e-commerce_analysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "2b41cca1",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import os\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sb\n",
    "from sklearn.preprocessing import StandardScaler"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9af810d4",
   "metadata": {},
   "source": [
    "# **Data Overview**\n",
    "### There are in total 11 datasets from LoMo, in csv.\n",
    "1. lomo_customers_dataset.csv\n",
    "2. lomo_sellers_dataset.csv\n",
    "3. lomo_geolocation_dataset.csv\n",
    "4. lomo_products_dataset.csv\n",
    "5. lomo_product_category_name_translation.csv\n",
    "6. lomo_orders_dataset.csv\n",
    "7. lomo_order_items_dataset.csv\n",
    "8. lomo_order_payments_dataset.csv\n",
    "9. lomo_order_reviews_dataset.csv\n",
    "10. lomo_marketing_qualified_leads_dataset.csv\n",
    "11. lomo_closed_deals_dataset.csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "44abbcd5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Setting the dir\n",
    "os.chdir('C:\\\\Users\\\\tyeck\\\\OneDrive\\\\Desktop\\\\School\\\\Internship\\\\data-analytics-case-study-dataset') \n",
    "# Load the datasets\n",
    "customers_df = pd.read_csv('001_lomo_customers_dataset.csv')\n",
    "sellers_df = pd.read_csv('002_lomo_sellers_dataset.csv')\n",
    "geolocation_df = pd.read_csv('003_lomo_geolocation_dataset.csv')\n",
    "products_df = pd.read_csv('004_lomo_products_dataset.csv')\n",
    "product_category_translation_df = pd.read_csv('005_lomo_product_category_name_translation.csv')\n",
    "orders_df = pd.read_csv('006_lomo_orders_dataset.csv')\n",
    "order_items_df = pd.read_csv('007_lomo_order_items_dataset.csv')\n",
    "order_payments_df = pd.read_csv('008_lomo_order_payments_dataset.csv')\n",
    "order_reviews_df = pd.read_csv('009_lomo_order_reviews_dataset.csv')\n",
    "marketing_qualified_leads_df = pd.read_csv('010_lomo_marketing_qualified_leads_dataset.csv')\n",
    "closed_deals_df = pd.read_csv('011_lomo_closed_deals_dataset.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "7a42bae0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Helper function to return df summary \n",
    "from scipy import stats \n",
    "def dfsummary(df):\n",
    "    print(f\"Dataset Shape: {df.shape}\")\n",
    "    summary = pd.DataFrame(df.dtypes,columns=['dtypes'])\n",
    "    summary = summary.reset_index()\n",
    "    summary['Name'] = summary['index']\n",
    "    summary = summary[['Name','dtypes']]\n",
    "    summary['Missing'] = df.isnull().sum().values    \n",
    "    summary['Uniques'] = df.nunique().values\n",
    "    summary['First Value'] = df.loc[0].values\n",
    "    summary['Second Value'] = df.loc[1].values\n",
    "    summary['Third Value'] = df.loc[2].values\n",
    "\n",
    "    for name in summary['Name'].value_counts().index:\n",
    "        summary.loc[summary['Name'] == name, 'Entropy'] = round(stats.entropy(df[name].value_counts(normalize=True), base=2),2) \n",
    "\n",
    "    return summary"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2f33cdaa",
   "metadata": {},
   "source": [
    "# **Problem Statement**\n",
    "### **CMO challenge: Sales performance and prediction**\n",
    "1. Which product category has the best performance and which has the highest potential? \n",
    "\n",
    "\n",
    "2. If you were the Chief Marketing Officer, how would you prioritise your (2C) marketing efforts? Would your strategy differ by region (state)? (Visualising your points on a map would help)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ab6e5eca",
   "metadata": {},
   "source": [
    "## **Data Preparation**\n",
    "I will first do a preliminary exploration of the data before doing data cleaning and preparation for dashboard purposes later on.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "4a9f8b10",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>product_id</th>\n",
       "      <th>product_category_name</th>\n",
       "      <th>product_name_lenght</th>\n",
       "      <th>product_description_lenght</th>\n",
       "      <th>product_photos_qty</th>\n",
       "      <th>product_weight_g</th>\n",
       "      <th>product_length_cm</th>\n",
       "      <th>product_height_cm</th>\n",
       "      <th>product_width_cm</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1e9e8ef04dbcff4541ed26657ea517e5</td>\n",
       "      <td>perfumaria</td>\n",
       "      <td>40</td>\n",
       "      <td>287</td>\n",
       "      <td>1</td>\n",
       "      <td>225</td>\n",
       "      <td>16</td>\n",
       "      <td>10</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3aa071139cb16b67ca9e5dea641aaa2f</td>\n",
       "      <td>artes</td>\n",
       "      <td>44</td>\n",
       "      <td>276</td>\n",
       "      <td>1</td>\n",
       "      <td>1000</td>\n",
       "      <td>30</td>\n",
       "      <td>18</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>96bd76ec8810374ed1b65e291975717f</td>\n",
       "      <td>esporte_lazer</td>\n",
       "      <td>46</td>\n",
       "      <td>250</td>\n",
       "      <td>1</td>\n",
       "      <td>154</td>\n",
       "      <td>18</td>\n",
       "      <td>9</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>cef67bcfe19066a932b7673e239eb23d</td>\n",
       "      <td>bebes</td>\n",
       "      <td>27</td>\n",
       "      <td>261</td>\n",
       "      <td>1</td>\n",
       "      <td>371</td>\n",
       "      <td>26</td>\n",
       "      <td>4</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>9dc1a7de274444849c219cff195d0b71</td>\n",
       "      <td>utilidades_domesticas</td>\n",
       "      <td>37</td>\n",
       "      <td>402</td>\n",
       "      <td>4</td>\n",
       "      <td>625</td>\n",
       "      <td>20</td>\n",
       "      <td>17</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         product_id  product_category_name  \\\n",
       "0  1e9e8ef04dbcff4541ed26657ea517e5             perfumaria   \n",
       "1  3aa071139cb16b67ca9e5dea641aaa2f                  artes   \n",
       "2  96bd76ec8810374ed1b65e291975717f          esporte_lazer   \n",
       "3  cef67bcfe19066a932b7673e239eb23d                  bebes   \n",
       "4  9dc1a7de274444849c219cff195d0b71  utilidades_domesticas   \n",
       "\n",
       "   product_name_lenght  product_description_lenght  product_photos_qty  \\\n",
       "0                   40                         287                   1   \n",
       "1                   44                         276                   1   \n",
       "2                   46                         250                   1   \n",
       "3                   27                         261                   1   \n",
       "4                   37                         402                   4   \n",
       "\n",
       "   product_weight_g  product_length_cm  product_height_cm  product_width_cm  \n",
       "0               225                 16                 10                14  \n",
       "1              1000                 30                 18                20  \n",
       "2               154                 18                  9                15  \n",
       "3               371                 26                  4                26  \n",
       "4               625                 20                 17                13  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "products_df.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1b964afa",
   "metadata": {},
   "source": [
    "### Product Category - Portuguese \n",
    "It is noted that the product category names are in Portuguese, which will require the translation to English, using the data from lomo_product_category_name_translation.csv "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "44718db1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset Shape: (32340, 11)\n"
     ]
    },
    {
     "data": {
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       "      <th>Missing</th>\n",
       "      <th>Uniques</th>\n",
       "      <th>First Value</th>\n",
       "      <th>Second Value</th>\n",
       "      <th>Third Value</th>\n",
       "      <th>Entropy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>product_id</td>\n",
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       "      <td>32340</td>\n",
       "      <td>1e9e8ef04dbcff4541ed26657ea517e5</td>\n",
       "      <td>3aa071139cb16b67ca9e5dea641aaa2f</td>\n",
       "      <td>96bd76ec8810374ed1b65e291975717f</td>\n",
       "      <td>14.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>product_category_name</td>\n",
       "      <td>object</td>\n",
       "      <td>0</td>\n",
       "      <td>73</td>\n",
       "      <td>perfumaria</td>\n",
       "      <td>artes</td>\n",
       "      <td>esporte_lazer</td>\n",
       "      <td>4.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>product_name_lenght</td>\n",
       "      <td>int64</td>\n",
       "      <td>0</td>\n",
       "      <td>66</td>\n",
       "      <td>40</td>\n",
       "      <td>44</td>\n",
       "      <td>46</td>\n",
       "      <td>5.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>product_description_lenght</td>\n",
       "      <td>int64</td>\n",
       "      <td>0</td>\n",
       "      <td>2960</td>\n",
       "      <td>287</td>\n",
       "      <td>276</td>\n",
       "      <td>250</td>\n",
       "      <td>10.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>product_photos_qty</td>\n",
       "      <td>int64</td>\n",
       "      <td>0</td>\n",
       "      <td>19</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>product_weight_g</td>\n",
       "      <td>int64</td>\n",
       "      <td>0</td>\n",
       "      <td>2202</td>\n",
       "      <td>225</td>\n",
       "      <td>1000</td>\n",
       "      <td>154</td>\n",
       "      <td>7.57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>product_length_cm</td>\n",
       "      <td>int64</td>\n",
       "      <td>0</td>\n",
       "      <td>99</td>\n",
       "      <td>16</td>\n",
       "      <td>30</td>\n",
       "      <td>18</td>\n",
       "      <td>5.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>product_height_cm</td>\n",
       "      <td>int64</td>\n",
       "      <td>0</td>\n",
       "      <td>102</td>\n",
       "      <td>10</td>\n",
       "      <td>18</td>\n",
       "      <td>9</td>\n",
       "      <td>5.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>product_width_cm</td>\n",
       "      <td>int64</td>\n",
       "      <td>0</td>\n",
       "      <td>95</td>\n",
       "      <td>14</td>\n",
       "      <td>20</td>\n",
       "      <td>15</td>\n",
       "      <td>4.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>product_category_name_portugese</td>\n",
       "      <td>object</td>\n",
       "      <td>13</td>\n",
       "      <td>71</td>\n",
       "      <td>perfumaria</td>\n",
       "      <td>artes</td>\n",
       "      <td>esporte_lazer</td>\n",
       "      <td>4.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>product_category_name_english</td>\n",
       "      <td>object</td>\n",
       "      <td>13</td>\n",
       "      <td>71</td>\n",
       "      <td>perfumery</td>\n",
       "      <td>arts</td>\n",
       "      <td>sports_leisure</td>\n",
       "      <td>4.80</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               Name  dtypes  Missing  Uniques  \\\n",
       "0                        product_id  object        0    32340   \n",
       "1             product_category_name  object        0       73   \n",
       "2               product_name_lenght   int64        0       66   \n",
       "3        product_description_lenght   int64        0     2960   \n",
       "4                product_photos_qty   int64        0       19   \n",
       "5                  product_weight_g   int64        0     2202   \n",
       "6                 product_length_cm   int64        0       99   \n",
       "7                 product_height_cm   int64        0      102   \n",
       "8                  product_width_cm   int64        0       95   \n",
       "9   product_category_name_portugese  object       13       71   \n",
       "10    product_category_name_english  object       13       71   \n",
       "\n",
       "                         First Value                      Second Value  \\\n",
       "0   1e9e8ef04dbcff4541ed26657ea517e5  3aa071139cb16b67ca9e5dea641aaa2f   \n",
       "1                         perfumaria                             artes   \n",
       "2                                 40                                44   \n",
       "3                                287                               276   \n",
       "4                                  1                                 1   \n",
       "5                                225                              1000   \n",
       "6                                 16                                30   \n",
       "7                                 10                                18   \n",
       "8                                 14                                20   \n",
       "9                         perfumaria                             artes   \n",
       "10                         perfumery                              arts   \n",
       "\n",
       "                         Third Value  Entropy  \n",
       "0   96bd76ec8810374ed1b65e291975717f    14.98  \n",
       "1                      esporte_lazer     4.80  \n",
       "2                                 46     5.09  \n",
       "3                                250    10.72  \n",
       "4                                  1     2.16  \n",
       "5                                154     7.57  \n",
       "6                                 18     5.07  \n",
       "7                                  9     5.15  \n",
       "8                                 15     4.86  \n",
       "9                      esporte_lazer     4.80  \n",
       "10                    sports_leisure     4.80  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "products_df = products_df.merge(product_category_translation_df, \n",
    "                                left_on='product_category_name', \n",
    "                                right_on='product_category_name_portugese',\n",
    "                                how='left')\n",
    "dfsummary(products_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8a267780",
   "metadata": {},
   "source": [
    "### Product Category - Missing Translation\n",
    "It is noted that there are missing product category names translations in English, where we will then do a manual override to the correct english translation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "461ca4ce",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>product_id</th>\n",
       "      <th>product_category_name</th>\n",
       "      <th>product_name_lenght</th>\n",
       "      <th>product_description_lenght</th>\n",
       "      <th>product_photos_qty</th>\n",
       "      <th>product_weight_g</th>\n",
       "      <th>product_length_cm</th>\n",
       "      <th>product_height_cm</th>\n",
       "      <th>product_width_cm</th>\n",
       "      <th>product_category_name_portugese</th>\n",
       "      <th>product_category_name_english</th>\n",
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       "    <tr>\n",
       "      <th>1581</th>\n",
       "      <td>0105b5323d24fc655f73052694dbbb3a</td>\n",
       "      <td>pc_gamer</td>\n",
       "      <td>59</td>\n",
       "      <td>621</td>\n",
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       "      <td>2839</td>\n",
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       "      <th>5703</th>\n",
       "      <td>6fd83eb3e0799b775e4f946bd66657c0</td>\n",
       "      <td>portateis_cozinha_e_preparadores_de_alimentos</td>\n",
       "      <td>52</td>\n",
       "      <td>280</td>\n",
       "      <td>1</td>\n",
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       "      <td>25</td>\n",
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       "      <th>7176</th>\n",
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       "      <td>portateis_cozinha_e_preparadores_de_alimentos</td>\n",
       "      <td>58</td>\n",
       "      <td>284</td>\n",
       "      <td>1</td>\n",
       "      <td>1200</td>\n",
       "      <td>25</td>\n",
       "      <td>33</td>\n",
       "      <td>25</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>7326</th>\n",
       "      <td>6727051471a0fc4a0e7737b57bff2549</td>\n",
       "      <td>pc_gamer</td>\n",
       "      <td>60</td>\n",
       "      <td>1532</td>\n",
       "      <td>3</td>\n",
       "      <td>650</td>\n",
       "      <td>16</td>\n",
       "      <td>22</td>\n",
       "      <td>20</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8647</th>\n",
       "      <td>bed164d9d628cf0593003389c535c6e0</td>\n",
       "      <td>portateis_cozinha_e_preparadores_de_alimentos</td>\n",
       "      <td>54</td>\n",
       "      <td>382</td>\n",
       "      <td>2</td>\n",
       "      <td>850</td>\n",
       "      <td>30</td>\n",
       "      <td>21</td>\n",
       "      <td>22</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10822</th>\n",
       "      <td>1220978a08a6b29a202bc015b18250e9</td>\n",
       "      <td>portateis_cozinha_e_preparadores_de_alimentos</td>\n",
       "      <td>46</td>\n",
       "      <td>280</td>\n",
       "      <td>1</td>\n",
       "      <td>1200</td>\n",
       "      <td>25</td>\n",
       "      <td>33</td>\n",
       "      <td>25</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13995</th>\n",
       "      <td>ae62bb0f95af63d64eae5f93dddea8d3</td>\n",
       "      <td>portateis_cozinha_e_preparadores_de_alimentos</td>\n",
       "      <td>59</td>\n",
       "      <td>927</td>\n",
       "      <td>1</td>\n",
       "      <td>10600</td>\n",
       "      <td>40</td>\n",
       "      <td>20</td>\n",
       "      <td>38</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15874</th>\n",
       "      <td>1954739d84629e7323a4295812a3e0ec</td>\n",
       "      <td>portateis_cozinha_e_preparadores_de_alimentos</td>\n",
       "      <td>58</td>\n",
       "      <td>792</td>\n",
       "      <td>4</td>\n",
       "      <td>750</td>\n",
       "      <td>30</td>\n",
       "      <td>30</td>\n",
       "      <td>30</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16608</th>\n",
       "      <td>dbe520fb381ad695a7e1f2807d20c765</td>\n",
       "      <td>pc_gamer</td>\n",
       "      <td>60</td>\n",
       "      <td>840</td>\n",
       "      <td>6</td>\n",
       "      <td>800</td>\n",
       "      <td>18</td>\n",
       "      <td>22</td>\n",
       "      <td>22</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17459</th>\n",
       "      <td>c7a3f1a7f9eef146cc499368b578b884</td>\n",
       "      <td>portateis_cozinha_e_preparadores_de_alimentos</td>\n",
       "      <td>52</td>\n",
       "      <td>1372</td>\n",
       "      <td>5</td>\n",
       "      <td>7350</td>\n",
       "      <td>40</td>\n",
       "      <td>30</td>\n",
       "      <td>23</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18251</th>\n",
       "      <td>7afdd65f79f63819ff5bee328843fa37</td>\n",
       "      <td>portateis_cozinha_e_preparadores_de_alimentos</td>\n",
       "      <td>48</td>\n",
       "      <td>305</td>\n",
       "      <td>1</td>\n",
       "      <td>750</td>\n",
       "      <td>20</td>\n",
       "      <td>20</td>\n",
       "      <td>20</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26397</th>\n",
       "      <td>a4756663d007b0cd1af865754d08d968</td>\n",
       "      <td>portateis_cozinha_e_preparadores_de_alimentos</td>\n",
       "      <td>60</td>\n",
       "      <td>1304</td>\n",
       "      <td>4</td>\n",
       "      <td>650</td>\n",
       "      <td>22</td>\n",
       "      <td>6</td>\n",
       "      <td>14</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29369</th>\n",
       "      <td>cb9d764f38ee4d0c00af64d5c388f837</td>\n",
       "      <td>portateis_cozinha_e_preparadores_de_alimentos</td>\n",
       "      <td>57</td>\n",
       "      <td>306</td>\n",
       "      <td>1</td>\n",
       "      <td>625</td>\n",
       "      <td>28</td>\n",
       "      <td>27</td>\n",
       "      <td>24</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             product_id  \\\n",
       "1581   0105b5323d24fc655f73052694dbbb3a   \n",
       "5703   6fd83eb3e0799b775e4f946bd66657c0   \n",
       "7176   5d923ead886c44b86845f69e50520c3e   \n",
       "7326   6727051471a0fc4a0e7737b57bff2549   \n",
       "8647   bed164d9d628cf0593003389c535c6e0   \n",
       "10822  1220978a08a6b29a202bc015b18250e9   \n",
       "13995  ae62bb0f95af63d64eae5f93dddea8d3   \n",
       "15874  1954739d84629e7323a4295812a3e0ec   \n",
       "16608  dbe520fb381ad695a7e1f2807d20c765   \n",
       "17459  c7a3f1a7f9eef146cc499368b578b884   \n",
       "18251  7afdd65f79f63819ff5bee328843fa37   \n",
       "26397  a4756663d007b0cd1af865754d08d968   \n",
       "29369  cb9d764f38ee4d0c00af64d5c388f837   \n",
       "\n",
       "                               product_category_name  product_name_lenght  \\\n",
       "1581                                        pc_gamer                   59   \n",
       "5703   portateis_cozinha_e_preparadores_de_alimentos                   52   \n",
       "7176   portateis_cozinha_e_preparadores_de_alimentos                   58   \n",
       "7326                                        pc_gamer                   60   \n",
       "8647   portateis_cozinha_e_preparadores_de_alimentos                   54   \n",
       "10822  portateis_cozinha_e_preparadores_de_alimentos                   46   \n",
       "13995  portateis_cozinha_e_preparadores_de_alimentos                   59   \n",
       "15874  portateis_cozinha_e_preparadores_de_alimentos                   58   \n",
       "16608                                       pc_gamer                   60   \n",
       "17459  portateis_cozinha_e_preparadores_de_alimentos                   52   \n",
       "18251  portateis_cozinha_e_preparadores_de_alimentos                   48   \n",
       "26397  portateis_cozinha_e_preparadores_de_alimentos                   60   \n",
       "29369  portateis_cozinha_e_preparadores_de_alimentos                   57   \n",
       "\n",
       "       product_description_lenght  product_photos_qty  product_weight_g  \\\n",
       "1581                          621                   4              2839   \n",
       "5703                          280                   1              1200   \n",
       "7176                          284                   1              1200   \n",
       "7326                         1532                   3               650   \n",
