1 Feature_Mining.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AwBEqZm2_hFS"
      },
      "source": [
        "# Решение задачи кластеризации торговых точек нейросетевыми методами"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PQs7rwA8_V5D"
      },
      "source": [
        "* [Тетрадка Егора](https://colab.research.google.com/drive/1AyiGImahqooigehAzoyOFR2FX1eFamik?usp=sharing#scrollTo=bo4utKnSOT9q) про каталог"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
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        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NIpwELoVb0wc",
        "outputId": "0ebf3fd8-d59f-4da7-d1c7-720a4a913c4b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive', force_remount=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "mMxRstUKdSiO"
      },
      "outputs": [],
      "source": [
        "from warnings import filterwarnings\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "import seaborn as sns\n",
        "import pandas as pd\n",
        "from collections import defaultdict\n",
        "from tqdm import tqdm\n",
        "from scipy.sparse import load_npz\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "\n",
        "sns.set(style='darkgrid')\n",
        "filterwarnings('ignore')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YnZfLubbxtmz"
      },
      "source": [
        "## Загрузка и Базовая очистка данных (от дубликатов, нулей)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
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        "id": "vYfPMelDCnEE",
        "outputId": "9ea8e383-72e9-42b4-b7de-85f35c9123b4"
      },
      "outputs": [
        {
          "output_type": "execute_result",
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              "     PRODUCT_ID                                PRODUCT_NM     PRODUCT_LVL_NM1  \\\n",
              "100      157448      Решетка радиаторная 600х1200 мм слон  Инженерные системы   \n",
              "101       75608      Лючок пластиковый 200х200 с защелкой  Инженерные системы   \n",
              "102       43328  Люк под плитку Слава 600х600 (ШхВ) станд  Инженерные системы   \n",
              "103      235018  Панель монтаж. для КСРМ 500х590 (уп.2шт)  Инженерные системы   \n",
              "104       84075  Решетка вентиляционная 600*600 нак.С-003  Инженерные системы   \n",
              "\n",
              "    PRODUCT_LVL_NM2 PRODUCT_LVL_NM3                 PRODUCT_LVL_NM4  FAKE_ID  \n",
              "100      Вентиляция      Вентиляция             Решетки радиаторные      100  \n",
              "101      Вентиляция      Вентиляция               Лючки ревизионные      101  \n",
              "102      Вентиляция      Вентиляция               Лючки ревизионные      102  \n",
              "103      Вентиляция      Вентиляция  Комплексные системы вентиляции      103  \n",
              "104      Вентиляция      Вентиляция          Решетки вентиляционные      104  "
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              "      <td>Инженерные системы</td>\n",
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              "      <td>Вентиляция</td>\n",
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              "      <td>Инженерные системы</td>\n",
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              "      <td>Вентиляция</td>\n",
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      ],
      "source": [
        "df_product = pd.read_csv('/content/drive/MyDrive/CourseWorkSAS/product_dict_df_sorted.csv', sep=';')\n",
        "df_product.fillna('Неизвестный класс', inplace=True)\n",
        "df_product.loc[df_product['PRODUCT_LVL_NM1'] == '04-10', 'PRODUCT_LVL_NM1'] = 'Неизвестный класс'\n",
        "df_product[100:105]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "p2UnlcD9x2xg"
      },
      "source": [
        "Всего записей в исходном наборе данных:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
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      "outputs": [
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              "45605040"
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          "execution_count": 6,
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      ],
      "source": [
        "chunksize = 10 ** 6\n",
        "counter = 0\n",
        "for chunk in pd.read_csv('/content/drive/MyDrive/CourseWorkSAS/df_fakeid_sended.csv', sep=';', chunksize=chunksize):\n",
        "    counter += chunk.shape[0]\n",
        "\n",
        "counter"
      ]
    },
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              "      <th>POS_ID</th>\n",
              "      <th>PICKUP_STORE_LOC_ID</th>\n",
              "      <th>PICKUP_DT</th>\n",
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              "      background-color: #E2EBFA;\n",
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              "      fill: #174EA6;\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",
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              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-5f925975-61a8-41d6-ab29-97181968b39c 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-5f925975-61a8-41d6-ab29-97181968b39c');\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",
              "  "
            ],
            "text/plain": [
              "              POS_ID  PICKUP_STORE_LOC_ID  PICKUP_DT  SALES_QTY UNIT_BASE  \\\n",
              "45000000  2034624967                  155  30DEC2019      0.376        KG   \n",
              "45000001  2034624967                  155  30DEC2019      0.208        KG   \n",
              "45000002  2034624971                  147  30DEC2019      2.000        ST   \n",
              "45000003  2034624978                  244  30DEC2019      2.000       MSH   \n",
              "45000004  2034624979                  237  30DEC2019      2.000        UP   \n",
              "...              ...                  ...        ...        ...       ...   \n",
              "45605035  2038026535                  220  17JUL2020     12.000       MSH   \n",
              "45605036  2038026535                  220  17JUL2020      2.000        ST   \n",
              "45605037  2038026535                  220  17JUL2020      2.000        ST   \n",
              "45605038  2038026538                  157  16JUL2020     20.000        ST   \n",
              "45605039  2038026544                  121  16JUL2020     20.000        ST   \n",
              "\n",
              "          SALES_QTY_BASE  FAKE_ID  REVENUE  FAKE_ID.1  \n",
              "45000000           0.188    83191    96.82      83191  \n",
              "45000001           0.104    88116    53.56      88116  \n",
              "45000002           1.000   259601   424.50     259601  \n",
              "45000003           1.000   212979   508.34     212979  \n",
              "45000004           1.000   223394   296.66     223394  \n",
              "...                  ...      ...      ...        ...  \n",
              "45605035           6.000   228365  3500.00     228365  \n",
              "45605036           1.000    50818   481.66      50818  \n",
              "45605037           1.000   205088   924.34     205088  \n",
              "45605038          10.000   223962   233.34     223962  \n",
              "45605039          10.000   223962   216.66     223962  \n",
              "\n",
              "[605040 rows x 9 columns]"
            ]
          },
          "execution_count": 7,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "chunk"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WLuecKm_5YFP"
      },
      "source": [
        "### Нулевая выручка - удалить из подсчетов"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Agh7mGZt5ITP",
        "outputId": "3bee3858-37e3-44f7-b577-db2135d64140"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "46it [00:51,  1.12s/it]\n"
          ]
        }
      ],
      "source": [
        "zero_revenue_trash = 0\n",
        "for chunk in tqdm(pd.read_csv('/content/drive/MyDrive/CourseWorkSAS/df_fakeid_sended.csv', sep=';', chunksize=chunksize)):\n",
        "    zero_revenue_trash += (chunk['REVENUE'] == 0).sum()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "o8JioC2UD-od",
        "outputId": "6220fb28-8804-41f2-fefa-df26d558d53a"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "227665"
            ]
          },
          "execution_count": 26,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "zero_revenue_trash"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tMXRtE8HqPFN"
      },
      "source": [
        "### Таблица id чека - список товаров"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5ROymt40yJZM"
      },
      "source": [
        "* Сколько различных товарных чеков (POS_ID) встречается в записях?\n",
        "\n",
        "* В скольких чеках встретилась определенная товарная позиция (FAKE_ID)?\n",
        "\n",
        "Заведем два словаря: для каждого чека список его товарных позиций, для товара число раз, когда его купили."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "L6CyFqiIzKW4",
        "outputId": "985d998f-be43-4de7-e3df-0d7efc1450b2"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "46it [01:47,  2.34s/it]\n"
          ]
        }
      ],
      "source": [
        "from collections import defaultdict\n",
        "\n",
        "receipts = defaultdict(list)\n",
        "goods = defaultdict(int)\n",
        "\n",
        "chunksize = 10 ** 6\n",
        "for chunk in tqdm(pd.read_csv('/content/drive/MyDrive/CourseWorkSAS/df_fakeid_sended.csv', sep=';', chunksize=chunksize)):\n",
        "    pos_ids = chunk['POS_ID']\n",
        "    good_ids = chunk['FAKE_ID']\n",
        "    for rec, good in zip(pos_ids, good_ids):\n",
        "      receipts[rec] += [good]\n",
        "      goods[good] += 1"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "49_24pW45LyS",
        "outputId": "d4823ce5-06bd-4c44-afa3-fc8a70ab114f"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "[227761, 83982, 209589, 41768, 227987, 93204]"
            ]
          },
          "execution_count": 5,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "receipts[2029755112]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UawAlYQl1S2L"
      },
      "source": [
        "**Итого,** в базе информация из 18,6 млн чеков по 43 358 товарам."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "xWAe6yWyO-Xi"
      },
      "outputs": [],
      "source": [
        "import csv\n",
        "\n",
        "# v = 0\n",
        "# with open('/content/drive/MyDrive/CourseWorkSAS/receipts_items.csv', 'w') as File_write:\n",
        "#   writer = csv.writer(File_write, delimiter=';')\n",
        "#   writer.writerow(['POS_ID', 'FAKE_ID', 'GOOD_CATEG', 'STORE_ID', 'MONTH_DT', 'REVENUE'])\n",
        "#   for r in tqdm(reader):\n",
        "#       writer.writerow()\n",
        "#       v += 1"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EKnvsn7X-n5Q"
      },
      "source": [
        "### Расшифровка чека в читаемом виде"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6N5ifnQf6Ng3"
      },
      "source": [
        "Ниже введена функция, переводящая список чисел - номеров sku_id в dataframe с названиями и исходными категориями соотвествующих товаров. Первая функция сопоставляет id с product_id продукта, вторая - с fake_id."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "XJ8Q9z94-wiJ"
      },
      "outputs": [],
      "source": [
        "# Индексы sku_id соотвествуют fake_id:\n",
        "def read_receipt(ids, df_descr):\n",
        "    descr_table = pd.DataFrame()\n",
        "    for id in ids:\n",
        "        id_descr = df_descr.loc[id]\n",
        "        if id_descr.shape[0] == 0:\n",
        "            print('Товар не найден, id = ', id)\n",
        "        else:\n",
        "            descr_table = descr_table.append(id_descr)\n",
        "    return descr_table"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "I_g_KRXZ7c3f"
      },
      "source": [
        "Пример работы функции."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 237
        },
        "id": "4Xgn22LzD-T5",
        "outputId": "ead070ea-942a-4e68-fe89-6626f9afc522"
      },
      "outputs": [
        {
          "data": {
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            "text/plain": [
              "        PRODUCT_ID                                PRODUCT_NM  \\\n",
              "227761    275826.0  Губки для мытья посуды Оптима 96х64х27мм   \n",
              "83982     232614.0              СММ 4,2х41 сверло SMMwD Yoko   \n",
              "209589    141947.0                 Перчатки х/б, ПВХ 5 нитей   \n",
              "41768     269664.0              Шпатель резиновый 40мм белый   \n",
              "227987     36682.0               Маркер перманентный, черный   \n",
              "93204     205108.0     Дюбель полипропилен. 6х42 тип U потай   \n",
              "\n",
              "             PRODUCT_LVL_NM1    PRODUCT_LVL_NM2  \\\n",
              "227761  Сопутствующие товары          Хозтовары   \n",
              "83982   Сопутствующие товары    Метизы и крепеж   \n",
              "209589  Сопутствующие товары    Средства защиты   \n",
              "41768   Сопутствующие товары  Инструмент ручной   \n",
              "227987  Сопутствующие товары          Хозтовары   \n",
              "93204   Сопутствующие товары    Метизы и крепеж   \n",
              "\n",
              "                         PRODUCT_LVL_NM3          PRODUCT_LVL_NM4   FAKE_ID  \n",
              "227761                         Хозтовары  Тряпки, салфетки, губки  227761.0  \n",
              "83982                             Метизы           Саморезы с п/ш   83982.0  \n",
              "209589                   Средства защиты      Средства защиты рук  209589.0  \n",
              "41768   Штукатурно-отделочный инструмент                  Шпатели   41768.0  \n",
              "227987                         Хозтовары         Хозтовары прочее  227987.0  \n",
              "93204                             Метизы                   Дюбели   93204.0  "
            ]
          },
          "execution_count": 7,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "read_receipt(receipts[2029755112], df_product)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GOwW-oiMFtXw"
      },
      "source": [
        "## **Обзор классификации товаров**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "aPkR6h4uflTU",
        "outputId": "55189d2e-bd2f-4e7f-cae4-aa25e8d0ecba"
      },
      "outputs": [
        {
          "data": {
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              "      <div>\n",
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>PRODUCT_ID</th>\n",
              "      <th>PRODUCT_NM</th>\n",
              "      <th>PRODUCT_LVL_NM1</th>\n",
              "      <th>PRODUCT_LVL_NM2</th>\n",
              "      <th>PRODUCT_LVL_NM3</th>\n",
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              "      <td>2100159</td>\n",
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              "      <td>Водоснабжение и Водоотведение</td>\n",
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              "      <td>Инженерные системы</td>\n",
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              "      <td>5401</td>\n",
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              "      <th>5402</th>\n",
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              "      <td>Инженерные системы</td>\n",
