notebook.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "H9wADwK78DCz"
      },
      "source": [
        "# Proyek Analisis Data: E-Commerce Public Dataset\n",
        "- **Nama:** Arif Wicaksono\n",
        "- **Email:** arifwicaksono947@gmail.com\n",
        "- **ID Dicoding:** arifwcksn26"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eE0raob58DC0"
      },
      "source": [
        "## Menentukan Pertanyaan Bisnis"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GmQeQ5YF8DC0"
      },
      "source": [
        "- Bagaimana Demografi pelanggan yang dimiliki ?\n",
        "- Bagaimana Performa Penjualan dan Revenue Perusahaan dalam Beberapa Tahun Terakhir?\n",
        "- Kategori Produk apa yang paling banyak terjual?"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "H-z4QGlO8DC1"
      },
      "source": [
        "## Import Semua Packages/Library yang Digunakan"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "FVYwaObI8DC1"
      },
      "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": {
        "id": "h_Sh51Xy8DC1"
      },
      "source": [
        "## Data Wrangling"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sXU2GBYu8DC1"
      },
      "source": [
        "### Gathering Data"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "#### Identifikasi tabel `customer_df`"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "zjCBk1BI8DC1"
      },
      "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",
              "    </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": 4,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "customers_df = pd.read_csv('data/customers_dataset.csv') \n",
        "customers_df.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "**Insight:**\n",
        "- kita memperoleh informasi bahwa terdapat 94,411 pelanggan yang terdaftar, dengan distribusi kode pos yang bervariasi. Data menunjukkan bahwa pelanggan berasal dari 4,119 kota dan 27 negara bagian di Brasil.\n",
        "- kita dapat melihat bahwa segmentasi pelanggan cukup luas, mencakup berbagai lokasi dan demografi. Rata-rata pelanggan berasal dari kode pos dengan nilai rata-rata 35,137.45, menunjukkan adanya konsentrasi pelanggan yang signifikan di wilayah tertentu, seperti são paulo, yang merupakan kota dengan frekuensi tertinggi."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "#### Identifikasi tabel `order_df`"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "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_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>2017-10-18 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>2018-08-13 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>2018-09-04 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>2017-12-15 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>2018-02-26 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_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": 5,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "orders_df = pd.read_csv('data/orders_dataset.csv') \n",
        "selected_columns = ['order_id', 'customer_id', 'order_status', 'order_purchase_timestamp','order_approved_at','order_estimated_delivery_date']\n",
        "orders_df = orders_df[selected_columns]\n",
        "orders_df.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "**Insight:**\n",
        "- Dengan membandingkan tanggal pengiriman yang diharapkan dan tanggal pengiriman yang sebenarnya, dapat dihitung persentase pengiriman tepat waktu. Ini penting untuk menilai efisiensi operasional dan meningkatkan kepuasan pelanggan.\n",
        "- Melalui analisis kolom customer_id, pola pembelian pelanggan dapat diidentifikasi, termasuk seberapa sering pelanggan melakukan order. Ini membantu dalam segmentasi pelanggan dan pengembangan strategi pemasaran yang lebih efektif."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "#### Identifikasi tabel `payment_df`"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "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",
              "    </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",
              "</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  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": 6,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "payment_df = pd.read_csv('data/order_payments_dataset.csv')\n",
        "payment_df.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "#### Identifikasi tabel `order_item_df`"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "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",
              "    </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_item_df = pd.read_csv ('data/order_items_dataset.csv')\n",
        "order_item_df.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "#### Identifikasi tabel `product_df`"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "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": [
        "product_df = pd.read_csv('data/products_dataset.csv')\n",
        "product_df.info()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "**Insight:**\n",
        "- Terdapat 32,951 produk yang terdaftar, menunjukkan variasi yang signifikan dalam penawaran produk. Ini dapat membantu dalam menganalisis keberagaman dan daya tarik produk di pasar.\n",
        "- Rata-rata panjang deskripsi produk dapat dianalisis untuk menentukan apakah informasi yang diberikan cukup untuk menarik minat pelanggan. Deskripsi yang lebih panjang dapat menunjukkan produk yang lebih kompleks, sementara deskripsi yang pendek mungkin memerlukan tambahan informasi untuk meningkatkan daya tarik."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "#### Identifikasi tabel `seller_df`"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "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": [
        "seller_df = pd.read_csv('data/sellers_dataset.csv')\n",
        "seller_df.info()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MMi6xGaDkbCi"
      },
      "source": [
        "**Insight:**\n",
        "- Terdapat 3,095 penjual yang terdaftar, dengan konsentrasi yang signifikan berada di wilayah São Paulo, SP. Ini menunjukkan potensi pasar yang besar di daerah tersebut, serta kemungkinan adanya cluster penjual yang dapat dimanfaatkan untuk strategi pemasaran lokal.\n",
        "- Rata-rata kode pos penjual adalah 32291.059 dengan rentang yang luas (min 10001.000 hingga max 99730.000). Ini menunjukkan bahwa penjual tersebar di berbagai area, yang dapat mempengaruhi aspek logistik dan pengiriman produk."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FHSiqaZp8DC1"
      },
      "source": [
        "### Assessing Data"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "#### Menilai tabel `customer_df`"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "id": "ax-3tEjc9Cj1"
      },
      "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"
          ]
        },
        {
          "data": {
            "text/plain": [
              "customer_id                 0\n",
              "customer_unique_id          0\n",
              "customer_zip_code_prefix    0\n",
              "customer_city               0\n",
              "customer_state              0\n",
              "dtype: int64"
            ]
          },
          "execution_count": 10,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "customers_df.info()\n",
        "customers_df.isna().sum()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Jumlah duplikasi:  0\n"
          ]
        },
        {
          "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": 11,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "print(\"Jumlah duplikasi: \", customers_df.duplicated().sum())\n",
        "customers_df.describe()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7dtxhAPrkhPL"
      },
      "source": [
        "**Insight:**\n",
        "- DataFrame customers_df memiliki 99,404 entri dengan tidak ada nilai yang hilang di semua kolom. Ini menunjukkan bahwa data pelanggan lengkap dan siap untuk analisis.\n",
        "- Kolom seperti customer_zip_code_prefix, customer_city, dan customer_state memungkinkan analisis distribusi geografis pelanggan. Ini dapat membantu dalam merumuskan strategi pemasaran yang lebih terfokus dan efisien berdasarkan lokasi."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "#### Menilai tabel `orders_df`"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 99441 entries, 0 to 99440\n",
            "Data columns (total 6 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_estimated_delivery_date  99441 non-null  object\n",
            "dtypes: object(6)\n",
            "memory usage: 4.6+ MB\n"
          ]
        },
        {
          "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_estimated_delivery_date      0\n",
              "dtype: int64"
            ]
          },
          "execution_count": 12,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "orders_df.info()\n",
        "orders_df.isna().sum()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Jumlah duplikasi:  0\n"
          ]
        },
        {
          "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_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>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>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>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>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_estimated_delivery_date  \n",
              "count                          99441  \n",
              "unique                           459  \n",
              "top              2017-12-20 00:00:00  \n",
              "freq                             522  "
            ]
          },
          "execution_count": 13,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "print(\"Jumlah duplikasi: \",orders_df.duplicated().sum())\n",
        "orders_df.describe()\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "**Insight:**\n",
        "- Kolom order_delivered_customer_date memiliki 94.476 entri non-null, menunjukkan bahwa ada 2.965 entri yang hilang. Ini bisa menunjukkan bahwa beberapa pesanan belum sampai ke pelanggan atau statusnya belum diperbarui\n",
        "- Kolom order_status menunjukkan berbagai status pesanan yang dapat memberikan wawasan tentang proses pemenuhan. Jika terdapat banyak pesanan yang berstatus \"dikirim\" tetapi belum sampai, ini bisa mengindikasikan masalah dalam pengiriman atau komunikasi dengan kurir."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "#### Menilai tabel `order_item_df`"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "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"
          ]
        },
        {
          "data": {
            "text/plain": [
              "order_id               0\n",
              "order_item_id          0\n",
              "product_id             0\n",
              "seller_id              0\n",
              "shipping_limit_date    0\n",
              "price                  0\n",
              "freight_value          0\n",
              "dtype: int64"
            ]
          },
          "execution_count": 14,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "order_item_df.info()\n",
        "order_item_df.isna().sum()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Jumlah duplikasi:  0\n"
          ]
        },
        {
          "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": 15,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "print(\"Jumlah duplikasi: \",order_item_df.duplicated().sum())\n",
        "order_item_df.describe()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "#### Menilai tabel `payment_df`"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "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"
          ]
        },
        {
          "data": {
            "text/plain": [
              "order_id                0\n",
              "payment_sequential      0\n",
              "payment_type            0\n",
              "payment_installments    0\n",
              "payment_value           0\n",
              "dtype: int64"
            ]
          },
          "execution_count": 16,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "payment_df.info()\n",
        "payment_df.isna().sum()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Jumlah duplikasi:  0\n"
          ]
        },
        {
          "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": 17,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "print(\"Jumlah duplikasi: \",payment_df.duplicated().sum())\n",
        "payment_df.describe()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "#### Menilai tabel `product_df`"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "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"
          ]
        },
        {
          "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": 18,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "product_df.info()\n",
        "product_df.isna().sum()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Jumlah duplikasi:  0\n"
          ]
        },
        {
          "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": 19,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "print(\"Jumlah duplikasi: \", product_df.duplicated().sum())\n",
        "product_df.describe()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "**Insight:**\n",
        "- Kolom product_weight_g dll dapat memberikan wawasan tentang kategori produk. Misalnya, kategori makanan mungkin memiliki berat yang berbeda dibandingkan dengan barang elektronik. Ini juga bisa digunakan untuk analisis logistik dan pengiriman.\n",
        "- Dengan 610 nilai hilang pada kolom product_category_name dll penting untuk menganalisis distribusi kategori yang ada. Ini bisa membantu dalam memahami proporsi produk dalam berbagai kategori dan mengidentifikasi kategori yang mungkin kurang terwakili."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "#### Menilai tabel `seller_df`"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "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"
          ]
        },
        {
          "data": {
            "text/plain": [
              "seller_id                 0\n",
              "seller_zip_code_prefix    0\n",
              "seller_city               0\n",
              "seller_state              0\n",
              "dtype: int64"
            ]
          },
          "execution_count": 20,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "seller_df.info()\n",
        "seller_df.isna().sum()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Jumlah duplikasi:  0\n"
          ]
        },
        {
          "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": 21,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "print(\"Jumlah duplikasi: \", seller_df.duplicated().sum())\n",
        "seller_df.describe()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EhN5R4hr8DC1"
      },
      "source": [
        "### Cleaning Data"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "#### Membersihkan tabel `orders_df`"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "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_estimated_delivery_date      0\n",
              "dtype: int64"
            ]
          },
          "execution_count": 22,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "orders_df.isna().sum()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "execution_count": 23,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "orders_df.duplicated().sum()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "order_approved_at\n",
              "2018-02-27 04:31:10    9\n",
              "2017-11-07 07:30:38    7\n",
              "2018-02-27 04:31:01    7\n",
              "2018-02-06 05:31:52    7\n",
              "2017-11-07 07:30:29    7\n",
              "                      ..\n",
              "2018-08-22 11:50:14    1\n",
              "2017-09-22 11:27:36    1\n",
              "2018-03-07 16:40:32    1\n",
              "2017-08-08 10:50:15    1\n",
              "2018-03-09 11:20:28    1\n",
              "Name: count, Length: 90733, dtype: int64"
            ]
          },
          "execution_count": 24,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "orders_df[orders_df.order_approved_at.isna()]\n",
        "orders_df.order_approved_at.value_counts()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "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                0\n",
              "order_estimated_delivery_date    0\n",
              "dtype: int64"
            ]
          },
          "execution_count": 25,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "orders_df.fillna(value=\"2018-02-27 04:31:10\", inplace=True)\n",
        "orders_df.isna().sum()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Mengubah tipe data object menjadi date time"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {},
      "outputs": [],
      "source": [
        "datetime_columns = [\"order_purchase_timestamp\", \"order_approved_at\",\"order_estimated_delivery_date\" ]\n",
        "for column in datetime_columns:\n",
        "    orders_df[column]=pd.to_datetime(orders_df[column])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 99441 entries, 0 to 99440\n",
            "Data columns (total 6 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              99441 non-null  datetime64[ns]\n",
