sbermarket_analytics_only.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from tqdm import tqdm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "folder_address = '/Users/macbook/Downloads/test_tasks/sbermarket/sbermarket-internship-competition/'\n",
    "data = pd.read_csv(folder_address + 'train.csv')\n",
    "sample = pd.read_csv(folder_address + 'sample_submission.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>order_completed_at</th>\n",
       "      <th>cart</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <td>0</td>\n",
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       "      <td>2015-03-22 09:25:46</td>\n",
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       "      <td>3</td>\n",
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       "      <td>2015-03-22 09:25:46</td>\n",
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       "      <td>2020-09-03 23:45:45</td>\n",
       "      <td>92</td>\n",
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       "      <td>2020-09-03 23:45:45</td>\n",
       "      <td>431</td>\n",
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       "      <td>3123062</td>\n",
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       "      <td>2020-09-03 23:45:45</td>\n",
       "      <td>24</td>\n",
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       "<p>3123064 rows × 3 columns</p>\n",
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      ],
      "text/plain": [
       "         user_id   order_completed_at  cart\n",
       "0              2  2015-03-22 09:25:46   399\n",
       "1              2  2015-03-22 09:25:46    14\n",
       "2              2  2015-03-22 09:25:46   198\n",
       "3              2  2015-03-22 09:25:46    88\n",
       "4              2  2015-03-22 09:25:46   157\n",
       "...          ...                  ...   ...\n",
       "3123059    12702  2020-09-03 23:45:45   441\n",
       "3123060    12702  2020-09-03 23:45:45    92\n",
       "3123061    12702  2020-09-03 23:45:45   431\n",
       "3123062    12702  2020-09-03 23:45:45    24\n",
       "3123063    12702  2020-09-03 23:45:45   430\n",
       "\n",
       "[3123064 rows x 3 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data\n",
    "#sample"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 3123064 entries, 0 to 3123063\n",
      "Data columns (total 3 columns):\n",
      "user_id               int64\n",
      "order_completed_at    object\n",
      "cart                  int64\n",
      "dtypes: int64(2), object(1)\n",
      "memory usage: 71.5+ MB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.order_completed_at = pd.to_datetime(data.order_completed_at)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Анализ данных\n",
    "### Сгруппируем данные в заказы"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def collect_carts(x):\n",
    "    return list(x['cart'])\n",
    "\n",
    "#data.groupby(['order_completed_at', 'user_id']).apply(collect_carts)\n",
    "#то же самое через лямбда-функцию\n",
    "#data.groupby(['order_completed_at', 'user_id']).apply(lambda x: list(x['cart']))    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
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       "      <td>2015-06-18 16:15:33</td>\n",
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       "      <td>2020-09-03 23:20:49</td>\n",
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       "      <td>2020-09-03 23:39:40</td>\n",
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       "<p>209406 rows × 3 columns</p>\n",
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      ],
      "text/plain": [
       "        order_completed_at  user_id  \\\n",
       "0      2015-03-22 09:25:46        2   \n",
       "1      2015-06-18 16:15:33        3   \n",
       "2      2015-07-04 14:05:22        3   \n",
       "3      2015-07-08 06:59:04        4   \n",
       "4      2015-07-22 08:16:24        5   \n",
       "...                    ...      ...   \n",
       "209401 2020-09-03 23:06:59    10137   \n",
       "209402 2020-09-03 23:17:54     7858   \n",
       "209403 2020-09-03 23:20:49    15887   \n",
       "209404 2020-09-03 23:39:40    19188   \n",
       "209405 2020-09-03 23:45:45    12702   \n",
       "\n",
       "                                           carts_in_order  \n",
       "0       [399, 14, 198, 88, 157, 82, 134, 16, 409, 384,...  \n",
       "1                                                   [399]  \n",
       "2                                                   [399]  \n",
       "3                                                [54, 55]  \n",
       "4       [409, 88, 376, 14, 423, 23, 92, 29, 386, 440, ...  \n",
       "...                                                   ...  \n",
       "209401                          [29, 411, 41, 42, 89, 43]  \n",
       "209402                              [89, 83, 712, 22, 27]  \n",
       "209403  [23, 25, 19, 799, 84, 9, 85, 57, 29, 26, 430, ...  \n",
