Analytics of Megaline tariffs.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "deletable": false
   },
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false
   },
   "source": [
    "### Откройте файл с данными и изучите общую информацию"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false,
    "tags": [
     "start_task"
    ]
   },
   "source": [
    "**Задание 1.** Откройте файл `/datasets/calls.csv`, сохраните датафрейм в переменную `calls`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "deletable": false
   },
   "outputs": [],
   "source": [
    "calls = pd.read_csv('/datasets/calls.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false,
    "tags": [
     "start_task"
    ]
   },
   "source": [
    "**Задание 2.** Выведите первые 5 строк датафрейма `calls`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "deletable": false
   },
   "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>call_date</th>\n",
       "      <th>duration</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>2018-07-25</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1000</td>\n",
       "    </tr>\n",
       "    <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",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "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": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "calls.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false,
    "tags": [
     "start_task"
    ]
   },
   "source": [
    "**Задание 3.** Выведите основную информацию для датафрейма `calls` с помощью метода `info()`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "deletable": false
   },
   "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": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false,
    "tags": [
     "start_task"
    ]
   },
   "source": [
    "**Задание 4.** С помощью метода `hist()` выведите гистограмму для столбца с продолжительностью звонков. Подумайте о том, как распределены данные."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "deletable": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 68,
     "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": [
    "calls['duration'].hist()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Очевидно, что преобладают звонки продолжительностью до 5 минут"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false,
    "tags": [
     "start_task"
    ]
   },
   "source": [
    "**Задание 5.** Откройте файл `/datasets/internet.csv`, сохраните датафрейм в переменную `sessions`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "deletable": false
   },
   "outputs": [],
   "source": [
    "sessions = pd.read_csv('/datasets/internet.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false,
    "tags": [
     "start_task"
    ]
   },
   "source": [
    "**Задание 6.** Выведите первые 5 строк датафрейма `sessions`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "deletable": false
   },
   "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>Unnamed: 0</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>0</td>\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>1</td>\n",
       "      <td>1000_1</td>\n",
       "      <td>1052.81</td>\n",
       "      <td>2018-09-07</td>\n",
       "      <td>1000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>1000_2</td>\n",
       "      <td>1197.26</td>\n",
       "      <td>2018-06-25</td>\n",
       "      <td>1000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\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>4</td>\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": [
       "   Unnamed: 0      id  mb_used session_date  user_id\n",
       "0           0  1000_0   112.95   2018-11-25     1000\n",
       "1           1  1000_1  1052.81   2018-09-07     1000\n",
       "2           2  1000_2  1197.26   2018-06-25     1000\n",
       "3           3  1000_3   550.27   2018-08-22     1000\n",
       "4           4  1000_4   302.56   2018-09-24     1000"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sessions.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false,
    "tags": [
     "start_task"
    ]
   },
   "source": [
    "**Задание 7.** Выведите основную информацию для датафрейма sessions с помощью метода `info()`. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "deletable": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 149396 entries, 0 to 149395\n",
      "Data columns (total 5 columns):\n",
      " #   Column        Non-Null Count   Dtype  \n",
      "---  ------        --------------   -----  \n",
      " 0   Unnamed: 0    149396 non-null  int64  \n",
      " 1   id            149396 non-null  object \n",
      " 2   mb_used       149396 non-null  float64\n",
      " 3   session_date  149396 non-null  object \n",
      " 4   user_id       149396 non-null  int64  \n",
      "dtypes: float64(1), int64(2), object(2)\n",
      "memory usage: 5.7+ MB\n"
     ]
    }
   ],
   "source": [
    "sessions.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false,
    "tags": [
     "start_task"
    ]
   },
   "source": [
    "**Задание 8.** С помощью метода `hist()` выведите гистограмму для столбца с количеством потраченных мегабайт."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "deletable": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 72,
     "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": [
    "sessions['mb_used'].hist()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false,
    "tags": [
     "start_task"
    ]
   },
   "source": [
    "**Задание 9.** Откройте файл `/datasets/messages.csv`, сохраните датафрейм в переменную `messages`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "deletable": false
   },
   "outputs": [],
   "source": [
    "messages = pd.read_csv('/datasets/messages.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false,
    "tags": [
     "start_task"
    ]
   },
   "source": [
    "**Задание 10.** Выведите первые 5 строк датафрейма `messages`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "deletable": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    <tr>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1000_3</td>\n",
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       "      <td>1000</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1000_4</td>\n",
       "      <td>2018-12-05</td>\n",
       "      <td>1000</td>\n",
       "    </tr>\n",
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      ],
      "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": 74,
     "metadata": {},
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    }
   ],
   "source": [
    "messages.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false,
    "tags": [
     "start_task"
    ]
   },
   "source": [
    "**Задание 11.** Выведите основную информацию для датафрейма `messages` с помощью метода `info()`. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "deletable": false
   },
   "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  object\n",
      " 1   message_date  123036 non-null  object\n",
      " 2   user_id       123036 non-null  int64 \n",
      "dtypes: int64(1), object(2)\n",
      "memory usage: 2.8+ MB\n"
     ]
    }
   ],
   "source": [
    "messages.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false,
    "tags": [
     "start_task"
    ]
   },
   "source": [
    "**Задание 12.** Откройте файл `/datasets/tariffs.csv`, сохраните датафрейм в переменную `tariffs`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "deletable": false
   },
   "outputs": [],
   "source": [
    "tariffs = pd.read_csv('/datasets/tariffs.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false,