            " 5   order_estimated_delivery_date  99441 non-null  datetime64[ns]\n",
            "dtypes: datetime64[ns](3), object(3)\n",
            "memory usage: 4.6+ MB\n",
            "Jumlah duplikasi:  0\n"
          ]
        }
      ],
      "source": [
        "orders_df.info()\n",
        "print(\"Jumlah duplikasi: \", orders_df.duplicated().sum())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "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_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>2017-10-18</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>2018-08-13</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>2018-09-04</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>2017-12-15</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>2018-02-26</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_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": 28,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "orders_df.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "#### Membersihkan tabel `order_item_df`"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "execution_count": 29,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "order_item_df.isna().sum()\n",
        "order_item_df.duplicated().sum()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "metadata": {},
      "outputs": [],
      "source": [
        "datetime_columns = [\"shipping_limit_date\" ]\n",
        "for column in datetime_columns:\n",
        "    order_item_df[column]=pd.to_datetime(order_item_df[column])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 31,
      "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_item_df.info()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "#### Membersihkan tabel `product_df`"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 32,
      "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": 32,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "product_df.isna().sum()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 33,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "product_name_lenght\n",
              "60.0    2182\n",
              "59.0    2025\n",
              "58.0    1887\n",
              "57.0    1719\n",
              "55.0    1683\n",
              "        ... \n",
              "66.0       1\n",
              "72.0       1\n",
              "67.0       1\n",
              "76.0       1\n",
              "6.0        1\n",
              "Name: count, Length: 66, dtype: int64"
            ]
          },
          "execution_count": 33,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "product_df[product_df.product_name_lenght.isna()]\n",
        "product_df.product_name_lenght.value_counts()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 34,
      "metadata": {},
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "C:\\Users\\hp\\AppData\\Local\\Temp\\ipykernel_16352\\3070792045.py:1: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise an error in a future version of pandas. Value '60.0' has dtype incompatible with float64, please explicitly cast to a compatible dtype first.\n",
            "  product_df.fillna(value=\"60.0\", inplace=True)\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "product_id                    0\n",
              "product_category_name         0\n",
              "product_name_lenght           0\n",
              "product_description_lenght    0\n",
              "product_photos_qty            0\n",
              "product_weight_g              0\n",
              "product_length_cm             0\n",
              "product_height_cm             0\n",
              "product_width_cm              0\n",
              "dtype: int64"
            ]
          },
          "execution_count": 34,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "product_df.fillna(value=\"60.0\", inplace=True)\n",
        "product_df.isna().sum()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 35,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "execution_count": 35,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "product_df.duplicated().sum()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 36,
      "metadata": {},
      "outputs": [],
      "source": [
        "float_columns = ['product_length_cm', 'product_height_cm', 'product_width_cm']\n",
        "\n",
        "# Loop untuk mengubah tipe data dari object ke float\n",
        "for column in float_columns:\n",
        "    product_df[column] = pd.to_numeric(product_df[column], errors='coerce')"
      ]
    },
    {
      "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_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": 37,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "product_df.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gp-Y6wU38DC1"
      },
      "source": [
        "## Exploratory Data Analysis (EDA)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MW7WF2kr8DC1"
      },
      "source": [
        "### Explore Data Customer "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 38,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 429
        },
        "id": "e9CQCZjk8DC2",
        "outputId": "0083ab92-dbe3-46c0-90a0-0596641b2c27"
      },
      "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",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>50643</th>\n",
              "      <td>61d502df98a7bd95bc3a0049bd504bef</td>\n",
              "      <td>7f582010ed8a1bf032ee4f5bab9f2498</td>\n",
              "      <td>13270</td>\n",
              "      <td>valinhos</td>\n",
              "      <td>SP</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>89742</th>\n",
              "      <td>e51f2a8949104c23d15abd26048a0af3</td>\n",
              "      <td>6341a01f140e37b96af71c218039bb1c</td>\n",
              "      <td>13390</td>\n",
              "      <td>rio das pedras</td>\n",
              "      <td>SP</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75447</th>\n",
              "      <td>ff46b4411cd7dd62403e3d6df6ea8059</td>\n",
              "      <td>c3599131cc089add5823cadec0ec88ee</td>\n",
              "      <td>98700</td>\n",
              "      <td>ijui</td>\n",
              "      <td>RS</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>52490</th>\n",
              "      <td>2300457c28afe54edb4da376808a8781</td>\n",
              "      <td>4c581027a6bd52c46aaca848e6e0aebe</td>\n",
              "      <td>23017</td>\n",
              "      <td>rio de janeiro</td>\n",
              "      <td>RJ</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50511</th>\n",
              "      <td>7e7a6bad55dc4c19a83581008f2e9d1e</td>\n",
              "      <td>074f7184fdd96018f97df50380e41f10</td>\n",
              "      <td>33010</td>\n",
              "      <td>santa luzia</td>\n",
              "      <td>MG</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                            customer_id                customer_unique_id  \\\n",
              "50643  61d502df98a7bd95bc3a0049bd504bef  7f582010ed8a1bf032ee4f5bab9f2498   \n",
              "89742  e51f2a8949104c23d15abd26048a0af3  6341a01f140e37b96af71c218039bb1c   \n",
              "75447  ff46b4411cd7dd62403e3d6df6ea8059  c3599131cc089add5823cadec0ec88ee   \n",
              "52490  2300457c28afe54edb4da376808a8781  4c581027a6bd52c46aaca848e6e0aebe   \n",
              "50511  7e7a6bad55dc4c19a83581008f2e9d1e  074f7184fdd96018f97df50380e41f10   \n",
              "\n",
              "       customer_zip_code_prefix   customer_city customer_state  \n",
              "50643                     13270        valinhos             SP  \n",
              "89742                     13390  rio das pedras             SP  \n",
              "75447                     98700            ijui             RS  \n",
              "52490                     23017  rio de janeiro             RJ  \n",
              "50511                     33010     santa luzia             MG  "
            ]
          },
          "execution_count": 38,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "customers_df.sample(5)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 39,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "execution_count": 39,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "customers_df.customer_id.is_unique"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 40,
      "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",
              "    </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": 40,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "customers_df.describe(include=\"all\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 41,
      "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": 41,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "customers_df.groupby(by=\"customer_city\").customer_id.nunique().sort_values(ascending=False)\n",
        "customers_df.groupby(by=\"customer_state\").customer_id.nunique().sort_values(ascending=False)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Explore Data Order"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 42,
      "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_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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>unique</th>\n",
              "      <td>99441</td>\n",
              "      <td>99441</td>\n",
              "      <td>8</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>top</th>\n",
              "      <td>e481f51cbdc54678b7cc49136f2d6af7</td>\n",
              "      <td>9ef432eb6251297304e76186b10a928d</td>\n",
              "      <td>delivered</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>freq</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>96478</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>NaN</td>\n",
              "      <td>2017-12-31 08:43:12.776581120</td>\n",
              "      <td>2017-12-31 20:48:25.588771072</td>\n",
              "      <td>2018-01-24 03:08:37.730111232</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</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>2016-09-30 00:00:00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2017-09-12 14:46:19</td>\n",
              "      <td>2017-09-13 03:35:20</td>\n",
              "      <td>2017-10-03 00:00:00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2018-01-18 23:04:36</td>\n",
              "      <td>2018-01-19 17:42:49</td>\n",
              "      <td>2018-02-15 00:00:00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2018-05-04 15:42:16</td>\n",
              "      <td>2018-05-04 17:10:24</td>\n",
              "      <td>2018-05-25 00:00:00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2018-10-17 17:30:18</td>\n",
              "      <td>2018-09-03 17:40:06</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",
              "mean                                 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_status       order_purchase_timestamp  \\\n",
              "count         99441                          99441   \n",
              "unique            8                            NaN   \n",
              "top       delivered                            NaN   \n",
              "freq          96478                            NaN   \n",
              "mean            NaN  2017-12-31 08:43:12.776581120   \n",
              "min             NaN            2016-09-04 21:15:19   \n",
              "25%             NaN            2017-09-12 14:46:19   \n",
              "50%             NaN            2018-01-18 23:04:36   \n",
              "75%             NaN            2018-05-04 15:42:16   \n",
              "max             NaN            2018-10-17 17:30:18   \n",
              "\n",
              "                    order_approved_at  order_estimated_delivery_date  \n",
              "count                           99441                          99441  \n",
              "unique                            NaN                            NaN  \n",
              "top                               NaN                            NaN  \n",
              "freq                              NaN                            NaN  \n",
              "mean    2017-12-31 20:48:25.588771072  2018-01-24 03:08:37.730111232  \n",
              "min               2016-09-15 12:16:38            2016-09-30 00:00:00  \n",
              "25%               2017-09-13 03:35:20            2017-10-03 00:00:00  \n",
              "50%               2018-01-19 17:42:49            2018-02-15 00:00:00  \n",
              "75%               2018-05-04 17:10:24            2018-05-25 00:00:00  \n",
              "max               2018-09-03 17:40:06            2018-11-12 00:00:00  "
            ]
          },
          "execution_count": 42,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "orders_df.describe(include=\"all\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 43,
      "metadata": {},
      "outputs": [],
      "source": [
        "delivery_time = orders_df['order_estimated_delivery_date'] - orders_df['order_approved_at']\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": 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>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_estimated_delivery_date</th>\n",
              "      <th>delivery_time</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>2017-10-18</td>\n",
              "      <td>16.0</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>2018-08-13</td>\n",
              "      <td>18.0</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>2018-09-04</td>\n",
              "      <td>27.0</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>2017-12-15</td>\n",
              "      <td>26.0</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>2018-02-26</td>\n",
              "      <td>12.0</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_estimated_delivery_date  delivery_time  \n",
              "0                    2017-10-18           16.0  \n",
              "1                    2018-08-13           18.0  \n",
              "2                    2018-09-04           27.0  \n",
              "3                    2017-12-15           26.0  \n",
              "4                    2018-02-26           12.0  "
            ]
          },
          "execution_count": 44,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "orders_df.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Explore Data Payment"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 45,
      "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",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>103886</td>\n",
              "      <td>103886.000000</td>\n",
              "      <td>103886</td>\n",
              "      <td>103886.000000</td>\n",
              "      <td>103886.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>unique</th>\n",
              "      <td>99440</td>\n",
              "      <td>NaN</td>\n",
              "      <td>5</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>top</th>\n",
              "      <td>fa65dad1b0e818e3ccc5cb0e39231352</td>\n",
              "      <td>NaN</td>\n",
              "      <td>credit_card</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>freq</th>\n",
              "      <td>29</td>\n",
              "      <td>NaN</td>\n",
              "      <td>76795</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>NaN</td>\n",
              "      <td>1.092679</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2.853349</td>\n",
              "      <td>154.100380</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>NaN</td>\n",
              "      <td>0.706584</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2.687051</td>\n",
              "      <td>217.494064</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>NaN</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>NaN</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>1.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>56.790000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>NaN</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>100.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>NaN</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>171.837500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>NaN</td>\n",
              "      <td>29.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>24.000000</td>\n",
              "      <td>13664.080000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</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": 45,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "payment_df.describe(include=\"all\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 46,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "False"
            ]
          },
          "execution_count": 46,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "payment_df.order_id.is_unique"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Menggabungkan Data Payment & Orders"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 47,
      "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>payment_type</th>\n",
              "      <th>payment_value</th>\n",
              "      <th>order_purchase_timestamp</th>\n",