       "209404  [57, 409, 82, 14, 198, 398, 31, 442, 151, 23, ...  \n",
       "209405  [382, 384, 54, 55, 392, 57, 409, 380, 412, 75,...  \n",
       "\n",
       "[209406 rows x 3 columns]"
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     "execution_count": 7,
     "metadata": {},
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   ],
   "source": [
    "orders = data.groupby(['order_completed_at', 'user_id']).apply(collect_carts).reset_index()\n",
    "orders = orders.rename(columns = {0: 'carts_in_order'})\n",
    "orders"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
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       "      <td>12</td>\n",
       "      <td>2016-04-24 15:59:48</td>\n",
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       "      <td>[92, 24, 383, 31, 103, 77, 396]</td>\n",
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       "      <td>41</td>\n",
       "      <td>2016-10-02 15:56:05</td>\n",
       "      <td>14</td>\n",
       "      <td>[165, 19, 17, 16, 112, 14, 198, 205, 5, 420, 403, 82, 85, 430, 99, 419, 23, 422, 169, 407, 409, 170, 57, 411, 66, 179, 398, 25, 396, 812, 406, 808, 157]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>42</td>\n",
       "      <td>2016-10-04 09:32:52</td>\n",
       "      <td>14</td>\n",
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       "      <td>47</td>\n",
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       "      <td>14</td>\n",
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       "      <td>61</td>\n",
       "      <td>2016-10-28 09:50:33</td>\n",
       "      <td>14</td>\n",
       "      <td>[41, 403, 42, 57, 396, 430, 382, 160, 395, 87, 394, 322, 820, 89, 316, 84, 426, 16, 22, 23, 318, 638, 25, 411, 438, 104, 99, 100, 102, 103]</td>\n",
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       "      <td>115845</td>\n",
       "      <td>2020-06-09 11:47:31</td>\n",
       "      <td>14</td>\n",
       "      <td>[804, 446, 169, 394, 55]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>148907</td>\n",
       "      <td>2020-07-09 10:42:44</td>\n",
       "      <td>14</td>\n",
       "      <td>[417, 408, 382, 0, 19, 170, 422, 67, 430, 256, 169]</td>\n",
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       "      <td>176768</td>\n",
       "      <td>2020-08-04 14:03:47</td>\n",
       "      <td>14</td>\n",
       "      <td>[394, 440, 807, 445, 169, 198, 19, 82, 87, 170, 84, 22, 253]</td>\n",
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       "      <td>192258</td>\n",
       "      <td>2020-08-18 11:26:07</td>\n",
       "      <td>14</td>\n",
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       "      <td>2020-08-29 06:00:19</td>\n",
       "      <td>14</td>\n",
       "      <td>[57, 55, 798, 169, 41, 431, 432, 441, 61, 430, 802, 22, 382, 402, 5, 104]</td>\n",
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       "  </tbody>\n",
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       "<p>81 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        order_completed_at  user_id  \\\n",
       "12     2016-04-24 15:59:48       14   \n",
       "41     2016-10-02 15:56:05       14   \n",
       "42     2016-10-04 09:32:52       14   \n",
       "47     2016-10-13 23:17:42       14   \n",
       "61     2016-10-28 09:50:33       14   \n",
       "...                    ...      ...   \n",
       "115845 2020-06-09 11:47:31       14   \n",
       "148907 2020-07-09 10:42:44       14   \n",
       "176768 2020-08-04 14:03:47       14   \n",
       "192258 2020-08-18 11:26:07       14   \n",
       "203165 2020-08-29 06:00:19       14   \n",
       "\n",
       "                                                                                                                                                  carts_in_order  \n",
       "12                                                                                                                               [92, 24, 383, 31, 103, 77, 396]  \n",
       "41      [165, 19, 17, 16, 112, 14, 198, 205, 5, 420, 403, 82, 85, 430, 99, 419, 23, 422, 169, 407, 409, 170, 57, 411, 66, 179, 398, 25, 396, 812, 406, 808, 157]  \n",
       "42                                                                                              [432, 57, 103, 380, 381, 99, 431, 102, 430, 100, 89, 171, 6, 61]  \n",
       "47                                                        [376, 169, 61, 165, 430, 170, 63, 411, 57, 394, 383, 379, 231, 377, 82, 140, 22, 203, 103, 99, 89, 27]  \n",
       "61                   [41, 403, 42, 57, 396, 430, 382, 160, 395, 87, 394, 322, 820, 89, 316, 84, 426, 16, 22, 23, 318, 638, 25, 411, 438, 104, 99, 100, 102, 103]  \n",
       "...                                                                                                                                                          ...  \n",
