Ecommerce Analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "001c51b4",
   "metadata": {},
   "source": [
    "# Ecommerce Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b30ae690",
   "metadata": {},
   "source": [
    "#### Author : Nicodemus Naisau (demsroadmap@gmail.com)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0d6d2c19",
   "metadata": {},
   "source": [
    "<a href=\"https://medium.com/@nicodemusnaisau/ecommerce-analytics-df427308bcd\" target=_blank>\n",
    "<img src=\"img/banner.jpg\"\n",
    "     alt=\"ecommerce img assets\"\n",
    "     style=\"float: center\" /></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fc577eb5",
   "metadata": {},
   "source": [
    "____"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "13491bda",
   "metadata": {},
   "source": [
    "## Import Liblary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "ff769481",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import datetime\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib\n",
    "%matplotlib inline\n",
    "from matplotlib.gridspec import GridSpec\n",
    "from plotly.offline import init_notebook_mode, iplot\n",
    "import plotly.graph_objs as go\n",
    "from plotly import tools\n",
    "import colorlover as cl \n",
    "\n",
    "from pathlib import Path\n",
    "from datetime import date\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\") "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c34ba253",
   "metadata": {},
   "source": [
    "## Read Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "180d0971",
   "metadata": {},
   "outputs": [],
   "source": [
    "#connection\n",
    "import sqlite3\n",
    "con = sqlite3.connect(\"../olist.db\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "d14ffd4e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[('olist_order_customer_dataset',), ('olist_order_dataset',), ('olist_order_reviews_dataset',), ('olist_order_payments_dataset',), ('olist_order_items_dataset',), ('olist_products_dataset',), ('olist_sellers_dataset',), ('olist_geolocation_dataset',), ('product_category_name_translation',)]\n"
     ]
    }
   ],
   "source": [
    "cursor = con.cursor()\n",
    "#query get name tables from database\n",
    "cursor.execute(\"SELECT name FROM sqlite_master WHERE type='table';\")\n",
    "\n",
    "#show data table\n",
    "\n",
    "print(cursor.fetchall())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "de87b9af",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "#read_data\n",
    "order_customer = pd.read_sql(\"SELECT * from olist_order_customer_dataset\", con)\n",
    "order = pd.read_sql(\"SELECT * from olist_order_dataset\", con)\n",
    "order_reviews = pd.read_sql(\"SELECT * from olist_order_reviews_dataset\", con)\n",
    "order_payments = pd.read_sql(\"SELECT * from olist_order_payments_dataset\", con)\n",
    "order_items = pd.read_sql(\"SELECT * from olist_order_items_dataset\", con)\n",
    "products = pd.read_sql(\"SELECT * from olist_products_dataset\", con)\n",
    "sellers = pd.read_sql(\"SELECT * from olist_sellers_dataset\", con)\n",
    "geolocation = pd.read_sql(\"SELECT * from olist_geolocation_dataset\", con)\n",
    "product_category_translate = pd.read_sql(\"SELECT * from product_category_name_translation\", con)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fcda8e5a",
   "metadata": {},
   "source": [
    "____"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "414d8e43",
   "metadata": {},
   "source": [
    "## Cleaning Data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "22747437",
   "metadata": {},
   "source": [
    "### Missing Value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "c517c68c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "index                            0\n",
       "product_category_name            0\n",
       "product_category_name_english    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "order_customer.isna().sum() \n",
    "order.isna().sum() # missing value\n",
    "order_reviews.isna().sum() #missing value\n",
    "order_payments.isna().sum()\n",
    "order_items.isna().sum()\n",
    "products.isna().sum() #missing value\n",
    "sellers.isna().sum()\n",
    "geolocation.isna().sum()\n",
    "product_category_translate.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d97560c0",
   "metadata": {},
   "source": [
    "After checking each table column, it was found that there were 3 tables with a null value, such as the order table, product table, and review table."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8c2b7848",
   "metadata": {
    "scrolled": true
   },
   "source": [
    "products.isna().sum() #missing value <br>\n",
    "order_reviews.isna().sum() #missing value<br>\n",
    "order.isna().sum() # missing value<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "babdc252",
   "metadata": {},
   "source": [
    "#### Order Table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "468d298e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
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       "      <th id=\"T_064d7_level0_row0\" class=\"row_heading level0 row0\" >index</th>\n",
       "      <td id=\"T_064d7_row0_col0\" class=\"data row0 col0\" >0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_064d7_level0_row1\" class=\"row_heading level0 row1\" >order_id</th>\n",
       "      <td id=\"T_064d7_row1_col0\" class=\"data row1 col0\" >0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_064d7_level0_row2\" class=\"row_heading level0 row2\" >customer_id</th>\n",
       "      <td id=\"T_064d7_row2_col0\" class=\"data row2 col0\" >0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_064d7_level0_row3\" class=\"row_heading level0 row3\" >order_status</th>\n",
       "      <td id=\"T_064d7_row3_col0\" class=\"data row3 col0\" >0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_064d7_level0_row4\" class=\"row_heading level0 row4\" >order_purchase_timestamp</th>\n",
       "      <td id=\"T_064d7_row4_col0\" class=\"data row4 col0\" >0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_064d7_level0_row5\" class=\"row_heading level0 row5\" >order_approved_at</th>\n",
       "      <td id=\"T_064d7_row5_col0\" class=\"data row5 col0\" >160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_064d7_level0_row6\" class=\"row_heading level0 row6\" >order_delivered_carrier_date</th>\n",
       "      <td id=\"T_064d7_row6_col0\" class=\"data row6 col0\" >1783</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_064d7_level0_row7\" class=\"row_heading level0 row7\" >order_delivered_customer_date</th>\n",
       "      <td id=\"T_064d7_row7_col0\" class=\"data row7 col0\" >2965</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_064d7_level0_row8\" class=\"row_heading level0 row8\" >order_estimated_delivery_date</th>\n",
       "      <td id=\"T_064d7_row8_col0\" class=\"data row8 col0\" >0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "order.isna().sum().to_frame().style.background_gradient(cmap='Blues')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "6fe63e8a",
   "metadata": {},
   "outputs": [],
   "source": [
    "#handling NaN\n",
    "order.dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "0747f4f2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_0fcf3_row0_col0, #T_0fcf3_row1_col0, #T_0fcf3_row2_col0, #T_0fcf3_row3_col0, #T_0fcf3_row4_col0, #T_0fcf3_row5_col0, #T_0fcf3_row6_col0, #T_0fcf3_row7_col0, #T_0fcf3_row8_col0 {\n",
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       "  <thead>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_0fcf3_level0_row1\" class=\"row_heading level0 row1\" >order_id</th>\n",
       "      <td id=\"T_0fcf3_row1_col0\" class=\"data row1 col0\" >0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_0fcf3_level0_row2\" class=\"row_heading level0 row2\" >customer_id</th>\n",
       "      <td id=\"T_0fcf3_row2_col0\" class=\"data row2 col0\" >0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_0fcf3_level0_row3\" class=\"row_heading level0 row3\" >order_status</th>\n",
       "      <td id=\"T_0fcf3_row3_col0\" class=\"data row3 col0\" >0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_0fcf3_level0_row4\" class=\"row_heading level0 row4\" >order_purchase_timestamp</th>\n",
       "      <td id=\"T_0fcf3_row4_col0\" class=\"data row4 col0\" >0</td>\n",
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       "      <th id=\"T_0fcf3_level0_row5\" class=\"row_heading level0 row5\" >order_approved_at</th>\n",
       "      <td id=\"T_0fcf3_row5_col0\" class=\"data row5 col0\" >0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_0fcf3_level0_row6\" class=\"row_heading level0 row6\" >order_delivered_carrier_date</th>\n",
       "      <td id=\"T_0fcf3_row6_col0\" class=\"data row6 col0\" >0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_0fcf3_level0_row7\" class=\"row_heading level0 row7\" >order_delivered_customer_date</th>\n",
       "      <td id=\"T_0fcf3_row7_col0\" class=\"data row7 col0\" >0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_0fcf3_level0_row8\" class=\"row_heading level0 row8\" >order_estimated_delivery_date</th>\n",
       "      <td id=\"T_0fcf3_row8_col0\" class=\"data row8 col0\" >0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "order.isna().sum().to_frame().style.background_gradient(cmap='Blues')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "48d1c392",
   "metadata": {},
   "source": [
    "#### Order Reviews Table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "3bc9df66",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <tbody>\n",
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       "      <th id=\"T_7f7e0_level0_row0\" class=\"row_heading level0 row0\" >index</th>\n",
       "      <td id=\"T_7f7e0_row0_col0\" class=\"data row0 col0\" >0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7f7e0_level0_row1\" class=\"row_heading level0 row1\" >review_id</th>\n",