              "      <td>Водоснабжение и Водоотведение</td>\n",
              "      <td>Трубы ПП и фитинги</td>\n",
              "      <td>ПП фитинги соединительные</td>\n",
              "      <td>5402</td>\n",
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              "    <tr>\n",
              "      <th>5403</th>\n",
              "      <td>160163</td>\n",
              "      <td>VT Кран под полипропилен с накидной гайк</td>\n",
              "      <td>Инженерные системы</td>\n",
              "      <td>Водоснабжение и Водоотведение</td>\n",
              "      <td>Краны шаровые бытовые</td>\n",
              "      <td>Краны шаровые Бытовые</td>\n",
              "      <td>5403</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5404</th>\n",
              "      <td>82243</td>\n",
              "      <td>ПП Угольник бел. 45град. 50 ФД Пласт</td>\n",
              "      <td>Инженерные системы</td>\n",
              "      <td>Водоснабжение и Водоотведение</td>\n",
              "      <td>Трубы ПП и фитинги</td>\n",
              "      <td>ПП фитинги соединительные</td>\n",
              "      <td>5404</td>\n",
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              "                                                     [key], {});\n",
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              "          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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            ],
            "text/plain": [
              "      PRODUCT_ID                                PRODUCT_NM  \\\n",
              "5400     2100159  Счетчик гор воды с имп.вых ВСГд-20 с КМЧ   \n",
              "5401      250673  МП-Пресс Муфта с гайкой 16х1/2 Comap (уп   \n",
              "5402       82244      ПП Угольник бел. 45град. 75 ФД Пласт   \n",
              "5403      160163  VT Кран под полипропилен с накидной гайк   \n",
              "5404       82243      ПП Угольник бел. 45град. 50 ФД Пласт   \n",
              "\n",
              "         PRODUCT_LVL_NM1                PRODUCT_LVL_NM2  \\\n",
              "5400  Инженерные системы  Водоснабжение и Водоотведение   \n",
              "5401  Инженерные системы  Водоснабжение и Водоотведение   \n",
              "5402  Инженерные системы  Водоснабжение и Водоотведение   \n",
              "5403  Инженерные системы  Водоснабжение и Водоотведение   \n",
              "5404  Инженерные системы  Водоснабжение и Водоотведение   \n",
              "\n",
              "            PRODUCT_LVL_NM3            PRODUCT_LVL_NM4  FAKE_ID  \n",
              "5400          Счетчики воды              Счетчики воды     5400  \n",
              "5401     Трубы МП и фитинги           МП пресс фитинги     5401  \n",
              "5402     Трубы ПП и фитинги  ПП фитинги соединительные     5402  \n",
              "5403  Краны шаровые бытовые      Краны шаровые Бытовые     5403  \n",
              "5404     Трубы ПП и фитинги  ПП фитинги соединительные     5404  "
            ]
          },
          "execution_count": 4,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df_product[5400:5405]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 322
        },
        "id": "g_iiHVpuY7WU",
        "outputId": "1eff873a-0364-44e8-c272-e69a95e733a4"
      },
      "outputs": [
        {
          "data": {
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\n",
            "text/plain": [
              "<Figure size 1080x360 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "lvl_1_count = df_product.groupby(['PRODUCT_LVL_NM1'])['FAKE_ID'].agg('count')\n",
        "\n",
        "fig, ax = plt.subplots(figsize=(15, 5))\n",
        "ax.barh(lvl_1_count.index, lvl_1_count.values)\n",
        "for i, v in enumerate(lvl_1_count):\n",
        "    ax.text(v + 100, i - .2, str(v))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UMnyd8_1q3hx",
        "outputId": "19a13cc0-241f-4b28-952d-db68a734625b"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array(['Неизвестный класс', 'Инженерные системы', 'Общестрой',\n",
              "       'Спецодежда', 'Внутренняя отделка', 'Наружная отделка',\n",
              "       'Сопутствующие товары', 'Услуги'], dtype=object)"
            ]
          },
          "execution_count": 28,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df_product['PRODUCT_LVL_NM1'].unique()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mUhciQupOZnm"
      },
      "source": [
        "## Майнинг признаков магазинов"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Halno4P0q--J"
      },
      "outputs": [],
      "source": [
        "categs = {'Неизвестный класс' : '0', 'Инженерные системы' : '1', \n",
        "          'Общестрой' : '2', 'Спецодежда' : '3',\n",
        "          'Внутренняя отделка' : '4', 'Наружная отделка' : '5',\n",
        "          'Сопутствующие товары' : '6', 'Услуги' : '7'}"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "48V-5rsrjyvL"
      },
      "outputs": [],
      "source": [
        "months = {'JAN' : '01', 'FEB' : '02', 'MAR' : '03', 'APR' : '04',\n",
        "          'MAY' : '05', 'JUN' : '06', 'JUL' : '07', 'AUG' : '08',\n",
        "          'SEP' : '09', 'OCT' : '10', 'NOV' : '11', 'DEC' : '12'}\n",
        "\n",
        "def data_formatting(s: str):\n",
        "    return s[5:] + '-' + months[s[2:5]] + '-' + s[:2]\n",
        "\n",
        "def data_crop(s: str):\n",
        "    if s == 'PICKUP_DT':\n",
        "      return s\n",
        "    return s[5:] + '_' + months[s[2:5]]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4KZ2FMeVh6lj",
        "outputId": "ed8f63d4-46c8-4617-e051-c0854dade4ce"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "2018-01-01 2021-05-12\n",
            "2017-10-27 2021-05-12\n",
            "2018-01-02 2021-05-12\n",
            "2018-01-02 2021-05-12\n",
            "2018-01-02 2021-05-12\n",
            "2018-01-02 2021-05-12\n",
            "2018-01-01 2021-05-12\n",
            "2018-01-02 2021-05-12\n",
            "2018-01-02 2021-05-12\n",
            "2018-01-02 2021-05-12\n",
            "2018-01-02 2021-05-12\n",
            "2018-01-02 2021-05-12\n",
            "2018-01-03 2021-05-12\n",
            "2016-09-30 2021-05-12\n",
            "2018-01-03 2021-05-12\n",
            "2018-01-01 2021-08-14\n",
            "2018-01-01 2021-05-12\n",
            "2018-01-01 2021-05-12\n",
            "2018-01-03 2021-05-12\n",
            "2018-01-02 2021-05-12\n",
            "2017-11-09 2021-05-12\n",
            "2018-01-02 2021-05-12\n",
            "2018-01-03 2021-05-12\n",
            "2018-01-02 2021-05-12\n",
            "2018-01-02 2021-05-12\n",
            "2018-01-02 2021-05-12\n",
            "2018-01-01 2021-05-12\n",
            "2018-01-04 2021-05-12\n",
            "2018-01-02 2021-05-12\n",
            "2017-05-05 2021-05-12\n",
            "2018-01-02 2021-05-12\n",
            "2018-01-03 2021-05-12\n",
            "2018-01-03 2021-05-12\n",
            "2018-01-02 2021-05-12\n",
            "2018-01-01 2021-05-12\n",
            "2018-01-02 2021-05-12\n",
            "2018-01-03 2021-05-12\n",
            "2018-01-01 2021-05-12\n",
            "2018-01-02 2021-05-12\n",
            "2018-01-01 2021-05-12\n",
            "2018-01-03 2021-05-12\n",
            "2018-01-03 2021-05-12\n",
            "2018-01-02 2021-05-12\n",
            "2018-01-01 2021-05-12\n",
            "2018-01-02 2021-05-12\n",
            "2018-01-01 2021-05-11\n"
          ]
        }
      ],
      "source": [
        "for chunk in pd.read_csv('/content/drive/MyDrive/CourseWorkSAS/df_fakeid_sended.csv', sep=';', chunksize=chunksize):\n",
        "    m = chunk['PICKUP_DT'].apply(lambda s: s[5:] + '-' + months[s[2:5]] + '-' + s[:2])\n",
        "    print(m.min(), m.max())"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "h07bNQwox-Vp"
      },
      "source": [
        "#### Количество чеков по категориям и периодам"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "o4fjjRucEWsL",
        "outputId": "112f440c-add9-403c-b6b2-b085caab9600"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "46it [01:13,  1.60s/it]\n"
          ]
        }
      ],
      "source": [
        "###\n",
        "# Словарь: магазин - список чеков\n",
        "###\n",
        "stores = defaultdict(int)\n",
        "for chunk in tqdm(pd.read_csv('/content/drive/MyDrive/CourseWorkSAS/df_fakeid_sended.csv', sep=';', chunksize=10**6)):\n",
        "    pos_ids = chunk['POS_ID']\n",
        "    stores_ids = chunk['PICKUP_STORE_LOC_ID']\n",
        "    for rec, place in zip(pos_ids, stores_ids):\n",
        "      stores[place] += 1"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4-PjpKEoGEyH",
        "outputId": "bfb3fc8d-635c-4f04-8741-1df07f35e4e4"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "75"
            ]
          },
          "execution_count": 16,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df_stores = pd.DataFrame.from_dict(stores, orient='index', columns=['rec_count'])\n",
        "(df_stores['rec_count'] < 5000).sum()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "KUYjEfDTP0Li"
      },
      "outputs": [],
      "source": [
        "bigs = [ 24,  22, 124, 144, 131, 123, 121, 127,  23,  21, 154, 25, 244, 146, 153]\n",
        "bigs = sorted(bigs)\n",
        "\n",
        "missing_stores = [258, 169, 62, 180, 268, 175, 267, 66, 168, 172, 257, 176, 179, \n",
        "                  44, 270, 78, 69, 275, 73, 65, 1007, 71, 8806, 77, 182, 74, 67,\n",
        "                  264, 72, 171, 272, 76, 173, 63, 266, 68, 273, 183]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "xkvNQ35LPYmF"
      },
      "outputs": [],
      "source": [
        "# main_stores_stat = df_stores.sort_values(by='rec_count', ascending=False)[:154]\n",
        "# main_stores = sorted(main_stores_stat.index)\n",
        "# for st in [62, 66, 168, 169, 172, 175, 176, 179, 180, 257, 258, 267, 268, 270]:\n",
        "#   main_stores.pop(main_stores.index(st))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "BIVh844jCYAM"
      },
      "outputs": [],
      "source": [
        "main_stores = pd.read_csv('/content/drive/MyDrive/CourseWorkSAS/df_140stores_500items.csv', sep=';', index_col='Unnamed: 0').columns"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 455
        },
        "id": "aJn4vG_QoZA2",
        "outputId": "85f906cb-d266-4608-dfe4-2c2f9a4801dc"
      },
      "outputs": [
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              "\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
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              "      <th></th>\n",
              "      <th>StoreName</th>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>БМ Тмн, 50 Лет Октября, 109</td>\n",
              "      <td>БИГ</td>\n",
              "      <td>676.80</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>БМ Тмн, Панфиловцев 86/1</td>\n",
              "      <td>БИГ</td>\n",
              "      <td>689.44</td>\n",
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              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>БМ Тмн, Пермякова, 2 к1</td>\n",
              "      <td>БИГ</td>\n",
              "      <td>417.89</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>БМ Тмн, Щербакова, 99</td>\n",
              "      <td>БИГ</td>\n",
              "      <td>421.84</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>БМ Тмн, Домостроителей, 32</td>\n",
              "      <td>БИГ</td>\n",
              "      <td>662.63</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "    <tr>\n",
              "      <th>5501</th>\n",
              "      <td>БМ Тмн Ставропольская 120 к2</td>\n",
              "      <td>БИГ</td>\n",
              "      <td>808.43</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6501</th>\n",
              "      <td>МОК Екб мкр Светлый 3</td>\n",
              "      <td>МОК</td>\n",
              "      <td>76.20</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6701</th>\n",
              "      <td>МОК Тмн Олимпийская 31</td>\n",
              "      <td>МОК</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>8401</th>\n",
              "      <td>МОКс Тмн Заводоуковская 12а</td>\n",
              "      <td>МОК</td>\n",
              "      <td>110.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8403</th>\n",
              "      <td>БМ Верхняя Пышма Бажова 28</td>\n",
              "      <td>БИГ</td>\n",
              "      <td>354.10</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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              "<p>140 rows × 3 columns</p>\n",
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            "text/plain": [
              "                            StoreName StoreType  StoreArea\n",
              "StoreID                                                   \n",
              "21        БМ Тмн, 50 Лет Октября, 109       БИГ     676.80\n",
              "22           БМ Тмн, Панфиловцев 86/1       БИГ     689.44\n",
              "23            БМ Тмн, Пермякова, 2 к1       БИГ     417.89\n",
              "24              БМ Тмн, Щербакова, 99       БИГ     421.84\n",
              "25         БМ Тмн, Домостроителей, 32       БИГ     662.63\n",
              "...                               ...       ...        ...\n",
              "5501     БМ Тмн Ставропольская 120 к2       БИГ     808.43\n",
              "6501            МОК Екб мкр Светлый 3       МОК      76.20\n",
              "6701           МОК Тмн Олимпийская 31       МОК      96.00\n",
              "8401      МОКс Тмн Заводоуковская 12а       МОК     110.00\n",
              "8403       БМ Верхняя Пышма Бажова 28       БИГ     354.10\n",
              "\n",
              "[140 rows x 3 columns]"
            ]
          },
          "execution_count": 44,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df_store_info = pd.read_csv('/content/drive/MyDrive/CourseWorkSAS/StoresDirectory.csv', index_col='StoreID')\n",
        "df_store_info.loc[main_stores]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qHzwD84ZhX70"
      },
      "source": [
        "#### Выручка по периодам и категориям"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EoNmmYd5n4xZ"
      },
      "source": [
        "Выделим признаки для месяцев с января 2018 по апрель 2021, предметы сгруппируем по 8 категориям верхнего уровня.\n",
        "\n",
        "Примеры названий:\n",
        "\n",
        "revenue_7_2018_05\n",
        "\n",
        "cnt_7_2018_05"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dKCuYvcNr43h",
        "outputId": "1563983c-f9c6-4fc4-8a6e-48227e98352d"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "['2020_12', '2021_01', '2021_02', '2021_03', '2021_04']"
            ]
          },
          "execution_count": 28,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "month_strs = []\n",
        "for y in range(2018, 2022):\n",
        "    for mon in range(1, 13):\n",
        "        if y == 2021 and mon == 5: \n",
        "            break\n",
        "        strm = str(mon) if mon > 9 else '0' + str(mon)\n",
        "        month_strs += [str(y) + '_' + strm]\n",
        "month_strs[-5:]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 299
        },
        "id": "K6Ksr791h6o-",
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            ],
            "text/plain": [
              "     rec_count  revenue_0_2018_01  revenue_1_2018_01  revenue_2_2018_01  \\\n",
