a_b_tests.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyNTjGSMfuFg0X0iT7jMAx3H",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Murolando/analysis-of-a_b-testing-kwork/blob/main/a_b_tests.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Usqc3zmPXHbK"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "import scipy.stats as stats"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "B5eBUSkZYUts",
        "outputId": "4aad8880-06ba-4eae-c280-780933f5a7a3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Задача: На сайте запущен А/В тест с целью увеличить доход. В приложенном excel файле ты найдешь сырые данные по результатам эксперимента (user_id), тип выборки (variant_name) и доход, принесенный пользователем (revenue).\n",
        "\n",
        "Проанализируй результаты эксперимента и напиши свои рекомендации менеджеру.\n",
        "\n",
        "Результат нужен в формате .ipynb файл, код на питоне с графиками и пр."
      ],
      "metadata": {
        "id": "e99GLEZlImG6"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Загрузка данных"
      ],
      "metadata": {
        "id": "83ZQzvgGb_9T"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_excel('/content/drive/MyDrive/ML Pr/Rabota 9 a b tests/AB_Test_Results.xlsx')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_PSf-Mq2YMwO",
        "outputId": "0450b3f2-c9d3-48c0-e022-4797a07059a0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.9/dist-packages/openpyxl/worksheet/_reader.py:312: UserWarning: Unknown extension is not supported and will be removed\n",
            "  warn(msg)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Анализ данных"
      ],
      "metadata": {
        "id": "iPSDGKX8cs55"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "n4ul4TWqYNF5",
        "outputId": "a962d48d-7d25-4a28-b0b9-800cc3d3940d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 10000 entries, 0 to 9999\n",
            "Data columns (total 3 columns):\n",
            " #   Column        Non-Null Count  Dtype  \n",
            "---  ------        --------------  -----  \n",
            " 0   USER_ID       10000 non-null  int64  \n",
            " 1   VARIANT_NAME  10000 non-null  object \n",
            " 2   REVENUE       10000 non-null  float64\n",
            "dtypes: float64(1), int64(1), object(1)\n",
            "memory usage: 234.5+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "len(df['USER_ID'].unique())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6OBr3ET_tyWA",
        "outputId": "80247676-064d-478f-ba30-65bd2fb1486c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "6324"
            ]
          },
          "metadata": {},
          "execution_count": 155
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['VARIANT_NAME'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OYTPoQFVY4oX",
        "outputId": "55df6da1-5c39-4924-a997-917c5efd46c2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "variant    5016\n",
              "control    4984\n",
              "Name: VARIANT_NAME, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 156
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Кол-во данных равно относительно классов, это значительно упрощает анализ"
      ],
      "metadata": {
        "id": "reoHHKw9dqtc"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "fig, ax = plt.subplots(figsize=(5,5))\n",
        "sns.boxplot(df.loc[df['REVENUE']>0],x = \"VARIANT_NAME\", y ='REVENUE')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 352
        },
        "id": "qfsgWnWQT79m",
        "outputId": "fab1aa17-670b-4243-cac6-bb07828fcf88"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='VARIANT_NAME', ylabel='REVENUE'>"
            ]
          },
          "metadata": {},
          "execution_count": 157
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.loc[(df['REVENUE']>0)].describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "tCGwnf_pLkNu",
        "outputId": "b6519db8-7c17-46bf-f618-73d1c241dd6d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           USER_ID     REVENUE\n",
              "count   152.000000  152.000000\n",
              "mean   4862.217105    6.542566\n",
              "std    2987.566745   17.706752\n",
              "min      56.000000    0.020000\n",
              "25%    2177.250000    1.250000\n",
              "50%    4980.500000    2.420000\n",
              "75%    7393.250000    4.607500\n",
              "max    9996.000000  196.010000"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-83ae4e9d-6493-4028-8532-9fb97bb90dfd\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>USER_ID</th>\n",
              "      <th>REVENUE</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>152.000000</td>\n",