       "      <td id=\"T_7f7e0_row1_col0\" class=\"data row1 col0\" >0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7f7e0_level0_row2\" class=\"row_heading level0 row2\" >order_id</th>\n",
       "      <td id=\"T_7f7e0_row2_col0\" class=\"data row2 col0\" >0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7f7e0_level0_row3\" class=\"row_heading level0 row3\" >review_score</th>\n",
       "      <td id=\"T_7f7e0_row3_col0\" class=\"data row3 col0\" >0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7f7e0_level0_row4\" class=\"row_heading level0 row4\" >review_comment_title</th>\n",
       "      <td id=\"T_7f7e0_row4_col0\" class=\"data row4 col0\" >87656</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7f7e0_level0_row5\" class=\"row_heading level0 row5\" >review_comment_message</th>\n",
       "      <td id=\"T_7f7e0_row5_col0\" class=\"data row5 col0\" >58247</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7f7e0_level0_row6\" class=\"row_heading level0 row6\" >review_creation_date</th>\n",
       "      <td id=\"T_7f7e0_row6_col0\" class=\"data row6 col0\" >0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7f7e0_level0_row7\" class=\"row_heading level0 row7\" >review_answer_timestamp</th>\n",
       "      <td id=\"T_7f7e0_row7_col0\" class=\"data row7 col0\" >0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
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     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "order_reviews.isna().sum().to_frame().style.background_gradient(cmap='Blues')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "186c3738",
   "metadata": {},
   "source": [
    "we don't need to drop data reviews that contain missing values, because from the order reviews sometimes people don't fill the review completely"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a71ca3b5",
   "metadata": {},
   "source": [
    "#### Products Table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "2719f442",
   "metadata": {},
   "outputs": [
    {
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       "      <th id=\"T_b6076_level0_row1\" class=\"row_heading level0 row1\" >product_id</th>\n",
       "      <td id=\"T_b6076_row1_col0\" class=\"data row1 col0\" >0</td>\n",
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       "    <tr>\n",
       "      <th id=\"T_b6076_level0_row2\" class=\"row_heading level0 row2\" >product_category_name</th>\n",
       "      <td id=\"T_b6076_row2_col0\" class=\"data row2 col0\" >610</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_b6076_level0_row3\" class=\"row_heading level0 row3\" >product_name_lenght</th>\n",
       "      <td id=\"T_b6076_row3_col0\" class=\"data row3 col0\" >610</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_b6076_level0_row4\" class=\"row_heading level0 row4\" >product_description_lenght</th>\n",
       "      <td id=\"T_b6076_row4_col0\" class=\"data row4 col0\" >610</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_b6076_level0_row5\" class=\"row_heading level0 row5\" >product_photos_qty</th>\n",
       "      <td id=\"T_b6076_row5_col0\" class=\"data row5 col0\" >610</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_b6076_level0_row6\" class=\"row_heading level0 row6\" >product_weight_g</th>\n",
       "      <td id=\"T_b6076_row6_col0\" class=\"data row6 col0\" >2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_b6076_level0_row7\" class=\"row_heading level0 row7\" >product_length_cm</th>\n",
       "      <td id=\"T_b6076_row7_col0\" class=\"data row7 col0\" >2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_b6076_level0_row8\" class=\"row_heading level0 row8\" >product_height_cm</th>\n",
       "      <td id=\"T_b6076_row8_col0\" class=\"data row8 col0\" >2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_b6076_level0_row9\" class=\"row_heading level0 row9\" >product_width_cm</th>\n",
       "      <td id=\"T_b6076_row9_col0\" class=\"data row9 col0\" >2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x2f038cd4d30>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "products.isna().sum().to_frame().style.background_gradient(cmap='Blues')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "465c0d58",
   "metadata": {},
   "outputs": [],
   "source": [
    "#handling NaN\n",
    "products.dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "b624fc30",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_16e2e_row0_col0, #T_16e2e_row1_col0, #T_16e2e_row2_col0, #T_16e2e_row3_col0, #T_16e2e_row4_col0, #T_16e2e_row5_col0, #T_16e2e_row6_col0, #T_16e2e_row7_col0, #T_16e2e_row8_col0, #T_16e2e_row9_col0 {\n",
       "  background-color: #f7fbff;\n",
       "  color: #000000;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_16e2e\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_16e2e_level0_col0\" class=\"col_heading level0 col0\" >0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_16e2e_level0_row0\" class=\"row_heading level0 row0\" >index</th>\n",
       "      <td id=\"T_16e2e_row0_col0\" class=\"data row0 col0\" >0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_16e2e_level0_row1\" class=\"row_heading level0 row1\" >product_id</th>\n",
       "      <td id=\"T_16e2e_row1_col0\" class=\"data row1 col0\" >0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_16e2e_level0_row2\" class=\"row_heading level0 row2\" >product_category_name</th>\n",
       "      <td id=\"T_16e2e_row2_col0\" class=\"data row2 col0\" >0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_16e2e_level0_row3\" class=\"row_heading level0 row3\" >product_name_lenght</th>\n",
       "      <td id=\"T_16e2e_row3_col0\" class=\"data row3 col0\" >0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_16e2e_level0_row4\" class=\"row_heading level0 row4\" >product_description_lenght</th>\n",
       "      <td id=\"T_16e2e_row4_col0\" class=\"data row4 col0\" >0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_16e2e_level0_row5\" class=\"row_heading level0 row5\" >product_photos_qty</th>\n",
       "      <td id=\"T_16e2e_row5_col0\" class=\"data row5 col0\" >0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_16e2e_level0_row6\" class=\"row_heading level0 row6\" >product_weight_g</th>\n",
       "      <td id=\"T_16e2e_row6_col0\" class=\"data row6 col0\" >0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_16e2e_level0_row7\" class=\"row_heading level0 row7\" >product_length_cm</th>\n",
       "      <td id=\"T_16e2e_row7_col0\" class=\"data row7 col0\" >0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_16e2e_level0_row8\" class=\"row_heading level0 row8\" >product_height_cm</th>\n",
       "      <td id=\"T_16e2e_row8_col0\" class=\"data row8 col0\" >0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_16e2e_level0_row9\" class=\"row_heading level0 row9\" >product_width_cm</th>\n",
       "      <td id=\"T_16e2e_row9_col0\" class=\"data row9 col0\" >0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x2f048cd0340>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "products.isna().sum().to_frame().style.background_gradient(cmap='Blues')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0fc1a09e",
   "metadata": {},
   "source": [
    "## Identify Duplicate Data & Inconsistency Format"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "55d04104",
   "metadata": {},
   "source": [
    "### Identify Duplicate Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "264e6a00",
   "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>index</th>\n",
       "      <th>product_category_name</th>\n",
       "      <th>product_category_name_english</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [index, product_category_name, product_category_name_english]\n",
       "Index: []"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "order_customer[order_customer.duplicated(keep='first')] \n",
    "order[order.duplicated(keep='first')]\n",
    "order_reviews[order_reviews.duplicated(keep='first')] \n",
    "order_payments[order_payments.duplicated(keep='first')]\n",
    "order_items[order_items.duplicated(keep='first')]\n",
    "products[products.duplicated(keep='first')]\n",
    "sellers[sellers.duplicated(keep='first')]\n",
    "geolocation[geolocation.duplicated(keep='first')]\n",
    "product_category_translate[product_category_translate.duplicated(keep='first')]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "895bd530",
   "metadata": {},
   "source": [
    "After checking on each table, it does not find duplicate values."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f711a777",
   "metadata": {},
   "source": [
    "### Inconsistency Format"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "f6bec947",
   "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>index</th>\n",
       "      <th>product_category_name</th>\n",
       "      <th>product_category_name_english</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>beleza_saude</td>\n",
       "      <td>health_beauty</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>informatica_acessorios</td>\n",
       "      <td>computers_accessories</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>automotivo</td>\n",
       "      <td>auto</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>cama_mesa_banho</td>\n",
       "      <td>bed_bath_table</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>moveis_decoracao</td>\n",
       "      <td>furniture_decor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>66</td>\n",
       "      <td>flores</td>\n",
       "      <td>flowers</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>67</td>\n",
       "      <td>artes_e_artesanato</td>\n",
       "      <td>arts_and_craftmanship</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>68</td>\n",
       "      <td>fraldas_higiene</td>\n",
       "      <td>diapers_and_hygiene</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>69</td>\n",
       "      <td>fashion_roupa_infanto_juvenil</td>\n",
       "      <td>fashion_childrens_clothes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>70</td>\n",
       "      <td>seguros_e_servicos</td>\n",
