287,
   "id": "dfcf86ce",
   "metadata": {},
   "outputs": [],
   "source": [
    "# критический уровень статистической значимости\n",
    "alpha = 0.05"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 288,
   "id": "b1077673",
   "metadata": {},
   "outputs": [],
   "source": [
    "# используем из библиотеки статистики  функцию ttest_ind () для вычисления  t-критерия Стьюдента\n",
    "results = st.ttest_ind(ultra, smart)\n",
    "# equal_var = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 289,
   "id": "5be73876",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "p-значение: 1.3166937589634654e-188\n",
      "Отвергаем нулевую гипотезу\n"
     ]
    }
   ],
   "source": [
    "print('p-значение:', results.pvalue)\n",
    "\n",
    "if (results.pvalue < alpha):\n",
    "    print(\"Отвергаем нулевую гипотезу\")\n",
    "else:\n",
    "    print(\"Не получилось отвергнуть нулевую гипотезу\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 290,
   "id": "b88a6ed3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# проверка гипотезы:средняя выручка пользователей из Москвы отличается от выручки пользователей из других регионов\n",
    "# Гипотеза о равенстве средних двух генеральных совокупностей"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "efe41ea2",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 291,
   "id": "01e23184",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Краснодар', 'Москва', 'Стерлитамак', 'Новокузнецк',\n",
       "       'Набережные Челны', 'Ульяновск', 'Челябинск', 'Пермь', 'Тюмень',\n",
       "       'Самара', 'Санкт-Петербург', 'Иваново', 'Чебоксары', 'Омск', 'Уфа',\n",
       "       'Томск', 'Чита', 'Мурманск', 'Петрозаводск', 'Тверь',\n",
       "       'Екатеринбург', 'Махачкала', 'Вологда', 'Череповец', 'Ярославль',\n",
       "       'Сочи', 'Хабаровск', 'Ставрополь', 'Рязань', 'Улан-Удэ', 'Тула',\n",
       "       'Саратов', 'Ростов-на-Дону', 'Казань', 'Иркутск', 'Курск',\n",
       "       'Калуга', 'Якутск', 'Астрахань', 'Химки', 'Владикавказ',\n",
       "       'Волгоград', 'Саранск', 'Ижевск', 'Новосибирск', 'Брянск',\n",
       "       'Тольятти', 'Нижний Тагил', 'Орёл', 'Белгород', 'Воронеж',\n",
       "       'Курган', 'Барнаул', 'Красноярск', 'Архангельск', 'Липецк',\n",
       "       'Кемерово', 'Киров', 'Сургут', 'Пенза', 'Волжский', 'Магнитогорск',\n",
       "       'Нижний Новгород', 'Кострома', 'Владивосток', 'Тамбов', 'Балашиха',\n",
       "       'Севастополь', 'Калининград', 'Оренбург', 'Смоленск',\n",
       "       'Нижневартовск', 'Владимир', 'Новороссийск', 'Грозный', 'Подольск'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 291,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Функция unique() находит уникальные элементы массива и возвращает их в отсортированном массиве, названия городов берем из таблицы\n",
    "df['city'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 292,
   "id": "0978fc78",
   "metadata": {},
   "outputs": [],
   "source": [
    "# в таблице все города, кроме Москвы, меняет на Провинцию\n",
    "df['region'] = np.where(df['city'] == 'Москва', 'Москва', 'Провинция')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 293,
   "id": "e888c7a0",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>month</th>\n",
       "      <th>month_calls_duration</th>\n",
       "      <th>calls_total_in_month</th>\n",
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       "      <th>reg_date</th>\n",
       "      <th>tariff</th>\n",
       "      <th>messages_included</th>\n",
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       "      <th>minutes_included</th>\n",
       "      <th>rub_monthly_fee</th>\n",
       "      <th>rub_per_gb</th>\n",
       "      <th>rub_per_message</th>\n",
       "      <th>rub_per_minute</th>\n",
       "      <th>above_the_tariff</th>\n",
       "      <th>total_paid</th>\n",
       "      <th>region</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1000</td>\n",
       "      <td>August</td>\n",
       "      <td>418.00</td>\n",
       "      <td>52.00</td>\n",
       "      <td>81.00</td>\n",
       "      <td>14,055.93</td>\n",
       "      <td>52</td>\n",
       "      <td>NaT</td>\n",
       "      <td>Краснодар</td>\n",
       "      <td>Рафаил</td>\n",
       "      <td>Верещагин</td>\n",
       "      <td>2018-05-25</td>\n",
       "      <td>ultra</td>\n",
       "      <td>1000</td>\n",
       "      <td>30720</td>\n",
       "      <td>3000</td>\n",
       "      <td>1950</td>\n",
       "      <td>150</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1,950.00</td>\n",
       "      <td>Провинция</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1000</td>\n",
       "      <td>December</td>\n",
       "      <td>341.00</td>\n",
       "      <td>46.00</td>\n",
       "      <td>70.00</td>\n",
       "      <td>9,817.61</td>\n",
       "      <td>52</td>\n",
       "      <td>NaT</td>\n",
       "      <td>Краснодар</td>\n",
       "      <td>Рафаил</td>\n",
       "      <td>Верещагин</td>\n",
       "      <td>2018-05-25</td>\n",
       "      <td>ultra</td>\n",
       "      <td>1000</td>\n",
       "      <td>30720</td>\n",
       "      <td>3000</td>\n",
       "      <td>1950</td>\n",
       "      <td>150</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1,950.00</td>\n",
       "      <td>Провинция</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1000</td>\n",
       "      <td>July</td>\n",
       "      <td>346.00</td>\n",
       "      <td>47.00</td>\n",
       "      <td>75.00</td>\n",
       "      <td>14,003.64</td>\n",
       "      <td>52</td>\n",
       "      <td>NaT</td>\n",
       "      <td>Краснодар</td>\n",
       "      <td>Рафаил</td>\n",
       "      <td>Верещагин</td>\n",
       "      <td>2018-05-25</td>\n",
       "      <td>ultra</td>\n",
       "      <td>1000</td>\n",
       "      <td>30720</td>\n",
