e_commerce_project.ipynb

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        "Задание:\n",
        "\n",
        "###1. Сколько у нас пользователей, которые совершили покупку только один раз?  \n",
        "###2. Сколько заказов в месяц в среднем не доставляется по разным причинам (вывести детализацию по причинам)?\n",
        "###3. По каждому товару определить, в какой день недели товар чаще всего покупается. \n",
        "###4. Сколько у каждого из пользователей в среднем покупок в неделю (по месяцам)? Не стоит забывать, что внутри месяца может быть не целое количество недель. Например, в ноябре 2021 года 4,28 недели. И внутри метрики это нужно учесть. \n",
        "###5. Напиши функцию на python, позволяющую строить когортный анализ. В период с января по декабрь выяви когорту с самым высоким retention на 3-й месяц. Описание подхода можно найти тут.\n",
        "###6. Построй RFM-кластеры для пользователей. Выведи для каждого кластера средние значения метрик R, F, M (и опиши подробно, как были построены метрики R, F, M). Описание подхода можно найти тут. "
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      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "from scipy import stats\n",
        "from operator import attrgetter\n",
        "import datetime as dt\n",
        "\n",
        "import seaborn as sns\n",
        "sns.set_palette('Set2')\n",
        "import matplotlib.pyplot as plt\n",
        "import matplotlib.colors as mcolors\n",
        "\n",
        "%matplotlib inline\n",
        "plt.rcParams[\"figure.figsize\"] = (20,8)"
      ]
    },
    {
      "cell_type": "markdown",
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      "source": [
        "# 1 Raw data"
      ]
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      "source": [
        "## 1.1 dataset 1 'уникальные идентификаторы пользователей'"
      ]
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      "source": [
        "customer_id — позаказный идентификатор пользователя (аналог номера паспорта)\n",
        "\n",
        "customer_unique_id —  уникальный идентификатор пользователя\n",
        "\n",
        "customer_zip_code_prefix —  почтовый индекс пользователя\n",
        "\n",
        "customer_city —  город доставки пользователя\n",
        "\n",
        "customer_state —  штат доставки пользователя"
      ]
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              "                        customer_id                customer_unique_id  \\\n",
              "0  06b8999e2fba1a1fbc88172c00ba8bc7  861eff4711a542e4b93843c6dd7febb0   \n",
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              "2  4e7b3e00288586ebd08712fdd0374a03  060e732b5b29e8181a18229c7b0b2b5e   \n",
              "\n",
              "   customer_zip_code_prefix          customer_city customer_state  \n",
              "0                     14409                 franca             SP  \n",
              "1                      9790  sao bernardo do campo             SP  \n",
              "2                      1151              sao paulo             SP  "
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      ],
      "source": [
        "#таблица с уникальными идентификаторами пользователей\n",
        "\n",
        "customers =  pd.read_csv('https://getfile.dokpub.com/yandex/get/https://disk.yandex.ru/d/FUi5uSd6BfG_ig', encoding='utf-8', sep=',')\n",
        "customers.head(3)"
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          "data": {
            "text/plain": [
              "(99441, 5)"
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          "execution_count": 5
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      "source": [
        "customers.shape"
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            "text/plain": [
              "customer_id                 0\n",
              "customer_unique_id          0\n",
              "customer_zip_code_prefix    0\n",
              "customer_city               0\n",
              "customer_state              0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ],
      "source": [
        "# Количество пропущенных значений\n",
        "customers.isnull().sum()"
      ]
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              "0"
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      "source": [
        "# Количество дублирующих значений \n",
        "customers.duplicated().sum()"
      ]
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          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 99441 entries, 0 to 99440\n",
            "Data columns (total 5 columns):\n",
            " #   Column                    Non-Null Count  Dtype \n",
            "---  ------                    --------------  ----- \n",
            " 0   customer_id               99441 non-null  object\n",
            " 1   customer_unique_id        99441 non-null  object\n",
            " 2   customer_zip_code_prefix  99441 non-null  int64 \n",
            " 3   customer_city             99441 non-null  object\n",
            " 4   customer_state            99441 non-null  object\n",
            "dtypes: int64(1), object(4)\n",
            "memory usage: 3.8+ MB\n"
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      "source": [
        "# Проверим количество записей и типы столбцов\n",
        "customers.info()"
      ]
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      "source": [
        "# Посмотрим на 2-й датасет с данными о заказах"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "e3w4LRuGcOLB"
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      "source": [
        "## 1.2 dataset 2 'данные о заказах'"
      ]
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      "source": [
        "order_id —  уникальный идентификатор заказа (номер чека)\n",
        "\n",
        "customer_id —  позаказный идентификатор пользователя (аналог номера паспорта)\n",
        "\n",
        "order_status —  статус заказа\n",
        "\n",
        "order_purchase_timestamp —  время создания заказа\n",
        "\n",
        "order_approved_at —  время подтверждения оплаты заказа\n",
        "\n",
        "order_delivered_carrier_date —  время передачи заказа в логистическую службу\n",
        "\n",
        "order_delivered_customer_date —  время доставки заказа\n",
        "\n",
        "order_estimated_delivery_date —  обещанная дата доставки"
      ]
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              "                           order_id                       customer_id  \\\n",
              "0  e481f51cbdc54678b7cc49136f2d6af7  9ef432eb6251297304e76186b10a928d   \n",
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              "\n",
              "  order_status order_purchase_timestamp   order_approved_at  \\\n",
              "0    delivered      2017-10-02 10:56:33 2017-10-02 11:07:15   \n",
              "1    delivered      2018-07-24 20:41:37 2018-07-26 03:24:27   \n",
              "2    delivered      2018-08-08 08:38:49 2018-08-08 08:55:23   \n",
              "\n",
              "  order_delivered_carrier_date order_delivered_customer_date  \\\n",
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              "1          2018-07-26 14:31:00           2018-08-07 15:27:45   \n",
              "2          2018-08-08 13:50:00           2018-08-17 18:06:29   \n",
              "\n",
              "  order_estimated_delivery_date  \n",
              "0                    2017-10-18  \n",
              "1                    2018-08-13  \n",
              "2                    2018-09-04  "
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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",
              "      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-3dade9c4-0811-41cc-99b1-d8c2294973f6 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-3dade9c4-0811-41cc-99b1-d8c2294973f6');\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": 10
        }
      ],
      "source": [
        "#таблица заказов\n",
        "orders = pd.read_csv('https://getfile.dokpub.com/yandex/get/https://disk.yandex.ru/d/t9Li4JOfgxuUrg',parse_dates=[3,4,5,6,7])\n",
        "orders.head(3)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Wb8SaYMicOLD",
        "outputId": "06990ab4-5de2-42b4-a3bb-70027f21a12c"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(99441, 8)"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ],
      "source": [
        "orders.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "V4soG7ZMcOLE",
        "outputId": "ad09981e-0538-4e3d-a98b-0eb870aa43a0"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ],
      "source": [
        "# Количество дублирующих значений \n",
        "orders.duplicated().sum()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "T7Nwg43KcOLG",
        "outputId": "7280e7e0-6a55-45f5-9580-7ed597fbb34d"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "order_id                            0\n",
              "customer_id                         0\n",
              "order_status                        0\n",
              "order_purchase_timestamp            0\n",
              "order_approved_at                 160\n",
              "order_delivered_carrier_date     1783\n",
              "order_delivered_customer_date    2965\n",
              "order_estimated_delivery_date       0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ],
      "source": [
        "# Количество пропущенных значений\n",
        "orders.isnull().sum()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gn3fmu7wcOLG",
        "outputId": "53dba2b6-7293-472d-f318-ccb3a7714522"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 99441 entries, 0 to 99440\n",
            "Data columns (total 8 columns):\n",
            " #   Column                         Non-Null Count  Dtype         \n",
            "---  ------                         --------------  -----         \n",
            " 0   order_id                       99441 non-null  object        \n",
            " 1   customer_id                    99441 non-null  object        \n",
            " 2   order_status                   99441 non-null  object        \n",
            " 3   order_purchase_timestamp       99441 non-null  datetime64[ns]\n",
            " 4   order_approved_at              99281 non-null  datetime64[ns]\n",
            " 5   order_delivered_carrier_date   97658 non-null  datetime64[ns]\n",
            " 6   order_delivered_customer_date  96476 non-null  datetime64[ns]\n",
            " 7   order_estimated_delivery_date  99441 non-null  datetime64[ns]\n",
            "dtypes: datetime64[ns](5), object(3)\n",
            "memory usage: 6.1+ MB\n"
          ]
        }
      ],
      "source": [
        "# Проверим количество записей и типы столбцов\n",
        "orders.info()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mGWZ8aKbcOLH",
        "outputId": "c5f48c63-bb83-4b6a-eda9-e62a16ec449f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "% пропущенных значений в колонке \"время подтверждения оплаты\": 0.16115873127788802\n",
            "% пропущенных значений в колонке \"время передачу в ЛС\": 1.8257592823936595\n",
            "% пропущенных значений в колонке \"время доставки заказа\": 3.0733032049421616\n"
          ]
        }
      ],
      "source": [
        "\n",
        "# % пропущенных значений\n",
        "\n",
        "order_approved_at_null = orders.isnull().query('order_approved_at == True').order_approved_at.count()\n",
        "order_approved_at_not_null = orders.isnull().query('order_approved_at == False').order_approved_at.count()\n",
        "print('% пропущенных значений в колонке \"время подтверждения оплаты\": {}'.format((order_approved_at_null * 100) / order_approved_at_not_null))\n",
        "\n",
        "order_delivered_carrier_date_null = orders.isnull().query('order_delivered_carrier_date == True').order_approved_at.count()\n",
        "order_delivered_carrier_date_not_null = orders.isnull().query('order_delivered_carrier_date == False').order_approved_at.count()\n",
        "print('% пропущенных значений в колонке \"время передачу в ЛС\": {}'.format((order_delivered_carrier_date_null * 100) / order_delivered_carrier_date_not_null))\n",
        "\n",
        "order_delivered_customer_date_null = orders.isnull().query('order_delivered_customer_date == True').order_approved_at.count()\n",
        "order_delivered_customer_date_not_null = orders.isnull().query('order_delivered_customer_date == False').order_approved_at.count()\n",
        "print('% пропущенных значений в колонке \"время доставки заказа\": {}'.format((order_delivered_customer_date_null * 100) /order_delivered_customer_date_not_null))\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 148
        },
        "id": "oPKW1EfacOLI",
        "outputId": "d55758b4-6471-4db9-d101-37490a161dbf"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 3 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "labels = ['Empty data','Full data']\n",
        "sizes1 = [order_approved_at_null, order_approved_at_not_null]\n",
        "sizes2 = [order_delivered_carrier_date_null, order_delivered_carrier_date_not_null]\n",
        "sizes3 = [order_delivered_customer_date_null, order_delivered_customer_date_not_null]\n",
        "explode = (0, 0.1) \n",
        "\n",
        "fig, (ax1, ax2, ax3) = plt.subplots(1, 3)\n",
        "ax1.pie(sizes1, explode=explode, labels=labels, autopct='%1.1f%%',\n",
        "        shadow=True, startangle=90)\n",
        "ax1.set_title('Empty values in order_approved_at %')\n",
        "\n",