              "      <th>order_approved_at</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>e481f51cbdc54678b7cc49136f2d6af7</td>\n",
              "      <td>9ef432eb6251297304e76186b10a928d</td>\n",
              "      <td>delivered</td>\n",
              "      <td>credit_card</td>\n",
              "      <td>18.12</td>\n",
              "      <td>2017-10-02 10:56:33</td>\n",
              "      <td>2017-10-02 11:07:15</td>\n",
              "      <td>2017-10-18</td>\n",
              "      <td>16.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>e481f51cbdc54678b7cc49136f2d6af7</td>\n",
              "      <td>9ef432eb6251297304e76186b10a928d</td>\n",
              "      <td>delivered</td>\n",
              "      <td>voucher</td>\n",
              "      <td>2.00</td>\n",
              "      <td>2017-10-02 10:56:33</td>\n",
              "      <td>2017-10-02 11:07:15</td>\n",
              "      <td>2017-10-18</td>\n",
              "      <td>16.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>e481f51cbdc54678b7cc49136f2d6af7</td>\n",
              "      <td>9ef432eb6251297304e76186b10a928d</td>\n",
              "      <td>delivered</td>\n",
              "      <td>voucher</td>\n",
              "      <td>18.59</td>\n",
              "      <td>2017-10-02 10:56:33</td>\n",
              "      <td>2017-10-02 11:07:15</td>\n",
              "      <td>2017-10-18</td>\n",
              "      <td>16.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>53cdb2fc8bc7dce0b6741e2150273451</td>\n",
              "      <td>b0830fb4747a6c6d20dea0b8c802d7ef</td>\n",
              "      <td>delivered</td>\n",
              "      <td>boleto</td>\n",
              "      <td>141.46</td>\n",
              "      <td>2018-07-24 20:41:37</td>\n",
              "      <td>2018-07-26 03:24:27</td>\n",
              "      <td>2018-08-13</td>\n",
              "      <td>18.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>47770eb9100c2d0c44946d9cf07ec65d</td>\n",
              "      <td>41ce2a54c0b03bf3443c3d931a367089</td>\n",
              "      <td>delivered</td>\n",
              "      <td>credit_card</td>\n",
              "      <td>179.12</td>\n",
              "      <td>2018-08-08 08:38:49</td>\n",
              "      <td>2018-08-08 08:55:23</td>\n",
              "      <td>2018-09-04</td>\n",
              "      <td>27.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                           order_id                       customer_id  \\\n",
              "0  e481f51cbdc54678b7cc49136f2d6af7  9ef432eb6251297304e76186b10a928d   \n",
              "1  e481f51cbdc54678b7cc49136f2d6af7  9ef432eb6251297304e76186b10a928d   \n",
              "2  e481f51cbdc54678b7cc49136f2d6af7  9ef432eb6251297304e76186b10a928d   \n",
              "3  53cdb2fc8bc7dce0b6741e2150273451  b0830fb4747a6c6d20dea0b8c802d7ef   \n",
              "4  47770eb9100c2d0c44946d9cf07ec65d  41ce2a54c0b03bf3443c3d931a367089   \n",
              "\n",
              "  order_status payment_type  payment_value order_purchase_timestamp  \\\n",
              "0    delivered  credit_card          18.12      2017-10-02 10:56:33   \n",
              "1    delivered      voucher           2.00      2017-10-02 10:56:33   \n",
              "2    delivered      voucher          18.59      2017-10-02 10:56:33   \n",
              "3    delivered       boleto         141.46      2018-07-24 20:41:37   \n",
              "4    delivered  credit_card         179.12      2018-08-08 08:38:49   \n",
              "\n",
              "    order_approved_at order_estimated_delivery_date  delivery_time  \n",
              "0 2017-10-02 11:07:15                    2017-10-18           16.0  \n",
              "1 2017-10-02 11:07:15                    2017-10-18           16.0  \n",
              "2 2017-10-02 11:07:15                    2017-10-18           16.0  \n",
              "3 2018-07-26 03:24:27                    2018-08-13           18.0  \n",
              "4 2018-08-08 08:55:23                    2018-09-04           27.0  "
            ]
          },
          "execution_count": 47,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "orders_payment_df = pd.merge(\n",
        "    left=orders_df,\n",
        "    right=payment_df,\n",
        "    how=\"inner\",\n",
        "    left_on=\"order_id\",\n",
        "    right_on=\"order_id\"\n",
        ")\n",
        "\n",
        "selected_columns = ['order_id', 'customer_id', 'order_status', 'payment_type','payment_value','order_purchase_timestamp','order_approved_at', 'order_estimated_delivery_date','delivery_time']\n",
        "orders_payment_df = orders_payment_df[selected_columns]\n",
        "orders_payment_df.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Menggabungkan Data Customer & Order_payment"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 48,
      "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>97973</th>\n",
              "      <td>72740dc2b626c46da39cd84cad2102bf</td>\n",
              "      <td>00a1d0d7d91c17c634d6896ce10ce1c6</td>\n",
              "      <td>60711</td>\n",
              "      <td>fortaleza</td>\n",
              "      <td>CE</td>\n",
              "      <td>Active</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>60932</th>\n",
              "      <td>e38b747f2d270d470bff9e3b25041b0b</td>\n",
              "      <td>f34b22f6c901c80bdaa7c85caa540ae4</td>\n",
              "      <td>72543</td>\n",
              "      <td>brasilia</td>\n",
              "      <td>DF</td>\n",
              "      <td>Active</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>80187</th>\n",
              "      <td>399cb3db1d7a4c442e4833b5f57a4429</td>\n",
              "      <td>f0cd91b4062c68c65d115f48f368718f</td>\n",
              "      <td>96810</td>\n",
              "      <td>santa cruz do sul</td>\n",
              "      <td>RS</td>\n",
              "      <td>Active</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>98738</th>\n",
              "      <td>5d298dce585ab9711f691fa5c1c22ee9</td>\n",
              "      <td>0c3a1c1585e96999bdaf83732d1fe234</td>\n",
              "      <td>65076</td>\n",
              "      <td>sao luis</td>\n",
              "      <td>MA</td>\n",
              "      <td>Active</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>39615</th>\n",
              "      <td>ec5637e1b926c79b5472bd41d67882f8</td>\n",
              "      <td>bdd88e5f6a6810d95baef27f2f6055fe</td>\n",
              "      <td>11713</td>\n",
              "      <td>praia grande</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",
              "97973  72740dc2b626c46da39cd84cad2102bf  00a1d0d7d91c17c634d6896ce10ce1c6   \n",
              "60932  e38b747f2d270d470bff9e3b25041b0b  f34b22f6c901c80bdaa7c85caa540ae4   \n",
              "80187  399cb3db1d7a4c442e4833b5f57a4429  f0cd91b4062c68c65d115f48f368718f   \n",
              "98738  5d298dce585ab9711f691fa5c1c22ee9  0c3a1c1585e96999bdaf83732d1fe234   \n",
              "39615  ec5637e1b926c79b5472bd41d67882f8  bdd88e5f6a6810d95baef27f2f6055fe   \n",
              "\n",
              "       customer_zip_code_prefix      customer_city customer_state  status  \n",
              "97973                     60711          fortaleza             CE  Active  \n",
              "60932                     72543           brasilia             DF  Active  \n",
              "80187                     96810  santa cruz do sul             RS  Active  \n",
              "98738                     65076           sao luis             MA  Active  \n",
              "39615                     11713       praia grande             SP  Active  "
            ]
          },
          "execution_count": 48,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "customer_id_in_orders_df =  orders_df.customer_id.tolist()\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": "code",
      "execution_count": 49,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "status\n",
              "Active    99441\n",
              "Name: customer_id, dtype: int64"
            ]
          },
          "execution_count": 49,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "customers_df.groupby(by=\"status\").customer_id.count()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 50,
      "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>payment_type</th>\n",
              "      <th>payment_value</th>\n",
              "      <th>order_purchase_timestamp</th>\n",
              "      <th>order_approved_at</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>credit_card</td>\n",
              "      <td>146.87</td>\n",
              "      <td>2017-05-16 15:05:35</td>\n",
              "      <td>2017-05-16 15:22:12</td>\n",
              "      <td>2017-06-05</td>\n",
              "      <td>19.0</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>credit_card</td>\n",
              "      <td>335.48</td>\n",
              "      <td>2018-01-12 20:48:24</td>\n",
              "      <td>2018-01-12 20:58:32</td>\n",
              "      <td>2018-02-06</td>\n",
              "      <td>24.0</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>credit_card</td>\n",
              "      <td>157.73</td>\n",
              "      <td>2018-05-19 16:07:45</td>\n",
              "      <td>2018-05-20 16:19:10</td>\n",
              "      <td>2018-06-13</td>\n",
              "      <td>23.0</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>credit_card</td>\n",
              "      <td>173.30</td>\n",
              "      <td>2018-03-13 16:06:38</td>\n",
              "      <td>2018-03-13 17:29:19</td>\n",
              "      <td>2018-04-10</td>\n",
              "      <td>27.0</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>credit_card</td>\n",
              "      <td>252.25</td>\n",
              "      <td>2018-07-29 09:51:30</td>\n",
              "      <td>2018-07-29 10:10:09</td>\n",
              "      <td>2018-08-15</td>\n",
              "      <td>17.0</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 payment_type  payment_value  \\\n",
              "0  00e7ee1b050b8499577073aeb2a297a1    delivered  credit_card         146.87   \n",
              "1  29150127e6685892b6eab3eec79f59c7    delivered  credit_card         335.48   \n",
              "2  b2059ed67ce144a36e2aa97d2c9e9ad2    delivered  credit_card         157.73   \n",
              "3  951670f92359f4fe4a63112aa7306eba    delivered  credit_card         173.30   \n",
              "4  6b7d50bd145f6fc7f33cebabd7e49d0f    delivered  credit_card         252.25   \n",
              "\n",
              "  order_purchase_timestamp   order_approved_at order_estimated_delivery_date  \\\n",
              "0      2017-05-16 15:05:35 2017-05-16 15:22:12                    2017-06-05   \n",
              "1      2018-01-12 20:48:24 2018-01-12 20:58:32                    2018-02-06   \n",
              "2      2018-05-19 16:07:45 2018-05-20 16:19:10                    2018-06-13   \n",
              "3      2018-03-13 16:06:38 2018-03-13 17:29:19                    2018-04-10   \n",
              "4      2018-07-29 09:51:30 2018-07-29 10:10:09                    2018-08-15   \n",
              "\n",
              "   delivery_time  \n",
              "0           19.0  \n",
              "1           24.0  \n",
              "2           23.0  \n",
              "3           27.0  \n",
              "4           17.0  "
            ]
          },
          "execution_count": 50,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "customer_order_payment_df = pd.merge(\n",
        "    left=customers_df,\n",
        "    right=orders_payment_df,\n",
        "    how='inner',\n",
        "    on='customer_id'\n",
        ")\n",
        "\n",
        "customer_order_payment_df.head()\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Menentukan Pengiriman Tepat Waktu"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 51,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "         order_approved_at order_estimated_delivery_date  on_time_delivery\n",
            "0      2017-05-16 15:22:12                    2017-06-05              True\n",
            "1      2018-01-12 20:58:32                    2018-02-06              True\n",
            "2      2018-05-20 16:19:10                    2018-06-13              True\n",
            "3      2018-03-13 17:29:19                    2018-04-10              True\n",
            "4      2018-07-29 10:10:09                    2018-08-15              True\n",
            "...                    ...                           ...               ...\n",
            "103881 2018-04-07 16:08:45                    2018-04-25              True\n",
            "103882 2018-04-04 08:35:12                    2018-04-20              True\n",
            "103883 2018-04-08 20:30:03                    2018-05-02              True\n",
            "103884 2017-11-03 21:31:20                    2017-12-05              True\n",
            "103885 2017-12-19 18:50:39                    2018-01-08              True\n",
            "\n",
            "[103886 rows x 3 columns]\n"
          ]
        }
      ],
      "source": [
        "customer_order_payment_df['actual_delivery_date'] = customer_order_payment_df['order_approved_at'] + pd.to_timedelta(customer_order_payment_df['delivery_time'], unit='h')\n",
        "\n",
        "# Tentukan apakah pengiriman tepat waktu\n",
        "customer_order_payment_df['on_time_delivery'] = customer_order_payment_df['actual_delivery_date'] <= customer_order_payment_df['order_estimated_delivery_date']\n",
        "\n",
        "# Menampilkan DataFrame dengan kolom yang diinginkan\n",
        "result = customer_order_payment_df[['order_approved_at', 'order_estimated_delivery_date', 'on_time_delivery']]\n",
        "print(result)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "**Insight:**\n",
        "- Jika sebagian besar pengiriman tepat waktu (True), ini menunjukkan bahwa perusahaan memiliki sistem logistik yang efisien dan dapat meningkatkan kepuasan pelanggan. Tingkat ketepatan pengiriman yang tinggi dapat menjadi faktor penting dalam mempertahankan dan menarik pelanggan baru."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Total pembayaran berdasarkan metode"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 52,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "payment_type\n",
            "boleto          2869361.27\n",
            "credit_card    12542084.19\n",
            "debit_card       217989.79\n",
            "not_defined           0.00\n",
            "voucher          379436.87\n",
            "Name: payment_value, dtype: float64\n"
          ]
        }
      ],
      "source": [
        "total_payment_by_type = customer_order_payment_df.groupby('payment_type')['payment_value'].sum()\n",
        "\n",
        "print(total_payment_by_type)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "**Insight:**\n",
        "- Jika satu metode pembayaran, seperti \"credit_card\", menunjukkan total yang jauh lebih tinggi dibandingkan dengan metode lain, ini menunjukkan preferensi pelanggan. Perusahaan dapat memanfaatkan informasi ini untuk meningkatkan kemudahan dan promosi terkait metode pembayaran yang paling banyak digunakan."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Jenis Pembayaran"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 53,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "payment_type\n",
              "boleto         1\n",
              "credit_card    1\n",
              "debit_card     1\n",
              "not_defined    1\n",
              "voucher        1\n",
              "Name: payment_type, dtype: int64"
            ]
          },
          "execution_count": 53,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "customer_order_payment_df.groupby(by=\"payment_type\").payment_type.nunique().sort_values(ascending=False)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Jumlah order berdasarkan kota"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 54,
      "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_city</th>\n",
              "      <th>order_id</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>sao paulo</td>\n",
              "      <td>15540</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>rio de janeiro</td>\n",
              "      <td>6882</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>belo horizonte</td>\n",
              "      <td>2773</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>brasilia</td>\n",
              "      <td>2131</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>curitiba</td>\n",
              "      <td>1521</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>campinas</td>\n",
              "      <td>1444</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>porto alegre</td>\n",
              "      <td>1379</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>salvador</td>\n",
              "      <td>1245</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>guarulhos</td>\n",
              "      <td>1189</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>sao bernardo do campo</td>\n",
              "      <td>938</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "           customer_city  order_id\n",
              "0              sao paulo     15540\n",
              "1         rio de janeiro      6882\n",
