AB_test_1.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# A/B тестирование\n",
    "Приоритизация гипотез и анализ результатов A/B теста."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Оглавление: \n",
    "<a class=\"anchor\" id=\"step_0\"></a>  \n",
    "\n",
    "* [1. Загрузка файлов с данными и изучение общей информации.](#step_1)\n",
    "* [2. Приоритизация](#step_2) \n",
    "* [3. Анализ  A/B-теста](#step_3) \n",
    " * [3.01. График кумулятивной выручки по группам](#step_3.01)\n",
    " * [3.02. График кумулятивного среднего чека по группам](#step_3.02)\n",
    " * [3.03. График относительного изменения кумулятивного среднего чека группы B к группе A](#step_3.03)\n",
    " * [3.04. График кумулятивной конверсии по группам](#step_3.04)\n",
    " * [3.05. График относительного изменения кумулятивной конверсии группы B к группе A](#step_3.05)\n",
    " * [3.06. Точечный график количества заказов по пользователям](#step_3.06)\n",
    " * [3.07. 95-й и 99-й перцентили количества заказов на пользователя](#step_3.07)\n",
    " * [3.08. Точечный график стоимостей заказов](#step_3.08)\n",
    " * [3.09. 95-й и 99-й перцентили стоимости заказов](#step_3.09)\n",
    " * [3.10. Рассчет статистической значимости различий в конверсии между группами по «сырым» данным](#step_3.10)\n",
    " * [3.11. Рассчет статистической значимости различий в среднем чеке заказа между группами по «сырым» данным](#step_3.11)\n",
    " * [3.12. Рассчет статистической значимости различий в конверсии между группами по «очищенным» данным](#step_3.12)\n",
    " * [3.13. Рассчет статистической значимости различий в среднем чеке заказа между группами по «очищенным» данным](#step_3.13)\n",
    " * [3.14. Решение по результатам теста](#step_3.14)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd # Импортируем необходимые библиотеки\n",
    "import numpy as np\n",
    "import datetime as dt\n",
    "from scipy import stats as st\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from math import sqrt, e\n",
    "plt.style.use('fivethirtyeight')\n",
    "plt.rcParams['figure.figsize']=(15,5) # Зададим размер графиков по умолчанию\n",
    "\n",
    "import warnings # отключим предупреждения анаконды\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "\n",
    "pd.set_option('max_columns', None) # Зададим вывод всех столбцов таблиц\n",
    "pd.options.display.float_format = '{:.2f}'.format # Зададим удобный формат отображения вещественных чисел"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Универсальная функция если понадобится получить имя датафрейма или серии\n",
    "def get_name(obj):\n",
    "    name =[x for x in globals() if globals()[x] is obj][0]\n",
    "    return name"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Загрузка данные и подготовка их к анализу  \n",
    "[к оглавлению](#step_0)\n",
    "<a class=\"anchor\" id=\"step_1\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Загрузим датасеты\n",
    "try:\n",
    "    hypotheses = pd.read_csv('https://code.s3.yandex.net/datasets/hypothesis.csv')\n",
    "    orders = pd.read_csv('https://code.s3.yandex.net/datasets/orders.csv')\n",
    "    visitors = pd.read_csv('https://code.s3.yandex.net/datasets/visitors.csv')    \n",
    "except:\n",
    "    hypotheses = pd.read_csv('/Users/victorchudnovskiy/Documents/raw_csv/hypothesis.csv')\n",
    "    orders = pd.read_csv('/Users/victorchudnovskiy/Documents/raw_csv/orders.csv')\n",
    "    visitors = pd.read_csv('/Users/victorchudnovskiy/Documents/raw_csv/visitors.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_list = [hypotheses, orders, visitors]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "hypotheses\n"
     ]
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Hypothesis</th>\n",
       "      <th>Reach</th>\n",
       "      <th>Impact</th>\n",
       "      <th>Confidence</th>\n",
       "      <th>Efforts</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Добавить два новых канала привлечения трафика,...</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Запустить собственную службу доставки, что сок...</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Добавить блоки рекомендаций товаров на сайт ин...</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Изменить структура категорий, что увеличит кон...</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Изменить цвет фона главной страницы, чтобы уве...</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "                                          Hypothesis  Reach  Impact  \\\n",
       "0  Добавить два новых канала привлечения трафика,...      3      10   \n",
       "1  Запустить собственную службу доставки, что сок...      2       5   \n",
       "2  Добавить блоки рекомендаций товаров на сайт ин...      8       3   \n",
       "3  Изменить структура категорий, что увеличит кон...      8       3   \n",
       "4  Изменить цвет фона главной страницы, чтобы уве...      3       1   \n",
       "\n",
       "   Confidence  Efforts  \n",
       "0           8        6  \n",
       "1           4       10  \n",
       "2           7        3  \n",
       "3           3        8  \n",
       "4           1        1  "
      ]
     },
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     "output_type": "display_data"
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 9 entries, 0 to 8\n",
      "Data columns (total 5 columns):\n",
      " #   Column      Non-Null Count  Dtype \n",
      "---  ------      --------------  ----- \n",
      " 0   Hypothesis  9 non-null      object\n",
      " 1   Reach       9 non-null      int64 \n",
      " 2   Impact      9 non-null      int64 \n",
      " 3   Confidence  9 non-null      int64 \n",
      " 4   Efforts     9 non-null      int64 \n",
      "dtypes: int64(4), object(1)\n",
      "memory usage: 488.0+ bytes\n",
      "None\n",
      "______________________________________________________________________________\n",
      "orders\n"
     ]
    },
    {
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       "      <th></th>\n",
       "      <th>transactionId</th>\n",
       "      <th>visitorId</th>\n",
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       "      <th>revenue</th>\n",
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       "      <td>3667963787</td>\n",
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       "      <td>1650</td>\n",
       "      <td>B</td>\n",
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       "      <th>1</th>\n",
       "      <td>2804400009</td>\n",
       "      <td>3642806036</td>\n",
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       "      <td>730</td>\n",
       "      <td>B</td>\n",
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       "      <th>2</th>\n",
       "      <td>2961555356</td>\n",
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       "      <td>400</td>\n",
       "      <td>A</td>\n",
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       "      <th>3</th>\n",
       "      <td>3797467345</td>\n",
       "      <td>1196621759</td>\n",
       "      <td>2019-08-15</td>\n",
       "      <td>9759</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2282983706</td>\n",
       "      <td>2322279887</td>\n",
       "      <td>2019-08-15</td>\n",
       "      <td>2308</td>\n",
       "      <td>B</td>\n",
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       "   transactionId   visitorId        date  revenue group\n",
       "0     3667963787  3312258926  2019-08-15     1650     B\n",
       "1     2804400009  3642806036  2019-08-15      730     B\n",
       "2     2961555356  4069496402  2019-08-15      400     A\n",
       "3     3797467345  1196621759  2019-08-15     9759     B\n",
       "4     2282983706  2322279887  2019-08-15     2308     B"
      ]
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1197 entries, 0 to 1196\n",
      "Data columns (total 5 columns):\n",
      " #   Column         Non-Null Count  Dtype \n",
      "---  ------         --------------  ----- \n",
      " 0   transactionId  1197 non-null   int64 \n",
      " 1   visitorId      1197 non-null   int64 \n",
      " 2   date           1197 non-null   object\n",
      " 3   revenue        1197 non-null   int64 \n",
      " 4   group          1197 non-null   object\n",
      "dtypes: int64(3), object(2)\n",
      "memory usage: 46.9+ KB\n",
      "None\n",
      "______________________________________________________________________________\n",
      "visitors\n"
     ]
    },
    {
     "data": {
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       "      <th>date</th>\n",
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       "      <th>visitors</th>\n",
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       "      <th>0</th>\n",
       "      <td>2019-08-01</td>\n",
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       "      <td>719</td>\n",
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       "      <th>1</th>\n",
       "      <td>2019-08-02</td>\n",
       "      <td>A</td>\n",
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      "text/plain": [
       "         date group  visitors\n",
       "0  2019-08-01     A       719\n",
       "1  2019-08-02     A       619\n",
       "2  2019-08-03     A       507\n",
       "3  2019-08-04     A       717\n",
       "4  2019-08-05     A       756"
      ]
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 62 entries, 0 to 61\n",
      "Data columns (total 3 columns):\n",
      " #   Column    Non-Null Count  Dtype \n",
      "---  ------    --------------  ----- \n",
      " 0   date      62 non-null     object\n",
      " 1   group     62 non-null     object\n",
      " 2   visitors  62 non-null     int64 \n",
      "dtypes: int64(1), object(2)\n",
      "memory usage: 1.6+ KB\n",
      "None\n",
      "______________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "for df in df_list:\n",
    "    print(get_name(df))\n",
    "    display(df.head())\n",
    "    print(df.info())\n",
    "    print('_' * 78)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "В исследуемых таблицах очевидно пропусков нет, но в тоблице hypotheses заголовки проименованы с заглавной буквы, а данные с датами представлены в строковом формате. Исправим это недоразумение, приведя даты к типу `datetime` а заголовки в удобный формат"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "for df in df_list:\n",
    "    df.columns = [x.lower() for x in df.columns]\n",
    "    for col in df.columns:\n",
    "        if 'date' in col:\n",
    "            df[col] = df[col].map(lambda x: dt.datetime.strptime(x, '%Y-%m-%d'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "hypotheses\n"
     ]
    },
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>hypothesis</th>\n",
       "      <th>reach</th>\n",
       "      <th>impact</th>\n",
       "      <th>confidence</th>\n",
       "      <th>efforts</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Добавить два новых канала привлечения трафика,...</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Запустить собственную службу доставки, что сок...</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Добавить блоки рекомендаций товаров на сайт ин...</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Изменить структура категорий, что увеличит кон...</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Изменить цвет фона главной страницы, чтобы уве...</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                          hypothesis  reach  impact  \\\n",
       "0  Добавить два новых канала привлечения трафика,...      3      10   \n",
       "1  Запустить собственную службу доставки, что сок...      2       5   \n",
       "2  Добавить блоки рекомендаций товаров на сайт ин...      8       3   \n",
       "3  Изменить структура категорий, что увеличит кон...      8       3   \n",
       "4  Изменить цвет фона главной страницы, чтобы уве...      3       1   \n",
       "\n",
       "   confidence  efforts  \n",
       "0           8        6  \n",
       "1           4       10  \n",
       "2           7        3  \n",
       "3           3        8  \n",
       "4           1        1  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 9 entries, 0 to 8\n",
      "Data columns (total 5 columns):\n",
      " #   Column      Non-Null Count  Dtype \n",
      "---  ------      --------------  ----- \n",
      " 0   hypothesis  9 non-null      object\n",
      " 1   reach       9 non-null      int64 \n",
      " 2   impact      9 non-null      int64 \n",
      " 3   confidence  9 non-null      int64 \n",
      " 4   efforts     9 non-null      int64 \n",
      "dtypes: int64(4), object(1)\n",
      "memory usage: 488.0+ bytes\n",
      "None\n",
      "__________________________________________________________________________________________\n",
      "orders\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "      <th></th>\n",
       "      <th>transactionid</th>\n",
       "      <th>visitorid</th>\n",
       "      <th>date</th>\n",
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       "      <th>0</th>\n",
       "      <td>3667963787</td>\n",
       "      <td>3312258926</td>\n",
       "      <td>2019-08-15</td>\n",
       "      <td>1650</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2804400009</td>\n",
       "      <td>3642806036</td>\n",
       "      <td>2019-08-15</td>\n",
       "      <td>730</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2961555356</td>\n",
       "      <td>4069496402</td>\n",
       "      <td>2019-08-15</td>\n",
       "      <td>400</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3797467345</td>\n",
       "      <td>1196621759</td>\n",
       "      <td>2019-08-15</td>\n",
       "      <td>9759</td>\n",
       "      <td>B</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2282983706</td>\n",
       "      <td>2322279887</td>\n",
       "      <td>2019-08-15</td>\n",
       "      <td>2308</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   transactionid   visitorid       date  revenue group\n",
       "0     3667963787  3312258926 2019-08-15     1650     B\n",
       "1     2804400009  3642806036 2019-08-15      730     B\n",
       "2     2961555356  4069496402 2019-08-15      400     A\n",
       "3     3797467345  1196621759 2019-08-15     9759     B\n",
       "4     2282983706  2322279887 2019-08-15     2308     B"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1197 entries, 0 to 1196\n",
      "Data columns (total 5 columns):\n",
      " #   Column         Non-Null Count  Dtype         \n",
      "---  ------         --------------  -----         \n",
      " 0   transactionid  1197 non-null   int64         \n",
      " 1   visitorid      1197 non-null   int64         \n",
      " 2   date           1197 non-null   datetime64[ns]\n",
      " 3   revenue        1197 non-null   int64         \n",
      " 4   group          1197 non-null   object        \n",
      "dtypes: datetime64[ns](1), int64(3), object(1)\n",
      "memory usage: 46.9+ KB\n",
      "None\n",
      "__________________________________________________________________________________________\n",
      "visitors\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "      <th>date</th>\n",
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       "      <th>visitors</th>\n",
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       "      <th>0</th>\n",
       "      <td>2019-08-01</td>\n",
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       "      <td>719</td>\n",
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       "      <td>2019-08-02</td>\n",
       "      <td>A</td>\n",
       "      <td>619</td>\n",
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       "      <th>2</th>\n",
       "      <td>2019-08-03</td>\n",
       "      <td>A</td>\n",
       "      <td>507</td>\n",
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       "      <th>3</th>\n",
       "      <td>2019-08-04</td>\n",