       "      <td>security_and_services</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>71 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    index          product_category_name product_category_name_english\n",
       "0       0                   beleza_saude                 health_beauty\n",
       "1       1         informatica_acessorios         computers_accessories\n",
       "2       2                     automotivo                          auto\n",
       "3       3                cama_mesa_banho                bed_bath_table\n",
       "4       4               moveis_decoracao               furniture_decor\n",
       "..    ...                            ...                           ...\n",
       "66     66                         flores                       flowers\n",
       "67     67             artes_e_artesanato         arts_and_craftmanship\n",
       "68     68                fraldas_higiene           diapers_and_hygiene\n",
       "69     69  fashion_roupa_infanto_juvenil     fashion_childrens_clothes\n",
       "70     70             seguros_e_servicos         security_and_services\n",
       "\n",
       "[71 rows x 3 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#checking the table where is datetimes values\n",
    "order_customer\n",
    "order #date\n",
    "order_reviews #date\n",
    "order_payments\n",
    "order_items #date\n",
    "products\n",
    "sellers\n",
    "geolocation\n",
    "product_category_translate"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d054a777",
   "metadata": {},
   "source": [
    "**some tables contains data which has timestamp values ​​but is has object data type, so we need to change the data type.**<br>\n",
    "order #date<br>\n",
    "order_reviews #date<br>\n",
    "order_items #date<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bd47436a",
   "metadata": {},
   "source": [
    "#### Order"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "71f7e714",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 96461 entries, 0 to 99440\n",
      "Data columns (total 9 columns):\n",
      " #   Column                         Non-Null Count  Dtype \n",
      "---  ------                         --------------  ----- \n",
      " 0   index                          96461 non-null  int64 \n",
      " 1   order_id                       96461 non-null  object\n",
      " 2   customer_id                    96461 non-null  object\n",
      " 3   order_status                   96461 non-null  object\n",
      " 4   order_purchase_timestamp       96461 non-null  object\n",
      " 5   order_approved_at              96461 non-null  object\n",
      " 6   order_delivered_carrier_date   96461 non-null  object\n",
      " 7   order_delivered_customer_date  96461 non-null  object\n",
      " 8   order_estimated_delivery_date  96461 non-null  object\n",
      "dtypes: int64(1), object(8)\n",
      "memory usage: 7.4+ MB\n"
     ]
    }
   ],
   "source": [
    "order.info() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "9ed88c72",
   "metadata": {},
   "outputs": [],
   "source": [
    "#looping for changing dateimes type\n",
    "for datetimes in ['order_purchase_timestamp',\n",
    "                'order_approved_at',\n",
    "                'order_delivered_carrier_date',\n",
    "                'order_delivered_customer_date',\n",
    "                'order_estimated_delivery_date']:\n",
    "    order[datetimes] = pd.to_datetime(order[datetimes], errors='raise',utc=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "413d9b75",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 96461 entries, 0 to 99440\n",
      "Data columns (total 9 columns):\n",
      " #   Column                         Non-Null Count  Dtype         \n",
      "---  ------                         --------------  -----         \n",
      " 0   index                          96461 non-null  int64         \n",
      " 1   order_id                       96461 non-null  object        \n",
      " 2   customer_id                    96461 non-null  object        \n",
      " 3   order_status                   96461 non-null  object        \n",
      " 4   order_purchase_timestamp       96461 non-null  datetime64[ns]\n",
      " 5   order_approved_at              96461 non-null  datetime64[ns]\n",
      " 6   order_delivered_carrier_date   96461 non-null  datetime64[ns]\n",
      " 7   order_delivered_customer_date  96461 non-null  datetime64[ns]\n",
      " 8   order_estimated_delivery_date  96461 non-null  datetime64[ns]\n",
      "dtypes: datetime64[ns](5), int64(1), object(3)\n",
      "memory usage: 7.4+ MB\n"
     ]
    }
   ],
   "source": [
    "order.info() "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4ece90e8",
   "metadata": {},
   "source": [
    "#### Order Review"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "b8d4cd99",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 99224 entries, 0 to 99223\n",
      "Data columns (total 8 columns):\n",
      " #   Column                   Non-Null Count  Dtype \n",
      "---  ------                   --------------  ----- \n",
      " 0   index                    99224 non-null  int64 \n",
      " 1   review_id                99224 non-null  object\n",
      " 2   order_id                 99224 non-null  object\n",
      " 3   review_score             99224 non-null  int64 \n",
      " 4   review_comment_title     11568 non-null  object\n",
      " 5   review_comment_message   40977 non-null  object\n",
      " 6   review_creation_date     99224 non-null  object\n",
      " 7   review_answer_timestamp  99224 non-null  object\n",
      "dtypes: int64(2), object(6)\n",
      "memory usage: 6.1+ MB\n"
     ]
    }
   ],
   "source": [
    "order_reviews.info() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "1e9d22f9",
   "metadata": {},
   "outputs": [],
   "source": [
    "for datetimes in ['review_creation_date','review_answer_timestamp']:\n",
    "    order_reviews[datetimes] = pd.to_datetime(order_reviews[datetimes], errors='raise',utc=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "d50c7892",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 99224 entries, 0 to 99223\n",
      "Data columns (total 8 columns):\n",
      " #   Column                   Non-Null Count  Dtype         \n",
      "---  ------                   --------------  -----         \n",
      " 0   index                    99224 non-null  int64         \n",
      " 1   review_id                99224 non-null  object        \n",
      " 2   order_id                 99224 non-null  object        \n",
      " 3   review_score             99224 non-null  int64         \n",
      " 4   review_comment_title     11568 non-null  object        \n",
      " 5   review_comment_message   40977 non-null  object        \n",
      " 6   review_creation_date     99224 non-null  datetime64[ns]\n",
      " 7   review_answer_timestamp  99224 non-null  datetime64[ns]\n",
      "dtypes: datetime64[ns](2), int64(2), object(4)\n",
      "memory usage: 6.1+ MB\n"
     ]
    }
   ],
   "source": [
    "order_reviews.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "62ea0863",
   "metadata": {},
   "source": [
    "#### Order Items"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "05c95ffa",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 112650 entries, 0 to 112649\n",
      "Data columns (total 8 columns):\n",
      " #   Column               Non-Null Count   Dtype  \n",
      "---  ------               --------------   -----  \n",
      " 0   index                112650 non-null  int64  \n",
      " 1   order_id             112650 non-null  object \n",
      " 2   order_item_id        112650 non-null  int64  \n",
      " 3   product_id           112650 non-null  object \n",
      " 4   seller_id            112650 non-null  object \n",
      " 5   shipping_limit_date  112650 non-null  object \n",
      " 6   price                112650 non-null  float64\n",
      " 7   freight_value        112650 non-null  float64\n",
      "dtypes: float64(2), int64(2), object(4)\n",
      "memory usage: 6.9+ MB\n"
     ]
    }
   ],
   "source": [
    "order_items.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "b5939390",
   "metadata": {},
   "outputs": [],
   "source": [
    "order_items['shipping_limit_date'] = pd.to_datetime(order_items['shipping_limit_date'], errors='raise',utc=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "808997b7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 112650 entries, 0 to 112649\n",
      "Data columns (total 8 columns):\n",
      " #   Column               Non-Null Count   Dtype         \n",
      "---  ------               --------------   -----         \n",
      " 0   index                112650 non-null  int64         \n",
      " 1   order_id             112650 non-null  object        \n",
      " 2   order_item_id        112650 non-null  int64         \n",
      " 3   product_id           112650 non-null  object        \n",
      " 4   seller_id            112650 non-null  object        \n",
      " 5   shipping_limit_date  112650 non-null  datetime64[ns]\n",
      " 6   price                112650 non-null  float64       \n",
      " 7   freight_value        112650 non-null  float64       \n",
      "dtypes: datetime64[ns](1), float64(2), int64(2), object(3)\n",
      "memory usage: 6.9+ MB\n"
     ]
    }
   ],
   "source": [
    "order_items.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "87079dac",
   "metadata": {},
   "source": [
    "the data format is suitable with the appropriate data type and the value of the attribute data itself."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e23e6303",
   "metadata": {},
   "source": [
    "___"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "72e82611",
   "metadata": {},
   "source": [
    "# Handling Outliers"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aa45e2c7",
   "metadata": {},
   "source": [
