BUSINESS2BUSINESS.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "0f167e59-6cf8-4597-ae86-ec82d7b62371",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "import matplotlib as plt\n",
    "# Load data from a CSV file\n",
    "data = pd.read_csv('B2B Online Shopping.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "7bdce799-0727-4b39-b7ab-fdf2e958ab68",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
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       "\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>order_id</th>\n",
       "      <th>customer_id</th>\n",
       "      <th>order_item_id</th>\n",
       "      <th>product_id</th>\n",
       "      <th>price</th>\n",
       "      <th>product_category_name_english</th>\n",
       "      <th>seller_id</th>\n",
       "      <th>order_status</th>\n",
       "      <th>delivery_status</th>\n",
       "      <th>...</th>\n",
       "      <th>order_purchase_timestamp</th>\n",
       "      <th>order_estimated_delivery_date</th>\n",
       "      <th>order_delivered_customer_date</th>\n",
       "      <th>payment_value</th>\n",
       "      <th>purchase_type</th>\n",
       "      <th>order_date</th>\n",
       "      <th>order_year</th>\n",
       "      <th>order_day</th>\n",
       "      <th>order_month</th>\n",
       "      <th>order_hour</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>e481f51cbdc54678b7cc49136f2d6af7</td>\n",
       "      <td>9ef432eb6251297304e76186b10a928d</td>\n",
       "      <td>1.0</td>\n",
       "      <td>87285b34884572647811a353c7ac498a</td>\n",
       "      <td>29.99</td>\n",
       "      <td>housewares</td>\n",
       "      <td>3504c0cb71d7fa48d967e0e4c94d59d9</td>\n",
       "      <td>delivered</td>\n",
       "      <td>Late</td>\n",
       "      <td>...</td>\n",
       "      <td>2017-10-02 10:56:33</td>\n",
       "      <td>2017-10-18 00:00:00</td>\n",
       "      <td>2017-10-10 21:25:13</td>\n",
       "      <td>18.12</td>\n",
       "      <td>small_purchase</td>\n",
       "      <td>2017-10-02 10:56:33</td>\n",
       "      <td>2017</td>\n",
       "      <td>Monday</td>\n",
       "      <td>October</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>e481f51cbdc54678b7cc49136f2d6af7</td>\n",
       "      <td>9ef432eb6251297304e76186b10a928d</td>\n",
       "      <td>1.0</td>\n",
       "      <td>87285b34884572647811a353c7ac498a</td>\n",
       "      <td>29.99</td>\n",
       "      <td>housewares</td>\n",
       "      <td>3504c0cb71d7fa48d967e0e4c94d59d9</td>\n",
       "      <td>delivered</td>\n",
       "      <td>Late</td>\n",
       "      <td>...</td>\n",
       "      <td>2017-10-02 10:56:33</td>\n",
       "      <td>2017-10-18 00:00:00</td>\n",
       "      <td>2017-10-10 21:25:13</td>\n",
       "      <td>2.00</td>\n",
       "      <td>small_purchase</td>\n",
       "      <td>2017-10-02 10:56:33</td>\n",
       "      <td>2017</td>\n",
       "      <td>Monday</td>\n",
       "      <td>October</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>e481f51cbdc54678b7cc49136f2d6af7</td>\n",
       "      <td>9ef432eb6251297304e76186b10a928d</td>\n",
       "      <td>1.0</td>\n",
       "      <td>87285b34884572647811a353c7ac498a</td>\n",
       "      <td>29.99</td>\n",
       "      <td>housewares</td>\n",
       "      <td>3504c0cb71d7fa48d967e0e4c94d59d9</td>\n",
       "      <td>delivered</td>\n",
       "      <td>Late</td>\n",
       "      <td>...</td>\n",
       "      <td>2017-10-02 10:56:33</td>\n",
       "      <td>2017-10-18 00:00:00</td>\n",
       "      <td>2017-10-10 21:25:13</td>\n",
       "      <td>18.59</td>\n",
       "      <td>small_purchase</td>\n",
       "      <td>2017-10-02 10:56:33</td>\n",
       "      <td>2017</td>\n",
       "      <td>Monday</td>\n",
       "      <td>October</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>53cdb2fc8bc7dce0b6741e2150273451</td>\n",
       "      <td>b0830fb4747a6c6d20dea0b8c802d7ef</td>\n",
       "      <td>1.0</td>\n",
       "      <td>595fac2a385ac33a80bd5114aec74eb8</td>\n",
       "      <td>118.70</td>\n",
       "      <td>perfumery</td>\n",
       "      <td>289cdb325fb7e7f891c38608bf9e0962</td>\n",
       "      <td>delivered</td>\n",
       "      <td>Late</td>\n",
       "      <td>...</td>\n",
       "      <td>2018-07-24 20:41:37</td>\n",
       "      <td>2018-08-13 00:00:00</td>\n",
       "      <td>2018-08-07 15:27:45</td>\n",
       "      <td>141.46</td>\n",
       "      <td>small_purchase</td>\n",
       "      <td>2018-07-24 20:41:37</td>\n",
       "      <td>2018</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>July</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>47770eb9100c2d0c44946d9cf07ec65d</td>\n",
       "      <td>41ce2a54c0b03bf3443c3d931a367089</td>\n",
       "      <td>1.0</td>\n",
       "      <td>aa4383b373c6aca5d8797843e5594415</td>\n",
       "      <td>159.90</td>\n",
       "      <td>auto</td>\n",
       "      <td>4869f7a5dfa277a7dca6462dcf3b52b2</td>\n",
       "      <td>delivered</td>\n",
       "      <td>Late</td>\n",
       "      <td>...</td>\n",
       "      <td>2018-08-08 08:38:49</td>\n",
       "      <td>2018-09-04 00:00:00</td>\n",
       "      <td>2018-08-17 18:06:29</td>\n",
       "      <td>179.12</td>\n",
       "      <td>normal_purchase</td>\n",
       "      <td>2018-08-08 08:38:49</td>\n",
       "      <td>2018</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>August</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0                          order_id  \\\n",
       "0           0  e481f51cbdc54678b7cc49136f2d6af7   \n",
       "1           1  e481f51cbdc54678b7cc49136f2d6af7   \n",
       "2           2  e481f51cbdc54678b7cc49136f2d6af7   \n",
       "3           3  53cdb2fc8bc7dce0b6741e2150273451   \n",
       "4           4  47770eb9100c2d0c44946d9cf07ec65d   \n",
       "\n",
       "                        customer_id  order_item_id  \\\n",
       "0  9ef432eb6251297304e76186b10a928d            1.0   \n",
       "1  9ef432eb6251297304e76186b10a928d            1.0   \n",
       "2  9ef432eb6251297304e76186b10a928d            1.0   \n",
       "3  b0830fb4747a6c6d20dea0b8c802d7ef            1.0   \n",
       "4  41ce2a54c0b03bf3443c3d931a367089            1.0   \n",
       "\n",
       "                         product_id   price product_category_name_english  \\\n",
       "0  87285b34884572647811a353c7ac498a   29.99                    housewares   \n",
       "1  87285b34884572647811a353c7ac498a   29.99                    housewares   \n",
       "2  87285b34884572647811a353c7ac498a   29.99                    housewares   \n",
       "3  595fac2a385ac33a80bd5114aec74eb8  118.70                     perfumery   \n",
       "4  aa4383b373c6aca5d8797843e5594415  159.90                          auto   \n",
       "\n",
       "                          seller_id order_status delivery_status  ...  \\\n",
       "0  3504c0cb71d7fa48d967e0e4c94d59d9    delivered            Late  ...   \n",
       "1  3504c0cb71d7fa48d967e0e4c94d59d9    delivered            Late  ...   \n",
       "2  3504c0cb71d7fa48d967e0e4c94d59d9    delivered            Late  ...   \n",
       "3  289cdb325fb7e7f891c38608bf9e0962    delivered            Late  ...   \n",
       "4  4869f7a5dfa277a7dca6462dcf3b52b2    delivered            Late  ...   \n",
       "\n",
       "  order_purchase_timestamp order_estimated_delivery_date  \\\n",
       "0      2017-10-02 10:56:33           2017-10-18 00:00:00   \n",
       "1      2017-10-02 10:56:33           2017-10-18 00:00:00   \n",
       "2      2017-10-02 10:56:33           2017-10-18 00:00:00   \n",
       "3      2018-07-24 20:41:37           2018-08-13 00:00:00   \n",
       "4      2018-08-08 08:38:49           2018-09-04 00:00:00   \n",
       "\n",
       "  order_delivered_customer_date payment_value    purchase_type  \\\n",
       "0           2017-10-10 21:25:13         18.12   small_purchase   \n",
       "1           2017-10-10 21:25:13          2.00   small_purchase   \n",
       "2           2017-10-10 21:25:13         18.59   small_purchase   \n",
       "3           2018-08-07 15:27:45        141.46   small_purchase   \n",
       "4           2018-08-17 18:06:29        179.12  normal_purchase   \n",
       "\n",
       "            order_date  order_year  order_day order_month  order_hour  \n",
       "0  2017-10-02 10:56:33        2017     Monday     October          10  \n",
       "1  2017-10-02 10:56:33        2017     Monday     October          10  \n",
       "2  2017-10-02 10:56:33        2017     Monday     October          10  \n",
       "3  2018-07-24 20:41:37        2018    Tuesday        July          20  \n",
       "4  2018-08-08 08:38:49        2018  Wednesday      August           8  \n",
       "\n",
       "[5 rows x 23 columns]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "ff168699-89fa-4f63-bfd4-4ac496a4ce60",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>order_id</th>\n",
       "      <th>customer_id</th>\n",
       "      <th>order_item_id</th>\n",
       "      <th>product_id</th>\n",
       "      <th>price</th>\n",
       "      <th>product_category_name_english</th>\n",
       "      <th>seller_id</th>\n",
       "      <th>order_status</th>\n",
       "      <th>delivery_status</th>\n",
       "      <th>...</th>\n",
       "      <th>order_purchase_timestamp</th>\n",
       "      <th>order_estimated_delivery_date</th>\n",
       "      <th>order_delivered_customer_date</th>\n",
       "      <th>payment_value</th>\n",
       "      <th>purchase_type</th>\n",
       "      <th>order_date</th>\n",
       "      <th>order_year</th>\n",
       "      <th>order_day</th>\n",
       "      <th>order_month</th>\n",
       "      <th>order_hour</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>118429</th>\n",
       "      <td>118429</td>\n",
       "      <td>63943bddc261676b46f01ca7ac2f7bd8</td>\n",
       "      <td>1fca14ff2861355f6e5f14306ff977a7</td>\n",
       "      <td>1.0</td>\n",
       "      <td>f1d4ce8c6dd66c47bbaa8c6781c2a923</td>\n",
       "      <td>174.90</td>\n",
       "      <td>baby</td>\n",
       "      <td>1f9ab4708f3056ede07124aad39a2554</td>\n",
       "      <td>delivered</td>\n",
       "      <td>Late</td>\n",
       "      <td>...</td>\n",
       "      <td>2018-02-06 12:58:58</td>\n",
       "      <td>2018-03-02 00:00:00</td>\n",
       "      <td>2018-02-28 17:37:56</td>\n",
       "      <td>195.00</td>\n",