       "      <td>A</td>\n",
       "      <td>717</td>\n",
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       "        date group  visitors\n",
       "0 2019-08-01     A       719\n",
       "1 2019-08-02     A       619\n",
       "2 2019-08-03     A       507\n",
       "3 2019-08-04     A       717\n",
       "4 2019-08-05     A       756"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 62 entries, 0 to 61\n",
      "Data columns (total 3 columns):\n",
      " #   Column    Non-Null Count  Dtype         \n",
      "---  ------    --------------  -----         \n",
      " 0   date      62 non-null     datetime64[ns]\n",
      " 1   group     62 non-null     object        \n",
      " 2   visitors  62 non-null     int64         \n",
      "dtypes: datetime64[ns](1), int64(1), object(1)\n",
      "memory usage: 1.6+ KB\n",
      "None\n",
      "__________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "for df in df_list:\n",
    "    print(get_name(df))\n",
    "    display(df.head())\n",
    "    print(df.info())\n",
    "    print('_' * 90)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "В датафрейме hypotheses выявлено 0 дубликатов\n",
      "В датафрейме orders выявлено 0 дубликатов\n",
      "В датафрейме visitors выявлено 0 дубликатов\n"
     ]
    }
   ],
   "source": [
    "for df in df_list:\n",
    "    print(f'В датафрейме {get_name(df)} выявлено {df.duplicated().sum()} дубликатов')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Вывод \n",
    "Таблицы загружены, типы данных приведены в соответствие содержимому. Пропусков и дубликатов нет. Приступим к нализу."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Приоритизация гипотез.\n",
    "[к оглавлению](#step_0)\n",
    "<a class=\"anchor\" id=\"step_2\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Добавить два новых канала привлечения трафика, что позволит привлекать на 30% больше пользователей',\n",
       "       'Запустить собственную службу доставки, что сократит срок доставки заказов',\n",
       "       'Добавить блоки рекомендаций товаров на сайт интернет магазина, чтобы повысить конверсию и средний чек заказа',\n",
       "       'Изменить структура категорий, что увеличит конверсию, т.к. пользователи быстрее найдут нужный товар',\n",
       "       'Изменить цвет фона главной страницы, чтобы увеличить вовлеченность пользователей',\n",
       "       'Добавить страницу отзывов клиентов о магазине, что позволит увеличить количество заказов',\n",
       "       'Показать на главной странице баннеры с актуальными акциями и распродажами, чтобы увеличить конверсию',\n",
       "       'Добавить форму подписки на все основные страницы, чтобы собрать базу клиентов для email-рассылок',\n",
       "       'Запустить акцию, дающую скидку на товар в день рождения'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hypotheses['hypothesis'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "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>hypothesis</th>\n",
       "      <th>reach</th>\n",
       "      <th>impact</th>\n",
       "      <th>confidence</th>\n",
       "      <th>efforts</th>\n",
       "      <th>ICE</th>\n",
       "      <th>RICE</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Добавить два новых канала привлечения трафика,...</td>\n",
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       "      <td>8</td>\n",
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       "      <td>13.33</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Запустить собственную службу доставки, что сок...</td>\n",
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       "      <td>5</td>\n",
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       "      <td>Добавить блоки рекомендаций товаров на сайт ин...</td>\n",
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       "      <td>56.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Изменить структура категорий, что увеличит кон...</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>1.12</td>\n",
       "      <td>9.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Изменить цвет фона главной страницы, чтобы уве...</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1.00</td>\n",
       "      <td>3.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Добавить страницу отзывов клиентов о магазине,...</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1.33</td>\n",
       "      <td>4.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Показать на главной странице баннеры с актуаль...</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>8.00</td>\n",
       "      <td>40.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Добавить форму подписки на все основные страни...</td>\n",
       "      <td>10</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "      <td>11.20</td>\n",
       "      <td>112.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Запустить акцию, дающую скидку на товар в день...</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>5</td>\n",
       "      <td>16.20</td>\n",
       "      <td>16.20</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                          hypothesis  reach  impact  \\\n",
       "0  Добавить два новых канала привлечения трафика,...      3      10   \n",
       "1  Запустить собственную службу доставки, что сок...      2       5   \n",
       "2  Добавить блоки рекомендаций товаров на сайт ин...      8       3   \n",
       "3  Изменить структура категорий, что увеличит кон...      8       3   \n",
       "4  Изменить цвет фона главной страницы, чтобы уве...      3       1   \n",
       "5  Добавить страницу отзывов клиентов о магазине,...      3       2   \n",
       "6  Показать на главной странице баннеры с актуаль...      5       3   \n",
       "7  Добавить форму подписки на все основные страни...     10       7   \n",
       "8  Запустить акцию, дающую скидку на товар в день...      1       9   \n",
       "\n",
       "   confidence  efforts   ICE   RICE  \n",
       "0           8        6 13.33  40.00  \n",
       "1           4       10  2.00   4.00  \n",
       "2           7        3  7.00  56.00  \n",
       "3           3        8  1.12   9.00  \n",
       "4           1        1  1.00   3.00  \n",
       "5           2        3  1.33   4.00  \n",
       "6           8        3  8.00  40.00  \n",
       "7           8        5 11.20 112.00  \n",
       "8           9        5 16.20  16.20  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hypotheses['ICE'] = (hypotheses['impact'] * hypotheses['confidence']) / hypotheses['efforts']\n",
    "hypotheses['RICE'] = (hypotheses['reach'] * hypotheses['impact'] * hypotheses['confidence']) / hypotheses['efforts']\n",
    "hypotheses"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>hypothesis</th>\n",
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       "  <tbody>\n",
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       "      <th>8</th>\n",
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       "      <td>16.20</td>\n",
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       "      <th>0</th>\n",
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       "      <th>6</th>\n",
       "      <td>Показать на главной странице баннеры с актуаль...</td>\n",
       "      <td>8.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Добавить блоки рекомендаций товаров на сайт ин...</td>\n",
       "      <td>7.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Запустить собственную службу доставки, что сок...</td>\n",
       "      <td>2.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Добавить страницу отзывов клиентов о магазине,...</td>\n",
       "      <td>1.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Изменить структура категорий, что увеличит кон...</td>\n",
       "      <td>1.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Изменить цвет фона главной страницы, чтобы уве...</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                          hypothesis   ICE\n",
       "8  Запустить акцию, дающую скидку на товар в день... 16.20\n",
       "0  Добавить два новых канала привлечения трафика,... 13.33\n",
       "7  Добавить форму подписки на все основные страни... 11.20\n",
       "6  Показать на главной странице баннеры с актуаль...  8.00\n",
       "2  Добавить блоки рекомендаций товаров на сайт ин...  7.00\n",
       "1  Запустить собственную службу доставки, что сок...  2.00\n",
       "5  Добавить страницу отзывов клиентов о магазине,...  1.33\n",
       "3  Изменить структура категорий, что увеличит кон...  1.12\n",
       "4  Изменить цвет фона главной страницы, чтобы уве...  1.00"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hypotheses[['hypothesis', 'ICE']].sort_values(by='ICE', ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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>hypothesis</th>\n",
       "      <th>RICE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Добавить форму подписки на все основные страни...</td>\n",
       "      <td>112.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Добавить блоки рекомендаций товаров на сайт ин...</td>\n",
       "      <td>56.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Добавить два новых канала привлечения трафика,...</td>\n",
       "      <td>40.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Показать на главной странице баннеры с актуаль...</td>\n",
       "      <td>40.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Запустить акцию, дающую скидку на товар в день...</td>\n",
       "      <td>16.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Изменить структура категорий, что увеличит кон...</td>\n",
       "      <td>9.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Запустить собственную службу доставки, что сок...</td>\n",
       "      <td>4.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Добавить страницу отзывов клиентов о магазине,...</td>\n",
       "      <td>4.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Изменить цвет фона главной страницы, чтобы уве...</td>\n",
       "      <td>3.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                          hypothesis   RICE\n",
       "7  Добавить форму подписки на все основные страни... 112.00\n",
       "2  Добавить блоки рекомендаций товаров на сайт ин...  56.00\n",
       "0  Добавить два новых канала привлечения трафика,...  40.00\n",
       "6  Показать на главной странице баннеры с актуаль...  40.00\n",
       "8  Запустить акцию, дающую скидку на товар в день...  16.20\n",
       "3  Изменить структура категорий, что увеличит кон...   9.00\n",
       "1  Запустить собственную службу доставки, что сок...   4.00\n",
       "5  Добавить страницу отзывов клиентов о магазине,...   4.00\n",
       "4  Изменить цвет фона главной страницы, чтобы уве...   3.00"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hypotheses[['hypothesis', 'RICE']].sort_values(by='RICE', ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Рразница в методах ICE и RICE заключается в балансе критериев **impact** и **reach/impact**. Поскольку параметры **confidence** и **efforts** в двух методах одинаковы, в ICE решающую роль играет сила воздействия, в RICE - сила воздействия скорректированная на охват.  \n",
    "#### **Иными словами ICE это стратегия, RICE это тактика**\n",
    "_______________________________  \n",
    "Например:  \n",
    "* **\"Запустить акцию, дающую скидку на товар в день рождения\"** reach = 1, т.е. улучшение каснется минимального количества пользователей в течении какого-то периода с точки зрения RICE. В тоже время это улучшение повлияет на удовлетворенность клиентов в целом - у каждого ДР случается раз в 365 дней.   \n",
    "* **\"Добавить форму подписки на все основные страницы, чтобы собрать базу клиентов для email-рассылок\"** reach = 10, говорит о том что в процессе эксперимента или за период, например за 3 месяца, все увядят это изменение. В рамках RICE этот параметр выдвигает гипотезу на превое место, но в долгосрочной перспективе в рамках ICE, будет большой просев в 30% и нивелируя фактор охвата мы отодвигаем приоритет этой гипотезы на третье место.  \n",
    "* **\"Добавить два новых канала привлечения трафика, что позволит привлекать на 30% больше пользователей\"** - reach = 3. Балл говорит сам за себя бизнес планирует охватить 30% аудитории, и это нововведение повлияет на эти 30% в полной мере, но с точки зрения ICE мы оцениваем эффект от процесса без корректировки на количество вовлеченных новых пользователей за некий период.    \n",
    "* **\"Показать на главной странице баннеры с актуальными акциями и распродажами, чтобы увеличить конверсию\"** Сила воздействия на пользователя оценена в 3 балла, причем охват - 5 говорят о так себе фиче, но поскольку мы на 8 баллов уверены в ее результативности оставляют эту гипотезу на 4 месте в приоритетах.  \n",
    "* **\"Добавить блоки рекомендаций товаров на сайт интернет магазина, чтобы повысить конверсию и средний чек заказа\"** и наконец в этой гипотезе параметр reach = 8 говорит не о внушительном охвате пользователей и в обозримом периоде RICE ставит приоритет гипотезы на второе место, но влияние - impact = 3 , оценивая гипотезу с точки зрения процесса, ICE отодвигает ее на пятое. В глобальном плане есть вопросы и посерьезней.  \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Анализ A/B-теста  \n",
    "[к оглавлению](#step_0)\n",
    "<a class=\"anchor\" id=\"step_3\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.01. График кумулятивной выручки по группам.  \n",
    "[к оглавлению](#step_0)\n",
    "<a class=\"anchor\" id=\"step_3.01\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Сперва взглянем на простой график выручки по дням"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "both_groups = orders.groupby(['visitorid', 'group']).agg({'transactionid':'count'}).unstack().reset_index()\n",
    "both_groups.columns = ['visitorid', 'A', 'B']\n",
    "both_groups['both'] = both_groups['A'] == both_groups['B']\n",
    "double_group_visitors = both_groups.query('both == True')['visitorid']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "orders = orders.query('visitorid not in @double_group_visitors').copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lineplot(data=orders.groupby(['date', 'group']).agg({'revenue':'sum'}).reset_index(), \n",
    "             x='date', y='revenue', \n",
    "             hue='group', palette='bright', ci=None, estimator='sum')\n",
    "plt.title('Revenue by groups')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "На протяжении всего эксперимента в 1 месяц, кроме сильного скачка выручки в группе B 19 августа, на первый взляд две группы не особо отличаются. Определенных выводов сделать невозможно. Наглядней будет картинка, если мы выведем метрики накопительно"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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>date</th>\n",
       "      <th>group</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2019-08-15</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2019-08-15</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>2019-08-16</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>2019-08-16</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>2019-08-01</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         date group\n",
       "0  2019-08-15     B\n",
       "7  2019-08-15     A\n",
       "45 2019-08-16     A\n",
       "47 2019-08-16     B\n",
       "55 2019-08-01     A"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# создаем массив уникальных пар значений дат и групп теста\n",
    "dates_groups = orders[['date','group']].drop_duplicates()\n",
    "dates_groups.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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>date</th>\n",
       "      <th>group</th>\n",
       "      <th>transactionid</th>\n",
       "      <th>visitorid</th>\n",
       "      <th>revenue</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>533</th>\n",