        "ax2.pie(sizes2, explode=explode, labels=labels, autopct='%1.1f%%',\n",
        "        shadow=True, startangle=90)\n",
        "ax2.set_title('Empty values in order_delivered_carrier_date %')\n",
        "\n",
        "ax3.pie(sizes3, explode=explode, labels=labels, autopct='%1.1f%%',\n",
        "        shadow=True, startangle=90)\n",
        "ax3.set_title('Empty values in order_delivered_customer_datet % ')\n",
        "\n",
        "\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "t61dlfb4cOLJ"
      },
      "source": [
        "-- В трех колонках \n",
        "\n",
        "        (- order_approved_at — время подтверждения оплаты заказа;\n",
        "        - order_delivered_carrier_date — время передачи заказа в логистическую службу;\n",
        "        - order_delivered_customer_date — время доставки заказа;) \n",
        "содержатся пустые значения, причем разное количество для каждой переменной.\n",
        "\n",
        "-- Однако % пропущенных значений для каждой переменной небольшой.\n",
        "\n",
        "Попробуем понять почему так, вероятно у нас есть \"свежие\" заказы по которым еще не успели доставить заказы. Нужно посмотреть на временной промежуток за который нам предоставленны данные       "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WwCjWwI-cOLJ",
        "outputId": "62182cb9-d16c-4bf3-cae6-a16d0a76a662"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2018-09-03 17:40:06\n",
            "2018-09-11 19:48:28\n",
            "2018-10-17 13:22:46\n",
            "2016-09-15 12:16:38\n",
            "2016-10-08 10:34:01\n",
            "2016-10-11 13:46:32\n"
          ]
        }
      ],
      "source": [
        "print(orders.order_approved_at.max())\n",
        "print(orders.order_delivered_carrier_date.max())\n",
        "print(orders.order_delivered_customer_date.max())\n",
        "\n",
        "print(orders.order_approved_at.min())\n",
        "print(orders.order_delivered_carrier_date.min())\n",
        "print(orders.order_delivered_customer_date.min())"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "o-Sx8vXVcOLK"
      },
      "source": [
        "Мы видим, что у нас представленны данные за 3 года с 2016-10 по 2018-10. Теперь посмотрим на сами пропущенные значения и их статусы"
      ]
    },
    {
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      },
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              "                               order_id                       customer_id  \\\n",
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              "\n",
              "      order_status order_purchase_timestamp order_approved_at  \\\n",
              "1130      canceled      2018-08-28 15:26:39               NaT   \n",
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              "99347     canceled      2018-09-06 18:45:47               NaT   \n",
              "\n",
              "      order_delivered_carrier_date order_delivered_customer_date  \\\n",
              "1130                           NaT                           NaT   \n",
              "1801                           NaT                           NaT   \n",
              "1868                           NaT                           NaT   \n",
              "2029                           NaT                           NaT   \n",
              "2161                           NaT                           NaT   \n",
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              "97696                          NaT                           NaT   \n",
              "98415                          NaT                           NaT   \n",
              "98909                          NaT                           NaT   \n",
              "99283                          NaT                           NaT   \n",
              "99347                          NaT                           NaT   \n",
              "\n",
              "      order_estimated_delivery_date  \n",
              "1130                     2018-09-12  \n",
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              "1868                     2017-04-10  \n",
              "2029                     2018-09-13  \n",
              "2161                     2017-05-30  \n",
              "...                             ...  \n",
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              "98415                    2017-10-16  \n",
              "98909                    2018-08-10  \n",
              "99283                    2018-10-01  \n",
              "99347                    2018-09-27  \n",
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              "<p>160 rows × 8 columns</p>\n",
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              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-d30999c4-f14d-4dfb-9148-510ea58f9dc4')\"\n",
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              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ],
      "source": [
        "# Посмотрим на 'время подтверждения оплаты заказа'\n",
        "orders.loc[orders.order_approved_at.isnull()]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8JRkYVzAcOLK"
      },
      "source": [
        "Есть предположение о том, что если все три колонки пусты, то значит заказ имел статус \"canceled\"."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "id": "Yz8fPRMGcOLL",
        "outputId": "7c590b7e-e83e-4bd9-ad55-561415fb5966"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "              order_id  customer_id  order_purchase_timestamp  \\\n",
              "order_status                                                    \n",
              "canceled           141          141                       141   \n",
              "created              5            5                         5   \n",
              "\n",
              "              order_approved_at  order_delivered_carrier_date  \\\n",
              "order_status                                                    \n",
              "canceled                      0                             0   \n",
              "created                       0                             0   \n",
              "\n",
              "              order_delivered_customer_date  order_estimated_delivery_date  \n",
              "order_status                                                                \n",
              "canceled                                  0                            141  \n",
              "created                                   0                              5  "
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
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              "\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",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ],
      "source": [
        "orders\\\n",
        "    .query('order_approved_at == \"NaT\" and order_delivered_carrier_date == \"NaT\" and order_delivered_customer_date == \"NaT\"')\\\n",
        "    .groupby(['order_status']).count()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yv5EmvLxcOLL"
      },
      "source": [
        "С вероятнотью 0.965 при условии что order_approved_at, order_delivered_carrier_date и order_delivered_customer_date будут пустыми значениями, статус заказа будет \"canceled\"."
      ]
    },
    {
      "cell_type": "code",
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        "id": "fyWhmeNocOLL",
        "outputId": "6825bcb2-1598-43a8-bb1a-25c685efb3e6"
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              "                               order_id                       customer_id  \\\n",
              "7434   b5359909123fa03c50bdb0cfed07f098  438449d4af8980d107bf04571413a8e7   \n",
              "9238   dba5062fbda3af4fb6c33b1e040ca38f  964a6df3d9bdf60fe3e7b8bb69ed893a   \n",
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              "\n",
              "      order_status order_purchase_timestamp order_approved_at  \\\n",
              "7434       created      2017-12-05 01:07:52               NaT   \n",
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              "21441      created      2017-11-25 11:10:33               NaT   \n",
              "55086      created      2017-12-05 01:07:58               NaT   \n",
              "58958      created      2017-11-06 13:12:34               NaT   \n",
              "\n",
              "      order_delivered_carrier_date order_delivered_customer_date  \\\n",
              "7434                           NaT                           NaT   \n",
              "9238                           NaT                           NaT   \n",
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              "55086                          NaT                           NaT   \n",
              "58958                          NaT                           NaT   \n",
              "\n",
              "      order_estimated_delivery_date  \n",
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          "metadata": {},
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      "source": [
        "orders\\\n",
        "    .query('order_approved_at == \"NaT\" and order_delivered_carrier_date == \"NaT\" \\\n",
        "            and order_delivered_customer_date == \"NaT\" and order_status == \"created\"')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bO02GGo9cOLL"
      },
      "source": [
        "Несколько заказов со статусом \"created\" были созданы в конце вероятно это \"свежие\" заказы. давайте посмотрим на дату создания заказа"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
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          "height": 488
        },
        "id": "9FKnJkCncOLL",
        "outputId": "ff126604-f43e-49b8-c789-3e4454620fb7"
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          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                               order_id                       customer_id  \\\n",
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              "\n",
              "      order_status order_purchase_timestamp   order_approved_at  \\\n",
              "6         invoiced      2017-04-11 12:22:08 2017-04-13 13:25:17   \n",
              "103       invoiced      2018-08-03 17:44:42 2018-08-07 06:15:14   \n",
              "128     processing      2017-09-03 14:22:03 2017-09-03 14:30:09   \n",
              "266    unavailable      2017-11-16 15:09:28 2017-11-16 15:26:57   \n",
              "324     processing      2016-10-05 22:44:13 2016-10-06 15:51:05   \n",
              "...            ...                      ...                 ...   \n",
              "99283     canceled      2018-08-31 16:13:44                 NaT   \n",
              "99313   processing      2018-01-05 23:07:24 2018-01-09 07:18:05   \n",
              "99347     canceled      2018-09-06 18:45:47                 NaT   \n",
              "99348  unavailable      2017-08-23 16:28:04 2017-08-28 15:44:47   \n",
              "99415  unavailable      2017-10-10 10:50:03 2017-10-14 18:35:57   \n",
              "\n",
              "      order_delivered_carrier_date order_delivered_customer_date  \\\n",
              "6                              NaT                           NaT   \n",
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              "266                            NaT                           NaT   \n",
              "324                            NaT                           NaT   \n",
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              "99283                          NaT                           NaT   \n",
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              "...                             ...  \n",
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              "99347                    2018-09-27  \n",
              "99348                    2017-09-15  \n",
              "99415                    2017-10-23  \n",
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              "      <td>NaT</td>\n",
              "      <td>NaT</td>\n",
              "      <td>NaT</td>\n",
              "      <td>2018-10-01</td>\n",
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              "    <tr>\n",
              "      <th>99313</th>\n",
              "      <td>e9e64a17afa9653aacf2616d94c005b8</td>\n",
              "      <td>b4cd0522e632e481f8eaf766a2646e86</td>\n",
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              "      <td>2018-01-05 23:07:24</td>\n",
              "      <td>2018-01-09 07:18:05</td>\n",
              "      <td>NaT</td>\n",
              "      <td>NaT</td>\n",
              "      <td>2018-02-06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>99347</th>\n",
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              "      <td>NaT</td>\n",
              "      <td>NaT</td>\n",
              "      <td>NaT</td>\n",
              "      <td>2018-09-27</td>\n",
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              "    <tr>\n",
              "      <th>99348</th>\n",
              "      <td>a69ba794cc7deb415c3e15a0a3877e69</td>\n",
              "      <td>726f0894b5becdf952ea537d5266e543</td>\n",
              "      <td>unavailable</td>\n",
              "      <td>2017-08-23 16:28:04</td>\n",
              "      <td>2017-08-28 15:44:47</td>\n",
              "      <td>NaT</td>\n",
              "      <td>NaT</td>\n",
              "      <td>2017-09-15</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>99415</th>\n",