       "      <td>normal_purchase</td>\n",
       "      <td>2018-02-06 12:58:58</td>\n",
       "      <td>2018</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>February</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>118430</th>\n",
       "      <td>118430</td>\n",
       "      <td>83c1379a015df1e13d02aae0204711ab</td>\n",
       "      <td>1aa71eb042121263aafbe80c1b562c9c</td>\n",
       "      <td>1.0</td>\n",
       "      <td>b80910977a37536adeddd63663f916ad</td>\n",
       "      <td>205.99</td>\n",
       "      <td>home_appliances_2</td>\n",
       "      <td>d50d79cb34e38265a8649c383dcffd48</td>\n",
       "      <td>delivered</td>\n",
       "      <td>Late</td>\n",
       "      <td>...</td>\n",
       "      <td>2017-08-27 14:46:43</td>\n",
       "      <td>2017-09-27 00:00:00</td>\n",
       "      <td>2017-09-21 11:24:17</td>\n",
       "      <td>271.01</td>\n",
       "      <td>normal_purchase</td>\n",
       "      <td>2017-08-27 14:46:43</td>\n",
       "      <td>2017</td>\n",
       "      <td>Sunday</td>\n",
       "      <td>August</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>118431</th>\n",
       "      <td>118431</td>\n",
       "      <td>11c177c8e97725db2631073c19f07b62</td>\n",
       "      <td>b331b74b18dc79bcdf6532d51e1637c1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>d1c427060a0f73f6b889a5c7c61f2ac4</td>\n",
       "      <td>179.99</td>\n",
       "      <td>computers_accessories</td>\n",
       "      <td>a1043bafd471dff536d0c462352beb48</td>\n",
       "      <td>delivered</td>\n",
       "      <td>Late</td>\n",
       "      <td>...</td>\n",
       "      <td>2018-01-08 21:28:27</td>\n",
       "      <td>2018-02-15 00:00:00</td>\n",
       "      <td>2018-01-25 23:32:54</td>\n",
       "      <td>441.16</td>\n",
       "      <td>normal_purchase</td>\n",
       "      <td>2018-01-08 21:28:27</td>\n",
       "      <td>2018</td>\n",
       "      <td>Monday</td>\n",
       "      <td>January</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>118432</th>\n",
       "      <td>118432</td>\n",
       "      <td>11c177c8e97725db2631073c19f07b62</td>\n",
       "      <td>b331b74b18dc79bcdf6532d51e1637c1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>d1c427060a0f73f6b889a5c7c61f2ac4</td>\n",
       "      <td>179.99</td>\n",
       "      <td>computers_accessories</td>\n",
       "      <td>a1043bafd471dff536d0c462352beb48</td>\n",
       "      <td>delivered</td>\n",
       "      <td>Late</td>\n",
       "      <td>...</td>\n",
       "      <td>2018-01-08 21:28:27</td>\n",
       "      <td>2018-02-15 00:00:00</td>\n",
       "      <td>2018-01-25 23:32:54</td>\n",
       "      <td>441.16</td>\n",
       "      <td>normal_purchase</td>\n",
       "      <td>2018-01-08 21:28:27</td>\n",
       "      <td>2018</td>\n",
       "      <td>Monday</td>\n",
       "      <td>January</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>118433</th>\n",
       "      <td>118433</td>\n",
       "      <td>66dea50a8b16d9b4dee7af250b4be1a5</td>\n",
       "      <td>edb027a75a1449115f6b43211ae02a24</td>\n",
       "      <td>1.0</td>\n",
       "      <td>006619bbed68b000c8ba3f8725d5409e</td>\n",
       "      <td>68.50</td>\n",
       "      <td>health_beauty</td>\n",
       "      <td>ececbfcff9804a2d6b40f589df8eef2b</td>\n",
       "      <td>delivered</td>\n",
       "      <td>Late</td>\n",
       "      <td>...</td>\n",
       "      <td>2018-03-08 20:57:30</td>\n",
       "      <td>2018-04-03 00:00:00</td>\n",
       "      <td>2018-03-16 13:08:30</td>\n",
       "      <td>86.86</td>\n",
       "      <td>small_purchase</td>\n",
       "      <td>2018-03-08 20:57:30</td>\n",
       "      <td>2018</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>March</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Unnamed: 0                          order_id  \\\n",
       "118429      118429  63943bddc261676b46f01ca7ac2f7bd8   \n",
       "118430      118430  83c1379a015df1e13d02aae0204711ab   \n",
       "118431      118431  11c177c8e97725db2631073c19f07b62   \n",
       "118432      118432  11c177c8e97725db2631073c19f07b62   \n",
       "118433      118433  66dea50a8b16d9b4dee7af250b4be1a5   \n",
       "\n",
       "                             customer_id  order_item_id  \\\n",
       "118429  1fca14ff2861355f6e5f14306ff977a7            1.0   \n",
       "118430  1aa71eb042121263aafbe80c1b562c9c            1.0   \n",
       "118431  b331b74b18dc79bcdf6532d51e1637c1            1.0   \n",
       "118432  b331b74b18dc79bcdf6532d51e1637c1            2.0   \n",
       "118433  edb027a75a1449115f6b43211ae02a24            1.0   \n",
       "\n",
       "                              product_id   price  \\\n",
       "118429  f1d4ce8c6dd66c47bbaa8c6781c2a923  174.90   \n",
       "118430  b80910977a37536adeddd63663f916ad  205.99   \n",
       "118431  d1c427060a0f73f6b889a5c7c61f2ac4  179.99   \n",
       "118432  d1c427060a0f73f6b889a5c7c61f2ac4  179.99   \n",
       "118433  006619bbed68b000c8ba3f8725d5409e   68.50   \n",
       "\n",
       "       product_category_name_english                         seller_id  \\\n",
       "118429                          baby  1f9ab4708f3056ede07124aad39a2554   \n",
       "118430             home_appliances_2  d50d79cb34e38265a8649c383dcffd48   \n",
       "118431         computers_accessories  a1043bafd471dff536d0c462352beb48   \n",
       "118432         computers_accessories  a1043bafd471dff536d0c462352beb48   \n",
       "118433                 health_beauty  ececbfcff9804a2d6b40f589df8eef2b   \n",
       "\n",
       "       order_status delivery_status  ... order_purchase_timestamp  \\\n",
       "118429    delivered            Late  ...      2018-02-06 12:58:58   \n",
       "118430    delivered            Late  ...      2017-08-27 14:46:43   \n",
       "118431    delivered            Late  ...      2018-01-08 21:28:27   \n",
       "118432    delivered            Late  ...      2018-01-08 21:28:27   \n",
       "118433    delivered            Late  ...      2018-03-08 20:57:30   \n",
       "\n",
       "       order_estimated_delivery_date order_delivered_customer_date  \\\n",
       "118429           2018-03-02 00:00:00           2018-02-28 17:37:56   \n",
       "118430           2017-09-27 00:00:00           2017-09-21 11:24:17   \n",
       "118431           2018-02-15 00:00:00           2018-01-25 23:32:54   \n",
       "118432           2018-02-15 00:00:00           2018-01-25 23:32:54   \n",
       "118433           2018-04-03 00:00:00           2018-03-16 13:08:30   \n",
       "\n",
       "       payment_value    purchase_type           order_date  order_year  \\\n",
       "118429        195.00  normal_purchase  2018-02-06 12:58:58        2018   \n",
       "118430        271.01  normal_purchase  2017-08-27 14:46:43        2017   \n",
       "118431        441.16  normal_purchase  2018-01-08 21:28:27        2018   \n",
       "118432        441.16  normal_purchase  2018-01-08 21:28:27        2018   \n",
       "118433         86.86   small_purchase  2018-03-08 20:57:30        2018   \n",
       "\n",
       "       order_day order_month  order_hour  \n",
       "118429   Tuesday    February          12  \n",
       "118430    Sunday      August          14  \n",
       "118431    Monday     January          21  \n",
       "118432    Monday     January          21  \n",
       "118433  Thursday       March          20  \n",
       "\n",
       "[5 rows x 23 columns]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "a38873b9-b3a9-45de-b759-f12e6bf1f377",
   "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>Unnamed: 0</th>\n",
       "      <th>order_item_id</th>\n",
       "      <th>price</th>\n",
       "      <th>payment_value</th>\n",
       "      <th>order_year</th>\n",
       "      <th>order_hour</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>118434.000000</td>\n",
       "      <td>117604.000000</td>\n",
       "      <td>117604.000000</td>\n",
       "      <td>118431.000000</td>\n",
       "      <td>118434.000000</td>\n",
       "      <td>118434.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>59216.500000</td>\n",
       "      <td>1.195920</td>\n",
       "      <td>120.822849</td>\n",
       "      <td>172.849395</td>\n",
       "      <td>2017.536324</td>\n",
       "      <td>14.75605</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>34189.095228</td>\n",
       "      <td>0.697721</td>\n",
       "      <td>184.477367</td>\n",
       "      <td>268.259831</td>\n",
       "      <td>0.505492</td>\n",
       "      <td>5.32724</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.850000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2016.000000</td>\n",
       "      <td>0.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>29608.250000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>39.900000</td>\n",
       "      <td>60.860000</td>\n",
       "      <td>2017.000000</td>\n",
       "      <td>11.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>59216.500000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>74.900000</td>\n",
       "      <td>108.200000</td>\n",
       "      <td>2018.000000</td>\n",
       "      <td>15.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>88824.750000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>134.900000</td>\n",
       "      <td>189.245000</td>\n",
       "      <td>2018.000000</td>\n",
       "      <td>19.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>118433.000000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>6735.000000</td>\n",
       "      <td>13664.080000</td>\n",
       "      <td>2018.000000</td>\n",
       "      <td>23.00000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Unnamed: 0  order_item_id          price  payment_value  \\\n",
       "count  118434.000000  117604.000000  117604.000000  118431.000000   \n",
       "mean    59216.500000       1.195920     120.822849     172.849395   \n",
       "std     34189.095228       0.697721     184.477367     268.259831   \n",
       "min         0.000000       1.000000       0.850000       0.000000   \n",
       "25%     29608.250000       1.000000      39.900000      60.860000   \n",
       "50%     59216.500000       1.000000      74.900000     108.200000   \n",
       "75%     88824.750000       1.000000     134.900000     189.245000   \n",
       "max    118433.000000      21.000000    6735.000000   13664.080000   \n",
       "\n",
