notebook.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Proyek Analisis Data: E-Commerce Public Dataset\n",
    "- Nama: Muhammad Alif Alfarizi\n",
    "- Email:maa.junior2001@gmail.com\n",
    "- Id Dicoding:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Menentukan Pertanyaan Bisnis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Bagaimana Performa Penjualan dan Revenue Perusahaan dalam tahun 2018?\n",
    "- Produk Apa yang Paling Banyak dan Paling Sedikit Terjual?\n",
    "- Bagaimana demografi pelanggan yang kita miliki?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Menyaipkan semua library yang dibuthkan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Wrangling"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Gathering Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</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>06b8999e2fba1a1fbc88172c00ba8bc7</td>\n",
       "      <td>861eff4711a542e4b93843c6dd7febb0</td>\n",
       "      <td>14409</td>\n",
       "      <td>franca</td>\n",
       "      <td>SP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>18955e83d337fd6b2def6b18a428ac77</td>\n",
       "      <td>290c77bc529b7ac935b93aa66c333dc3</td>\n",
       "      <td>9790</td>\n",
       "      <td>sao bernardo do campo</td>\n",
       "      <td>SP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4e7b3e00288586ebd08712fdd0374a03</td>\n",
       "      <td>060e732b5b29e8181a18229c7b0b2b5e</td>\n",
       "      <td>1151</td>\n",
       "      <td>sao paulo</td>\n",
       "      <td>SP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>b2b6027bc5c5109e529d4dc6358b12c3</td>\n",
       "      <td>259dac757896d24d7702b9acbbff3f3c</td>\n",
       "      <td>8775</td>\n",
       "      <td>mogi das cruzes</td>\n",
       "      <td>SP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4f2d8ab171c80ec8364f7c12e35b23ad</td>\n",
       "      <td>345ecd01c38d18a9036ed96c73b8d066</td>\n",
       "      <td>13056</td>\n",
       "      <td>campinas</td>\n",
       "      <td>SP</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        customer_id                customer_unique_id  \\\n",
       "0  06b8999e2fba1a1fbc88172c00ba8bc7  861eff4711a542e4b93843c6dd7febb0   \n",
       "1  18955e83d337fd6b2def6b18a428ac77  290c77bc529b7ac935b93aa66c333dc3   \n",
       "2  4e7b3e00288586ebd08712fdd0374a03  060e732b5b29e8181a18229c7b0b2b5e   \n",
       "3  b2b6027bc5c5109e529d4dc6358b12c3  259dac757896d24d7702b9acbbff3f3c   \n",
       "4  4f2d8ab171c80ec8364f7c12e35b23ad  345ecd01c38d18a9036ed96c73b8d066   \n",
       "\n",
       "   customer_zip_code_prefix          customer_city customer_state  \n",
       "0                     14409                 franca             SP  \n",
       "1                      9790  sao bernardo do campo             SP  \n",
       "2                      1151              sao paulo             SP  \n",
       "3                      8775        mogi das cruzes             SP  \n",
       "4                     13056               campinas             SP  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "customers_df = pd.read_csv(\"Data\\E-commerce-public-dataset\\E-Commerce Public Dataset\\customers_dataset.csv\", delimiter=\",\")\n",
    "customers_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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>geolocation_zip_code_prefix</th>\n",
       "      <th>geolocation_lat</th>\n",
       "      <th>geolocation_lng</th>\n",
       "      <th>geolocation_city</th>\n",
       "      <th>geolocation_state</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1037</td>\n",
       "      <td>-23.545621</td>\n",
       "      <td>-46.639292</td>\n",
       "      <td>sao paulo</td>\n",
       "      <td>SP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1046</td>\n",
       "      <td>-23.546081</td>\n",
       "      <td>-46.644820</td>\n",
       "      <td>sao paulo</td>\n",
       "      <td>SP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1046</td>\n",
       "      <td>-23.546129</td>\n",
       "      <td>-46.642951</td>\n",
       "      <td>sao paulo</td>\n",
       "      <td>SP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1041</td>\n",
       "      <td>-23.544392</td>\n",
       "      <td>-46.639499</td>\n",
       "      <td>sao paulo</td>\n",
       "      <td>SP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1035</td>\n",
       "      <td>-23.541578</td>\n",
       "      <td>-46.641607</td>\n",
       "      <td>sao paulo</td>\n",
       "      <td>SP</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   geolocation_zip_code_prefix  geolocation_lat  geolocation_lng  \\\n",
       "0                         1037       -23.545621       -46.639292   \n",
       "1                         1046       -23.546081       -46.644820   \n",
       "2                         1046       -23.546129       -46.642951   \n",
       "3                         1041       -23.544392       -46.639499   \n",
       "4                         1035       -23.541578       -46.641607   \n",
       "\n",
       "  geolocation_city geolocation_state  \n",
       "0        sao paulo                SP  \n",
       "1        sao paulo                SP  \n",
       "2        sao paulo                SP  \n",
       "3        sao paulo                SP  \n",
       "4        sao paulo                SP  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "geolocation_df = pd.read_csv(\"Data\\E-commerce-public-dataset\\E-Commerce Public Dataset\\geolocation_dataset.csv\", delimiter=\",\")\n",
    "geolocation_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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>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",
       "    </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>2017-09-19 09:45:35</td>\n",
       "      <td>58.90</td>\n",
       "      <td>13.29</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>2017-05-03 11:05:13</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>2018-01-18 14:48:30</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>2018-08-15 10:10:18</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>2017-02-13 13:57:51</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  2017-09-19 09:45:35   58.90          13.29  \n",
       "1  2017-05-03 11:05:13  239.90          19.93  \n",
       "2  2018-01-18 14:48:30  199.00          17.87  \n",
       "3  2018-08-15 10:10:18   12.99          12.79  \n",
       "4  2017-02-13 13:57:51  199.90          18.14  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "order_items_df = pd.read_csv(\"Data\\E-commerce-public-dataset\\E-Commerce Public Dataset\\order_items_dataset.csv\", delimiter=\",\")\n",
    "order_items_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>order_id</th>\n",
       "      <th>payment_sequential</th>\n",
       "      <th>payment_type</th>\n",
       "      <th>payment_installments</th>\n",
       "      <th>payment_value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>b81ef226f3fe1789b1e8b2acac839d17</td>\n",
       "      <td>1</td>\n",
       "      <td>credit_card</td>\n",
       "      <td>8</td>\n",
       "      <td>99.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>a9810da82917af2d9aefd1278f1dcfa0</td>\n",
       "      <td>1</td>\n",
       "      <td>credit_card</td>\n",
       "      <td>1</td>\n",
       "      <td>24.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>25e8ea4e93396b6fa0d3dd708e76c1bd</td>\n",
       "      <td>1</td>\n",
       "      <td>credit_card</td>\n",
       "      <td>1</td>\n",
       "      <td>65.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ba78997921bbcdc1373bb41e913ab953</td>\n",
       "      <td>1</td>\n",
       "      <td>credit_card</td>\n",
       "      <td>8</td>\n",
       "      <td>107.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>42fdf880ba16b47b59251dd489d4441a</td>\n",
       "      <td>1</td>\n",
       "      <td>credit_card</td>\n",
       "      <td>2</td>\n",
       "      <td>128.45</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "                           order_id  payment_sequential payment_type  \\\n",
       "0  b81ef226f3fe1789b1e8b2acac839d17                   1  credit_card   \n",
       "1  a9810da82917af2d9aefd1278f1dcfa0                   1  credit_card   \n",
       "2  25e8ea4e93396b6fa0d3dd708e76c1bd                   1  credit_card   \n",
       "3  ba78997921bbcdc1373bb41e913ab953                   1  credit_card   \n",
       "4  42fdf880ba16b47b59251dd489d4441a                   1  credit_card   \n",
       "\n",
       "   payment_installments  payment_value  \n",
       "0                     8          99.33  \n",
       "1                     1          24.39  \n",
       "2                     1          65.71  \n",
       "3                     8         107.78  \n",
       "4                     2         128.45  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "order_pay_df = pd.read_csv(\"Data\\E-commerce-public-dataset\\E-Commerce Public Dataset\\order_payments_dataset.csv\", delimiter=\",\")\n",
    "order_pay_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>review_id</th>\n",
       "      <th>order_id</th>\n",
       "      <th>review_score</th>\n",
       "      <th>review_comment_title</th>\n",
       "      <th>review_comment_message</th>\n",
       "      <th>review_creation_date</th>\n",
       "      <th>review_answer_timestamp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7bc2406110b926393aa56f80a40eba40</td>\n",
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       "      <td>4</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>2018-01-18 00:00:00</td>\n",
       "      <td>2018-01-18 21:46:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>80e641a11e56f04c1ad469d5645fdfde</td>\n",
       "      <td>a548910a1c6147796b98fdf73dbeba33</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>2018-03-10 00:00:00</td>\n",
       "      <td>2018-03-11 03:05:13</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2018-02-17 00:00:00</td>\n",
       "      <td>2018-02-18 14:36:24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>e64fb393e7b32834bb789ff8bb30750e</td>\n",
       "      <td>658677c97b385a9be170737859d3511b</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Recebi bem antes do prazo estipulado.</td>\n",
       "      <td>2017-04-21 00:00:00</td>\n",
       "      <td>2017-04-21 22:02:06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>f7c4243c7fe1938f181bec41a392bdeb</td>\n",
       "      <td>8e6bfb81e283fa7e4f11123a3fb894f1</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Parabéns lojas lannister adorei comprar pela I...</td>\n",
       "      <td>2018-03-01 00:00:00</td>\n",
       "      <td>2018-03-02 10:26:53</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          review_id                          order_id  \\\n",
       "0  7bc2406110b926393aa56f80a40eba40  73fc7af87114b39712e6da79b0a377eb   \n",
       "1  80e641a11e56f04c1ad469d5645fdfde  a548910a1c6147796b98fdf73dbeba33   \n",
       "2  228ce5500dc1d8e020d8d1322874b6f0  f9e4b658b201a9f2ecdecbb34bed034b   \n",
       "3  e64fb393e7b32834bb789ff8bb30750e  658677c97b385a9be170737859d3511b   \n",
       "4  f7c4243c7fe1938f181bec41a392bdeb  8e6bfb81e283fa7e4f11123a3fb894f1   \n",
       "\n",
       "   review_score review_comment_title  \\\n",
       "0             4                  NaN   \n",
       "1             5                  NaN   \n",
       "2             5                  NaN   \n",
       "3             5                  NaN   \n",
       "4             5                  NaN   \n",
       "\n",
       "                              review_comment_message review_creation_date  \\\n",
       "0                                                NaN  2018-01-18 00:00:00   \n",
       "1                                                NaN  2018-03-10 00:00:00   \n",
       "2                                                NaN  2018-02-17 00:00:00   \n",
       "3              Recebi bem antes do prazo estipulado.  2017-04-21 00:00:00   \n",
       "4  Parabéns lojas lannister adorei comprar pela I...  2018-03-01 00:00:00   \n",
       "\n",
       "  review_answer_timestamp  \n",
       "0     2018-01-18 21:46:59  \n",
       "1     2018-03-11 03:05:13  \n",
       "2     2018-02-18 14:36:24  \n",
       "3     2017-04-21 22:02:06  \n",
       "4     2018-03-02 10:26:53  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "order_rev_df = pd.read_csv(\"Data\\E-commerce-public-dataset\\E-Commerce Public Dataset\\order_reviews_dataset.csv\", delimiter=\",\")\n",
    "order_rev_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\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>order_id</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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>e481f51cbdc54678b7cc49136f2d6af7</td>\n",
       "      <td>9ef432eb6251297304e76186b10a928d</td>\n",
       "      <td>delivered</td>\n",
       "      <td>2017-10-02 10:56:33</td>\n",
       "      <td>2017-10-02 11:07:15</td>\n",
       "      <td>2017-10-04 19:55:00</td>\n",
       "      <td>2017-10-10 21:25:13</td>\n",
       "      <td>2017-10-18 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>53cdb2fc8bc7dce0b6741e2150273451</td>\n",
       "      <td>b0830fb4747a6c6d20dea0b8c802d7ef</td>\n",
       "      <td>delivered</td>\n",
       "      <td>2018-07-24 20:41:37</td>\n",
       "      <td>2018-07-26 03:24:27</td>\n",
       "      <td>2018-07-26 14:31:00</td>\n",
       "      <td>2018-08-07 15:27:45</td>\n",
       "      <td>2018-08-13 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>47770eb9100c2d0c44946d9cf07ec65d</td>\n",
       "      <td>41ce2a54c0b03bf3443c3d931a367089</td>\n",
       "      <td>delivered</td>\n",
       "      <td>2018-08-08 08:38:49</td>\n",
       "      <td>2018-08-08 08:55:23</td>\n",
       "      <td>2018-08-08 13:50:00</td>\n",
       "      <td>2018-08-17 18:06:29</td>\n",
       "      <td>2018-09-04 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>949d5b44dbf5de918fe9c16f97b45f8a</td>\n",
       "      <td>f88197465ea7920adcdbec7375364d82</td>\n",
       "      <td>delivered</td>\n",
       "      <td>2017-11-18 19:28:06</td>\n",
       "      <td>2017-11-18 19:45:59</td>\n",
       "      <td>2017-11-22 13:39:59</td>\n",
       "      <td>2017-12-02 00:28:42</td>\n",
       "      <td>2017-12-15 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ad21c59c0840e6cb83a9ceb5573f8159</td>\n",
       "      <td>8ab97904e6daea8866dbdbc4fb7aad2c</td>\n",
       "      <td>delivered</td>\n",
       "      <td>2018-02-13 21:18:39</td>\n",
       "      <td>2018-02-13 22:20:29</td>\n",
       "      <td>2018-02-14 19:46:34</td>\n",
       "      <td>2018-02-16 18:17:02</td>\n",
       "      <td>2018-02-26 00:00:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           order_id                       customer_id  \\\n",
       "0  e481f51cbdc54678b7cc49136f2d6af7  9ef432eb6251297304e76186b10a928d   \n",
       "1  53cdb2fc8bc7dce0b6741e2150273451  b0830fb4747a6c6d20dea0b8c802d7ef   \n",
       "2  47770eb9100c2d0c44946d9cf07ec65d  41ce2a54c0b03bf3443c3d931a367089   \n",
       "3  949d5b44dbf5de918fe9c16f97b45f8a  f88197465ea7920adcdbec7375364d82   \n",
       "4  ad21c59c0840e6cb83a9ceb5573f8159  8ab97904e6daea8866dbdbc4fb7aad2c   \n",
       "\n",
       "  order_status order_purchase_timestamp    order_approved_at  \\\n",
       "0    delivered      2017-10-02 10:56:33  2017-10-02 11:07:15   \n",
       "1    delivered      2018-07-24 20:41:37  2018-07-26 03:24:27   \n",
       "2    delivered      2018-08-08 08:38:49  2018-08-08 08:55:23   \n",
       "3    delivered      2017-11-18 19:28:06  2017-11-18 19:45:59   \n",
       "4    delivered      2018-02-13 21:18:39  2018-02-13 22:20:29   \n",
       "\n",
       "  order_delivered_carrier_date order_delivered_customer_date  \\\n",
       "0          2017-10-04 19:55:00           2017-10-10 21:25:13   \n",
       "1          2018-07-26 14:31:00           2018-08-07 15:27:45   \n",
       "2          2018-08-08 13:50:00           2018-08-17 18:06:29   \n",
       "3          2017-11-22 13:39:59           2017-12-02 00:28:42   \n",
       "4          2018-02-14 19:46:34           2018-02-16 18:17:02   \n",
       "\n",
       "  order_estimated_delivery_date  \n",
       "0           2017-10-18 00:00:00  \n",
       "1           2018-08-13 00:00:00  \n",
       "2           2018-09-04 00:00:00  \n",
       "3           2017-12-15 00:00:00  \n",
       "4           2018-02-26 00:00:00  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "orders_df = pd.read_csv(\"Data\\E-commerce-public-dataset\\E-Commerce Public Dataset\\orders_dataset.csv\", delimiter=\",\")\n",
    "orders_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\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</th>\n",
       "      <th>product_category_name_english</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>beleza_saude</td>\n",
       "      <td>health_beauty</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>informatica_acessorios</td>\n",
       "      <td>computers_accessories</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>automotivo</td>\n",
       "      <td>auto</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>cama_mesa_banho</td>\n",
       "      <td>bed_bath_table</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>moveis_decoracao</td>\n",
       "      <td>furniture_decor</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    product_category_name product_category_name_english\n",
       "0            beleza_saude                 health_beauty\n",
       "1  informatica_acessorios         computers_accessories\n",
       "2              automotivo                          auto\n",
       "3         cama_mesa_banho                bed_bath_table\n",
       "4        moveis_decoracao               furniture_decor"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "product_cat_df = pd.read_csv(\"Data\\E-commerce-public-dataset\\E-Commerce Public Dataset\\product_category_name_translation.csv\", delimiter=\",\")\n",
    "product_cat_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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_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>40.0</td>\n",
       "      <td>287.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>225.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>14.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3aa071139cb16b67ca9e5dea641aaa2f</td>\n",
       "      <td>artes</td>\n",
       "      <td>44.0</td>\n",
       "      <td>276.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>96bd76ec8810374ed1b65e291975717f</td>\n",
       "      <td>esporte_lazer</td>\n",
       "      <td>46.0</td>\n",
       "      <td>250.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>154.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>cef67bcfe19066a932b7673e239eb23d</td>\n",
       "      <td>bebes</td>\n",
       "      <td>27.0</td>\n",
       "      <td>261.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>371.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>26.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>9dc1a7de274444849c219cff195d0b71</td>\n",
       "      <td>utilidades_domesticas</td>\n",
       "      <td>37.0</td>\n",
       "      <td>402.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>625.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>13.0</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                 40.0                       287.0                 1.0   \n",
       "1                 44.0                       276.0                 1.0   \n",
       "2                 46.0                       250.0                 1.0   \n",
       "3                 27.0                       261.0                 1.0   \n",
       "4                 37.0                       402.0                 4.0   \n",
       "\n",
       "   product_weight_g  product_length_cm  product_height_cm  product_width_cm  \n",
       "0             225.0               16.0               10.0              14.0  \n",
       "1            1000.0               30.0               18.0              20.0  \n",
       "2             154.0               18.0                9.0              15.0  \n",
       "3             371.0               26.0                4.0              26.0  \n",
       "4             625.0               20.0               17.0              13.0  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "products_df = pd.read_csv(\"Data\\E-commerce-public-dataset\\E-Commerce Public Dataset\\products_dataset.csv\", delimiter=\",\")\n",
    "products_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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>seller_id</th>\n",
       "      <th>seller_zip_code_prefix</th>\n",
       "      <th>seller_city</th>\n",
       "      <th>seller_state</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3442f8959a84dea7ee197c632cb2df15</td>\n",
       "      <td>13023</td>\n",
       "      <td>campinas</td>\n",
       "      <td>SP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>d1b65fc7debc3361ea86b5f14c68d2e2</td>\n",
       "      <td>13844</td>\n",
       "      <td>mogi guacu</td>\n",
       "      <td>SP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ce3ad9de960102d0677a81f5d0bb7b2d</td>\n",
       "      <td>20031</td>\n",
       "      <td>rio de janeiro</td>\n",
       "      <td>RJ</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>c0f3eea2e14555b6faeea3dd58c1b1c3</td>\n",
       "      <td>4195</td>\n",
       "      <td>sao paulo</td>\n",
       "      <td>SP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>51a04a8a6bdcb23deccc82b0b80742cf</td>\n",
       "      <td>12914</td>\n",
       "      <td>braganca paulista</td>\n",
       "      <td>SP</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          seller_id  seller_zip_code_prefix  \\\n",
       "0  3442f8959a84dea7ee197c632cb2df15                   13023   \n",
       "1  d1b65fc7debc3361ea86b5f14c68d2e2                   13844   \n",
       "2  ce3ad9de960102d0677a81f5d0bb7b2d                   20031   \n",
       "3  c0f3eea2e14555b6faeea3dd58c1b1c3                    4195   \n",
       "4  51a04a8a6bdcb23deccc82b0b80742cf                   12914   \n",
       "\n",
       "         seller_city seller_state  \n",
       "0           campinas           SP  \n",
       "1         mogi guacu           SP  \n",
       "2     rio de janeiro           RJ  \n",
       "3          sao paulo           SP  \n",
       "4  braganca paulista           SP  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sellers_df = pd.read_csv(\"Data\\E-commerce-public-dataset\\E-Commerce Public Dataset\\sellers_dataset.csv\", delimiter=\",\")\n",
    "sellers_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Assessing Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 99441 entries, 0 to 99440\n",
      "Data columns (total 5 columns):\n",
      " #   Column                    Non-Null Count  Dtype \n",
      "---  ------                    --------------  ----- \n",
      " 0   customer_id               99441 non-null  object\n",
      " 1   customer_unique_id        99441 non-null  object\n",
      " 2   customer_zip_code_prefix  99441 non-null  int64 \n",
      " 3   customer_city             99441 non-null  object\n",
      " 4   customer_state            99441 non-null  object\n",
      "dtypes: int64(1), object(4)\n",
      "memory usage: 3.8+ MB\n"
     ]
    }
   ],
   "source": [
    "customers_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Jumlah duplikasi:  0\n"
     ]
    }
   ],
   "source": [
    "print(\"Jumlah duplikasi: \", customers_df.duplicated().sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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>customer_zip_code_prefix</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>99441.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>35137.474583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>29797.938996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1003.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>11347.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>24416.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>58900.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>99990.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       customer_zip_code_prefix\n",
       "count              99441.000000\n",
       "mean               35137.474583\n",
       "std                29797.938996\n",
       "min                 1003.000000\n",
       "25%                11347.000000\n",
       "50%                24416.000000\n",
       "75%                58900.000000\n",
       "max                99990.000000"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "customers_df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1000163 entries, 0 to 1000162\n",
      "Data columns (total 5 columns):\n",
      " #   Column                       Non-Null Count    Dtype  \n",
      "---  ------                       --------------    -----  \n",
      " 0   geolocation_zip_code_prefix  1000163 non-null  int64  \n",
      " 1   geolocation_lat              1000163 non-null  float64\n",
      " 2   geolocation_lng              1000163 non-null  float64\n",
      " 3   geolocation_city             1000163 non-null  object \n",
      " 4   geolocation_state            1000163 non-null  object \n",
      "dtypes: float64(2), int64(1), object(2)\n",
      "memory usage: 38.2+ MB\n"
     ]
    }
   ],
   "source": [
    "geolocation_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Jumlah duplikasi:  261831\n"
     ]
    }
   ],
   "source": [
    "print(\"Jumlah duplikasi: \", geolocation_df.duplicated().sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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>geolocation_zip_code_prefix</th>\n",
       "      <th>geolocation_lat</th>\n",
       "      <th>geolocation_lng</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1.000163e+06</td>\n",
       "      <td>1.000163e+06</td>\n",
       "      <td>1.000163e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.657417e+04</td>\n",
       "      <td>-2.117615e+01</td>\n",
       "      <td>-4.639054e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.054934e+04</td>\n",
       "      <td>5.715866e+00</td>\n",
       "      <td>4.269748e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.001000e+03</td>\n",
       "      <td>-3.660537e+01</td>\n",
       "      <td>-1.014668e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.107500e+04</td>\n",
       "      <td>-2.360355e+01</td>\n",
       "      <td>-4.857317e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2.653000e+04</td>\n",
       "      <td>-2.291938e+01</td>\n",
       "      <td>-4.663788e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>6.350400e+04</td>\n",
       "      <td>-1.997962e+01</td>\n",
       "      <td>-4.376771e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>9.999000e+04</td>\n",
       "      <td>4.506593e+01</td>\n",
       "      <td>1.211054e+02</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       geolocation_zip_code_prefix  geolocation_lat  geolocation_lng\n",
       "count                 1.000163e+06     1.000163e+06     1.000163e+06\n",
       "mean                  3.657417e+04    -2.117615e+01    -4.639054e+01\n",
       "std                   3.054934e+04     5.715866e+00     4.269748e+00\n",
       "min                   1.001000e+03    -3.660537e+01    -1.014668e+02\n",
       "25%                   1.107500e+04    -2.360355e+01    -4.857317e+01\n",
       "50%                   2.653000e+04    -2.291938e+01    -4.663788e+01\n",
       "75%                   6.350400e+04    -1.997962e+01    -4.376771e+01\n",
       "max                   9.999000e+04     4.506593e+01     1.211054e+02"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "geolocation_df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 112650 entries, 0 to 112649\n",
      "Data columns (total 7 columns):\n",
      " #   Column               Non-Null Count   Dtype  \n",
      "---  ------               --------------   -----  \n",
      " 0   order_id             112650 non-null  object \n",
      " 1   order_item_id        112650 non-null  int64  \n",
      " 2   product_id           112650 non-null  object \n",
      " 3   seller_id            112650 non-null  object \n",
      " 4   shipping_limit_date  112650 non-null  object \n",
      " 5   price                112650 non-null  float64\n",
      " 6   freight_value        112650 non-null  float64\n",
      "dtypes: float64(2), int64(1), object(4)\n",
      "memory usage: 6.0+ MB\n"
     ]
    }
   ],
   "source": [
    "order_items_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Jumlah duplikasi:  0\n"
     ]
    }
   ],
   "source": [
    "print(\"Jumlah duplikasi: \", order_items_df.duplicated().sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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_item_id</th>\n",
       "      <th>price</th>\n",
       "      <th>freight_value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>112650.000000</td>\n",
       "      <td>112650.000000</td>\n",
       "      <td>112650.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.197834</td>\n",
       "      <td>120.653739</td>\n",
       "      <td>19.990320</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.705124</td>\n",
       "      <td>183.633928</td>\n",
       "      <td>15.806405</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.850000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>39.900000</td>\n",
       "      <td>13.080000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>74.990000</td>\n",
       "      <td>16.260000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>134.900000</td>\n",
       "      <td>21.150000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>21.000000</td>\n",
       "      <td>6735.000000</td>\n",
       "      <td>409.680000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       order_item_id          price  freight_value\n",
       "count  112650.000000  112650.000000  112650.000000\n",
       "mean        1.197834     120.653739      19.990320\n",
       "std         0.705124     183.633928      15.806405\n",
       "min         1.000000       0.850000       0.000000\n",
       "25%         1.000000      39.900000      13.080000\n",
       "50%         1.000000      74.990000      16.260000\n",
       "75%         1.000000     134.900000      21.150000\n",
       "max        21.000000    6735.000000     409.680000"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "order_items_df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 103886 entries, 0 to 103885\n",
      "Data columns (total 5 columns):\n",
      " #   Column                Non-Null Count   Dtype  \n",
      "---  ------                --------------   -----  \n",
      " 0   order_id              103886 non-null  object \n",
      " 1   payment_sequential    103886 non-null  int64  \n",
      " 2   payment_type          103886 non-null  object \n",
      " 3   payment_installments  103886 non-null  int64  \n",
      " 4   payment_value         103886 non-null  float64\n",
      "dtypes: float64(1), int64(2), object(2)\n",
      "memory usage: 4.0+ MB\n"
     ]
    }
   ],
   "source": [
    "order_pay_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Jumlah duplikasi:  0\n"
     ]
    }
   ],
   "source": [
    "print(\"Jumlah duplikasi: \", order_pay_df.duplicated().sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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>payment_sequential</th>\n",
       "      <th>payment_installments</th>\n",
       "      <th>payment_value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>103886.000000</td>\n",
       "      <td>103886.000000</td>\n",
       "      <td>103886.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.092679</td>\n",
       "      <td>2.853349</td>\n",
       "      <td>154.100380</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.706584</td>\n",
       "      <td>2.687051</td>\n",
       "      <td>217.494064</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>56.790000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>100.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>171.837500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>29.000000</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>13664.080000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       payment_sequential  payment_installments  payment_value\n",
       "count       103886.000000         103886.000000  103886.000000\n",
       "mean             1.092679              2.853349     154.100380\n",
       "std              0.706584              2.687051     217.494064\n",
       "min              1.000000              0.000000       0.000000\n",
       "25%              1.000000              1.000000      56.790000\n",
       "50%              1.000000              1.000000     100.000000\n",
       "75%              1.000000              4.000000     171.837500\n",
       "max             29.000000             24.000000   13664.080000"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "order_pay_df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 99224 entries, 0 to 99223\n",
      "Data columns (total 7 columns):\n",
      " #   Column                   Non-Null Count  Dtype \n",
      "---  ------                   --------------  ----- \n",
      " 0   review_id                99224 non-null  object\n",
      " 1   order_id                 99224 non-null  object\n",
      " 2   review_score             99224 non-null  int64 \n",
      " 3   review_comment_title     11568 non-null  object\n",
      " 4   review_comment_message   40977 non-null  object\n",
      " 5   review_creation_date     99224 non-null  object\n",
      " 6   review_answer_timestamp  99224 non-null  object\n",
      "dtypes: int64(1), object(6)\n",
      "memory usage: 5.3+ MB\n"
     ]
    }
   ],
   "source": [
    "order_rev_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "review_id                      0\n",
       "order_id                       0\n",
       "review_score                   0\n",
       "review_comment_title       87656\n",
       "review_comment_message     58247\n",
       "review_creation_date           0\n",
       "review_answer_timestamp        0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "order_rev_df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Jumlah duplikasi:  0\n"
     ]
    }
   ],
   "source": [