    "tags": [
     "start_task"
    ]
   },
   "source": [
    "**Задание 13.** Выведите весь датафрейм `tariffs`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "deletable": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>messages_included</th>\n",
       "      <th>mb_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",
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       "      <th>0</th>\n",
       "      <td>50</td>\n",
       "      <td>15360</td>\n",
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       "      <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",
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       "      <td>150</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>ultra</td>\n",
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       "  </tbody>\n",
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      "text/plain": [
       "   messages_included  mb_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": 77,
     "metadata": {},
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   ],
   "source": [
    "tariffs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false,
    "tags": [
     "start_task"
    ]
   },
   "source": [
    "**Задание 14.** Выведите основную информацию для датафрейма `tariffs` с помощью метода `info()`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "deletable": false
   },
   "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   mb_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_name            2 non-null      object\n",
      "dtypes: int64(7), object(1)\n",
      "memory usage: 256.0+ bytes\n"
     ]
    }
   ],
   "source": [
    "tariffs.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false,
    "tags": [
     "start_task"
    ]
   },
   "source": [
    "**Задание 15.** Откройте файл `/datasets/users.csv`, сохраните датафрейм в переменную `users`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "deletable": false
   },
   "outputs": [],
   "source": [
    "users = pd.read_csv('/datasets/users.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false,
    "tags": [
     "start_task"
    ]
   },
   "source": [
    "**Задание 16.** Выведите первые 5 строк датафрейма `users`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "deletable": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
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       "      <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",
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       "      <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",
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       "      <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",
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       "      <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": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "users.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false,
    "tags": [
     "start_task"
    ]
   },
   "source": [
    "**Задание 17.** Выведите основную информацию для датафрейма `users` с помощью метода `info()`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "deletable": false
   },
   "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     object\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    object\n",
      " 7   tariff      500 non-null    object\n",
      "dtypes: int64(2), object(6)\n",
      "memory usage: 31.4+ KB\n"
     ]
    }
   ],
   "source": [
    "users.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false
   },
   "source": [
    "### Подготовьте данные"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false,
    "tags": [
     "start_task"
    ]
   },
   "source": [
    "**Задание 18.**  Приведите столбцы\n",
    "\n",
    "- `reg_date` из таблицы `users`\n",
    "- `churn_date` из таблицы `users`\n",
    "- `call_date` из таблицы `calls`\n",
    "- `message_date` из таблицы `messages`\n",
    "- `session_date` из таблицы `sessions`\n",
    "\n",
    "к новому типу с помощью метода `to_datetime()`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "deletable": false
   },
   "outputs": [],
   "source": [
    "users['reg_date'] = pd.to_datetime(users['reg_date']) \n",
    "#обработка столбца reg_date\n",
    "users['churn_date'] = pd.to_datetime(users['churn_date']) \n",
    "#обработка столбца churn_date\n",
    "calls['call_date'] = pd.to_datetime(calls['call_date'])\n",
    "# обработка столбца call_date\n",
    "messages['message_date'] = pd.to_datetime(messages['message_date'])\n",
    "# обработка столбца message_date\n",
    "sessions['session_date'] = pd.to_datetime(sessions['session_date'])                                          \n",
    "# обработка столбца session_date"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 149396 entries, 0 to 149395\n",
      "Data columns (total 5 columns):\n",
      " #   Column        Non-Null Count   Dtype         \n",
      "---  ------        --------------   -----         \n",
      " 0   Unnamed: 0    149396 non-null  int64         \n",
      " 1   id            149396 non-null  object        \n",
      " 2   mb_used       149396 non-null  float64       \n",
      " 3   session_date  149396 non-null  datetime64[ns]\n",
      " 4   user_id       149396 non-null  int64         \n",
      "dtypes: datetime64[ns](1), float64(1), int64(2), object(1)\n",
      "memory usage: 5.7+ MB\n"
     ]
    }
   ],
   "source": [
    "sessions.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false,
    "tags": [
     "start_task"
    ]
   },
   "source": [
    "**Задание 19.** В данных вы найдёте звонки с нулевой продолжительностью. Это не ошибка: нулями обозначены пропущенные звонки, поэтому их не нужно удалять.\n",
    "\n",
    "Однако в столбце `duration` датафрейма `calls` значения дробные. Округлите значения столбца `duration` вверх с помощью метода `numpy.ceil()` и приведите столбец `duration` к типу `int`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "deletable": false
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "calls['duration'] = np.ceil(calls['duration']).astype(int)\n",
    "\n",
    "# округление значений столбца duration с помощью np.ceil() и приведение типа к int"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false,
    "tags": [
     "start_task"
    ]
   },
   "source": [
    "**Задание 20.** Удалите столбец `Unnamed: 0` из датафрейма `sessions`. Столбец с таким названием возникает, когда данные сохраняют с указанием индекса (`df.to_csv(..., index=column)`). Он сейчас не понадобится."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "deletable": false
   },
   "outputs": [],
   "source": [
    "sessions = sessions.drop(columns = ['Unnamed: 0'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false,
    "tags": [
     "start_task"
    ]
   },
   "source": [
    "**Задание 21.** Создайте столбец `month` в датафрейме `calls` с номером месяца из столбца `call_date`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>2018-08-17</td>\n",
       "      <td>0</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>3</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>14</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>6</td>\n",
       "      <td>1000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       id  call_date  duration  user_id\n",
       "0  1000_0 2018-07-25         0     1000\n",
       "1  1000_1 2018-08-17         0     1000\n",
       "2  1000_2 2018-06-11         3     1000\n",
       "3  1000_3 2018-09-21        14     1000\n",
       "4  1000_4 2018-12-15         6     1000"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "calls.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {
    "deletable": false
   },
   "outputs": [],
   "source": [
    "calls['month'] = calls['call_date'].apply(lambda x: x.month)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</div>"
      ],
      "text/plain": [
       "       id  call_date  duration  user_id  month\n",
       "0  1000_0 2018-07-25         0     1000      7\n",
       "1  1000_1 2018-08-17         0     1000      8\n",
       "2  1000_2 2018-06-11         3     1000      6\n",
       "3  1000_3 2018-09-21        14     1000      9\n",
       "4  1000_4 2018-12-15         6     1000     12"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Проверим, что получилось:\n",
    "\n",
    "calls.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false,
    "tags": [
     "start_task"
    ]
   },
   "source": [
    "**Задание 22.** Создайте столбец `month` в датафрейме `messages` с номером месяца из столбца `message_date`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {
    "deletable": false
   },
   "outputs": [],
   "source": [
    "messages['month'] = messages['message_date'].apply(lambda x: x.month)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false,
    "tags": [
     "start_task"
    ]
   },
   "source": [
    "**Задание 23.** Создайте столбец `month` в датафрейме `sessions` с номером месяца из столбца `session_date`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {
    "deletable": false
   },
   "outputs": [],
   "source": [
    "sessions['month'] = sessions['session_date'].apply(lambda x: x.month)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false,
    "tags": [
     "start_task"
    ]
   },
   "source": [
    "**Задание 24.** Посчитайте количество сделанных звонков разговора для каждого пользователя по месяцам."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {
    "deletable": false
   },
   "outputs": [],
   "source": [
    "# подсчёт количества звонков для каждого пользователя по месяцам\n",
    "\n",
    "calls_per_month = calls.groupby(['user_id','month']).agg(calls=('duration', 'count'))\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "deletable": false
   },
   "outputs": [
    {
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       "      <th rowspan=\"8\" valign=\"top\">1004</th>\n",
       "      <th>5</th>\n",
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       "      <th>6</th>\n",
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       "      <td>21</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               calls\n",
       "user_id month       \n",
       "1000    5         22\n",
       "        6         43\n",
       "        7         47\n",
       "        8         52\n",
       "        9         58\n",
       "        10        57\n",
       "        11        43\n",
       "        12        46\n",
       "1001    11        59\n",
       "        12        63\n",
       "1002    6         15\n",
       "        7         26\n",
       "        8         42\n",
       "        9         36\n",
       "        10        33\n",
       "        11        32\n",
       "        12        33\n",
       "1003    8         55\n",
       "        9        134\n",
       "        10       108\n",
       "        11       115\n",
       "        12       108\n",
       "1004    5          9\n",
       "        6         31\n",
       "        7         22\n",
       "        8         19\n",
       "        9         26\n",
       "        10        29\n",
       "        11        19\n",
       "        12        21"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# вывод 30 первых строк на экран\n",
    "\n",
    "calls_per_month.head(30)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false,
    "tags": [
     "start_task"
    ]
   },
   "source": [
    "**Задание 25.** Посчитайте количество израсходованных минут разговора для каждого пользователя по месяцам и сохраните в переменную `minutes_per_month`. Вам понадобится\n",
    "\n",
    "- сгруппировать датафрейм с информацией о звонках по двум столбцам — с идентификаторами пользователей и номерами месяцев;\n",
    "- после группировки выбрать столбец `duration`\n",
    "- затем применить метод для подсчёта суммы.\n",
    "\n",
    "Выведите первые 30 строчек `minutes_per_month`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {
    "deletable": false
   },
   "outputs": [],
   "source": [
    "# подсчёт израсходованных минут для каждого пользователя по месяцам\n",
    "\n",
    "minutes_per_month = calls.groupby(['user_id','month']).agg(minutes = ('duration','sum'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {
    "deletable": false
   },
   "outputs": [
    {
     "data": {
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       "<p>3174 rows × 1 columns</p>\n",
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      ],
      "text/plain": [
       "               minutes\n",
       "user_id month         \n",
       "1000    5          159\n",
       "        6          172\n",
       "        7          340\n",
       "        8          408\n",
       "        9          466\n",
       "...                ...\n",
       "1498    10         247\n",
       "1499    9           70\n",
       "        10         449\n",
       "        11         612\n",
       "        12         492\n",
       "\n",
       "[3174 rows x 1 columns]"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# вывод первых 30 строк на экран\n",
    "minutes_per_month"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false,
    "tags": [
     "start_task"
    ]
   },
   "source": [
    "**Задание 26.** Посчитайте количество отправленных сообщений по месяцам для каждого пользователя и сохраните в переменную `messages_per_month`. Вам понадобится\n",
    "\n",
    "- сгруппировать датафрейм с информацией о сообщениях по двум столбцам — с идентификаторами пользователей и номерами месяцев;\n",
    "- после группировки выбрать столбец `message_date`;\n",
    "- затем применить метод для подсчёта количества.\n",
    "\n",
    "Выведите первые 30 строчек `messages_per_month`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {
    "deletable": false
   },
   "outputs": [],
   "source": [
    "# подсчёт количества отправленных сообщений для каждого пользователя по месяцам #{\"id\":lambda x: len(np.unique(x)) }\n",
    "\n",
    "messages_per_month = messages.groupby(['user_id','month']).agg(messages=('message_date', 'count'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {
    "deletable": false
   },
   "outputs": [
    {
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       "      <th rowspan=\"7\" valign=\"top\">1002</th>\n",
       "      <th>6</th>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">1003</th>\n",
       "      <th>8</th>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"8\" valign=\"top\">1004</th>\n",
       "      <th>5</th>\n",
       "      <td>95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>134</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>181</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>151</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>146</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>165</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>158</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>162</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">1005</th>\n",
       "      <th>1</th>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               messages\n",
       "user_id month          \n",
       "1000    5            22\n",
       "        6            60\n",
       "        7            75\n",
       "        8            81\n",
       "        9            57\n",