       "          order_year    order_hour  \n",
       "count  118434.000000  118434.00000  \n",
       "mean     2017.536324      14.75605  \n",
       "std         0.505492       5.32724  \n",
       "min      2016.000000       0.00000  \n",
       "25%      2017.000000      11.00000  \n",
       "50%      2018.000000      15.00000  \n",
       "75%      2018.000000      19.00000  \n",
       "max      2018.000000      23.00000  "
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "8b3e5fff-c089-4cc9-9e6e-ba2d24f7f8d4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 118434 entries, 0 to 118433\n",
      "Data columns (total 23 columns):\n",
      " #   Column                         Non-Null Count   Dtype  \n",
      "---  ------                         --------------   -----  \n",
      " 0   Unnamed: 0                     118434 non-null  int64  \n",
      " 1   order_id                       118434 non-null  object \n",
      " 2   customer_id                    118434 non-null  object \n",
      " 3   order_item_id                  117604 non-null  float64\n",
      " 4   product_id                     117604 non-null  object \n",
      " 5   price                          117604 non-null  float64\n",
      " 6   product_category_name_english  115881 non-null  object \n",
      " 7   seller_id                      117604 non-null  object \n",
      " 8   order_status                   118434 non-null  object \n",
      " 9   delivery_status                118434 non-null  object \n",
      " 10  customer_state                 118434 non-null  object \n",
      " 11  customer_city                  118434 non-null  object \n",
      " 12  payment_type                   118431 non-null  object \n",
      " 13  order_purchase_timestamp       118434 non-null  object \n",
      " 14  order_estimated_delivery_date  118434 non-null  object \n",
      " 15  order_delivered_customer_date  115037 non-null  object \n",
      " 16  payment_value                  118431 non-null  float64\n",
      " 17  purchase_type                  118434 non-null  object \n",
      " 18  order_date                     118434 non-null  object \n",
      " 19  order_year                     118434 non-null  int64  \n",
      " 20  order_day                      118434 non-null  object \n",
      " 21  order_month                    118434 non-null  object \n",
      " 22  order_hour                     118434 non-null  int64  \n",
      "dtypes: float64(3), int64(3), object(17)\n",
      "memory usage: 20.8+ MB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c418453c-a03c-46aa-96ee-1b12937eb835",
   "metadata": {},
   "source": [
    "OBSERVATIONS\n",
    "    I observed that there are missing values in the following row\n",
    "3   order_item_id                  117604 non-null  float64\n",
    " 4   product_id                     117604 non-null  object \n",
    " 5   price                          117604 non-null  float64\n",
    " 6   product_category_name_english  115881 non-null  object \n",
    " 7   seller_id                      117604 non-null  object \n",
    "12  payment_type                   118431 non-null  object \n",
    "15  order_delivered_customer_date  115037 non-null  object \n",
    " 16  payment_value                  118431 non-null  float64"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d48d3a3e-d14e-459d-bc70-5b99f0ddf5f3",
   "metadata": {},
   "source": [
    "HANDLING THE MISSING VALUES FOR THE CATEGORICAL VALUES"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "e8a53907-8a09-4289-a9a3-20b3a16a7676",
   "metadata": {},
   "outputs": [],
   "source": [
    "data.order_item_id= data.order_item_id.fillna(data.order_item_id.mean())\n",
    "data.price =data.price.fillna(data.price.mean())\n",
    "data.payment_value=data.payment_value.fillna(data.payment_value.mean())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d04831ef-5694-4721-bd2b-385b55e88ddb",
   "metadata": {},
   "source": [
    "HANDLING MISSING VALUES IN NUMERICAL VALUES"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "0073d4bb-f9df-4ce5-87b1-667e07e9da73",
   "metadata": {},
   "outputs": [],
   "source": [
    "data['product_id'].fillna(data['product_id'].mode()[0],inplace=True)\n",
    "data['product_category_name_english'].fillna(data['product_category_name_english'].mode()[0],inplace=True)\n",
    "data['seller_id'].fillna(data['seller_id'].mode()[0],inplace=True)\n",
    "data['payment_type'].fillna(data['payment_type'].mode()[0],inplace=True)\n",
    "data['order_delivered_customer_date'].fillna(data['order_delivered_customer_date'].mode()[0], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "2810d5ff-8364-428c-8d28-4e37059e3fd4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 118434 entries, 0 to 118433\n",
      "Data columns (total 23 columns):\n",
      " #   Column                         Non-Null Count   Dtype  \n",
      "---  ------                         --------------   -----  \n",
      " 0   Unnamed: 0                     118434 non-null  int64  \n",
      " 1   order_id                       118434 non-null  object \n",
      " 2   customer_id                    118434 non-null  object \n",
      " 3   order_item_id                  118434 non-null  float64\n",
      " 4   product_id                     118434 non-null  object \n",
      " 5   price                          118434 non-null  float64\n",
      " 6   product_category_name_english  118434 non-null  object \n",
      " 7   seller_id                      118434 non-null  object \n",
      " 8   order_status                   118434 non-null  object \n",
      " 9   delivery_status                118434 non-null  object \n",
      " 10  customer_state                 118434 non-null  object \n",
      " 11  customer_city                  118434 non-null  object \n",
      " 12  payment_type                   118434 non-null  object \n",
      " 13  order_purchase_timestamp       118434 non-null  object \n",
      " 14  order_estimated_delivery_date  118434 non-null  object \n",
      " 15  order_delivered_customer_date  118434 non-null  object \n",
      " 16  payment_value                  118434 non-null  float64\n",
      " 17  purchase_type                  118434 non-null  object \n",
      " 18  order_date                     118434 non-null  object \n",
      " 19  order_year                     118434 non-null  int64  \n",
      " 20  order_day                      118434 non-null  object \n",
      " 21  order_month                    118434 non-null  object \n",
      " 22  order_hour                     118434 non-null  int64  \n",
      "dtypes: float64(3), int64(3), object(17)\n",
      "memory usage: 20.8+ MB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "c83522e4-65a0-4be6-bbe9-fe86f748b587",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ai-lab-002/anaconda3/lib/python3.10/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "/home/ai-lab-002/anaconda3/lib/python3.10/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "/home/ai-lab-002/anaconda3/lib/python3.10/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='order_status', ylabel='count'>"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x='order_status',data=data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "468852fc-882c-4e9c-8187-dddd6b342abe",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ai-lab-002/anaconda3/lib/python3.10/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "/home/ai-lab-002/anaconda3/lib/python3.10/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "/home/ai-lab-002/anaconda3/lib/python3.10/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='purchase_type', ylabel='count'>"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x='purchase_type',data=data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "c156fa15-94ec-45cb-b436-8d70a2fa1506",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ai-lab-002/anaconda3/lib/python3.10/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "/home/ai-lab-002/anaconda3/lib/python3.10/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "/home/ai-lab-002/anaconda3/lib/python3.10/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='delivery_status', ylabel='count'>"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x='delivery_status',data=data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "f4c378c9-64d1-402f-86de-0352e233a0a2",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ai-lab-002/anaconda3/lib/python3.10/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "/home/ai-lab-002/anaconda3/lib/python3.10/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "/home/ai-lab-002/anaconda3/lib/python3.10/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='order_day', ylabel='count'>"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x='order_day',data=data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "fbd4e5a7-8319-4d4f-be77-62342e0a4d72",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ai-lab-002/anaconda3/lib/python3.10/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "/home/ai-lab-002/anaconda3/lib/python3.10/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "/home/ai-lab-002/anaconda3/lib/python3.10/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='order_month', ylabel='count'>"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x='order_month',data=data)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cf9e96c1-d3b3-4697-8809-72f88fda21e9",
   "metadata": {},
   "source": [
    "HANDLING OUTLIERS\n",
    "I observed that there were outliers in the following columns\n",
    "1.order_item_id\n",
    "2.price\n",
    "3.payment_value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "2f99c74b-e1e1-4f0b-91ab-6ee06788992d",
   "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>Unnamed: 0</th>\n",
       "      <th>order_item_id</th>\n",
       "      <th>price</th>\n",
       "      <th>payment_value</th>\n",
       "      <th>order_year</th>\n",
       "      <th>order_hour</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>118434.000000</td>\n",
       "      <td>118434.000000</td>\n",
       "      <td>118434.000000</td>\n",
       "      <td>118434.000000</td>\n",
       "      <td>118434.000000</td>\n",
       "      <td>118434.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>59216.500000</td>\n",
       "      <td>1.195920</td>\n",
       "      <td>120.822849</td>\n",
       "      <td>172.849395</td>\n",
       "      <td>2017.536324</td>\n",