    "print(\"Jumlah duplikasi: \", order_rev_df.duplicated().sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "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>review_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>99224.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>4.086421</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.347579</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       review_score\n",
       "count  99224.000000\n",
       "mean       4.086421\n",
       "std        1.347579\n",
       "min        1.000000\n",
       "25%        4.000000\n",
       "50%        5.000000\n",
       "75%        5.000000\n",
       "max        5.000000"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "order_rev_df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 99441 entries, 0 to 99440\n",
      "Data columns (total 8 columns):\n",
      " #   Column                         Non-Null Count  Dtype \n",
      "---  ------                         --------------  ----- \n",
      " 0   order_id                       99441 non-null  object\n",
      " 1   customer_id                    99441 non-null  object\n",
      " 2   order_status                   99441 non-null  object\n",
      " 3   order_purchase_timestamp       99441 non-null  object\n",
      " 4   order_approved_at              99281 non-null  object\n",
      " 5   order_delivered_carrier_date   97658 non-null  object\n",
      " 6   order_delivered_customer_date  96476 non-null  object\n",
      " 7   order_estimated_delivery_date  99441 non-null  object\n",
      "dtypes: object(8)\n",
      "memory usage: 6.1+ MB\n"
     ]
    }
   ],
   "source": [
    "orders_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "order_id                            0\n",
       "customer_id                         0\n",
       "order_status                        0\n",
       "order_purchase_timestamp            0\n",
       "order_approved_at                 160\n",
       "order_delivered_carrier_date     1783\n",
       "order_delivered_customer_date    2965\n",
       "order_estimated_delivery_date       0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "orders_df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Jumlah duplikasi:  0\n"
     ]
    }
   ],
   "source": [
    "print(\"Jumlah duplikasi: \", orders_df.duplicated().sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "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>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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>99441</td>\n",
       "      <td>99441</td>\n",
       "      <td>99441</td>\n",
       "      <td>99441</td>\n",
       "      <td>99281</td>\n",
       "      <td>97658</td>\n",
       "      <td>96476</td>\n",
       "      <td>99441</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>99441</td>\n",
       "      <td>99441</td>\n",
       "      <td>8</td>\n",
       "      <td>98875</td>\n",
       "      <td>90733</td>\n",
       "      <td>81018</td>\n",
       "      <td>95664</td>\n",
       "      <td>459</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>e481f51cbdc54678b7cc49136f2d6af7</td>\n",
       "      <td>9ef432eb6251297304e76186b10a928d</td>\n",
       "      <td>delivered</td>\n",
       "      <td>2018-04-11 10:48:14</td>\n",
       "      <td>2018-02-27 04:31:10</td>\n",
       "      <td>2018-05-09 15:48:00</td>\n",
       "      <td>2018-05-08 23:38:46</td>\n",
       "      <td>2017-12-20 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>96478</td>\n",
       "      <td>3</td>\n",
       "      <td>9</td>\n",
       "      <td>47</td>\n",
       "      <td>3</td>\n",
       "      <td>522</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                order_id                       customer_id  \\\n",
       "count                              99441                             99441   \n",
       "unique                             99441                             99441   \n",
       "top     e481f51cbdc54678b7cc49136f2d6af7  9ef432eb6251297304e76186b10a928d   \n",
       "freq                                   1                                 1   \n",
       "\n",
       "       order_status order_purchase_timestamp    order_approved_at  \\\n",
       "count         99441                    99441                99281   \n",
       "unique            8                    98875                90733   \n",
       "top       delivered      2018-04-11 10:48:14  2018-02-27 04:31:10   \n",
       "freq          96478                        3                    9   \n",
       "\n",
       "       order_delivered_carrier_date order_delivered_customer_date  \\\n",
       "count                         97658                         96476   \n",
       "unique                        81018                         95664   \n",
       "top             2018-05-09 15:48:00           2018-05-08 23:38:46   \n",
       "freq                             47                             3   \n",
       "\n",
       "       order_estimated_delivery_date  \n",
       "count                          99441  \n",
       "unique                           459  \n",
       "top              2017-12-20 00:00:00  \n",
       "freq                             522  "
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "orders_df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 71 entries, 0 to 70\n",
      "Data columns (total 2 columns):\n",
      " #   Column                         Non-Null Count  Dtype \n",
      "---  ------                         --------------  ----- \n",
      " 0   product_category_name          71 non-null     object\n",
      " 1   product_category_name_english  71 non-null     object\n",
      "dtypes: object(2)\n",
      "memory usage: 1.2+ KB\n"
     ]
    }
   ],
   "source": [
    "product_cat_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "product_category_name            0\n",
       "product_category_name_english    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "product_cat_df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Jumlah duplikasi:  0\n"
     ]
    }
   ],
   "source": [
    "print(\"Jumlah duplikasi: \", product_cat_df.duplicated().sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>product_category_name</th>\n",
       "      <th>product_category_name_english</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>71</td>\n",
       "      <td>71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>71</td>\n",
       "      <td>71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>beleza_saude</td>\n",
       "      <td>health_beauty</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       product_category_name product_category_name_english\n",
       "count                     71                            71\n",
       "unique                    71                            71\n",
       "top             beleza_saude                 health_beauty\n",
       "freq                       1                             1"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "product_cat_df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 32951 entries, 0 to 32950\n",
      "Data columns (total 9 columns):\n",
      " #   Column                      Non-Null Count  Dtype  \n",
      "---  ------                      --------------  -----  \n",
      " 0   product_id                  32951 non-null  object \n",
      " 1   product_category_name       32341 non-null  object \n",
      " 2   product_name_lenght         32341 non-null  float64\n",
      " 3   product_description_lenght  32341 non-null  float64\n",
      " 4   product_photos_qty          32341 non-null  float64\n",
      " 5   product_weight_g            32949 non-null  float64\n",
      " 6   product_length_cm           32949 non-null  float64\n",
      " 7   product_height_cm           32949 non-null  float64\n",
      " 8   product_width_cm            32949 non-null  float64\n",
      "dtypes: float64(7), object(2)\n",
      "memory usage: 2.3+ MB\n"
     ]
    }
   ],
   "source": [
    "products_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "product_id                      0\n",
       "product_category_name         610\n",
       "product_name_lenght           610\n",
       "product_description_lenght    610\n",
       "product_photos_qty            610\n",
       "product_weight_g                2\n",
       "product_length_cm               2\n",
       "product_height_cm               2\n",
       "product_width_cm                2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "products_df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Jumlah duplikasi:  0\n"
     ]
    }
   ],
   "source": [
    "print(\"Jumlah duplikasi: \", products_df.duplicated().sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>product_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>count</th>\n",
       "      <td>32341.000000</td>\n",
       "      <td>32341.000000</td>\n",
       "      <td>32341.000000</td>\n",
       "      <td>32949.000000</td>\n",
       "      <td>32949.000000</td>\n",
       "      <td>32949.000000</td>\n",
       "      <td>32949.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>48.476949</td>\n",
       "      <td>771.495285</td>\n",
       "      <td>2.188986</td>\n",
       "      <td>2276.472488</td>\n",
       "      <td>30.815078</td>\n",
       "      <td>16.937661</td>\n",
       "      <td>23.196728</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>10.245741</td>\n",
       "      <td>635.115225</td>\n",
       "      <td>1.736766</td>\n",
       "      <td>4282.038731</td>\n",
       "      <td>16.914458</td>\n",
       "      <td>13.637554</td>\n",
       "      <td>12.079047</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>5.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>6.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>42.000000</td>\n",
       "      <td>339.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>300.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>15.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>51.000000</td>\n",
       "      <td>595.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>700.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>20.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>57.000000</td>\n",
       "      <td>972.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1900.000000</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>30.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>76.000000</td>\n",
       "      <td>3992.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>40425.000000</td>\n",
       "      <td>105.000000</td>\n",
       "      <td>105.000000</td>\n",
       "      <td>118.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       product_name_lenght  product_description_lenght  product_photos_qty  \\\n",
       "count         32341.000000                32341.000000        32341.000000   \n",
       "mean             48.476949                  771.495285            2.188986   \n",
       "std              10.245741                  635.115225            1.736766   \n",
       "min               5.000000                    4.000000            1.000000   \n",
       "25%              42.000000                  339.000000            1.000000   \n",
       "50%              51.000000                  595.000000            1.000000   \n",
       "75%              57.000000                  972.000000            3.000000   \n",
       "max              76.000000                 3992.000000           20.000000   \n",
       "\n",
       "       product_weight_g  product_length_cm  product_height_cm  \\\n",
       "count      32949.000000       32949.000000       32949.000000   \n",
       "mean        2276.472488          30.815078          16.937661   \n",
       "std         4282.038731          16.914458          13.637554   \n",
       "min            0.000000           7.000000           2.000000   \n",
       "25%          300.000000          18.000000           8.000000   \n",
       "50%          700.000000          25.000000          13.000000   \n",
       "75%         1900.000000          38.000000          21.000000   \n",
       "max        40425.000000         105.000000         105.000000   \n",
       "\n",
       "       product_width_cm  \n",
       "count      32949.000000  \n",
       "mean          23.196728  \n",
       "std           12.079047  \n",
       "min            6.000000  \n",
       "25%           15.000000  \n",
       "50%           20.000000  \n",
       "75%           30.000000  \n",
       "max          118.000000  "
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "products_df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 3095 entries, 0 to 3094\n",
      "Data columns (total 4 columns):\n",
      " #   Column                  Non-Null Count  Dtype \n",
      "---  ------                  --------------  ----- \n",
      " 0   seller_id               3095 non-null   object\n",
      " 1   seller_zip_code_prefix  3095 non-null   int64 \n",
      " 2   seller_city             3095 non-null   object\n",
      " 3   seller_state            3095 non-null   object\n",
      "dtypes: int64(1), object(3)\n",
      "memory usage: 96.8+ KB\n"
     ]
    }
   ],
   "source": [
    "sellers_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Jumlah duplikasi:  0\n"
     ]
    }
   ],
   "source": [
    "print(\"Jumlah duplikasi: \", sellers_df.duplicated().sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "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>seller_zip_code_prefix</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>3095.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>32291.059451</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>32713.453830</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1001.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>7093.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>14940.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>64552.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>99730.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       seller_zip_code_prefix\n",
       "count             3095.000000\n",
       "mean             32291.059451\n",
       "std              32713.453830\n",
       "min               1001.000000\n",
       "25%               7093.500000\n",
       "50%              14940.000000\n",
       "75%              64552.500000\n",
       "max              99730.000000"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sellers_df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Cleaning Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "datetime_orems = [\"shipping_limit_date\"]\n",
    "\n",
    "for column in datetime_orems:\n",
    "    order_items_df[column] = pd.to_datetime(order_items_df[column])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 112650 entries, 0 to 112649\n",
      "Data columns (total 7 columns):\n",
      " #   Column               Non-Null Count   Dtype         \n",
      "---  ------               --------------   -----         \n",
      " 0   order_id             112650 non-null  object        \n",
      " 1   order_item_id        112650 non-null  int64         \n",
      " 2   product_id           112650 non-null  object        \n",
      " 3   seller_id            112650 non-null  object        \n",
      " 4   shipping_limit_date  112650 non-null  datetime64[ns]\n",
      " 5   price                112650 non-null  float64       \n",
      " 6   freight_value        112650 non-null  float64       \n",
      "dtypes: datetime64[ns](1), float64(2), int64(1), object(3)\n",
      "memory usage: 6.0+ MB\n"
     ]
    }
   ],
   "source": [
    "order_items_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "datetime_orrev = [\"review_creation_date\", \"review_answer_timestamp\"]\n",
    "\n",
    "for column in datetime_orrev:\n",
    "    order_rev_df[column] = pd.to_datetime(order_rev_df[column])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 99224 entries, 0 to 99223\n",
      "Data columns (total 7 columns):\n",
      " #   Column                   Non-Null Count  Dtype         \n",
      "---  ------                   --------------  -----         \n",
      " 0   review_id                99224 non-null  object        \n",
      " 1   order_id                 99224 non-null  object        \n",
      " 2   review_score             99224 non-null  int64         \n",
      " 3   review_comment_title     11568 non-null  object        \n",
      " 4   review_comment_message   40977 non-null  object        \n",
      " 5   review_creation_date     99224 non-null  datetime64[ns]\n",
      " 6   review_answer_timestamp  99224 non-null  datetime64[ns]\n",
      "dtypes: datetime64[ns](2), int64(1), object(4)\n",
      "memory usage: 5.3+ MB\n"
     ]
    }
   ],
   "source": [
    "order_rev_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>review_id</th>\n",
       "      <th>order_id</th>\n",
       "      <th>review_score</th>\n",
       "      <th>review_comment_title</th>\n",
       "      <th>review_comment_message</th>\n",
       "      <th>review_creation_date</th>\n",
       "      <th>review_answer_timestamp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7bc2406110b926393aa56f80a40eba40</td>\n",
       "      <td>73fc7af87114b39712e6da79b0a377eb</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2018-01-18</td>\n",
       "      <td>2018-01-18 21:46:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>80e641a11e56f04c1ad469d5645fdfde</td>\n",
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       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2018-03-10</td>\n",
       "      <td>2018-03-11 03:05:13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>228ce5500dc1d8e020d8d1322874b6f0</td>\n",
       "      <td>f9e4b658b201a9f2ecdecbb34bed034b</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2018-02-17</td>\n",
       "      <td>2018-02-18 14:36:24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>e64fb393e7b32834bb789ff8bb30750e</td>\n",
       "      <td>658677c97b385a9be170737859d3511b</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Recebi bem antes do prazo estipulado.</td>\n",
       "      <td>2017-04-21</td>\n",
       "      <td>2017-04-21 22:02:06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>f7c4243c7fe1938f181bec41a392bdeb</td>\n",
       "      <td>8e6bfb81e283fa7e4f11123a3fb894f1</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Parabéns lojas lannister adorei comprar pela I...</td>\n",
       "      <td>2018-03-01</td>\n",
       "      <td>2018-03-02 10:26:53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <th>99219</th>\n",
       "      <td>574ed12dd733e5fa530cfd4bbf39d7c9</td>\n",
       "      <td>2a8c23fee101d4d5662fa670396eb8da</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2018-07-07</td>\n",
       "      <td>2018-07-14 17:18:30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99220</th>\n",
       "      <td>f3897127253a9592a73be9bdfdf4ed7a</td>\n",
       "      <td>22ec9f0669f784db00fa86d035cf8602</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017-12-09</td>\n",
       "      <td>2017-12-11 20:06:42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99221</th>\n",
       "      <td>b3de70c89b1510c4cd3d0649fd302472</td>\n",
       "      <td>55d4004744368f5571d1f590031933e4</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Excelente mochila, entrega super rápida. Super...</td>\n",
       "      <td>2018-03-22</td>\n",
       "      <td>2018-03-23 09:10:43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99222</th>\n",
       "      <td>1adeb9d84d72fe4e337617733eb85149</td>\n",
       "      <td>7725825d039fc1f0ceb7635e3f7d9206</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2018-07-01</td>\n",
       "      <td>2018-07-02 12:59:13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99223</th>\n",
       "      <td>efe49f1d6f951dd88b51e6ccd4cc548f</td>\n",
       "      <td>90531360ecb1eec2a1fbb265a0db0508</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>meu produto chegou e ja tenho que devolver, po...</td>\n",
       "      <td>2017-07-03</td>\n",
       "      <td>2017-07-03 21:01:49</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>87656 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                              review_id                          order_id  \\\n",
       "0      7bc2406110b926393aa56f80a40eba40  73fc7af87114b39712e6da79b0a377eb   \n",
       "1      80e641a11e56f04c1ad469d5645fdfde  a548910a1c6147796b98fdf73dbeba33   \n",
       "2      228ce5500dc1d8e020d8d1322874b6f0  f9e4b658b201a9f2ecdecbb34bed034b   \n",
       "3      e64fb393e7b32834bb789ff8bb30750e  658677c97b385a9be170737859d3511b   \n",
       "4      f7c4243c7fe1938f181bec41a392bdeb  8e6bfb81e283fa7e4f11123a3fb894f1   \n",
       "...                                 ...                               ...   \n",
       "99219  574ed12dd733e5fa530cfd4bbf39d7c9  2a8c23fee101d4d5662fa670396eb8da   \n",
       "99220  f3897127253a9592a73be9bdfdf4ed7a  22ec9f0669f784db00fa86d035cf8602   \n",
       "99221  b3de70c89b1510c4cd3d0649fd302472  55d4004744368f5571d1f590031933e4   \n",
       "99222  1adeb9d84d72fe4e337617733eb85149  7725825d039fc1f0ceb7635e3f7d9206   \n",
       "99223  efe49f1d6f951dd88b51e6ccd4cc548f  90531360ecb1eec2a1fbb265a0db0508   \n",
       "\n",
       "       review_score review_comment_title  \\\n",
       "0                 4                  NaN   \n",
       "1                 5                  NaN   \n",
       "2                 5                  NaN   \n",
       "3                 5                  NaN   \n",
       "4                 5                  NaN   \n",
       "...             ...                  ...   \n",
       "99219             5                  NaN   \n",
       "99220             5                  NaN   \n",
       "99221             5                  NaN   \n",
       "99222             4                  NaN   \n",
       "99223             1                  NaN   \n",
       "\n",
       "                                  review_comment_message review_creation_date  \\\n",
       "0                                                    NaN           2018-01-18   \n",
       "1                                                    NaN           2018-03-10   \n",
       "2                                                    NaN           2018-02-17   \n",
       "3                  Recebi bem antes do prazo estipulado.           2017-04-21   \n",
       "4      Parabéns lojas lannister adorei comprar pela I...           2018-03-01   \n",
       "...                                                  ...                  ...   \n",
       "99219                                                NaN           2018-07-07   \n",
       "99220                                                NaN           2017-12-09   \n",
       "99221  Excelente mochila, entrega super rápida. Super...           2018-03-22   \n",
       "99222                                                NaN           2018-07-01   \n",
       "99223  meu produto chegou e ja tenho que devolver, po...           2017-07-03   \n",
       "\n",
       "      review_answer_timestamp  \n",
       "0         2018-01-18 21:46:59  \n",
       "1         2018-03-11 03:05:13  \n",
       "2         2018-02-18 14:36:24  \n",
       "3         2017-04-21 22:02:06  \n",
       "4         2018-03-02 10:26:53  \n",
       "...                       ...  \n",
       "99219     2018-07-14 17:18:30  \n",
       "99220     2017-12-11 20:06:42  \n",
       "99221     2018-03-23 09:10:43  \n",
       "99222     2018-07-02 12:59:13  \n",
       "99223     2017-07-03 21:01:49  \n",
       "\n",
       "[87656 rows x 7 columns]"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "order_rev_df[order_rev_df.review_comment_title.isna()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Recomendo                    423\n",
       "recomendo                    345\n",
       "Bom                          293\n",
       "super recomendo              270\n",
       "Excelente                    248\n",
       "                            ... \n",
       "Luminaria sobrepor led 18      1\n",
       "Não recebi.                    1\n",
       "produto com lacre violado      1\n",
       "Atraso Entrega                 1\n",
       "Foto enganosa                  1\n",
       "Name: review_comment_title, Length: 4527, dtype: int64"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "order_rev_df.review_comment_title.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
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       "      <td>2018-01-18 21:46:59</td>\n",
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       "      <th>1</th>\n",
       "      <td>80e641a11e56f04c1ad469d5645fdfde</td>\n",
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       "      <td>2018-03-11 03:05:13</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>228ce5500dc1d8e020d8d1322874b6f0</td>\n",
       "      <td>f9e4b658b201a9f2ecdecbb34bed034b</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2018-02-17</td>\n",
       "      <td>2018-02-18 14:36:24</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>15197aa66ff4d0650b5434f1b46cda19</td>\n",
       "      <td>b18dcdf73be66366873cd26c5724d1dc</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2018-04-13</td>\n",
       "      <td>2018-04-16 00:39:37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>07f9bee5d1b850860defd761afa7ff16</td>\n",
       "      <td>e48aa0d2dcec3a2e87348811bcfdf22b</td>\n",
       "      <td>5</td>\n",
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       "      <td>2017-07-16</td>\n",
       "      <td>2017-07-18 19:30:34</td>\n",
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       "    <tr>\n",
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       "      <th>99217</th>\n",
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       "      <td>48f7ee67313eda32bfcf5b9c1dd9522d</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017-12-13</td>\n",
       "      <td>2017-12-14 11:09:36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99218</th>\n",
       "      <td>af2dc0519de6e0720ef0c74292fb4114</td>\n",
       "      <td>d699c734a0b1c8111f2272a3f36d398c</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2018-04-27</td>\n",
       "      <td>2018-04-30 01:18:57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99219</th>\n",
       "      <td>574ed12dd733e5fa530cfd4bbf39d7c9</td>\n",
       "      <td>2a8c23fee101d4d5662fa670396eb8da</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2018-07-07</td>\n",
       "      <td>2018-07-14 17:18:30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99220</th>\n",
       "      <td>f3897127253a9592a73be9bdfdf4ed7a</td>\n",
       "      <td>22ec9f0669f784db00fa86d035cf8602</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017-12-09</td>\n",
       "      <td>2017-12-11 20:06:42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99222</th>\n",
       "      <td>1adeb9d84d72fe4e337617733eb85149</td>\n",
       "      <td>7725825d039fc1f0ceb7635e3f7d9206</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2018-07-01</td>\n",
       "      <td>2018-07-02 12:59:13</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>58247 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                              review_id                          order_id  \\\n",
       "0      7bc2406110b926393aa56f80a40eba40  73fc7af87114b39712e6da79b0a377eb   \n",
       "1      80e641a11e56f04c1ad469d5645fdfde  a548910a1c6147796b98fdf73dbeba33   \n",
       "2      228ce5500dc1d8e020d8d1322874b6f0  f9e4b658b201a9f2ecdecbb34bed034b   \n",
       "5      15197aa66ff4d0650b5434f1b46cda19  b18dcdf73be66366873cd26c5724d1dc   \n",
       "6      07f9bee5d1b850860defd761afa7ff16  e48aa0d2dcec3a2e87348811bcfdf22b   \n",
       "...                                 ...                               ...   \n",
       "99217  c6b270c61f67c9f7cb07d84ea8aeaf8b  48f7ee67313eda32bfcf5b9c1dd9522d   \n",
       "99218  af2dc0519de6e0720ef0c74292fb4114  d699c734a0b1c8111f2272a3f36d398c   \n",
       "99219  574ed12dd733e5fa530cfd4bbf39d7c9  2a8c23fee101d4d5662fa670396eb8da   \n",
       "99220  f3897127253a9592a73be9bdfdf4ed7a  22ec9f0669f784db00fa86d035cf8602   \n",
       "99222  1adeb9d84d72fe4e337617733eb85149  7725825d039fc1f0ceb7635e3f7d9206   \n",
       "\n",
       "       review_score review_comment_title review_comment_message  \\\n",
       "0                 4                  NaN                    NaN   \n",
       "1                 5                  NaN                    NaN   \n",
       "2                 5                  NaN                    NaN   \n",
       "5                 1                  NaN                    NaN   \n",
       "6                 5                  NaN                    NaN   \n",
       "...             ...                  ...                    ...   \n",
       "99217             5                  NaN                    NaN   \n",
       "99218             5                  NaN                    NaN   \n",
       "99219             5                  NaN                    NaN   \n",
       "99220             5                  NaN                    NaN   \n",
       "99222             4                  NaN                    NaN   \n",
       "\n",
       "      review_creation_date review_answer_timestamp  \n",
       "0               2018-01-18     2018-01-18 21:46:59  \n",
       "1               2018-03-10     2018-03-11 03:05:13  \n",
       "2               2018-02-17     2018-02-18 14:36:24  \n",
       "5               2018-04-13     2018-04-16 00:39:37  \n",
       "6               2017-07-16     2017-07-18 19:30:34  \n",
       "...                    ...                     ...  \n",
       "99217           2017-12-13     2017-12-14 11:09:36  \n",
       "99218           2018-04-27     2018-04-30 01:18:57  \n",
       "99219           2018-07-07     2018-07-14 17:18:30  \n",
       "99220           2017-12-09     2017-12-11 20:06:42  \n",
       "99222           2018-07-01     2018-07-02 12:59:13  \n",
       "\n",
       "[58247 rows x 7 columns]"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "order_rev_df[order_rev_df.review_comment_message.isna()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Muito bom                                                                                                                                                                                              230\n",
       "Bom                                                                                                                                                                                                    189\n",
       "muito bom                                                                                                                                                                                              122\n",
       "bom                                                                                                                                                                                                    107\n",
       "Recomendo                                                                                                                                                                                              100\n",
       "                                                                                                                                                                                                      ... \n",
       "PRODUTO FOI ENTREGUE VIA CORREIOS\\r\\nANTES DO PRAZO PREVISTO.                                                                                                                                            1\n",
       "Devido incompatibilidade vou devolver, ótimo produto!                                                                                                                                                    1\n",
       "Infelizmente não! A qualidade do produto não agradou.Impressão da foto em exposição remete a um tamanho maior e a recebida sequer atende a uma criança. PRODUTO NÃO JUSTIFICA O VALOR INFELIZMENTE!      1\n",
       "entrega na data prevista,somente a qualidade do material não é o mesmo que eu pensava mas pelo preço ta valido                                                                                           1\n",
       "meu produto chegou e ja tenho que devolver, pois está com defeito , não segurar carga                                                                                                                    1\n",
       "Name: review_comment_message, Length: 36159, dtype: int64"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "order_rev_df.review_comment_message.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "order_rev_df.fillna(value=\"no comment\", inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "datetime_orders = [\"order_purchase_timestamp\", \"order_approved_at\", \n",
    "                   \"order_delivered_carrier_date\", \"order_delivered_customer_date\",\n",
    "                   \"order_estimated_delivery_date\"]\n",
    "\n",
    "for column in datetime_orders:\n",
    "    orders_df[column] = pd.to_datetime(orders_df[column])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 99441 entries, 0 to 99440\n",
      "Data columns (total 8 columns):\n",
      " #   Column                         Non-Null Count  Dtype         \n",
      "---  ------                         --------------  -----         \n",
      " 0   order_id                       99441 non-null  object        \n",
      " 1   customer_id                    99441 non-null  object        \n",
      " 2   order_status                   99441 non-null  object        \n",
      " 3   order_purchase_timestamp       99441 non-null  datetime64[ns]\n",
      " 4   order_approved_at              99281 non-null  datetime64[ns]\n",
      " 5   order_delivered_carrier_date   97658 non-null  datetime64[ns]\n",
      " 6   order_delivered_customer_date  96476 non-null  datetime64[ns]\n",
      " 7   order_estimated_delivery_date  99441 non-null  datetime64[ns]\n",
      "dtypes: datetime64[ns](5), object(3)\n",
      "memory usage: 6.1+ MB\n"
     ]
    }
   ],
   "source": [
    "orders_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "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>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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>e481f51cbdc54678b7cc49136f2d6af7</td>\n",
       "      <td>9ef432eb6251297304e76186b10a928d</td>\n",
       "      <td>delivered</td>\n",
       "      <td>2017-10-02 10:56:33</td>\n",
       "      <td>2017-10-02 11:07:15</td>\n",
       "      <td>2017-10-04 19:55:00</td>\n",
       "      <td>2017-10-10 21:25:13</td>\n",
       "      <td>2017-10-18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>53cdb2fc8bc7dce0b6741e2150273451</td>\n",
       "      <td>b0830fb4747a6c6d20dea0b8c802d7ef</td>\n",