       "115845                                                                                                                                  [804, 446, 169, 394, 55]  \n",
       "148907                                                                                                       [417, 408, 382, 0, 19, 170, 422, 67, 430, 256, 169]  \n",
       "176768                                                                                              [394, 440, 807, 445, 169, 198, 19, 82, 87, 170, 84, 22, 253]  \n",
       "192258                                                                                                                            [19, 169, 85, 5, 67, 382, 209]  \n",
       "203165                                                                                 [57, 55, 798, 169, 41, 431, 432, 441, 61, 430, 802, 22, 382, 402, 5, 104]  \n",
       "\n",
       "[81 rows x 3 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# посмотрим на заказы некоторых пользователей\n",
    "pd.set_option('display.max_colwidth', 1000)\n",
    "orders[orders.user_id == 14]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.reset_option('display.max_columns')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Посчитаем среднее количество категорий в заказе у каждого пользователя и частоту заказов каждой категории"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def count_carts_number(x):\n",
    "    count = 0\n",
    "    for order in x['carts_in_order']:\n",
    "        count += len(order)\n",
    "    return count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
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       "      <td>4</td>\n",
       "      <td>4</td>\n",
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       "      <td>19995</td>\n",
       "      <td>19995</td>\n",
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       "    <tr>\n",
       "      <td>19996</td>\n",
       "      <td>19996</td>\n",
       "      <td>5.666667</td>\n",
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       "    <tr>\n",
       "      <td>19997</td>\n",
       "      <td>19997</td>\n",
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       "    <tr>\n",
       "      <td>19998</td>\n",
       "      <td>19998</td>\n",
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       "    <tr>\n",
       "      <td>19999</td>\n",
       "      <td>19999</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>20000 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       user_id  mean_carts_number\n",
       "0            0          14.666667\n",
       "1            1           4.111111\n",
       "2            2          11.466667\n",
       "3            3           8.857143\n",
       "4            4          14.000000\n",
       "...        ...                ...\n",
       "19995    19995           3.000000\n",
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       "19997    19997          14.000000\n",
       "19998    19998           6.333333\n",
       "19999    19999           1.000000\n",
       "\n",
       "[20000 rows x 2 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "users_carts_mean_number = orders.groupby('user_id').apply(count_carts_number)/orders.groupby('user_id')['carts_in_order'].count()\n",
    "users_carts_mean_number = users_carts_mean_number.reset_index()\n",
    "users_carts_mean_number.rename(columns = {0:'mean_carts_number'}, inplace = True)\n",
    "users_carts_mean_number"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "from itertools import groupby\n",
    "import collections\n",
    "\n",
    "def list_of_favourite_carts(x): # создает отсортированный (по частоте) список заказанных категорий пользователя\n",
    "    carts_list = []\n",
    "    for order in x['carts_in_order']:\n",
    "        carts_list.extend(order)\n",
    "    counts = collections.Counter(carts_list)\n",
    "    new_list = sorted(carts_list, key=lambda y: (counts[y], y), reverse=True)\n",
    "    \n",
    "    cart_list = [el for el, _ in groupby(new_list)]\n",
    "    \n",
    "    return cart_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
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       "      <td>19999</td>\n",
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      "text/plain": [
       "       user_id  \\\n",
       "0            0   \n",
       "1            1   \n",
       "2            2   \n",
       "3            3   \n",
       "4            4   \n",
       "...        ...   \n",
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       "0                                                                                                                                                                                   [57, 441, 405, 379, 84, 82, 14, 821, 803, 799, 440, 432, 430, 411, 409, 402, 401, 399, 398, 396, 383, 382, 170, 169, 157, 133, 83, 61, 55, 41, 26, 25, 22, 20, 10, 5]  \n",