       "      <td>14.75605</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>34189.095228</td>\n",
       "      <td>0.695271</td>\n",
       "      <td>183.829805</td>\n",
       "      <td>268.256434</td>\n",
       "      <td>0.505492</td>\n",
       "      <td>5.32724</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.850000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2016.000000</td>\n",
       "      <td>0.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>29608.250000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>39.900000</td>\n",
       "      <td>60.860000</td>\n",
       "      <td>2017.000000</td>\n",
       "      <td>11.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>59216.500000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>75.000000</td>\n",
       "      <td>108.200000</td>\n",
       "      <td>2018.000000</td>\n",
       "      <td>15.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>88824.750000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>133.000000</td>\n",
       "      <td>189.240000</td>\n",
       "      <td>2018.000000</td>\n",
       "      <td>19.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>118433.000000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>6735.000000</td>\n",
       "      <td>13664.080000</td>\n",
       "      <td>2018.000000</td>\n",
       "      <td>23.00000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Unnamed: 0  order_item_id          price  payment_value  \\\n",
       "count  118434.000000  118434.000000  118434.000000  118434.000000   \n",
       "mean    59216.500000       1.195920     120.822849     172.849395   \n",
       "std     34189.095228       0.695271     183.829805     268.256434   \n",
       "min         0.000000       1.000000       0.850000       0.000000   \n",
       "25%     29608.250000       1.000000      39.900000      60.860000   \n",
       "50%     59216.500000       1.000000      75.000000     108.200000   \n",
       "75%     88824.750000       1.000000     133.000000     189.240000   \n",
       "max    118433.000000      21.000000    6735.000000   13664.080000   \n",
       "\n",
       "          order_year    order_hour  \n",
       "count  118434.000000  118434.00000  \n",
       "mean     2017.536324      14.75605  \n",
       "std         0.505492       5.32724  \n",
       "min      2016.000000       0.00000  \n",
       "25%      2017.000000      11.00000  \n",
       "50%      2018.000000      15.00000  \n",
       "75%      2018.000000      19.00000  \n",
       "max      2018.000000      23.00000  "
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "13568104-b57e-4538-9e53-edc1f72fa10f",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ai-lab-002/anaconda3/lib/python3.10/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "/home/ai-lab-002/anaconda3/lib/python3.10/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='order_item_id', ylabel='order_hour'>"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.scatterplot(x='order_item_id',y='order_hour', data=data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "7191638c-8d4c-4bea-9949-e4040521c1e9",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ai-lab-002/anaconda3/lib/python3.10/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "/home/ai-lab-002/anaconda3/lib/python3.10/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='price', ylabel='order_hour'>"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.scatterplot(x='price',y='order_hour', data=data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "faec09a1-0361-438c-a64c-418121a13434",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ai-lab-002/anaconda3/lib/python3.10/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "/home/ai-lab-002/anaconda3/lib/python3.10/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='payment_value', ylabel='order_hour'>"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.scatterplot(x='payment_value',y='order_hour', data=data)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b4abfcd0-f319-4403-a3a9-468be1c28033",
   "metadata": {},
   "source": [
    "USING CAPPING AND FLOORING METHOD TO HANLE THE OUTLIERS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "87ab0f41-c0f5-453e-987b-95c6fbd63657",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([890.])"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.percentile(data.price,[99])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "26bf02c1-88b4-4d5d-a4ed-da4c79204745",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "890.0"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.percentile(data.price,[99])[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "e5532a79-4532-450e-a8ff-35f9015a0833",
   "metadata": {},
   "outputs": [],
   "source": [
    "uv = np.percentile(data.price,[99])[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "e0f4e5da-3f5e-40f6-ba0f-50ff1d01bd10",
   "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>Unnamed: 0</th>\n",
       "      <th>order_id</th>\n",
       "      <th>customer_id</th>\n",
       "      <th>order_item_id</th>\n",
       "      <th>product_id</th>\n",
       "      <th>price</th>\n",
       "      <th>product_category_name_english</th>\n",
       "      <th>seller_id</th>\n",
       "      <th>order_status</th>\n",
       "      <th>delivery_status</th>\n",
       "      <th>...</th>\n",
       "      <th>order_purchase_timestamp</th>\n",
       "      <th>order_estimated_delivery_date</th>\n",
       "      <th>order_delivered_customer_date</th>\n",
       "      <th>payment_value</th>\n",
       "      <th>purchase_type</th>\n",
       "      <th>order_date</th>\n",
       "      <th>order_year</th>\n",
       "      <th>order_day</th>\n",
       "      <th>order_month</th>\n",
       "      <th>order_hour</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>20</td>\n",
       "      <td>403b97836b0c04a622354cf531062e5f</td>\n",
       "      <td>738b086814c6fcc74b8cc583f8516ee3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>638bbb2a5e4f360b71f332ddfebfd672</td>\n",
       "      <td>1299.00</td>\n",
       "      <td>construction_tools_construction</td>\n",
       "      <td>c4af86330efa7a2620772227d2d670c9</td>\n",
       "      <td>delivered</td>\n",
       "      <td>Late</td>\n",
       "      <td>...</td>\n",
       "      <td>2018-01-02 19:00:43</td>\n",
       "      <td>2018-02-06 00:00:00</td>\n",
       "      <td>2018-01-20 01:38:59</td>\n",
       "      <td>1376.45</td>\n",
       "      <td>normal_purchase</td>\n",
       "      <td>2018-01-02 19:00:43</td>\n",
       "      <td>2018</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>January</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>393</th>\n",
       "      <td>393</td>\n",
       "      <td>2b72a32a2c3e93fa260ca7655073886f</td>\n",
       "      <td>5265029daa29cc08d86b9f96cd99d03f</td>\n",
       "      <td>1.0</td>\n",
       "      <td>cc3181a81384b6fc34602d49cb6068a6</td>\n",
       "      <td>1148.00</td>\n",
       "      <td>home_appliances_2</td>\n",
       "      <td>e882b2a25a10b9c057cc49695f222c19</td>\n",
       "      <td>delivered</td>\n",
       "      <td>Late</td>\n",
       "      <td>...</td>\n",
       "      <td>2018-04-18 15:40:36</td>\n",
       "      <td>2018-05-18 00:00:00</td>\n",
       "      <td>2018-05-03 13:03:03</td>\n",
       "      <td>1255.43</td>\n",
       "      <td>normal_purchase</td>\n",
       "      <td>2018-04-18 15:40:36</td>\n",
       "      <td>2018</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>April</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>407</th>\n",
       "      <td>407</td>\n",
       "      <td>ac64f79a33bfa575fc5a17e7926df3d5</td>\n",
       "      <td>51a8d592336a414fea21f12534b0f797</td>\n",
       "      <td>1.0</td>\n",
       "      <td>bd9fca6bf4bdcbf4094d7bc11415dfe5</td>\n",
       "      <td>1099.00</td>\n",
       "      <td>small_appliances</td>\n",
       "      <td>ba90964cff9b9e0e6f32b23b82465f7b</td>\n",
       "      <td>delivered</td>\n",
       "      <td>Late</td>\n",
       "      <td>...</td>\n",
       "      <td>2018-04-01 18:27:01</td>\n",
       "      <td>2018-04-12 00:00:00</td>\n",
       "      <td>2018-04-04 19:47:44</td>\n",
       "      <td>1119.60</td>\n",
       "      <td>normal_purchase</td>\n",
       "      <td>2018-04-01 18:27:01</td>\n",
       "      <td>2018</td>\n",
       "      <td>Sunday</td>\n",
       "      <td>April</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>411</th>\n",
       "      <td>411</td>\n",
       "      <td>6cb134bb285a64b0425d1fdaa00d4214</td>\n",
       "      <td>d854800a320bbb07b484f76894e4638f</td>\n",
       "      <td>1.0</td>\n",
       "      <td>463cdd8acfe23847f35e29076df0bbbd</td>\n",
       "      <td>1999.00</td>\n",
       "      <td>watches_gifts</td>\n",
       "      <td>c3acdfac4e3e97ff87529454fbc03642</td>\n",
       "      <td>delivered</td>\n",
       "      <td>Late</td>\n",
       "      <td>...</td>\n",
       "      <td>2018-07-01 20:54:21</td>\n",
       "      <td>2018-07-26 00:00:00</td>\n",
       "      <td>2018-07-10 22:12:13</td>\n",
       "      <td>2031.09</td>\n",
       "      <td>normal_purchase</td>\n",
       "      <td>2018-07-01 20:54:21</td>\n",
       "      <td>2018</td>\n",
       "      <td>Sunday</td>\n",
       "      <td>July</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>482</th>\n",
       "      <td>482</td>\n",
       "      <td>da8be3bb62e9bf01e2e1a3bfd74ebd1a</td>\n",
       "      <td>3f083b9f62e687be8d84684c7d54c8db</td>\n",
       "      <td>1.0</td>\n",
       "      <td>cea7edf64d9e93fbf6b89cc3ba537cd9</td>\n",
       "      <td>2690.00</td>\n",
       "      <td>garden_tools</td>\n",
       "      <td>a4b6b9b992b46e9ef863637af96e04bc</td>\n",
       "      <td>delivered</td>\n",
       "      <td>Not late</td>\n",
       "      <td>...</td>\n",
       "      <td>2017-04-06 13:44:08</td>\n",
       "      <td>2017-05-04 00:00:00</td>\n",
       "      <td>2017-05-08 10:52:06</td>\n",
       "      <td>2751.24</td>\n",
       "      <td>normal_purchase</td>\n",
       "      <td>2017-04-06 13:44:08</td>\n",
       "      <td>2017</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>April</td>\n",
       "      <td>13</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>117931</th>\n",
       "      <td>117931</td>\n",
       "      <td>25a93076658202368073fb3697a3f4ef</td>\n",