       "      <td>delivered</td>\n",
       "      <td>2018-07-24 20:41:37</td>\n",
       "      <td>2018-07-26 03:24:27</td>\n",
       "      <td>2018-07-26 14:31:00</td>\n",
       "      <td>2018-08-07 15:27:45</td>\n",
       "      <td>2018-08-13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>47770eb9100c2d0c44946d9cf07ec65d</td>\n",
       "      <td>41ce2a54c0b03bf3443c3d931a367089</td>\n",
       "      <td>delivered</td>\n",
       "      <td>2018-08-08 08:38:49</td>\n",
       "      <td>2018-08-08 08:55:23</td>\n",
       "      <td>2018-08-08 13:50:00</td>\n",
       "      <td>2018-08-17 18:06:29</td>\n",
       "      <td>2018-09-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>949d5b44dbf5de918fe9c16f97b45f8a</td>\n",
       "      <td>f88197465ea7920adcdbec7375364d82</td>\n",
       "      <td>delivered</td>\n",
       "      <td>2017-11-18 19:28:06</td>\n",
       "      <td>2017-11-18 19:45:59</td>\n",
       "      <td>2017-11-22 13:39:59</td>\n",
       "      <td>2017-12-02 00:28:42</td>\n",
       "      <td>2017-12-15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ad21c59c0840e6cb83a9ceb5573f8159</td>\n",
       "      <td>8ab97904e6daea8866dbdbc4fb7aad2c</td>\n",
       "      <td>delivered</td>\n",
       "      <td>2018-02-13 21:18:39</td>\n",
       "      <td>2018-02-13 22:20:29</td>\n",
       "      <td>2018-02-14 19:46:34</td>\n",
       "      <td>2018-02-16 18:17:02</td>\n",
       "      <td>2018-02-26</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           order_id                       customer_id  \\\n",
       "0  e481f51cbdc54678b7cc49136f2d6af7  9ef432eb6251297304e76186b10a928d   \n",
       "1  53cdb2fc8bc7dce0b6741e2150273451  b0830fb4747a6c6d20dea0b8c802d7ef   \n",
       "2  47770eb9100c2d0c44946d9cf07ec65d  41ce2a54c0b03bf3443c3d931a367089   \n",
       "3  949d5b44dbf5de918fe9c16f97b45f8a  f88197465ea7920adcdbec7375364d82   \n",
       "4  ad21c59c0840e6cb83a9ceb5573f8159  8ab97904e6daea8866dbdbc4fb7aad2c   \n",
       "\n",
       "  order_status order_purchase_timestamp   order_approved_at  \\\n",
       "0    delivered      2017-10-02 10:56:33 2017-10-02 11:07:15   \n",
       "1    delivered      2018-07-24 20:41:37 2018-07-26 03:24:27   \n",
       "2    delivered      2018-08-08 08:38:49 2018-08-08 08:55:23   \n",
       "3    delivered      2017-11-18 19:28:06 2017-11-18 19:45:59   \n",
       "4    delivered      2018-02-13 21:18:39 2018-02-13 22:20:29   \n",
       "\n",
       "  order_delivered_carrier_date order_delivered_customer_date  \\\n",
       "0          2017-10-04 19:55:00           2017-10-10 21:25:13   \n",
       "1          2018-07-26 14:31:00           2018-08-07 15:27:45   \n",
       "2          2018-08-08 13:50:00           2018-08-17 18:06:29   \n",
       "3          2017-11-22 13:39:59           2017-12-02 00:28:42   \n",
       "4          2018-02-14 19:46:34           2018-02-16 18:17:02   \n",
       "\n",
       "  order_estimated_delivery_date  \n",
       "0                    2017-10-18  \n",
       "1                    2018-08-13  \n",
       "2                    2018-09-04  \n",
       "3                    2017-12-15  \n",
       "4                    2018-02-26  "
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "orders_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "products_df.rename(columns={\n",
    "    'product_name_lenght' : 'product_name_length',\n",
    "    'product_description_lenght' : 'product_description_length'\n",
    "}, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "approval_timeframe = pd.Timedelta(days=2)\n",
    "delivery_timeframe = pd.Timedelta(days=7)\n",
    "\n",
    "orders_df['order_approved_at'].fillna(orders_df['order_purchase_timestamp'] + approval_timeframe, inplace=True)\n",
    "\n",
    "orders_df['order_delivered_carrier_date'].fillna(orders_df['order_approved_at'] + delivery_timeframe, inplace=True)\n",
    "\n",
    "orders_df['order_delivered_customer_date'].fillna(orders_df['order_delivered_carrier_date'] + delivery_timeframe, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "products_df[\"product_category_name\"].fillna('Other', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "products_df[\"product_name_length\"].fillna(products_df[\"product_name_length\"].mean(), inplace=True)\n",
    "products_df[\"product_description_length\"].fillna(products_df[\"product_description_length\"].mean(), inplace=True)\n",
    "products_df[\"product_photos_qty\"].fillna(products_df[\"product_photos_qty\"].mean(), inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "products_df[\"product_weight_g\"].fillna(0, inplace=True)\n",
    "products_df[\"product_length_cm\"].fillna(0, inplace=True)\n",
    "products_df[\"product_height_cm\"].fillna(0, inplace=True)\n",
    "products_df[\"product_width_cm\"].fillna(0, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 32951 entries, 0 to 32950\n",
      "Data columns (total 9 columns):\n",
      " #   Column                      Non-Null Count  Dtype  \n",
      "---  ------                      --------------  -----  \n",
      " 0   product_id                  32951 non-null  object \n",
      " 1   product_category_name       32951 non-null  object \n",
      " 2   product_name_length         32951 non-null  float64\n",
      " 3   product_description_length  32951 non-null  float64\n",
      " 4   product_photos_qty          32951 non-null  float64\n",
      " 5   product_weight_g            32951 non-null  float64\n",
      " 6   product_length_cm           32951 non-null  float64\n",
      " 7   product_height_cm           32951 non-null  float64\n",
      " 8   product_width_cm            32951 non-null  float64\n",
      "dtypes: float64(7), object(2)\n",
      "memory usage: 2.3+ MB\n"
     ]
    }
   ],
   "source": [
    "products_df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exploratory Data Analysis (EDA)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Explore Customers df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\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>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>count</th>\n",
       "      <td>99441</td>\n",
       "      <td>99441</td>\n",
       "      <td>99441.000000</td>\n",
       "      <td>99441</td>\n",
       "      <td>99441</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>99441</td>\n",
       "      <td>96096</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4119</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>06b8999e2fba1a1fbc88172c00ba8bc7</td>\n",
       "      <td>8d50f5eadf50201ccdcedfb9e2ac8455</td>\n",
       "      <td>NaN</td>\n",
       "      <td>sao paulo</td>\n",
       "      <td>SP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>1</td>\n",
       "      <td>17</td>\n",
       "      <td>NaN</td>\n",
       "      <td>15540</td>\n",
       "      <td>41746</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>35137.474583</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>29797.938996</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1003.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11347.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>24416.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>58900.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>99990.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             customer_id                customer_unique_id  \\\n",
       "count                              99441                             99441   \n",
       "unique                             99441                             96096   \n",
       "top     06b8999e2fba1a1fbc88172c00ba8bc7  8d50f5eadf50201ccdcedfb9e2ac8455   \n",
       "freq                                   1                                17   \n",
       "mean                                 NaN                               NaN   \n",
       "std                                  NaN                               NaN   \n",
       "min                                  NaN                               NaN   \n",
       "25%                                  NaN                               NaN   \n",
       "50%                                  NaN                               NaN   \n",
       "75%                                  NaN                               NaN   \n",
       "max                                  NaN                               NaN   \n",
       "\n",
       "        customer_zip_code_prefix customer_city customer_state  \n",
       "count               99441.000000         99441          99441  \n",
       "unique                       NaN          4119             27  \n",
       "top                          NaN     sao paulo             SP  \n",
       "freq                         NaN         15540          41746  \n",
       "mean                35137.474583           NaN            NaN  \n",
       "std                 29797.938996           NaN            NaN  \n",
       "min                  1003.000000           NaN            NaN  \n",
       "25%                 11347.000000           NaN            NaN  \n",
       "50%                 24416.000000           NaN            NaN  \n",
       "75%                 58900.000000           NaN            NaN  \n",
       "max                 99990.000000           NaN            NaN  "
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "customers_df.describe(include=\"all\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "customer_city\n",
       "sao paulo         15540\n",
       "rio de janeiro     6882\n",
       "belo horizonte     2773\n",
       "brasilia           2131\n",
       "curitiba           1521\n",
       "                  ...  \n",
       "ibiara                1\n",
       "rio espera            1\n",
       "rio dos indios        1\n",
       "rio dos cedros        1\n",
       "lagoao                1\n",
       "Name: customer_id, Length: 4119, dtype: int64"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "customers_df.groupby(by=\"customer_city\").customer_id.nunique().sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "customer_state\n",
       "SP    41746\n",
       "RJ    12852\n",
       "MG    11635\n",
       "RS     5466\n",
       "PR     5045\n",
       "SC     3637\n",
       "BA     3380\n",
       "DF     2140\n",
       "ES     2033\n",
       "GO     2020\n",
       "PE     1652\n",
       "CE     1336\n",
       "PA      975\n",
       "MT      907\n",
       "MA      747\n",
       "MS      715\n",
       "PB      536\n",
       "PI      495\n",
       "RN      485\n",
       "AL      413\n",
       "SE      350\n",
       "TO      280\n",
       "RO      253\n",
       "AM      148\n",
       "AC       81\n",
       "AP       68\n",
       "RR       46\n",
       "Name: customer_id, dtype: int64"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "customers_df.groupby(by=\"customer_state\").customer_id.nunique().sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Explore Geolocation df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "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>geolocation_zip_code_prefix</th>\n",
       "      <th>geolocation_lat</th>\n",
       "      <th>geolocation_lng</th>\n",
       "      <th>geolocation_city</th>\n",
       "      <th>geolocation_state</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1037</td>\n",
       "      <td>-23.545621</td>\n",
       "      <td>-46.639292</td>\n",
       "      <td>sao paulo</td>\n",
       "      <td>SP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1046</td>\n",
       "      <td>-23.546081</td>\n",
       "      <td>-46.644820</td>\n",
       "      <td>sao paulo</td>\n",
       "      <td>SP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1046</td>\n",
       "      <td>-23.546129</td>\n",
       "      <td>-46.642951</td>\n",
       "      <td>sao paulo</td>\n",
       "      <td>SP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1041</td>\n",
       "      <td>-23.544392</td>\n",
       "      <td>-46.639499</td>\n",
       "      <td>sao paulo</td>\n",
       "      <td>SP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1035</td>\n",
       "      <td>-23.541578</td>\n",
       "      <td>-46.641607</td>\n",
       "      <td>sao paulo</td>\n",
       "      <td>SP</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   geolocation_zip_code_prefix  geolocation_lat  geolocation_lng  \\\n",
       "0                         1037       -23.545621       -46.639292   \n",
       "1                         1046       -23.546081       -46.644820   \n",
       "2                         1046       -23.546129       -46.642951   \n",
       "3                         1041       -23.544392       -46.639499   \n",
       "4                         1035       -23.541578       -46.641607   \n",
       "\n",
       "  geolocation_city geolocation_state  \n",
       "0        sao paulo                SP  \n",
       "1        sao paulo                SP  \n",
       "2        sao paulo                SP  \n",
       "3        sao paulo                SP  \n",
       "4        sao paulo                SP  "
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "geolocation_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "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>geolocation_zip_code_prefix</th>\n",
       "      <th>geolocation_lat</th>\n",
       "      <th>geolocation_lng</th>\n",
       "      <th>geolocation_city</th>\n",
       "      <th>geolocation_state</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1.000163e+06</td>\n",
       "      <td>1.000163e+06</td>\n",
       "      <td>1.000163e+06</td>\n",
       "      <td>1000163</td>\n",
       "      <td>1000163</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8011</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>sao paulo</td>\n",
       "      <td>SP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>135800</td>\n",
       "      <td>404268</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.657417e+04</td>\n",
       "      <td>-2.117615e+01</td>\n",
       "      <td>-4.639054e+01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.054934e+04</td>\n",
       "      <td>5.715866e+00</td>\n",
       "      <td>4.269748e+00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.001000e+03</td>\n",
       "      <td>-3.660537e+01</td>\n",
       "      <td>-1.014668e+02</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.107500e+04</td>\n",
       "      <td>-2.360355e+01</td>\n",
       "      <td>-4.857317e+01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2.653000e+04</td>\n",
       "      <td>-2.291938e+01</td>\n",
       "      <td>-4.663788e+01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>6.350400e+04</td>\n",
       "      <td>-1.997962e+01</td>\n",
       "      <td>-4.376771e+01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>9.999000e+04</td>\n",
       "      <td>4.506593e+01</td>\n",
       "      <td>1.211054e+02</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        geolocation_zip_code_prefix  geolocation_lat  geolocation_lng  \\\n",
       "count                  1.000163e+06     1.000163e+06     1.000163e+06   \n",
       "unique                          NaN              NaN              NaN   \n",
       "top                             NaN              NaN              NaN   \n",
       "freq                            NaN              NaN              NaN   \n",
       "mean                   3.657417e+04    -2.117615e+01    -4.639054e+01   \n",
       "std                    3.054934e+04     5.715866e+00     4.269748e+00   \n",
       "min                    1.001000e+03    -3.660537e+01    -1.014668e+02   \n",
       "25%                    1.107500e+04    -2.360355e+01    -4.857317e+01   \n",
       "50%                    2.653000e+04    -2.291938e+01    -4.663788e+01   \n",
       "75%                    6.350400e+04    -1.997962e+01    -4.376771e+01   \n",
       "max                    9.999000e+04     4.506593e+01     1.211054e+02   \n",
       "\n",
       "       geolocation_city geolocation_state  \n",
       "count           1000163           1000163  \n",
       "unique             8011                27  \n",
       "top           sao paulo                SP  \n",
       "freq             135800            404268  \n",
       "mean                NaN               NaN  \n",
       "std                 NaN               NaN  \n",
       "min                 NaN               NaN  \n",
       "25%                 NaN               NaN  \n",
       "50%                 NaN               NaN  \n",
       "75%                 NaN               NaN  \n",
       "max                 NaN               NaN  "
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "geolocation_df.describe(include=\"all\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "import unicodedata\n",
    "import re\n",
    "\n",
    "def clean_string(column):\n",
    "    normalized_column = unicodedata.normalize('NFKD', column)\n",
    "    cleaned_column = re.sub(r'[^a-zA-Z0-9\\s]', '', normalized_column)\n",
    "    cleaned_column = cleaned_column.lower()\n",
    "    cleaned_column = ' '.join(cleaned_column.split())\n",
    "\n",
    "    return cleaned_column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "geolocation_df['geolocation_city'] = geolocation_df['geolocation_city'].apply(clean_string)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "geolocation_lat\n",
       "-27.102099    314\n",
       "-23.495901    190\n",
       "-23.506049    141\n",
       "-23.490618    127\n",
       "-23.005514    102\n",
       "             ... \n",
       "-23.465020      1\n",
       "-23.465018      1\n",
       "-23.465010      1\n",
       "-23.465008      1\n",
       " 45.065933      1\n",
       "Length: 717360, dtype: int64"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "geolocation_df.groupby(by='geolocation_lat').size().sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "geolocation_zip_code_prefix\n",
       "24220    1146\n",
       "24230    1102\n",
       "38400     965\n",
       "35500     907\n",
       "11680     879\n",
       "         ... \n",
       "20056       1\n",
       "76370       1\n",
       "63012       1\n",
       "76372       1\n",
       "32635       1\n",
       "Length: 19015, dtype: int64"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "geolocation_df.groupby(by='geolocation_zip_code_prefix').size().sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Explore Order Items df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "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",
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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>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",
       "    </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>2017-09-19 09:45:35</td>\n",
       "      <td>58.90</td>\n",
       "      <td>13.29</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>2017-05-03 11:05:13</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>2018-01-18 14:48:30</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>2018-08-15 10:10:18</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>2017-02-13 13:57:51</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 2017-09-19 09:45:35   58.90          13.29  \n",
       "1 2017-05-03 11:05:13  239.90          19.93  \n",
       "2 2018-01-18 14:48:30  199.00          17.87  \n",
       "3 2018-08-15 10:10:18   12.99          12.79  \n",
       "4 2017-02-13 13:57:51  199.90          18.14  "
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "order_items_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\muham\\AppData\\Local\\Temp\\ipykernel_16060\\3210315173.py:1: FutureWarning: Treating datetime data as categorical rather than numeric in `.describe` is deprecated and will be removed in a future version of pandas. Specify `datetime_is_numeric=True` to silence this warning and adopt the future behavior now.\n",
      "  order_items_df.describe(include=\"all\")\n"
     ]
    },
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       "      <td>112650.000000</td>\n",
       "      <td>112650.000000</td>\n",
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       "      <td>98666</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "                                order_id  order_item_id  \\\n",
       "count                             112650  112650.000000   \n",
       "unique                             98666            NaN   \n",
       "top     8272b63d03f5f79c56e9e4120aec44ef            NaN   \n",
       "freq                                  21            NaN   \n",
       "first                                NaN            NaN   \n",
       "last                                 NaN            NaN   \n",
       "mean                                 NaN       1.197834   \n",
       "std                                  NaN       0.705124   \n",
       "min                                  NaN       1.000000   \n",
       "25%                                  NaN       1.000000   \n",
       "50%                                  NaN       1.000000   \n",
       "75%                                  NaN       1.000000   \n",
       "max                                  NaN      21.000000   \n",
       "\n",
       "                              product_id                         seller_id  \\\n",
       "count                             112650                            112650   \n",
       "unique                             32951                              3095   \n",
       "top     aca2eb7d00ea1a7b8ebd4e68314663af  6560211a19b47992c3666cc44a7e94c0   \n",
       "freq                                 527                              2033   \n",
       "first                                NaN                               NaN   \n",
       "last                                 NaN                               NaN   \n",
       "mean                                 NaN                               NaN   \n",
       "std                                  NaN                               NaN   \n",
       "min                                  NaN                               NaN   \n",
       "25%                                  NaN                               NaN   \n",
       "50%                                  NaN                               NaN   \n",
       "75%                                  NaN                               NaN   \n",
       "max                                  NaN                               NaN   \n",
       "\n",
       "        shipping_limit_date          price  freight_value  \n",
       "count                112650  112650.000000  112650.000000  \n",
       "unique                93318            NaN            NaN  \n",
       "top     2017-07-21 18:25:23            NaN            NaN  \n",
       "freq                     21            NaN            NaN  \n",
       "first   2016-09-19 00:15:34            NaN            NaN  \n",
       "last    2020-04-09 22:35:08            NaN            NaN  \n",
       "mean                    NaN     120.653739      19.990320  \n",
       "std                     NaN     183.633928      15.806405  \n",
       "min                     NaN       0.850000       0.000000  \n",
       "25%                     NaN      39.900000      13.080000  \n",
       "50%                     NaN      74.990000      16.260000  \n",
       "75%                     NaN     134.900000      21.150000  \n",
       "max                     NaN    6735.000000     409.680000  "
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "order_items_df.describe(include=\"all\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "price\n",
       "59.90      2087\n",
       "69.90      1821\n",
       "49.90      1675\n",
       "89.90      1419\n",
       "99.90      1319\n",
       "           ... \n",
       "189.42        1\n",
       "46.63         1\n",
       "189.49        1\n",
       "189.59        1\n",
       "6735.00       1\n",
       "Name: order_id, Length: 5968, dtype: int64"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "order_items_df.groupby(by=\"price\").order_id.nunique().sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Explore Order Payments df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>max</th>\n",
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       "  </tbody>\n",
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       "</div>"
      ],
      "text/plain": [
       "                                order_id  payment_sequential payment_type  \\\n",
       "count                             103886       103886.000000       103886   \n",
       "unique                             99440                 NaN            5   \n",
       "top     fa65dad1b0e818e3ccc5cb0e39231352                 NaN  credit_card   \n",
       "freq                                  29                 NaN        76795   \n",
       "mean                                 NaN            1.092679          NaN   \n",
       "std                                  NaN            0.706584          NaN   \n",
       "min                                  NaN            1.000000          NaN   \n",
       "25%                                  NaN            1.000000          NaN   \n",
       "50%                                  NaN            1.000000          NaN   \n",
       "75%                                  NaN            1.000000          NaN   \n",
       "max                                  NaN           29.000000          NaN   \n",
       "\n",
       "        payment_installments  payment_value  \n",
       "count          103886.000000  103886.000000  \n",
       "unique                   NaN            NaN  \n",
       "top                      NaN            NaN  \n",
       "freq                     NaN            NaN  \n",
       "mean                2.853349     154.100380  \n",
       "std                 2.687051     217.494064  \n",
       "min                 0.000000       0.000000  \n",
       "25%                 1.000000      56.790000  \n",
       "50%                 1.000000     100.000000  \n",
       "75%                 4.000000     171.837500  \n",
       "max                24.000000   13664.080000  "
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "order_pay_df.describe(include=\"all\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>order_id</th>\n",
       "      <th colspan=\"2\" halign=\"left\">payment_value</th>\n",
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       "      <th></th>\n",
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       "      <th>boleto</th>\n",
       "      <td>19784</td>\n",
       "      <td>7274.88</td>\n",
       "      <td>11.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>credit_card</th>\n",
       "      <td>76505</td>\n",
       "      <td>13664.08</td>\n",
       "      <td>0.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>debit_card</th>\n",
       "      <td>1528</td>\n",
       "      <td>4445.50</td>\n",
       "      <td>13.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>not_defined</th>\n",
       "      <td>3</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>voucher</th>\n",
       "      <td>3866</td>\n",
       "      <td>3184.34</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             order_id payment_value       \n",
       "              nunique           max    min\n",
       "payment_type                              \n",
       "boleto          19784       7274.88  11.62\n",
       "credit_card     76505      13664.08   0.01\n",
       "debit_card       1528       4445.50  13.38\n",
       "not_defined         3          0.00   0.00\n",
       "voucher          3866       3184.34   0.00"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "order_pay_df.groupby(by=\"payment_type\").agg({\n",
    "    \"order_id\" : \"nunique\",\n",
    "    \"payment_value\" : [\"max\", \"min\"]\n",
    "})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Explore Order Reviews df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>review_id</th>\n",
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       "      <th>review_comment_title</th>\n",
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       "      <td>a548910a1c6147796b98fdf73dbeba33</td>\n",
       "      <td>5</td>\n",
       "      <td>no comment</td>\n",
       "      <td>no comment</td>\n",
       "      <td>2018-03-10</td>\n",
       "      <td>2018-03-11 03:05:13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>228ce5500dc1d8e020d8d1322874b6f0</td>\n",
       "      <td>f9e4b658b201a9f2ecdecbb34bed034b</td>\n",
       "      <td>5</td>\n",
       "      <td>no comment</td>\n",
       "      <td>no comment</td>\n",
       "      <td>2018-02-17</td>\n",
       "      <td>2018-02-18 14:36:24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>e64fb393e7b32834bb789ff8bb30750e</td>\n",
       "      <td>658677c97b385a9be170737859d3511b</td>\n",
       "      <td>5</td>\n",
       "      <td>no comment</td>\n",
       "      <td>Recebi bem antes do prazo estipulado.</td>\n",
       "      <td>2017-04-21</td>\n",
       "      <td>2017-04-21 22:02:06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>f7c4243c7fe1938f181bec41a392bdeb</td>\n",
       "      <td>8e6bfb81e283fa7e4f11123a3fb894f1</td>\n",
       "      <td>5</td>\n",
       "      <td>no comment</td>\n",
       "      <td>Parabéns lojas lannister adorei comprar pela I...</td>\n",
       "      <td>2018-03-01</td>\n",
       "      <td>2018-03-02 10:26:53</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          review_id                          order_id  \\\n",
       "0  7bc2406110b926393aa56f80a40eba40  73fc7af87114b39712e6da79b0a377eb   \n",
       "1  80e641a11e56f04c1ad469d5645fdfde  a548910a1c6147796b98fdf73dbeba33   \n",
       "2  228ce5500dc1d8e020d8d1322874b6f0  f9e4b658b201a9f2ecdecbb34bed034b   \n",
       "3  e64fb393e7b32834bb789ff8bb30750e  658677c97b385a9be170737859d3511b   \n",
       "4  f7c4243c7fe1938f181bec41a392bdeb  8e6bfb81e283fa7e4f11123a3fb894f1   \n",
       "\n",
       "   review_score review_comment_title  \\\n",
       "0             4           no comment   \n",
       "1             5           no comment   \n",
       "2             5           no comment   \n",
       "3             5           no comment   \n",
       "4             5           no comment   \n",
       "\n",
       "                              review_comment_message review_creation_date  \\\n",
       "0                                         no comment           2018-01-18   \n",
       "1                                         no comment           2018-03-10   \n",
       "2                                         no comment           2018-02-17   \n",
       "3              Recebi bem antes do prazo estipulado.           2017-04-21   \n",
       "4  Parabéns lojas lannister adorei comprar pela I...           2018-03-01   \n",
       "\n",
       "  review_answer_timestamp  \n",
       "0     2018-01-18 21:46:59  \n",
       "1     2018-03-11 03:05:13  \n",
       "2     2018-02-18 14:36:24  \n",
       "3     2017-04-21 22:02:06  \n",
       "4     2018-03-02 10:26:53  "
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "order_rev_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\muham\\AppData\\Local\\Temp\\ipykernel_16060\\2793593606.py:1: FutureWarning: Treating datetime data as categorical rather than numeric in `.describe` is deprecated and will be removed in a future version of pandas. Specify `datetime_is_numeric=True` to silence this warning and adopt the future behavior now.\n",
      "  order_rev_df.describe(include=\"all\")\n",
      "C:\\Users\\muham\\AppData\\Local\\Temp\\ipykernel_16060\\2793593606.py:1: FutureWarning: Treating datetime data as categorical rather than numeric in `.describe` is deprecated and will be removed in a future version of pandas. Specify `datetime_is_numeric=True` to silence this warning and adopt the future behavior now.\n",
      "  order_rev_df.describe(include=\"all\")\n"
     ]
    },
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       "      <td>98410</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>no comment</td>\n",
       "      <td>2017-12-19 00:00:00</td>\n",
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       "      <td>2016-10-07 18:32:28</td>\n",
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       "      <td>NaN</td>\n",
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      "text/plain": [
       "                               review_id                          order_id  \\\n",
       "count                              99224                             99224   \n",
       "unique                             98410                             98673   \n",
       "top     7b606b0d57b078384f0b58eac1d41d78  c88b1d1b157a9999ce368f218a407141   \n",
       "freq                                   3                                 3   \n",
       "first                                NaN                               NaN   \n",
       "last                                 NaN                               NaN   \n",
       "mean                                 NaN                               NaN   \n",
       "std                                  NaN                               NaN   \n",
       "min                                  NaN                               NaN   \n",
       "25%                                  NaN                               NaN   \n",
       "50%                                  NaN                               NaN   \n",
       "75%                                  NaN                               NaN   \n",
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       "\n",
       "        review_score review_comment_title review_comment_message  \\\n",
       "count   99224.000000                99224                  99224   \n",
       "unique           NaN                 4528                  36160   \n",
       "top              NaN           no comment             no comment   \n",
       "freq             NaN                87656                  58247   \n",
       "first            NaN                  NaN                    NaN   \n",
       "last             NaN                  NaN                    NaN   \n",
       "mean        4.086421                  NaN                    NaN   \n",
       "std         1.347579                  NaN                    NaN   \n",
       "min         1.000000                  NaN                    NaN   \n",
       "25%         4.000000                  NaN                    NaN   \n",
       "50%         5.000000                  NaN                    NaN   \n",
       "75%         5.000000                  NaN                    NaN   \n",
       "max         5.000000                  NaN                    NaN   \n",
       "\n",