              "      <td>5fabc81b6322c8443648e1b21a6fef21</td>\n",
              "      <td>32c9df889d41b0ee8309a5efb6855dcb</td>\n",
              "      <td>unavailable</td>\n",
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              "      <td>2017-10-14 18:35:57</td>\n",
              "      <td>NaT</td>\n",
              "      <td>NaT</td>\n",
              "      <td>2017-10-23</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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              "<p>1783 rows × 8 columns</p>\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "          const element = document.querySelector('#df-0535496d-501c-4aac-aa11-2086d1980c2d');\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",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ],
      "source": [
        "# Посмотрим на 'время передачи заказа в логистическую службу'\n",
        "orders.loc[orders.order_delivered_carrier_date.isnull()]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 488
        },
        "id": "jvgge_PvcOLM",
        "outputId": "f7657a63-4656-4dcb-a2ab-faa682e32d2d"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "                               order_id                       customer_id  \\\n",
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              "\n",
              "      order_status order_purchase_timestamp   order_approved_at  \\\n",
              "6         invoiced      2017-04-11 12:22:08 2017-04-13 13:25:17   \n",
              "44         shipped      2018-06-04 16:44:48 2018-06-05 04:31:18   \n",
              "103       invoiced      2018-08-03 17:44:42 2018-08-07 06:15:14   \n",
              "128     processing      2017-09-03 14:22:03 2017-09-03 14:30:09   \n",
              "154        shipped      2018-01-10 11:33:07 2018-01-11 02:32:30   \n",
              "...            ...                      ...                 ...   \n",
              "99283     canceled      2018-08-31 16:13:44                 NaT   \n",
              "99313   processing      2018-01-05 23:07:24 2018-01-09 07:18:05   \n",
              "99347     canceled      2018-09-06 18:45:47                 NaT   \n",
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              "99415  unavailable      2017-10-10 10:50:03 2017-10-14 18:35:57   \n",
              "\n",
              "      order_delivered_carrier_date order_delivered_customer_date  \\\n",
              "6                              NaT                           NaT   \n",
              "44             2018-06-05 14:32:00                           NaT   \n",
              "103                            NaT                           NaT   \n",
              "128                            NaT                           NaT   \n",
              "154            2018-01-11 19:39:23                           NaT   \n",
              "...                            ...                           ...   \n",
              "99283                          NaT                           NaT   \n",
              "99313                          NaT                           NaT   \n",
              "99347                          NaT                           NaT   \n",
              "99348                          NaT                           NaT   \n",
              "99415                          NaT                           NaT   \n",
              "\n",
              "      order_estimated_delivery_date  \n",
              "6                        2017-05-09  \n",
              "44                       2018-06-28  \n",
              "103                      2018-08-21  \n",
              "128                      2017-10-03  \n",
              "154                      2018-02-07  \n",
              "...                             ...  \n",
              "99283                    2018-10-01  \n",
              "99313                    2018-02-06  \n",
              "99347                    2018-09-27  \n",
              "99348                    2017-09-15  \n",
              "99415                    2017-10-23  \n",
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              "      <td>NaT</td>\n",
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              "<p>2965 rows × 8 columns</p>\n",
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              "          const element = document.querySelector('#df-5ef2f67c-da90-406a-8553-5d5fdc940e8c');\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
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              "\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",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ],
      "source": [
        "# Посмотрим на 'время доставки заказа'\n",
        "orders.loc[orders.order_delivered_customer_date.isnull()]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pDNRLJrfcOLM"
      },
      "source": [
        "## 1.3 dataset 3 'товарные позиции, входящие в заказы'"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CFxWTLJfcOLM"
      },
      "source": [
        "Описание переменных:\n",
        "\n",
        "order_id —  уникальный идентификатор заказа (номер чека)\n",
        "\n",
        "order_item_id —  идентификатор товара внутри одного заказа\n",
        "\n",
        "product_id —  ид товара (аналог штрихкода)\n",
        "\n",
        "seller_id — ид производителя товара\n",
        "\n",
        "shipping_limit_date —  максимальная дата доставки продавцом для передачи заказа партнеру по логистике\n",
        "\n",
        "price —  цена за единицу товара\n",
        "\n",
        "freight_value —  вес товара"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
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        "id": "a8qacFeOcOLM",
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            "text/plain": [
              "                           order_id  order_item_id  \\\n",
              "0  00010242fe8c5a6d1ba2dd792cb16214              1   \n",
              "1  00018f77f2f0320c557190d7a144bdd3              1   \n",
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              "                         product_id                         seller_id  \\\n",
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              "\n",
              "  shipping_limit_date  price  freight_value  \n",
              "0 2017-09-19 09:45:35   58.9          13.29  \n",
              "1 2017-05-03 11:05:13  239.9          19.93  \n",
              "2 2018-01-18 14:48:30  199.0          17.87  "
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ],
      "source": [
        "# товарные позиции, входящие в заказы\n",
        "products = pd.read_csv('https://getfile.dokpub.com/yandex/get/https://disk.yandex.ru/d/Gbt-yAcQrOe3Pw',parse_dates=[4])\n",
        "products.head(3)"
      ]
    },
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      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "V0eCm1mncOLM",
        "outputId": "ee0d8896-74ef-4cbb-dbda-a3e3793a60e3"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(112650, 7)"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ],
      "source": [
        "products.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mP6Xq8OLcOLN",
        "outputId": "a70902a2-122f-4420-bafe-9f87d179919e"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "order_id                       object\n",
              "order_item_id                   int64\n",
              "product_id                     object\n",
              "seller_id                      object\n",
              "shipping_limit_date    datetime64[ns]\n",
              "price                         float64\n",
              "freight_value                 float64\n",
              "dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ],
      "source": [
        "products.dtypes"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VmJEK_b3cOLN",
        "outputId": "b3b2308f-f08d-42f5-d2ed-2d6251135b24"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ],
      "source": [
        "# Количество дублирующих значений \n",
        "products.duplicated().sum()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4-pai-OVcOLN",
        "outputId": "b58b3aa3-48d9-458f-b6b6-4f828f6f6fc1"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "order_id               0\n",
              "order_item_id          0\n",
              "product_id             0\n",
              "seller_id              0\n",
              "shipping_limit_date    0\n",
              "price                  0\n",
              "freight_value          0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ],
      "source": [
        "# Количество пропущенных значений\n",
        "products.isnull().sum()\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SK-1WjntcOLN"
      },
      "source": [
        "## 2.1 Сколько у нас пользователей, которые совершили покупку только один раз?"
      ]
    },
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              "                        customer_id                customer_unique_id  \\\n",
              "0  06b8999e2fba1a1fbc88172c00ba8bc7  861eff4711a542e4b93843c6dd7febb0   \n",
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              "\n",
              "   customer_zip_code_prefix          customer_city customer_state  \\\n",
              "0                     14409                 franca             SP   \n",
              "1                      9790  sao bernardo do campo             SP   \n",
              "2                      1151              sao paulo             SP   \n",
              "\n",
              "                           order_id order_status order_purchase_timestamp  \\\n",
              "0  00e7ee1b050b8499577073aeb2a297a1    delivered      2017-05-16 15:05:35   \n",
              "1  29150127e6685892b6eab3eec79f59c7    delivered      2018-01-12 20:48:24   \n",
              "2  b2059ed67ce144a36e2aa97d2c9e9ad2    delivered      2018-05-19 16:07:45   \n",
              "\n",
              "    order_approved_at order_delivered_carrier_date  \\\n",
              "0 2017-05-16 15:22:12          2017-05-23 10:47:57   \n",
              "1 2018-01-12 20:58:32          2018-01-15 17:14:59   \n",
              "2 2018-05-20 16:19:10          2018-06-11 14:31:00   \n",
              "\n",
              "  order_delivered_customer_date order_estimated_delivery_date  \n",
              "0           2017-05-25 10:35:35                    2017-06-05  \n",
              "1           2018-01-29 12:41:19                    2018-02-06  \n",
              "2           2018-06-14 17:58:51                    2018-06-13  "
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              "                                                     [key], {});\n",
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              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "            + ' to learn more about interactive tables.';\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ],
      "source": [
        "# объединим 2-е таблицы вместе по customer_id — позаказный идентификатор пользователя (аналог номера паспорта)\n",
        "customers_orders = customers.merge(orders, how='inner', on='customer_id')\n",
        "customers_orders.head(3)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sFv4XB_AcOLO",
        "outputId": "04bd5328-6743-4f0b-d8ec-81ac7e8d4f0b"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(99441, 12)"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ],
      "source": [
        "customers_orders.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nhc6NQstcOLO",
        "outputId": "5005e3f0-a16a-489a-871c-914eaee46840"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "customer_id                         0\n",
              "customer_unique_id                  0\n",
              "customer_zip_code_prefix            0\n",
              "customer_city                       0\n",
              "customer_state                      0\n",
              "order_id                            0\n",
              "order_status                        0\n",
              "order_purchase_timestamp            0\n",
              "order_approved_at                 160\n",
              "order_delivered_carrier_date     1783\n",
              "order_delivered_customer_date    2965\n",
              "order_estimated_delivery_date       0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ],
      "source": [
        "#customers_orders.describe().transpose()\n",
        "customers_orders.isnull().sum()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FbEJT9vAcOLO",
        "outputId": "d41819cb-d05b-4a10-9fd3-ee4e60f278a8"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['delivered', 'canceled', 'invoiced', 'shipped', 'processing',\n",
              "       'unavailable', 'approved', 'created'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ],
      "source": [
        "# уникальные статусы доставки\n",
        "customers_orders.order_status.unique()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JtghWF4CcOLO",
        "outputId": "f0ed99ff-aa52-4d35-b645-dc2606966bd6"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "order_status\n",
              "approved           2\n",
              "canceled         625\n",
              "created            5\n",
              "delivered      96478\n",
              "invoiced         314\n",
              "processing       301\n",
              "shipped         1107\n",
              "unavailable      609\n",
              "Name: order_id, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ],