              "2         belo horizonte      2773\n",
              "3               brasilia      2131\n",
              "4               curitiba      1521\n",
              "5               campinas      1444\n",
              "6           porto alegre      1379\n",
              "7               salvador      1245\n",
              "8              guarulhos      1189\n",
              "9  sao bernardo do campo       938"
            ]
          },
          "execution_count": 54,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "customer_order_payment_df.groupby(by=\"customer_city\").order_id.nunique().sort_values(ascending=False).reset_index().head(10)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "**Insight:**\n",
        "- Kota seperti \"São Paulo\" dan \"Rio de Janeiro\" menunjukkan jumlah order yang sangat tinggi. Ini menunjukkan potensi pasar yang signifikan di daerah tersebut. Perusahaan dapat mempertimbangkan untuk meningkatkan upaya pemasaran dan promosi di kota-kota ini untuk lebih memanfaatkan potensi permintaan."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Jumlah order berdasarkan state"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 55,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "customer_state\n",
              "SP    41745\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: order_id, dtype: int64"
            ]
          },
          "execution_count": 55,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "customer_order_payment_df.groupby(by=\"customer_state\").order_id.nunique().sort_values(ascending=False)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Jenis Pembayaran terbanyak"
      ]
    },
    {
      "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>payment_type</th>\n",
              "      <th>order_id</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>credit_card</td>\n",
              "      <td>76505</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>boleto</td>\n",
              "      <td>19784</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>voucher</td>\n",
              "      <td>3866</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>debit_card</td>\n",
              "      <td>1528</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>not_defined</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  payment_type  order_id\n",
              "0  credit_card     76505\n",
              "1       boleto     19784\n",
              "2      voucher      3866\n",
              "3   debit_card      1528\n",
              "4  not_defined         3"
            ]
          },
          "execution_count": 56,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "customer_order_payment_df.groupby(by=\"payment_type\").order_id.nunique().sort_values(ascending=False).reset_index()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "**Insight:**\n",
        "- Metode pembayaran \"credit_card\" dan \"boleto\" memiliki jumlah order yang jauh lebih tinggi dibandingkan dengan metode lain. Ini menunjukkan bahwa pelanggan lebih suka menggunakan metode tersebut, yang dapat menjadi fokus dalam promosi dan komunikasi pemasaran untuk meningkatkan konversi."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Menggabungkan Data Product & Item"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 57,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>product_id</th>\n",
              "      <th>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>count</th>\n",
              "      <td>32951</td>\n",
              "      <td>32951</td>\n",
              "      <td>32951.0</td>\n",
              "      <td>32951</td>\n",
              "      <td>32951.0</td>\n",
              "      <td>32951.0</td>\n",
              "      <td>32951.000000</td>\n",
              "      <td>32951.000000</td>\n",
              "      <td>32951.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>unique</th>\n",
              "      <td>32951</td>\n",
              "      <td>74</td>\n",
              "      <td>67.0</td>\n",
              "      <td>2961</td>\n",
              "      <td>20.0</td>\n",
              "      <td>2205.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>top</th>\n",
              "      <td>1e9e8ef04dbcff4541ed26657ea517e5</td>\n",
              "      <td>cama_mesa_banho</td>\n",
              "      <td>60.0</td>\n",
              "      <td>60.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>200.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>freq</th>\n",
              "      <td>1</td>\n",
              "      <td>3029</td>\n",
              "      <td>2182.0</td>\n",
              "      <td>610</td>\n",
              "      <td>16489.0</td>\n",
              "      <td>2084.0</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>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>30.816849</td>\n",
              "      <td>16.940275</td>\n",
              "      <td>23.198962</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\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>16.915473</td>\n",
              "      <td>13.641266</td>\n",
              "      <td>12.082083</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\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>7.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>6.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\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>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>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</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>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</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>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</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_id product_category_name  \\\n",
              "count                              32951                 32951   \n",
              "unique                             32951                    74   \n",
              "top     1e9e8ef04dbcff4541ed26657ea517e5       cama_mesa_banho   \n",
              "freq                                   1                  3029   \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_lenght product_description_lenght  product_photos_qty  \\\n",
              "count               32951.0                      32951             32951.0   \n",
              "unique                 67.0                       2961                20.0   \n",
              "top                    60.0                       60.0                 1.0   \n",
              "freq                 2182.0                        610             16489.0   \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_weight_g  product_length_cm  product_height_cm  \\\n",
              "count            32951.0       32951.000000       32951.000000   \n",
              "unique            2205.0                NaN                NaN   \n",
              "top                200.0                NaN                NaN   \n",
              "freq              2084.0                NaN                NaN   \n",
              "mean                 NaN          30.816849          16.940275   \n",
              "std                  NaN          16.915473          13.641266   \n",
              "min                  NaN           7.000000           2.000000   \n",
              "25%                  NaN          18.000000           8.000000   \n",
              "50%                  NaN          25.000000          13.000000   \n",
              "75%                  NaN          38.000000          21.000000   \n",
              "max                  NaN         105.000000         105.000000   \n",
              "\n",
              "        product_width_cm  \n",
              "count       32951.000000  \n",
              "unique               NaN  \n",
              "top                  NaN  \n",
              "freq                 NaN  \n",
              "mean           23.198962  \n",
              "std            12.082083  \n",
              "min             6.000000  \n",
              "25%            15.000000  \n",
              "50%            20.000000  \n",
              "75%            30.000000  \n",
              "max           118.000000  "
            ]
          },
          "execution_count": 57,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "product_df.describe(include=\"all\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 58,
      "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",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>112650</td>\n",
              "      <td>112650.000000</td>\n",
              "      <td>112650</td>\n",
              "      <td>112650</td>\n",
              "      <td>112650</td>\n",
              "      <td>112650.000000</td>\n",
              "      <td>112650.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>unique</th>\n",
              "      <td>98666</td>\n",
              "      <td>NaN</td>\n",
              "      <td>32951</td>\n",
              "      <td>3095</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>top</th>\n",
              "      <td>8272b63d03f5f79c56e9e4120aec44ef</td>\n",
              "      <td>NaN</td>\n",
              "      <td>aca2eb7d00ea1a7b8ebd4e68314663af</td>\n",
              "      <td>6560211a19b47992c3666cc44a7e94c0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>freq</th>\n",
              "      <td>21</td>\n",
              "      <td>NaN</td>\n",
              "      <td>527</td>\n",
              "      <td>2033</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>1.197834</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2018-01-07 15:36:52.192685312</td>\n",
              "      <td>120.653739</td>\n",
              "      <td>19.990320</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>NaN</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2016-09-19 00:15:34</td>\n",
              "      <td>0.850000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>NaN</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2017-09-20 20:57:27.500000</td>\n",
              "      <td>39.900000</td>\n",
              "      <td>13.080000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>NaN</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2018-01-26 13:59:35</td>\n",
              "      <td>74.990000</td>\n",
              "      <td>16.260000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>NaN</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2018-05-10 14:34:00.750000128</td>\n",
              "      <td>134.900000</td>\n",
              "      <td>21.150000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>NaN</td>\n",
              "      <td>21.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2020-04-09 22:35:08</td>\n",
              "      <td>6735.000000</td>\n",
              "      <td>409.680000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>NaN</td>\n",
              "      <td>0.705124</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>183.633928</td>\n",
              "      <td>15.806405</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                order_id  order_item_id  \\\n",
              "count                             112650  112650.000000   \n",
              "unique                             98666            NaN   \n",
              "top     8272b63d03f5f79c56e9e4120aec44ef            NaN   \n",
              "freq                                  21            NaN   \n",
              "mean                                 NaN       1.197834   \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",
              "std                                  NaN       0.705124   \n",
              "\n",
              "                              product_id                         seller_id  \\\n",
              "count                             112650                            112650   \n",
              "unique                             32951                              3095   \n",
              "top     aca2eb7d00ea1a7b8ebd4e68314663af  6560211a19b47992c3666cc44a7e94c0   \n",
              "freq                                 527                              2033   \n",
              "mean                                 NaN                               NaN   \n",
              "min                                  NaN                               NaN   \n",
              "25%                                  NaN                               NaN   \n",
              "50%                                  NaN                               NaN   \n",
              "75%                                  NaN                               NaN   \n",
              "max                                  NaN                               NaN   \n",
              "std                                  NaN                               NaN   \n",
              "\n",
              "                  shipping_limit_date          price  freight_value  \n",
              "count                          112650  112650.000000  112650.000000  \n",
              "unique                            NaN            NaN            NaN  \n",
              "top                               NaN            NaN            NaN  \n",
              "freq                              NaN            NaN            NaN  \n",
              "mean    2018-01-07 15:36:52.192685312     120.653739      19.990320  \n",
              "min               2016-09-19 00:15:34       0.850000       0.000000  \n",
              "25%        2017-09-20 20:57:27.500000      39.900000      13.080000  \n",
              "50%               2018-01-26 13:59:35      74.990000      16.260000  \n",
              "75%     2018-05-10 14:34:00.750000128     134.900000      21.150000  \n",
              "max               2020-04-09 22:35:08    6735.000000     409.680000  \n",
              "std                               NaN     183.633928      15.806405  "
            ]
          },
          "execution_count": 58,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "order_item_df.describe(include=\"all\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 59,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>product_id</th>\n",
              "      <th>product_category_name</th>\n",
              "      <th>order_item_id</th>\n",
              "      <th>seller_id</th>\n",
              "      <th>shipping_limit_date</th>\n",
              "      <th>freight_value</th>\n",
              "      <th>price</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1e9e8ef04dbcff4541ed26657ea517e5</td>\n",
              "      <td>perfumaria</td>\n",
              "      <td>1</td>\n",
              "      <td>5670f4db5b62c43d542e1b2d56b0cf7c</td>\n",
              "      <td>2018-04-30 17:33:54</td>\n",
              "      <td>7.39</td>\n",
              "      <td>10.91</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>3aa071139cb16b67ca9e5dea641aaa2f</td>\n",
              "      <td>artes</td>\n",
              "      <td>1</td>\n",
              "      <td>b561927807645834b59ef0d16ba55a24</td>\n",
              "      <td>2018-02-06 19:11:15</td>\n",
              "      <td>17.99</td>\n",
              "      <td>248.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>96bd76ec8810374ed1b65e291975717f</td>\n",
              "      <td>esporte_lazer</td>\n",
              "      <td>1</td>\n",
              "      <td>7b07b3c7487f0ea825fc6df75abd658b</td>\n",
              "      <td>2018-07-11 21:30:20</td>\n",
              "      <td>7.82</td>\n",
              "      <td>79.80</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>cef67bcfe19066a932b7673e239eb23d</td>\n",
              "      <td>bebes</td>\n",
              "      <td>1</td>\n",
              "      <td>c510bc1718f0f2961eaa42a23330681a</td>\n",
              "      <td>2018-08-07 09:10:13</td>\n",
              "      <td>9.54</td>\n",
              "      <td>112.30</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>9dc1a7de274444849c219cff195d0b71</td>\n",
              "      <td>utilidades_domesticas</td>\n",
              "      <td>1</td>\n",
              "      <td>0be8ff43f22e456b4e0371b2245e4d01</td>\n",
              "      <td>2018-04-17 01:30:23</td>\n",
              "      <td>8.29</td>\n",
              "      <td>37.90</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                         product_id  product_category_name  order_item_id  \\\n",
              "0  1e9e8ef04dbcff4541ed26657ea517e5             perfumaria              1   \n",
              "1  3aa071139cb16b67ca9e5dea641aaa2f                  artes              1   \n",
              "2  96bd76ec8810374ed1b65e291975717f          esporte_lazer              1   \n",
              "3  cef67bcfe19066a932b7673e239eb23d                  bebes              1   \n",
              "4  9dc1a7de274444849c219cff195d0b71  utilidades_domesticas              1   \n",
              "\n",
              "                          seller_id shipping_limit_date  freight_value   price  \n",
              "0  5670f4db5b62c43d542e1b2d56b0cf7c 2018-04-30 17:33:54           7.39   10.91  \n",
              "1  b561927807645834b59ef0d16ba55a24 2018-02-06 19:11:15          17.99  248.00  \n",
              "2  7b07b3c7487f0ea825fc6df75abd658b 2018-07-11 21:30:20           7.82   79.80  \n",