       "8647                          382                   2               850   \n",
       "10822                         280                   1              1200   \n",
       "13995                         927                   1             10600   \n",
       "15874                         792                   4               750   \n",
       "16608                         840                   6               800   \n",
       "17459                        1372                   5              7350   \n",
       "18251                         305                   1               750   \n",
       "26397                        1304                   4               650   \n",
       "29369                         306                   1               625   \n",
       "\n",
       "       product_length_cm  product_height_cm  product_width_cm  \\\n",
       "1581                  19                 16                18   \n",
       "5703                  25                 33                25   \n",
       "7176                  25                 33                25   \n",
       "7326                  16                 22                20   \n",
       "8647                  30                 21                22   \n",
       "10822                 25                 33                25   \n",
       "13995                 40                 20                38   \n",
       "15874                 30                 30                30   \n",
       "16608                 18                 22                22   \n",
       "17459                 40                 30                23   \n",
       "18251                 20                 20                20   \n",
       "26397                 22                  6                14   \n",
       "29369                 28                 27                24   \n",
       "\n",
       "      product_category_name_portugese product_category_name_english  \n",
       "1581                              NaN                           NaN  \n",
       "5703                              NaN                           NaN  \n",
       "7176                              NaN                           NaN  \n",
       "7326                              NaN                           NaN  \n",
       "8647                              NaN                           NaN  \n",
       "10822                             NaN                           NaN  \n",
       "13995                             NaN                           NaN  \n",
       "15874                             NaN                           NaN  \n",
       "16608                             NaN                           NaN  \n",
       "17459                             NaN                           NaN  \n",
       "18251                             NaN                           NaN  \n",
       "26397                             NaN                           NaN  \n",
       "29369                             NaN                           NaN  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "products_df[products_df['product_category_name_english'].isnull()]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f91d6c34",
   "metadata": {},
   "source": [
    "The product category names that do not have an english translation are: ***'pc_gamer'*** and ***'portateis_cozinha_e_preparadores_de_alimentos'***. \n",
    "\n",
    "***'pc_gamer'*** can remain as is, while ***'portateis_cozinha_e_preparadores_de_alimentos'*** is actually ***'portable_kitchen_and_food_preparation'***."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "8d6d21e0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset Shape: (32340, 11)\n"
     ]
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>dtypes</th>\n",
       "      <th>Missing</th>\n",
       "      <th>Uniques</th>\n",
       "      <th>First Value</th>\n",
       "      <th>Second Value</th>\n",
       "      <th>Third Value</th>\n",
       "      <th>Entropy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>product_id</td>\n",
       "      <td>object</td>\n",
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       "      <td>96bd76ec8810374ed1b65e291975717f</td>\n",
       "      <td>14.98</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>product_category_name</td>\n",
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       "      <td>perfumaria</td>\n",
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       "      <td>esporte_lazer</td>\n",
       "      <td>4.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>product_name_lenght</td>\n",
       "      <td>int64</td>\n",
       "      <td>0</td>\n",
       "      <td>66</td>\n",
       "      <td>40</td>\n",
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       "      <td>46</td>\n",
       "      <td>5.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>product_description_lenght</td>\n",
       "      <td>int64</td>\n",
       "      <td>0</td>\n",
       "      <td>2960</td>\n",
       "      <td>287</td>\n",
       "      <td>276</td>\n",
       "      <td>250</td>\n",
       "      <td>10.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>product_photos_qty</td>\n",
       "      <td>int64</td>\n",
       "      <td>0</td>\n",
       "      <td>19</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>product_weight_g</td>\n",
       "      <td>int64</td>\n",
       "      <td>0</td>\n",
       "      <td>2202</td>\n",
       "      <td>225</td>\n",
       "      <td>1000</td>\n",
       "      <td>154</td>\n",
       "      <td>7.57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>product_length_cm</td>\n",
       "      <td>int64</td>\n",
       "      <td>0</td>\n",
       "      <td>99</td>\n",
       "      <td>16</td>\n",
       "      <td>30</td>\n",
       "      <td>18</td>\n",
       "      <td>5.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>product_height_cm</td>\n",
       "      <td>int64</td>\n",
       "      <td>0</td>\n",
       "      <td>102</td>\n",
       "      <td>10</td>\n",
       "      <td>18</td>\n",
       "      <td>9</td>\n",
       "      <td>5.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>product_width_cm</td>\n",
       "      <td>int64</td>\n",
       "      <td>0</td>\n",
       "      <td>95</td>\n",
       "      <td>14</td>\n",
       "      <td>20</td>\n",
       "      <td>15</td>\n",
       "      <td>4.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>product_category_name_portugese</td>\n",
       "      <td>object</td>\n",
       "      <td>13</td>\n",
       "      <td>71</td>\n",
       "      <td>perfumaria</td>\n",
       "      <td>artes</td>\n",
       "      <td>esporte_lazer</td>\n",
       "      <td>4.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>product_category_name_english</td>\n",
       "      <td>object</td>\n",
       "      <td>0</td>\n",
       "      <td>73</td>\n",
       "      <td>perfumery</td>\n",
       "      <td>arts</td>\n",
       "      <td>sports_leisure</td>\n",
       "      <td>4.80</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               Name  dtypes  Missing  Uniques  \\\n",
       "0                        product_id  object        0    32340   \n",
       "1             product_category_name  object        0       73   \n",
       "2               product_name_lenght   int64        0       66   \n",
       "3        product_description_lenght   int64        0     2960   \n",
       "4                product_photos_qty   int64        0       19   \n",
       "5                  product_weight_g   int64        0     2202   \n",
       "6                 product_length_cm   int64        0       99   \n",
       "7                 product_height_cm   int64        0      102   \n",
       "8                  product_width_cm   int64        0       95   \n",
       "9   product_category_name_portugese  object       13       71   \n",
       "10    product_category_name_english  object        0       73   \n",
       "\n",
       "                         First Value                      Second Value  \\\n",
       "0   1e9e8ef04dbcff4541ed26657ea517e5  3aa071139cb16b67ca9e5dea641aaa2f   \n",
       "1                         perfumaria                             artes   \n",
       "2                                 40                                44   \n",
       "3                                287                               276   \n",
       "4                                  1                                 1   \n",
       "5                                225                              1000   \n",
       "6                                 16                                30   \n",
       "7                                 10                                18   \n",
       "8                                 14                                20   \n",
       "9                         perfumaria                             artes   \n",
       "10                         perfumery                              arts   \n",
       "\n",
       "                         Third Value  Entropy  \n",
       "0   96bd76ec8810374ed1b65e291975717f    14.98  \n",
       "1                      esporte_lazer     4.80  \n",
       "2                                 46     5.09  \n",
       "3                                250    10.72  \n",
       "4                                  1     2.16  \n",
       "5                                154     7.57  \n",
       "6                                 18     5.07  \n",
       "7                                  9     5.15  \n",
       "8                                 15     4.86  \n",
       "9                      esporte_lazer     4.80  \n",
       "10                    sports_leisure     4.80  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# To update 'product_category_name_english' in the 'products_df' DataFrame\n",
    "products_df.loc[products_df['product_category_name'] == 'pc_gamer', 'product_category_name_english'] = 'pc_gamer'\n",
    "products_df.loc[products_df['product_category_name'] == 'portateis_cozinha_e_preparadores_de_alimentos', 'product_category_name_english'] = 'portable_kitchen_and_food_preparation'\n",
    "\n",
    "dfsummary(products_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "e153f4bf",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Do not need multiple category names fields, so we will drop the rest.\n",
    "# Drop 'product_category_name' and 'product_category_name_portugese' columns from the DataFrame\n",
    "products_df = products_df.drop(['product_category_name', 'product_category_name_portugese'], axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8d945688",
   "metadata": {},
   "source": [
    "## Merge Related DataFrames to evaluate performance and potential.\n",
    "The DFs to be merged are:\n",
    "1. order_items_df\n",
    "2. products_df\n",
    "3. orders_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "79bf07fa",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "      <th>shipping_limit_date</th>\n",
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       "      <td>19/09/2017 09:45</td>\n",
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       "      <td>dd7ddc04e1b6c2c614352b383efe2d36</td>\n",
       "      <td>03/05/2017 11:05</td>\n",
       "      <td>239.90</td>\n",
       "      <td>19.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>000229ec398224ef6ca0657da4fc703e</td>\n",
       "      <td>1</td>\n",
       "      <td>c777355d18b72b67abbeef9df44fd0fd</td>\n",
       "      <td>5b51032eddd242adc84c38acab88f23d</td>\n",
       "      <td>18/01/2018 14:48</td>\n",
       "      <td>199.00</td>\n",
       "      <td>17.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>00024acbcdf0a6daa1e931b038114c75</td>\n",
       "      <td>1</td>\n",
       "      <td>7634da152a4610f1595efa32f14722fc</td>\n",
       "      <td>9d7a1d34a5052409006425275ba1c2b4</td>\n",
       "      <td>15/08/2018 10:10</td>\n",
       "      <td>12.99</td>\n",
       "      <td>12.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>00042b26cf59d7ce69dfabb4e55b4fd9</td>\n",
       "      <td>1</td>\n",
       "      <td>ac6c3623068f30de03045865e4e10089</td>\n",
       "      <td>df560393f3a51e74553ab94004ba5c87</td>\n",
       "      <td>13/02/2017 13:57</td>\n",
       "      <td>199.90</td>\n",
       "      <td>18.14</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           order_id  order_item_id  \\\n",
       "0  00010242fe8c5a6d1ba2dd792cb16214              1   \n",
       "1  00018f77f2f0320c557190d7a144bdd3              1   \n",
       "2  000229ec398224ef6ca0657da4fc703e              1   \n",
       "3  00024acbcdf0a6daa1e931b038114c75              1   \n",
       "4  00042b26cf59d7ce69dfabb4e55b4fd9              1   \n",
       "\n",
       "                         product_id                         seller_id  \\\n",
       "0  4244733e06e7ecb4970a6e2683c13e61  48436dade18ac8b2bce089ec2a041202   \n",
       "1  e5f2d52b802189ee658865ca93d83a8f  dd7ddc04e1b6c2c614352b383efe2d36   \n",
       "2  c777355d18b72b67abbeef9df44fd0fd  5b51032eddd242adc84c38acab88f23d   \n",
       "3  7634da152a4610f1595efa32f14722fc  9d7a1d34a5052409006425275ba1c2b4   \n",
       "4  ac6c3623068f30de03045865e4e10089  df560393f3a51e74553ab94004ba5c87   \n",
       "\n",
       "  shipping_limit_date   price  freight_value  \n",
       "0    19/09/2017 09:45   58.90          13.29  \n",
       "1    03/05/2017 11:05  239.90          19.93  \n",
       "2    18/01/2018 14:48  199.00          17.87  \n",
       "3    15/08/2018 10:10   12.99          12.79  \n",
       "4    13/02/2017 13:57  199.90          18.14  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "order_items_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2789deeb",
   "metadata": {},
   "source": [
    "### Merging of orders_items and products_df\n",
    "to ensure that every item ordered is categorised into product categories."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "2201ab5e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset Shape: (112650, 15)\n"
     ]
    },
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>dtypes</th>\n",
       "      <th>Missing</th>\n",
       "      <th>Uniques</th>\n",
       "      <th>First Value</th>\n",
       "      <th>Second Value</th>\n",
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       "      <td>16.49</td>\n",
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       "      <td>product_id</td>\n",
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       "      <td>13.63</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>seller_id</td>\n",
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       "      <td>9.48</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>shipping_limit_date</td>\n",
       "      <td>object</td>\n",
       "      <td>0</td>\n",
       "      <td>54615</td>\n",
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       "      <td>03/05/2017 11:05</td>\n",
       "      <td>18/01/2018 14:48</td>\n",
       "      <td>15.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>price</td>\n",
       "      <td>float64</td>\n",
       "      <td>0</td>\n",
       "      <td>5968</td>\n",
       "      <td>58.9</td>\n",
       "      <td>239.9</td>\n",
       "      <td>199.0</td>\n",
       "      <td>9.58</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>freight_value</td>\n",
       "      <td>float64</td>\n",
       "      <td>0</td>\n",
       "      <td>6999</td>\n",
       "      <td>13.29</td>\n",
       "      <td>19.93</td>\n",
       "      <td>17.87</td>\n",
       "      <td>10.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>product_name_lenght</td>\n",
       "      <td>float64</td>\n",
       "      <td>1604</td>\n",
       "      <td>66</td>\n",
       "      <td>58.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>5.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>product_description_lenght</td>\n",
       "      <td>float64</td>\n",
       "      <td>1604</td>\n",
       "      <td>2960</td>\n",
       "      <td>598.0</td>\n",
       "      <td>239.0</td>\n",
       "      <td>695.0</td>\n",
       "      <td>10.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>product_photos_qty</td>\n",
       "      <td>float64</td>\n",
       "      <td>1604</td>\n",
       "      <td>19</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>product_weight_g</td>\n",
       "      <td>float64</td>\n",
       "      <td>1604</td>\n",
       "      <td>2202</td>\n",
       "      <td>650.0</td>\n",
       "      <td>30000.0</td>\n",
       "      <td>3050.0</td>\n",
       "      <td>7.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>product_length_cm</td>\n",
       "      <td>float64</td>\n",
       "      <td>1604</td>\n",
       "      <td>99</td>\n",
       "      <td>28.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>5.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>product_height_cm</td>\n",
       "      <td>float64</td>\n",
       "      <td>1604</td>\n",
       "      <td>102</td>\n",
       "      <td>9.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>5.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>product_width_cm</td>\n",
       "      <td>float64</td>\n",
       "      <td>1604</td>\n",
       "      <td>95</td>\n",
       "      <td>14.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>4.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>product_category_name_english</td>\n",
       "      <td>object</td>\n",
       "      <td>1604</td>\n",
       "      <td>73</td>\n",
       "      <td>cool_stuff</td>\n",
       "      <td>pet_shop</td>\n",
       "      <td>furniture_decor</td>\n",
       "      <td>4.72</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             Name   dtypes  Missing  Uniques  \\\n",
       "0                        order_id   object        0    98666   \n",
       "1                   order_item_id    int64        0       21   \n",
       "2                      product_id   object        0    32951   \n",
       "3                       seller_id   object        0     3095   \n",
       "4             shipping_limit_date   object        0    54615   \n",
       "5                           price  float64        0     5968   \n",
       "6                   freight_value  float64        0     6999   \n",
       "7             product_name_lenght  float64     1604       66   \n",
       "8      product_description_lenght  float64     1604     2960   \n",
       "9              product_photos_qty  float64     1604       19   \n",
       "10               product_weight_g  float64     1604     2202   \n",
       "11              product_length_cm  float64     1604       99   \n",
       "12              product_height_cm  float64     1604      102   \n",
       "13               product_width_cm  float64     1604       95   \n",
       "14  product_category_name_english   object     1604       73   \n",
       "\n",
       "                         First Value                      Second Value  \\\n",
       "0   00010242fe8c5a6d1ba2dd792cb16214  00018f77f2f0320c557190d7a144bdd3   \n",
       "1                                  1                                 1   \n",
       "2   4244733e06e7ecb4970a6e2683c13e61  e5f2d52b802189ee658865ca93d83a8f   \n",
       "3   48436dade18ac8b2bce089ec2a041202  dd7ddc04e1b6c2c614352b383efe2d36   \n",
       "4                   19/09/2017 09:45                  03/05/2017 11:05   \n",
       "5                               58.9                             239.9   \n",
       "6                              13.29                             19.93   \n",
       "7                               58.0                              56.0   \n",
       "8                              598.0                             239.0   \n",
       "9                                4.0                               2.0   \n",
       "10                             650.0                           30000.0   \n",
       "11                              28.0                              50.0   \n",
       "12                               9.0                              30.0   \n",
       "13                              14.0                              40.0   \n",
       "14                        cool_stuff                          pet_shop   \n",
       "\n",
       "                         Third Value  Entropy  \n",
       "0   000229ec398224ef6ca0657da4fc703e    16.49  \n",
       "1                                  1     0.72  \n",
       "2   c777355d18b72b67abbeef9df44fd0fd    13.63  \n",
       "3   5b51032eddd242adc84c38acab88f23d     9.48  \n",
       "4                   18/01/2018 14:48    15.28  \n",
       "5                              199.0     9.58  \n",
       "6                              17.87    10.51  \n",
       "7                               59.0     5.03  \n",
       "8                              695.0    10.44  \n",
       "9                                2.0     2.17  \n",
       "10                            3050.0     7.64  \n",
       "11                              33.0     5.02  \n",
       "12                              13.0     5.09  \n",
       "13                              33.0     4.80  \n",
       "14                   furniture_decor     4.72  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Merge datasets to get a full picture of the product performance on sales and volume\n",
    "LoMoDF = (order_items_df.merge(products_df, on='product_id',how =\"left\"))\n",
    "\n",
    "dfsummary(LoMoDF)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9bcca37f",
   "metadata": {},
   "source": [
    "### Missing Product Categorisation and details.\n",
    "There are 1604 products that do not have categorisation. Noting that these products accounts for about 4.86% (1604/32951) of the total products, we will drop these records.\n",
    "\n",
    "Additionally, since the product physical details such as the photos, weight and length will not be as useful in the current analysis, I will be dropping these fields as well."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "48ead6dc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset Shape: (111046, 8)\n"
     ]
    },
    {
     "data": {
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       "      <th>1</th>\n",
       "      <td>order_item_id</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>seller_id</td>\n",
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       "      <td>3035</td>\n",
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       "      <td>5b51032eddd242adc84c38acab88f23d</td>\n",
       "      <td>9.45</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>shipping_limit_date</td>\n",
       "      <td>object</td>\n",
       "      <td>0</td>\n",
       "      <td>54106</td>\n",
       "      <td>19/09/2017 09:45</td>\n",
