ab-project.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  1. A/B–тестирование"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "В ходе тестирования одной гипотезы целевой группе была предложена новая механика оплаты услуг на сайте, у контрольной группы оставалась базовая механика. Необходимо проанализировать итоги эксперимента и сделать вывод, стоит ли запускать новую механику оплаты на всех пользователей."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import scipy.stats as ss\n",
    "from tqdm.auto import tqdm\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "groups = pd.read_csv('/mnt/HC_Volume_18315164/home-jupyter/jupyter-b-dzhumaeva-37/final_project_my/Проект_2_groups.csv', sep=';')\n",
    "groups_add = pd.read_csv('/mnt/HC_Volume_18315164/home-jupyter/jupyter-b-dzhumaeva-37/final_project_my/Проект_2_group_add.csv', \n",
    "                         sep=',')\n",
    "active_studs = pd.read_csv('/mnt/HC_Volume_18315164/home-jupyter/jupyter-b-dzhumaeva-37/final_project_my/Проект_2_active_studs.csv',\n",
    "                          sep=';')\n",
    "checks = pd.read_csv('/mnt/HC_Volume_18315164/home-jupyter/jupyter-b-dzhumaeva-37/final_project_my/Проект_2_checks.csv', sep=';')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Посмотрим на входные данные"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***Таблица groups***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>grp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1489</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1627</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1768</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1783</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1794</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     id grp\n",
       "0  1489   B\n",
       "1  1627   A\n",
       "2  1768   B\n",
       "3  1783   B\n",
       "4  1794   A"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "groups.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id      int64\n",
       "grp    object\n",
       "dtype: object"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "groups.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(74484, 2)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "groups.shape #количество записей"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id     74484\n",
       "grp        2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "groups.nunique() #уникальных значений"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "groups.duplicated().sum() #количество дупликатов"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id     0\n",
       "grp    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "groups.isna().sum() #количество пустых строк"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***Таблица groups_add***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\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>grp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5694584</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5694830</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5695057</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5698872</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5699067</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        id grp\n",
       "0  5694584   B\n",
       "1  5694830   B\n",
       "2  5695057   B\n",
       "3  5698872   B\n",
       "4  5699067   B"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "groups_add.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id      int64\n",
       "grp    object\n",
       "dtype: object"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "groups_add.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(92, 2)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "groups_add.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id     92\n",
       "grp     2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "groups_add.nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "groups_add.duplicated().sum() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id     0\n",
       "grp    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "groups_add.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***Таблица active_studs***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>student_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>581585</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5723133</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3276743</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4238589</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4475369</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   student_id\n",
       "0      581585\n",
       "1     5723133\n",
       "2     3276743\n",
       "3     4238589\n",
       "4     4475369"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "active_studs.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "student_id    int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "active_studs.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(8341, 1)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "active_studs.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "student_id    8341\n",
       "dtype: int64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "active_studs.nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "active_studs.duplicated().sum() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "student_id    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "active_studs.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***Таблица checks***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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>student_id</th>\n",
       "      <th>rev</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1627</td>\n",
       "      <td>990.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3185</td>\n",
       "      <td>690.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>25973</td>\n",
       "      <td>690.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>26280</td>\n",
       "      <td>690.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>100300</td>\n",
       "      <td>990.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   student_id    rev\n",
       "0        1627  990.0\n",
       "1        3185  690.0\n",
       "2       25973  690.0\n",
       "3       26280  690.0\n",
       "4      100300  990.0"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "checks.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "student_id      int64\n",
       "rev           float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "checks.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(541, 2)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "checks.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "student_id    541\n",
       "rev            39\n",
       "dtype: int64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "checks.nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "checks.duplicated().sum() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "student_id    0\n",
       "rev           0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "checks.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Соединю дополнительный файл groups_add с groups и посмотрю на распределение пользователей по группам"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(74576, 2)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "groups_total = pd.concat([groups, groups_add])\n",