              "      <td>152.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>4862.217105</td>\n",
              "      <td>6.542566</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>2987.566745</td>\n",
              "      <td>17.706752</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>56.000000</td>\n",
              "      <td>0.020000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>2177.250000</td>\n",
              "      <td>1.250000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>4980.500000</td>\n",
              "      <td>2.420000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>7393.250000</td>\n",
              "      <td>4.607500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>9996.000000</td>\n",
              "      <td>196.010000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-83ae4e9d-6493-4028-8532-9fb97bb90dfd')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
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              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-83ae4e9d-6493-4028-8532-9fb97bb90dfd button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-83ae4e9d-6493-4028-8532-9fb97bb90dfd');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 158
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.loc[(df['REVENUE']>0) & (df[\"VARIANT_NAME\"] == 'variant')].describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "WGunmrpeT73z",
        "outputId": "b087f58b-c8f2-4871-83da-26b428942092"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           USER_ID    REVENUE\n",
              "count    72.000000  72.000000\n",
              "mean   5306.958333   4.881528\n",
              "std    3116.544256   9.913973\n",
              "min      56.000000   0.020000\n",
              "25%    2707.500000   1.245000\n",
              "50%    5721.000000   2.170000\n",
              "75%    7963.750000   3.972500\n",
              "max    9996.000000  58.630000"
            ],
            "text/html": [
              "\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>USER_ID</th>\n",
              "      <th>REVENUE</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>72.000000</td>\n",
              "      <td>72.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>5306.958333</td>\n",
              "      <td>4.881528</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>3116.544256</td>\n",
              "      <td>9.913973</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>56.000000</td>\n",
              "      <td>0.020000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>2707.500000</td>\n",
              "      <td>1.245000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>5721.000000</td>\n",
              "      <td>2.170000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>7963.750000</td>\n",
              "      <td>3.972500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>9996.000000</td>\n",
              "      <td>58.630000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-52cc36f4-b876-4fab-bd0a-3a088e0c7a2c')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
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              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
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              "      width: 32px;\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-52cc36f4-b876-4fab-bd0a-3a088e0c7a2c button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-52cc36f4-b876-4fab-bd0a-3a088e0c7a2c');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 159
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.loc[(df['REVENUE']>0) & (df[\"VARIANT_NAME\"] != 'variant')].describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "1vdsqKrDVB25",
        "outputId": "0eae7577-fa34-47db-b716-d851817ba2fb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           USER_ID     REVENUE\n",
              "count    80.000000   80.000000\n",
              "mean   4461.950000    8.037500\n",
              "std    2826.386242   22.498124\n",
              "min     124.000000    0.020000\n",
              "25%    2037.500000    1.515000\n",
              "50%    4327.000000    2.960000\n",
              "75%    6636.750000    6.657500\n",
              "max    9766.000000  196.010000"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-4ff27bf6-fc48-4b2e-b532-db7f546435a7\">\n",
              "    <div class=\"colab-df-container\">\n",
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              "<style scoped>\n",
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              "\n",
