generate.ipynb

{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Старая структура\n",
    "\n",
    "Структура имела дефект в сущности sup_to_prod. По-сути она являлась бесползной, так как не давала информации сколько товаров за день продал каждый постащик, ввиду остуствия привязки каждого товара к конкретному поставщику.\n",
    "\n",
    "**product_categories**  \n",
    "|- id  \n",
    "|- product_category  \n",
    "\n",
    "**products**  \n",
    "|- id  \n",
    "|- name  \n",
    "|- product_category_id  \n",
    "|- country_from  \n",
    "\n",
    "**sup_to_prod**  \n",
    "|- id  \n",
    "|- product_id  \n",
    "|- supplier_id  \n",
    "\n",
    "**sell**  \n",
    "|- id  \n",
    "|- product_id  \n",
    "|- quantity  \n",
    "|- sell_date  \n",
    "\n",
    "**suppliers**  \n",
    "|- id  \n",
    "|- supplier_name  \n",
    "|- address  \n",
    "\n",
    "### Новая структура\n",
    "\n",
    "**product_categories**  \n",
    "|- id  \n",
    "|- product_category \n",
    "\n",
    "**products**  \n",
    "|- id  \n",
    "|- name  \n",
    "|- product_category_id  \n",
    "|- country_from \n",
    "\n",
    "**sell**  \n",
    "|- id  \n",
    "|- product_id  \n",
    "|- <span style=\"color: red\">supplier_id</span>  \n",
    "|- quantity  \n",
    "|- sell_date  \n",
    "\n",
    "**suppliers**  \n",
    "|- id  \n",
    "|- supplier_name  \n",
    "|- address "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 365,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from random import randint\n",
    "from random import sample\n",
    "from random import uniform\n",
    "import datetime\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 366,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>product_category</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Телефон</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Монитор</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Мышка</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Клавиатура</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Ноутбук</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Игровая приставка</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Наушники</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     product_category\n",
       "id                   \n",
       "1             Телефон\n",
       "2             Монитор\n",
       "3               Мышка\n",
       "4          Клавиатура\n",
       "5             Ноутбук\n",
       "6   Игровая приставка\n",
       "7            Наушники"
      ]
     },
     "execution_count": 366,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Считываем сущность product_category\n",
    "categories = pd.read_excel('NewData.xlsx', sheet_name='sr.1', index_col='id')\n",
    "categories"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 367,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(157, 3)\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>product_category_id</th>\n",
       "      <th>country_from</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Realme 9 Pro 8/128 Gb</td>\n",
       "      <td>1</td>\n",
       "      <td>Китай</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Redmi Note 10 Pro 8/128 Gb</td>\n",
       "      <td>1</td>\n",
       "      <td>Китай</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Poco X3 Pro 8/256 Gb</td>\n",
       "      <td>1</td>\n",
       "      <td>Китай</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Redmi Note 11 Pro 5G 8/128 GB</td>\n",
       "      <td>1</td>\n",
       "      <td>Китай</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Honor 50 8/128 Gb</td>\n",
       "      <td>1</td>\n",
       "      <td>Китай</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             name  product_category_id country_from\n",
       "id                                                                 \n",
       "1           Realme 9 Pro 8/128 Gb                    1        Китай\n",
       "2      Redmi Note 10 Pro 8/128 Gb                    1        Китай\n",
       "3           Poco X3 Pro 8/256 Gb                     1        Китай\n",
       "4   Redmi Note 11 Pro 5G 8/128 GB                    1        Китай\n",
       "5               Honor 50 8/128 Gb                    1        Китай"
      ]
     },
     "execution_count": 367,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Считываем сущность products\n",
    "products = pd.read_excel('NewData.xlsx', sheet_name='sr.2', index_col='id')\n",
    "print(products.shape)\n",
    "products.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 368,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>product_id</th>\n",
       "      <th>supplier_id</th>\n",
       "      <th>quantity</th>\n",
       "      <th>sell_date</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [product_id, supplier_id, quantity, sell_date]\n",
       "Index: []"
      ]
     },
     "execution_count": 368,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Считываем сущность sells - она пустая\n",
    "sells = pd.read_excel('NewData.xlsx', sheet_name='sr.3', index_col='id')\n",
    "sells"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 369,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>supplier_name</th>\n",