    "groups_total.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "A    14693\n",
       "B    59883\n",
       "Name: grp, dtype: int64"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#распределение по группам\n",
    "groups_total.grp.value_counts().sort_values()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Таким образом, у нас всего 74576 пользователей и распределение по группам явно неравномерное.\\\n",
    "Проверим все ли пользователи заходили в приложение и совершали оплату, так как посещение сайта является условием для использования новой механики оплаты."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#количество активных пользователей, не вошедших в общий список\n",
    "active_studs.query('student_id not in @groups_total.id').shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#количество совершивших оплату пользователей, но отсутствующих в общем списке\n",
    "checks.query('student_id not in @groups_total.id').shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "149"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#количество совершивших оплату пользователей, отсутствующих в списке активных в дни проведения теста\n",
    "checks.query('student_id not in @active_studs.student_id').shape[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Видим, что в период проведения экспериментов были оплаты и от тех студентов (149), которые не посещали платформу. Возможно, это автоматические оплаты или сбой в системе. \\\n",
    "Посмотрим на распределение групп в checks и active_studs:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "A    1538\n",
       "B    6803\n",
       "Name: grp, dtype: int64"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#распределение активных пользователей по группа\n",
    "groups_total.query('id in @active_studs.student_id').grp.value_counts().sort_values()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "B    434\n",
       "A    107\n",
       "Name: grp, dtype: int64"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#совершившие оплату в группах\n",
    "groups_total.query('id in @checks.student_id').grp.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "B    314\n",
       "A     78\n",
       "Name: grp, dtype: int64"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "groups_total.query('id in @active_studs.student_id & id in @checks.student_id').grp.value_counts() \n",
    "#распределение активных  и совершивих оплату пользователей, всего 392"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Промежуточный вывод \n",
    "* Пользователи распределены по группам неравномерно. Группа B значительно больше, чем группа А.\n",
    "* У нас 74576 пользователей, из них 8341 являются активными, 541 совершили оплату. Из общего количества совершивших оплату (541), 392 являются активными - оплатившими. Не ясно, это сбой системы или автоматические платежи , поэтому не стану включать этих пользователей в AB-тест."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Я буду проверять гипотезы на активных и совершивших оплату пользователей, так как нецелесообразно проверять гипотезы эксперимента на пользователях, которые были отобраны, но в нем не участвовали\n",
    "#### Объединим все таблицы в один df и отфильтруем пользователей для AB-теста"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>rev</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>1184303</td>\n",
       "      <td>1530.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>3354191</td>\n",
       "      <td>1900.0001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>3278734</td>\n",
       "      <td>1900.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>720651</td>\n",
       "      <td>1980.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>193</th>\n",
       "      <td>1232221</td>\n",
       "      <td>1900.0000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          id        rev\n",
       "62   1184303  1530.0000\n",
       "67   3354191  1900.0001\n",
       "77   3278734  1900.0000\n",
       "99    720651  1980.0000\n",
       "193  1232221  1900.0000"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "active_checks = (\n",
    "                active_studs\n",
    "                .merge(checks, how='left', on='student_id')\n",
    "                .rename(columns={'student_id': 'id'})\n",
    "                )\n",
    "active_checks.query('rev.notna()').head() #список активных пользователей со списком оплативших"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "#объединим активных оплативших из предыдущего шага с groups_total, оставив только активных\n",
    "active_total = groups_total.merge(active_checks, how='right', on='id')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>grp</th>\n",
       "      <th>rev</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>581585</td>\n",
       "      <td>A</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5723133</td>\n",
       "      <td>A</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4238589</td>\n",
       "      <td>A</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1394612</td>\n",
       "      <td>A</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>5194768</td>\n",
       "      <td>A</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         id grp  rev\n",
       "0    581585   A  NaN\n",
       "1   5723133   A  NaN\n",
       "3   4238589   A  NaN\n",
       "7   1394612   A  NaN\n",
       "21  5194768   A  NaN"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#активные пользователи группы А\n",
    "group_a = active_total.query('grp == \"A\"')\n",
    "group_a.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\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>id</th>\n",
       "      <th>grp</th>\n",
       "      <th>rev</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>1184303</td>\n",
       "      <td>A</td>\n",
       "      <td>1530.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>720651</td>\n",
       "      <td>A</td>\n",
       "      <td>1980.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>306</th>\n",
       "      <td>4337103</td>\n",
       "      <td>A</td>\n",
       "      <td>580.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>392</th>\n",
       "      <td>332387</td>\n",
       "      <td>A</td>\n",
       "      <td>2910.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>439</th>\n",
       "      <td>1659434</td>\n",
       "      <td>A</td>\n",
       "      <td>690.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          id grp     rev\n",
       "62   1184303   A  1530.0\n",
       "99    720651   A  1980.0\n",
       "306  4337103   A   580.0\n",
       "392   332387   A  2910.0\n",
       "439  1659434   A   690.0"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#активные и совершившие оплату пользователи группы А\n",
    "group_a_paid = group_a.query('rev > 0')\n",
    "group_a_paid.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "        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>grp</th>\n",
       "      <th>rev</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3276743</td>\n",
       "      <td>B</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4475369</td>\n",
       "      <td>B</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5481015</td>\n",
       "      <td>B</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1353433</td>\n",
       "      <td>B</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1170611</td>\n",