    "#### Order Payments"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "bab0353d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0.5, 1.0, 'Outlier Into Median')]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# changing outliers values into median\n",
    "\n",
    "# identify quartile\n",
    "q1 = order_payments[\"payment_value\"].quantile(q=0.25) * 1.5\n",
    "q3 = order_payments[\"payment_value\"].quantile(q=0.75) * 1.5\n",
    "# select the data where is < q1\n",
    "\n",
    "order_payments_without_outlier = order_payments[order_payments[\"payment_value\"] < q1]\n",
    "median = order_payments[\"payment_value\"].median()\n",
    "\n",
    "# changking values into median where the data more than value q3\n",
    "order_payments.loc[order_payments[\"payment_value\"] > q3, \"payment_value\"]= median\n",
    "\n",
    "sns.histplot(order_payments[\"payment_value\"]).set(title='Outlier Into Median')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f071b717",
   "metadata": {},
   "source": [
    "## Exploratory Data Anlaysis"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "22620a36",
   "metadata": {},
   "source": [
    "### Define Tables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "35ea7529",
   "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>index</th>\n",
       "      <th>product_category_name</th>\n",
       "      <th>product_category_name_english</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>beleza_saude</td>\n",
       "      <td>health_beauty</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>informatica_acessorios</td>\n",
       "      <td>computers_accessories</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>automotivo</td>\n",
       "      <td>auto</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>cama_mesa_banho</td>\n",
       "      <td>bed_bath_table</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>moveis_decoracao</td>\n",
       "      <td>furniture_decor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>66</td>\n",
       "      <td>flores</td>\n",
       "      <td>flowers</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>67</td>\n",
       "      <td>artes_e_artesanato</td>\n",
       "      <td>arts_and_craftmanship</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>68</td>\n",
       "      <td>fraldas_higiene</td>\n",
       "      <td>diapers_and_hygiene</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>69</td>\n",
       "      <td>fashion_roupa_infanto_juvenil</td>\n",
       "      <td>fashion_childrens_clothes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>70</td>\n",
       "      <td>seguros_e_servicos</td>\n",
       "      <td>security_and_services</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>71 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    index          product_category_name product_category_name_english\n",
       "0       0                   beleza_saude                 health_beauty\n",
       "1       1         informatica_acessorios         computers_accessories\n",
       "2       2                     automotivo                          auto\n",
       "3       3                cama_mesa_banho                bed_bath_table\n",
       "4       4               moveis_decoracao               furniture_decor\n",
       "..    ...                            ...                           ...\n",
       "66     66                         flores                       flowers\n",
       "67     67             artes_e_artesanato         arts_and_craftmanship\n",
       "68     68                fraldas_higiene           diapers_and_hygiene\n",
       "69     69  fashion_roupa_infanto_juvenil     fashion_childrens_clothes\n",
       "70     70             seguros_e_servicos         security_and_services\n",
       "\n",
       "[71 rows x 3 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "order_customer\n",
    "order \n",
    "order_reviews \n",
    "order_payments\n",
    "order_items \n",
    "products\n",
    "sellers\n",
    "geolocation\n",
    "product_category_translate"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "de8c3bd7",
   "metadata": {},
   "source": [
    "## Product Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d6c6db43",
   "metadata": {},
   "source": [
    "Product Categories Most Popular"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "2231513c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    .dataframe thead th {\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</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>0</td>\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>1</td>\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>2</td>\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>3</td>\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>4</td>\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",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32946</th>\n",
       "      <td>32946</td>\n",
       "      <td>a0b7d5a992ccda646f2d34e418fff5a0</td>\n",
       "      <td>moveis_decoracao</td>\n",
       "      <td>45.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>12300.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>40.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32947</th>\n",
       "      <td>32947</td>\n",
       "      <td>bf4538d88321d0fd4412a93c974510e6</td>\n",
       "      <td>construcao_ferramentas_iluminacao</td>\n",
       "      <td>41.0</td>\n",
       "      <td>971.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1700.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>16.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32948</th>\n",
       "      <td>32948</td>\n",
       "      <td>9a7c6041fa9592d9d9ef6cfe62a71f8c</td>\n",
       "      <td>cama_mesa_banho</td>\n",
       "      <td>50.0</td>\n",
       "      <td>799.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1400.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>27.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32949</th>\n",
       "      <td>32949</td>\n",
       "      <td>83808703fc0706a22e264b9d75f04a2e</td>\n",
       "      <td>informatica_acessorios</td>\n",
       "      <td>60.0</td>\n",
       "      <td>156.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>700.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32950</th>\n",
       "      <td>32950</td>\n",
       "      <td>106392145fca363410d287a815be6de4</td>\n",
       "      <td>cama_mesa_banho</td>\n",
       "      <td>58.0</td>\n",
       "      <td>309.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2083.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>32340 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       index                        product_id  \\\n",
       "0          0  1e9e8ef04dbcff4541ed26657ea517e5   \n",
       "1          1  3aa071139cb16b67ca9e5dea641aaa2f   \n",
       "2          2  96bd76ec8810374ed1b65e291975717f   \n",
       "3          3  cef67bcfe19066a932b7673e239eb23d   \n",
       "4          4  9dc1a7de274444849c219cff195d0b71   \n",
       "...      ...                               ...   \n",
       "32946  32946  a0b7d5a992ccda646f2d34e418fff5a0   \n",
       "32947  32947  bf4538d88321d0fd4412a93c974510e6   \n",
       "32948  32948  9a7c6041fa9592d9d9ef6cfe62a71f8c   \n",
       "32949  32949  83808703fc0706a22e264b9d75f04a2e   \n",
       "32950  32950  106392145fca363410d287a815be6de4   \n",
       "\n",
       "                   product_category_name  product_name_lenght  \\\n",
       "0                             perfumaria                 40.0   \n",
       "1                                  artes                 44.0   \n",
       "2                          esporte_lazer                 46.0   \n",
       "3                                  bebes                 27.0   \n",
       "4                  utilidades_domesticas                 37.0   \n",
       "...                                  ...                  ...   \n",
       "32946                   moveis_decoracao                 45.0   \n",
       "32947  construcao_ferramentas_iluminacao                 41.0   \n",
       "32948                    cama_mesa_banho                 50.0   \n",
       "32949             informatica_acessorios                 60.0   \n",
       "32950                    cama_mesa_banho                 58.0   \n",
       "\n",
       "       product_description_lenght  product_photos_qty  product_weight_g  \\\n",
       "0                           287.0                 1.0             225.0   \n",
       "1                           276.0                 1.0            1000.0   \n",
       "2                           250.0                 1.0             154.0   \n",
       "3                           261.0                 1.0             371.0   \n",
       "4                           402.0                 4.0             625.0   \n",
       "...                           ...                 ...               ...   \n",
       "32946                        67.0                 2.0           12300.0   \n",
       "32947                       971.0                 1.0            1700.0   \n",
       "32948                       799.0                 1.0            1400.0   \n",
       "32949                       156.0                 2.0             700.0   \n",
       "32950                       309.0                 1.0            2083.0   \n",
       "\n",
       "       product_length_cm  product_height_cm  product_width_cm  \n",
       "0                   16.0               10.0              14.0  \n",
       "1                   30.0               18.0              20.0  \n",
       "2                   18.0                9.0              15.0  \n",
       "3                   26.0                4.0              26.0  \n",
       "4                   20.0               17.0              13.0  \n",
       "...                  ...                ...               ...  \n",
       "32946               40.0               40.0              40.0  \n",
       "32947               16.0               19.0              16.0  \n",