              "129     350359                0.0                0.0                0.0   \n",
              "46      268252                0.0                0.0                0.0   \n",
              "125     317273                0.0                0.0                0.0   \n",
              "130     358758                0.0                0.0                0.0   \n",
              "24      845969                0.0                0.0                0.0   \n",
              "\n",
              "     revenue_3_2018_01  revenue_4_2018_01  revenue_5_2018_01  \\\n",
              "129                0.0                0.0                0.0   \n",
              "46                 0.0                0.0                0.0   \n",
              "125                0.0                0.0                0.0   \n",
              "130                0.0                0.0                0.0   \n",
              "24                 0.0                0.0                0.0   \n",
              "\n",
              "     revenue_6_2018_01  revenue_7_2018_01  revenue_0_2018_02  ...  \\\n",
              "129                0.0                0.0                0.0  ...   \n",
              "46                 0.0                0.0                0.0  ...   \n",
              "125                0.0                0.0                0.0  ...   \n",
              "130                0.0                0.0                0.0  ...   \n",
              "24                 0.0                0.0                0.0  ...   \n",
              "\n",
              "     revenue_6_2021_03  revenue_7_2021_03  revenue_0_2021_04  \\\n",
              "129                0.0                0.0                0.0   \n",
              "46                 0.0                0.0                0.0   \n",
              "125                0.0                0.0                0.0   \n",
              "130                0.0                0.0                0.0   \n",
              "24                 0.0                0.0                0.0   \n",
              "\n",
              "     revenue_1_2021_04  revenue_2_2021_04  revenue_3_2021_04  \\\n",
              "129                0.0                0.0                0.0   \n",
              "46                 0.0                0.0                0.0   \n",
              "125                0.0                0.0                0.0   \n",
              "130                0.0                0.0                0.0   \n",
              "24                 0.0                0.0                0.0   \n",
              "\n",
              "     revenue_4_2021_04  revenue_5_2021_04  revenue_6_2021_04  \\\n",
              "129                0.0                0.0                0.0   \n",
              "46                 0.0                0.0                0.0   \n",
              "125                0.0                0.0                0.0   \n",
              "130                0.0                0.0                0.0   \n",
              "24                 0.0                0.0                0.0   \n",
              "\n",
              "     revenue_7_2021_04  \n",
              "129                0.0  \n",
              "46                 0.0  \n",
              "125                0.0  \n",
              "130                0.0  \n",
              "24                 0.0  \n",
              "\n",
              "[5 rows x 321 columns]"
            ]
          },
          "execution_count": 51,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "for mon in month_strs:\n",
        "  for categ in range(8):\n",
        "    df_stores['revenue_' + str(categ) + '_' + mon] = np.zeros(247)\n",
        "    # df_stores['cnt_' + str(categ) + '_' + mon] = np.zeros(247)\n",
        "\n",
        "df_stores.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8pC7PiLlh6tB",
        "outputId": "9946f1c6-1222-4ffd-accd-3a6a935fcf97"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "46it [43:29, 56.73s/it]\n"
          ]
        }
      ],
      "source": [
        "# for chunk in tqdm(pd.read_csv('/content/drive/MyDrive/CourseWorkSAS/df_categ_months_revenue.csv', sep=';', chunksize=10**6)):\n",
        "#     for store_id in df_stores.index:\n",
        "#         for categ in range(8):\n",
        "#             df_curr = chunk.loc[(chunk['GOOD_CATEG'] == categ) & (chunk['STORE_ID'] == store_id), ['MONTH_DT', 'REVENUE']]\n",
        "#             for mon in month_strs:\n",
        "#                 sum_value = df_curr.loc[(df_curr['MONTH_DT'] == mon), 'REVENUE'].sum()\n",
        "#                 df_stores.loc[store_id, 'revenue_' + str(categ) + '_' + mon] += sum_value"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "o6Jzpxm_bRBF"
      },
      "outputs": [],
      "source": [
        "col_names = {\n",
        "    'rev_0' : ['revenue_0_' + mon for mon in month_strs],\n",
        "    'rev_1' : ['revenue_1_' + mon for mon in month_strs],\n",
        "    'rev_2' : ['revenue_2_' + mon for mon in month_strs],\n",
        "    'rev_3' : ['revenue_3_' + mon for mon in month_strs],\n",
        "    'rev_4' : ['revenue_4_' + mon for mon in month_strs],\n",
        "    'rev_5' : ['revenue_5_' + mon for mon in month_strs],\n",
        "    'rev_6' : ['revenue_6_' + mon for mon in month_strs],\n",
        "    'rev_7' : ['revenue_7_' + mon for mon in month_strs]\n",
        "    }"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 456
        },
        "id": "vFbKXJsn2e0Z",
        "outputId": "52c7ac24-ab08-4778-b747-39940cc1acd7"
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      "outputs": [
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              "      <td>1190163.08</td>\n",
              "      <td>1551352.24</td>\n",
              "      <td>1436113.42</td>\n",
              "      <td>1956076.96</td>\n",
              "      <td>1740168.88</td>\n",
              "      <td>1966931.42</td>\n",
              "      <td>1718645.78</td>\n",
              "      <td>...</td>\n",
              "      <td>3248194.62</td>\n",
              "      <td>2727429.74</td>\n",
              "      <td>2231883.02</td>\n",
              "      <td>2368305.18</td>\n",
              "      <td>2439340.84</td>\n",
              "      <td>2835561.70</td>\n",
              "      <td>2082844.40</td>\n",
              "      <td>2591763.40</td>\n",
              "      <td>3517360.36</td>\n",
              "      <td>2948860.56</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>489528</td>\n",
              "      <td>1840990.52</td>\n",
              "      <td>1512196.44</td>\n",
              "      <td>1698578.64</td>\n",
              "      <td>1737270.82</td>\n",
              "      <td>2202120.50</td>\n",
              "      <td>2299746.86</td>\n",
              "      <td>2794516.88</td>\n",
              "      <td>2775962.92</td>\n",
              "      <td>2843438.92</td>\n",
              "      <td>...</td>\n",
              "      <td>4282897.12</td>\n",
              "      <td>3706035.56</td>\n",
              "      <td>3555833.32</td>\n",
              "      <td>3691889.06</td>\n",
              "      <td>3107757.86</td>\n",
              "      <td>3440701.06</td>\n",
              "      <td>3466478.66</td>\n",
              "      <td>2975040.66</td>\n",
              "      <td>3404218.20</td>\n",
              "      <td>3420555.68</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>45</th>\n",
              "      <td>205666</td>\n",
              "      <td>364739.28</td>\n",
              "      <td>417383.84</td>\n",
              "      <td>404561.12</td>\n",
              "      <td>423656.28</td>\n",
              "      <td>610139.60</td>\n",
              "      <td>962587.36</td>\n",
              "      <td>792034.96</td>\n",
              "      <td>721129.70</td>\n",
              "      <td>862474.52</td>\n",
              "      <td>...</td>\n",
              "      <td>843177.86</td>\n",
              "      <td>834773.50</td>\n",
              "      <td>782482.72</td>\n",
              "      <td>805391.40</td>\n",
              "      <td>820677.82</td>\n",
              "      <td>866091.48</td>\n",
              "      <td>717447.72</td>\n",
              "      <td>831898.54</td>\n",
              "      <td>681854.90</td>\n",
              "      <td>818139.50</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>775722</td>\n",
              "      <td>3196192.72</td>\n",
              "      <td>2685317.38</td>\n",
              "      <td>2868647.56</td>\n",
              "      <td>3345300.80</td>\n",
              "      <td>4388790.20</td>\n",
              "      <td>4829252.12</td>\n",
              "      <td>5642865.70</td>\n",
              "      <td>6470105.86</td>\n",
              "      <td>6114931.26</td>\n",
              "      <td>...</td>\n",
              "      <td>7356722.58</td>\n",
              "      <td>5624370.88</td>\n",
              "      <td>6090728.74</td>\n",
              "      <td>6534113.92</td>\n",
              "      <td>4916608.72</td>\n",
              "      <td>5884711.26</td>\n",
              "      <td>5160817.82</td>\n",
              "      <td>4024992.72</td>\n",
              "      <td>4456752.52</td>\n",
              "      <td>4794204.14</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>121</th>\n",
              "      <td>523320</td>\n",
              "      <td>1950155.36</td>\n",
              "      <td>1540003.54</td>\n",
              "      <td>1466336.20</td>\n",
              "      <td>1830124.76</td>\n",
              "      <td>2501885.16</td>\n",
              "      <td>2687545.56</td>\n",
              "      <td>2674853.40</td>\n",
              "      <td>3022713.64</td>\n",
              "      <td>3310056.74</td>\n",
              "      <td>...</td>\n",
              "      <td>4418814.68</td>\n",
              "      <td>3579290.24</td>\n",
              "      <td>3780011.22</td>\n",
              "      <td>4212016.34</td>\n",
              "      <td>3582962.22</td>\n",
              "      <td>3486868.40</td>\n",
              "      <td>3315815.22</td>\n",
              "      <td>2468636.40</td>\n",
              "      <td>2933618.20</td>\n",
              "      <td>3150093.78</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>10 rows × 41 columns</p>\n",
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              "  "
            ],
            "text/plain": [
              "     rec_count  revenue_1_2018_01  revenue_1_2018_02  revenue_1_2018_03  \\\n",
              "129     350359          733413.96          714143.74          553315.84   \n",
              "46      268252          927563.96          665164.76          746204.86   \n",
              "125     317273         1194867.74         1299332.46         1150429.90   \n",
              "130     358758         1067969.78         1167235.54         1087280.30   \n",
              "24      845969         1062645.96          996930.60          938634.46   \n",
              "120     404693         1190437.08         1310109.84         1190163.08   \n",
              "23      489528         1840990.52         1512196.44         1698578.64   \n",
              "45      205666          364739.28          417383.84          404561.12   \n",
              "22      775722         3196192.72         2685317.38         2868647.56   \n",
              "121     523320         1950155.36         1540003.54         1466336.20   \n",
              "\n",
              "     revenue_1_2018_04  revenue_1_2018_05  revenue_1_2018_06  \\\n",
              "129          802250.42         1135512.24         1237357.28   \n",
              "46           914836.58         1264118.06         1340742.80   \n",
              "125         1336317.54         1306231.00         1579099.60   \n",
              "130         1207028.94         1540627.42         1191961.30   \n",
              "24          1323042.42         1322777.60         1579646.20   \n",
              "120         1551352.24         1436113.42         1956076.96   \n",
              "23          1737270.82         2202120.50         2299746.86   \n",
              "45           423656.28          610139.60          962587.36   \n",
              "22          3345300.80         4388790.20         4829252.12   \n",
              "121         1830124.76         2501885.16         2687545.56   \n",
              "\n",
              "     revenue_1_2018_07  revenue_1_2018_08  revenue_1_2018_09  ...  \\\n",
              "129         1378073.86         1404924.24         1472601.64  ...   \n",
              "46          1269568.48         1459626.60         1277517.26  ...   \n",
              "125         1746014.96         2019318.20         1783689.44  ...   \n",
              "130         1457454.50         1172001.98         1560546.24  ...   \n",
              "24          1670135.98         1650452.12         1597007.30  ...   \n",
              "120         1740168.88         1966931.42         1718645.78  ...   \n",
              "23          2794516.88         2775962.92         2843438.92  ...   \n",
              "45           792034.96          721129.70          862474.52  ...   \n",
              "22          5642865.70         6470105.86         6114931.26  ...   \n",
              "121         2674853.40         3022713.64         3310056.74  ...   \n",
              "\n",
              "     revenue_1_2020_07  revenue_1_2020_08  revenue_1_2020_09  \\\n",
              "129         2169945.58         1986885.16         2381663.20   \n",
              "46          1191781.12         1020190.08         1098007.18   \n",
              "125         2506695.10         1895555.70         1770741.66   \n",
              "130         2479861.00         2043399.08         2125029.78   \n",
              "24         10568192.42        10385675.04        11050016.56   \n",
              "120         3248194.62         2727429.74         2231883.02   \n",
              "23          4282897.12         3706035.56         3555833.32   \n",
              "45           843177.86          834773.50          782482.72   \n",
              "22          7356722.58         5624370.88         6090728.74   \n",
              "121         4418814.68         3579290.24         3780011.22   \n",
              "\n",
              "     revenue_1_2020_10  revenue_1_2020_11  revenue_1_2020_12  \\\n",
              "129         2197663.44         1681608.96         1546799.46   \n",
              "46          1192635.70          777683.92          984661.58   \n",
              "125         1922306.04         1714900.50         1908443.94   \n",
              "130         1937207.60         1829025.38         1803869.68   \n",
              "24         11509780.42         9371161.38         9504694.30   \n",
              "120         2368305.18         2439340.84         2835561.70   \n",
              "23          3691889.06         3107757.86         3440701.06   \n",
              "45           805391.40          820677.82          866091.48   \n",
              "22          6534113.92         4916608.72         5884711.26   \n",
              "121         4212016.34         3582962.22         3486868.40   \n",
              "\n",
              "     revenue_1_2021_01  revenue_1_2021_02  revenue_1_2021_03  \\\n",
              "129         1427280.96         1220828.14         1508943.10   \n",
              "46           912960.36          738996.44         1090807.86   \n",