       "      <td>3000</td>\n",
       "      <td>1950</td>\n",
       "      <td>150</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1,950.00</td>\n",
       "      <td>Провинция</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1000</td>\n",
       "      <td>June</td>\n",
       "      <td>187.00</td>\n",
       "      <td>43.00</td>\n",
       "      <td>60.00</td>\n",
       "      <td>23,233.77</td>\n",
       "      <td>52</td>\n",
       "      <td>NaT</td>\n",
       "      <td>Краснодар</td>\n",
       "      <td>Рафаил</td>\n",
       "      <td>Верещагин</td>\n",
       "      <td>2018-05-25</td>\n",
       "      <td>ultra</td>\n",
       "      <td>1000</td>\n",
       "      <td>30720</td>\n",
       "      <td>3000</td>\n",
       "      <td>1950</td>\n",
       "      <td>150</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1,950.00</td>\n",
       "      <td>Провинция</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1000</td>\n",
       "      <td>May</td>\n",
       "      <td>164.00</td>\n",
       "      <td>22.00</td>\n",
       "      <td>22.00</td>\n",
       "      <td>2,253.49</td>\n",
       "      <td>52</td>\n",
       "      <td>NaT</td>\n",
       "      <td>Краснодар</td>\n",
       "      <td>Рафаил</td>\n",
       "      <td>Верещагин</td>\n",
       "      <td>2018-05-25</td>\n",
       "      <td>ultra</td>\n",
       "      <td>1000</td>\n",
       "      <td>30720</td>\n",
       "      <td>3000</td>\n",
       "      <td>1950</td>\n",
       "      <td>150</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1,950.00</td>\n",
       "      <td>Провинция</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   user_id     month  month_calls_duration  calls_total_in_month  \\\n",
       "0     1000    August                418.00                 52.00   \n",
       "1     1000  December                341.00                 46.00   \n",
       "2     1000      July                346.00                 47.00   \n",
       "3     1000      June                187.00                 43.00   \n",
       "4     1000       May                164.00                 22.00   \n",
       "\n",
       "   sms_total_in_month  mb_total_in_month  age churn_date       city  \\\n",
       "0               81.00          14,055.93   52        NaT  Краснодар   \n",
       "1               70.00           9,817.61   52        NaT  Краснодар   \n",
       "2               75.00          14,003.64   52        NaT  Краснодар   \n",
       "3               60.00          23,233.77   52        NaT  Краснодар   \n",
       "4               22.00           2,253.49   52        NaT  Краснодар   \n",
       "\n",
       "  first_name  last_name   reg_date tariff  messages_included  \\\n",
       "0     Рафаил  Верещагин 2018-05-25  ultra               1000   \n",
       "1     Рафаил  Верещагин 2018-05-25  ultra               1000   \n",
       "2     Рафаил  Верещагин 2018-05-25  ultra               1000   \n",
       "3     Рафаил  Верещагин 2018-05-25  ultra               1000   \n",
       "4     Рафаил  Верещагин 2018-05-25  ultra               1000   \n",
       "\n",
       "   mg_per_month_included  minutes_included  rub_monthly_fee  rub_per_gb  \\\n",
       "0                  30720              3000             1950         150   \n",
       "1                  30720              3000             1950         150   \n",
       "2                  30720              3000             1950         150   \n",
       "3                  30720              3000             1950         150   \n",
       "4                  30720              3000             1950         150   \n",
       "\n",
       "   rub_per_message  rub_per_minute  above_the_tariff  total_paid     region  \n",
       "0                1               1              0.00    1,950.00  Провинция  \n",
       "1                1               1              0.00    1,950.00  Провинция  \n",
       "2                1               1              0.00    1,950.00  Провинция  \n",
       "3                1               1              0.00    1,950.00  Провинция  \n",
       "4                1               1              0.00    1,950.00  Провинция  "
      ]
     },
     "execution_count": 293,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 294,
   "id": "910d2926",
   "metadata": {},
   "outputs": [],
   "source": [
    "# выделяем две таблицы из исходной\n",
    "moscow = df[df['region'] == 'Москва']\n",
    "province = df[df['region'] == 'Провинция']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 295,
   "id": "43a152a5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# удаляем строки с нулевой оплатой, вызывая функцию delete_outliers\n",
    "moscow = delete_outliers(moscow, 'total_paid', 0)\n",
    "moscow = moscow['total_paid']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 296,
   "id": "a0fcfd79",
   "metadata": {},
   "outputs": [],
   "source": [
    "province = delete_outliers(province, 'total_paid', 0)\n",
    "province = province['total_paid']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 297,
   "id": "ce3b502c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# используем из библиотеки статистики  функцию ttest_ind () для вычисления  t-критерия Стьюдента\n",
    "results = st.ttest_ind(moscow, province)\n",
    "# equal_var = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 298,
   "id": "0641e91a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "p-значение: 0.3093521905092109\n",
      "Не получилось отвергнуть нулевую гипотезу\n"
     ]
    }
   ],
   "source": [
    "print('p-значение:', results.pvalue)\n",
    "\n",
    "if (results.pvalue < alpha):\n",
    "    print(\"Отвергаем нулевую гипотезу\")\n",
    "else:\n",
    "    print(\"Не получилось отвергнуть нулевую гипотезу\")"
   ]
  }
 ],
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