       "        10           73\n",
       "        11           58\n",
       "        12           70\n",
       "1002    6             4\n",
       "        7            11\n",
       "        8            13\n",
       "        9             4\n",
       "        10           10\n",
       "        11           16\n",
       "        12           12\n",
       "1003    8            37\n",
       "        9            91\n",
       "        10           83\n",
       "        11           94\n",
       "        12           75\n",
       "1004    5            95\n",
       "        6           134\n",
       "        7           181\n",
       "        8           151\n",
       "        9           146\n",
       "        10          165\n",
       "        11          158\n",
       "        12          162\n",
       "1005    1             7\n",
       "        2            38"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# вывод первых 30 строк на экран\n",
    "messages_per_month=messages_per_month.rename(columns = {'id': 'messages_count'})\n",
    "messages_per_month.head(30)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false,
    "tags": [
     "start_task"
    ]
   },
   "source": [
    "**Задание 27.** Посчитайте количество потраченных мегабайт по месяцам для каждого пользователя и сохраните в переменную `sessions_per_month`. Вам понадобится\n",
    "\n",
    "- сгруппировать датафрейм с информацией о сообщениях по двум столбцам — с идентификаторами пользователей и номерами месяцев;\n",
    "- затем применить метод для подсчёта суммы: `.agg({'mb_used': 'sum'})`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {
    "deletable": false
   },
   "outputs": [],
   "source": [
    "# подсчёт потраченных мегабайт для каждого пользователя по месяцам\n",
    "\n",
    "sessions_per_month = sessions.groupby(['user_id','month']).agg({'mb_used': 'sum'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {
    "deletable": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>mb_used</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user_id</th>\n",
       "      <th>month</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"8\" valign=\"top\">1000</th>\n",
       "      <th>5</th>\n",
       "      <td>2253.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>23233.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>14003.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>14055.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>14568.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>14702.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>14756.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>9817.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">1001</th>\n",
       "      <th>11</th>\n",
       "      <td>18429.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>14036.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"7\" valign=\"top\">1002</th>\n",
       "      <th>6</th>\n",
       "      <td>10856.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>17580.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>20319.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>16691.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>13888.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>18587.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>18113.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">1003</th>\n",
       "      <th>8</th>\n",
       "      <td>8565.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>12468.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>14768.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>11356.89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>10121.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"8\" valign=\"top\">1004</th>\n",
       "      <th>5</th>\n",
       "      <td>13403.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>17600.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>22229.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>28584.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>15109.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>18475.44</td>\n",
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       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>15616.02</td>\n",
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       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>18021.04</td>\n",
       "    </tr>\n",
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      ],
      "text/plain": [
       "                mb_used\n",
       "user_id month          \n",
       "1000    5       2253.49\n",
       "        6      23233.77\n",
       "        7      14003.64\n",
       "        8      14055.93\n",
       "        9      14568.91\n",
       "        10     14702.49\n",
       "        11     14756.47\n",
       "        12      9817.61\n",
       "1001    11     18429.34\n",
       "        12     14036.66\n",
       "1002    6      10856.82\n",
       "        7      17580.10\n",
       "        8      20319.26\n",
       "        9      16691.08\n",
       "        10     13888.25\n",
       "        11     18587.28\n",
       "        12     18113.73\n",
       "1003    8       8565.21\n",
       "        9      12468.87\n",
       "        10     14768.14\n",
       "        11     11356.89\n",
       "        12     10121.53\n",
       "1004    5      13403.98\n",
       "        6      17600.02\n",
       "        7      22229.58\n",
       "        8      28584.37\n",
       "        9      15109.03\n",
       "        10     18475.44\n",
       "        11     15616.02\n",
       "        12     18021.04"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# вывод первых 30 строк на экран\n",
    "\n",
    "sessions_per_month.head(30)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false
   },
   "source": [
    "### Анализ данных и подсчёт выручки"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false
   },
   "source": [
    "Объединяем все посчитанные выше значения в один датафрейм `user_behavior`.\n",
    "Для каждой пары \"пользователь - месяц\" будут доступны информация о тарифе, количестве звонков, сообщений и потраченных мегабайтах."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {
    "deletable": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7.6"
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "users['churn_date'].count() / users['churn_date'].shape[0] * 100"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false
   },
   "source": [
    "Расторгли договор 7.6% клиентов из датасета"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {
    "scrolled": true
   },
   "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",
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       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>1000</td>\n",
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       "      <td>NaT</td>\n",
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       "      <td>Рафаил</td>\n",
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       "      <td>ultra</td>\n",
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       "      <td>1001</td>\n",
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       "      <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",
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       "      <td>Евгений</td>\n",
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       "      <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",
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       "      <td>2018-08-17</td>\n",
       "      <td>ultra</td>\n",