       "      <td>B</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        id grp  rev\n",
       "2  3276743   B  NaN\n",
       "4  4475369   B  NaN\n",
       "5  5481015   B  NaN\n",
       "6  1353433   B  NaN\n",
       "8  1170611   B  NaN"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#активные пользователи группы B\n",
    "group_b = active_total.query('grp == \"B\"')\n",
    "group_b.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "        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>grp</th>\n",
       "      <th>rev</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>3354191</td>\n",
       "      <td>B</td>\n",
       "      <td>1900.0001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>3278734</td>\n",
       "      <td>B</td>\n",
       "      <td>1900.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>193</th>\n",
       "      <td>1232221</td>\n",
       "      <td>B</td>\n",
       "      <td>1900.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>195</th>\n",
       "      <td>3821501</td>\n",
       "      <td>B</td>\n",
       "      <td>1900.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>211</th>\n",
       "      <td>1036096</td>\n",
       "      <td>B</td>\n",
       "      <td>1900.0000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          id grp        rev\n",
       "67   3354191   B  1900.0001\n",
       "77   3278734   B  1900.0000\n",
       "193  1232221   B  1900.0000\n",
       "195  3821501   B  1900.0000\n",
       "211  1036096   B  1900.0000"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#активные и совершившие оплату пользователи группы B\n",
    "group_b_paid = group_b.query('rev > 0')\n",
    "group_b_paid.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='rev', ylabel='Count'>"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 792x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#посмотрим на распределение оплат активных пользователей\n",
    "sns.set(rc={'figure.figsize' : (11, 8)}, style=\"whitegrid\", palette=\"Set2\") #установлю размеры и стиль\n",
    "sns.histplot(data=active_total, x='rev', hue='grp', bins=39, multiple=\"stack\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Видим неравномерное распределение, посмотрим на выбросы с помощью боксплот"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 792x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#посмотрим на выборосы:\n",
    "sns.boxplot(data=active_total, y='rev', x='grp')\n",
    "sns.despine()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Из боксплот мы видим, что в наших данных есть выбросы, распределения оплат выглядят не равномерными, не унимодальными.\\\n",
    "\n",
    "В нашем случае, где присутствует оплата и проверяется новая механика оплаты, будет неправильным удалять выбросы, так как мы можем удалить из анализа клиентов с большими суммами оплат.\n",
    "\n",
    "Так же из-за выбросов сравнение средних в наших выборках будет некорректно. Значит, применение Т-критерия, который сравнивает средние в распределениях, а выбросы сильно влияют на значение среднего, будет неправильным."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Сформулирю предварительную гипотезу"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* H0 - Новая механика оплаты услуг на сайте в группе B не отличается от базовой механики котрольной группы A.\n",
    "* H1 - Новая механика оплаты услуг на сайте в группе B имеет значимые различия в сравнении с базовой механикой в группе A."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Выберу метрики"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Так как у нас есть две категориальные переменные - принадлежность к группе и факт оплаты (1 или 0)-  следует проверить коэффициент конверсии в целевое действие, а конкретно в оплату услуги на сайте (CR). Также проверим степень воздействия новой механики оплаты на среднее значение суммы оплаты (ARPPU). Ещё посчитаю ARPU вместе с ARPPU для просмотра степени воздействия на всех активных пользователей. \\\n",
    "Таким образом:\n",
    "\n",
    "* CR в оплату (сonversion rate) — отношение количества платящих пользователей к общему количеству пользователей.\\\n",
    "Переход в оплату услуги. CR будет выше в том варианте, где пользователю проще проивзести оплату.\n",
    "\n",
    "* ARPU - отношение всей выручки на количество активных пользователей.\n",
    "\n",
    "* ARPPU - отношение выручки на количество активных оплативших пользователей. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "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>students</th>\n",
       "      <th>paying_students</th>\n",
       "      <th>CR</th>\n",
       "      <th>average_check</th>\n",
       "      <th>repeat_purchases</th>\n",
       "      <th>arppu</th>\n",
       "      <th>arpu</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <td>1538</td>\n",
       "      <td>78</td>\n",
       "      <td>5.07</td>\n",
       "      <td>933.59</td>\n",
       "      <td>1.0</td>\n",
       "      <td>933.59</td>\n",
       "      <td>4733.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>6803</td>\n",
       "      <td>314</td>\n",
       "      <td>4.62</td>\n",
       "      <td>1257.88</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1257.88</td>\n",
       "      <td>5811.41</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   students  paying_students    CR  average_check  repeat_purchases    arppu  \\\n",
       "A      1538               78  5.07         933.59               1.0   933.59   \n",
       "B      6803              314  4.62        1257.88               1.0  1257.88   \n",
       "\n",
       "      arpu  \n",
       "A  4733.30  \n",
       "B  5811.41  "
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#построим наглядную таблицу \n",
    "metrics = pd.DataFrame()\n",
    "metrics['students'] = active_total.groupby('grp').id.count()  #количество активных пользователей\n",
    "metrics['paying_students'] = active_total.groupby('grp').rev.count() #количество совершивших оплату\n",
    "metrics['CR'] = round(active_total.groupby('grp').rev.count() / active_total.groupby('grp').id.count() *100, 2) #конверсия \n",
    "metrics['average_check'] = round(active_total.groupby('grp').rev.mean(), 2) #средний чек\n",
    "metrics['repeat_purchases'] = active_total.groupby('grp').rev.count() / active_total.query('rev.notna()')\\\n",
    "                                          .groupby('grp').rev.count() #повторные покупки\n",
    "metrics['arppu'] = round(metrics['repeat_purchases'] * active_total.groupby('grp').rev.mean(), 2) #ARPPU\n",
    "metrics['arpu'] = round(metrics['CR']*metrics['arppu'], 2) #ARPU\n",
    "metrics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Имеются ли различия в показателях и с чем они могут быть связаны?\n",
    "* Опять же видим, как пользователи распределены по группам неравномерно. Группа B значительно больше, чем группа А.\n",
    "* Наблюдаем небольшой отрицательный эффект CR (конверсия в покупку у группы А больше, чем у группы В), и положительные эффекты в ARPU и ARPPU. То есть, конверсия падает, но доход на пользователя/покупателя растёт. Такое может происходить, если пользователь попадает на сайт и начинает осуществлять ряд целевых действий. Конечный этап заключается в оплате услуги. До момента перехода пользователя на этап оплаты, новая механика оплаты не оказывает на него влияния, либо как в нашем случае конверсия перехода в оплату упал на 0.45 пунктов. Но когда пользователь совершил целевое действие и перешел на следующий этап (хочет оплатить услугу), то новая механика начинает работать. Причем в данном случае положительно, так как средний чек увеличился. Но этого не додостаточно, и отрицательные и положительные различия в показателях требуют статистических подтверждений.\n",