       "      <td>1a0fba6c64f792517a1f8d8c88b8bbab</td>\n",
       "      <td>1.0</td>\n",
       "      <td>c1e8014cae93306629b1de89dfa5a1bb</td>\n",
       "      <td>899.99</td>\n",
       "      <td>watches_gifts</td>\n",
       "      <td>966cb4760537b1404caedd472cc610a5</td>\n",
       "      <td>delivered</td>\n",
       "      <td>Late</td>\n",
       "      <td>...</td>\n",
       "      <td>2018-05-06 23:32:46</td>\n",
       "      <td>2018-05-22 00:00:00</td>\n",
       "      <td>2018-05-14 18:52:05</td>\n",
       "      <td>914.05</td>\n",
       "      <td>normal_purchase</td>\n",
       "      <td>2018-05-06 23:32:46</td>\n",
       "      <td>2018</td>\n",
       "      <td>Sunday</td>\n",
       "      <td>May</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>117996</th>\n",
       "      <td>117996</td>\n",
       "      <td>4f2a29346d25613d2e553a8d6381b006</td>\n",
       "      <td>3043e244f2c35c95554a613c30a7a332</td>\n",
       "      <td>1.0</td>\n",
       "      <td>25c38557cf793876c5abdd5931f922db</td>\n",
       "      <td>949.90</td>\n",
       "      <td>baby</td>\n",
       "      <td>5dceca129747e92ff8ef7a997dc4f8ca</td>\n",
       "      <td>delivered</td>\n",
       "      <td>Not late</td>\n",
       "      <td>...</td>\n",
       "      <td>2018-03-15 23:18:16</td>\n",
       "      <td>2018-04-10 00:00:00</td>\n",
       "      <td>2018-04-20 11:12:30</td>\n",
       "      <td>987.20</td>\n",
       "      <td>normal_purchase</td>\n",
       "      <td>2018-03-15 23:18:16</td>\n",
       "      <td>2018</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>March</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>117998</th>\n",
       "      <td>117998</td>\n",
       "      <td>f3d113574c163b8642fb44302c8fe0c9</td>\n",
       "      <td>7ca7b6bb5ab0b1ac2529e5c489cf3592</td>\n",
       "      <td>1.0</td>\n",
       "      <td>9fa7e05ae5cddeb74ff2fe5046b742db</td>\n",
       "      <td>990.00</td>\n",
       "      <td>health_beauty</td>\n",
       "      <td>17f51e7198701186712e53a39c564617</td>\n",
       "      <td>delivered</td>\n",
       "      <td>Late</td>\n",
       "      <td>...</td>\n",
       "      <td>2017-06-23 21:50:23</td>\n",
       "      <td>2017-07-24 00:00:00</td>\n",
       "      <td>2017-07-13 18:23:47</td>\n",
       "      <td>1090.68</td>\n",
       "      <td>normal_purchase</td>\n",
       "      <td>2017-06-23 21:50:23</td>\n",
       "      <td>2017</td>\n",
       "      <td>Friday</td>\n",
       "      <td>June</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>118096</th>\n",
       "      <td>118096</td>\n",
       "      <td>bfb1d0f7664c36a532d74c4cf0da37af</td>\n",
       "      <td>811e55e5f48c8f37832963ff509ce85b</td>\n",
       "      <td>1.0</td>\n",
       "      <td>9aedf557945e816d48539ee56293f860</td>\n",
       "      <td>1200.00</td>\n",
       "      <td>computers</td>\n",
       "      <td>53243585a1d6dc2643021fd1853d8905</td>\n",
       "      <td>delivered</td>\n",
       "      <td>Late</td>\n",
       "      <td>...</td>\n",
       "      <td>2017-08-31 15:05:50</td>\n",
       "      <td>2017-09-22 00:00:00</td>\n",
       "      <td>2017-09-11 20:45:01</td>\n",
       "      <td>1240.85</td>\n",
       "      <td>normal_purchase</td>\n",
       "      <td>2017-08-31 15:05:50</td>\n",
       "      <td>2017</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>August</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>118340</th>\n",
       "      <td>118340</td>\n",
       "      <td>2ff4dba9b7e65ee15034ed2b0004048d</td>\n",
       "      <td>f3721694bcec86501e4a19cf459d3b28</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6b0894fbd8143c541f3c40622d8a9603</td>\n",
       "      <td>980.00</td>\n",
       "      <td>garden_tools</td>\n",
       "      <td>9df5750209b83567ec144eb299d7d634</td>\n",
       "      <td>delivered</td>\n",
       "      <td>Late</td>\n",
       "      <td>...</td>\n",
       "      <td>2018-05-23 18:35:24</td>\n",
       "      <td>2018-06-28 00:00:00</td>\n",
       "      <td>2018-06-08 13:38:45</td>\n",
       "      <td>1013.59</td>\n",
       "      <td>normal_purchase</td>\n",
       "      <td>2018-05-23 18:35:24</td>\n",
       "      <td>2018</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>May</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1184 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Unnamed: 0                          order_id  \\\n",
       "20              20  403b97836b0c04a622354cf531062e5f   \n",
       "393            393  2b72a32a2c3e93fa260ca7655073886f   \n",
       "407            407  ac64f79a33bfa575fc5a17e7926df3d5   \n",
       "411            411  6cb134bb285a64b0425d1fdaa00d4214   \n",
       "482            482  da8be3bb62e9bf01e2e1a3bfd74ebd1a   \n",
       "...            ...                               ...   \n",
       "117931      117931  25a93076658202368073fb3697a3f4ef   \n",
       "117996      117996  4f2a29346d25613d2e553a8d6381b006   \n",
       "117998      117998  f3d113574c163b8642fb44302c8fe0c9   \n",
       "118096      118096  bfb1d0f7664c36a532d74c4cf0da37af   \n",
       "118340      118340  2ff4dba9b7e65ee15034ed2b0004048d   \n",
       "\n",
       "                             customer_id  order_item_id  \\\n",
       "20      738b086814c6fcc74b8cc583f8516ee3            1.0   \n",
       "393     5265029daa29cc08d86b9f96cd99d03f            1.0   \n",
       "407     51a8d592336a414fea21f12534b0f797            1.0   \n",
       "411     d854800a320bbb07b484f76894e4638f            1.0   \n",
       "482     3f083b9f62e687be8d84684c7d54c8db            1.0   \n",
       "...                                  ...            ...   \n",
       "117931  1a0fba6c64f792517a1f8d8c88b8bbab            1.0   \n",
       "117996  3043e244f2c35c95554a613c30a7a332            1.0   \n",
       "117998  7ca7b6bb5ab0b1ac2529e5c489cf3592            1.0   \n",
       "118096  811e55e5f48c8f37832963ff509ce85b            1.0   \n",
       "118340  f3721694bcec86501e4a19cf459d3b28            1.0   \n",
       "\n",
       "                              product_id    price  \\\n",
       "20      638bbb2a5e4f360b71f332ddfebfd672  1299.00   \n",
       "393     cc3181a81384b6fc34602d49cb6068a6  1148.00   \n",
       "407     bd9fca6bf4bdcbf4094d7bc11415dfe5  1099.00   \n",
       "411     463cdd8acfe23847f35e29076df0bbbd  1999.00   \n",
       "482     cea7edf64d9e93fbf6b89cc3ba537cd9  2690.00   \n",
       "...                                  ...      ...   \n",
       "117931  c1e8014cae93306629b1de89dfa5a1bb   899.99   \n",
       "117996  25c38557cf793876c5abdd5931f922db   949.90   \n",
       "117998  9fa7e05ae5cddeb74ff2fe5046b742db   990.00   \n",
       "118096  9aedf557945e816d48539ee56293f860  1200.00   \n",
       "118340  6b0894fbd8143c541f3c40622d8a9603   980.00   \n",
       "\n",
       "          product_category_name_english                         seller_id  \\\n",
       "20      construction_tools_construction  c4af86330efa7a2620772227d2d670c9   \n",
       "393                   home_appliances_2  e882b2a25a10b9c057cc49695f222c19   \n",
       "407                    small_appliances  ba90964cff9b9e0e6f32b23b82465f7b   \n",
       "411                       watches_gifts  c3acdfac4e3e97ff87529454fbc03642   \n",
       "482                        garden_tools  a4b6b9b992b46e9ef863637af96e04bc   \n",
       "...                                 ...                               ...   \n",
       "117931                    watches_gifts  966cb4760537b1404caedd472cc610a5   \n",
       "117996                             baby  5dceca129747e92ff8ef7a997dc4f8ca   \n",
       "117998                    health_beauty  17f51e7198701186712e53a39c564617   \n",
       "118096                        computers  53243585a1d6dc2643021fd1853d8905   \n",
       "118340                     garden_tools  9df5750209b83567ec144eb299d7d634   \n",
       "\n",
       "       order_status delivery_status  ... order_purchase_timestamp  \\\n",
       "20        delivered            Late  ...      2018-01-02 19:00:43   \n",
       "393       delivered            Late  ...      2018-04-18 15:40:36   \n",
       "407       delivered            Late  ...      2018-04-01 18:27:01   \n",
       "411       delivered            Late  ...      2018-07-01 20:54:21   \n",
       "482       delivered        Not late  ...      2017-04-06 13:44:08   \n",
       "...             ...             ...  ...                      ...   \n",
       "117931    delivered            Late  ...      2018-05-06 23:32:46   \n",
       "117996    delivered        Not late  ...      2018-03-15 23:18:16   \n",
       "117998    delivered            Late  ...      2017-06-23 21:50:23   \n",
       "118096    delivered            Late  ...      2017-08-31 15:05:50   \n",
       "118340    delivered            Late  ...      2018-05-23 18:35:24   \n",
       "\n",
       "       order_estimated_delivery_date order_delivered_customer_date  \\\n",
       "20               2018-02-06 00:00:00           2018-01-20 01:38:59   \n",
       "393              2018-05-18 00:00:00           2018-05-03 13:03:03   \n",
       "407              2018-04-12 00:00:00           2018-04-04 19:47:44   \n",
       "411              2018-07-26 00:00:00           2018-07-10 22:12:13   \n",
       "482              2017-05-04 00:00:00           2017-05-08 10:52:06   \n",
       "...                              ...                           ...   \n",
       "117931           2018-05-22 00:00:00           2018-05-14 18:52:05   \n",
       "117996           2018-04-10 00:00:00           2018-04-20 11:12:30   \n",
       "117998           2017-07-24 00:00:00           2017-07-13 18:23:47   \n",
       "118096           2017-09-22 00:00:00           2017-09-11 20:45:01   \n",
       "118340           2018-06-28 00:00:00           2018-06-08 13:38:45   \n",
       "\n",
       "       payment_value    purchase_type           order_date  order_year  \\\n",
       "20           1376.45  normal_purchase  2018-01-02 19:00:43        2018   \n",
       "393          1255.43  normal_purchase  2018-04-18 15:40:36        2018   \n",
       "407          1119.60  normal_purchase  2018-04-01 18:27:01        2018   \n",
       "411          2031.09  normal_purchase  2018-07-01 20:54:21        2018   \n",
       "482          2751.24  normal_purchase  2017-04-06 13:44:08        2017   \n",
       "...              ...              ...                  ...         ...   \n",
