AB_test_retail.ipynb

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    "colab": {
      "name": "AB_test_retail.ipynb",
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    },
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BTFryFnNDDr-"
      },
      "source": [
        "# A/B test \n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tIx4JRLl2YO1"
      },
      "source": [
        "**Цель проекта**: увеличение выручки интернет-магазина.\n",
        "\n",
        "**Задачи проекта**: приоритизация гипотез, запуск A/B-теста и анализ его результатов."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3bhAOT2_3Huj"
      },
      "source": [
        "**Описание данных**\n",
        "\n",
        "**Данные для первой части**\n",
        "\n",
        "Hypothesis — краткое описание гипотезы;\n",
        "\n",
        "Reach — охват пользователей по 10-балльной шкале;\n",
        "\n",
        "Impact — влияние на пользователей по 10-балльной шкале;\n",
        "\n",
        "Confidence — уверенность в гипотезе по 10-балльной шкале;\n",
        "\n",
        "Efforts — затраты ресурсов на проверку гипотезы по 10-балльной шкале. Чем больше значение Efforts, тем дороже проверка гипотезы.\n",
        "\n",
        "**Данные для второй части**\n",
        "\n",
        "transactionId — идентификатор заказа;\n",
        "\n",
        "visitorId — идентификатор пользователя, совершившего заказ;\n",
        "\n",
        "date — дата, когда был совершён заказ;\n",
        "\n",
        "revenue — выручка заказа;\n",
        "\n",
        "group — группа A/B-теста, в которую попал заказ.\n",
        "\n",
        "date — дата;\n",
        "\n",
        "group — группа A/B-теста;\n",
        "\n",
        "visitors — количество пользователей в указанную дату в указанной группе A/B-теста"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sa5lO6cX3x1y"
      },
      "source": [
        "Установим библиотеки:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dnoCwrbT2dwP",
        "trusted": false
      },
      "source": [
        "import datetime as dt\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "import scipy.stats as stats"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "F9hNIlBz38-M"
      },
      "source": [
        "Загрузим данные, для дальнешего изучения нам предоставлено 3 датасета:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fZmlY3NK2-Cp",
        "trusted": false
      },
      "source": [
        "hypothesis = pd.read_csv('.../datasets/hypothesis.csv')\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 328
        },
        "id": "o6LSyWwuCXOM",
        "trusted": false,
        "outputId": "3804fbdc-430f-400f-a356-a96411699c50"
      },
      "source": [
        "hypothesis"
      ],
      "execution_count": null,
      "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>Reach</th>\n      <th>Impact</th>\n      <th>Confidence</th>\n      <th>Efforts</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td>0</td>\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      <td>1</td>\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      <td>2</td>\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      <td>3</td>\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      <td>4</td>\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    <tr>\n      <td>5</td>\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    </tr>\n    <tr>\n      <td>6</td>\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    </tr>\n    <tr>\n      <td>7</td>\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    </tr>\n    <tr>\n      <td>8</td>\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    </tr>\n  </tbody>\n</table>\n</div>",
            "text/plain": "                                          Hypothesis  Reach  Impact  \\\n0  Добавить два новых канала привлечения трафика,...      3      10   \n1  Запустить собственную службу доставки, что сок...      2       5   \n2  Добавить блоки рекомендаций товаров на сайт ин...      8       3   \n3  Изменить структура категорий, что увеличит кон...      8       3   \n4  Изменить цвет фона главной страницы, чтобы уве...      3       1   \n5  Добавить страницу отзывов клиентов о магазине,...      3       2   \n6  Показать на главной странице баннеры с актуаль...      5       3   \n7  Добавить форму подписки на все основные страни...     10       7   \n8  Запустить акцию, дающую скидку на товар в день...      1       9   \n\n   Confidence  Efforts  \n0           8        6  \n1           4       10  \n2           7        3  \n3           3        8  \n4           1        1  \n5           2        3  \n6           8        3  \n7           8        5  \n8           9        5  "
          },
          "execution_count": 3,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_uI5Sxl-CqsF"
      },
      "source": [
        "Заменим названия столбцов:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "4SQY0ymlCwfW",
        "trusted": false,
        "outputId": "c20cbc1c-711e-45d8-a377-f1003b8ffaed"
      },
      "source": [
        "hypothesis.columns = hypothesis.columns.str.lower()\n",
        "hypothesis.head(5)"
      ],
      "execution_count": null,
      "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>reach</th>\n      <th>impact</th>\n      <th>confidence</th>\n      <th>efforts</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td>0</td>\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      <td>1</td>\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      <td>2</td>\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      <td>3</td>\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      <td>4</td>\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  \\\n0  Добавить два новых канала привлечения трафика,...      3      10   \n1  Запустить собственную службу доставки, что сок...      2       5   \n2  Добавить блоки рекомендаций товаров на сайт ин...      8       3   \n3  Изменить структура категорий, что увеличит кон...      8       3   \n4  Изменить цвет фона главной страницы, чтобы уве...      3       1   \n\n   confidence  efforts  \n0           8        6  \n1           4       10  \n2           7        3  \n3           3        8  \n4           1        1  "
          },
          "execution_count": 4,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "b8RnJxvb4iZA",
        "trusted": false
      },
      "source": [
        "orders = pd.read_csv('.../datasets/orders.csv', sep=',')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        },
        "id": "MArjkmSgDGi2",
        "trusted": false,
        "outputId": "566a7e0e-ffe4-4ca9-9f70-fcf940a72c33"
      },
      "source": [
        "orders"
      ],
      "execution_count": null,
      "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>transactionId</th>\n      <th>visitorId</th>\n      <th>date</th>\n      <th>revenue</th>\n      <th>group</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td>0</td>\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      <td>1</td>\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      <td>2</td>\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      <td>3</td>\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      <td>4</td>\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    <tr>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <td>1192</td>\n      <td>2662137336</td>\n      <td>3733762160</td>\n      <td>2019-08-14</td>\n      <td>6490</td>\n      <td>B</td>\n    </tr>\n    <tr>\n      <td>1193</td>\n      <td>2203539145</td>\n      <td>370388673</td>\n      <td>2019-08-14</td>\n      <td>3190</td>\n      <td>A</td>\n    </tr>\n    <tr>\n      <td>1194</td>\n      <td>1807773912</td>\n      <td>573423106</td>\n      <td>2019-08-14</td>\n      <td>10550</td>\n      <td>A</td>\n    </tr>\n    <tr>\n      <td>1195</td>\n      <td>1947021204</td>\n      <td>1614305549</td>\n      <td>2019-08-14</td>\n      <td>100</td>\n      <td>A</td>\n    </tr>\n    <tr>\n      <td>1196</td>\n      <td>3936777065</td>\n      <td>2108080724</td>\n      <td>2019-08-15</td>\n      <td>202740</td>\n      <td>B</td>\n    </tr>\n  </tbody>\n</table>\n<p>1197 rows × 5 columns</p>\n</div>",
            "text/plain": "      transactionId   visitorId        date  revenue group\n0        3667963787  3312258926  2019-08-15     1650     B\n1        2804400009  3642806036  2019-08-15      730     B\n2        2961555356  4069496402  2019-08-15      400     A\n3        3797467345  1196621759  2019-08-15     9759     B\n4        2282983706  2322279887  2019-08-15     2308     B\n...             ...         ...         ...      ...   ...\n1192     2662137336  3733762160  2019-08-14     6490     B\n1193     2203539145   370388673  2019-08-14     3190     A\n1194     1807773912   573423106  2019-08-14    10550     A\n1195     1947021204  1614305549  2019-08-14      100     A\n1196     3936777065  2108080724  2019-08-15   202740     B\n\n[1197 rows x 5 columns]"
          },
          "execution_count": 6,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "COrX7FwGD24D",
        "trusted": false,
        "outputId": "f5a2485c-521b-4061-cfb3-8c6b6f382faf"
      },
      "source": [
        "orders.info()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": "<class 'pandas.core.frame.DataFrame'>\nRangeIndex: 1197 entries, 0 to 1196\nData columns (total 5 columns):\ntransactionId    1197 non-null int64\nvisitorId        1197 non-null int64\ndate             1197 non-null object\nrevenue          1197 non-null int64\ngroup            1197 non-null object\ndtypes: int64(3), object(2)\nmemory usage: 46.9+ KB\n"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6DywzTcKFEbK",
        "trusted": false,
        "outputId": "d1f3df57-0e7f-4c00-d21c-68a9cf5e1f0e"
      },
      "source": [
        "orders.duplicated().sum()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "data": {
            "text/plain": "0"
          },
          "execution_count": 8,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lZaC8JuH5GpI",
        "trusted": false
      },
      "source": [
        "visitors = pd.read_csv('.../datasets/visitors.csv', sep=',')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        },
        "id": "6V9fyIhMD-3K",
        "trusted": false,
        "outputId": "fb1aee87-6a08-4059-e1f3-7a205e002d38"
      },
      "source": [
        "visitors"
      ],
      "execution_count": null,