       "      <td>2019-08-29</td>\n",
       "      <td>B</td>\n",
       "      <td>565</td>\n",
       "      <td>513</td>\n",
       "      <td>5266434</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>757</th>\n",
       "      <td>2019-08-30</td>\n",
       "      <td>A</td>\n",
       "      <td>512</td>\n",
       "      <td>460</td>\n",
       "      <td>3643218</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>690</th>\n",
       "      <td>2019-08-30</td>\n",
       "      <td>B</td>\n",
       "      <td>587</td>\n",
       "      <td>534</td>\n",
       "      <td>5477657</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>958</th>\n",
       "      <td>2019-08-31</td>\n",
       "      <td>A</td>\n",
       "      <td>522</td>\n",
       "      <td>468</td>\n",
       "      <td>3702201</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>930</th>\n",
       "      <td>2019-08-31</td>\n",
       "      <td>B</td>\n",
       "      <td>605</td>\n",
       "      <td>551</td>\n",
       "      <td>5592646</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          date group  transactionid  visitorid  revenue\n",
       "533 2019-08-29     B            565        513  5266434\n",
       "757 2019-08-30     A            512        460  3643218\n",
       "690 2019-08-30     B            587        534  5477657\n",
       "958 2019-08-31     A            522        468  3702201\n",
       "930 2019-08-31     B            605        551  5592646"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"Код работает так: \n",
    "для каждой строки dates_groups проверяем равна ли группа и не больше ли дата из orders\n",
    "и если условие выполняется, выводим: \n",
    "число уникальных пользователей, \n",
    "число уникальных заказов,\n",
    "накопленную выручку\"\"\"\n",
    "orders_aggregated = dates_groups.apply(\n",
    "    lambda x: orders[np.logical_and(orders['date'] <= x['date'], orders['group'] == x['group'])].agg({\n",
    "        'date':'max', \n",
    "        'group':'max', \n",
    "        'transactionid':pd.Series.nunique, \n",
    "        'visitorid':pd.Series.nunique, \n",
    "        'revenue':'sum'}), axis=1).sort_values(by=['date','group']) \n",
    "orders_aggregated.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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>date</th>\n",
       "      <th>group</th>\n",
       "      <th>visitors</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>533</th>\n",
       "      <td>2019-08-29</td>\n",
       "      <td>B</td>\n",
       "      <td>17708</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>757</th>\n",
       "      <td>2019-08-30</td>\n",
       "      <td>A</td>\n",
       "      <td>18037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>690</th>\n",
       "      <td>2019-08-30</td>\n",
       "      <td>B</td>\n",
       "      <td>18198</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>958</th>\n",
       "      <td>2019-08-31</td>\n",
       "      <td>A</td>\n",
       "      <td>18736</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>930</th>\n",
       "      <td>2019-08-31</td>\n",
       "      <td>B</td>\n",
       "      <td>18916</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          date group  visitors\n",
       "533 2019-08-29     B     17708\n",
       "757 2019-08-30     A     18037\n",
       "690 2019-08-30     B     18198\n",
       "958 2019-08-31     A     18736\n",
       "930 2019-08-31     B     18916"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "visitors_aggregated = dates_groups.apply(\n",
    "    lambda x: visitors[np.logical_and(visitors['date'] <= x['date'], visitors['group'] == x['group'])].agg({\n",
    "        'date':'max', \n",
    "        'group':'max', \n",
    "        'visitors':'sum'}), axis=1).sort_values(by=['date','group'])\n",
    "visitors_aggregated.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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>date</th>\n",
       "      <th>group</th>\n",
       "      <th>orders</th>\n",
       "      <th>buyers</th>\n",
       "      <th>revenue</th>\n",
       "      <th>visitors</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>2019-08-29</td>\n",
       "      <td>B</td>\n",
       "      <td>565</td>\n",
       "      <td>513</td>\n",
       "      <td>5266434</td>\n",
       "      <td>17708</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>2019-08-30</td>\n",
       "      <td>A</td>\n",
       "      <td>512</td>\n",
       "      <td>460</td>\n",
       "      <td>3643218</td>\n",
       "      <td>18037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>2019-08-30</td>\n",
       "      <td>B</td>\n",
       "      <td>587</td>\n",
       "      <td>534</td>\n",
       "      <td>5477657</td>\n",
       "      <td>18198</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>2019-08-31</td>\n",
       "      <td>A</td>\n",
       "      <td>522</td>\n",
       "      <td>468</td>\n",
       "      <td>3702201</td>\n",
       "      <td>18736</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>2019-08-31</td>\n",
       "      <td>B</td>\n",
       "      <td>605</td>\n",
       "      <td>551</td>\n",
       "      <td>5592646</td>\n",
       "      <td>18916</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         date group  orders  buyers  revenue  visitors\n",
       "57 2019-08-29     B     565     513  5266434     17708\n",
       "58 2019-08-30     A     512     460  3643218     18037\n",
       "59 2019-08-30     B     587     534  5477657     18198\n",
       "60 2019-08-31     A     522     468  3702201     18736\n",
       "61 2019-08-31     B     605     551  5592646     18916"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cumulative_data = pd.merge(orders_aggregated, visitors_aggregated, left_on=['date', 'group'], right_on=['date', 'group'])\n",
    "cumulative_data.columns = ['date', 'group', 'orders', 'buyers', 'revenue', 'visitors']\n",
    "cumulative_data.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Для просмотра результата приемлемей выводить конец таблицы.  \n",
    "Самое время построить графики кумулятивной выручки"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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6JCQk1O4CqiAIQrX+myy99l27dpU7MqAmUlNTy92elpZmds5S3bp1Q48ePbBhwwakp6fjwIEDSEpKwqOPPlpmZEtFSr8QKz2HpTUBFY/WcXd3R1ZWFgoLC8sN96XTeG69nuq+/6UyMjIgSVKZcF/RezZ58mRMnjwZubm5OHLkCDZv3oxly5Zh8uTJ2Lt3L1q3bl3huYiIyDoa1Rz7OXPmYN++ffj666+xf/9+TJ8+HdOmTTPNA925cyckSUJqair69OmD9u3bY8aMGbhy5YptCyciKjFjxgxoNBqsX78eZ86cqXTfW3utS+dJJyYmltnPYDCYDZ2tK5XVcOLECYuPU3pLtnHjxpV5rvS2drcrDSCyLFt8HsA4QmLYsGE4ffo0oqOjsXLlSgDmC80BgKurKyIjIxEbG1vnC4L17NkToiiaFlK0lIODAyZOnIjMzExs2bIFv/zyCwoKCsymFJTq1asXbty4UafrJADG/ybK++8BKP+/idIFEat77ZVJTEw0u61cqdL/ljp37lzmudL59ytWrDAtmvfQQw9ZfM7SY5Z3HQkJCTUakdOlSxcAwN69e8s8d+bMGaSlpaF169ZmvfXVff9LGQwG07SIW1X2ngHGwD98+HC89957ePLJJ1FUVIRt27ZVfFFERGQ1jSbYX758GWvWrMG3336LAQMGICwsDI8//jjuuusu0yI4V65cgSzLeP/99/HWW29hxYoVMBgMGDNmDAoKCmx7AUREMM6v/b//+z/o9XpMmTIFR44cKXe/gwcPmi265ubmhnbt2uHQoUM4ffq0abuiKHj77bcr/HBvTaWhbMWKFdDr9abtGRkZeOWVVyw+TuliY7ffXzs6Ohr/+9//yn1N6SJdNemBLh2m/sMPP5S70FypOXPmQK/XY/bs2cjKyirz/I0bN3D06NFqn/92Pj4+mDp1Kv7++28sXLiw3CkP165dw4ULFyq8lpUrV5Y7paBU6S3enn766XJDZlFRkVXCda9evZCYmIitW7eabV+2bFm5wfH++++Hp6cn3nvvPRw+fLjM84qi4MCBA+Xes70ikiRh/vz5Zl/6XL582XSnh8mTJ5d5zYQJE+Dt7Y3Fixdj586d6Nu3b7VG902ePBlqtRpLliwxG3KvKAreeOONGk1ZeOCBBwAACxYsMJsiodfr8X//938AUGaUT3Xf/1u98cYbZl8eZmRk4MMPPwRgPrXjzz//NPv7Xqp0BIGjo2OV10ZERLVnV0PxKxMdHQ1FUdC3b1+z7cXFxbjjjjsAGHty9Ho93nnnHQwbNgwA8NVXX6Ft27bYvHkzJkyYUO91ExHd7umnn4bBYMB///tf3HXXXejZsye6d+8ONzc3ZGRk4PDhwzhz5kyZFaf/85//YObMmRg1ahTGjx8PZ2dnHDp0CNeuXcPAgQPL7emzpu7du2Pw4MH466+/MGTIEAwZMgRZWVnYunUrBg8ejFOnTll0nGnTpuGTTz7BvHnzsHfvXoSHh+PixYvYsmULxo4di3Xr1pV5zdChQ/HLL7/g3//+N+655x64uLjAw8MDjz/+eJXnGzVqFLy8vPDtt9+avlApb22BGTNmIDo6Gl999RW6du2K4cOHIyQkBDk5Obh69Sr279+PoUOHmnr9a+Pdd9/FpUuX8M4772DVqlXo378//P39kZKSgri4OBw5cgRvvfUW2rRpY/a67t27o3379vjzzz8hSVKZKQWl7rjjDrzxxht47bXX0KNHD9M93IuKipCQkID9+/cjJCSk1v/NPPXUU9i2bRvuv/9+jB8/Hr6+vqa1A0aMGIEtW7aY7e/l5YXvv/8e999/P6KionDHHXegXbt20Gg0uHbtGo4ePYrExERcuXKl3FXYy9OhQwccO3YMQ4YMwbBhw5CVlYVffvkFubm5eOutt8q9PaKDgwMeeOABfPzxxwCAhx9+uFrX3bJlS8ybNw8LFizAoEGDMGHCBHh4eGDXrl3IzMxEx44dcerUqTIjKSozceJEbN68GatXr0bfvn0xevRo033s4+LiMHjw4DK36Kvu+18qICAAxcXF6N+/P0aNGoXi4mKsX78eKSkpmDlzptnCeo888gi0Wi369euHkJAQCIJguiViWFgYxo8fX633joiIaqbRBHtZliEIAnbs2GF2Wybg5rfFpfdXbdu2rek5Dw8PBAQE1EtvFhGRpZ577jmMHz8eS5Yswe7du/HTTz+hoKAAnp6eiIyMxDvvvFOmJ3bq1KlQFAWffPIJfvrpJ7i6umLYsGFYvnw53nrrrXqpe/ny5Xj99dexceNGfP311wgJCcG//vUvPPnkk1i7dq1Fx2jevDk2bdqE+fPn4+DBg9ixYwciIiLwwQcfYPDgweUG+/vvvx/Xrl3Dzz//jEWLFkGv1yM4ONiiYO/g4IBJkybh66+/BlB2oblbvfvuu4iKisLSpUuxd+9eZGVlwcPDAy1atMAjjzxSbu9vTbi5uWHjxo1Yvnw5Vq9ejY0bN6KoqAi+vr4ICQnBq6++WmFguu+++0wjJG6fUnCrf/3rX+jbty+++OILHDhwAJs3b4arqyuaN2+OyZMnW+XL7oEDB2LVqlV4++23sX79emi1WvTv3x9//vknfvvtt3KD5R133IF9+/bhs88+w/bt23H48GGo1Wr4+/ujV69eZW73VpXSdQdee+01LF++HHl5eWjXrh2eeuopTJw4scLX3X///fj444/RrFkz3HPPPdW+9meeeQYtWrTAokWLsHLlSri6umL48OF4/fXXTe9tda4DAL788kv0798fy5cvx/LlyyHLMsLDw7FgwQI88cQTZT7/1OT9BwCNRoNffvkFb7zxBtasWYPMzEy0bNkSzz77LB577DGzfefPn48dO3bg77//xvbt26FWqxEUFIS5c+di5syZZW6nSEREdUPIzs5WbF1ETQQGBuLdd981fQCLi4tDz549sX79elMP/e1K9/ntt98wePBgAMYVfyMiIvD555+XO/SSiIiImp7Vq1fjsccew1NPPYUFCxZY7bi5ublo06YNPD09ce7cOasd11pKb3FZ1+svEBGRddnVHPu8vDzExMQgJiYGsiwjMTERMTExSEhIQOvWrTFlyhTMnj0bv/32G65cuYITJ07g008/xfr16wEArVu3xt13340XX3wRBw8exLlz5zBnzhz4+PhYfD9cIiIiatwkScInn3wClUpV7WH4pTIyMsrMPTcYDHj55ZdRVFRU7uKQRERENWVXPfZ79uzB2LFjy2yfPn06Fi9eDL1ej/fffx8//fQTrl+/Di8vL3Tv3h0vvvii6bY5N27cwLx587BhwwbTnPy3334bLVu2rOerISIiooZk37592LNnD/bv34/du3fjoYceqnDBxqp8//33WLBgAYYMGYLAwEBkZWVh//79iIuLQ1hYGHbs2AFvb28rX0HtsceeiMg+2VWwJyIiIqorCxcuxDvvvAMvLy+MHTsWb7/9NpydnWt0rL///hsffvghjh07hrS0NMiyjKCgIIwcORLPPPNMmcUvGwoGeyIi+8RgT0RERERERGTH7GqOPRERERERERGZY7AnIiIiIiIismMM9kRERERERER2jMHeymJjY21dAlkB27FxYDs2DmzHxoHt2DiwHRsHtmPjwHa0Pnt+TxnsiYiIiIiIiOwYgz0RERERERGRHWOwJyIiIiIiIrJjDPZEREREREREdkxt6wKsJT8/HwaDwdZlwNHRETk5OXV2fBcXF6jVjabZiIiIiIiIqJYaRUIsLi4GAHh4eNi4EsDBwQGOjo51cmxFUZCdnQ03NzeGeyIiIiIiIgLQSIbiFxUVwdnZ2dZl1DlBEODp6Yn8/Hxbl0JEREREREQNRKMI9oAx9DYFTeU6iYiIiIioCVMUW1dgVziem4iIiIiIiGxDUSDkJkCVGgNVSrTpd/7E1ZD9Otm6OrvBYE9ERERERER1T5EhZl2CKjUaqpQYiKnRUKVEQyzKKrOrKiWawb4aGOyJiIiIiIjIumQDxIzzJb3wpT3xf0PQ51n0clVqDPR1XGJjwmBPRERERERENWcogirtDMTUmJLe+Gio0k5DkIprfEhV2mkrFtj4MdhXQafTQavV2roMIiIiIiIi29PdgCr11M0AnxoDMf0cBEWq8SEVjSskv06Q/DtD8usCyb8LZO82Viy68WtywT4/Px/PPPMMNm7cCGdnZ8yaNQuHDh2Ct7c3Fi9ejE6dOuG+++5DYmIiNmzYgKFDh2LZsmVYv3493n77bcTFxcHX1xcPPfQQnn32WdMq9Z06dcLjjz+Oxx57zHSu0aNHIzIyEu+9955pn/vuuw+XL1/G77//DhcXF/zrX//Cv/71L5u8F0RERERERJURchOgid0AVdIx43z4rIsQUPMV62VHL8h+nSH5GwO85NcFslcrQGg0N2yziSYX7F9++WXs27cPK1asQEBAAN577z0cOHAAo0ePNu3z+eef47nnnsOuXbugKApOnjyJf/7zn3juuecwZcoUHD9+HP/5z3/g5uaGmTNnVuv8n3/+Of7973/jhRdewJ49e/DCCy8gNDQU48aNs/alEhERERERVV9RNjQXfoP2zCqoE/fX+DCyS0BJeO9kCvGKezDAW3hbXZMK9nl5eVixYgW++OILDB06FADw6aefIjIy0my//v3749///rfp8WOPPYYBAwZg3rx5AIDWrVvj4sWL+Pjjj6sd7Hv06IHnnnvOdJzjx4/j888/Z7AnIiIiIiLbMRRBfWkLtGd+hvrynxAkXbVeLruHmPXCS/6dobj411GxdLsmFewvX74MvV6PHj16mLa5uLiUCfbdunUze3z+/HlERUWZbevXrx/eeecd5Obmwt3d3eIaevXqVebxhg0bLH49ERERERGRVSgyVAl7oT27GpoLv0Eozq36JRAge0dAMg2n7wzZtzMUJ696KJgq0qSCvaJYNhfExcWlzOuECoaLlG4XRbHM8Q0GQw2qJCIiIiIiqiOKAjHtlDHMn10DMe96lS8xBPSAvu14SC16QfLtCGhd66FQqo4mFexbtWoFjUaD48ePIywsDABQUFCAM2fOmB6Xp127djh48KDZtgMHDiAwMBBubm4AAB8fHyQnJ5ueLyoqwoULF9C5c2ez1x09erTM47Zt29biqoiIiIiIiCon5CZAe3YNNGd+hirjbJX7S56toG8/GfrIKZC9wuuhQqqNJhXsXV1dcf/99+O1115Ds2bN4O/vj/fff7/SHnkAmDNnDoYNG4aFCxdi8uTJOH78OBYtWoRXXnnFtM8dd9yBFStWYPjw4WjRogU++OCDcnvsjx49ig8//BD33HMP9u7di59++glff/11nVwvERERERE1XUJhFtQXfoP27CqoEw9Uub/s5AN9uwnQR06FFNCdi9zZkSYV7AHgjTfeQH5+PqZPnw4XFxfMnj0bqampcHR0rPA1Xbt2xXfffYe3334bH374Ifz8/PD000/j8ccfN+3zn//8B1evXsU///lPuLq64tlnn0VSUlKZY82ePRunT5/GBx98AGdnZ8ybNw/33HNPnVwrERERERE1MYYiqC9ugfbsz1Bf2gpB1le6u6J2hj5iDPTtJ8MQOgRQaeqnTrKqJhfsXV1d8dVXX5keFxcXY/HixbjrrrsAAH///Xe5rxs3blylK9e7u7tj6dKlKCoqMn1J8Oijj5Z7/qVLl9bmEoiIiIiIiG6SJagS90J7ZjU0F9ZD0FW+CJ4iqGAIGwZ95BTow+8GtC6V7k8NX5ML9tHR0bhw4QJ69OiBGzdu4OOPP0ZeXh4mTJhg69KIiIiIiIgsoygQU2OMYf7cWssWwWve0zhvvu0EKC6+9VAk1ZcmF+wBYNGiRYiLi4NKpUKnTp3wxx9/IDAw0NZlERERERERVUzSQUw/C82V7ehwcgWcblyq+iVe4cYw334KZK9W9VAk2UKTC/ZdunTBrl27bHLuiob5ExERERERmSkJ8aqUk8af5JNQpZ+GIOmqfKns7GtcBK/9VEgB3bgIXhPQ5II9ERERERFRgyLpIKafgSolutohvpSicYE+YjT07acYF8ETGfWaErY2ERERERFRfSkT4k9AlX6mWiG+lHERvOEli+CN4iJ4TRiDPRERERERUV0oDfHJJ28Oqa9hiC8lu7aA5N8F1507wmvg41wEjwAw2BMREREREdWepIOYduZmgLdiiJf8u0IK6AbJvysUFz8AQFpsLDwZ6qkEgz0REREREZGlFAXCjUSoMs5DzDhv/J0aY50QH9DVGOJLfkpDPFFVGOyJiIiIiIhuJ0sQc65AzDgHVcYFiBnnIGZcgCrzAgR9fu0O7RZ4syeeIZ6sgMGeiIiIiIiaLkMxxKyLEDMvQFUa3jPOQcyKq1UPfCnzEN8Nkn8XhniyOgZ7G4uOjsbQoUPRq1cvbNmyxdblEBERERE1Trp8iJmxUGXeMoQ+4zzE7MsQFMkqpzCG+NuH03MePNU9Bnsb+/777/HII49g1apVOH/+PNq2bWvrkoiIiIiI7FdRNlSZxqHzqvTzN3vicxOsdgrFwR1Ss3aQvdtAatYWsk97SH5dGOLJZmwW7BcuXIh33nnHbJufnx8uXLhgleN7j/OwynEslbk+p9qvKSwsxOrVq/HHH3+gsLAQy5cvx5tvvlkH1RERERERNUL6QqhSY6BKOgpV8nGoko5BlXPFaoeXnX0hN2sLybut8Xcz42/FxR8QBKudh6i2bNpjHxERgY0bN5oeq1QqG1ZT/3777TcEBwejY8eOmDp1Kh566CG89tpr0Gg0ti6NiIiIiKhhkSXjUPqSEK9OOgox/QwE2VD7Q7sFQWrWpiS8G3vi5WZtoTh5W6Fworpn02CvVqvh7+9vyxJs6vvvv8e0adMAAAMHDoSTkxP++OMP3HPPPTaujIiIiIjItoQb16FKPgZVkjHEq1JOQtDdqPHxFEGE7BEGuVmbW8J7O0jNIgCtmxUrJ6p/Ng32V65cQfv27aHRaNCzZ0+8+uqrCAsLs2VJ9ebSpUs4dOgQli5dCgAQBAFTpkzB8uXLGeyJiIiIqGkpzoUq5STUScdMYV7Mu16jQymCCrJ3hNnQealZW8herQG1o5ULJ2oYhOzsbMUWJ/7zzz+Rl5eHiIgIpKen47333kNsbCwOHjwIb++Kh7zExsaW2ebo6AhfX/taqOLNN9/EZ599Zjb9QFGMTXHkyBEEBgZW+Nq0tDQUFRXVeY1ERERERNYmyAY45l6Ea9YpuGSfhkvWKTjeuAIBNYslRc6ByPfqYPop8GgLRcUAT/UjIiLC1iUAsGGwv11eXh66du2Kp59+Gk8++WS1XpuTkwMPj/pdLK8iRUVFcHSs/B8Sg8GADh064IknnsCIESPMnps5cybGjBmDuXPnVvj6hnS9jVVsbGyD+UtKNcd2bBzYjo0D27FxYDs2DvXajooCISce6uRjxoXtko5BlRoNwVCzTirZyRtSQA9IAd0hNe8BKaAHFOdmVi7aPvDvo/XZ83vaYG535+rqinbt2uHSpUu2LqXObdmyBRkZGfjHP/5RZnTCxIkTsXTpUjz//PMQRdFGFRIRERER1ZC+AOqrf0ETtwnqS1sg5qfU6DCKygGSf5eSEN8TUvMekD3CuBo9UTkaTLAvKipCbGwsBg0aZOtS6tzy5csxaNCgcqccjB8/HvPnz8euXbswbNgwG1RHRERERFQ9Qn4q1Jc2G8N8/C4IhsJqvV6BYLwnfPMekJr3gCGgB2TfSEClraOKiRoXmwX7l19+GSNHjkRQUJBpjn1BQQGmT59uq5LqzU8//VThc2FhYcjOzq6/YoiIiIiIqktRIGach+biJqgvboLq+pFqzZGXXQJuhvjmPSD5dwUcONWUqKZsFuyvX7+ORx99FBkZGfDx8UHPnj3x559/IiQkxFYlERERERFRRWQDVIkHbob57MsWvUzRuEAK6AZD856mufGKW8ULRRNR9dks2H/zzTe2OjUREREREVmiOBeay9uhvrgJ6stbIRZlW/Qy2S0I+vBRMLS+G4bgARxST1THGswceyIiIiIisj0h5yo0Fzcbw3zCXgiy3qLXGfy7whA+CvrWoyD7duIid0T1iMGeiIiIiKgpUxSIKdHQXPwDmouboEr927KXqbQwhAw2hvnwERxeT2RDDPZERERERE2NoQjqq3ugvrgJmoubIeZdt+hlspM3DC2joG89CoawYYDWrY4LJSJLMNgTERERETV2sgFi9mWorh9GePQauP9xGII+36KXSl7hMITfDX3rUZBa9AZERgiihoZ/K4mIiIiIGgtFgZB3Haq0MxDTz0KVfhqq9LMQM85DkIoBAM5VHUIQIbXoU7L43SjI3hF1XzcR1QqDPRERERGRPSrKhiqtJLinn4Eq/SxU6WcgFOdU+1CKxgWGsGHGMN8qCoqzTx0UTER1hcGeiIiIiKgh0xdCzDwPVdoZqNJLe+LPQMxLqtVhZZcA41z58FEwhNwBqB2tVDAR1TcGeyIiIiKihqBkHrxYEuBLQ7yYfQmCItf+8M6+kH3aI9WxNdx7PwDJvwsgiFYonIhsjcHehmbNmoUff/zR9Njb2xu9evXCG2+8gTZt2tiwMiIiIiKqM5IeYu5ViJlxEDPOlzsPvjYUjQskn/aQfSIh+bSH5BsJuVkkFBdfAMD12Fi4BHDePFFjwmBvY0OGDMGXX34JAEhKSsKrr76K+++/H4cPH7ZxZURERERUY4oCIS8JYlYcVFkXjSE+Kw5i1kWIOVcgyIban0JUQ/ZuA8knEnJJgJeatYfiEcKeeKImptEGe4/3Pev1fDnPZdfodQ4ODvD39wcA+Pv7Y/bs2Zg2bRoKCwvh5ORkxQqJiIiIyOqKsqHKijML7qqsOIhZlyy+nZwlZI9QSD6RxhBfEuBl79aASmu1cxCR/Wq0wd4e3bhxA+vWrUNkZCRDPREREVFDoS+EmH3ptt73i8YgX5hh1VMZ58HfMoTeJxJSs3aA1tWq5yGixoXB3sa2bduGwMBAAEB+fj6CgoLw888/27gqIiIioiZG0kPMTbgZ2Et+qzLjIN5ItPrpZBd/yF7hkL1aG4fQ+7Q3mwdPRFQdDPY21r9/f3z88ccAgKysLCxZsgQTJkzAtm3bEBQUZOPqiIiIiBqR4hzjqvPZ8cbfOVegKvkt5CZCUCSrnk7RukPybg3Zq3VJiA+H5NUaslcrwMHdqucioqat0Qb7ms55r2/Ozs5o1aqV6XHXrl0REhKC7777Di+//LINKyMiIiKyM7IEIe+6KbSL2SU/OVeM24qyrH5KRaWF7BkO2bs0tIeXBPnWUJx9AEGw+jmJiG7XaIO9vRIEAaIoorCw0NalEBERETU8unxjUC8N67f0vou5VyFIOqufUoEAxSPEPLh7t4bkFQ7FLQgQVVY/JxFRdTDY21hxcTFSUlIAANnZ2fj666+Rl5eHkSNH2rgyIiIiIhuRJYhpfxvv7Z59BWLOLQG+ILXuTuviD9mrlXHe+60h3jMMUDvW2XmJiGqLwd7Gdu3ahbZt2wIA3NzcEBERge+++w6DBg2ycWVERERE9USWIKadgjphL9QJe6BO3A+hONfqp1FUWsgeoZA9wiB7hpX8bmn87REKaF2sfk4iovrAYG9DixcvxuLFi21dBhEREVH9UuSbQf5qaZDPscqhZSdvyB4tjcHds6UxyJeEd8WtBSCIVjkPEVFDwmBPRERERHVLkSGmnzGG+IS9UCXug1iUXbNDCSrI7sG3BPdbe9/DAAcPq5ZORGQPGOyJiIiIyLoUGWL6WdPQelXCvmqvSC87ekEK7AvZuw1kzzBIpb3u7kGAyI+wRES34r+KRERERFQ7igwx/VxJkC/pkS/MqN4hHDxgCB4AQ/AgGIIHQvbtwGHzREQWYrAnIiIioupRFIgZ540L3SXshSphb82CfFB/GEJKgrxPB942joiohhjsiYiIiKhyigIx8wJ8L6+D09nzUCfshViYXr1DOLgbg7ypR74jgzwRkZU0mmCvKAoEQbB1GXVOURRbl0BERERNgFCYCXX8LqivbIf6yk6IedfhVo3XK1p3GIL6wRA8EIaQQZB9OzHIExHVkUYR7B0dHVFQUAAXl8Z971FFUZCdnQ03t+r8b5WIiIjIApIeqqQjUF/ZAfWVHVAln4AAyzsUFK1bSY/8QBiCB0H2Y5AnIqovjSLYOzg4wGAwICfHOvc/rY3c3Fy4u7vX2fHd3NygVjeKZiMiIiIbE7MvlwT57VBf3QNBd8Pi1ypaNxgCjT3yUvBASP6duVo9EZGNNJp/fRtKb31qaiqCg4NtXQYRERFRWcW5xgXvLu+AOn4HVNmXLX6ponZGrncXaNuNLAnyXRjkiYgaCP5rTERERNRYyRJUKdFQxxt75VXXj0CQDRa/XPLrBH3YcBjChkJq0Rexl68iIiKiDgsmIqKaYLAnIiIiakSEG9dN8+TVV3dBLMy0+LWysy8MYUNhCBsOQ+gQKC7+dVgpERFZC4M9ERERkT3TF0KduN+0er0q46zFL1VUWkiBfU298rJvR0AQ67BYIiKqCwz2RERERPZEkSGmn4H6yk5jr3zifghSscUvl7zbwBA2zPgTNADQNox1ioiIqOYY7ImIiIgaMkWGmHYK6oR9UCfshSpxP8SiLMtf7uABQ+gQU6+84s5FfomIGhsGeyIiIqKGRJZKgvxeqBP3lQT5bItfrggipOa9THPlpYDuvJ88EVEjx2BPREREZEuyBDHtb2OQT9gLdeIBCMU51TuEe3BJj/wwGELuABw966ZWIiJqkBjsiYiIiOqTLEFM/dt4P/mEvVBfOwChOLdah1AcPGAI6gdDqLFXXvYKBwShjgomIqKGjsGeiIiIqC7JBqhS/4bq1h55XfWCvOzoCSmoPwzBA2EIHgjZpwOH1xMRkQmDPREREZE1yQaoUqKhKlnsTn3tAATdjeodwtHLPMj7duBt6IiIqEINJth/8MEHeOONN/DYY4/hvffes3U5RERERJZRFKhSTkIV/xfUifugvnaw+kHeyRtS0AAYggeU9MhHMsgTEZHFGkSwP3LkCJYtW4YOHTrYuhQiIiIiyykKnP54HNqzq6v1MtmpWUmQHwhD8ADIPu0Z5ImIqMZsHuxzcnLw2GOP4dNPP8W7775r63KIiIiILKZK2G1RqJedmkEqGVZvCB4IuVlbBnkiIrIamwf7p59+Gvfccw8GDx7MYE9ERER2xeHE0nK3y04+MAQPNIV5Y5DnqvVERFQ3hOzsbMVWJ1+2bBm++eYb/Pnnn9BqtRg9ejQiIyMrnWMfGxtbjxUSERERlU9TmIrOf46DoEimbdfazUJWi6Eocg1jkCciagIiIiJsXQIAG/bYx8bGYsGCBdi0aRO0Wq3Fr2sob1xFYmNjG3yNVDW2Y+PAdmwc2I6NQ2NsR4d9a8xCvdSsPVxH/xeujTjQN8Z2bIrYjo0D29H67Pk9tVmwP3z4MDIyMtCvXz/TNkmSsH//fnzzzTe4fv06HBwcbFUeERERUcUkPbQxy8w26bo+wl56IiKyCZsF+9GjR6Nbt25m2+bMmYPw8HA888wz1erFJyIiIqpP6rg/IOYnmx4rGlfoIqfYsCIiImrKbBbsPT094enpabbN2dkZXl5eiIyMtE1RRERERBZwOLnE7LEucirg4G6jaoiIqKnjfVaIiIiIqkHMOA91wh6zbbquD9uoGiIiogZwu7tb/f7777YugYiIiKhS2pPmt7gzBPWD7NvBRtUQERGxx56IiIjIcro8aE//ZL6p66M2KoaIiMiIwZ6IiIjIQpqzayDock2PZWdf6CPG2rAiIiIiBnsiIiIiyygKHE5+bbZJ1/kfgIp38iEiIttisCciIiKygOr6YajSTpseK4JoDPZEREQ2xmBPREREZIEyi+aFj4TiHmyjaoiIGi+9wfhDlmtQq+ITERERNURCfho0F34128ZF84iIqkdvAFKyBKRkiUjOFJCSKSIp0/xxcqaA9FwB384twLj+TPeWYrAnIiIiqoL21AoIks70WPJsBUPoENsVRETUgOj0QGq2gOSSYF4a1G9/nJErQFEEi46ZksnB5dXBYE9ERERUGVmCNvobs026rg8DAj90ElHjpihA5g0BV5JFXE+vLLBb/9/D5EzLvgAgIwZ7IiIiokqoL/8JMTfB9FhRO0LfYYYNKyIisp4iHZCQKuJKioj4ZBFXkkXEpxh/riSLyCu0TcC+UcBgXx0M9kRERESV0J40763Xt5sIxcnLRtUQEVWPohjntceXBvdbAvyVFGOPu6XD42tLEBT4uCsI8Fbg7y0bf3vJaH7bYz9PBVpNvZTUaDDYExEREVVAyL4C9eU/zbbpujxio2qIiMqXX4SbwT1ZRHzqzT9fTRFRqKvb4C4ICnw9KgjsXjICmt0M7Bom0DrBt5WIiIioAg7R30KAYnps8O8GqXl3G1ZERE2ZQQL2nVJh3yk1/r7QEpmFLohPFpGaXXdrfjg7KAgNkBHke7Nn3RTYSx77eSpQq+qsBLIAgz0RERFReQxF0JxabrZJ15W99URUvyQJ2H9ahV/3arDhgAbpOaUh3tEqxxcEBS2aKQgLkBHqLyMswPgT4i8jzF+Gr6cCgdPdGzwGeyIiIqJyaM7/CrEw0/RYdvSEvt0EG1ZERE2FJAGHzhrD/Pr9mlr3yLs5KwjzlxFaEtpv/XOQrwwHzme3ewz2REREROXQnlxq9ljfYQagcbZRNUTU2MkycPjczTCfXI37uKtEBcF+MkL9FVOve2hJgA8LkOHpyl73xo7BnoiIiOg2YspJqJOOmG3TdX3YRtUQUWOlKMCR88Yw/9s+DZIyqg7z7i4K7u6tR0uf6+jd2Qdh/jICfTnHvaljsCciIiK6jcPtt7gLGwbZK9xG1RBRY6IowPHYm2E+Ma3qMO/qpGB0Xz3GD9RjSFcDHDRAbGw6IiJ4600yqlWwlyQJOTk5cHd3h1rN7wiIiIioESjKhubsarNNXDSPiGpDUYDoiyJ+3avFr3s1uJpqWZgf2VuP8QP0GNbdAEdtPRRKdqtGafz48eNYsGABDhw4AIPBgHXr1mHw4MHIyMjArFmzMGfOHAwePNjatRIRERHVOe3pHyEYCk2PZbcgGFqNsGFFRGSPFAU4dVnEL3s0+HWfBleSqx4r7+ygYERvPe4dqMfw7gY4OdRDodQoVDvYHz58GOPGjYO/vz+mTZuG77//3vRcs2bNkJeXh+XLlzPYExERkf1RlDKL5um6/BMQOTKRiKqmKMDZeBG/7NXg170aXLxedZh30iq4q6cB9w7S4a6eBjgzzFMNVPv/Um+88QbCw8Oxfft25OfnmwV7ABg0aBBWrVpltQKJiIiI6ovq6m6osuJMjxVRA12nB2xYERHZg7NXRfxaEuZjE6sO845aBXf2MGD8QD2ieurh6lQPRVKjVu1gf/z4cbz88stwdHREQUFBmecDAwORkpJileKIiIiI6pPDySVmj/VtxkFx8bdRNUTU0BTpgIvXRJxLUOHcVRHnE1Q4c0XEpaSqw7xWfTPMj+ilhxvvnklWVO1gL4oiRLHixR5SUlLg5MSvnIiIiMi+CDeuQR33h9k2LppH1DQVFgOxicbgfj7B+PvcVRGXk0XIsuU3hNeoFQzrZgzzo3rr4e5Sh0VTk1btYN+1a1ds3rwZM2fOLPOcTqfD6tWr0bt3b6sUR0RERFRftDHLICiS6bHkEwkpsJ8NKyKiupZfdDPAl/bAn78q4kqKCEWxPMDfSq1SMKSrAfcO1OPuPnp4uFq5aKJyVDvYP/PMM5g0aRKefPJJTJ48GQCQnJyMbdu24f3338fly5exaNEiqxdKREREVGckPbQxy8w26bo+Agg1+2BPRA3LjQLgQmJJ7/tV4+9zV1UW3XbOEipRwR1djGF+dF8DvNwUqxyXyFLVDvZDhw7Fl19+ieeffx4rV64EAMyaNQuKosDDwwNLlixBr169rF4oERERUV1Rx/0OMf/mGkGKxhW6yCk2rIiIakKnB2IuGXvfz129OYw+Mc06AR4Agv1ktA2W0Db45u92IRLnzJNN1ejeLZMmTcLdd9+NHTt24NKlS5BlGS1btsTw4cPh6sqxJkRERGRfbl80T9dhGqB1s1E1RFQdRTpg5wk1NuzXYNNhDXLyrTPSJtTfGNzbhZQE+BAZEYEM8NQw1fimrM7OzhgzZow1ayEiIiKqd2L6OagT9ppt03V52EbVEJEl8ouAbceMYX7rUQ3yCmsW5gVBQcsA2dT73i5ERtsQCa0DZbg4WrloojpU7WCfkJBg0X7BwcHVLoaIiIiovmmjl5o9NgT1h+wbaaNqiKgiuQXA1iMarN+vwfZjahTqLA/zKlFBq+YlAT7k5jD61oEynBzqsGiielLtYN+5c2cIFiwkk5mZWaOCiIiIiOqNLg/a0z+Zb+r6qI2KIaLbZd0QsOmwsWd+5wk1dIaqc4iPh4x+HSS0Kwnw7YIlhAfKcNDUQ8FENlLtYP/ZZ5+VCfaSJCE+Ph4//fQT/Pz88Oij/B8iERERNXzas6sh6G6YHsvOftBHcKohkS2l5wj4/aAa6/drsCdGDYNUdZgP8JYxtp8eY/vr0S9SgkpVD4USNSDVDvYzZsyo8Lmnn34aw4YNQ15eXq2KIiIiIqpzigLt7YvmdX4QUGltVBBR05WUIeD3g8Zh9vtPqyDLVYf5IF8ZY/vrMa6/Hr3aShCtt/A9kd2p8eJ55XF1dcWMGTPw+eef4/HHH7fmoYmIiIisSnX9MFRpp02PFUGErvM/bFgRUdOSkCpgwwEN1u/T4Mh5FRSl6jDfMkDCuAF6jO1vQLfWEiyYIUzUJFg12AOARqNBUlKStQ9LREREZFXak7ctmhc+Eoo7F/8lqkuXrovYcMA4Z/54rGVRpE2whHElPfMdwmSGeaJyWDXY//333/jiiy/Qtm1bax6WiIiIyKqE/DRoLvxqto2L5hHVjXNXRWzYr8GGAxqcumzZ5PeOLSXTnPl2IXIdV0hk/6y2Kn5OTg5yc3Ph6uqKRYsWWaU4IiIiorqgPbUCgqQzPZY8W8EQOsR2BRE1Irn5wL5TavwVrcbOE2rEXrMszHePMGBsfz3G9jOgVQuGeaLqqHawHzBgQJlgLwgCPD090apVK0ycOBGenp7Wqo+IiIjIumQJ2uhvzDbpuj4MCFx5i6gmdHrg6HkVdkUbw/zxCypIFix+BwC92xmMc+b76RHsp9RxpUSNV7WD/eLFi61y4q+//hrffvstEhISAADt2rXDc889hxEjRljl+ERERETlUV/+E2JugumxonaEvkPFd/0hInOyDJyNF01Bfv8pNQqKLQvyoqigfwfjnPnRffVo3oxhnsgarL54nqVatGiB119/HeHh4ZBlGT/++CNmzJiBXbt2oWPHjrYqi4iIiBq52xfN07ebCMXJy0bVENmHxDQBu04ag/zuaDXSciwf4aISFdzRxYCx/fQY3dcAX0+GeSJrq1Gwz87Oxtq1a3HlyhVkZWVBUcz/cgqCgM8++6zSY4wePdrs8SuvvIKlS5fiyJEjDPZERERUJ8Tsy1Bf3ma2jYvmEZWVnQfsiTEG+b+i1bh43bJ58qXaBEsY0sWAwV0MGNDRAHeXOiqUiADUINj/9ddfeOCBB3Djxg24ubmVO5++vMX1KiNJEn799Vfk5+ejd+/e1S2JiIiIyCLa6G8h4GaHhCGgO6SAbjasiKhhKNIBh8+qTEH+5EUVZAvnyQNAgLeMwSVB/o4uBrTgEHuieiVkZ2dX62/dgAEDkJeXhxUrVqBTp061Ovnp06cRFRWFoqIiuLi44Ouvv65yjn1sbGytzklERERNkyAVocuWMVDrc0zbLnd7FRkhY21YFZFtyDJw4bozjlxwx+FYN0RfckOxwfLh9c4OEnqE30CvNrnoFZGLlv5FvL88NUkRERG2LgFADYK9v78/Xn/9dTzxxBO1PrlOp0NiYiJycnKwfv16LFu2DBs3bkRkZGStj20rsbGxDaZxqebYjo0D27FxYDs2Dg2hHTWnf4Tzplmmx7KjJ27MPAtonGxYlX1pCO1I1WOQgIxcAWnZAtKyRcSniPh9XyFOXPJE5g3Lg7xapaBXWwmDuxp75btHSNDYbLUuAvj3sS7Y83ta7b+OoaGhKCoqssrJtVotWrVqBQDo1q0bjh8/js8//7zK+flERERE1VVm0byO9zPUk10qKAbSswWkZotIzxGQmi0gPVtEao6A9JIAn5ZjDPOZNwQoyu1d6Zb9dx8ZagzyQ7oY0K+DAa7860LUYFU72D/zzDNYuHAh/vGPf8DLy7oryMqyDJ1OZ9VjEhEREYnJJ6FOOmq2TdflYRtVQ2ROUYDsPMEUxtNuC+fpOWJJeBeQliMir7Buxry38JExpIsBQ7oaMKizAf5enCdPZC+qHexTUlLg7e2N7t27495770VQUBBUKvNVMgVBwFNPPVXpcebPn4+oqCgEBgYiLy8Pa9aswd69e/Hzzz9XtyQiIiKiSjlE39ZbHzYcslcrG1VDTZWiAFdTBByLVeNErArHL6hwJcXY66431P8EdXcXBXd0MpiG14e3kDlPnshOVTvYz58/3/Tnb7/9ttx9LAn2KSkpePzxx5Gamgp3d3d06NABa9aswfDhw6tbEhEREVHFirKhObvGbJOu6yM2KoaakoxcAcdLAnzp74xcy+e1W4u3mwwfTwV+ngp8PGQEuKVh4nAPdAmXoK7eXeyIqIGqdrCPjo62yokXL15sleMQERERVUZ7eiUEQ6HpsewWBEOryu/CQ1RdBcVAzEUVjplCvBrxKXUT4tUqBb4eCnw9Ffh6yqY/+3jKJeFdgZ+nDF9PBc3clTKL3MXGJiEiwrVOaiMi26h2sA8JCamLOoiIiIisT5HLLJqn6/IQILKbkmrOIAHnr4o4VhLgj8eqcDZehFSN+77fzsWxJKR7loZ2uSSgKyW97Tef83RVOGSeiMzU+CYVCQkJ2LdvH9LS0kxz7Q0GA7KysuDl5QW1mve/ICIiIttSXd0NVdZF02NF1EDX6QEbVkT25tZ58aVD6mMuqlBQXLNk7eKooEu4hO5tJHSPMKBzKxn+3jJcHK1cOBE1KTVK3/PmzcNXX30FSZIgCAI6d+6MoKAgFBQUoHv37njxxRcxZ84ca9dKREREVC0OJ5eYPda3GQfFxc9G1ZA9SM8RjAvbxdZ+XrxKVNAhTEb3Nsb7vndvI6FtkAwVB4wQkZVVO9h/8sknWLx4MZ566ikMGzYM48ePNz3n7u6O0aNHY+PGjQz2REREZFPCjWtQx/1hto2L5pGiGBe1i08REZ8i4mqKiPgU4+NL11W4mlrzefGtmpf2xBt/d2opwcnBisUTEVWg2sF+2bJlmDJlCl5//XVkZmaWeb5Dhw7YsWOHVYojIiIiqilt9HcQFNn0WPKJhBTYz4YVUX3JK0RJYBdvC/AirqZa5z7wvh4yerSV0K21McR3ay3B2533fSci26h2sE9MTKz0VnZubm7IycmpVVFEREREtSLpof37e7NNuq6PgCuONQ46PZCQVrbHvTS8W/uWcq5OJfPiIyR0b2NAjzYSAn24gB0RNRzVDvbe3t5ITk6u8PnTp0+jefPmtSqKiIiIqDbUcb9DzE8xPVY0rtBFTrFhRVRd6TkCYhPL9rjHp4i4niFAUeomVatVJfPiIwzGYfVtJLQJ5Lx4ImrYqh3so6KisGzZMjz66KMQbvuaMjo6GitWrMDDDz9stQKJiIiIquv2RfN0HaYBWjcbVUOVURQgPkVAzCUV/r7lJymzbu4BDxhXpg/xlxHqLxt/+xn/HOovo1ULmfPiicjuVDvYz5s3D9u3b0f//v0xYsQICIKAH374AcuWLcPGjRsRHByM559/vi5qJSIiIqqSmH4O6oS9Ztt0Xdjp0BDoDcCFBBExl1SIuaTCqcsq/H1Zhdx86/a+q1UKgn1LQru/cjPAl/z4eHAYPRE1LtUO9v7+/ti1axfeeOMNrF+/HoqiYPXq1XBzc8PUqVMxf/58eHp61kGpRERERFXTRi81e2wI6g/ZN9JG1TRdeYXA6SvG3vfS3viz8SJ0htonakFQEOCtmIL6raE91F9Gc2+FQ+eJqEmp0X3sfXx88PHHH+Pjjz9Geno6ZFmGj48PRLHuhkwRERERlaEoQHEOxMJMCIUZEArToT39k9kuuq6P2qi4piMtWyjpgRdNIf7idbFW8+C1agXtQmSEBchmAT4sQEawnwwHjRUvgIjIzlU72O/evRuDBg0yza/38fGxelFERETUBCkKoC8oCeiZEAszSv6cUXZbQcnvokwIsqHCQ8rOftBHjKnHi2jcFAW4kmz9+fDuLgo6tZTQqZWEzq2M939vEyxDU6MuKCKipqfa/1zec8898Pf3x/jx4zFhwgT07t27LuoiIiKixkCXDzEnHm5pJ6CR/i4T1IXCjJKwXtLjbiiy7uk7PwiotFY9ZlOiKMCpyyK2HtXgr2g1TsR2RX5R7dJ2c28ZnVrdEuJbSQj155x3IqLaqPa/zN999x3Wrl2L77//Hl999RUCAwMxceJE3HvvvejSpUtd1EhEREQNlaIYw3n2ZfOfnCvG3yW3nGtrg9JkJ28Ow6+BvELgr2g1th7VYNtRdY174wVBQXgL+ZYAL6NTSwm+noqVKyYiohr12N9zzz3Iz8/H77//jrVr12Lx4sX45JNP0KpVK0ycOBETJkxA27a2+F84ERERWZ1sgJCbCDHnClRlAnw8BN0Nm5anaFygOHlDdmoGpeRHdguEvsN0KK4BNq3NXly6LmLrUTW2HlVj/yl1tRe406oVtA+9GeI7tjT+uDrVUcFERGSmxmOpXFxcMGXKFEyZMgU5OTn47bff8Ouvv+KDDz7A+++/j4yMDGvWSURERHVJXwAx+wrEnNLQfuVmeM+9Wuk8dmtSVFooTj5QnLyNAd0U1o2PFefSbSWPHb0BDdNjden0wIEzKmw9qsGfR9WIu2b5EvJuzrfNh28loU2QDC0XsyMishmrLEni5OQEb29veHp6QqvVoqjIuvPjiIiIyDrErEtQJR+7pde9dMh8cp2cTxFUUNyDkK/2hqN3UNmgXtrD7uQNxbkZoHEFJ1vXjZQsAX8eNQ6x33VSjbxCy95nZwcFg7sYcFdPPUI8LmJInxDwRkhERA1LjYO9JEnYuXMn1q5diz/++AM3btyAv78/HnzwQUyaNMmaNRIREVFNGYqgTtwP9aWtUF/+E6qsi1Y/haJ2huwZBtmzJWSPkt+lP+7BgEqD2NhYREREWP3cVDFZBk7EqbD1qBp/HlXjZJzlH/vCAiRE9TTgrp4GDOhogGPJ+oOxscUM9UREDVCNbne3bt06bNiwAVlZWfD09MS9996LCRMmmN0Gj4iIiGxDyLkKzeVtUF/eCnX8bgiGglofU3byuSWwm4d3xdmPvewNRE4esOOkcdG7bcfUSMuxLIWrVQr6RUq4q6ceUb0MiAiU2aRERHakRovnubm5YdSoUZg4cSKGDRsGtZo3GSUiIrIZSQfVtYPQXPrT2Cufca7ah1AEEYpbEGTPlpBuD/AeYYCDu/XrplpTFOB8gljSK6/BobMqGCTLErmfp4w7exgQ1UuPIV0NcHeu42KJiKjO1Oh2dyNHjoSDg0Nd1ENEREQWEPKSoL78pzHMx++yeGV6RdRACuwLya9TOUPmeb/3hkxRgMwbAuJTRFxJFnHwtHHxu6uplo+N7x5hHF4f1dOALuESh9UTETUSNeqxLyVJEnJycuDu7s5eeyIiorokG6BKOgr1pT+hubwVqtS/LX+pWyAMLe+EvuVdMIQOBrRudVgo1UZhMXA1VcTVkvAenyKagnx8imjxgnel3JwVDO1q7JW/s7sBfl68hzwRUWNUozR+/PhxLFiwAAcOHIDBYMC6deswePBgZGRkYNasWZgzZw4GDx5s7VqJiIiaFCE/Deor26G+/CfUV7ZDLMq26HWKoIIU2Af6llEwtLoLsk8k58A3ELIMJGUKiC8J6ldSzEN8cmbtu9AjgowL30X11KNvpAQN+16IiBq9av9Tf/jwYYwbNw7+/v6YNm0avv/+e9NzzZo1Q15eHpYvX85gT0REVF2KDFXyiZsr2CefgADLelhlZz9jr3yrKBhChwCOnnVaKlUsNx+4kmwM7fEpoinEx5eEeJ3Bul+yOGgUDOhoQFQvY5gPC2CvPBFRU1PtYP/GG28gPDwc27dvR35+vlmwB4BBgwZh1apVViuQiIioMRMKs6CO31ES5rdDLEy36HUKBEjNe8LQ6i7oW0ZB9u8MCJwwXd+ybgjYfFiN7cfVuJRk7HnPzqu7dnDSKgjxlxEWIKNlcxmDOhlwRxcDXBzr7JRERGQHqh3sjx8/jpdffhmOjo4oKCh7+5zAwECkpKRYpTgiIqLGSrhxHY7734bm9EoIssGi18hO3jCE3QlDq7tgCB0GxblZHVdJ5UnOFPDHQQ02HFBj799qSLL1euAFQUGgj4JQf9n0ExYgIzTA+Gc/T4WzKoiIqIxqB3tRFCFWsoRqSkoKnJycalUUERFRo1WUDYfDH8Ph+GIIhqIqdzf4d4Wh5V0wtIqCFNAdEFX1UCTd7kqygI0HNNh4QIMj51VQlJqna09XY1gP8VOMod3fGNzD/GUE+crQaqxYOBERNQnVDvZdu3bF5s2bMXPmzDLP6XQ6rF69Gr1797ZKcURERI2GoQjaE1/D4dAHlS6Cpzi4Qx86zNgr3/JOKC7+9VcjmSgKcC5BxMb9xjD/92XLv1DRqo3D5U297iXhPazksYdrHRZORERNUrWD/TPPPINJkybhySefxOTJkwEAycnJ2LZtG95//31cvnwZixYtsnqhREREdkmWoDn7Mxz3vgXxRmK5u0geYdC3HQ9Dy7sgtegNqNhlawuKApyMU2HjATU2HtAg9pplYb5diIQx/fS4o7MBLZvLaO6t8P7wRERUr6od7IcOHYqvvvoKzz33HFauXAkAmDVrFhRFgYeHB5YsWYJevXpZvVAiIiK7oihQX/4TjrvnQ5V+ptxdZBd/FPV/CfpO9wMi70lmC5IEHDqrwoaSYfbX0i1L5N1aGzCmnwFj+ukRESTXcZVERESVq9aniMLCQvTr1w9PPPEETp8+jR07duDSpUuQZRktW7bE8OHD4erK8WVERNS0qZKOwvGv16BO3Ffu84rWDcW9/43i7rMArUs9V0c6PbA7xtgr/8chNdJzqg7zgqCgX6SxZ35MPz2CfHlLOSIiajiqFeydnJyQm5sLrVYLZ2dnjBkzpq7qIiIisjtiZhwc974BzYXfyn1eETXQdXsUxX2e44r29Sy/CNhx3BjmtxzVIDe/6sXvNGoFd3Q29sqP6m2AnxfDPBERNUzVHvcXFRWFrVu34uGHH66LeoiIiOyOkJcMhwPvQhuzDIIilXlegQB95GQU9Z8HxTOs/gtsonLygK1HNdhwQIPtx9Qo1FUd5p20Cob3MGBsPz2ieuq50B0REdmFagf7//znP3jooYfwz3/+Ew899BBatmxZ7u3tfH19rVIgERFRg1WcC4cjn8Dh6OcQDAXl7qIPG46iO16D7Ne5notrGmQZyLwhIDlTQFKmiJRMAcmZIg6dVWF3jBp6Q9Vh3t1FwchexiH2w7ob4OxQD4UTERFZUbWDfd++fQEAZ8+exfr16yvcLzMzs+ZVERERNWSGYmhjvoXDgfcgFmaUv4t/VxTd8Tqk0MH1XFzjoCjGwJ5SEtiTSwJ7aXBPKvmdmi1YFN5v5+sh4+6+eozpZ8CgTgbeO56IiOxatYP9Cy+8AEGo/v9AiYiI7J4iQ3NuLRz3vgkxJ77cXSTPlige+Ar0bccDAu95djtFAbLzBFMwLw3qph73rJvbdTUI7JUJ8pUxpp8eY/vp0budBJXlt6YnIiJq0Kod7F966aW6qIOIiKhBU1/ZCcfdr0GVGlPu87KzL4r7vQBd538AKm09V2cbsgzkFhiDenaegKwbIrLzBeTkCci6YdyWnW/889WkdsgucEZKloBiff11EEQEShjb3zjMvku4DPZNEBFRY2Szm+Z++OGH2LBhA+Li4qDVatGzZ0+89tpriIyMtFVJREREZYjJJ+G4Zz408bvKfV7RuKC4179Q3HMOoHWr3+KsQFGAvEIgJ780nAs3g3qeMaSX/jm75LHpz/kCFMXSpFx3Y93dXRQEeMto7q3A30tGgLeCFj4y7uhsQLsQ3mOeiIgaP5sF+7179+KRRx5B9+7doSgK/vvf/2L8+PE4dOgQvLy8bFUWERERAEDMvgyHvW9Ce25tuc8rohq6zg+huN/zUFz86rk6y0kSEJ8i4txVEeeuqnAuQcSVZNEU3rPzBBikhtmN7eqkoLm3MagHeMvwL/kd4G3c7u+lwN9bhoujrSslIiKyLZsF+3Xr1pk9/vLLLxESEoKDBw9i1KhRNqqKiIiaOiE/DQ4H34U2+lsIsqHcfXRtJ6B44MuQvVrVc3UVk2UgIVXAuQSVKcSfjVfhQqKIIgtu81afXBxvBvSAkoBe2uMe0My43d9LhmvZm+4QERFROWwW7G+Xl5cHWZbh6elp61KIiKipKc6BOvEA1PG7oP17BQR9Xrm7GULuMK50H9Ctngu8SVGAa+kCzl69GeDPXxVxPkGF/KL6D/CuTgo8Xcv/8Sr57eGqoPhGAnp2bgF/bxluTuBcdyIiIisSsrOzFVsXAQD//Oc/cfHiRezatQuqSpapjY2NrceqiIioMRL1+XDNPAn39GNwSz8G5+xzEFDxXOwC9wgkdvgXcn371lsiVRQgPVeDi8lOuJzshEvJjriU4oTLKY7IL7Lu9/IOGgnuThLcnCW4Oxvg5mQwe+zuZICbU8lzzhLcnQxwd5bg5iRBrWoQHyOIiIhsIiIiwtYlAGggPfbz5s3DwYMHsXnz5kpDPdBw3riKxMbGNvgaqWpsx8aB7dg4WKUd9QVQXzsEVcIeqK/ugSr5OARFqvJlsnsIiga+DH37SfAXRPjXropyKQqQli3gbOkceNNvFXLyrfMlgrebjPahMtqFSGgXIqNNkARfz5s9645lFvEXS36st+Ad/z42DmzHxoHt2DiwHa3Pnt9Tmwf7l156CevWrcOGDRsQFhZm63KIiKgx0BdClXQY6qt7oU7YC1XSUQiy3uKXy07eKO77PHRdHgbUDlYpqaAYuHxdRNx1ERevqXCx5M9x10Rk3bDO/e49XBS0D5VMAb5dsPG3r6fCoe9ERESNmE2D/dy5c7Fu3Tps3LgRbdq0sWUpRERkzwzFUCUdhbq0Rz7pKASpuFqHkJq1gyF4EAwhA2EIGw5oXatfhgRcTRGNof2a8ffF6yrEXRNxLd064R0wzmtvFyIZe+FLwnu7EAkB3gzwRERETZHNgv1zzz2HVatWYcWKFfD09ERKSgoAwMXFBa6u1f8wRURETYikhyr5+M0gf/0wBENh9Q7h1RqGkEGQggfBEDzQ4lvWKQqQnCncEt6Nve8Xr4m4nCxa9dZxLo4K2gZLaFsS3NuX/A70YYAnIiKim2wW7JcsWQIAuOeee8y2z507Fy+99JItSiIiooZKNkCVctIY4hP2Qn3tIAR9frUOIXm2NIb4kJIg79q80v1z8oC46ypcvFYyfP6WIfTWXn3eSaugTXDpHHhjD3z7EAlBvgpE63X0ExERUSNls2CfnZ1tq1MTEVFDV5wDVUo0VMkn0Pr8VrhvioGgu1GtQ8juwSVD6wfBEDwIinsQDBKQkSsgI11A+kUB6bkiMnIEpOcKyMwVkJ4jIClDxKUkEek51k3UoqggxE9GeItbfgJltG5h7IGvYu1YIiIiogrZfPE8IiJq4oqyjSE+5eTNn+zLpqedLDxMgUMgrrncgQvqOxAtDcGF/FbIPCEgfZeAjJLQnp1X993f/l43Q3t4CwmtS/4cFiDDwXqLzBMRERGZMNgTEVG9EQqzIKZGQ5V8S4jPuVKjYyXrArAzeyh2ZQ/FzuyhuFgUDqB+Jp67OSto3UIqCe9ySXiX0KqFDHfneimBiIiIyITBnoiI6oRQmHlLL7yxR17Mia/x8VJ1vvgrZ4gpyJ8vbIu6DPJatYKWzWW0DiwdOi+Z/szbxxEREVFDwmBPRES1JhRkmA+lTzkJMTehxseTFBFnCiJxIq87Duf2xl85Q3CmIBK1DfKCoMDLVUEzDwU+7iW/PWQ0c1fgU7LNx1NGywCZ896JiIjIbjDYExFRtQgF6eYhPvkkxBuJNT6eLKhwxdAeu9N64lheDxy/0QPR+V1QKFc9pl2tUtDMXTEF82Ye8s3A7l7y2OPm816uDOtERETU+DDYExFRxWQJYvppqBMPQJV4AOqko7UK8YqgguzTHjrfLjic3QNfHOuN9Re7o0guf4k8dxcFwzunoWNrd/PQ7qGgmbsMDxdwSDwRERE1eQz2RER0k6EIquQTUF8rCfLXDkHQ5dboUIqohuzTHpJ/V9NPiqYjlv7pjm9+1CKtktvJhfrLeGJcMe4brkPytauIiIio6RURERERNXoM9kRETVlxDtTXDkN17YCxVz75OASpuNqHUUQNZN9ISH5dbgZ530hA7QgAOHdVxOK1Dvh5pwbF+oq72Pu0N2D2PcW4u4/BNGQ+uUYXRkRERNR0MNgTETUhQn6KsSc+cT/U1w5ATDsNQZGrdQxFpYXkE2nWEy/7tAfUDub7KcBfJ1VY9KsDth+v+AbuKlHBuAF6zBqnQ8+2Uo2ui4iIiKgpY7AnImqsFAVi9mWoSkK8KvEAVNmXqn0Y2ckbUmBfGAL7QQrqD8mvE6DSVrh/sR5Yu1uDz391wJn4ileqc3VS8GCUDjPHFiPYT6l2XURERERkxGBPRNRY3L7Q3bUDEPNTqn8YtyAYgvpDCuoHQ2A/yM3aAELF8+FLZeQK+HaTFkt+1yI1u+L9g/1kzBxbjPvv0sG96oXviYiIiKgKDPZERPZKXwhVygmorx2s1UJ3UrN2MAT1gxTYD4agflDcg6v1+guJIr5Yr8VPO7Qo0lU8f75nW+P8+TH9DFDzlnNEREREVsNgT0TUkOgLIOSnQixIg1CQCiE/DeKtv2/9c3H1Q7wiqo0L3AX1M4V5xcm7+sdRgD0xKnz+mwO2Hq14/rwoKhjT14DZ44vRux3nzxMRERHVBQZ7IqK6pCiA7oYxqOcbg7mYXxLaC9Ig5qdCKEi/uV2fZ93Tq50htegJQ2A/4/D65j0BrUuNj6fTA+v2aPD5bw44dbny+fP336XDzDHFCA3g/HkiIiKiusRgT0RUG5IeYuYFqNJOQ8y8UBLUb+1hT4NgKKq3cswXuusHya8LoKq4R91SKVkCVvypxdI/tEjOrHj+fKCPcf78g1E6uNf8+wMiIiIiqgYGeyIiCwkF6RDTTkOV9jdUqaegSj8NMeM8BElnk3oUQQXFLRCGwD6QgvpXa6E7i46vAPtPq/DNJi027NfAIFU8f757hAGzx+swtp8eGv6fhYiIiKhe8eMXEdHtJD3ErDio0k5BlXqqJMyfgpifXOenVkQNFGdfKC6+kJ39oDj7QnbxK9nmZ9qmuPgZ58ZbKcTfKjcfWLVTi282aXE+oeLh9oKgYHRf44J4fdpLECrO/URERERUhxjsiahJEwoyIKYbA7wq7ZRxSH3GOav2witqR2NAd/aD4uILxdnvZlh39oN8yzY4eMBWCfnUZRHfbNJi9S4t8osqrsHFUcGMu3SYOUaHls3leqyQiIiIiMrDYE9ETYNsgJhp7IUXSwK8Ku0UxLyk2h/atTkknw6QfTtAdg82BXVjD7svoHWzWVivSrEeWL9Pg6V/aHH4XOX/S2gdKOHhUTpMH6aDh2s9FUhEREREVWKwJ6LGR9JBlXQMfhe3wuliqjHAp5+DIBXX6rCKSgu5WVtIvh1NP7JvByjOPlYqvP7EJwv4bosWK/7UIiO34uH8KlHB3X0NeHhUMe7ozOH2RERERA0Rgz0R2T9FgZgZC3X8Tqiv7IA6YS8EfT5q06ksuwRA8u0A2bcjJN8OxhDvHWGVFeZtRZKA7cfVWLpJi23H1FCUilN6c28ZD47Q4YEoHVo04+3qiIiIiBoyBnsisktCQQbUV/8yBvn4XRBvJNboOIqoudkL71fSC+/TAYqLr5Urtp20bAE/bNPi281aJKRWvtje4C7G3vmRvQ1c3Z6IiIjITvBjGxHZB0MxVNcPQX1lJ9TxO6FKiYaA6vUky85+kPw63tYL38aue+EroijAobPGW9X9tk8DvaHi3nl3FwX3DdfhoZE6RARxMTwiIiIie8NgT0QNk6JAzDhXMrx+J9QJ+yAYCix+uezijyzPrnBuPdAU5BUXvzosuGG4UQCs+UuLpX9ocSa+4lvVAUDX1gY8PEqHCXfo4exQTwUSERERkdUx2BNRgyHkp0F9ddfN4fXVWLFeUTvCEDQAhtAhMIQNg+wTictxcYiIiKjDihuOM/Eivt2kxaqdWuQVVtw776hVcO8gPR65W4fuEVI9VkhEREREdYXBnohsx1AE1bWD0FzZYRxen/p3tV4u+XWCIXQY9GHDIAX2AdSOdVRow6TTAxsOaPDNH1ocOFP5P+etmkt4aJQO9w3Xw8uNi+ERERERNSYM9kRUfxQFYvpp0zx5deJ+CIYii18uuzaHIXQoDGFDYQgZ0qgWuKuITg8kpImITxERn2z8fSVZxNVUAXHXVJX2zouiglG9jcPtB3cxQKx83TwiIiIislMM9kRUtyQd1Je2QhO7Hur4vyDmp1j8UkXtDEPwQBjChsAQOgxys7ZobDdSVxQgNVswhvWS0B6fcjPAJ2UKkOXqXbO/l4wHonR4MEqHIF/2zhMRERE1dgz2RGR9igIxNRra0z9Cc3YNxMIMy14GAZJ/V2OPfOhQSC16A2r7X9UtrxBlQrup5z1FRKHOOl9WDOxkvFXd6L68VR0RERFRU8KPfkRkNUJ+CjRnfob29I9QpZ+x6DWyW1BJkB8GQ8gdUJyb1XGVdSO/CDh+QYXL5fS8p+fU3Rh4T1cZU4bo8c+ROrQL4a3qiIiIiJoiBnsiqh1DMdQXN0N7eiXUl7dBUCpfaV3RuMIQMrBkrvwwyF6t7XZ4vd4A7Dyhxuq/NNh0SIOC4rq5jubeMkIDZIT63/wJC5AR4i+jubfCufNERERETRyDPRFVn6JAlXwCmtMroTm3BmJRdqW7y46e0LebBH3be43D61Wa+qmzDigKcPicCmv+0uDXvRpk5NY+Vbs5K8aw7n9LgC/5HeInw1FrhcKJiIiIqNFisCciiwl5Scah9qdWQpV5vtJ9FUEFQ8s7oetwHwzhI+1+rvyFRBGrd2mwdrcGV5JV1XqtWqUg2PdmWDf2tivGIO8vw8tNsddBC0RERETUADDYE1Hl9IXQXPwDmlM/Qh2/A4JS+TxuyScSug7ToY+cAsXFv56KrBtJGQLW7dFgzV9aRF+sOsy38JHRL9KAsFsCfKi/jObNFKir910AEREREZHFGOyJqCxFgSrpCDSnVkJ7fh2E4txKd5edvKFvNwm6jvdB9utit3PmASA3H9hwwBjmd8eooCiVX4uHi4J7BugxeYgO/SIlzncnIiIionrHYE9EJkJuIrRnVkFz+keosuIq3VcR1TC0ioKuw3QYWo0AVPY7EbxYD2w7psbqXVpsOaJGsb7yMO+gUTCilwGTh+hwZw8DHOx3yQAiIiIiagQY7ImaOn0BNLEboTm9Eur4vyBAqXR3ya8TdB3ug77dJCguvvVUpPXJMnDwjAo/79Ji/X41svMq72oXBAWDOkmYPESHsf30cHepp0KJiIiIiKpg02C/b98+fPrpp4iOjkZSUhIWLVqEGTNm2LIkoqZBUaC6dgDa0z9Cc/5XCLoble4uO/tC334ydB2mQ/brVE9F1o0zV0Ss/ss41P5aetXj5ju3kjBpsA4T7tCjRbPKv/QgIiIiIrIFmwb7/Px8REZGYvr06XjiiSdsWQpRkyDkJhrD/OmVUGVfrnRfRdTAED4Suo73wRB2p13foi4xTcDa3Rqs3qXFmfiqV7EL8ZMxeYgOkwbr0Ta48sUCiYiIiIhszabBPioqClFRUQCA2bNn27IUosbLUARN3B/QnFoB9ZWdVQ61N/h3g77DdOjbT4Li5F1PRVpfdh7wywEf7P7GBftPV/1PnbebjHsH6TFpsB6920n2vP4fERERETUxnGNP1BgpCsSUaGhP/QDNudUQi7Ir3V128Yc+cqpxqL1P+/qpsQ7EJwvYfESDzYc12HdKBYPkUen+TloFo/roMWWIHkO7GaDhv4hEREREZIeE7OzsBjFpNDAwEO+++26Vc+xjY2PrqSIi+6MuzoJ34ib4XN0I59zK/67IogbZAYORHjIGub59ANH+Uq0sA2cTXbDntAf2nPZEXJJzla8RBQW92+RiRPcMDO6YDRdHDrUnIiIiopqJiIiwdQkA7LDHvqG8cRWJjY1t8DVS1eyqHWUD1Je3Q3tqBdQXN0OQ9ZXubvDvCn3HGdC3mwSVkxf8AfjXT6VWUVAM7I5WY/NhDbYcUSMly7Ibx3dvY8DkwXrcO1APPy8AaFbyQw2dXf19pAqxHRsHtmPjwHZsHNiO1mfP76ndBXsiMhIzY6E59QO0p3+CmJ9c6b6yUzPoI6dA13EGZN+O9VSh9aRmCdhyxBjmd51Uo1Bn2QT4ML9CTB0uYNJgPcJbsGeeiIiIiBonBnsie1KcC835X6E99QPU1w9VuqsiiDC0vAu6jjNgCB8JqLT1VGTtKQpwLkHE5kMabDqsxrELKihK1WFeFBX0bS9hZG89RvY2QCk4b7ffuhIRERERWcqmwT4vLw+XLl0CAMiyjMTERMTExMDLywvBwcG2LI2o4VBkqBL2GRfCu7AegqGg0t0l7wjoOt4PfeRUKK4B9VRk7ekNwMEzKmw6rMHmw2pcSa76tnQA4OqkYFg3A0b10eOuHgZ4u99cNoRLchARERFRU2DTYH/ixAmMHTvW9HjhwoVYuHAhpk+fjsWLF9uwMiLbE3ITjPecP7USqpwrle6raN2gb3svdJ3uh9S8F+zlXm05ecC248Ygv+2YBjn5ltUd6CNjVG89RvYxYEBHAxw0dVwoEREREVEDZtNgP2jQIGRnZ9uyBKKGxVAETdzv0Jz6wbJ7zgcPhK7jDOgjxgFal3oqsnbK3pLOsjDftbUBI3sbMKq3Hh1byvby3QURERERUZ3jHHsiW1MUqFJOGhfCO7saQnFOpbvLbkHQdZgOfcf7IHu2rKcia0ZvAK4ki4hNFHE8VoXNhzU4E2/ZEHsHjYJBnQ0Y1duAqF56BPo0iDtzEhERERE1OAz2RLZSnAvt2dXQRn8LVdqpSndVVA7QR4yBvuP9MITcAYiWheP6kpMHXEhUIfaaiLhEEbHXVLiQKOJykmhxjzwANHOXEdXL2Cs/pKsBrk51WDQRERERUSPBYE9UnxQFquQT0MZ8C83ZtVUuhGfw7wZ9xxnQtZ8EOHrWT40VkGUgIVXAhWuqkvAuIjZRhdhEEanZlt1LvjwRQRJG9TZgZG89erWVoGpY31kQERERETV4DPZE9UF3A9qza4y986kxle4qO/nccs/5DvVU4E35RcDFktB+4ZpxGH1sogoXr4sosvD+8ZURRQX9IiWM6GW8JV3rQN5fnoiIiIioNhjsieqQmHwS2pjvoD27BoI+r8L9TPec73Q/DK1G1Pk95xUFSM4UjKH9mrHX/UJJgL+WXvPe9/I095YRESSjdaCEXu2kMrekIyIiIiKi2mGwJ7I2XR4059ZCG/0t1CknK91Vdm0BXacHoOv0ABT3oDorSW8Aoi+qcOCMCgdPq3HorAqZN6wX4DVqBa2aGwN8myAJEUE3w7y7s9VOQ0RERERE5WCwJ7ISMTUG2ujvjCvb625UuJ8CAYZWUdB1/gcMraIA0fp/DfMKgaPnVThwRo1DZ9Q4el6FguLaD6P3cpPRpiS0RwRJxj8Hygjxl6Hm3HgiIiIiIptgsCeqDV0+NOfXQhu9DOrkY5XuKrs2h67j/dB1fhCKe7BVy8jIFXDwjAoHTqtx8IwK0RdVkOSaBXlRVBDqJ5t63U0BPkhGMw6hJyIiIiJqcBjsiWpATDtl7J0/8zMEXW6F+ykQYGh5p7F3PnykVXrnFcW4Ov2BM2ocOK3GgTMqxCZWv7vcxVExGzYfUfLnVs1lOGhqXSYREREREdUTBnsiS+kLoDm3DtqYZVAnHal0V9nF/+bceY/QWp1WloFzCSIOloT4A2fUuF6DBe78vWT0jTSgXwcJ/SINiAyVeWs5IiIiIqJGgMGeqApi2hloY76F9swqCMUV984DgD5sGHSd/wlD+ChAVbNub50eOHlRhYOnS+bIn1UhO6/6QT68hYS+kRL6dTCgX6SEsAAZQu2n2RMRERERUQPDYE9UDkEqgub0j9BGfwf19UOV7is7+5bMnf8HFM8wi89RrAcycwVk5Qm4ni7i8DkVDp5R49h5FQqreb94UVTQqWVpj7wBfdpL8PfifHgiIiIioqaAwZ5IliDkJkCVGQsx8wJU6WfR5fxvUOsrXtkeAPShQ6Dr/BDyQ0chq8ABWbkCsq8JyLwhIOuG8Xf2DWNwz7wh3vJn4/barFLvqFXQo42EvpEG9I2U0KudgbeVIyIiIiJqohjsqenQ5UPMijMFeDEzFqrMCxCzLkIwFFl0iBzFF38U/wMrsx5F9JkIZK8WkF9U9+PbPVyUkhBvDPJdW0tc4I6IiIiIiAAw2FNjoygQ8pONPe8ZJQE+Kw6qjAsQbyTW+LA7sofi66SZ+DV9PHSKgxULLl/zZjL63bLQXbsQGWL1p9kTEREREVETwGBP9knSQcy6ZAzwZj3wsRB0lQ+ht1S6vhm+S34IS5IfQ2xhG6sc81ZqlQIvVwVe7sbfbYPlkoXuDAj2U7jQHRERERERWYTBnhq2omyoMs6XCfBi9hUIimS106TpfHC+sC3OF7TDucJ2OJPfATuyh1nUO68SFXi5GcO5p5sCbzfzwO7ldvN5b3fZtJ+bExjeiYiIiIio1hjsqWHQ3TAG+PSzUKWfhZhxDqr0cxDzrlvtFJIi4lJRK2N4L2iH84UlvwvaIsPgY9rPzVlBcLMCDA4X4e2mg2dJOC8b2GV4uylwdQKHyRMRERERkc0w2FP90hdAzLgAVUZJgE8/B1XGWYi5CVY7Ra7B7Wbv+y0BPq6wtVkPvLODgrbBEqJCZbQPKUS7EBntQyW0aKYgLi4WERERVquJiIiIiIiorjDYU90wFBnnvJt6342/xewrEGCd+6tfLQo2hfZbA3ySrjmAm2PctWoFbYJljAuR0D6kCO1DJbQLkRDip7CnnYiIiIiI7B6DPdWOpIOYdbGk9/0sVBnnIKafhZh9CYIi1/rwOlmDC4VtcLYgsmTYvHEO/IWCNsiXXc32VYkKwgNl9A7Rm3rf24XIaNVchlpV61KIiIiIiIgaJAZ7soyiQMhNgCo1BkLqaeiTjL3wzvlxEBVDrQ9vUFSIK2yN0/kdcbqgA87kd8Cpgo6ILYyAQTG/YbsgKAjzl9E+VI92IRLal4T48ECZ93YnIiIiIqImh8GeypIlYy98agx0CdHQJfwN15wYOMuZtT+0IuBSUasyAf58QdsyK9B7ucmIbKkgyFeP8BayMcSHSmgTLMO57m8lT0REREREZBcY7Js6SWccQp8Sg4IrMZCT/oZH3t9wUPIBAM61OPSVotAyAf5cQTsUys4QRQXNvRUE+cpo6yfjTl8ZQb6FCPKTEewrI8hXhquTdS6RiIiIiIioMWOwb0p0+VClnQKSY5B/OQZiSgy8Cs9ADT2Amof4xOJAU3A/k98Bpws64LK+PTx9XBDkKyM4UEGIn4z+vqWhPRfNmynQ8L8+IiIiIiKiWmO0aqSEwiyIqTGQrsWg4HIMtBkx8NJdgFiyIr1rFa8vzw2DK07md0V0Xldcljsg0zESxR7t4eXvbgzwvjJ6+MkI8lXQzF2BIORZ96KIiIiIiIioDAZ7e6coEPKToUqJRlH83yi6Gg2XrBh4SFdNu3jU4LDp+mY4kdcdMQVdkebUFbJ/Z/i1bYlO4QruCZPgbta9X1TbqyAiIiIiIqIaYrC3Q3npKciJ3gXtlW3wy9kNdzkFAOBSw+MlFAfhZF43nC7uhhzXLhADOyM4vDk6h8t4MEiG1rTSfO1XvyciIiIiIiLrYrBvoGQZSEwTEHtNhYsJBuDqIbTI3o4u2IqOTicRWMPjXiiIwMm8bohTuiHfozO0IZ0Q3tobnVpJGOSvQBBK92SIJyIiIiIisgcM9jaWVwhcvC4iNlGF2GsiYhONf5Yy4jHEdQuivLbgCc/tcFPnAdVYJV4vq3GmIBIn87shUdUVRc06wzWsA9q0dkX3lhJGeCq37M0QT0REREREZK8Y7OuBogDX0gXEJqoQVxrer4mIvabC9XQRAOAs5mOwxw6M8N6MhT6b0SYk1uLjF0qOiM7vgr8LuiLFsSskvy7wbNUOka01uCtMgovjrXszxBMRERERETUmDPZWpCjAhetO+DtZg9hEsSTEq3Dxuoj8IuH2vdHB+TSmBm7BSO/NGOSxGw6izqLzyIqAU8U9cEYdhVSf4XBs2ROdwgXcGyTfdgs5yVqXRkRERERERA0Ug72Vzf68LW4Ulv+2eqqzcKfnNozw3owory0Icrhm8XHzVH5Ia3YnlPDh8Og8BKFuzRBqelYp+SEiIiIiIqKmhsHeigQBCPUrwql4413iRUjo4XYMI7w2Y4TXZvRxPwSVIFt0LEVQQwrqC0PYcOhbDofs2xHegliX5RMREREREZEdYrC3sq5+V9Cj6CiivLbgLq+taKbJtPi1skco9GF3wtByOAwhgwCtWx1WSkRERERERI0Bg70VCdlXsMypK9DWsv0VtTMMIYNgCBsGQ8s7IXu2wi33myMiIiIiIiKqEoO9FSkeoSh2bgGHgusV7iP5RJqG10uBfQG1Y4X7EhEREREREVXF5pO2lyxZgs6dO8Pf3x+DBw/G/v37bV1SzQkCcvz6mW1SHDygazMeBSM+Re7M08j7534UDXkDUugQhnoiIiIiIiKqNZv22K9btw4vvvgiPvjgA/Tt2xdLlizB5MmTcfDgQQQHB9uytBrL8e8P7+J40/B6KaA7IHJgBBEREREREdUNm/bYL1q0CPfddx/+8Y9/oG3btnjvvffg7++Pb775xpZl1UpOwB3In7ENxQPmQWrRm6GeiIiIiIiI6pTNgr1Op8PJkycxbNgws+3Dhg3DoUOHbFQVERERERERkX2xWXdyRkYGJEmCr6+v2XZfX1+kpqZW+LrY2Ni6Lq3W7KFGqhrbsXFgOzYObMfGge3YOLAdGwe2Y+PAdrS+6r6nERERdVRJ9dh8nLhw2+3dFEUps+1WDeWNq0hsbGyDr5GqxnZsHNiOjQPbsXFgOzYObMfGge3YOLAdrc+e31ObDcVv1qwZVCpVmd759PT0Mr34RERERERERFQ+mwV7rVaLrl27YufOnWbbd+7ciT59+tioKiIiIiIiIiL7YtOh+HPmzMHMmTPRo0cP9OnTB9988w2Sk5Px0EMP2bIsIiIiIiIiIrth02A/YcIEZGZm4r333kNKSgrat2+Pn3/+GSEhIbYsi4iIiIiIiMhu2HzxvEcffRSPPvqorcsgIiIiIiIisks2m2NPRERERERERLUnZGdnK7YugoiIiIiIiIhqhj32RERERERERHaMwZ6IiIiIiIjIjjHYExEREREREdkxBnsiIiIiIiIiO8ZgT0RERERERGTH7DLYf/jhhxg6dCiCg4MRHh6OqVOn4syZM2b7KIqChQsXol27dggICMDo0aNx9uxZs32+++47jBkzBiEhIfD09ER8fHyZc508eRLjx49HSEgIWrZsiX//+9/Iy8urssbTp0/j7rvvRkBAANq3b4933nkHimJ+A4LVq1dj4MCBaN68Odq0aYPHH38cKSkpVR57yZIl6Ny5M/z9/TF48GDs37/f7Pn169djwoQJCA8Ph6enJ/bs2VPlMW2B7Vh5O86aNQuenp5mP3feeWeVx61vbMfK2zE1NRWzZs1Cu3bt0Lx5c0ycOBEXL16s8rj1rSm34759+zBt2jS0b98enp6e+OGHH8rs8+abb6JXr15o0aIFQkNDMW7cOBw6dKjKmusT27DyNrz939PSn+eee67KuutTU25HS66dn3EaRzvyM07jaMeafsZpLO/r119/jd69eyMgIAA9e/bEjz/+WOVxgbrLcnYZ7Pfu3YtHHnkEW7Zswfr166FWqzF+/HhkZWWZ9vn444+xaNEivPPOO9ixYwd8fX1x77334saNG6Z9CgoKMGzYMLz44ovlnicpKQnjx49HWFgYtm/fjrVr1+LcuXOYPXt2pfXl5ubi3nvvhZ+fH3bs2IG3334bn376KT777DPTPgcPHsTMmTMxffp0HDhwAD/88APOnTuHxx57rNJjr1u3Di+++CKeffZZ7N69G71798bkyZORkJBgdl29e/fGW2+9VemxbI3tWHk7AsCQIUNw/vx508/q1asrPa4tsB0rbkdFUTBjxgxcunQJP/zwA3bv3o3g4GDcc889yM/Pr/K9rU9NuR3z8/MRGRmJt99+G05OTuXuExERgffffx/79+/H5s2bERoaikmTJiE1NbXSY9cntmHlbXjrv6Xnz5/HTz/9BAAYP358pceub025HS25dn7GMWev7QjwM86t7LEda/MZpzG8r0uXLsX8+fPxwgsv4ODBg3jppZfw/PPPY9OmTZUeuy6zXKO4j31eXh5CQkLwww8/YNSoUVAUBe3atcNjjz1m+ia+sLAQEREReOONN/DQQw+Zvf7EiRMYOnQooqOjERoaatr+3XffYcGCBYiNjYVKpQJg/PZmwIABOH78OFq1alVuPaUNfeHCBdMHjPfeew/ffPMNzpw5A0EQ8Omnn+LLL7/EqVOnTK9bsWIF5s6di2vXrlV4rcOHD0eHDh3wySefmLZ1794d99xzD1577TWzfTMyMhAeHo4NGzZg0KBBlryVNsV2NG/HWbNmITMzE6tWrarO22hzbMeb7RgXF4eePXtiz5496NSpEwBAlmW0adMGr776Kh588MHqvLX1qim1460CAwPx7rvvYsaMGZXul5ubi5CQEKxduxbDhw+36Nj1jW1YeRs+9dRT2L9/P44ePWrRcW2lqbZjedd+K37GMbLXduRnHPtvR2t+xrHH9zUqKgo9evTAwoULTa/7v//7Pxw7dgybN2+u8FrrMsvZZY/97fLy8iDLMjw9PQEA8fHxSElJwbBhw0z7ODk5oX///tUaOllcXAyNRmP6D6H0OABw4MCBCl93+PBh9OvXz6zXYPjw4UhKSjINEenTpw9SUlKwadMmKIqCjIwMrFu3DnfddVeFx9XpdDh58qTZdQHAsGHDGtyQ0JpgO5ZtxwMHDqB169bo0aMHnnrqKaSlpVl83bbCdrzZjsXFxQAAR0dH0/OiKMLBwaHSmhuCptKONaHT6bBs2TK4u7ubPsw0RGzDiuXl5WHdunX4xz/+YdXj1oWm3I63X7s9YzuWbUd+xrnJHtvRmp9x7PF9LS4uNrv20mMfO3YMer2+3OPWdZZrFMH+xRdfRKdOndC7d28AMM0Z8fX1NdvP19e3WsMm77jjDmRkZOB///sfdDodsrOzMX/+fLNzlCc1NbXcc5c+BwC9e/fGkiVL8Pjjj8PX1xfh4eFQFAWLFy+u8LgZGRmQJKnW19VQsR3Nr+vOO+/EF198gd9++w1vvvkmjh07hnHjxpn+IW2o2I43r6tNmzYIDg7GggULkJWVBZ1Oh48++gjXrl2zaP6+LTWVdqyOzZs3IzAwEP7+/vj888/xyy+/wM/PzyrHrgtsw4qtWbMGxcXFmD59ulWPWxeacjvefu32jO1o3o78jGPOHtvRmp9x7PF9HT58OFasWIHjx49DURScOHEC33//PfR6PTIyMso9bl1nObsP9vPmzcPBgwexfPlys29jAEAQBLPHiqKU2VaZ9u3bY/HixVi8eLFpsYnQ0FD4+fmZztW3b18EBgYiMDAQkyZNqvTct24/d+4cXnzxRTz//PPYtWsX1q5di5SUFDz99NMAgP3795uOGxgYiJ9//tlq19UQsR3LXtfEiRNx9913o0OHDhg1ahTWrFmD2NhYbNmyxeJrr29sR/Pr0mg0WL58OS5fvoyWLVuiefPm2LNnD+66664y709D0hTb0RKDBg3Cnj17sHXrVgwfPhz//Oc/kZycXK1j1Be2YeWWLVuG0aNHw8fHp0avry9NuR0ru3Z7w3Yse+38jGPOHtvRWp9x7PV9ff755xEVFYWoqCj4+PjgvvvuM31ZrFKpbJLl1LU+gg299NJLWLduHTZs2ICwsDDTdn9/fwDGb1SCgoJM29PT08t8Q1KVyZMnY/LkyUhNTYWzszMEQcCiRYtM8zd+/vlnGAwGADeHovj5+ZX51iU9PR3AzW97PvzwQ3Tv3h1PPfUUAKBjx45wdnbGqFGj8Morr6Bbt25mKyD6+vrCwcEBKpWq3GNX97oaErajZdfVvHlztGjRApcuXarWtdcXtmP519W1a1fs3bsXOTk50Ov18PHxwfDhw9GtW7dqXXt9aWrtWB0uLi5o1aoVWrVqhV69eqF79+74/vvv8cILL1TrOHWNbVi5mJgYnDhxAq+++mq1X1ufmnI7VnTt9ojtaFk78jOOfbZjbT/j2PP76uTkhEWLFuGjjz5CamoqAgIC8N1338HNzQ3NmjWDq6trvWc5u+2xnzt3LtasWYP169ejTZs2Zs+FhobC398fO3fuNG0rKirCgQMH0KdPnxqdz8/PD66urli3bh0cHR0xZMgQAEBISIjpg16LFi0AGIe8HDhwAEVFRabX79y5E82bNzf9R1RYWFjmW6nSx4qiwMnJyXTcVq1awc3NDVqtFl27djW7rtJj1/S6bI3tCLNjV3ZdGRkZSEpKMv1j15CwHWF27PKuy8PDAz4+Prh48SJOnDiBu+++u0bXXpeaYjvWhizL0Ol0tTqGtbENq7Zs2TKEhISYam2ImnI7Vnbt9obtaHk78jPOTfbYjjX5jGPv72spjUaDwMBAqFQqrF27FiNGjIAoijbJcnYZ7J977jmsXLkSS5YsgaenJ1JSUpCSkmK6J6EgCJg1axY++ugjrF+/HmfOnMHs2bPh4uJiNsQiJSUFMTExiIuLA2C8FU5MTIzZrRa++uornDx5EnFxcfj666/xwgsv4NVXX610IZdJkybByckJs2fPxpkzZ7B+/Xp89NFHmD17tmmYxciRI/HHH39g6dKluHLlCg4ePIi5c+eiS5cuCA4OrvDYc+bMwcqVK/H999/j/PnzmDt3LpKTk81Wh8zKykJMTIzpfpCXL19GTExMg5vTy3asuB3z8vLw8ssv4/Dhw4iPj8eePXswbdo0+Pr6YsyYMTV+z+sC27Hyv4+//vordu/ejStXruD333/H+PHjMXr06DILp9haU27HvLw8xMTEICYmBrIsIzExETExMaZbz+Tm5uLNN9/E0aNHkZCQgJMnT2LOnDm4fv16g7pVGtuw4jYsVVBQgNWrV+PBBx9ssFPYmnI7VnXtAD/jNIZ25GecxtGOQM0/4zSG9zUuLg4//fQTLl68iGPHjuHhhx/G2bNn8corr1R67XWa5bKzsxV7+wFQ7s/cuXNN+2RlZSlz585V/P39FQcHB6V///7K/v37zY4zd+7cco+zaNEi0z5Tp05VvLy8FK1Wq3To0EH54osvLKpx3759Sr9+/RQHBwfF399fefHFF5WsrCyzfd555x2lXbt2ipOTk+Lv769MmjRJOX36dJXHfv/995Xg4GBFq9UqXbp0UX7//Xez5xctWlTl+9MQftiOFbdjUlKSMmzYMMXHx0fRaDRKUFCQMn36dOXUqVM2bze2Y/X+Pr799ttKYGCgqR2fe+45JTU11ebtxna8+bNhw4Zya54+fbqSnZ2tXL9+XRk9erQSEBCgaLVaJSAgQBk1apSybds2m7cb29CyNiz9+eyzzxSVSqWcPXvW5u3FdqzZtfMzjv23Iz/jNI52zM6u+WecxvC+Hjp0SOnUqZPi5OSkuLu7K3fffbdy5MgRi45dV1muUdzHnoiIiIiIiKipssuh+ERERERERERkxGBPREREREREZMcY7ImIiIiIiIjsGIM9ERERERERkR1jsCciIiIiIiKyYwz2RERERERERHaMwZ6IiKgR+OGHH+Dp6Yn4+Hhbl0JERET1jMGeiIioCdu8eTMWLlxo6zKIiIioFhjsiYiImrAtW7bgnXfesXUZREREVAsM9kRERERERER2jMGeiIjIzhw5cgRRUVHw9/dHx44d8b///Q+Kopjt88cff2Dq1Klo3749/Pz80LFjR7z22msoLi427TNr1ix8++23AABPT0/Tz63z9NeuXYvhw4ejefPmCAkJwdSpU3Hu3Ln6uVAiIiKyiNrWBRAREZHlzp07h/Hjx8PNzQ3PPfcctFotvvvuO7i4uJjtt2LFCqhUKjz++OPw9PTEoUOH8Omnn+LatWtYsmQJAOChhx7CtWvXsHv3bnz55Zem1/r4+AAAPvroI8yfPx9jx47FtGnTkJ+fjyVLlmDEiBH466+/EBYWVm/XTURERBUTsrOzlap3IyIioobggQcewObNm3H48GG0bNkSAJCeno7u3bsjNzcX0dHRCA0NRUFBAZydnc1e+9577+G///0vTp06hcDAQADAf/7zH3z77bfIzs422zchIQHdunXDs88+i5deesm0PTk5Gb1798a4cePw2Wef1e3FEhERkUU4FJ+IiMhOSJKE7du3Y+TIkaZQDxh72KdMmWK2b2mol2UZOTk5yMjIQP/+/aEoCqKjo6s814YNG2AwGDBx4kRkZGSYfjQaDXr27Indu3db9+KIiIioxjgUn4iIyE6kp6ejoKAAERERZZ5r3bq12eOzZ8/i1Vdfxd69e1FYWGj2XE5OTpXnunjxIgCgd+/e5T5/+2gAIiIish0GeyIiIjtRukCeIAgVPgcYg/vYsWPh5OSEV155BS1btoSTkxOuX7+O2bNnQ5blKs9Vus+aNWugVpf9uCCKHPRHRETUUDDYExER2QlfX184OzvjwoULZZ4r7WEHgD179iA9PR0bN27EwIEDTdt37txZ5nXlfUkAwDTUPygoCO3atatt6URERFSH+HU7ERGRnVCpVBg2bBg2b96My5cvm7anp6dj9erVZvsB5r34sixj0aJFZY5ZOqT+9sXzxo0bB7VajYULF5bbw5+enl6rayEiIiLrYY89ERGRHZk3bx527NiBUaNG4dFHH4VGo8F3332H4OBg09z5vn37wtvbG7NmzcLMmTOhVquxfv165OXllTlet27dAADPP/887rzzTqjVaowcORJhYWF4/fXX8X//93+48847MXbsWHh5eSEhIQFbt25Fz5498b///a9er52IiIjKx9vdERER2ZlDhw7h5ZdfRkxMDHx9ffHII4/A19cXTz75pOl2d0ePHjXt4+LignHjxuHhhx/GgAEDsGjRIsyYMQOAcaX9efPm4ZdffkFaWppp1fzQ0FAAwJYtW/Dpp58iOjoaBoMBzZs3R9++ffHoo4+ie/futnwbiIiIqASDPREREREREZEd4xx7IiIiIiIiIjvGYE9ERERERERkxxjsiYiIiIiIiOwYgz0RERERERGRHWOwJyIiIiIiIrJjDPZEREREREREdozBnoiIiIiIiMiOMdgTERERERER2TEGeyIiIiIiIiI7xmBPREREREREZMf+H5GdYlMPRgtTAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lineplot(data=cumulative_data, x='date', y='revenue', hue='group', palette='bright', ci=None, estimator='sum')\n",
    "plt.title('Cumulative revenue by groups')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Выручка в обеих группах растут практически из одной точки, но в группе B с 5.08 выручка начанает расти активнее. Впрочем 13.08 они снова пересекаются. Произошел едва заметный подскок в группе A и провал в группе B. Но начиная с 13.08 линии окончательно расходятся. Примерно 19.08 в группе B произошел резкий всплеск, который был заметен и на предыдущем графике. Наблюдаемый всплеск существенно отдалил линию B от A, но оставил линии параллельными. Однозначно это выброс. В ходе дальнейшего анализа пропробуем его нейтрализовать и посмотрим как повели бы себя графики без его участия. В любом случае, даже без всплеска видимая параллельность линий накопительной выручки мало очем говорит, т.к. выручка это метрика более высокого уровня. Понять причину событий поможет декомпозиция на более низкоуровневые метрики, типа число заказов, число клиентов, средний чек и т.п."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.02. График кумулятивного среднего чека по группам. \n",
    "[к оглавлению](#step_0)\n",
    "<a class=\"anchor\" id=\"step_3.02\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Средний чек (AOV — Average Order Value) — это отношение общей стоимости совершенных заказов (т.е. полученного дохода) к суммарному количеству заказов за определенный период. Измеряется в денежном эквиваленте."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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>date</th>\n",
       "      <th>group</th>\n",
       "      <th>orders</th>\n",
       "      <th>buyers</th>\n",
       "      <th>revenue</th>\n",
       "      <th>visitors</th>\n",
       "      <th>aov</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>2019-08-29</td>\n",
       "      <td>B</td>\n",
       "      <td>565</td>\n",
       "      <td>513</td>\n",
       "      <td>5266434</td>\n",
       "      <td>17708</td>\n",
       "      <td>9321.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>2019-08-30</td>\n",
       "      <td>A</td>\n",
       "      <td>512</td>\n",
       "      <td>460</td>\n",
       "      <td>3643218</td>\n",