       "      <td>03/05/2017 11:05</td>\n",
       "      <td>18/01/2018 14:48</td>\n",
       "      <td>15.27</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>price</td>\n",
       "      <td>float64</td>\n",
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       "      <td>5901</td>\n",
       "      <td>58.9</td>\n",
       "      <td>239.9</td>\n",
       "      <td>199.0</td>\n",
       "      <td>9.57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>freight_value</td>\n",
       "      <td>float64</td>\n",
       "      <td>0</td>\n",
       "      <td>6977</td>\n",
       "      <td>13.29</td>\n",
       "      <td>19.93</td>\n",
       "      <td>17.87</td>\n",
       "      <td>10.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>product_category_name_english</td>\n",
       "      <td>object</td>\n",
       "      <td>0</td>\n",
       "      <td>73</td>\n",
       "      <td>cool_stuff</td>\n",
       "      <td>pet_shop</td>\n",
       "      <td>furniture_decor</td>\n",
       "      <td>4.72</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "                            Name   dtypes  Missing  Uniques  \\\n",
       "0                       order_id   object        0    97276   \n",
       "1                  order_item_id    int64        0       21   \n",
       "2                     product_id   object        0    32340   \n",
       "3                      seller_id   object        0     3035   \n",
       "4            shipping_limit_date   object        0    54106   \n",
       "5                          price  float64        0     5901   \n",
       "6                  freight_value  float64        0     6977   \n",
       "7  product_category_name_english   object        0       73   \n",
       "\n",
       "                        First Value                      Second Value  \\\n",
       "0  00010242fe8c5a6d1ba2dd792cb16214  00018f77f2f0320c557190d7a144bdd3   \n",
       "1                                 1                                 1   \n",
       "2  4244733e06e7ecb4970a6e2683c13e61  e5f2d52b802189ee658865ca93d83a8f   \n",
       "3  48436dade18ac8b2bce089ec2a041202  dd7ddc04e1b6c2c614352b383efe2d36   \n",
       "4                  19/09/2017 09:45                  03/05/2017 11:05   \n",
       "5                              58.9                             239.9   \n",
       "6                             13.29                             19.93   \n",
       "7                        cool_stuff                          pet_shop   \n",
       "\n",
       "                        Third Value  Entropy  \n",
       "0  000229ec398224ef6ca0657da4fc703e    16.47  \n",
       "1                                 1     0.72  \n",
       "2  c777355d18b72b67abbeef9df44fd0fd    13.61  \n",
       "3  5b51032eddd242adc84c38acab88f23d     9.45  \n",
       "4                  18/01/2018 14:48    15.27  \n",
       "5                             199.0     9.57  \n",
       "6                             17.87    10.51  \n",
       "7                   furniture_decor     4.72  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Dropping columns that are not used in this analysis.\n",
    "LoMoDF = LoMoDF.drop(['product_name_lenght', 'product_description_lenght',\n",
    "                      'product_photos_qty','product_weight_g','product_length_cm',\n",
    "                      'product_height_cm','product_width_cm'], axis=1)\n",
    "# Dropping records that do not have a product category, since it would not be useful for the analysis.\n",
    "LoMoDF = LoMoDF.dropna(subset=['product_category_name_english'])\n",
    "\n",
    "dfsummary(LoMoDF)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9e965d81",
   "metadata": {},
   "source": [
    "### Merging of orders_df\n",
    "to ensure that every item ordered has the related purchase timestamp and order status."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "0d17b9ba",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset Shape: (111046, 15)\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>dtypes</th>\n",
       "      <th>Missing</th>\n",
       "      <th>Uniques</th>\n",
       "      <th>First Value</th>\n",
       "      <th>Second Value</th>\n",
       "      <th>Third Value</th>\n",
       "      <th>Entropy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>order_id</td>\n",
       "      <td>object</td>\n",
       "      <td>0</td>\n",
       "      <td>97276</td>\n",
       "      <td>00010242fe8c5a6d1ba2dd792cb16214</td>\n",
       "      <td>00018f77f2f0320c557190d7a144bdd3</td>\n",
       "      <td>000229ec398224ef6ca0657da4fc703e</td>\n",
       "      <td>16.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>order_item_id</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>product_id</td>\n",
       "      <td>object</td>\n",
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       "      <td>32340</td>\n",
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       "      <td>c777355d18b72b67abbeef9df44fd0fd</td>\n",
       "      <td>13.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>seller_id</td>\n",
       "      <td>object</td>\n",
       "      <td>0</td>\n",
       "      <td>3035</td>\n",
       "      <td>48436dade18ac8b2bce089ec2a041202</td>\n",
       "      <td>dd7ddc04e1b6c2c614352b383efe2d36</td>\n",
       "      <td>5b51032eddd242adc84c38acab88f23d</td>\n",
       "      <td>9.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>shipping_limit_date</td>\n",
       "      <td>object</td>\n",
       "      <td>0</td>\n",
       "      <td>54106</td>\n",
       "      <td>19/09/2017 09:45</td>\n",
       "      <td>03/05/2017 11:05</td>\n",
       "      <td>18/01/2018 14:48</td>\n",
       "      <td>15.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>price</td>\n",
       "      <td>float64</td>\n",
       "      <td>0</td>\n",
       "      <td>5901</td>\n",
       "      <td>58.9</td>\n",
       "      <td>239.9</td>\n",
       "      <td>199.0</td>\n",
       "      <td>9.57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>freight_value</td>\n",
       "      <td>float64</td>\n",
       "      <td>0</td>\n",
       "      <td>6977</td>\n",
       "      <td>13.29</td>\n",
       "      <td>19.93</td>\n",
       "      <td>17.87</td>\n",
       "      <td>10.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>product_category_name_english</td>\n",
       "      <td>object</td>\n",
       "      <td>0</td>\n",
       "      <td>73</td>\n",
       "      <td>cool_stuff</td>\n",
       "      <td>pet_shop</td>\n",
       "      <td>furniture_decor</td>\n",
       "      <td>4.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>customer_id</td>\n",
       "      <td>object</td>\n",
       "      <td>0</td>\n",
       "      <td>97276</td>\n",
       "      <td>3ce436f183e68e07877b285a838db11a</td>\n",
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       "      <td>6489ae5e4333f3693df5ad4372dab6d3</td>\n",
       "      <td>16.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>order_status</td>\n",
       "      <td>object</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>delivered</td>\n",
       "      <td>delivered</td>\n",
       "      <td>delivered</td>\n",
       "      <td>0.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>order_purchase_timestamp</td>\n",
       "      <td>object</td>\n",
       "      <td>0</td>\n",
       "      <td>87027</td>\n",
       "      <td>13/09/2017 08:59</td>\n",
       "      <td>26/04/2017 10:53</td>\n",
       "      <td>14/01/2018 14:33</td>\n",
       "      <td>16.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>order_approved_at</td>\n",
       "      <td>object</td>\n",
       "      <td>14</td>\n",
       "      <td>49734</td>\n",
       "      <td>13/09/2017 09:45</td>\n",
       "      <td>26/04/2017 11:05</td>\n",
       "      <td>14/01/2018 14:48</td>\n",
       "      <td>15.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>order_delivered_carrier_date</td>\n",
       "      <td>object</td>\n",
       "      <td>1157</td>\n",
       "      <td>60814</td>\n",
       "      <td>19/09/2017 18:34</td>\n",
       "      <td>04/05/2017 14:35</td>\n",
       "      <td>16/01/2018 12:36</td>\n",
       "      <td>15.57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>order_delivered_customer_date</td>\n",
       "      <td>object</td>\n",
       "      <td>2388</td>\n",
       "      <td>74811</td>\n",
       "      <td>20/09/2017 23:43</td>\n",
       "      <td>12/05/2017 16:04</td>\n",
       "      <td>22/01/2018 13:19</td>\n",
       "      <td>15.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>order_estimated_delivery_date</td>\n",
       "      <td>object</td>\n",
       "      <td>0</td>\n",
       "      <td>450</td>\n",
       "      <td>29/09/2017 00:00</td>\n",
       "      <td>15/05/2017 00:00</td>\n",
       "      <td>05/02/2018 00:00</td>\n",
       "      <td>8.46</td>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             Name   dtypes  Missing  Uniques  \\\n",
       "0                        order_id   object        0    97276   \n",
       "1                   order_item_id    int64        0       21   \n",
       "2                      product_id   object        0    32340   \n",
       "3                       seller_id   object        0     3035   \n",
       "4             shipping_limit_date   object        0    54106   \n",
       "5                           price  float64        0     5901   \n",
       "6                   freight_value  float64        0     6977   \n",
       "7   product_category_name_english   object        0       73   \n",
       "8                     customer_id   object        0    97276   \n",
       "9                    order_status   object        0        7   \n",
       "10       order_purchase_timestamp   object        0    87027   \n",
       "11              order_approved_at   object       14    49734   \n",
       "12   order_delivered_carrier_date   object     1157    60814   \n",
       "13  order_delivered_customer_date   object     2388    74811   \n",
       "14  order_estimated_delivery_date   object        0      450   \n",
       "\n",
       "                         First Value                      Second Value  \\\n",
       "0   00010242fe8c5a6d1ba2dd792cb16214  00018f77f2f0320c557190d7a144bdd3   \n",
       "1                                  1                                 1   \n",
       "2   4244733e06e7ecb4970a6e2683c13e61  e5f2d52b802189ee658865ca93d83a8f   \n",
       "3   48436dade18ac8b2bce089ec2a041202  dd7ddc04e1b6c2c614352b383efe2d36   \n",
       "4                   19/09/2017 09:45                  03/05/2017 11:05   \n",
       "5                               58.9                             239.9   \n",
       "6                              13.29                             19.93   \n",
       "7                         cool_stuff                          pet_shop   \n",
       "8   3ce436f183e68e07877b285a838db11a  f6dd3ec061db4e3987629fe6b26e5cce   \n",
       "9                          delivered                         delivered   \n",
       "10                  13/09/2017 08:59                  26/04/2017 10:53   \n",
       "11                  13/09/2017 09:45                  26/04/2017 11:05   \n",
       "12                  19/09/2017 18:34                  04/05/2017 14:35   \n",
       "13                  20/09/2017 23:43                  12/05/2017 16:04   \n",
       "14                  29/09/2017 00:00                  15/05/2017 00:00   \n",
       "\n",
       "                         Third Value  Entropy  \n",
       "0   000229ec398224ef6ca0657da4fc703e    16.47  \n",
       "1                                  1     0.72  \n",
       "2   c777355d18b72b67abbeef9df44fd0fd    13.61  \n",
       "3   5b51032eddd242adc84c38acab88f23d     9.45  \n",
       "4                   18/01/2018 14:48    15.27  \n",
       "5                              199.0     9.57  \n",
       "6                              17.87    10.51  \n",
       "7                    furniture_decor     4.72  \n",
       "8   6489ae5e4333f3693df5ad4372dab6d3    16.47  \n",
       "9                          delivered     0.19  \n",
       "10                  14/01/2018 14:33    16.25  \n",
       "11                  14/01/2018 14:48    15.04  \n",
       "12                  16/01/2018 12:36    15.57  \n",
       "13                  22/01/2018 13:19    15.99  \n",
       "14                  05/02/2018 00:00     8.46  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Merge dataset orders_df to get a full picture of the product performance on sales and volume\n",
    "LoMoDF = (LoMoDF.merge(orders_df, on='order_id',how =\"left\"))\n",
    "\n",
    "dfsummary(LoMoDF)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3030b12f",
   "metadata": {},
   "source": [
    "### Data Cleaning for Orders_df\n",
    "There are orders that ends up being 'canceled' and hence, should be removed from the df."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "1886eb95",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "528"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(LoMoDF['order_status'] == 'canceled')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "caf2e4d5",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset Shape: (110518, 15)\n"
     ]
    },
    {
     "data": {
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       "      <td>product_id</td>\n",
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       "      <td>13.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>seller_id</td>\n",
       "      <td>object</td>\n",
       "      <td>0</td>\n",
       "      <td>2995</td>\n",
       "      <td>48436dade18ac8b2bce089ec2a041202</td>\n",
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       "      <td>5b51032eddd242adc84c38acab88f23d</td>\n",
       "      <td>9.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>shipping_limit_date</td>\n",
       "      <td>object</td>\n",
       "      <td>0</td>\n",
       "      <td>53909</td>\n",
       "      <td>19/09/2017 09:45</td>\n",
       "      <td>03/05/2017 11:05</td>\n",
       "      <td>18/01/2018 14:48</td>\n",
       "      <td>15.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>price</td>\n",
       "      <td>float64</td>\n",
       "      <td>0</td>\n",
       "      <td>5867</td>\n",
       "      <td>58.9</td>\n",
       "      <td>239.9</td>\n",
       "      <td>199.0</td>\n",
       "      <td>9.57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>freight_value</td>\n",
       "      <td>float64</td>\n",
       "      <td>0</td>\n",
       "      <td>6964</td>\n",
       "      <td>13.29</td>\n",
       "      <td>19.93</td>\n",
       "      <td>17.87</td>\n",
       "      <td>10.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>product_category_name_english</td>\n",
       "      <td>object</td>\n",
       "      <td>0</td>\n",
       "      <td>73</td>\n",
       "      <td>cool_stuff</td>\n",
       "      <td>pet_shop</td>\n",
       "      <td>furniture_decor</td>\n",
       "      <td>4.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>customer_id</td>\n",
       "      <td>object</td>\n",
       "      <td>0</td>\n",
       "      <td>96829</td>\n",
       "      <td>3ce436f183e68e07877b285a838db11a</td>\n",
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       "      <td>16.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>order_status</td>\n",
       "      <td>object</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>delivered</td>\n",
       "      <td>delivered</td>\n",
       "      <td>delivered</td>\n",
       "      <td>0.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>order_purchase_timestamp</td>\n",
       "      <td>object</td>\n",
       "      <td>0</td>\n",
       "      <td>86642</td>\n",
       "      <td>13/09/2017 08:59</td>\n",
       "      <td>26/04/2017 10:53</td>\n",
       "      <td>14/01/2018 14:33</td>\n",
       "      <td>16.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>order_approved_at</td>\n",
       "      <td>object</td>\n",
       "      <td>14</td>\n",
       "      <td>49556</td>\n",
       "      <td>13/09/2017 09:45</td>\n",
       "      <td>26/04/2017 11:05</td>\n",
       "      <td>14/01/2018 14:48</td>\n",
       "      <td>15.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>order_delivered_carrier_date</td>\n",
       "      <td>object</td>\n",
       "      <td>703</td>\n",
       "      <td>60779</td>\n",
       "      <td>19/09/2017 18:34</td>\n",
       "      <td>04/05/2017 14:35</td>\n",
       "      <td>16/01/2018 12:36</td>\n",
       "      <td>15.57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>order_delivered_customer_date</td>\n",
       "      <td>object</td>\n",
       "      <td>1867</td>\n",
       "      <td>74807</td>\n",
       "      <td>20/09/2017 23:43</td>\n",
       "      <td>12/05/2017 16:04</td>\n",
       "      <td>22/01/2018 13:19</td>\n",
       "      <td>15.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>order_estimated_delivery_date</td>\n",
       "      <td>object</td>\n",
       "      <td>0</td>\n",
       "      <td>447</td>\n",
       "      <td>29/09/2017 00:00</td>\n",
       "      <td>15/05/2017 00:00</td>\n",
       "      <td>05/02/2018 00:00</td>\n",
       "      <td>8.46</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             Name   dtypes  Missing  Uniques  \\\n",
       "0                        order_id   object        0    96829   \n",
       "1                   order_item_id    int64        0       21   \n",
       "2                      product_id   object        0    32134   \n",
       "3                       seller_id   object        0     2995   \n",
       "4             shipping_limit_date   object        0    53909   \n",
       "5                           price  float64        0     5867   \n",
       "6                   freight_value  float64        0     6964   \n",
       "7   product_category_name_english   object        0       73   \n",
       "8                     customer_id   object        0    96829   \n",
       "9                    order_status   object        0        6   \n",
       "10       order_purchase_timestamp   object        0    86642   \n",
       "11              order_approved_at   object       14    49556   \n",
       "12   order_delivered_carrier_date   object      703    60779   \n",
       "13  order_delivered_customer_date   object     1867    74807   \n",
       "14  order_estimated_delivery_date   object        0      447   \n",
       "\n",
       "                         First Value                      Second Value  \\\n",
       "0   00010242fe8c5a6d1ba2dd792cb16214  00018f77f2f0320c557190d7a144bdd3   \n",
       "1                                  1                                 1   \n",
       "2   4244733e06e7ecb4970a6e2683c13e61  e5f2d52b802189ee658865ca93d83a8f   \n",
       "3   48436dade18ac8b2bce089ec2a041202  dd7ddc04e1b6c2c614352b383efe2d36   \n",
       "4                   19/09/2017 09:45                  03/05/2017 11:05   \n",
       "5                               58.9                             239.9   \n",
       "6                              13.29                             19.93   \n",
       "7                         cool_stuff                          pet_shop   \n",
       "8   3ce436f183e68e07877b285a838db11a  f6dd3ec061db4e3987629fe6b26e5cce   \n",
       "9                          delivered                         delivered   \n",
       "10                  13/09/2017 08:59                  26/04/2017 10:53   \n",
       "11                  13/09/2017 09:45                  26/04/2017 11:05   \n",
       "12                  19/09/2017 18:34                  04/05/2017 14:35   \n",
       "13                  20/09/2017 23:43                  12/05/2017 16:04   \n",
       "14                  29/09/2017 00:00                  15/05/2017 00:00   \n",
       "\n",
       "                         Third Value  Entropy  \n",
       "0   000229ec398224ef6ca0657da4fc703e    16.46  \n",
       "1                                  1     0.72  \n",
       "2   c777355d18b72b67abbeef9df44fd0fd    13.59  \n",
       "3   5b51032eddd242adc84c38acab88f23d     9.44  \n",
       "4                   18/01/2018 14:48    15.26  \n",
       "5                              199.0     9.57  \n",
       "6                              17.87    10.51  \n",
       "7                    furniture_decor     4.72  \n",
       "8   6489ae5e4333f3693df5ad4372dab6d3    16.46  \n",
       "9                          delivered     0.15  \n",
       "10                  14/01/2018 14:33    16.25  \n",
       "11                  14/01/2018 14:48    15.04  \n",
       "12                  16/01/2018 12:36    15.57  \n",
       "13                  22/01/2018 13:19    15.99  \n",
       "14                  05/02/2018 00:00     8.46  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "LoMoDF = LoMoDF[LoMoDF['order_status'] != 'canceled']\n",
    "\n",
    "dfsummary(LoMoDF)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eae53e09",
   "metadata": {},
   "source": [
    "### Merging of Reviews\n",
    "to include a review score (1-5) for items ordered. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "b3ca22aa",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset Shape: (100000, 7)\n"
     ]
    },
    {
     "data": {
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       "      <th>dtypes</th>\n",
       "      <th>Missing</th>\n",
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       "      <th>First Value</th>\n",
       "      <th>Second Value</th>\n",
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       "  <tbody>\n",
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       "      <th>0</th>\n",
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       "      <td>228ce5500dc1d8e020d8d1322874b6f0</td>\n",
       "      <td>16.59</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>order_id</td>\n",
       "      <td>object</td>\n",
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       "      <td>16.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>review_score</td>\n",
       "      <td>int64</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>1.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>review_comment_title</td>\n",
       "      <td>object</td>\n",
       "      <td>88285</td>\n",
       "      <td>4588</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>review_comment_message</td>\n",
       "      <td>object</td>\n",
       "      <td>58247</td>\n",
       "      <td>36917</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>review_creation_date</td>\n",
       "      <td>object</td>\n",
       "      <td>0</td>\n",
       "      <td>637</td>\n",
       "      <td>18/01/2018 00:00</td>\n",
       "      <td>10/03/2018 00:00</td>\n",