      "source": [
        "customers_orders.groupby(['order_status']).order_id.count()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ivp3mJUycOLP"
      },
      "source": [
        "За выполненные заказы будем считать все заказы со статусом \"delivered\" "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gaERC-p7cOLP",
        "outputId": "4215b924-0505-4b46-f24f-d5545eab7c76"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "canceled     141\n",
              "delivered     14\n",
              "created        5\n",
              "Name: order_status, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ],
      "source": [
        "customers_orders[customers_orders.order_approved_at.isnull()].order_status.value_counts()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tHvtv6jPcOLP",
        "outputId": "928f71a3-e3d7-44ab-8852-7446fe709f6d"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "unavailable    609\n",
              "canceled       550\n",
              "invoiced       314\n",
              "processing     301\n",
              "created          5\n",
              "approved         2\n",
              "delivered        2\n",
              "Name: order_status, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 35
        }
      ],
      "source": [
        "customers_orders[customers_orders.order_delivered_carrier_date.isnull()].order_status.value_counts()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1pPe4g8gcOLP",
        "outputId": "8775c4cb-5636-4daa-f367-a16276a8d65f"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "shipped        1107\n",
              "canceled        619\n",
              "unavailable     609\n",
              "invoiced        314\n",
              "processing      301\n",
              "delivered         8\n",
              "created           5\n",
              "approved          2\n",
              "Name: order_status, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ],
      "source": [
        "customers_orders[customers_orders.order_delivered_customer_date.isnull()].order_status.value_counts()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
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          "height": 424
        },
        "id": "LHM47InjcOLP",
        "outputId": "6e71eda4-3cf4-4766-c89e-768af635307c"
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      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                     customer_unique_id  order_id\n",
              "0      0000366f3b9a7992bf8c76cfdf3221e2         1\n",
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          },
          "metadata": {},
          "execution_count": 37
        }
      ],
      "source": [
        "customers_orders.query('order_status == \"delivered\"')\\\n",
        "             .groupby(['customer_unique_id'],as_index=False)\\\n",
        "            .agg({'order_id':'count'})\\\n",
        "            .query('order_id == 1')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ns6Yg-QBcOLQ"
      },
      "source": [
        "Ответ 2.1: \n",
        "90557 пользователей совершили покупку один раз при условии, что статус \"delivered\" присваивался без ошибок. \n",
        "\n",
        "Тем неменее, если обращать внимание на пропуски timestamps в, то в order_approved_at пропущенно 14 значений со статусом \"delivered\", в order_delivered_carrier_date - 2 значения, а в order_delivered_customer_date - 8 значений. \n",
        "\n",
        "Если статус \"delivered\" присваивался некорректно, то ошибка должна составить 0,015%."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-AfJNYX7cOLQ"
      },
      "source": [
        "## 2.2 Сколько заказов в месяц в среднем не доставляется по разным причинам (вывести детализацию по причинам)? "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 176
        },
        "id": "kjGCJCoucOLQ",
        "outputId": "c250691b-dc24-413d-e2c2-c278da267e78"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                         customer_id                customer_unique_id  \\\n",
              "46  f34a6e874087ec1f0e3dab9fdf659c5d  233896de79986082f1f479f1f85281cb   \n",
              "47  c132855c926907970dcf6f2bf0b33a24  a8ae36a2bb6c2bbc3b5d62ede131c9ef   \n",
              "\n",
              "    customer_zip_code_prefix customer_city customer_state  \\\n",
              "46                     38300     ituiutaba             MG   \n",
              "47                     18740   taquarituba             SP   \n",
              "\n",
              "                            order_id order_status order_purchase_timestamp  \\\n",
              "46  6e98de3a85c84ead6689189b825d35b5     canceled      2018-03-15 10:07:02   \n",
              "47  b5172d4a722c0ebb34c29bbbb7a3cb42     invoiced      2018-04-17 13:14:13   \n",
              "\n",
              "     order_approved_at order_delivered_carrier_date  \\\n",
              "46 2018-03-15 10:29:33                          NaT   \n",
              "47 2018-04-18 05:31:51                          NaT   \n",
              "\n",
              "   order_delivered_customer_date order_estimated_delivery_date  \n",
              "46                           NaT                    2018-04-09  \n",
              "47                           NaT                    2018-05-24  "
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              "      <td>NaT</td>\n",
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              "      <td>NaT</td>\n",
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              "  </div>\n",
              "  "
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          },
          "metadata": {},
          "execution_count": 38
        }
      ],
      "source": [
        "tmp_cust_ord = customers_orders.query('order_status != \"delivered\"')\n",
        "tmp_cust_ord.head(2)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
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        },
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        "outputId": "688b1ecc-9b07-4c85-a299-00f6502992e3"
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        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(2963, 12)"
            ]
          },
          "metadata": {},
          "execution_count": 39
        }
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      "source": [
        "tmp_cust_ord.shape"
      ]
    },
    {
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      "execution_count": null,
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        },
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      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  \"\"\"Entry point for launching an IPython kernel.\n"
          ]
        }
      ],
      "source": [
        "tmp_cust_ord['month_year'] = tmp_cust_ord.order_estimated_delivery_date.apply(lambda x: x.strftime('%B-%Y')) "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
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        "id": "v2a1vA3CcOLQ",
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      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(2963, 13)"
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          },
          "metadata": {},
          "execution_count": 41
        }
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      "source": [
        "tmp_cust_ord.shape"
      ]
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        "id": "TlEVxaNNcOLR",
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        {
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            "text/plain": [
              "                         customer_id                customer_unique_id  \\\n",
              "46  f34a6e874087ec1f0e3dab9fdf659c5d  233896de79986082f1f479f1f85281cb   \n",
              "47  c132855c926907970dcf6f2bf0b33a24  a8ae36a2bb6c2bbc3b5d62ede131c9ef   \n",
              "70  4c7241af24b5344cb01fe687643de4fe  b157c176c3fe04914fde33f2dc8b878a   \n",
              "\n",
              "    customer_zip_code_prefix customer_city customer_state  \\\n",
              "46                     38300     ituiutaba             MG   \n",
              "47                     18740   taquarituba             SP   \n",
              "70                     60336     fortaleza             CE   \n",
              "\n",
              "                            order_id order_status order_purchase_timestamp  \\\n",
              "46  6e98de3a85c84ead6689189b825d35b5     canceled      2018-03-15 10:07:02   \n",
              "47  b5172d4a722c0ebb34c29bbbb7a3cb42     invoiced      2018-04-17 13:14:13   \n",
              "70  ad380680e87dea0f2abf5cd5bace626c      shipped      2018-01-21 15:54:02   \n",
              "\n",
              "     order_approved_at order_delivered_carrier_date  \\\n",
              "46 2018-03-15 10:29:33                          NaT   \n",
              "47 2018-04-18 05:31:51                          NaT   \n",
              "70 2018-01-22 13:52:41          2018-01-24 18:26:40   \n",
              "\n",
              "   order_delivered_customer_date order_estimated_delivery_date     month_year  \n",
              "46                           NaT                    2018-04-09     April-2018  \n",
              "47                           NaT                    2018-05-24       May-2018  \n",
              "70                           NaT                    2018-02-20  February-2018  "
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              "      <td>2018-01-24 18:26:40</td>\n",
              "      <td>NaT</td>\n",
              "      <td>2018-02-20</td>\n",
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              "                                                     [key], {});\n",
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              "\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",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 42
        }
      ],
      "source": [
        "tmp_cust_ord.head(3)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yVvJSod-cOLR",
        "outputId": "30d88a66-6774-4dbb-ca9d-233d868dfc1a"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "113.96153846153847"
            ]
          },
          "metadata": {},
          "execution_count": 43
        }
      ],
      "source": [
        "tmp_cust_ord.groupby(['month_year']).order_id.count().mean()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UuWw2R1zcOLR"
      },
      "source": [
        "Теперь посмотрим среднее значение в зависимости от разных причин"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
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        },
        "id": "Y_1tCw9WcOLR",
        "outputId": "8d2716d5-f46c-4679-9408-d82ce004ff34"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "24.03846153846154"
            ]
          },
          "metadata": {},
          "execution_count": 44
        }
      ],
      "source": [
        "# если заказ был отменен\n",
        "tmp_cust_ord.query('order_status == \"canceled\"')\\\n",
        "                    .groupby(['month_year'])\\\n",
        "                    .order_id.count().mean()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BOg2toGxcOLS",
        "outputId": "76d1007c-62ef-4bfd-d23e-51d1cd8f1b0a"
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        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "29.0"
            ]
          },
          "metadata": {},
          "execution_count": 45
        }
      ],
      "source": [
        "# если заказ был недоступен\n",
        "tmp_cust_ord.query('order_status == \"unavailable\"')\\\n",
        "                    .groupby(['month_year'])\\\n",
        "                    .order_id.count().mean()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
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        "id": "YtDu8eyscOLS",
        "outputId": "983339b4-fbd4-46b4-84f9-41ff844b75e8"
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        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1.6666666666666667"
            ]
          },
          "metadata": {},
          "execution_count": 46
        }
      ],
      "source": [
        "# если заказ был только что создан и его не успели собрать и доставить\n",
        "tmp_cust_ord.query('order_status == \"created\"')\\\n",
        "                    .groupby(['month_year'])\\\n",
        "                    .order_id.count().mean()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "E94E_R0ccOLS"
      },
      "source": [
        "Ответ 2.2:\n",
        "\n",
        "Примерно ~114 заказов не доставляются в среднем за месяц. \n",
        "Возможными причинами могут быть:\n",
        "\n",
        "1) Заказ не доставили, т.к. он был отменен, в среднем 24 заказа в месяц;\n",
        "2) Заказ не доставили, т.к. он был недоступен, в среднем 29 заказа в месяц;\n",
        "3) Заказ не доставили, т.к. он был только что создан и его не успели собрать и доставить, в среднем ~2 заказа в месяц;"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FNC9-ElEcOLS"
      },
      "source": [
        "## 2.3 По каждому товару определить, в какой день недели товар чаще всего покупается. "
      ]
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              "                           order_id  order_item_id  \\\n",
              "0  00010242fe8c5a6d1ba2dd792cb16214              1   \n",
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              "  shipping_limit_date  price  freight_value  \n",