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              "        text-align: right;\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>USER_ID</th>\n",
              "      <th>REVENUE</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>80.000000</td>\n",
              "      <td>80.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>4461.950000</td>\n",
              "      <td>8.037500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>2826.386242</td>\n",
              "      <td>22.498124</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>124.000000</td>\n",
              "      <td>0.020000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>2037.500000</td>\n",
              "      <td>1.515000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>4327.000000</td>\n",
              "      <td>2.960000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>6636.750000</td>\n",
              "      <td>6.657500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>9766.000000</td>\n",
              "      <td>196.010000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-4ff27bf6-fc48-4b2e-b532-db7f546435a7')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-4ff27bf6-fc48-4b2e-b532-db7f546435a7 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-4ff27bf6-fc48-4b2e-b532-db7f546435a7');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 160
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Избавимся от единственного заметного выбросы"
      ],
      "metadata": {
        "id": "3qEcW5f9RgGA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df = df.loc[df['REVENUE']<196]"
      ],
      "metadata": {
        "id": "-7I_g-QYjyJh"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_variant = df.loc[(df['VARIANT_NAME'] == 'variant') & (0<df['REVENUE'])]\n",
        "df_control = df.loc[(df['VARIANT_NAME'] != 'variant' ) & (0<df['REVENUE'])]"
      ],
      "metadata": {
        "id": "yLdHLM4VVe9W"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Анализ экономических метрик\n"
      ],
      "metadata": {
        "id": "WuXW_cWEg560"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Изменение количества покупок"
      ],
      "metadata": {
        "id": "XFW3HqrjMD_o"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(len(df_variant))\n",
        "print(len(df_control))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "65QbKU5gKItZ",
        "outputId": "9e3d683a-fc7e-4e32-f6e1-34c5589cdb4a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "72\n",
            "79\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Суммарная выручка\n"
      ],
      "metadata": {
        "id": "Q_0A7F1WkKl-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sum(df_control['REVENUE'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "txvnnWpXbWFK",
        "outputId": "d4493a37-2540-4146-96d5-455e653bd47b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "446.9900000000001"
            ]
          },
          "metadata": {},
          "execution_count": 164
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sum(df_variant['REVENUE'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "grqCxqKubUDL",
        "outputId": "73d7d7ff-7f74-4f51-a89b-85dd50792239"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "351.4700000000001"
            ]
          },
          "metadata": {},
          "execution_count": 165
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Вывод: суммарная выручка за месяц уменьшилась на почти 100 долларов"
      ],
      "metadata": {
        "id": "7nmi7dIQjr2h"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Величина среднего чека\n",
        "\n",
        "H0 - В ходе изменений на сайте величина среднего чека не изменилась\n",
        "\n",
        "H1 - В ходе изменений на сайте величина среднего чека уменьшилась\n",
        "\n",
        "Средний чек - выручки компании за конкретный период времени, например за месяц, делённая на количество покупок"
      ],
      "metadata": {
        "id": "hooz9NNZvcQm"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# variant\n",
        "df_mean_check_varient = sum(df_variant['REVENUE'] / len(df_variant))\n",
        "# control\n",
        "df_mean_check_control = sum(df_control['REVENUE']) / len(df_control)\n",
        "print(df_mean_check_varient,df_mean_check_control)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "p6xPX9sztk4d",
        "outputId": "ad711f4d-795e-472d-d1d4-e5b8b75955c8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "4.8815277777777775 5.658101265822786\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fig, ax = plt.subplots(figsize=(5,5))\n",
        "sns.histplot(df_variant,x ='REVENUE',stat='count',binwidth=1,multiple=\"dodge\",element =\"poly\")\n",