              "3  c510bc1718f0f2961eaa42a23330681a 2018-08-07 09:10:13           9.54  112.30  \n",
              "4  0be8ff43f22e456b4e0371b2245e4d01 2018-04-17 01:30:23           8.29   37.90  "
            ]
          },
          "execution_count": 59,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "product_item_df = pd.merge(\n",
        "    left=product_df,\n",
        "    right=order_item_df,\n",
        "    how=\"inner\",\n",
        "    left_on=\"product_id\",  # Kolom yang sesuai di orders_df\n",
        "    right_on=\"product_id\"  # Kolom yang sesuai di order_items_df\n",
        ")\n",
        "selected_columns = ['product_id', 'product_category_name', 'order_item_id','seller_id', 'shipping_limit_date','freight_value', 'price']\n",
        "\n",
        "product_item_df = product_item_df[selected_columns]\n",
        "\n",
        "product_item_df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 60,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>product_id</th>\n",
              "      <th>product_category_name</th>\n",
              "      <th>order_item_id</th>\n",
              "      <th>seller_id</th>\n",
              "      <th>shipping_limit_date</th>\n",
              "      <th>freight_value</th>\n",
              "      <th>price</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>20528</th>\n",
              "      <td>489ae2aa008f021502940f251d4cce7f</td>\n",
              "      <td>utilidades_domesticas</td>\n",
              "      <td>1</td>\n",
              "      <td>e3b4998c7a498169dc7bce44e6bb6277</td>\n",
              "      <td>2017-02-16 20:37:36</td>\n",
              "      <td>194.31</td>\n",
              "      <td>6735.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>73922</th>\n",
              "      <td>69c590f7ffc7bf8db97190b6cb6ed62e</td>\n",
              "      <td>pcs</td>\n",
              "      <td>1</td>\n",
              "      <td>80ceebb4ee9b31afb6c6a916a574a1e2</td>\n",
              "      <td>2018-08-02 04:05:13</td>\n",
              "      <td>193.21</td>\n",
              "      <td>6729.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>49503</th>\n",
              "      <td>1bdf5e6731585cf01aa8169c7028d6ad</td>\n",
              "      <td>artes</td>\n",
              "      <td>1</td>\n",
              "      <td>ee27a8f15b1dded4d213a468ba4eb391</td>\n",
              "      <td>2017-06-15 02:45:17</td>\n",
              "      <td>227.66</td>\n",
              "      <td>6499.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>71835</th>\n",
              "      <td>a6492cc69376c469ab6f61d8f44de961</td>\n",
              "      <td>eletroportateis</td>\n",
              "      <td>1</td>\n",
              "      <td>59417c56835dd8e2e72f91f809cd4092</td>\n",
              "      <td>2017-04-18 13:25:18</td>\n",
              "      <td>151.34</td>\n",
              "      <td>4799.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32232</th>\n",
              "      <td>c3ed642d592594bb648ff4a04cee2747</td>\n",
              "      <td>eletroportateis</td>\n",
              "      <td>1</td>\n",
              "      <td>59417c56835dd8e2e72f91f809cd4092</td>\n",
              "      <td>2017-05-09 15:50:15</td>\n",
              "      <td>74.34</td>\n",
              "      <td>4690.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>55911</th>\n",
              "      <td>05b515fdc76e888aada3c6d66c201dff</td>\n",
              "      <td>beleza_saude</td>\n",
              "      <td>11</td>\n",
              "      <td>2709af9587499e95e803a6498a5a56e9</td>\n",
              "      <td>2017-07-21 18:25:23</td>\n",
              "      <td>7.89</td>\n",
              "      <td>1.20</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>55903</th>\n",
              "      <td>05b515fdc76e888aada3c6d66c201dff</td>\n",
              "      <td>beleza_saude</td>\n",
              "      <td>3</td>\n",
              "      <td>2709af9587499e95e803a6498a5a56e9</td>\n",
              "      <td>2017-07-21 18:25:23</td>\n",
              "      <td>7.89</td>\n",
              "      <td>1.20</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29610</th>\n",
              "      <td>8a3254bee785a526d548a81a9bc3c9be</td>\n",
              "      <td>construcao_ferramentas_construcao</td>\n",
              "      <td>2</td>\n",
              "      <td>96804ea39d96eb908e7c3afdb671bb9e</td>\n",
              "      <td>2018-05-07 02:55:22</td>\n",
              "      <td>22.30</td>\n",
              "      <td>0.85</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29608</th>\n",
              "      <td>8a3254bee785a526d548a81a9bc3c9be</td>\n",
              "      <td>construcao_ferramentas_construcao</td>\n",
              "      <td>1</td>\n",
              "      <td>96804ea39d96eb908e7c3afdb671bb9e</td>\n",
              "      <td>2018-05-04 03:55:26</td>\n",
              "      <td>18.23</td>\n",
              "      <td>0.85</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29609</th>\n",
              "      <td>8a3254bee785a526d548a81a9bc3c9be</td>\n",
              "      <td>construcao_ferramentas_construcao</td>\n",
              "      <td>1</td>\n",
              "      <td>96804ea39d96eb908e7c3afdb671bb9e</td>\n",
              "      <td>2018-05-02 20:30:34</td>\n",
              "      <td>18.23</td>\n",
              "      <td>0.85</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>112650 rows × 7 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                             product_id              product_category_name  \\\n",
              "20528  489ae2aa008f021502940f251d4cce7f              utilidades_domesticas   \n",
              "73922  69c590f7ffc7bf8db97190b6cb6ed62e                                pcs   \n",
              "49503  1bdf5e6731585cf01aa8169c7028d6ad                              artes   \n",
              "71835  a6492cc69376c469ab6f61d8f44de961                    eletroportateis   \n",
              "32232  c3ed642d592594bb648ff4a04cee2747                    eletroportateis   \n",
              "...                                 ...                                ...   \n",
              "55911  05b515fdc76e888aada3c6d66c201dff                       beleza_saude   \n",
              "55903  05b515fdc76e888aada3c6d66c201dff                       beleza_saude   \n",
              "29610  8a3254bee785a526d548a81a9bc3c9be  construcao_ferramentas_construcao   \n",
              "29608  8a3254bee785a526d548a81a9bc3c9be  construcao_ferramentas_construcao   \n",
              "29609  8a3254bee785a526d548a81a9bc3c9be  construcao_ferramentas_construcao   \n",
              "\n",
              "       order_item_id                         seller_id shipping_limit_date  \\\n",
              "20528              1  e3b4998c7a498169dc7bce44e6bb6277 2017-02-16 20:37:36   \n",
              "73922              1  80ceebb4ee9b31afb6c6a916a574a1e2 2018-08-02 04:05:13   \n",
              "49503              1  ee27a8f15b1dded4d213a468ba4eb391 2017-06-15 02:45:17   \n",
              "71835              1  59417c56835dd8e2e72f91f809cd4092 2017-04-18 13:25:18   \n",
              "32232              1  59417c56835dd8e2e72f91f809cd4092 2017-05-09 15:50:15   \n",
              "...              ...                               ...                 ...   \n",
              "55911             11  2709af9587499e95e803a6498a5a56e9 2017-07-21 18:25:23   \n",
              "55903              3  2709af9587499e95e803a6498a5a56e9 2017-07-21 18:25:23   \n",
              "29610              2  96804ea39d96eb908e7c3afdb671bb9e 2018-05-07 02:55:22   \n",
              "29608              1  96804ea39d96eb908e7c3afdb671bb9e 2018-05-04 03:55:26   \n",
              "29609              1  96804ea39d96eb908e7c3afdb671bb9e 2018-05-02 20:30:34   \n",
              "\n",
              "       freight_value    price  \n",
              "20528         194.31  6735.00  \n",
              "73922         193.21  6729.00  \n",
              "49503         227.66  6499.00  \n",
              "71835         151.34  4799.00  \n",
              "32232          74.34  4690.00  \n",
              "...              ...      ...  \n",
              "55911           7.89     1.20  \n",
              "55903           7.89     1.20  \n",
              "29610          22.30     0.85  \n",
              "29608          18.23     0.85  \n",
              "29609          18.23     0.85  \n",
              "\n",
              "[112650 rows x 7 columns]"
            ]
          },
          "execution_count": 60,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "product_item_df.sort_values(by=\"price\", ascending=False)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "**Insight:**\n",
        "- Dengan melihat produk yang memiliki harga lebih rendah, perusahaan dapat melakukan analisis persaingan untuk menentukan apakah ada peluang untuk menyesuaikan harga atau meningkatkan nilai produk. Ini juga dapat membantu dalam mengidentifikasi segmen pasar yang mungkin kurang terlayani, memungkinkan perusahaan untuk menyesuaikan penawaran produk atau layanan"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Menggabungkan Data Product Item & Seller"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 61,
      "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": 61,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "seller_df.describe(include=\"all\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 62,
      "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",
              "      <th>product_id</th>\n",
              "      <th>product_category_name</th>\n",
              "      <th>order_item_id</th>\n",
              "      <th>shipping_limit_date</th>\n",
              "      <th>freight_value</th>\n",
              "      <th>price</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",
              "      <td>ffb64e34a37740dafb6c88f1abd1fa61</td>\n",
              "      <td>esporte_lazer</td>\n",
              "      <td>1</td>\n",
              "      <td>2017-08-25 20:50:19</td>\n",
              "      <td>9.56</td>\n",
              "      <td>106.20</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>3442f8959a84dea7ee197c632cb2df15</td>\n",
              "      <td>13023</td>\n",
              "      <td>campinas</td>\n",
              "      <td>SP</td>\n",
              "      <td>325a06bcce0da45b7f4ecf2797dd40e4</td>\n",
              "      <td>esporte_lazer</td>\n",
              "      <td>2</td>\n",
              "      <td>2017-09-05 12:50:19</td>\n",
              "      <td>2.42</td>\n",
              "      <td>10.80</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3442f8959a84dea7ee197c632cb2df15</td>\n",
              "      <td>13023</td>\n",
              "      <td>campinas</td>\n",
              "      <td>SP</td>\n",
              "      <td>f4621f8ad6f54a2e3c408884068be46d</td>\n",
              "      <td>esporte_lazer</td>\n",
              "      <td>1</td>\n",
              "      <td>2017-05-11 16:25:11</td>\n",
              "      <td>15.92</td>\n",
              "      <td>101.70</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>d1b65fc7debc3361ea86b5f14c68d2e2</td>\n",
              "      <td>13844</td>\n",
              "      <td>mogi guacu</td>\n",
              "      <td>SP</td>\n",
              "      <td>55103cbbe0851d3a303a022cc4140628</td>\n",
              "      <td>malas_acessorios</td>\n",
              "      <td>1</td>\n",
              "      <td>2017-08-17 23:30:18</td>\n",
              "      <td>23.40</td>\n",
              "      <td>199.90</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>d1b65fc7debc3361ea86b5f14c68d2e2</td>\n",
              "      <td>13844</td>\n",
              "      <td>mogi guacu</td>\n",
              "      <td>SP</td>\n",
              "      <td>1c36a5285f7f3b1ed2637d7c528ce5ff</td>\n",
              "      <td>malas_acessorios</td>\n",
              "      <td>1</td>\n",
              "      <td>2017-11-22 04:30:29</td>\n",
              "      <td>45.09</td>\n",
              "      <td>89.99</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                          seller_id  seller_zip_code_prefix seller_city  \\\n",
              "0  3442f8959a84dea7ee197c632cb2df15                   13023    campinas   \n",
              "1  3442f8959a84dea7ee197c632cb2df15                   13023    campinas   \n",
              "2  3442f8959a84dea7ee197c632cb2df15                   13023    campinas   \n",
              "3  d1b65fc7debc3361ea86b5f14c68d2e2                   13844  mogi guacu   \n",
              "4  d1b65fc7debc3361ea86b5f14c68d2e2                   13844  mogi guacu   \n",
              "\n",
              "  seller_state                        product_id product_category_name  \\\n",
              "0           SP  ffb64e34a37740dafb6c88f1abd1fa61         esporte_lazer   \n",
              "1           SP  325a06bcce0da45b7f4ecf2797dd40e4         esporte_lazer   \n",
              "2           SP  f4621f8ad6f54a2e3c408884068be46d         esporte_lazer   \n",
              "3           SP  55103cbbe0851d3a303a022cc4140628      malas_acessorios   \n",
              "4           SP  1c36a5285f7f3b1ed2637d7c528ce5ff      malas_acessorios   \n",
              "\n",
              "   order_item_id shipping_limit_date  freight_value   price  \n",
              "0              1 2017-08-25 20:50:19           9.56  106.20  \n",
              "1              2 2017-09-05 12:50:19           2.42   10.80  \n",
              "2              1 2017-05-11 16:25:11          15.92  101.70  \n",
              "3              1 2017-08-17 23:30:18          23.40  199.90  \n",
              "4              1 2017-11-22 04:30:29          45.09   89.99  "
            ]
          },
          "execution_count": 62,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "seller_product_df = pd.merge(\n",
        "    left=seller_df,\n",
        "    right=product_item_df,\n",
        "    how=\"left\",\n",
        "    left_on=\"seller_id\",  # Kolom yang sesuai di orders_df\n",
        "    right_on=\"seller_id\"  # Kolom yang sesuai di order_items_df\n",
        ")\n",
        "\n",
        "\n",
        "seller_product_df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 63,
      "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 tr th {\n",
              "        text-align: left;\n",
              "    }\n",
              "\n",
              "    .dataframe thead tr:last-of-type th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th></th>\n",
              "      <th>product_id</th>\n",
              "      <th>order_item_id</th>\n",
              "      <th colspan=\"2\" halign=\"left\">price</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th></th>\n",
              "      <th>nunique</th>\n",
              "      <th>sum</th>\n",
              "      <th>min</th>\n",
              "      <th>max</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>product_category_name</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>60.0</th>\n",
              "      <td>610</td>\n",
              "      <td>1878</td>\n",
              "      <td>3.00</td>\n",
              "      <td>3980.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>agro_industria_e_comercio</th>\n",
              "      <td>74</td>\n",
              "      <td>265</td>\n",
              "      <td>12.99</td>\n",
              "      <td>2990.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>alimentos</th>\n",
              "      <td>82</td>\n",
              "      <td>609</td>\n",
              "      <td>9.99</td>\n",
              "      <td>274.99</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>alimentos_bebidas</th>\n",
              "      <td>104</td>\n",
              "      <td>365</td>\n",
              "      <td>6.50</td>\n",
              "      <td>699.90</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>artes</th>\n",
              "      <td>55</td>\n",
              "      <td>230</td>\n",
              "      <td>3.50</td>\n",
              "      <td>6499.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>sinalizacao_e_seguranca</th>\n",
              "      <td>93</td>\n",
              "      <td>307</td>\n",
              "      <td>4.50</td>\n",
              "      <td>740.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>tablets_impressao_imagem</th>\n",
              "      <td>9</td>\n",
              "      <td>89</td>\n",
              "      <td>14.90</td>\n",
              "      <td>889.99</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>telefonia</th>\n",
              "      <td>1134</td>\n",
              "      <td>5161</td>\n",
              "      <td>5.00</td>\n",
              "      <td>2428.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>telefonia_fixa</th>\n",
              "      <td>116</td>\n",
              "      <td>366</td>\n",
              "      <td>6.00</td>\n",
              "      <td>1790.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>utilidades_domesticas</th>\n",
              "      <td>2335</td>\n",
              "      <td>9051</td>\n",
              "      <td>3.06</td>\n",