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              "                        customer_id                customer_unique_id  \\\n",
              "0  06b8999e2fba1a1fbc88172c00ba8bc7  861eff4711a542e4b93843c6dd7febb0   \n",
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          "metadata": {},
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      ],
      "source": [
        "# объединим customers_orders и products\n",
        "common_data = customers_orders.merge(products, how='inner', on='order_id')\n",
        "common_data.head(2)"
      ]
    },
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        "id": "e6di9Sj6cOLT",
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              "(112650, 18)"
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          "metadata": {},
          "execution_count": 49
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        "common_data.shape"
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    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Ioxmujw4cOLT"
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      "outputs": [],
      "source": [
        "common_data['day_name'] = common_data.order_purchase_timestamp.dt.day_name()"
      ]
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    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "14rwgtQrcOLT"
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        "common_data['orders_data'] = common_data.order_purchase_timestamp.isnull()"
      ]
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    {
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              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
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              "      border-radius: 50%;\n",
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              "      display: none;\n",
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              "      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",
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              "\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",
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              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-ef226b0a-c934-4e37-a50b-c6e46f489a39 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-ef226b0a-c934-4e37-a50b-c6e46f489a39');\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": 53
        }
      ],
      "source": [
        "pivot_data = common_data.pivot_table(index='product_id',\n",
        "                        columns='day_name', \n",
        "                        values='order_purchase_timestamp',\n",
        "                        aggfunc='count', fill_value=0).reset_index()\n",
        "pivot_data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "wpXbIXd_cOLU"
      },
      "outputs": [],
      "source": [
        "# подсчитаем дни недели "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "riQibKp1cOLU"
      },
      "outputs": [],
      "source": [
        "pivot_data['week_days'] = pivot_data.loc[:, pivot_data.columns != 'product_id'].idxmax(axis=1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "dchq7kUqcOLU",
        "outputId": "8dccd1ff-1508-4999-f253-abf78342a7a8"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "day_name                        product_id  week_days\n",
              "0         00066f42aeeb9f3007548bb9d3f33c38     Sunday\n",
              "1         00088930e925c41fd95ebfe695fd2655    Tuesday\n",
              "2         0009406fd7479715e4bef61dd91f2462   Thursday\n",
              "3         000b8f95fcb9e0096488278317764d19     Friday\n",
              "4         000d9be29b5207b54e86aa1b1ac54872    Tuesday\n",
              "...                                    ...        ...\n",
              "32946     fff6177642830a9a94a0f2cba5e476d1   Saturday\n",
              "32947     fff81cc3158d2725c0655ab9ba0f712c     Monday\n",
              "32948     fff9553ac224cec9d15d49f5a263411f     Friday\n",
              "32949     fffdb2d0ec8d6a61f0a0a0db3f25b441    Tuesday\n",
              "32950     fffe9eeff12fcbd74a2f2b007dde0c58  Wednesday\n",
              "\n",
              "[32951 rows x 2 columns]"
            ],
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              "\n",
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              "      <th>day_name</th>\n",
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              "      <th>week_days</th>\n",
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              "  <tbody>\n",
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              "      <th>0</th>\n",
              "      <td>00066f42aeeb9f3007548bb9d3f33c38</td>\n",
              "      <td>Sunday</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>00088930e925c41fd95ebfe695fd2655</td>\n",
              "      <td>Tuesday</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0009406fd7479715e4bef61dd91f2462</td>\n",
              "      <td>Thursday</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>000b8f95fcb9e0096488278317764d19</td>\n",
              "      <td>Friday</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>000d9be29b5207b54e86aa1b1ac54872</td>\n",
              "      <td>Tuesday</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "      <th>32946</th>\n",
              "      <td>fff6177642830a9a94a0f2cba5e476d1</td>\n",
              "      <td>Saturday</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32947</th>\n",
              "      <td>fff81cc3158d2725c0655ab9ba0f712c</td>\n",
              "      <td>Monday</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32948</th>\n",
              "      <td>fff9553ac224cec9d15d49f5a263411f</td>\n",
              "      <td>Friday</td>\n",
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              "    <tr>\n",
              "      <th>32949</th>\n",
              "      <td>fffdb2d0ec8d6a61f0a0a0db3f25b441</td>\n",
              "      <td>Tuesday</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32950</th>\n",
              "      <td>fffe9eeff12fcbd74a2f2b007dde0c58</td>\n",
              "      <td>Wednesday</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>32951 rows × 2 columns</p>\n",
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              "\n",
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              "  </style>\n",
              "\n",
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              "          const element = document.querySelector('#df-eb238a49-3f95-4ad9-894e-47355b59eeb0');\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": 56
        }
      ],
      "source": [
        "# найдем дни недели в которых товар чаще всего покупается\n",
        "pivot_data[['product_id','week_days']]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3sCAHStCcOLU"
      },
      "source": [
        "## 2.4 Сколько у каждого из пользователей в среднем покупок в неделю (по месяцам)? Не стоит забывать, что внутри месяца может быть не целое количество недель. Например, в ноябре 2021 года 4,28 недели. И внутри метрики это нужно учесть. "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "id": "boa3R8O3cOLU",
        "outputId": "3c7563e9-eb03-4e6b-e1a5-9b3b55f97221"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                 customer_unique_id                          order_id  \\\n",
              "0  861eff4711a542e4b93843c6dd7febb0  00e7ee1b050b8499577073aeb2a297a1   \n",
              "1  290c77bc529b7ac935b93aa66c333dc3  29150127e6685892b6eab3eec79f59c7   \n",
              "\n",
              "    order_approved_at  \n",
              "0 2017-05-16 15:22:12  \n",
              "1 2018-01-12 20:58:32  "
            ],
            "text/html": [
              "\n",
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              "      <div>\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>customer_unique_id</th>\n",
              "      <th>order_id</th>\n",
              "      <th>order_approved_at</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
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              "      <th>0</th>\n",
              "      <td>861eff4711a542e4b93843c6dd7febb0</td>\n",
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              "      <td>2017-05-16 15:22:12</td>\n",
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              "      <th>1</th>\n",
              "      <td>290c77bc529b7ac935b93aa66c333dc3</td>\n",
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              "      <td>2018-01-12 20:58:32</td>\n",
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              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
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              "      </button>\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-180fa41c-89b4-40c2-96dd-d339364342b3 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-180fa41c-89b4-40c2-96dd-d339364342b3');\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",
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              "          element.appendChild(docLink);\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 57
        }
      ],
      "source": [
        "# объединим customers и orders\n",
        "# За время покупки будем считать 'order_approved_at' время подтверждения оплаты заказа\n",
        "purchase_by_month = customers.merge(orders, how='inner', on='customer_id')[['customer_unique_id','order_id','order_approved_at']]\n",
        "purchase_by_month.head(2)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YtIGy_ZAcOLV",
        "outputId": "0d24e408-5387-4e7d-d33f-41bf59f35159"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(99441, 3)"
            ]
          },
          "metadata": {},
          "execution_count": 58
        }
      ],
      "source": [
        "purchase_by_month.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VlGQuRprcOLV",
        "outputId": "1d1a38bf-994d-418c-d4b7-7c37da64422c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2016-09-15 12:16:38\n",
            "2018-09-03 17:40:06\n"
          ]
        }
      ],
      "source": [
        "print(purchase_by_month.order_approved_at.min())\n",
        "print(purchase_by_month.order_approved_at.max())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QYDttdBrcOLV",
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      "outputs": [
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              "customer_unique_id            object\n",
              "order_id                      object\n",
              "order_approved_at     datetime64[ns]\n",
              "dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 60
        }
      ],
      "source": [
        "purchase_by_month.dtypes"
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        "id": "0yijkDKDcOLV"
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      "outputs": [],
      "source": [
        "purchase_by_month['number_of_weeks'] = purchase_by_month.order_approved_at.dt.daysinmonth/7"
      ]
    },
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              "                 customer_unique_id                          order_id  \\\n",
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              "                     customer_unique_id  number_of_weeks  order_id\n",
              "53512  8d50f5eadf50201ccdcedfb9e2ac8455         4.428571        14\n",
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          "metadata": {},
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      ],
      "source": [
        "purchase_by_month_process = purchase_by_month.groupby(['customer_unique_id','number_of_weeks'],as_index=False)\\\n",
        "                        .agg({'order_id':'count'})\\\n",
        "                        .sort_values('order_id',ascending=False)\n",
        "\n",
        "purchase_by_month_process.head(3)"
      ]
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              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
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              "    }\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-92720d18-5297-48c3-b663-51f58d172f3c 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-92720d18-5297-48c3-b663-51f58d172f3c');\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": 65
        }
      ],
      "source": [
        "purchase_by_month_process['metrics'] = purchase_by_month_process.order_id/purchase_by_month_process.number_of_weeks\n",
        "purchase_by_month_process.head(5)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Znz9hY1KcOLW"
      },
      "source": [
        "Ответ 2.4. Среднее кол-во покупок в неделю (по месяцам) для каждого пользователя рассчитано в таблице 'purchase_by_month_process'  в столбце \"metrics\" "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yz40uEiecOLW"
      },
      "source": [
        "## 2.5 Напиши функцию на python, позволяющую строить когортный анализ. В период с января по декабрь выяви когорту с самым высоким retention на 3-й месяц. Описание подхода можно найти тут. "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 192
        },
        "id": "KdxLnOcecOLW",
        "outputId": "a507d63f-c97f-4d91-f8e4-04de4d162a47"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(99441, 3)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                 customer_unique_id                          order_id  \\\n",
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              "\n",