       "1                                                                                                                                                                                                                                                            [55, 798, 169, 88, 14, 812, 803, 421, 307, 302, 231, 204, 171, 170, 149, 86, 82, 54, 23, 19]  \n",
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       "3                                                                                                                                                                             [399, 383, 382, 57, 43, 41, 19, 430, 398, 179, 84, 64, 61, 27, 22, 16, 15, 804, 712, 442, 412, 411, 404, 402, 380, 376, 148, 142, 134, 100, 92, 91, 88, 77, 62, 42, 17, 14]  \n",
       "4                                                                                                                    [398, 57, 420, 98, 54, 712, 102, 100, 84, 61, 29, 22, 16, 425, 400, 391, 388, 17, 619, 430, 421, 383, 50, 5, 812, 805, 800, 799, 443, 431, 409, 387, 384, 379, 204, 179, 134, 133, 107, 101, 99, 89, 88, 87, 85, 77, 55, 26, 21, 19]  \n",
       "...                                                                                                                                                                                                                                                                                                                                                   ...  \n",
       "19995                                                                                                                                                                                                                                                                                                              [712, 398, 393, 84, 67, 57, 31, 14, 0]  \n",
       "19996                                                                                                                                                                                                                                                                       [812, 804, 443, 431, 409, 404, 396, 393, 231, 92, 84, 82, 61, 57, 22, 21, 14]  \n",
       "19997                                                                                                                                                                    [61, 57, 55, 49, 479, 443, 430, 420, 398, 383, 382, 244, 243, 239, 232, 231, 227, 223, 219, 218, 203, 198, 197, 160, 135, 131, 107, 106, 103, 99, 98, 87, 84, 77, 23, 17, 14, 0]  \n",
       "19998                                                                                                                                                                                                                                                                                            [420, 415, 398, 84, 57, 6, 798, 409, 61, 31, 29, 26, 19]  \n",
       "19999                                                                                                                                                                                                                                                                                                                                               [326]  \n",
       "\n",
       "[20000 rows x 2 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "users_favourite_carts = orders.groupby('user_id').apply(list_of_favourite_carts).reset_index()\n",
    "pd.set_option('display.max_colwidth', 1000)\n",
    "users_favourite_carts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    </tr>\n",
       "    <tr>\n",
       "      <td>19998</td>\n",
       "      <td>19998</td>\n",
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       "<p>20000 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       user_id  mean_carts_number  \\\n",
       "0            0          14.666667   \n",
       "1            1           4.111111   \n",
       "2            2          11.466667   \n",
       "3            3           8.857143   \n",
       "4            4          14.000000   \n",
       "...        ...                ...   \n",
       "19995    19995           3.000000   \n",
       "19996    19996           5.666667   \n",
       "19997    19997          14.000000   \n",
       "19998    19998           6.333333   \n",
       "19999    19999           1.000000   \n",
       "\n",
       "                                                                                                                                                                                                                                                                                                                                          favourite_carts  \n",
       "0                                                                                                                                                                                   [57, 441, 405, 379, 84, 82, 14, 821, 803, 799, 440, 432, 430, 411, 409, 402, 401, 399, 398, 396, 383, 382, 170, 169, 157, 133, 83, 61, 55, 41, 26, 25, 22, 20, 10, 5]  \n",