              "125         1304944.34         1367349.08         1506066.78   \n",
              "130         2106909.84         1958662.90         2203637.36   \n",
              "24          9514868.66         7275936.52         7559371.46   \n",
              "120         2082844.40         2591763.40         3517360.36   \n",
              "23          3466478.66         2975040.66         3404218.20   \n",
              "45           717447.72          831898.54          681854.90   \n",
              "22          5160817.82         4024992.72         4456752.52   \n",
              "121         3315815.22         2468636.40         2933618.20   \n",
              "\n",
              "     revenue_1_2021_04  \n",
              "129         1751028.92  \n",
              "46          1010932.20  \n",
              "125         1722546.14  \n",
              "130         2118512.56  \n",
              "24          7941589.92  \n",
              "120         2948860.56  \n",
              "23          3420555.68  \n",
              "45           818139.50  \n",
              "22          4794204.14  \n",
              "121         3150093.78  \n",
              "\n",
              "[10 rows x 41 columns]"
            ]
          },
          "execution_count": 59,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df_stores[['rec_count'] + col_names['rev_1']][:10]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "jZDP7l7Ygh4_"
      },
      "outputs": [],
      "source": [
        "df_stores.to_csv('/content/drive/MyDrive/CourseWorkSAS/df_stores_revenues.csv', sep=';')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ksjdYe416tdd"
      },
      "source": [
        "#### Цена за единицу товарной позиции"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "2fuOJhFQIRDc"
      },
      "outputs": [],
      "source": [
        "df_items = pd.DataFrame()\n",
        "for chunk in pd.read_csv('/content/drive/MyDrive/CourseWorkSAS/df_fakeid_sended.csv', sep=';', chunksize=chunksize):\n",
        "    df_items = chunk\n",
        "    break"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "42UUgGDR7OWp"
      },
      "source": [
        "НАДО ДЛЯ ВСЕЙ ТАБЛИЦЫ ДОБАВЛЯТЬ"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "ipHAo6_21QnI",
        "outputId": "33409437-888f-4f85-b00c-782b11e7ea0f"
      },
      "outputs": [
        {
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              "      <td>41768</td>\n",
              "      <td>30.00</td>\n",
              "      <td>30.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>999997</th>\n",
              "      <td>2029755112</td>\n",
              "      <td>131</td>\n",
              "      <td>15APR2019</td>\n",
              "      <td>2.000</td>\n",
              "      <td>ST</td>\n",
              "      <td>1.000</td>\n",
              "      <td>227987</td>\n",
              "      <td>53.34</td>\n",
              "      <td>53.340000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>999998</th>\n",
              "      <td>2029755112</td>\n",
              "      <td>131</td>\n",
              "      <td>15APR2019</td>\n",
              "      <td>40.000</td>\n",
              "      <td>ST</td>\n",
              "      <td>20.000</td>\n",
              "      <td>93204</td>\n",
              "      <td>49.66</td>\n",
              "      <td>2.483000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>999999</th>\n",
              "      <td>2029755114</td>\n",
              "      <td>22</td>\n",
              "      <td>15APR2019</td>\n",
              "      <td>4.000</td>\n",
              "      <td>ST</td>\n",
              "      <td>2.000</td>\n",
              "      <td>14061</td>\n",
              "      <td>333.00</td>\n",
              "      <td>166.500000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1000000 rows × 9 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-31a88d5a-524c-4d98-af7a-8a50f54648b6')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
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              "  </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-31a88d5a-524c-4d98-af7a-8a50f54648b6 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-31a88d5a-524c-4d98-af7a-8a50f54648b6');\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",
              "  "
            ],
            "text/plain": [
              "            POS_ID  PICKUP_STORE_LOC_ID  PICKUP_DT  SALES_QTY UNIT_BASE  \\\n",
              "0       2030069017                  129  04MAY2019      2.000        ST   \n",
              "1       2030069018                   46  04MAY2019      0.896        KG   \n",
              "2       2030069020                  125  04MAY2019      2.000       MSH   \n",
              "3       2030069021                  130  04MAY2019      2.000        ST   \n",
              "4       2030069022                   24  04MAY2019     12.000         M   \n",
              "...            ...                  ...        ...        ...       ...   \n",
              "999995  2029755112                  131  15APR2019      8.000       PAA   \n",
              "999996  2029755112                  131  15APR2019      2.000        ST   \n",
              "999997  2029755112                  131  15APR2019      2.000        ST   \n",
              "999998  2029755112                  131  15APR2019     40.000        ST   \n",
              "999999  2029755114                   22  15APR2019      4.000        ST   \n",
              "\n",
              "        SALES_QTY_BASE  FAKE_ID  REVENUE       PRICE  \n",
              "0                1.000    59180   105.00  105.000000  \n",
              "1                0.448    82691     1.02    2.276786  \n",
              "2                1.000   212269   739.20  739.200000  \n",
              "3                1.000   205772   110.00  110.000000  \n",
              "4               12.000    16505  1129.20   94.100000  \n",
              "...                ...      ...      ...         ...  \n",
              "999995           4.000   209589   133.34   33.335000  \n",
              "999996           1.000    41768    30.00   30.000000  \n",
              "999997           1.000   227987    53.34   53.340000  \n",
              "999998          20.000    93204    49.66    2.483000  \n",
              "999999           2.000    14061   333.00  166.500000  \n",
              "\n",
              "[1000000 rows x 9 columns]"
            ]
          },
          "execution_count": 65,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df_items.drop('FAKE_ID.1', 1, inplace=True)\n",
        "df_items['PRICE'] = df_items['REVENUE'] / df_items['SALES_QTY_BASE']\n",
        "df_items"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "je8jLzluwHwJ"
      },
      "outputs": [],
      "source": [
        ""
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZscxfSJCYDV4"
      },
      "source": [
        "### Количество проданного понедельно и по категориям"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "IELotGWJop_2"
      },
      "outputs": [],
      "source": [
        "# количество проданного понедельно\n",
        "\n",
        "# сначала надо получить список недель датами"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "UUjyO7wToqqB"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import datetime as dt\n",
        "from datetime import datetime\n",
        "\n",
        "daterange_from = pd.date_range(datetime.strptime('2017-12-26', '%Y-%m-%d'), freq='W-MON', periods=118)\n",
        "daterange_to = pd.date_range(datetime.strptime('2018-01-01', '%Y-%m-%d'), freq='W-SUN', periods=118)\n",
        "\n",
        "datestr_from = [date_obj.strftime('%Y-%m-%d') for date_obj in daterange_from]\n",
        "datestr_to = [date_obj.strftime('%Y-%m-%d') for date_obj in daterange_to]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5VDIkAcAueGo",
        "outputId": "3f054970-516a-47e9-daa8-bd353f64f378"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "('2018-01-01', '2020-03-30', '2018-01-07', '2020-04-05')"
            ]
          },
          "execution_count": 5,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "datestr_from[0], datestr_from[-1], datestr_to[0], datestr_to[-1]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        },
        "id": "OE1xuhPjYOdt",
        "outputId": "5c8d1dec-35e2-4efa-c0ee-8db1a316e9d1"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'2018-01-01'"
            ]
          },
          "execution_count": 6,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "currday = datetime.strptime('2018-01-04', '%Y-%m-%d')\n",
        "mon = currday - dt.timedelta(days=currday.weekday())\n",
        "mon.strftime('%Y-%m-%d')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6ZOq0vhol4da",
        "outputId": "3a0af8c9-e29e-439e-e5bf-161270c67b95"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "1.000      553676\n",
              "2.000      143463\n",
              "3.000       46732\n",
              "4.000       39902\n",
              "10.000      26735\n",
              "            ...  \n",
              "76.738          1\n",
              "43.697          1\n",
              "3.308           1\n",
              "19.516          1\n",
              "532.032         1\n",
              "Name: SALES_QTY_BASE, Length: 14685, dtype: int64"
            ]
          },
          "execution_count": 60,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "chunk['SALES_QTY_BASE'].value_counts()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "eELGEVPgkeF4"
      },
      "outputs": [],
      "source": [
        "import csv\n",
        "\n",
        "# v = 0\n",
        "# with open('/content/drive/MyDrive/CourseWorkSAS/df_fakeid_sended.csv', 'r') as File_read:\n",
        "#   with open('/content/drive/MyDrive/CourseWorkSAS/df_fakeid_lvl1_categ.csv', 'w') as File_write:\n",
        "#     reader = csv.reader(File_read, delimiter=';')\n",
        "#     writer = csv.writer(File_write, delimiter=';')\n",
        "#     writer.writerow(['POS_ID', 'FAKE_ID', 'LVL1_CATEG', 'STORE_ID', 'PICKUP_DT', 'SALES_QTY_BASE'])\n",
        "#     for r in tqdm(reader):\n",
        "#         if v > 0:\n",
        "#             lvl_name = df_product.loc[int(r[6]), 'PRODUCT_LVL_NM1']\n",
        "#             c_num = categs[lvl_name]\n",
        "#             writer.writerow([r[0], r[6], c_num, r[1], r[2], r[5]])\n",
        "#         v += 1"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "oP3fZrO0Br1s"
      },
      "outputs": [],
      "source": [
        "months = {'JAN' : '01', 'FEB' : '02', 'MAR' : '03', 'APR' : '04',\n",
        "          'MAY' : '05', 'JUN' : '06', 'JUL' : '07', 'AUG' : '08',\n",
        "          'SEP' : '09', 'OCT' : '10', 'NOV' : '11', 'DEC' : '12'}\n",
        "\n",
        "def data_extract(s: str):\n",
        "    return s[5:] + '-' + months[s[2:5]] + '-' + s[:2]\n",
        "\n",
        "def monday_align(ugly_date):\n",
        "    currday = datetime.strptime(data_extract(ugly_date), '%Y-%m-%d')\n",
        "    mon = currday - dt.timedelta(days=currday.weekday())\n",
        "    return mon.strftime('%Y-%m-%d')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        },
        "id": "7SGDeQZrbJXs",
        "outputId": "c254f6a3-3ae0-4cce-aa4a-967838fe691d"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'2018-01-01'"
            ]
          },
          "execution_count": 19,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "monday_align('05JAN2018')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "zGshhZ0KeN6-"
      },
      "outputs": [],
      "source": [
        "# создаем dataframe ответа\n",
        "# столбцы - названия категорий\n",
        "# индекс строки - неделя, номер магазина\n",
        "\n",
        "categs = {'Неизвестный класс' : '0', 'Инженерные системы' : '1', \n",
        "          'Общестрой' : '2', 'Спецодежда' : '3',\n",
        "          'Внутренняя отделка' : '4', 'Наружная отделка' : '5',\n",
        "          'Сопутствующие товары' : '6', 'Услуги' : '7'}\n",
        "\n",
        "iterables = [datestr_from, main_stores]\n",
        "mltindex = pd.MultiIndex.from_product(iterables, names=[\"week\", \"store\"])\n",
        "\n",
        "final_df = pd.DataFrame(columns=range(8), index=mltindex)\n",
        "final_df.fillna(0, inplace=True)\n",
        "final_df.head(3)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qv6kCl4uDRQ8"
      },
      "source": [
        "### Признаки встречаемости в чеках"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "sChWFvTD8vjD"
      },
      "outputs": [],
      "source": [
        "main_stores = list(pd.read_csv('/content/drive/MyDrive/CourseWorkSAS/df_140stores_500items.csv', sep=';', index_col='Unnamed: 0').columns.astype(int))\n",
        "popular_products = list(pd.read_csv('/content/drive/MyDrive/CourseWorkSAS/df_140stores_500items.csv', sep=';', index_col='Unnamed: 0').index.astype(int))\n",
        "main_stores_set = set(main_stores)\n",
        "popular_prods_set = set(popular_products)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "yyNR8PLGBHED"
      },
      "outputs": [],
      "source": [
        "months = {'JAN' : 1, 'FEB' : 2, 'MAR' : 3, 'APR' : 4,\n",
        "          'MAY' : 5, 'JUN' : 6, 'JUL' : 7, 'AUG' : 8,\n",
        "          'SEP' : 9, 'OCT' : 10, 'NOV' : 11, 'DEC' : 12}\n",
        "\n",
        "def calc_yearhalf(date_str):\n",
        "  m = months[date_str[2:5]]\n",
        "  y = int(date_str[5:])\n",
        "  return 2 * (y - 2018) + int(m > 6)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "calc_yearhalf('31NOV2019')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6k5XEmoeYrvM",
        "outputId": "31fec616-023b-4667-bf41-7e8116785e83"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "3"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "APwhgJky8oUD",
        "outputId": "9cb137eb-ccaa-4772-f62d-175e4e44cf33"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "46it [49:50, 65.01s/it]\n"
          ]
        }
      ],
      "source": [
        "item_freq = dict()\n",
        "item_quants = dict()\n",
        "item_revenue = dict()\n",
        "dates = defaultdict(list)\n",
        "\n",
        "for item in sorted(popular_products):\n",
        "    item_freq[item] = np.array([[0] * 140] * 6)\n",
        "    item_quants[item] = np.array([[0] * 140] * 6)\n",
        "    item_revenue[item] = np.array([[0.0] * 140] * 6)\n",
        "\n",
        "for chunk in tqdm(pd.read_csv('/content/drive/MyDrive/CourseWorkSAS/df_fakeid_sended.csv', sep=';', chunksize=10**6)):\n",
        "  for i, row in chunk.iterrows():\n",
        "    if row['FAKE_ID'] in popular_prods_set and row['PICKUP_STORE_LOC_ID'] in main_stores_set:\n",
        "      y = int(row['PICKUP_DT'][5:])\n",
        "      if y > 2017 and y < 2021:\n",
        "        yearhalf = calc_yearhalf(row['PICKUP_DT'])\n",
        "        dates[yearhalf].append(row['PICKUP_DT'])\n",
        "        ind = main_stores.index(row['PICKUP_STORE_LOC_ID'])\n",
        "        item_freq[row['FAKE_ID']][yearhalf, ind] += 1\n",
        "        item_quants[row['FAKE_ID']][yearhalf, ind] += abs(row['SALES_QTY_BASE'])\n",
        "        item_revenue[row['FAKE_ID']][yearhalf, ind] += abs(row['REVENUE'])"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "7HMHI12XsUQt"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "item_revenue_0 = dict()\n",
        "item_revenue_1 = dict()\n",