              "  order_purchase_timestamp  \n",
              "0      2017-05-16 15:05:35  \n",
              "1      2018-01-12 20:48:24  \n",
              "2      2018-05-19 16:07:45  \n",
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              "                                                     [key], {});\n",
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              "\n",
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              "            '<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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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 66
        }
      ],
      "source": [
        "# Так как нас интересует метррика retention, то нам необходимо знатьвремя создания заказа'order_purchase_timestamp'\n",
        "data_for_cohort = customers.merge(orders, on='customer_id')[['customer_unique_id','order_id','order_purchase_timestamp']]\n",
        "print(data_for_cohort.shape)\n",
        "data_for_cohort.head(4)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GFr4scE2cOLX",
        "outputId": "a2e24ade-b15f-43c1-ee34-0660ce12c876"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(99441, 3)\n"
          ]
        }
      ],
      "source": [
        "data_for_cohort.drop_duplicates(inplace=True)\n",
        "print(data_for_cohort.shape)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MDhVoAykcOLX",
        "outputId": "1fb72e8e-809d-414d-aa0f-5bf161f79c14"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2016-09-04 21:15:19\n",
            "2018-10-17 17:30:18\n"
          ]
        }
      ],
      "source": [
        "print(data_for_cohort.order_purchase_timestamp.min())\n",
        "print(data_for_cohort.order_purchase_timestamp.max())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "qaRg54J-cOLX"
      },
      "outputs": [],
      "source": [
        "data_for_2017 = data_for_cohort[(data_for_cohort.order_purchase_timestamp >= dt.datetime(year=2017, month=1, day=1)) & \\\n",
        "                               (data_for_cohort.order_purchase_timestamp <= dt.datetime(year=2017, month=12, day=31))]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "kgzhSMDpcOLX",
        "outputId": "535810d3-8e6b-45bf-f845-ea5915994da6"
      },
      "outputs": [
        {
          "output_type": "execute_result",
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            "text/plain": [
              "                  customer_unique_id                          order_id  \\\n",
              "0   861eff4711a542e4b93843c6dd7febb0  00e7ee1b050b8499577073aeb2a297a1   \n",
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              "\n",
              "   order_purchase_timestamp  \n",
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              "7       2017-11-16 19:29:02  \n",
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              "                                                     [key], {});\n",
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          },
          "metadata": {},
          "execution_count": 70
        }
      ],
      "source": [
        "data_for_2017.head(5)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Md6ZFAr1cOLX"
      },
      "outputs": [],
      "source": [
        "def cohort_analysis_visualisation(retention_matrix_, cohort_size_):\n",
        "    with sns.axes_style(\"white\"):\n",
        "        fig, ax = plt.subplots(1, 2, figsize=(12, 8), sharey=True, gridspec_kw={'width_ratios': [1, 11]})\n",
        "\n",
        "        sns.heatmap(retention_matrix_, mask=retention_matrix_.isnull(),annot=True, fmt='.0%', cmap= 'coolwarm', ax=ax[1])\n",
        "        ax[1].set_title('Monthly Cohorts: User Retention', fontsize=16)\n",
        "        ax[1].set(xlabel='# of periods', ylabel='')\n",
        "\n",
        "        cohort_size_df = pd.DataFrame(cohort_size_).rename(columns={0: 'cohort_size'})\n",
        "        white_cmap = mcolors.ListedColormap(['white'])\n",
        "        sns.heatmap(cohort_size_df, annot=True, cbar=False, fmt='g', cmap=white_cmap, ax=ax[0]) \n",
        "        fig.tight_layout()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "pIo8qdU4cOLX"
      },
      "outputs": [],
      "source": [
        "retention_matrix = None\n",
        "\n",
        "def cohort_analysis(df):\n",
        "    \"\"\" Датасет должен состоять из трех колонок (идентификатор покупателя, идентификатор заказа, время первой покупки)\n",
        "    для более детального просмотра метррики 'retention', вызовите 'retention_matrix'\"\"\"\n",
        "    df['order_month'] = df.order_purchase_timestamp.dt.to_period('M')\n",
        "    df['cohort'] = df.groupby(['customer_unique_id']).order_purchase_timestamp \\\n",
        "                 .transform('min') \\\n",
        "                 .dt.to_period('M') \n",
        "    cohort = df.groupby(['cohort', 'order_month'])\\\n",
        "              .agg(n_customers=('customer_unique_id', 'nunique'))\\\n",
        "              .reset_index(drop=False)\n",
        "    cohort['period_number'] = (cohort.order_month - cohort.cohort).apply(attrgetter('n'))\n",
        "    cohort_pivot = cohort.pivot_table(index = 'cohort',\n",
        "                                     columns = 'period_number',\n",
        "                                     values = 'n_customers')\n",
        "    cohort_size = cohort_pivot.iloc[:,0]\n",
        "    global retention_matrix\n",
        "    retention_matrix = cohort_pivot.divide(cohort_size, axis = 0)\n",
        "    \n",
        "    cohort_analysis_visualisation(retention_matrix, cohort_size)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 793
        },
        "id": "juL-CkqbcOLY",
        "outputId": "e19e1503-5939-453d-e42f-8220f34b2599"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:6: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  \n",
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:7: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  import sys\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x576 with 3 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "cohort_analysis(data_for_2017)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 457
        },
        "id": "pNVqWzgGcOLY",
        "outputId": "aa848305-a21f-4f15-ee0f-abbb78c01065"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "period_number   0         1         2         3         4         5   \\\n",
              "cohort                                                                 \n",
              "2017-01        1.0  0.003922  0.002614  0.001307  0.003922  0.001307   \n",
              "2017-02        1.0  0.002283  0.002854  0.001142  0.003995  0.001142   \n",
              "2017-03        1.0  0.004932  0.003794  0.003794  0.003414  0.001517   \n",
              "2017-04        1.0  0.005950  0.002125  0.001700  0.003400  0.002550   \n",
              "2017-05        1.0  0.005006  0.005006  0.003893  0.003059  0.003337   \n",
              "2017-06        1.0  0.004779  0.003504  0.004141  0.002549  0.003823   \n",
              "2017-07        1.0  0.005135  0.003594  0.002567  0.002824  0.002054   \n",
              "2017-08        1.0  0.006931  0.003346  0.002629  0.003585       NaN   \n",
              "2017-09        1.0  0.006778  0.005326  0.002663       NaN       NaN   \n",
              "2017-10        1.0  0.006935  0.002461       NaN       NaN       NaN   \n",
              "2017-11        1.0  0.005476       NaN       NaN       NaN       NaN   \n",
              "2017-12        1.0       NaN       NaN       NaN       NaN       NaN   \n",
              "\n",
              "period_number        6         7         8         9         10        11  \n",
              "cohort                                                                     \n",
              "2017-01        0.005229  0.001307  0.001307       NaN  0.003922  0.001307  \n",
              "2017-02        0.002283  0.001712  0.001712  0.002283  0.001142       NaN  \n",
              "2017-03        0.001517  0.003035  0.003414  0.000759       NaN       NaN  \n",
              "2017-04        0.003400  0.002975  0.002975       NaN       NaN       NaN  \n",
              "2017-05        0.004171  0.001669       NaN       NaN       NaN       NaN  \n",
              "2017-06        0.003823       NaN       NaN       NaN       NaN       NaN  \n",
              "2017-07             NaN       NaN       NaN       NaN       NaN       NaN  \n",
              "2017-08             NaN       NaN       NaN       NaN       NaN       NaN  \n",
              "2017-09             NaN       NaN       NaN       NaN       NaN       NaN  \n",
              "2017-10             NaN       NaN       NaN       NaN       NaN       NaN  \n",
              "2017-11             NaN       NaN       NaN       NaN       NaN       NaN  \n",
              "2017-12             NaN       NaN       NaN       NaN       NaN       NaN  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-f3eada40-4c0e-48ed-9186-963fcaeb7873\">\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>period_number</th>\n",
              "      <th>0</th>\n",
              "      <th>1</th>\n",
              "      <th>2</th>\n",
              "      <th>3</th>\n",
              "      <th>4</th>\n",
              "      <th>5</th>\n",
              "      <th>6</th>\n",
              "      <th>7</th>\n",
              "      <th>8</th>\n",
              "      <th>9</th>\n",
              "      <th>10</th>\n",
              "      <th>11</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>cohort</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2017-01</th>\n",
              "      <td>1.0</td>\n",
              "      <td>0.003922</td>\n",
              "      <td>0.002614</td>\n",
              "      <td>0.001307</td>\n",
              "      <td>0.003922</td>\n",
              "      <td>0.001307</td>\n",
              "      <td>0.005229</td>\n",
              "      <td>0.001307</td>\n",
              "      <td>0.001307</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.003922</td>\n",
              "      <td>0.001307</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-02</th>\n",
              "      <td>1.0</td>\n",
              "      <td>0.002283</td>\n",
              "      <td>0.002854</td>\n",
              "      <td>0.001142</td>\n",
              "      <td>0.003995</td>\n",
              "      <td>0.001142</td>\n",
              "      <td>0.002283</td>\n",
              "      <td>0.001712</td>\n",
              "      <td>0.001712</td>\n",
              "      <td>0.002283</td>\n",
              "      <td>0.001142</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-03</th>\n",
              "      <td>1.0</td>\n",
              "      <td>0.004932</td>\n",
              "      <td>0.003794</td>\n",
              "      <td>0.003794</td>\n",
              "      <td>0.003414</td>\n",
              "      <td>0.001517</td>\n",
              "      <td>0.001517</td>\n",
              "      <td>0.003035</td>\n",
              "      <td>0.003414</td>\n",
              "      <td>0.000759</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-04</th>\n",
              "      <td>1.0</td>\n",
              "      <td>0.005950</td>\n",
              "      <td>0.002125</td>\n",
              "      <td>0.001700</td>\n",
              "      <td>0.003400</td>\n",
              "      <td>0.002550</td>\n",
              "      <td>0.003400</td>\n",
              "      <td>0.002975</td>\n",
              "      <td>0.002975</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-05</th>\n",
              "      <td>1.0</td>\n",
              "      <td>0.005006</td>\n",
              "      <td>0.005006</td>\n",
              "      <td>0.003893</td>\n",
              "      <td>0.003059</td>\n",
              "      <td>0.003337</td>\n",
              "      <td>0.004171</td>\n",
              "      <td>0.001669</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-06</th>\n",
              "      <td>1.0</td>\n",
              "      <td>0.004779</td>\n",
              "      <td>0.003504</td>\n",
              "      <td>0.004141</td>\n",
              "      <td>0.002549</td>\n",
              "      <td>0.003823</td>\n",
              "      <td>0.003823</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-07</th>\n",
              "      <td>1.0</td>\n",
              "      <td>0.005135</td>\n",
              "      <td>0.003594</td>\n",
              "      <td>0.002567</td>\n",
              "      <td>0.002824</td>\n",
              "      <td>0.002054</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "    <tr>\n",
              "      <th>2017-08</th>\n",
              "      <td>1.0</td>\n",
              "      <td>0.006931</td>\n",
              "      <td>0.003346</td>\n",
              "      <td>0.002629</td>\n",
              "      <td>0.003585</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-09</th>\n",
              "      <td>1.0</td>\n",
              "      <td>0.006778</td>\n",
              "      <td>0.005326</td>\n",
              "      <td>0.002663</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-10</th>\n",
              "      <td>1.0</td>\n",
              "      <td>0.006935</td>\n",
              "      <td>0.002461</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-11</th>\n",
              "      <td>1.0</td>\n",
              "      <td>0.005476</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "    <tr>\n",
              "      <th>2017-12</th>\n",