       "       review_creation_date review_answer_timestamp  \n",
       "count                 99224                   99224  \n",
       "unique                  636                   98248  \n",
       "top     2017-12-19 00:00:00     2017-06-15 23:21:05  \n",
       "freq                    463                       4  \n",
       "first   2016-10-02 00:00:00     2016-10-07 18:32:28  \n",
       "last    2018-08-31 00:00:00     2018-10-29 12:27:35  \n",
       "mean                    NaN                     NaN  \n",
       "std                     NaN                     NaN  \n",
       "min                     NaN                     NaN  \n",
       "25%                     NaN                     NaN  \n",
       "50%                     NaN                     NaN  \n",
       "75%                     NaN                     NaN  \n",
       "max                     NaN                     NaN  "
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "order_rev_df.describe(include=\"all\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Explore Orders df "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>2018-07-26 14:31:00</td>\n",
       "      <td>2018-08-07 15:27:45</td>\n",
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       "      <td>2018-08-17 18:06:29</td>\n",
       "      <td>2018-09-04</td>\n",
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       "      <td>2018-02-13 21:18:39</td>\n",
       "      <td>2018-02-13 22:20:29</td>\n",
       "      <td>2018-02-14 19:46:34</td>\n",
       "      <td>2018-02-16 18:17:02</td>\n",
       "      <td>2018-02-26</td>\n",
       "    </tr>\n",
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       "</div>"
      ],
      "text/plain": [
       "                           order_id                       customer_id  \\\n",
       "0  e481f51cbdc54678b7cc49136f2d6af7  9ef432eb6251297304e76186b10a928d   \n",
       "1  53cdb2fc8bc7dce0b6741e2150273451  b0830fb4747a6c6d20dea0b8c802d7ef   \n",
       "2  47770eb9100c2d0c44946d9cf07ec65d  41ce2a54c0b03bf3443c3d931a367089   \n",
       "3  949d5b44dbf5de918fe9c16f97b45f8a  f88197465ea7920adcdbec7375364d82   \n",
       "4  ad21c59c0840e6cb83a9ceb5573f8159  8ab97904e6daea8866dbdbc4fb7aad2c   \n",
       "\n",
       "  order_status order_purchase_timestamp   order_approved_at  \\\n",
       "0    delivered      2017-10-02 10:56:33 2017-10-02 11:07:15   \n",
       "1    delivered      2018-07-24 20:41:37 2018-07-26 03:24:27   \n",
       "2    delivered      2018-08-08 08:38:49 2018-08-08 08:55:23   \n",
       "3    delivered      2017-11-18 19:28:06 2017-11-18 19:45:59   \n",
       "4    delivered      2018-02-13 21:18:39 2018-02-13 22:20:29   \n",
       "\n",
       "  order_delivered_carrier_date order_delivered_customer_date  \\\n",
       "0          2017-10-04 19:55:00           2017-10-10 21:25:13   \n",
       "1          2018-07-26 14:31:00           2018-08-07 15:27:45   \n",
       "2          2018-08-08 13:50:00           2018-08-17 18:06:29   \n",
       "3          2017-11-22 13:39:59           2017-12-02 00:28:42   \n",
       "4          2018-02-14 19:46:34           2018-02-16 18:17:02   \n",
       "\n",
       "  order_estimated_delivery_date  \n",
       "0                    2017-10-18  \n",
       "1                    2018-08-13  \n",
       "2                    2018-09-04  \n",
       "3                    2017-12-15  \n",
       "4                    2018-02-26  "
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "orders_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\muham\\AppData\\Local\\Temp\\ipykernel_16060\\2273736046.py:1: FutureWarning: Treating datetime data as categorical rather than numeric in `.describe` is deprecated and will be removed in a future version of pandas. Specify `datetime_is_numeric=True` to silence this warning and adopt the future behavior now.\n",
      "  orders_df.describe(include=\"all\")\n",
      "C:\\Users\\muham\\AppData\\Local\\Temp\\ipykernel_16060\\2273736046.py:1: FutureWarning: Treating datetime data as categorical rather than numeric in `.describe` is deprecated and will be removed in a future version of pandas. Specify `datetime_is_numeric=True` to silence this warning and adopt the future behavior now.\n",
      "  orders_df.describe(include=\"all\")\n",
      "C:\\Users\\muham\\AppData\\Local\\Temp\\ipykernel_16060\\2273736046.py:1: FutureWarning: Treating datetime data as categorical rather than numeric in `.describe` is deprecated and will be removed in a future version of pandas. Specify `datetime_is_numeric=True` to silence this warning and adopt the future behavior now.\n",
      "  orders_df.describe(include=\"all\")\n",
      "C:\\Users\\muham\\AppData\\Local\\Temp\\ipykernel_16060\\2273736046.py:1: FutureWarning: Treating datetime data as categorical rather than numeric in `.describe` is deprecated and will be removed in a future version of pandas. Specify `datetime_is_numeric=True` to silence this warning and adopt the future behavior now.\n",
      "  orders_df.describe(include=\"all\")\n",
      "C:\\Users\\muham\\AppData\\Local\\Temp\\ipykernel_16060\\2273736046.py:1: FutureWarning: Treating datetime data as categorical rather than numeric in `.describe` is deprecated and will be removed in a future version of pandas. Specify `datetime_is_numeric=True` to silence this warning and adopt the future behavior now.\n",
      "  orders_df.describe(include=\"all\")\n"
     ]
    },
    {
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       "      <th>order_id</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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>99441</td>\n",
       "      <td>99441</td>\n",
       "      <td>99441</td>\n",
       "      <td>99441</td>\n",
       "      <td>99441</td>\n",
       "      <td>99441</td>\n",
       "      <td>99441</td>\n",
       "      <td>99441</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>99441</td>\n",
       "      <td>99441</td>\n",
       "      <td>8</td>\n",
       "      <td>98875</td>\n",
       "      <td>90893</td>\n",
       "      <td>82793</td>\n",
       "      <td>98609</td>\n",
       "      <td>459</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>e481f51cbdc54678b7cc49136f2d6af7</td>\n",
       "      <td>9ef432eb6251297304e76186b10a928d</td>\n",
       "      <td>delivered</td>\n",
       "      <td>2018-04-11 10:48:14</td>\n",
       "      <td>2018-02-27 04:31:10</td>\n",
       "      <td>2018-05-09 15:48:00</td>\n",
       "      <td>2018-05-08 23:38:46</td>\n",
       "      <td>2017-12-20 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>96478</td>\n",
       "      <td>3</td>\n",
       "      <td>9</td>\n",
       "      <td>47</td>\n",
       "      <td>3</td>\n",
       "      <td>522</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>first</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016-09-04 21:15:19</td>\n",
       "      <td>2016-09-15 12:16:38</td>\n",
       "      <td>2016-10-08 10:34:01</td>\n",
       "      <td>2016-10-11 13:46:32</td>\n",
       "      <td>2016-09-30 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>last</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2018-10-17 17:30:18</td>\n",
       "      <td>2018-10-19 17:30:18</td>\n",
       "      <td>2018-10-26 17:30:18</td>\n",
       "      <td>2018-11-02 17:30:18</td>\n",
       "      <td>2018-11-12 00:00:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                order_id                       customer_id  \\\n",
       "count                              99441                             99441   \n",
       "unique                             99441                             99441   \n",
       "top     e481f51cbdc54678b7cc49136f2d6af7  9ef432eb6251297304e76186b10a928d   \n",
       "freq                                   1                                 1   \n",
       "first                                NaN                               NaN   \n",
       "last                                 NaN                               NaN   \n",
       "\n",
       "       order_status order_purchase_timestamp    order_approved_at  \\\n",
       "count         99441                    99441                99441   \n",
       "unique            8                    98875                90893   \n",
       "top       delivered      2018-04-11 10:48:14  2018-02-27 04:31:10   \n",
       "freq          96478                        3                    9   \n",
       "first           NaN      2016-09-04 21:15:19  2016-09-15 12:16:38   \n",
       "last            NaN      2018-10-17 17:30:18  2018-10-19 17:30:18   \n",
       "\n",
       "       order_delivered_carrier_date order_delivered_customer_date  \\\n",
       "count                         99441                         99441   \n",
       "unique                        82793                         98609   \n",
       "top             2018-05-09 15:48:00           2018-05-08 23:38:46   \n",
       "freq                             47                             3   \n",
       "first           2016-10-08 10:34:01           2016-10-11 13:46:32   \n",
       "last            2018-10-26 17:30:18           2018-11-02 17:30:18   \n",
       "\n",
       "       order_estimated_delivery_date  \n",
       "count                          99441  \n",
       "unique                           459  \n",
       "top              2017-12-20 00:00:00  \n",
       "freq                             522  \n",
       "first            2016-09-30 00:00:00  \n",
       "last             2018-11-12 00:00:00  "
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "orders_df.describe(include=\"all\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "delivery_time = orders_df[\"order_delivered_customer_date\"] - orders_df[\"order_delivered_carrier_date\"]\n",
    "delivery_time = delivery_time.apply(lambda x: x.total_seconds())\n",
    "orders_df[\"delivery_time\"] = round(delivery_time/86400)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "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>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>delivery_time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>81503</th>\n",
       "      <td>1e05735291d8969fddb706f4fabe8725</td>\n",
       "      <td>0764fd70cf180925814aac76754a1a65</td>\n",
       "      <td>delivered</td>\n",
       "      <td>2018-01-11 21:41:57</td>\n",
       "      <td>2018-01-12 10:28:15</td>\n",
       "      <td>2018-01-12 22:07:05</td>\n",
       "      <td>2018-01-17 17:43:17</td>\n",
       "      <td>2018-02-05</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71259</th>\n",
       "      <td>cb86e7fa5dcf2f86b0a038518514d891</td>\n",
       "      <td>678e29da385819d20ed00a3928143d54</td>\n",
       "      <td>delivered</td>\n",
       "      <td>2018-03-15 12:11:58</td>\n",
       "      <td>2018-03-16 03:50:11</td>\n",
       "      <td>2018-03-19 21:22:06</td>\n",
       "      <td>2018-03-21 23:12:15</td>\n",
       "      <td>2018-04-11</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1333</th>\n",
       "      <td>5596ba1b51a8d6801a60712a250471a8</td>\n",
       "      <td>c38477a5b6ae3d9b123c14c74e258210</td>\n",
       "      <td>delivered</td>\n",
       "      <td>2017-06-16 15:16:41</td>\n",
       "      <td>2017-06-16 15:26:39</td>\n",
       "      <td>2017-06-17 09:17:29</td>\n",
       "      <td>2017-06-19 21:18:44</td>\n",
       "      <td>2017-07-11</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36289</th>\n",
       "      <td>414b227d34e12d2c95093f0bdb4c44bd</td>\n",
       "      <td>c05135e931996796d1b5bff60251825a</td>\n",
       "      <td>delivered</td>\n",
       "      <td>2018-05-18 11:04:32</td>\n",
       "      <td>2018-05-18 13:07:00</td>\n",
       "      <td>2018-05-21 16:43:00</td>\n",
       "      <td>2018-05-25 13:30:58</td>\n",
       "      <td>2018-06-07</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63488</th>\n",
       "      <td>cc2e6cf82767599713d657cd5cf8b300</td>\n",
       "      <td>fb5a37c5751065b31f48a501d47eb1bc</td>\n",
       "      <td>delivered</td>\n",
       "      <td>2017-09-12 00:31:07</td>\n",
       "      <td>2017-09-12 00:44:04</td>\n",
       "      <td>2017-09-14 21:44:30</td>\n",
       "      <td>2017-09-26 19:23:30</td>\n",
       "      <td>2017-10-20</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               order_id                       customer_id  \\\n",
       "81503  1e05735291d8969fddb706f4fabe8725  0764fd70cf180925814aac76754a1a65   \n",
       "71259  cb86e7fa5dcf2f86b0a038518514d891  678e29da385819d20ed00a3928143d54   \n",
       "1333   5596ba1b51a8d6801a60712a250471a8  c38477a5b6ae3d9b123c14c74e258210   \n",
       "36289  414b227d34e12d2c95093f0bdb4c44bd  c05135e931996796d1b5bff60251825a   \n",
       "63488  cc2e6cf82767599713d657cd5cf8b300  fb5a37c5751065b31f48a501d47eb1bc   \n",
       "\n",
       "      order_status order_purchase_timestamp   order_approved_at  \\\n",
       "81503    delivered      2018-01-11 21:41:57 2018-01-12 10:28:15   \n",
       "71259    delivered      2018-03-15 12:11:58 2018-03-16 03:50:11   \n",
       "1333     delivered      2017-06-16 15:16:41 2017-06-16 15:26:39   \n",
       "36289    delivered      2018-05-18 11:04:32 2018-05-18 13:07:00   \n",
       "63488    delivered      2017-09-12 00:31:07 2017-09-12 00:44:04   \n",
       "\n",
       "      order_delivered_carrier_date order_delivered_customer_date  \\\n",
       "81503          2018-01-12 22:07:05           2018-01-17 17:43:17   \n",
       "71259          2018-03-19 21:22:06           2018-03-21 23:12:15   \n",
       "1333           2017-06-17 09:17:29           2017-06-19 21:18:44   \n",
       "36289          2018-05-21 16:43:00           2018-05-25 13:30:58   \n",
       "63488          2017-09-14 21:44:30           2017-09-26 19:23:30   \n",
       "\n",
       "      order_estimated_delivery_date  delivery_time  \n",
       "81503                    2018-02-05            5.0  \n",
       "71259                    2018-04-11            2.0  \n",
       "1333                     2017-07-11            3.0  \n",
       "36289                    2018-06-07            4.0  \n",
       "63488                    2017-10-20           12.0  "
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "orders_df.sample(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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AAAAjEXIAAICRCDkAAMBIhBwAAGAkQg4AADASIQcAABiJkAMAAIxEyAEAAEYi5AAAACMRcgAAgJEIOQAAwEiEHAAAYCRCDgAAMBIhBwAAGImQAwAAjETIAQAARiLkAAAAIxFyAACAkQg5AADASIQcAABgJEIOAAAwEiEHAAAYiZADAACMRMgBAABGIuQAAAAjEXIAAICRCDkAAMBIhBwAAGAkQg4AADASIQcAABiJkAMAAIxEyAEAAEYi5AAAACMRcgAAgJEIOQAAwEiEHAAAYCRCDgAAMBIhBwAAGImQAwAAjETIAQAARiLkAAAAIxFyAACAkQg5AADASIQcAABgpLBCzquvvqoxY8bI6XTK6XTK5XLpd7/7nb3+woULKigoUP/+/dW7d2/l5eWpoaEhZBv19fXKzc1VUlKSUlJStGDBAl28eDGkprq6WuPHj5fD4dBtt92m8vLydr2UlpZq+PDhSkxMVFZWlvbs2RPOoQAAAMOFFXIGDx6sF154QbW1tfrwww9133336cEHH9TBgwclSfPnz9e7776rTZs2qaamRseOHdPDDz9sP7+lpUW5ublqbm7Wzp07tX79epWXl6ukpMSuOXr0qHJzc3Xvvfeqrq5O8+bN05NPPqlt27bZNRs2bFBRUZEWLVqkvXv3auzYsXK73WpsbLze8wEAAAwRVsh54IEH9N3vfle333677rjjDj3//PPq3bu3du3apdOnT+v111/XqlWrdN999ykzM1Pr1q3Tzp07tWvXLklSZWWlDh06pDfeeEPjxo3TtGnTtGzZMpWWlqq5uVmSVFZWpvT0dK1cuVIjR45UYWGhHnnkEa1evdruY9WqVZozZ45mzZqljIwMlZWVKSkpSWvXru3EUwMAALqza74mp6WlRW+99ZaamprkcrlUW1urYDCo7Oxsu2bEiBEaOnSovF6vJMnr9Wr06NFKTU21a9xut/x+v/1ukNfrDdlGW03bNpqbm1VbWxtSExsbq+zsbLsGAACgR7hP2L9/v1wuly5cuKDevXvr7bffVkZGhurq6pSQkKDk5OSQ+tTUVPl8PkmSz+cLCTht69vWXanG7/fr/Pnz+uKLL9TS0nLJmsOHD1+x90AgoEAgYD/2+/2SpGAwqGAw2MEz0DGOOKtTtxf2/mOtkF9N1dlz62pt/Xa3vk3HXKITc4lO0TCXju477JBz5513qq6uTqdPn9Z//Md/KD8/XzU1NWE3GAnLly/XkiVL2i2vrKxUUlJSp+5rxcRO3dw1WzahNdItdKmtW7dGuoVr4vF4It0CLoG5RCfmEp0iOZdz5851qC7skJOQkKDbbrtNkpSZmakPPvhAa9as0YwZM9Tc3KxTp06FvJvT0NCgtLQ0SVJaWlq7u6Da7r76as3X78hqaGiQ0+lUz549FRcXp7i4uEvWtG3jcoqLi1VUVGQ/9vv9GjJkiHJycuR0OsM4C1c3avG2qxd1IUespWUTWvWTD2MVaI2JaC9d6cBid6RbCEswGJTH49GUKVMUHx8f6XbwJeYSnZhLdIqGubR9EnM1YYecr2ttbVUgEFBmZqbi4+NVVVWlvLw8SdKRI0dUX18vl8slSXK5XHr++efV2NiolJQUSX9Ngk6nUxkZGXbN1/917vF47G0kJCQoMzNTVVVVmj59ut1DVVWVCgsLr9irw+GQw+Fotzw+Pr7TBxVoiY5gEWiNiZpeukJ3feHrij9zuH7MJToxl+gUybl0dL9hhZzi4mJNmzZNQ4cO1ZkzZ/Tmm2+qurpa27ZtU9++fTV79mwVFRWpX79+cjqdeuaZZ+RyuTRp0iRJUk5OjjIyMvT4449rxYoV8vl8WrhwoQoKCuzw8fTTT+vll1/Ws88+qyeeeELbt2/Xxo0btWXLFruPoqIi5efna8KECZo4caJefPFFNTU1adasWeEcDgAAMFhYIaexsVHf//73dfz4cfXt21djxozRtm3bNGXKFEnS6tWrFRsbq7y8PAUCAbndbr3yyiv28+Pi4rR582bNnTtXLpdLvXr1Un5+vpYuXWrXpKena8uWLZo/f77WrFmjwYMH67XXXpPb/bePJWbMmKHPP/9cJSUl8vl8GjdunCoqKtpdjAwAAG5eYYWc119//YrrExMTVVpaqtLS0svWDBs27KoXi06ePFn79u27Yk1hYeFVP54CAAA3L352FQAAMBIhBwAAGImQAwAAjETIAQAARiLkAAAAIxFyAACAkQg5AADASIQcAABgJEIOAAAwEiEHAAAYiZADAACMRMgBAABGIuQAAAAjEXIAAICRCDkAAMBIhBwAAGAkQg4AADASIQcAABiJkAMAAIxEyAEAAEYi5AAAACMRcgAAgJEIOQAAwEiEHAAAYCRCDgAAMBIhBwAAGImQAwAAjETIAQAARiLkAAAAIxFyAACAkQg5AADASIQcAABgJEIOAAAwEiEHAAAYiZADAACMRMgBAABGIuQAAAAjEXIAAICRCDkAAMBIhBwAAGAkQg4AADASIQcAABiJkAMAAIxEyAEAAEYi5AAAACMRcgAAgJEIOQAAwEiEHAAAYCRCDgAAMBIhBwAAGImQAwAAjETIAQAARiLkAAAAIxFyAACAkQg5AADASGGFnOXLl+ub3/ym+vTpo5SUFE2fPl1HjhwJqblw4YIKCgrUv39/9e7dW3l5eWpoaAipqa+vV25urpKSkpSSkqIFCxbo4sWLITXV1dUaP368HA6HbrvtNpWXl7frp7S0VMOHD1diYqKysrK0Z8+ecA4HAAAYLKyQU1NTo4KCAu3atUsej0fBYFA5OTlqamqya+bPn693331XmzZtUk1NjY4dO6aHH37YXt/S0qLc3Fw1Nzdr586dWr9+vcrLy1VSUmLXHD16VLm5ubr33ntVV1enefPm6cknn9S2bdvsmg0bNqioqEiLFi3S3r17NXbsWLndbjU2Nl7P+QAAAIboEU5xRUVFyOPy8nKlpKSotrZW3/nOd3T69Gm9/vrrevPNN3XfffdJktatW6eRI0dq165dmjRpkiorK3Xo0CH9/ve/V2pqqsaNG6dly5bpn//5n7V48WIlJCSorKxM6enpWrlypSRp5MiRev/997V69Wq53W5J0qpVqzRnzhzNmjVLklRWVqYtW7Zo7dq1eu655677xAAAgO7tuq7JOX36tCSpX79+kqTa2loFg0FlZ2fbNSNGjNDQoUPl9XolSV6vV6NHj1Zqaqpd43a75ff7dfDgQbvmq9toq2nbRnNzs2pra0NqYmNjlZ2dbdcAAICbW1jv5HxVa2ur5s2bp29961saNWqUJMnn8ykhIUHJyckhtampqfL5fHbNVwNO2/q2dVeq8fv9On/+vL744gu1tLRcsubw4cOX7TkQCCgQCNiP/X6/JCkYDCoYDHb00DvEEWd16vbC3n+sFfKrqTp7bl2trd/u1rfpmEt0Yi7RKRrm0tF9X3PIKSgo0IEDB/T+++9f6yZuuOXLl2vJkiXtlldWViopKalT97ViYqdu7potm9Aa6Ra61NatWyPdwjXxeDyRbgGXwFyiE3OJTpGcy7lz5zpUd00hp7CwUJs3b9aOHTs0ePBge3laWpqam5t16tSpkHdzGhoalJaWZtd8/S6otruvvlrz9TuyGhoa5HQ61bNnT8XFxSkuLu6SNW3buJTi4mIVFRXZj/1+v4YMGaKcnBw5nc4wzsDVjVq87epFXcgRa2nZhFb95MNYBVpjItpLVzqw2B3pFsISDAbl8Xg0ZcoUxcfHR7odfIm5RCfmEp2iYS5tn8RcTVghx7IsPfPMM3r77bdVXV2t9PT0kPWZmZmKj49XVVWV8vLyJElHjhxRfX29XC6XJMnlcun5559XY2OjUlJSJP01DTqdTmVkZNg1X/8XusfjsbeRkJCgzMxMVVVVafr06ZL++vFZVVWVCgsLL9u/w+GQw+Fotzw+Pr7TBxVoiY5gEWiNiZpeukJ3feHrij9zuH7MJToxl+gUybl0dL9hhZyCggK9+eab+u1vf6s+ffrY19D07dtXPXv2VN++fTV79mwVFRWpX79+cjqdeuaZZ+RyuTRp0iRJUk5OjjIyMvT4449rxYoV8vl8WrhwoQoKCuwA8vTTT+vll1/Ws88+qyeeeELbt2/Xxo0btWXLFruXoqIi5efna8KECZo4caJefPFFNTU12XdbAQCAm1tYIefVV1+VJE2ePDlk+bp16/SDH/xAkrR69WrFxsYqLy9PgUBAbrdbr7zyil0bFxenzZs3a+7cuXK5XOrVq5fy8/O1dOlSuyY9PV1btmzR/PnztWbNGg0ePFivvfaaffu4JM2YMUOff/65SkpK5PP5NG7cOFVUVLS7GBkAANycwv646moSExNVWlqq0tLSy9YMGzbsqheMTp48Wfv27btiTWFh4RU/ngIAADcvfnYVAAAwEiEHAAAYiZADAACMRMgBAABGIuQAAAAjEXIAAICRCDkAAMBIhBwAAGAkQg4AADASIQcAABiJkAMAAIxEyAEAAEYi5AAAACMRcgAAgJEIOQAAwEiEHAAAYCRCDgAAMBIhBwAAGImQAwAAjETIAQAARiLkAAAAIxFyAACAkQg5AADASIQcAABgJEIOAAAwEiEHAAAYiZADAACMRMgBAABGIuQAAAAjEXIAAICRCDkAAMBIhBwAAGAkQg4AADASIQcAABiJkAMAAIxEyAEAAEYi5AAAACMRcgAAgJEIOQAAwEiEHAAAYCRCDgAAMBIhBwAAGImQAwAAjETIAQAARiLkAAAAIxFyAACAkQg5AADASIQcAABgJEIOAAAwEiEHAAAYiZADAACMRMgBAABGIuQAAAAjEXIAAICRCDkAAMBIYYecHTt26IEHHtCgQYMUExOjd955J2S9ZVkqKSnRwIED1bNnT2VnZ+vjjz8OqTl58qRmzpwpp9Op5ORkzZ49W2fPng2p+eMf/6h77rlHiYmJGjJkiFasWNGul02bNmnEiBFKTEzU6NGjtXXr1nAPBwAAGCrskNPU1KSxY8eqtLT0kutXrFihl156SWVlZdq9e7d69eolt9utCxcu2DUzZ87UwYMH5fF4tHnzZu3YsUNPPfWUvd7v9ysnJ0fDhg1TbW2tfv7zn2vx4sX693//d7tm586deuyxxzR79mzt27dP06dP1/Tp03XgwIFwDwkAABioR7hPmDZtmqZNm3bJdZZl6cUXX9TChQv14IMPSpJ+9atfKTU1Ve+8844effRRffTRR6qoqNAHH3ygCRMmSJJ+8Ytf6Lvf/a7+9V//VYMGDdKvf/1rNTc3a+3atUpISNBdd92luro6rVq1yg5Da9as0dSpU7VgwQJJ0rJly+TxePTyyy+rrKzsmk4GAAAwR9gh50qOHj0qn8+n7Oxse1nfvn2VlZUlr9erRx99VF6vV8nJyXbAkaTs7GzFxsZq9+7deuihh+T1evWd73xHCQkJdo3b7dbPfvYzffHFF7rlllvk9XpVVFQUsn+3293u47OvCgQCCgQC9mO/3y9JCgaDCgaD13v4IRxxVqduL+z9x1ohv5qqs+fW1dr67W59m465RCfmEp2iYS4d3XenhhyfzydJSk1NDVmemppqr/P5fEpJSQltokcP9evXL6QmPT293Tba1t1yyy3y+XxX3M+lLF++XEuWLGm3vLKyUklJSR05xA5bMbFTN3fNlk1ojXQLXaq7Xofl8Xgi3QIugblEJ+YSnSI5l3PnznWorlNDTrQrLi4OeffH7/dryJAhysnJkdPp7NR9jVq8rVO3Fy5HrKVlE1r1kw9jFWiNiWgvXenAYnekWwhLMBiUx+PRlClTFB8fH+l28CXmEp2YS3SKhrm0fRJzNZ0actLS0iRJDQ0NGjhwoL28oaFB48aNs2saGxtDnnfx4kWdPHnSfn5aWpoaGhpCatoeX62mbf2lOBwOORyOdsvj4+M7fVCBlugIFoHWmKjppSt01xe+rvgzh+vHXKITc4lOkZxLR/fbqf9PTnp6utLS0lRVVWUv8/v92r17t1wulyTJ5XLp1KlTqq2ttWu2b9+u1tZWZWVl2TU7duwI+czN4/Hozjvv1C233GLXfHU/bTVt+wEAADe3sEPO2bNnVVdXp7q6Okl/vdi4rq5O9fX1iomJ0bx58/TTn/5U//mf/6n9+/fr+9//vgYNGqTp06dLkkaOHKmpU6dqzpw52rNnj/7whz+osLBQjz76qAYNGiRJ+sd//EclJCRo9uzZOnjwoDZs2KA1a9aEfNT0wx/+UBUVFVq5cqUOHz6sxYsX68MPP1RhYeH1nxUAANDthf1x1Ycffqh7773XftwWPPLz81VeXq5nn31WTU1Neuqpp3Tq1Cl9+9vfVkVFhRITE+3n/PrXv1ZhYaHuv/9+xcbGKi8vTy+99JK9vm/fvqqsrFRBQYEyMzM1YMAAlZSUhPxfOv/wD/+gN998UwsXLtS//Mu/6Pbbb9c777yjUaNGXdOJAAAAZgk75EyePFmWdfnbkmNiYrR06VItXbr0sjX9+vXTm2++ecX9jBkzRv/1X/91xZrvfe97+t73vnflhgEAwE2Jn10FAACMRMgBAABGIuQAAAAjEXIAAICRCDkAAMBIhBwAAGAkQg4AADASIQcAABiJkAMAAIxEyAEAAEYi5AAAACMRcgAAgJEIOQAAwEiEHAAAYCRCDgAAMBIhBwAAGImQAwAAjETIAQAARiLkAAAAIxFyAACAkQg5AADASIQcAABgJEIOAAAwEiEHAAAYiZADAACMRMgBAABGIuQAAAAjEXIAAICRCDkAAMBIhBwAAGAkQg4AADASIQcAABiJkAMAAIxEyAEAAEYi5AAAACMRcgAAgJEIOQAAwEiEHAAAYCRCDgAAMBIhBwAAGImQAwAAjNQj0g0A12P4c1si3UJYHHGWVkyMdBcAcHPgnRwAAGAkQg4AADASIQcAABiJkAMAAIxEyAEAAEYi5AAAACMRcgAAgJEIOQAAwEiEHAAAYCRCDgAAMBIhBwAAGImQAwAAjETIAQAARur2Iae0tFTDhw9XYmKisrKytGfPnki3BAAAokC3DjkbNmxQUVGRFi1apL1792rs2LFyu91qbGyMdGsAACDCekS6geuxatUqzZkzR7NmzZIklZWVacuWLVq7dq2ee+65CHcHXN6oxdsUaImJdBth+fSF3Ei3AABh6bYhp7m5WbW1tSouLraXxcbGKjs7W16v95LPCQQCCgQC9uPTp09Lkk6ePKlgMNip/fW42NSp2wt7/62Wzp1rVY9grFpau9dfpibrznM5ceJEpFvoMsFgUOfOndOJEycUHx8f6XbwJeYSnaJhLmfOnJEkWZZ1xbpuG3L+8pe/qKWlRampqSHLU1NTdfjw4Us+Z/ny5VqyZEm75enp6V3SY6T9Y6QbwCV117kMWBnpDgAg1JkzZ9S3b9/Lru+2IedaFBcXq6ioyH7c2tqqkydPqn///oqJ6V7/qr4av9+vIUOG6LPPPpPT6Yx0O/gSc4lOzCU6MZfoFA1zsSxLZ86c0aBBg65Y121DzoABAxQXF6eGhoaQ5Q0NDUpLS7vkcxwOhxwOR8iy5OTkrmoxKjidTl4cohBziU7MJToxl+gU6blc6R2cNt327qqEhARlZmaqqqrKXtba2qqqqiq5XK4IdgYAAKJBt30nR5KKioqUn5+vCRMmaOLEiXrxxRfV1NRk320FAABuXt065MyYMUOff/65SkpK5PP5NG7cOFVUVLS7GPlm5HA4tGjRonYfzyGymEt0Yi7RiblEp+40lxjravdfAQAAdEPd9pocAACAKyHkAAAAIxFyAACAkQg5AADASIQcA5WWlmr48OFKTExUVlaW9uzZE+mWbiqLFy9WTExMyNeIESPs9RcuXFBBQYH69++v3r17Ky8vr91/aonrt2PHDj3wwAMaNGiQYmJi9M4774SstyxLJSUlGjhwoHr27Kns7Gx9/PHHITUnT57UzJkz5XQ6lZycrNmzZ+vs2bM38CjMc7W5/OAHP2j3/TN16tSQGubS+ZYvX65vfvOb6tOnj1JSUjR9+nQdOXIkpKYjr1319fXKzc1VUlKSUlJStGDBAl28ePFGHkoIQo5hNmzYoKKiIi1atEh79+7V2LFj5Xa71djYGOnWbip33XWXjh8/bn+9//779rr58+fr3Xff1aZNm1RTU6Njx47p4YcfjmC3ZmpqatLYsWNVWlp6yfUrVqzQSy+9pLKyMu3evVu9evWS2+3WhQsX7JqZM2fq4MGD8ng82rx5s3bs2KGnnnrqRh2Cka42F0maOnVqyPfPb37zm5D1zKXz1dTUqKCgQLt27ZLH41EwGFROTo6amv72w6av9trV0tKi3NxcNTc3a+fOnVq/fr3Ky8tVUlISiUP6KwtGmThxolVQUGA/bmlpsQYNGmQtX748gl3dXBYtWmSNHTv2kutOnTplxcfHW5s2bbKXffTRR5Yky+v13qAObz6SrLffftt+3NraaqWlpVk///nP7WWnTp2yHA6H9Zvf/MayLMs6dOiQJcn64IMP7Jrf/e53VkxMjPV///d/N6x3k319LpZlWfn5+daDDz542ecwlxujsbHRkmTV1NRYltWx166tW7dasbGxls/ns2teffVVy+l0WoFA4MYewJd4J8cgzc3Nqq2tVXZ2tr0sNjZW2dnZ8nq9Eezs5vPxxx9r0KBB+sY3vqGZM2eqvr5eklRbW6tgMBgyoxEjRmjo0KHM6AY6evSofD5fyBz69u2rrKwsew5er1fJycmaMGGCXZOdna3Y2Fjt3r37hvd8M6murlZKSoruvPNOzZ07VydOnLDXMZcb4/Tp05Kkfv36SerYa5fX69Xo0aND/kNet9stv9+vgwcP3sDu/4aQY5C//OUvamlpafc/Pqempsrn80Woq5tPVlaWysvLVVFRoVdffVVHjx7VPffcozNnzsjn8ykhIaHdD4ZlRjdW27m+0veKz+dTSkpKyPoePXqoX79+zKoLTZ06Vb/61a9UVVWln/3sZ6qpqdG0adPU0tIiibncCK2trZo3b56+9a1vadSoUZLUodcun893ye+ptnWR0K1/rAMQjaZNm2b/fsyYMcrKytKwYcO0ceNG9ezZM4KdAdHv0UcftX8/evRojRkzRn//93+v6upq3X///RHs7OZRUFCgAwcOhFxL2F3xTo5BBgwYoLi4uHZXuzc0NCgtLS1CXSE5OVl33HGHPvnkE6Wlpam5uVmnTp0KqWFGN1bbub7S90paWlq7C/YvXryokydPMqsb6Bvf+IYGDBigTz75RBJz6WqFhYXavHmz3nvvPQ0ePNhe3pHXrrS0tEt+T7WtiwRCjkESEhKUmZmpqqoqe1lra6uqqqrkcrki2NnN7ezZs/rTn/6kgQMHKjMzU/Hx8SEzOnLkiOrr65nRDZSenq60tLSQOfj9fu3evdueg8vl0qlTp1RbW2vXbN++Xa2trcrKyrrhPd+s/vznP+vEiRMaOHCgJObSVSzLUmFhod5++21t375d6enpIes78trlcrm0f//+kBDq8XjkdDqVkZFxYw7k6yJyuTO6zFtvvWU5HA6rvLzcOnTokPXUU09ZycnJIVe7o2v96Ec/sqqrq62jR49af/jDH6zs7GxrwIABVmNjo2VZlvX0009bQ4cOtbZv3259+OGHlsvlslwuV4S7Ns+ZM2esffv2Wfv27bMkWatWrbL27dtn/e///q9lWZb1wgsvWMnJydZvf/tb649//KP14IMPWunp6db58+ftbUydOtW6++67rd27d1vvv/++dfvtt1uPPfZYpA7JCFeay5kzZ6wf//jHltfrtY4ePWr9/ve/t8aPH2/dfvvt1oULF+xtMJfON3fuXKtv375WdXW1dfz4cfvr3Llzds3VXrsuXrxojRo1ysrJybHq6uqsiooK69Zbb7WKi4sjcUiWZVkWIcdAv/jFL6yhQ4daCQkJ1sSJE61du3ZFuqWbyowZM6yBAwdaCQkJ1t/93d9ZM2bMsD755BN7/fnz561/+qd/sm655RYrKSnJeuihh6zjx49HsGMzvffee5akdl/5+fmWZf31NvKf/OQnVmpqquVwOKz777/fOnLkSMg2Tpw4YT322GNW7969LafTac2aNcs6c+ZMBI7GHFeay7lz56ycnBzr1ltvteLj461hw4ZZc+bMafePNObS+S41E0nWunXr7JqOvHZ9+umn1rRp06yePXtaAwYMsH70ox9ZwWDwBh/N38RYlmXd6HePAAAAuhrX5AAAACMRcgAAgJEIOQAAwEiEHAAAYCRCDgAAMBIhBwAAGImQAwAAjETIAQAARiLkAAAAIxFyAACAkQg5AADASIQcAABgpP8HkcWQgA27x3cAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "orders_df.delivery_time.hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "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>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>status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>23892</th>\n",
       "      <td>6399526f1057f40e8454d9614e16d77a</td>\n",
       "      <td>f483ce4e87afdc082b44a89f6eb0fefe</td>\n",
       "      <td>8530</td>\n",
       "      <td>ferraz de vasconcelos</td>\n",
       "      <td>SP</td>\n",
       "      <td>Active</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17345</th>\n",
       "      <td>8bb46af2da65493427799b95d0739d34</td>\n",
       "      <td>877a47a9c3f87b14581bd7ad4b6207ea</td>\n",
       "      <td>6310</td>\n",
       "      <td>carapicuiba</td>\n",
       "      <td>SP</td>\n",
       "      <td>Active</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20075</th>\n",
       "      <td>418398a534ddffb19a62d282e8285db4</td>\n",
       "      <td>b30bcdb9ae45a5a96f13d9b17826988d</td>\n",
       "      <td>37750</td>\n",
       "      <td>machado</td>\n",
       "      <td>MG</td>\n",
       "      <td>Active</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77770</th>\n",
       "      <td>ef14bc7bc7f8a385f119d6d67e9d7fa9</td>\n",
       "      <td>fdd6bd902f6948b21330b111b89ccc4b</td>\n",
       "      <td>8051</td>\n",
       "      <td>sao paulo</td>\n",
       "      <td>SP</td>\n",
       "      <td>Active</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98440</th>\n",
       "      <td>fd296940ea15b437d576eb81a265d3ad</td>\n",
       "      <td>6e13d840dffe7d6f5eb7d31b66a78c04</td>\n",
       "      <td>1241</td>\n",
       "      <td>sao paulo</td>\n",
       "      <td>SP</td>\n",
       "      <td>Active</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            customer_id                customer_unique_id  \\\n",
       "23892  6399526f1057f40e8454d9614e16d77a  f483ce4e87afdc082b44a89f6eb0fefe   \n",
       "17345  8bb46af2da65493427799b95d0739d34  877a47a9c3f87b14581bd7ad4b6207ea   \n",
       "20075  418398a534ddffb19a62d282e8285db4  b30bcdb9ae45a5a96f13d9b17826988d   \n",
       "77770  ef14bc7bc7f8a385f119d6d67e9d7fa9  fdd6bd902f6948b21330b111b89ccc4b   \n",
       "98440  fd296940ea15b437d576eb81a265d3ad  6e13d840dffe7d6f5eb7d31b66a78c04   \n",
       "\n",
       "       customer_zip_code_prefix          customer_city customer_state  status  \n",
       "23892                      8530  ferraz de vasconcelos             SP  Active  \n",
       "17345                      6310            carapicuiba             SP  Active  \n",
       "20075                     37750                machado             MG  Active  \n",
       "77770                      8051              sao paulo             SP  Active  \n",
       "98440                      1241              sao paulo             SP  Active  "
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "customer_id_in_orders_df = orders_df.customer_id.values\n",
    "customers_df[\"status\"] = customers_df[\"customer_id\"].apply(lambda x: \"Active\" if x in customer_id_in_orders_df else \"Non Active\")\n",
    "customers_df.sample(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Explore Product Category df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>product_category_name</th>\n",
       "      <th>product_category_name_english</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>71</td>\n",
       "      <td>71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>71</td>\n",
       "      <td>71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>beleza_saude</td>\n",
       "      <td>health_beauty</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       product_category_name product_category_name_english\n",
       "count                     71                            71\n",
       "unique                    71                            71\n",
       "top             beleza_saude                 health_beauty\n",
       "freq                       1                             1"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "product_cat_df.describe(include=\"all\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 71 entries, 0 to 70\n",
      "Data columns (total 2 columns):\n",
      " #   Column                         Non-Null Count  Dtype \n",
      "---  ------                         --------------  ----- \n",
      " 0   product_category_name          71 non-null     object\n",
      " 1   product_category_name_english  71 non-null     object\n",
      "dtypes: object(2)\n",
      "memory usage: 1.2+ KB\n"
     ]
    }
   ],
   "source": [
    "product_cat_df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Explore Sellers df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "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>seller_id</th>\n",
       "      <th>seller_zip_code_prefix</th>\n",
       "      <th>seller_city</th>\n",
       "      <th>seller_state</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>3095</td>\n",
       "      <td>3095.000000</td>\n",
       "      <td>3095</td>\n",
       "      <td>3095</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>3095</td>\n",
       "      <td>NaN</td>\n",
       "      <td>611</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>3442f8959a84dea7ee197c632cb2df15</td>\n",
       "      <td>NaN</td>\n",
       "      <td>sao paulo</td>\n",
       "      <td>SP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>694</td>\n",
       "      <td>1849</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>NaN</td>\n",
       "      <td>32291.059451</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>NaN</td>\n",
       "      <td>32713.453830</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1001.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>7093.500000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>14940.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>64552.500000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>NaN</td>\n",
       "      <td>99730.000000</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",
       "count                               3095             3095.000000        3095   \n",
       "unique                              3095                     NaN         611   \n",
       "top     3442f8959a84dea7ee197c632cb2df15                     NaN   sao paulo   \n",
       "freq                                   1                     NaN         694   \n",
       "mean                                 NaN            32291.059451         NaN   \n",
       "std                                  NaN            32713.453830         NaN   \n",
       "min                                  NaN             1001.000000         NaN   \n",
       "25%                                  NaN             7093.500000         NaN   \n",
       "50%                                  NaN            14940.000000         NaN   \n",
       "75%                                  NaN            64552.500000         NaN   \n",
       "max                                  NaN            99730.000000         NaN   \n",
       "\n",
       "       seller_state  \n",
       "count          3095  \n",
       "unique           23  \n",
       "top              SP  \n",
       "freq           1849  \n",
       "mean            NaN  \n",
       "std             NaN  \n",
       "min             NaN  \n",
       "25%             NaN  \n",
       "50%             NaN  \n",
       "75%             NaN  \n",
       "max             NaN  "
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sellers_df.describe(include=\"all\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "seller_city\n",
       "sao paulo         694\n",
       "curitiba          127\n",
       "rio de janeiro     96\n",
       "belo horizonte     68\n",
       "ribeirao preto     52\n",
       "                 ... \n",
       "ivoti               1\n",
       "itirapina           1\n",
       "itau de minas       1\n",
       "itapui              1\n",
       "xaxim               1\n",
       "Name: seller_id, Length: 611, dtype: int64"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sellers_df.groupby(by='seller_city').seller_id.nunique().sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "seller_state\n",
       "SP    1849\n",
       "PR     349\n",
       "MG     244\n",
       "SC     190\n",
       "RJ     171\n",
       "RS     129\n",
       "GO      40\n",
       "DF      30\n",
       "ES      23\n",
       "BA      19\n",
       "CE      13\n",
       "PE       9\n",
       "PB       6\n",
       "MS       5\n",
       "RN       5\n",
       "MT       4\n",
       "RO       2\n",
       "SE       2\n",
       "AC       1\n",
       "PI       1\n",
       "AM       1\n",
       "MA       1\n",
       "PA       1\n",
       "Name: seller_id, dtype: int64"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sellers_df.groupby(by='seller_state').seller_id.nunique().sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Explore & Merge 2 df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Merge Customers df & Orders df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "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>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>status</th>\n",
       "      <th>order_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>delivery_time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>06b8999e2fba1a1fbc88172c00ba8bc7</td>\n",
       "      <td>861eff4711a542e4b93843c6dd7febb0</td>\n",
       "      <td>14409</td>\n",
       "      <td>franca</td>\n",
       "      <td>SP</td>\n",
       "      <td>Active</td>\n",
       "      <td>00e7ee1b050b8499577073aeb2a297a1</td>\n",
       "      <td>delivered</td>\n",
       "      <td>2017-05-16 15:05:35</td>\n",
       "      <td>2017-05-16 15:22:12</td>\n",
       "      <td>2017-05-23 10:47:57</td>\n",
       "      <td>2017-05-25 10:35:35</td>\n",
       "      <td>2017-06-05</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>18955e83d337fd6b2def6b18a428ac77</td>\n",
       "      <td>290c77bc529b7ac935b93aa66c333dc3</td>\n",
       "      <td>9790</td>\n",
       "      <td>sao bernardo do campo</td>\n",
       "      <td>SP</td>\n",
       "      <td>Active</td>\n",