        "sns.histplot(df_control,x ='REVENUE',stat='count',binwidth=1,multiple=\"dodge\",element =\"poly\",color ='orange')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MmnubdegxznY",
        "outputId": "f96e1c2b-a813-4d22-cac9-ddf72fc78bb7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='REVENUE', ylabel='Count'>"
            ]
          },
          "metadata": {},
          "execution_count": 167
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Вывод: Средний чек упал почти на 80 долларов, что также говорит не в пользу изменений"
      ],
      "metadata": {
        "id": "a5FsiB6qm30B"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Но для полного вывода проверим статистическую значимость этого различия\n",
        "\n",
        "Распределение отлично от нормального поэтому\n",
        "Проверим верность гипотезы с помощью критерия Манна Уитни"
      ],
      "metadata": {
        "id": "L_E_x2y2dVrH"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# variant\n",
        "x = df_variant['REVENUE']\n",
        "x.head(3)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iO0-dB4dfPnm",
        "outputId": "3c61165b-a4f5-43f3-9e5e-fff16da62021"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "13     2.15\n",
              "139    4.27\n",
              "149    1.75\n",
              "Name: REVENUE, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 168
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# control\n",
        "y = df_control['REVENUE']\n",
        "y.head(3)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_Qm0QpUnfSIA",
        "outputId": "4666800f-62ff-47f0-ff3d-83ea867a8df2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "49     3.25\n",
              "152    2.99\n",
              "333    9.91\n",
              "Name: REVENUE, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 169
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "u1,p = stats.mannwhitneyu(x,y,method = 'exact',alternative = 'less')\n",
        "u2 = len(x)*len(y) - u1\n",
        "print(u1,u2,p)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hLBAaLDbfSE9",
        "outputId": "56b1d342-af46-43d7-aa3b-3b6ff3a7319c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2404.0 3284.0 0.05083135934993447\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Конечно, порог в 0.05 уровня значимости не был преодолен, но я считаю, что для интернет магазина, недобор в 0.0008, не значителен => эти результаты следует отнести к статистически значимым => у нас есть основания отмести 0 гипотезу и принять альтернативную"
      ],
      "metadata": {
        "id": "Sb05WPNMgNnD"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Конверсия\n",
        "Конверсия - количество реальных клиентов на количество потенциальных и умножить на 100%"
      ],
      "metadata": {
        "id": "atAVh4bNO5Wg"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df_conversion_variant = len(df_variant['REVENUE'])/len(df.loc[(df[\"VARIANT_NAME\"] == 'variant')])\n",
        "df_conversion_control = len(df_control['REVENUE'])/len(df.loc[(df[\"VARIANT_NAME\"] != 'variant')])\n",
        "print(df_conversion_control*100,df_conversion_variant*100)"
      ],
      "metadata": {
        "id": "Jc43JcKZQxfh",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "369482f7-a887-4e68-eb71-fdf7f9d31400"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1.5853903271121814 1.4354066985645932\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Конверсия упала примерно на 0,15%, чтобы проверить значимость таких различий используем критерий Хи-квадрат\n",
        "\n",
        "H0 - В ходе изменений на сайте величина конверсии не изменилась\n",
        "\n",
        "H1 - В ходе изменений на сайте величина конверсии уменьшилась"
      ],
      "metadata": {
        "id": "_zw5uTfso-nS"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Составляю таблицу сопряженности"
      ],
      "metadata": {
        "id": "OSjl565XGPgV"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "control_buy = len(df_control['REVENUE'])\n",
        "control_nbuy = len(df.loc[(df[\"VARIANT_NAME\"] != 'variant') & (df[\"REVENUE\"] == 0) ])\n",
        "variant_buy = len(df_variant['REVENUE'])\n",
        "variant_nbuy = len(df.loc[(df[\"VARIANT_NAME\"] == 'variant') & (df[\"REVENUE\"] == 0) ])"
      ],
      "metadata": {
        "id": "7yygbUsgAPol"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "contingency_table = pd.DataFrame({'_':['Купили','Не купили','Total'],\n",
        "                                  'Control':[control_buy,control_nbuy,control_buy+control_nbuy],\n",