    "* ARPPU ожидаемо равна среднему чеку, так как на каждого совершившего оплату пользователя приходится по одной покупке (повторных покупок не было)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Являются ли эти различия статистически значимыми?\n",
    "Для проверки стат значимости различий я выберу bootstrap, так как нет объективных причин НЕ использовать его. Объем данных ползволяет, размеры групп очень сильно отличаются, поэтому классические тесты могут давать большие погрешности. В таких случаях идеально подходит bootstrap. Гавным критерием будет, чтобы доверительный интервал не включал в себя 0\n",
    "\n",
    "* Строим bootstrap распределения для групп А и B.\n",
    "* Вычислим их разницу.\n",
    "* В получившемся рапределении разницы находим доверительных интервал.\n",
    "* Проверяем, попадает ли доверительный интервал в 0. Если да, то нулевая гипотеза на данном уровне значимости принимается, если нет - отвергается."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Напишем функцию, которая позволит проверять гипотезы с помощью bootstrap:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [],
   "source": [
    "# функция для проверки гипотез с помощью бутстрапа\n",
    "def get_bootstrap(\n",
    "    data_column_1,                       # числовые значения контрольной выборки\n",
    "    data_column_2,                       # числовые значения целевой выборки\n",
    "    col_1,                               #название колонки первой выборки\n",
    "    col_2,                               #название колонки второй выборки\n",
    "    sample_count_1 = 0,                  #количество наблюдений для подвыборок\n",
    "    sample_count_2 = 0,                  #количество наблюдений для подвыборок\n",
    "    boot_it = 10000,                     #количество бутстрэп-подвыборок\n",
    "    statistic = 'mean',                  #интересующая нас статистика\n",
    "    bootstrap_conf_level = 0.95,         #уровень значимости\n",
    "    replace = True,                      #параметр возвращения\n",
    "    max_len = True\n",
    "):\n",
    "    from scipy.stats import norm\n",
    "    \n",
    "    if sample_count_1 == 0:\n",
    "        sample_count_1 = len(data_column_1[col_1])\n",
    "    \n",
    "    if sample_count_2 == 0:\n",
    "        sample_count_2 = len(data_column_1[col_2])\n",
    "    if max_len:\n",
    "        boot_len = max([sample_count_1, sample_count_2])\n",
    "    else:\n",
    "        boot_len = min([sample_count_1, sample_count_2])\n",
    "    \n",
    "    boot_data = []\n",
    "    smpl_1_data = []\n",
    "    smpl_2_data = []\n",
    "    for i in tqdm(range(boot_it)):         #извлекаем подвыборки\n",
    "        if statistic == 'arpu':\n",
    "            samples_1 = arpu(data_column_1.sample(boot_len, replace=replace))\n",
    "            samples_2 = arpu(data_column_2.sample(boot_len, replace=replace))            \n",
    "        else:\n",
    "            samples_1 = np.nanmean(data_column_1[col_1].sample(boot_len, replace=replace).values)\n",
    "            samples_2 = np.nanmean(data_column_2[col_2].sample(boot_len, replace=replace).values)\n",
    "        \n",
    "        \n",
    "        boot_data.append(samples_1-samples_2)\n",
    "        smpl_1_data.append(samples_1)\n",
    "        smpl_2_data.append(samples_2)\n",
    "        \n",
    "    pd_boot_data = pd.DataFrame(boot_data)\n",
    "    \n",
    "    # доверительный интервал \n",
    "    left_quant = (1 - bootstrap_conf_level)/2\n",
    "    right_quant = 1 - (1 - bootstrap_conf_level) / 2\n",
    "    quants = pd_boot_data.quantile([left_quant, right_quant])\n",
    "        \n",
    "    p_1 = norm.cdf(\n",
    "        x = 0, \n",
    "        loc = np.nanmean(boot_data), \n",
    "        scale = np.nanstd(boot_data)\n",
    "    )\n",
    "    p_2 = norm.cdf(\n",
    "        x = 0, \n",
    "        loc = -np.nanmean(boot_data), \n",
    "        scale = np.nanstd(boot_data)\n",
    "    )\n",
    "    p_value = min(p_1, p_2) * 2\n",
    "        \n",
    "    #настройка визуализации\n",
    "    _, _, bars = plt.hist(pd_boot_data[0], bins = 30)\n",
    "    for bar in bars:\n",
    "        if bar.get_x() <= quants.iloc[0][0] or bar.get_x() >= quants.iloc[1][0]:\n",
    "            bar.set_facecolor('lightcoral')\n",
    "        else: \n",
    "            bar.set_facecolor('steelblue')\n",
    "            bar.set_edgecolor('lightskyblue')\n",
    "    \n",
    "    plt.style.use('ggplot')\n",
    "    plt.vlines(quants,ymin=0,ymax=50,linestyle='--')\n",
    "    plt.xlabel('boot_data')\n",
    "    plt.ylabel('frequency')\n",
    "    plt.title(\"Histogram of bootstrap\")\n",
    "    plt.show()\n",
    "    \n",
    "    print('pvalue = ', p_value)\n",
    "       \n",
    "    return {\"boot_data\" : boot_data, \n",
    "            \"quants\" : quants, \n",
    "            \"p_value\" : p_value,\n",
    "           \"smpl_1_data\" : smpl_1_data,\n",
    "           \"smpl_2_data\" : smpl_2_data}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Определим функции для метрик. \\\n",
    "Для CR в качестве аргумента нужно будет передавать серию о факте оплаты со значениями  1 и 0, или True и False. В функции ARPPU нужно будет передавать серию, где отобраны только платежи."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [],
   "source": [
    "#определим функции для метрик\n",
    "def cr_func(x):\n",
    "    return sum(x) / len(x)\n",
    "\n",
    "def arpu_func(x):\n",
    "    return np.mean(x)\n",
    "\n",
    "def arppu_func(y):\n",
    "    return np.mean(y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Бутстрап по CR** \\\n",
    "Гипотеза:\\\n",
    "Н0: Конверсия в оплату в двух группах не отличается \\\n",
    "Н1: Конверсия в оплату в двух группах отличается"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>grp</th>\n",
       "      <th>rev</th>\n",
       "      <th>payment</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>581585</td>\n",
       "      <td>A</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5723133</td>\n",
       "      <td>A</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3276743</td>\n",
       "      <td>B</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4238589</td>\n",
       "      <td>A</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4475369</td>\n",
       "      <td>B</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        id grp  rev  payment\n",
       "0   581585   A  NaN        0\n",
       "1  5723133   A  NaN        0\n",
       "2  3276743   B  NaN        0\n",
       "3  4238589   A  NaN        0\n",
       "4  4475369   B  NaN        0"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#добавим колонку со статусом оплаты (0 - не оплачен, 1 - оплачен):\n",
    "active_total['payment'] = active_total.rev.apply(lambda x: 0 if np.isnan(x) else 1) \n",
    "active_total.query('rev.isna()').head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
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       "model_id": "4e7153b368264258a9b0e30b9f20f20b",
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      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, max=5000.0), HTML(value='')))"