       "117931        914.05  normal_purchase  2018-05-06 23:32:46        2018   \n",
       "117996        987.20  normal_purchase  2018-03-15 23:18:16        2018   \n",
       "117998       1090.68  normal_purchase  2017-06-23 21:50:23        2017   \n",
       "118096       1240.85  normal_purchase  2017-08-31 15:05:50        2017   \n",
       "118340       1013.59  normal_purchase  2018-05-23 18:35:24        2018   \n",
       "\n",
       "        order_day order_month  order_hour  \n",
       "20        Tuesday     January          19  \n",
       "393     Wednesday       April          15  \n",
       "407        Sunday       April          18  \n",
       "411        Sunday        July          20  \n",
       "482      Thursday       April          13  \n",
       "...           ...         ...         ...  \n",
       "117931     Sunday         May          23  \n",
       "117996   Thursday       March          23  \n",
       "117998     Friday        June          21  \n",
       "118096   Thursday      August          15  \n",
       "118340  Wednesday         May          18  \n",
       "\n",
       "[1184 rows x 23 columns]"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[(data.price>uv)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "e3a580fa-58ab-432f-9b1d-603e88d1d5c0",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_3377/3144864312.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  data.price[(data.price> 3*uv)] = 3*uv\n"
     ]
    }
   ],
   "source": [
    "data.price[(data.price> 3*uv)] = 3*uv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "50c2a322-3794-441d-90e4-2c4a0b90d423",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7.2433"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.percentile(data.payment_value,[1])[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "83fc9b92-6b92-4148-bc6b-ee398e4227ad",
   "metadata": {},
   "outputs": [],
   "source": [
    "lv = np.percentile(data.payment_value,[1])[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "1a0fc03a-61b8-46e7-aa49-647b41c4fe0c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>order_id</th>\n",
       "      <th>customer_id</th>\n",
       "      <th>order_item_id</th>\n",
       "      <th>product_id</th>\n",
       "      <th>price</th>\n",
       "      <th>product_category_name_english</th>\n",
       "      <th>seller_id</th>\n",
       "      <th>order_status</th>\n",
       "      <th>delivery_status</th>\n",
       "      <th>...</th>\n",
       "      <th>order_purchase_timestamp</th>\n",
       "      <th>order_estimated_delivery_date</th>\n",
       "      <th>order_delivered_customer_date</th>\n",
       "      <th>payment_value</th>\n",
       "      <th>purchase_type</th>\n",
       "      <th>order_date</th>\n",
       "      <th>order_year</th>\n",
       "      <th>order_day</th>\n",
       "      <th>order_month</th>\n",
       "      <th>order_hour</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>e481f51cbdc54678b7cc49136f2d6af7</td>\n",
       "      <td>9ef432eb6251297304e76186b10a928d</td>\n",
       "      <td>1.0</td>\n",
       "      <td>87285b34884572647811a353c7ac498a</td>\n",
       "      <td>29.99</td>\n",
       "      <td>housewares</td>\n",
       "      <td>3504c0cb71d7fa48d967e0e4c94d59d9</td>\n",
       "      <td>delivered</td>\n",
       "      <td>Late</td>\n",
       "      <td>...</td>\n",
       "      <td>2017-10-02 10:56:33</td>\n",
       "      <td>2017-10-18 00:00:00</td>\n",
       "      <td>2017-10-10 21:25:13</td>\n",
       "      <td>2.00</td>\n",
       "      <td>small_purchase</td>\n",
       "      <td>2017-10-02 10:56:33</td>\n",
       "      <td>2017</td>\n",
       "      <td>Monday</td>\n",
       "      <td>October</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>25</td>\n",
       "      <td>83018ec114eee8641c97e08f7b4e926f</td>\n",
       "      <td>7f8c8b9c2ae27bf3300f670c3d478be8</td>\n",
       "      <td>1.0</td>\n",
       "      <td>c35498fbb4358837ae16850f50c3fd22</td>\n",
       "      <td>76.00</td>\n",
       "      <td>telephony</td>\n",
       "      <td>70a12e78e608ac31179aea7f8422044b</td>\n",
       "      <td>delivered</td>\n",
       "      <td>Late</td>\n",
       "      <td>...</td>\n",
       "      <td>2017-10-26 15:54:26</td>\n",
       "      <td>2017-11-23 00:00:00</td>\n",
       "      <td>2017-11-08 22:22:00</td>\n",
       "      <td>5.96</td>\n",
       "      <td>small_purchase</td>\n",
       "      <td>2017-10-26 15:54:26</td>\n",
       "      <td>2017</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>October</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>97</td>\n",
       "      <td>634e8f4c0f6744a626f77f39770ac6aa</td>\n",
       "      <td>05e996469a2bf9559c7122b87e156724</td>\n",
       "      <td>1.0</td>\n",
       "      <td>69d980b4120a76616d7b237d731d6156</td>\n",
       "      <td>219.00</td>\n",
       "      <td>watches_gifts</td>\n",
       "      <td>744dac408745240a2c2528fb1b6028f3</td>\n",
       "      <td>delivered</td>\n",
       "      <td>Late</td>\n",
       "      <td>...</td>\n",
       "      <td>2017-08-09 18:32:47</td>\n",
       "      <td>2017-08-31 00:00:00</td>\n",
       "      <td>2017-08-16 18:17:54</td>\n",
       "      <td>7.08</td>\n",
       "      <td>small_purchase</td>\n",
       "      <td>2017-08-09 18:32:47</td>\n",
       "      <td>2017</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>August</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>120</th>\n",
       "      <td>120</td>\n",
       "      <td>f7959f8385f34c4f645327465a1c9fc4</td>\n",
       "      <td>0bf19317b1830a69e55b40710576aa7a</td>\n",
       "      <td>1.0</td>\n",
       "      <td>c1234c80dafde7ef3311b3eabd5069ed</td>\n",
       "      <td>17.90</td>\n",
       "      <td>bed_bath_table</td>\n",
       "      <td>dc4a0fc896dc34b0d5bfec8438291c80</td>\n",
       "      <td>delivered</td>\n",
       "      <td>Late</td>\n",
       "      <td>...</td>\n",
       "      <td>2017-03-30 07:50:33</td>\n",
       "      <td>2017-04-26 00:00:00</td>\n",
       "      <td>2017-04-10 02:59:52</td>\n",
       "      <td>6.83</td>\n",
       "      <td>small_purchase</td>\n",
       "      <td>2017-03-30 07:50:33</td>\n",
       "      <td>2017</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>March</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>379</th>\n",
       "      <td>379</td>\n",
       "      <td>dc400373e624e9b0435847a79813c658</td>\n",
       "      <td>4a00f00d951c167739888a1f16c56e8e</td>\n",
       "      <td>1.0</td>\n",
       "      <td>91910bbde68f7027e850dcd82b25c5bb</td>\n",
       "      <td>39.90</td>\n",
       "      <td>perfumery</td>\n",
       "      <td>2e1a7d075abe038c1b2743005fe42ff1</td>\n",
       "      <td>delivered</td>\n",
       "      <td>Late</td>\n",
       "      <td>...</td>\n",
       "      <td>2018-01-14 18:49:14</td>\n",
       "      <td>2018-01-30 00:00:00</td>\n",
       "      <td>2018-01-20 14:28:43</td>\n",
       "      <td>0.33</td>\n",
       "      <td>small_purchase</td>\n",
       "      <td>2018-01-14 18:49:14</td>\n",
       "      <td>2018</td>\n",
       "      <td>Sunday</td>\n",
       "      <td>January</td>\n",
       "      <td>18</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>117621</th>\n",
       "      <td>117621</td>\n",
       "      <td>4b09fc170c84e10e4caeff4d880db358</td>\n",
       "      <td>13527074814c80fd3f739a54ee91672c</td>\n",
       "      <td>1.0</td>\n",
       "      <td>39a0257f2ddaad536f01a4d2cacf7392</td>\n",
       "      <td>36.00</td>\n",
       "      <td>computers_accessories</td>\n",
       "      <td>70a12e78e608ac31179aea7f8422044b</td>\n",
       "      <td>delivered</td>\n",
       "      <td>Late</td>\n",
       "      <td>...</td>\n",
       "      <td>2017-07-30 17:47:40</td>\n",
       "      <td>2017-08-22 00:00:00</td>\n",
       "      <td>2017-08-05 15:46:47</td>\n",
       "      <td>1.10</td>\n",
       "      <td>small_purchase</td>\n",
       "      <td>2017-07-30 17:47:40</td>\n",
       "      <td>2017</td>\n",
       "      <td>Sunday</td>\n",
       "      <td>July</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>117713</th>\n",
       "      <td>117713</td>\n",
       "      <td>4134d857ea0e2009e02f6f90888fa583</td>\n",
       "      <td>58ea963c191e6d553f030709aabcda9c</td>\n",
       "      <td>1.0</td>\n",
       "      <td>dd60cac04e6bcbc919cb1c7dcc473211</td>\n",
       "      <td>29.90</td>\n",
       "      <td>housewares</td>\n",
       "      <td>febab0275244b9a49a623f0bd613ca2f</td>\n",
       "      <td>delivered</td>\n",
       "      <td>Late</td>\n",
       "      <td>...</td>\n",
       "      <td>2017-07-30 20:32:10</td>\n",
       "      <td>2017-08-22 00:00:00</td>\n",
       "      <td>2017-08-03 14:19:55</td>\n",
       "      <td>2.65</td>\n",
       "      <td>small_purchase</td>\n",
       "      <td>2017-07-30 20:32:10</td>\n",
       "      <td>2017</td>\n",
       "      <td>Sunday</td>\n",
       "      <td>July</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>117830</th>\n",
       "      <td>117830</td>\n",
       "      <td>a0fd703a1c6a122520246bd8d481e528</td>\n",
       "      <td>cfa2494edba0a27c258d43d40f190a4b</td>\n",
       "      <td>1.0</td>\n",
       "      <td>b7ebdc826d0220dc16c813b9682d63dd</td>\n",
       "      <td>105.60</td>\n",
       "      <td>cool_stuff</td>\n",
       "      <td>8cbac7e12637ed9cffa18c7875207478</td>\n",
       "      <td>delivered</td>\n",
       "      <td>Late</td>\n",
       "      <td>...</td>\n",
       "      <td>2017-05-16 10:29:47</td>\n",
       "      <td>2017-06-07 00:00:00</td>\n",
       "      <td>2017-05-23 11:53:44</td>\n",
       "      <td>2.35</td>\n",
       "      <td>small_purchase</td>\n",
       "      <td>2017-05-16 10:29:47</td>\n",
       "      <td>2017</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>May</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>117934</th>\n",
       "      <td>117934</td>\n",
       "      <td>d591a97fad2791ee9d973b748fd1c4d9</td>\n",
       "      <td>49c66275a0b693c4414d9b1cc41477c2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>b09790edda54333116f9cf1decc580a1</td>\n",
       "      <td>24.90</td>\n",
       "      <td>bed_bath_table</td>\n",
       "      <td>d2374cbcbb3ca4ab1086534108cc3ab7</td>\n",
       "      <td>delivered</td>\n",
       "      <td>Late</td>\n",
       "      <td>...</td>\n",