        "                                  'Variant':[variant_buy,variant_nbuy,variant_buy+variant_nbuy],\n",
        "                                  'Total':[control_buy+variant_buy,control_nbuy+variant_nbuy,control_buy+control_nbuy+variant_buy+variant_nbuy]})\n"
      ],
      "metadata": {
        "id": "UlajNaBs9wAd"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "contingency_table.head(5)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "id": "KQxY5p5K9v-S",
        "outputId": "72103cef-38dd-4629-9c3c-d43b97178ee6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           _  Control  Variant  Total\n",
              "0     Купили       79       72    151\n",
              "1  Не купили     4904     4944   9848\n",
              "2      Total     4983     5016   9999"
            ],
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              "\n",
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              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
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              "      <th>0</th>\n",
              "      <td>Купили</td>\n",
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              "      <td>151</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Не купили</td>\n",
              "      <td>4904</td>\n",
              "      <td>4944</td>\n",
              "      <td>9848</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Total</td>\n",
              "      <td>4983</td>\n",
              "      <td>5016</td>\n",
              "      <td>9999</td>\n",
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              "  </tbody>\n",
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              "  </style>\n",
              "\n",
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              "          document.querySelector('#df-d1df58cd-1bc3-4169-8abc-37c9ad9527c4 button.colab-df-convert');\n",
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              "\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 174
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Ожидаемая частота = (сумма строк * сумма столбцов) / сумма таблицы\n",
        "\n"
      ],
      "metadata": {
        "id": "XIs996LVGNV4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Ожидаемая частота купивших control\n",
        "expected_per_control = (contingency_table['Total'][0] * contingency_table['Control'][2])/contingency_table['Total'][2]\n",
        "# Ожидаемая частота купивших variant\n",
        "expected_per_variant = (contingency_table['Total'][0] * contingency_table['Variant'][2])/contingency_table['Total'][2]\n",
        "print(expected_per_control,expected_per_variant)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-Hl__I-RF_WL",
        "outputId": "eed53306-d836-404c-f31b-90cb8fa10fc2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "75.25082508250826 75.74917491749174\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "stats.chisquare(f_obs=[contingency_table['Control'][0],contingency_table['Variant'][0]],\n",
        "    f_exp=[expected_per_control,expected_per_variant])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HK7D0IJ-F_SU",
        "outputId": "993db114-ab3b-4870-c504-a2d9e55b943e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Power_divergenceResult(statistic=0.3723567064233089, pvalue=0.54172245539968)"
            ]
          },
          "metadata": {},
          "execution_count": 176
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Вывод: Изменения конверсии выявленные нами ранее, не являются статистически значимыми"
      ],
      "metadata": {
        "id": "H5l2v5JRPRbK"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Изменения количества покупок взависимости от ценовой категории"
      ],
      "metadata": {
        "id": "btxCsifWLYI-"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Т.к конверсия не изменилась, а средний чек упал, то логично, что пользователи стали чаще покупать более дешевые товары и реже более дорогие, разделим на 4 категории по перцентилям:\n",
        "\n",
        "* First -  Покупки больше 0 - меньше или равно 1.25 Долларов\n",
        "* Second - Покупки больше 1.25 - меньше или равно 2.34 Долларов\n",
        "* Third - Покупки больше 2.34 - и меньше или равно 4.52 Долларов\n",
        "* Fourth - Покупки больше 4.52 - и выше"
      ],
      "metadata": {
        "id": "9ZnSW2TgLQMT"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df['GROUPS_REV'] = df['REVENUE']\n",
        "df.loc[(df['REVENUE'] > 0) & (df['REVENUE'] <= 1.25),('GROUPS_REV')] = 'first'\n",
        "df.loc[(df['REVENUE'] > 1.25) & (df['REVENUE'] <= 2.34),('GROUPS_REV')] ='second'\n",
        "df.loc[(df['REVENUE'] > 2.34) & (df['REVENUE'] <= 4.52),('GROUPS_REV')]= 'third'\n",
        "df.loc[(df['REVENUE'] > 4.52),('GROUPS_REV')] = 'fourth'"
      ],
      "metadata": {
        "id": "pbigkZ0-Megv"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_control_cases = df.loc[(df['VARIANT_NAME'] == 'variant') & (0<df['REVENUE'])]\n",
        "df_variant_cases = df.loc[(df['VARIANT_NAME'] != 'variant' ) & (0<df['REVENUE'])]"
      ],
      "metadata": {
        "id": "fYoeXrXJNt8a"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_variant_cases\n",
        "df_variant_cases.head(3)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "id": "Scm-CO_1N3AA",
        "outputId": "215ac7c9-2c6b-450e-daac-e51c48d3ef1b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     USER_ID VARIANT_NAME  REVENUE GROUPS_REV\n",
              "49      6429      control     3.25      third\n",
              "152     8607      control     2.99      third\n",
              "333     8020      control     9.91     fourth"
            ],
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              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-98794cb0-4fd2-4f0e-915f-cccb66a4fbbb button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-98794cb0-4fd2-4f0e-915f-cccb66a4fbbb');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 214
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_control_cases\n",
        "df_control_cases.head(3)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "id": "juVSFAL3N3dw",
        "outputId": "584b82f5-0193-4273-c658-19275ce8faf0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     USER_ID VARIANT_NAME  REVENUE GROUPS_REV\n",
              "13      2529      variant     2.15     second\n",
              "139     3331      variant     4.27      third\n",
              "149     8110      variant     1.75     second"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-ee4df593-4896-4e70-8e0d-33f5abe32b82\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>USER_ID</th>\n",
              "      <th>VARIANT_NAME</th>\n",
              "      <th>REVENUE</th>\n",
              "      <th>GROUPS_REV</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>2529</td>\n",
              "      <td>variant</td>\n",
              "      <td>2.15</td>\n",
              "      <td>second</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>139</th>\n",
              "      <td>3331</td>\n",
              "      <td>variant</td>\n",
              "      <td>4.27</td>\n",
              "      <td>third</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>149</th>\n",
              "      <td>8110</td>\n",
              "      <td>variant</td>\n",
              "      <td>1.75</td>\n",
              "      <td>second</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-ee4df593-4896-4e70-8e0d-33f5abe32b82')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-ee4df593-4896-4e70-8e0d-33f5abe32b82 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-ee4df593-4896-4e70-8e0d-33f5abe32b82');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 215
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "control_fr = len(df_control_cases.loc[df_control_cases['GROUPS_REV'] == 'first'])\n",
        "control_sc = len(df_control_cases.loc[df_control_cases['GROUPS_REV'] == 'second'])\n",
        "control_th = len(df_control_cases.loc[df_control_cases['GROUPS_REV'] == 'third'])\n",
        "control_fo = len(df_control_cases.loc[df_control_cases['GROUPS_REV'] == 'fourth'])\n",
        "\n",
        "variant_fr = len(df_variant_cases.loc[df_variant_cases['GROUPS_REV'] == 'first'])\n",
        "variant_sc = len(df_variant_cases.loc[df_variant_cases['GROUPS_REV'] == 'second'])\n",
        "variant_th = len(df_variant_cases.loc[df_variant_cases['GROUPS_REV'] == 'third'])\n",
        "variant_fo = len(df_variant_cases.loc[df_variant_cases['GROUPS_REV'] == 'fourth'])"
      ],
      "metadata": {
        "id": "et28bYXAU0J5"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(control_fr,variant_fr)\n",
        "print(control_sc,variant_sc)\n",
        "print(control_th,variant_th)\n",
        "print(control_fo,variant_fo)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6mc1aYWXVIS3",
        "outputId": "37c8c890-2091-438e-885e-c51fde31fc71"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "23 17\n",
            "16 20\n",
            "19 19\n",
            "14 23\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fig, ax = plt.subplots(figsize=(6,6))\n",
        "sns.histplot(df.loc[(df['REVENUE'] > 0)],x ='GROUPS_REV',stat='count',hue = 'VARIANT_NAME',multiple=\"dodge\",color = 'green')\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 406