       "      <td>18037</td>\n",
       "      <td>7115.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>2019-08-30</td>\n",
       "      <td>B</td>\n",
       "      <td>587</td>\n",
       "      <td>534</td>\n",
       "      <td>5477657</td>\n",
       "      <td>18198</td>\n",
       "      <td>9331.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>2019-08-31</td>\n",
       "      <td>A</td>\n",
       "      <td>522</td>\n",
       "      <td>468</td>\n",
       "      <td>3702201</td>\n",
       "      <td>18736</td>\n",
       "      <td>7092.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>2019-08-31</td>\n",
       "      <td>B</td>\n",
       "      <td>605</td>\n",
       "      <td>551</td>\n",
       "      <td>5592646</td>\n",
       "      <td>18916</td>\n",
       "      <td>9244.04</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         date group  orders  buyers  revenue  visitors     aov\n",
       "57 2019-08-29     B     565     513  5266434     17708 9321.12\n",
       "58 2019-08-30     A     512     460  3643218     18037 7115.66\n",
       "59 2019-08-30     B     587     534  5477657     18198 9331.61\n",
       "60 2019-08-31     A     522     468  3702201     18736 7092.34\n",
       "61 2019-08-31     B     605     551  5592646     18916 9244.04"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cumulative_data['aov'] = cumulative_data['revenue'] / cumulative_data['orders']\n",
    "cumulative_data.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lineplot(data=cumulative_data, x='date', y='aov', hue='group', palette='bright', ci=None, estimator='sum')\n",
    "plt.title('Cumulative average order value')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Вычленив из выручки влияние среднего чека более заметно сближение (и даже прогиб) линии B 13.05. Похоже на выброс в группе A, им мы займемся позже. Так же заметна аномалия 19.08. Теперь мы знаем что причиной тому поскочивший средний чек. Впрочем дальнейшее направлении линии B направлено в сторону уменьшения, а линия A фиксируется на уровне 7000, даже с небольшим ростом. Продолжим наблюдения."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.03. График относительного изменения кумулятивного среднего чека группы B к группе A.  \n",
    "[к оглавлению](#step_0)\n",
    "<a class=\"anchor\" id=\"step_3.03\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Преобразуем таблицу в горизонтальное положение кумулятивных выручек и заказов, чтобы можно посчитать относительное изменение кумулятивного среднего чека. назовем эту метрику Relative Change of Average Order Value и расчитаем по формуле  \n",
    "\n",
    "$$\n",
    "rc\\_{aov} = \\frac{revenueB}{ordersB} : \\frac{revenueA}{ordersA} - 1\n",
    "$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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>date</th>\n",
       "      <th>ordersA</th>\n",
       "      <th>ordersB</th>\n",
       "      <th>revenueA</th>\n",
       "      <th>revenueB</th>\n",
       "      <th>visitorsA</th>\n",
       "      <th>visitorsB</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>2019-08-27</td>\n",
       "      <td>468</td>\n",
       "      <td>520</td>\n",
       "      <td>3267966</td>\n",
       "      <td>5052226</td>\n",
       "      <td>16376</td>\n",
       "      <td>16523</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>2019-08-28</td>\n",
       "      <td>484</td>\n",
       "      <td>548</td>\n",
       "      <td>3365628</td>\n",
       "      <td>5148234</td>\n",
       "      <td>16970</td>\n",
       "      <td>17177</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>2019-08-29</td>\n",
       "      <td>503</td>\n",
       "      <td>565</td>\n",
       "      <td>3565038</td>\n",
       "      <td>5266434</td>\n",
       "      <td>17547</td>\n",
       "      <td>17708</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>2019-08-30</td>\n",
       "      <td>512</td>\n",
       "      <td>587</td>\n",
       "      <td>3643218</td>\n",
       "      <td>5477657</td>\n",
       "      <td>18037</td>\n",
       "      <td>18198</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>2019-08-31</td>\n",
       "      <td>522</td>\n",
       "      <td>605</td>\n",
       "      <td>3702201</td>\n",
       "      <td>5592646</td>\n",
       "      <td>18736</td>\n",
       "      <td>18916</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         date  ordersA  ordersB  revenueA  revenueB  visitorsA  visitorsB\n",
       "26 2019-08-27      468      520   3267966   5052226      16376      16523\n",
       "27 2019-08-28      484      548   3365628   5148234      16970      17177\n",
       "28 2019-08-29      503      565   3565038   5266434      17547      17708\n",
       "29 2019-08-30      512      587   3643218   5477657      18037      18198\n",
       "30 2019-08-31      522      605   3702201   5592646      18736      18916"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cumulative_data_horizont = cumulative_data.pivot_table(\n",
    "    index='date', columns=['group'], values=['revenue', 'orders', 'visitors']).reset_index()\n",
    "cumulative_data_horizont.columns = [''.join(col).strip() for col in cumulative_data_horizont.columns.values]\n",
    "cumulative_data_horizont.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\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>date</th>\n",
       "      <th>ordersA</th>\n",
       "      <th>ordersB</th>\n",
       "      <th>revenueA</th>\n",
       "      <th>revenueB</th>\n",
       "      <th>visitorsA</th>\n",
       "      <th>visitorsB</th>\n",
       "      <th>rc_aov</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>2019-08-27</td>\n",
       "      <td>468</td>\n",
       "      <td>520</td>\n",
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       "      <td>5052226</td>\n",
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       "      <td>0.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
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       "      <td>16970</td>\n",
       "      <td>17177</td>\n",
       "      <td>0.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>2019-08-29</td>\n",
       "      <td>503</td>\n",
       "      <td>565</td>\n",
       "      <td>3565038</td>\n",
       "      <td>5266434</td>\n",
       "      <td>17547</td>\n",
       "      <td>17708</td>\n",
       "      <td>0.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>2019-08-30</td>\n",
       "      <td>512</td>\n",
       "      <td>587</td>\n",
       "      <td>3643218</td>\n",
       "      <td>5477657</td>\n",
       "      <td>18037</td>\n",
       "      <td>18198</td>\n",
       "      <td>0.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>2019-08-31</td>\n",
       "      <td>522</td>\n",
       "      <td>605</td>\n",
       "      <td>3702201</td>\n",
       "      <td>5592646</td>\n",
       "      <td>18736</td>\n",
       "      <td>18916</td>\n",
       "      <td>0.30</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         date  ordersA  ordersB  revenueA  revenueB  visitorsA  visitorsB  \\\n",
       "26 2019-08-27      468      520   3267966   5052226      16376      16523   \n",
       "27 2019-08-28      484      548   3365628   5148234      16970      17177   \n",
       "28 2019-08-29      503      565   3565038   5266434      17547      17708   \n",
       "29 2019-08-30      512      587   3643218   5477657      18037      18198   \n",
       "30 2019-08-31      522      605   3702201   5592646      18736      18916   \n",
       "\n",
       "    rc_aov  \n",
       "26    0.39  \n",
       "27    0.35  \n",
       "28    0.32  \n",
       "29    0.31  \n",
       "30    0.30  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cumulative_data_horizont['rc_aov'] = (\n",
    "    cumulative_data_horizont['revenueB'] / cumulative_data_horizont['ordersB']) / (\n",
    "    cumulative_data_horizont['revenueA'] / cumulative_data_horizont['ordersA']) - 1\n",
    "cumulative_data_horizont.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lineplot(data=cumulative_data_horizont, x='date', y='rc_aov', palette='bright', ci=None, estimator='mean')\n",
    "plt.axhline(y=0, color='black', linestyle='--')\n",
    "plt.title('Relative change of average order value of group B to group A')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "После относительно роста среднего чека группы B 6.08 и 7.08, 13.08 снова наблюдается провал и резкий подъем 19.08. Все эти выбросы в обеих группах заставляют скакать линию относительного изменения среднего чека вокруг нуля. Иначе при стабильном росте линия была бы параллельна пунктирной оси и располагалась сверху, в случае стабильного падения снизу, а без изменений слилась бы с ней. Займемся выбросами дальше.\n",
    "<a class=\"anchor\" id=\"step_8\"></a> [обратно](#step_10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.04 График кумулятивной конверсии по группам. \n",
    "[к оглавлению](#step_0)\n",
    "<a class=\"anchor\" id=\"step_3.04\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "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>date</th>\n",
       "      <th>group</th>\n",
       "      <th>orders</th>\n",
       "      <th>buyers</th>\n",
       "      <th>revenue</th>\n",
       "      <th>visitors</th>\n",
       "      <th>aov</th>\n",
       "      <th>cr</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>2019-08-29</td>\n",
       "      <td>B</td>\n",
       "      <td>565</td>\n",
       "      <td>513</td>\n",
       "      <td>5266434</td>\n",
       "      <td>17708</td>\n",
       "      <td>9321.12</td>\n",
       "      <td>0.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>2019-08-30</td>\n",
       "      <td>A</td>\n",
       "      <td>512</td>\n",
       "      <td>460</td>\n",
       "      <td>3643218</td>\n",
       "      <td>18037</td>\n",
       "      <td>7115.66</td>\n",
       "      <td>0.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>2019-08-30</td>\n",
       "      <td>B</td>\n",
       "      <td>587</td>\n",
       "      <td>534</td>\n",
       "      <td>5477657</td>\n",
       "      <td>18198</td>\n",
       "      <td>9331.61</td>\n",
       "      <td>0.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>2019-08-31</td>\n",
       "      <td>A</td>\n",
       "      <td>522</td>\n",
       "      <td>468</td>\n",
       "      <td>3702201</td>\n",
       "      <td>18736</td>\n",
       "      <td>7092.34</td>\n",
       "      <td>0.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>2019-08-31</td>\n",
       "      <td>B</td>\n",
       "      <td>605</td>\n",
       "      <td>551</td>\n",
       "      <td>5592646</td>\n",
       "      <td>18916</td>\n",
       "      <td>9244.04</td>\n",
       "      <td>0.03</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         date group  orders  buyers  revenue  visitors     aov   cr\n",
       "57 2019-08-29     B     565     513  5266434     17708 9321.12 0.03\n",
       "58 2019-08-30     A     512     460  3643218     18037 7115.66 0.03\n",
       "59 2019-08-30     B     587     534  5477657     18198 9331.61 0.03\n",
       "60 2019-08-31     A     522     468  3702201     18736 7092.34 0.03\n",
       "61 2019-08-31     B     605     551  5592646     18916 9244.04 0.03"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cumulative_data['cr'] = cumulative_data['orders'] / cumulative_data['visitors']\n",
    "cumulative_data.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lineplot(data=cumulative_data, x='date', y='cr', hue='group', palette='bright', ci=None, estimator='mean')\n",
    "plt.axis([cumulative_data.date.min(),\n",
    "          cumulative_data.date.max(), 0, 0.05])\n",
    "plt.title('Cumulative conversion by groups')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Коневерсия группы B по началу несколько отстает от группы A, но после 10.08 устойчиво превышает и фиксируется на одном значении. Это значение определим в следующем пункте."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.05. График относительного изменения кумулятивной конверсии группы B к группе A. \n",
    "[к оглавлению](#step_0)\n",
    "<a class=\"anchor\" id=\"step_3.05\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "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>date</th>\n",
       "      <th>ordersA</th>\n",
       "      <th>ordersB</th>\n",
       "      <th>revenueA</th>\n",
       "      <th>revenueB</th>\n",
       "      <th>visitorsA</th>\n",
       "      <th>visitorsB</th>\n",
       "      <th>rc_aov</th>\n",
       "      <th>rc_cr</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>2019-08-27</td>\n",
       "      <td>468</td>\n",
       "      <td>520</td>\n",
       "      <td>3267966</td>\n",
       "      <td>5052226</td>\n",
       "      <td>16376</td>\n",
       "      <td>16523</td>\n",
       "      <td>0.39</td>\n",
       "      <td>0.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>2019-08-28</td>\n",
       "      <td>484</td>\n",
       "      <td>548</td>\n",
       "      <td>3365628</td>\n",
       "      <td>5148234</td>\n",
       "      <td>16970</td>\n",
       "      <td>17177</td>\n",
       "      <td>0.35</td>\n",
       "      <td>0.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>2019-08-29</td>\n",
       "      <td>503</td>\n",
       "      <td>565</td>\n",
       "      <td>3565038</td>\n",
       "      <td>5266434</td>\n",
       "      <td>17547</td>\n",
       "      <td>17708</td>\n",
       "      <td>0.32</td>\n",
       "      <td>0.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>2019-08-30</td>\n",
       "      <td>512</td>\n",
       "      <td>587</td>\n",
       "      <td>3643218</td>\n",
       "      <td>5477657</td>\n",
       "      <td>18037</td>\n",
       "      <td>18198</td>\n",
       "      <td>0.31</td>\n",
       "      <td>0.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>2019-08-31</td>\n",
       "      <td>522</td>\n",
       "      <td>605</td>\n",
       "      <td>3702201</td>\n",
       "      <td>5592646</td>\n",
       "      <td>18736</td>\n",
       "      <td>18916</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.15</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         date  ordersA  ordersB  revenueA  revenueB  visitorsA  visitorsB  \\\n",
       "26 2019-08-27      468      520   3267966   5052226      16376      16523   \n",
       "27 2019-08-28      484      548   3365628   5148234      16970      17177   \n",
       "28 2019-08-29      503      565   3565038   5266434      17547      17708   \n",
       "29 2019-08-30      512      587   3643218   5477657      18037      18198   \n",
       "30 2019-08-31      522      605   3702201   5592646      18736      18916   \n",
       "\n",
       "    rc_aov  rc_cr  \n",
       "26    0.39   0.10  \n",
       "27    0.35   0.12  \n",
       "28    0.32   0.11  \n",
       "29    0.31   0.14  \n",
       "30    0.30   0.15  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cumulative_data_horizont['rc_cr'] = (\n",
    "    cumulative_data_horizont['ordersB'] / cumulative_data_horizont['visitorsB']) / (\n",
    "    cumulative_data_horizont['ordersA'] / cumulative_data_horizont['visitorsA']) - 1\n",
    "cumulative_data_horizont.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lineplot(data=cumulative_data_horizont, x='date', y='rc_cr', palette='bright', ci=None, estimator='mean')\n",
    "plt.axhline(y=0, color='black', linestyle='--')\n",
    "plt.axhline(y=float(cumulative_data_horizont.rc_cr[-1:]), color='green', linestyle='--') \n",
    "plt.title('Relative change of conversion of group B to group A')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Конверсия группы B в первые дни эксперимента вырвавшись вперед на некоторое время просела относительно группы A. Но дальше - примерно после 10.08 она всегда превышала группу A примерно на 15% <a class=\"anchor\" id=\"step_5\"></a> [обратно](#step_10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.06. Точечный график количества заказов по пользователям.  \n",