        "item_revenue_2 = dict()\n",
        "item_revenue_3 = dict()\n",
        "item_revenue_4 = dict()\n",
        "item_revenue_5 = dict()\n",
        "\n",
        "for item in popular_products:\n",
        "    item_revenue_0[item] = item_revenue[item][0]\n",
        "    item_revenue_1[item] = item_revenue[item][1]\n",
        "    item_revenue_2[item] = item_revenue[item][2]\n",
        "    item_revenue_3[item] = item_revenue[item][3]\n",
        "    item_revenue_4[item] = item_revenue[item][4]\n",
        "    item_revenue_5[item] = item_revenue[item][5]"
      ],
      "metadata": {
        "id": "Pyc4o-IjZ0OC"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_freq = pd.DataFrame.from_dict(item_revenue_5, orient='index', columns=main_stores)\n",
        "df_freq.to_csv('/content/drive/MyDrive/CourseWorkSAS/revenues/df_revenue_yearhalf_5.csv', sep=';')"
      ],
      "metadata": {
        "id": "QFvWQmtuuU3a"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "pd.DataFrame.from_dict(item_freq_5, orient='index', columns=main_stores)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
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              "                                                     [key], {});\n",
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          },
          "metadata": {},
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "zNcwbbXWWegE"
      },
      "outputs": [],
      "source": [
        "item_freq_sum = {item : sum(mags) for item, mags in item_freq.items()}\n",
        "popular_products = list(reversed(sorted(item_freq_sum, key=item_freq_sum.get)))[:500]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "4gaPOps9gFH7"
      },
      "outputs": [],
      "source": [
        "popular_products = sorted(popular_products)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "sBr2LbjYb13l"
      },
      "outputs": [],
      "source": [
        "item_freq_pop = dict()\n",
        "for item in popular_products:\n",
        "    item_freq_pop[item] = item_freq[item]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
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        "id": "UPtrl-SjcJjl",
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              "      <td>8</td>\n",
              "      <td>5</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>405</th>\n",
              "      <td>525</td>\n",
              "      <td>722</td>\n",
              "      <td>679</td>\n",
              "      <td>1124</td>\n",
              "      <td>625</td>\n",
              "      <td>295</td>\n",
              "      <td>134</td>\n",
              "      <td>783</td>\n",
              "      <td>346</td>\n",
              "      <td>24</td>\n",
              "      <td>...</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>7</td>\n",
              "      <td>23</td>\n",
              "      <td>8</td>\n",
              "      <td>24</td>\n",
              "      <td>14</td>\n",
              "      <td>4</td>\n",
              "      <td>11</td>\n",
              "      <td>9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>410</th>\n",
              "      <td>597</td>\n",
              "      <td>681</td>\n",
              "      <td>490</td>\n",
              "      <td>930</td>\n",
              "      <td>398</td>\n",
              "      <td>150</td>\n",
              "      <td>77</td>\n",
              "      <td>365</td>\n",
              "      <td>144</td>\n",
              "      <td>9</td>\n",
              "      <td>...</td>\n",
              "      <td>4</td>\n",
              "      <td>12</td>\n",
              "      <td>7</td>\n",
              "      <td>20</td>\n",
              "      <td>7</td>\n",
              "      <td>38</td>\n",
              "      <td>8</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>506</th>\n",
              "      <td>350</td>\n",
              "      <td>553</td>\n",
              "      <td>552</td>\n",
              "      <td>774</td>\n",
              "      <td>289</td>\n",
              "      <td>228</td>\n",
              "      <td>66</td>\n",
              "      <td>416</td>\n",
              "      <td>284</td>\n",
              "      <td>12</td>\n",
              "      <td>...</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>5</td>\n",
              "      <td>17</td>\n",
              "      <td>2</td>\n",
              "      <td>21</td>\n",
              "      <td>5</td>\n",
              "      <td>3</td>\n",
              "      <td>8</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>507</th>\n",
              "      <td>524</td>\n",
              "      <td>924</td>\n",
              "      <td>1013</td>\n",
              "      <td>1111</td>\n",
              "      <td>606</td>\n",
              "      <td>173</td>\n",
              "      <td>82</td>\n",
              "      <td>712</td>\n",
              "      <td>249</td>\n",
              "      <td>18</td>\n",
              "      <td>...</td>\n",
              "      <td>4</td>\n",
              "      <td>11</td>\n",
              "      <td>8</td>\n",
              "      <td>24</td>\n",
              "      <td>9</td>\n",
              "      <td>39</td>\n",
              "      <td>11</td>\n",
              "      <td>6</td>\n",
              "      <td>9</td>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>259313</th>\n",
              "      <td>2498</td>\n",
              "      <td>1128</td>\n",
              "      <td>1357</td>\n",
              "      <td>2135</td>\n",
              "      <td>1274</td>\n",
              "      <td>1367</td>\n",
              "      <td>636</td>\n",
              "      <td>1606</td>\n",
              "      <td>1054</td>\n",
              "      <td>81</td>\n",
              "      <td>...</td>\n",
              "      <td>44</td>\n",
              "      <td>28</td>\n",
              "      <td>54</td>\n",
              "      <td>99</td>\n",
              "      <td>21</td>\n",
              "      <td>36</td>\n",
              "      <td>63</td>\n",
              "      <td>56</td>\n",
              "      <td>26</td>\n",
              "      <td>9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>259718</th>\n",
              "      <td>2358</td>\n",
              "      <td>1113</td>\n",
              "      <td>918</td>\n",
              "      <td>1437</td>\n",
              "      <td>797</td>\n",
              "      <td>563</td>\n",
              "      <td>393</td>\n",
              "      <td>883</td>\n",
              "      <td>535</td>\n",
              "      <td>35</td>\n",
              "      <td>...</td>\n",
              "      <td>16</td>\n",
              "      <td>15</td>\n",
              "      <td>27</td>\n",
              "      <td>51</td>\n",
              "      <td>15</td>\n",
              "      <td>57</td>\n",
              "      <td>9</td>\n",
              "      <td>10</td>\n",
              "      <td>17</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>260634</th>\n",
              "      <td>677</td>\n",
              "      <td>1162</td>\n",
              "      <td>673</td>\n",
              "      <td>1289</td>\n",
              "      <td>783</td>\n",
              "      <td>621</td>\n",
              "      <td>240</td>\n",
              "      <td>74</td>\n",
              "      <td>434</td>\n",
              "      <td>51</td>\n",
              "      <td>...</td>\n",
              "      <td>20</td>\n",
              "      <td>12</td>\n",
              "      <td>32</td>\n",
              "      <td>51</td>\n",
              "      <td>50</td>\n",
              "      <td>26</td>\n",
              "      <td>27</td>\n",
              "      <td>25</td>\n",
              "      <td>15</td>\n",
              "      <td>12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>262752</th>\n",
              "      <td>1448</td>\n",
              "      <td>2150</td>\n",
              "      <td>1728</td>\n",
              "      <td>2419</td>\n",
              "      <td>1580</td>\n",
              "      <td>1352</td>\n",
              "      <td>989</td>\n",
              "      <td>1551</td>\n",
              "      <td>1415</td>\n",
              "      <td>109</td>\n",
              "      <td>...</td>\n",
              "      <td>15</td>\n",
              "      <td>13</td>\n",
              "      <td>60</td>\n",
              "      <td>93</td>\n",
              "      <td>46</td>\n",
              "      <td>37</td>\n",
              "      <td>39</td>\n",
              "      <td>42</td>\n",
              "      <td>34</td>\n",
              "      <td>15</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>263059</th>\n",
              "      <td>715</td>\n",
              "      <td>1141</td>\n",
              "      <td>1068</td>\n",
              "      <td>1431</td>\n",
              "      <td>681</td>\n",
              "      <td>560</td>\n",
              "      <td>249</td>\n",
              "      <td>787</td>\n",
              "      <td>542</td>\n",
              "      <td>24</td>\n",
              "      <td>...</td>\n",
              "      <td>11</td>\n",
              "      <td>11</td>\n",
              "      <td>17</td>\n",
              "      <td>36</td>\n",
              "      <td>10</td>\n",
              "      <td>30</td>\n",
              "      <td>15</td>\n",
              "      <td>18</td>\n",
              "      <td>11</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>500 rows × 140 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-019f702e-3c37-4e20-9ec6-174a7a683020')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
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              "\n",
              "      <script>\n",
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              "          document.querySelector('#df-019f702e-3c37-4e20-9ec6-174a7a683020 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\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",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
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              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "        21    22    23    24    25    26    27    28    29    32    ...  3106  \\\n",
              "221      498   836   845   869   539     7    50   500    44    12  ...     1   \n",
              "405      525   722   679  1124   625   295   134   783   346    24  ...     2   \n",
              "410      597   681   490   930   398   150    77   365   144     9  ...     4   \n",
              "506      350   553   552   774   289   228    66   416   284    12  ...     1   \n",
              "507      524   924  1013  1111   606   173    82   712   249    18  ...     4   \n",
              "...      ...   ...   ...   ...   ...   ...   ...   ...   ...   ...  ...   ...   \n",
              "259313  2498  1128  1357  2135  1274  1367   636  1606  1054    81  ...    44   \n",
              "259718  2358  1113   918  1437   797   563   393   883   535    35  ...    16   \n",
              "260634   677  1162   673  1289   783   621   240    74   434    51  ...    20   \n",
              "262752  1448  2150  1728  2419  1580  1352   989  1551  1415   109  ...    15   \n",
              "263059   715  1141  1068  1431   681   560   249   787   542    24  ...    11   \n",
              "\n",
              "        3301  4401  4502  4601  5501  6501  6701  8401  8403  \n",
              "221       14     6    19     9    40     2     8     5     3  \n",
              "405        3     7    23     8    24    14     4    11     9  \n",
              "410       12     7    20     7    38     8     5     5     5  \n",
              "506        3     5    17     2    21     5     3     8     6  \n",
              "507       11     8    24     9    39    11     6     9    10  \n",
              "...      ...   ...   ...   ...   ...   ...   ...   ...   ...  \n",
              "259313    28    54    99    21    36    63    56    26     9  \n",
              "259718    15    27    51    15    57     9    10    17     2  \n",
              "260634    12    32    51    50    26    27    25    15    12  \n",
              "262752    13    60    93    46    37    39    42    34    15  \n",
              "263059    11    17    36    10    30    15    18    11     1  \n",
              "\n",
              "[500 rows x 140 columns]"
            ]
          },
          "execution_count": 51,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df_big_popular = pd.DataFrame.from_dict(item_freq_pop, orient='index', columns=main_stores)\n",
        "df_big_popular"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "3TdtDH3QczaI"
      },
      "outputs": [],
      "source": [
        "df_big_popular.to_csv('/content/drive/MyDrive/CourseWorkSAS/df_140stores_500items.csv', sep=';')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "h8brIL-XDWDi"
      },
      "source": [
        "Признаки количества в базовых ЕИ"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "LcSuTV3JCKX8"
      },
      "outputs": [],
      "source": [
        "main_stores = list(pd.read_csv('/content/drive/MyDrive/CourseWorkSAS/df_140stores_500items.csv', sep=';', index_col='Unnamed: 0').columns.astype(int))\n",
        "popular_products = list(pd.read_csv('/content/drive/MyDrive/CourseWorkSAS/df_140stores_500items.csv', sep=';', index_col='Unnamed: 0').index.astype(int))\n",
        "main_stores_set = set(main_stores)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "n0b_ZPk8CF61",
        "outputId": "99e21768-f05c-487a-fbc1-21400d25dd68"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "46it [53:03, 69.20s/it]\n"
          ]
        }
      ],
      "source": [
        "item_quants = dict()\n",
        "item_revenue = dict()\n",
        "for item in sorted(df_product['FAKE_ID']):\n",
        "    item_quants[item] = [0] * 140\n",
        "    item_revenue[item] = [0.0] * 140\n",
        "\n",
        "for chunk in tqdm(pd.read_csv('/content/drive/MyDrive/CourseWorkSAS/df_fakeid_sended.csv', sep=';', chunksize=10**6)):\n",
        "  for i, row in chunk.iterrows():\n",
        "    if row['PICKUP_STORE_LOC_ID'] in main_stores_set:\n",
        "      pos = main_stores.index(row['PICKUP_STORE_LOC_ID'])\n",
        "      item_quants[row['FAKE_ID']][pos] += abs(row['SALES_QTY_BASE'])\n",
        "      item_revenue[row['FAKE_ID']][pos] += abs(row['REVENUE'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "pjNzFgRSSt39"
      },
      "outputs": [],
      "source": [
        "item_quant_pop = dict()\n",
        "for item in popular_products:\n",
        "    item_quant_pop[item] = item_quants[item]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "IKcG0NETSNrm",
        "outputId": "dc7f3b43-81f6-4586-dddf-077d5dbd4a2e"
      },
      "outputs": [
        {
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              "      <td>303.0</td>\n",
              "      <td>137.00</td>\n",
              "      <td>810.0</td>\n",
              "      <td>375.0</td>\n",
              "      <td>24.0</td>\n",
              "      <td>...</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>23.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>25.0</td>\n",
              "      <td>14.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>12.0</td>\n",
              "      <td>9.0</td>\n",
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              "    <tr>\n",
              "      <th>410</th>\n",