       "1                                                                                                                                                                                                                                                            [55, 798, 169, 88, 14, 812, 803, 421, 307, 302, 231, 204, 171, 170, 149, 86, 82, 54, 23, 19]  \n",
       "2      [23, 57, 61, 403, 82, 430, 409, 382, 804, 398, 383, 22, 15, 396, 380, 197, 87, 67, 432, 420, 412, 402, 393, 376, 199, 198, 165, 160, 157, 134, 100, 88, 84, 54, 41, 29, 24, 19, 17, 16, 5, 820, 808, 743, 431, 425, 408, 406, 399, 395, 392, 384, 381, 379, 377, 219, 203, 168, 159, 158, 139, 135, 129, 99, 89, 85, 64, 55, 43, 42, 25, 14, 9, 0]  \n",
       "3                                                                                                                                                                             [399, 383, 382, 57, 43, 41, 19, 430, 398, 179, 84, 64, 61, 27, 22, 16, 15, 804, 712, 442, 412, 411, 404, 402, 380, 376, 148, 142, 134, 100, 92, 91, 88, 77, 62, 42, 17, 14]  \n",
       "4                                                                                                                    [398, 57, 420, 98, 54, 712, 102, 100, 84, 61, 29, 22, 16, 425, 400, 391, 388, 17, 619, 430, 421, 383, 50, 5, 812, 805, 800, 799, 443, 431, 409, 387, 384, 379, 204, 179, 134, 133, 107, 101, 99, 89, 88, 87, 85, 77, 55, 26, 21, 19]  \n",
       "...                                                                                                                                                                                                                                                                                                                                                   ...  \n",
       "19995                                                                                                                                                                                                                                                                                                              [712, 398, 393, 84, 67, 57, 31, 14, 0]  \n",
       "19996                                                                                                                                                                                                                                                                       [812, 804, 443, 431, 409, 404, 396, 393, 231, 92, 84, 82, 61, 57, 22, 21, 14]  \n",
       "19997                                                                                                                                                                    [61, 57, 55, 49, 479, 443, 430, 420, 398, 383, 382, 244, 243, 239, 232, 231, 227, 223, 219, 218, 203, 198, 197, 160, 135, 131, 107, 106, 103, 99, 98, 87, 84, 77, 23, 17, 14, 0]  \n",
       "19998                                                                                                                                                                                                                                                                                            [420, 415, 398, 84, 57, 6, 798, 409, 61, 31, 29, 26, 19]  \n",
       "19999                                                                                                                                                                                                                                                                                                                                               [326]  \n",
       "\n",
       "[20000 rows x 3 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "users_data = users_carts_mean_number.merge(users_favourite_carts, on = 'user_id')\n",
    "users_data.rename(columns = {0: 'favourite_carts'}, inplace = True)\n",
    "users_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>4</td>\n",
       "      <td>14</td>\n",
       "      <td>[398, 57, 420, 98, 54, 712, 102, 100, 84, 61, 29, 22, 16, 425, 400, 391, 388, 17, 619, 430, 421, 383, 50, 5, 812, 805, 800, 799, 443, 431, 409, 387, 384, 379, 204, 179, 134, 133, 107, 101, 99, 89, 88, 87, 85, 77, 55, 26, 21, 19]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>19995</td>\n",
       "      <td>19995</td>\n",
       "      <td>3</td>\n",
       "      <td>[712, 398, 393, 84, 67, 57, 31, 14, 0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>19996</td>\n",
       "      <td>19996</td>\n",
       "      <td>6</td>\n",
       "      <td>[812, 804, 443, 431, 409, 404, 396, 393, 231, 92, 84, 82, 61, 57, 22, 21, 14]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>19997</td>\n",
       "      <td>19997</td>\n",
       "      <td>14</td>\n",
       "      <td>[61, 57, 55, 49, 479, 443, 430, 420, 398, 383, 382, 244, 243, 239, 232, 231, 227, 223, 219, 218, 203, 198, 197, 160, 135, 131, 107, 106, 103, 99, 98, 87, 84, 77, 23, 17, 14, 0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>19998</td>\n",