       "32948               27.0                7.0              27.0  \n",
       "32949               31.0               13.0              20.0  \n",
       "32950               12.0                2.0               7.0  \n",
       "\n",
       "[32340 rows x 10 columns]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#join table\n",
    "products"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "9efaaafa",
   "metadata": {},
   "outputs": [],
   "source": [
    "#jointable\n",
    "products = pd.merge(products, product_category_translate, on='product_category_name')\n",
    "products.drop(columns='product_category_name', inplace=True)\n",
    "\n",
    "#rename columns\n",
    "products.rename(columns={'product_category_name_english': 'product_category'}, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "0a2d3ce6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  background-color: #08306b;\n",
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       "#T_d2807_row1_col1 {\n",
       "  background-color: #1966ad;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_d2807_row2_col1 {\n",
       "  background-color: #3e8ec4;\n",
       "  color: #f1f1f1;\n",
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       "#T_d2807_row3_col1 {\n",
       "  background-color: #4b98ca;\n",
       "  color: #f1f1f1;\n",
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       "#T_d2807_row4_col1 {\n",
       "  background-color: #64a9d3;\n",
       "  color: #f1f1f1;\n",
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       "#T_d2807_row5_col1 {\n",
       "  background-color: #95c5df;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_d2807_row6_col1 {\n",
       "  background-color: #c4daee;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_d2807_row7_col1 {\n",
       "  background-color: #eef5fc;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_d2807_row8_col1 {\n",
       "  background-color: #f4f9fe;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_d2807_row9_col1 {\n",
       "  background-color: #f7fbff;\n",
       "  color: #000000;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_d2807\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_d2807_level0_col0\" class=\"col_heading level0 col0\" >product_category</th>\n",
       "      <th id=\"T_d2807_level0_col1\" class=\"col_heading level0 col1\" >order_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_d2807_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "      <td id=\"T_d2807_row0_col0\" class=\"data row0 col0\" >bed_bath_table</td>\n",
       "      <td id=\"T_d2807_row0_col1\" class=\"data row0 col1\" >11115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_d2807_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "      <td id=\"T_d2807_row1_col0\" class=\"data row1 col0\" >health_beauty</td>\n",
       "      <td id=\"T_d2807_row1_col1\" class=\"data row1 col1\" >9670</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_d2807_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "      <td id=\"T_d2807_row2_col0\" class=\"data row2 col0\" >sports_leisure</td>\n",
       "      <td id=\"T_d2807_row2_col1\" class=\"data row2 col1\" >8641</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_d2807_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
       "      <td id=\"T_d2807_row3_col0\" class=\"data row3 col0\" >furniture_decor</td>\n",
       "      <td id=\"T_d2807_row3_col1\" class=\"data row3 col1\" >8334</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_d2807_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
       "      <td id=\"T_d2807_row4_col0\" class=\"data row4 col0\" >computers_accessories</td>\n",
       "      <td id=\"T_d2807_row4_col1\" class=\"data row4 col1\" >7827</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_d2807_level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
       "      <td id=\"T_d2807_row5_col0\" class=\"data row5 col0\" >housewares</td>\n",
       "      <td id=\"T_d2807_row5_col1\" class=\"data row5 col1\" >6964</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_d2807_level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
       "      <td id=\"T_d2807_row6_col0\" class=\"data row6 col0\" >watches_gifts</td>\n",
       "      <td id=\"T_d2807_row6_col1\" class=\"data row6 col1\" >5991</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_d2807_level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
       "      <td id=\"T_d2807_row7_col0\" class=\"data row7 col0\" >telephony</td>\n",
       "      <td id=\"T_d2807_row7_col1\" class=\"data row7 col1\" >4545</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_d2807_level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
       "      <td id=\"T_d2807_row8_col0\" class=\"data row8 col0\" >garden_tools</td>\n",
       "      <td id=\"T_d2807_row8_col1\" class=\"data row8 col1\" >4347</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_d2807_level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
       "      <td id=\"T_d2807_row9_col0\" class=\"data row9 col0\" >auto</td>\n",
       "      <td id=\"T_d2807_row9_col1\" class=\"data row9 col1\" >4235</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x2f017e354c0>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#jointable\n",
    "product_most_bought=pd.merge(order_items,products,on='product_id',how='left')\n",
    "\n",
    "#aggreagtion count every order_id\n",
    "product_most_bought = product_most_bought.groupby('product_category').aggregate({'order_id':'count'}).rename(columns={'order_id':'order_count'}).sort_values(by='order_count',ascending=False).reset_index()\n",
    "\n",
    "#product category most bought\n",
    "product_most_bought.head(10).style.background_gradient(cmap='Blues')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "2b31c6a5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "### Visualising top 10 most bought product categories:\n",
    "\n",
    "ax = sns.barplot(x='order_count',y='product_category',data=product_most_bought[:10],palette=\"YlGnBu_r\")\n",
    "plt.ylabel(\"product category\")\n",
    "plt.xlabel(\"total number of orders\")\n",
    "plt.xticks(rotation='horizontal')\n",
    "total = product_most_bought['order_count'].sum()\n",
    "\n",
    "#comma formatter\n",
    "ax.get_xaxis().set_major_formatter(matplotlib.ticker.FuncFormatter(lambda x, p: format(int(x), ',')))\n",
    "\n",
    "#percantage\n",
    "for c in ax.containers:\n",
    "        labels = [f\" {(value.get_width() / total):.1%}\" for value in c]\n",
    "        ax.bar_label(c, labels=labels, label_type=\"edge\",fontsize='smaller', fontname='Sans',color='grey')    \n",
    "\n",
    "\n",
    "for spine in ['top', 'right','bottom']:\n",
    "    ax.spines[spine].set_visible(False)\n",
    "    \n",
    "ax.set_title(\n",
    "        \"Top 10 Product Category that Sells the Most\",\n",
    "        fontdict={\"fontweight\": \"bold\"},\n",
    "    )\n",
    "\n",
    "ax.set(\n",
    "        xlim=(None, product_most_bought[\"order_count\"].max() * 1.2)\n",
    "    )  # gives room for the annotations\n",
    "\n",
    "plt.savefig(\n",
    "        Path(\".\") / \"plots\" / f\"most_bought_products.png\",\n",
    "        bbox_inches=\"tight\",\n",
    "        dpi=200,\n",
    "    )\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "881c97d3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_64629_row0_col1, #T_64629_row1_col1 {\n",
       "  background-color: #08306b;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_64629_row2_col1 {\n",
       "  background-color: #0a549e;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_64629_row3_col1, #T_64629_row4_col1 {\n",
       "  background-color: #1b69af;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_64629_row5_col1 {\n",
       "  background-color: #4695c8;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_64629_row6_col1, #T_64629_row7_col1 {\n",
       "  background-color: #abd0e6;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_64629_row8_col1 {\n",
       "  background-color: #d6e6f4;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_64629_row9_col1 {\n",
       "  background-color: #f7fbff;\n",
       "  color: #000000;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_64629\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_64629_level0_col0\" class=\"col_heading level0 col0\" >product_category</th>\n",
       "      <th id=\"T_64629_level0_col1\" class=\"col_heading level0 col1\" >order_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_64629_level0_row0\" class=\"row_heading level0 row0\" >61</th>\n",
       "      <td id=\"T_64629_row0_col0\" class=\"data row0 col0\" >furniture_mattress_and_upholstery</td>\n",
       "      <td id=\"T_64629_row0_col1\" class=\"data row0 col1\" >38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_64629_level0_row1\" class=\"row_heading level0 row1\" >62</th>\n",
       "      <td id=\"T_64629_row1_col0\" class=\"data row1 col0\" >music</td>\n",
       "      <td id=\"T_64629_row1_col1\" class=\"data row1 col1\" >38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_64629_level0_row2\" class=\"row_heading level0 row2\" >63</th>\n",
       "      <td id=\"T_64629_row2_col0\" class=\"data row2 col0\" >flowers</td>\n",
       "      <td id=\"T_64629_row2_col1\" class=\"data row2 col1\" >33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_64629_level0_row3\" class=\"row_heading level0 row3\" >64</th>\n",
       "      <td id=\"T_64629_row3_col0\" class=\"data row3 col0\" >fashion_sport</td>\n",
       "      <td id=\"T_64629_row3_col1\" class=\"data row3 col1\" >30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_64629_level0_row4\" class=\"row_heading level0 row4\" >65</th>\n",
       "      <td id=\"T_64629_row4_col0\" class=\"data row4 col0\" >home_comfort_2</td>\n",
       "      <td id=\"T_64629_row4_col1\" class=\"data row4 col1\" >30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_64629_level0_row5\" class=\"row_heading level0 row5\" >66</th>\n",