              "      <td>1.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-f3eada40-4c0e-48ed-9186-963fcaeb7873')\"\n",
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              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "\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",
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              "  </style>\n",
              "\n",
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              "          document.querySelector('#df-f3eada40-4c0e-48ed-9186-963fcaeb7873 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-f3eada40-4c0e-48ed-9186-963fcaeb7873');\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",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 74
        }
      ],
      "source": [
        "retention_matrix"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bcKFFU3gcOLY"
      },
      "source": [
        "В период с января по декабрь выяви когорту с самым высоким retention на 3-й месяц."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GlqcYl0CcOLY",
        "outputId": "6dcc86b0-65c8-45b7-dca1-3d6114f56498"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "period_number\n",
              "2    2017-09\n",
              "dtype: period[M]"
            ]
          },
          "metadata": {},
          "execution_count": 75
        }
      ],
      "source": [
        "# так как нужно выявить когорту с самым высоким retention в марте (на 3-й месяц) выведем\n",
        "retention_matrix[[2]].idxmax()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BbzvZDHAcOLY"
      },
      "source": [
        "Ответ 2.5: когорта с самым высоким retention 2017-09 "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zrA3jyWccOLZ"
      },
      "source": [
        "## 2.6 Построй RFM-кластеры для пользователей. Выведи для каждого кластера средние значения метрик R, F, M (и опиши подробно, как были построены метрики R, F, M). Описание подхода можно найти тут."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Wj90TOGicOLZ"
      },
      "source": [
        "Подготовим итоговую таблицу"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "id": "heQMzLsxcOLZ",
        "outputId": "9d664269-cf24-4da4-a321-e0892f96bf93"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                           order_id  price  product_id\n",
              "0  00010242fe8c5a6d1ba2dd792cb16214   58.9           1\n",
              "1  00018f77f2f0320c557190d7a144bdd3  239.9           1"
            ],
            "text/html": [
              "\n",
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              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\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>order_id</th>\n",
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              "</table>\n",
              "</div>\n",
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              "              style=\"display:none;\">\n",
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              "       width=\"24px\">\n",
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              "\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-4f62d4e1-bcda-4000-add5-84d14957b970 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-4f62d4e1-bcda-4000-add5-84d14957b970');\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",
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              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 76
        }
      ],
      "source": [
        "purchase_costs = products.groupby(['order_id', 'price'],as_index=False).agg({'product_id': 'count'})\n",
        "purchase_costs.head(2)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "h-dkP1XqcOLZ"
      },
      "outputs": [],
      "source": [
        "purchase_costs['amount'] = purchase_costs.price * purchase_costs.product_id"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "id": "SC-DZnTecOLZ",
        "outputId": "c3a8191c-1577-4d09-ef5f-cf94fee337b3"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                           order_id  price  product_id  amount\n",
              "0  00010242fe8c5a6d1ba2dd792cb16214   58.9           1    58.9\n",
              "1  00018f77f2f0320c557190d7a144bdd3  239.9           1   239.9"
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              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
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              "                                                     [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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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 78
        }
      ],
      "source": [
        "purchase_costs.head(2)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
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        "id": "b_dL7Iq8cOLa",
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      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                 customer_unique_id                          order_id  \\\n",
              "0  861eff4711a542e4b93843c6dd7febb0  00e7ee1b050b8499577073aeb2a297a1   \n",
              "1  290c77bc529b7ac935b93aa66c333dc3  29150127e6685892b6eab3eec79f59c7   \n",
              "\n",
              "  order_purchase_timestamp  \n",
              "0      2017-05-16 15:05:35  \n",
              "1      2018-01-12 20:48:24  "
            ],
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              "\n",
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              "        vertical-align: top;\n",
              "    }\n",
              "\n",
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              "</style>\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>customer_unique_id</th>\n",
              "      <th>order_id</th>\n",
              "      <th>order_purchase_timestamp</th>\n",
              "    </tr>\n",
              "  </thead>\n",
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              "    <tr>\n",
              "      <th>0</th>\n",
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              "      <td>2017-05-16 15:05:35</td>\n",
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              "      <td>290c77bc529b7ac935b93aa66c333dc3</td>\n",
              "      <td>29150127e6685892b6eab3eec79f59c7</td>\n",
              "      <td>2018-01-12 20:48:24</td>\n",
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              "  </tbody>\n",
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              "    }\n",
              "  </style>\n",
              "\n",
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              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-b4f1d448-0b65-4794-94a0-07553700a2ae');\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",
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              "        }\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 79
        }
      ],
      "source": [
        "tmp = customers.merge(orders, on='customer_id')[['customer_unique_id','order_id','order_purchase_timestamp']]\n",
        "tmp.head(2)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "id": "O13OCee0cOLa",
        "outputId": "6f75a6a2-fc65-47dc-81c4-aa3cf60b9764"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                 customer_unique_id                          order_id  \\\n",
              "0  861eff4711a542e4b93843c6dd7febb0  00e7ee1b050b8499577073aeb2a297a1   \n",
              "1  290c77bc529b7ac935b93aa66c333dc3  29150127e6685892b6eab3eec79f59c7   \n",
              "\n",
              "  order_purchase_timestamp  amount  \n",
              "0      2017-05-16 15:05:35  124.99  \n",
              "1      2018-01-12 20:48:24  289.00  "
            ],
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              "\n",
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              "        vertical-align: top;\n",
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              "\n",
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              "</style>\n",
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              "      <th></th>\n",
              "      <th>customer_unique_id</th>\n",
              "      <th>order_id</th>\n",
              "      <th>order_purchase_timestamp</th>\n",
              "      <th>amount</th>\n",
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              "      <td>2018-01-12 20:48:24</td>\n",
              "      <td>289.00</td>\n",
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              "  </tbody>\n",
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              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-5b0d19e0-531a-455e-b796-06487b14b716')\"\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "\n",
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              "      fill: #FFFFFF;\n",
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              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-5b0d19e0-531a-455e-b796-06487b14b716 button.colab-df-convert');\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-5b0d19e0-531a-455e-b796-06487b14b716');\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": 80
        }
      ],
      "source": [
        "data_rfm = tmp.merge(purchase_costs, on='order_id')[['customer_unique_id','order_id','order_purchase_timestamp','amount']]\n",
        "data_rfm.head(2)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZMw-NEsYcOLa",
        "outputId": "8f1ca319-3737-412b-97eb-47dcdcd8a332"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "customer_unique_id                  object\n",
              "order_id                            object\n",
              "order_purchase_timestamp    datetime64[ns]\n",
              "amount                             float64\n",
              "dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 81
        }
      ],
      "source": [
        "data_rfm.dtypes"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3EGL7-17cOLa",
        "outputId": "77a32064-ca1d-4fc0-f375-ac0978c64edd"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(101270, 4)"
            ]
          },
          "metadata": {},
          "execution_count": 82
        }
      ],
      "source": [
        "data_rfm.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "HlaQ-H51cOLb"
      },
      "outputs": [],
      "source": [
        "data_rfm.drop_duplicates(inplace=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eaCpKh3TcOLb",
        "outputId": "05880f28-eb34-4317-ff41-eaacdbcdba7f"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(101269, 4)"
            ]
          },
          "metadata": {},
          "execution_count": 84
        }
      ],
      "source": [
        "data_rfm.shape"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "skN17AI2cOLb"
      },
      "source": [
        "Посмотрим за кокой период у нас данные"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8_Yfmy5DcOLb",
        "outputId": "1ffc1133-507e-42ed-ddac-5e952c1f1969"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2016-09-04 21:15:19\n",
            "2018-09-03 09:06:57\n"
          ]
        }
      ],
      "source": [
        "print(data_rfm.order_purchase_timestamp.min())\n",
        "print(data_rfm.order_purchase_timestamp.max())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "tgC5p1-ucOLb",
        "outputId": "f1d04730-a19c-4805-d1a6-41930adcd97b"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                      customer_unique_id                          order_id  \\\n",
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              "...                                  ...                               ...   \n",
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              "101269  84732c5050c01db9b23e19ba39899398  28db69209a75e59f20ccbb5c36a20b90   \n",
              "\n",
              "       order_purchase_timestamp  amount  \n",
              "0           2017-05-16 15:05:35  124.99  \n",
              "1           2018-01-12 20:48:24  289.00  \n",
              "2           2018-05-19 16:07:45  139.94  \n",
              "3           2018-03-13 16:06:38  149.94  \n",
              "4           2018-07-29 09:51:30  230.00  \n",
              "...                         ...     ...  \n",
              "101265      2018-04-07 15:48:17   74.90  \n",
              "101266      2018-04-04 08:20:22  114.90  \n",
              "101267      2018-04-08 20:11:50   37.00  \n",
              "101268      2017-11-03 21:08:33  689.00  \n",
              "101269      2017-12-19 14:27:23   13.99  \n",
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              "      <th>101268</th>\n",
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              "      <th>101269</th>\n",
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              "      <td>2017-12-19 14:27:23</td>\n",
              "      <td>13.99</td>\n",
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              "\n",
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            ]
          },
          "metadata": {},
          "execution_count": 86
        }
      ],
      "source": [
        "data_rfm"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "cTGuuWYXcOLb"
      },
      "outputs": [],
      "source": [
        "# Возьмем срез данных за 1 год с 2017.09.04 - 2018.09.04\n",
        "\n",
        "rfm = data_rfm[(data_rfm.order_purchase_timestamp >= dt.datetime(year=2017, month=9, day=4)) & \\\n",