              "      <td>940.0</td>\n",
              "      <td>1189.00</td>\n",
              "      <td>831.00</td>\n",
              "      <td>1485.00</td>\n",
              "      <td>673.000</td>\n",
              "      <td>210.0</td>\n",
              "      <td>97.00</td>\n",
              "      <td>602.0</td>\n",
              "      <td>216.0</td>\n",
              "      <td>12.0</td>\n",
              "      <td>...</td>\n",
              "      <td>4.0</td>\n",
              "      <td>16.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>33.0</td>\n",
              "      <td>15.0</td>\n",
              "      <td>72.0</td>\n",
              "      <td>14.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>5.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>506</th>\n",
              "      <td>417.0</td>\n",
              "      <td>650.00</td>\n",
              "      <td>656.00</td>\n",
              "      <td>925.00</td>\n",
              "      <td>352.000</td>\n",
              "      <td>287.0</td>\n",
              "      <td>72.00</td>\n",
              "      <td>474.0</td>\n",
              "      <td>339.0</td>\n",
              "      <td>15.0</td>\n",
              "      <td>...</td>\n",
              "      <td>1.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>18.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>24.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>6.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>507</th>\n",
              "      <td>757.0</td>\n",
              "      <td>1368.00</td>\n",
              "      <td>1490.00</td>\n",
              "      <td>1584.00</td>\n",
              "      <td>879.000</td>\n",
              "      <td>218.0</td>\n",
              "      <td>104.00</td>\n",
              "      <td>1030.0</td>\n",
              "      <td>360.0</td>\n",
              "      <td>24.0</td>\n",
              "      <td>...</td>\n",
              "      <td>4.0</td>\n",
              "      <td>17.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>36.0</td>\n",
              "      <td>13.0</td>\n",
              "      <td>68.0</td>\n",
              "      <td>15.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>12.0</td>\n",
              "      <td>13.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>259313</th>\n",
              "      <td>50069.5</td>\n",
              "      <td>17308.10</td>\n",
              "      <td>20151.06</td>\n",
              "      <td>28256.73</td>\n",
              "      <td>14810.450</td>\n",
              "      <td>12077.3</td>\n",
              "      <td>5771.90</td>\n",
              "      <td>15144.5</td>\n",
              "      <td>9400.0</td>\n",
              "      <td>539.0</td>\n",
              "      <td>...</td>\n",
              "      <td>242.5</td>\n",
              "      <td>309.8</td>\n",
              "      <td>359.5</td>\n",
              "      <td>868.0</td>\n",
              "      <td>512.0</td>\n",
              "      <td>451.0</td>\n",
              "      <td>296.0</td>\n",
              "      <td>384.0</td>\n",
              "      <td>420.0</td>\n",
              "      <td>86.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>259718</th>\n",
              "      <td>62141.5</td>\n",
              "      <td>18695.40</td>\n",
              "      <td>18468.00</td>\n",
              "      <td>23556.30</td>\n",
              "      <td>9979.900</td>\n",
              "      <td>5124.8</td>\n",
              "      <td>4298.50</td>\n",
              "      <td>9552.1</td>\n",
              "      <td>5654.0</td>\n",
              "      <td>301.0</td>\n",
              "      <td>...</td>\n",
              "      <td>184.0</td>\n",
              "      <td>263.0</td>\n",
              "      <td>197.0</td>\n",
              "      <td>611.5</td>\n",
              "      <td>167.0</td>\n",
              "      <td>1247.0</td>\n",
              "      <td>53.0</td>\n",
              "      <td>76.5</td>\n",
              "      <td>164.0</td>\n",
              "      <td>32.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>260634</th>\n",
              "      <td>980.0</td>\n",
              "      <td>1577.00</td>\n",
              "      <td>907.00</td>\n",
              "      <td>1768.00</td>\n",
              "      <td>1006.000</td>\n",
              "      <td>761.0</td>\n",
              "      <td>283.00</td>\n",
              "      <td>89.0</td>\n",
              "      <td>538.0</td>\n",
              "      <td>64.0</td>\n",
              "      <td>...</td>\n",
              "      <td>23.0</td>\n",
              "      <td>14.0</td>\n",
              "      <td>41.0</td>\n",
              "      <td>62.0</td>\n",
              "      <td>89.0</td>\n",
              "      <td>39.0</td>\n",
              "      <td>27.0</td>\n",
              "      <td>31.0</td>\n",
              "      <td>15.0</td>\n",
              "      <td>14.0</td>\n",
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              "    <tr>\n",
              "      <th>262752</th>\n",
              "      <td>2288.0</td>\n",
              "      <td>3068.00</td>\n",
              "      <td>2369.00</td>\n",
              "      <td>3416.00</td>\n",
              "      <td>2050.000</td>\n",
              "      <td>1691.0</td>\n",
              "      <td>1156.00</td>\n",
              "      <td>1869.0</td>\n",
              "      <td>1657.0</td>\n",
              "      <td>144.0</td>\n",
              "      <td>...</td>\n",
              "      <td>15.0</td>\n",
              "      <td>15.0</td>\n",
              "      <td>68.0</td>\n",
              "      <td>108.0</td>\n",
              "      <td>63.0</td>\n",
              "      <td>49.0</td>\n",
              "      <td>39.0</td>\n",
              "      <td>44.0</td>\n",
              "      <td>41.0</td>\n",
              "      <td>18.0</td>\n",
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              "    <tr>\n",
              "      <th>263059</th>\n",
              "      <td>1083.0</td>\n",
              "      <td>1720.01</td>\n",
              "      <td>1507.03</td>\n",
              "      <td>1990.00</td>\n",
              "      <td>910.000</td>\n",
              "      <td>700.0</td>\n",
              "      <td>298.01</td>\n",
              "      <td>967.2</td>\n",
              "      <td>700.0</td>\n",
              "      <td>39.0</td>\n",
              "      <td>...</td>\n",
              "      <td>11.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>21.0</td>\n",
              "      <td>38.0</td>\n",
              "      <td>13.0</td>\n",
              "      <td>40.0</td>\n",
              "      <td>16.0</td>\n",
              "      <td>20.0</td>\n",
              "      <td>15.0</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>500 rows × 140 columns</p>\n",
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            "text/plain": [
              "           21        22        23        24         25       26       27    \\\n",
              "221       609.0   1019.00   1025.00   1042.00    661.000      9.0    55.00   \n",
              "405       556.0    767.00    709.00   1180.00    657.044    303.0   137.00   \n",
              "410       940.0   1189.00    831.00   1485.00    673.000    210.0    97.00   \n",
              "506       417.0    650.00    656.00    925.00    352.000    287.0    72.00   \n",
              "507       757.0   1368.00   1490.00   1584.00    879.000    218.0   104.00   \n",
              "...         ...       ...       ...       ...        ...      ...      ...   \n",
              "259313  50069.5  17308.10  20151.06  28256.73  14810.450  12077.3  5771.90   \n",
              "259718  62141.5  18695.40  18468.00  23556.30   9979.900   5124.8  4298.50   \n",
              "260634    980.0   1577.00    907.00   1768.00   1006.000    761.0   283.00   \n",
              "262752   2288.0   3068.00   2369.00   3416.00   2050.000   1691.0  1156.00   \n",
              "263059   1083.0   1720.01   1507.03   1990.00    910.000    700.0   298.01   \n",
              "\n",
              "           28      29     32    ...   3106   3301   4401   4502   4601  \\\n",
              "221       552.0    49.0   12.0  ...    1.0   16.0    8.0   20.0   11.0   \n",
              "405       810.0   375.0   24.0  ...    3.0    3.0    7.0   23.0    8.0   \n",
              "410       602.0   216.0   12.0  ...    4.0   16.0   10.0   33.0   15.0   \n",
              "506       474.0   339.0   15.0  ...    1.0    4.0    5.0   18.0    4.0   \n",
              "507      1030.0   360.0   24.0  ...    4.0   17.0   10.0   36.0   13.0   \n",
              "...         ...     ...    ...  ...    ...    ...    ...    ...    ...   \n",
              "259313  15144.5  9400.0  539.0  ...  242.5  309.8  359.5  868.0  512.0   \n",
              "259718   9552.1  5654.0  301.0  ...  184.0  263.0  197.0  611.5  167.0   \n",
              "260634     89.0   538.0   64.0  ...   23.0   14.0   41.0   62.0   89.0   \n",
              "262752   1869.0  1657.0  144.0  ...   15.0   15.0   68.0  108.0   63.0   \n",
              "263059    967.2   700.0   39.0  ...   11.0   11.0   21.0   38.0   13.0   \n",
              "\n",
              "          5501   6501   6701   8401  8403  \n",
              "221       53.0    2.0   10.0    8.0   3.0  \n",
              "405       25.0   14.0    4.0   12.0   9.0  \n",
              "410       72.0   14.0    6.0    7.0   5.0  \n",
              "506       24.0    5.0    3.0   10.0   6.0  \n",
              "507       68.0   15.0    9.0   12.0  13.0  \n",
              "...        ...    ...    ...    ...   ...  \n",
              "259313   451.0  296.0  384.0  420.0  86.0  \n",
              "259718  1247.0   53.0   76.5  164.0  32.0  \n",
              "260634    39.0   27.0   31.0   15.0  14.0  \n",
              "262752    49.0   39.0   44.0   41.0  18.0  \n",
              "263059    40.0   16.0   20.0   15.0   2.0  \n",
              "\n",
              "[500 rows x 140 columns]"
            ]
          },
          "execution_count": 33,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df_main_quants = pd.DataFrame.from_dict(item_quant_pop, orient='index', columns=main_stores)\n",
        "df_main_quants"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ptPHYEO1h9aZ"
      },
      "outputs": [],
      "source": [
        "df_main_quants.to_csv('/content/drive/MyDrive/CourseWorkSAS/df_140stores_500quants.csv', sep=';')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3R52-v9MDZjG"
      },
      "source": [
        " И \n",
        "Признаки выручки по 500 товарам"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "STG5YDjHiF0v"
      },
      "outputs": [],
      "source": [
        "item_rev_pop = dict()\n",
        "for item in popular_products:\n",
        "    item_rev_pop[item] = item_revenue[item]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 468
        },
        "id": "5i6v18K8iF5e",
        "outputId": "8f0fc031-4851-420b-a966-933c7e653ba5"
      },
      "outputs": [
        {
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              "    <tr>\n",
              "      <th>405</th>\n",
              "      <td>119200.08</td>\n",
              "      <td>160984.50</td>\n",
              "      <td>154409.96</td>\n",
              "      <td>271037.26</td>\n",
              "      <td>146907.72</td>\n",
              "      <td>65552.86</td>\n",
              "      <td>29910.74</td>\n",
              "      <td>189228.04</td>\n",
              "      <td>88148.90</td>\n",
              "      <td>8830.40</td>\n",
              "      <td>...</td>\n",
              "      <td>1104.06</td>\n",
              "      <td>1291.04</td>\n",
              "      <td>3172.68</td>\n",
              "      <td>9240.86</td>\n",
              "      <td>1819.66</td>\n",
              "      <td>11055.82</td>\n",
              "      <td>4400.02</td>\n",
              "      <td>1876.68</td>\n",
              "      <td>5079.50</td>\n",
              "      <td>2506.20</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>410</th>\n",
              "      <td>172054.72</td>\n",
              "      <td>219817.80</td>\n",
              "      <td>161885.50</td>\n",
              "      <td>286727.38</td>\n",
              "      <td>128905.12</td>\n",
              "      <td>42092.70</td>\n",
              "      <td>20146.62</td>\n",
              "      <td>115115.24</td>\n",
              "      <td>42500.60</td>\n",
              "      <td>3969.42</td>\n",
              "      <td>...</td>\n",
              "      <td>1189.60</td>\n",
              "      <td>3801.54</td>\n",
              "      <td>3362.80</td>\n",
              "      <td>12402.60</td>\n",
              "      <td>2914.66</td>\n",
              "      <td>24746.88</td>\n",
              "      <td>5111.86</td>\n",
              "      <td>2320.00</td>\n",
              "      <td>2666.66</td>\n",
              "      <td>1504.04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>506</th>\n",
              "      <td>95079.30</td>\n",
              "      <td>140015.20</td>\n",
              "      <td>145993.52</td>\n",
              "      <td>205046.00</td>\n",
              "      <td>76639.42</td>\n",
              "      <td>69091.00</td>\n",
              "      <td>16002.22</td>\n",
              "      <td>116710.02</td>\n",
              "      <td>78114.94</td>\n",
              "      <td>5173.38</td>\n",
              "      <td>...</td>\n",
              "      <td>291.66</td>\n",
              "      <td>875.30</td>\n",
              "      <td>2241.68</td>\n",
              "      <td>7985.68</td>\n",
              "      <td>780.00</td>\n",
              "      <td>9231.56</td>\n",
              "      <td>1413.32</td>\n",
              "      <td>1315.02</td>\n",
              "      <td>3718.34</td>\n",
              "      <td>1538.32</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>507</th>\n",
              "      <td>175787.86</td>\n",
              "      <td>311156.28</td>\n",
              "      <td>359600.02</td>\n",
              "      <td>381366.06</td>\n",
              "      <td>206268.76</td>\n",
              "      <td>55949.34</td>\n",
              "      <td>23785.16</td>\n",
              "      <td>267688.64</td>\n",
              "      <td>92609.96</td>\n",
              "      <td>9062.54</td>\n",
              "      <td>...</td>\n",
              "      <td>1271.32</td>\n",
              "      <td>4842.08</td>\n",
              "      <td>3967.46</td>\n",
              "      <td>17228.16</td>\n",
              "      <td>3049.14</td>\n",
              "      <td>30061.52</td>\n",
              "      <td>4491.66</td>\n",
              "      <td>4377.52</td>\n",
              "      <td>5111.70</td>\n",
              "      <td>3660.82</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>259313</th>\n",
              "      <td>1689132.26</td>\n",
              "      <td>584191.98</td>\n",
              "      <td>673138.30</td>\n",
              "      <td>996936.90</td>\n",
              "      <td>501299.54</td>\n",
              "      <td>419211.02</td>\n",
              "      <td>198326.74</td>\n",
              "      <td>543442.92</td>\n",
              "      <td>332117.70</td>\n",
              "      <td>18413.90</td>\n",
              "      <td>...</td>\n",