       "      <td id=\"T_64629_row5_col0\" class=\"data row5 col0\" >arts_and_craftmanship</td>\n",
       "      <td id=\"T_64629_row5_col1\" class=\"data row5 col1\" >24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_64629_level0_row6\" class=\"row_heading level0 row6\" >67</th>\n",
       "      <td id=\"T_64629_row6_col0\" class=\"data row6 col0\" >la_cuisine</td>\n",
       "      <td id=\"T_64629_row6_col1\" class=\"data row6 col1\" >14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_64629_level0_row7\" class=\"row_heading level0 row7\" >68</th>\n",
       "      <td id=\"T_64629_row7_col0\" class=\"data row7 col0\" >cds_dvds_musicals</td>\n",
       "      <td id=\"T_64629_row7_col1\" class=\"data row7 col1\" >14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_64629_level0_row8\" class=\"row_heading level0 row8\" >69</th>\n",
       "      <td id=\"T_64629_row8_col0\" class=\"data row8 col0\" >fashion_childrens_clothes</td>\n",
       "      <td id=\"T_64629_row8_col1\" class=\"data row8 col1\" >8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_64629_level0_row9\" class=\"row_heading level0 row9\" >70</th>\n",
       "      <td id=\"T_64629_row9_col0\" class=\"data row9 col0\" >security_and_services</td>\n",
       "      <td id=\"T_64629_row9_col1\" class=\"data row9 col1\" >2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x2f040bd2be0>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#product lowest bought \n",
    "product_most_bought.tail(10).style.background_gradient(cmap='Blues')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "760c7c8c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "### Visualising top 10 lowest bought product categories:\n",
    "ax = sns.barplot(x='order_count',y='product_category',data=product_most_bought[61:71],palette=\"YlOrRd\")\n",
    "plt.ylabel(\"product category\")\n",
    "plt.xlabel(\"total number of orders\")\n",
    "plt.xticks(rotation='horizontal')\n",
    "total = product_most_bought['order_count'].sum()\n",
    "#formmater comma\n",
    "ax.get_xaxis().set_major_formatter(matplotlib.ticker.FuncFormatter(lambda x, p: format(int(x), ',')))\n",
    "#percantage\n",
    "for c in ax.containers:\n",
    "        labels = [f\" {(value.get_width() / total):.2%}\" for value in c]\n",
    "        ax.bar_label(c, labels=labels, label_type=\"edge\",fontsize='smaller', fontname='Sans',color='grey')    \n",
    "\n",
    "\n",
    "for spine in ['top', 'right','bottom']:\n",
    "    ax.spines[spine].set_visible(False)\n",
    "    \n",
    "ax.set_title(\n",
    "        \"Top 10 Product Category that Sells the Lowest\",\n",
    "        fontdict={\"fontweight\": \"bold\"},\n",
    "        color='red'\n",
    "    )\n",
    "plt.savefig(\n",
    "        Path(\".\") / \"plots\" / f\"lowest_bought_products.png\",\n",
    "        bbox_inches=\"tight\",\n",
    "        dpi=200,\n",
    "    )\n",
    "ax.set(\n",
    "        xlim=(None, 50)\n",
    "    )  # gives room for the annotations\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f80540bd",
   "metadata": {},
   "source": [
    "## Sales"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0af2ee8b",
   "metadata": {},
   "source": [
    "### Time series Order"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "9beea3f3",
   "metadata": {},
   "outputs": [],
   "source": [
    "#get date purhcase\n",
    "order['order_purchase_date'] = pd.to_datetime(order['order_purchase_timestamp']).dt.date\n",
    "order['day_name'] = pd.to_datetime(order['order_purchase_timestamp']).dt.day_name()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "9780fd27",
   "metadata": {},
   "outputs": [],
   "source": [
    "#aggregate count every date how much order \n",
    "order_count = order.groupby(['order_purchase_date','day_name'])[['order_id']].count().rename(columns={'order_id':'total_order'}).reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "ba4ec629",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x2f03aa8dee0>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 2000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "### Visualising time-series number of order \n",
    "sns.set(style = 'whitegrid', font_scale = 1.5)\n",
    "sns.set_palette('PuBu_r')\n",
    "fig, axes = plt.subplots(figsize=(20,5))\n",
    "plt.plot(order_count['order_purchase_date'], order_count['total_order'], label = 'Number of order for different Month (Oct 2016- Ags 2017)', linewidth = 3)\n",
    "axes.get_yaxis().set_major_formatter(matplotlib.ticker.FuncFormatter(lambda x, p: format(int(x), ',')))\n",
    "axes.grid()\n",
    "\n",
    "plt.savefig(\n",
    "        Path(\".\") / \"plots\" / f\"time_series_sales.png\",\n",
    "        bbox_inches=\"tight\",\n",
    "        dpi=200,\n",
    "    )\n",
    "\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0683a277",
   "metadata": {},
   "source": [
    "spike in transactions in 2017 lets see it details"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "3a6eca87",
   "metadata": {},
   "outputs": [],
   "source": [
    "order['purchase_month_date'] = order['order_purchase_timestamp'].astype(str).str.slice(0, 7)\n",
    "order_in_2017 = order.groupby('purchase_month_date')[['order_id']].count().rename(columns={'order_id':'total_order'}).reset_index()\n",
    "order_in_2017['purchase_month_date'] = order_in_2017['purchase_month_date'].astype('datetime64[ns]')\n",
    "order_in_2017 = order_in_2017[(order_in_2017['purchase_month_date'] >= '2017-01-01') & (order_in_2017['purchase_month_date'] < '2018-01-01')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "c727dad3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  background-color: #f7fbff;\n",
       "  color: #000000;\n",
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       "#T_b5df0_row1_col1 {\n",
       "  background-color: #dceaf6;\n",
       "  color: #000000;\n",
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       "#T_b5df0_row2_col1 {\n",
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       "  background-color: #74b3d8;\n",
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       "#T_b5df0_row7_col1 {\n",
       "  background-color: #63a8d3;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_b5df0_row8_col1 {\n",
       "  background-color: #64a9d3;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_b5df0_row9_col1 {\n",
       "  background-color: #539ecd;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_b5df0_row10_col1 {\n",
       "  background-color: #08306b;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_b5df0_row11_col1 {\n",
       "  background-color: #2676b8;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_b5df0\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_b5df0_level0_col0\" class=\"col_heading level0 col0\" >purchase_month_date</th>\n",
       "      <th id=\"T_b5df0_level0_col1\" class=\"col_heading level0 col1\" >total_order</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_b5df0_level0_row0\" class=\"row_heading level0 row0\" >3</th>\n",
       "      <td id=\"T_b5df0_row0_col0\" class=\"data row0 col0\" >2017-01-01 00:00:00</td>\n",
       "      <td id=\"T_b5df0_row0_col1\" class=\"data row0 col1\" >748</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_b5df0_level0_row1\" class=\"row_heading level0 row1\" >4</th>\n",
       "      <td id=\"T_b5df0_row1_col0\" class=\"data row1 col0\" >2017-02-01 00:00:00</td>\n",
       "      <td id=\"T_b5df0_row1_col1\" class=\"data row1 col1\" >1641</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_b5df0_level0_row2\" class=\"row_heading level0 row2\" >5</th>\n",
       "      <td id=\"T_b5df0_row2_col0\" class=\"data row2 col0\" >2017-03-01 00:00:00</td>\n",
       "      <td id=\"T_b5df0_row2_col1\" class=\"data row2 col1\" >2546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_b5df0_level0_row3\" class=\"row_heading level0 row3\" >6</th>\n",
       "      <td id=\"T_b5df0_row3_col0\" class=\"data row3 col0\" >2017-04-01 00:00:00</td>\n",
       "      <td id=\"T_b5df0_row3_col1\" class=\"data row3 col1\" >2303</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_b5df0_level0_row4\" class=\"row_heading level0 row4\" >7</th>\n",
       "      <td id=\"T_b5df0_row4_col0\" class=\"data row4 col0\" >2017-05-01 00:00:00</td>\n",
       "      <td id=\"T_b5df0_row4_col1\" class=\"data row4 col1\" >3545</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_b5df0_level0_row5\" class=\"row_heading level0 row5\" >8</th>\n",
       "      <td id=\"T_b5df0_row5_col0\" class=\"data row5 col0\" >2017-06-01 00:00:00</td>\n",
       "      <td id=\"T_b5df0_row5_col1\" class=\"data row5 col1\" >3135</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_b5df0_level0_row6\" class=\"row_heading level0 row6\" >9</th>\n",
       "      <td id=\"T_b5df0_row6_col0\" class=\"data row6 col0\" >2017-07-01 00:00:00</td>\n",
       "      <td id=\"T_b5df0_row6_col1\" class=\"data row6 col1\" >3872</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_b5df0_level0_row7\" class=\"row_heading level0 row7\" >10</th>\n",
       "      <td id=\"T_b5df0_row7_col0\" class=\"data row7 col0\" >2017-08-01 00:00:00</td>\n",
       "      <td id=\"T_b5df0_row7_col1\" class=\"data row7 col1\" >4193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_b5df0_level0_row8\" class=\"row_heading level0 row8\" >11</th>\n",
       "      <td id=\"T_b5df0_row8_col0\" class=\"data row8 col0\" >2017-09-01 00:00:00</td>\n",
       "      <td id=\"T_b5df0_row8_col1\" class=\"data row8 col1\" >4149</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_b5df0_level0_row9\" class=\"row_heading level0 row9\" >12</th>\n",
       "      <td id=\"T_b5df0_row9_col0\" class=\"data row9 col0\" >2017-10-01 00:00:00</td>\n",
       "      <td id=\"T_b5df0_row9_col1\" class=\"data row9 col1\" >4478</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_b5df0_level0_row10\" class=\"row_heading level0 row10\" >13</th>\n",
       "      <td id=\"T_b5df0_row10_col0\" class=\"data row10 col0\" >2017-11-01 00:00:00</td>\n",