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     "output_type": "display_data"
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 792x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "pvalue =  0.5650643760546425\n"
     ]
    }
   ],
   "source": [
    "cr_boot = get_bootstrap(data_column_1 = active_total.query('grp == \"A\"'), col_1 = 'payment',\n",
    "                        data_column_2 = active_total.query('grp == \"B\"'), col_2 = 'payment',\n",
    "                        boot_it = 5000,\n",
    "                        statistic = 'cr_func')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Группы всё-таки не отличаются друг от друга. Это видно по доверительному интервалу, который попадает в 0. Недостаточно оснований отвергнуть нулевую гипотезу. Нельзя утверждать, что различия CR в двух группах есть."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Бутстрап по ARPU**\\\n",
    "Гипотеза:\\\n",
    "Н0: ARPU в двух группах не отличается \\\n",
    "Н1: ARPU в двух группах отличается"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "2843962d0e1343489f140a985b81af68",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, max=5000.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 792x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "pvalue =  0.02177110717135273\n"
     ]
    }
   ],
   "source": [
    "arpu_boot = get_bootstrap(data_column_1 = group_a, col_1 = 'rev',\n",
    "                        data_column_2 = group_b, col_2 = 'rev',\n",
    "                        boot_it = 5000,\n",
    "                        statistic = 'arpu_func')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Вывод по бутстрапу по ARPU**:\\\n",
    "0 не попадает в доверительный интервал. Отвергаем нулевую гипотезу. ARPU в двух группах различаются."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Бутстрап ARPPU**\\\n",
    "Гипотеза:\\\n",
    "Н0: ARPPU в двух группах не отличается \\\n",
    "Н1: ARPPU в двух группах отличается"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "0f2337ff984e4fa9bbc6203fdc8a6376",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, max=5000.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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c7YdmAADnhTAKoNrLyCvR6vVbK1yne6/OfugGAHA+OE0PAAAAawijAAAAsIYwCgAAAGsIowAAALCGMAoAAABrCKMAAACwhjAKAAAAawijAAAAsIYwCgAAAGsIowAAALCGMAoAAABrCKMAAACwhjAKAAAAawijAAAAsCbIdgMAgLKd9ARp6Z6SCtdp6grSwEiPHzoCAP8hjAJAFVdYVKwNm7ZVuM7Q2C7ihBiAqoafSgAAALCGMAoAAABrCKMAAACwhjAKAAAAawijAAAAsIYwCgAAAGsIowAAALCGMAoAAABrCKMAAACwhjAKAAAAawijAAAAsIYwCgAAAGsIowAAALCGMAoAAABrCKMAAACwhjAKAAAAawijAAAAsIYwCgAAAGsIowAAALCGMAoAAABrCKMAAACwhjAKAAAAawijAAAAsIYwCgAAAGsIowAAALCGMAoAAABrCKMAAACwhjAKAAAAawijAAAAsIYwCgAAAGsIowAAALCGMAoAAABrgmw3AKB2+vyIUxl5JT6tW/dAhoqKPOccP+72V1cAgEAjjAKwIiOvRKvXb/VLre69OvulDgAg8DhNDwAAAGsIowAAALAm4Kfp33vvPb333nt66aWX9Jvf/Ea7d+9WQkKC3G63IiIiNGnSJIWGhkpSmWMAAACo/gJ6ZDQ1NVV79uxRRESEJMnj8WjWrFkaPXq0Zs6cqQ4dOmjRokXljgEAAKBmCFgYLS4u1rx58zRmzBjvstTUVIWEhCgmJkaSNHjwYG3cuLHcMQAAANQMATtN/9e//lVXXnmlmjZt6l2WmZmp8PBw7+OGDRvKGKO8vLwyx1wul8/PGxUVVeHe/VEDdjGHVU/dAxl+q+Vw+ufvan/Vqaq16tYNUVRU0/JXrCS8Dqs/5rD6q4pzGJAwunv3bqWmpmrUqFGBeLpS0tPTK7R9VFRUhWvALuawairrvqHny3j8U8tfdapqraIit7XXAq/D6o85rP5sz+G5gnBAwujOnTt1+PBhxcXFSZKysrL03HPPadiwYcrMzPSul5ubK4fDIZfLpfDw8HOOAQAAoGYISBgdMWKERowY4X08ceJETZ48WZdeeqk+++wzpaSkKCYmRmvWrFHfvn0lSa1bt5bb7T7rGAAAAGoGq5/A5HQ6FRcXp7lz56q4uNh7+6byxgAAAFAzWAmj8fHx3q+jo6M1Y8aMs65X1hgAAACqPz6BCQAAANYQRgEAAGANYRQAAADWEEYBAABgDWEUAAAA1hBGAQAAYA1hFAAAANYQRgEAAGANYRQAAADWEEYBAABgDWEUAAAA1hBGAQAAYA1hFAAAANYQRgEAAGANYRQAAADWEEYBAABgDWEUAAAA1hBGAQAAYA1hFAAAANYQRgEAAGANYRQAAADWEEYBAABgDWEUAAAA1hBGAQAAYA1hFAAAANYQRgEAAGANYRQAAADWEEYBAABgDWEUAAAA1gTZbgAAEBgnPUFauqfEL7WauoI0MNLjl1oAajfCKADUEoVFxdqwaZtfag2N7SJOrgHwB36SAAAAwBrCKAAAAKwhjAIAAMAawigAAACsIYwCAADAGsIoAAAArOHWTgDOy+dHnMrIq/i9Ko+7/dAMAKDaI4wCOC8ZeSVavX5rhet079XZD90AAKo7TtMDAADAGsIoAAAArCGMAgAAwBrCKAAAAKwhjAIAAMAawigAAACsIYwCAADAGsIoAAAArCGMAgAAwBrCKAAAAKwhjAIAAMAawigAAACsIYwCAADAGsIoAAAArCGMAgAAwBrCKAAAAKwhjAIAAMAawigAAACsIYwCAADAGsIoAAAArCGMAgAAwJog2w0AAKqfk54gLd1T4vP6dQ9kqKjIc8bypq4gDYw8czmA2oMwCgA4b4VFxdqwaVuF6wyN7SJO0gG1Gz8BAAAAYA1hFAAAANYQRgEAAGANYRQAAADWEEYBAABgDWEUAAAA1hBGAQAAYI1PYXT69OlKSkpSSYnvNzgGAAAAyuNTGO3QoYNWrFihcePGKSEhQbt27arsvgAAAFAL+PQJTNdff72uv/56HTx4UP/61780c+ZMBQUFKTY2VgMGDFDz5s3LrfHiiy/q2LFjcjgcqlevnu6++261bNlS6enpio+PV15enlwul+Li4hQZGSlJZY4BAACg+juva0ZbtGihW2+9VZMmTVLdunX13nvvafLkyfrzn/+sH374ocxt4+LiNH36dL344osaPny45syZI0lKSEjQ0KFDNXPmTA0dOlRz5871blPWGAAAAKo/n8Noenq6li5dqkmTJmnu3Lnq27ev4uPjlZCQoG7dumn69Ollbn/RRRd5v87Pz5fD4dCJEyeUlpamAQMGSJIGDBigtLQ05ebmljkGAACAmsGn0/SPPPKIjh07pr59++q+++5Tu3btSo1ff/31WrVqVbl13njjDW3dulWSNGXKFGVlZSksLExO50+Z2Ol0qnHjxsrMzJSkc441bNjQ5x2Mioryed3KrAG7mEP/qXsgwy91HE7/3czDX7WqYk/+rFUVe6pbN0RRUU39UguVj5+l1V9VnEOfwuiIESPUs2dPBQWde/X4+Phy69xzzz2SpPXr1+vdd9/VyJEjfWzzwqWnp1do+6ioqArXgF3MoX8VFXn8Usd4/FPHn7WqYk/+rFUVeyoqcvP6rCb4WVr92Z7DcwVhn/60rV+/vjIySh8NSU9P17Zt2y6omdjYWG3fvl1hYWHKzs6W598/1Dwej3JychQeHq4mTZqccwwAAAA1g09hdN68eapfv36pZfXq1dO8efN8epLCwkLvqXdJ2rx5s1wul0JDQ9WyZUslJiZKkhITE9WqVSs1bNiwzDEAAADUDD6dpj9x4oQaN25calnjxo11/Phxn56ksLBQr7zyigoLC+V0OuVyuTR58mQ5HA6NHTtW8fHxWrFihRo0aKC4uDjvdmWNAQAAoPrzKYw2a9ZM27dvV6dOnbzLduzYoaZNfbvovFGjRnruuefOOnbJJZfo+eefP+8xAAAAVH8+hdFbbrlFL730kgYOHKhmzZrpxx9/1BdffKEJEyZUdn8AAACowXy6ZrRXr156/PHHVVhYqG+//VaFhYV67LHH1KtXr8ruDwAAADWYT0dGJalt27Zq27ZtZfYCAACAWsanMFpSUqJ//vOf+uGHH1RYWFhqjDcVAQAA4EL5FEZnz56t/fv3q0ePHgoNDa3sngAAAFBL+BRGt27dqtmzZ6tBgwaV3Q8AAABqEZ/ewBQeHq7i4uLK7gUAAAC1jE9HRmNjYzV9+nQNGzZMjRo1KjX2y3uPAqiaPj/iVEZeiV9qHXf7pQwAAJJ8DKOffPKJJGnJkiWlljscDs2ePdv/XQHwq4y8Eq1ev9Uvtbr36uyXOgAASD6G0fj4+MruAwAAALWQT9eMSj/d3ik5OVkbNmyQ9NPnzf/6Nk8AAADA+fDpyOiBAwc0bdo0BQcHKysrS/369dPOnTu1bt063X///ZXdIwAAAGoon46MJiQkaOTIkXr11VcVFPRTfr3sssuUkpJSqc0BAACgZvMpjB46dEhXXnllqWX16tWT283bagEAAHDhfAqjERERSk1NLbVs7969at68eaU0BQAAgNrBp2tGR44cqRdeeEGDBw9WSUmJPvjgA61Zs0bjx4+v7P4AAABQg/l0ZLRHjx6aMmWKcnNzddlll+nYsWN66KGH1KVLl8ruDwAAADWYT0dGJalVq1YaM2ZMZfYCAACAWsanMPrXv/71nGMjR470WzMAAACoXXwKo1lZWaUeHz9+XDt37lTv3r0rpSkAAADUDj6F0QkTJpyxbMuWLUpMTPR7QwAAAKg9fP440F/r3LmzNm3a5M9eAAAAUMv4dGT0xx9/LPW4qKhIiYmJCg8Pr5SmAAAAUDv4FEbvu+++Uo9DQkLUqlUrTZw4sVKaAgAAQO1Q4XfTAwAAABfqgq8ZBQAAACrKpyOj9957r0/F5syZU6FmAAAAULv4FEaHDRumdevWadiwYYqIiNCxY8e0evVqxcbGqk2bNpXdIwAAAGoon8LounXr9NhjjyksLMy7rFu3bnr++ec1fPjwSmsOAAAANZtP14xmZ2erXr16pZbVq1dP2dnZldIUAAAAagefjoz27NlT06ZN0+9+9zuFhYUpKytLK1euVI8ePSq7PwAAANRgPoXRsWPH6r333lNCQoKys7MVFhamPn366JZbbqns/gAAAFCD+RRGQ0JCNGrUKI0aNaqy+wEAAEAt4lMYlaRt27bpyy+/1IkTJ/TII49o3759KigoUKdOnSqzPwAAANRgPr2BadWqVUpISFBkZKSSk5Ml/XS0dOnSpZXaHAAAAGo2n8LoP/7xDz3xxBMaMWKEnM6fNrnkkkuUnp5eqc0BAACgZvMpjBYUFCg8PLzUspKSEgUF+XyWHwAAADiDT2G0Q4cOWrlyZallq1atUseOHSujJwAAANQSPoXRu+++W0lJSZo4caIKCwv1pz/9SRs3btQdd9xR2f0BAACgBiv3PLvH49Hhw4f17LPP6sCBAzp27JiaNGmitm3beq8fBQAAAC5EuWnS6XTqxRdfVEhIiNq2bau+ffuqffv2BFEAAABUmM/XjO7evbuyewEAAEAt49Pb4SMiIjR16lT17NlTTZo0kcPh8I6NHDmy0poDAABAzXbOI6OffPKJ9+v8/Hz16tVLDodD2dnZysrK8v4DAAAALtQ5j4wuWbJE1157rSTpm2++0dtvvx2wpgAAAFA7nDOMNmvWTAsXLtSll16qkpISffHFFzLGnLHewIEDK7VBAAAA1FznDKP/8z//o7/97W/68ssvdfr0aa1fv/6s6xFGAQAAcKHOGUajoqJ0zz33SJKeffZZPfnkkwFrCgAAALWDT7d2IogCAACgMnDnegAAAFjj031GAQCoDCc9QVq6p8QvtZq6gjQw0uOXWgAChzAKALCmsKhYGzZt80utobFdxAk/oPrhVQsAAABrCKMAAACwhjAKAAAAawijAAAAsIYwCgAAAGsIowAAALCGMAoAAABrCKMAAACwhjAKAAAAawijAAAAsIYwCgAAAGsIowAAALCGMAoAAABrCKMAAACwhjAKAAAAawijAAAAsIYwCgAAAGsIowAAALAmKBBPcvLkSc2ePVtHjx5VUFCQIiMjNW7cODVs2FC7d+9WQkKC3G63IiIiNGnSJIWGhkpSmWMAAACo/gJyZNThcOiGG27QzJkzNWPGDDVr1kyLFi2Sx+PRrFmzNHr0aM2cOVMdOnTQokWLJKnMMQAAANQMAQmjLpdLHTt29D5u166dMjMzlZqaqpCQEMXExEiSBg8erI0bN0pSmWMAAACoGQJ+zajH49GaNWvUo0cPZWZmKjw83DvWsGFDGWOUl5dX5hgAAABqhoBcM/pL8+fPV926dXXttdcqKSmp0p8vKiqqStSAXbV9DuseyPBbLYfTP3/D+quOP2tVxZ78Waum91S3boiiopr6rR7OVNt/ltYEVXEOAxpGFy5cqKNHj2ry5MlyOp0KDw9XZmamdzw3N1cOh0Mul6vMsfORnp5eoZ6joqIqXAN2MYdSUZHHb7WMxz+1/FXHn7WqYk/+rFXTeyoqctf613pl4mdp9Wd7Ds8VhAN2mn7x4sVKS0vTww8/rODgYElS69at5Xa7lZKSIklas2aN+vbtW+4YAAAAaoaAHBk9ePCgVq5cqcjISD3++OOSpKZNm+rhhx9WXFyc5s6dq+LiYu/tmyTJ6XSecwwAAAA1Q0DCaIsWLbRs2bKzjkVHR2vGjBnnPQYAAIDqj09gAgAAgDWEUQAAAFhDGAUAAIA1Ab/PKADffH7EqYy8Er/UOu72SxkAAPyOMApUURl5JVq9fqtfanXv1dkvdQAA8DdO0wMAAMAawigAAACs4TQ9AKBGOOkJ0tI9/rnOuqkrSAMj/fdRpQDOjTAKAKgRCouKtWHTNr/UGhrbRZw8BAKDVxoAAACsIYwCAADAGsIoAAAArCGMAgAAwBrCKAAAAKwhjAIAAMAawigAAACsIYwCAADAGsIoAAAArCGMAgAAwBrCKAAAAKwhjAIAAMAawigAAACsIYwCAADAGsIoAAAArCGMAgAAwBrCKAAAAKwhjAIAAMAawigAAACsIYwCAADAmiDbDQA1zedHnMrIK6lwneNuPzQDAEAVRxgF/Cwjr0Sr12+tcJ3uvTr7oRsAAKo2TtMDAADAGsIoAAAArCGMAgAAwBrCKAAAAKwhjAIAAMAawigAAACsIYwCAADAGsIoAAAArCGMAgAAwBrCKAAAAKwhjAIAAMAawigAAACsIYwCAADAGsIoAAAArCGMAgAAwBrCKAAAAKwhjAIAAMAawigAAACsIYwCAADAGsIoAAAArAmy3QAAAFXNSU+Qlu4pqXCdpq4gDYz0+KEjoOYijAIA8CuFRcXasGlbhesMje0iTkICZeMVAgAAAGsIowAAALCGMAoAAABrCKMAAACwhjAKAAAAawijAAAAsIYwCgAAAGsIowAAALCGMAoAAABrCKMAAACwhjAKAAAAawijAAAAsIYwCgAAAGsIowAAALCGMAoAAABrCKMAAACwhjAKAAAAawijAAAAsCYoEE+ycOFCff311zp27Jheeukl/eY3v5EkpaenKz4+Xnl5eXK5XIqLi1NkZGS5Y4C/Befny3nqlF9qOTyN/FIHAIDaICBhtHfv3rruuuv01FNPlVqekJCgoUOHKjY2VuvXr9fcuXO965Q1Bvib89QpBa1c6Zdajv63+6UOAAC1QUBO08fExCg8PLzUshMnTigtLU0DBgyQJA0YMEBpaWnKzc0tcwwAAAA1R0COjJ5NVlaWwsLC5HT+lIedTqcaN26szMxMSTrnWMOGDc/reaKioircqz9qwK7y5vDkiRPy+Om5HA7//I3ncPrvb8WqWIueAl+LngJfq27dEEVFNfVLraqA34fVX1WcQ2thNFDS09MrtH1UVFSFa8AuX+awrtvttxeDMf6Jtcbjr3hcNWvRU+Br0VPgaxUVuWvM7xB+H1Z/tufwXEHYWhht0qSJsrOz5fF45HQ65fF4lJOTo/DwcBljzjkGAACAmsParZ1CQ0PVsmVLJSYmSpISExPVqlUrNWzYsMwxAAAA1BwBOTI6f/58JSUl6fjx4/rzn/+siy++WC+//LLGjh2r+Ph4rVixQg0aNFBcXJx3m7LGAAAAUDMEJIzefffduvvuu89Yfskll+j5558/6zZljQEAAKBm4BOYAAAAYA1hFAAAANYQRgEAAGANYRQAAADWEEYBAABgDWEUAAAA1hBGAQAAYA1hFAAAANYQRgEAAGBNQD6BCQCA2uikJ0hL95T4pVZTV5AGRnr8UguoSgijAABUksKiYm3YtM0vtYbGdhEnNFET8b8aAAAA1hBGAQAAYA1hFAAAANYQRgEAAGANYRQAAADWEEYBAABgDWEUAAAA1hBGAQAAYA03vQcAoBrw16c58UlOqGoIowAAVAP++jQnPskJVQ3/GwEAAGANYRQAAADWEEYBAABgDWEUAAAA1hBGAQAAYA1hFAAAANYQRgEAAGANYRQAAADWEEYBAABgDWEUAAAA1hBGAQAAYA1hFAAAANYQRgEAAGANYRSQ5Ek/oNOnTthuAwB81uLUEbU4dcR2G0CFBdluAKiI4Px8OU+dKnOdkydOqK7bXeY6jmaXqE6DUH+2BgCV6mCDSNstAH5BGEW15jx1SkErV5a5jkfl/0d3R7eSOXFMQaER/moNACpVhxP7JEnJoW0sdwJUDGEUkORev0qO7GzCKIBq4/pD6yURRlH9cc0oAAAArCGMAgAAwBrCKAAAAKzhmlEAAGqRfq4C1T2Wf97bne3OJJ4GDVR80UX+ag21FGEUAefL7Zh85Th92i916g2/VY516/1SCwACYWHr4Re0Xaj7lII+/vC8tzvbnUlKRoyQCKOoIMIoAs6X2zH5ynPddX6p4wxvLmd9l19qAUAg/Fg/3HYLgF9wzSggqWTXNhXnHLXdBgD4rEvOLnXJ2WW7DaDCODIKSHJvWCtHdraCGze33QoA+GRI+gZJ0tbG0ZY7ASqGI6MAAACwhjAKAAAAawijAAAAsIYwCgAAAGt4AxMgqd5Nd8r5+Re22wAAn73Z9ibbLQB+QRgFJDlDw+SsW992GwDgs5y6obZbAPyC0/SApOLtm1Wcddh2GwDgs16Z29Urc7vtNoAKI4wCkoo3rZc7Y7/tNgDAZ1f9uElX/bjJdhtAhRFGAQAAYA1hFAAAANYQRgEAAGANYRQAAADWcGsnQFL934+TY+0a220AgM/mtP+97RYAvyCMApIcDVxyBte13QYA+CwvuIHtFgC/4DQ9IKn4u41yHztouw0A8Fm/jO/UL+M7220AFUYYBSQVb9mo4kzCKIDqo/+xLep/bIvtNoAK4zQ9AAC4IA6nU3WPHfNLLU+DBiq+6CK/1EL1QhiFT4Lz8+U8dcovtRynT/ulDgDALkdhoer84x9+qVUyYoREGK2VCKM1nL9CpOP0adX56CM/dCR5rrvOL3UAADWHv46ycoS1+iGM1nDOU6cUtHJlhesQIAEAlclfR1k5wlr9EEYBSfVHxcm5+hPbbQCAz2bGjLLdAuAXhFFAkiMkRI46vBwAVB/uOiG2WwD8gls7AZKKk9bJ/eMPttsAAJ9ddTRJVx1Nst0GUGGEUUBS8Y5vVJydbrsNAPBZr6wd6pW1w3YbQIVxXhIAANQY3Pu0+qnyYTQ9PV3x8fHKy8uTy+VSXFycIiMjbbd1Vv66jRL/+QEAuDDc+7T6qfJhNCEhQUOHDlVsbKzWr1+vuXPn6qmnnrLd1ln56zZKp2+6iRvMAwBgGfc+DYwqHUZPnDihtLQ0PfHEE5KkAQMGaP78+crNzVXDhg0td1d5/PlXHfcHBQDgwnDv08BwGGOM7SbOJTU1VbNnz9bLL7/sXXb//fdr0qRJat26tcXOAAAA4A+8mx4AAADWVOkw2qRJE2VnZ8vj8UiSPB6PcnJyFB4ebrkzAAAA+EOVDqOhoaFq2bKlEhMTJUmJiYlq1apVjb5eFAAAoDap0teMStLhw4cVHx+vU6dOqUGDBoqLi1NUVJTttgAAAOAHVT6MAgAAoOaq0qfpAQAAULMRRgEAAGANYRQAAADWEEYBAABgDWEUAAAA1lTpz6YPpPT0dM2dO1enTp1SSUmJ+vbtq9///veSpKKiIr3++utKTU1VnTp1dPvtt6tHjx7ljiHwVq1apdWrV6tOnTpyOp2aPn26JOawutmxY4eeffZZ3XXXXbr22mslScePH9fs2bN17NgxhYSEaNy4cWrXrl25YwisN998U9u3b1dwcLDq1aunO++8U23atJHEHFZX6enpio+PV15enlwul+Li4hQZGWm7LfzCyZMnNXv2bB09elRBQUGKjIzUuHHj1LBhQ+3evVsJCQlyu92KiIjQpEmTFBoaKklljgWUgTHGmGnTpplVq1YZY4wpKCgw9957r9mzZ48xxpj33nvPzJkzxxhjTHp6uhkzZowpKCgodwyB9dVXX5knn3zS5OfnG2OMycnJ8Y4xh9VHfn6+efTRR83UqVO9r0ljjImPjzfLly83xhiTnJxsJk2aZDweT7ljCKzNmzeb4uJi79dxcXHeMeawenr66afNunXrjDHGrFu3zjz99NOWO8KvnTx50mzfvt37eOHCheb11183p0+fNnFxcSY5OdkYY8zy5ctNfHy8McaUORZonKb/N4fDofz8fEk/HSlzOBzeT3rasGGDBg8eLEmKjIxUmzZt9N1335U7hsD6+OOPdcstt6h+/fqSpEaNGnnHmMPq4+2339YNN9ygiy++uNTyjRs3eucpJiZGwcHB2rdvX7ljCKwePXooKOink27t27dXVlaW9yOdmcPq58SJE0pLS9OAAQMkSQMGDFBaWppyc3Mtd4Zfcrlc6tixo/dxu3btlJmZqdTUVIWEhCgmJkaSNHjwYG3cuFGSyhwLNMLov915553asGGDxo8fr4kTJ2r48OFq2rSpJCkzM1MRERHedcPDw5WVlVXuGALr0KFD2r17tx5//HE98sgjWrt2rXeMOawevvvuO+Xn56tPnz6llp88eVKSSn0U8M/zVNYY7Prkk0/UvXt3OZ1O5rCaysrKUlhYmJzOn+KC0+lU48aNlZmZabkznIvH49GaNWvUo0cPZWZmKjw83DvWsGFDGWOUl5dX5lig1ZprRidPnnzOF09CQoLWrFmj2NhY3XDDDcrJydHTTz+tNm3acM1SFVLeHHo8HmVlZenZZ5/VyZMn9cQTTygqKkqXXXZZgDvFuZQ1h6+++qoWL16sxx9/PMBd4XyU9zr8ObR8+eWXSkxM1DPPPBPI9oBab/78+apbt66uvfZaJSUl2W7HJ7UmjE6bNq3M8VWrVmn27NmSpMaNG6tTp05KTk5Wu3btFB4ermPHjnn/as/MzPQeDi9rDP5V3hyGh4erf//+cjqdCg0NVefOnbV3715ddtllzGEVUdYcpqSkKCcnR1OmTJEk5ebm6ptvvlFeXp5uvvlm77JfzlOTJk28p/PPNgb/K+91KElJSUlaunSpnnjiCe/lMmXNE3NYdTVp0kTZ2dnyeDxyOp3yeDzKyckpdUQNVcfChQt19OhRTZ48WU6nU+Hh4aX+eMzNzZXD4ZDL5SpzLNA4Tf9vTZs21ZYtWyRJBQUFSklJUYsWLSRJffr00Zo1ayRJR44c0b59+9S1a9dyxxBY/fv3985hYWGhkpOT1bJlS0nMYXUQExOjN998U/Hx8YqPj1efPn30+9//3htEfzlPKSkpcrvdat26dbljCKxvvvlGb7/9th577DHvpU4/Yw6rn9DQULVs2VKJiYmSpMTERLVq1arUJRWoGhYvXqy0tDQ9/PDDCg4OliS1bt1abrdbKSkpkqQ1a9aob9++5Y4FmsMYY6w8cxWTmpqq+fPnq6ioSCUlJerfv7/3l2BhYaFef/11paWlyel06rbbblOvXr3KHUNgud1u/eUvf1FaWpokKTY2ViNGjJDEHFZH8fHxatOmTalbO82aNct765+xY8cqOjq63DEE1ujRoxUUFFQqrDz55JO6+OKLmcNq6vDhw4qPj9epU6fUoEEDxcXFKSoqynZb+IWDBw/qwQcfVGRkpEJCQiT9dJDt4Ycf1q5duzR37lwVFxd7b9/08xmLssYCiTAKAAAAazhNDwAAAGsIowAAALCGMAoAAABrCKMAAACwhjAKAAAAawijAAAAsIYwCgDnYeLEidq2bZvtNryefvppffbZZ7bbAIALRhgFAMsCFSirWpAGAIkwCgAAAIuCbDcAANXNvn379NZbb+n48ePq1auXxowZo5CQEK1du1Yffvih8vLyFBMTo7FjxyosLEzSTx+7t2DBAqWnpysqKkp33nmnoqOjtWTJEiUnJ2vPnj1asGCBrrrqKo0ePfqcz71t2zbNnz9fOTk5io2N1S8/RO/o0aP6y1/+ov3798vhcKhLly4aPXq0GjRooFmzZikzM1PTpk2T0+nUzTffrP/6r//Syy+/rOTkZLndbrVs2VJjxoxRixYtKv17CAA/4+NAAeA8TJw4UfXq1dOjjz6qevXqadq0aerYsaM6deqkV155RY899phatGihd955R/v379czzzyjvLw8TZo0SXfddZf69++vjRs3at68eXrttdd08cUX6+mnn9aVV16pQYMGlfncubm5iouL04QJE9SzZ0998sknevfddzV27FgNGjRIR48eVUZGhjp06KCCggLNmDFDrVq10p133untffz48ercubO35ueff66+ffsqKChIixYt0o4dOzR9+vTK/BYCQCmcpgeA8zR06FCFh4fL5XLpxhtv1Jdffql//etfuvrqq9W6dWsFBwfr1ltv1e7du5WRkaFvv/1WzZs3V2xsrOrUqaMBAwYoKipK33zzzXk973fffacWLVqoT58+CgoK0m9/+1s1atTIO968eXN17txZwcHBatiwoX77299q586dZdYcOHCg6tevr+DgYN1yyy3av3+/8vPzL+TbAgAXhNP0AHCewsPDvV9HREQoOztbOTk5atWqlXd5vXr15HK5lJ2drezsbEVERJSq8fN25yMnJ0dNmjTxPnY4HKUeHz9+XAsWLFBycrIKCwvl8XjkcrnOWc/j8WjJkiX66quvlJubK4fDIemnI7AXXXTRefUGABeKMAoA5ykzM7PU12FhYWrcuHGp5YWFhcrLy1NYWJjCwsL09ddfn1Gja9eukuQNgeVp1KiRsrKyvI+NMaUeL1myRJI0Y8YMuVwuJSUlaf78+eesl5iYqM2bN+uJJ55QRESE8vPzddddd/nUCwD4C6fpAeA8rV69WllZWcrLy9P777+vvn37qn///vriiy/0ww8/qLi4WEuWLFHbtm3VtGlTdevWTUeOHFFiYqJOnz6tDRs26NChQ+revbskKTQ0VD/++GO5z9u9e3cdPHhQX3/9tU6fPq1Vq1bp+PHj3vGCggLVq1dPF110kbKzs/XRRx+V2r5Ro0bKyMgotX5QUJBcLpeKioq8YRYAAok3MAHAeZg4caKuueYarV+/Xjk5OerZs6fGjh2runXr6tNPP9VHH32kvLw8RUdHa+zYsd7T6CkpKXrrrbd09OhRNW/eXHfddZdiYmIkSbt371Z8fLxyc3N15ZVX6u677z7n82/ZssX7Tv7Y2FgdOHBAsbGxGjRokA4ePKjZs2crPT3de43q3//+d73xxhuSpE2bNmn+/PkqKCjQTTfdpCFDhui1117T9u3b5XK5NHLkSMXHx+u1115T8+bNK/+bCQAijAIAAMAiTtMDAADAGt7ABABVSHJysp5//vmzjr3zzjsB7gYAKh+n6QEAAGANp+kBAABgDWEUAAAA1hBGAQAAYA1hFAAAANb8PzaAExmspJ8HAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 792x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "pvalue =  0.016688592241146598\n"