        },
        "id": "Rt5wkEweSOC1",
        "outputId": "94bdb473-9383-4ba9-a0ff-ff3406dd9544"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='GROUPS_REV', ylabel='Count'>"
            ]
          },
          "metadata": {},
          "execution_count": 228
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x432 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "На графике мы видим, что количество товаров из второй и четвертой группой уменьшилось, в третьей группе все осталось без именений,а вот в количество проданных товаров из первой группы значительно увеличилось\n",
        "\n",
        "Проверим значимость таких результатов для каждой из групп также через критерий Хи-квадрат\n",
        "\n",
        "* First\n",
        "  - H0: В ходе изменений на сайте количество проданых товаров из первой ценовой категории не изменилось\n",
        "  - H1: В ходе изменений на сайте количество проданых товаров из первой ценовой категории увеличилось\n",
        "\n",
        "* Second\n",
        "  - H0: В ходе изменений на сайте количество проданых товаров из второй ценовой категории не изменилось\n",
        "  - H1: В ходе изменений на сайте количество проданых товаров из первой ценовой категории уменьшилось\n",
        "\n",
        "* Third\n",
        "  - H0: В ходе изменений на сайте количество проданых товаров из третьей ценовой категории не изменилось\n",
        "  - H1: В ходе изменений на сайте количество проданых товаров из третьей ценовой категории изменилось\n",
        "\n",
        "* Fourth\n",
        "  - H0: В ходе изменений на сайте количество проданых товаров из четвертой ценовой категории не изменилось\n",
        "  - H1: В ходе изменений на сайте количество проданых товаров из четвертой ценовой категории уменьшилось"
      ],
      "metadata": {
        "id": "ldqoE4KTRTpy"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Таблица сопряженности"
      ],
      "metadata": {
        "id": "HuZ1LLK1WScF"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "contingency_table_2 = pd.DataFrame({'_':['First','Second','Third','Fourht','Total'],\n",
        "                                    'Control':[control_fr,control_sc,control_th,control_fo,control_buy],\n",
        "                                    'Variant':[variant_fr,variant_sc,variant_th,variant_fo,variant_buy],\n",
        "                                    'Total':[control_fr+variant_fr,control_sc+variant_sc,control_th+variant_th,control_fo+variant_fo,control_buy+variant_buy],\n",
        "                                    })\n"
      ],
      "metadata": {
        "id": "BDa-711FQvPi"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "contingency_table_2.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "09x2EyDmQvKJ",
        "outputId": "54f5d962-98e6-4622-9713-e48e5c26b0eb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        _  Control  Variant  Total\n",
              "0   First       23       17     40\n",
              "1  Second       16       20     36\n",
              "2   Third       19       19     38\n",
              "3  Fourht       14       23     37\n",
              "4   Total       79       72    151"
            ],
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            ]
          },
          "metadata": {},
          "execution_count": 236
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Ожидаемая частота = (сумма строк * сумма столбцов) / сумма таблицы\n"
      ],
      "metadata": {
        "id": "FkaOxnpXXv__"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "First"
      ],
      "metadata": {
        "id": "VwueWXSgXX2R"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Ожидаемая частота купивших control\n",
        "expected_per_control = (contingency_table_2['Total'][0] * contingency_table_2['Control'][4])/contingency_table_2['Total'][4]\n",
        "# Ожидаемая частота купивших variant\n",
        "expected_per_variant = (contingency_table_2['Total'][0] * contingency_table_2['Variant'][4])/contingency_table_2['Total'][4]\n",
        "print(expected_per_control,expected_per_variant)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GKIgESyxV23S",
        "outputId": "7dbd1551-2ad4-411f-be2c-1391aa765cb6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "20.927152317880793 19.072847682119207\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "stats.chisquare(f_obs=[contingency_table_2['Control'][0],contingency_table_2['Variant'][0]],\n",
        "    f_exp=[expected_per_control,expected_per_variant])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "m0Hk-pIFXpei",