       "      <td id=\"T_b5df0_row10_col1\" class=\"data row10 col1\" >7288</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_b5df0_level0_row11\" class=\"row_heading level0 row11\" >14</th>\n",
       "      <td id=\"T_b5df0_row11_col0\" class=\"data row11 col0\" >2017-12-01 00:00:00</td>\n",
       "      <td id=\"T_b5df0_row11_col1\" class=\"data row11 col1\" >5513</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x2f0472aaa90>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "order_in_2017.style.background_gradient(cmap='Blues')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "68add795",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x2f044affc40>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "### Visualising time-series number of order (jan 17 - des 17)\n",
    "sns.set(style = 'whitegrid', font_scale = 1.5)\n",
    "sns.set_palette('PuBu_r')\n",
    "fig, axes = plt.subplots(figsize=(20,5))\n",
    "plt.plot(order_in_2017['purchase_month_date'], order_in_2017['total_order'], \n",
    "         label = 'Number of order for different Months (Jan 2017- Des 2017)', marker = '.', markersize = 15, linewidth = 3)\n",
    "\n",
    "#formatter comma\n",
    "axes.get_yaxis().set_major_formatter(matplotlib.ticker.FuncFormatter(lambda x, p: format(int(x), ',')))\n",
    "axes.grid()\n",
    "\n",
    "\n",
    "plt.savefig(\n",
    "        Path(\".\") / \"plots\" / f\"time_series_sales_2017.png\",\n",
    "        bbox_inches=\"tight\",\n",
    "        dpi=200,\n",
    "    )\n",
    "\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6e693530",
   "metadata": {},
   "source": [
    "it can be seen that november reached 7000 orders. what happened???"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7d82e002",
   "metadata": {},
   "source": [
    "lets checkout on nov 2017"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bafd3d7a",
   "metadata": {},
   "source": [
    "<img src=\"https://media.makeameme.org/created/what-is-happening-5c6d40.jpg\"\n",
    "     alt=\"Markdown Monster icon\"\n",
    "     style=\"float: center\" />"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "e8f7e55d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_cf76b_row0_col2 {\n",
       "  background-color: #08306b;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_cf76b_row1_col2 {\n",
       "  background-color: #bad6eb;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_cf76b_row2_col2 {\n",
       "  background-color: #d2e3f3;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_cf76b_row3_col2 {\n",
       "  background-color: #d5e5f4;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_cf76b_row4_col2 {\n",
       "  background-color: #d7e6f5;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_cf76b_row5_col2 {\n",
       "  background-color: #e3eef8;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_cf76b_row6_col2 {\n",
       "  background-color: #edf4fc;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_cf76b_row7_col2, #T_cf76b_row8_col2, #T_cf76b_row9_col2 {\n",
       "  background-color: #f7fbff;\n",
       "  color: #000000;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_cf76b\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_cf76b_level0_col0\" class=\"col_heading level0 col0\" >order_purchase_date</th>\n",
       "      <th id=\"T_cf76b_level0_col1\" class=\"col_heading level0 col1\" >day_name</th>\n",
       "      <th id=\"T_cf76b_level0_col2\" class=\"col_heading level0 col2\" >total_order</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_cf76b_level0_row0\" class=\"row_heading level0 row0\" >333</th>\n",
       "      <td id=\"T_cf76b_row0_col0\" class=\"data row0 col0\" >2017-11-24 00:00:00</td>\n",
       "      <td id=\"T_cf76b_row0_col1\" class=\"data row0 col1\" >Friday</td>\n",
       "      <td id=\"T_cf76b_row0_col2\" class=\"data row0 col2\" >1147</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_cf76b_level0_row1\" class=\"row_heading level0 row1\" >334</th>\n",
       "      <td id=\"T_cf76b_row1_col0\" class=\"data row1 col0\" >2017-11-25 00:00:00</td>\n",
       "      <td id=\"T_cf76b_row1_col1\" class=\"data row1 col1\" >Saturday</td>\n",
       "      <td id=\"T_cf76b_row1_col2\" class=\"data row1 col2\" >487</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_cf76b_level0_row2\" class=\"row_heading level0 row2\" >336</th>\n",
       "      <td id=\"T_cf76b_row2_col0\" class=\"data row2 col0\" >2017-11-27 00:00:00</td>\n",
       "      <td id=\"T_cf76b_row2_col1\" class=\"data row2 col1\" >Monday</td>\n",
       "      <td id=\"T_cf76b_row2_col2\" class=\"data row2 col2\" >395</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_cf76b_level0_row3\" class=\"row_heading level0 row3\" >335</th>\n",
       "      <td id=\"T_cf76b_row3_col0\" class=\"data row3 col0\" >2017-11-26 00:00:00</td>\n",
       "      <td id=\"T_cf76b_row3_col1\" class=\"data row3 col1\" >Sunday</td>\n",
       "      <td id=\"T_cf76b_row3_col2\" class=\"data row3 col2\" >382</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_cf76b_level0_row4\" class=\"row_heading level0 row4\" >337</th>\n",
       "      <td id=\"T_cf76b_row4_col0\" class=\"data row4 col0\" >2017-11-28 00:00:00</td>\n",
       "      <td id=\"T_cf76b_row4_col1\" class=\"data row4 col1\" >Tuesday</td>\n",
       "      <td id=\"T_cf76b_row4_col2\" class=\"data row4 col2\" >371</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_cf76b_level0_row5\" class=\"row_heading level0 row5\" >338</th>\n",
       "      <td id=\"T_cf76b_row5_col0\" class=\"data row5 col0\" >2017-11-29 00:00:00</td>\n",
       "      <td id=\"T_cf76b_row5_col1\" class=\"data row5 col1\" >Wednesday</td>\n",
       "      <td id=\"T_cf76b_row5_col2\" class=\"data row5 col2\" >314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_cf76b_level0_row6\" class=\"row_heading level0 row6\" >332</th>\n",
       "      <td id=\"T_cf76b_row6_col0\" class=\"data row6 col0\" >2017-11-23 00:00:00</td>\n",
       "      <td id=\"T_cf76b_row6_col1\" class=\"data row6 col1\" >Thursday</td>\n",
       "      <td id=\"T_cf76b_row6_col2\" class=\"data row6 col2\" >268</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_cf76b_level0_row7\" class=\"row_heading level0 row7\" >330</th>\n",
       "      <td id=\"T_cf76b_row7_col0\" class=\"data row7 col0\" >2017-11-21 00:00:00</td>\n",
       "      <td id=\"T_cf76b_row7_col1\" class=\"data row7 col1\" >Tuesday</td>\n",
       "      <td id=\"T_cf76b_row7_col2\" class=\"data row7 col2\" >222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_cf76b_level0_row8\" class=\"row_heading level0 row8\" >329</th>\n",
       "      <td id=\"T_cf76b_row8_col0\" class=\"data row8 col0\" >2017-11-20 00:00:00</td>\n",
       "      <td id=\"T_cf76b_row8_col1\" class=\"data row8 col1\" >Monday</td>\n",
       "      <td id=\"T_cf76b_row8_col2\" class=\"data row8 col2\" >219</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_cf76b_level0_row9\" class=\"row_heading level0 row9\" >325</th>\n",
       "      <td id=\"T_cf76b_row9_col0\" class=\"data row9 col0\" >2017-11-16 00:00:00</td>\n",
       "      <td id=\"T_cf76b_row9_col1\" class=\"data row9 col1\" >Thursday</td>\n",
       "      <td id=\"T_cf76b_row9_col2\" class=\"data row9 col2\" >219</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x2f04de2da90>"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "order_count['order_purchase_date']= pd.to_datetime(order_count['order_purchase_date']).astype('datetime64[ns]')\n",
    "order_count_nov_17 = order_count[(order_count['order_purchase_date'] >= '2017-01-01') & (order_count['order_purchase_date']<'2017-11-30')]\n",
    "order_count_nov_17.sort_values(by='total_order', ascending=False).head(10).style.background_gradient(cmap='Blues')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e7766cbe",
   "metadata": {},
   "source": [
    "<img src=\"img/black_friday.png\"\n",
    "     alt=\"news about black friday\"\n",
    "     style=\"float: center\" />"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6b66c8c1",
   "metadata": {},
   "source": [
    "aaa i see because of public enthusiasm for the black friday event, it causes a spike in transactions."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a6d7f2ba",
   "metadata": {},
   "source": [
    "## Customer"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a37ed4dc",
   "metadata": {},
   "source": [
    "### Payment Method"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "5a97da22",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 96461 entries, 0 to 99440\n",
      "Data columns (total 12 columns):\n",
      " #   Column                         Non-Null Count  Dtype         \n",
      "---  ------                         --------------  -----         \n",
      " 0   index                          96461 non-null  int64         \n",
      " 1   order_id                       96461 non-null  object        \n",
      " 2   customer_id                    96461 non-null  object        \n",
      " 3   order_status                   96461 non-null  object        \n",
      " 4   order_purchase_timestamp       96461 non-null  datetime64[ns]\n",
      " 5   order_approved_at              96461 non-null  datetime64[ns]\n",
      " 6   order_delivered_carrier_date   96461 non-null  datetime64[ns]\n",
      " 7   order_delivered_customer_date  96461 non-null  datetime64[ns]\n",
      " 8   order_estimated_delivery_date  96461 non-null  datetime64[ns]\n",
      " 9   order_purchase_date            96461 non-null  object        \n",
      " 10  day_name                       96461 non-null  object        \n",
      " 11  purchase_month_date            96461 non-null  object        \n",
      "dtypes: datetime64[ns](5), int64(1), object(6)\n",
      "memory usage: 9.6+ MB\n"
     ]
    }
   ],
   "source": [
    "order.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "8a23b937",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 103886 entries, 0 to 103885\n",