      "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      <td>0</td>\n      <td>2019-08-01</td>\n      <td>A</td>\n      <td>719</td>\n    </tr>\n    <tr>\n      <td>1</td>\n      <td>2019-08-02</td>\n      <td>A</td>\n      <td>619</td>\n    </tr>\n    <tr>\n      <td>2</td>\n      <td>2019-08-03</td>\n      <td>A</td>\n      <td>507</td>\n    </tr>\n    <tr>\n      <td>3</td>\n      <td>2019-08-04</td>\n      <td>A</td>\n      <td>717</td>\n    </tr>\n    <tr>\n      <td>4</td>\n      <td>2019-08-05</td>\n      <td>A</td>\n      <td>756</td>\n    </tr>\n    <tr>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <td>57</td>\n      <td>2019-08-27</td>\n      <td>B</td>\n      <td>720</td>\n    </tr>\n    <tr>\n      <td>58</td>\n      <td>2019-08-28</td>\n      <td>B</td>\n      <td>654</td>\n    </tr>\n    <tr>\n      <td>59</td>\n      <td>2019-08-29</td>\n      <td>B</td>\n      <td>531</td>\n    </tr>\n    <tr>\n      <td>60</td>\n      <td>2019-08-30</td>\n      <td>B</td>\n      <td>490</td>\n    </tr>\n    <tr>\n      <td>61</td>\n      <td>2019-08-31</td>\n      <td>B</td>\n      <td>718</td>\n    </tr>\n  </tbody>\n</table>\n<p>62 rows × 3 columns</p>\n</div>",
            "text/plain": "          date group  visitors\n0   2019-08-01     A       719\n1   2019-08-02     A       619\n2   2019-08-03     A       507\n3   2019-08-04     A       717\n4   2019-08-05     A       756\n..         ...   ...       ...\n57  2019-08-27     B       720\n58  2019-08-28     B       654\n59  2019-08-29     B       531\n60  2019-08-30     B       490\n61  2019-08-31     B       718\n\n[62 rows x 3 columns]"
          },
          "execution_count": 10,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fl4dS5Gw5Rts",
        "trusted": false,
        "outputId": "f6654601-ad10-4f61-8aa9-fd95ffc3a181"
      },
      "source": [
        "visitors.info()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": "<class 'pandas.core.frame.DataFrame'>\nRangeIndex: 62 entries, 0 to 61\nData columns (total 3 columns):\ndate        62 non-null object\ngroup       62 non-null object\nvisitors    62 non-null int64\ndtypes: int64(1), object(2)\nmemory usage: 1.6+ KB\n"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ueULhf5fESKn",
        "trusted": false,
        "outputId": "71e4c43f-4353-4d08-db19-0fd08d5df939"
      },
      "source": [
        "visitors.duplicated().sum()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "data": {
            "text/plain": "0"
          },
          "execution_count": 12,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jieSqa3iFTqG"
      },
      "source": [
        "Данные чистые, пропусков и дубликатов нет. Мы изменили названия столбцов в ДФ hypothesis. Можно приступать к решению поставленных задач."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WWwT1fklKG_P"
      },
      "source": [
        "**ЧАСТЬ 1. Приоритизация гипотез.**\n",
        "\n",
        "**Фреймворк ICE для приоритизации гипотез.**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 328
        },
        "id": "aZequ31iFShN",
        "trusted": false,
        "outputId": "2180e8ab-dcb9-4006-dd26-d5ce397ba663"
      },
      "source": [
        "hypothesis['ICE'] = (hypothesis['impact'] * hypothesis['confidence']) / hypothesis['efforts']\n",
        "hypothesis.sort_values(by='ICE', ascending=False)\n"
      ],
      "execution_count": null,
      "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>reach</th>\n      <th>impact</th>\n      <th>confidence</th>\n      <th>efforts</th>\n      <th>ICE</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td>8</td>\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>16.200000</td>\n    </tr>\n    <tr>\n      <td>0</td>\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      <td>13.333333</td>\n    </tr>\n    <tr>\n      <td>7</td>\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>11.200000</td>\n    </tr>\n    <tr>\n      <td>6</td>\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>8.000000</td>\n    </tr>\n    <tr>\n      <td>2</td>\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      <td>7.000000</td>\n    </tr>\n    <tr>\n      <td>1</td>\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      <td>2.000000</td>\n    </tr>\n    <tr>\n      <td>5</td>\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>1.333333</td>\n    </tr>\n    <tr>\n      <td>3</td>\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>1.125000</td>\n    </tr>\n    <tr>\n      <td>4</td>\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>1.000000</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
            "text/plain": "                                          hypothesis  reach  impact  \\\n8  Запустить акцию, дающую скидку на товар в день...      1       9   \n0  Добавить два новых канала привлечения трафика,...      3      10   \n7  Добавить форму подписки на все основные страни...     10       7   \n6  Показать на главной странице баннеры с актуаль...      5       3   \n2  Добавить блоки рекомендаций товаров на сайт ин...      8       3   \n1  Запустить собственную службу доставки, что сок...      2       5   \n5  Добавить страницу отзывов клиентов о магазине,...      3       2   \n3  Изменить структура категорий, что увеличит кон...      8       3   \n4  Изменить цвет фона главной страницы, чтобы уве...      3       1   \n\n   confidence  efforts        ICE  \n8           9        5  16.200000  \n0           8        6  13.333333  \n7           8        5  11.200000  \n6           8        3   8.000000  \n2           7        3   7.000000  \n1           4       10   2.000000  \n5           2        3   1.333333  \n3           3        8   1.125000  \n4           1        1   1.000000  "
          },
          "execution_count": 13,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qUgdB0wTQRmu"
      },
      "source": [
        "По ICE самыми приоритетными являются гипотезы 8, 0, 7."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "q6nDSup_QeNM"
      },
      "source": [
        "**Фреймворк RICE для приоритизации гипотез.**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 328
        },
        "id": "EtObMDSGOrTO",
        "trusted": false,
        "outputId": "9c38cb76-1d70-4363-d509-c9c57ccef11d"
      },
      "source": [
        "hypothesis['RICE'] = (hypothesis['impact'] * hypothesis['reach'] * hypothesis['confidence']) / hypothesis['efforts']\n",
        "hypothesis.sort_values(by='RICE', ascending=False)"
      ],
      "execution_count": null,
      "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>reach</th>\n      <th>impact</th>\n      <th>confidence</th>\n      <th>efforts</th>\n      <th>ICE</th>\n      <th>RICE</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td>7</td>\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>11.200000</td>\n      <td>112.0</td>\n    </tr>\n    <tr>\n      <td>2</td>\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      <td>7.000000</td>\n      <td>56.0</td>\n    </tr>\n    <tr>\n      <td>0</td>\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      <td>13.333333</td>\n      <td>40.0</td>\n    </tr>\n    <tr>\n      <td>6</td>\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>8.000000</td>\n      <td>40.0</td>\n    </tr>\n    <tr>\n      <td>8</td>\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>16.200000</td>\n      <td>16.2</td>\n    </tr>\n    <tr>\n      <td>3</td>\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>1.125000</td>\n      <td>9.0</td>\n    </tr>\n    <tr>\n      <td>1</td>\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      <td>2.000000</td>\n      <td>4.0</td>\n    </tr>\n    <tr>\n      <td>5</td>\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>1.333333</td>\n      <td>4.0</td>\n    </tr>\n    <tr>\n      <td>4</td>\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>1.000000</td>\n      <td>3.0</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
            "text/plain": "                                          hypothesis  reach  impact  \\\n7  Добавить форму подписки на все основные страни...     10       7   \n2  Добавить блоки рекомендаций товаров на сайт ин...      8       3   \n0  Добавить два новых канала привлечения трафика,...      3      10   \n6  Показать на главной странице баннеры с актуаль...      5       3   \n8  Запустить акцию, дающую скидку на товар в день...      1       9   \n3  Изменить структура категорий, что увеличит кон...      8       3   \n1  Запустить собственную службу доставки, что сок...      2       5   \n5  Добавить страницу отзывов клиентов о магазине,...      3       2   \n4  Изменить цвет фона главной страницы, чтобы уве...      3       1   \n\n   confidence  efforts        ICE   RICE  \n7           8        5  11.200000  112.0  \n2           7        3   7.000000   56.0  \n0           8        6  13.333333   40.0  \n6           8        3   8.000000   40.0  \n8           9        5  16.200000   16.2  \n3           3        8   1.125000    9.0  \n1           4       10   2.000000    4.0  \n5           2        3   1.333333    4.0  \n4           1        1   1.000000    3.0  "
          },
          "execution_count": 14,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WW3pUjS4QhXk"
      },
      "source": [
        "По RICE самыми приоритетными являются гипотезы 7, 2, 0."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "D5hzAnWWQ-Nj"
      },
      "source": [
        "По RICE гипотеза №8 уже не стоит в топе приоритетных. В формуле этого фреймворка присутствует параметр охвата reach, он и повлиял на разницу результатов. То есть гипотеза №7 будет самой приоритетной, так как охватит большее число пользователей. За ней пойдут 2 и 0."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "e4226PBzSapL"
      },
      "source": [
        "**ЧАСТЬ 2. Анализ A/B-теста**\n",
        "\n",
        "**Кумулятивная выручка по группам.**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AhAJvP41UWLX"
      },
      "source": [
        "Чтобы построить графики, нужно собрать кумулятивные данные. Объявим датафрейм cumulativeData со столбцами:\n",
        "\n",
        "date — дата;\n",
        "\n",
        "group — группа A/B-теста (A или B);\n",
        "\n",
        "orders — кумулятивное количество заказов на указанную дату в указанной группе;\n",
        "\n",
        "buyers — кумулятивное количество пользователей, совершивших хотя бы один заказ, на указанную дату в указанной группе;\n",
        "\n",
        "revenue — кумулятивная выручка на указанную дату в указанной группе (средний чек);\n",
        "\n",
        "visitors — кумулятивное количество посетителей интернет-магазина на указанную дату в определённой группе.\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fp-zJV8sWDf0",
        "trusted": false
      },
      "source": [