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       "    <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": 141,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "users.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(500, 8)"
      ]
     },
     "execution_count": 142,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "users.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {
    "deletable": false
   },
   "outputs": [
    {
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       "      <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>8</td>\n",
       "      <td>52.0</td>\n",
       "      <td>14055.93</td>\n",
       "      <td>408.0</td>\n",
       "      <td>81.0</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>9</td>\n",
       "      <td>58.0</td>\n",
       "      <td>14568.91</td>\n",
       "      <td>466.0</td>\n",
       "      <td>57.0</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  calls   mb_used  minutes  messages  age churn_date  \\\n",
       "0     1000      5   22.0   2253.49    159.0      22.0   52        NaT   \n",
       "1     1000      6   43.0  23233.77    172.0      60.0   52        NaT   \n",
       "2     1000      7   47.0  14003.64    340.0      75.0   52        NaT   \n",
       "3     1000      8   52.0  14055.93    408.0      81.0   52        NaT   \n",
       "4     1000      9   58.0  14568.91    466.0      57.0   52        NaT   \n",
       "\n",
       "        city first_name  last_name   reg_date tariff  \n",
       "0  Краснодар     Рафаил  Верещагин 2018-05-25  ultra  \n",
       "1  Краснодар     Рафаил  Верещагин 2018-05-25  ultra  \n",
       "2  Краснодар     Рафаил  Верещагин 2018-05-25  ultra  \n",
       "3  Краснодар     Рафаил  Верещагин 2018-05-25  ultra  \n",
       "4  Краснодар     Рафаил  Верещагин 2018-05-25  ultra  "
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "user_behavior = calls_per_month.merge(sessions_per_month, left_index=True, right_index=True, how='outer')\\\n",
    "    .merge(minutes_per_month, left_index=True, right_index=True, how='outer')\\\n",
    "    .merge(messages_per_month, left_index=True, right_index=True, how='outer')\\\n",
    "    .reset_index()\\\n",
    "    .merge(users, how='left', left_on='user_id', right_on='user_id')\\\n",
    "    \n",
    "\n",
    "\n",
    "\n",
    "#(calls,  how='outer', on =['user_id','month']).merge(\n",
    "#sessions_per_month, how = 'outer', on = ['user_id','month']).merge(\n",
    "#minutes_per_month,how = 'outer', on = ['user_id','month']).merge(\n",
    "#users, how = 'outer', on = ['user_id'])\n",
    "\n",
    "user_behavior.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false
   },
   "source": [
    "Проверим пропуски в таблице `user_behavior` после объединения:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false
   },
   "source": [
    "Заполним образовавшиеся пропуски в данных:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {
    "deletable": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    .dataframe tbody tr th {\n",
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       "    }\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>calls</th>\n",
       "      <th>mb_used</th>\n",
       "      <th>minutes</th>\n",
       "      <th>messages</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>5</td>\n",
       "      <td>22.0</td>\n",
       "      <td>2253.49</td>\n",
       "      <td>159.0</td>\n",
       "      <td>22.0</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>6</td>\n",
       "      <td>43.0</td>\n",
       "      <td>23233.77</td>\n",
       "      <td>172.0</td>\n",
       "      <td>60.0</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>7</td>\n",
       "      <td>47.0</td>\n",
       "      <td>14003.64</td>\n",
       "      <td>340.0</td>\n",
       "      <td>75.0</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>8</td>\n",
       "      <td>52.0</td>\n",
       "      <td>14055.93</td>\n",
       "      <td>408.0</td>\n",
       "      <td>81.0</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>9</td>\n",
       "      <td>58.0</td>\n",
       "      <td>14568.91</td>\n",
       "      <td>466.0</td>\n",
       "      <td>57.0</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  calls   mb_used  minutes  messages  age churn_date  \\\n",
       "0     1000      5   22.0   2253.49    159.0      22.0   52        NaT   \n",
       "1     1000      6   43.0  23233.77    172.0      60.0   52        NaT   \n",
       "2     1000      7   47.0  14003.64    340.0      75.0   52        NaT   \n",
       "3     1000      8   52.0  14055.93    408.0      81.0   52        NaT   \n",
       "4     1000      9   58.0  14568.91    466.0      57.0   52        NaT   \n",
       "\n",
       "        city first_name  last_name   reg_date tariff  \n",
       "0  Краснодар     Рафаил  Верещагин 2018-05-25  ultra  \n",
       "1  Краснодар     Рафаил  Верещагин 2018-05-25  ultra  \n",
       "2  Краснодар     Рафаил  Верещагин 2018-05-25  ultra  \n",
       "3  Краснодар     Рафаил  Верещагин 2018-05-25  ultra  \n",
       "4  Краснодар     Рафаил  Верещагин 2018-05-25  ultra  "
      ]
     },
     "execution_count": 144,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "user_behavior['calls'] = user_behavior['calls'].fillna(0)\n",
    "user_behavior['minutes'] = user_behavior['minutes'].fillna(0)\n",
    "user_behavior['messages'] = user_behavior['messages'].fillna(0)\n",
    "user_behavior['mb_used'] = user_behavior['mb_used'].fillna(0)\n",
    "user_behavior = user_behavior.dropna(subset=['month'])\n",
    "\n",
    "user_behavior.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {
    "deletable": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "user_id          0\n",
       "month            0\n",
       "calls            0\n",
       "mb_used          0\n",
       "minutes          0\n",
       "messages         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",
       "dtype: int64"
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "user_behavior.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false
   },
   "source": [
    "Присоединяем информацию о тарифах"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {
    "deletable": false
   },
   "outputs": [],
   "source": [
    "# переименование столбца tariff_name на более простое tariff\n",
    "\n",
    "tariffs = tariffs.rename(columns={'tariff_name': 'tariff'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {
    "deletable": false
   },
   "outputs": [],
   "source": [
    "user_behavior = user_behavior.merge(tariffs, on='tariff')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false
   },
   "source": [
    "Считаем количество минут разговора, сообщений и мегабайт, превышающих включенные в тариф\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {
    "deletable": false
   },
   "outputs": [],
   "source": [
    "user_behavior['paid_minutes'] = user_behavior['minutes'] - user_behavior['minutes_included']\n",
    "user_behavior['paid_messages'] = user_behavior['messages'] - user_behavior['messages_included']\n",
    "user_behavior['paid_mb'] = user_behavior['mb_used'] - user_behavior['mb_per_month_included']\n",
    "\n",
    "for col in ['paid_messages', 'paid_minutes', 'paid_mb']:\n",
    "    user_behavior.loc[user_behavior[col] < 0, col] = 0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false
   },
   "source": [
    "Переводим превышающие тариф мегабайты в гигабайты и сохраняем в столбец `paid_gb`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {
    "deletable": false
   },
   "outputs": [],
   "source": [