        "outputId": "a0abf1d0-2362-47a3-cb54-659c802804f9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Power_divergenceResult(statistic=0.43059511251758154, pvalue=0.5116970800160716)"
            ]
          },
          "metadata": {},
          "execution_count": 238
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Second"
      ],
      "metadata": {
        "id": "X0rY68z1Xao3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Ожидаемая частота купивших control\n",
        "expected_per_control = (contingency_table_2['Total'][1] * contingency_table_2['Control'][4])/contingency_table_2['Total'][4]\n",
        "# Ожидаемая частота купивших variant\n",
        "expected_per_variant = (contingency_table_2['Total'][1] * contingency_table_2['Variant'][4])/contingency_table_2['Total'][4]\n",
        "print(expected_per_control,expected_per_variant)\n",
        "stats.chisquare(f_obs=[contingency_table_2['Control'][1],contingency_table_2['Variant'][1]],\n",
        "    f_exp=[expected_per_control,expected_per_variant])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "H0UdJeZnXdWD",
        "outputId": "413c5856-e2c6-4a24-bb43-96b633242d05"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "18.834437086092716 17.165562913907284\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Power_divergenceResult(statistic=0.8945929051414291, pvalue=0.3442357015842661)"
            ]
          },
          "metadata": {},
          "execution_count": 240
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Third"
      ],
      "metadata": {
        "id": "-aJpSpLYXait"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Ожидаемая частота купивших control\n",
        "expected_per_control = (contingency_table_2['Total'][2] * contingency_table_2['Control'][4])/contingency_table_2['Total'][4]\n",
        "# Ожидаемая частота купивших variant\n",
        "expected_per_variant = (contingency_table_2['Total'][2] * contingency_table_2['Variant'][4])/contingency_table_2['Total'][4]\n",
        "print(expected_per_control,expected_per_variant)\n",
        "stats.chisquare(f_obs=[contingency_table_2['Control'][2],contingency_table_2['Variant'][2]],\n",
        "    f_exp=[expected_per_control,expected_per_variant])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1wvqZkMtXdum",
        "outputId": "2310ced8-b320-4a03-deb5-1c9e97688fb3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "19.880794701986755 18.119205298013245\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Power_divergenceResult(statistic=0.08183895921237691, pvalue=0.7748206071147562)"
            ]
          },
          "metadata": {},
          "execution_count": 241
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Fourth"
      ],
      "metadata": {
        "id": "9Pfc6CjiXack"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Ожидаемая частота купивших control\n",
        "expected_per_control = (contingency_table_2['Total'][3] * contingency_table_2['Control'][4])/contingency_table_2['Total'][4]\n",
        "# Ожидаемая частота купивших variant\n",
        "expected_per_variant = (contingency_table_2['Total'][3] * contingency_table_2['Variant'][4])/contingency_table_2['Total'][4]\n",
        "print(expected_per_control,expected_per_variant)\n",
        "stats.chisquare(f_obs=[contingency_table_2['Control'][3],contingency_table_2['Variant'][3]],\n",
        "    f_exp=[expected_per_control,expected_per_variant])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "V_xLxEawXeIj",
        "outputId": "1c4fb5e8-7372-4fa7-f850-e250f468fa64"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "19.357615894039736 17.642384105960264\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Power_divergenceResult(statistic=3.109823431025963, pvalue=0.07782139654409342)"
            ]
          },
          "metadata": {},
          "execution_count": 242
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Для степеней свободы 3 значение при p = 0.05 = 7.8\n",
        "\n",
        "Отсюда Вывод: не одно из изменений не является статистически значимым => Эксперимент никак не повлиял на изменения в количестве покупок в разных ценовых категориях"
      ],
      "metadata": {
        "id": "Z1zNtzVaZC7y"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Результаты"
      ],
      "metadata": {
        "id": "stACCZSJcSum"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Рекомендация для менеджера:\n",
        "\n",
        "Т.к Средний чек уменьшился, конверсия осталась на прежнем уровне, а изменение количества покупок в пределах ценовой категории статистически не значимы то рекомендация может быть одна - признать эксперимент неудачным"
      ],
      "metadata": {
        "id": "jKrNfFKHHm_I"
      }
    }
  ]
}