       "      <td>29150127e6685892b6eab3eec79f59c7</td>\n",
       "      <td>delivered</td>\n",
       "      <td>2018-01-12 20:48:24</td>\n",
       "      <td>2018-01-12 20:58:32</td>\n",
       "      <td>2018-01-15 17:14:59</td>\n",
       "      <td>2018-01-29 12:41:19</td>\n",
       "      <td>2018-02-06</td>\n",
       "      <td>14.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4e7b3e00288586ebd08712fdd0374a03</td>\n",
       "      <td>060e732b5b29e8181a18229c7b0b2b5e</td>\n",
       "      <td>1151</td>\n",
       "      <td>sao paulo</td>\n",
       "      <td>SP</td>\n",
       "      <td>Active</td>\n",
       "      <td>b2059ed67ce144a36e2aa97d2c9e9ad2</td>\n",
       "      <td>delivered</td>\n",
       "      <td>2018-05-19 16:07:45</td>\n",
       "      <td>2018-05-20 16:19:10</td>\n",
       "      <td>2018-06-11 14:31:00</td>\n",
       "      <td>2018-06-14 17:58:51</td>\n",
       "      <td>2018-06-13</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>b2b6027bc5c5109e529d4dc6358b12c3</td>\n",
       "      <td>259dac757896d24d7702b9acbbff3f3c</td>\n",
       "      <td>8775</td>\n",
       "      <td>mogi das cruzes</td>\n",
       "      <td>SP</td>\n",
       "      <td>Active</td>\n",
       "      <td>951670f92359f4fe4a63112aa7306eba</td>\n",
       "      <td>delivered</td>\n",
       "      <td>2018-03-13 16:06:38</td>\n",
       "      <td>2018-03-13 17:29:19</td>\n",
       "      <td>2018-03-27 23:22:42</td>\n",
       "      <td>2018-03-28 16:04:25</td>\n",
       "      <td>2018-04-10</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4f2d8ab171c80ec8364f7c12e35b23ad</td>\n",
       "      <td>345ecd01c38d18a9036ed96c73b8d066</td>\n",
       "      <td>13056</td>\n",
       "      <td>campinas</td>\n",
       "      <td>SP</td>\n",
       "      <td>Active</td>\n",
       "      <td>6b7d50bd145f6fc7f33cebabd7e49d0f</td>\n",
       "      <td>delivered</td>\n",
       "      <td>2018-07-29 09:51:30</td>\n",
       "      <td>2018-07-29 10:10:09</td>\n",
       "      <td>2018-07-30 15:16:00</td>\n",
       "      <td>2018-08-09 20:55:48</td>\n",
       "      <td>2018-08-15</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        customer_id                customer_unique_id  \\\n",
       "0  06b8999e2fba1a1fbc88172c00ba8bc7  861eff4711a542e4b93843c6dd7febb0   \n",
       "1  18955e83d337fd6b2def6b18a428ac77  290c77bc529b7ac935b93aa66c333dc3   \n",
       "2  4e7b3e00288586ebd08712fdd0374a03  060e732b5b29e8181a18229c7b0b2b5e   \n",
       "3  b2b6027bc5c5109e529d4dc6358b12c3  259dac757896d24d7702b9acbbff3f3c   \n",
       "4  4f2d8ab171c80ec8364f7c12e35b23ad  345ecd01c38d18a9036ed96c73b8d066   \n",
       "\n",
       "   customer_zip_code_prefix          customer_city customer_state  status  \\\n",
       "0                     14409                 franca             SP  Active   \n",
       "1                      9790  sao bernardo do campo             SP  Active   \n",
       "2                      1151              sao paulo             SP  Active   \n",
       "3                      8775        mogi das cruzes             SP  Active   \n",
       "4                     13056               campinas             SP  Active   \n",
       "\n",
       "                           order_id order_status order_purchase_timestamp  \\\n",
       "0  00e7ee1b050b8499577073aeb2a297a1    delivered      2017-05-16 15:05:35   \n",
       "1  29150127e6685892b6eab3eec79f59c7    delivered      2018-01-12 20:48:24   \n",
       "2  b2059ed67ce144a36e2aa97d2c9e9ad2    delivered      2018-05-19 16:07:45   \n",
       "3  951670f92359f4fe4a63112aa7306eba    delivered      2018-03-13 16:06:38   \n",
       "4  6b7d50bd145f6fc7f33cebabd7e49d0f    delivered      2018-07-29 09:51:30   \n",
       "\n",
       "    order_approved_at order_delivered_carrier_date  \\\n",
       "0 2017-05-16 15:22:12          2017-05-23 10:47:57   \n",
       "1 2018-01-12 20:58:32          2018-01-15 17:14:59   \n",
       "2 2018-05-20 16:19:10          2018-06-11 14:31:00   \n",
       "3 2018-03-13 17:29:19          2018-03-27 23:22:42   \n",
       "4 2018-07-29 10:10:09          2018-07-30 15:16:00   \n",
       "\n",
       "  order_delivered_customer_date order_estimated_delivery_date  delivery_time  \n",
       "0           2017-05-25 10:35:35                    2017-06-05            2.0  \n",
       "1           2018-01-29 12:41:19                    2018-02-06           14.0  \n",
       "2           2018-06-14 17:58:51                    2018-06-13            3.0  \n",
       "3           2018-03-28 16:04:25                    2018-04-10            1.0  \n",
       "4           2018-08-09 20:55:48                    2018-08-15           10.0  "
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cust_orders_df = pd.merge(\n",
    "    left=customers_df,\n",
    "    right=orders_df,\n",
    "    how=\"left\",\n",
    "    left_on=\"customer_id\",\n",
    "    right_on=\"customer_id\"\n",
    ")\n",
    "cust_orders_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "order_status\n",
       "delivered      96478\n",
       "shipped         1107\n",
       "canceled         625\n",
       "unavailable      609\n",
       "invoiced         314\n",
       "processing       301\n",
       "created            5\n",
       "approved           2\n",
       "Name: customer_id, dtype: int64"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cust_orders_df.groupby(by=\"order_status\").customer_id.nunique().sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "customer_state\n",
       "SP    41746\n",
       "RJ    12852\n",
       "MG    11635\n",
       "RS     5466\n",
       "PR     5045\n",
       "SC     3637\n",
       "BA     3380\n",
       "DF     2140\n",
       "ES     2033\n",
       "GO     2020\n",
       "PE     1652\n",
       "CE     1336\n",
       "PA      975\n",
       "MT      907\n",
       "MA      747\n",
       "MS      715\n",
       "PB      536\n",
       "PI      495\n",
       "RN      485\n",
       "AL      413\n",
       "SE      350\n",
       "TO      280\n",
       "RO      253\n",
       "AM      148\n",
       "AC       81\n",
       "AP       68\n",
       "RR       46\n",
       "Name: customer_id, dtype: int64"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cust_orders_df.groupby(by=\"customer_state\").customer_id.nunique().sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "customer_city\n",
       "sao paulo         15540\n",
       "rio de janeiro     6882\n",
       "belo horizonte     2773\n",
       "brasilia           2131\n",
       "curitiba           1521\n",
       "                  ...  \n",
       "ibiara                1\n",
       "rio espera            1\n",
       "rio dos indios        1\n",
       "rio dos cedros        1\n",
       "lagoao                1\n",
       "Name: customer_id, Length: 4119, dtype: int64"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cust_orders_df.groupby(by=\"customer_city\").customer_id.nunique().sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Merge Order Items df & Sellers df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "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>seller_zip_code_prefix</th>\n",
       "      <th>seller_city</th>\n",
       "      <th>seller_state</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>2017-09-19 09:45:35</td>\n",
       "      <td>58.90</td>\n",
       "      <td>13.29</td>\n",
       "      <td>27277</td>\n",
       "      <td>volta redonda</td>\n",
       "      <td>SP</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>2017-05-03 11:05:13</td>\n",
       "      <td>239.90</td>\n",
       "      <td>19.93</td>\n",
       "      <td>3471</td>\n",
       "      <td>sao paulo</td>\n",
       "      <td>SP</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>2018-01-18 14:48:30</td>\n",
       "      <td>199.00</td>\n",
       "      <td>17.87</td>\n",
       "      <td>37564</td>\n",
       "      <td>borda da mata</td>\n",
       "      <td>MG</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>2018-08-15 10:10:18</td>\n",
       "      <td>12.99</td>\n",
       "      <td>12.79</td>\n",
       "      <td>14403</td>\n",
       "      <td>franca</td>\n",
       "      <td>SP</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>2017-02-13 13:57:51</td>\n",
       "      <td>199.90</td>\n",
       "      <td>18.14</td>\n",
       "      <td>87900</td>\n",
       "      <td>loanda</td>\n",
       "      <td>PR</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  seller_zip_code_prefix  \\\n",
       "0 2017-09-19 09:45:35   58.90          13.29                   27277   \n",
       "1 2017-05-03 11:05:13  239.90          19.93                    3471   \n",
       "2 2018-01-18 14:48:30  199.00          17.87                   37564   \n",
       "3 2018-08-15 10:10:18   12.99          12.79                   14403   \n",
       "4 2017-02-13 13:57:51  199.90          18.14                   87900   \n",
       "\n",
       "     seller_city seller_state  \n",
       "0  volta redonda           SP  \n",
       "1      sao paulo           SP  \n",
       "2  borda da mata           MG  \n",
       "3         franca           SP  \n",
       "4         loanda           PR  "
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "items_sellers_df = pd.merge(\n",
    "    left=order_items_df,\n",
    "    right=sellers_df,\n",
    "    how=\"left\",\n",
    "    left_on=\"seller_id\",\n",
    "    right_on=\"seller_id\"\n",
    ")\n",
    "items_sellers_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "seller_city\n",
       "sao paulo         694\n",
       "curitiba          127\n",
       "rio de janeiro     96\n",
       "belo horizonte     68\n",
       "ribeirao preto     52\n",
       "                 ... \n",
       "ivoti               1\n",
       "itirapina           1\n",
       "itau de minas       1\n",
       "itapui              1\n",
       "xaxim               1\n",
       "Name: seller_id, Length: 611, dtype: int64"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "items_sellers_df.groupby(by='seller_city').seller_id.nunique().sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "seller_state\n",
       "SP    1849\n",
       "PR     349\n",
       "MG     244\n",
       "SC     190\n",
       "RJ     171\n",
       "RS     129\n",
       "GO      40\n",
       "DF      30\n",
       "ES      23\n",
       "BA      19\n",
       "CE      13\n",
       "PE       9\n",
       "PB       6\n",
       "MS       5\n",
       "RN       5\n",
       "MT       4\n",
       "RO       2\n",
       "SE       2\n",
       "AC       1\n",
       "PI       1\n",
       "AM       1\n",
       "MA       1\n",
       "PA       1\n",
       "Name: seller_id, dtype: int64"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "items_sellers_df.groupby(by=\"seller_state\").seller_id.nunique().sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Merge Order Payments df & Order Reviews df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "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>payment_sequential</th>\n",
       "      <th>payment_type</th>\n",
       "      <th>payment_installments</th>\n",
       "      <th>payment_value</th>\n",
       "      <th>review_id</th>\n",
       "      <th>review_score</th>\n",
       "      <th>review_comment_title</th>\n",
       "      <th>review_comment_message</th>\n",
       "      <th>review_creation_date</th>\n",
       "      <th>review_answer_timestamp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>b81ef226f3fe1789b1e8b2acac839d17</td>\n",
       "      <td>1</td>\n",
       "      <td>credit_card</td>\n",
       "      <td>8</td>\n",
       "      <td>99.33</td>\n",
       "      <td>4ce650e117a5b292bf5ea980a61eb61e</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Não recomendo</td>\n",
       "      <td>Produto com qualidade ruim, amassado e descasc...</td>\n",
       "      <td>2018-05-10</td>\n",
       "      <td>2018-05-15 23:38:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>a9810da82917af2d9aefd1278f1dcfa0</td>\n",
       "      <td>1</td>\n",
       "      <td>credit_card</td>\n",
       "      <td>1</td>\n",
       "      <td>24.39</td>\n",
       "      <td>ad2036caca61484fc435d7e673c020f2</td>\n",
       "      <td>5.0</td>\n",
       "      <td>no comment</td>\n",
       "      <td>no comment</td>\n",
       "      <td>2018-06-30</td>\n",
       "      <td>2018-07-03 11:23:16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>25e8ea4e93396b6fa0d3dd708e76c1bd</td>\n",
       "      <td>1</td>\n",
       "      <td>credit_card</td>\n",
       "      <td>1</td>\n",
       "      <td>65.71</td>\n",
       "      <td>bcddfa0f7d2e54cfc74bae4f01b84d92</td>\n",
       "      <td>5.0</td>\n",
       "      <td>no comment</td>\n",
       "      <td>Aprovada</td>\n",
       "      <td>2017-12-19</td>\n",
       "      <td>2017-12-22 17:55:30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ba78997921bbcdc1373bb41e913ab953</td>\n",
       "      <td>1</td>\n",
       "      <td>credit_card</td>\n",
       "      <td>8</td>\n",
       "      <td>107.78</td>\n",
       "      <td>c37a0e51e5df82bdb55d38ff0ffb6af3</td>\n",
       "      <td>5.0</td>\n",
       "      <td>no comment</td>\n",
       "      <td>no comment</td>\n",
       "      <td>2017-12-21</td>\n",
       "      <td>2017-12-21 21:42:31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ba78997921bbcdc1373bb41e913ab953</td>\n",
       "      <td>1</td>\n",
       "      <td>credit_card</td>\n",
       "      <td>8</td>\n",
       "      <td>107.78</td>\n",
       "      <td>5bc7680443b4ef765fec11a8201eeb0c</td>\n",
       "      <td>5.0</td>\n",
       "      <td>no comment</td>\n",
       "      <td>no comment</td>\n",
       "      <td>2017-12-21</td>\n",
       "      <td>2017-12-22 01:10:39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>42fdf880ba16b47b59251dd489d4441a</td>\n",
       "      <td>1</td>\n",
       "      <td>credit_card</td>\n",
       "      <td>2</td>\n",
       "      <td>128.45</td>\n",
       "      <td>d4f36356231f2e8a28c0209df9181c29</td>\n",
       "      <td>5.0</td>\n",
       "      <td>no comment</td>\n",
       "      <td>no comment</td>\n",
       "      <td>2018-06-02</td>\n",
       "      <td>2018-06-05 15:19:55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>298fcdf1f73eb413e4d26d01b25bc1cd</td>\n",
       "      <td>1</td>\n",
       "      <td>credit_card</td>\n",
       "      <td>2</td>\n",
       "      <td>96.12</td>\n",
       "      <td>d9a5d41bc78d0f565a7b8ac13585e9a9</td>\n",
       "      <td>5.0</td>\n",
       "      <td>10</td>\n",
       "      <td>Muito bom!!\\r\\n</td>\n",
       "      <td>2018-05-15</td>\n",
       "      <td>2018-05-15 22:41:43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>771ee386b001f06208a7419e4fc1bbd7</td>\n",
       "      <td>1</td>\n",
       "      <td>credit_card</td>\n",
       "      <td>1</td>\n",
       "      <td>81.16</td>\n",
       "      <td>35f54313f077af011b5e3a8f0dafb9d7</td>\n",
       "      <td>5.0</td>\n",
       "      <td>no comment</td>\n",
       "      <td>Fiquei muito satisfeita chegou bom rápido!</td>\n",
       "      <td>2017-07-04</td>\n",
       "      <td>2017-07-05 00:12:17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>3d7239c394a212faae122962df514ac7</td>\n",
       "      <td>1</td>\n",
       "      <td>credit_card</td>\n",
       "      <td>3</td>\n",
       "      <td>51.84</td>\n",
       "      <td>1e23005084e0ca486cb6be7f343ab1ef</td>\n",
       "      <td>4.0</td>\n",
       "      <td>no comment</td>\n",
       "      <td>no comment</td>\n",
       "      <td>2017-06-24</td>\n",
       "      <td>2017-06-27 11:04:19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1f78449c87a54faf9e96e88ba1491fa9</td>\n",
       "      <td>1</td>\n",
       "      <td>credit_card</td>\n",
       "      <td>6</td>\n",
       "      <td>341.09</td>\n",
       "      <td>59398fe630bce3bce7dd6faf226a3d2f</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Muito bom</td>\n",
       "      <td>Gostei</td>\n",
       "      <td>2018-07-29</td>\n",
       "      <td>2018-07-31 18:50:06</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           order_id  payment_sequential payment_type  \\\n",
       "0  b81ef226f3fe1789b1e8b2acac839d17                   1  credit_card   \n",
       "1  a9810da82917af2d9aefd1278f1dcfa0                   1  credit_card   \n",
       "2  25e8ea4e93396b6fa0d3dd708e76c1bd                   1  credit_card   \n",
       "3  ba78997921bbcdc1373bb41e913ab953                   1  credit_card   \n",
       "4  ba78997921bbcdc1373bb41e913ab953                   1  credit_card   \n",
       "5  42fdf880ba16b47b59251dd489d4441a                   1  credit_card   \n",
       "6  298fcdf1f73eb413e4d26d01b25bc1cd                   1  credit_card   \n",
       "7  771ee386b001f06208a7419e4fc1bbd7                   1  credit_card   \n",
       "8  3d7239c394a212faae122962df514ac7                   1  credit_card   \n",
       "9  1f78449c87a54faf9e96e88ba1491fa9                   1  credit_card   \n",
       "\n",
       "   payment_installments  payment_value                         review_id  \\\n",
       "0                     8          99.33  4ce650e117a5b292bf5ea980a61eb61e   \n",
       "1                     1          24.39  ad2036caca61484fc435d7e673c020f2   \n",
       "2                     1          65.71  bcddfa0f7d2e54cfc74bae4f01b84d92   \n",
       "3                     8         107.78  c37a0e51e5df82bdb55d38ff0ffb6af3   \n",
       "4                     8         107.78  5bc7680443b4ef765fec11a8201eeb0c   \n",
       "5                     2         128.45  d4f36356231f2e8a28c0209df9181c29   \n",
       "6                     2          96.12  d9a5d41bc78d0f565a7b8ac13585e9a9   \n",
       "7                     1          81.16  35f54313f077af011b5e3a8f0dafb9d7   \n",
       "8                     3          51.84  1e23005084e0ca486cb6be7f343ab1ef   \n",
       "9                     6         341.09  59398fe630bce3bce7dd6faf226a3d2f   \n",
       "\n",
       "   review_score review_comment_title  \\\n",
       "0           1.0        Não recomendo   \n",
       "1           5.0           no comment   \n",
       "2           5.0           no comment   \n",
       "3           5.0           no comment   \n",
       "4           5.0           no comment   \n",
       "5           5.0           no comment   \n",
       "6           5.0                   10   \n",
       "7           5.0           no comment   \n",
       "8           4.0           no comment   \n",
       "9           5.0            Muito bom   \n",
       "\n",
       "                              review_comment_message review_creation_date  \\\n",
       "0  Produto com qualidade ruim, amassado e descasc...           2018-05-10   \n",
       "1                                         no comment           2018-06-30   \n",
       "2                                           Aprovada           2017-12-19   \n",
       "3                                         no comment           2017-12-21   \n",
       "4                                         no comment           2017-12-21   \n",
       "5                                         no comment           2018-06-02   \n",
       "6                                    Muito bom!!\\r\\n           2018-05-15   \n",
       "7         Fiquei muito satisfeita chegou bom rápido!           2017-07-04   \n",
       "8                                         no comment           2017-06-24   \n",
       "9                                             Gostei           2018-07-29   \n",
       "\n",
       "  review_answer_timestamp  \n",
       "0     2018-05-15 23:38:00  \n",
       "1     2018-07-03 11:23:16  \n",
       "2     2017-12-22 17:55:30  \n",
       "3     2017-12-21 21:42:31  \n",
       "4     2017-12-22 01:10:39  \n",
       "5     2018-06-05 15:19:55  \n",
       "6     2018-05-15 22:41:43  \n",
       "7     2017-07-05 00:12:17  \n",
       "8     2017-06-27 11:04:19  \n",
       "9     2018-07-31 18:50:06  "
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "order_payrev_df = pd.merge(\n",
    "    left=order_pay_df,\n",
    "    right=order_rev_df,\n",
    "    how=\"left\",\n",
    "    left_on=\"order_id\",\n",
    "    right_on=\"order_id\"\n",
    ")\n",
    "order_payrev_df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\muham\\AppData\\Local\\Temp\\ipykernel_16060\\3409310786.py:1: FutureWarning: Treating datetime data as categorical rather than numeric in `.describe` is deprecated and will be removed in a future version of pandas. Specify `datetime_is_numeric=True` to silence this warning and adopt the future behavior now.\n",
      "  order_payrev_df.describe(include=\"all\")\n",
      "C:\\Users\\muham\\AppData\\Local\\Temp\\ipykernel_16060\\3409310786.py:1: FutureWarning: Treating datetime data as categorical rather than numeric in `.describe` is deprecated and will be removed in a future version of pandas. Specify `datetime_is_numeric=True` to silence this warning and adopt the future behavior now.\n",
      "  order_payrev_df.describe(include=\"all\")\n"
     ]
    },
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      ],
      "text/plain": [
       "                                order_id  payment_sequential payment_type  \\\n",
       "count                             104477       104477.000000       104477   \n",
       "unique                             99440                 NaN            5   \n",
       "top     fa65dad1b0e818e3ccc5cb0e39231352                 NaN  credit_card   \n",
       "freq                                  29                 NaN        77198   \n",
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       "mean                                 NaN            1.093379          NaN   \n",
       "std                                  NaN            0.709289          NaN   \n",
       "min                                  NaN            1.000000          NaN   \n",
       "25%                                  NaN            1.000000          NaN   \n",
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       "max                                  NaN           29.000000          NaN   \n",
       "\n",
       "        payment_installments  payment_value                         review_id  \\\n",
       "count          104477.000000  104477.000000                            103677   \n",
       "unique                   NaN            NaN                             98409   \n",
       "top                      NaN            NaN  f28281373ab8815bafafe371218f02ce   \n",
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       "max                24.000000   13664.080000                               NaN   \n",
       "\n",
       "         review_score review_comment_title review_comment_message  \\\n",
       "count   103677.000000               103677                 103677   \n",
       "unique            NaN                 4528                  36159   \n",
       "top               NaN           no comment             no comment   \n",
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       "\n",
       "       review_creation_date review_answer_timestamp  \n",
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       "top     2017-12-19 00:00:00     2017-05-24 16:21:27  \n",
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      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "order_payrev_df.describe(include=\"all\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
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       "      <td>2017-04-18</td>\n",
       "      <td>2017-04-21 15:17:33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70713</th>\n",
       "      <td>b4c4b76c642808cbe472a32b86cddc95</td>\n",
       "      <td>1</td>\n",
       "      <td>credit_card</td>\n",
       "      <td>5</td>\n",
       "      <td>4809.44</td>\n",
       "      <td>0f39c8201a4782ed03470ed525e7d68b</td>\n",
       "      <td>1.0</td>\n",
       "      <td>no comment</td>\n",
       "      <td>no comment</td>\n",
       "      <td>2018-08-03</td>\n",
       "      <td>2018-08-05 05:34:57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6481</th>\n",
       "      <td>199af31afc78c699f0dbf71fb178d4d4</td>\n",
       "      <td>1</td>\n",
       "      <td>credit_card</td>\n",
       "      <td>8</td>\n",
       "      <td>4764.34</td>\n",
       "      <td>dbf39e1ecc23738d2e7b56875fb49fac</td>\n",
       "      <td>4.0</td>\n",
       "      <td>no comment</td>\n",
       "      <td>no comment</td>\n",
       "      <td>2017-05-08</td>\n",
       "      <td>2017-05-09 02:00:10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67923</th>\n",
       "      <td>8dbc85d1447242f3b127dda390d56e19</td>\n",
       "      <td>1</td>\n",
       "      <td>credit_card</td>\n",
       "      <td>8</td>\n",
       "      <td>4681.78</td>\n",
       "      <td>5740810e4d16fe8fc6a1b959558f63a5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Satisfeito</td>\n",
       "      <td>Cumpriu o prometido</td>\n",
       "      <td>2018-07-06</td>\n",
       "      <td>2018-07-06 21:30:22</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               order_id  payment_sequential payment_type  \\\n",
       "52415  03caa2c082116e1d31e67e9ae3700499                   1  credit_card   \n",
       "34565  736e1922ae60d0d6a89247b851902527                   1       boleto   \n",
       "41660  0812eb902a67711a1cb742b3cdaa65ae                   1  credit_card   \n",
       "49871  fefacc66af859508bf1a7934eab1e97f                   1       boleto   \n",
       "86018  f5136e38d1a14a4dbd87dff67da82701                   1       boleto   \n",
       "62765  2cc9089445046817a7539d90805e6e5a                   1       boleto   \n",
       "43483  a96610ab360d42a2e5335a3998b4718a                   1  credit_card   \n",
       "70713  b4c4b76c642808cbe472a32b86cddc95                   1  credit_card   \n",
       "6481   199af31afc78c699f0dbf71fb178d4d4                   1  credit_card   \n",
       "67923  8dbc85d1447242f3b127dda390d56e19                   1  credit_card   \n",
       "\n",
       "       payment_installments  payment_value                         review_id  \\\n",
       "52415                     1       13664.08  211fa81a22870cc634265ea85b751e40   \n",
       "34565                     1        7274.88  6b9a600f1bb824da4ca38f65baf42526   \n",
       "41660                     8        6929.31  e2fbd9f0ef7b724c7ecb351601a857e7   \n",
       "49871                     1        6922.21                               NaN   \n",
       "86018                     1        6726.66  1b73e6c7cd135008ebeb18404b776ddc   \n",
       "62765                     1        6081.54  4069f5f76abb1cf501f2d4e614e1eaa4   \n",
       "43483                    10        4950.34  52632252782892317727260a87bc0663   \n",
       "70713                     5        4809.44  0f39c8201a4782ed03470ed525e7d68b   \n",
       "6481                      8        4764.34  dbf39e1ecc23738d2e7b56875fb49fac   \n",
       "67923                     8        4681.78  5740810e4d16fe8fc6a1b959558f63a5   \n",
       "\n",
       "       review_score review_comment_title  \\\n",
       "52415           1.0           no comment   \n",
       "34565           1.0   Entrega incompleta   \n",
       "41660           5.0           no comment   \n",
       "49871           NaN                  NaN   \n",
       "86018           5.0           no comment   \n",
       "62765           1.0           no comment   \n",
       "43483           5.0           no comment   \n",
       "70713           1.0           no comment   \n",
       "6481            4.0           no comment   \n",
       "67923           5.0           Satisfeito   \n",
       "\n",
       "                                  review_comment_message review_creation_date  \\\n",
       "52415  Peço por favor verificar a entrega do pedido, ...           2017-10-18   \n",
       "34565  Comprei 04 unidades, e foram entregues apenas ...           2018-07-27   \n",
       "41660  Mesmo a máquina ter vindo errada, já se pronti...           2017-03-04   \n",
       "49871                                                NaN                  NaT   \n",
       "86018                                         no comment           2017-06-06   \n",
       "62765                                         no comment           2017-12-14   \n",
       "43483                                         no comment           2017-04-18   \n",
       "70713                                         no comment           2018-08-03   \n",
       "6481                                          no comment           2017-05-08   \n",
       "67923                                Cumpriu o prometido           2018-07-06   \n",
       "\n",
       "      review_answer_timestamp  \n",
       "52415     2017-10-19 13:10:39  \n",
       "34565     2018-07-28 02:36:10  \n",
       "41660     2017-03-04 21:40:41  \n",
       "49871                     NaT  \n",
       "86018     2017-06-07 19:35:23  \n",
       "62765     2017-12-14 15:30:42  \n",
       "43483     2017-04-21 15:17:33  \n",
       "70713     2018-08-05 05:34:57  \n",
       "6481      2017-05-09 02:00:10  \n",
       "67923     2018-07-06 21:30:22  "
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "order_payrev_df.sort_values(by='payment_value', ascending=False).head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "payment_type\n",
       "credit_card    76505\n",
       "boleto         19784\n",
       "voucher         3866\n",
       "debit_card      1528\n",
       "not_defined        3\n",
       "Name: order_id, dtype: int64"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "order_payrev_df.groupby(by='payment_type').order_id.nunique().sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Merge Product Category df & Product df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\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>product_category_name</th>\n",
       "      <th>product_category_name_english</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>beleza_saude</td>\n",
       "      <td>health_beauty</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>informatica_acessorios</td>\n",
       "      <td>computers_accessories</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>automotivo</td>\n",
       "      <td>auto</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>cama_mesa_banho</td>\n",
       "      <td>bed_bath_table</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>moveis_decoracao</td>\n",
       "      <td>furniture_decor</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    product_category_name product_category_name_english\n",
       "0            beleza_saude                 health_beauty\n",
       "1  informatica_acessorios         computers_accessories\n",
       "2              automotivo                          auto\n",
       "3         cama_mesa_banho                bed_bath_table\n",
       "4        moveis_decoracao               furniture_decor"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "product_cat_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
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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_id</th>\n",
       "      <th>product_category_name</th>\n",
       "      <th>product_name_length</th>\n",
       "      <th>product_description_length</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>40.0</td>\n",
       "      <td>287.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>225.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>14.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3aa071139cb16b67ca9e5dea641aaa2f</td>\n",
       "      <td>artes</td>\n",
       "      <td>44.0</td>\n",
       "      <td>276.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>96bd76ec8810374ed1b65e291975717f</td>\n",
       "      <td>esporte_lazer</td>\n",
       "      <td>46.0</td>\n",
       "      <td>250.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>154.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>cef67bcfe19066a932b7673e239eb23d</td>\n",
       "      <td>bebes</td>\n",
       "      <td>27.0</td>\n",
       "      <td>261.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>371.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>26.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>9dc1a7de274444849c219cff195d0b71</td>\n",
       "      <td>utilidades_domesticas</td>\n",
       "      <td>37.0</td>\n",
       "      <td>402.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>625.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>13.0</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_length  product_description_length  product_photos_qty  \\\n",
       "0                 40.0                       287.0                 1.0   \n",
       "1                 44.0                       276.0                 1.0   \n",
       "2                 46.0                       250.0                 1.0   \n",
       "3                 27.0                       261.0                 1.0   \n",
       "4                 37.0                       402.0                 4.0   \n",
       "\n",
       "   product_weight_g  product_length_cm  product_height_cm  product_width_cm  \n",
       "0             225.0               16.0               10.0              14.0  \n",
       "1            1000.0               30.0               18.0              20.0  \n",
       "2             154.0               18.0                9.0              15.0  \n",
       "3             371.0               26.0                4.0              26.0  \n",
       "4             625.0               20.0               17.0              13.0  "
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "products_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>product_category_name</th>\n",
       "      <th>product_category_name_english</th>\n",
       "      <th>product_id</th>\n",
       "      <th>product_name_length</th>\n",
       "      <th>product_description_length</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>beleza_saude</td>\n",
       "      <td>health_beauty</td>\n",
       "      <td>e3e020af31d4d89d2602272b315c3f6e</td>\n",
       "      <td>58.0</td>\n",
       "      <td>1987.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>13.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>beleza_saude</td>\n",
       "      <td>health_beauty</td>\n",
       "      <td>c5d8079278e912d7e3b6beb48ecb56e8</td>\n",
       "      <td>60.0</td>\n",
       "      <td>330.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1300.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>17.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>beleza_saude</td>\n",
       "      <td>health_beauty</td>\n",