    "user_behavior['paid_gb'] = np.ceil(user_behavior['paid_mb'] / 1024).astype(int)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false
   },
   "source": [
    "Считаем выручку за минуты разговора, сообщения и интернет"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {
    "deletable": false
   },
   "outputs": [],
   "source": [
    "user_behavior['cost_minutes'] = user_behavior['paid_minutes'] * user_behavior['rub_per_minute']\n",
    "user_behavior['cost_messages'] = user_behavior['paid_messages'] * user_behavior['rub_per_message']\n",
    "user_behavior['cost_gb'] = user_behavior['paid_gb'] * user_behavior['rub_per_gb']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false
   },
   "source": [
    "Считаем помесячную выручку с каждого пользователя, она будет храниться в столбец `total_cost`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {
    "deletable": false
   },
   "outputs": [],
   "source": [
    "user_behavior['total_cost'] = \\\n",
    "      user_behavior['rub_monthly_fee']\\\n",
    "    + user_behavior['cost_minutes']\\\n",
    "    + user_behavior['cost_messages']\\\n",
    "    + user_behavior['cost_gb']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false
   },
   "source": [
    "Датафрейм `stats_df` для каждой пары \"месяц-тариф\" будет хранить основные характеристики"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {
    "deletable": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>month</th>\n",
       "      <th>tariff</th>\n",
       "      <th>calls_mean</th>\n",
       "      <th>sessions_mean</th>\n",
       "      <th>messages_mean</th>\n",
       "      <th>minutes_mean</th>\n",
       "      <th>calls_std</th>\n",
       "      <th>sessions_std</th>\n",
       "      <th>messages_std</th>\n",
       "      <th>minutes_std</th>\n",
       "      <th>calls_median</th>\n",
       "      <th>sessions_median</th>\n",
       "      <th>messages_median</th>\n",
       "      <th>minutes_median</th>\n",
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       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>smart</td>\n",
       "      <td>27.68</td>\n",
       "      <td>8513.72</td>\n",
       "      <td>18.24</td>\n",
       "      <td>203.85</td>\n",
       "      <td>20.81</td>\n",
       "      <td>6444.68</td>\n",
       "      <td>16.20</td>\n",
       "      <td>154.23</td>\n",
       "      <td>20.5</td>\n",
       "      <td>7096.18</td>\n",
       "      <td>15.0</td>\n",
       "      <td>162.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>ultra</td>\n",
       "      <td>59.44</td>\n",
       "      <td>13140.68</td>\n",
       "      <td>33.78</td>\n",
       "      <td>428.11</td>\n",
       "      <td>41.64</td>\n",
       "      <td>6865.35</td>\n",
       "      <td>30.67</td>\n",
       "      <td>269.76</td>\n",
       "      <td>51.0</td>\n",
       "      <td>14791.37</td>\n",
       "      <td>32.0</td>\n",
       "      <td>382.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>smart</td>\n",
       "      <td>40.19</td>\n",
       "      <td>11597.05</td>\n",
       "      <td>24.09</td>\n",
       "      <td>298.69</td>\n",
       "      <td>25.39</td>\n",
       "      <td>6247.35</td>\n",
       "      <td>21.75</td>\n",
       "      <td>190.82</td>\n",
       "      <td>38.5</td>\n",
       "      <td>12553.71</td>\n",
       "      <td>20.0</td>\n",
       "      <td>258.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>ultra</td>\n",
       "      <td>41.54</td>\n",
       "      <td>11775.94</td>\n",
       "      <td>21.96</td>\n",
       "      <td>297.12</td>\n",
       "      <td>40.97</td>\n",
       "      <td>10644.64</td>\n",
       "      <td>26.77</td>\n",
       "      <td>296.51</td>\n",
       "      <td>25.0</td>\n",
       "      <td>7327.12</td>\n",
       "      <td>5.5</td>\n",
       "      <td>168.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>smart</td>\n",
       "      <td>54.32</td>\n",
       "      <td>15104.16</td>\n",
       "      <td>31.86</td>\n",
       "      <td>390.05</td>\n",
       "      <td>25.54</td>\n",
       "      <td>5828.24</td>\n",
       "      <td>26.80</td>\n",
       "      <td>191.89</td>\n",
       "      <td>59.0</td>\n",
       "      <td>15670.25</td>\n",
       "      <td>23.0</td>\n",
       "      <td>409.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>3</td>\n",
       "      <td>ultra</td>\n",
       "      <td>67.68</td>\n",
       "      <td>17535.55</td>\n",
       "      <td>32.30</td>\n",
       "      <td>489.65</td>\n",
       "      <td>44.84</td>\n",
       "      <td>10951.79</td>\n",
       "      <td>41.62</td>\n",
       "      <td>333.74</td>\n",
       "      <td>57.0</td>\n",
       "      <td>17495.18</td>\n",
       "      <td>20.0</td>\n",
       "      <td>403.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>4</td>\n",
       "      <td>smart</td>\n",
       "      <td>51.31</td>\n",
       "      <td>13462.18</td>\n",
       "      <td>30.74</td>\n",
       "      <td>367.13</td>\n",
       "      <td>25.70</td>\n",
       "      <td>5698.25</td>\n",
       "      <td>24.54</td>\n",
       "      <td>186.49</td>\n",
       "      <td>52.0</td>\n",
       "      <td>14087.65</td>\n",
       "      <td>28.0</td>\n",
       "      <td>368.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>4</td>\n",
       "      <td>ultra</td>\n",
       "      <td>64.09</td>\n",
       "      <td>16828.13</td>\n",
       "      <td>31.56</td>\n",
       "      <td>458.02</td>\n",
       "      <td>36.27</td>\n",
       "      <td>9718.65</td>\n",
       "      <td>37.51</td>\n",
       "      <td>267.68</td>\n",
       "      <td>61.0</td>\n",
       "      <td>16645.78</td>\n",
       "      <td>17.0</td>\n",
       "      <td>453.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>5</td>\n",
       "      <td>smart</td>\n",
       "      <td>55.24</td>\n",
       "      <td>15805.18</td>\n",
       "      <td>33.77</td>\n",
       "      <td>387.36</td>\n",
       "      <td>25.38</td>\n",
       "      <td>5978.23</td>\n",
       "      <td>27.04</td>\n",
       "      <td>186.60</td>\n",
       "      <td>59.0</td>\n",
       "      <td>16323.94</td>\n",
       "      <td>30.0</td>\n",
       "      <td>433.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>5</td>\n",
       "      <td>ultra</td>\n",
       "      <td>72.51</td>\n",
       "      <td>19363.15</td>\n",
       "      <td>37.85</td>\n",
       "      <td>510.33</td>\n",
       "      <td>41.08</td>\n",
       "      <td>10046.11</td>\n",
       "      <td>40.31</td>\n",
       "      <td>289.60</td>\n",
       "      <td>75.0</td>\n",
       "      <td>18696.43</td>\n",
       "      <td>25.0</td>\n",
       "      <td>519.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   month tariff  calls_mean  sessions_mean  messages_mean  minutes_mean  \\\n",
       "0      1  smart       27.68        8513.72          18.24        203.85   \n",
       "1      1  ultra       59.44       13140.68          33.78        428.11   \n",
       "2      2  smart       40.19       11597.05          24.09        298.69   \n",
       "3      2  ultra       41.54       11775.94          21.96        297.12   \n",
       "4      3  smart       54.32       15104.16          31.86        390.05   \n",
       "5      3  ultra       67.68       17535.55          32.30        489.65   \n",
       "6      4  smart       51.31       13462.18          30.74        367.13   \n",
       "7      4  ultra       64.09       16828.13          31.56        458.02   \n",
       "8      5  smart       55.24       15805.18          33.77        387.36   \n",