       "      <td>17/02/2018 00:00</td>\n",
       "      <td>8.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>review_answer_timestamp</td>\n",
       "      <td>object</td>\n",
       "      <td>0</td>\n",
       "      <td>89500</td>\n",
       "      <td>18/01/2018 21:46</td>\n",
       "      <td>11/03/2018 03:05</td>\n",
       "      <td>18/02/2018 14:36</td>\n",
       "      <td>16.39</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      Name  dtypes  Missing  Uniques  \\\n",
       "0                review_id  object        0    99173   \n",
       "1                 order_id  object        0    99441   \n",
       "2             review_score   int64        0        5   \n",
       "3     review_comment_title  object    88285     4588   \n",
       "4   review_comment_message  object    58247    36917   \n",
       "5     review_creation_date  object        0      637   \n",
       "6  review_answer_timestamp  object        0    89500   \n",
       "\n",
       "                        First Value                      Second Value  \\\n",
       "0  7bc2406110b926393aa56f80a40eba40  80e641a11e56f04c1ad469d5645fdfde   \n",
       "1  73fc7af87114b39712e6da79b0a377eb  a548910a1c6147796b98fdf73dbeba33   \n",
       "2                                 4                                 5   \n",
       "3                               NaN                               NaN   \n",
       "4                               NaN                               NaN   \n",
       "5                  18/01/2018 00:00                  10/03/2018 00:00   \n",
       "6                  18/01/2018 21:46                  11/03/2018 03:05   \n",
       "\n",
       "                        Third Value  Entropy  \n",
       "0  228ce5500dc1d8e020d8d1322874b6f0    16.59  \n",
       "1  f9e4b658b201a9f2ecdecbb34bed034b    16.60  \n",
       "2                                 5     1.74  \n",
       "3                               NaN     9.95  \n",
       "4                               NaN    14.84  \n",
       "5                  17/02/2018 00:00     8.77  \n",
       "6                  18/02/2018 14:36    16.39  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfsummary(order_reviews_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "79c43afe",
   "metadata": {},
   "source": [
    "### Multiple review entries\n",
    "It is however noted that there can be multiple reviews for the same order id. As such, an aggregation is done to achieve a mean review score for the specific order id, before merging.\n",
    "\n",
    "Additionally, review comment details such as title, message, date and timestamp will also be dropped as it is not useful in this analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "a9b48f70",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "559"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(order_reviews_df.duplicated(subset='order_id'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "60663342",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Dropping the columns that are not needed in this analysis\n",
    "order_reviews_df = order_reviews_df.drop(['review_comment_title', 'review_comment_message',\n",
    "                                          'review_creation_date', 'review_answer_timestamp'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "df1f2503",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset Shape: (99441, 2)\n"
     ]
    },
    {
     "data": {
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       "      <td>16.60</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>review_score</td>\n",
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       "  </tbody>\n",
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      ],
      "text/plain": [
       "           Name   dtypes  Missing  Uniques                       First Value  \\\n",
       "0      order_id   object        0    99441  00010242fe8c5a6d1ba2dd792cb16214   \n",
       "1  review_score  float64        0       11                               5.0   \n",
       "\n",
       "                       Second Value                       Third Value  Entropy  \n",
       "0  00018f77f2f0320c557190d7a144bdd3  000229ec398224ef6ca0657da4fc703e    16.60  \n",
       "1                               4.0                               5.0     1.75  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Aggregating the review scores per order id, before merging\n",
    "order_reviews_agg = order_reviews_df.groupby('order_id').agg({\n",
    "    'review_score': 'mean'\n",
    "}).reset_index()\n",
    "\n",
    "dfsummary(order_reviews_agg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "9bc936a4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([5.        , 4.        , 1.        , 3.        , 2.        ,\n",
       "       4.5       , 3.33333333, 2.5       , 3.5       , 1.5       ,\n",
       "       4.33333333])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "order_reviews_agg['review_score'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "5aabb0db",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset Shape: (110518, 16)\n"
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    },
    {
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>product_id</td>\n",
       "      <td>object</td>\n",
       "      <td>0</td>\n",
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       "      <td>13.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>seller_id</td>\n",
       "      <td>object</td>\n",
       "      <td>0</td>\n",
       "      <td>2995</td>\n",
       "      <td>48436dade18ac8b2bce089ec2a041202</td>\n",
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       "      <td>5b51032eddd242adc84c38acab88f23d</td>\n",
       "      <td>9.44</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>shipping_limit_date</td>\n",
       "      <td>object</td>\n",
       "      <td>0</td>\n",
       "      <td>53909</td>\n",
       "      <td>19/09/2017 09:45</td>\n",
       "      <td>03/05/2017 11:05</td>\n",
       "      <td>18/01/2018 14:48</td>\n",
       "      <td>15.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>price</td>\n",
       "      <td>float64</td>\n",
       "      <td>0</td>\n",
       "      <td>5867</td>\n",
       "      <td>58.9</td>\n",
       "      <td>239.9</td>\n",
       "      <td>199.0</td>\n",
       "      <td>9.57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>freight_value</td>\n",
       "      <td>float64</td>\n",
       "      <td>0</td>\n",
       "      <td>6964</td>\n",
       "      <td>13.29</td>\n",
       "      <td>19.93</td>\n",
       "      <td>17.87</td>\n",
       "      <td>10.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>product_category_name_english</td>\n",
       "      <td>object</td>\n",
       "      <td>0</td>\n",
       "      <td>73</td>\n",
       "      <td>cool_stuff</td>\n",
       "      <td>pet_shop</td>\n",
       "      <td>furniture_decor</td>\n",
       "      <td>4.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>customer_id</td>\n",
       "      <td>object</td>\n",
       "      <td>0</td>\n",
       "      <td>96829</td>\n",
       "      <td>3ce436f183e68e07877b285a838db11a</td>\n",
       "      <td>f6dd3ec061db4e3987629fe6b26e5cce</td>\n",
       "      <td>6489ae5e4333f3693df5ad4372dab6d3</td>\n",
       "      <td>16.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>order_status</td>\n",
       "      <td>object</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>delivered</td>\n",
       "      <td>delivered</td>\n",
       "      <td>delivered</td>\n",
       "      <td>0.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>order_purchase_timestamp</td>\n",
       "      <td>object</td>\n",
       "      <td>0</td>\n",
       "      <td>86642</td>\n",
       "      <td>13/09/2017 08:59</td>\n",
       "      <td>26/04/2017 10:53</td>\n",
       "      <td>14/01/2018 14:33</td>\n",
       "      <td>16.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>order_approved_at</td>\n",
       "      <td>object</td>\n",
       "      <td>14</td>\n",
       "      <td>49556</td>\n",
       "      <td>13/09/2017 09:45</td>\n",
       "      <td>26/04/2017 11:05</td>\n",
       "      <td>14/01/2018 14:48</td>\n",
       "      <td>15.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>order_delivered_carrier_date</td>\n",
       "      <td>object</td>\n",
       "      <td>703</td>\n",
       "      <td>60779</td>\n",
       "      <td>19/09/2017 18:34</td>\n",
       "      <td>04/05/2017 14:35</td>\n",
       "      <td>16/01/2018 12:36</td>\n",
       "      <td>15.57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>order_delivered_customer_date</td>\n",
       "      <td>object</td>\n",
       "      <td>1867</td>\n",
       "      <td>74807</td>\n",
       "      <td>20/09/2017 23:43</td>\n",
       "      <td>12/05/2017 16:04</td>\n",
       "      <td>22/01/2018 13:19</td>\n",
       "      <td>15.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>order_estimated_delivery_date</td>\n",
       "      <td>object</td>\n",
       "      <td>0</td>\n",
       "      <td>447</td>\n",
       "      <td>29/09/2017 00:00</td>\n",
       "      <td>15/05/2017 00:00</td>\n",
       "      <td>05/02/2018 00:00</td>\n",
       "      <td>8.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>review_score</td>\n",
       "      <td>float64</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.78</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             Name   dtypes  Missing  Uniques  \\\n",
       "0                        order_id   object        0    96829   \n",
       "1                   order_item_id    int64        0       21   \n",
       "2                      product_id   object        0    32134   \n",
       "3                       seller_id   object        0     2995   \n",
       "4             shipping_limit_date   object        0    53909   \n",
       "5                           price  float64        0     5867   \n",
       "6                   freight_value  float64        0     6964   \n",
       "7   product_category_name_english   object        0       73   \n",
       "8                     customer_id   object        0    96829   \n",
       "9                    order_status   object        0        6   \n",
       "10       order_purchase_timestamp   object        0    86642   \n",
       "11              order_approved_at   object       14    49556   \n",
       "12   order_delivered_carrier_date   object      703    60779   \n",
       "13  order_delivered_customer_date   object     1867    74807   \n",
       "14  order_estimated_delivery_date   object        0      447   \n",
       "15                   review_score  float64        0       11   \n",
       "\n",
       "                         First Value                      Second Value  \\\n",
       "0   00010242fe8c5a6d1ba2dd792cb16214  00018f77f2f0320c557190d7a144bdd3   \n",
       "1                                  1                                 1   \n",
       "2   4244733e06e7ecb4970a6e2683c13e61  e5f2d52b802189ee658865ca93d83a8f   \n",
       "3   48436dade18ac8b2bce089ec2a041202  dd7ddc04e1b6c2c614352b383efe2d36   \n",
       "4                   19/09/2017 09:45                  03/05/2017 11:05   \n",
       "5                               58.9                             239.9   \n",
       "6                              13.29                             19.93   \n",
       "7                         cool_stuff                          pet_shop   \n",
       "8   3ce436f183e68e07877b285a838db11a  f6dd3ec061db4e3987629fe6b26e5cce   \n",
       "9                          delivered                         delivered   \n",
       "10                  13/09/2017 08:59                  26/04/2017 10:53   \n",
       "11                  13/09/2017 09:45                  26/04/2017 11:05   \n",
       "12                  19/09/2017 18:34                  04/05/2017 14:35   \n",
       "13                  20/09/2017 23:43                  12/05/2017 16:04   \n",
       "14                  29/09/2017 00:00                  15/05/2017 00:00   \n",
       "15                               5.0                               4.0   \n",
       "\n",
       "                         Third Value  Entropy  \n",
       "0   000229ec398224ef6ca0657da4fc703e    16.46  \n",
       "1                                  1     0.72  \n",
       "2   c777355d18b72b67abbeef9df44fd0fd    13.59  \n",
       "3   5b51032eddd242adc84c38acab88f23d     9.44  \n",
       "4                   18/01/2018 14:48    15.26  \n",
       "5                              199.0     9.57  \n",
       "6                              17.87    10.51  \n",
       "7                    furniture_decor     4.72  \n",
       "8   6489ae5e4333f3693df5ad4372dab6d3    16.46  \n",
       "9                          delivered     0.15  \n",
       "10                  14/01/2018 14:33    16.25  \n",
       "11                  14/01/2018 14:48    15.04  \n",
       "12                  16/01/2018 12:36    15.57  \n",
       "13                  22/01/2018 13:19    15.99  \n",
       "14                  05/02/2018 00:00     8.46  \n",
       "15                               5.0     1.78  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Merge dataset order_reviews\n",
    "LoMoDF = (LoMoDF.merge(order_reviews_agg, on='order_id',how =\"left\"))\n",
    "\n",
    "dfsummary(LoMoDF)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "db137a8f",
   "metadata": {},
   "source": [
    "### Performance Analysis of Product Categories\n",
    "Performance can be evaluated using 3 identifiable metrics:\n",
    "- Sales Revenue\n",
    "- Sales Volume\n",
    "- Average Review Score "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "67b5f57c",
   "metadata": {},
   "source": [
    "### Sales Revenue\n",
    "is calculated by taking the aggregated sum of 'price', grouped by the product category.\n",
    "We will then evaluate based on the top 5 Product Categories with highest revenue."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "230b79a3",
   "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>total_sales</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>product_category_name_english</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>health_beauty</th>\n",
       "      <td>1255695.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>watches_gifts</th>\n",
       "      <td>1198185.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bed_bath_table</th>\n",
       "      <td>1035964.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sports_leisure</th>\n",
       "      <td>979740.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>computers_accessories</th>\n",
       "      <td>904322.02</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               total_sales\n",
       "product_category_name_english             \n",
       "health_beauty                   1255695.13\n",
       "watches_gifts                   1198185.21\n",
       "bed_bath_table                  1035964.06\n",
       "sports_leisure                   979740.92\n",
       "computers_accessories            904322.02"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Analyze sales performance per product category\n",
    "sales_performance = LoMoDF.groupby('product_category_name_english').agg({\n",
    "    'price': 'sum'\n",
    "}).rename(columns={\n",
    "    'price': 'total_sales'\n",
    "})\n",
    "# Sort the total_sales amount, descending order.\n",
    "sales_performers = sales_performance.sort_values(by='total_sales', ascending=False)\n",
    "\n",
    "sales_performers.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "d46ef7df",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Set the figure size for better readability\n",
    "plt.figure(figsize=(10, 6))\n",
    "\n",
    "# Create a bar chart\n",
    "sales_performers['total_sales'].head(5).plot(kind='bar', color = sb.color_palette(\"YlOrBr_r\"))\n",
    "\n",
    "# Set the title and labels\n",
    "plt.title('Top 5 Performing Categories by Sales')\n",
    "plt.xlabel('Product Category')\n",
    "plt.ylabel('Total Sales')\n",
    "plt.xticks(rotation=0)\n",
    "\n",
    "# Display the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "27a22322",
   "metadata": {},
   "source": [
    "### Sales Volume\n",
    "is calculated by taking the aggregated order quantity, grouped by the product category.\n",
    "We will then evaluate based on the top 5 Product Categories with highest sales volume."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "35773518",
   "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>order_quantity</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>product_category_name_english</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>bed_bath_table</th>\n",
       "      <td>11097</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>health_beauty</th>\n",
       "      <td>9634</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sports_leisure</th>\n",
       "      <td>8590</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>furniture_decor</th>\n",
       "      <td>8298</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>computers_accessories</th>\n",
       "      <td>7781</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               order_quantity\n",
       "product_category_name_english                \n",
       "bed_bath_table                          11097\n",
       "health_beauty                            9634\n",
       "sports_leisure                           8590\n",
       "furniture_decor                          8298\n",
       "computers_accessories                    7781"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Group by product category and calculate the sales volume\n",
    "volume_performance = LoMoDF.groupby('product_category_name_english').agg({\n",
    "    'order_id': 'count',\n",
    "}).rename(columns={\n",
    "    'order_id': 'order_quantity',\n",
    "})\n",
    "\n",
    "volume_performance = volume_performance.sort_values('order_quantity',ascending = False)\n",
    "volume_performance.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "0e5d6364",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Set the figure size for better readability\n",
    "plt.figure(figsize=(10, 6))\n",
    "\n",
    "# Create a bar chart\n",
    "volume_performance['order_quantity'].head(5).plot(kind='bar', color=sb.color_palette(\"rocket\"))\n",
    "\n",
    "# Set the title and labels\n",
    "plt.title('Top 5 Categories by Sales Volume')\n",
    "plt.xlabel('Product Category')\n",
    "plt.ylabel('Order Quantity')\n",
    "plt.xticks(rotation=0)\n",
    "\n",
    "# Display the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3728f931",
   "metadata": {},
   "source": [
    "### Review Score\n",
    "is calculated by taking the aggregated review score, grouped by the product category.\n",
    "We will then evaluate based on the top 5 Product Categories with highest review score."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "f088072a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "    }\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>avg_review_score</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>product_category_name_english</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>cds_dvds_musicals</th>\n",
       "      <td>4.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fashion_children_clothes</th>\n",
       "      <td>4.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>books_general_interest</th>\n",
       "      <td>4.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>books_imported</th>\n",
       "      <td>4.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>construction_tools_tools</th>\n",
       "      <td>4.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>office_furniture</th>\n",
       "      <td>3.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>portable_kitchen_and_food_preparation</th>\n",
       "      <td>3.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>home_comfort_2</th>\n",
       "      <td>3.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>diapers_and_hygiene</th>\n",
       "      <td>3.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>security_and_services</th>\n",
       "      <td>2.50</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>73 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                       avg_review_score\n",
       "product_category_name_english                          \n",
       "cds_dvds_musicals                                  4.64\n",
       "fashion_children_clothes                           4.50\n",
       "books_general_interest                             4.48\n",
       "books_imported                                     4.40\n",
       "construction_tools_tools                           4.36\n",
       "...                                                 ...\n",
       "office_furniture                                   3.48\n",
       "portable_kitchen_and_food_preparation              3.43\n",
       "home_comfort_2                                     3.37\n",
       "diapers_and_hygiene                                3.32\n",
       "security_and_services                              2.50\n",
       "\n",
       "[73 rows x 1 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Group by product category and calculate the average review score\n",
    "review_score_cat = LoMoDF.groupby('product_category_name_english').agg({\n",
    "    'review_score': 'mean',\n",
    "}).rename(columns={\n",
    "    'review_score': 'avg_review_score',\n",
    "})\n",
    "\n",
    "review_score_cat = review_score_cat.sort_values('avg_review_score',ascending = False).round(2)\n",
    "review_score_cat"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d61e81f1",
   "metadata": {},
   "source": [
    "### Performance Evaluation\n",
    "based on sales revenue, sales volume and average review score, we can obtain a overview table with the 3 metrics."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "3a3953e9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>product_category_name_english</th>\n",
       "      <th>total_sales</th>\n",
       "      <th>sales_rank</th>\n",
       "      <th>order_quantity</th>\n",
       "      <th>volume_rank</th>\n",
       "      <th>avg_review_score</th>\n",
       "      <th>average_revenue_per_order</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>health_beauty</td>\n",
       "      <td>1255695.13</td>\n",
       "      <td>1.0</td>\n",
       "      <td>9634</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.13</td>\n",
       "      <td>130.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>watches_gifts</td>\n",
       "      <td>1198185.21</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5970</td>\n",