       "      <th>address</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Пост_1</td>\n",
       "      <td>США</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Пост_1</td>\n",
       "      <td>Англия</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Пост_1</td>\n",
       "      <td>Швейцария</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Пост_1</td>\n",
       "      <td>Нидерланды</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Пост_1</td>\n",
       "      <td>Швейцария</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Пост_1</td>\n",
       "      <td>Корея</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Пост_1</td>\n",
       "      <td>Индия</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Пост_2</td>\n",
       "      <td>США</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Пост_2</td>\n",
       "      <td>Англия</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Пост_2</td>\n",
       "      <td>Турция</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Пост_2</td>\n",
       "      <td>Казахстан</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Пост_2</td>\n",
       "      <td>Армения</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Пост_2</td>\n",
       "      <td>Корея</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Пост_2</td>\n",
       "      <td>Индия</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Пост_2</td>\n",
       "      <td>Япония</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Пост_3</td>\n",
       "      <td>Китай</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Пост_3</td>\n",
       "      <td>Тайвань</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Пост_3</td>\n",
       "      <td>Корея</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Пост_3</td>\n",
       "      <td>Индия</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Пост_3</td>\n",
       "      <td>Япония</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   supplier_name     address\n",
       "id                          \n",
       "1         Пост_1         США\n",
       "2         Пост_1      Англия\n",
       "3         Пост_1   Швейцария\n",
       "4         Пост_1  Нидерланды\n",
       "5         Пост_1   Швейцария\n",
       "6         Пост_1       Корея\n",
       "7         Пост_1       Индия\n",
       "8         Пост_2         США\n",
       "9         Пост_2      Англия\n",
       "10        Пост_2      Турция\n",
       "11        Пост_2   Казахстан\n",
       "12        Пост_2     Армения\n",
       "13        Пост_2       Корея\n",
       "14        Пост_2       Индия\n",
       "15        Пост_2      Япония\n",
       "16        Пост_3       Китай\n",
       "17        Пост_3     Тайвань\n",
       "18        Пост_3       Корея\n",
       "19        Пост_3       Индия\n",
       "20        Пост_3      Япония"
      ]
     },
     "execution_count": 369,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Считываем сущность suppliers \n",
    "suppliers = pd.read_excel('NewData.xlsx', sheet_name='sr.4', index_col='id')\n",
    "suppliers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 370,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>supplier_id</th>\n",
       "      <th>product_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [supplier_id, product_id]\n",
       "Index: []"
      ]
     },
     "execution_count": 370,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Считываем сущность sup_to_prod - она пустая\n",
    "# sup_to_prod = pd.read_excel('Data.xlsx', sheet_name='sr.5', index_col='id')\n",
    "# sup_to_prod\n",
    "\n",
    "# Создаем рабочую таблицу sup_to_prod\n",
    "sup_to_prod = pd.DataFrame(columns=['supplier_id', 'product_id'])\n",
    "sup_to_prod"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 371,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Заполняем sup_to_prod\n",
    "# Пусть каждый Поставщик имеет в своем каталоге от 30 до 45 наименований товаров\n",
    "for sup_id in suppliers['supplier_name'].index:\n",
    "    sup_products = sample(products.index.to_list(), randint(30, 45))\n",
    "    for prod_id in sup_products:\n",
    "        sup_to_prod.loc[len(sup_to_prod.index)] = [sup_id, prod_id]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 372,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
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       "      <th>product_id</th>\n",
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       "      <th>25</th>\n",
       "      <td>1</td>\n",
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       "      <td>1</td>\n",
       "      <td>127</td>\n",
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       "      <th>27</th>\n",
       "      <td>1</td>\n",
       "      <td>16</td>\n",
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       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>1</td>\n",
       "      <td>117</td>\n",
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       "      <th>29</th>\n",
       "      <td>1</td>\n",
       "      <td>129</td>\n",
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       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>1</td>\n",