              "      <td>9686.62</td>\n",
              "      <td>13884.26</td>\n",
              "      <td>14200.50</td>\n",
              "      <td>35648.68</td>\n",
              "      <td>17367.72</td>\n",
              "      <td>17121.52</td>\n",
              "      <td>14070.62</td>\n",
              "      <td>16613.52</td>\n",
              "      <td>18127.48</td>\n",
              "      <td>3155.40</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>259718</th>\n",
              "      <td>1236659.88</td>\n",
              "      <td>367928.24</td>\n",
              "      <td>392854.46</td>\n",
              "      <td>471735.40</td>\n",
              "      <td>208576.44</td>\n",
              "      <td>109540.16</td>\n",
              "      <td>88644.18</td>\n",
              "      <td>217812.66</td>\n",
              "      <td>130569.30</td>\n",
              "      <td>9860.76</td>\n",
              "      <td>...</td>\n",
              "      <td>6729.28</td>\n",
              "      <td>9241.50</td>\n",
              "      <td>8191.32</td>\n",
              "      <td>24890.44</td>\n",
              "      <td>4564.36</td>\n",
              "      <td>45568.74</td>\n",
              "      <td>1963.34</td>\n",
              "      <td>3576.00</td>\n",
              "      <td>6661.68</td>\n",
              "      <td>1220.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>260634</th>\n",
              "      <td>92261.30</td>\n",
              "      <td>146218.96</td>\n",
              "      <td>91249.04</td>\n",
              "      <td>164630.30</td>\n",
              "      <td>97093.68</td>\n",
              "      <td>75281.20</td>\n",
              "      <td>27944.44</td>\n",
              "      <td>7831.86</td>\n",
              "      <td>52653.72</td>\n",
              "      <td>7275.42</td>\n",
              "      <td>...</td>\n",
              "      <td>2723.50</td>\n",
              "      <td>1703.18</td>\n",
              "      <td>4745.04</td>\n",
              "      <td>6920.18</td>\n",
              "      <td>10993.30</td>\n",
              "      <td>4346.34</td>\n",
              "      <td>3154.74</td>\n",
              "      <td>3590.54</td>\n",
              "      <td>1723.30</td>\n",
              "      <td>1656.72</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>262752</th>\n",
              "      <td>122515.98</td>\n",
              "      <td>174412.42</td>\n",
              "      <td>136464.10</td>\n",
              "      <td>195772.78</td>\n",
              "      <td>117938.74</td>\n",
              "      <td>99538.66</td>\n",
              "      <td>68577.96</td>\n",
              "      <td>107329.58</td>\n",
              "      <td>100478.02</td>\n",
              "      <td>10571.42</td>\n",
              "      <td>...</td>\n",
              "      <td>1300.94</td>\n",
              "      <td>1028.10</td>\n",
              "      <td>5537.34</td>\n",
              "      <td>8714.72</td>\n",
              "      <td>5158.20</td>\n",
              "      <td>3629.76</td>\n",
              "      <td>3235.20</td>\n",
              "      <td>3651.02</td>\n",
              "      <td>3285.02</td>\n",
              "      <td>1438.88</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>263059</th>\n",
              "      <td>255399.56</td>\n",
              "      <td>415407.60</td>\n",
              "      <td>375802.90</td>\n",
              "      <td>496817.70</td>\n",
              "      <td>212518.08</td>\n",
              "      <td>167668.20</td>\n",
              "      <td>69554.56</td>\n",
              "      <td>233536.14</td>\n",
              "      <td>169119.56</td>\n",
              "      <td>10001.86</td>\n",
              "      <td>...</td>\n",
              "      <td>3373.24</td>\n",
              "      <td>3302.80</td>\n",
              "      <td>5564.28</td>\n",
              "      <td>10540.90</td>\n",
              "      <td>3003.34</td>\n",
              "      <td>9772.54</td>\n",
              "      <td>4021.80</td>\n",
              "      <td>5621.46</td>\n",
              "      <td>4204.94</td>\n",
              "      <td>487.34</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>500 rows × 140 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-0107dd21-9cdf-432c-8dee-89730d234829')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "    }\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",
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              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-0107dd21-9cdf-432c-8dee-89730d234829');\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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              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "              21         22         23         24         25         26    \\\n",
              "221      131736.66  214870.40  226352.88  239664.10  139431.82    2970.84   \n",
              "405      119200.08  160984.50  154409.96  271037.26  146907.72   65552.86   \n",
              "410      172054.72  219817.80  161885.50  286727.38  128905.12   42092.70   \n",
              "506       95079.30  140015.20  145993.52  205046.00   76639.42   69091.00   \n",
              "507      175787.86  311156.28  359600.02  381366.06  206268.76   55949.34   \n",
              "...            ...        ...        ...        ...        ...        ...   \n",
              "259313  1689132.26  584191.98  673138.30  996936.90  501299.54  419211.02   \n",
              "259718  1236659.88  367928.24  392854.46  471735.40  208576.44  109540.16   \n",
              "260634    92261.30  146218.96   91249.04  164630.30   97093.68   75281.20   \n",
              "262752   122515.98  174412.42  136464.10  195772.78  117938.74   99538.66   \n",
              "263059   255399.56  415407.60  375802.90  496817.70  212518.08  167668.20   \n",
              "\n",
              "             27         28         29        32    ...     3106      3301  \\\n",
              "221      11735.48  124619.08   16410.50   4669.46  ...   341.34   4767.98   \n",
              "405      29910.74  189228.04   88148.90   8830.40  ...  1104.06   1291.04   \n",
              "410      20146.62  115115.24   42500.60   3969.42  ...  1189.60   3801.54   \n",
              "506      16002.22  116710.02   78114.94   5173.38  ...   291.66    875.30   \n",
              "507      23785.16  267688.64   92609.96   9062.54  ...  1271.32   4842.08   \n",
              "...           ...        ...        ...       ...  ...      ...       ...   \n",
              "259313  198326.74  543442.92  332117.70  18413.90  ...  9686.62  13884.26   \n",
              "259718   88644.18  217812.66  130569.30   9860.76  ...  6729.28   9241.50   \n",
              "260634   27944.44    7831.86   52653.72   7275.42  ...  2723.50   1703.18   \n",
              "262752   68577.96  107329.58  100478.02  10571.42  ...  1300.94   1028.10   \n",
              "263059   69554.56  233536.14  169119.56  10001.86  ...  3373.24   3302.80   \n",
              "\n",
              "            4401      4502      4601      5501      6501      6701      8401  \\\n",
              "221      2473.32   9771.64   2781.64  22009.66    553.32   4171.62   2828.32   \n",
              "405      3172.68   9240.86   1819.66  11055.82   4400.02   1876.68   5079.50   \n",
              "410      3362.80  12402.60   2914.66  24746.88   5111.86   2320.00   2666.66   \n",
              "506      2241.68   7985.68    780.00   9231.56   1413.32   1315.02   3718.34   \n",
              "507      3967.46  17228.16   3049.14  30061.52   4491.66   4377.52   5111.70   \n",
              "...          ...       ...       ...       ...       ...       ...       ...   \n",
              "259313  14200.50  35648.68  17367.72  17121.52  14070.62  16613.52  18127.48   \n",
              "259718   8191.32  24890.44   4564.36  45568.74   1963.34   3576.00   6661.68   \n",
              "260634   4745.04   6920.18  10993.30   4346.34   3154.74   3590.54   1723.30   \n",
              "262752   5537.34   8714.72   5158.20   3629.76   3235.20   3651.02   3285.02   \n",
              "263059   5564.28  10540.90   3003.34   9772.54   4021.80   5621.46   4204.94   \n",
              "\n",
              "           8403  \n",
              "221      661.82  \n",
              "405     2506.20  \n",
              "410     1504.04  \n",
              "506     1538.32  \n",
              "507     3660.82  \n",
              "...         ...  \n",
              "259313  3155.40  \n",
              "259718  1220.00  \n",
              "260634  1656.72  \n",
              "262752  1438.88  \n",
              "263059   487.34  \n",
              "\n",
              "[500 rows x 140 columns]"
            ]
          },
          "execution_count": 36,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df_main_revenue = pd.DataFrame.from_dict(item_rev_pop, orient='index', columns=main_stores)\n",
        "df_main_revenue"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "JBYuoCl0iZiu"
      },
      "outputs": [],
      "source": [
        "df_main_revenue.to_csv('/content/drive/MyDrive/CourseWorkSAS/df_140stores_500revenue.csv', sep=';')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "M5e3fJUpjxQm"
      },
      "source": [
        "## Понедельная сборка"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qQ2HOay9u7G0",
        "outputId": "9883bcc0-7a08-4767-9fc8-437599f4dd16"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "46it [44:38, 58.22s/it]\n"
          ]
        }
      ],
      "source": [
        "items_set = set(popular_products)\n",
        "shops_set = set(bigs)\n",
        "date_matching = dict()\n",
        "\n",
        "for chunk in tqdm(pd.read_csv('/content/drive/MyDrive/CourseWorkSAS/df_fakeid_sended.csv', sep=';', chunksize=10**6)):\n",
        "  for i, row in chunk.iterrows():\n",
        "      if row['PICKUP_STORE_LOC_ID'] not in shops_set: # big shop\n",
        "          continue\n",
        "      if row['FAKE_ID'] not in items_set:             # popular item\n",
        "          continue\n",
        "      mon_date = ''\n",
        "      if row['PICKUP_DT'] in date_matching:\n",
        "          mon_date = date_matching[row['PICKUP_DT']]\n",
        "      else:\n",
        "          mon_date = monday_align(row['PICKUP_DT'])\n",
        "          date_matching[row['PICKUP_DT']] = mon_date\n",
        "      if mon_date < '2018-01-01' or mon_date > '2020-03-30':\n",
        "          continue\n",
        "      # bin search\n",
        "      l, r = 0, 500\n",
        "      while l < r - 1:\n",
        "        m = (l + r) // 2\n",
        "        if row['FAKE_ID'] < popular_products[m]:\n",
        "            r = m\n",
        "        else:\n",
        "            l = m\n",
        "      if popular_products[l] != row['FAKE_ID']:\n",
        "        print('alarm')\n",
        "      final_dict[(mon_date, row['PICKUP_STORE_LOC_ID'])][l] += row['REVENUE']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
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              "      <th>507</th>\n",
              "      <th>746</th>\n",
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              "      <th>1207</th>\n",
              "      <th>1532</th>\n",
              "      <th>1882</th>\n",
              "      <th>...</th>\n",
              "      <th>256746</th>\n",
              "      <th>256861</th>\n",
              "      <th>258055</th>\n",
              "      <th>258580</th>\n",
              "      <th>259313</th>\n",
              "      <th>259718</th>\n",
              "      <th>260634</th>\n",
              "      <th>261354</th>\n",
              "      <th>262752</th>\n",
              "      <th>263059</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>WEEK</th>\n",
              "      <th>STORE_ID</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",
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              "    <tr>\n",
              "      <th rowspan=\"5\" valign=\"top\">2018-01-01</th>\n",
              "      <th>21</th>\n",
              "      <td>503.40</td>\n",
              "      <td>271.52</td>\n",
              "      <td>1052.54</td>\n",
              "      <td>164.40</td>\n",
              "      <td>380.0</td>\n",
              "      <td>1812.88</td>\n",
              "      <td>305.08</td>\n",
              "      <td>1308.82</td>\n",
              "      <td>151.02</td>\n",
              "      <td>0.00</td>\n",
              "      <td>...</td>\n",
              "      <td>117.46</td>\n",
              "      <td>752.54</td>\n",
              "      <td>5949.84</td>\n",
              "      <td>466.10</td>\n",
              "      <td>5207.00</td>\n",
              "      <td>2227.12</td>\n",
              "      <td>374.40</td>\n",
              "      <td>255.94</td>\n",
              "      <td>542.40</td>\n",
              "      <td>1838.16</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>0.00</td>\n",
              "      <td>431.40</td>\n",
              "      <td>419.32</td>\n",
              "      <td>591.84</td>\n",
              "      <td>360.0</td>\n",
              "      <td>932.32</td>\n",
              "      <td>457.62</td>\n",
              "      <td>3344.74</td>\n",
              "      <td>295.08</td>\n",
              "      <td>324.92</td>\n",
              "      <td>...</td>\n",
              "      <td>43.90</td>\n",
              "      <td>267.36</td>\n",
              "      <td>448.48</td>\n",
              "      <td>966.30</td>\n",
              "      <td>3333.68</td>\n",
              "      <td>583.06</td>\n",
              "      <td>367.24</td>\n",
              "      <td>425.44</td>\n",
              "      <td>101.70</td>\n",
              "      <td>1372.90</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>186.44</td>\n",
              "      <td>407.28</td>\n",
              "      <td>549.14</td>\n",
              "      <td>0.00</td>\n",
              "      <td>180.0</td>\n",
              "      <td>1456.78</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>318.82</td>\n",
              "      <td>0.00</td>\n",
              "      <td>...</td>\n",
              "      <td>760.52</td>\n",
              "      <td>457.80</td>\n",
              "      <td>1793.90</td>\n",
              "      <td>161.02</td>\n",
              "      <td>1893.22</td>\n",
              "      <td>2366.12</td>\n",
              "      <td>169.50</td>\n",
              "      <td>967.44</td>\n",
              "      <td>203.40</td>\n",
              "      <td>2079.68</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>171.52</td>\n",
              "      <td>150.84</td>\n",
              "      <td>549.16</td>\n",
              "      <td>164.40</td>\n",
              "      <td>740.0</td>\n",
              "      <td>906.44</td>\n",
              "      <td>616.94</td>\n",
              "      <td>668.94</td>\n",
              "      <td>318.82</td>\n",
              "      <td>1696.80</td>\n",
              "      <td>...</td>\n",
              "      <td>32.04</td>\n",
              "      <td>597.68</td>\n",
              "      <td>2657.62</td>\n",
              "      <td>398.32</td>\n",
              "      <td>3816.14</td>\n",
              "      <td>1894.10</td>\n",
              "      <td>159.32</td>\n",
              "      <td>946.96</td>\n",