       "9      5  ultra       72.51       19363.15          37.85        510.33   \n",
       "\n",
       "   calls_std  sessions_std  messages_std  minutes_std  calls_median  \\\n",
       "0      20.81       6444.68         16.20       154.23          20.5   \n",
       "1      41.64       6865.35         30.67       269.76          51.0   \n",
       "2      25.39       6247.35         21.75       190.82          38.5   \n",
       "3      40.97      10644.64         26.77       296.51          25.0   \n",
       "4      25.54       5828.24         26.80       191.89          59.0   \n",
       "5      44.84      10951.79         41.62       333.74          57.0   \n",
       "6      25.70       5698.25         24.54       186.49          52.0   \n",
       "7      36.27       9718.65         37.51       267.68          61.0   \n",
       "8      25.38       5978.23         27.04       186.60          59.0   \n",
       "9      41.08      10046.11         40.31       289.60          75.0   \n",
       "\n",
       "   sessions_median  messages_median  minutes_median  \n",
       "0          7096.18             15.0           162.5  \n",
       "1         14791.37             32.0           382.0  \n",
       "2         12553.71             20.0           258.0  \n",
       "3          7327.12              5.5           168.0  \n",
       "4         15670.25             23.0           409.0  \n",
       "5         17495.18             20.0           403.0  \n",
       "6         14087.65             28.0           368.5  \n",
       "7         16645.78             17.0           453.0  \n",
       "8         16323.94             30.0           433.0  \n",
       "9         18696.43             25.0           519.0  "
      ]
     },
     "execution_count": 153,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# сохранение статистических метрик для каждой пары месяц-тариф\n",
    "# в одной таблице stats_df (среднее значение, стандартное отклонение, медиана)\n",
    "\n",
    "stats_df = user_behavior.pivot_table(\n",
    "            index=['month', 'tariff'],\\\n",
    "            values=['calls', 'minutes', 'messages', 'mb_used'],\\\n",
    "            aggfunc=['mean', 'std', 'median']\\\n",
    ").round(2).reset_index()\n",
    "\n",
    "stats_df.columns=['month','tariff', 'calls_mean', 'sessions_mean', 'messages_mean', 'minutes_mean',\n",
    "                                     'calls_std',  'sessions_std', 'messages_std', 'minutes_std', \n",
    "                                     'calls_median', 'sessions_median', 'messages_median',  'minutes_median']\n",
    "\n",
    "stats_df.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false
   },
   "source": [
    "Распределение среднего количества звонков по видам тарифов и месяцам"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {
    "deletable": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "\n",
    "ax = sns.barplot(x='month',\n",
    "            y='calls_mean',\n",
    "            hue=\"tariff\",\n",
    "            data=stats_df,\n",
    "            palette=['lightblue', 'blue'])\n",
    "\n",
    "ax.set_title('Распределение количества звонков по видам тарифов и месяцам')\n",
    "ax.set(xlabel='Номер месяца', ylabel='Среднее количество звонков');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {
    "deletable": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "user_behavior.groupby('tariff')['calls'].plot(kind='hist', bins=35, alpha=0.5)\n",
    "plt.legend(['Smart', 'Ultra'])\n",
    "plt.xlabel('Количество звонков')\n",
    "plt.ylabel('Количество клиентов')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false
   },
   "source": [
    "Распределение средней продолжительности звонков по видам тарифов и месяцам"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {
    "deletable": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.barplot(x='month',\n",
    "            y='minutes_mean',\n",
    "            hue=\"tariff\",\n",
    "            data=stats_df,\n",
    "            palette=['lightblue', 'blue'])\n",
    "\n",
    "ax.set_title('Распределение продолжительности звонков по видам тарифов и месяцам')\n",
    "ax.set(xlabel='Номер месяца', ylabel='Средняя продолжительность звонков');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {
    "deletable": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "user_behavior[user_behavior['tariff'] =='smart']['minutes'].hist(bins=35, alpha=0.5, color='green')\n",
    "user_behavior[user_behavior['tariff'] =='ultra']['minutes'].hist(bins=35, alpha=0.5, color='blue');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false
   },
   "source": [
    "Средняя длительность разговоров у абонентов тарифа Ultra больше, чем у абонентов тарифа Smart. В течение года пользователи обоих тарифов увеличивают среднюю продолжительность своих разговоров. Рост средней длительности разговоров у абонентов тарифа Smart равномерный в течение года. Пользователи тарифа Ultra не проявляют подобной линейной стабильности. Стоит отметить, что феврале у абонентов обоих тарифных планов наблюдались самые низкие показатели."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false
   },
   "source": [
    "Распределение среднего количества сообщений по видам тарифов и месяцам"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {
    "deletable": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.barplot(x='month',\n",
    "            y='messages_mean',\n",
    "            hue=\"tariff\",\n",
    "            data=stats_df,\n",
    "            palette=['lightblue', 'blue']\n",
    ")\n",
    "\n",
    "ax.set_title('Распределение количества сообщений по видам тарифов и месяцам')\n",
    "ax.set(xlabel='Номер месяца', ylabel='Среднее количество сообщений');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {
    "deletable": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "user_behavior[user_behavior['tariff'] =='smart']['messages'].hist(bins=35, alpha=0.5, color='green')\n",
    "user_behavior[user_behavior['tariff'] =='ultra']['messages'].hist(bins=35, alpha=0.5, color='blue');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "В среднем количество сообщений пользователи тарифа Ultra отправляют больше - почти на 20 сообщений больше, чем пользователи тарифа Smart. Количество сообщений в течение года на обоих тарифак растет. Динамика по отправке сообщений схожа с тенденциями по длительности разговоров: в феврале отмечено наименьшее количество сообщений за год и пользователи тарифа Ultra также проявляют нелинейную положительную динамику."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {
    "deletable": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.barplot(x='month',\n",
    "            y='sessions_mean',\n",
    "            hue=\"tariff\",\n",
    "            data=stats_df,\n",
    "            palette=['lightblue', 'blue']\n",
    ")\n",
    "\n",
    "ax.set_title('Распределение количества потраченного трафика (Мб) по видам тарифов и месяцам')\n",
    "ax.set(xlabel='Номер месяца', ylabel='Среднее количество мегабайт');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false
   },
   "source": [
    "Сравнение потраченных мегабайт среди пользователей тарифов Smart и Ultra"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {
    "deletable": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "user_behavior[user_behavior['tariff'] =='smart']['mb_used'].hist(bins=35, alpha=0.5, color='green')\n",
    "user_behavior[user_behavior['tariff'] =='ultra']['mb_used'].hist(bins=35, alpha=0.5, color='blue');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false
   },
   "source": [