       "      <td>7.0</td>\n",
       "      <td>4.01</td>\n",
       "      <td>200.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>bed_bath_table</td>\n",
       "      <td>1035964.06</td>\n",
       "      <td>3.0</td>\n",
       "      <td>11097</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.88</td>\n",
       "      <td>93.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>sports_leisure</td>\n",
       "      <td>979740.92</td>\n",
       "      <td>4.0</td>\n",
       "      <td>8590</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.11</td>\n",
       "      <td>114.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>computers_accessories</td>\n",
       "      <td>904322.02</td>\n",
       "      <td>5.0</td>\n",
       "      <td>7781</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.94</td>\n",
       "      <td>116.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>furniture_decor</td>\n",
       "      <td>727465.05</td>\n",
       "      <td>6.0</td>\n",
       "      <td>8298</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.90</td>\n",
       "      <td>87.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>housewares</td>\n",
       "      <td>626825.80</td>\n",
       "      <td>7.0</td>\n",
       "      <td>6915</td>\n",
       "      <td>6.0</td>\n",
       "      <td>4.06</td>\n",
       "      <td>90.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>cool_stuff</td>\n",
       "      <td>620835.39</td>\n",
       "      <td>8.0</td>\n",
       "      <td>3780</td>\n",
       "      <td>12.0</td>\n",
       "      <td>4.14</td>\n",
       "      <td>164.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>auto</td>\n",
       "      <td>586620.73</td>\n",
       "      <td>9.0</td>\n",
       "      <td>4205</td>\n",
       "      <td>10.0</td>\n",
       "      <td>4.05</td>\n",
       "      <td>139.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>garden_tools</td>\n",
       "      <td>481009.94</td>\n",
       "      <td>10.0</td>\n",
       "      <td>4328</td>\n",
       "      <td>9.0</td>\n",
       "      <td>4.04</td>\n",
       "      <td>111.14</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   product_category_name_english  total_sales  sales_rank  order_quantity  \\\n",
       "43                 health_beauty   1255695.13         1.0            9634   \n",
       "72                 watches_gifts   1198185.21         2.0            5970   \n",
       "7                 bed_bath_table   1035964.06         3.0           11097   \n",
       "66                sports_leisure    979740.92         4.0            8590   \n",
       "14         computers_accessories    904322.02         5.0            7781   \n",
       "39               furniture_decor    727465.05         6.0            8298   \n",
       "49                    housewares    626825.80         7.0            6915   \n",
       "21                    cool_stuff    620835.39         8.0            3780   \n",
       "5                           auto    586620.73         9.0            4205   \n",
       "42                  garden_tools    481009.94        10.0            4328   \n",
       "\n",
       "    volume_rank  avg_review_score  average_revenue_per_order  \n",
       "43          2.0              4.13                     130.34  \n",
       "72          7.0              4.01                     200.70  \n",
       "7           1.0              3.88                      93.36  \n",
       "66          3.0              4.11                     114.06  \n",
       "14          5.0              3.94                     116.22  \n",
       "39          4.0              3.90                      87.67  \n",
       "49          6.0              4.06                      90.65  \n",
       "21         12.0              4.14                     164.24  \n",
       "5          10.0              4.05                     139.51  \n",
       "42          9.0              4.04                     111.14  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create ranks for sales and volume performance\n",
    "sales_performance['sales_rank'] = sales_performance['total_sales'].rank(ascending=False)\n",
    "volume_performance['volume_rank'] = volume_performance['order_quantity'].rank(ascending=False)\n",
    "\n",
    "# Merge the DataFrames\n",
    "performance_df = sales_performance.merge(volume_performance, left_index=True, right_index=True)\n",
    "performance_df = performance_df.merge(review_score_cat, left_index=True, right_index=True)\n",
    "\n",
    "# Selecting relevant columns and rename\n",
    "performance_df = performance_df[['total_sales', 'sales_rank', 'order_quantity', \n",
    "                                 'volume_rank', 'avg_review_score']].reset_index()\n",
    "performance_df.rename(columns={'index': 'category'}, inplace=True)\n",
    "performance_df['average_revenue_per_order'] = (performance_df['total_sales']/performance_df['order_quantity']).round(2)\n",
    "\n",
    "# Display the DataFrame\n",
    "performance_df.sort_values('sales_rank').head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "451ec057",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Saving the DF into a CSV.\n",
    "LoMoDF.to_csv('lomo_key_info.csv', index = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "2c0903c3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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>product_category_name_english</th>\n",
       "      <th>total_sales</th>\n",
       "      <th>sales_rank</th>\n",
       "      <th>order_quantity</th>\n",
       "      <th>volume_rank</th>\n",
       "      <th>avg_review_score</th>\n",
       "      <th>average_revenue_per_order</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>cds_dvds_musicals</td>\n",
       "      <td>730.00</td>\n",
       "      <td>71.0</td>\n",
       "      <td>14</td>\n",
       "      <td>69.0</td>\n",
       "      <td>4.64</td>\n",
       "      <td>52.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>fashion_children_clothes</td>\n",
       "      <td>569.85</td>\n",
       "      <td>72.0</td>\n",
       "      <td>8</td>\n",
       "      <td>71.5</td>\n",
       "      <td>4.50</td>\n",
       "      <td>71.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>books_general_interest</td>\n",
       "      <td>45622.29</td>\n",
       "      <td>37.0</td>\n",
       "      <td>546</td>\n",
       "      <td>27.0</td>\n",
       "      <td>4.48</td>\n",
       "      <td>83.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>books_imported</td>\n",
       "      <td>4639.85</td>\n",
       "      <td>58.0</td>\n",
       "      <td>60</td>\n",
       "      <td>58.0</td>\n",
       "      <td>4.40</td>\n",
       "      <td>77.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>construction_tools_tools</td>\n",
       "      <td>15903.95</td>\n",
       "      <td>50.0</td>\n",
       "      <td>103</td>\n",
       "      <td>53.0</td>\n",
       "      <td>4.36</td>\n",
       "      <td>154.41</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>office_furniture</td>\n",
       "      <td>273580.70</td>\n",
       "      <td>15.0</td>\n",
       "      <td>1690</td>\n",
       "      <td>19.0</td>\n",
       "      <td>3.48</td>\n",
       "      <td>161.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>portable_kitchen_and_food_preparation</td>\n",
       "      <td>3933.63</td>\n",
       "      <td>62.0</td>\n",
       "      <td>14</td>\n",
       "      <td>69.0</td>\n",
       "      <td>3.43</td>\n",
       "      <td>280.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>home_comfort_2</td>\n",
       "      <td>760.27</td>\n",
       "      <td>70.0</td>\n",
       "      <td>30</td>\n",
       "      <td>65.5</td>\n",
       "      <td>3.37</td>\n",
       "      <td>25.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>diapers_and_hygiene</td>\n",
       "      <td>1538.79</td>\n",
       "      <td>67.0</td>\n",
       "      <td>38</td>\n",
       "      <td>62.0</td>\n",
       "      <td>3.32</td>\n",
       "      <td>40.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>security_and_services</td>\n",
       "      <td>283.29</td>\n",
       "      <td>73.0</td>\n",
       "      <td>2</td>\n",
       "      <td>73.0</td>\n",
       "      <td>2.50</td>\n",
       "      <td>141.64</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>73 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            product_category_name_english  total_sales  sales_rank  \\\n",
       "11                      cds_dvds_musicals       730.00        71.0   \n",
       "28               fashion_children_clothes       569.85        72.0   \n",
       "8                  books_general_interest     45622.29        37.0   \n",
       "9                          books_imported      4639.85        58.0   \n",
       "20               construction_tools_tools     15903.95        50.0   \n",
       "..                                    ...          ...         ...   \n",
       "56                       office_furniture    273580.70        15.0   \n",
       "61  portable_kitchen_and_food_preparation      3933.63        62.0   \n",
       "47                         home_comfort_2       760.27        70.0   \n",
       "23                    diapers_and_hygiene      1538.79        67.0   \n",
       "62                  security_and_services       283.29        73.0   \n",
       "\n",
       "    order_quantity  volume_rank  avg_review_score  average_revenue_per_order  \n",
       "11              14         69.0              4.64                      52.14  \n",
       "28               8         71.5              4.50                      71.23  \n",
       "8              546         27.0              4.48                      83.56  \n",
       "9               60         58.0              4.40                      77.33  \n",
       "20             103         53.0              4.36                     154.41  \n",
       "..             ...          ...               ...                        ...  \n",
       "56            1690         19.0              3.48                     161.88  \n",
       "61              14         69.0              3.43                     280.97  \n",
       "47              30         65.5              3.37                      25.34  \n",
       "23              38         62.0              3.32                      40.49  \n",
       "62               2         73.0              2.50                     141.64  \n",
       "\n",
       "[73 rows x 7 columns]"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "performance_df.sort_values('avg_review_score', ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ab3e3c85",
   "metadata": {},
   "source": [
    "## Merging the other datasets for geolocation information. \n",
    "The analysis and findings from geolocation details will be presented on a dashboard."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "c0658080",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>object_id</th>\n",
       "      <th>shape</th>\n",
       "      <th>admin0_name_en</th>\n",
       "      <th>admin0_name_th</th>\n",
       "      <th>admin0_pcode</th>\n",
       "      <th>admin1_name_en</th>\n",
       "      <th>admin1_name_th</th>\n",
       "      <th>admin1_pcode</th>\n",
       "      <th>admin2_name_en</th>\n",
       "      <th>admin2_name_th</th>\n",
       "      <th>admin2_pcode</th>\n",
       "      <th>admin3_name_en</th>\n",
       "      <th>admin3_name_th</th>\n",
       "      <th>admin3_pcode</th>\n",
       "      <th>shape_length</th>\n",
       "      <th>shape_area</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Polygon</td>\n",
       "      <td>Thailand</td>\n",
       "      <td>ประเทศไทย</td>\n",
       "      <td>TH</td>\n",
       "      <td>Loei</td>\n",
       "      <td>เลย</td>\n",
       "      <td>TH42</td>\n",
       "      <td>Tha Li</td>\n",
       "      <td>ท่าลี่</td>\n",
       "      <td>TH4208</td>\n",
       "      <td>A Hi</td>\n",
       "      <td>อาฮี</td>\n",
       "      <td>TH420803</td>\n",
       "      <td>0.360041</td>\n",
       "      <td>0.005078</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Polygon</td>\n",
       "      <td>Thailand</td>\n",
       "      <td>ประเทศไทย</td>\n",
       "      <td>TH</td>\n",
       "      <td>Pattani</td>\n",
       "      <td>ปัตตานี</td>\n",
       "      <td>TH94</td>\n",
       "      <td>Mueang Pattani</td>\n",
       "      <td>เมืองปัตตานี</td>\n",
       "      <td>TH9401</td>\n",
       "      <td>A Noru</td>\n",
       "      <td>อาเนาะรู</td>\n",
       "      <td>TH940102</td>\n",
       "      <td>0.044900</td>\n",
       "      <td>0.000120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Polygon</td>\n",
       "      <td>Thailand</td>\n",
       "      <td>ประเทศไทย</td>\n",
       "      <td>TH</td>\n",
       "      <td>Surin</td>\n",
       "      <td>สุรินทร์</td>\n",
       "      <td>TH32</td>\n",
       "      <td>Buachet</td>\n",
       "      <td>บัวเชด</td>\n",
       "      <td>TH3213</td>\n",
       "      <td>A Phon</td>\n",
       "      <td>อาโพน</td>\n",
       "      <td>TH321305</td>\n",
       "      <td>0.365838</td>\n",
       "      <td>0.004086</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Polygon</td>\n",
       "      <td>Thailand</td>\n",
       "      <td>ประเทศไทย</td>\n",
       "      <td>TH</td>\n",
       "      <td>Yala</td>\n",
       "      <td>ยะลา</td>\n",
       "      <td>TH95</td>\n",
       "      <td>Raman</td>\n",
       "      <td>รามัน</td>\n",
       "      <td>TH9506</td>\n",
       "      <td>A Song</td>\n",
       "      <td>อาซ่อง</td>\n",
       "      <td>TH950615</td>\n",
       "      <td>0.314589</td>\n",
       "      <td>0.003177</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Polygon</td>\n",
       "      <td>Thailand</td>\n",
       "      <td>ประเทศไทย</td>\n",
       "      <td>TH</td>\n",
       "      <td>Nan</td>\n",
       "      <td>น่าน</td>\n",
       "      <td>TH55</td>\n",
       "      <td>Wiang Sa</td>\n",
       "      <td>เวียงสา</td>\n",
       "      <td>TH5507</td>\n",
       "      <td>Ai Na Lai</td>\n",
       "      <td>อ่ายนาไลย</td>\n",
       "      <td>TH550711</td>\n",
       "      <td>1.102764</td>\n",
       "      <td>0.014955</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   object_id    shape admin0_name_en admin0_name_th admin0_pcode  \\\n",
       "0          1  Polygon       Thailand      ประเทศไทย           TH   \n",
       "1          2  Polygon       Thailand      ประเทศไทย           TH   \n",
       "2          3  Polygon       Thailand      ประเทศไทย           TH   \n",
       "3          4  Polygon       Thailand      ประเทศไทย           TH   \n",
       "4          5  Polygon       Thailand      ประเทศไทย           TH   \n",
       "\n",
       "  admin1_name_en admin1_name_th admin1_pcode  admin2_name_en admin2_name_th  \\\n",
       "0           Loei            เลย         TH42          Tha Li         ท่าลี่   \n",
       "1        Pattani        ปัตตานี         TH94  Mueang Pattani   เมืองปัตตานี   \n",
       "2          Surin       สุรินทร์         TH32         Buachet         บัวเชด   \n",
       "3           Yala           ยะลา         TH95           Raman          รามัน   \n",
       "4            Nan           น่าน         TH55        Wiang Sa        เวียงสา   \n",
       "\n",
       "  admin2_pcode admin3_name_en admin3_name_th admin3_pcode  shape_length  \\\n",
       "0       TH4208           A Hi           อาฮี     TH420803      0.360041   \n",
       "1       TH9401         A Noru       อาเนาะรู     TH940102      0.044900   \n",
       "2       TH3213         A Phon          อาโพน     TH321305      0.365838   \n",
       "3       TH9506         A Song         อาซ่อง     TH950615      0.314589   \n",
       "4       TH5507      Ai Na Lai      อ่ายนาไลย     TH550711      1.102764   \n",
       "\n",
       "   shape_area  \n",
       "0    0.005078  \n",
       "1    0.000120  \n",
       "2    0.004086  \n",
       "3    0.003177  \n",
       "4    0.014955  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "geolocation_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6eb993fa",
   "metadata": {},
   "source": [
    "### Dropping columns that are not needed.\n",
    "These includes the administrative names in thai, the pcodes for admin0 to admin2, and shape details. The dashboard will only require essential informations such as the administrative names 0 to 3."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "89234e8c",
   "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>admin0_name_en</th>\n",
       "      <th>admin1_name_en</th>\n",
       "      <th>admin2_name_en</th>\n",
       "      <th>admin3_name_en</th>\n",
       "      <th>admin3_pcode</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Thailand</td>\n",
       "      <td>Loei</td>\n",
       "      <td>Tha Li</td>\n",
       "      <td>A Hi</td>\n",
       "      <td>TH420803</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Thailand</td>\n",
       "      <td>Pattani</td>\n",
       "      <td>Mueang Pattani</td>\n",
       "      <td>A Noru</td>\n",
       "      <td>TH940102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Thailand</td>\n",
       "      <td>Surin</td>\n",
       "      <td>Buachet</td>\n",
       "      <td>A Phon</td>\n",
       "      <td>TH321305</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Thailand</td>\n",
       "      <td>Yala</td>\n",
       "      <td>Raman</td>\n",
       "      <td>A Song</td>\n",
       "      <td>TH950615</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Thailand</td>\n",
       "      <td>Nan</td>\n",
       "      <td>Wiang Sa</td>\n",
       "      <td>Ai Na Lai</td>\n",
       "      <td>TH550711</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  admin0_name_en admin1_name_en  admin2_name_en admin3_name_en admin3_pcode\n",
       "0       Thailand           Loei          Tha Li           A Hi     TH420803\n",
       "1       Thailand        Pattani  Mueang Pattani         A Noru     TH940102\n",
       "2       Thailand          Surin         Buachet         A Phon     TH321305\n",
       "3       Thailand           Yala           Raman         A Song     TH950615\n",
       "4       Thailand            Nan        Wiang Sa      Ai Na Lai     TH550711"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "geolocation_df = geolocation_df.drop(['shape','admin0_name_th','admin0_pcode',\n",
    "                                      'admin1_name_th','admin1_pcode','admin2_name_th','admin2_pcode',\n",
    "                                      'admin3_name_th','shape_length','shape_area','object_id'], axis = 1)\n",
    "\n",
    "geolocation_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "92d38fec",
   "metadata": {},
   "source": [
    "### Merging the geolocation data with customer data.\n",
    "This will be done by matching of the primary key 'admin3_pcode', which is the zip code prefix in customer data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "9c05f697",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\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",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>customer_id</th>\n",
       "      <th>customer_unique_id</th>\n",
       "      <th>customer_zip_code_prefix</th>\n",
       "      <th>customer_city</th>\n",
       "      <th>customer_state</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>8535e7d171732dcdb3de48ff99040f6c</td>\n",
       "      <td>05c0c68fbdf283e56b37df9f2d20245c</td>\n",
       "      <td>TH100101</td>\n",
       "      <td>Phra Nakhon</td>\n",
       "      <td>Bangkok</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>045ce65dc053765839733057c287dbcb</td>\n",
       "      <td>1f69219bc86e6918b4751692323de642</td>\n",
       "      <td>TH100101</td>\n",
       "      <td>Phra Nakhon</td>\n",
       "      <td>Bangkok</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>c754052065714c0e304de22a0e5b7749</td>\n",
       "      <td>31b5e7b0290cf70ac603e77f85efb53f</td>\n",
       "      <td>TH100101</td>\n",
       "      <td>Phra Nakhon</td>\n",
       "      <td>Bangkok</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>d707092bd024342cd484143f8fd20a55</td>\n",
       "      <td>3f6faa55fa7e82f1245fff7e2118663b</td>\n",
       "      <td>TH100101</td>\n",
       "      <td>Phra Nakhon</td>\n",
       "      <td>Bangkok</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6a899e55865de6549a58d2c6845e5604</td>\n",
       "      <td>3fb97204945ca0c01bcf3eee6031c5f1</td>\n",
       "      <td>TH100101</td>\n",
       "      <td>Phra Nakhon</td>\n",
       "      <td>Bangkok</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        customer_id                customer_unique_id  \\\n",
       "0  8535e7d171732dcdb3de48ff99040f6c  05c0c68fbdf283e56b37df9f2d20245c   \n",
       "1  045ce65dc053765839733057c287dbcb  1f69219bc86e6918b4751692323de642   \n",
       "2  c754052065714c0e304de22a0e5b7749  31b5e7b0290cf70ac603e77f85efb53f   \n",
       "3  d707092bd024342cd484143f8fd20a55  3f6faa55fa7e82f1245fff7e2118663b   \n",
       "4  6a899e55865de6549a58d2c6845e5604  3fb97204945ca0c01bcf3eee6031c5f1   \n",
       "\n",
       "  customer_zip_code_prefix customer_city customer_state  \n",
       "0                 TH100101   Phra Nakhon        Bangkok  \n",
       "1                 TH100101   Phra Nakhon        Bangkok  \n",
       "2                 TH100101   Phra Nakhon        Bangkok  \n",
       "3                 TH100101   Phra Nakhon        Bangkok  \n",
       "4                 TH100101   Phra Nakhon        Bangkok  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "customers_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "ef7542f9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    }\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>customer_id</th>\n",
       "      <th>customer_unique_id</th>\n",
       "      <th>customer_zip_code_prefix</th>\n",
       "      <th>customer_city</th>\n",
       "      <th>customer_state</th>\n",
       "      <th>admin0_name_en</th>\n",
       "      <th>admin1_name_en</th>\n",
       "      <th>admin2_name_en</th>\n",
       "      <th>admin3_name_en</th>\n",
       "      <th>admin3_pcode</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>8535e7d171732dcdb3de48ff99040f6c</td>\n",
       "      <td>05c0c68fbdf283e56b37df9f2d20245c</td>\n",
       "      <td>TH100101</td>\n",
       "      <td>Phra Nakhon</td>\n",
       "      <td>Bangkok</td>\n",
       "      <td>Thailand</td>\n",
       "      <td>Bangkok</td>\n",
       "      <td>Phra Nakhon</td>\n",
       "      <td>Phraborom Maharatchawang</td>\n",
       "      <td>TH100101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>045ce65dc053765839733057c287dbcb</td>\n",