              "      <td>6735.00</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>74 rows × 4 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                          product_id order_item_id  price         \n",
              "                             nunique           sum    min      max\n",
              "product_category_name                                             \n",
              "60.0                             610          1878   3.00  3980.00\n",
              "agro_industria_e_comercio         74           265  12.99  2990.00\n",
              "alimentos                         82           609   9.99   274.99\n",
              "alimentos_bebidas                104           365   6.50   699.90\n",
              "artes                             55           230   3.50  6499.00\n",
              "...                              ...           ...    ...      ...\n",
              "sinalizacao_e_seguranca           93           307   4.50   740.00\n",
              "tablets_impressao_imagem           9            89  14.90   889.99\n",
              "telefonia                       1134          5161   5.00  2428.00\n",
              "telefonia_fixa                   116           366   6.00  1790.00\n",
              "utilidades_domesticas           2335          9051   3.06  6735.00\n",
              "\n",
              "[74 rows x 4 columns]"
            ]
          },
          "execution_count": 63,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "seller_product_df.groupby(by='product_category_name').agg({\n",
        "    'product_id': 'nunique',  \n",
        "    'order_item_id': 'sum',   \n",
        "    'price': ['min', 'max']   \n",
        "})\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "**Insight:**\n",
        "- Dengan adanya rentang harga yang luas di beberapa kategori, perusahaan bisa mengeksplorasi peluang untuk memperkenalkan produk baru dalam berbagai segmen harga. Misalnya, kategori dengan harga maksimum yang tinggi dapat menjadi area untuk inovasi produk atau peluncuran barang premium yang menarik pelanggan yang mencari kualitas lebih."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 64,
      "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>average_price</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>product_category_name</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>60.0</th>\n",
              "      <td>111.999551</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>agro_industria_e_comercio</th>\n",
              "      <td>342.124858</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>alimentos</th>\n",
              "      <td>57.634137</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>alimentos_bebidas</th>\n",
              "      <td>54.602446</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>artes</th>\n",
              "      <td>115.802105</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>sinalizacao_e_seguranca</th>\n",
              "      <td>108.086583</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>tablets_impressao_imagem</th>\n",
              "      <td>90.703735</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>telefonia</th>\n",
              "      <td>71.213978</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>telefonia_fixa</th>\n",
              "      <td>225.693182</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>utilidades_domesticas</th>\n",
              "      <td>90.788148</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>74 rows × 1 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                           average_price\n",
              "product_category_name                   \n",
              "60.0                          111.999551\n",
              "agro_industria_e_comercio     342.124858\n",
              "alimentos                      57.634137\n",
              "alimentos_bebidas              54.602446\n",
              "artes                         115.802105\n",
              "...                                  ...\n",
              "sinalizacao_e_seguranca       108.086583\n",
              "tablets_impressao_imagem       90.703735\n",
              "telefonia                      71.213978\n",
              "telefonia_fixa                225.693182\n",
              "utilidades_domesticas          90.788148\n",
              "\n",
              "[74 rows x 1 columns]"
            ]
          },
          "execution_count": 64,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "seller_product_df.groupby('product_category_name').agg({\n",
        "    'price': 'mean'  # Rata-rata harga\n",
        "}).rename(columns={'price': 'average_price'})"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 65,
      "metadata": {},
      "outputs": [],
      "source": [
        "customer_order_payment_df.to_csv(\"customer_order.csv\", index=False)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 66,
      "metadata": {},
      "outputs": [],
      "source": [
        "seller_product_df.to_csv(\"seller_product.csv\", index=False)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zsyZjqak8DC2"
      },
      "source": [
        "## Visualization & Explanatory Analysis"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HZxOiQ6n8DC2"
      },
      "source": [
        "### Pertanyaan 1:Bagaimana Demografi pelanggan yang dimiliki ?"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 67,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "1swJUdAD8DC2",
        "outputId": "6d042b85-bc08-4fc8-a8a3-1095720ae1b7"
      },
      "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_city</th>\n",
              "      <th>customer_count</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>3597</th>\n",
              "      <td>sao paulo</td>\n",
              "      <td>15540</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3155</th>\n",
              "      <td>rio de janeiro</td>\n",
              "      <td>6882</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>453</th>\n",
              "      <td>belo horizonte</td>\n",
              "      <td>2773</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>558</th>\n",
              "      <td>brasilia</td>\n",
              "      <td>2131</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1143</th>\n",
              "      <td>curitiba</td>\n",
              "      <td>1521</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       customer_city  customer_count\n",
              "3597       sao paulo           15540\n",
              "3155  rio de janeiro            6882\n",
              "453   belo horizonte            2773\n",
              "558         brasilia            2131\n",
              "1143        curitiba            1521"
            ]
          },
          "execution_count": 67,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "bycity_df = customer_order_payment_df.groupby(by=\"customer_city\").customer_id.nunique().reset_index()\n",
        "\n",
        "bycity_df.rename(columns={\n",
        "    \"customer_id\": \"customer_count\"\n",
        "}, inplace=True)\n",
        "bycity_df = bycity_df.sort_values(by=\"customer_count\", ascending=False).head(5)\n",
        "bycity_df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 68,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "image/png": "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",
            "text/plain": [
              "<Figure size 1000x500 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.figure(figsize=(10, 5))\n",
        "\n",
        "sns.barplot(\n",
        "    y=\"customer_count\", \n",
        "    x=\"customer_city\",\n",
        "    data=bycity_df.sort_values(by=\"customer_count\", ascending=False).head(5),\n",
        "    color='skyblue'\n",
        ")\n",
        "\n",
        "# Mengatur batas sumbu y\n",
        "plt.ylim(0, bycity_df['customer_count'].max() * 1.1)\n",
        "\n",
        "# Menambahkan judul dan label\n",
        "plt.title(\"Number of Customers by City\", loc=\"center\", fontsize=15)\n",
        "plt.ylabel(None)\n",
        "plt.xlabel(None)\n",
        "plt.tick_params(axis='x', labelsize=12)\n",
        "\n",
        "# Menampilkan grafik\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "**Insight:**\n",
        "- Kota São Paulo memiliki jumlah pelanggan unik yang sangat tinggi, yaitu 15.540, yang jauh lebih banyak dibandingkan dengan kota lainnya. Hal ini menunjukkan bahwa São Paulo adalah pasar yang sangat penting dan potensial bagi bisnis.\n",
        "- Meskipun kota-kota seperti Rio de Janeiro dan Belo Horizonte memiliki jumlah pelanggan yang signifikan (6.882 dan 2.773, masing-masing), ada peluang untuk meningkatkan jumlah pelanggan di kota yang lebih kecil seperti Brasília dan Curitiba, yang memiliki jumlah pelanggan di bawah 2.500. Meningkatkan upaya pemasaran dan penawaran khusus di kota-kota ini dapat membantu menarik lebih banyak pelanggan dan memperluas pangsa pasar di wilayah tersebut."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DgHI7CiU8DC2"
      },
      "source": [
        "### Pertanyaan 2: Bagaimana Performa Penjualan dan Revenue Perusahaan dalam Beberapa Tahun Terakhir?"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 69,
      "metadata": {
        "id": "Go0lCsvO8DC2"
      },
      "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_purchase_timestamp</th>\n",
              "      <th>order_count</th>\n",
              "      <th>revenue</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2016</td>\n",
              "      <td>328</td>\n",
              "      <td>59362.34</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2017</td>\n",
              "      <td>45101</td>\n",
              "      <td>7249746.73</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2018</td>\n",
              "      <td>54011</td>\n",
              "      <td>8699763.05</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  order_purchase_timestamp  order_count     revenue\n",
              "0                     2016          328    59362.34\n",
              "1                     2017        45101  7249746.73\n",
              "2                     2018        54011  8699763.05"
            ]
          },
          "execution_count": 69,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "yearly_orders_df = customer_order_payment_df.resample(rule='YE', on='order_purchase_timestamp').agg({\n",
        "    \"order_id\": \"nunique\",\n",
        "    \"payment_value\": \"sum\"\n",
        "})\n",
        "\n",
        "yearly_orders_df.index = yearly_orders_df.index.strftime('%Y')\n",
        "\n",
        "yearly_orders_df = yearly_orders_df.reset_index()\n",
        "\n",
        "yearly_orders_df.rename(columns={\n",
        "    \"order_id\": \"order_count\",\n",
        "    \"payment_value\": \"revenue\"\n",
        "}, inplace=True)\n",
        "\n",
        "yearly_orders_df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 70,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "image/png": "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",
            "text/plain": [
              "<Figure size 1000x500 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.figure(figsize=(10, 5)) \n",
        "plt.plot(yearly_orders_df[\"order_purchase_timestamp\"], yearly_orders_df[\"order_count\"], marker='o', linewidth=2, color=\"#72BCD4\") \n",
        "plt.title(\"Number of Orders per Year\", loc=\"center\", fontsize=20) \n",
        "plt.xticks(fontsize=10) \n",
        "plt.yticks(fontsize=10) \n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_0-36BDLklRg"
      },
      "source": [
        "**Insight:**\n",
        "- Terlihat ada tren peningkatan yang signifikan dalam jumlah pesanan dari tahun ke tahun. Ini menunjukkan bahwa bisnis sedang berkembang dan semakin banyak pelanggan yang melakukan pembelian.\n",
        "- Berdasarkan tren ini, perusahaan dapat memanfaatkan momentum pertumbuhan untuk merencanakan strategi pemasaran dan pengembangan produk lebih lanjut, serta mempertimbangkan investasi dalam kapasitas produksi atau layanan pelanggan."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Pertanyaan 3: Kategori Produk apa yang paling banyak terjual?"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 71,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Product Category</th>\n",
              "      <th>Unique Product Count</th>\n",
              "      <th>Total Price</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>beleza_saude</td>\n",
              "      <td>2444</td>\n",
              "      <td>1258681.34</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>67</th>\n",
              "      <td>relogios_presentes</td>\n",
              "      <td>1329</td>\n",
              "      <td>1205005.68</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>cama_mesa_banho</td>\n",
              "      <td>3029</td>\n",
              "      <td>1036988.68</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>33</th>\n",
              "      <td>esporte_lazer</td>\n",
              "      <td>2867</td>\n",
              "      <td>988048.97</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>45</th>\n",
              "      <td>informatica_acessorios</td>\n",
              "      <td>1639</td>\n",
              "      <td>911954.32</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "          Product Category  Unique Product Count  Total Price\n",
              "12            beleza_saude                  2444   1258681.34\n",
              "67      relogios_presentes                  1329   1205005.68\n",
              "14         cama_mesa_banho                  3029   1036988.68\n",
              "33           esporte_lazer                  2867    988048.97\n",
              "45  informatica_acessorios                  1639    911954.32"
            ]
          },
          "execution_count": 71,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "category_summary = seller_product_df.groupby('product_category_name').agg({\n",
        "    'product_id': 'nunique',  \n",
        "    'price': 'sum'            \n",
        "}).reset_index()\n",
        "\n",
        "category_summary.columns = ['Product Category', 'Unique Product Count', 'Total Price']\n",
        "category_summary_sorted = category_summary.sort_values(by='Total Price', ascending=False)\n",
        "category_summary_sorted.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 72,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "image/png": "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",
            "text/plain": [
              "<Figure size 1000x600 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "categories = category_summary_sorted.head(5)\n",
        "\n",
        "fig, ax1 = plt.subplots(figsize=(10, 6))\n",
        "\n",
        "ax1.bar(categories['Product Category'], categories['Total Price'], color='skyblue', alpha=0.7, label='Total Price')\n",
        "ax1.set_ylabel('Total Price', color='skyblue')\n",
        "ax1.tick_params(axis='y', labelcolor='skyblue')\n",
        "\n",
        "ax2 = ax1.twinx()\n",
        "ax2.plot(categories['Product Category'], categories['Unique Product Count'], color='orange', marker='o', linewidth=2, label='Unique Product Count')\n",
        "ax2.set_ylabel('Unique Product Count', color='orange')\n",
        "ax2.tick_params(axis='y', labelcolor='orange')\n",
        "\n",
        "plt.title('Top Product Categories: Total Price and Unique Product Count', fontsize=16)\n",
        "fig.tight_layout() \n",
        "ax1.legend(loc='upper left')\n",
        "ax2.legend(loc='upper right')\n",
        "\n",
        "plt.xticks(rotation=45)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "**Insight:**\n",
        "- Kategori \"beleza_saude\" memiliki jumlah produk unik tertinggi (2,444) dan total harga yang signifikan (Rp 1,258,681.34). Ini menunjukkan bahwa pasar untuk produk kesehatan dan kecantikan memiliki permintaan yang kuat, dan mungkin menjadi area yang baik untuk memperluas penawaran produk atau meningkatkan pemasaran.\n",
        "- Kategori \"cama_mesa_banho\" juga menunjukkan total harga yang tinggi (Rp 1,215,623.96) dengan jumlah produk unik yang cukup besar (3,639). Ini menunjukkan bahwa konsumen aktif berinvestasi dalam produk untuk kebutuhan rumah tangga. Perusahaan bisa mempertimbangkan untuk menawarkan lebih banyak varian produk atau meningkatkan kualitas produk dalam kategori ini untuk menarik perhatian lebih banyak pelanggan."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9y4VUsmcYNZ5"
      },
      "source": [
        "## Analisis Lanjutan (Opsional)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 73,
      "metadata": {
        "id": "iWhnzsJGYUCO"
      },
      "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>max_order_timestamp</th>\n",
              "      <th>frequency</th>\n",
              "      <th>monetary</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>00012a2ce6f8dcda20d059ce98491703</td>\n",
              "      <td>2017-11-14 16:08:26</td>\n",
              "      <td>1</td>\n",
              "      <td>114.74</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>000161a058600d5901f007fab4c27140</td>\n",
              "      <td>2017-07-16 09:40:32</td>\n",
              "      <td>1</td>\n",
              "      <td>67.41</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0001fd6190edaaf884bcaf3d49edf079</td>\n",