        "orders['date'] = orders['date'].map(lambda x: dt.datetime.strptime(x, '%Y-%m-%d'))\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "07QE3I_pWD2Z",
        "trusted": false
      },
      "source": [
        "visitors['date'] = visitors['date'].map(lambda x: dt.datetime.strptime(x, '%Y-%m-%d'))\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "SDHe9eIoU9pI",
        "trusted": false,
        "outputId": "3a52fc5a-dfc3-421a-9261-29d0ff6ed28f"
      },
      "source": [
        "#cоздадим датафрейм datesGroups с уникальными парами значений 'date' и 'group', таблицы orders. Избавимся от дубликатов\n",
        "datesGroups = orders[['date','group']].drop_duplicates() \n",
        "\n",
        "#объявим переменную ordersAggregated\n",
        "ordersAggregated = datesGroups.apply(lambda x: orders[np.logical_and(orders['date'] <= x['date'], orders['group'] == x['group'])]\\\n",
        ".agg({'date' : 'max', 'group' : 'max', 'transactionId' : pd.Series.nunique, 'visitorId' : pd.Series.nunique, 'revenue' : 'sum'}), axis=1).sort_values(by=['date','group'])\n",
        "\n",
        "#объявим переменную visitorsAggregated. ordersAggregated и visitorsAggregated отсортируем по столбцам 'date', 'group'\n",
        "visitorsAggregated = datesGroups.apply(lambda x: visitors[np.logical_and(visitors['date'] <= x['date'], visitors['group'] == x['group'])]\\\n",
        "                                       .agg({'date' : 'max', 'group' : 'max', 'visitors' : 'sum'}), axis=1).sort_values(by=['date','group'])\n",
        "\n",
        "#определим переменную cumulativeData, объединив ordersAggregated и visitorsAggregated по колонкам 'date', 'group' \n",
        "cumulativeData = ordersAggregated.merge(visitorsAggregated, left_on=['date', 'group'], right_on=['date', 'group'])\n",
        "\n",
        "#присвоим столбцам cumulativeData необходимые названия\n",
        "cumulativeData.columns = ['date', 'group', 'orders', 'buyers', 'revenue', 'visitors']\n",
        " \n",
        "cumulativeData.head(5) \n"
      ],
      "execution_count": null,
      "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      <td>0</td>\n      <td>2019-08-01</td>\n      <td>A</td>\n      <td>24</td>\n      <td>20</td>\n      <td>148579</td>\n      <td>719</td>\n    </tr>\n    <tr>\n      <td>1</td>\n      <td>2019-08-01</td>\n      <td>B</td>\n      <td>21</td>\n      <td>20</td>\n      <td>101217</td>\n      <td>713</td>\n    </tr>\n    <tr>\n      <td>2</td>\n      <td>2019-08-02</td>\n      <td>A</td>\n      <td>44</td>\n      <td>38</td>\n      <td>242401</td>\n      <td>1338</td>\n    </tr>\n    <tr>\n      <td>3</td>\n      <td>2019-08-02</td>\n      <td>B</td>\n      <td>45</td>\n      <td>43</td>\n      <td>266748</td>\n      <td>1294</td>\n    </tr>\n    <tr>\n      <td>4</td>\n      <td>2019-08-03</td>\n      <td>A</td>\n      <td>68</td>\n      <td>62</td>\n      <td>354874</td>\n      <td>1845</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
            "text/plain": "        date group  orders  buyers  revenue  visitors\n0 2019-08-01     A      24      20   148579       719\n1 2019-08-01     B      21      20   101217       713\n2 2019-08-02     A      44      38   242401      1338\n3 2019-08-02     B      45      43   266748      1294\n4 2019-08-03     A      68      62   354874      1845"
          },
          "execution_count": 17,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Vglo5RQRdye8"
      },
      "source": [
        "Объявим переменные cumulativeRevenueA и cumulativeRevenueB, в которых сохраним данные о датах, выручке и числе заказов в группах A и B.\n",
        "Построим графики кумулятивной выручки по дням по группам.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QoBYJzd9XzLU",
        "trusted": false
      },
      "source": [
        "cumulativeRevenueA = cumulativeData[cumulativeData['group']=='A'][['date','revenue', 'orders']]\n",
        "cumulativeRevenueB = cumulativeData[cumulativeData['group']=='B'][['date','revenue', 'orders']]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 350
        },
        "id": "XirHkC2TeBP8",
        "trusted": false,
        "outputId": "ede33198-a92e-4382-8b63-ef380d5f21ef"
      },
      "source": [
        "plt.figure(figsize=(12,5))\n",
        "plt.plot(cumulativeRevenueA['date'], cumulativeRevenueA['revenue'], label='A')\n",
        "plt.plot(cumulativeRevenueB['date'], cumulativeRevenueB['revenue'], label='B')\n",
        "plt.ylabel('Выручка')\n",
        "plt.xlabel('Дата')\n",
        "plt.title('Графики кумулятивной выручки по группам A/B')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": "/opt/conda/lib/python3.7/site-packages/pandas/plotting/_matplotlib/converter.py:103: FutureWarning: Using an implicitly registered datetime converter for a matplotlib plotting method. The converter was registered by pandas on import. Future versions of pandas will require you to explicitly register matplotlib converters.\n\nTo register the converters:\n\t>>> from pandas.plotting import register_matplotlib_converters\n\t>>> register_matplotlib_converters()\n  warnings.warn(msg, FutureWarning)\n"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": "<Figure size 864x360 with 1 Axes>"
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kXUWU9gxg4Bd"
      },
      "source": [
        "Первые 2 дня наблюдался одинаковый рост выручки в двух группах. С 5го по 13ое августа группа В стала показывать немногим бОльшую выручку. 18 августа в группе В произошел резкий скачок, и дальше рост продолжился, выручка накапливалась. К сентябрю выручка группы А составила 4 млн., группы В - 6 млн. Возможно, имеет место выброс, например, заказ на большую сумму или огромная закупка. Тем не менее, в целом в обоих группах мы наблюдаем линейный рост, но в группе В он выше."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "I3GjrttYGcT8"
      },
      "source": [
        "**Кумулятивный средний чек по группам A/B**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 350
        },
        "id": "QuD1pA_XfECj",
        "trusted": false,
        "outputId": "3d6f5863-882f-4528-a4ce-3db093df05e6"
      },
      "source": [
        "plt.figure(figsize=(12,5))\n",
        "plt.plot(cumulativeRevenueA['date'], cumulativeRevenueA['revenue']/cumulativeRevenueA['orders'], label='A')\n",
        "plt.plot(cumulativeRevenueB['date'], cumulativeRevenueB['revenue']/cumulativeRevenueB['orders'], label='B')\n",
        "plt.ylabel('Средний чек')\n",
        "plt.xlabel('Дата')\n",
        "plt.title('Графики кумулятивного среднего чека по группам A/B')\n",
        "plt.legend()\n",
        "plt.show()\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": "<Figure size 864x360 with 1 Axes>"
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZdoBoIpHp7Su"
      },
      "source": [
        "До 18 августа средние чеки в двух группах колебались, в каждой наблюдаем и рост, и падение. Однако, в группе В резкий скачок все же был связан с покупкой или покупками на круппную сумму, после чего мы видим небольшой спад, и это похоже на аномалию. Чек группы А после роста 13 августа стабильно держится в районе 7000."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_R-14GlleNoa"
      },
      "source": [
        "**Относительное изменение кумулятивного среднего чека группы B к группе A.**\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 336
        },
        "id": "jL7D6oiDrMXK",
        "trusted": false,
        "outputId": "e99b3006-6dab-45b8-bd4d-34e4738129a8"
      },
      "source": [
        "#объединим таблицы cumulativeRevenueA и cumulativeRevenueB \n",
        "mergedCumulativeRevenue = cumulativeRevenueA.merge(cumulativeRevenueB, left_on='date', right_on='date', how='left', suffixes=['A', 'B'])\n",
        "plt.figure(figsize=(12,5))\n",
        "plt.plot(mergedCumulativeRevenue['date'], (mergedCumulativeRevenue['revenueB']/mergedCumulativeRevenue['ordersB'])/(mergedCumulativeRevenue['revenueA']/mergedCumulativeRevenue['ordersA'])-1)\n",
        "plt.axhline(y=0, color='black', linestyle='--')\n",
        "plt.title('График относительного изменения кумулятивного среднего чека группы B к группе A')\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "data": {
            "image/png": 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Q8C/nVXfsCKV431VH9OPBLxA5FVjTymIr/ZL8nwNj/e/lo/EN/xqSgXddCq2r/Qze3eVv8fE7ZO3xF7xEejwcbPx+aQvLfgF4Ce+ac5z/NwHvOhCWdkyixLsreBSvMVUJXgPCxltpL+DdMl2Pd8uqikNvY92Ed9Hchlf3G7xbqvl4VTNCb8P3AG5v5TZuqKvw6sd9rAGUc26dH98deCVN5+E1nGlXPWj/9efhnfjFeCUOn3fOfegv8l280rmlePvpNxx6bP/HObfhCLeZj9eA8Id4P27y8S5McXj1m78IXHCYi8hC83oxeBP4lV9n7nDb3YOXNHwH79b3jcC5zrniw7z0frwL2wK8hkJVHFq61VRrn/mlQL6ZleMlLV93zm1pEuc+59wVLezXz+PVUf4A72L4JIe/9d7oNryqKy/621+Clyi2VWvv60Zgq3PuX83MO5wbgHPt0J4SHsRLmpu9de68Ordfaq5kyfc0ftUa51wFgH9n4UHgu86rH3sI/wftX/HaNOw7ivfRD7jJtdJOw8z24x174/CO/7a4Ba9u8ft45+IKf1qL2nBeN/U5vLsrH+JVl/t2yLydeMfadry7D9eErOd7wEZgiV+l4mU+agvzH7yGZD80v0cIvPrXN1pIL1GtvIfWvidac6Tna7u375xbj1dafp9/N/LneIleGV6d+KeavuYItHSdatTqudJOW/3v2XnA1S3cyWnqRv/zPoD3A/kBDv9jFOfVYf8i8Cf/B+aRetTf3ma86pEHzxH/WvIvvEbN7fksDuGcexrvmviYf/yvppkk2i9smQvc4ZzbGfK3Be9zU3WTMDHnXNAxSAvM6zapwDl30+GWPcx6huJ9uSceRYmKiDTDzAbjJYH9jzIJxsw24d0GbmujX2nC/zH0sPuox5Ajee3rwJXOua1Npt+EV1L8ehhCjGptuU75dyceBoa4GE06zOse96rWznX/zvVo55y68ItiqrMjInKE/HrJN+BVdzrapPsSvOocr4YzNjkiRXhV9prah1dNTtrJv0vzLeC+WE2628KvfvJlDt8bkHRxSrxFRI6A34hvF141rjlHuY7X8apyfM410ye+dA7nXLN1XZ1zh+vxRtrAvAHUluE1Bv5iwOFELDP7Cl63hg855xYEHY90LFU1ERERERHpBGpcKSIiIiLSCZR4i4iIiIh0goit452ZmemGDh0adBgiIiIiEuWWL19e7Jxrz8ikbRKxiffQoUNZtmxZ0GGIiIiISJQzs+ZG7g47VTUREREREekESrxFRERERDqBEm8RERERkU6gxFtEREREpBMo8RYRERER6QRhSbzNbI6ZrTOzjWb2/WbmX2lmRWa20v+7KhzbFRERERHpKtrdnaCZxQN3AWcABcBSM3vWOfdBk0Ufd85d297tiYiIiIh0ReEo8T4B2Oic2+ycqwEeAy4Iw3pFRERERKJGOBLvQUB+yPMCf1pTl5jZ+2b2pJnlhGG7IiIiIiJdRmc1rnwOGOqcmwS8BDzY3EJmdrWZLTOzZUVFRZ0UmoiIdFU7yip5Ze0uyqtqgw5FROSwwjFkfCEQWoKd7U87yDm3J+TpfcBvm1uRc+5e4F6A3NxcF4bYREQkiv32f+t4+t1CEuONqUMzmDWmL7PG9mVEVipmFnR4IiKHCEfivRQYZWbD8BLuy4FPhy5gZgOcczv8p+cDa8OwXRERiXGbiw9wzIAezBidyWsf7uaX89fyy/lrGZzRnVljsjhtbF9OGt6HlMT4oEMVEWl/4u2cqzOza4EXgHjgfufcGjO7GVjmnHsWuM7MzgfqgBLgyvZuV0REJL+kgk+O788PzjqGH5x1DAWlFby2rojXP9zN48vyeXDxNlIS4zh5RCazxvZl1pgssnt3DzpsEYlR5lxk1ujIzc11y5YtCzoMERGJUPur65jw0xf43pyxfO20ER+bX1Vbz5LNe3jtw928um43+SWVAIzul3awSsrxQ3qTGK+x5ERinZktd87ldvR2wlHVREREpNPll1QAkJPRrdn5KYnxnDamL6eN6cvPnGNT0QFeX7eb19bt5v43t3DPgs2kpyQwY1QWp43J4rQxfclKT+7MtyAiMUaJt4iIdEl5fuI9OOPwVUfMjJF90xjZN42rpg+nvKqWNzd6peGvrdvN86u8ZkiTsntyyshMendPpFtSAt0S4+meFE+3xHi6+f+7J8WT0jg9KZ6UhHji4tSQU0QOT4m3iIh0SflHkHg3lZ6SyJwJ/ZkzoT/OOdZs3+eXhhdxzxubaDjCWpgpiXF09xP1bk0S9bTkBM4c34/zJg1Ugi4S45R4i4hIl5RfUkF6cgI9uyW2az1mxoRBPZkwqCfXnj6K+gZHVW09FTX1B/9X1tZTUVP30XN/WmXNx5erDFl+b2UtG3fv5/lVO7jrtY1cP3s0nxzfXwm4SIxS4i0iIl1SXkkFORndw95fd3yckZqcQGpyeC6RDQ2O51ft4E8vr+drj6xg3IAefOfM0Zw+tq/6GheJMWrKLSIiXVJeScVRVTPpbHFxxnnHDuTF62dy69xjOVBTx5cfXMZFd7/FgvVFRGrvYiISfkq8RUSky2locBSUVrbYo0kkio8zLp6Szcs3zOQ3l0ykqLyaz9//Dpfds4Qlm/ccfgUi0uWpqomIiHQ5Rfurqa5r6BIl3k0lxsdx2dTBXDh5EPOW5nPnaxu5/N4lnDKyDzecMYbjh/TulDi2Fh9g4YYi3lhfTPH+aq6aPoyzJwxQ/XORDqTEW0REupy8g314d73Eu1FyQjyfO2kol+bm8PCSbfzljU1c8ue3mDUmixvOGMPE7J5h3d7+6jre2ljMgg1FLFhfHLIPu5EUH8e1j77LxEGb+d6csZw6KjOs2xYRjxJvERHpcvKjIPFulJIYz1XTh/PpEwfz4FvbuGfBJs67cxFnjuvH9WeM5pgBPY5qvQ0NXjeJCzYU8cb6IlZsK6WuwdE9KZ6TR/ThqunDmDEqi6GZqdQ3OJ55t5BbX1rPZ//2NqeOzOTGOWOYlN0rzO9WJLZpyHgREely/vTyem57ZQNrb55DSmJ80OGEVXlVLfcv2sp9CzdTXl3HOZMGcP3sUYzsm37Y1+7eV8XCDV6p9qINxew5UAPA+IE9mDE6ixmjsjh+SG+SEppv4lVdV8/DS/K489UNlFbUcs7EAXz3k2MYlpka1vcoEmk6a8h4Jd4iItLl3DBvJYs37WHxDz4RdCgdZm9FDX9duJkH3txKVW09Fx43iG/NHsWQPh8lwdV19SzbWsqC9V6p9oc7ywHITEti+qgsZozO5NSRWWSlJx/Rtsuravnrgs3ct2gL1XUNXDY1h29/YhR9e6SE9T2KRAol3kq8RUSkBXP/shiAedecFHAkHW/P/mruWbCZB9/aSl2D41NTshk7IJ0F64tYsrmEytp6EuON44f0PliqPW5Aj7A0kiwqr+bOVzfw6Dt5xMcZXzplGF+dOaLdgxaJRBol3kq8RUSkBdN+9QqnjMzkD3OPDTqUTrN7XxV3v76JR9/Oo6a+gWGZqcwYlcmM0VlMG94nbAP+NCdvTwV/eGkd/165nZ7dEvnGrBF8/qShUVfNR2KXEm8l3iIi0oyq2nrG/vh/XD97NN+aPSrocDpdUXk1VbX1gTQsXbO9jN/+bx1vrC9iQM8Urp89mounDCIhXsOCSNfWWYm3zhQREelSCvdWAnSpwXPCKSs9ObDeXMYP7MmDXzqBf35lGn17pHDjv95nzm0L+d/qnRqBU6QNlHiLiEiX0tj/dFccPCdanDSiD898/WT+8tkpNDjHNQ8v5+I/v6UROEUOQ4m3iIh0KflKvCOCmTFnwgBe/PYM/t/FE9mxt4rL713ClQ+8wwfb9wUdnkhE0gA6IiLSpeSXVJCcEHfEXeRJx0iIj+PyEwZz4eRBPPjWVu5+fRNn376Q/j1SGN0/ndF90xjdL53R/dMZ1TetQxuBikQ6Hf0iItKl5JVUkJPRHbP2d5cn4ZOSGM9XZ47g8qmDeWJ5Ph9s38e6XeU8tHkP1XUNB5cb1Ksbo/ul+Ul5OqP7pTOybxrdktRDikQ/Jd4iItKl5JVUqppJBOvZPZGrpg8/+Ly+wZFfUsH6XeX+337W7yrnzY17qKn3EnIzr+rQqL7pjO6Xxpj+6Yzqm87wrFR1WShRRYm3iIh0Gc45CkoqOHFYRtChSBvFxxlDM1MZmpnKmeP7H5xeV9/A1j0VbNhVzrpd5WzwE/LX1+2mrsHrISXOYGhmKqP7pnPesQM5e2J/3emQLk2Jt4iIdBl7K2opr64ju3dsdiUYTRLi4xjZN42RfdM4a+KAg9Nr6hrYUnyA9bvKDyblqwrL+N+ancwak8UvLpxAdm/d8ZCuSYm3iIh0GepKMPolJcQxpn86Y/qnH5xW3+B48K2t/P7FdZxx6wK+c+Zorjx5qAbukS5HR6yIiHQZ+aV+4t1HiXcsiY8zvnTqMF66YSYnjejDLc+v5aK732J1YVnQoYkckbAk3mY2x8zWmdlGM/t+K8tdYmbOzDp8SE4REYk+jSXeOapqEJMG9erG376Qy52fnsyOsiouuOtNfjV/LRU1dUGHJtIm7U68zSweuAs4CxgHXGFm45pZLh34FvB2e7cpIiKxKb+kgj6pSeoLOoaZGedOGsgrN8xkbm4O9y7YzJl/XMDr63YHHZrIYYWjxPsEYKNzbrNzrgZ4DLigmeV+AfwGqArDNkVEJAbll1SSo/rdgtdt4a8vnsi8r55EckIcVz6wlOv++S5F5dVBhybSonAk3oOA/JDnBf60g8xsCpDjnHs+DNsTEZEY1Th4jkijE4ZlMP9b0/n27FH8b/VOZt/6BvOW5uOcCzo0kY/p8MaVZhYH3Ap8pw3LXm1my8xsWVFRUUeHJiIiXUhdfQOFeysZnKGuBOVQyQnxfHv2aOZ/61TG9Evnxn+9zxV/XcLmov1BhyZyiHAk3oVATsjzbH9ao3RgAvC6mW0FpgHPNtfA0jl3r3Mu1zmXm5WVFYbQREQkWuwoq6K+wakrQWnRyL7pPHb1NH598UTWbN/HnNsWcscrG6gJGbJeJEjhSLyXAqPMbJiZJQGXA882znTOlTnnMp1zQ51zQ4ElwPnOuWVh2LaIiMSIfPVoIm0QF2dcccJgXvnOTM4c148/vLSec25fyPJtJUGHJtL+xNs5VwdcC7wArAXmOefWmNnNZnZ+e9cvIiICIV0JqsRb2qBvegp3fnoK91+ZS0VNPZf8eTE3PbOKfVW1QYcmMSws/TE55+YD85tM+0kLy54Wjm2KiEhsyS+tICHOGNAzJehQpAs5fWw/Try+D7e+tJ4H3tzCi2t2cfMF4/nk+P6YWdDhSYzRyJUiItIl5JVUMrBXNw0TLkcsNTmBH587jme+cQqZaclc8/AKrn5oOTvL1MOxdC59e4mISJeQV1KhhpXSLpOye/Hstafww7PHsnBDEWfc+gaPvp1HQ4O6HpTOocRbRES6hAL14S1hkBAfx9UzRvDCt2cwYVBPfvj0Kj593xK2Fh8IOjSJAUq8RUQk4u2vrmPPgRpy1Ie3hMmQPqk8+pUT+X9+14Of/NMC7nljE3X16npQOo4SbxERiXiNXQmqqomEk5lx+QmDefmGmcwcncWv//shF939Fh9s3xd0aBKllHiLiEjEU+ItHalfjxTu+dzx3P2ZKewoq+T8Oxfx+xfWUVVbH3RoEmWUeIuISMTL0+A50sHMjLMnDuDlG2Zy4eRB3PnaRs65fSHLtmrgHQkfJd4iIhLx8ksqSE9OoFf3xKBDkSjXq3sSv7/0WP7xpROormvg0nsW89N/r2Z/dV3QoUkUUOItEgZVtfXUqzsqkQ6TX1pJTkZ3DXginWbG6Cxe+PYMrjx5KP9Yso0zb32D19btDjos6eLCMnKlSEcqKK3g729uZUz/dHKHZjC0T/AX34qaOpZtLWXx5j0s2byH9wvKOP/YgfzxsuMCjUskWuWVVDAiKzXoMCTGpCYn8NPzxnPupIF871/v88UHlnLR5EH8+NxxZKQmBR2edEFKvCXiPbR4G/ct2nLweUZqElMG9yZ3aG+OH9KbiYN6kpIY36ExVNbUs3xbKUs272Hx5j28l7+XugZHQpwxKbsnx+X04j/vb+emc9TNNzQAACAASURBVI6hT1pyh8YiEmucc+SXVDBrTFbQoUiMOn5Ib56/7lTuem0Td7+2kQXri/jZ+eM5d9KAwAuCpGtR4i0R7431RZw4LINbLpzA8m2lLNtWyoptpby8dhcAifHGhEE9OX6wl4gfP6Q3fXuktGubVbX1rAhJtFfm76W23hEfZ0wc1JOvzBjOtOF9yB3Sm9TkBNbtLOeTf1rA0+8WctX04eF42yLiKyqvprquQT2aSKCSE+K54YzRnD2xP9978n2++c93+ffKQn5x4QQG9FT/8tI2Srwlou3eV8WHO8u5cc4YRvVLZ1S/dC4/YTAAe/ZXsyJvL8v9RPyhJR+VjOdkdAtJxDMY0z+d+LiWSyWqaut5N2/vR4l23l5q6huIM5g4qCdfOnUY04b3YerQDNKSP37ajOmfzrE5vZi3LJ8vnzpMJSAiYdTYo0m2Em+JAGP79+Cpr5/CA29u4fcvruPMWxfw/bPHcsXUwcS1cp0RASXeEuEWbSwGYMaoj99i7pOWzBnj+nHGuH4A1NQ1sGZ7mZeI55Xy1qY9PLNyOwBpyQkcl9PrYIn4+IE92Lh7P0s2l7B4czEr8vZSU+cl2uMH9uTKU4YybXgGU4dmkJ7Stl4ULsvN4YdPr+K9gjKOy+kVpj0gInnqw1siTHyccdX04Zw5rj/ff+p9fvT0ap5duZ1bLpzAqH7pQYcnEUyJt0S0BeuL6JOaxLgBPQ67bFJCHJMH92by4N6AVy+0oLSSFXmlLNtayvJtpdzx6gZCOx8xg3EDevD5aUO8Eu1hGfTsdnTdlZ137ABu/s8aHl+ar8RbJIzySyoxg0G9dDtfIsvgPt155KoTeWJZAb+cv5azblvIlScP5VuzR7W50EZiixJviVgNDY5FG4s5dVTmUd2+MzNyMrqTk9GdC44bBMD+6jrey9/L6sIyhmWmcuKwPvQMU7/A6SmJnD1xAM+9t52fnDuObkkd2+BTJFbklVTQLz2lwxtRixwNM2Pu1Bxmj+vH715Yx9/e3MIzK7fzg7PGctHkQap+IodQP94Ssdbu3Efx/ppmq5kcrbTkBE4ZmclXZ47gzPH9w5Z0N7osN4f91XXMX7UjrOsViWX5JRWqZiIRLyM1iV9fPJF/f+MUsnt34ztPvMen/vIWqwvLgg5NIogSb4lYC9Z79bunj8oMOJK2O2GY18/448vygw5FJGrkl1aQo8RbuohJ2b146msn87tPTSKvpILz7lzEj55eRemBmqBDkwigxFsi1sINRYztn97urgE7k5lxaW4O72wpYUvxgaDDEenyqmrr2bmvipwM1e+WriMuzrsWvPKd07jy5KE8tjSfWX94nYeXbNMoxzFOibdEpMaRIWeM7noDZnzq+GziDJ5QqbdIuxXurcQ59WgiXVPPbon89LzxzL9uOmP7p3PTM6s5/85FLN9WEnRoEhAl3hKR3t5cQk19Q5eqZtKoX48UZo3py5PLC6irbwg6HJEuLV9dCUoUGNM/nX9+ZRp3XDGZPftruOTPi7lh3kp2l1cFHZp0MiXeEpEWbCgiOSGOqUMzgg7lqFyam8Pu8moWbCgKOhSRLq0x8VYdb+nqzIzzjh3IK9+ZyddPG8Fz723n9N+/wX0LN1OrQpqYocRbItLCDcWcOLxPl+0+7BPH9CUzLYnHl6q6iUh75JVUkJwQR1ZactChiIRFanICN84ZywvfnkHu0N7c8rzX//eb/oBxEt2UeEvE2b63ko279zOjC1YzaZQYH8fFU7J5Ze1uisqrgw5HpMvKL6kkJ6O7+kKWqDM8K40HrpzKfZ/Ppbquns/c9zZff2Q5hXsrgw5NOpASb4k4C/3qGdPD2H93EObmZlPX4Hjm3cKgQxHpsvJKKsjprR5NJDqZGbPH9eOl62dywxmjefXD3XziD69zxysbqKqtDzo86QBKvCXiLNhQTL8eyYzulxZ0KO0ysm86Uwb34vFl+Tin7qNEjpRzToPnSExISYznuk+M4uUbZjJrTF/+8NJ6zvzjAt7apOon0SYsibeZzTGzdWa20cy+38z8a8xslZmtNLNFZjYuHNuV6FPf4Fi0oZjpo7Iw6/q3li+bmsPG3ftZkbc36FBEupyyylrKq+vUsFJiRnbv7vz5s8fz8JdPJD7O+Ox9b3P7KxtoUN/fUaPdibeZxQN3AWcB44ArmkmsH3XOTXTOHQf8Fri1vduV6LSqsIyyytou2Y1gc86ZNJDuSfHq01vkKOSpRxOJUaeOyuQ/3zyV844dyK0vrecLD7zDnv1qLxQNwlHifQKw0Tm32TlXAzwGXBC6gHNuX8jTVEA/3aRZC9cXYdb163c3SktO4JyJA3juve0cqK4LOhyRLiVPfXhLDEtNTuBPlx3HLy+awNtbSjjn9kUs3aqBd7q6cCTeg4DQ4rwCf9ohzOwbZrYJr8T7ujBsV6LQgg1FTBjYk4zUpKBDCZvLpuZwoKae51ftCDoUkS4lv8Tr3UEl3hKrzIzPnDiEp752MsmJcVx+7xLueWOTqp50YZ3WuNI5d5dzbgTwPeCm5pYxs6vNbJmZLSsq0sAjsaa8qpYVeXujpppJo+OH9GZ4Vqqqm4gcobySCjJSk0hLTgg6FJFATRjUk+e+eSpnjuvHr//7IVc/tIy9FTVBhyVHIRyJdyGQE/I825/WkseAC5ub4Zy71zmX65zLzcqKjqoG0naLN+2hvsExY3R0ffZmxtzcHJZuLWVT0f6gwxHpMvJLKlTaLeLrkZLI3Z+Zws/OG8cb64s45/ZFrMxXw/2uJhyJ91JglJkNM7Mk4HLg2dAFzGxUyNNzgA1h2K5EmQUbiuieFM+Uwb2DDiXsLp4yiPg4Y55KvUXaLL9UXQmKhDIzrjxlGE9cczIAl/7lLR54c4u6rO1C2p14O+fqgGuBF4C1wDzn3Bozu9nMzvcXu9bM1pjZSuAG4Avt3a5En4UbijlpeB+SEqKve/m+6SmcPrYv/1peSG19Q9DhiES8uvoGCksrNXiOSDOOy+nF89edyszRWfz8uQ/4+iMr2FdVG3RY0gZhyXCcc/Odc6OdcyOcc7/0p/3EOfes//hbzrnxzrnjnHOznHNrwrFdiR7b9hxg256KqKtmEmpubg7F+6t5fZ3aL4gczo6yKuoanEq8RVrQq3sSf/18Lj88eywvfrCL8+5YxOrCsqDDksOIvqJF6ZIWbPBG54q2hpWhZo3JIis9mceXqrqJyOHkl6orQZHDMTOunjGCx6+eRnVtAxf/+S0eeXubqp5EMCXeEhEWri9iUK9uDMtMDTqUDpMQH8clU7J5bd1udpdXBR2OSETL1+A5Im2WOzSD5687lWnD+/Cjp1fz7cdXauyICKXEWwJXW9/A4k17mDE6OoaJb82ludnUNzieWtFaxz8ikldSQXycMaBnStChiHQJfdKS+fuVU/numaN57r3tnH/nItbtLA86LGlCibcEbmX+Xsqr65gRxdVMGo3ISmPq0N7MW5qvW4EircgvqWRQr24kxOsyJdJWcXHGtaeP4uGrTqSsso4L7lqkMSQijL7RJHAL1xcRZ3DyiOhPvMFrZLm5+ADLt5UGHYpIxMorUVeCIkfr5BGZzP/WqRyX04v/e/J9/u+J96isqQ86LEGJt0SABRuKOS6nFz27JwYdSqc4e+IAUpPi1chSpBXe4DnqSlDkaPVNT+GRq6Zx3ekjeXJFARfe9SYL1hexdsc+Ckor2FdVq6HnA6BxeCVQeytqeL9gL988fdThF44SqckJnHfsQJ59bzs/PX+8hsMWaeJAdR17DtSoYaVIO8XHGTecOYbjh2Zw/eMr+fz97xwy3wzSkhPokZJIekoCPbol0iOl6fOPHqc3mZeekkByQnxA765r0hVfAvXmxj00OJgxOjaqmTSaOzWHx5bm8/z727ls6uCgwxGJKOpKUCS8Zo7O4pUbZvLBjn2UV9Wyr7KOfVW17KuqY19lLeVV3vPyqlq2761iXXU5+yrrKK+q5XCF4onxRlpyAqnJCaT5f6kH/8eTlpxIWnK8Ny3Fn54U8jjkdSmJcVHfyYISbwnUwg1FpKckcGx2r6BD6VSTc3oxsm8ajy/NV+It0kTeHr8rwd5KvEXCpXdqEqeMPLJCLuccB2rqP5acNybu5VV17K+u40B1Hfv9x/ur69hbUUNBaYU/r54DNXW0pT+B+Djj95dO4qLJ2Uf5LiOfEm8JjHOOBeuLOGVEZsz1XGBmXJabwy/nr2Xj7nJG9k0POiSRiJFfWgmoxFskaGZ2sDS6PRoaHBW19V6C7ifpBx83Ju7V9eyvrmV0v+i+HirxlsBsKjrA9rIqvnF6bFUzaXTRlEH85n8fMm9ZAT88+5igwxGJGPklFaQnJ9ArRhpci0S7uLiPEvh+QQcTsNgqZpSIsnBDEQAzRmUFHEkwMtOS+cQxfXlqRQG19Q1BhyMSMfJKKsjO6B71dT1FJPYo8ZbALFhfxLDM1JjuueCyqTkU76/hlbW7gw5FJGLkl1QwWF0JikgUUuItgaiuq2fJ5hKmx8Bola2ZMSqLfj2SNbKYiM85p8FzRCRqKfGWQCzfVkplbX3MVjNplBAfxyVTsnlt3W527asKOhyRwBWVV1Nd1xDTd8JEJHop8Y5xr63bzam/eZW/LdrSqdtdsL6YhDhj2og+nbrdSDQ3N4cGB08uLwg6FJHANfbhrcRbRKKREu8YVVVbz0//vZovPrCU3fuq+e3/PjzYd25nWLihiClDemvURmBoZionDsvgiWX5uLZ0dCoSxfJKNHiOiEQvJd4xaM32Ms67YxEPLt7GF08Zyks3zCAhzvjps6s7JfEr3l/Nmu37mDk6tquZhJqbm8PWPRW8s6Uk6FBEApW3x+vDe1AvNa4UkeijxDuGNDQ4/rpgMxfd9RZ7K2t58Esn8NPzxjOkTyrXnzGa19YV8eIHuzo8jkUbigFivmFlqLMnDiA9OYHH1chSYlx+aQX9e6SQkhgfdCgiImGnxDtG7Cyr4nP3v80v569l5pgs/vet6YeUOF958lDG9k/n58+u4UB1XYfGsmBDEb27JzJ+YM8O3U5X0i0pnvOOG8j8VTsor6oNOhyRwKhHExGJZkq8Y8B/V+1gzm0LWLFtL7+6aCL3fu54+qQlH7JMQnwct1w4ge1lVdz+6oYOi8U5x8INxZw6Kov4OA2OEWpubg5VtQ08996OoEMRCUx+SQXZ6sNbRKKUEu8odqC6jhuffI+vPbKCnN7def66U/n0iYNbHA0ud2gGc3Oz+dvCLazfVd4hMX24s5yi8mpVM2nGsdk9GdMvXdVNJGZV19Wzc1+VSrxFJGop8Y5SK/P3cs7tC3lieQFfP20E//rayQzPSjvs675/1jGkpSRw0zMd09CycZh4Jd4fZ2bMnZrDe/l7WbezY374iESywtJKnFOPJiISvZR4R5n6Bscdr2zgkj+/RU1dA//8yjRunDOWpIS2fdQZqUl8f85Y3tlSwlMrCsMe38INxYzul8aAnrqV3JyLJg8iMd6Yp1JviUGNXQmqD28RiVZKvKNIfkkFl92zmD+8tJ5zJg7gv9+ewbThRz5AzdzcHKYM7sWv5q+lrCJ8Df0qa+p5e0sJ02N8tMrWZKQmcca4fjz9biE1dQ1BhyPSqfJLva4EVeItItEqLIm3mc0xs3VmttHMvt/M/BvM7AMze9/MXjGzIeHYrnicczz9bgFn37aQdTvL+dNlx3H7FZPp2S3xqNYXF2fccuFESitq+N2LH4Ytzne2llBT16BqJocxNzeHkgM1vLy247t2FIkk+SUVJCfEkdWk8beISLRod+JtZvHAXcBZwDjgCjMb12Sxd4Fc59wk4Engt+3drnjKKmu57rGVXP/4e4zpn878b03nwsmD2r3ecQN7cOXJw3jk7TxW5u8NQ6SwcH0RSQlxnDhMw8S3ZvqoLAb0TFF1E4k5eXsqyO7djTj1eCQiUSocJd4nABudc5udczXAY8AFoQs4515zzjWOR74EyA7DdmPe25v3cPZtC5m/agffOWM0j109Lax1I68/YxRZacnc9Mwq6hva39BywYYiThiaQbckDYzRmvg441PHZ/PG+iK2760MOhyRTpNfqj68RSS6hSPxHgSEFs0V+NNa8mXgv2HYbsyqqWvgt//7kMv/uoTEeOPJa07im58YRUJ8eKvsp6ck8uNzx7G6cB+PvL2tXevaWVbF+l37Vc2kjebm5uAcPLm8IOhQRDqFc468PUq8RSS6dWrjSjP7LJAL/K6F+Veb2TIzW1ZUVNSZoXUZJQdq+NRf3uLu1zcx9/gcnr9uOpMH9+6w7Z07aQDTR2XyuxfWsbu86qjXs8DvRnDGaDWsbIucjO6cOjKTecvyaQjD3QaRSFdWWUt5dZ16NBGRqBaOxLsQyAl5nu1PO4SZzQZ+BJzvnKtubkXOuXudc7nOudysLCVozfnz6xtZXVjGnz8zhd98ahKpyQkduj0z4+fnj6e6toFfPb/2qNezcEMxmWnJjO2fHsbootvcqTkUlFby1qY9QYci0uHyS7xqVUq8RSSahSPxXgqMMrNhZpYEXA48G7qAmU0G7sFLuneHYZsxac/+ah5ekscFxw3irIkDOm27w7PSuGbmcJ5ZuZ23NhUf8esbGhyLNhQxY1Rmi6NmysedOa4fvbon8tjSvKBDEelwjX14q6qJiESzdifezrk64FrgBWAtMM85t8bMbjaz8/3FfgekAU+Y2Uoze7aF1Ukr/rZoC1V19Xxj1ohO3/bXZ41kcEZ3fvzM6iPuX3r19jJKK2pVzeQIpSTGc9HkQby4ZhelB2qCDkekQ2nwHBGJBWGp4+2cm++cG+2cG+Gc+6U/7SfOuWf9x7Odc/2cc8f5f+e3vkZpqqyiln8s3sbZEwYwsm/nV9dISYzn5xeMZ1PRAf66cPMRvXbhBq+U/JSRalh5pC6bmkNNfQNPvxv+UURFIkl+aQUZqUmkdXD1ORGRIGnkyi7igbe2sL+6jmtPHxlYDLPG9GXO+P7c8eoG8ksqDv8C34L1RYwb0IOsdA2KcaTG9u/BsTm9eHxpPs6pkaVEr/ySCpV2i0jUU+LdBZRX1fLAm1s5Y1w/jhnQI9BYfnLeOOLM+PlzH7Rp+f3VdSzfVqpqJu1w+dQc1u0qD9tARiKRKK+kgpze3YIOQ0SkQynx7gIeWrKNsspavhlgaXejgb268e3Zo3h57S5e+uDwQ5ov2bSHugbHDPXffdTOnTSAbonxPL5UI1lKdKpvcBSWVqphpYhEPSXeEa6ipo77Fm5h5ugsJmX3CjocAL54yjBG90vjZ8+uoaKmrtVlF24ooltiPMcP7bi+xqNdekoi504awHPvbedAdev7W6Qr2lFWSV2DU+ItIlFPiXeEe/TtPEoO1EREaXejxPg4brlwIoV7K7nz1Y2tLrtgQzHThmeQnKBh4tvj8hNyOFBTz/Pv7wg6FJGwU48mIhIrlHhHsKraeu5dsJmThvchd2hG0OEc4oRhGVwyJZu/LtzMxt3lzS6TX1LBluIDTB+l+t3tNWVwb0ZkpapPb4lKBf7gOSrxFpFop8Q7gj2xLJ/d5dV88xORU9od6odnj6V7UgI3PbO62R43GrsRnDFa9bvby8y4fOpgVuTtZcOu5n/oiHRVeSUVxMcZA3qmBB2KiEiHUuIdoWrqGvjz65s4fkhvThreJ+hwmtUnLZkb54xhyeYS/r1y+8fmL1hfxMCeKYzISgsguuhz0ZRBJMabGllK1MkrqWBgrxQS4nVJEpHopm+5CPXUigK2l1XxzdNHRvQw61dMHcyxOb245fm1lFXWHpxeV9/Am5uKmT4qK6Lj70oy05I5Y1w/nnq3kOq6+qDDEQmb/NIKVTMRkZigxDsC1dU3cPfrm5iU3ZOZEd7/dVyc8csLJ1ByoJpbX1x3cPp7BWWUV9UxXdVMwmpubg4lB2p4+YPdQYciEjb5JUq8RSQ2KPGOQM++t528kgqunRXZpd2NJgzqyedPGspDS7axqqAM8KqZmMGpGiY+rKaPymJgzxQeX6bqJhIdDlTXUby/huzeSrxFJPop8Y4w9Q2Ou17byNj+6cw+pl/Q4bTZDWeOJiM1mZueWUV9g2PhhiImZfeiV/ekoEOLKvFxxqW5OSzcUERBaUXQ4Yi0W0GpejQRkdihxDvC/Hf1DjYVHeDa00cSFxf5pd2NeqQk8uNzj+G9gjLuWbCJlfl7NVplB7k0NxuAJ5YVBByJSPs19uGtxFtEYoES7wjS0OC489WNjMhK5awJA4IO54idf+xATh7Rh9/+bx0NDmZEeP30riq7d3dOHZnJk8sLqG/4eDeOIl2JBs8RkViixDuCvLx2Fx/uLOcbs0YS34VKuxuZGTdfMIHEeCMtOYHjciJjiPtodPnUwRTurWTRxuKgQxFpl/ySCtKSE+jdPTHoUEREOlxC0AGIxznHHa9uZEif7px/7MCgwzlqI/um8YsLJlBRU0+i+uTtMLPH9aV390QeX5oX8T3fiLQmv6SCnIzuXaIhuYhIeynxjhBvrC9iVWEZv7lkYpcfROLyEwYHHULUS06I5+Ip2fxj8Vb27K+mT1py0CGJHJW8kgqGZaYGHYaISKfo2hlelGgs7R7UqxsXTc4OOhzpIi6bmkNtvePpdwuDDkXkqDjnNHiOiMQUJd4RYPHmPSzfVso1M4eTlKCPRNpmdL90pgzuxWNL83FOjSyl6ynaX01VbQOD+yjxFpHYoCwvAtzxykb6pidzaW5O0KFIF3PZ1Bw27t7PirzSoEMROWL5jT2aaPAcEYkRSrwDtmxrCYs37+HqGcNJSYwPOhzpYs6dNJDUpHgeX6qRLKXryS/xBs9RV4IiEiuUeAfsjlc30ic1iU+fqAaJcuRSkxM479iBPPfeDsqraoMOR+SINPbhnd27W8CRiIh0DiXeAXovfy9vrC/iy9OH0T1JHczI0Zk7NYfK2nr+8/6OoEMROSJ5JRX065Gsu30iEjOUeAfoztc20rNbIp+bNiToUKQLm5zTi9H90lTdRLqc/BL1aCIisUWJd0DW7tjHSx/s4ounDCU9RSO2ydEzMy6bOpiV+Xv5cOe+oMMRabPGwXNERGKFEu+A3PnqRtKSE/jiycOCDkWiwEWTB5EYbyr1li6juq6eHfuqVOItIjElLIm3mc0xs3VmttHMvt/M/BlmtsLM6szsU+HYZle2cXc581fv4AsnD6Fnd5V2S/tlpCZx5vj+PP1uIdV19UGHI3JY2/dW4Zy6EhSR2NLuxNvM4oG7gLOAccAVZjauyWJ5wJXAo+3dXjS467VNpCTE86VTVNot4XP51Bz2VtTy4ppdQYcicliNPZpo8BwRiSXhKPE+AdjonNvsnKsBHgMuCF3AObfVOfc+0BCG7XVp2/Yc4N8rC/nstMH0SUsOOhyJIqeMyGRQr26qbiJdwsHEW1VNRCSGhCPxHgSEXukL/GnSjLtf20RCfBxfmT486FAkysTFGXNzc1i0sfjgiIAikaqgpIKkhDiyVAAhIjEkohpXmtnVZrbMzJYVFRUFHU7YFZRW8K8VBVwxNYe+PVKCDkei0KW52ZjBE8tU6i2RLa+kgpze3YiLs6BDERHpNOFIvAuBnJDn2f60I+acu9c5l+ucy83KygpDaJHlnjc2YwZfnTki6FAkSg3s1Y2Zo7OYt6yA+gYXdDgiLcpTH94iEoPCkXgvBUaZ2TAzSwIuB54Nw3qjyq59VTy+LJ9PHZ/NwF4aHlk6zmW5OezcV8WC9dF310iih/rwFpFY1O7E2zlXB1wLvACsBeY559aY2c1mdj6AmU01swLgUuAeM1vT3u12Nfcu2Ex9g+NrM0cGHYpEuU8c048+qUk8tjQv6FBEmlVWUcu+qjqVeItIzEkIx0qcc/OB+U2m/STk8VK8KigxqXh/NY+8vY0LjhuorrOkwyUlxHHJ8dncv2gLReXVZKWr8ZpElsYeTVTiLSKxJqIaV0ar+xZuobqugW/MUmm3dI65uTnUNTieWlEQdCgiH5Nf6ifeGjxHRGKMEu8OtreihocWb+XcSQMZkZUWdDgSI0b2TSN3SG8eX5qPc2pkKZHloxJvtXcRkdiixLuDPfJ2Hgdq6vnGLPVkIp3rsqk5bC4+wLJtpUGHInKIvJIKMlKTSE9JDDoUEZFOpcS7gy3ZvIdxA3owtn+PoEORGHPOpAGkJSfw2Dvq01siS77fh7eISKxR4t2BnHOsLixj4qCeQYciMah7UgLnHTuQ51dtZ19VbdDhiBykrgRFJFYp8e5AhXsrKa2oZUK2Em8JxuVTc6iqbeDZlduDDkUEgPoGR+HeSnUlKCIxSYl3B1pduA+ACQNVzUSCMSm7J2P7pzNPQ8hLhNi5r4raeqcSbxGJSUq8O9DqwjLi44xjBijxlmCYGZdNzeH9gjLWbC8LOhwR8vZ4PZqoxFtEYpES7w60qrCMUX3TSEmMDzoUiWEXTR5EUkIc85aq1FuCl1+ixFtEYpcS7w7S2LByghpWSsB6dU9izvj+PP1uITvKKoMOR2JcfmkF8XHGgJ4pQYciItLplHh3kJ37qthzoEY9mkhE+NxJQ9hfXcdJv36V8+9cxO2vbGDN9jINriOdLq+kgoG9UkiI1+VHRGJPQtABRKuDDSsHqX63BG/q0AxevH4GL6zZxctrd/HHl9dz60vrGdgzhdnj+jH7mH6cODyD5ARVi5KOlVdSoWomIhKzlHh3kFWFZcQZjBugEm+JDCP7pjOybzrfmDWSovJqXvtwNy+t3cW8Zfn8Y/E2UpPimTkmi9nH9GPWmL70Tk0KOmSJEjV1Dby+bjdPrSjk/YIy5ubmBB2SiEgglHh3kNWFZYzsm0a3JJUgSuTJSk9m7tQc5k7Noaq2njc3FvPy2t28snYX81ftJM4gd2gGs4/py+xj+jE8Ky3okKWLcc7xXkEZT60o4Ln3tlNaUUtmWhJfOGkoX5kxLOjwREQCocS7g6wuLOPUMBjzewAAHfdJREFUkZlBhyFyWCmJ8XzimH584ph+NDRMYFVhGS+v3cXLa3fzq/kf8qv5HzI8K5UzjunH7HH9mDK4N/FxFnTYEqEKSit45t1Cnnq3kM1FB0hOiOOMcf24ZEo200dlqm63iMQ0Jd4dYPe+KnaXV6tHE+ly4uKMY3N6cWxOL75z5hgKSit4Ze1uXl67i/vf3MI9CzbTu3sis8b25Qw/WU9KUCIV68qravnvqp089W4BSzaXAHDCsAy+OmM4Z00cQI+UxIAjFBGJDEq8O8CqQm+gEiXe0tVl9+7OF04eyhdOHkp5VS0L1hfz8tpdvPqhV193xugs7v9CrkoxY1BdfQOLNhbz1IpCXlizk+q6BoZlpvKdM0Zz4eRBGplSRKQZSrw7wOrCfZjBeA0VL1EkPSWRcyYN4JxJA6irb+CRt/P46bNruPk/H3DzBROCDk86yQfb9/HUigKeWbmd4v3V9OqeyNzcHC6aMojJOb0wUzUkEZGWKPHuAKsKyxiemUpqsnavRKeE+Di+cPJQtu+t5J4FmxmemcqVp6jBXLTata+Kf68s5KkVhXy4s5zEeOP0sX25aHI2s8ZmqRtKEZE2UmbYAdZsL+OEYRlBhyHS4W6cM5bNxQe4+T8fMCQzlVlj+gYdUkxzzlFyoIaqugaqa+uprmugpq6B6roGquvqDz6u8Z8fMr+2nur6BqprD12meH8172wpocHB5MG9+MWFEzh34gB1NykichSUeIdZ8f5qdpRVacRKiQnxccZtlx/HpX9ZzDcffZd/fe1kxvRPDzqsmFNZU88zKwv5+5tbWber/KjWYQbJCXEkxceRnBjvPU6IIzUpgW/MGslFkwepW0kRkXZS4h1mjQ0rxw9U4i2xoXtSAvd9IZcL73qTL/19Kc984xSy0pODDismbN9byT8Wb+OxpXnsrahl3IAe3HTOMfTolkhyQpz/F0+S/zjJf/7RYy/JToqPIzHeVD9bRKSDKfEOszWNibeGipcYMqBnN+77/FQuvectrn5oGf/8yjRSElXvtyM451i2rZQH3tzCC2t24Zzjk+P788VThjF1aG8lzyIiEUyJd5itKixjWGaq+q2VmDMxuyd/uuw4rnl4Bf/35PvcfvlxSgLDqKq2nv+8v4MH3tzCmu376NktkaumD+Nz04aQ3Vtd94mIdAVKvMNsdeE+Jg/uFXQYIoGYM2EA35szlv/f3p2HV1Weex//3iQkEuZZIIzKIERFCSj0WH0LThwtohRpX5FYPbTS9kiP2qKtvlZFra3H4bXaqsio1Yp6cECtUD3HKsqgSDYgAWQMkVESQiDjc/7YCw0hIcPa2Wvvze9zXblYe03PvW723rmz1vOs9fu3v6BPh+b88sJ+QYcU93YVHGbex1t47pOt7D1YQt9OLZg+NoOxZ3UjLUVf4SIi8SQi39pmdgnwKJAEPOOce6DK8lRgDjAE2Atc7ZzbHIm2Y8m+gyXk7j/EtcN7Bh2KSGB+en4fvtxdyKOL19OnY3PGDO4WdEhxaeW2/cz8cBNvrsqj3DlGDuhE1ojefOfU9rqSICISp3wX3maWBPwJuBDYDiwzs9ecc2sqrXY98LVz7lQzmwD8Hrjab9uxJuT179YdTeREZmZMH3s6W/YVcev8VaS3bcaQnrq9Zl2UlFXwViiPmR9uZuW2/bRITWbi8J5MGt6LXh2aBx2eiIj4FIkz3sOADc65LwHM7AVgDFC58B4D3OVNzwceNzNzzrkItB8zQjt0RxMRgJTkJvzlmiGMfeJDJs9ZwX/97Dt6hPhx7C0s5vlPtjL34y3sOlBM7w7NuevygYzL7E4LPYhLRCRhROIbvRuwrdLr7cA5Na3jnCszs3ygPbCnpp2uW7eOCy644Kh548ePZ8qUKRQVFTF69OhjtsnKyiIrK4s9e/Ywbty4Y5bfeOONXH311Wzbto2JEyces/zmm2/m8ssvZ926dfzkJz85Zvlvf/tbRo0axcqVK5k6deoxy9udfy092vVm9cpl3H777ccsf+SRRxg8eDCLFi3i3nvvPWb5X/7yF/r378/rr7/OQw89dMzyuXPn0r17d1588UWefPLJY5bPnz+fDh06MGvWLGbNmnXM8oULF5KWlsYTTzzB3/72t2OWv//++wD88Y9/5I033jhqWbNmzXjrrbcAuOeee1i8ePFRy9u3b8/LL78MwG233caSJUuOWp6ens68efMAmDp1KitXrjxqeb9+/XjqqacAmDx5Mjk5OUctHzx4MI888ggA11xzDdu3bz9q+fDhw7n//vsBuOqqq9i7d+9Ry0eOHMkdd9wBwKWXXsqhQ4eOWn7ZZZdxyy23ABzzvoPYf+/dd999jBgxgo8++iim3nuHSstZl5tPVsofePWm7zHv2af13qv03jtUWs5JfYaS1/NCSsoqOPTqHXRp3QyX1pRZb8As9N7T956+9/Te03uvqsZ+7zWmJlFtrRZmNtnMlpvZ8tLS0qDDqbeNuw+SodsIinyjWdMk+nVuyaY9B/nF859RXpFQF7l8OVRSTig3n7V5BYzPTGfRf3yX07q0ok2a7ogkIpKozG9vDzMbDtzlnLvYe30bgHPu/krrvOOts8TMkoGvgI7H62qSmZnpli9f7iu2aMovKuXMu//Ory7pz5QLTg06HJGY8sLSrUx7JZtJw3vyuzEZQYcTuJ0Fhxn7pw8prXC8cuMIdcMREQmYma1wzmU2djuROOO9DOhrZr3NLAWYALxWZZ3XgEne9DjgH4nav1sDK0WONWFYD/7tvN7MXrKF2R9tDjqcQB04XErWzGXkHyplZtZQFd0iIicQ3328vT7bPwfeIXw7wWedc6vN7G5guXPuNWAGMNfMNgD7CBfnCeXIHU0yNLBSpFrTLj2NTXuK+N3rq+nZPo0L+ncKOqSoKy2vYMpzn5Kz8wDPZg0lQ3+oi4icUCLSx9s5t9A51885d4pzbro3706v6MY5d9g59wPn3KnOuWFH7oCSSLJz8+nWphltm6cEHYpITEpqYjw6YTADTm7Fz5//jHVfHQg6pKhyznHbK9l8sH4P9195Ouf36xh0SCIiEmUxNbgynoVy8zWwUqQWzVOTmZGVSVpKEtfPXsaewuKgQ4qahxetZ/6K7Uwd1Zfxmd2DDkdERAKgwjsCCg6Xsnlvkfp3i9RBl9bNeGZSJnsKi5k8ZzmHS8uDDqnRvbhsK48tXs/4zHRuGtk36HBERCQgKrwjYHVuAYD6a4rU0RnpbXh4/GA+3bqfX7+8igQba32U99bt4vZXQ3y3X0emjz1dj3sXETmBqfCOgG8GVqrwFqmzS0/vwq0X92fByh08tnhD0OE0ilBuPj977lMGnNySJ/7v2TRN0leuiMiJTM8ijoDQjny6tD6JDi1Sgw5FJK5MueAUvtx9kIcX5dC7Y3O+f2bXoEOKmG37isiauYy2aSnMzBqqR7+LiIjOeEdCdm6+znaLNICZcd+VGQzr1Y5bXvqcT7d+HXRIEbG/qISsmUspKStn1nVD6dTqpKBDEhGRGKDC26fC4jI27Tmo+3eLNFBqchJ/njiELq1PYsq8TzlYXBZ0SL4cLi1n8pwVbNt3iKevzaRv55ZBhyQiIjFChbdPa3YU4Bycnq5bCYo0VLvmKfzn+MF8VXCYx/6xPuhwGqyiwnHzS5+zdPM+Hhp/Juf0aR90SCIiEkNUePuUrSdWikTEkJ5tGZ+ZzowPNrFhV3w+XOf+t9by5qo8bh89gMsTqL+6iIhEhgpvn1bn5tOpZar6cIpEwK8vGUBaShJ3Llgdd7cYnPnhJp7+YBNZI3rxb+f1CTocERGJQSq8fcrOzdeDc0QipH2LVG69ZAAfbdzLG6vygg6nzt4O5XH3G2u4eFBn7rhsoO7VLSIi1VLh7UNRSRkbdxcySIW3SMT8aFgPMrq14t4311AYBwMtV2zZx00vrOSs7m14dMJZJDVR0S0iItVT4e3D2rwCKhw64y0SQUlNjHvGZLCzoJjHFsf2QMsvdxdyw+zldG3TjGcmDeWkpklBhyQiIjFMhbcP2duPPLFSdzQRiaSzerRlwtDuPPvPTeTsjM2BlrsPFDNp5lKamDHruqG0a54SdEgiIhLjVHj7ENpRQIcWKZysgZUiEferSwbQPDWZOxeEYm6gZVFJGdfPXsbuA8XMyBpKz/bNgw5JRETigApvH0LeEys1kEok8to1T+FXl/Tn4y/38drnO4IO5xtl5RX84vnPCOXm8/gPz2Zw9zZBhyQiInFChXcDHS4tZ/2uQt2/W6QRTRjagzPSWzP9zbUcOFwadDg457jztdUs/mIXd4/JYNTAzkGHJCIicUSFdwOtzSugvMKRoYGVIo3myEDL3YXFPLoo+IGWT7y/kec/2cqNF5zCNef2DDocERGJMyq8GyjkPbHy9HQV3iKN6czubZgwtAczP9rMuq+CG2j54rKt/OGddVwxuCu3XtQ/sDhERCR+qfBuoOzcfNqmNaVraw2sFGlsv7q4Py1PSuaOgAZaLszO47ZXsjm/X0ceHHcmTXSvbhERaQAV3g0Uyi3QwEqRKGnbPIVfXzKApZv2sWBldAdafrB+Nze98Bln92jLk9ecTUqyvjZFRKRh9BukAQ6XlpOz84D6d4tE0dWZ3TmzexumL1xLQZQGWq7Y8jWT56zg1E4tmZE1lLSU5Ki0KyIiiUmFdwPk7DxAWYXTEytFoqhJE+OeMYPYU1jMI+82/kDLL74q4LqZS+ncKpU5Px5G62ZNG71NERFJbCq8GyD7yMBKFd4iUXVGeht+NKwHs5dsZm1eQaO1s2XvQSbOWEpaSjJzrz+Hji1TG60tERE5cajwboBQbj6tmzUlvW2zoEMROeHcenF/Wp3UeE+03FlwmGtmfEJZeQVzrx9G93ZpEW9DREROTCq8GyA8sLKVBlaKBKBNWgrTLh3Ass1f8+pnuRHd99cHS5g44xP2FZYw67ph9O3cMqL7FxGRE5uvwtvM2pnZu2a23vu3bQ3rvW1m+83sDT/txYKSsgrWfaWBlSJB+sGQ7gzu3ob7Fq4l/1BkBloeLC4ja9YyNu8t4ulJmZypR8GLiEiE+T3jPQ1Y7JzrCyz2XlfnD8BEn23FhJydBygpr9Cj4kUC1KSJce8VGew9WMLD7+b43l9xWTmT5y4nlJvP4z88ixGndIhAlCIiIkfzW3iPAWZ707OBK6pbyTm3GAjukXMRFNLASpGYkNGtNdec05M5SzazZkfDB1qWlVfw73/9jA837OXBq87gokEnRy5IERGRSvwW3p2dc3ne9FdAZ5/7i3nZufm0TE2mhwZciQTulov60yYthTsXhKioqP9Ay4oKx7RXsnln9U7+3+UDuWpIeiNEKSIiElZr4W1mi8wsVM3PmMrrufDtBXzdYsDMJpvZcjNbvnv3bj+7ajShHQUM6tZKj4wWiQGt05oy7dIBLN/yNa/Uc6Clc47pC9cyf8V2po7qy3Xf6d1IUYqIiITVWng750Y55zKq+VkA7DSzLgDev7v8BOOce8o5l+mcy+zYsaOfXTWK0vIK1uYVqJuJSAwZd3Y6Z/dow/0L15JfVPeBlo//YwMz/rmJrBG9uGlk30aMUEREJMxvV5PXgEne9CRggc/9xbT1OwspKavQHU1EYkiTJsbdYzL4uqiEh95dV6dt5izZzEPv5nDl2d2487KBujWoiIhEhd/C+wHgQjNbD4zyXmNmmWb2zJGVzOwD4CVgpJltN7OLfbYbiNCO8MBKFd4isSWjW2smntuTeR9v+WYAdE3+67Nc7lywmlGndebBq85QtzEREYkaX4W3c26vc26kc66v1yVlnzd/uXPuhkrrneec6+ica+acS3fOveM38CCEcvNpnpJE7/bNgw5FRKr4j4v60zYthTuOM9By8dqd3PzS5wzv057Hf3QWyUl6hpiIiESPfuvUQyg3n0FdW+sMmUgMat2sKbeNPo3Ptu5n/ortxyz/+Mu9THnuUzK6tuLpSZmc1DQpgChFROREpsK7jsrKK1iTV6BuJiIx7MqzupHZsy0PvP0F+4tKvpkfys3nhtnL6d4ujZnXDaNFanKAUYqIyIlKhXcdbdx9kMOlFWR0axV0KCJSgyMDLfcXlfDHv4cHWm7YVci1zy6ldbOmzL1+GO2apwQcpYiInKh02qeO9MRKkfgwsGsrrh3ei9lLNvMvp3bkd6+vpokZz91wDl1aNws6PBEROYHpjHcdZefm06xpEn06tgg6FBGpxS8v7Ef75qn8dN4KCovLmPPjYfTqoEHRIiISLBXedRTKzWdg11YkaWClSMxr3awpv/v+IDq1TGVm1lAGdlUXMRERCZ66mtRBeYVjTV4B4zO7Bx2KiNTRv57RhdGnn6yH44iISMzQGe862LSnkKKScgbprJlIXFHRLSIisUSFdx2EcgsAOD1dAytFREREpGFUeNdBdm4+qclNOFUDK0VERESkgVR410F2bj6ndWmlx0uLiIiISIOpkqxFRYVjzY4C3b9bRERERHxR4V2LzXsPUlhcpidWioiIiIgvKrxrEdoRHliZoTPeIiIiIuKDCu9ahHLzSUlqQr/OLYMORURERETimArvWmRvz2dAl5Y01cBKEREREfFB1eRxOOcI7chXNxMRERER8U2F93Fs3VfEgcNluqOJiIiIiPimwvs4snPzAcjoqsJbRERERPxR4X0codwCmiYZ/U7WEytFRERExB8V3scRys2nX+eWpCYnBR2KiIiIiMQ5Fd41ODKwUv27RURERCQSVHjXYPvXh9hfVKo7moiIiIhIRKjwrkHoyMBKFd4iIiIiEgEqvGsQ2pFPUhNjwMl6YqWIiIiI+Oer8Dazdmb2rpmt9/5tW806g81siZmtNrNVZna1nzajJTu3gL6dWnBSUw2sFBERERH//J7xngYsds71BRZ7r6sqAq51zg0CLgEeMbM2PtttVM45QrkaWCkiIiIikeO38B4DzPamZwNXVF3BOZfjnFvvTe8AdgEdfbbbqPLyD7PvYAmnp6vwFhEREZHI8Ft4d3bO5XnTXwGdj7eymQ0DUoCNPtttVEeeWDlIT6wUERERkQhJrm0FM1sEnFzNot9UfuGcc2bmjrOfLsBcYJJzrqKGdSYDkwF69OhRW2iNZnVuPk0MBnZpFVgMIiIiIpJYai28nXOjalpmZjvNrItzLs8rrHfVsF4r4E3gN865j4/T1lPAUwCZmZk1FvGNLTs3n76dWtIsRQMrRURERCQy/HY1eQ2Y5E1PAhZUXcHMUoBXgTnOufk+22t0zjmycwsY1E1nu0VEREQkcvwW3g8AF5rZemCU9xozyzSzZ7x1xgPfBbLMbKX3M9hnu41m14Fi9hQW644mIiIiIhJRtXY1OR7n3F5gZDXzlwM3eNPzgHl+2omm7O16YqWIiIiIRJ6eXFlFaEc+poGVIiIiIhJhKryrCOXmc0rHFjRP9XUxQERERETkKCq8q8jOzSejq852i4iIiEhk6bRuFS/9ZATlLrA7GYqIiIhIglLhXUWP9mlBhyAiIiIiCUhdTUREREREokCFt4iIiIhIFKjwFhERERGJAhXeIiIiIiJRoMJbRERERCQKVHiLiIiIiESBCm8RERERkShQ4S0iIiIiEgUqvEVEREREokCFt4iIiIhIFJhzLugYqmVmu4EtATXfAdgTUNuJQjn0Tzn0TzmMDOXRP+XQP+XQvxMxh3U95p7OuY6NHUzMFt5BMrPlzrnMoOOIZ8qhf8qhf8phZCiP/imH/imH/p2IOYy1Y1ZXExERERGRKFDhLSIiIiISBSq8q/dU0AEkAOXQP+XQP+UwMpRH/5RD/5RD/07EHMbUMauPt4iIiIhIFOiMt4iIiIhIFMRF4W1m3c3sPTNbY2arzewmb347M3vXzNZ7/7b15g8wsyVmVmxmt1TZ101mFvL2M/U4bV5iZuvMbIOZTas0f6SZfWpmK83sn2Z2ag3bDzGzbG/7x8zMvPk/8NquMLOojbJNsBzeZWa53vYrzWx0JHJUmwTL4ZlebNlm9rqZtYpEjmoTpzmcbmbbzKywyvyfevk7sv1AP7mpqwTL4cOVPsc5ZrbfT27qI97yaGZpZvammX3htfNApWXf9bYvM7NxkchPXSRYDrPMbHel9+MNkchRbRIshz3NbLGZrTKz980sPQ6O+XveMYfMbLaZJdewfW8z+8Tb/kUzS/Hm1/+z55yL+R+gC3C2N90SyAEGAg8C07z504Dfe9OdgKHAdOCWSvvJAEJAGpAMLAJOraa9JGAj0AdIAT4HBnrLcoDTvOkpwKwaYl4KnAsY8BZwqTf/NKA/8D6QqRw2KId3VY5JOWxQDpcB53vTPwbuUQ5rzOG5XtyFVea3qjT9feBt5bB+Oayyzi+AZ6ORw3jMo7f//+NNpwAf8O3nuRdwBjAHGKccNiiHWcDj0cpdgubwJWCSN/09YG4sHzPhk8/bgH7eencD19cQ89+ACd70n4EbG/rZi4sz3s65POfcp970AWAt0A0YA8z2VpsNXOGts8s5twworbKr04BPnHNFzrky4L+BK6tpchiwwTn3pXOuBHjBawvAAUfODrYGdlTd2My6EP6l/LEL/8/MqRTbWufcuvrmwK9EymFQEiyH/YD/8abfBa6qWxb8ibccejF87JzLq2Z+QaWXzb39NbpEymEVPwT+Wss6ERNvefT2/543XQJ8CqR7rzc751YBFfXLgj+JlMOgJFgOBwL/8Kbfq7TfWD3m9kCJcy7HW6/a34VmZoT/kJhfTWz1/uzFReFdmZn1As4CPgE6V/oy/wroXMvmIeA8M2tvZmnAaKB7Net1I/xX0BHbvXkANwALzWw7MBF4gGN187apbvvAJUgOf+5dznr2yOWoaEqAHK7m2y/FH9TQfqOKkxzWdgw/M7ONhM/U/Ht9t/crEXII4UvUQG++/aUdVfGWRzNrA1wOLK4ltqhJkBxe5f1emW9m+k6sfw4/59vCdyzQ0sza17KPXgR3zHuAZPu22++4GrZvD+z3ivvK2zdIXBXeZtYCeBmYWuVsE94ZveOecXLOrQV+D/wdeBtYCZTXM4xfAqOdc+nATOA/67l9oBIkh08CpwCDgTzgoXpu70uC5PDHwBQzW0H4Ul9JPbf3JUFyiHPuT865U4BfA7+t7/Z+JEoOPROA+c65+rbvW7zl0euD+lfgMefcl/Vsp1EkSA5fB3o5584gfOZzdk3bN4YEyeEtwPlm9hlwPpB7vBiCPmavjQnAw2a2FDhQn+0bKm4KbzNrSvg/6Dnn3Cve7J3e5fQjl9V31bYf59wM59wQ59x3ga+BHK+j/5EBFT8l/Gap/FdPOpBrZh2BM51zn3jzXwRGmFlSpe3v9rZPr7p9gw8+QhIlh865nc65cudcBfA04ctIUZFAOfzCOXeRc24I4S/PjQ1KSAPEWQ7r6gWi2BUqAXM4gSh2MzkiTvP4FLDeOfeIj0OPmETJoXNur3Ou2Hv5DDCknqlosATK4Q7n3JXOubOA33jzqh0wHQvH7G2/xDl3nnNuGOHulzle++942z8D7AXa2LcDL33VdNWO3ow1ZmbADGCtc67yX2CvAZMIXw6ZBCyow746Oed2mVkPwpdEzvXeGIMrrZMM9DWz3oSTOwH4EeH/1NZm1s+F+wRd6MVUXnl7bx8FZnYu4csn1wL/v2FHHxmJlEMz61LpctRYwpebGl2C5fBI+00In6n9c/0zUn/xmMPjtN/XObfee/mvwPrjrR8piZRDb/8DgLbAkrpuEwnxmEczu5dw39uo3HGjNomUwyq/V75PuN9xo0uwHHYA9nknxW4Dno3xY668fSrhK5fTAZxzF1dp5z3CXVFeqGtsNXJRHsHbkB/gXwhfclhF+FLCSsJ9edoT7lu0nvBo1nbe+icT7oNTAOz3plt5yz4A1hDuizTyOG2OJvyXz0bgN5XmjwWyve3fB/rUsH0m4YJwI/A4fPOworFePMXATuAd5bDeOZzrbb+K8Ae1i3JY7xze5O03h/CXnCmHNebwQa/dCu/fu7z5jxLuK7+S8ECiQcph/XLoLbsLeCAauYvnPBI+y+YIF4RH4r3BWzbUi+cg4bNzq5XDeufwfsKf588Jf54HKIf1zuE4L94cwlcNUuPgmP/gHcs6wl1eatq+D+G7hG0gfPeW1IZ+9vTkShERERGRKIibPt4iIiIiIvFMhbeIiIiISBSo8BYRERERiQIV3iIiIiIiUaDCW0REREQkClR4i4iIiIhEgQpvEREREZEoUOEtIiIiIhIF/ws5DpH+gUqwOgAAAABJRU5ErkJggg==\n",
            "text/plain": "<Figure size 864x360 with 1 Axes>"
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "K_VJiUGKhoVE"
      },
      "source": [
        "На большей части графика средний чек по группе B выше, чем по группе A. Главное, на что нам стоит обратить внимание - резкие колебания, как на рост, так и на спад. Следует отфильтровать аномальные значения. Здесь мы также видим описанную выше дату 18 августа, когда произошла очень крупная покупка. "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4aXo4-sqi-IW"
      },
      "source": [
        "**Кумулятивная конверсия по группам.**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 336
        },
        "id": "9tx-lhkHf4ST",
        "trusted": false,
        "outputId": "69aaa0bd-c8fe-465c-ede8-bc85ebf187b9"
      },
      "source": [
        "cumulativeData['conversion'] = cumulativeData['orders']/cumulativeData['visitors']\n",
        "cumulativeDataA = cumulativeData[cumulativeData['group']=='A']\n",
        "cumulativeDataB = cumulativeData[cumulativeData['group']=='B']\n",
        "plt.figure(figsize=(12,5)) \n",
        "plt.plot(cumulativeDataA['date'], cumulativeDataA['conversion'], label='A')\n",
        "plt.plot(cumulativeDataB['date'], cumulativeDataB['conversion'], label='B')\n",
        "plt.legend()\n",
        "plt.title('График кумулятивной конверсии по группам')\n",
        "plt.show()\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": "<Figure size 864x360 with 1 Axes>"
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4L03KqeklBs7"
      },
      "source": [
        "Накопительная конверсия по группе B практически на всем протяжении лежит значительно выше накопительной конверсии группы A. В первые дни конверсия группы А показала резкий скачок, за ним пошел резкий спад, и после 8 августа кривая стабилизировалась. Аналогичный процесс мы наблюдаем в группе В, только после первой недели кривая В пошла вверх и стабильно держалась в районе 0,35, а В пошла вниз, и стабильно держалась в районе 0,3. График в целом выглядит симметричным."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jG4BgIHWnOIl"
      },
      "source": [
        "**Относительное изменение кумулятивной конверсии группы B к группе A.**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 336
        },
        "id": "-kVSl5sykY0t",
        "trusted": false,
        "outputId": "cd13b165-b553-4bd6-b32b-a3a219d70edb"
      },
      "source": [
        "mergedCumulativeConversions = cumulativeDataA[['date','conversion']]\\\n",
        ".merge(cumulativeDataB[['date','conversion']], left_on='date', right_on='date', how='left', suffixes=['A', 'B'])\n",
        "plt.figure(figsize=(12,5))  \n",
        "plt.plot(mergedCumulativeConversions['date'], mergedCumulativeConversions['conversionB']/mergedCumulativeConversions['conversionA']-1,\\\n",
        "         label=\"Относительный прирост конверсии группы B относительно группы A\")\n",
        "\n",
        "plt.legend()\n",
        "plt.title('График относительного изменения кумулятивной конверсии группы B к группе A')\n",
        "plt.axhline(y=0, color='black', linestyle='--')\n",
        "plt.axhline(y=0.2, color='grey', linestyle='--')\n",
        "plt.axhline(y=0.1, color='grey', linestyle='--')\n",
        "plt.show() "
      ],
      "execution_count": null,
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": "<Figure size 864x360 with 1 Axes>"
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WUrcwXaVoJ4u"
      },
      "source": [
        "Конверсия группы В после нестабильных первых дней теста уверено растет с 7 августа, далее демонстрирует относительный прирост на уровне 0,1-0,2. Таким образом, конверсия в группе B стабильно лучше конверсии в группе A на 10%."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zLUB3CWLpIz1"
      },
      "source": [
        "**Количество заказов по пользователям.**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 359
        },
        "id": "PESsq0p4m4IE",
        "trusted": false,
        "outputId": "2b23a596-b007-49f3-dc45-5743811c0cdc"
      },
      "source": [
        "ordersByUsers = (orders.drop(['group', 'revenue', 'date'], axis=1).groupby('visitorId', as_index=False).agg({'transactionId': pd.Series.nunique}))\n",
        "ordersByUsers.columns = ['transactionId', 'orders']\n",
        "ordersByUsers.sort_values(by='orders', ascending=False).head(10)\n"
      ],
      "execution_count": null,
      "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>transactionId</th>\n      <th>orders</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td>1023</td>\n      <td>4256040402</td>\n      <td>11</td>\n    </tr>\n    <tr>\n      <td>591</td>\n      <td>2458001652</td>\n      <td>11</td>\n    </tr>\n    <tr>\n      <td>569</td>\n      <td>2378935119</td>\n      <td>9</td>\n    </tr>\n    <tr>\n      <td>487</td>\n      <td>2038680547</td>\n      <td>8</td>\n    </tr>\n    <tr>\n      <td>44</td>\n      <td>199603092</td>\n      <td>5</td>\n    </tr>\n    <tr>\n      <td>744</td>\n      <td>3062433592</td>\n      <td>5</td>\n    </tr>\n    <tr>\n      <td>55</td>\n      <td>237748145</td>\n      <td>5</td>\n    </tr>\n    <tr>\n      <td>917</td>\n      <td>3803269165</td>\n      <td>5</td>\n    </tr>\n    <tr>\n      <td>299</td>\n      <td>1230306981</td>\n      <td>5</td>\n    </tr>\n    <tr>\n      <td>897</td>\n      <td>3717692402</td>\n      <td>5</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
            "text/plain": "      transactionId  orders\n1023     4256040402      11\n591      2458001652      11\n569      2378935119       9\n487      2038680547       8\n44        199603092       5\n744      3062433592       5\n55        237748145       5\n917      3803269165       5\n299      1230306981       5\n897      3717692402       5"
          },
          "execution_count": 24,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 336
        },
        "id": "8lDxNtp8yYAI",
        "trusted": false,
        "outputId": "e4b9fea8-e7ad-4a15-d5cb-a1336806dd03"
      },
      "source": [
        "o_values = pd.Series(range(0, len(ordersByUsers)))\n",
        "plt.figure(figsize=(12,5)) \n",
        "plt.scatter(o_values, ordersByUsers['orders']) \n",
        "plt.title('График количества заказов по пользователям')\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": "<Figure size 864x360 with 1 Axes>"
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 336
        },
        "id": "b51Kw64PyssL",
        "trusted": false,
        "outputId": "171f72b3-cf71-4821-ccea-d8c1aad5ab90"
      },
      "source": [
        "plt.figure(figsize=(7,5))\n",
        "plt.hist(ordersByUsers['orders'])\n",
        "plt.title('Гистрограмма распределения количества заказов на одного пользователя')\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "data": {
            "image/png": "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\n",
            "text/plain": "<Figure size 504x360 with 1 Axes>"
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "437v4PSgzoMS"
      },
      "source": [