       "      <td>2017-03-25 15:34:23</td>\n",
       "      <td>2017-05-02 00:00:00</td>\n",
       "      <td>2017-04-10 11:26:36</td>\n",
       "      <td>5.86</td>\n",
       "      <td>small_purchase</td>\n",
       "      <td>2017-03-25 15:34:23</td>\n",
       "      <td>2017</td>\n",
       "      <td>Saturday</td>\n",
       "      <td>March</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>117938</th>\n",
       "      <td>117938</td>\n",
       "      <td>580e9410cdbeb1ec66a48d3d0454dd17</td>\n",
       "      <td>e64813a1e1c56c5f628d71d62b69fe7c</td>\n",
       "      <td>1.0</td>\n",
       "      <td>f71f42e2381752836563b70beb542f80</td>\n",
       "      <td>31.90</td>\n",
       "      <td>computers_accessories</td>\n",
       "      <td>85d9eb9ddc5d00ca9336a2219c97bb13</td>\n",
       "      <td>delivered</td>\n",
       "      <td>Late</td>\n",
       "      <td>...</td>\n",
       "      <td>2017-11-24 02:08:40</td>\n",
       "      <td>2018-01-05 00:00:00</td>\n",
       "      <td>2017-12-21 20:05:34</td>\n",
       "      <td>0.08</td>\n",
       "      <td>small_purchase</td>\n",
       "      <td>2017-11-24 02:08:40</td>\n",
       "      <td>2017</td>\n",
       "      <td>Friday</td>\n",
       "      <td>November</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1185 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Unnamed: 0                          order_id  \\\n",
       "1                1  e481f51cbdc54678b7cc49136f2d6af7   \n",
       "25              25  83018ec114eee8641c97e08f7b4e926f   \n",
       "97              97  634e8f4c0f6744a626f77f39770ac6aa   \n",
       "120            120  f7959f8385f34c4f645327465a1c9fc4   \n",
       "379            379  dc400373e624e9b0435847a79813c658   \n",
       "...            ...                               ...   \n",
       "117621      117621  4b09fc170c84e10e4caeff4d880db358   \n",
       "117713      117713  4134d857ea0e2009e02f6f90888fa583   \n",
       "117830      117830  a0fd703a1c6a122520246bd8d481e528   \n",
       "117934      117934  d591a97fad2791ee9d973b748fd1c4d9   \n",
       "117938      117938  580e9410cdbeb1ec66a48d3d0454dd17   \n",
       "\n",
       "                             customer_id  order_item_id  \\\n",
       "1       9ef432eb6251297304e76186b10a928d            1.0   \n",
       "25      7f8c8b9c2ae27bf3300f670c3d478be8            1.0   \n",
       "97      05e996469a2bf9559c7122b87e156724            1.0   \n",
       "120     0bf19317b1830a69e55b40710576aa7a            1.0   \n",
       "379     4a00f00d951c167739888a1f16c56e8e            1.0   \n",
       "...                                  ...            ...   \n",
       "117621  13527074814c80fd3f739a54ee91672c            1.0   \n",
       "117713  58ea963c191e6d553f030709aabcda9c            1.0   \n",
       "117830  cfa2494edba0a27c258d43d40f190a4b            1.0   \n",
       "117934  49c66275a0b693c4414d9b1cc41477c2            1.0   \n",
       "117938  e64813a1e1c56c5f628d71d62b69fe7c            1.0   \n",
       "\n",
       "                              product_id   price  \\\n",
       "1       87285b34884572647811a353c7ac498a   29.99   \n",
       "25      c35498fbb4358837ae16850f50c3fd22   76.00   \n",
       "97      69d980b4120a76616d7b237d731d6156  219.00   \n",
       "120     c1234c80dafde7ef3311b3eabd5069ed   17.90   \n",
       "379     91910bbde68f7027e850dcd82b25c5bb   39.90   \n",
       "...                                  ...     ...   \n",
       "117621  39a0257f2ddaad536f01a4d2cacf7392   36.00   \n",
       "117713  dd60cac04e6bcbc919cb1c7dcc473211   29.90   \n",
       "117830  b7ebdc826d0220dc16c813b9682d63dd  105.60   \n",
       "117934  b09790edda54333116f9cf1decc580a1   24.90   \n",
       "117938  f71f42e2381752836563b70beb542f80   31.90   \n",
       "\n",
       "       product_category_name_english                         seller_id  \\\n",
       "1                         housewares  3504c0cb71d7fa48d967e0e4c94d59d9   \n",
       "25                         telephony  70a12e78e608ac31179aea7f8422044b   \n",
       "97                     watches_gifts  744dac408745240a2c2528fb1b6028f3   \n",
       "120                   bed_bath_table  dc4a0fc896dc34b0d5bfec8438291c80   \n",
       "379                        perfumery  2e1a7d075abe038c1b2743005fe42ff1   \n",
       "...                              ...                               ...   \n",
       "117621         computers_accessories  70a12e78e608ac31179aea7f8422044b   \n",
       "117713                    housewares  febab0275244b9a49a623f0bd613ca2f   \n",
       "117830                    cool_stuff  8cbac7e12637ed9cffa18c7875207478   \n",
       "117934                bed_bath_table  d2374cbcbb3ca4ab1086534108cc3ab7   \n",
       "117938         computers_accessories  85d9eb9ddc5d00ca9336a2219c97bb13   \n",
       "\n",
       "       order_status delivery_status  ... order_purchase_timestamp  \\\n",
       "1         delivered            Late  ...      2017-10-02 10:56:33   \n",
       "25        delivered            Late  ...      2017-10-26 15:54:26   \n",
       "97        delivered            Late  ...      2017-08-09 18:32:47   \n",
       "120       delivered            Late  ...      2017-03-30 07:50:33   \n",
       "379       delivered            Late  ...      2018-01-14 18:49:14   \n",
       "...             ...             ...  ...                      ...   \n",
       "117621    delivered            Late  ...      2017-07-30 17:47:40   \n",
       "117713    delivered            Late  ...      2017-07-30 20:32:10   \n",
       "117830    delivered            Late  ...      2017-05-16 10:29:47   \n",
       "117934    delivered            Late  ...      2017-03-25 15:34:23   \n",
       "117938    delivered            Late  ...      2017-11-24 02:08:40   \n",
       "\n",
       "       order_estimated_delivery_date order_delivered_customer_date  \\\n",
       "1                2017-10-18 00:00:00           2017-10-10 21:25:13   \n",
       "25               2017-11-23 00:00:00           2017-11-08 22:22:00   \n",
       "97               2017-08-31 00:00:00           2017-08-16 18:17:54   \n",
       "120              2017-04-26 00:00:00           2017-04-10 02:59:52   \n",
       "379              2018-01-30 00:00:00           2018-01-20 14:28:43   \n",
       "...                              ...                           ...   \n",
       "117621           2017-08-22 00:00:00           2017-08-05 15:46:47   \n",
       "117713           2017-08-22 00:00:00           2017-08-03 14:19:55   \n",
       "117830           2017-06-07 00:00:00           2017-05-23 11:53:44   \n",
       "117934           2017-05-02 00:00:00           2017-04-10 11:26:36   \n",
       "117938           2018-01-05 00:00:00           2017-12-21 20:05:34   \n",
       "\n",
       "       payment_value   purchase_type           order_date  order_year  \\\n",
       "1               2.00  small_purchase  2017-10-02 10:56:33        2017   \n",
       "25              5.96  small_purchase  2017-10-26 15:54:26        2017   \n",
       "97              7.08  small_purchase  2017-08-09 18:32:47        2017   \n",
       "120             6.83  small_purchase  2017-03-30 07:50:33        2017   \n",
       "379             0.33  small_purchase  2018-01-14 18:49:14        2018   \n",
       "...              ...             ...                  ...         ...   \n",
       "117621          1.10  small_purchase  2017-07-30 17:47:40        2017   \n",
       "117713          2.65  small_purchase  2017-07-30 20:32:10        2017   \n",
       "117830          2.35  small_purchase  2017-05-16 10:29:47        2017   \n",
       "117934          5.86  small_purchase  2017-03-25 15:34:23        2017   \n",
       "117938          0.08  small_purchase  2017-11-24 02:08:40        2017   \n",
       "\n",
       "        order_day order_month  order_hour  \n",
       "1          Monday     October          10  \n",
       "25       Thursday     October          15  \n",
       "97      Wednesday      August          18  \n",
       "120      Thursday       March           7  \n",
       "379        Sunday     January          18  \n",
       "...           ...         ...         ...  \n",
       "117621     Sunday        July          17  \n",
       "117713     Sunday        July          20  \n",
       "117830    Tuesday         May          10  \n",
       "117934   Saturday       March          15  \n",
       "117938     Friday    November           2  \n",
       "\n",
       "[1185 rows x 23 columns]"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[(data.payment_value< lv)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "04a0cace-ac7a-46e3-b1b6-dc9219c57a81",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_3377/2204459969.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  data.payment_value[(data.payment_value <0.3*lv)] = 0.3*lv\n"
     ]
    }
   ],
   "source": [
    "data.payment_value[(data.payment_value <0.3*lv)] = 0.3*lv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "fdd38466-2e5f-44ca-a07b-d7d58d3c3dcf",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = data.drop('Unnamed: 0', axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "d11b67fb-10f1-4177-bf34-36c63b71c672",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>order_id</th>\n",
       "      <th>customer_id</th>\n",
       "      <th>order_item_id</th>\n",
       "      <th>product_id</th>\n",
       "      <th>price</th>\n",
       "      <th>product_category_name_english</th>\n",
       "      <th>seller_id</th>\n",
       "      <th>order_status</th>\n",
       "      <th>delivery_status</th>\n",
       "      <th>customer_state</th>\n",
       "      <th>...</th>\n",
       "      <th>order_purchase_timestamp</th>\n",
       "      <th>order_estimated_delivery_date</th>\n",
       "      <th>order_delivered_customer_date</th>\n",
       "      <th>payment_value</th>\n",
       "      <th>purchase_type</th>\n",
       "      <th>order_date</th>\n",
       "      <th>order_year</th>\n",
       "      <th>order_day</th>\n",
       "      <th>order_month</th>\n",
       "      <th>order_hour</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>e481f51cbdc54678b7cc49136f2d6af7</td>\n",
       "      <td>9ef432eb6251297304e76186b10a928d</td>\n",
       "      <td>1.0</td>\n",
       "      <td>87285b34884572647811a353c7ac498a</td>\n",
       "      <td>29.99</td>\n",
       "      <td>housewares</td>\n",