       "      <td>19998</td>\n",
       "      <td>6</td>\n",
       "      <td>[420, 415, 398, 84, 57, 6, 798, 409, 61, 31, 29, 26, 19]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>19999</td>\n",
       "      <td>19999</td>\n",
       "      <td>1</td>\n",
       "      <td>[326]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>20000 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       user_id  mean_carts_number  \\\n",
       "0            0                 15   \n",
       "1            1                  4   \n",
       "2            2                 11   \n",
       "3            3                  9   \n",
       "4            4                 14   \n",
       "...        ...                ...   \n",
       "19995    19995                  3   \n",
       "19996    19996                  6   \n",
       "19997    19997                 14   \n",
       "19998    19998                  6   \n",
       "19999    19999                  1   \n",
       "\n",
       "                                                                                                                                                                                                                                                                                                                                          favourite_carts  \n",
       "0                                                                                                                                                                                   [57, 441, 405, 379, 84, 82, 14, 821, 803, 799, 440, 432, 430, 411, 409, 402, 401, 399, 398, 396, 383, 382, 170, 169, 157, 133, 83, 61, 55, 41, 26, 25, 22, 20, 10, 5]  \n",
       "1                                                                                                                                                                                                                                                            [55, 798, 169, 88, 14, 812, 803, 421, 307, 302, 231, 204, 171, 170, 149, 86, 82, 54, 23, 19]  \n",
       "2      [23, 57, 61, 403, 82, 430, 409, 382, 804, 398, 383, 22, 15, 396, 380, 197, 87, 67, 432, 420, 412, 402, 393, 376, 199, 198, 165, 160, 157, 134, 100, 88, 84, 54, 41, 29, 24, 19, 17, 16, 5, 820, 808, 743, 431, 425, 408, 406, 399, 395, 392, 384, 381, 379, 377, 219, 203, 168, 159, 158, 139, 135, 129, 99, 89, 85, 64, 55, 43, 42, 25, 14, 9, 0]  \n",
       "3                                                                                                                                                                             [399, 383, 382, 57, 43, 41, 19, 430, 398, 179, 84, 64, 61, 27, 22, 16, 15, 804, 712, 442, 412, 411, 404, 402, 380, 376, 148, 142, 134, 100, 92, 91, 88, 77, 62, 42, 17, 14]  \n",
       "4                                                                                                                    [398, 57, 420, 98, 54, 712, 102, 100, 84, 61, 29, 22, 16, 425, 400, 391, 388, 17, 619, 430, 421, 383, 50, 5, 812, 805, 800, 799, 443, 431, 409, 387, 384, 379, 204, 179, 134, 133, 107, 101, 99, 89, 88, 87, 85, 77, 55, 26, 21, 19]  \n",
       "...                                                                                                                                                                                                                                                                                                                                                   ...  \n",
       "19995                                                                                                                                                                                                                                                                                                              [712, 398, 393, 84, 67, 57, 31, 14, 0]  \n",
       "19996                                                                                                                                                                                                                                                                       [812, 804, 443, 431, 409, 404, 396, 393, 231, 92, 84, 82, 61, 57, 22, 21, 14]  \n",
       "19997                                                                                                                                                                    [61, 57, 55, 49, 479, 443, 430, 420, 398, 383, 382, 244, 243, 239, 232, 231, 227, 223, 219, 218, 203, 198, 197, 160, 135, 131, 107, 106, 103, 99, 98, 87, 84, 77, 23, 17, 14, 0]  \n",
       "19998                                                                                                                                                                                                                                                                                            [420, 415, 398, 84, 57, 6, 798, 409, 61, 31, 29, 26, 19]  \n",