       "      <td>36555a2f528d7b2a255c504191445d39</td>\n",
       "      <td>51.0</td>\n",
       "      <td>1069.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1467.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>beleza_saude</td>\n",
       "      <td>health_beauty</td>\n",
       "      <td>e586ebb6022265ae1eea38f46ffe3ead</td>\n",
       "      <td>40.0</td>\n",
       "      <td>1469.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1050.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>27.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>beleza_saude</td>\n",
       "      <td>health_beauty</td>\n",
       "      <td>75b4372e69a42f8ae1d908c076f547b2</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1313.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>938.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>23.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>beleza_saude</td>\n",
       "      <td>health_beauty</td>\n",
       "      <td>3569d4374a919941a50f57371b1dc93d</td>\n",
       "      <td>37.0</td>\n",
       "      <td>1168.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2700.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>31.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>beleza_saude</td>\n",
       "      <td>health_beauty</td>\n",
       "      <td>3a6a0247ced9dcb444b46caafdcdd368</td>\n",
       "      <td>60.0</td>\n",
       "      <td>2497.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3600.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>beleza_saude</td>\n",
       "      <td>health_beauty</td>\n",
       "      <td>adf591c625cb265c12bc6749d3a2f757</td>\n",
       "      <td>60.0</td>\n",
       "      <td>1146.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>33.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>beleza_saude</td>\n",
       "      <td>health_beauty</td>\n",
       "      <td>50556c630443502c11acde1c320fe278</td>\n",
       "      <td>59.0</td>\n",
       "      <td>257.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>250.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>beleza_saude</td>\n",
       "      <td>health_beauty</td>\n",
       "      <td>88d2c501ec765f5d7e8038fa6aab0e62</td>\n",
       "      <td>51.0</td>\n",
       "      <td>863.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>10700.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>40.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  product_category_name product_category_name_english  \\\n",
       "0          beleza_saude                 health_beauty   \n",
       "1          beleza_saude                 health_beauty   \n",
       "2          beleza_saude                 health_beauty   \n",
       "3          beleza_saude                 health_beauty   \n",
       "4          beleza_saude                 health_beauty   \n",
       "5          beleza_saude                 health_beauty   \n",
       "6          beleza_saude                 health_beauty   \n",
       "7          beleza_saude                 health_beauty   \n",
       "8          beleza_saude                 health_beauty   \n",
       "9          beleza_saude                 health_beauty   \n",
       "\n",
       "                         product_id  product_name_length  \\\n",
       "0  e3e020af31d4d89d2602272b315c3f6e                 58.0   \n",
       "1  c5d8079278e912d7e3b6beb48ecb56e8                 60.0   \n",
       "2  36555a2f528d7b2a255c504191445d39                 51.0   \n",
       "3  e586ebb6022265ae1eea38f46ffe3ead                 40.0   \n",
       "4  75b4372e69a42f8ae1d908c076f547b2                 22.0   \n",
       "5  3569d4374a919941a50f57371b1dc93d                 37.0   \n",
       "6  3a6a0247ced9dcb444b46caafdcdd368                 60.0   \n",
       "7  adf591c625cb265c12bc6749d3a2f757                 60.0   \n",
       "8  50556c630443502c11acde1c320fe278                 59.0   \n",
       "9  88d2c501ec765f5d7e8038fa6aab0e62                 51.0   \n",
       "\n",
       "   product_description_length  product_photos_qty  product_weight_g  \\\n",
       "0                      1987.0                 3.0              75.0   \n",
       "1                       330.0                 1.0            1300.0   \n",
       "2                      1069.0                 1.0            1467.0   \n",
       "3                      1469.0                 3.0            1050.0   \n",
       "4                      1313.0                 1.0             938.0   \n",
       "5                      1168.0                 1.0            2700.0   \n",
       "6                      2497.0                 1.0            3600.0   \n",
       "7                      1146.0                 6.0             150.0   \n",
       "8                       257.0                 1.0             250.0   \n",
       "9                       863.0                 1.0           10700.0   \n",
       "\n",
       "   product_length_cm  product_height_cm  product_width_cm  \n",
       "0               21.0                7.0              13.0  \n",
       "1               19.0               28.0              17.0  \n",
       "2               23.0               16.0              20.0  \n",
       "3               18.0               21.0              27.0  \n",
       "4               29.0               22.0              23.0  \n",
       "5               21.0               16.0              31.0  \n",
       "6               20.0               20.0              20.0  \n",
       "7               20.0               24.0              33.0  \n",
       "8               16.0                2.0              20.0  \n",
       "9               40.0               40.0              40.0  "
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "product_merge_df = pd.merge(\n",
    "    left=product_cat_df,\n",
    "    right=products_df,\n",
    "    how=\"left\",\n",
    "    left_on=\"product_category_name\",\n",
    "    right_on=\"product_category_name\"\n",
    ")\n",
    "product_merge_df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "product_category_name\n",
       "cama_mesa_banho                  3029\n",
       "esporte_lazer                    2867\n",
       "moveis_decoracao                 2657\n",
       "beleza_saude                     2444\n",
       "utilidades_domesticas            2335\n",
       "                                 ... \n",
       "tablets_impressao_imagem            9\n",
       "casa_conforto_2                     5\n",
       "fashion_roupa_infanto_juvenil       5\n",
       "seguros_e_servicos                  2\n",
       "cds_dvds_musicais                   1\n",
       "Name: product_id, Length: 71, dtype: int64"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "product_merge_df.groupby(by='product_category_name').product_id.nunique().sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "product_category_name_english\n",
       "bed_bath_table               3029\n",
       "sports_leisure               2867\n",
       "furniture_decor              2657\n",
       "health_beauty                2444\n",
       "housewares                   2335\n",
       "                             ... \n",
       "tablets_printing_image          9\n",
       "home_comfort_2                  5\n",
       "fashion_childrens_clothes       5\n",
       "security_and_services           2\n",
       "cds_dvds_musicals               1\n",
       "Name: product_id, Length: 71, dtype: int64"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "product_merge_df.groupby(by='product_category_name_english').product_id.nunique().sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Explore & Merge 4 df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Merge Customers Orders df & Order Payments Order Reviews df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "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>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>status</th>\n",
       "      <th>order_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>delivery_time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>06b8999e2fba1a1fbc88172c00ba8bc7</td>\n",
       "      <td>861eff4711a542e4b93843c6dd7febb0</td>\n",
       "      <td>14409</td>\n",
       "      <td>franca</td>\n",
       "      <td>SP</td>\n",
       "      <td>Active</td>\n",
       "      <td>00e7ee1b050b8499577073aeb2a297a1</td>\n",
       "      <td>delivered</td>\n",
       "      <td>2017-05-16 15:05:35</td>\n",
       "      <td>2017-05-16 15:22:12</td>\n",
       "      <td>2017-05-23 10:47:57</td>\n",
       "      <td>2017-05-25 10:35:35</td>\n",
       "      <td>2017-06-05</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>18955e83d337fd6b2def6b18a428ac77</td>\n",
       "      <td>290c77bc529b7ac935b93aa66c333dc3</td>\n",
       "      <td>9790</td>\n",
       "      <td>sao bernardo do campo</td>\n",
       "      <td>SP</td>\n",
       "      <td>Active</td>\n",
       "      <td>29150127e6685892b6eab3eec79f59c7</td>\n",
       "      <td>delivered</td>\n",
       "      <td>2018-01-12 20:48:24</td>\n",
       "      <td>2018-01-12 20:58:32</td>\n",
       "      <td>2018-01-15 17:14:59</td>\n",
       "      <td>2018-01-29 12:41:19</td>\n",
       "      <td>2018-02-06</td>\n",
       "      <td>14.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4e7b3e00288586ebd08712fdd0374a03</td>\n",
       "      <td>060e732b5b29e8181a18229c7b0b2b5e</td>\n",
       "      <td>1151</td>\n",
       "      <td>sao paulo</td>\n",
       "      <td>SP</td>\n",
       "      <td>Active</td>\n",
       "      <td>b2059ed67ce144a36e2aa97d2c9e9ad2</td>\n",
       "      <td>delivered</td>\n",
       "      <td>2018-05-19 16:07:45</td>\n",
       "      <td>2018-05-20 16:19:10</td>\n",
       "      <td>2018-06-11 14:31:00</td>\n",
       "      <td>2018-06-14 17:58:51</td>\n",
       "      <td>2018-06-13</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>b2b6027bc5c5109e529d4dc6358b12c3</td>\n",
       "      <td>259dac757896d24d7702b9acbbff3f3c</td>\n",
       "      <td>8775</td>\n",
       "      <td>mogi das cruzes</td>\n",
       "      <td>SP</td>\n",
       "      <td>Active</td>\n",
       "      <td>951670f92359f4fe4a63112aa7306eba</td>\n",
       "      <td>delivered</td>\n",
       "      <td>2018-03-13 16:06:38</td>\n",
       "      <td>2018-03-13 17:29:19</td>\n",
       "      <td>2018-03-27 23:22:42</td>\n",
       "      <td>2018-03-28 16:04:25</td>\n",
       "      <td>2018-04-10</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4f2d8ab171c80ec8364f7c12e35b23ad</td>\n",
       "      <td>345ecd01c38d18a9036ed96c73b8d066</td>\n",
       "      <td>13056</td>\n",
       "      <td>campinas</td>\n",
       "      <td>SP</td>\n",
       "      <td>Active</td>\n",
       "      <td>6b7d50bd145f6fc7f33cebabd7e49d0f</td>\n",
       "      <td>delivered</td>\n",
       "      <td>2018-07-29 09:51:30</td>\n",
       "      <td>2018-07-29 10:10:09</td>\n",
       "      <td>2018-07-30 15:16:00</td>\n",
       "      <td>2018-08-09 20:55:48</td>\n",
       "      <td>2018-08-15</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        customer_id                customer_unique_id  \\\n",
       "0  06b8999e2fba1a1fbc88172c00ba8bc7  861eff4711a542e4b93843c6dd7febb0   \n",
       "1  18955e83d337fd6b2def6b18a428ac77  290c77bc529b7ac935b93aa66c333dc3   \n",
       "2  4e7b3e00288586ebd08712fdd0374a03  060e732b5b29e8181a18229c7b0b2b5e   \n",
       "3  b2b6027bc5c5109e529d4dc6358b12c3  259dac757896d24d7702b9acbbff3f3c   \n",
       "4  4f2d8ab171c80ec8364f7c12e35b23ad  345ecd01c38d18a9036ed96c73b8d066   \n",
       "\n",
       "   customer_zip_code_prefix          customer_city customer_state  status  \\\n",
       "0                     14409                 franca             SP  Active   \n",
       "1                      9790  sao bernardo do campo             SP  Active   \n",
       "2                      1151              sao paulo             SP  Active   \n",
       "3                      8775        mogi das cruzes             SP  Active   \n",
       "4                     13056               campinas             SP  Active   \n",
       "\n",
       "                           order_id order_status order_purchase_timestamp  \\\n",
       "0  00e7ee1b050b8499577073aeb2a297a1    delivered      2017-05-16 15:05:35   \n",
       "1  29150127e6685892b6eab3eec79f59c7    delivered      2018-01-12 20:48:24   \n",
       "2  b2059ed67ce144a36e2aa97d2c9e9ad2    delivered      2018-05-19 16:07:45   \n",
       "3  951670f92359f4fe4a63112aa7306eba    delivered      2018-03-13 16:06:38   \n",
       "4  6b7d50bd145f6fc7f33cebabd7e49d0f    delivered      2018-07-29 09:51:30   \n",
       "\n",
       "    order_approved_at order_delivered_carrier_date  \\\n",
       "0 2017-05-16 15:22:12          2017-05-23 10:47:57   \n",
       "1 2018-01-12 20:58:32          2018-01-15 17:14:59   \n",
       "2 2018-05-20 16:19:10          2018-06-11 14:31:00   \n",
       "3 2018-03-13 17:29:19          2018-03-27 23:22:42   \n",
       "4 2018-07-29 10:10:09          2018-07-30 15:16:00   \n",
       "\n",
       "  order_delivered_customer_date order_estimated_delivery_date  delivery_time  \n",
       "0           2017-05-25 10:35:35                    2017-06-05            2.0  \n",
       "1           2018-01-29 12:41:19                    2018-02-06           14.0  \n",
       "2           2018-06-14 17:58:51                    2018-06-13            3.0  \n",
       "3           2018-03-28 16:04:25                    2018-04-10            1.0  \n",
       "4           2018-08-09 20:55:48                    2018-08-15           10.0  "
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cust_orders_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>order_id</th>\n",
       "      <th>payment_sequential</th>\n",
       "      <th>payment_type</th>\n",
       "      <th>payment_installments</th>\n",
       "      <th>payment_value</th>\n",
       "      <th>review_id</th>\n",
       "      <th>review_score</th>\n",
       "      <th>review_comment_title</th>\n",
       "      <th>review_comment_message</th>\n",
       "      <th>review_creation_date</th>\n",
       "      <th>review_answer_timestamp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>b81ef226f3fe1789b1e8b2acac839d17</td>\n",
       "      <td>1</td>\n",
       "      <td>credit_card</td>\n",
       "      <td>8</td>\n",
       "      <td>99.33</td>\n",
       "      <td>4ce650e117a5b292bf5ea980a61eb61e</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Não recomendo</td>\n",
       "      <td>Produto com qualidade ruim, amassado e descasc...</td>\n",
       "      <td>2018-05-10</td>\n",
       "      <td>2018-05-15 23:38:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>a9810da82917af2d9aefd1278f1dcfa0</td>\n",
       "      <td>1</td>\n",
       "      <td>credit_card</td>\n",
       "      <td>1</td>\n",
       "      <td>24.39</td>\n",
       "      <td>ad2036caca61484fc435d7e673c020f2</td>\n",
       "      <td>5.0</td>\n",
       "      <td>no comment</td>\n",
       "      <td>no comment</td>\n",
       "      <td>2018-06-30</td>\n",
       "      <td>2018-07-03 11:23:16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>25e8ea4e93396b6fa0d3dd708e76c1bd</td>\n",
       "      <td>1</td>\n",
       "      <td>credit_card</td>\n",
       "      <td>1</td>\n",
       "      <td>65.71</td>\n",
       "      <td>bcddfa0f7d2e54cfc74bae4f01b84d92</td>\n",
       "      <td>5.0</td>\n",
       "      <td>no comment</td>\n",
       "      <td>Aprovada</td>\n",
       "      <td>2017-12-19</td>\n",
       "      <td>2017-12-22 17:55:30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ba78997921bbcdc1373bb41e913ab953</td>\n",
       "      <td>1</td>\n",
       "      <td>credit_card</td>\n",
       "      <td>8</td>\n",
       "      <td>107.78</td>\n",
       "      <td>c37a0e51e5df82bdb55d38ff0ffb6af3</td>\n",
       "      <td>5.0</td>\n",
       "      <td>no comment</td>\n",
       "      <td>no comment</td>\n",
       "      <td>2017-12-21</td>\n",
       "      <td>2017-12-21 21:42:31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ba78997921bbcdc1373bb41e913ab953</td>\n",
       "      <td>1</td>\n",
       "      <td>credit_card</td>\n",
       "      <td>8</td>\n",
       "      <td>107.78</td>\n",
       "      <td>5bc7680443b4ef765fec11a8201eeb0c</td>\n",
       "      <td>5.0</td>\n",
       "      <td>no comment</td>\n",
       "      <td>no comment</td>\n",
       "      <td>2017-12-21</td>\n",
       "      <td>2017-12-22 01:10:39</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           order_id  payment_sequential payment_type  \\\n",
       "0  b81ef226f3fe1789b1e8b2acac839d17                   1  credit_card   \n",
       "1  a9810da82917af2d9aefd1278f1dcfa0                   1  credit_card   \n",
       "2  25e8ea4e93396b6fa0d3dd708e76c1bd                   1  credit_card   \n",
       "3  ba78997921bbcdc1373bb41e913ab953                   1  credit_card   \n",
       "4  ba78997921bbcdc1373bb41e913ab953                   1  credit_card   \n",
       "\n",
       "   payment_installments  payment_value                         review_id  \\\n",
       "0                     8          99.33  4ce650e117a5b292bf5ea980a61eb61e   \n",
       "1                     1          24.39  ad2036caca61484fc435d7e673c020f2   \n",
       "2                     1          65.71  bcddfa0f7d2e54cfc74bae4f01b84d92   \n",
       "3                     8         107.78  c37a0e51e5df82bdb55d38ff0ffb6af3   \n",
       "4                     8         107.78  5bc7680443b4ef765fec11a8201eeb0c   \n",
       "\n",
       "   review_score review_comment_title  \\\n",
       "0           1.0        Não recomendo   \n",
       "1           5.0           no comment   \n",
       "2           5.0           no comment   \n",
       "3           5.0           no comment   \n",
       "4           5.0           no comment   \n",
       "\n",
       "                              review_comment_message review_creation_date  \\\n",
       "0  Produto com qualidade ruim, amassado e descasc...           2018-05-10   \n",
       "1                                         no comment           2018-06-30   \n",
       "2                                           Aprovada           2017-12-19   \n",
       "3                                         no comment           2017-12-21   \n",
       "4                                         no comment           2017-12-21   \n",
       "\n",
       "  review_answer_timestamp  \n",
       "0     2018-05-15 23:38:00  \n",
       "1     2018-07-03 11:23:16  \n",
       "2     2017-12-22 17:55:30  \n",
       "3     2017-12-21 21:42:31  \n",
       "4     2017-12-22 01:10:39  "
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "order_payrev_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\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>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>status</th>\n",
       "      <th>order_id</th>\n",
       "      <th>order_status</th>\n",
       "      <th>order_purchase_timestamp</th>\n",
       "      <th>order_approved_at</th>\n",
       "      <th>...</th>\n",
       "      <th>payment_sequential</th>\n",
       "      <th>payment_type</th>\n",
       "      <th>payment_installments</th>\n",
       "      <th>payment_value</th>\n",
       "      <th>review_id</th>\n",
       "      <th>review_score</th>\n",
       "      <th>review_comment_title</th>\n",
       "      <th>review_comment_message</th>\n",
       "      <th>review_creation_date</th>\n",
       "      <th>review_answer_timestamp</th>\n",
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       "      <td>06b8999e2fba1a1fbc88172c00ba8bc7</td>\n",
       "      <td>861eff4711a542e4b93843c6dd7febb0</td>\n",
       "      <td>14409</td>\n",
       "      <td>franca</td>\n",
       "      <td>SP</td>\n",
       "      <td>Active</td>\n",
       "      <td>00e7ee1b050b8499577073aeb2a297a1</td>\n",
       "      <td>delivered</td>\n",
       "      <td>2017-05-16 15:05:35</td>\n",
       "      <td>2017-05-16 15:22:12</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>credit_card</td>\n",
       "      <td>2.0</td>\n",
       "      <td>146.87</td>\n",
       "      <td>88b8b52d46df026a9d1ad2136a59b30b</td>\n",
       "      <td>4.0</td>\n",
       "      <td>no comment</td>\n",
       "      <td>no comment</td>\n",
       "      <td>2017-05-26</td>\n",
       "      <td>2017-05-30 22:34:40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>18955e83d337fd6b2def6b18a428ac77</td>\n",
       "      <td>290c77bc529b7ac935b93aa66c333dc3</td>\n",
       "      <td>9790</td>\n",
       "      <td>sao bernardo do campo</td>\n",
       "      <td>SP</td>\n",
       "      <td>Active</td>\n",
       "      <td>29150127e6685892b6eab3eec79f59c7</td>\n",
       "      <td>delivered</td>\n",
       "      <td>2018-01-12 20:48:24</td>\n",
       "      <td>2018-01-12 20:58:32</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>credit_card</td>\n",
       "      <td>8.0</td>\n",
       "      <td>335.48</td>\n",
       "      <td>02fc48a9efa3e3d0f1a8ea26507eeec3</td>\n",
       "      <td>5.0</td>\n",
       "      <td>no comment</td>\n",
       "      <td>no comment</td>\n",
       "      <td>2018-01-30</td>\n",
       "      <td>2018-02-10 22:43:29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4e7b3e00288586ebd08712fdd0374a03</td>\n",
       "      <td>060e732b5b29e8181a18229c7b0b2b5e</td>\n",
       "      <td>1151</td>\n",
       "      <td>sao paulo</td>\n",
       "      <td>SP</td>\n",
       "      <td>Active</td>\n",
       "      <td>b2059ed67ce144a36e2aa97d2c9e9ad2</td>\n",
       "      <td>delivered</td>\n",
       "      <td>2018-05-19 16:07:45</td>\n",
       "      <td>2018-05-20 16:19:10</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>credit_card</td>\n",
       "      <td>7.0</td>\n",
       "      <td>157.73</td>\n",
       "      <td>5ad6695d76ee186dc473c42706984d87</td>\n",
       "      <td>5.0</td>\n",
       "      <td>no comment</td>\n",
       "      <td>no comment</td>\n",
       "      <td>2018-06-15</td>\n",
       "      <td>2018-06-15 12:10:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>b2b6027bc5c5109e529d4dc6358b12c3</td>\n",
       "      <td>259dac757896d24d7702b9acbbff3f3c</td>\n",
       "      <td>8775</td>\n",
       "      <td>mogi das cruzes</td>\n",
       "      <td>SP</td>\n",
       "      <td>Active</td>\n",
       "      <td>951670f92359f4fe4a63112aa7306eba</td>\n",
       "      <td>delivered</td>\n",
       "      <td>2018-03-13 16:06:38</td>\n",
       "      <td>2018-03-13 17:29:19</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>credit_card</td>\n",
       "      <td>1.0</td>\n",
       "      <td>173.30</td>\n",
       "      <td>059a801bb31f6aab2266e672cab87bc5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>no comment</td>\n",
       "      <td>no comment</td>\n",
       "      <td>2018-03-29</td>\n",
       "      <td>2018-04-02 18:36:47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4f2d8ab171c80ec8364f7c12e35b23ad</td>\n",
       "      <td>345ecd01c38d18a9036ed96c73b8d066</td>\n",
       "      <td>13056</td>\n",
       "      <td>campinas</td>\n",
       "      <td>SP</td>\n",
       "      <td>Active</td>\n",
       "      <td>6b7d50bd145f6fc7f33cebabd7e49d0f</td>\n",
       "      <td>delivered</td>\n",
       "      <td>2018-07-29 09:51:30</td>\n",
       "      <td>2018-07-29 10:10:09</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>credit_card</td>\n",
       "      <td>8.0</td>\n",
       "      <td>252.25</td>\n",
       "      <td>8490879d58d6c5d7773f2739a03f089a</td>\n",
       "      <td>5.0</td>\n",
       "      <td>a melhor nota</td>\n",
       "      <td>O baratheon è esxelente Amo adoro o baratheon</td>\n",
       "      <td>2018-08-10</td>\n",
       "      <td>2018-08-17 01:59:52</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                        customer_id                customer_unique_id  \\\n",
       "0  06b8999e2fba1a1fbc88172c00ba8bc7  861eff4711a542e4b93843c6dd7febb0   \n",
       "1  18955e83d337fd6b2def6b18a428ac77  290c77bc529b7ac935b93aa66c333dc3   \n",
       "2  4e7b3e00288586ebd08712fdd0374a03  060e732b5b29e8181a18229c7b0b2b5e   \n",
       "3  b2b6027bc5c5109e529d4dc6358b12c3  259dac757896d24d7702b9acbbff3f3c   \n",
       "4  4f2d8ab171c80ec8364f7c12e35b23ad  345ecd01c38d18a9036ed96c73b8d066   \n",
       "\n",
       "   customer_zip_code_prefix          customer_city customer_state  status  \\\n",
       "0                     14409                 franca             SP  Active   \n",
       "1                      9790  sao bernardo do campo             SP  Active   \n",
       "2                      1151              sao paulo             SP  Active   \n",
       "3                      8775        mogi das cruzes             SP  Active   \n",
       "4                     13056               campinas             SP  Active   \n",
       "\n",
       "                           order_id order_status order_purchase_timestamp  \\\n",
       "0  00e7ee1b050b8499577073aeb2a297a1    delivered      2017-05-16 15:05:35   \n",
       "1  29150127e6685892b6eab3eec79f59c7    delivered      2018-01-12 20:48:24   \n",
       "2  b2059ed67ce144a36e2aa97d2c9e9ad2    delivered      2018-05-19 16:07:45   \n",
       "3  951670f92359f4fe4a63112aa7306eba    delivered      2018-03-13 16:06:38   \n",
       "4  6b7d50bd145f6fc7f33cebabd7e49d0f    delivered      2018-07-29 09:51:30   \n",
       "\n",
       "    order_approved_at  ... payment_sequential payment_type  \\\n",
       "0 2017-05-16 15:22:12  ...                1.0  credit_card   \n",
       "1 2018-01-12 20:58:32  ...                1.0  credit_card   \n",
       "2 2018-05-20 16:19:10  ...                1.0  credit_card   \n",
       "3 2018-03-13 17:29:19  ...                1.0  credit_card   \n",
       "4 2018-07-29 10:10:09  ...                1.0  credit_card   \n",
       "\n",
       "  payment_installments  payment_value                         review_id  \\\n",
       "0                  2.0         146.87  88b8b52d46df026a9d1ad2136a59b30b   \n",
       "1                  8.0         335.48  02fc48a9efa3e3d0f1a8ea26507eeec3   \n",
       "2                  7.0         157.73  5ad6695d76ee186dc473c42706984d87   \n",
       "3                  1.0         173.30  059a801bb31f6aab2266e672cab87bc5   \n",
       "4                  8.0         252.25  8490879d58d6c5d7773f2739a03f089a   \n",
       "\n",
       "  review_score  review_comment_title  \\\n",
       "0          4.0            no comment   \n",
       "1          5.0            no comment   \n",
       "2          5.0            no comment   \n",
       "3          5.0            no comment   \n",
       "4          5.0         a melhor nota   \n",
       "\n",
       "                          review_comment_message review_creation_date  \\\n",
       "0                                     no comment           2017-05-26   \n",
       "1                                     no comment           2018-01-30   \n",
       "2                                     no comment           2018-06-15   \n",
       "3                                     no comment           2018-03-29   \n",
       "4  O baratheon è esxelente Amo adoro o baratheon           2018-08-10   \n",
       "\n",
       "   review_answer_timestamp  \n",
       "0      2017-05-30 22:34:40  \n",
       "1      2018-02-10 22:43:29  \n",
       "2      2018-06-15 12:10:59  \n",
       "3      2018-04-02 18:36:47  \n",
       "4      2018-08-17 01:59:52  \n",
       "\n",
       "[5 rows x 24 columns]"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "customers_orders_df = pd.merge(\n",
    "    left=cust_orders_df,\n",
    "    right=order_payrev_df,\n",
    "    how=\"left\",\n",
    "    left_on=\"order_id\",\n",
    "    right_on=\"order_id\"\n",
    ")\n",
    "customers_orders_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Merge Order Items Sellers df & Product Category Products df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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>seller_zip_code_prefix</th>\n",
       "      <th>seller_city</th>\n",
       "      <th>seller_state</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>00010242fe8c5a6d1ba2dd792cb16214</td>\n",
       "      <td>1</td>\n",
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       "      <td>48436dade18ac8b2bce089ec2a041202</td>\n",
       "      <td>2017-09-19 09:45:35</td>\n",
       "      <td>58.90</td>\n",
       "      <td>13.29</td>\n",
       "      <td>27277</td>\n",
       "      <td>volta redonda</td>\n",
       "      <td>SP</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>2017-05-03 11:05:13</td>\n",
       "      <td>239.90</td>\n",
       "      <td>19.93</td>\n",
       "      <td>3471</td>\n",
       "      <td>sao paulo</td>\n",
       "      <td>SP</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>2018-01-18 14:48:30</td>\n",
       "      <td>199.00</td>\n",
       "      <td>17.87</td>\n",
       "      <td>37564</td>\n",
       "      <td>borda da mata</td>\n",
       "      <td>MG</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>2018-08-15 10:10:18</td>\n",
       "      <td>12.99</td>\n",
       "      <td>12.79</td>\n",
       "      <td>14403</td>\n",
       "      <td>franca</td>\n",
       "      <td>SP</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>2017-02-13 13:57:51</td>\n",
       "      <td>199.90</td>\n",
       "      <td>18.14</td>\n",
       "      <td>87900</td>\n",
       "      <td>loanda</td>\n",
       "      <td>PR</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  seller_zip_code_prefix  \\\n",
       "0 2017-09-19 09:45:35   58.90          13.29                   27277   \n",
       "1 2017-05-03 11:05:13  239.90          19.93                    3471   \n",
       "2 2018-01-18 14:48:30  199.00          17.87                   37564   \n",
       "3 2018-08-15 10:10:18   12.99          12.79                   14403   \n",
       "4 2017-02-13 13:57:51  199.90          18.14                   87900   \n",
       "\n",
       "     seller_city seller_state  \n",
       "0  volta redonda           SP  \n",
       "1      sao paulo           SP  \n",
       "2  borda da mata           MG  \n",
       "3         franca           SP  \n",
       "4         loanda           PR  "
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "items_sellers_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>product_category_name</th>\n",
       "      <th>product_category_name_english</th>\n",
       "      <th>product_id</th>\n",
       "      <th>product_name_length</th>\n",
       "      <th>product_description_length</th>\n",
       "      <th>product_photos_qty</th>\n",
       "      <th>product_weight_g</th>\n",
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       "      <th>product_height_cm</th>\n",
       "      <th>product_width_cm</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
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       "      <td>58.0</td>\n",
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       "      <td>3.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>13.0</td>\n",
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       "      <th>1</th>\n",
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       "      <td>60.0</td>\n",
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       "      <td>1300.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>17.0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>beleza_saude</td>\n",
       "      <td>health_beauty</td>\n",
       "      <td>36555a2f528d7b2a255c504191445d39</td>\n",
       "      <td>51.0</td>\n",
       "      <td>1069.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1467.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>beleza_saude</td>\n",
       "      <td>health_beauty</td>\n",
       "      <td>e586ebb6022265ae1eea38f46ffe3ead</td>\n",
       "      <td>40.0</td>\n",
       "      <td>1469.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1050.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>27.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>beleza_saude</td>\n",
       "      <td>health_beauty</td>\n",
       "      <td>75b4372e69a42f8ae1d908c076f547b2</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1313.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>938.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>23.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  product_category_name product_category_name_english  \\\n",
       "0          beleza_saude                 health_beauty   \n",
       "1          beleza_saude                 health_beauty   \n",
       "2          beleza_saude                 health_beauty   \n",
       "3          beleza_saude                 health_beauty   \n",
       "4          beleza_saude                 health_beauty   \n",
       "\n",
       "                         product_id  product_name_length  \\\n",
       "0  e3e020af31d4d89d2602272b315c3f6e                 58.0   \n",
       "1  c5d8079278e912d7e3b6beb48ecb56e8                 60.0   \n",
       "2  36555a2f528d7b2a255c504191445d39                 51.0   \n",
       "3  e586ebb6022265ae1eea38f46ffe3ead                 40.0   \n",
       "4  75b4372e69a42f8ae1d908c076f547b2                 22.0   \n",
       "\n",
       "   product_description_length  product_photos_qty  product_weight_g  \\\n",
       "0                      1987.0                 3.0              75.0   \n",
       "1                       330.0                 1.0            1300.0   \n",
       "2                      1069.0                 1.0            1467.0   \n",
       "3                      1469.0                 3.0            1050.0   \n",
       "4                      1313.0                 1.0             938.0   \n",
       "\n",
       "   product_length_cm  product_height_cm  product_width_cm  \n",
       "0               21.0                7.0              13.0  \n",
       "1               19.0               28.0              17.0  \n",
       "2               23.0               16.0              20.0  \n",
       "3               18.0               21.0              27.0  \n",
       "4               29.0               22.0              23.0  "
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "product_merge_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<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>seller_zip_code_prefix</th>\n",
       "      <th>seller_city</th>\n",
       "      <th>seller_state</th>\n",
       "      <th>product_category_name</th>\n",
       "      <th>product_category_name_english</th>\n",
       "      <th>product_name_length</th>\n",