              "      <td>296.64</td>\n",
              "      <td>915.28</td>\n",
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              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>540.68</td>\n",
              "      <td>844.72</td>\n",
              "      <td>549.16</td>\n",
              "      <td>470.20</td>\n",
              "      <td>972.0</td>\n",
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              "      <td>959.32</td>\n",
              "      <td>1454.24</td>\n",
              "      <td>151.02</td>\n",
              "      <td>696.76</td>\n",
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              "      <td>208.82</td>\n",
              "      <td>151.02</td>\n",
              "      <td>7408.14</td>\n",
              "      <td>659.60</td>\n",
              "      <td>280.16</td>\n",
              "      <td>279.66</td>\n",
              "      <td>302.72</td>\n",
              "      <td>486.28</td>\n",
              "      <td>635.66</td>\n",
              "      <td>686.46</td>\n",
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            "text/plain": [
              "                     221     405      410     506     507      746     978     \\\n",
              "WEEK       STORE_ID                                                             \n",
              "2018-01-01 21        503.40  271.52  1052.54  164.40   380.0  1812.88  305.08   \n",
              "           22          0.00  431.40   419.32  591.84   360.0   932.32  457.62   \n",
              "           23        186.44  407.28   549.14    0.00   180.0  1456.78    0.00   \n",
              "           24        171.52  150.84   549.16  164.40   740.0   906.44  616.94   \n",
              "           25        540.68  844.72   549.16  470.20   972.0  1780.50  959.32   \n",
              "\n",
              "                      1207    1532     1882    ...  256746  256861   258055  \\\n",
              "WEEK       STORE_ID                            ...                            \n",
              "2018-01-01 21        1308.82  151.02     0.00  ...  117.46  752.54  5949.84   \n",
              "           22        3344.74  295.08   324.92  ...   43.90  267.36   448.48   \n",
              "           23           0.00  318.82     0.00  ...  760.52  457.80  1793.90   \n",
              "           24         668.94  318.82  1696.80  ...   32.04  597.68  2657.62   \n",
              "           25        1454.24  151.02   696.76  ...  208.82  151.02  7408.14   \n",
              "\n",
              "                     258580   259313   259718  260634  261354  262752   263059  \n",
              "WEEK       STORE_ID                                                             \n",
              "2018-01-01 21        466.10  5207.00  2227.12  374.40  255.94  542.40  1838.16  \n",
              "           22        966.30  3333.68   583.06  367.24  425.44  101.70  1372.90  \n",
              "           23        161.02  1893.22  2366.12  169.50  967.44  203.40  2079.68  \n",
              "           24        398.32  3816.14  1894.10  159.32  946.96  296.64   915.28  \n",
              "           25        659.60   280.16   279.66  302.72  486.28  635.66   686.46  \n",
              "\n",
              "[5 rows x 500 columns]"
            ]
          },
          "execution_count": 139,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "iterables = [datestr_from, bigs]\n",
        "mltindex = pd.MultiIndex.from_product(iterables, names=[\"WEEK\", \"STORE_ID\"])\n",
        "\n",
        "final_df = pd.DataFrame(data=final_dict.values(), columns=popular_products, index=mltindex)\n",
        "final_df.head(5)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
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        },
        "id": "bkEZAtGZyq6N",
        "outputId": "31228330-7ff6-4af5-889e-c52f27ca329d"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "221       123\n",
              "405       125\n",
              "410       138\n",
              "506       207\n",
              "507        76\n",
              "         ... \n",
              "259718    103\n",
              "260634     97\n",
              "261354    167\n",
              "262752     73\n",
              "263059    128\n",
              "Length: 500, dtype: int64"
            ]
          },
          "execution_count": 140,
          "metadata": {},
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      ],
      "source": [
        "(final_df == 0).sum(axis=0)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "B6sR6GvezW5a"
      },
      "outputs": [],
      "source": [
        "final_df.to_csv('/content/drive/MyDrive/CourseWorkSAS/df_pop_week_revenue.csv', sep=';')"
      ]
    },
    {
      "cell_type": "code",
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          "data": {
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              "14962426.917"
            ]
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          "execution_count": 120,
          "metadata": {},
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      "source": [
        "final_df[87251].sum()"
      ]
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    {
      "cell_type": "code",
      "execution_count": null,
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        "outputId": "c2290e13-13cb-4712-8640-d95beefb0a7e"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "35263"
            ]
          },
          "execution_count": 122,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df_big_popular.loc[87251].sum()"
      ]
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    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
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        "id": "57CZslipwoyV",
        "outputId": "e9c757b8-e939-490f-9ed4-922def5eef81"
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      "outputs": [
        {
          "data": {
            "text/plain": [
              "PRODUCT_ID                                        70120\n",
              "PRODUCT_NM         Саморез п/металлу TN 3,5х25 мм Knauf\n",
              "PRODUCT_LVL_NM1                    Сопутствующие товары\n",
              "PRODUCT_LVL_NM2                         Метизы и крепеж\n",
              "PRODUCT_LVL_NM3                                  Метизы\n",
              "PRODUCT_LVL_NM4                     Саморезы по металлу\n",
              "FAKE_ID                                           87251\n",
              "Name: 87251, dtype: object"
            ]
          },
          "execution_count": 121,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df_product.loc[87251]"
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    {
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        "id": "3mMqg42mxwoJ",
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              "      <td>Пиломатериал и погонажные изделия</td>\n",
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              "      <td>219860.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>223394</th>\n",
              "      <td>238272.0</td>\n",
              "      <td>Мешки для мусора 120л, рул/25шт 40мкм</td>\n",
              "      <td>Сопутствующие товары</td>\n",
              "      <td>Хозтовары</td>\n",
              "      <td>Хозтовары</td>\n",
              "      <td>Мешки для строительного мусора</td>\n",
              "      <td>223394.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>43152</th>\n",
              "      <td>5831.0</td>\n",
              "      <td>Набор для газовой пайки 58кВт, 1 насадка</td>\n",
              "      <td>Сопутствующие товары</td>\n",
              "      <td>Инструмент ручной</td>\n",
              "      <td>Инструмент специализированный</td>\n",
              "      <td>Инструмент специализированный прочий</td>\n",
              "      <td>43152.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>100391</th>\n",
              "      <td>123296.0</td>\n",
              "      <td>Угол внутренний Wimar 58мм Дуб ведре 2ш</td>\n",
              "      <td>Внутренняя отделка</td>\n",
              "      <td>Напольные покрытия</td>\n",
              "      <td>Плинтусы и комплектующие</td>\n",
              "      <td>Углы для плинтусов</td>\n",
              "      <td>100391.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>124738</th>\n",
              "      <td>123250.0</td>\n",
              "      <td>Заглушки Wimar 58мм Дуб ведре 2ш</td>\n",
              "      <td>Внутренняя отделка</td>\n",
              "      <td>Напольные покрытия</td>\n",
              "      <td>Плинтусы и комплектующие</td>\n",
              "      <td>Заглушки для плинтусов</td>\n",
              "      <td>124738.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>91313</th>\n",
              "      <td>32300.0</td>\n",
              "      <td>Хомут обжимной 25х40 (2 шт)</td>\n",
              "      <td>Сопутствующие товары</td>\n",
              "      <td>Метизы и крепеж</td>\n",
              "      <td>Такелаж</td>\n",
              "      <td>Хомуты червячные</td>\n",
              "      <td>91313.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>241699</th>\n",
              "      <td>33162.0</td>\n",
              "      <td>Бита с торцевой головкой 8х45мм, 2шт</td>\n",
              "      <td>Сопутствующие товары</td>\n",
              "      <td>Электроинструмент и расходники</td>\n",
              "      <td>Биты</td>\n",
              "      <td>Насадки-биты</td>\n",
              "      <td>241699.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-3363f9f0-e07e-4d4e-aec2-274a35564db9')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "  </svg>\n",
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              "      \n",
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              "    .colab-df-container {\n",
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              "    .colab-df-convert:hover {\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
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              "          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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              "  "
            ],
            "text/plain": [
              "        PRODUCT_ID                                PRODUCT_NM  \\\n",
              "223394    238272.0     Мешки для мусора 120л, рул/25шт 40мкм   \n",
              "159240     58775.0         Брикеты топливные 12 шт. Pini-Kay   \n",
              "160840    170066.0            Евродрова РУФ уп. 12 шт. 10 кг   \n",
              "219860     49008.0  Пленка полиэтилен. фас. ТУ 60мкр (3х10м)   \n",
              "223394    238272.0     Мешки для мусора 120л, рул/25шт 40мкм   \n",
              "43152       5831.0  Набор для газовой пайки 58кВт, 1 насадка   \n",
              "100391    123296.0   Угол внутренний Wimar 58мм Дуб ведре 2ш   \n",
              "124738    123250.0          Заглушки Wimar 58мм Дуб ведре 2ш   \n",
              "91313      32300.0               Хомут обжимной 25х40 (2 шт)   \n",
              "241699     33162.0      Бита с торцевой головкой 8х45мм, 2шт   \n",
              "\n",
              "             PRODUCT_LVL_NM1                    PRODUCT_LVL_NM2  \\\n",
              "223394  Сопутствующие товары                          Хозтовары   \n",
              "159240             Общестрой  Пиломатериал и погонажные изделия   \n",
              "160840             Общестрой  Пиломатериал и погонажные изделия   \n",
              "219860             Общестрой                      Теплоизоляция   \n",
              "223394  Сопутствующие товары                          Хозтовары   \n",
              "43152   Сопутствующие товары                  Инструмент ручной   \n",
              "100391    Внутренняя отделка                 Напольные покрытия   \n",
              "124738    Внутренняя отделка                 Напольные покрытия   \n",
              "91313   Сопутствующие товары                    Метизы и крепеж   \n",
              "241699  Сопутствующие товары     Электроинструмент и расходники   \n",
              "\n",
              "                          PRODUCT_LVL_NM3  \\\n",
              "223394                          Хозтовары   \n",
              "159240  Пиломатериал и погонажные изделия   \n",
              "160840  Пиломатериал и погонажные изделия   \n",
              "219860  Изоляционный, укрывочный материал   \n",
              "223394                          Хозтовары   \n",
              "43152       Инструмент специализированный   \n",
              "100391           Плинтусы и комплектующие   \n",
              "124738           Плинтусы и комплектующие   \n",
              "91313                             Такелаж   \n",
              "241699                               Биты   \n",
              "\n",
              "                             PRODUCT_LVL_NM4   FAKE_ID  \n",
              "223394        Мешки для строительного мусора  223394.0  \n",
              "159240                                 Дрова  159240.0  \n",
              "160840                                 Дрова  160840.0  \n",
              "219860                   Укрывочный материал  219860.0  \n",
              "223394        Мешки для строительного мусора  223394.0  \n",
              "43152   Инструмент специализированный прочий   43152.0  \n",
              "100391                    Углы для плинтусов  100391.0  \n",
              "124738                Заглушки для плинтусов  124738.0  \n",
              "91313                       Хомуты червячные   91313.0  \n",
              "241699                          Насадки-биты  241699.0  "
            ]
          },
          "execution_count": 135,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "read_receipt(chunk[chunk['UNIT_BASE'] == 'UP']['FAKE_ID'].values[:10], df_product)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sydLvUKRxxS1",
        "outputId": "aa780332-eabd-4e8a-f4ff-b18cdab8d52a"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "ST     456932\n",
              "UP      47750\n",
              "MSH     31668\n",
              "KG      17492\n",
              "PAA     13575\n",
              "M        9853\n",
              "LST      7651\n",
              "PM       5334\n",
              "VDR      3964\n",
              "ROL      3892\n",
              "KMP      2808\n",
              "BAG      1425\n",
              "10        827\n",
              "L         729\n",
              "M2        600\n",
              "BN        215\n",
              "KAR       144\n",
              "TJK        58\n",
              "MKR        56\n",
              "BCH        29\n",
              "MST        28\n",
              "TO          6\n",
              "PAL         4\n",
              "Name: UNIT_BASE, dtype: int64"
            ]
          },
          "execution_count": 128,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "chunk['UNIT_BASE'].value_counts()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "f-QzaQfO0e1R"
      },
      "outputs": [],
      "source": [
        ""
      ]
    }
  ],
  "metadata": {
    "colab": {
      "collapsed_sections": [],
      "name": "1 Feature Mining.ipynb",
      "toc_visible": true,
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
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    "language_info": {
      "name": "python"
    }
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}