       "      <td>1f69219bc86e6918b4751692323de642</td>\n",
       "      <td>TH100101</td>\n",
       "      <td>Phra Nakhon</td>\n",
       "      <td>Bangkok</td>\n",
       "      <td>Thailand</td>\n",
       "      <td>Bangkok</td>\n",
       "      <td>Phra Nakhon</td>\n",
       "      <td>Phraborom Maharatchawang</td>\n",
       "      <td>TH100101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>c754052065714c0e304de22a0e5b7749</td>\n",
       "      <td>31b5e7b0290cf70ac603e77f85efb53f</td>\n",
       "      <td>TH100101</td>\n",
       "      <td>Phra Nakhon</td>\n",
       "      <td>Bangkok</td>\n",
       "      <td>Thailand</td>\n",
       "      <td>Bangkok</td>\n",
       "      <td>Phra Nakhon</td>\n",
       "      <td>Phraborom Maharatchawang</td>\n",
       "      <td>TH100101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>d707092bd024342cd484143f8fd20a55</td>\n",
       "      <td>3f6faa55fa7e82f1245fff7e2118663b</td>\n",
       "      <td>TH100101</td>\n",
       "      <td>Phra Nakhon</td>\n",
       "      <td>Bangkok</td>\n",
       "      <td>Thailand</td>\n",
       "      <td>Bangkok</td>\n",
       "      <td>Phra Nakhon</td>\n",
       "      <td>Phraborom Maharatchawang</td>\n",
       "      <td>TH100101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6a899e55865de6549a58d2c6845e5604</td>\n",
       "      <td>3fb97204945ca0c01bcf3eee6031c5f1</td>\n",
       "      <td>TH100101</td>\n",
       "      <td>Phra Nakhon</td>\n",
       "      <td>Bangkok</td>\n",
       "      <td>Thailand</td>\n",
       "      <td>Bangkok</td>\n",
       "      <td>Phra Nakhon</td>\n",
       "      <td>Phraborom Maharatchawang</td>\n",
       "      <td>TH100101</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        customer_id                customer_unique_id  \\\n",
       "0  8535e7d171732dcdb3de48ff99040f6c  05c0c68fbdf283e56b37df9f2d20245c   \n",
       "1  045ce65dc053765839733057c287dbcb  1f69219bc86e6918b4751692323de642   \n",
       "2  c754052065714c0e304de22a0e5b7749  31b5e7b0290cf70ac603e77f85efb53f   \n",
       "3  d707092bd024342cd484143f8fd20a55  3f6faa55fa7e82f1245fff7e2118663b   \n",
       "4  6a899e55865de6549a58d2c6845e5604  3fb97204945ca0c01bcf3eee6031c5f1   \n",
       "\n",
       "  customer_zip_code_prefix customer_city customer_state admin0_name_en  \\\n",
       "0                 TH100101   Phra Nakhon        Bangkok       Thailand   \n",
       "1                 TH100101   Phra Nakhon        Bangkok       Thailand   \n",
       "2                 TH100101   Phra Nakhon        Bangkok       Thailand   \n",
       "3                 TH100101   Phra Nakhon        Bangkok       Thailand   \n",
       "4                 TH100101   Phra Nakhon        Bangkok       Thailand   \n",
       "\n",
       "  admin1_name_en admin2_name_en            admin3_name_en admin3_pcode  \n",
       "0        Bangkok    Phra Nakhon  Phraborom Maharatchawang     TH100101  \n",
       "1        Bangkok    Phra Nakhon  Phraborom Maharatchawang     TH100101  \n",
       "2        Bangkok    Phra Nakhon  Phraborom Maharatchawang     TH100101  \n",
       "3        Bangkok    Phra Nakhon  Phraborom Maharatchawang     TH100101  \n",
       "4        Bangkok    Phra Nakhon  Phraborom Maharatchawang     TH100101  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "customers_df = pd.merge(customers_df, geolocation_df, \n",
    "                        left_on='customer_zip_code_prefix', \n",
    "                        right_on='admin3_pcode', how='left')\n",
    "\n",
    "customers_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "007dce80",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>customer_id</th>\n",
       "      <th>customer_unique_id</th>\n",
       "      <th>customer_zip_code_prefix</th>\n",
       "      <th>customer_city</th>\n",
       "      <th>customer_state</th>\n",
       "      <th>admin0_name_en</th>\n",
       "      <th>admin1_name_en</th>\n",
       "      <th>admin2_name_en</th>\n",
       "      <th>admin3_name_en</th>\n",
       "      <th>admin3_pcode</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>8535e7d171732dcdb3de48ff99040f6c</td>\n",
       "      <td>05c0c68fbdf283e56b37df9f2d20245c</td>\n",
       "      <td>TH100101</td>\n",
       "      <td>Phra Nakhon</td>\n",
       "      <td>Bangkok</td>\n",
       "      <td>Thailand</td>\n",
       "      <td>Bangkok</td>\n",
       "      <td>Phra Nakhon</td>\n",
       "      <td>Phraborom Maharatchawang</td>\n",
       "      <td>TH100101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>045ce65dc053765839733057c287dbcb</td>\n",
       "      <td>1f69219bc86e6918b4751692323de642</td>\n",
       "      <td>TH100101</td>\n",
       "      <td>Phra Nakhon</td>\n",
       "      <td>Bangkok</td>\n",
       "      <td>Thailand</td>\n",
       "      <td>Bangkok</td>\n",
       "      <td>Phra Nakhon</td>\n",
       "      <td>Phraborom Maharatchawang</td>\n",
       "      <td>TH100101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>c754052065714c0e304de22a0e5b7749</td>\n",
       "      <td>31b5e7b0290cf70ac603e77f85efb53f</td>\n",
       "      <td>TH100101</td>\n",
       "      <td>Phra Nakhon</td>\n",
       "      <td>Bangkok</td>\n",
       "      <td>Thailand</td>\n",
       "      <td>Bangkok</td>\n",
       "      <td>Phra Nakhon</td>\n",
       "      <td>Phraborom Maharatchawang</td>\n",
       "      <td>TH100101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>d707092bd024342cd484143f8fd20a55</td>\n",
       "      <td>3f6faa55fa7e82f1245fff7e2118663b</td>\n",
       "      <td>TH100101</td>\n",
       "      <td>Phra Nakhon</td>\n",
       "      <td>Bangkok</td>\n",
       "      <td>Thailand</td>\n",
       "      <td>Bangkok</td>\n",
       "      <td>Phra Nakhon</td>\n",
       "      <td>Phraborom Maharatchawang</td>\n",
       "      <td>TH100101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6a899e55865de6549a58d2c6845e5604</td>\n",
       "      <td>3fb97204945ca0c01bcf3eee6031c5f1</td>\n",
       "      <td>TH100101</td>\n",
       "      <td>Phra Nakhon</td>\n",
       "      <td>Bangkok</td>\n",
       "      <td>Thailand</td>\n",
       "      <td>Bangkok</td>\n",
       "      <td>Phra Nakhon</td>\n",
       "      <td>Phraborom Maharatchawang</td>\n",
       "      <td>TH100101</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        customer_id                customer_unique_id  \\\n",
       "0  8535e7d171732dcdb3de48ff99040f6c  05c0c68fbdf283e56b37df9f2d20245c   \n",
       "1  045ce65dc053765839733057c287dbcb  1f69219bc86e6918b4751692323de642   \n",
       "2  c754052065714c0e304de22a0e5b7749  31b5e7b0290cf70ac603e77f85efb53f   \n",
       "3  d707092bd024342cd484143f8fd20a55  3f6faa55fa7e82f1245fff7e2118663b   \n",
       "4  6a899e55865de6549a58d2c6845e5604  3fb97204945ca0c01bcf3eee6031c5f1   \n",
       "\n",
       "  customer_zip_code_prefix customer_city customer_state admin0_name_en  \\\n",
       "0                 TH100101   Phra Nakhon        Bangkok       Thailand   \n",
       "1                 TH100101   Phra Nakhon        Bangkok       Thailand   \n",
       "2                 TH100101   Phra Nakhon        Bangkok       Thailand   \n",
       "3                 TH100101   Phra Nakhon        Bangkok       Thailand   \n",
       "4                 TH100101   Phra Nakhon        Bangkok       Thailand   \n",
       "\n",
       "  admin1_name_en admin2_name_en            admin3_name_en admin3_pcode  \n",
       "0        Bangkok    Phra Nakhon  Phraborom Maharatchawang     TH100101  \n",
       "1        Bangkok    Phra Nakhon  Phraborom Maharatchawang     TH100101  \n",
       "2        Bangkok    Phra Nakhon  Phraborom Maharatchawang     TH100101  \n",
       "3        Bangkok    Phra Nakhon  Phraborom Maharatchawang     TH100101  \n",
       "4        Bangkok    Phra Nakhon  Phraborom Maharatchawang     TH100101  "
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "customers_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a89ec77f",
   "metadata": {},
   "source": [
    "### Duplicated columns\n",
    "Noting that 'admin1_name' is the same as 'customer_state' and 'admin2_name' is the same as 'customer_city', I will drop the duplicated columns. \n",
    "\n",
    "I will also rename 'admin0_name_en' as 'customer_country' and 'admin3_name_en' as 'customer_district', for clearer representation.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "68ff8096",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>customer_id</th>\n",
       "      <th>customer_unique_id</th>\n",
       "      <th>customer_zip_code_prefix</th>\n",
       "      <th>customer_city</th>\n",
       "      <th>customer_state</th>\n",
       "      <th>customer_country</th>\n",
       "      <th>customer_district</th>\n",
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       "      <td>05c0c68fbdf283e56b37df9f2d20245c</td>\n",
       "      <td>TH100101</td>\n",
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       "      <td>Phraborom Maharatchawang</td>\n",
       "    </tr>\n",
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       "      <th>1</th>\n",
       "      <td>045ce65dc053765839733057c287dbcb</td>\n",
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       "      <td>TH100101</td>\n",
       "      <td>Phra Nakhon</td>\n",
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       "      <td>Thailand</td>\n",
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       "      <th>2</th>\n",
       "      <td>c754052065714c0e304de22a0e5b7749</td>\n",
       "      <td>31b5e7b0290cf70ac603e77f85efb53f</td>\n",
       "      <td>TH100101</td>\n",
       "      <td>Phra Nakhon</td>\n",
       "      <td>Bangkok</td>\n",
       "      <td>Thailand</td>\n",
       "      <td>Phraborom Maharatchawang</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>d707092bd024342cd484143f8fd20a55</td>\n",
       "      <td>3f6faa55fa7e82f1245fff7e2118663b</td>\n",
       "      <td>TH100101</td>\n",
       "      <td>Phra Nakhon</td>\n",
       "      <td>Bangkok</td>\n",
       "      <td>Thailand</td>\n",
       "      <td>Phraborom Maharatchawang</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6a899e55865de6549a58d2c6845e5604</td>\n",
       "      <td>3fb97204945ca0c01bcf3eee6031c5f1</td>\n",
       "      <td>TH100101</td>\n",
       "      <td>Phra Nakhon</td>\n",
       "      <td>Bangkok</td>\n",
       "      <td>Thailand</td>\n",
       "      <td>Phraborom Maharatchawang</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        customer_id                customer_unique_id  \\\n",
       "0  8535e7d171732dcdb3de48ff99040f6c  05c0c68fbdf283e56b37df9f2d20245c   \n",
       "1  045ce65dc053765839733057c287dbcb  1f69219bc86e6918b4751692323de642   \n",
       "2  c754052065714c0e304de22a0e5b7749  31b5e7b0290cf70ac603e77f85efb53f   \n",
       "3  d707092bd024342cd484143f8fd20a55  3f6faa55fa7e82f1245fff7e2118663b   \n",
       "4  6a899e55865de6549a58d2c6845e5604  3fb97204945ca0c01bcf3eee6031c5f1   \n",
       "\n",
       "  customer_zip_code_prefix customer_city customer_state customer_country  \\\n",
       "0                 TH100101   Phra Nakhon        Bangkok         Thailand   \n",
       "1                 TH100101   Phra Nakhon        Bangkok         Thailand   \n",
       "2                 TH100101   Phra Nakhon        Bangkok         Thailand   \n",
       "3                 TH100101   Phra Nakhon        Bangkok         Thailand   \n",
       "4                 TH100101   Phra Nakhon        Bangkok         Thailand   \n",
       "\n",
       "          customer_district  \n",
       "0  Phraborom Maharatchawang  \n",
       "1  Phraborom Maharatchawang  \n",
       "2  Phraborom Maharatchawang  \n",
       "3  Phraborom Maharatchawang  \n",
       "4  Phraborom Maharatchawang  "
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Drop the duplicated columns 'admin1_name_en' and 'admin2_name_en'\n",
    "customers_df.drop(['admin1_name_en', 'admin2_name_en','admin3_pcode'], axis = 1, inplace=True)\n",
    "\n",
    "# Rename 'admin0_name_en' to 'customer_country' and 'admin3_name_en' to 'customer_district'\n",
    "customers_df.rename(columns={\n",
    "    'admin0_name_en': 'customer_country',\n",
    "    'admin3_name_en': 'customer_district'\n",
    "}, inplace=True)\n",
    "\n",
    "customers_df.head()  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "ddd24ab8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Saving the DF into a CSV.\n",
    "customers_df.to_csv('customers_key_info.csv', index = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "00f217d1",
   "metadata": {},
   "source": [
    "## Merging the geolocation data with seller data, closed deals and leads data.\n",
    "### Geolocation data merging\n",
    "This will be done by matching of the primary key 'admin3_pcode', which is the zip code prefix in seller data. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "f072ce01",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>seller_id</th>\n",
       "      <th>seller_zip_code_prefix_prep</th>\n",
       "      <th>seller_city_prep</th>\n",
       "      <th>seller_state_prep</th>\n",
       "      <th>seller_zip_code_prefix</th>\n",
       "      <th>seller_city</th>\n",
       "      <th>seller_state</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <td>TH100101</td>\n",
       "      <td>Phra Nakhon</td>\n",
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       "      <td>TH100101</td>\n",
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       "      <td>TH100102</td>\n",
       "      <td>Phra Nakhon</td>\n",
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       "      <td>Phra Nakhon</td>\n",
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       "      <td>TH100102</td>\n",
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       "      <td>TH100102</td>\n",
       "      <td>Phra Nakhon</td>\n",
       "      <td>Bangkok</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>a425f92c199eb576938df686728acd20</td>\n",
       "      <td>TH100102</td>\n",
       "      <td>Phra Nakhon</td>\n",
       "      <td>Bangkok</td>\n",
       "      <td>TH100102</td>\n",
       "      <td>Phra Nakhon</td>\n",
       "      <td>Bangkok</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          seller_id seller_zip_code_prefix_prep  \\\n",
       "0  c2bda99904207edd1834f03e0022b007                    TH100101   \n",
       "1  788e857f317e53de488d393e65a80f45                    TH100101   \n",
       "2  713e3a23ca5e47786eb6f0d99f94cfac                    TH100102   \n",
       "3  cc80e2f693fa6e2408f47ccf62413fc1                    TH100102   \n",
       "4  a425f92c199eb576938df686728acd20                    TH100102   \n",
       "\n",
       "  seller_city_prep seller_state_prep seller_zip_code_prefix  seller_city  \\\n",
       "0      Phra Nakhon           Bangkok               TH100101  Phra Nakhon   \n",
       "1      Phra Nakhon           Bangkok               TH100101  Phra Nakhon   \n",
       "2      Phra Nakhon           Bangkok               TH100102  Phra Nakhon   \n",
       "3      Phra Nakhon           Bangkok               TH100102  Phra Nakhon   \n",
       "4      Phra Nakhon           Bangkok               TH100102  Phra Nakhon   \n",
       "\n",
       "  seller_state  \n",
       "0      Bangkok  \n",
       "1      Bangkok  \n",
       "2      Bangkok  \n",
       "3      Bangkok  \n",
       "4      Bangkok  "
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sellers_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "199f6daa",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>seller_zip_code_prefix</th>\n",
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       "      <th>seller_state</th>\n",
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       "      <td>TH100102</td>\n",
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       "      <td>TH100102</td>\n",
       "      <td>Phra Nakhon</td>\n",
       "      <td>Bangkok</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>a425f92c199eb576938df686728acd20</td>\n",
       "      <td>TH100102</td>\n",
       "      <td>Phra Nakhon</td>\n",
       "      <td>Bangkok</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          seller_id seller_zip_code_prefix  seller_city  \\\n",
       "0  c2bda99904207edd1834f03e0022b007               TH100101  Phra Nakhon   \n",
       "1  788e857f317e53de488d393e65a80f45               TH100101  Phra Nakhon   \n",
       "2  713e3a23ca5e47786eb6f0d99f94cfac               TH100102  Phra Nakhon   \n",
       "3  cc80e2f693fa6e2408f47ccf62413fc1               TH100102  Phra Nakhon   \n",
       "4  a425f92c199eb576938df686728acd20               TH100102  Phra Nakhon   \n",
       "\n",
       "  seller_state  \n",
       "0      Bangkok  \n",
       "1      Bangkok  \n",
       "2      Bangkok  \n",
       "3      Bangkok  \n",
       "4      Bangkok  "
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Removing duplicate prep columns\n",
    "sellers_df = sellers_df.drop(['seller_zip_code_prefix_prep','seller_city_prep',\n",
    "                              'seller_state_prep'], axis = 1)\n",
    "\n",
    "sellers_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "8597a113",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>seller_id</th>\n",
       "      <th>seller_zip_code_prefix</th>\n",
       "      <th>seller_city</th>\n",
       "      <th>seller_state</th>\n",
       "      <th>seller_country</th>\n",
       "      <th>seller_district</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>c2bda99904207edd1834f03e0022b007</td>\n",
       "      <td>TH100101</td>\n",
       "      <td>Phra Nakhon</td>\n",
       "      <td>Bangkok</td>\n",
       "      <td>Thailand</td>\n",
       "      <td>Phraborom Maharatchawang</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>788e857f317e53de488d393e65a80f45</td>\n",
       "      <td>TH100101</td>\n",
       "      <td>Phra Nakhon</td>\n",
       "      <td>Bangkok</td>\n",
       "      <td>Thailand</td>\n",
       "      <td>Phraborom Maharatchawang</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>713e3a23ca5e47786eb6f0d99f94cfac</td>\n",
       "      <td>TH100102</td>\n",
       "      <td>Phra Nakhon</td>\n",
       "      <td>Bangkok</td>\n",
       "      <td>Thailand</td>\n",
       "      <td>Wang Burapha Phirom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>cc80e2f693fa6e2408f47ccf62413fc1</td>\n",
       "      <td>TH100102</td>\n",
       "      <td>Phra Nakhon</td>\n",
       "      <td>Bangkok</td>\n",
       "      <td>Thailand</td>\n",
       "      <td>Wang Burapha Phirom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>a425f92c199eb576938df686728acd20</td>\n",
       "      <td>TH100102</td>\n",
       "      <td>Phra Nakhon</td>\n",
       "      <td>Bangkok</td>\n",
       "      <td>Thailand</td>\n",
       "      <td>Wang Burapha Phirom</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          seller_id seller_zip_code_prefix  seller_city  \\\n",
       "0  c2bda99904207edd1834f03e0022b007               TH100101  Phra Nakhon   \n",
       "1  788e857f317e53de488d393e65a80f45               TH100101  Phra Nakhon   \n",
       "2  713e3a23ca5e47786eb6f0d99f94cfac               TH100102  Phra Nakhon   \n",
       "3  cc80e2f693fa6e2408f47ccf62413fc1               TH100102  Phra Nakhon   \n",
       "4  a425f92c199eb576938df686728acd20               TH100102  Phra Nakhon   \n",
       "\n",
       "  seller_state seller_country           seller_district  \n",
       "0      Bangkok       Thailand  Phraborom Maharatchawang  \n",
       "1      Bangkok       Thailand  Phraborom Maharatchawang  \n",
       "2      Bangkok       Thailand       Wang Burapha Phirom  \n",
       "3      Bangkok       Thailand       Wang Burapha Phirom  \n",
       "4      Bangkok       Thailand       Wang Burapha Phirom  "
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Merging with geolocation data to include district and country\n",
    "sellers_merge = pd.merge(\n",
    "    sellers_df,\n",
    "    geolocation_df[['admin3_pcode', 'admin0_name_en', 'admin3_name_en']],\n",
    "    left_on='seller_zip_code_prefix',\n",
    "    right_on='admin3_pcode', how='left)\n",
    "\n",
    "# Drop the 'admin3_pcode' as it is a duplicate of 'customer_zip_code_prefix'\n",
    "sellers_merge.drop(columns=['admin3_pcode'], inplace=True)\n",
    "\n",
    "# Rename 'admin0_name_en' to 'customer_country' and 'admin3_name_en' to 'customer_district'\n",
    "sellers_merge.rename(\n",
    "    columns={\n",
    "        'admin0_name_en': 'seller_country',\n",
    "        'admin3_name_en': 'seller_district'\n",
    "    },inplace=True)\n",
    "\n",
    "sellers_merge.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "4fd62ebb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset Shape: (3095, 6)\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "      <th>First Value</th>\n",
       "      <th>Second Value</th>\n",
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       "      <th>Entropy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>seller_id</td>\n",
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       "      <td>3095</td>\n",
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       "      <td>713e3a23ca5e47786eb6f0d99f94cfac</td>\n",
       "      <td>11.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>seller_zip_code_prefix</td>\n",
       "      <td>object</td>\n",
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       "      <td>2014</td>\n",
       "      <td>TH100101</td>\n",
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       "      <td>10.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>seller_city</td>\n",
       "      <td>object</td>\n",
       "      <td>0</td>\n",
       "      <td>382</td>\n",
       "      <td>Phra Nakhon</td>\n",
       "      <td>Phra Nakhon</td>\n",
       "      <td>Phra Nakhon</td>\n",
       "      <td>8.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>seller_state</td>\n",
       "      <td>object</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>Bangkok</td>\n",
       "      <td>Bangkok</td>\n",
       "      <td>Bangkok</td>\n",
       "      <td>5.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>seller_country</td>\n",
       "      <td>object</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Thailand</td>\n",
       "      <td>Thailand</td>\n",
       "      <td>Thailand</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>seller_district</td>\n",
       "      <td>object</td>\n",
       "      <td>0</td>\n",
       "      <td>1759</td>\n",
       "      <td>Phraborom Maharatchawang</td>\n",
       "      <td>Phraborom Maharatchawang</td>\n",
       "      <td>Wang Burapha Phirom</td>\n",
       "      <td>10.46</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Name  dtypes  Missing  Uniques  \\\n",