       "      <th>product_description_length</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>00010242fe8c5a6d1ba2dd792cb16214</td>\n",
       "      <td>1</td>\n",
       "      <td>4244733e06e7ecb4970a6e2683c13e61</td>\n",
       "      <td>48436dade18ac8b2bce089ec2a041202</td>\n",
       "      <td>2017-09-19 09:45:35</td>\n",
       "      <td>58.90</td>\n",
       "      <td>13.29</td>\n",
       "      <td>27277</td>\n",
       "      <td>volta redonda</td>\n",
       "      <td>SP</td>\n",
       "      <td>cool_stuff</td>\n",
       "      <td>cool_stuff</td>\n",
       "      <td>58.0</td>\n",
       "      <td>598.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>650.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>14.0</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>2017-05-03 11:05:13</td>\n",
       "      <td>239.90</td>\n",
       "      <td>19.93</td>\n",
       "      <td>3471</td>\n",
       "      <td>sao paulo</td>\n",
       "      <td>SP</td>\n",
       "      <td>pet_shop</td>\n",
       "      <td>pet_shop</td>\n",
       "      <td>56.0</td>\n",
       "      <td>239.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>30000.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>40.0</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>2018-01-18 14:48:30</td>\n",
       "      <td>199.00</td>\n",
       "      <td>17.87</td>\n",
       "      <td>37564</td>\n",
       "      <td>borda da mata</td>\n",
       "      <td>MG</td>\n",
       "      <td>moveis_decoracao</td>\n",
       "      <td>furniture_decor</td>\n",
       "      <td>59.0</td>\n",
       "      <td>695.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3050.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>33.0</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>2018-08-15 10:10:18</td>\n",
       "      <td>12.99</td>\n",
       "      <td>12.79</td>\n",
       "      <td>14403</td>\n",
       "      <td>franca</td>\n",
       "      <td>SP</td>\n",
       "      <td>perfumaria</td>\n",
       "      <td>perfumery</td>\n",
       "      <td>42.0</td>\n",
       "      <td>480.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>200.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>15.0</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>2017-02-13 13:57:51</td>\n",
       "      <td>199.90</td>\n",
       "      <td>18.14</td>\n",
       "      <td>87900</td>\n",
       "      <td>loanda</td>\n",
       "      <td>PR</td>\n",
       "      <td>ferramentas_jardim</td>\n",
       "      <td>garden_tools</td>\n",
       "      <td>59.0</td>\n",
       "      <td>409.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3750.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>30.0</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  seller_zip_code_prefix  \\\n",
       "0 2017-09-19 09:45:35   58.90          13.29                   27277   \n",
       "1 2017-05-03 11:05:13  239.90          19.93                    3471   \n",
       "2 2018-01-18 14:48:30  199.00          17.87                   37564   \n",
       "3 2018-08-15 10:10:18   12.99          12.79                   14403   \n",
       "4 2017-02-13 13:57:51  199.90          18.14                   87900   \n",
       "\n",
       "     seller_city seller_state product_category_name  \\\n",
       "0  volta redonda           SP            cool_stuff   \n",
       "1      sao paulo           SP              pet_shop   \n",
       "2  borda da mata           MG      moveis_decoracao   \n",
       "3         franca           SP            perfumaria   \n",
       "4         loanda           PR    ferramentas_jardim   \n",
       "\n",
       "  product_category_name_english  product_name_length  \\\n",
       "0                    cool_stuff                 58.0   \n",
       "1                      pet_shop                 56.0   \n",
       "2               furniture_decor                 59.0   \n",
       "3                     perfumery                 42.0   \n",
       "4                  garden_tools                 59.0   \n",
       "\n",
       "   product_description_length  product_photos_qty  product_weight_g  \\\n",
       "0                       598.0                 4.0             650.0   \n",
       "1                       239.0                 2.0           30000.0   \n",
       "2                       695.0                 2.0            3050.0   \n",
       "3                       480.0                 1.0             200.0   \n",
       "4                       409.0                 1.0            3750.0   \n",
       "\n",
       "   product_length_cm  product_height_cm  product_width_cm  \n",
       "0               28.0                9.0              14.0  \n",
       "1               50.0               30.0              40.0  \n",
       "2               33.0               13.0              33.0  \n",
       "3               16.0               10.0              15.0  \n",
       "4               35.0               40.0              30.0  "
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "products_itemsells_df = pd.merge(\n",
    "    left=items_sellers_df,\n",
    "    right=product_merge_df,\n",
    "    how=\"left\",\n",
    "    left_on=\"product_id\",\n",
    "    right_on=\"product_id\"\n",
    "\n",
    ")\n",
    "products_itemsells_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Explore & Merge all df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "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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       "</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>status</th>\n",
       "      <th>order_id</th>\n",
       "      <th>order_status</th>\n",
       "      <th>order_purchase_timestamp</th>\n",
       "      <th>order_approved_at</th>\n",
       "      <th>...</th>\n",
       "      <th>seller_state</th>\n",
       "      <th>product_category_name</th>\n",
       "      <th>product_category_name_english</th>\n",
       "      <th>product_name_length</th>\n",
       "      <th>product_description_length</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>06b8999e2fba1a1fbc88172c00ba8bc7</td>\n",
       "      <td>861eff4711a542e4b93843c6dd7febb0</td>\n",
       "      <td>14409</td>\n",
       "      <td>franca</td>\n",
       "      <td>SP</td>\n",
       "      <td>Active</td>\n",
       "      <td>00e7ee1b050b8499577073aeb2a297a1</td>\n",
       "      <td>delivered</td>\n",
       "      <td>2017-05-16 15:05:35</td>\n",
       "      <td>2017-05-16 15:22:12</td>\n",
       "      <td>...</td>\n",
       "      <td>SP</td>\n",
       "      <td>moveis_escritorio</td>\n",
       "      <td>office_furniture</td>\n",
       "      <td>41.0</td>\n",
       "      <td>1141.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8683.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>31.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>18955e83d337fd6b2def6b18a428ac77</td>\n",
       "      <td>290c77bc529b7ac935b93aa66c333dc3</td>\n",
       "      <td>9790</td>\n",
       "      <td>sao bernardo do campo</td>\n",
       "      <td>SP</td>\n",
       "      <td>Active</td>\n",
       "      <td>29150127e6685892b6eab3eec79f59c7</td>\n",
       "      <td>delivered</td>\n",
       "      <td>2018-01-12 20:48:24</td>\n",
       "      <td>2018-01-12 20:58:32</td>\n",
       "      <td>...</td>\n",
       "      <td>SC</td>\n",
       "      <td>utilidades_domesticas</td>\n",
       "      <td>housewares</td>\n",
       "      <td>43.0</td>\n",
       "      <td>1002.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>10150.0</td>\n",
       "      <td>89.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>40.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4e7b3e00288586ebd08712fdd0374a03</td>\n",
       "      <td>060e732b5b29e8181a18229c7b0b2b5e</td>\n",
       "      <td>1151</td>\n",
       "      <td>sao paulo</td>\n",
       "      <td>SP</td>\n",
       "      <td>Active</td>\n",
       "      <td>b2059ed67ce144a36e2aa97d2c9e9ad2</td>\n",
       "      <td>delivered</td>\n",
       "      <td>2018-05-19 16:07:45</td>\n",
       "      <td>2018-05-20 16:19:10</td>\n",
       "      <td>...</td>\n",
       "      <td>SP</td>\n",
       "      <td>moveis_escritorio</td>\n",
       "      <td>office_furniture</td>\n",
       "      <td>55.0</td>\n",
       "      <td>955.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8267.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>17.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>b2b6027bc5c5109e529d4dc6358b12c3</td>\n",
       "      <td>259dac757896d24d7702b9acbbff3f3c</td>\n",
       "      <td>8775</td>\n",
       "      <td>mogi das cruzes</td>\n",
       "      <td>SP</td>\n",
       "      <td>Active</td>\n",
       "      <td>951670f92359f4fe4a63112aa7306eba</td>\n",
       "      <td>delivered</td>\n",
       "      <td>2018-03-13 16:06:38</td>\n",
       "      <td>2018-03-13 17:29:19</td>\n",
       "      <td>...</td>\n",
       "      <td>SP</td>\n",
       "      <td>moveis_escritorio</td>\n",
       "      <td>office_furniture</td>\n",
       "      <td>48.0</td>\n",
       "      <td>1066.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>12160.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>28.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4f2d8ab171c80ec8364f7c12e35b23ad</td>\n",
       "      <td>345ecd01c38d18a9036ed96c73b8d066</td>\n",
       "      <td>13056</td>\n",
       "      <td>campinas</td>\n",
       "      <td>SP</td>\n",
       "      <td>Active</td>\n",
       "      <td>6b7d50bd145f6fc7f33cebabd7e49d0f</td>\n",
       "      <td>delivered</td>\n",
       "      <td>2018-07-29 09:51:30</td>\n",
       "      <td>2018-07-29 10:10:09</td>\n",
       "      <td>...</td>\n",
       "      <td>SP</td>\n",
       "      <td>casa_conforto</td>\n",
       "      <td>home_confort</td>\n",
       "      <td>61.0</td>\n",
       "      <td>407.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5200.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>35.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>879864dab9bc3047522c92c82e1212b8</td>\n",
       "      <td>4c93744516667ad3b8f1fb645a3116a4</td>\n",
       "      <td>89254</td>\n",
       "      <td>jaragua do sul</td>\n",
       "      <td>SC</td>\n",
       "      <td>Active</td>\n",
       "      <td>5741ea1f91b5fbab2bd2dc653a5b5099</td>\n",
       "      <td>delivered</td>\n",
       "      <td>2017-09-14 18:14:31</td>\n",
       "      <td>2017-09-14 18:25:11</td>\n",
       "      <td>...</td>\n",
       "      <td>SP</td>\n",
       "      <td>esporte_lazer</td>\n",
       "      <td>sports_leisure</td>\n",
       "      <td>32.0</td>\n",
       "      <td>282.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4450.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>fd826e7cf63160e536e0908c76c3f441</td>\n",
       "      <td>addec96d2e059c80c30fe6871d30d177</td>\n",
       "      <td>4534</td>\n",
       "      <td>sao paulo</td>\n",
       "      <td>SP</td>\n",
       "      <td>Active</td>\n",
       "      <td>36e694cf4cbc2a4803200c35e84abdc4</td>\n",
       "      <td>delivered</td>\n",
       "      <td>2018-02-19 14:38:35</td>\n",
       "      <td>2018-02-19 14:50:37</td>\n",
       "      <td>...</td>\n",
       "      <td>SP</td>\n",
       "      <td>informatica_acessorios</td>\n",
       "      <td>computers_accessories</td>\n",
       "      <td>37.0</td>\n",
       "      <td>322.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>13.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>5e274e7a0c3809e14aba7ad5aae0d407</td>\n",
       "      <td>57b2a98a409812fe9618067b6b8ebe4f</td>\n",
       "      <td>35182</td>\n",
       "      <td>timoteo</td>\n",
       "      <td>MG</td>\n",
       "      <td>Active</td>\n",
       "      <td>1093c8304c7a003280dd34598194913d</td>\n",
       "      <td>delivered</td>\n",
       "      <td>2017-11-16 19:29:02</td>\n",
       "      <td>2017-11-16 19:55:41</td>\n",
       "      <td>...</td>\n",
       "      <td>SP</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>8</th>\n",
       "      <td>5adf08e34b2e993982a47070956c5c65</td>\n",
       "      <td>1175e95fb47ddff9de6b2b06188f7e0d</td>\n",
       "      <td>81560</td>\n",
       "      <td>curitiba</td>\n",
       "      <td>PR</td>\n",
       "      <td>Active</td>\n",
       "      <td>1ebeea841c590e86a14a0d7a48e7d062</td>\n",
       "      <td>delivered</td>\n",
       "      <td>2018-01-18 12:35:44</td>\n",
       "      <td>2018-01-18 12:56:32</td>\n",
       "      <td>...</td>\n",
       "      <td>RJ</td>\n",
       "      <td>brinquedos</td>\n",
       "      <td>toys</td>\n",
       "      <td>58.0</td>\n",
       "      <td>739.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>350.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>4b7139f34592b3a31687243a302fa75b</td>\n",
       "      <td>9afe194fb833f79e300e37e580171f22</td>\n",
       "      <td>30575</td>\n",
       "      <td>belo horizonte</td>\n",
       "      <td>MG</td>\n",
       "      <td>Active</td>\n",
       "      <td>7433cbcc783205509d66a5260da5b574</td>\n",
       "      <td>delivered</td>\n",
       "      <td>2018-01-08 11:22:34</td>\n",
       "      <td>2018-01-08 11:35:27</td>\n",
       "      <td>...</td>\n",
       "      <td>PR</td>\n",
       "      <td>moveis_decoracao</td>\n",
       "      <td>furniture_decor</td>\n",
       "      <td>57.0</td>\n",
       "      <td>272.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2500.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>30.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 42 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                        customer_id                customer_unique_id  \\\n",
       "0  06b8999e2fba1a1fbc88172c00ba8bc7  861eff4711a542e4b93843c6dd7febb0   \n",
       "1  18955e83d337fd6b2def6b18a428ac77  290c77bc529b7ac935b93aa66c333dc3   \n",
       "2  4e7b3e00288586ebd08712fdd0374a03  060e732b5b29e8181a18229c7b0b2b5e   \n",
       "3  b2b6027bc5c5109e529d4dc6358b12c3  259dac757896d24d7702b9acbbff3f3c   \n",
       "4  4f2d8ab171c80ec8364f7c12e35b23ad  345ecd01c38d18a9036ed96c73b8d066   \n",
       "5  879864dab9bc3047522c92c82e1212b8  4c93744516667ad3b8f1fb645a3116a4   \n",
       "6  fd826e7cf63160e536e0908c76c3f441  addec96d2e059c80c30fe6871d30d177   \n",
       "7  5e274e7a0c3809e14aba7ad5aae0d407  57b2a98a409812fe9618067b6b8ebe4f   \n",
       "8  5adf08e34b2e993982a47070956c5c65  1175e95fb47ddff9de6b2b06188f7e0d   \n",
       "9  4b7139f34592b3a31687243a302fa75b  9afe194fb833f79e300e37e580171f22   \n",
       "\n",
       "   customer_zip_code_prefix          customer_city customer_state  status  \\\n",
       "0                     14409                 franca             SP  Active   \n",
       "1                      9790  sao bernardo do campo             SP  Active   \n",
       "2                      1151              sao paulo             SP  Active   \n",
       "3                      8775        mogi das cruzes             SP  Active   \n",
       "4                     13056               campinas             SP  Active   \n",
       "5                     89254         jaragua do sul             SC  Active   \n",
       "6                      4534              sao paulo             SP  Active   \n",
       "7                     35182                timoteo             MG  Active   \n",
       "8                     81560               curitiba             PR  Active   \n",
       "9                     30575         belo horizonte             MG  Active   \n",
       "\n",
       "                           order_id order_status order_purchase_timestamp  \\\n",
       "0  00e7ee1b050b8499577073aeb2a297a1    delivered      2017-05-16 15:05:35   \n",
       "1  29150127e6685892b6eab3eec79f59c7    delivered      2018-01-12 20:48:24   \n",
       "2  b2059ed67ce144a36e2aa97d2c9e9ad2    delivered      2018-05-19 16:07:45   \n",
       "3  951670f92359f4fe4a63112aa7306eba    delivered      2018-03-13 16:06:38   \n",
       "4  6b7d50bd145f6fc7f33cebabd7e49d0f    delivered      2018-07-29 09:51:30   \n",
       "5  5741ea1f91b5fbab2bd2dc653a5b5099    delivered      2017-09-14 18:14:31   \n",
       "6  36e694cf4cbc2a4803200c35e84abdc4    delivered      2018-02-19 14:38:35   \n",
       "7  1093c8304c7a003280dd34598194913d    delivered      2017-11-16 19:29:02   \n",
       "8  1ebeea841c590e86a14a0d7a48e7d062    delivered      2018-01-18 12:35:44   \n",
       "9  7433cbcc783205509d66a5260da5b574    delivered      2018-01-08 11:22:34   \n",
       "\n",
       "    order_approved_at  ... seller_state   product_category_name  \\\n",
       "0 2017-05-16 15:22:12  ...           SP       moveis_escritorio   \n",
       "1 2018-01-12 20:58:32  ...           SC   utilidades_domesticas   \n",
       "2 2018-05-20 16:19:10  ...           SP       moveis_escritorio   \n",
       "3 2018-03-13 17:29:19  ...           SP       moveis_escritorio   \n",
       "4 2018-07-29 10:10:09  ...           SP           casa_conforto   \n",
       "5 2017-09-14 18:25:11  ...           SP           esporte_lazer   \n",
       "6 2018-02-19 14:50:37  ...           SP  informatica_acessorios   \n",
       "7 2017-11-16 19:55:41  ...           SP                     NaN   \n",
       "8 2018-01-18 12:56:32  ...           RJ              brinquedos   \n",
       "9 2018-01-08 11:35:27  ...           PR        moveis_decoracao   \n",
       "\n",
       "  product_category_name_english  product_name_length  \\\n",
       "0              office_furniture                 41.0   \n",
       "1                    housewares                 43.0   \n",
       "2              office_furniture                 55.0   \n",
       "3              office_furniture                 48.0   \n",
       "4                  home_confort                 61.0   \n",
       "5                sports_leisure                 32.0   \n",
       "6         computers_accessories                 37.0   \n",
       "7                           NaN                  NaN   \n",
       "8                          toys                 58.0   \n",
       "9               furniture_decor                 57.0   \n",
       "\n",
       "   product_description_length product_photos_qty  product_weight_g  \\\n",
       "0                      1141.0                1.0            8683.0   \n",
       "1                      1002.0                3.0           10150.0   \n",
       "2                       955.0                1.0            8267.0   \n",
       "3                      1066.0                1.0           12160.0   \n",
       "4                       407.0                1.0            5200.0   \n",
       "5                       282.0                2.0            4450.0   \n",
       "6                       322.0                3.0             100.0   \n",
       "7                         NaN                NaN               NaN   \n",
       "8                       739.0                5.0             350.0   \n",
       "9                       272.0                1.0            2500.0   \n",
       "\n",
       "   product_length_cm product_height_cm  product_width_cm  \n",
       "0               54.0              64.0              31.0  \n",
       "1               89.0              15.0              40.0  \n",
       "2               52.0              52.0              17.0  \n",
       "3               56.0              51.0              28.0  \n",
       "4               45.0              15.0              35.0  \n",
       "5               60.0              15.0              15.0  \n",
       "6               16.0               3.0              13.0  \n",
       "7                NaN               NaN               NaN  \n",
       "8               28.0              10.0              12.0  \n",
       "9               40.0              10.0              30.0  \n",
       "\n",
       "[10 rows x 42 columns]"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_df = pd.merge(\n",
    "    left=customers_orders_df,\n",
    "    right=products_itemsells_df,\n",
    "    how=\"left\",\n",
    "    left_on=\"order_id\",\n",
    "    right_on=\"order_id\"\n",
    ")\n",
    "all_df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\muham\\AppData\\Local\\Temp\\ipykernel_16060\\3453494195.py:1: FutureWarning: Treating datetime data as categorical rather than numeric in `.describe` is deprecated and will be removed in a future version of pandas. Specify `datetime_is_numeric=True` to silence this warning and adopt the future behavior now.\n",
      "  all_df.describe(include=\"all\")\n",
      "C:\\Users\\muham\\AppData\\Local\\Temp\\ipykernel_16060\\3453494195.py:1: FutureWarning: Treating datetime data as categorical rather than numeric in `.describe` is deprecated and will be removed in a future version of pandas. Specify `datetime_is_numeric=True` to silence this warning and adopt the future behavior now.\n",
      "  all_df.describe(include=\"all\")\n",
      "C:\\Users\\muham\\AppData\\Local\\Temp\\ipykernel_16060\\3453494195.py:1: FutureWarning: Treating datetime data as categorical rather than numeric in `.describe` is deprecated and will be removed in a future version of pandas. Specify `datetime_is_numeric=True` to silence this warning and adopt the future behavior now.\n",
      "  all_df.describe(include=\"all\")\n",
      "C:\\Users\\muham\\AppData\\Local\\Temp\\ipykernel_16060\\3453494195.py:1: FutureWarning: Treating datetime data as categorical rather than numeric in `.describe` is deprecated and will be removed in a future version of pandas. Specify `datetime_is_numeric=True` to silence this warning and adopt the future behavior now.\n",
      "  all_df.describe(include=\"all\")\n",
      "C:\\Users\\muham\\AppData\\Local\\Temp\\ipykernel_16060\\3453494195.py:1: FutureWarning: Treating datetime data as categorical rather than numeric in `.describe` is deprecated and will be removed in a future version of pandas. Specify `datetime_is_numeric=True` to silence this warning and adopt the future behavior now.\n",
      "  all_df.describe(include=\"all\")\n",
      "C:\\Users\\muham\\AppData\\Local\\Temp\\ipykernel_16060\\3453494195.py:1: FutureWarning: Treating datetime data as categorical rather than numeric in `.describe` is deprecated and will be removed in a future version of pandas. Specify `datetime_is_numeric=True` to silence this warning and adopt the future behavior now.\n",
      "  all_df.describe(include=\"all\")\n",
      "C:\\Users\\muham\\AppData\\Local\\Temp\\ipykernel_16060\\3453494195.py:1: FutureWarning: Treating datetime data as categorical rather than numeric in `.describe` is deprecated and will be removed in a future version of pandas. Specify `datetime_is_numeric=True` to silence this warning and adopt the future behavior now.\n",
      "  all_df.describe(include=\"all\")\n",
      "C:\\Users\\muham\\AppData\\Local\\Temp\\ipykernel_16060\\3453494195.py:1: FutureWarning: Treating datetime data as categorical rather than numeric in `.describe` is deprecated and will be removed in a future version of pandas. Specify `datetime_is_numeric=True` to silence this warning and adopt the future behavior now.\n",
      "  all_df.describe(include=\"all\")\n"
     ]
    },
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>customer_id</th>\n",
       "      <th>customer_unique_id</th>\n",
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       "      <th>product_width_cm</th>\n",
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       "      <td>...</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>mean</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>35033.451298</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>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>48.766032</td>\n",
       "      <td>785.973151</td>\n",
       "      <td>2.205008</td>\n",
       "      <td>2115.460961</td>\n",
       "      <td>30.318239</td>\n",
       "      <td>16.645442</td>\n",
       "      <td>23.116422</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>29823.198969</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>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.033893</td>\n",
       "      <td>652.619510</td>\n",
       "      <td>1.717440</td>\n",
       "      <td>3783.553574</td>\n",
       "      <td>16.223268</td>\n",
       "      <td>13.474412</td>\n",
       "      <td>11.758658</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1003.000000</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>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11250.000000</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>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>42.000000</td>\n",
       "      <td>346.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>300.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>15.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>24240.000000</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>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>52.000000</td>\n",
       "      <td>600.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>700.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>20.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>58475.000000</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>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>57.000000</td>\n",
       "      <td>983.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1800.000000</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>30.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>99990.000000</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>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>76.000000</td>\n",
       "      <td>3992.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>40425.000000</td>\n",
       "      <td>105.000000</td>\n",
       "      <td>105.000000</td>\n",
       "      <td>118.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>13 rows × 42 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                             customer_id                customer_unique_id  \\\n",
       "count                             119143                            119143   \n",
       "unique                             99441                             96096   \n",
       "top     270c23a11d024a44c896d1894b261a83  9a736b248f67d166d2fbb006bcb877c3   \n",
       "freq                                  63                                75   \n",
       "first                                NaN                               NaN   \n",
       "last                                 NaN                               NaN   \n",
       "mean                                 NaN                               NaN   \n",
       "std                                  NaN                               NaN   \n",
       "min                                  NaN                               NaN   \n",
       "25%                                  NaN                               NaN   \n",
       "50%                                  NaN                               NaN   \n",
       "75%                                  NaN                               NaN   \n",
       "max                                  NaN                               NaN   \n",
       "\n",
       "        customer_zip_code_prefix customer_city customer_state  status  \\\n",
       "count              119143.000000        119143         119143  119143   \n",
       "unique                       NaN          4119             27       1   \n",
       "top                          NaN     sao paulo             SP  Active   \n",
       "freq                         NaN         18875          50265  119143   \n",
       "first                        NaN           NaN            NaN     NaN   \n",
       "last                         NaN           NaN            NaN     NaN   \n",
       "mean                35033.451298           NaN            NaN     NaN   \n",
       "std                 29823.198969           NaN            NaN     NaN   \n",
       "min                  1003.000000           NaN            NaN     NaN   \n",
       "25%                 11250.000000           NaN            NaN     NaN   \n",
       "50%                 24240.000000           NaN            NaN     NaN   \n",
       "75%                 58475.000000           NaN            NaN     NaN   \n",
       "max                 99990.000000           NaN            NaN     NaN   \n",
       "\n",
       "                                order_id order_status  \\\n",
       "count                             119143       119143   \n",
       "unique                             99441            8   \n",
       "top     895ab968e7bb0d5659d16cd74cd1650c    delivered   \n",
       "freq                                  63       115723   \n",
       "first                                NaN          NaN   \n",
       "last                                 NaN          NaN   \n",
       "mean                                 NaN          NaN   \n",
       "std                                  NaN          NaN   \n",
       "min                                  NaN          NaN   \n",
       "25%                                  NaN          NaN   \n",
       "50%                                  NaN          NaN   \n",
       "75%                                  NaN          NaN   \n",
       "max                                  NaN          NaN   \n",
       "\n",
       "       order_purchase_timestamp    order_approved_at  ... seller_state  \\\n",
       "count                    119143               119143  ...       118310   \n",
       "unique                    98875                90893  ...           23   \n",
       "top         2017-08-08 20:26:31  2017-08-08 20:43:31  ...           SP   \n",
       "freq                         63                   63  ...        84377   \n",
       "first       2016-09-04 21:15:19  2016-09-15 12:16:38  ...          NaN   \n",
       "last        2018-10-17 17:30:18  2018-10-19 17:30:18  ...          NaN   \n",
       "mean                        NaN                  NaN  ...          NaN   \n",
       "std                         NaN                  NaN  ...          NaN   \n",
       "min                         NaN                  NaN  ...          NaN   \n",
       "25%                         NaN                  NaN  ...          NaN   \n",
       "50%                         NaN                  NaN  ...          NaN   \n",
       "75%                         NaN                  NaN  ...          NaN   \n",
       "max                         NaN                  NaN  ...          NaN   \n",
       "\n",
       "       product_category_name product_category_name_english  \\\n",
       "count                 116576                        116576   \n",
       "unique                    71                            71   \n",
       "top          cama_mesa_banho                bed_bath_table   \n",
       "freq                   11988                         11988   \n",
       "first                    NaN                           NaN   \n",
       "last                     NaN                           NaN   \n",
       "mean                     NaN                           NaN   \n",
       "std                      NaN                           NaN   \n",
       "min                      NaN                           NaN   \n",
       "25%                      NaN                           NaN   \n",
       "50%                      NaN                           NaN   \n",
       "75%                      NaN                           NaN   \n",
       "max                      NaN                           NaN   \n",
       "\n",
       "        product_name_length  product_description_length product_photos_qty  \\\n",
       "count         116576.000000               116576.000000      116576.000000   \n",
       "unique                  NaN                         NaN                NaN   \n",
       "top                     NaN                         NaN                NaN   \n",
       "freq                    NaN                         NaN                NaN   \n",
       "first                   NaN                         NaN                NaN   \n",
       "last                    NaN                         NaN                NaN   \n",
       "mean              48.766032                  785.973151           2.205008   \n",
       "std               10.033893                  652.619510           1.717440   \n",
       "min                5.000000                    4.000000           1.000000   \n",
       "25%               42.000000                  346.000000           1.000000   \n",
       "50%               52.000000                  600.000000           1.000000   \n",
       "75%               57.000000                  983.000000           3.000000   \n",
       "max               76.000000                 3992.000000          20.000000   \n",
       "\n",
       "        product_weight_g  product_length_cm product_height_cm  \\\n",
       "count      116576.000000      116576.000000     116576.000000   \n",
       "unique               NaN                NaN               NaN   \n",
       "top                  NaN                NaN               NaN   \n",
       "freq                 NaN                NaN               NaN   \n",
       "first                NaN                NaN               NaN   \n",
       "last                 NaN                NaN               NaN   \n",
       "mean         2115.460961          30.318239         16.645442   \n",
       "std          3783.553574          16.223268         13.474412   \n",
       "min             0.000000           0.000000          0.000000   \n",
       "25%           300.000000          18.000000          8.000000   \n",
       "50%           700.000000          25.000000         13.000000   \n",
       "75%          1800.000000          38.000000         20.000000   \n",
       "max         40425.000000         105.000000        105.000000   \n",
       "\n",
       "        product_width_cm  \n",
       "count      116576.000000  \n",
       "unique               NaN  \n",
       "top                  NaN  \n",
       "freq                 NaN  \n",
       "first                NaN  \n",
       "last                 NaN  \n",
       "mean           23.116422  \n",
       "std            11.758658  \n",
       "min             0.000000  \n",
       "25%            15.000000  \n",
       "50%            20.000000  \n",
       "75%            30.000000  \n",
       "max           118.000000  \n",
       "\n",
       "[13 rows x 42 columns]"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_df.describe(include=\"all\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     2.0\n",
       "1    14.0\n",
       "2     3.0\n",
       "3     1.0\n",
       "4    10.0\n",
       "Name: delivery_time, dtype: float64"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_df.delivery_time.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 119143 entries, 0 to 119142\n",