    "[к оглавлению](#step_0)\n",
    "<a class=\"anchor\" id=\"step_3.06\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Начиная с этого раздела приступим к анализу выбросов в искажающих чистоту нашего эксперимента.  \n",
    "Сперва оценим количество заказов, совершенных клиентами на предмет нестандартных значений"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count   996.00\n",
       "mean      1.13\n",
       "std       0.72\n",
       "min       1.00\n",
       "25%       1.00\n",
       "50%       1.00\n",
       "75%       1.00\n",
       "max      11.00\n",
       "Name: orders, dtype: float64"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Создадим датафрейм, в котором будет лишь информация о клиенте и количество созданых им заказов\n",
    "orders_by_users = orders.groupby('visitorid', as_index=False).agg({'transactionid':pd.Series.nunique})\n",
    "orders_by_users.columns = ['visitorid', 'orders']\n",
    "orders_by_users['orders'].describe().T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Судя по всему клиенты чаще всего совершают 1 заказ - не менее 75%, но бывает что и 11. Проверим эти данные гистограммой."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Vzz//vHr06CFXV1ft27dP+/btU6dOnbRp0yab9i4uLvL391d8fLyCgoLk5+enEiVKqGXLltnevJRp1qxZ6ty5s9577z3t3LlTzZo1sz6H9saNG5ozZ06hvUrY09NT06ZN0+jRo9W+fXv16NFDZrNZCQkJiouLU/Xq1TVz5swHPk7z5s01evRozZo1S0888YR69Oih8uXLKzY2Vj///LPq1aunt99+O8/7c3Jy0ty5c9WzZ08FBwfbPIc2NjZWTz31lLZu3WqzTcmSJfXpp5+qV69eeu6559S0aVM1btxYLi4uSkpK0v79+5WYmKidO3fm+QkF9lzbORk8eLCWLl2q4OBgPfPMM6pataoOHTqkrVu3qmfPnlq9enWeP5f8KKproH379vrxxx81cOBAdezYUaVLl5aHh4f69+9fAGcBOA6BFviLmzZtmqR7QeCRRx5RtWrV1L17dwUGBqpz5855Hk0bM2aM1q1bp3//+9/WUahq1aopJCREI0aMsAlrbdu21fTp07Vo0SItWLDAejNMQQXaKVOmaO3atVq8eLFOnTqlypUra8SIEQoLC8vyUoXWrVtr+fLlmjp1qtasWaNSpUqpZcuW2rJli77++ussgVa6NwI8YcIEfffdd9qyZYsyMjI0bty4XAOtl5eXduzYocjISG3cuFF79uyRi4uLWrVqpX/84x+5Po+3IAQHB6tWrVqaO3eu1q1bp2vXrqlq1aoaOnRolsdCPYiJEyeqUaNGmj9/vr744gvdunVLXl5eGjt2rF577TW7b8bz9/fXhg0bNGnSJG3btk3btm1TkyZN9M0332jbtm1ZAq1072atuLg4RUdHa/369Vq2bJksFovMZrN8fX316quv5nnag2TftZ2TBg0aaO3atZo8ebI2b96s9PR0NWjQQEuWLFGFChUKPdBKRXMNjBkzRpcvX9b69es1Z84c3b17V61atSLQwvCc0tLS8vZOQAAAAOAviDm0AAAAMDQCLQAAAAyNQAsAAABDI9ACAADA0Ai0AAAAMDQCLQAAAAyNQAsAAABDI9DigSUmJjq6BBQA+rF4oB+Njz4sHujHokWgBQAAgKERaAEAAGBoBFoAAAAYGoEWAAAAhkagBQAAgKERaAEAAGBoBFoAAAAYGoEWAAAAhlbC0QUUd6ZFSY4uodB939rRFQAAgP9ljNACAADA0Ai0AAAAMDQCLQAAAAyNQAsAAABDI9ACAADA0Ai0AAAAMDQCLQAAAAyNQAsAAABDI9ACAADA0Ai0AAAAMDQCLQAAAAyNQAsAAABDI9ACAADA0Ai0AAAAMDQCLQAAAAyNQAsAAABDI9ACAADA0Ai0AAAAMDQCLQAAAAyNQAsAAABDI9ACAADA0Ai0AAAAMDQCLQAAAAyNQAsAAABDI9ACAADA0Ai0AAAAMDQCLQAAAAzNYYE2PT1dkydPVqNGjWQ2m9WoUSNNnjxZd+/etbaxWCyKiIiQr6+v3NzcFBgYqEOHDtns59atWwoNDVWtWrVUrVo19e/fX0lJSUV9OgAAAHAQhwXa2bNna8GCBZo2bZr27t2rqVOn6sMPP9SsWbOsbebMmaOoqChNmzZN27dvl6urq3r27KkrV65Y24SFhWnt2rVauHCh1q9frytXrigoKEjp6emOOC0AAAAUMYcF2r1796pz587q0qWLvLy81LVrV3Xp0kU//vijpHujs9HR0Ro1apS6d++uevXqKTo6WlevXtXKlSslSZcuXdKSJUsUHh6u9u3by8/PTzExMTp48KB27NjhqFMDAABAEXJYoPX399fu3bt19OhRSdLhw4e1a9cuPf3005KkkydPKjk5WQEBAdZtypQpo5YtWyohIUGStG/fPt25c8emjbu7u+rWrWttAwAAgOKthKMOPGrUKF29elUtWrSQs7Oz7t69q7Fjx2rIkCGSpOTkZEmSq6urzXaurq46e/asJCklJUXOzs6qVKlSljYpKSlFcBYAAABwNIcF2tWrV+vzzz/XggUL5OvrqwMHDmj8+PHy9PTUoEGDrO2cnJxstrNYLFmW/dn92iQmJj5Y8XYpW4THcpyi/UxRWOjH4oF+ND76sHigHwuWj49PjuscFmgnTpyokSNHqnfv3pKk+vXr6/Tp03r//fc1aNAgmc1mSfdGYd3d3a3bpaamWkdtq1SpovT0dJ0/f16VK1e2adOyZcscj53bB1Lgdv9vPHGhSD9TFIrExET6sRigH42PPiwe6Mei5bA5tNevX5ezs7PNMmdnZ2VkZEiSvLy8ZDabFRsba11/8+ZNxcfHq0WLFpIkPz8/lSxZ0qZNUlKSjhw5Ym0DAACA4s1hI7SdO3fW7Nmz5eXlJV9fX+3fv19RUVHq37+/pHtTDUJCQjRz5kz5+PjI29tbkZGRcnFxUZ8+fSRJFSpU0MCBAzVx4kS5urqqYsWKmjBhgurXr6927do56tQAAABQhBwWaKdPn6733ntPY8aMUWpqqsxms1544QW98cYb1javvfaabty4odDQUKWlpalJkyZavXq1ypUrZ20zZcoUOTs7Kzg4WDdv3tSTTz6pefPmZRn9BQAAQPHklJaWZnF0EcWZaVHxn0P7fevrzBMqBpjvVTzQj8ZHHxYP9GPRctgcWgAAAKAgEGgBAABgaARaAAAAGBqBFgAAAIZGoAUAAIChEWgBAABgaARaAAAAGBqBFgAAAIZGoAUAAIChEWgBAABgaARaAAAAGBqBFgAAAIZGoAUAAIChEWgBAABgaARaAAAAGBqBFgAAAIZGoAUAAIChEWgBAABgaARaAAAAGBqBFgAAAIZGoAUAAIChEWgBAABgaARaAAAAGBqBFgAAAIZGoAUAAIChEWgBAABgaARaAAAAGJpdgfbFF1/Uxo0blZ6eXlj1AAAAAHaxK9DGx8frueeeU926dfXGG2/op59+Kqy6AAAAgDyxK9AeOnRIX3zxhQICAvTZZ5/pqaeeUtOmTRUZGalTp04VVo0AAABAjuwKtA899JA6dOig+fPn6+jRo4qOjpaXl5emTp0qPz8/de3aVZ988okuXbpUWPUCAAAANvJ9U1jZsmUVFBSkVatW6ZdfflGPHj0UHx+vUaNGydfXV4MHD2ZKAgAAAApdiQfZ+MSJE1qxYoVWrFihX3/9VVWqVFHfvn1VqlQpLVu2TF9++aWmTJmi4cOHF1S9AAAAgA27A21aWppWr16t5cuX6/vvv1epUqXUpUsXTZkyRR06dJCzs7MkKSwsTEOHDtWsWbMItAAAACg0dgXaAQMGaOvWrbp9+7aaNWummTNnqmfPnjKZTFnalixZUl27dtVXX31VQKUCAAAAWdkVaH/++We9+uqrevbZZ1W7du37tm/fvr3Wrl2b7+IAAACA+7Er0B44cEBOTk55bl+5cmW1bt3a7qIAAACAvLLrKQdHjx7V8uXLc1y/YsUKHT16NM/7O3funIYPH67atWvLbDarRYsW2r17t3W9xWJRRESEfH195ebmpsDAQB06dMhmH7du3VJoaKhq1aqlatWqqX///kpKSrLntAAAAGBgdgXad999V6tWrcpx/apVqxQeHp6nfaWlpalTp06yWCxasWKFEhISNH36dLm6ulrbzJkzR1FRUZo2bZq2b98uV1dX9ezZU1euXLG2CQsL09q1a7Vw4UKtX79eV65cUVBQEK/nBQAA+B9hV6D94Ycf1KZNmxzXt2nTRj/88EOe9vXBBx/Izc1NMTExatKkiWrUqKG2bduqbt26ku6NzkZHR2vUqFHq3r276tWrp+joaF29elUrV66UJF26dElLlixReHi42rdvLz8/P8XExOjgwYPasWOHPacGAAAAg7Ir0F66dEllypTJcX3p0qV18eLFPO1r3bp1atKkiYKDg+Xt7a3WrVtr/vz5slgskqSTJ08qOTlZAQEB1m3KlCmjli1bKiEhQZK0b98+3blzx6aNu7u76tata20DAACA4s2um8K8vLwUFxenIUOGZLs+Li5O7u7uedrXiRMntHDhQo0YMUKjRo3SgQMHNG7cOEnS0KFDlZycLEk2UxAyfz579qwkKSUlRc7OzqpUqVKWNikpKTkeOzExMU81FoyyRXgsxynazxSFhX4sHuhH46MPiwf6sWD5+PjkuM6uQNu3b19FRETo/fff18iRI1WyZElJ0t27dxUVFaWvv/5aoaGhedpXRkaGHnvsMb3zzjuSpMaNG+v48eNasGCBhg4dam3356cqWCyW+z5p4X5tcvtACtzu/40b1Ir0M0WhSExMpB+LAfrR+OjD4oF+LFp2BdpRo0Zpz549Cg8P1wcffCBvb285OTnp2LFjunjxotq2basxY8bkaV9ms9k6XzZTnTp1dObMGet66d4o7B9HfVNTU62jtlWqVFF6errOnz+vypUr27Rp2bKlPacGAAAAg7JrDm3JkiW1cuVK/d///Z+aN2+uy5cvKy0tTc2aNVNUVJRWr16tUqVK5Wlf/v7+OnbsmM2yY8eOycPDQ9K96Q1ms1mxsbHW9Tdv3lR8fLxatGghSfLz81PJkiVt2iQlJenIkSPWNgAAACje7Bqhle5NARgwYIAGDBjwQAceMWKEOnbsqMjISPXq1Uv79+/X/Pnz9fbbb1uPExISopkzZ8rHx0fe3t6KjIyUi4uL+vTpI0mqUKGCBg4cqIkTJ8rV1VUVK1bUhAkTVL9+fbVr1+6B6gMAAIAx2B1oC8rjjz+upUuXKjw8XDNmzJC7u7vefPNNmxvOXnvtNd24cUOhoaFKS0tTkyZNtHr1apUrV87aZsqUKXJ2dlZwcLBu3rypJ598UvPmzZOzs7MjTgsAAABFzCktLc1izwY7duzQ4sWLdeLECV28eNH6mC3rDp2ctG/fvoKs0dBMi4r/TWHft77OxPdigBsYigf60fjow+KBfixado3QRkdHa8KECapcubKaNm2qRx99tLDqAgAAAPLErkAbFRWlVq1aadWqVXm++QsAAAAoTHY95eD8+fPq1asXYRYAAAB/GXYFWj8/P506daqwagEAAADsZlegfe+99/TZZ59p586dhVUPAAAAYBe75tBGRESofPny6tGjh2rXri0PD48sj8dycnLSihUrCrRIAAAAICd2BdrDhw/LyclJ7u7uunXrVpY3fUn3Ai0AAABQVOwKtAcOHCisOgAAAIB8sWsOLQAAAPBXY3egTU9P14oVKzRy5EgFBQXpP//5jyQpLS1NX375pc6dO1fgRQIAAAA5sSvQXrp0SR07dtSwYcP09ddfa8uWLTp//rwkqVy5cpowYYLmz59fKIUCAAAA2bEr0L777rs6fPiwvvjiC+3bt08Wi8W6ztnZWd26ddOWLVsKvEgAAAAgJ3YF2nXr1mno0KF66qmnsn2aQe3atXX69OkCKw4AAAC4H7sCbVpammrWrJnjeovFotu3bz9wUQAAAEBe2RVoPT099csvv+S4Pi4uTt7e3g9cFAAAAJBXdgXavn376pNPPlFcXJx1WebUg5iYGH3zzTd67rnnCrZCAAAAIBd2vVjh9ddf1w8//KBnnnlG3t7ecnJy0vjx43XhwgUlJycrMDBQw4YNK6xaAQAAgCzsCrQlS5bUihUr9MUXX+irr76Sk5OT7t69q8aNG6tXr17q168fr74FAABAkbIr0Gbq27ev+vbtW9C1AAAAAHbj1bcAAAAwNLtGaLt163bfNk5OTlqzZk2+CwIAAADsYVegzcjIyDJHNj09XadPn1ZSUpJq1aqlqlWrFmiBAAAAQG7sCrTr1q3Ldd3rr7+uRYsWPXBRAAAAQF4V2BzawMBA9enTR2+++WZB7RIAAAC4rwK9KaxOnTr66aefCnKXAAAAQK4KNNBu2bJF5cuXL8hdAgAAALmyaw7ttGnTsl1+6dIl7d69WwcOHNDYsWMLpDAAAAAgL+wKtFOnTs12uclkUq1atfTBBx/o+eefL5DCAAAAgLywK9BevHixsOoAAAAA8oU3hQEAAMDQ7BqhPX36dL4O4uHhka/tAAAAgPuxK9A2atQoy5vC8uLChQt2bwMAAADkhV2Bdu7cuYqJidGpU6fUr18/eXt7y2Kx6NixY1q5cqU8PT01bNiwfIVeAAAAID/sCrSpqam6fv26/v3vf+tvf/ubzbrx48erY8eOOn/+vP7xj38UaJEAAABATuy6KWzBggV68cUXs4RZSapcubJeeOEFffjhhwVWHAAAAHA/dgXa1NRU3blzJ8f1d+/eVWpq6gMXBQAAAOSVXYG2UaNGWrBggU6ePJll3YkTJ7RgwQI1atQoX4XMnDlTJpNJoaGh1mUWi0URERHy9fWVm5ubAgMDdejQIZvtbt26pdDQUNWqVUvVqlVT//79lZSUlK8aAAAAYDx2Bdr33ntPly9fVvPmzfXiiy9q8uTJmjx5sl544QW1aNFCly9f1uTJk+0u4vvvv9fixYtVv359m+Vz5sxRVFSUpk2bpu3bt8vV1VU9e/bUlStXrG3CwsK0du1aLVy4UOvXr9eVK1cUFBSk9PR0u+sAAACA8dgVaJs1a6Zt27apY8eO2rJli2bOnKmZM2dq69at6tixo7Zu3apmzZrZVcClS5f08ssva+7cuTKZTNblFotF0dHRGjVqlLp376569eopOjpaV69e1cqVK63bLlmyROHh4Wrfvr38/PwUExOjgwcPaseOHXbVAQAAAGOy+01hdevW1ZIlS3T69GkdOXJEhw8f1unTp7VkyRI9+uijdheQGVjbtm1rs/zkyZNKTk5WQECAdVmZMmXUsmVLJSQkSJL27dunO3fu2LRxd3dX3bp1rW0AAABQvNn12K4/slgsKlGihMqXL6+HHsrfG3QXL16s48ePKyYmJsu65ORkSZKrq6vNcldXV509e1aSlJKSImdnZ1WqVClLm5SUlByPm5iYmK9686dsER7LcYr2M0VhoR+LB/rR+OjD4oF+LFg+Pj45rrM70P70008KDw9XfHy87ty5oy+//FJt27bV+fPnFRISoldeeSXLaGt2EhMTFR4erg0bNqhUqVI5tvvzSxosFst9X9xwvza5fSAFbvf/xg1qRfqZolAkJibSj8UA/Wh89GHxQD8WLbuGVvfu3auuXbvqv//9r/r37y+LxWJdV6lSJV29elVLlizJ877Onz+vJ554QpUqVVKlSpUUFxenBQsWqFKlStZn3f55pDU1NdU6alulShWlp6fr/PnzObYBAABA8WZXoJ00aZJq166thIQETZw4Mcv6Nm3a6IcffsjTvgIDA/Xdd99p165d1n+PPfaYevfurV27dsnb21tms1mxsbHWbW7evKn4+Hi1aNFCkuTn56eSJUvatElKStKRI0esbQAAAFC82TXl4KefftJbb72l0qVL6/r161nWV69e3Tr39X5MJpPNUw0kqWzZsqpYsaLq1asnSQoJCdHMmTPl4+Mjb29vRUZGysXFRX369JEkVahQQQMHDtTEiRPl6uqqihUrasKECapfv77atWtnz6kBAADAoOwKtA899FCuN4AlJyerTJkyD1xUptdee003btxQaGio0tLS1KRJE61evVrlypWztpkyZYqcnZ0VHBysmzdv6sknn9S8efPk7OxcYHUAAADgr8uuQOvn56eNGzdq2LBhWdbdvn1bX3zxhZo3b57vYtatW2fzs5OTk8LCwhQWFpbjNqVLl9aMGTM0Y8aMfB8XAAAAxmXXHNrRo0dr586dGjlypA4cOCBJOnfunLZu3apnnnlG//3vfzVmzJhCKRQAAADIjl0jtO3bt1dMTIxCQ0P12WefSbo3z9VisahChQpasGCB3W8KAwAAAB6E3c+h7dOnj7p27arY2Fj9+uuvysjIUM2aNdWhQwc98sgjhVEjAAAAkKM8B9obN27oiSee0PDhwzV8+HAFBgYWZl0AAABAnuR5Dm2ZMmV0+fLlXN/qBQAAABQ1u24K69ixozZv3lxYtQAAAAB2syvQvv766zp16pRefPFFffvttzp16pR+//33LP8AAACAomLXTWH+/v6SpEOHDmnNmjU5trtw4cKDVQUAAADkUa6BNi4uTnXr1lXlypUlSePGjSuSogAAAIC8yjXQduvWTTExMerbt68kadmyZYqIiFDXrl2LpDgAAADgfnKdQ+vi4qJr165Zfz516pTNzwAAAICj5TpC26BBA82ZM0e3bt1S+fLlJUnx8fG6e/durjt99tlnC65CAAAAIBe5BtqIiAgFBwdr/PjxkiQnJyctWrRIixYtynEbJycnAi0AAACKTK6B1s/PTz/++KPOnDmj33//XU899ZTCwsIUEBBQVPUBAAAAubrvY7seeugheXp6ytPTU88++6wCAgLUtGnToqgNAAAAuC+7nkP7r3/9q7DqAAAAAPLFrjeFAQAAAH81BFoAAAAYGoEWAAAAhkagBQAAgKERaAEAAGBoBFoAAAAYGoEWAAAAhkagBQAAgKERaAEAAGBoBFoAAAAYGoEWAAAAhkagBQAAgKERaAEAAGBoBFoAAAAYGoEWAAAAhkagBQAAgKERaAEAAGBoBFoAAAAYGoEWAAAAhkagBQAAgKERaAEAAGBoDgu0s2bNUvv27eXh4aHatWsrKChIv/zyi00bi8WiiIgI+fr6ys3NTYGBgTp06JBNm1u3bik0NFS1atVStWrV1L9/fyUlJRXlqQAAAMCBHBZod+/ercGDB2vTpk1as2aNSpQooR49eujixYvWNnPmzFFUVJSmTZum7du3y9XVVT179tSVK1esbcLCwrR27VotXLhQ69ev15UrVxQUFKT09HRHnBYAAACKWAlHHXj16tU2P8fExMjT01N79uxRly5dZLFYFB0drVGjRql79+6SpOjoaPn4+GjlypUKDg7WpUuXtGTJEkVFRal9+/bW/TRs2FA7duxQhw4divy8AAAAULT+MnNor169qoyMDJlMJknSyZMnlZycrICAAGubMmXKqGXLlkpISJAk7du3T3fu3LFp4+7urrp161rbAAAAoHhz2Ajtn40fP14NGzZU8+bNJUnJycmSJFdXV5t2rq6uOnv2rCQpJSVFzs7OqlSpUpY2KSkpOR4rMTGxIEu/j7JFeCzHKdrPFIWFfiwe6Efjow+LB/qxYPn4+OS47i8RaN98803t2bNHGzdulLOzs806Jycnm58tFkuWZX92vza5fSAFbvf/xg1qRfqZolAkJibSj8UA/Wh89GHxQD8WLYdPOQgLC9OqVau0Zs0a1ahRw7rcbDZLUpaR1tTUVOuobZUqVZSenq7z58/n2AYAAADFm0MD7bhx47Ry5UqtWbNGderUsVnn5eUls9ms2NhY67KbN28qPj5eLVq0kCT5+fmpZMmSNm2SkpJ05MgRaxsAAAAUbw6bcjB27FgtX75cn376qUwmk3XOrIuLix555BE5OTkpJCREM2fOlI+Pj7y9vRUZGSkXFxf16dNHklShQgUNHDhQEydOlKurqypWrKgJEyaofv36ateunaNODQAAAEXIYYF2wYIFkmR9JFemcePGKSwsTJL02muv6caNGwoNDVVaWpqaNGmi1atXq1y5ctb2U6ZMkbOzs4KDg3Xz5k09+eSTmjdvXpa5uAAAACienNLS0iyOLqI4My0q/jeFfd/6OhPfiwFuYCge6Efjow+LB/qxaDn8pjAAAADgQRBoAQAAYGgEWgAAABgagRYAAACGRqAFAACAoRFoAQAAYGgEWgAAABgagRYAAACGRqAFAACAoRFoAQAAYGgEWgAAABgagRYAAACGRqAFAACAoRFoAQAAYGgEWgAAABgagRYAAACGRqAFAACAoRFoAQAAYGgEWgAAABgagRYAAACGRqAFAACAoRFoAQAAYGgEWgAAABgagRYAAACGRqAFAACAoRFoAQAAYGgEWgAAABgagRYAAACGRqAFAACAoRFoAQAAYGgEWgAAABgagRYAAACGRqAFAACAoZVwdAEwvma7y0q7kxxdRqFKC67u6BIAAEAOGKEFAACAoRFoAQAAYGjFZsrBggUL9MEHHyg5OVm+vr6KiIhQy5YtHV0WignTouI9pUKSvm/t6AoAAMifYjFCu3r1ao0fP15jxozRzp071bx5c/Xt21enT592dGkAAAAoZMUi0EZFRem5557TCy+8oLp162rGjBkym8366KOPHF0aAAAACpnhpxzcvn1b+/bt06uvvmqzPCAgQAkJCQ6q6v/j7ngARcnHx8fRJeAB0YfFA/1YtAw/Qnv+/Hmlp6fL1dXVZrmrq6tSUlIcVBUAAACKiuEDbSYnJyebny0WS5ZlAAAAKH4MH2grVaokZ2fnLKOxqampWUZtAQAAUPwYPtCWKlVKfn5+io2NtVkeGxurFi1aOKgqAAAAFBXD3xQmSa+88oqGDRumJk2aqEWLFvroo4907tw5BQcHO7o0AAAAFDLDj9BKUq9evRQREaEZM2aoTZs22rNnj1asWCFPT09Hl1YszZo1S+3bt5eHh4dq166toKAg/fLLL44uCw9o5syZMplMCg0NdXQpsNO5c+c0fPhw1a5dW2azWS1atNDu3bsdXRbskJ6ersmTJ6tRo0Yym81q1KiRJk+erLt37zq6NOQiLi5O/fv316OPPiqTyaSlS5farLdYLIqIiJCvr6/c3NwUGBioQ4cOOaja4q1YBFpJGjJkiA4cOKCUlBR9++23atWqlaNLKrZ2796twYMHa9OmTVqzZo1KlCihHj166OLFi44uDfn0/fffa/Hixapfv76jS4Gd0tLS1KlTJ1ksFq1YsUIJCQmaPn069xAYzOzZs7VgwQJNmzZNe/fu1dSpU/Xhhx9q1qxZji4Nubh27Zrq1aunqVOnqkyZMlnWz5kzR1FRUZo2bZq2b98uV1dX9ezZU1euXHFAtcWbU1pamsXRRcDYrl69Kk9PTy1dulRdunRxdDmw06VLl9S2bVvNmTNH06dPV7169TRjxgxHl4U8Cg8PV1xcnDZt2uToUvAAgoKCVLFiRc2bN8+6bPjw4bp48aKWL1/uwMqQV9WrV9f06dM1YMAASfdGZ319ffXyyy9r7NixkqQbN27Ix8dHkyZNYlpkASs2I7RwnKtXryojI0Mmk8nRpSAfRo0ape7du6tt27aOLgX5sG7dOjVp0kTBwcHy9vZW69atNX/+fFksjFUYib+/v3bv3q2jR49Kkg4fPqxdu3bp6aefdnBlyK+TJ08qOTlZAQEB1mVlypRRy5Yt/xIvfipuisVNYXCs8ePHq2HDhmrevLmjS4GdFi9erOPHjysmJsbRpSCfTpw4oYULF2rEiBEaNWqUDhw4oHHjxkmShg4d6uDqkFejRo3S1atX1aJFCzk7O+vu3bsaO3ashgwZ4ujSkE/JycmSlO2Ln86ePeuIkoo1Ai0eyJtvvqk9e/Zo48aNcnZ2dnQ5sENiYqLCw8O1YcMGlSpVytHlIJ8yMjL02GOP6Z133pEkNW7cWMePH9eCBQsItAayevVqff7551qwYIF8fX114MABjR8/Xp6enho0aJCjy8MD4MVPRYNAi3wLCwvT6tWrtXbtWtWoUcPR5cBOe/fu1fnz5/XEE09Yl6Wnp+u7777TRx99pN9++00PP/ywAytEXpjNZtWtW9dmWZ06dXTmzBkHVYT8mDhxokaOHKnevXtLkurXr6/Tp0/r/fffJ9AalNlsliSlpKTI3d3dupwXPxUOAi3yZdy4cVq9erW++eYb1alTx9HlIB8CAwP12GOP2Sx75ZVXVLt2bY0ePZpRW4Pw9/fXsWPHbJYdO3ZMHh4eDqoI+XH9+vUs33I5OzsrIyPDQRXhQXl5eclsNis2NlaPP/64JOnmzZuKj49XeHi4g6srfgi0sNvYsWO1fPlyffrppzKZTNZ5Qi4uLnrkkUccXB3yymQyZbmRr2zZsqpYsaLq1avnmKJgtxEjRqhjx46KjIxUr169tH//fs2fP19vv/22o0uDHTp37qzZs2fLy8tLvr6+2r9/v6KiotS/f39Hl4ZcXL16VcePH5d0b/rPmTNntH//flWsWFEeHh4KCQnRzJkz5ePjI29vb0VGRsrFxUV9+vRxcOXFD4/tgt1yeprBuHHjFBYWVrTFoEAFBgby2C4D2rRpk8LDw3Xs2DG5u7vr5Zdf1rBhw5inZyBXrlzRe++9p2+++Uapqakym83q3bu33njjDZUuXdrR5SEHu3btUrdu3bIsf/bZZxUdHS2LxaKpU6fq448/Vlpampo0aaLIyEgGDQoBgRYAAACGxnNoAQAAYGgEWgAAABgagRYAAACGRqAFAACAoRFoAQAAYGgEWgAAABgagRYADG7p0qUymUw6efKko0sBAIcg0AIAAMDQCLQAAAAwNAItAPzF3b59W+np6UV2vOvXrxfZsQCgIBBoAaAI/fLLL+rfv788PT1VtWpVPf3009qyZYt1/a5du2QymbRixQpNnz5dDRo0kJubm86cOSNJ+v7779WxY0eZzWY1aNBA77//viyW7N9gHhsbq7///e9yd3dXtWrV9Pe//10JCQk2bSIiImQymXT48GENHz5cNWvWlL+/vyTp6tWreuutt9SoUSOZzWb5+PioW7du2rVrVyF9OgCQPyUcXQAA/K84duyYOnfurFKlSmnEiBFycXHRZ599pqCgIC1evFjdunWztn3//ffl5OSkYcOGSZIeeeQRHT58WD169FC5cuU0duxYlSpVSh9//LFcXFyyHGvlypUaOnSo2rRpowkTJigjI0NLly7VM888o3Xr1qlp06Y27YODg+Xp6akJEybo9u3bkqTRo0frq6++0pAhQ+Tr66tLly7phx9+0IEDB9SmTZtC/KQAwD5OaWlp2f9pDwAoUIMGDdL69ev13XffqU6dOpKky5cvq2XLlpKk/fv3Ky4uTt26dZO7u7sSEhJswurAgQO1ceNG7d27VzVr1pQkpaam6vHHH9fly5f1888/y8vLS9euXVP9+vXVpUsXRUdHW7e/fv26/P39VaNGDa1Zs0bSvRHaadOmqVu3blqyZIlNvV5eXurXr59mzJhRqJ8LADwophwAQBFIT0/Xtm3b1LlzZ2uYlaTy5cvrpZde0pkzZ3Tw4EHr8v79+9uE2T9unxlmJaly5crq16+fzbFiY2OVlpamfv366fz589Z/N27cULt27RQfH687d+7YbDN48OAsNZcrV04//vijfvvttwc+fwAoTEw5AIAikJqaqmvXrtmE2Ux169aVJJ06dUrly5eXJNWoUSPL9tevX5ePj0+W7b29vW1+/vXXXyVJPXv2zLGeS5cuqXLlytaf/3w8SXr33Xf1yiuvqEGDBmrUqJGeeuop9e3b11ovAPxVEGgBwMGyu6mrTJky2bZxcnK67/YZGRmSpH/961+qVq1atsfMDM45HU+SevfurVatWmnDhg3avn27YmJiNHv2bEVFRSkoKCiXMwKAokWgBYAiULlyZbm4uOjo0aNZ1iUmJkqSPD09lZaWlu32rq6uKlu2bLbbZ47IZsqcklC5cmW1a9fugep2c3NTcHCwgoODlZaWpqefflrTpk0j0AL4S2EOLQAUAWdnZ3Xo0EGbNm3SsWPHrMuvXLmiRYsWyd3dXfXr1891+4CAAG3cuFH//e9/rctTU1P1xRdf2LTt0KGDKlSooMjISN26dSvLvlJTU+9bb3p6ui5dumSzzGQyycvLK8fQDQCOwggtABSRt99+Wzt27FCXLl00ZMgQ62O7zpw5o48//lgPPZT7GMObb76p7du3W7cvWbKkPv74Y3l4eNiEz3LlymnOnDkaPHiwWrdurb59+8psNispKUm7du2Si4uLVq5cmeuxrly5onr16qlbt25q0KCBypcvrz179mjr1q16+eWXC+TzAICCQqAFgCLi4+OjjRs36t1331VUVJRu376thg0b6vPPP1fHjh3vu329evX05Zdf6q233tKMGTPk6uqqwYMHy9XVVSNHjrRp26NHD1WtWlWzZs3Sv/71L924cUNms1lNmzbVoEGD7nussmXLasiQIYqNjdWGDRt09+5deXl5adKkSQoJCcn3ZwAAhYHn0AIAAMDQmEMLAAAAQyPQAgAAwNAItAAAADA0Ai0AAAAMjUALAAAAQyPQAgAAwNAItAAAADA0Ai0AAAAMjUALAAAAQyPQAgAAwND+H3TdLl1UY2qcAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 720x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,4))\n",
    "plt.hist(orders_by_users['orders'])\n",
    "plt.title('Distribution of orders amont')\n",
    "plt.xlabel('orders')\n",
    "plt.ylabel('frequency')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Все верно, практически многие делают по 1 заказу, **но все же не все**. Ессть и 2 и 3 заказа, и даже 11.  \n",
    "Рассмотрим данные о количестве заказов с иной стороны. Постоим диаграмму рассеяния где по оси x будет порядковый номер клиента, а по оси y - сколько он совершил заказов"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Присвоим порядковый номер каждому из клиентов\n",