        "Большинство покупателей заказывали только один раз. Однако доля пользователей с 2-3 заказами тоже значительна. На точечном графике видно, что есть пользователи, которые заказывали 8 раз и больше, таких 4 человека. Тех, кто заказывал больше трех раз, мало. Их точная доля не ясна — непонятно, считать их аномалиями или нет. Посчитаем выборочные перцентили количества заказов на одного пользователя\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xaPh9OCT0rK0"
      },
      "source": [
        "**Определение аномальных пользователей.**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yggWXjejzHM3",
        "trusted": false,
        "outputId": "3260a6f5-7d5d-4c90-d19d-11d24ddcff8c"
      },
      "source": [
        "np.percentile(ordersByUsers['orders'], [95, 99])\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "data": {
            "text/plain": "array([2., 4.])"
          },
          "execution_count": 27,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hR-ow1Al5OMd"
      },
      "source": [
        "5% пользователей сделало больше двух заказов, 1% пользователей сделало больше четырех заказов. Исходя из распределений количества заказов на точечном графике, будем считать аномальным значением выше трех заказов, т.е. 4-11."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zKML2X-X6BtL"
      },
      "source": [
        "**Стоимости заказов.**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 545
        },
        "id": "2hg1tLYD4Yw3",
        "trusted": false,
        "outputId": "4fb531b3-3a47-4ca7-86fe-7859544c7436"
      },
      "source": [
        "plt.figure(figsize=(10,4))\n",
        "plt.hist(orders['revenue'], range=(0,100000), bins = 100)\n",
        "plt.title('Гистрограмма распределения сумм заказов на одного пользователя')\n",
        "plt.show()\n",
        "r_values = pd.Series(range(0, len(orders)))\n",
        "plt.figure(figsize=(10,4))\n",
        "plt.scatter(r_values, orders['revenue'])\n",
        "plt.title('График стоимостей заказов')\n",
        "plt.axis([0, 1200, 0, 200000])\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": "<Figure size 720x288 with 1 Axes>"
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": "<Figure size 720x288 with 1 Axes>"
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ghWUbOVmNTTo"
      },
      "source": [
        "Основная масса пользователей совершает заказы на сумму до 20-25 тыс.руб. Выбросами кажутся покупки выше 75 тыс.руб. Сделаем график крупнее."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 499
        },
        "id": "svgLouIANXV2",
        "trusted": false,
        "outputId": "3fabf97e-e496-4836-e77d-f2398df028db"
      },
      "source": [
        "plt.figure(figsize=(16,8))\n",
        "sns.scatterplot(x=r_values, y=orders['revenue'])\n",
        "plt.ylim(0, 100000);\n",
        "plt.title('График стоимостей заказов')\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": "<Figure size 1152x576 with 1 Axes>"
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SX2dS8KUPwS-"
      },
      "source": [
        "Большинство покупок совершается на сумму 10 000 руб. Выше 50 000 есть лишь единичные покупки."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DNkMb0d7QEKg"
      },
      "source": [
        "**Определение аномальных заказов.**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "003V5weEO5Pu",
        "trusted": false,
        "outputId": "1f9b9516-8b10-4921-85b5-f83c0d0947db"
      },
      "source": [
        "np.percentile(orders['revenue'], [95, 99])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "data": {
            "text/plain": "array([28000. , 58233.2])"
          },
          "execution_count": 30,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aaln5SaaQ7tv"
      },
      "source": [
        "5% пользователей совершили заказ на сумму выше 28 000 руб., 1% пользователей совершили заказ на сумму выше 58233,2 руб. Исходя из распределения на точечном графике, обозначим границу аномальных заказов свыше 40 000 руб."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tYZw8KgZRlwb"
      },
      "source": [
        "**Статистическая значимость различий в конверсии между группами по «сырым» данным.**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XQObG8ELjeiN"
      },
      "source": [
        "Создадим переменные\n",
        "ordersByUsersA и ordersByUsersB со столбцами ['visitorId', 'orders'], где для пользователей, совершивших хотя бы 1 заказ, будет указано число заказов.\n",
        "Объявим переменные sampleA и sampleB, в которых пользователям с заказами будет соответствовать число заказов пользователя. А пользователям без заказов — нули. Посчитаем статистическую значимость различия конверсии по результатам двух недель теста. Применим тест Манна-Уитни.\n",
        "Выведем p-value для сравнения конверсии между группами. Вычислим и выведем относительное различие в конверсии между группами."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "palVt2kvNwRy",
        "trusted": false,
        "outputId": "c119dc0b-134c-4046-96c6-4ff1791f3e40"
      },
      "source": [
        "visitorsADaily = visitors[visitors['group'] == 'A'][['date', 'visitors']]\n",
        "visitorsADaily.columns = ['date', 'visitorsPerDateA']\n",
        " \n",
        "visitorsACummulative = visitorsADaily.apply(\n",
        "    lambda x: visitorsADaily[visitorsADaily['date'] <= x['date']].agg(\n",
        "        {'date': 'max', 'visitorsPerDateA': 'sum'}\n",
        "    ),\n",
        "    axis=1,\n",
        ")\n",
        "visitorsACummulative.columns = ['date', 'visitorsCummulativeA']\n",
        " \n",
        "visitorsBDaily = visitors[visitors['group'] == 'B'][['date', 'visitors']]\n",
        "visitorsBDaily.columns = ['date', 'visitorsPerDateB']\n",
        " \n",
        "visitorsBCummulative = visitorsBDaily.apply(\n",
        "    lambda x: visitorsBDaily[visitorsBDaily['date'] <= x['date']].agg(\n",
        "        {'date': 'max', 'visitorsPerDateB': 'sum'}\n",
        "    ),\n",
        "    axis=1,\n",
        ")\n",
        "visitorsBCummulative.columns = ['date', 'visitorsCummulativeB']\n",
        " \n",
        "ordersADaily = (\n",
        "    orders[orders['group'] == 'A'][['date', 'transactionId', 'visitorId', 'revenue']]\n",
        "    .groupby('date', as_index=False)\n",
        "    .agg({'transactionId': pd.Series.nunique, 'revenue': 'sum'})\n",
        ")\n",
        "ordersADaily.columns = ['date', 'ordersPerDateA', 'revenuePerDateA']\n",
        " \n",
        "ordersACummulative = ordersADaily.apply(\n",
        "    lambda x: ordersADaily[ordersADaily['date'] <= x['date']].agg(\n",
        "        {'date': 'max', 'ordersPerDateA': 'sum', 'revenuePerDateA': 'sum'}\n",
        "    ),\n",
        "    axis=1,\n",
        ").sort_values(by=['date'])\n",
        "ordersACummulative.columns = [\n",
        "    'date',\n",
        "    'ordersCummulativeA',\n",
        "    'revenueCummulativeA',\n",
        "]\n",
        " \n",
        "ordersBDaily = (\n",
        "    orders[orders['group'] == 'B'][['date', 'transactionId', 'visitorId', 'revenue']]\n",
        "    .groupby('date', as_index=False)\n",
        "    .agg({'transactionId': pd.Series.nunique, 'revenue': 'sum'})\n",
        ")\n",
        "ordersBDaily.columns = ['date', 'ordersPerDateB', 'revenuePerDateB']\n",
        " \n",
        "ordersBCummulative = ordersBDaily.apply(\n",
        "    lambda x: ordersBDaily[ordersBDaily['date'] <= x['date']].agg(\n",
        "        {'date': 'max', 'ordersPerDateB': 'sum', 'revenuePerDateB': 'sum'}\n",
        "    ),\n",
        "    axis=1,\n",
        ").sort_values(by=['date'])\n",
        "ordersBCummulative.columns = [\n",
        "    'date',\n",
        "    'ordersCummulativeB',\n",
        "    'revenueCummulativeB',\n",
        "]\n",
        " \n",
        "data = (\n",
        "    ordersADaily.merge(\n",
        "        ordersBDaily, left_on='date', right_on='date', how='left'\n",
        "    )\n",
        "    .merge(ordersACummulative, left_on='date', right_on='date', how='left')\n",
        "    .merge(ordersBCummulative, left_on='date', right_on='date', how='left')\n",
        "    .merge(visitorsADaily, left_on='date', right_on='date', how='left')\n",
        "    .merge(visitorsBDaily, left_on='date', right_on='date', how='left')\n",
        "    .merge(visitorsACummulative, left_on='date', right_on='date', how='left')\n",
        "    .merge(visitorsBCummulative, left_on='date', right_on='date', how='left')\n",
        ")\n",
        " \n",
        "print(data.head(5))\n",
        " \n",
        "ordersByUsersA = (\n",
        "    orders[orders['group'] == 'A']\n",
        "    .groupby('visitorId', as_index=False)\n",
        "    .agg({'transactionId': pd.Series.nunique})\n",
        ")\n",
        "ordersByUsersA.columns = ['visitorId', 'orders']\n",
        " \n",
        "ordersByUsersB = (\n",
        "    orders[orders['group'] == 'B']\n",
        "    .groupby('visitorId', as_index=False)\n",
        "    .agg({'transactionId': pd.Series.nunique})\n",
        ")\n",
        "ordersByUsersB.columns = ['visitorId', 'orders'] \n",
        " \n",
        "pd.Series(0, index=np.arange(data['visitorsPerDateA'].sum() - len(ordersByUsersA['orders'])), name='orders')\n",
        "[ordersByUsersA['orders'],pd.Series(0, index=np.arange(data['visitorsPerDateA'].sum() - len(ordersByUsersA['orders'])), name='orders')] \n",
        " \n",
        "sampleA = pd.concat([ordersByUsersA['orders'],pd.Series(0, index=np.arange(data['visitorsPerDateA'].sum() - len(ordersByUsersA['orders'])), name='orders')],axis=0)\n",
        " \n",
        "sampleB = pd.concat([ordersByUsersB['orders'],pd.Series(0, index=np.arange(data['visitorsPerDateB'].sum() - len(ordersByUsersB['orders'])), name='orders')],axis=0)\n",
        " \n",
        "ordersByUsersA = (\n",
        "    orders[orders['group'] == 'A']\n",
        "    .groupby('visitorId', as_index=False)\n",
        "    .agg({'transactionId': pd.Series.nunique})\n",
        ")\n",
        "ordersByUsersA.columns = ['visitorId', 'orders']\n",
        " \n",
        "ordersByUsersB = (\n",
        "    orders[orders['group'] == 'B']\n",
        "    .groupby('visitorId', as_index=False)\n",
        "    .agg({'transactionId': pd.Series.nunique})\n",
        ")\n",
        "ordersByUsersB.columns = ['visitorId', 'orders']\n",
        " \n",
        " \n",
        "print(\"{0:.3f}\".format(stats.mannwhitneyu(sampleA, sampleB)[1]))\n",
        " \n",