      "Data columns (total 42 columns):\n",
      " #   Column                         Non-Null Count   Dtype         \n",
      "---  ------                         --------------   -----         \n",
      " 0   customer_id                    119143 non-null  object        \n",
      " 1   customer_unique_id             119143 non-null  object        \n",
      " 2   customer_zip_code_prefix       119143 non-null  int64         \n",
      " 3   customer_city                  119143 non-null  object        \n",
      " 4   customer_state                 119143 non-null  object        \n",
      " 5   status                         119143 non-null  object        \n",
      " 6   order_id                       119143 non-null  object        \n",
      " 7   order_status                   119143 non-null  object        \n",
      " 8   order_purchase_timestamp       119143 non-null  datetime64[ns]\n",
      " 9   order_approved_at              119143 non-null  datetime64[ns]\n",
      " 10  order_delivered_carrier_date   119143 non-null  datetime64[ns]\n",
      " 11  order_delivered_customer_date  119143 non-null  datetime64[ns]\n",
      " 12  order_estimated_delivery_date  119143 non-null  datetime64[ns]\n",
      " 13  delivery_time                  119143 non-null  float64       \n",
      " 14  payment_sequential             119140 non-null  float64       \n",
      " 15  payment_type                   119140 non-null  object        \n",
      " 16  payment_installments           119140 non-null  float64       \n",
      " 17  payment_value                  119140 non-null  float64       \n",
      " 18  review_id                      118143 non-null  object        \n",
      " 19  review_score                   118143 non-null  float64       \n",
      " 20  review_comment_title           118143 non-null  object        \n",
      " 21  review_comment_message         118143 non-null  object        \n",
      " 22  review_creation_date           118143 non-null  datetime64[ns]\n",
      " 23  review_answer_timestamp        118143 non-null  datetime64[ns]\n",
      " 24  order_item_id                  118310 non-null  float64       \n",
      " 25  product_id                     118310 non-null  object        \n",
      " 26  seller_id                      118310 non-null  object        \n",
      " 27  shipping_limit_date            118310 non-null  datetime64[ns]\n",
      " 28  price                          118310 non-null  float64       \n",
      " 29  freight_value                  118310 non-null  float64       \n",
      " 30  seller_zip_code_prefix         118310 non-null  float64       \n",
      " 31  seller_city                    118310 non-null  object        \n",
      " 32  seller_state                   118310 non-null  object        \n",
      " 33  product_category_name          116576 non-null  object        \n",
      " 34  product_category_name_english  116576 non-null  object        \n",
      " 35  product_name_length            116576 non-null  float64       \n",
      " 36  product_description_length     116576 non-null  float64       \n",
      " 37  product_photos_qty             116576 non-null  float64       \n",
      " 38  product_weight_g               116576 non-null  float64       \n",
      " 39  product_length_cm              116576 non-null  float64       \n",
      " 40  product_height_cm              116576 non-null  float64       \n",
      " 41  product_width_cm               116576 non-null  float64       \n",
      "dtypes: datetime64[ns](8), float64(16), int64(1), object(17)\n",
      "memory usage: 39.1+ MB\n"
     ]
    }
   ],
   "source": [
    "all_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "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>payment_value</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>customer_city</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>sao paulo</th>\n",
       "      <td>15540</td>\n",
       "      <td>2901789.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rio de janeiro</th>\n",
       "      <td>6882</td>\n",
       "      <td>1581736.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>belo horizonte</th>\n",
       "      <td>2773</td>\n",
       "      <td>509165.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>brasilia</th>\n",
       "      <td>2131</td>\n",
       "      <td>435971.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>curitiba</th>\n",
       "      <td>1521</td>\n",
       "      <td>333582.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>santo antonio do rio abaixo</th>\n",
       "      <td>1</td>\n",
       "      <td>24.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tamboara</th>\n",
       "      <td>1</td>\n",
       "      <td>24.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>jenipapo de minas</th>\n",
       "      <td>1</td>\n",
       "      <td>22.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>polo petroquimico de triunfo</th>\n",
       "      <td>1</td>\n",
       "      <td>20.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sabaudia</th>\n",
       "      <td>1</td>\n",
       "      <td>20.42</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4119 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                              order_id  payment_value\n",
       "customer_city                                        \n",
       "sao paulo                        15540     2901789.67\n",
       "rio de janeiro                    6882     1581736.07\n",
       "belo horizonte                    2773      509165.81\n",
       "brasilia                          2131      435971.02\n",
       "curitiba                          1521      333582.09\n",
       "...                                ...            ...\n",
       "santo antonio do rio abaixo          1          24.23\n",
       "tamboara                             1          24.09\n",
       "jenipapo de minas                    1          22.58\n",
       "polo petroquimico de triunfo         1          20.70\n",
       "sabaudia                             1          20.42\n",
       "\n",
       "[4119 rows x 2 columns]"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_df.groupby(by=\"customer_city\").agg({\n",
    "    \"order_id\" : \"nunique\",\n",
    "    \"payment_value\" : \"sum\"\n",
    "}).sort_values(by=\"payment_value\", ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "        text-align: right;\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>order_id</th>\n",
       "      <th>payment_value</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>customer_state</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>SP</th>\n",
       "      <td>41746</td>\n",
       "      <td>7726078.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RJ</th>\n",
       "      <td>12852</td>\n",
       "      <td>2795615.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MG</th>\n",
       "      <td>11635</td>\n",
       "      <td>2351221.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RS</th>\n",
       "      <td>5466</td>\n",
       "      <td>1160175.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PR</th>\n",
       "      <td>5045</td>\n",
       "      <td>1079795.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BA</th>\n",
       "      <td>3380</td>\n",
       "      <td>805070.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SC</th>\n",
       "      <td>3637</td>\n",
       "      <td>801276.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GO</th>\n",
       "      <td>2020</td>\n",
       "      <td>520481.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DF</th>\n",
       "      <td>2140</td>\n",
       "      <td>438095.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ES</th>\n",
       "      <td>2033</td>\n",
       "      <td>408611.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PE</th>\n",
       "      <td>1652</td>\n",
       "      <td>380294.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CE</th>\n",
       "      <td>1336</td>\n",
       "      <td>349290.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PA</th>\n",
       "      <td>975</td>\n",
       "      <td>263217.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MT</th>\n",
       "      <td>907</td>\n",
       "      <td>257802.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MA</th>\n",
       "      <td>747</td>\n",
       "      <td>200363.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PB</th>\n",
       "      <td>536</td>\n",
       "      <td>181660.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MS</th>\n",
       "      <td>715</td>\n",
       "      <td>167670.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PI</th>\n",
       "      <td>495</td>\n",
       "      <td>137158.89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RN</th>\n",
       "      <td>485</td>\n",
       "      <td>117192.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AL</th>\n",
       "      <td>413</td>\n",
       "      <td>112296.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SE</th>\n",
       "      <td>350</td>\n",
       "      <td>90651.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TO</th>\n",
       "      <td>280</td>\n",
       "      <td>72412.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RO</th>\n",
       "      <td>253</td>\n",
       "      <td>69184.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AM</th>\n",
       "      <td>148</td>\n",
       "      <td>34956.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AC</th>\n",
       "      <td>81</td>\n",
       "      <td>24984.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AP</th>\n",
       "      <td>68</td>\n",
       "      <td>21642.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RR</th>\n",
       "      <td>46</td>\n",
       "      <td>12462.21</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                order_id  payment_value\n",
       "customer_state                         \n",
       "SP                 41746     7726078.35\n",
       "RJ                 12852     2795615.67\n",
       "MG                 11635     2351221.09\n",
       "RS                  5466     1160175.66\n",
       "PR                  5045     1079795.49\n",
       "BA                  3380      805070.98\n",
       "SC                  3637      801276.45\n",
       "GO                  2020      520481.65\n",
       "DF                  2140      438095.32\n",
       "ES                  2033      408611.64\n",
       "PE                  1652      380294.18\n",
       "CE                  1336      349290.36\n",
       "PA                   975      263217.70\n",
       "MT                   907      257802.26\n",
       "MA                   747      200363.24\n",
       "PB                   536      181660.73\n",
       "MS                   715      167670.27\n",
       "PI                   495      137158.89\n",
       "RN                   485      117192.91\n",
       "AL                   413      112296.47\n",
       "SE                   350       90651.44\n",
       "TO                   280       72412.08\n",
       "RO                   253       69184.48\n",
       "AM                   148       34956.93\n",
       "AC                    81       24984.86\n",
       "AP                    68       21642.70\n",
       "RR                    46       12462.21"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_df.groupby(by=\"customer_state\").agg({\n",
    "    \"order_id\" : \"nunique\",\n",
    "    \"payment_value\" : \"sum\"\n",
    "}).sort_values(by=\"payment_value\", ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "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></th>\n",
       "      <th>order_id</th>\n",
       "      <th>price</th>\n",
       "      <th>freight_value</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>customer_city</th>\n",
       "      <th>product_category_name_english</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">sao paulo</th>\n",
       "      <th>health_beauty</th>\n",
       "      <td>1533</td>\n",
       "      <td>195263.02</td>\n",
       "      <td>23444.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bed_bath_table</th>\n",
       "      <td>1653</td>\n",
       "      <td>184140.58</td>\n",
       "      <td>32057.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>watches_gifts</th>\n",
       "      <td>778</td>\n",
       "      <td>172269.07</td>\n",
       "      <td>9985.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sports_leisure</th>\n",
       "      <td>1266</td>\n",
       "      <td>154139.05</td>\n",
       "      <td>21159.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>computers_accessories</th>\n",
       "      <td>1004</td>\n",
       "      <td>147079.12</td>\n",
       "      <td>17043.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>areal</th>\n",
       "      <th>pet_shop</th>\n",
       "      <td>1</td>\n",
       "      <td>5.50</td>\n",
       "      <td>15.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tupa</th>\n",
       "      <th>construction_tools_construction</th>\n",
       "      <td>1</td>\n",
       "      <td>3.99</td>\n",
       "      <td>12.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>palmeira d'oeste</th>\n",
       "      <th>computers_accessories</th>\n",
       "      <td>1</td>\n",
       "      <td>3.90</td>\n",
       "      <td>11.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>caldas novas</th>\n",
       "      <th>fashion_underwear_beach</th>\n",
       "      <td>1</td>\n",
       "      <td>3.90</td>\n",
       "      <td>15.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hortolandia</th>\n",
       "      <th>construction_tools_construction</th>\n",
       "      <td>1</td>\n",
       "      <td>0.85</td>\n",
       "      <td>18.23</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>25199 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                  order_id      price  \\\n",
       "customer_city    product_category_name_english                          \n",
       "sao paulo        health_beauty                        1533  195263.02   \n",
       "                 bed_bath_table                       1653  184140.58   \n",
       "                 watches_gifts                         778  172269.07   \n",
       "                 sports_leisure                       1266  154139.05   \n",
       "                 computers_accessories                1004  147079.12   \n",
       "...                                                    ...        ...   \n",
       "areal            pet_shop                                1       5.50   \n",
       "tupa             construction_tools_construction         1       3.99   \n",
       "palmeira d'oeste computers_accessories                   1       3.90   \n",
       "caldas novas     fashion_underwear_beach                 1       3.90   \n",
       "hortolandia      construction_tools_construction         1       0.85   \n",
       "\n",
       "                                                  freight_value  \n",
       "customer_city    product_category_name_english                   \n",
       "sao paulo        health_beauty                         23444.32  \n",
       "                 bed_bath_table                        32057.43  \n",
       "                 watches_gifts                          9985.67  \n",
       "                 sports_leisure                        21159.67  \n",
       "                 computers_accessories                 17043.64  \n",
       "...                                                         ...  \n",
       "areal            pet_shop                                 15.10  \n",
       "tupa             construction_tools_construction          12.79  \n",
       "palmeira d'oeste computers_accessories                    11.85  \n",
       "caldas novas     fashion_underwear_beach                  15.10  \n",
       "hortolandia      construction_tools_construction          18.23  \n",
       "\n",
       "[25199 rows x 3 columns]"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_df.groupby(by=[\"customer_city\", \"product_category_name_english\"]).agg({\n",
    "    \"order_id\" : \"nunique\",\n",
    "    \"price\": \"sum\",\n",
    "    \"freight_value\": \"sum\"\n",
    "}).sort_values(by=\"price\", ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        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></th>\n",
       "      <th>order_id</th>\n",
       "      <th>price</th>\n",
       "      <th>freight_value</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>customer_state</th>\n",
       "      <th>product_category_name_english</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">SP</th>\n",
       "      <th>bed_bath_table</th>\n",
       "      <td>4416</td>\n",
       "      <td>509313.30</td>\n",
       "      <td>84389.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>health_beauty</th>\n",
       "      <td>3789</td>\n",
       "      <td>475928.29</td>\n",
       "      <td>59159.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>watches_gifts</th>\n",
       "      <td>2135</td>\n",
       "      <td>450822.51</td>\n",
       "      <td>28614.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sports_leisure</th>\n",
       "      <td>3296</td>\n",
       "      <td>403732.12</td>\n",
       "      <td>57092.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>computers_accessories</th>\n",
       "      <td>2679</td>\n",
       "      <td>360210.34</td>\n",
       "      <td>47486.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AL</th>\n",
       "      <th>drinks</th>\n",
       "      <td>1</td>\n",
       "      <td>15.49</td>\n",
       "      <td>34.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BA</th>\n",
       "      <th>home_comfort_2</th>\n",
       "      <td>1</td>\n",
       "      <td>12.90</td>\n",
       "      <td>16.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ES</th>\n",
       "      <th>home_comfort_2</th>\n",
       "      <td>1</td>\n",
       "      <td>12.90</td>\n",
       "      <td>15.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PR</th>\n",
       "      <th>home_comfort_2</th>\n",
       "      <td>1</td>\n",
       "      <td>12.90</td>\n",
       "      <td>15.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MS</th>\n",
       "      <th>costruction_tools_tools</th>\n",
       "      <td>1</td>\n",
       "      <td>6.80</td>\n",
       "      <td>19.32</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1356 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                              order_id      price  \\\n",
       "customer_state product_category_name_english                        \n",
       "SP             bed_bath_table                     4416  509313.30   \n",
       "               health_beauty                      3789  475928.29   \n",
       "               watches_gifts                      2135  450822.51   \n",
       "               sports_leisure                     3296  403732.12   \n",
       "               computers_accessories              2679  360210.34   \n",
       "...                                                ...        ...   \n",
       "AL             drinks                                1      15.49   \n",
       "BA             home_comfort_2                        1      12.90   \n",
       "ES             home_comfort_2                        1      12.90   \n",
       "PR             home_comfort_2                        1      12.90   \n",
       "MS             costruction_tools_tools               1       6.80   \n",
       "\n",
       "                                              freight_value  \n",
       "customer_state product_category_name_english                 \n",
       "SP             bed_bath_table                      84389.54  \n",
       "               health_beauty                       59159.36  \n",
       "               watches_gifts                       28614.27  \n",
       "               sports_leisure                      57092.38  \n",
       "               computers_accessories               47486.69  \n",
       "...                                                     ...  \n",
       "AL             drinks                                 34.15  \n",
       "BA             home_comfort_2                         16.79  \n",
       "ES             home_comfort_2                         15.10  \n",
       "PR             home_comfort_2                         15.10  \n",
       "MS             costruction_tools_tools                19.32  \n",
       "\n",
       "[1356 rows x 3 columns]"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_df.groupby(by=[\"customer_state\", \"product_category_name_english\"]).agg({\n",
    "    \"order_id\" : \"nunique\",\n",
    "    \"price\": \"sum\",\n",
    "    \"freight_value\": \"sum\"\n",
    "}).sort_values(by=\"price\", ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_df.to_csv(\"all_df.csv\", index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 119143 entries, 0 to 119142\n",
      "Data columns (total 42 columns):\n",
      " #   Column                         Non-Null Count   Dtype         \n",
      "---  ------                         --------------   -----         \n",
      " 0   customer_id                    119143 non-null  object        \n",
      " 1   customer_unique_id             119143 non-null  object        \n",
      " 2   customer_zip_code_prefix       119143 non-null  int64         \n",
      " 3   customer_city                  119143 non-null  object        \n",
      " 4   customer_state                 119143 non-null  object        \n",
      " 5   status                         119143 non-null  object        \n",
      " 6   order_id                       119143 non-null  object        \n",
      " 7   order_status                   119143 non-null  object        \n",
      " 8   order_purchase_timestamp       119143 non-null  datetime64[ns]\n",
      " 9   order_approved_at              119143 non-null  datetime64[ns]\n",
      " 10  order_delivered_carrier_date   119143 non-null  datetime64[ns]\n",
      " 11  order_delivered_customer_date  119143 non-null  datetime64[ns]\n",
      " 12  order_estimated_delivery_date  119143 non-null  datetime64[ns]\n",
      " 13  delivery_time                  119143 non-null  float64       \n",
      " 14  payment_sequential             119140 non-null  float64       \n",
      " 15  payment_type                   119140 non-null  object        \n",
      " 16  payment_installments           119140 non-null  float64       \n",
      " 17  payment_value                  119140 non-null  float64       \n",
      " 18  review_id                      118143 non-null  object        \n",
      " 19  review_score                   118143 non-null  float64       \n",
      " 20  review_comment_title           118143 non-null  object        \n",
      " 21  review_comment_message         118143 non-null  object        \n",
      " 22  review_creation_date           118143 non-null  datetime64[ns]\n",
      " 23  review_answer_timestamp        118143 non-null  datetime64[ns]\n",
      " 24  order_item_id                  118310 non-null  float64       \n",
      " 25  product_id                     118310 non-null  object        \n",
      " 26  seller_id                      118310 non-null  object        \n",
      " 27  shipping_limit_date            118310 non-null  datetime64[ns]\n",
      " 28  price                          118310 non-null  float64       \n",
      " 29  freight_value                  118310 non-null  float64       \n",
      " 30  seller_zip_code_prefix         118310 non-null  float64       \n",
      " 31  seller_city                    118310 non-null  object        \n",
      " 32  seller_state                   118310 non-null  object        \n",
      " 33  product_category_name          116576 non-null  object        \n",
      " 34  product_category_name_english  116576 non-null  object        \n",
      " 35  product_name_length            116576 non-null  float64       \n",
      " 36  product_description_length     116576 non-null  float64       \n",
      " 37  product_photos_qty             116576 non-null  float64       \n",
      " 38  product_weight_g               116576 non-null  float64       \n",
      " 39  product_length_cm              116576 non-null  float64       \n",
      " 40  product_height_cm              116576 non-null  float64       \n",
      " 41  product_width_cm               116576 non-null  float64       \n",
      "dtypes: datetime64[ns](8), float64(16), int64(1), object(17)\n",
      "memory usage: 39.1+ MB\n"
     ]
    }
   ],
   "source": [
    "all_df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualization & Explanatory Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Pertanyaan 1: Bagaimana performa penjualan dan revenue perusahaan dalam tahun 2018?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "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_approved_at</th>\n",
       "      <th>order_count</th>\n",
       "      <th>revenue</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>September</td>\n",
       "      <td>1</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>October</td>\n",
       "      <td>326</td>\n",
       "      <td>76830.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>November</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>December</td>\n",
       "      <td>1</td>\n",
       "      <td>19.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>January</td>\n",
       "      <td>763</td>\n",
       "      <td>182416.54</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  order_approved_at  order_count    revenue\n",
       "0         September            1       0.00\n",
       "1           October          326   76830.04\n",
       "2          November            0       0.00\n",
       "3          December            1      19.62\n",
       "4           January          763  182416.54"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "monthly_orders_df = all_df.resample(rule='M', on='order_approved_at').agg({\n",
    "    \"order_id\": \"nunique\",\n",
    "    \"payment_value\" : \"sum\"\n",
    "})\n",
    "monthly_orders_df.index = monthly_orders_df.index.strftime('%B')\n",
    "monthly_orders_df = monthly_orders_df.reset_index()\n",
    "monthly_orders_df.rename(columns={\n",
    "    \"order_id\": \"order_count\",\n",
    "    \"payment_value\": \"revenue\"\n",
    "}, inplace=True)\n",
    "monthly_orders_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   order_approved_at  order_count     revenue\n",
      "12         September         4307   929028.47\n",
      "13           October         4595  1140747.26\n",
      "15          December         5839  1094611.30\n",
      "21              June         6164  1306785.03\n",
      "22              July         6177  1334330.00\n",
      "23            August         6668  1285777.82\n",
      "17          February         6710  1315270.82\n",
      "19             April         6778  1476502.25\n",
      "20               May         7067  1545914.50\n",
      "16           January         7188  1406715.96\n",
      "18             March         7291  1501016.67\n",
      "14          November         7404  1589298.59\n"
     ]
    }
   ],
   "source": [
    "monthly_orders_df = monthly_orders_df.sort_values('order_count').drop_duplicates('order_approved_at', keep='last')\n",
    "print(monthly_orders_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [],
   "source": [
    "month_mapping = {\n",
    "    \"January\": 1, \"February\": 2, \"March\": 3, \"April\": 4, \"May\": 5, \"June\": 6,\n",
    "    \"July\": 7, \"August\": 8, \"September\": 9, \"October\": 10, \"November\": 11, \"December\": 12\n",
    "}\n",
    "\n",
    "monthly_orders_df[\"month_numeric\"] = monthly_orders_df[\"order_approved_at\"].map(month_mapping)\n",
    "monthly_orders_df.sort_values(\"month_numeric\", inplace=True)\n",
    "monthly_orders_df.drop(\"month_numeric\", axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12, 6))\n",
    "plt.plot(monthly_orders_df['order_approved_at'], monthly_orders_df['order_count'], marker='o', color='blue')\n",
    "plt.xlabel('Bulan')\n",
    "plt.ylabel('Jumlah Pesanan')\n",
    "plt.title('Performa Penjualan Perusahaan dalam tahun 2018')\n",
    "plt.xticks(rotation=45)\n",
    "plt.grid(True)\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pada grafik di atas, terlihat bahwa terjadi penurunan yang signifikan pada penjualan pada bulan September dan mengalami kenaikan yang juga signifikan pada bulan November."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12, 6))\n",
    "plt.plot(monthly_orders_df['order_approved_at'], monthly_orders_df['revenue'], marker='o', color='green')\n",
    "plt.xlabel('Bulan')\n",
    "plt.ylabel('Pendapatan (Revenue)')\n",
    "plt.title('Pendapatan Perusahaan dalam tahun 2018')\n",
    "plt.xticks(rotation=45)\n",
    "plt.grid(True)\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Begitu juga pada grafik revenue di atas, terlihat bahwa terjadi penurunan yang signifikan pada pendapatan dari hasil penjualan pada bulan September dan mengalami kenaikan yang juga signifikan pada bulan November."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Pertanyaan 2: Produk apa yang paling banyak dan paling sedikit terjual?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   product_category_name_english  product_id\n",
      "7                 bed_bath_table       11988\n",
      "43                 health_beauty       10032\n",
      "65                sports_leisure        9004\n",
      "39               furniture_decor        8832\n",
      "15         computers_accessories        8150\n",
      "49                    housewares        7380\n",
      "70                 watches_gifts        6213\n",
      "68                     telephony        4726\n",
      "42                  garden_tools        4590\n",
      "5                           auto        4400\n"
     ]
    }
   ],
   "source": [
    "sum_orders_items_df = all_df.groupby(by=\"product_category_name_english\").product_id.count().reset_index()\n",
    "sum_orders_items_df = sum_orders_items_df.sort_values(by=\"product_id\", ascending=False).head(10)\n",
    "print(sum_orders_items_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2400x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(nrows=1, ncols=2, figsize=(24, 6))\n",
    "\n",
    "colors = [\"#72BCD4\", \"#D3D3D3\", \"#D3D3D3\", \"#D3D3D3\", \"#D3D3D3\"]\n",
    "\n",
    "sns.barplot(x=\"product_id\", y=\"product_category_name_english\", data=sum_orders_items_df.head(5), palette=colors, ax=ax[0])\n",
    "ax[0].set_ylabel(None)\n",
    "ax[0].set_xlabel(None)\n",
    "ax[0].set_title(\"Best Performing Product\", loc=\"center\", fontsize=15)\n",
    "ax[0].tick_params(axis = 'y', labelsize=12)\n",
    "\n",
    "sns.barplot(x=\"product_id\", y=\"product_category_name_english\", data=sum_orders_items_df.sort_values(by=\"product_id\", ascending=True).head(5), palette=colors, ax=ax[1])\n",
    "ax[1].set_ylabel(None)\n",
    "ax[1].set_xlabel(None)\n",
    "ax[1].invert_xaxis()\n",
    "ax[1].yaxis.set_label_position(\"right\")\n",
    "ax[1].yaxis.tick_right()\n",
    "ax[1].set_title(\"Worst Performing Product\", loc=\"center\", fontsize=18)\n",
    "ax[1].tick_params(axis='y', labelsize=15)\n",
    "\n",
    "plt.suptitle(\"Best and Worst Performing Product by Number of Sales\", fontsize=20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pada grafik diatas, produk yang paling banyak terjual adalah bed_bath_table dan produk yang paling sedikit terjual adalah auto"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Pertanyaan 3: Bagaimana demografi pelanggan yang kita miliki?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "bystate_df = all_df.groupby(by=\"customer_state\")['customer_id'].nunique().reset_index()\n",
    "bystate_df.rename(columns={\"customer_id\": \"customer_count\"}, inplace=True)\n",
    "most_customer_state = bystate_df.loc[bystate_df['customer_count'].idxmax(), 'customer_state']\n",
    "bystate_df = bystate_df.sort_values(by='customer_count', ascending=False)\n",
    "\n",
    "plt.figure(figsize=(12, 6))\n",
    "palette = [\"#72BCD4\" if state == most_customer_state else \"#D3D3D3\" for state in bystate_df['customer_state']]\n",
    "\n",
    "sns.barplot(x=\"customer_count\", y=\"customer_state\", data=bystate_df, palette=palette)\n",
    "plt.title(\"Number of Customers by State\", loc=\"center\", fontsize=15)\n",
    "plt.ylabel(\"State\")\n",
    "plt.xlabel(\"Number of Customers\")\n",
    "plt.tick_params(axis='y', labelsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pada grafik diatas berdasarkan customers dari jumlah state, SP merupakan state dengan jumlah customers yang paling banyak."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bycity_df = all_df['customer_city'].value_counts().head(10)\n",
    "most_customer_city = bycity_df.idxmax()\n",
    "bycity_df = bycity_df.sort_values(ascending=False)\n",
    "\n",
    "plt.figure(figsize=(12, 6))\n",
    "sns.barplot(x=bycity_df.index, y=bycity_df.values, \n",
    "            palette=[\"#72BCD4\" if city == most_customer_city else \"#D3D3D3\" for city in bycity_df.index])\n",
    "\n",
    "plt.title(\"Number of Customers by City\", loc=\"center\", fontsize=15)\n",
    "plt.xlabel(\"City\")\n",
    "plt.ylabel(\"Number of Customers\")\n",
    "plt.xticks(rotation=30, fontsize=10)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pada grafik diatas berdasarkan customers jumlah dari kota, Sao Paulo merupakan kota dengan jumlah customers yang paling banyak."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conclusion"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Conclution pertanyaan 1\n",
    "> Berdasarkan hasil dari visualisasi pada pertanyaan pertama, dapat disimpulkan bahwa penjualan dan pendapatan tertinggi pada tahun 2018 tercatat pada bulan November, sementara penjualan paling rendah terjadi pada bulan September pada tahun yang sama.\n",
    "- Conclution pertanyaan 2\n",
    "> Berdasarkan hasil visualisasi pada pertanyaan kedua, dapat disimpulkan bahwa kategori produk \"bed_bath_table\" memiliki kinerja penjualan terbaik, sementara kategori produk \"auto\" memiliki kinerja penjualan yang paling rendah.\n",
    "- Conclution pertanyaan 3\n",
    "> Berdasarkan hasil visualisasi pada pertanyaan ketiga, dapat disimpulkan bahwa sebagian besar pelanggan berasal dari negara bagian SP (Sao Paulo), sementara kota dengan jumlah pelanggan terbanyak adalah Sao Paulo."
   ]
  }
 ],
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  "language_info": {
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