     ]
    }
   ],
   "source": [
    "arppu_boot = get_bootstrap(data_column_1 = group_a_paid, col_1 = 'rev',\n",
    "                        data_column_2 = group_b_paid, col_2 = 'rev',\n",
    "                        boot_it = 5000,\n",
    "                        statistic = 'arppu_func')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Вывод по бутстрапу по ARPPU**:\\\n",
    "Доверительный интервал не включает в себя 0. Отвергаем нулевую гипотезу. ARPPU в двух группах различаются. Очень малое количество наблюдений из двух выборок пересекаются между собой."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Стоит ли запускать новую механику на всех пользователей?\n",
    "Напомним основные гипотезы:\n",
    "* H0 - Новая механика оплаты услуг на сайте в группе B не отличается от старой механики, оставленной в группе A.\n",
    "* H1 - Новая механика оплаты услуг на сайте в группе B имеет значимые различия в сравнении с механикой в группе A. \\"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Напомним выводы по тестам:\n",
    "* Бутстрап по CR показал pvalue > 0.05. Это не дало нам основания отвергнуть нулевую гипотезу.\n",
    "* Бутстрап по ARPU показал pvalue < 0.05. Есть основания отвергнуть нулевую гипотезу.\n",
    "* Бутстрап по ARPPU показал pvalue < 0.05. Это дает нам основания отвергнуть нулевую гипотезу."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Таким образом, согласно проведенным тестам  новая механика оплаты даёт статистически значимый прирост в метриках ARPU и ARPPU. Имеет смысл применить новую механику оплаты услуг на всех пользователей. При этом в дальнейшем следить за метриками. Особенно за коэффициентом конверсии перехода к оплате. \\\n",
    "Однако стоит еще отметить, что так как система сплитования делит пользователей на группы неравномерно, в результате мы тестируем изменения на 85% наших активных пользователей. В будущем неудачный эксперимент в таком объеме может привести к значительному падению выручки.\\\n",
    "В идеале нужно было выделить больше пользователей для группы А, потому что из-за малого количества уникальных пользователей в группе А мы можем столкнуться с ситуацией, когда тесты показали статистически значимые различия, а в генеральной совокупности эти различий может не оказаться, либо различия будут в пользу базовой механики оплаты услуг на сайте."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. SQL"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2.1 Очень усердные ученики\n",
    "Необходимо написать оптимальный запрос, который даст информацию о количестве очень усердных студентов. \\\n",
    "NB! Под усердным студентом мы понимаем студента, который правильно решил 20 задач за текущий месяц."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandahouse as ph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Установим соединение с Clickhouse\n",
    "connection_default = {'host': 'https://clickhouse.lab.karpov.courses/',\n",
    "                      'database':'default',\n",
    "                      'user':'student', \n",
    "                      'password':'dpo_python_2020'\n",
    "                     }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>hardworking_students</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>136</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   hardworking_students\n",
       "0                   136"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "query_1 = \"\"\"\n",
    "    SELECT COUNT(DISTINCT st_id) AS hardworking_students         --- считаем количество отобранных уникальных студентов \n",
    "    FROM(\n",
    "        SELECT st_id,\n",
    "               SUM(correct) AS peas                              --- считаем кличество правильно решённых горошин\n",
    "        FROM default.peas\n",
    "        WHERE correct = 1\n",
    "        group by st_id) t1\n",
    "    where peas >= 20  \n",
    "    \"\"\"\n",
    "studs_count = ph.read_clickhouse(query_1, connection=connection_default)\n",
    "studs_count"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Данные в default.peas за 10-2021, всего 2 дня."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2.2 Оптимизация воронки\n",
    "Необходимо в одном запросе выгрузить следующие метрики: \n",
    "* ARPU (Average Revenue Per User) — средняя выручка на одного пользователя за период.\n",
    "* ARPAU (Average Revenue Per Active User) - средняя выручка на одного активного пользователя за период.\n",
    "* CR в покупку (сonversion rate) —  отношение количества платящих пользователей к общему количеству пользователей.\n",
    "* СR активного пользователя в покупку - отношение количества платящих пользователей к общему количеству активных пользователей.\n",
    "* CR пользователя из активности по математике (subject = ’math’) в покупку курса по математике - отношение количества платящих  в математике пользователей к общему количеству активных в математике пользователей."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Активным* считается пользователь, за все время решивший больше 10 задач правильно в любых дисциплинах.\\\n",
    "*Активным по математике* считается пользователь, за все время решивший 2 или больше задач правильно по математике."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "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>test_grp</th>\n",
       "      <th>ARPU</th>\n",
       "      <th>ARPAU</th>\n",
       "      <th>CR</th>\n",
       "      <th>CR_active</th>\n",
       "      <th>CR_active_math</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>control</td>\n",
       "      <td>4540.98</td>\n",
       "      <td>10905.51</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.11</td>\n",
       "      <td>0.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>pilot</td>\n",
       "      <td>11508.47</td>\n",
       "      <td>35364.58</td>\n",
       "      <td>0.11</td>\n",
       "      <td>0.26</td>\n",
       "      <td>0.10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  test_grp      ARPU     ARPAU    CR  CR_active  CR_active_math\n",
       "0  control   4540.98  10905.51  0.05       0.11            0.06\n",
       "1    pilot  11508.47  35364.58  0.11       0.26            0.10"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "query_2 = \"\"\"\n",
    "    SELECT \n",
    "    test_grp, \n",
    "    ROUND(SUM(revenue)/COUNT(studs.st_id), 2) AS ARPU, \n",
    "    ROUND(SUM(revenue)/SUM(active_total), 2) as ARPAU, \n",
    "    ROUND(SUM(paid) / COUNT(studs.st_id), 2) AS CR, \n",
    "    ROUND(SUM(CASE WHEN active_total > 0 THEN paid ELSE 0 END) / SUM(active_total), 2) AS CR_active, \n",
    "    ROUND(SUM(CASE WHEN cnt_math_payed = 1 and active_in_math = 1 THEN 1 ELSE 0 END) / SUM(active_in_math), 2) AS CR_active_math \n",
    "    FROM \n",
    "    ( \n",
    "        SELECT studs.st_id, test_grp, revenue, cnt_math_payed, \n",
    "        CASE WHEN math_tasks >= 2 THEN 1 ELSE 0 END active_in_math, ---условие для активных по Math\n",
    "        CASE WHEN total_tasks > 10 THEN 1 ELSE 0 END active_total, --- условие для активных пользователей \n",
    "        CASE WHEN revenue > 0 THEN 1 ELSE 0 end paid --- отбираем платящих пользователей по любым предметам и по математике\n",
    "        FROM default.studs \n",
    "        left join \n",