              "      <td>2017-02-28 11:06:43</td>\n",
              "      <td>1</td>\n",
              "      <td>195.42</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0002414f95344307404f0ace7a26f1d5</td>\n",
              "      <td>2017-08-16 13:09:20</td>\n",
              "      <td>1</td>\n",
              "      <td>179.35</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>000379cdec625522490c315e70c7a9fb</td>\n",
              "      <td>2018-04-02 13:42:17</td>\n",
              "      <td>1</td>\n",
              "      <td>107.01</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                        customer_id max_order_timestamp  frequency  monetary\n",
              "0  00012a2ce6f8dcda20d059ce98491703 2017-11-14 16:08:26          1    114.74\n",
              "1  000161a058600d5901f007fab4c27140 2017-07-16 09:40:32          1     67.41\n",
              "2  0001fd6190edaaf884bcaf3d49edf079 2017-02-28 11:06:43          1    195.42\n",
              "3  0002414f95344307404f0ace7a26f1d5 2017-08-16 13:09:20          1    179.35\n",
              "4  000379cdec625522490c315e70c7a9fb 2018-04-02 13:42:17          1    107.01"
            ]
          },
          "execution_count": 73,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "rfm_df = customer_order_payment_df.groupby(by=\"customer_id\", as_index=False).agg({\n",
        "    \"order_purchase_timestamp\": \"max\", #mengambil tanggal order terakhir\n",
        "    \"order_id\": \"nunique\",\n",
        "    \"payment_value\": \"sum\"\n",
        "})\n",
        "rfm_df.columns = [\"customer_id\", \"max_order_timestamp\", \"frequency\", \"monetary\"]\n",
        "rfm_df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 74,
      "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>max_order_timestamp</th>\n",
              "      <th>frequency</th>\n",
              "      <th>monetary</th>\n",
              "      <th>recency</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>00012a2ce6f8dcda20d059ce98491703</td>\n",
              "      <td>2017-11-14 16:08:26</td>\n",
              "      <td>1</td>\n",
              "      <td>114.74</td>\n",
              "      <td>337</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>000161a058600d5901f007fab4c27140</td>\n",
              "      <td>2017-07-16 09:40:32</td>\n",
              "      <td>1</td>\n",
              "      <td>67.41</td>\n",
              "      <td>458</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0001fd6190edaaf884bcaf3d49edf079</td>\n",
              "      <td>2017-02-28 11:06:43</td>\n",
              "      <td>1</td>\n",
              "      <td>195.42</td>\n",
              "      <td>596</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0002414f95344307404f0ace7a26f1d5</td>\n",
              "      <td>2017-08-16 13:09:20</td>\n",
              "      <td>1</td>\n",
              "      <td>179.35</td>\n",
              "      <td>427</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>000379cdec625522490c315e70c7a9fb</td>\n",
              "      <td>2018-04-02 13:42:17</td>\n",
              "      <td>1</td>\n",
              "      <td>107.01</td>\n",
              "      <td>198</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                        customer_id max_order_timestamp  frequency  monetary  \\\n",
              "0  00012a2ce6f8dcda20d059ce98491703 2017-11-14 16:08:26          1    114.74   \n",
              "1  000161a058600d5901f007fab4c27140 2017-07-16 09:40:32          1     67.41   \n",
              "2  0001fd6190edaaf884bcaf3d49edf079 2017-02-28 11:06:43          1    195.42   \n",
              "3  0002414f95344307404f0ace7a26f1d5 2017-08-16 13:09:20          1    179.35   \n",
              "4  000379cdec625522490c315e70c7a9fb 2018-04-02 13:42:17          1    107.01   \n",
              "\n",
              "   recency  \n",
              "0      337  \n",
              "1      458  \n",
              "2      596  \n",
              "3      427  \n",
              "4      198  "
            ]
          },
          "execution_count": 74,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "rfm_df['max_order_timestamp'] = pd.to_datetime(rfm_df['max_order_timestamp'])\n",
        "\n",
        "# Menghitung recency\n",
        "recent_date = orders_df['order_purchase_timestamp'].max().date()  # Pastikan ini adalah tanggal terbaru dari pembelian\n",
        "rfm_df['recency'] = (recent_date - rfm_df['max_order_timestamp'].dt.date).apply(lambda x: x.days)\n",
        "\n",
        "# Lihat hasil akhir\n",
        "rfm_df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 75,
      "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>frequency</th>\n",
              "      <th>monetary</th>\n",
              "      <th>recency</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>00012a2ce6f8dcda20d059ce98491703</td>\n",
              "      <td>1</td>\n",
              "      <td>114.74</td>\n",
              "      <td>337</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>000161a058600d5901f007fab4c27140</td>\n",
              "      <td>1</td>\n",
              "      <td>67.41</td>\n",
              "      <td>458</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0001fd6190edaaf884bcaf3d49edf079</td>\n",
              "      <td>1</td>\n",
              "      <td>195.42</td>\n",
              "      <td>596</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0002414f95344307404f0ace7a26f1d5</td>\n",
              "      <td>1</td>\n",
              "      <td>179.35</td>\n",
              "      <td>427</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>000379cdec625522490c315e70c7a9fb</td>\n",
              "      <td>1</td>\n",
              "      <td>107.01</td>\n",
              "      <td>198</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                        customer_id  frequency  monetary  recency\n",
              "0  00012a2ce6f8dcda20d059ce98491703          1    114.74      337\n",
              "1  000161a058600d5901f007fab4c27140          1     67.41      458\n",
              "2  0001fd6190edaaf884bcaf3d49edf079          1    195.42      596\n",
              "3  0002414f95344307404f0ace7a26f1d5          1    179.35      427\n",
              "4  000379cdec625522490c315e70c7a9fb          1    107.01      198"
            ]
          },
          "execution_count": 75,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "rfm_df.drop(\"max_order_timestamp\", axis=1, inplace=True)\n",
        "rfm_df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 76,
      "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>frequency</th>\n",
              "      <th>monetary</th>\n",
              "      <th>recency</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>99440.0</td>\n",
              "      <td>99440.000000</td>\n",
              "      <td>99440.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>1.0</td>\n",
              "      <td>160.990267</td>\n",
              "      <td>290.268212</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>0.0</td>\n",
              "      <td>221.951257</td>\n",
              "      <td>153.664106</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>1.0</td>\n",
              "      <td>62.010000</td>\n",
              "      <td>166.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>1.0</td>\n",
              "      <td>105.290000</td>\n",
              "      <td>272.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>1.0</td>\n",
              "      <td>176.970000</td>\n",
              "      <td>400.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>1.0</td>\n",
              "      <td>13664.080000</td>\n",
              "      <td>773.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       frequency      monetary       recency\n",
              "count    99440.0  99440.000000  99440.000000\n",
              "mean         1.0    160.990267    290.268212\n",
              "std          0.0    221.951257    153.664106\n",
              "min          1.0      0.000000      0.000000\n",
              "25%          1.0     62.010000    166.000000\n",
              "50%          1.0    105.290000    272.000000\n",
              "75%          1.0    176.970000    400.000000\n",
              "max          1.0  13664.080000    773.000000"
            ]
          },
          "execution_count": 76,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "rfm_df.describe()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 77,
      "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>frequency</th>\n",
              "      <th>monetary</th>\n",
              "      <th>recency</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>63969</th>\n",
              "      <td>a4b417188addbc05b26b72d5e44837a1</td>\n",
              "      <td>1</td>\n",
              "      <td>89.71</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>51713</th>\n",
              "      <td>856336203359aa6a61bf3826f7d84c49</td>\n",
              "      <td>1</td>\n",
              "      <td>222.03</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29531</th>\n",
              "      <td>4c2ec60c29d10c34bd49cb88aa85cfc4</td>\n",
              "      <td>1</td>\n",
              "      <td>197.55</td>\n",
              "      <td>14</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>74610</th>\n",
              "      <td>bf6181a85bbb4115736c0a8db1a53be3</td>\n",
              "      <td>1</td>\n",
              "      <td>80.38</td>\n",
              "      <td>16</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15595</th>\n",
              "      <td>2823ffda607a2316375088e0d00005ec</td>\n",
              "      <td>1</td>\n",
              "      <td>137.03</td>\n",
              "      <td>18</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                            customer_id  frequency  monetary  recency\n",
              "63969  a4b417188addbc05b26b72d5e44837a1          1     89.71        0\n",
              "51713  856336203359aa6a61bf3826f7d84c49          1    222.03        1\n",
              "29531  4c2ec60c29d10c34bd49cb88aa85cfc4          1    197.55       14\n",
              "74610  bf6181a85bbb4115736c0a8db1a53be3          1     80.38       16\n",
              "15595  2823ffda607a2316375088e0d00005ec          1    137.03       18"
            ]
          },
          "execution_count": 77,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "rfm_df.sort_values(by=\"recency\", ascending=True).head(5)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 78,
      "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>frequency</th>\n",
              "      <th>monetary</th>\n",
              "      <th>recency</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>00012a2ce6f8dcda20d059ce98491703</td>\n",
              "      <td>1</td>\n",
              "      <td>114.74</td>\n",
              "      <td>337</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>66278</th>\n",
              "      <td>aa62a16b10e3fb24ecdefd7e4dc3fa65</td>\n",
              "      <td>1</td>\n",
              "      <td>206.56</td>\n",
              "      <td>115</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>66300</th>\n",
              "      <td>aa738cdb72af39e38482fa25b992c67c</td>\n",
              "      <td>1</td>\n",
              "      <td>76.86</td>\n",
              "      <td>222</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>66299</th>\n",
              "      <td>aa7210736aad65a84e964cfa8d64aa66</td>\n",
              "      <td>1</td>\n",
              "      <td>141.90</td>\n",
              "      <td>202</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>66298</th>\n",
              "      <td>aa72002446dfbac0691304465c1b982d</td>\n",
              "      <td>1</td>\n",
              "      <td>20.95</td>\n",
              "      <td>612</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                            customer_id  frequency  monetary  recency\n",
              "0      00012a2ce6f8dcda20d059ce98491703          1    114.74      337\n",
              "66278  aa62a16b10e3fb24ecdefd7e4dc3fa65          1    206.56      115\n",
              "66300  aa738cdb72af39e38482fa25b992c67c          1     76.86      222\n",
              "66299  aa7210736aad65a84e964cfa8d64aa66          1    141.90      202\n",
              "66298  aa72002446dfbac0691304465c1b982d          1     20.95      612"
            ]
          },
          "execution_count": 78,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "rfm_df.sort_values(by=\"frequency\", ascending=False).head(5)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 79,
      "metadata": {},
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "C:\\Users\\hp\\AppData\\Local\\Temp\\ipykernel_16352\\4286489001.py:5: FutureWarning: \n",
            "\n",
            "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n",
            "\n",
            "  sns.barplot(y=\"recency\", x=\"customer_id\", data=rfm_df.sort_values(by=\"recency\", ascending=True).head(5), palette=colors, ax=ax[0])\n",
            "C:\\Users\\hp\\AppData\\Local\\Temp\\ipykernel_16352\\4286489001.py:11: FutureWarning: \n",
            "\n",
            "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n",
            "\n",
            "  sns.barplot(y=\"frequency\", x=\"customer_id\", data=rfm_df.sort_values(by=\"frequency\", ascending=False).head(5), palette=colors, ax=ax[1])\n",
            "C:\\Users\\hp\\AppData\\Local\\Temp\\ipykernel_16352\\4286489001.py:17: FutureWarning: \n",
            "\n",
            "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n",
            "\n",
            "  sns.barplot(y=\"monetary\", x=\"customer_id\", data=rfm_df.sort_values(by=\"monetary\", ascending=False).head(5), palette=colors, ax=ax[2])\n"
          ]
        },
        {
          "data": {
            "image/png": "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",
            "text/plain": [
              "<Figure size 3000x600 with 3 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "fig, ax = plt.subplots(nrows=1, ncols=3, figsize=(30, 6))\n",
        "\n",
        "colors = [\"#72BCD4\", \"#72BCD4\", \"#72BCD4\", \"#72BCD4\", \"#72BCD4\"]\n",
        "\n",
        "sns.barplot(y=\"recency\", x=\"customer_id\", data=rfm_df.sort_values(by=\"recency\", ascending=True).head(5), palette=colors, ax=ax[0])\n",
        "ax[0].set_ylabel(None)\n",
        "ax[0].set_xlabel(None)\n",
        "ax[0].set_title(\"By Recency (days)\", loc=\"center\", fontsize=18)\n",
        "ax[0].tick_params(axis ='x', labelsize=15)\n",
        "\n",
        "sns.barplot(y=\"frequency\", x=\"customer_id\", data=rfm_df.sort_values(by=\"frequency\", ascending=False).head(5), palette=colors, ax=ax[1])\n",
        "ax[1].set_ylabel(None)\n",
        "ax[1].set_xlabel(None)\n",
        "ax[1].set_title(\"By Frequency\", loc=\"center\", fontsize=18)\n",
        "ax[1].tick_params(axis='x', labelsize=15)\n",
        "\n",
        "sns.barplot(y=\"monetary\", x=\"customer_id\", data=rfm_df.sort_values(by=\"monetary\", ascending=False).head(5), palette=colors, ax=ax[2])\n",
        "ax[2].set_ylabel(None)\n",
        "ax[2].set_xlabel(None)\n",
        "ax[2].set_title(\"By Monetary\", loc=\"center\", fontsize=18)\n",
        "ax[2].tick_params(axis='x', labelsize=15)\n",
        "\n",
        "plt.suptitle(\"Best Customer Based on RFM Parameters (customer_id)\", fontsize=20)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "**Insight:**\n",
        "- Pelanggan yang lebih baru (dengan nilai recency lebih rendah) biasanya menunjukkan keterlibatan yang lebih besar dengan merek. Hal ini bisa berarti bahwa strategi pemasaran yang baru-baru ini diterapkan berhasil menarik perhatian pelanggan baru. Penting untuk terus memantau nilai recency untuk memastikan bahwa pelanggan tetap terlibat.\n",
        "- Jika grafik menunjukkan bahwa pelanggan dengan nilai monetary yang lebih tinggi adalah yang terbaik, ini menunjukkan bahwa mereka mengeluarkan lebih banyak uang dalam pembelian. Ini mengindikasikan potensi untuk meningkatkan loyalitas pelanggan. Strategi untuk mempertahankan pelanggan ini, seperti program loyalitas atau penawaran khusus, bisa jadi sangat efektif."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "#### Mengurutkan customer berdasarkan recency, frequency, & monetary score"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 80,
      "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>frequency</th>\n",
              "      <th>monetary</th>\n",
              "      <th>recency</th>\n",
              "      <th>r_rank</th>\n",
              "      <th>f_rank</th>\n",
              "      <th>m_rank</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>00012a2ce6f8dcda20d059ce98491703</td>\n",
              "      <td>1</td>\n",
              "      <td>114.74</td>\n",
              "      <td>337</td>\n",
              "      <td>34363.5</td>\n",
              "      <td>49720.5</td>\n",
              "      <td>53951.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>000161a058600d5901f007fab4c27140</td>\n",
              "      <td>1</td>\n",
              "      <td>67.41</td>\n",
              "      <td>458</td>\n",
              "      <td>16870.5</td>\n",
              "      <td>49720.5</td>\n",