    "Меньше всего пользователи использовали интернет в январе, феврале и апреле. Чаще всего абоненты тарифа Smart тратят 15-17 Гб, а абоненты тарифного плана Ultra - 19-21 ГБ."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false
   },
   "source": [
    "### Проверка гипотез"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false,
    "tags": [
     "start_task"
    ]
   },
   "source": [
    "**Задание 28.** Проверка гипотезы: средняя выручка пользователей тарифов «Ультра» и «Смарт» различаются;\n",
    "\n",
    "```\n",
    "H_0: Выручка (total_cost) пользователей \"Ультра\" = выручка (total_cost) пользователей \"Смарт\"`\n",
    "H_a: Выручка (total_cost) пользователей \"Ультра\" ≠ выручка (total_cost) пользователей \"Смарт\"`\n",
    "alpha = 0.05\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {
    "deletable": false
   },
   "outputs": [],
   "source": [
    "from scipy import stats as st"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {
    "deletable": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4.2606313931076085e-250\n",
      "Отвергаем нулевую гипотезу\n"
     ]
    }
   ],
   "source": [
    "#array1 = user_behavior[user_behavior['tariff'] =='ultra'].total_cost.values\n",
    "\n",
    "#array2 = user_behavior[user_behavior['tariff'] =='smart'].total_cost.values #вызов метода для проверки гипотезы\n",
    "\n",
    "results = st.ttest_ind(\n",
    "    user_behavior.loc[user_behavior.tariff =='ultra','total_cost'],\n",
    "    user_behavior.loc[user_behavior.tariff =='smart','total_cost'], \n",
    "    equal_var=False)\n",
    "\n",
    "alpha = .05   #задайте значение уровня значимости\n",
    "\n",
    "print(results.pvalue)\n",
    "#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\") \n",
    "\n",
    "# вывод значения p-value на экран \n",
    "# условный оператор с выводом строки с ответом"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false,
    "tags": [
     "start_task"
    ]
   },
   "source": [
    "**Задание 29.** Проверка гипотезы: пользователи из Москвы приносят больше выручки, чем пользователи из других городов;\n",
    "\n",
    "```\n",
    "H_0: Выручка (total_cost) пользователей из Москвы = выручка (total_cost) пользователей не из Москвы`\n",
    "H_1: Выручка (total_cost) пользователей из Москвы ≠ выручка (total_cost) пользователей не из Москвы`\n",
    "alpha = 0.05\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "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>calls</th>\n",
       "      <th>mb_used</th>\n",
       "      <th>minutes</th>\n",
       "      <th>messages</th>\n",
       "      <th>age</th>\n",
       "      <th>churn_date</th>\n",
       "      <th>city</th>\n",
       "      <th>first_name</th>\n",
       "      <th>...</th>\n",
       "      <th>rub_per_message</th>\n",
       "      <th>rub_per_minute</th>\n",
       "      <th>paid_minutes</th>\n",
       "      <th>paid_messages</th>\n",
       "      <th>paid_mb</th>\n",
       "      <th>paid_gb</th>\n",
       "      <th>cost_minutes</th>\n",
       "      <th>cost_messages</th>\n",
       "      <th>cost_gb</th>\n",
       "      <th>total_cost</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1000</td>\n",
       "      <td>5</td>\n",
       "      <td>22.0</td>\n",
       "      <td>2253.49</td>\n",
       "      <td>159.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>52</td>\n",
       "      <td>NaT</td>\n",
       "      <td>Краснодар</td>\n",
       "      <td>Рафаил</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1950.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1000</td>\n",
       "      <td>6</td>\n",
       "      <td>43.0</td>\n",
       "      <td>23233.77</td>\n",
       "      <td>172.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>52</td>\n",
       "      <td>NaT</td>\n",
       "      <td>Краснодар</td>\n",
       "      <td>Рафаил</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1950.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 28 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   user_id  month  calls   mb_used  minutes  messages  age churn_date  \\\n",
       "0     1000      5   22.0   2253.49    159.0      22.0   52        NaT   \n",
       "1     1000      6   43.0  23233.77    172.0      60.0   52        NaT   \n",
       "\n",
       "        city first_name  ... rub_per_message rub_per_minute paid_minutes  \\\n",
       "0  Краснодар     Рафаил  ...               1              1          0.0   \n",
       "1  Краснодар     Рафаил  ...               1              1          0.0   \n",
       "\n",
       "   paid_messages  paid_mb  paid_gb  cost_minutes  cost_messages  cost_gb  \\\n",
       "0            0.0      0.0        0           0.0            0.0        0   \n",
       "1            0.0      0.0        0           0.0            0.0        0   \n",
       "\n",
       "   total_cost  \n",
       "0      1950.0  \n",
       "1      1950.0  \n",
       "\n",
       "[2 rows x 28 columns]"
      ]
     },
     "execution_count": 164,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "user_behavior.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "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>calls</th>\n",
       "      <th>mb_used</th>\n",
       "      <th>minutes</th>\n",
       "      <th>messages</th>\n",
       "      <th>age</th>\n",
       "      <th>churn_date</th>\n",
       "      <th>city</th>\n",
       "      <th>first_name</th>\n",
       "      <th>...</th>\n",
       "      <th>rub_per_message</th>\n",
       "      <th>rub_per_minute</th>\n",
       "      <th>paid_minutes</th>\n",
       "      <th>paid_messages</th>\n",
       "      <th>paid_mb</th>\n",
       "      <th>paid_gb</th>\n",
       "      <th>cost_minutes</th>\n",
       "      <th>cost_messages</th>\n",
       "      <th>cost_gb</th>\n",
       "      <th>total_cost</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>0 rows × 28 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [user_id, month, calls, mb_used, minutes, messages, age, churn_date, city, first_name, last_name, reg_date, tariff, messages_included, mb_per_month_included, minutes_included, rub_monthly_fee, rub_per_gb, rub_per_message, rub_per_minute, paid_minutes, paid_messages, paid_mb, paid_gb, cost_minutes, cost_messages, cost_gb, total_cost]\n",
       "Index: []\n",
       "\n",
       "[0 rows x 28 columns]"
      ]
     },
     "execution_count": 165,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "user_behavior[user_behavior['city'] =='Москвы']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {
    "deletable": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "p-значение: 0.2720076455835112\n",
      "Не получилось отвергнуть нулевую гипотезу\n"
     ]
    }
   ],
   "source": [
    "array1 = user_behavior[user_behavior['city'] =='Москва'].total_cost.values\n",
    "\n",
    "array2 = user_behavior[user_behavior['city'] != 'Москва'].total_cost.values #вызов метода для проверки гипотезы\n",
    "\n",
    "results = st.ttest_ind (array1, array2)\n",
    "\n",
    "alpha = .05   #задайте значение уровня значимости\n",
    "\n",
    "\n",
    "print('p-значение:', results.pvalue/2)\n",
    "\n",
    "if results.pvalue/2 < alpha and array1.mean() < array2.mean():\n",
    "    print(\"Отвергаем нулевую гипотезу\")\n",
    "else:\n",
    "    print(\"Не получилось отвергнуть нулевую гипотезу\")\n",
    "\n",
    "\n",
    "# results = вызов метода для проверки гипотезы\n",
    "\n",
    "# alpha = задайте значение уровня значимости\n",
    "\n",
    "# вывод значения p-value на экран \n",
    "# условный оператор с выводом строки с ответом"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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