    "        (   --- отбираем платящих пользователей по любым предметам и по математике\n",
    "            SELECT st_id, \n",
    "            MAX(CASE WHEN subject = 'Math' THEN 1 ELSE 0 END) AS cnt_math_payed, --- количество оплативших по Math\n",
    "            SUM(money) AS revenue --- общая выручка\n",
    "            FROM default.final_project_check \n",
    "            GROUP BY st_id \n",
    "        ) AS a ON studs.st_id = a.st_id \n",
    "        left join ( \n",
    "                    SELECT \n",
    "                    st_id, \n",
    "                    SUM(CASE WHEN subject = 'Math' THEN 1 ELSE 0 END) math_tasks, \n",
    "                    SUM(correct) total_tasks \n",
    "                    FROM peas \n",
    "                    WHERE correct = 1 \n",
    "                    GROUP BY st_id \n",
    "                    HAVING math_tasks >= 2 OR total_tasks > 10 ---условие для активных и активных по Math\"\n",
    "                    ) AS b ON studs.st_id = b.st_id \n",
    "                  ) \n",
    "    GROUP BY test_grp\n",
    "    \"\"\"\n",
    "studs_metrics = ph.read_clickhouse(query_2, connection=connection_default)\n",
    "studs_metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3. Python"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. Реализую функцию, которая будет автоматически подгружать информацию из дополнительного файла groups_add.csv и на основании дополнительных параметров пересчитывать метрики."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_update(groups_data, groups_add_data, active_studs_data, checks_data):\n",
    "\n",
    "    \n",
    "    #считывание данных\n",
    "    groups = pd.read_csv('/mnt/HC_Volume_18315164/home-jupyter/jupyter-b-dzhumaeva-37/final_project/Проект_2_groups.csv', sep=';')\n",
    "    groups_add = pd.read_csv('/mnt/HC_Volume_18315164/home-jupyter/jupyter-b-dzhumaeva-37/final_project/Проект_2_group_add.csv', \n",
    "                         sep=',')\n",
    "    active_studs = pd.read_csv('/mnt/HC_Volume_18315164/home-jupyter/jupyter-b-dzhumaeva-37/final_project/Проект_2_active_studs.csv',\n",
    "                          sep=';')\n",
    "    checks = pd.read_csv('/mnt/HC_Volume_18315164/home-jupyter/jupyter-b-dzhumaeva-37/final_project/Проект_2_checks.csv', sep=';')\n",
    "        \n",
    "    #замена заголовков в новом датафрейме\n",
    "    original_names = groups_add.columns.values\n",
    "    new_names = groups.columns.values\n",
    "    new_columns = dict(zip(original_names, new_names))\n",
    "    groups_add.rename(columns=new_columns)\n",
    "\n",
    "    #соединение groups и groups_add\n",
    "    groups_all = pd.concat([groups, groups_add]).reset_index(drop=True)\n",
    "    \n",
    "    #соединение оставшихся \n",
    "    active_studs = active_studs.merge(groups_all, how='left', left_on='student_id', right_on='id').drop(columns=['id'])\n",
    "    checks = checks.merge(groups_all, how='left', left_on='student_id', right_on='id').drop(columns=['id'])\n",
    "    \n",
    "    # расчет метрик\n",
    "    metrics = {}\n",
    "    \n",
    "    # ARPPU\n",
    "    ARPPU_table = checks.groupby('grp', as_index=False).agg({'rev': 'sum', 'student_id': 'nunique'})\n",
    "    ARPPU_table.rename(columns={'rev': 'total_revenue', 'student_id': 'paying_users_count'}, inplace=True)\n",
    "    ARPPU_table['ARPPU'] = ARPPU_table['total_revenue'] / ARPPU_table['paying_users_count']\n",
    "    metrics['ARPPU'] = {'A': ARPPU_table.query('grp == \"A\"')['ARPPU'].values[0], \n",
    "                        'B': ARPPU_table.query('grp == \"B\"')['ARPPU'].values[0]}\n",
    "    \n",
    "    # ARPU\n",
    "    # количество пользователей по группам\n",
    "    ARPU_table = groups_all.groupby('grp', as_index=False).agg(users_count=('id', 'nunique'))\n",
    "    ARPU_table['total_revenue'] = ARPPU_table['total_revenue']\n",
    "    ARPU_table['ARPU'] = ARPU_table['total_revenue'] / ARPU_table['users_count']\n",
    "    metrics['ARPU'] = {'A': ARPU_table.query('grp == \"A\"')['ARPU'].values[0], \n",
    "                       'B': ARPU_table.query('grp == \"B\"')['ARPU'].values[0]}\n",
    "    \n",
    "    # CR\n",
    "    CR_table = ARPPU_table[['grp', 'paying_users_count']].merge(ARPU_table[['grp', 'users_count']], how='left', on='grp')\n",
    "    CR_table['CR'] = CR_table['paying_users_count'] * 100 / CR_table['users_count']\n",
    "    metrics['CR'] = {'A': CR_table.query('grp == \"A\"')['CR'].values[0], \n",
    "                     'B': CR_table.query('grp == \"B\"')['CR'].values[0]}\n",
    "    \n",
    "\n",
    "    return metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "result_metrics = get_update(groups, groups_add, active_studs, checks)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "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>group</th>\n",
       "      <th>ARPPU</th>\n",
       "      <th>ARPU</th>\n",
       "      <th>CR</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A</td>\n",
       "      <td>860.710280</td>\n",
       "      <td>6.268019</td>\n",
       "      <td>0.728238</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>B</td>\n",
       "      <td>1108.824893</td>\n",
       "      <td>8.036171</td>\n",
       "      <td>0.724747</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  group        ARPPU      ARPU        CR\n",
       "0     A   860.710280  6.268019  0.728238\n",
       "1     B  1108.824893  8.036171  0.724747"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# вывод в виде датафрейма\n",
    "result_metrics = pd.DataFrame(result_metrics).reset_index().rename(columns={'index': 'group'})\n",
    "result_metrics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2. Реализую функцию, которая будет строить графики по получаемым метрикам."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "def metrics_charts(metrics):  \n",
    "    #установим размер\n",
    "    plt.figure(figsize=(20, 6))\n",
    "    #график ARPPU\n",
    "    plt.subplot(1, 3, 1)\n",
    "    bar = sns.barplot(x='group', y='ARPPU', data=result_metrics)\n",
    "    #назначаем названия\n",
    "    bar.axes.set_title('График ARPPU', fontsize=16) \n",
    "    bar.set_xlabel('Group', fontsize=12)\n",
    "    bar.set_ylabel('ARPPU', fontsize=12)\n",
    "    bar.bar_label(bar.containers[0]) #показываем значения столбцов\n",
    "    plt.xticks(fontsize=12)\n",
    "    plt.yticks(fontsize=12) \n",
    "    #график ARPU\n",
    "    plt.subplot(1, 3, 2)\n",
    "    bar = sns.barplot(x='group', y='ARPU', data=result_metrics)\n",
    "    bar.axes.set_title('График ARPU', fontsize=16)\n",
    "    bar.set_xlabel('Group', fontsize=12)\n",
    "    bar.set_ylabel('ARPU', fontsize=12)\n",
    "    bar.bar_label(bar.containers[0]) #показываем значения столбцов\n",
    "    plt.xticks(fontsize=12)\n",
    "    plt.yticks(fontsize=12)  \n",
    "    #график CR\n",
    "    plt.subplot(1, 3, 3)\n",
    "    bar = sns.barplot(x='group', y='CR', data=result_metrics)\n",
    "    bar.axes.set_title(\"График CR\", fontsize=16)\n",
    "    bar.set_xlabel('Group', fontsize=12)\n",
    "    bar.set_ylabel('CR ', fontsize=12)\n",
    "    bar.bar_label(bar.containers[0]) #показываем значения столбцов\n",
    "    plt.xticks(fontsize=12)\n",
    "    plt.yticks(fontsize=12) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1440x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# вызов функции для визуализации\n",
    "metrics_charts(result_metrics)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}