       "0               seller_id  object        0     3095   \n",
       "1  seller_zip_code_prefix  object        0     2014   \n",
       "2             seller_city  object        0      382   \n",
       "3            seller_state  object        0       40   \n",
       "4          seller_country  object        0        1   \n",
       "5         seller_district  object        0     1759   \n",
       "\n",
       "                        First Value                      Second Value  \\\n",
       "0  c2bda99904207edd1834f03e0022b007  788e857f317e53de488d393e65a80f45   \n",
       "1                          TH100101                          TH100101   \n",
       "2                       Phra Nakhon                       Phra Nakhon   \n",
       "3                           Bangkok                           Bangkok   \n",
       "4                          Thailand                          Thailand   \n",
       "5          Phraborom Maharatchawang          Phraborom Maharatchawang   \n",
       "\n",
       "                        Third Value  Entropy  \n",
       "0  713e3a23ca5e47786eb6f0d99f94cfac    11.60  \n",
       "1                          TH100102    10.74  \n",
       "2                       Phra Nakhon     8.17  \n",
       "3                           Bangkok     5.10  \n",
       "4                          Thailand     0.00  \n",
       "5               Wang Burapha Phirom    10.46  "
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfsummary(sellers_merge)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "07890a2c",
   "metadata": {},
   "source": [
    "### Merging of closed deals and MQL data.\n",
    "this is done by the matching of primary key mql_id."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "1e442515",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>mql_id</th>\n",
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       "      <th>sdr_id</th>\n",
       "      <th>sr_id</th>\n",
       "      <th>won_date</th>\n",
       "      <th>business_segment</th>\n",
       "      <th>lead_type</th>\n",
       "      <th>lead_behaviour_profile</th>\n",
       "      <th>has_company</th>\n",
       "      <th>average_stock</th>\n",
       "      <th>business_type_org</th>\n",
       "      <th>declared_product_catalog_size</th>\n",
       "      <th>declared_monthly_revenue</th>\n",
       "      <th>first_contact_date</th>\n",
       "      <th>origin</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <td>26/02/2018 19:58</td>\n",
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       "      <td>08/05/2018 20:17</td>\n",
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       "      <td>hawkeye</td>\n",
       "      <td>True</td>\n",
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       "      <td>600</td>\n",
       "      <td>6,000</td>\n",
       "      <td>04/04/2018</td>\n",
       "      <td>referral</td>\n",
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       "      <td>d3d1e91a157ea7f90548eef82f1955e3</td>\n",
       "      <td>17/01/2018 13:51</td>\n",
       "      <td>food_drink</td>\n",
       "      <td>online_small</td>\n",
       "      <td>scarlet_witch</td>\n",
       "      <td>True</td>\n",
       "      <td>above 100</td>\n",
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       "      <td>700</td>\n",
       "      <td>2,000</td>\n",
       "      <td>14/01/2018</td>\n",
       "      <td>paid_search</td>\n",
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       "      <td>iron_man</td>\n",
       "      <td>False</td>\n",
       "      <td>below 100</td>\n",
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      ],
      "text/plain": [
       "                             mql_id                         seller_id  \\\n",
       "0  5420aad7fec3549a85876ba1c529bd84  2c43fb513632d29b3b58df74816f1b06   \n",
       "1  a555fb36b9368110ede0f043dfc3b9a0  bbb7d7893a450660432ea6652310ebb7   \n",
       "2  327174d3648a2d047e8940d7d15204ca  612170e34b97004b3ba37eae81836b4c   \n",
       "3  f5fee8f7da74f4887f5bcae2bafb6dd6  21e1781e36faf92725dde4730a88ca0f   \n",
       "4  ffe640179b554e295c167a2f6be528e0  ed8cb7b190ceb6067227478e48cf8dde   \n",
       "\n",
       "                             sdr_id                             sr_id  \\\n",
       "0  a8387c01a09e99ce014107505b92388c  4ef15afb4b2723d8f3d81e51ec7afefe   \n",
       "1  09285259593c61296eef10c734121d5b  d3d1e91a157ea7f90548eef82f1955e3   \n",
       "2  b90f87164b5f8c2cfa5c8572834dbe3f  6565aa9ce3178a5caf6171827af3a9ba   \n",
       "3  56bf83c4bb35763a51c2baab501b4c67  d3d1e91a157ea7f90548eef82f1955e3   \n",
       "4  4b339f9567d060bcea4f5136b9f5949e  d3d1e91a157ea7f90548eef82f1955e3   \n",
       "\n",
       "           won_date business_segment      lead_type lead_behaviour_profile  \\\n",
       "0  26/02/2018 19:58              pet  online_medium        captain_america   \n",
       "1  08/05/2018 20:17  car_accessories       industry                hawkeye   \n",
       "2  05/06/2018 17:27  home_appliances     online_big        captain_america   \n",
       "3  17/01/2018 13:51       food_drink   online_small          scarlet_witch   \n",
       "4  03/07/2018 20:17  home_appliances       industry               iron_man   \n",
       "\n",
       "   has_company average_stock business_type_org declared_product_catalog_size  \\\n",
       "0        False     above 100          reseller                           400   \n",
       "1         True     above 100          reseller                           600   \n",
       "2        False     above 100          reseller                           700   \n",
       "3         True     above 100          reseller                           700   \n",
       "4        False     below 100      manufacturer                           300   \n",
       "\n",
       "  declared_monthly_revenue first_contact_date          origin  \n",
       "0                    3,000         21/02/2018  organic_search  \n",
       "1                    6,000         04/04/2018        referral  \n",
       "2                    4,000         03/04/2018  organic_search  \n",
       "3                    2,000         14/01/2018     paid_search  \n",
       "4                    3,000         09/10/2017         unknown  "
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Merge the 2 dfs\n",
    "closed_deals_leads_df = pd.merge(closed_deals_df,\n",
    "                                 marketing_qualified_leads_df[['mql_id','first_contact_date','origin']],\n",
    "                                 on= 'mql_id', how = 'left')\n",
    "\n",
    "closed_deals_leads_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "2e1b0e51",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Drop the columns not needed\n",
    "closed_deals_leads_df = closed_deals_leads_df.drop(['sdr_id','sr_id'], axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5746cbec",
   "metadata": {},
   "source": [
    "### Merging of closed deals with leads and seller data.\n",
    "this is done by the matching of primary key seller_id."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "6735a690",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>mql_id</th>\n",
       "      <th>won_date</th>\n",
       "      <th>business_segment</th>\n",
       "      <th>lead_type</th>\n",
       "      <th>lead_behaviour_profile</th>\n",
       "      <th>has_company</th>\n",
       "      <th>average_stock</th>\n",
       "      <th>business_type_org</th>\n",
       "      <th>declared_product_catalog_size</th>\n",
       "      <th>declared_monthly_revenue</th>\n",
       "      <th>first_contact_date</th>\n",
       "      <th>origin</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>c2bda99904207edd1834f03e0022b007</td>\n",
       "      <td>TH100101</td>\n",
       "      <td>Phra Nakhon</td>\n",
       "      <td>Bangkok</td>\n",
       "      <td>Thailand</td>\n",
       "      <td>Phraborom Maharatchawang</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>788e857f317e53de488d393e65a80f45</td>\n",
       "      <td>TH100101</td>\n",
       "      <td>Phra Nakhon</td>\n",
       "      <td>Bangkok</td>\n",
       "      <td>Thailand</td>\n",
       "      <td>Phraborom Maharatchawang</td>\n",
       "      <td>c14a716bc18a0f87296c2ec10bfe8929</td>\n",
       "      <td>05/04/2018 14:44</td>\n",
       "      <td>bags_backpacks</td>\n",
       "      <td>online_medium</td>\n",
       "      <td>iron_man</td>\n",
       "      <td>True</td>\n",
       "      <td>above 100</td>\n",
       "      <td>manufacturer</td>\n",
       "      <td>600</td>\n",
       "      <td>8,000</td>\n",
       "      <td>03/04/2018</td>\n",
       "      <td>organic_search</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>713e3a23ca5e47786eb6f0d99f94cfac</td>\n",
       "      <td>TH100102</td>\n",
       "      <td>Phra Nakhon</td>\n",
       "      <td>Bangkok</td>\n",
       "      <td>Thailand</td>\n",
       "      <td>Wang Burapha Phirom</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>cc80e2f693fa6e2408f47ccf62413fc1</td>\n",
       "      <td>TH100102</td>\n",
       "      <td>Phra Nakhon</td>\n",
       "      <td>Bangkok</td>\n",
       "      <td>Thailand</td>\n",
       "      <td>Wang Burapha Phirom</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>a425f92c199eb576938df686728acd20</td>\n",
       "      <td>TH100102</td>\n",
       "      <td>Phra Nakhon</td>\n",
       "      <td>Bangkok</td>\n",
       "      <td>Thailand</td>\n",
       "      <td>Wang Burapha Phirom</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          seller_id seller_zip_code_prefix  seller_city  \\\n",
       "0  c2bda99904207edd1834f03e0022b007               TH100101  Phra Nakhon   \n",
       "1  788e857f317e53de488d393e65a80f45               TH100101  Phra Nakhon   \n",
       "2  713e3a23ca5e47786eb6f0d99f94cfac               TH100102  Phra Nakhon   \n",
       "3  cc80e2f693fa6e2408f47ccf62413fc1               TH100102  Phra Nakhon   \n",
       "4  a425f92c199eb576938df686728acd20               TH100102  Phra Nakhon   \n",
       "\n",
       "  seller_state seller_country           seller_district  \\\n",
       "0      Bangkok       Thailand  Phraborom Maharatchawang   \n",
       "1      Bangkok       Thailand  Phraborom Maharatchawang   \n",
       "2      Bangkok       Thailand       Wang Burapha Phirom   \n",
       "3      Bangkok       Thailand       Wang Burapha Phirom   \n",
       "4      Bangkok       Thailand       Wang Burapha Phirom   \n",
       "\n",
       "                             mql_id          won_date business_segment  \\\n",
       "0                               NaN               NaN              NaN   \n",
       "1  c14a716bc18a0f87296c2ec10bfe8929  05/04/2018 14:44   bags_backpacks   \n",
       "2                               NaN               NaN              NaN   \n",
       "3                               NaN               NaN              NaN   \n",
       "4                               NaN               NaN              NaN   \n",
       "\n",
       "       lead_type lead_behaviour_profile has_company average_stock  \\\n",
       "0            NaN                    NaN         NaN           NaN   \n",
       "1  online_medium               iron_man        True     above 100   \n",
       "2            NaN                    NaN         NaN           NaN   \n",
       "3            NaN                    NaN         NaN           NaN   \n",
       "4            NaN                    NaN         NaN           NaN   \n",
       "\n",
       "  business_type_org declared_product_catalog_size declared_monthly_revenue  \\\n",
       "0               NaN                           NaN                      NaN   \n",
       "1      manufacturer                           600                    8,000   \n",
       "2               NaN                           NaN                      NaN   \n",
       "3               NaN                           NaN                      NaN   \n",
       "4               NaN                           NaN                      NaN   \n",
       "\n",
       "  first_contact_date          origin  \n",
       "0                NaN             NaN  \n",
       "1         03/04/2018  organic_search  \n",
       "2                NaN             NaN  \n",
       "3                NaN             NaN  \n",
       "4                NaN             NaN  "
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "seller_key_info = pd.merge(sellers_merge,\n",
    "                           closed_deals_leads_df,\n",
    "                           on= 'seller_id',\n",
    "                           how= 'left')\n",
    "seller_key_info.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "54963d1e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Saving the DF into a CSV.\n",
    "seller_key_info.to_csv('sellers_key_info.csv', index = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "062f364c",
   "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>order_id</th>\n",
       "      <th>order_item_id</th>\n",
       "      <th>product_id</th>\n",
       "      <th>seller_id</th>\n",
       "      <th>shipping_limit_date</th>\n",
       "      <th>price</th>\n",
       "      <th>freight_value</th>\n",
       "      <th>product_category_name_english</th>\n",
       "      <th>customer_id</th>\n",
       "      <th>order_status</th>\n",
       "      <th>order_purchase_timestamp</th>\n",
       "      <th>order_approved_at</th>\n",
       "      <th>order_delivered_carrier_date</th>\n",
       "      <th>order_delivered_customer_date</th>\n",
       "      <th>order_estimated_delivery_date</th>\n",
       "      <th>review_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>00010242fe8c5a6d1ba2dd792cb16214</td>\n",
       "      <td>1</td>\n",
       "      <td>4244733e06e7ecb4970a6e2683c13e61</td>\n",
       "      <td>48436dade18ac8b2bce089ec2a041202</td>\n",
       "      <td>19/09/2017 09:45</td>\n",
       "      <td>58.90</td>\n",
       "      <td>13.29</td>\n",
       "      <td>cool_stuff</td>\n",
       "      <td>3ce436f183e68e07877b285a838db11a</td>\n",
       "      <td>delivered</td>\n",
       "      <td>13/09/2017 08:59</td>\n",
       "      <td>13/09/2017 09:45</td>\n",
       "      <td>19/09/2017 18:34</td>\n",
       "      <td>20/09/2017 23:43</td>\n",
       "      <td>29/09/2017 00:00</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>00018f77f2f0320c557190d7a144bdd3</td>\n",
       "      <td>1</td>\n",
       "      <td>e5f2d52b802189ee658865ca93d83a8f</td>\n",
       "      <td>dd7ddc04e1b6c2c614352b383efe2d36</td>\n",
       "      <td>03/05/2017 11:05</td>\n",
       "      <td>239.90</td>\n",
       "      <td>19.93</td>\n",
       "      <td>pet_shop</td>\n",
       "      <td>f6dd3ec061db4e3987629fe6b26e5cce</td>\n",
       "      <td>delivered</td>\n",
       "      <td>26/04/2017 10:53</td>\n",
       "      <td>26/04/2017 11:05</td>\n",
       "      <td>04/05/2017 14:35</td>\n",
       "      <td>12/05/2017 16:04</td>\n",
       "      <td>15/05/2017 00:00</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>000229ec398224ef6ca0657da4fc703e</td>\n",
       "      <td>1</td>\n",
       "      <td>c777355d18b72b67abbeef9df44fd0fd</td>\n",
       "      <td>5b51032eddd242adc84c38acab88f23d</td>\n",
       "      <td>18/01/2018 14:48</td>\n",
       "      <td>199.00</td>\n",
       "      <td>17.87</td>\n",
       "      <td>furniture_decor</td>\n",
       "      <td>6489ae5e4333f3693df5ad4372dab6d3</td>\n",
       "      <td>delivered</td>\n",
       "      <td>14/01/2018 14:33</td>\n",
       "      <td>14/01/2018 14:48</td>\n",
       "      <td>16/01/2018 12:36</td>\n",
       "      <td>22/01/2018 13:19</td>\n",
       "      <td>05/02/2018 00:00</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>00024acbcdf0a6daa1e931b038114c75</td>\n",
       "      <td>1</td>\n",
       "      <td>7634da152a4610f1595efa32f14722fc</td>\n",
       "      <td>9d7a1d34a5052409006425275ba1c2b4</td>\n",
       "      <td>15/08/2018 10:10</td>\n",
       "      <td>12.99</td>\n",
       "      <td>12.79</td>\n",
       "      <td>perfumery</td>\n",
       "      <td>d4eb9395c8c0431ee92fce09860c5a06</td>\n",
       "      <td>delivered</td>\n",
       "      <td>08/08/2018 10:00</td>\n",
       "      <td>08/08/2018 10:10</td>\n",
       "      <td>10/08/2018 13:28</td>\n",
       "      <td>14/08/2018 13:32</td>\n",
       "      <td>20/08/2018 00:00</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>00042b26cf59d7ce69dfabb4e55b4fd9</td>\n",
       "      <td>1</td>\n",
       "      <td>ac6c3623068f30de03045865e4e10089</td>\n",
       "      <td>df560393f3a51e74553ab94004ba5c87</td>\n",
       "      <td>13/02/2017 13:57</td>\n",
       "      <td>199.90</td>\n",
       "      <td>18.14</td>\n",
       "      <td>garden_tools</td>\n",
       "      <td>58dbd0b2d70206bf40e62cd34e84d795</td>\n",
       "      <td>delivered</td>\n",
       "      <td>04/02/2017 13:57</td>\n",
       "      <td>04/02/2017 14:10</td>\n",
       "      <td>16/02/2017 09:46</td>\n",
       "      <td>01/03/2017 16:42</td>\n",
       "      <td>17/03/2017 00:00</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110513</th>\n",
       "      <td>fffc94f6ce00a00581880bf54a75a037</td>\n",
       "      <td>1</td>\n",
       "      <td>4aa6014eceb682077f9dc4bffebc05b0</td>\n",
       "      <td>b8bc237ba3788b23da09c0f1f3a3288c</td>\n",
       "      <td>02/05/2018 04:11</td>\n",
       "      <td>299.99</td>\n",
       "      <td>43.41</td>\n",
       "      <td>housewares</td>\n",
       "      <td>b51593916b4b8e0d6f66f2ae24f2673d</td>\n",
       "      <td>delivered</td>\n",
       "      <td>23/04/2018 13:57</td>\n",
       "      <td>25/04/2018 04:11</td>\n",
       "      <td>25/04/2018 12:09</td>\n",
       "      <td>10/05/2018 22:56</td>\n",
       "      <td>18/05/2018 00:00</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110514</th>\n",
       "      <td>fffcd46ef2263f404302a634eb57f7eb</td>\n",
       "      <td>1</td>\n",
       "      <td>32e07fd915822b0765e448c4dd74c828</td>\n",
       "      <td>f3c38ab652836d21de61fb8314b69182</td>\n",
       "      <td>20/07/2018 04:31</td>\n",
       "      <td>350.00</td>\n",
       "      <td>36.53</td>\n",
       "      <td>computers_accessories</td>\n",
       "      <td>84c5d4fbaf120aae381fad077416eaa0</td>\n",
       "      <td>delivered</td>\n",
       "      <td>14/07/2018 10:26</td>\n",
       "      <td>17/07/2018 04:31</td>\n",
       "      <td>17/07/2018 08:05</td>\n",
       "      <td>23/07/2018 20:31</td>\n",
       "      <td>01/08/2018 00:00</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110515</th>\n",
       "      <td>fffce4705a9662cd70adb13d4a31832d</td>\n",
       "      <td>1</td>\n",
       "      <td>72a30483855e2eafc67aee5dc2560482</td>\n",
       "      <td>c3cfdc648177fdbbbb35635a37472c53</td>\n",
       "      <td>30/10/2017 17:14</td>\n",
       "      <td>99.90</td>\n",
       "      <td>16.95</td>\n",
       "      <td>sports_leisure</td>\n",
       "      <td>29309aa813182aaddc9b259e31b870e6</td>\n",
       "      <td>delivered</td>\n",
       "      <td>23/10/2017 17:07</td>\n",
       "      <td>24/10/2017 17:14</td>\n",
       "      <td>26/10/2017 15:13</td>\n",
       "      <td>28/10/2017 12:22</td>\n",
       "      <td>10/11/2017 00:00</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110516</th>\n",
       "      <td>fffe18544ffabc95dfada21779c9644f</td>\n",
       "      <td>1</td>\n",
       "      <td>9c422a519119dcad7575db5af1ba540e</td>\n",
       "      <td>2b3e4a2a3ea8e01938cabda2a3e5cc79</td>\n",
       "      <td>21/08/2017 00:04</td>\n",
       "      <td>55.99</td>\n",
       "      <td>8.72</td>\n",
       "      <td>computers_accessories</td>\n",
       "      <td>b5e6afd5a41800fdf401e0272ca74655</td>\n",
       "      <td>delivered</td>\n",
       "      <td>14/08/2017 23:02</td>\n",
       "      <td>15/08/2017 00:04</td>\n",
       "      <td>15/08/2017 19:02</td>\n",
       "      <td>16/08/2017 21:59</td>\n",
       "      <td>25/08/2017 00:00</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110517</th>\n",
       "      <td>fffe41c64501cc87c801fd61db3f6244</td>\n",
       "      <td>1</td>\n",
       "      <td>350688d9dc1e75ff97be326363655e01</td>\n",
       "      <td>f7ccf836d21b2fb1de37564105216cc1</td>\n",
       "      <td>12/06/2018 17:10</td>\n",
       "      <td>43.00</td>\n",
       "      <td>12.79</td>\n",
       "      <td>bed_bath_table</td>\n",
       "      <td>96d649da0cc4ff33bb408b199d4c7dcf</td>\n",
       "      <td>delivered</td>\n",
       "      <td>09/06/2018 17:00</td>\n",
       "      <td>09/06/2018 17:10</td>\n",
       "      <td>11/06/2018 14:11</td>\n",
       "      <td>14/06/2018 17:56</td>\n",
       "      <td>28/06/2018 00:00</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>110518 rows × 16 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                order_id  order_item_id  \\\n",
       "0       00010242fe8c5a6d1ba2dd792cb16214              1   \n",
       "1       00018f77f2f0320c557190d7a144bdd3              1   \n",
       "2       000229ec398224ef6ca0657da4fc703e              1   \n",
       "3       00024acbcdf0a6daa1e931b038114c75              1   \n",
       "4       00042b26cf59d7ce69dfabb4e55b4fd9              1   \n",
       "...                                  ...            ...   \n",
       "110513  fffc94f6ce00a00581880bf54a75a037              1   \n",
       "110514  fffcd46ef2263f404302a634eb57f7eb              1   \n",
       "110515  fffce4705a9662cd70adb13d4a31832d              1   \n",
       "110516  fffe18544ffabc95dfada21779c9644f              1   \n",
       "110517  fffe41c64501cc87c801fd61db3f6244              1   \n",
       "\n",
       "                              product_id                         seller_id  \\\n",
       "0       4244733e06e7ecb4970a6e2683c13e61  48436dade18ac8b2bce089ec2a041202   \n",
       "1       e5f2d52b802189ee658865ca93d83a8f  dd7ddc04e1b6c2c614352b383efe2d36   \n",
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       "\n",
       "       shipping_limit_date   price  freight_value  \\\n",
       "0         19/09/2017 09:45   58.90          13.29   \n",
       "1         03/05/2017 11:05  239.90          19.93   \n",
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       "...                    ...     ...            ...   \n",
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       "110514    20/07/2018 04:31  350.00          36.53   \n",
       "110515    30/10/2017 17:14   99.90          16.95   \n",
       "110516    21/08/2017 00:04   55.99           8.72   \n",
       "110517    12/06/2018 17:10   43.00          12.79   \n",
       "\n",
       "       product_category_name_english                       customer_id  \\\n",