       "      <td>96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
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       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>1</td>\n",
       "      <td>72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>1</td>\n",
       "      <td>73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>1</td>\n",
       "      <td>56</td>\n",
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       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>1</td>\n",
       "      <td>21</td>\n",
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       "      <th>37</th>\n",
       "      <td>1</td>\n",
       "      <td>147</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>1</td>\n",
       "      <td>152</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>1</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    supplier_id  product_id\n",
       "25            1          19\n",
       "26            1         127\n",
       "27            1          16\n",
       "28            1         117\n",
       "29            1         129\n",
       "30            1          96\n",
       "31            1           6\n",
       "32            1          72\n",
       "33            1          73\n",
       "34            1           9\n",
       "35            1          56\n",
       "36            1          21\n",
       "37            1         147\n",
       "38            1         152\n",
       "39            1           3\n",
       "40            1          27"
      ]
     },
     "execution_count": 372,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sup_to_prod.loc[25:40]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 373,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1109\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "    }\n",
       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>StartDate</th>\n",
       "      <th>FinishDate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2019-01-01</td>\n",
       "      <td>2018-12-25</td>\n",
       "      <td>2019-01-08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2019-02-23</td>\n",
       "      <td>2019-02-16</td>\n",
       "      <td>2019-03-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2019-03-08</td>\n",
       "      <td>2019-03-01</td>\n",
       "      <td>2019-03-15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2019-05-09</td>\n",
       "      <td>2019-05-02</td>\n",
       "      <td>2019-05-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2019-06-12</td>\n",
       "      <td>2019-06-05</td>\n",
       "      <td>2019-06-19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2020-01-01</td>\n",
       "      <td>2019-12-25</td>\n",
       "      <td>2020-01-08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2020-02-23</td>\n",
       "      <td>2020-02-16</td>\n",
       "      <td>2020-03-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2020-03-07</td>\n",
       "      <td>2020-02-29</td>\n",
       "      <td>2020-03-14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2020-05-08</td>\n",
       "      <td>2020-05-01</td>\n",
       "      <td>2020-05-15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2020-06-11</td>\n",
       "      <td>2020-06-04</td>\n",
       "      <td>2020-06-18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2020-12-31</td>\n",
       "      <td>2020-12-24</td>\n",
       "      <td>2021-01-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2021-02-22</td>\n",
       "      <td>2021-02-15</td>\n",
       "      <td>2021-03-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2021-03-07</td>\n",
       "      <td>2021-02-28</td>\n",
       "      <td>2021-03-14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2021-05-08</td>\n",
       "      <td>2021-05-01</td>\n",
       "      <td>2021-05-15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2021-06-11</td>\n",
       "      <td>2021-06-04</td>\n",
       "      <td>2021-06-18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2021-12-31</td>\n",
       "      <td>2021-12-24</td>\n",
       "      <td>2022-01-07</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Date  StartDate FinishDate\n",
       "0  2019-01-01 2018-12-25 2019-01-08\n",
       "1  2019-02-23 2019-02-16 2019-03-02\n",
       "2  2019-03-08 2019-03-01 2019-03-15\n",
       "3  2019-05-09 2019-05-02 2019-05-16\n",
       "4  2019-06-12 2019-06-05 2019-06-19\n",
       "5  2020-01-01 2019-12-25 2020-01-08\n",
       "6  2020-02-23 2020-02-16 2020-03-01\n",
       "7  2020-03-07 2020-02-29 2020-03-14\n",
       "8  2020-05-08 2020-05-01 2020-05-15\n",
       "9  2020-06-11 2020-06-04 2020-06-18\n",
       "10 2020-12-31 2020-12-24 2021-01-07\n",
       "11 2021-02-22 2021-02-15 2021-03-01\n",
       "12 2021-03-07 2021-02-28 2021-03-14\n",
       "13 2021-05-08 2021-05-01 2021-05-15\n",
       "14 2021-06-11 2021-06-04 2021-06-18\n",
       "15 2021-12-31 2021-12-24 2022-01-07"
      ]
     },
     "execution_count": 373,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# задаем праздничные даты за 2019 год\n",
    "important_dates_list_first = ['2019-01-01', '2019-02-23', '2019-03-08', '2019-05-09', '2019-06-12']\n",