    "n_user = pd.Series(range(0,orders_by_users.shape[0]))\n",
    "plt.figure(figsize=(5,5))\n",
    "plt.scatter(n_user, orders_by_users['orders'])\n",
    "plt.title('Orders by users')\n",
    "plt.xlabel('user')\n",
    "plt.ylabel('amont')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "На таком графике более наглядно распределение клиентов по количеству заказов. \n",
    "* Приктически все по 1 заказу, \n",
    "* чуть меньше по 2 заказа,\n",
    "* еще меньше по 3 заказа, \n",
    "* и т.д. вплоть до 11 заказов, которые сделали 2 человека.  \n",
    "\n",
    "\n",
    "Очевидно что большинство пользователей делают по 1-2 заказа, но точно определить значение в качестве границы нам помогут перцентили."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.07. 95-й и 99-й перцентили количества заказов на пользователя.  \n",
    "[к оглавлению](#step_0)\n",
    "<a class=\"anchor\" id=\"step_3.07\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Процентили помогут разбить наших клиентов по количеству заказов на 100 частей. В текущем контексте нас интересует количество заказов в 95 и 99 перцентилях"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Как видим не более 1% клиентов сделали 4 и более заказов, и не более 5% клиентов сделали свыше 2 заказов\n"
     ]
    }
   ],
   "source": [
    "orders_pp = np.percentile(orders_by_users['orders'], [95, 99])\n",
    "print(f'Как видим не более 1% клиентов сделали {orders_pp[1]:.0f} \\\n",
    "и более заказов, и не более 5% клиентов сделали свыше {orders_pp[0]:.0f} заказов')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Далее в процессе анализа отбросим всех клиентов совершивших более 2 заказов"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.08. Точечный график стоимостей заказов.  \n",
    "[к оглавлению](#step_0)\n",
    "<a class=\"anchor\" id=\"step_3.08\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Как и в случае с количеством заказов на клиента определим какие в принципе заказы у нас встречаются"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count      1127.00\n",
       "mean       8247.42\n",
       "std       40144.90\n",
       "min          50.00\n",
       "25%        1210.00\n",
       "50%        2970.00\n",
       "75%        8245.00\n",
       "max     1294500.00\n",
       "Name: revenue, dtype: float64"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Создадим датафрейм, в котором будет лишь информация о заказе и количество созданых им заказов\n",
    "revenue_by_orders = orders.groupby('transactionid', as_index=False).agg({'revenue':'sum'})\n",
    "revenue_by_orders['revenue'].describe().T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Посмотрим на распределение стоимостей заказов на гистограмме"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,4))\n",
    "plt.hist(revenue_by_orders['revenue'], bins=30, range=(0, 100000))\n",
    "plt.title('Distribution of revenues')\n",
    "plt.xlabel('revenue')\n",
    "plt.ylabel('frequency')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Явно видно, что наиболее частые заказы около 1000-1500р., но есть и дорогущие около 1.2 млн. Проверим как стоимости заказов распределились на точечной диагремме"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Присвоим порядковый номер каждому из заказов\n",
    "n_orders = pd.Series(range(0,revenue_by_orders.shape[0]))\n",
    "plt.figure(figsize=(5,5))\n",
    "plt.scatter(n_orders, revenue_by_orders['revenue'])\n",
    "plt.title('Revenue by orders')\n",
    "plt.xlabel('order')\n",
    "plt.ylabel('revenue')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Выводы согласуются с предыдущими. \n",
    "* В основном заказывают на пару тысяч р. \n",
    "* Несколько заказов на 100-200 тысяч р.\n",
    "* Один на 200 тысяч р. или около того\n",
    "* И один на 1.2 млн р. (скорее всего тот самый заказ, который вывел группу B сильно вперед в начале  проекта)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.09. 95-й и 99-й перцентили стоимости заказов.\n",
    "[к оглавлению](#step_0)\n",
    "<a class=\"anchor\" id=\"step_3.09\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Как видим не более 1% заказов стоят дороже 55936р.,и не более 5% заказов дороже 27460р.\n"
     ]
    }
   ],
   "source": [
    "orders_value_pp = np.percentile(revenue_by_orders['revenue'], [95, 99])\n",
    "print(f'Как видим не более 1% заказов стоят дороже {orders_value_pp[1]:.0f}р.,\\\n",
    "и не более 5% заказов дороже {orders_value_pp[0]:.0f}р.')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Далее отбросим всех клиентвов совершивших заказы дороже 55936р."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.10. Рассчет статистической значимости различий в конверсии между группами по «сырым» данным  \n",
    "[к оглавлению](#step_0)\n",
    "<a class=\"anchor\" id=\"step_3.10\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Сформируем гипотезы:\n",
    "______________\n",
    "**H0** разницы в конверсиях между двумя группами нет  \n",
    "**H1** разница в конверсиях есть"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Для проверки гипотезы что конверсии групп A и B не отличаются создадим два массива. Длина кажого из массивов равна числу посещений в каждой из групп. Если посещение завершилось заказом, добавим соответствующее число заказов, несли нет, то **НОЛЬ**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"Создадим две таблицы для групп A и B, где будут id посетителей и сколько каждый сделал заказов\n",
    "соответсвенно длиной таблицы будет количество результативных визитов\n",
    "\"\"\"\n",
    "ordersByUsersA = orders.query('group == \"A\"').groupby(\n",
    "    'visitorid', as_index=False).agg({'transactionid' : pd.Series.nunique})\n",
    "ordersByUsersA.columns = ['userId', 'orders']\n",
    "\n",
    "ordersByUsersB = orders.query('group == \"B\"').groupby(\n",
    "    'visitorid', as_index=False).agg({'transactionid' : pd.Series.nunique})\n",
    "ordersByUsersB.columns = ['userId', 'orders'] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"Если из количества всех визитов отнять длину предыдущей таблицы, \n",
    "получим количество нерезультативных визитов, \n",
    "создадим серию из этого количества нулей.\n",
    "Далее соединим серию из нулей и серию из результативных визитов,\n",
    "это и будет массив передаваемый в тест Манна-Уитни.\n",
    "Проделаем эту процедуру для двух групп.\n",
    "\"\"\"\n",
    "sampleA = pd.concat(\n",
    "    [ordersByUsersA['orders'], pd.Series(\n",
    "        0, index=np.arange(cumulative_data_horizont['visitorsA'].iloc[-1] - \\\n",
    "                           len(ordersByUsersA['orders'])), name='orders')])\n",
    "\n",
    "sampleB = pd.concat(\n",
    "    [ordersByUsersB['orders'], pd.Series(\n",
    "        0, index=np.arange(cumulative_data_horizont['visitorsB'].iloc[-1] - \\\n",
    "                           len(ordersByUsersB['orders'])), name='orders')])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "alpha = .05"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "p-значение: 0.007\n",
      "\n",
      "Отвергаем нулевую гипотезу: разница статистически значима\n",
      "\n",
      "Относительный +выигрыш/-проигрыш по конверсии группы B против группы A 14.798%\n"
     ]
    }
   ],
   "source": [
    "cr_raw = st.mannwhitneyu(sampleA, sampleB)\n",
    "print('p-значение: {0:.3f}'.format(cr_raw.pvalue))\n",
    "print()\n",
    "if (cr_raw.pvalue < alpha):\n",
    "    print(\"Отвергаем нулевую гипотезу: разница статистически значима\")\n",
    "else:\n",
    "    print(\"Не получилось отвергнуть нулевую гипотезу, вывод о различии сделать нельзя\")\n",
    "print()\n",
    "print(\"Относительный +выигрыш/-проигрыш по конверсии группы B против группы A {0:.3%}\".format(sampleB.mean()/sampleA.mean()-1)) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "По конверсиям эффект от эксперимента явно есть [обратно](#step_10)\n",
    "<a class=\"anchor\" id=\"step_6\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.11. Рассчет статистической значимости различий в среднем чеке заказа между группами по «сырым» данным  \n",
    "[к оглавлению](#step_0)\n",
    "<a class=\"anchor\" id=\"step_3.11\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Сформируем гипотезы:\n",
    "______________\n",
    "**H0** разницы в среднем чеке между двумя группами нет  \n",
    "**H1** разница в среднем чеке есть"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "p-значение: 0.339\n",
      "\n",
      "Не получилось отвергнуть нулевую гипотезу, вывод о различии сделать нельзя\n",
      "\n",
      "Относительный +выигрыш/-проигрыш по среднему чеку группы B против группы A  30.338%\n"
     ]
    }
   ],
   "source": [
    "aov_raw = st.mannwhitneyu(orders.query('group == \"A\"')['revenue'], \n",
    "                          orders.query('group == \"B\"')['revenue'])\n",
    "print('p-значение: {0:.3f}'.format(aov_raw.pvalue))\n",
    "print()\n",
    "if (aov_raw.pvalue < alpha):\n",
    "    print(\"Отвергаем нулевую гипотезу: разница статистически значима\")\n",
    "else:\n",
    "    print(\"Не получилось отвергнуть нулевую гипотезу, вывод о различии сделать нельзя\")\n",
    "print()\n",
    "print(\"Относительный +выигрыш/-проигрыш по среднему чеку группы B против группы A  {0:.3%}\".format(\n",
    "    orders.query('group == \"B\"')['revenue'].mean()/orders.query('group == \"A\"')['revenue'].mean()-1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "По среднему чеку вероятность в 34% случайно получить такие различия - довольно высокая. Но такой большой относительный выигыш... Не связанли ли он с чеком в 12000000р? [обратно](#step_10)\n",
    "<a class=\"anchor\" id=\"step_9\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.12. Рассчет статистической значимости различий в конверсии между группами по «очищенным» данным  \n",
    "[к оглавлению](#step_0)\n",
    "<a class=\"anchor\" id=\"step_3.12\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Сформируем гипотезы: \n",
    "___________  \n",
    "После удаления клиентов совершивших аномально высокое количество заказов  \n",
    "\n",
    "**H0** разницы в конверсиях между двумя группами нет   \n",
    "**H1** разница в конверсиях есть"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "q_id = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "568     113298937\n",
      "1099    148427295\n",
      "17      199603092\n",
      "928     204675465\n",
      "21      237748145\n",
      "dtype: int64\n",
      "(72,)\n"
     ]
    }
   ],
   "source": [
    "usersWithManyOrders = pd.concat(\n",
    "    [ordersByUsersA.query('orders > @orders_pp[@q_id]')['userId'], \n",
    "     ordersByUsersB.query('orders > @orders_pp[@q_id]')['userId']], axis = 0)\n",
    "usersWithExpensiveOrders = orders.query('revenue > @orders_value_pp[@q_id]')['visitorid']\n",
    "abnormalUsers = pd.concat([usersWithManyOrders, \n",
    "                           usersWithExpensiveOrders], axis = 0).drop_duplicates().sort_values()\n",
    "print(abnormalUsers.head(5))\n",
    "print(abnormalUsers.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "sampleAFiltered = pd.concat(\n",
    "    [ordersByUsersA[np.logical_not(ordersByUsersA['userId'].isin(abnormalUsers))]['orders'],\\\n",
    "     pd.Series(0, index=np.arange(\n",
    "        cumulative_data_horizont['visitorsA'].iloc[-1] - len(ordersByUsersA['orders'])),name='orders')],axis=0)\n",
    "\n",
    "sampleBFiltered = pd.concat(\n",
    "    [ordersByUsersB[np.logical_not(ordersByUsersB['userId'].isin(abnormalUsers))]['orders'],\\\n",
    "     pd.Series(0, index=np.arange(\n",
    "        cumulative_data_horizont['visitorsB'].iloc[-1] - len(ordersByUsersB['orders'])),name='orders')],axis=0) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "p-значение: 0.007\n",
      "\n",
      "Отвергаем нулевую гипотезу: разница статистически значима\n",
      "\n",
      "Относительный +выигрыш/-проигрыш по конверсии группы B против группы A  17.852%\n"
     ]
    }
   ],
   "source": [
    "cr_filtered = st.mannwhitneyu(\n",
    "    sampleAFiltered, \n",
    "    sampleBFiltered)\n",
    "print('p-значение: {0:.3f}'.format(cr_filtered.pvalue))\n",
    "print()\n",
    "if (cr_filtered.pvalue < alpha):\n",
    "    print(\"Отвергаем нулевую гипотезу: разница статистически значима\")\n",
    "else:\n",
    "    print(\"Не получилось отвергнуть нулевую гипотезу, вывод о различии сделать нельзя\")\n",
    "print()\n",
    "print(\"Относительный +выигрыш/-проигрыш по конверсии группы B против группы A  {0:.3%}\".format(\n",
    "    sampleBFiltered.mean()/\\\n",
    "    sampleAFiltered.mean()-1)) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a class=\"anchor\" id=\"step_7\"></a> [обратно](#step_10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.13. Рассчет статистической значимости различий в среднем чеке заказа между группами по «очищенным» данным  \n",
    "[к оглавлению](#step_0)\n",
    "<a class=\"anchor\" id=\"step_3.13\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Сформируем гипотезы:\n",
    "______________\n",
    "После удаления клиентов со сверхдорогими заказами\n",
    "\n",
    "**H0** разницы в среднем чеке между двумя группами нет  \n",
    "**H1** разница в среднем чеке есть"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "p-значение: 0.316\n",
      "\n",
      "Не получилось отвергнуть нулевую гипотезу, вывод о различии сделать нельзя\n",
      "\n",
      "Относительный +выигрыш/-проигрыш по среднему чеку группы B против группы A  4.699%\n"
     ]
    }
   ],
   "source": [
    "aov_filtered = st.mannwhitneyu(\n",
    "    orders.query('group == \"A\" and visitorid not in @abnormalUsers')['revenue'], \n",
    "    orders.query('group == \"B\" and visitorid not in @abnormalUsers')['revenue'])\n",
    "print('p-значение: {0:.3f}'.format(aov_filtered.pvalue))\n",
    "print()\n",
    "if (aov_filtered.pvalue < alpha):\n",
    "    print(\"Отвергаем нулевую гипотезу: разница статистически значима\")\n",
    "else:\n",
    "    print(\"Не получилось отвергнуть нулевую гипотезу, вывод о различии сделать нельзя\")\n",
    "print()\n",
    "print(\"Относительный +выигрыш/-проигрыш по среднему чеку группы B против группы A  {0:.3%}\".format(\n",
    "    orders.query('group == \"A\" and visitorid not in @abnormalUsers')['revenue'].mean()/\\\n",
    "    orders.query('group == \"B\" and visitorid not in @abnormalUsers')['revenue'].mean()-1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.14. Решение по результатам теста.\n",
    "[к оглавлению](#step_0)\n",
    "<a class=\"anchor\" id=\"step_3.14\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### В результате теста:   \n",
    "<a class=\"anchor\" id=\"step_10\"></a>\n",
    "* Обнаружилось статистически значимое различие по конверсии между группами как по сырым данным, так и после фильтрации аномалий.\n",
    "* Cтатистически значимого различия по среднему чеку между группами нет. Не обнаружилось оно и после удаления аномалийзначений.\n",
    "* Согласно [графику](#step_5) конверсия группы B лучше чем группы A зафиксировались на уровне [13%](#step_6)\n",
    "* После очистки данных от выбросов конверсия группы B улучшилась и зафиксировались на уровне [17%](#step_7)\n",
    "* [График](#step_8) различия среднего чека колеблется: очень сильно повлиял выбров в 1200000р. На данных этого графика определённые выводы нельзя.\n",
    "* Согласно статистическому анализу по сырым данных гипотезу об отсутсвии изменений в среденм чеке отклонить не удалось. Причем относительный выигрыш группы B против группы A составил [25%](#step_9)\n",
    "* После удаления выбросов относительный выигрыш по чеку приобрел адекватные 4%\n",
    "___________\n",
    "**В результате изложенного, тест можно признать удавшимся и состоявшимся**  \n",
    "Клиенты стали покупать чаще - выручка растет, но тратят столько же. Самое время поэкспериментировать как бы заставить клиента больше платить, не снижая количество заказов"
   ]
  }
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