       "      <td>3504c0cb71d7fa48d967e0e4c94d59d9</td>\n",
       "      <td>delivered</td>\n",
       "      <td>Late</td>\n",
       "      <td>SP</td>\n",
       "      <td>...</td>\n",
       "      <td>2017-10-02 10:56:33</td>\n",
       "      <td>2017-10-18 00:00:00</td>\n",
       "      <td>2017-10-10 21:25:13</td>\n",
       "      <td>18.12000</td>\n",
       "      <td>small_purchase</td>\n",
       "      <td>2017-10-02 10:56:33</td>\n",
       "      <td>2017</td>\n",
       "      <td>Monday</td>\n",
       "      <td>October</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>e481f51cbdc54678b7cc49136f2d6af7</td>\n",
       "      <td>9ef432eb6251297304e76186b10a928d</td>\n",
       "      <td>1.0</td>\n",
       "      <td>87285b34884572647811a353c7ac498a</td>\n",
       "      <td>29.99</td>\n",
       "      <td>housewares</td>\n",
       "      <td>3504c0cb71d7fa48d967e0e4c94d59d9</td>\n",
       "      <td>delivered</td>\n",
       "      <td>Late</td>\n",
       "      <td>SP</td>\n",
       "      <td>...</td>\n",
       "      <td>2017-10-02 10:56:33</td>\n",
       "      <td>2017-10-18 00:00:00</td>\n",
       "      <td>2017-10-10 21:25:13</td>\n",
       "      <td>2.17299</td>\n",
       "      <td>small_purchase</td>\n",
       "      <td>2017-10-02 10:56:33</td>\n",
       "      <td>2017</td>\n",
       "      <td>Monday</td>\n",
       "      <td>October</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>e481f51cbdc54678b7cc49136f2d6af7</td>\n",
       "      <td>9ef432eb6251297304e76186b10a928d</td>\n",
       "      <td>1.0</td>\n",
       "      <td>87285b34884572647811a353c7ac498a</td>\n",
       "      <td>29.99</td>\n",
       "      <td>housewares</td>\n",
       "      <td>3504c0cb71d7fa48d967e0e4c94d59d9</td>\n",
       "      <td>delivered</td>\n",
       "      <td>Late</td>\n",
       "      <td>SP</td>\n",
       "      <td>...</td>\n",
       "      <td>2017-10-02 10:56:33</td>\n",
       "      <td>2017-10-18 00:00:00</td>\n",
       "      <td>2017-10-10 21:25:13</td>\n",
       "      <td>18.59000</td>\n",
       "      <td>small_purchase</td>\n",
       "      <td>2017-10-02 10:56:33</td>\n",
       "      <td>2017</td>\n",
       "      <td>Monday</td>\n",
       "      <td>October</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>53cdb2fc8bc7dce0b6741e2150273451</td>\n",
       "      <td>b0830fb4747a6c6d20dea0b8c802d7ef</td>\n",
       "      <td>1.0</td>\n",
       "      <td>595fac2a385ac33a80bd5114aec74eb8</td>\n",
       "      <td>118.70</td>\n",
       "      <td>perfumery</td>\n",
       "      <td>289cdb325fb7e7f891c38608bf9e0962</td>\n",
       "      <td>delivered</td>\n",
       "      <td>Late</td>\n",
       "      <td>BA</td>\n",
       "      <td>...</td>\n",
       "      <td>2018-07-24 20:41:37</td>\n",
       "      <td>2018-08-13 00:00:00</td>\n",
       "      <td>2018-08-07 15:27:45</td>\n",
       "      <td>141.46000</td>\n",
       "      <td>small_purchase</td>\n",
       "      <td>2018-07-24 20:41:37</td>\n",
       "      <td>2018</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>July</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>47770eb9100c2d0c44946d9cf07ec65d</td>\n",
       "      <td>41ce2a54c0b03bf3443c3d931a367089</td>\n",
       "      <td>1.0</td>\n",
       "      <td>aa4383b373c6aca5d8797843e5594415</td>\n",
       "      <td>159.90</td>\n",
       "      <td>auto</td>\n",
       "      <td>4869f7a5dfa277a7dca6462dcf3b52b2</td>\n",
       "      <td>delivered</td>\n",
       "      <td>Late</td>\n",
       "      <td>GO</td>\n",
       "      <td>...</td>\n",
       "      <td>2018-08-08 08:38:49</td>\n",
       "      <td>2018-09-04 00:00:00</td>\n",
       "      <td>2018-08-17 18:06:29</td>\n",
       "      <td>179.12000</td>\n",
       "      <td>normal_purchase</td>\n",
       "      <td>2018-08-08 08:38:49</td>\n",
       "      <td>2018</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>August</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                           order_id                       customer_id  \\\n",
       "0  e481f51cbdc54678b7cc49136f2d6af7  9ef432eb6251297304e76186b10a928d   \n",
       "1  e481f51cbdc54678b7cc49136f2d6af7  9ef432eb6251297304e76186b10a928d   \n",
       "2  e481f51cbdc54678b7cc49136f2d6af7  9ef432eb6251297304e76186b10a928d   \n",
       "3  53cdb2fc8bc7dce0b6741e2150273451  b0830fb4747a6c6d20dea0b8c802d7ef   \n",
       "4  47770eb9100c2d0c44946d9cf07ec65d  41ce2a54c0b03bf3443c3d931a367089   \n",
       "\n",
       "   order_item_id                        product_id   price  \\\n",
       "0            1.0  87285b34884572647811a353c7ac498a   29.99   \n",
       "1            1.0  87285b34884572647811a353c7ac498a   29.99   \n",
       "2            1.0  87285b34884572647811a353c7ac498a   29.99   \n",
       "3            1.0  595fac2a385ac33a80bd5114aec74eb8  118.70   \n",
       "4            1.0  aa4383b373c6aca5d8797843e5594415  159.90   \n",
       "\n",
       "  product_category_name_english                         seller_id  \\\n",
       "0                    housewares  3504c0cb71d7fa48d967e0e4c94d59d9   \n",
       "1                    housewares  3504c0cb71d7fa48d967e0e4c94d59d9   \n",
       "2                    housewares  3504c0cb71d7fa48d967e0e4c94d59d9   \n",
       "3                     perfumery  289cdb325fb7e7f891c38608bf9e0962   \n",
       "4                          auto  4869f7a5dfa277a7dca6462dcf3b52b2   \n",
       "\n",
       "  order_status delivery_status customer_state  ... order_purchase_timestamp  \\\n",
       "0    delivered            Late             SP  ...      2017-10-02 10:56:33   \n",
       "1    delivered            Late             SP  ...      2017-10-02 10:56:33   \n",
       "2    delivered            Late             SP  ...      2017-10-02 10:56:33   \n",
       "3    delivered            Late             BA  ...      2018-07-24 20:41:37   \n",
       "4    delivered            Late             GO  ...      2018-08-08 08:38:49   \n",
       "\n",
       "  order_estimated_delivery_date order_delivered_customer_date payment_value  \\\n",
       "0           2017-10-18 00:00:00           2017-10-10 21:25:13      18.12000   \n",
       "1           2017-10-18 00:00:00           2017-10-10 21:25:13       2.17299   \n",
       "2           2017-10-18 00:00:00           2017-10-10 21:25:13      18.59000   \n",
       "3           2018-08-13 00:00:00           2018-08-07 15:27:45     141.46000   \n",
       "4           2018-09-04 00:00:00           2018-08-17 18:06:29     179.12000   \n",
       "\n",
       "     purchase_type           order_date order_year  order_day  order_month  \\\n",
       "0   small_purchase  2017-10-02 10:56:33       2017     Monday      October   \n",
       "1   small_purchase  2017-10-02 10:56:33       2017     Monday      October   \n",
       "2   small_purchase  2017-10-02 10:56:33       2017     Monday      October   \n",
       "3   small_purchase  2018-07-24 20:41:37       2018    Tuesday         July   \n",
       "4  normal_purchase  2018-08-08 08:38:49       2018  Wednesday       August   \n",
       "\n",
       "  order_hour  \n",
       "0         10  \n",
       "1         10  \n",
       "2         10  \n",
       "3         20  \n",
       "4          8  \n",
       "\n",
       "[5 rows x 22 columns]"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "7f92b2dd-6ad6-41e2-b998-77bc0dc95f8a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>order_item_id</th>\n",
       "      <th>price</th>\n",
       "      <th>payment_value</th>\n",
       "      <th>order_year</th>\n",
       "      <th>order_hour</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>118434.000000</td>\n",
       "      <td>118434.000000</td>\n",
       "      <td>118434.000000</td>\n",
       "      <td>118434.000000</td>\n",
       "      <td>118434.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.195920</td>\n",
       "      <td>120.451349</td>\n",
       "      <td>172.853189</td>\n",
       "      <td>2017.536324</td>\n",
       "      <td>14.75605</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.695271</td>\n",
       "      <td>176.566582</td>\n",
       "      <td>268.254009</td>\n",
       "      <td>0.505492</td>\n",
       "      <td>5.32724</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.850000</td>\n",
       "      <td>2.172990</td>\n",
       "      <td>2016.000000</td>\n",
       "      <td>0.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>39.900000</td>\n",
       "      <td>60.860000</td>\n",
       "      <td>2017.000000</td>\n",
       "      <td>11.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>75.000000</td>\n",
       "      <td>108.200000</td>\n",
       "      <td>2018.000000</td>\n",
       "      <td>15.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>133.000000</td>\n",
       "      <td>189.240000</td>\n",
       "      <td>2018.000000</td>\n",
       "      <td>19.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>21.000000</td>\n",
       "      <td>2670.000000</td>\n",
       "      <td>13664.080000</td>\n",
       "      <td>2018.000000</td>\n",
       "      <td>23.00000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       order_item_id          price  payment_value     order_year  \\\n",
       "count  118434.000000  118434.000000  118434.000000  118434.000000   \n",
       "mean        1.195920     120.451349     172.853189    2017.536324   \n",
       "std         0.695271     176.566582     268.254009       0.505492   \n",
       "min         1.000000       0.850000       2.172990    2016.000000   \n",
       "25%         1.000000      39.900000      60.860000    2017.000000   \n",
       "50%         1.000000      75.000000     108.200000    2018.000000   \n",
       "75%         1.000000     133.000000     189.240000    2018.000000   \n",
       "max        21.000000    2670.000000   13664.080000    2018.000000   \n",
       "\n",
       "         order_hour  \n",
       "count  118434.00000  \n",
       "mean       14.75605  \n",
       "std         5.32724  \n",
       "min         0.00000  \n",
       "25%        11.00000  \n",
       "50%        15.00000  \n",
       "75%        19.00000  \n",
       "max        23.00000  "
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2ef4a6eb-6e05-4ae2-ad09-5da43c19e5fd",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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