        "print(\"{0:.3f}\".format((data['ordersPerDateB'].sum()/data['visitorsPerDateB'].sum())/(data['ordersPerDateA'].sum()/data['visitorsPerDateA'].sum())-1))\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": "        date  ordersPerDateA  revenuePerDateA  ordersPerDateB  \\\n0 2019-08-01              24           148579              21   \n1 2019-08-02              20            93822              24   \n2 2019-08-03              24           112473              16   \n3 2019-08-04              16            70825              17   \n4 2019-08-05              25           124218              23   \n\n   revenuePerDateB  ordersCummulativeA  revenueCummulativeA  \\\n0           101217                  24               148579   \n1           165531                  44               242401   \n2           114248                  68               354874   \n3           108571                  84               425699   \n4            92428                 109               549917   \n\n   ordersCummulativeB  revenueCummulativeB  visitorsPerDateA  \\\n0                  21               101217               719   \n1                  45               266748               619   \n2                  61               380996               507   \n3                  78               489567               717   \n4                 101               581995               756   \n\n   visitorsPerDateB  visitorsCummulativeA  visitorsCummulativeB  \n0               713                   719                   713  \n1               581                  1338                  1294  \n2               509                  1845                  1803  \n3               770                  2562                  2573  \n4               707                  3318                  3280  \n0.008\n0.138\n"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IFJEaeYWmd9B"
      },
      "source": [
        "P-value < 0.05, поэтому мы отвергаем гипотезу об отсутствии статистически значимых различий в конверсии - конверсия группы А отличается от конверсии группы В. Конверсия группы В на сырых данных статистически выше на 13.8%."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hqZ6hOlJnmVm"
      },
      "source": [
        "**Статистическая значимость различий в среднем чеке заказа между группами по «сырым» данным.**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DWms91zPn0CK"
      },
      "source": [
        "Выведем p-value для сравнения средних чеков между группами. Округлим p-value до трёх знаков после запятой.\n",
        "Выведем относительный прирост среднего чека группы B, округлив до трёх знаков после запятой.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0qjkDoavnlQA",
        "trusted": false,
        "outputId": "ea9c95b2-d484-40bb-b98d-a3eda594c07c"
      },
      "source": [
        "print('{0:.3f}'.format(stats.mannwhitneyu(orders[orders['group']=='A']['revenue'], orders[orders['group']=='B']['revenue'])[1]))\n",
        "print('{0:.3f}'.format(orders[orders['group']=='B']['revenue'].mean()/orders[orders['group']=='A']['revenue'].mean()-1))\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": "0.365\n0.259\n"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tKeDJ_0-o0v9"
      },
      "source": [
        "P-value значительно больше 0,05, поэтому мы принимаем гипотезу об отсутствии статистически значимых различий в среднем чеке заказа между группами А и В по «сырым» данным. При этом относительное различие между средними чеками 25%."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "f4o8b1EFqzIA"
      },
      "source": [
        "**Статистическая значимость различий в конверсии между группами по «очищенным» данным.**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "USx5G-0crYqS"
      },
      "source": [
        "Очистим данные от аномалий. Мы определили, что аномалиями будем считать:\n",
        "\n",
        "количество заказов выше трех,\n",
        "\n",
        "стоимость заказов свыше 40 000 руб.\n",
        "\n",
        "Сделаем срезы пользователей с числом заказов больше 3 — usersWithManyOrders и пользователей, совершивших заказы дороже 40 000 — usersWithExpensiveOrders. Объединим их в таблице abnormalUsers, удалим дубликаты, отсортируем по возрастанию."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "d_5_viB6lYX7",
        "trusted": false,
        "outputId": "612b7490-e013-40d8-95e6-975bbead0c73"
      },
      "source": [
        "usersWithManyOrders = pd.concat(\n",
        "    [\n",
        "        ordersByUsersA[ordersByUsersA['orders'] > 3]['visitorId'],\n",
        "        ordersByUsersB[ordersByUsersB['orders'] > 3]['visitorId'],\n",
        "    ],\n",
        "    axis=0,\n",
        ")\n",
        "usersWithExpensiveOrders = orders[orders['revenue'] > 40000]['visitorId']\n",
        "abnormalUsers = (\n",
        "    pd.concat([usersWithManyOrders, usersWithExpensiveOrders], axis=0)\n",
        "    .drop_duplicates()\n",
        "    .sort_values()\n",
        ")\n",
        "abnormalUsers.head(5)\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "data": {
            "text/plain": "1099    148427295\n18      199603092\n23      237748145\n684     358944393\n648     382846391\nName: visitorId, dtype: int64"
          },
          "execution_count": 33,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pLRmsdf90HjP"
      },
      "source": [
        "Теперь у нас есть список аномальных пользователей. После их удаления мы сможем посчитать статистическую значимость, чтобы выяснить, есть ли существенные различия между группами с отфильтрованными данными.\n",
        "\n",
        "Посчитаем статистическую значимость различия конверсии после удаления аномальных пользователей. Создадим переменные sampleAFiltered и sampleBFiltered, в которых будут очищенные данные о заказах, не включая аномальных пользователей.\n",
        "Выведем p-value для сравнения конверсии между очищенными группами. Округлим p-value до 3 знаков после запятой. Выведем относительный прирост конверсии очищенной группы B, округлив до 3 знаков после запятой.\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sSzH9Hdsz9BD",
        "trusted": false,
        "outputId": "477d6e21-2259-4eb1-acde-f62c2bb9d673"
      },
      "source": [
        "sampleAFiltered = pd.concat(\n",
        "    [\n",
        "        ordersByUsersA[\n",
        "            np.logical_not(ordersByUsersA['visitorId'].isin(abnormalUsers))\n",
        "        ]['orders'],\n",
        "        pd.Series(\n",
        "            0,\n",
        "            index=np.arange(\n",
        "                data['visitorsPerDateA'].sum() - len(ordersByUsersA['orders'])\n",
        "            ),\n",
        "            name='orders',\n",
        "        ),\n",
        "    ],\n",
        "    axis=0,\n",
        ")\n",
        " \n",
        "sampleBFiltered = pd.concat(\n",
        "    [\n",
        "        ordersByUsersB[\n",
        "            np.logical_not(ordersByUsersB['visitorId'].isin(abnormalUsers))\n",
        "        ]['orders'],\n",
        "        pd.Series(\n",
        "            0,\n",
        "            index=np.arange(\n",
        "                data['visitorsPerDateB'].sum() - len(ordersByUsersB['orders'])\n",
        "            ),\n",
        "            name='orders',\n",
        "        ),\n",
        "    ],\n",
        "    axis=0,\n",
        ")\n",
        "print('{0:.3f}'.format(stats.mannwhitneyu(sampleAFiltered, sampleBFiltered)[1]))\n",
        "print('{0:.3f}'.format(sampleBFiltered.mean()/sampleAFiltered.mean()-1))\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": "0.005\n0.164\n"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7BMUtBK51i5i"
      },
      "source": [
        "p-value < 0.05, это означает, что по очищенным данным в конверсии групп A и B разница статистически значима. Относительный прирост конверсии группы B по отношению к группе A после очистки данных: 16.4%. После очистки данных показатель конверсии группы В увеличился."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9GCfb5934LgG"
      },
      "source": [
        "**Статистическая значимость различий в среднем чеке заказа между группами по «очищенным» данным.**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1xLuZM2G4dZ9"
      },
      "source": [
        "Выведем p-value для сравнения средних чеков между очищенными группами. Округлим p-value до трёх знаков после запятой. Выведем относительный прирост среднего чека очищенной группы B, округлив до трёх знаков после запятой.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "F858D3Dg1LMk",
        "trusted": false,
        "outputId": "119f7034-9a57-425b-b096-8ac0ccfa453f"
      },
      "source": [
        "print('{0:.3f}'.format(stats.mannwhitneyu(orders[np.logical_and(\n",
        "    orders['group'] == 'A', np.logical_not(orders['visitorId'].isin(abnormalUsers)),)\n",
        "    ]['revenue'], orders[np.logical_and(orders['group'] == 'B',np.logical_not(orders['visitorId'].isin(abnormalUsers)),) ]['revenue'],)[1]))      \n",
        "print(\"{0:.3f}\".format(orders[np.logical_and(orders['group'] == 'B',\\\n",
        "                                             np.logical_not(orders['visitorId'].isin(abnormalUsers)),)]['revenue'].mean()\n",
        "    /orders[np.logical_and(orders['group'] == 'A',np.logical_not(orders['visitorId'].isin(abnormalUsers)),)]['revenue'].mean()- 1))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": "0.355\n0.060\n"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UVIGhvI76DoQ"
      },
      "source": [
        "p-value больше уровня значимости 0.05, значит в этом случае нельзя отвергнуть нулевую гипотезу об отсутствии статистической значимости между размером среднего чека сравниваемых групп. То есть нет статистически значимых различий. Cредний чек группы В выше среднего чека группы А всего на 6%, а не на 25%, как в случае с сырыми данными."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fmYQu4uI-C1z"
      },
      "source": [
        "**Общий вывод по результатам А/В теста:**\n",
        "\n",
        "Конверсия группы А отличается от конверсии группы В, и эта разница является статистически значимой. Конверсия группы В на сырых данных выше на 13.8%. Относительный прирост конверсии группы B по отношению к группе A после очистки данных: 16.4%. После фильтрации данных от аномальных значений показатель конверсии группы В увеличился.\n",
        "\n",
        "Относительное различие между средними чеками групп А и В составляет 25% на “сырых” данных и 6% на очищенных. Но статистически значимыми эти  различия не являются.\n",
        "\n",
        "Данный тест можно остановить и считать успешно проведенным. Конверсия группы B статистически значимо выше, чем у группы A на 16,4%, это дает нам основания считать победителем группу В.\n"
      ]
    }
  ]
}