        "                               (data_rfm.order_purchase_timestamp <= dt.datetime(year=2018, month=9, day=4))]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "f1Y6nFLGcOLc",
        "outputId": "19dfe695-95bf-40a9-de2d-3f732230fa55"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Timestamp('2018-09-03 09:06:57')"
            ]
          },
          "metadata": {},
          "execution_count": 88
        }
      ],
      "source": [
        "last_date = rfm.order_purchase_timestamp.max()\n",
        "last_date"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "id": "O5j4Bbq2cOLc",
        "outputId": "6c1d43df-aac4-46ca-b172-f0988f487e98"
      },
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        {
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            "text/plain": [
              "                 customer_unique_id                          order_id  \\\n",
              "1  290c77bc529b7ac935b93aa66c333dc3  29150127e6685892b6eab3eec79f59c7   \n",
              "2  060e732b5b29e8181a18229c7b0b2b5e  b2059ed67ce144a36e2aa97d2c9e9ad2   \n",
              "\n",
              "  order_purchase_timestamp  amount  \n",
              "1      2018-01-12 20:48:24  289.00  \n",
              "2      2018-05-19 16:07:45  139.94  "
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              "      <th></th>\n",
              "      <th>customer_unique_id</th>\n",
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              "\n",
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              "          const element = document.querySelector('#df-73924709-899f-4a47-bb5d-d4940d82104b');\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",
              "          element.innerHTML = '';\n",
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              "          element.appendChild(docLink);\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 89
        }
      ],
      "source": [
        "rfm.head(2)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ygGeYI__cOLc"
      },
      "outputs": [],
      "source": [
        "rfmTable = rfm.groupby('customer_unique_id').agg({'order_purchase_timestamp': lambda x: (last_date - x.max()).days, \n",
        "                                        'order_id': lambda x: len(x),      \n",
        "                                        'amount': lambda x: x.sum()})"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "V3ZLBMzucOLc"
      },
      "outputs": [],
      "source": [
        "# Recency количество дней с последнего заказа\n",
        "# Frequency количество заказов\n",
        "# Monetary Value общая сумма по всем заказам\n",
        "\n",
        "rfmTable['order_purchase_timestamp'] = rfmTable['order_purchase_timestamp'].astype(int)\n",
        "rfmTable.rename(columns={'order_purchase_timestamp': 'recency', \n",
        "                         'order_id': 'frequency', \n",
        "                         'amount': 'monetary_value'}, inplace=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "id": "JM6ZyyYAcOLc",
        "outputId": "e50aa85e-d407-479a-ceff-f6647d913a1f"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                  recency  frequency  monetary_value\n",
              "customer_unique_id                                                  \n",
              "0000366f3b9a7992bf8c76cfdf3221e2      115          1          129.90\n",
              "0000b849f77a49e4a4ce2b2a4ca5be3f      118          1           18.90\n",
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              "                                                     [key], {});\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 92
        }
      ],
      "source": [
        "rfmTable.head(3)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "F0gYa5o0cOLd",
        "outputId": "574eaf35-b13d-43db-be53-af635c0063ea"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(73252, 3)"
            ]
          },
          "metadata": {},
          "execution_count": 93
        }
      ],
      "source": [
        "rfmTable.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_dqB-P6fcOLd",
        "outputId": "f9fe1088-9c06-4ba7-8a7b-f44b195e67ae"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "73252"
            ]
          },
          "metadata": {},
          "execution_count": 94
        }
      ],
      "source": [
        "rfm.customer_unique_id.nunique()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "S52NMp02cOLd"
      },
      "source": [
        "Посмотрим на распределение recency, frequency, monetary_value"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "iip00_NucOLd",
        "outputId": "e09f6ace-3e6d-44a3-d791-b766c9fb8389"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7ff21ecf6d10>"
            ]
          },
          "metadata": {},
          "execution_count": 95
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "rfmTable.recency.hist(bins=50)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rpRnJZaqcOLd"
      },
      "source": [
        "Давность распределена более равномено чем частотность и доходность."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "s8VouOKjcOLd",
        "outputId": "b7e791ec-34c4-411f-adf5-7901fc9139f3"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7ff21c266f90>"
            ]
          },
          "metadata": {},
          "execution_count": 96
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "rfmTable.frequency.hist(bins=20)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UedQ4BCGcOLe"
      },
      "source": [
        "Частота покупок распределена неравномерна. Больше половины покупок совершались 1 раз."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "iX71wBORcOLe",
        "outputId": "e594579c-a590-44c6-f33c-0577587cf86a"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7ff21c271390>"
            ]
          },
          "metadata": {},
          "execution_count": 97
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "rfmTable.monetary_value.hist(bins=300)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XgbOTKUycOLe"
      },
      "source": [
        "Доходность распределена неравномерно"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "id": "j0x8MOPHcOLe",
        "outputId": "aafae610-e3e8-4364-cf72-dda8cd2308c5"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                  recency  frequency  monetary_value\n",
              "customer_unique_id                                                  \n",
              "0000366f3b9a7992bf8c76cfdf3221e2      115          1           129.9\n",
              "0000b849f77a49e4a4ce2b2a4ca5be3f      118          1            18.9"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-4c3421cf-446d-4f6a-a686-c2470e7bbbac\">\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>recency</th>\n",
              "      <th>frequency</th>\n",
              "      <th>monetary_value</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>customer_unique_id</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0000366f3b9a7992bf8c76cfdf3221e2</th>\n",
              "      <td>115</td>\n",
              "      <td>1</td>\n",
              "      <td>129.9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0000b849f77a49e4a4ce2b2a4ca5be3f</th>\n",
              "      <td>118</td>\n",
              "      <td>1</td>\n",
              "      <td>18.9</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-4c3421cf-446d-4f6a-a686-c2470e7bbbac')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \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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              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 98
        }
      ],
      "source": [
        "rfmTable.head(2)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zydJzHhLcOLe"
      },
      "source": [
        "Делим на сегменты"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "2vFoozjMcOLe"
      },
      "outputs": [],
      "source": [
        "# где 1- давние клиенты\n",
        "# 2 - средне-давные клиенты\n",
        "# 3 - актуальные клиенты\n",
        "rfmTable['R']= pd.cut(rfmTable.recency, bins=[-1, np.percentile(rfmTable.recency,35), np.percentile(rfmTable.recency,65),\n",
        "            rfmTable.recency.max()],labels=[3,2,1]).astype(str)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "xARS7OhCcOLf"
      },
      "outputs": [],
      "source": [
        "# где 1- редкие клиенты (совершили покупку 1 раз)\n",
        "# 2 - средне-частые клиенты\n",
        "# 3 - лояльные клиенты\n",
        "rfmTable['F'] = pd.cut(rfmTable.frequency,\\\n",
        "       bins=[-1, np.percentile(rfmTable.frequency,90),\n",
        "            np.percentile(rfmTable.frequency,98),\n",
        "            rfmTable.frequency.max()],\\\n",
        "       labels=[1,2,3]).astype(str)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "3p2faiudcOLf"
      },
      "outputs": [],
      "source": [
        "# где 1- низкий чек\n",
        "# 2 - средний чек\n",
        "# 3 - высокий чек\n",
        "rfmTable['M'] = pd.cut(rfmTable.monetary_value,\\\n",
        "       bins=[0, np.percentile(rfmTable.monetary_value,50),\n",
        "            np.percentile(rfmTable.monetary_value,90),\n",
        "            rfmTable.monetary_value.max()],\\\n",
        "       labels=[1,2,3]).astype(str)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "NGw0s15VcOLf"
      },
      "outputs": [],
      "source": [
        "rfmTable['RFM'] = rfmTable.R + rfmTable.F + rfmTable.M"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
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        "id": "SUStkbXjcOLf",
        "outputId": "5944401b-e596-49f4-a3a5-40289b9ed89b"
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              "                                  recency  frequency  monetary_value  R  F  M  \\\n",
              "customer_unique_id                                                              \n",
              "0000366f3b9a7992bf8c76cfdf3221e2      115          1          129.90  3  1  2   \n",
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              "0004aac84e0df4da2b147fca70cf8255      292          1          180.00  1  1  2   \n",
              "0004bd2a26a76fe21f786e4fbd80607f      150          1          154.00  2  1  2   \n",
              "\n",
              "                                  RFM  \n",
              "customer_unique_id                     \n",
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              "0000b849f77a49e4a4ce2b2a4ca5be3f  311  \n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
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              "\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",
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          "metadata": {},
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        "rfmTable.head(5)"
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        "id": "TewxYjFwcOLf",
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        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "F      1     2    3\n",
              "R                  \n",
              "1  24352   977  106\n",
              "2  20961   963  113\n",
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              "          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": 104
        }
      ],
      "source": [
        "pd.crosstab(index = rfmTable.R, columns = rfmTable.F)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "id": "JfeYG56tcOLg",
        "outputId": "4c67c72e-c2c3-4b51-e6a9-88c5c1bac81a"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7ff21efc4790>"
            ]
          },
          "metadata": {},
          "execution_count": 105
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "rfm_table = rfmTable.pivot_table(\n",
        "                        index='R', \n",
        "                        columns='F', \n",
        "                        values='monetary_value', \n",
        "                        aggfunc=np.mean).applymap(int)\n",
        "sns.heatmap(rfm_table, cmap=\"YlGnBu\", annot=True, fmt=\".0f\", linewidths=4.15, annot_kws={\"size\": 10},yticklabels=4)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FvZ14JRbcOLg",
        "outputId": "3603b5d5-2234-4059-8a2a-d0f47717a4f5"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "111    12968\n",
              "311    12689\n",
              "211    10982\n",
              "312     9512\n",
              "112     9118\n",
              "212     8044\n",
              "113     2266\n",
              "313     2209\n",
              "213     1935\n",
              "322      685\n",
              "222      560\n",
              "122      555\n",
              "323      291\n",
              "321      227\n",
              "223      221\n",
              "121      217\n",
              "123      205\n",
              "221      182\n",
              "333       79\n",
              "332       77\n",
              "132       57\n",
              "232       53\n",
              "233       53\n",
              "133       44\n",
              "331       11\n",
              "231        7\n",
              "131        5\n",
              "Name: RFM, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 106
        }
      ],
      "source": [
        "rfmTable.RFM.value_counts()"
      ]
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.7.3"
    },
    "colab": {
      "name": "e-commerce_project.ipynb",
      "provenance": []
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}