       "19999                                                                                                                                                                                                                                                                                                                                               [326]  \n",
       "\n",
       "[20000 rows x 3 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#округлим средние количества товаров в заказе (округление в традиционном математическом смысле)\n",
    "users_data.mean_carts_number += 0.5\n",
    "users_data = users_data.astype({'mean_carts_number': int})\n",
    "users_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "20000it [00:04, 4611.78it/s]\n"
     ]
    }
   ],
   "source": [
    "d = {}\n",
    "indexer = 0\n",
    "\n",
    "for index, row in tqdm(users_data.iterrows()):\n",
    "    mean_carts_number = row.mean_carts_number\n",
    "    user_id = row.user_id\n",
    "    favourite_carts = row.favourite_carts\n",
    "        \n",
    "    for i in range(len(favourite_carts)):\n",
    "        id_to_add = str(user_id) + ';' + str(favourite_carts[i])\n",
    "        if i < mean_carts_number:\n",
    "            target_to_add = 1\n",
    "        else:\n",
    "            target_to_add = 0\n",
    "        d[indexer] = {'id':id_to_add, 'target': target_to_add}\n",
    "        indexer +=1\n",
    "        \n",
    "submission = pd.DataFrame.from_dict(d, 'index')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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>id</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0;57</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>0;441</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0;405</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>0;379</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>0;84</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1117595</td>\n",
       "      <td>19998;31</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1117596</td>\n",
       "      <td>19998;29</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1117597</td>\n",
       "      <td>19998;26</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1117598</td>\n",
       "      <td>19998;19</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1117599</td>\n",
       "      <td>19999;326</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1117600 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                id  target\n",
       "0             0;57       1\n",
       "1            0;441       1\n",
       "2            0;405       1\n",
       "3            0;379       1\n",
       "4             0;84       1\n",
       "...            ...     ...\n",
       "1117595   19998;31       0\n",
       "1117596   19998;29       0\n",
       "1117597   19998;26       0\n",
       "1117598   19998;19       0\n",
       "1117599  19999;326       1\n",
       "\n",
       "[1117600 rows x 2 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "submission.astype({'target':int})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "sample.drop('target', axis = 1, inplace = True)\n",
    "submission = submission.merge(sample, how = 'right')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "submission.to_csv('/Users/macbook/Downloads/test_tasks/sbermarket/sbermarket-internship-competition/submission.csv', index = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Score = 0.46475, 40-е место - неплохо, для чисто аналитического решения\n",
    "### лучшие решения у лидеров соревнования ~ 0,49\n",
    "### нужно поэкспериментировать с глубиной анализируемой истории\n",
    "### Релизовать ML-решение: прикрутить и обучить какой-нибудь классификатор"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Еще немного аналитики"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "orders.user_id.value_counts().hist(bins = 210)\n",
    "plt.title('Распределение пользователей по количеству заказов')\n",
    "plt.xlabel('количество заказов')\n",
    "plt.ylabel('количество пользователей')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    20000.000000\n",
       "mean        10.470300\n",
       "std         11.905856\n",
       "min          3.000000\n",
       "25%          4.000000\n",
       "50%          6.000000\n",
       "75%         12.000000\n",
       "max        213.000000\n",
       "Name: user_id, dtype: float64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "orders.user_id.value_counts().describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count                 3123064\n",
       "unique                 208155\n",
       "top       2019-10-04 06:39:01\n",
       "freq                      120\n",
       "first     2015-03-22 09:25:46\n",
       "last      2020-09-03 23:45:45\n",
       "Name: order_completed_at, dtype: object"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.order_completed_at.describe()"
   ]
  }
 ],
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