    "important_dates_list_first = list(map(lambda x: datetime.datetime.strptime(x, '%Y-%m-%d'), important_dates_list_first))\n",
    "\n",
    "# задаем праздничные даты за 2019, 2020, 2021, 2022 годы\n",
    "important_dates_list = []\n",
    "for i in range(4):\n",
    "    for day in important_dates_list_first:\n",
    "        important_dates_list.append(day + datetime.timedelta(days=365*i))\n",
    "\n",
    "# создаем датафрейм и задаем для каждой праздничной даты двух недельный период акций\n",
    "important_dates = pd.DataFrame(important_dates_list, columns=['Date'])[:16]\n",
    "important_dates['StartDate'] = important_dates['Date'].apply(lambda x: x - datetime.timedelta(days=7))\n",
    "important_dates['FinishDate'] = important_dates['Date'].apply(lambda x: x + datetime.timedelta(days=7))\n",
    "\n",
    "# вытаскиваем начало и конец временного среза\n",
    "start_day = important_dates.loc[0, 'StartDate']\n",
    "finish_day = important_dates.loc[important_dates.index[-1], 'FinishDate']\n",
    "\n",
    "# считаем количество дней во временном срезе и задаем все дни\n",
    "all_time = (finish_day - start_day).days\n",
    "all_days = [start_day + datetime.timedelta(days=day_num) for day_num in range(all_time+1)]\n",
    "\n",
    "print(all_time)\n",
    "important_dates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 374,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>product_id</th>\n",
       "      <th>quality</th>\n",
       "      <th>mean_day_quality</th>\n",
       "      <th>power</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>175964</td>\n",
       "      <td>158</td>\n",
       "      <td>2.904839</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>137522</td>\n",
       "      <td>124</td>\n",
       "      <td>2.113865</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>109358</td>\n",
       "      <td>98</td>\n",
       "      <td>2.743129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>15275</td>\n",
       "      <td>13</td>\n",
       "      <td>2.943780</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>39323</td>\n",
       "      <td>35</td>\n",
       "      <td>2.984666</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   product_id  quality  mean_day_quality     power\n",
       "0           1   175964               158  2.904839\n",
       "1           2   137522               124  2.113865\n",
       "2           3   109358                98  2.743129\n",
       "3           4    15275                13  2.943780\n",
       "4           5    39323                35  2.984666"
      ]
     },
     "execution_count": 374,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# создаем вспомагательную таблицу quality\n",
    "# сзадаем каждому товару общее количество продаж\n",
    "quality =pd.DataFrame(\n",
    "    {\n",
    "        'product_id': products.index,\n",
    "        'quality': [randint(10000, 200000) for i in products.index] # количество продаж от 10 тыс. до 200 тыс.\n",
    "    }\n",
    ")\n",
    "\n",
    "# задаем среднее количество продаж за день для каждого товара\n",
    "quality['mean_day_quality'] = quality['quality'].apply(lambda x: x // all_time)\n",
    "\n",
    "# задаем мощность товара, т.е. восколько раз максимально увеличатся продажи в праздничные дни\n",
    "quality['power'] = [uniform(1.5, 3) for i in products.index]\n",
    "\n",
    "quality.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 375,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Добавляем технические колонки tmp и заполняем 1, чтобы произвести cross join\n",
    "sup_to_prod['tmp'] = 1\n",
    "all_days_df = pd.DataFrame(all_days, columns=['Date'])\n",
    "all_days_df['tmp'] = 1\n",
    "\n",
    "# Cross join всех записей из sup_to_prod для всех дат\n",
    "buf_sells = pd.merge(sup_to_prod, all_days_df, on=['tmp'])\n",
    "buf_sells = buf_sells.drop('tmp', axis=1)\n",
    "\n",
    "# Заполняем таблицу sells\n",
    "sells['supplier_id'] = buf_sells['supplier_id']\n",
    "sells['product_id'] = buf_sells['product_id']\n",
    "sells['sell_date'] = buf_sells['Date']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 376,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>42</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>42</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>2018-12-26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>42</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>2018-12-27</td>\n",
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       "      <th>3</th>\n",
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       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2018-12-28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>42</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2018-12-29</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    product_id  supplier_id quantity  sell_date\n",
       "id                                             \n",
       "0           42            1      NaN 2018-12-25\n",
       "1           42            1      NaN 2018-12-26\n",
       "2           42            1      NaN 2018-12-27\n",
       "3           42            1      NaN 2018-12-28\n",
       "4           42            1      NaN 2018-12-29"