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       "110514         computers_accessories  84c5d4fbaf120aae381fad077416eaa0   \n",
       "110515                sports_leisure  29309aa813182aaddc9b259e31b870e6   \n",
       "110516         computers_accessories  b5e6afd5a41800fdf401e0272ca74655   \n",
       "110517                bed_bath_table  96d649da0cc4ff33bb408b199d4c7dcf   \n",
       "\n",
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       "0         delivered         13/09/2017 08:59  13/09/2017 09:45   \n",
       "1         delivered         26/04/2017 10:53  26/04/2017 11:05   \n",
       "2         delivered         14/01/2018 14:33  14/01/2018 14:48   \n",
       "3         delivered         08/08/2018 10:00  08/08/2018 10:10   \n",
       "4         delivered         04/02/2017 13:57  04/02/2017 14:10   \n",
       "...             ...                      ...               ...   \n",
       "110513    delivered         23/04/2018 13:57  25/04/2018 04:11   \n",
       "110514    delivered         14/07/2018 10:26  17/07/2018 04:31   \n",
       "110515    delivered         23/10/2017 17:07  24/10/2017 17:14   \n",
       "110516    delivered         14/08/2017 23:02  15/08/2017 00:04   \n",
       "110517    delivered         09/06/2018 17:00  09/06/2018 17:10   \n",
       "\n",
       "       order_delivered_carrier_date order_delivered_customer_date  \\\n",
       "0                  19/09/2017 18:34              20/09/2017 23:43   \n",
       "1                  04/05/2017 14:35              12/05/2017 16:04   \n",
       "2                  16/01/2018 12:36              22/01/2018 13:19   \n",
       "3                  10/08/2018 13:28              14/08/2018 13:32   \n",
       "4                  16/02/2017 09:46              01/03/2017 16:42   \n",
       "...                             ...                           ...   \n",
       "110513             25/04/2018 12:09              10/05/2018 22:56   \n",
       "110514             17/07/2018 08:05              23/07/2018 20:31   \n",
       "110515             26/10/2017 15:13              28/10/2017 12:22   \n",
       "110516             15/08/2017 19:02              16/08/2017 21:59   \n",
       "110517             11/06/2018 14:11              14/06/2018 17:56   \n",
       "\n",
       "       order_estimated_delivery_date  review_score  \n",
       "0                   29/09/2017 00:00           5.0  \n",
       "1                   15/05/2017 00:00           4.0  \n",
       "2                   05/02/2018 00:00           5.0  \n",
       "3                   20/08/2018 00:00           4.0  \n",
       "4                   17/03/2017 00:00           5.0  \n",
       "...                              ...           ...  \n",
       "110513              18/05/2018 00:00           5.0  \n",
       "110514              01/08/2018 00:00           5.0  \n",
       "110515              10/11/2017 00:00           5.0  \n",
       "110516              25/08/2017 00:00           5.0  \n",
       "110517              28/06/2018 00:00           5.0  \n",
       "\n",
       "[110518 rows x 16 columns]"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "LoMoDF"
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  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "f91e96bd",
   "metadata": {},
   "outputs": [
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       "                            customer_id                customer_unique_id  \\\n",
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       "...                        ...           ...            ...              ...   \n",
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       "99437                 TH960905      Sukhirin     Narathiwat         Thailand   \n",
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       "99439                 TH960905      Sukhirin     Narathiwat         Thailand   \n",
       "99440                 TH960905      Sukhirin     Narathiwat         Thailand   \n",
       "\n",
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       "\n",
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     "execution_count": 54,
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  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "bafe6666",
   "metadata": {},
   "outputs": [],
   "source": []
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  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "e1fb8ff2",
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   "outputs": [
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       "      <td>perfumery</td>\n",
       "      <td>d4eb9395c8c0431ee92fce09860c5a06</td>\n",
       "      <td>delivered</td>\n",
       "      <td>...</td>\n",
       "      <td>10/08/2018 13:28</td>\n",
       "      <td>14/08/2018 13:32</td>\n",
       "      <td>20/08/2018 00:00</td>\n",
       "      <td>4.0</td>\n",
       "      <td>af861d436cfc08b2c2ddefd0ba074622</td>\n",
       "      <td>TH320904</td>\n",
       "      <td>Sikhoraphum</td>\n",
       "      <td>Surin</td>\n",
       "      <td>Thailand</td>\n",
       "      <td>Yang</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>00042b26cf59d7ce69dfabb4e55b4fd9</td>\n",
       "      <td>1</td>\n",
       "      <td>ac6c3623068f30de03045865e4e10089</td>\n",
       "      <td>df560393f3a51e74553ab94004ba5c87</td>\n",
       "      <td>13/02/2017 13:57</td>\n",
       "      <td>199.90</td>\n",
       "      <td>18.14</td>\n",
       "      <td>garden_tools</td>\n",
       "      <td>58dbd0b2d70206bf40e62cd34e84d795</td>\n",
       "      <td>delivered</td>\n",
       "      <td>...</td>\n",
       "      <td>16/02/2017 09:46</td>\n",
       "      <td>01/03/2017 16:42</td>\n",
       "      <td>17/03/2017 00:00</td>\n",
       "      <td>5.0</td>\n",
       "      <td>64b576fb70d441e8f1b2d7d446e483c5</td>\n",
       "      <td>TH810303</td>\n",
       "      <td>Ko Lanta</td>\n",
       "      <td>Krabi</td>\n",
       "      <td>Thailand</td>\n",
       "      <td>Ko Klang</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>110513</th>\n",
       "      <td>fffc94f6ce00a00581880bf54a75a037</td>\n",
       "      <td>1</td>\n",
       "      <td>4aa6014eceb682077f9dc4bffebc05b0</td>\n",
       "      <td>b8bc237ba3788b23da09c0f1f3a3288c</td>\n",
       "      <td>02/05/2018 04:11</td>\n",
       "      <td>299.99</td>\n",
       "      <td>43.41</td>\n",
       "      <td>housewares</td>\n",
       "      <td>b51593916b4b8e0d6f66f2ae24f2673d</td>\n",
       "      <td>delivered</td>\n",
       "      <td>...</td>\n",
       "      <td>25/04/2018 12:09</td>\n",
       "      <td>10/05/2018 22:56</td>\n",
       "      <td>18/05/2018 00:00</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0c9aeda10a71f369396d0c04dce13a64</td>\n",
       "      <td>TH470401</td>\n",
       "      <td>Phanna Nikhom</td>\n",
       "      <td>Sakon Nakhon</td>\n",
       "      <td>Thailand</td>\n",
       "      <td>Phanna</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110514</th>\n",
       "      <td>fffcd46ef2263f404302a634eb57f7eb</td>\n",
       "      <td>1</td>\n",
       "      <td>32e07fd915822b0765e448c4dd74c828</td>\n",
       "      <td>f3c38ab652836d21de61fb8314b69182</td>\n",
       "      <td>20/07/2018 04:31</td>\n",
       "      <td>350.00</td>\n",
       "      <td>36.53</td>\n",
       "      <td>computers_accessories</td>\n",
       "      <td>84c5d4fbaf120aae381fad077416eaa0</td>\n",
       "      <td>delivered</td>\n",
       "      <td>...</td>\n",
       "      <td>17/07/2018 08:05</td>\n",
       "      <td>23/07/2018 20:31</td>\n",
       "      <td>01/08/2018 00:00</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0da9fe112eae0c74d3ba1fe16de0988b</td>\n",
       "      <td>TH380401</td>\n",
       "      <td>Seka</td>\n",
       "      <td>Bueng Kan</td>\n",
       "      <td>Thailand</td>\n",
       "      <td>Seka</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110515</th>\n",
       "      <td>fffce4705a9662cd70adb13d4a31832d</td>\n",
       "      <td>1</td>\n",
       "      <td>72a30483855e2eafc67aee5dc2560482</td>\n",
       "      <td>c3cfdc648177fdbbbb35635a37472c53</td>\n",
       "      <td>30/10/2017 17:14</td>\n",
       "      <td>99.90</td>\n",
       "      <td>16.95</td>\n",
       "      <td>sports_leisure</td>\n",
       "      <td>29309aa813182aaddc9b259e31b870e6</td>\n",
       "      <td>delivered</td>\n",
       "      <td>...</td>\n",
       "      <td>26/10/2017 15:13</td>\n",
       "      <td>28/10/2017 12:22</td>\n",
       "      <td>10/11/2017 00:00</td>\n",
       "      <td>5.0</td>\n",
       "      <td>cd79b407828f02fdbba457111c38e4c4</td>\n",
       "      <td>TH620301</td>\n",
       "      <td>Khlong Lan</td>\n",
       "      <td>Kamphaeng Phet</td>\n",
       "      <td>Thailand</td>\n",
       "      <td>Khlong Nam Lai</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110516</th>\n",
       "      <td>fffe18544ffabc95dfada21779c9644f</td>\n",
       "      <td>1</td>\n",
       "      <td>9c422a519119dcad7575db5af1ba540e</td>\n",
       "      <td>2b3e4a2a3ea8e01938cabda2a3e5cc79</td>\n",
       "      <td>21/08/2017 00:04</td>\n",
       "      <td>55.99</td>\n",
       "      <td>8.72</td>\n",
       "      <td>computers_accessories</td>\n",
       "      <td>b5e6afd5a41800fdf401e0272ca74655</td>\n",
       "      <td>delivered</td>\n",
       "      <td>...</td>\n",
       "      <td>15/08/2017 19:02</td>\n",
       "      <td>16/08/2017 21:59</td>\n",
       "      <td>25/08/2017 00:00</td>\n",
       "      <td>5.0</td>\n",
       "      <td>eb803377c9315b564bdedad672039306</td>\n",
       "      <td>TH361009</td>\n",
       "      <td>Phu Khiao</td>\n",
       "      <td>Chaiyaphum</td>\n",
       "      <td>Thailand</td>\n",
       "      <td>O Lo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110517</th>\n",
       "      <td>fffe41c64501cc87c801fd61db3f6244</td>\n",
       "      <td>1</td>\n",
       "      <td>350688d9dc1e75ff97be326363655e01</td>\n",
       "      <td>f7ccf836d21b2fb1de37564105216cc1</td>\n",
       "      <td>12/06/2018 17:10</td>\n",
       "      <td>43.00</td>\n",
       "      <td>12.79</td>\n",
       "      <td>bed_bath_table</td>\n",
       "      <td>96d649da0cc4ff33bb408b199d4c7dcf</td>\n",
       "      <td>delivered</td>\n",
       "      <td>...</td>\n",
       "      <td>11/06/2018 14:11</td>\n",
       "      <td>14/06/2018 17:56</td>\n",
       "      <td>28/06/2018 00:00</td>\n",
       "      <td>5.0</td>\n",
       "      <td>cd76a00d8e3ca5e6ab9ed9ecb6667ac4</td>\n",
       "      <td>TH380305</td>\n",
       "      <td>So Phisai</td>\n",
       "      <td>Bueng Kan</td>\n",
       "      <td>Thailand</td>\n",
       "      <td>Bua Tum</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>110518 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                order_id  order_item_id  \\\n",
       "0       00010242fe8c5a6d1ba2dd792cb16214              1   \n",
       "1       00018f77f2f0320c557190d7a144bdd3              1   \n",
       "2       000229ec398224ef6ca0657da4fc703e              1   \n",
       "3       00024acbcdf0a6daa1e931b038114c75              1   \n",
       "4       00042b26cf59d7ce69dfabb4e55b4fd9              1   \n",
       "...                                  ...            ...   \n",
       "110513  fffc94f6ce00a00581880bf54a75a037              1   \n",
       "110514  fffcd46ef2263f404302a634eb57f7eb              1   \n",
       "110515  fffce4705a9662cd70adb13d4a31832d              1   \n",
       "110516  fffe18544ffabc95dfada21779c9644f              1   \n",
       "110517  fffe41c64501cc87c801fd61db3f6244              1   \n",
       "\n",
       "                              product_id                         seller_id  \\\n",
       "0       4244733e06e7ecb4970a6e2683c13e61  48436dade18ac8b2bce089ec2a041202   \n",
       "1       e5f2d52b802189ee658865ca93d83a8f  dd7ddc04e1b6c2c614352b383efe2d36   \n",
       "2       c777355d18b72b67abbeef9df44fd0fd  5b51032eddd242adc84c38acab88f23d   \n",
       "3       7634da152a4610f1595efa32f14722fc  9d7a1d34a5052409006425275ba1c2b4   \n",
       "4       ac6c3623068f30de03045865e4e10089  df560393f3a51e74553ab94004ba5c87   \n",
       "...                                  ...                               ...   \n",
       "110513  4aa6014eceb682077f9dc4bffebc05b0  b8bc237ba3788b23da09c0f1f3a3288c   \n",
       "110514  32e07fd915822b0765e448c4dd74c828  f3c38ab652836d21de61fb8314b69182   \n",
       "110515  72a30483855e2eafc67aee5dc2560482  c3cfdc648177fdbbbb35635a37472c53   \n",
       "110516  9c422a519119dcad7575db5af1ba540e  2b3e4a2a3ea8e01938cabda2a3e5cc79   \n",
       "110517  350688d9dc1e75ff97be326363655e01  f7ccf836d21b2fb1de37564105216cc1   \n",
       "\n",
       "       shipping_limit_date   price  freight_value  \\\n",
       "0         19/09/2017 09:45   58.90          13.29   \n",
       "1         03/05/2017 11:05  239.90          19.93   \n",
       "2         18/01/2018 14:48  199.00          17.87   \n",
       "3         15/08/2018 10:10   12.99          12.79   \n",
       "4         13/02/2017 13:57  199.90          18.14   \n",
       "...                    ...     ...            ...   \n",
       "110513    02/05/2018 04:11  299.99          43.41   \n",
       "110514    20/07/2018 04:31  350.00          36.53   \n",
       "110515    30/10/2017 17:14   99.90          16.95   \n",
       "110516    21/08/2017 00:04   55.99           8.72   \n",
       "110517    12/06/2018 17:10   43.00          12.79   \n",
       "\n",
       "       product_category_name_english                       customer_id  \\\n",
       "0                         cool_stuff  3ce436f183e68e07877b285a838db11a   \n",
       "1                           pet_shop  f6dd3ec061db4e3987629fe6b26e5cce   \n",
       "2                    furniture_decor  6489ae5e4333f3693df5ad4372dab6d3   \n",
       "3                          perfumery  d4eb9395c8c0431ee92fce09860c5a06   \n",
       "4                       garden_tools  58dbd0b2d70206bf40e62cd34e84d795   \n",
       "...                              ...                               ...   \n",
       "110513                    housewares  b51593916b4b8e0d6f66f2ae24f2673d   \n",
       "110514         computers_accessories  84c5d4fbaf120aae381fad077416eaa0   \n",
       "110515                sports_leisure  29309aa813182aaddc9b259e31b870e6   \n",
       "110516         computers_accessories  b5e6afd5a41800fdf401e0272ca74655   \n",
       "110517                bed_bath_table  96d649da0cc4ff33bb408b199d4c7dcf   \n",
       "\n",
       "       order_status  ... order_delivered_carrier_date  \\\n",
       "0         delivered  ...             19/09/2017 18:34   \n",
       "1         delivered  ...             04/05/2017 14:35   \n",
       "2         delivered  ...             16/01/2018 12:36   \n",
       "3         delivered  ...             10/08/2018 13:28   \n",
       "4         delivered  ...             16/02/2017 09:46   \n",
       "...             ...  ...                          ...   \n",
       "110513    delivered  ...             25/04/2018 12:09   \n",
       "110514    delivered  ...             17/07/2018 08:05   \n",
       "110515    delivered  ...             26/10/2017 15:13   \n",
       "110516    delivered  ...             15/08/2017 19:02   \n",
       "110517    delivered  ...             11/06/2018 14:11   \n",
       "\n",
       "       order_delivered_customer_date order_estimated_delivery_date  \\\n",
       "0                   20/09/2017 23:43              29/09/2017 00:00   \n",
       "1                   12/05/2017 16:04              15/05/2017 00:00   \n",
       "2                   22/01/2018 13:19              05/02/2018 00:00   \n",
       "3                   14/08/2018 13:32              20/08/2018 00:00   \n",
       "4                   01/03/2017 16:42              17/03/2017 00:00   \n",
       "...                              ...                           ...   \n",
       "110513              10/05/2018 22:56              18/05/2018 00:00   \n",
       "110514              23/07/2018 20:31              01/08/2018 00:00   \n",
       "110515              28/10/2017 12:22              10/11/2017 00:00   \n",
       "110516              16/08/2017 21:59              25/08/2017 00:00   \n",
       "110517              14/06/2018 17:56              28/06/2018 00:00   \n",
       "\n",
       "       review_score                customer_unique_id  \\\n",
       "0               5.0  871766c5855e863f6eccc05f988b23cb   \n",
       "1               4.0  eb28e67c4c0b83846050ddfb8a35d051   \n",
       "2               5.0  3818d81c6709e39d06b2738a8d3a2474   \n",
       "3               4.0  af861d436cfc08b2c2ddefd0ba074622   \n",
       "4               5.0  64b576fb70d441e8f1b2d7d446e483c5   \n",
       "...             ...                               ...   \n",
       "110513          5.0  0c9aeda10a71f369396d0c04dce13a64   \n",
       "110514          5.0  0da9fe112eae0c74d3ba1fe16de0988b   \n",
       "110515          5.0  cd79b407828f02fdbba457111c38e4c4   \n",
       "110516          5.0  eb803377c9315b564bdedad672039306   \n",
       "110517          5.0  cd76a00d8e3ca5e6ab9ed9ecb6667ac4   \n",
       "\n",
       "        customer_zip_code_prefix  customer_city  customer_state  \\\n",
       "0                       TH350805  Loeng Nok Tha        Yasothon   \n",
       "1                       TH350802  Loeng Nok Tha        Yasothon   \n",
       "2                       TH190210     Kaeng Khoi        Saraburi   \n",
       "3                       TH320904    Sikhoraphum           Surin   \n",
       "4                       TH810303       Ko Lanta           Krabi   \n",
       "...                          ...            ...             ...   \n",
       "110513                  TH470401  Phanna Nikhom    Sakon Nakhon   \n",
       "110514                  TH380401           Seka       Bueng Kan   \n",
       "110515                  TH620301     Khlong Lan  Kamphaeng Phet   \n",
       "110516                  TH361009      Phu Khiao      Chaiyaphum   \n",
       "110517                  TH380305      So Phisai       Bueng Kan   \n",
       "\n",
       "       customer_country customer_district  \n",
       "0              Thailand        Hong Saeng  \n",
       "1              Thailand          Bung Kha  \n",
       "2              Thailand            Cha-Om  \n",
       "3              Thailand              Yang  \n",
       "4              Thailand          Ko Klang  \n",
       "...                 ...               ...  \n",
       "110513         Thailand            Phanna  \n",
       "110514         Thailand              Seka  \n",
       "110515         Thailand    Khlong Nam Lai  \n",
       "110516         Thailand              O Lo  \n",
       "110517         Thailand           Bua Tum  \n",
       "\n",
       "[110518 rows x 22 columns]"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best_selling_df = pd.merge(LoMoDF, customers_df,\n",
    "                           on='customer_id', how='left')\n",
    "\n",
    "best_selling_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "cde25d45",
   "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",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>customer_state</th>\n",
       "      <th>product_category_name_english</th>\n",
       "      <th>order_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Amnat Charoen</td>\n",
       "      <td>health_beauty</td>\n",
       "      <td>183</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Ang Thong</td>\n",
       "      <td>computers_accessories</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Bangkok</td>\n",
       "      <td>bed_bath_table</td>\n",
       "      <td>46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Bueng Kan</td>\n",
       "      <td>bed_bath_table</td>\n",
       "      <td>179</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Buri Ram</td>\n",
       "      <td>bed_bath_table</td>\n",
       "      <td>329</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>Udon Thani</td>\n",
       "      <td>bed_bath_table</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>Uthai Thani</td>\n",
       "      <td>health_beauty</td>\n",
       "      <td>93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>Uttaradit</td>\n",
       "      <td>health_beauty</td>\n",
       "      <td>67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>Yala</td>\n",
       "      <td>health_beauty</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>Yasothon</td>\n",
       "      <td>bed_bath_table</td>\n",
       "      <td>193</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>69 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   customer_state product_category_name_english  order_count\n",
       "0   Amnat Charoen                 health_beauty          183\n",
       "1       Ang Thong         computers_accessories           19\n",
       "2         Bangkok                bed_bath_table           46\n",
       "3       Bueng Kan                bed_bath_table          179\n",
       "4        Buri Ram                bed_bath_table          329\n",
       "..            ...                           ...          ...\n",
       "64     Udon Thani                bed_bath_table           39\n",
       "65    Uthai Thani                 health_beauty           93\n",
       "66      Uttaradit                 health_beauty           67\n",
       "67           Yala                 health_beauty           12\n",
       "68       Yasothon                bed_bath_table          193\n",
       "\n",
       "[69 rows x 3 columns]"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Count the number of unique orders for each product category within each customer state\n",
    "order_counts = best_selling_df.groupby(['customer_state', 'product_category_name_english'])['order_id'].nunique().reset_index(name='order_count')\n",
    "\n",
    "# Determine the best-selling category for each state by finding the category with the highest order count\n",
    "best_selling_categories = order_counts.loc[order_counts.groupby('customer_state')['order_count'].idxmax()]\n",
    "\n",
    "# Selecting only the relevant columns for the final output\n",
    "best_selling_categories = best_selling_categories[['customer_state', 'product_category_name_english', 'order_count']]\n",
    "best_selling_categories.reset_index(drop=True, inplace=True)\n",
    "\n",
    "best_selling_categories\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "8f96ca44",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Saving the DF into a CSV.\n",
    "best_selling_categories.to_csv('best_selling_categories.csv', index = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "35ba3074",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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