              "      <td>28878.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0001fd6190edaaf884bcaf3d49edf079</td>\n",
              "      <td>1</td>\n",
              "      <td>195.42</td>\n",
              "      <td>596</td>\n",
              "      <td>2881.0</td>\n",
              "      <td>49720.5</td>\n",
              "      <td>78306.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0002414f95344307404f0ace7a26f1d5</td>\n",
              "      <td>1</td>\n",
              "      <td>179.35</td>\n",
              "      <td>427</td>\n",
              "      <td>21180.5</td>\n",
              "      <td>49720.5</td>\n",
              "      <td>75142.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>000379cdec625522490c315e70c7a9fb</td>\n",
              "      <td>1</td>\n",
              "      <td>107.01</td>\n",
              "      <td>198</td>\n",
              "      <td>66985.5</td>\n",
              "      <td>49720.5</td>\n",
              "      <td>50617.5</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                        customer_id  frequency  monetary  recency   r_rank  \\\n",
              "0  00012a2ce6f8dcda20d059ce98491703          1    114.74      337  34363.5   \n",
              "1  000161a058600d5901f007fab4c27140          1     67.41      458  16870.5   \n",
              "2  0001fd6190edaaf884bcaf3d49edf079          1    195.42      596   2881.0   \n",
              "3  0002414f95344307404f0ace7a26f1d5          1    179.35      427  21180.5   \n",
              "4  000379cdec625522490c315e70c7a9fb          1    107.01      198  66985.5   \n",
              "\n",
              "    f_rank   m_rank  \n",
              "0  49720.5  53951.5  \n",
              "1  49720.5  28878.0  \n",
              "2  49720.5  78306.0  \n",
              "3  49720.5  75142.0  \n",
              "4  49720.5  50617.5  "
            ]
          },
          "execution_count": 80,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "rfm_df['r_rank'] = rfm_df['recency'].rank(ascending=False)\n",
        "rfm_df['f_rank'] = rfm_df['frequency'].rank(ascending=True)\n",
        "rfm_df['m_rank'] = rfm_df['monetary'].rank(ascending=True)\n",
        "\n",
        "rfm_df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 81,
      "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>frequency</th>\n",
              "      <th>monetary</th>\n",
              "      <th>recency</th>\n",
              "      <th>r_rank_norm</th>\n",
              "      <th>f_rank_norm</th>\n",
              "      <th>m_rank_norm</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>00012a2ce6f8dcda20d059ce98491703</td>\n",
              "      <td>1</td>\n",
              "      <td>114.74</td>\n",
              "      <td>337</td>\n",
              "      <td>34.557019</td>\n",
              "      <td>100.0</td>\n",
              "      <td>54.255330</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>000161a058600d5901f007fab4c27140</td>\n",
              "      <td>1</td>\n",
              "      <td>67.41</td>\n",
              "      <td>458</td>\n",
              "      <td>16.965507</td>\n",
              "      <td>100.0</td>\n",
              "      <td>29.040628</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0001fd6190edaaf884bcaf3d49edf079</td>\n",
              "      <td>1</td>\n",
              "      <td>195.42</td>\n",
              "      <td>596</td>\n",
              "      <td>2.897224</td>\n",
              "      <td>100.0</td>\n",
              "      <td>78.746983</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0002414f95344307404f0ace7a26f1d5</td>\n",
              "      <td>1</td>\n",
              "      <td>179.35</td>\n",
              "      <td>427</td>\n",
              "      <td>21.299779</td>\n",
              "      <td>100.0</td>\n",
              "      <td>75.565165</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>000379cdec625522490c315e70c7a9fb</td>\n",
              "      <td>1</td>\n",
              "      <td>107.01</td>\n",
              "      <td>198</td>\n",
              "      <td>67.362731</td>\n",
              "      <td>100.0</td>\n",
              "      <td>50.902554</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                        customer_id  frequency  monetary  recency  \\\n",
              "0  00012a2ce6f8dcda20d059ce98491703          1    114.74      337   \n",
              "1  000161a058600d5901f007fab4c27140          1     67.41      458   \n",
              "2  0001fd6190edaaf884bcaf3d49edf079          1    195.42      596   \n",
              "3  0002414f95344307404f0ace7a26f1d5          1    179.35      427   \n",
              "4  000379cdec625522490c315e70c7a9fb          1    107.01      198   \n",
              "\n",
              "   r_rank_norm  f_rank_norm  m_rank_norm  \n",
              "0    34.557019        100.0    54.255330  \n",
              "1    16.965507        100.0    29.040628  \n",
              "2     2.897224        100.0    78.746983  \n",
              "3    21.299779        100.0    75.565165  \n",
              "4    67.362731        100.0    50.902554  "
            ]
          },
          "execution_count": 81,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "rfm_df['r_rank_norm'] = (rfm_df['r_rank']/rfm_df['r_rank'].max())*100\n",
        "rfm_df['f_rank_norm'] = (rfm_df['f_rank']/rfm_df['f_rank'].max())*100\n",
        "rfm_df['m_rank_norm'] = (rfm_df['m_rank']/rfm_df['m_rank'].max())*100\n",
        " \n",
        "rfm_df.drop(columns=['r_rank', 'f_rank', 'm_rank'], inplace=True)\n",
        " \n",
        "rfm_df.head()"
      ]
    },
    {
      "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>customer_id</th>\n",
              "      <th>RFM_score</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>00012a2ce6f8dcda20d059ce98491703</td>\n",
              "      <td>3.21</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>000161a058600d5901f007fab4c27140</td>\n",
              "      <td>2.35</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0001fd6190edaaf884bcaf3d49edf079</td>\n",
              "      <td>3.67</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0002414f95344307404f0ace7a26f1d5</td>\n",
              "      <td>3.71</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>000379cdec625522490c315e70c7a9fb</td>\n",
              "      <td>3.36</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>0004164d20a9e969af783496f3408652</td>\n",
              "      <td>2.36</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>000419c5494106c306a97b5635748086</td>\n",
              "      <td>2.33</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                        customer_id  RFM_score\n",
              "0  00012a2ce6f8dcda20d059ce98491703       3.21\n",
              "1  000161a058600d5901f007fab4c27140       2.35\n",
              "2  0001fd6190edaaf884bcaf3d49edf079       3.67\n",
              "3  0002414f95344307404f0ace7a26f1d5       3.71\n",
              "4  000379cdec625522490c315e70c7a9fb       3.36\n",
              "5  0004164d20a9e969af783496f3408652       2.36\n",
              "6  000419c5494106c306a97b5635748086       2.33"
            ]
          },
          "execution_count": 82,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "rfm_df['RFM_score'] = 0.15*rfm_df['r_rank_norm']+0.28 * \\\n",
        "    rfm_df['f_rank_norm']+0.57*rfm_df['m_rank_norm']\n",
        "rfm_df['RFM_score'] *= 0.05\n",
        "rfm_df = rfm_df.round(2)\n",
        "rfm_df[['customer_id', 'RFM_score']].head(7)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "#### Segmentasi customer berdasarkan RFM_score"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 83,
      "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>RFM_score</th>\n",
              "      <th>customer_segment</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>00012a2ce6f8dcda20d059ce98491703</td>\n",
              "      <td>3.21</td>\n",
              "      <td>Medium value customer</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>000161a058600d5901f007fab4c27140</td>\n",
              "      <td>2.35</td>\n",
              "      <td>Low value customers</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0001fd6190edaaf884bcaf3d49edf079</td>\n",
              "      <td>3.67</td>\n",
              "      <td>Medium value customer</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0002414f95344307404f0ace7a26f1d5</td>\n",
              "      <td>3.71</td>\n",
              "      <td>Medium value customer</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>000379cdec625522490c315e70c7a9fb</td>\n",
              "      <td>3.36</td>\n",
              "      <td>Medium value customer</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>0004164d20a9e969af783496f3408652</td>\n",
              "      <td>2.36</td>\n",
              "      <td>Low value customers</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>000419c5494106c306a97b5635748086</td>\n",
              "      <td>2.33</td>\n",
              "      <td>Low value customers</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>00046a560d407e99b969756e0b10f282</td>\n",
              "      <td>3.79</td>\n",
              "      <td>Medium value customer</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>00050bf6e01e69d5c0fd612f1bcfb69c</td>\n",
              "      <td>2.73</td>\n",
              "      <td>Low value customers</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>000598caf2ef4117407665ac33275130</td>\n",
              "      <td>4.95</td>\n",
              "      <td>Top customers</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>0005aefbb696d34b3424dccd0a0e9fd0</td>\n",
              "      <td>3.93</td>\n",
              "      <td>Medium value customer</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>00062b33cb9f6fe976afdcff967ea74d</td>\n",
              "      <td>2.09</td>\n",
              "      <td>Low value customers</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>00066ccbe787a588c52bd5ff404590e3</td>\n",
              "      <td>4.31</td>\n",
              "      <td>High value customer</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>00072d033fe2e59061ae5c3aff1a2be5</td>\n",
              "      <td>3.03</td>\n",
              "      <td>Medium value customer</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>0009a69b72033b2d0ec8c69fc70ef768</td>\n",
              "      <td>3.57</td>\n",
              "      <td>Medium value customer</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>000bf8121c3412d3057d32371c5d3395</td>\n",
              "      <td>2.02</td>\n",
              "      <td>Low value customers</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>000e943451fc2788ca6ac98a682f2f49</td>\n",
              "      <td>2.89</td>\n",
              "      <td>Low value customers</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>000f17e290c26b28549908a04cfe36c1</td>\n",
              "      <td>3.48</td>\n",
              "      <td>Medium value customer</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>000fd45d6fedae68fc6676036610f879</td>\n",
              "      <td>2.74</td>\n",
              "      <td>Low value customers</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>0010068a73b7c56da5758c3f9e5c7375</td>\n",
              "      <td>2.02</td>\n",
              "      <td>Low value customers</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                         customer_id  RFM_score       customer_segment\n",
              "0   00012a2ce6f8dcda20d059ce98491703       3.21  Medium value customer\n",
              "1   000161a058600d5901f007fab4c27140       2.35    Low value customers\n",
              "2   0001fd6190edaaf884bcaf3d49edf079       3.67  Medium value customer\n",
              "3   0002414f95344307404f0ace7a26f1d5       3.71  Medium value customer\n",
              "4   000379cdec625522490c315e70c7a9fb       3.36  Medium value customer\n",
              "5   0004164d20a9e969af783496f3408652       2.36    Low value customers\n",
              "6   000419c5494106c306a97b5635748086       2.33    Low value customers\n",
              "7   00046a560d407e99b969756e0b10f282       3.79  Medium value customer\n",
              "8   00050bf6e01e69d5c0fd612f1bcfb69c       2.73    Low value customers\n",
              "9   000598caf2ef4117407665ac33275130       4.95          Top customers\n",
              "10  0005aefbb696d34b3424dccd0a0e9fd0       3.93  Medium value customer\n",
              "11  00062b33cb9f6fe976afdcff967ea74d       2.09    Low value customers\n",
              "12  00066ccbe787a588c52bd5ff404590e3       4.31    High value customer\n",
              "13  00072d033fe2e59061ae5c3aff1a2be5       3.03  Medium value customer\n",
              "14  0009a69b72033b2d0ec8c69fc70ef768       3.57  Medium value customer\n",
              "15  000bf8121c3412d3057d32371c5d3395       2.02    Low value customers\n",
              "16  000e943451fc2788ca6ac98a682f2f49       2.89    Low value customers\n",
              "17  000f17e290c26b28549908a04cfe36c1       3.48  Medium value customer\n",
              "18  000fd45d6fedae68fc6676036610f879       2.74    Low value customers\n",
              "19  0010068a73b7c56da5758c3f9e5c7375       2.02    Low value customers"
            ]
          },
          "execution_count": 83,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "rfm_df[\"customer_segment\"] = np.where(\n",
        "    rfm_df['RFM_score'] > 4.5, \"Top customers\", (np.where(\n",
        "        rfm_df['RFM_score'] > 4, \"High value customer\",(np.where(\n",
        "            rfm_df['RFM_score'] > 3, \"Medium value customer\", np.where(\n",
        "                rfm_df['RFM_score'] > 1.6, 'Low value customers', 'lost customers'))))))\n",
        "\n",
        "rfm_df[['customer_id', 'RFM_score', 'customer_segment']].head(20)"
      ]
    },
    {
      "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_segment</th>\n",
              "      <th>customer_id</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>High value customer</td>\n",
              "      <td>16194</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Low value customers</td>\n",
              "      <td>41904</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Medium value customer</td>\n",
              "      <td>34697</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Top customers</td>\n",
              "      <td>5572</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>lost customers</td>\n",
              "      <td>1073</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        customer_segment  customer_id\n",
              "0    High value customer        16194\n",
              "1    Low value customers        41904\n",
              "2  Medium value customer        34697\n",
              "3          Top customers         5572\n",
              "4         lost customers         1073"
            ]
          },
          "execution_count": 84,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "customer_segment_df = rfm_df.groupby(by=\"customer_segment\", as_index=False).customer_id.nunique()\n",
        "customer_segment_df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 85,
      "metadata": {},
      "outputs": [],
      "source": [
        "customer_segment_df['customer_segment'] = pd.Categorical(customer_segment_df['customer_segment'], [\n",
        "    \"lost customers\", \"Low value customers\", \"Medium value customer\",\n",
        "    \"High value customer\", \"Top customers\"\n",
        "])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 86,
      "metadata": {},
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "C:\\Users\\hp\\AppData\\Local\\Temp\\ipykernel_16352\\1016365800.py:4: FutureWarning: \n",
            "\n",
            "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `y` variable to `hue` and set `legend=False` for the same effect.\n",
            "\n",
            "  sns.barplot(\n"
          ]
        },
        {
          "data": {
            "image/png": "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",
            "text/plain": [
              "<Figure size 1000x500 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.figure(figsize=(10, 5))\n",
        "colors_ = [\"#72BCD4\", \"#72BCD4\", \"#D3D3D3\", \"#D3D3D3\", \"#D3D3D3\"]\n",
        "\n",
        "sns.barplot(\n",
        "    x=\"customer_id\", \n",
        "    y=\"customer_segment\",\n",
        "    data=customer_segment_df.sort_values(by=\"customer_segment\", ascending=False),\n",
        "    palette=colors_\n",
        ")\n",
        "plt.title(\"Number of Customer for Each Segment\", loc=\"center\", fontsize=15)\n",
        "plt.ylabel(None)\n",
        "plt.xlabel(None)\n",
        "plt.tick_params(axis='y', labelsize=12)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_WeHlCeX8DC2"
      },
      "source": [
        "## Conclusion"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZTcyR48Y8DC2"
      },
      "source": [
        "- Conclution pertanyaan 1\n",
        "- Conclution pertanyaan 2"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "main-ds",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.9.21"
    },
    "orig_nbformat": 4
  },
  "nbformat": 4,
  "nbformat_minor": 0
}