      ]
     },
     "execution_count": 376,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sells.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 377,
   "metadata": {},
   "outputs": [],
   "source": [
    "# заполняем знаковые даты\n",
    "\n",
    "for prod_id in quality.product_id:\n",
    "    for imp_day in important_dates.index:\n",
    "        # ищем праздничные периоды для товара в таблице sells\n",
    "        sdt = important_dates.loc[imp_day].StartDate\n",
    "        fdt = important_dates.loc[imp_day].FinishDate\n",
    "        imp_prd = sells.loc[(sells.sell_date >= sdt) & (sells.sell_date <= fdt) & (sells.product_id == prod_id)]\n",
    "        imp_prd_ind = imp_prd.index\n",
    "        imp_prd_nsup = imp_prd.supplier_id.nunique()\n",
    "\n",
    "        # достаем из нормального распредления продажи для праздничных дней\n",
    "        size = len(imp_prd_ind)\n",
    "        norm_value = quality.loc[quality.product_id == prod_id, 'mean_day_quality']\n",
    "        norm_value = norm_value / imp_prd_nsup\n",
    "        mu = norm_value * quality.loc[quality.product_id == prod_id, 'power'] # увеличенные продажи\n",
    "        sigma = mu - norm_value # разброс до значений обыкновенного дня\n",
    "        sells.loc[imp_prd_ind, 'quantity'] = [int(abs(value)) for value in np.random.normal(loc=mu, scale=sigma, size=size)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 378,
   "metadata": {},
   "outputs": [],
   "source": [
    "# заполняем остальные данные\n",
    "\n",
    "for prod_id in quality.product_id:\n",
    "    # находим обыкновенные дни\n",
    "\n",
    "    smp_day = sells.loc[(sells.quantity.isna()) & (sells.product_id==prod_id)]\n",
    "    smp_day_ind = smp_day.index\n",
    "    smp_day_nsup = smp_day.supplier_id.nunique()\n",
    "\n",
    "    # достаем из нормального распредления продажи для обыкновенных дней\n",
    "    size = len(smp_day_ind)\n",
    "    mu = quality.loc[quality.product_id == prod_id, 'mean_day_quality'] # уровень прожаж обыкновенного дня\n",
    "    mu = mu / smp_day_nsup\n",
    "    sigma = mu * 0.2 # разброс в 20%\n",
    "    sells.loc[smp_day_ind, 'quantity'] = [int(abs(value)) for value in np.random.normal(loc=mu, scale=sigma, size=size)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 379,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>supplier_id</th>\n",
       "      <th>quantity</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
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       "      <td>33</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>42</td>\n",
       "      <td>1</td>\n",
       "      <td>34</td>\n",
       "      <td>2018-12-26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>42</td>\n",
       "      <td>1</td>\n",
       "      <td>68</td>\n",
       "      <td>2018-12-27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>42</td>\n",
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       "      <td>91</td>\n",
       "      <td>2018-12-28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>42</td>\n",
       "      <td>1</td>\n",
       "      <td>81</td>\n",
       "      <td>2018-12-29</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    product_id  supplier_id quantity  sell_date\n",
       "id                                             \n",
       "0           42            1       33 2018-12-25\n",
       "1           42            1       34 2018-12-26\n",
       "2           42            1       68 2018-12-27\n",
       "3           42            1       91 2018-12-28\n",
       "4           42            1       81 2018-12-29"
      ]
     },
     "execution_count": 379,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sells.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 380,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.figure(figsize=(16,4))\n",
    "prd_id = 52\n",
    "plt.plot(sells[sells.product_id==prd_id].sort_values('sell_date').sell_date, sells[sells.product_id==prd_id].sort_values('sell_date').quantity)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 381,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/7g/1dqbz5hx5994z2r91kw1_k2m0000gn/T/ipykernel_64847/3297861658.py:11: FutureWarning: save is not part of the public API, usage can give unexpected results and will be removed in a future version\n",
      "  writer.save()\n"
     ]
    }
   ],
   "source": [
    "# записываем данные в файл DataFull.xlsx\n",
    "\n",
    "writer = pd.ExcelWriter('NewDataFull.xlsx', engine='xlsxwriter')\n",
    "\n",
    "categories.to_excel(writer, sheet_name='sr.1')\n",
    "products.to_excel(writer, sheet_name='sr.2')\n",
    "sells.to_excel(writer, sheet_name='sr.3')\n",
    "suppliers.to_excel(writer, sheet_name='sr.4')\n",
    "sup_to_prod.to_excel(writer, sheet_name='sr.5')\n",
    "\n",
    "writer.save()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.9.7 ('venv': venv)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7 (default, Sep 16 2021, 08:50:36) \n[Clang 10.0.0 ]"
  },
  "orig_nbformat": 4,
  "vscode": {
   "interpreter": {
    "hash": "40ca304b2191cffc02cb85f40aa0186dcb7185b239ab460b99d4b6058071a0bb"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}