      "Data columns (total 6 columns):\n",
      " #   Column                Non-Null Count   Dtype  \n",
      "---  ------                --------------   -----  \n",
      " 0   index                 103886 non-null  int64  \n",
      " 1   order_id              103886 non-null  object \n",
      " 2   payment_sequential    103886 non-null  int64  \n",
      " 3   payment_type          103886 non-null  object \n",
      " 4   payment_installments  103886 non-null  int64  \n",
      " 5   payment_value         103886 non-null  float64\n",
      "dtypes: float64(1), int64(3), object(2)\n",
      "memory usage: 4.8+ MB\n"
     ]
    }
   ],
   "source": [
    "order_payments.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "5c912f4c",
   "metadata": {},
   "outputs": [],
   "source": [
    "#join table order and payment \n",
    "orders_payments_method = pd.merge(order, order_payments, on= 'order_id', how ='left')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "1f707cf2",
   "metadata": {},
   "outputs": [],
   "source": [
    "orders_payments_method = orders_payments_method[(orders_payments_method['order_purchase_date'] >= datetime.date(2017,1,1)) & (orders_payments_method['order_purchase_date'] < datetime.date(2018,1,1))]\n",
    "orders_payments_method = orders_payments_method.groupby(['purchase_month_date', 'payment_type'])[['order_id']].count().reset_index()\n",
    "orders_payments_method = pd.pivot_table(orders_payments_method, index = 'purchase_month_date', columns = 'payment_type', values = 'order_id', aggfunc = sum).fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "c48348c2",
   "metadata": {},
   "outputs": [
    {
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       "<table id=\"T_54c6c\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"index_name level0\" >payment_type</th>\n",
       "      <th id=\"T_54c6c_level0_col0\" class=\"col_heading level0 col0\" >boleto</th>\n",
       "      <th id=\"T_54c6c_level0_col1\" class=\"col_heading level0 col1\" >credit_card</th>\n",
       "      <th id=\"T_54c6c_level0_col2\" class=\"col_heading level0 col2\" >debit_card</th>\n",
       "      <th id=\"T_54c6c_level0_col3\" class=\"col_heading level0 col3\" >voucher</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th class=\"index_name level0\" >purchase_month_date</th>\n",
       "      <th class=\"blank col0\" >&nbsp;</th>\n",
       "      <th class=\"blank col1\" >&nbsp;</th>\n",
       "      <th class=\"blank col2\" >&nbsp;</th>\n",
       "      <th class=\"blank col3\" >&nbsp;</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_54c6c_level0_row0\" class=\"row_heading level0 row0\" >2017-01</th>\n",
       "      <td id=\"T_54c6c_row0_col0\" class=\"data row0 col0\" >186</td>\n",
       "      <td id=\"T_54c6c_row0_col1\" class=\"data row0 col1\" >542</td>\n",
       "      <td id=\"T_54c6c_row0_col2\" class=\"data row0 col2\" >9</td>\n",
       "      <td id=\"T_54c6c_row0_col3\" class=\"data row0 col3\" >60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_54c6c_level0_row1\" class=\"row_heading level0 row1\" >2017-02</th>\n",
       "      <td id=\"T_54c6c_row1_col0\" class=\"data row1 col0\" >359</td>\n",
       "      <td id=\"T_54c6c_row1_col1\" class=\"data row1 col1\" >1257</td>\n",
       "      <td id=\"T_54c6c_row1_col2\" class=\"data row1 col2\" >13</td>\n",
       "      <td id=\"T_54c6c_row1_col3\" class=\"data row1 col3\" >108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_54c6c_level0_row2\" class=\"row_heading level0 row2\" >2017-03</th>\n",
       "      <td id=\"T_54c6c_row2_col0\" class=\"data row2 col0\" >565</td>\n",
       "      <td id=\"T_54c6c_row2_col1\" class=\"data row2 col1\" >1908</td>\n",
       "      <td id=\"T_54c6c_row2_col2\" class=\"data row2 col2\" >30</td>\n",
       "      <td id=\"T_54c6c_row2_col3\" class=\"data row2 col3\" >197</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_54c6c_level0_row3\" class=\"row_heading level0 row3\" >2017-04</th>\n",
       "      <td id=\"T_54c6c_row3_col0\" class=\"data row3 col0\" >474</td>\n",
       "      <td id=\"T_54c6c_row3_col1\" class=\"data row3 col1\" >1772</td>\n",
       "      <td id=\"T_54c6c_row3_col2\" class=\"data row3 col2\" >25</td>\n",
       "      <td id=\"T_54c6c_row3_col3\" class=\"data row3 col3\" >165</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_54c6c_level0_row4\" class=\"row_heading level0 row4\" >2017-05</th>\n",
       "      <td id=\"T_54c6c_row4_col0\" class=\"data row4 col0\" >740</td>\n",
       "      <td id=\"T_54c6c_row4_col1\" class=\"data row4 col1\" >2732</td>\n",
       "      <td id=\"T_54c6c_row4_col2\" class=\"data row4 col2\" >29</td>\n",
       "      <td id=\"T_54c6c_row4_col3\" class=\"data row4 col3\" >285</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_54c6c_level0_row5\" class=\"row_heading level0 row5\" >2017-06</th>\n",
       "      <td id=\"T_54c6c_row5_col0\" class=\"data row5 col0\" >689</td>\n",
       "      <td id=\"T_54c6c_row5_col1\" class=\"data row5 col1\" >2373</td>\n",
       "      <td id=\"T_54c6c_row5_col2\" class=\"data row5 col2\" >26</td>\n",
       "      <td id=\"T_54c6c_row5_col3\" class=\"data row5 col3\" >235</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_54c6c_level0_row6\" class=\"row_heading level0 row6\" >2017-07</th>\n",
       "      <td id=\"T_54c6c_row6_col0\" class=\"data row6 col0\" >811</td>\n",
       "      <td id=\"T_54c6c_row6_col1\" class=\"data row6 col1\" >2974</td>\n",
       "      <td id=\"T_54c6c_row6_col2\" class=\"data row6 col2\" >20</td>\n",
       "      <td id=\"T_54c6c_row6_col3\" class=\"data row6 col3\" >342</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_54c6c_level0_row7\" class=\"row_heading level0 row7\" >2017-08</th>\n",
       "      <td id=\"T_54c6c_row7_col0\" class=\"data row7 col0\" >902</td>\n",
       "      <td id=\"T_54c6c_row7_col1\" class=\"data row7 col1\" >3186</td>\n",
       "      <td id=\"T_54c6c_row7_col2\" class=\"data row7 col2\" >33</td>\n",
       "      <td id=\"T_54c6c_row7_col3\" class=\"data row7 col3\" >272</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_54c6c_level0_row8\" class=\"row_heading level0 row8\" >2017-09</th>\n",
       "      <td id=\"T_54c6c_row8_col0\" class=\"data row8 col0\" >868</td>\n",
       "      <td id=\"T_54c6c_row8_col1\" class=\"data row8 col1\" >3182</td>\n",
       "      <td id=\"T_54c6c_row8_col2\" class=\"data row8 col2\" >43</td>\n",
       "      <td id=\"T_54c6c_row8_col3\" class=\"data row8 col3\" >277</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_54c6c_level0_row9\" class=\"row_heading level0 row9\" >2017-10</th>\n",
       "      <td id=\"T_54c6c_row9_col0\" class=\"data row9 col0\" >955</td>\n",
       "      <td id=\"T_54c6c_row9_col1\" class=\"data row9 col1\" >3416</td>\n",
       "      <td id=\"T_54c6c_row9_col2\" class=\"data row9 col2\" >51</td>\n",
       "      <td id=\"T_54c6c_row9_col3\" class=\"data row9 col3\" >276</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_54c6c_level0_row10\" class=\"row_heading level0 row10\" >2017-11</th>\n",
       "      <td id=\"T_54c6c_row10_col0\" class=\"data row10 col0\" >1445</td>\n",
       "      <td id=\"T_54c6c_row10_col1\" class=\"data row10 col1\" >5715</td>\n",
       "      <td id=\"T_54c6c_row10_col2\" class=\"data row10 col2\" >65</td>\n",
       "      <td id=\"T_54c6c_row10_col3\" class=\"data row10 col3\" >367</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_54c6c_level0_row11\" class=\"row_heading level0 row11\" >2017-12</th>\n",
       "      <td id=\"T_54c6c_row11_col0\" class=\"data row11 col0\" >1134</td>\n",
       "      <td id=\"T_54c6c_row11_col1\" class=\"data row11 col1\" >4245</td>\n",
       "      <td id=\"T_54c6c_row11_col2\" class=\"data row11 col2\" >62</td>\n",
       "      <td id=\"T_54c6c_row11_col3\" class=\"data row11 col3\" >288</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x2f04dfd8cd0>"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "orders_payments_method.style.background_gradient(cmap='Blues')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "4f329e39",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x2f04dfb7a60>"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1700x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(style = 'whitegrid')\n",
    "sns.set_palette('Paired')\n",
    "ax.grid(False)\n",
    "axes = orders_payments_method.plot.area(figsize=(17,5))\n",
    "#remove line \n",
    "axes.spines['top'].set_visible(False)\n",
    "axes.spines['right'].set_visible(False)\n",
    "axes.set_xlabel('transaction date')\n",
    "axes.set_ylabel('number of transaction')\n",
    "#formater coma\n",
    "axes.get_yaxis().set_major_formatter(matplotlib.ticker.FuncFormatter(lambda x, p: format(int(x), ',')))\n",
    "\n",
    "plt.savefig(\n",
    "        Path(\".\") / \"plots\" / f\"payment_method.png\",\n",
    "        bbox_inches=\"tight\",\n",
    "        dpi=200,\n",
    "    )\n",
    "\n",
    "axes.grid()\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5273b3b6",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "bd0df45a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# join tables\n",
    "order_customer_data = pd.merge(order, order_customer, on= 'customer_id', how ='left')\n",
    "order_customer_data = pd.merge(order_customer_data, order_payments, on= 'order_id', how ='inner')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "86d88836",
   "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>payment_value</th>\n",
       "      <th>customer_state</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>18.12</td>\n",
       "      <td>SP</td>\n",
       "      <td>e481f51cbdc54678b7cc49136f2d6af7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>voucher</td>\n",
       "      <td>2.00</td>\n",
       "      <td>SP</td>\n",
       "      <td>e481f51cbdc54678b7cc49136f2d6af7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>voucher</td>\n",
       "      <td>18.59</td>\n",
       "      <td>SP</td>\n",
       "      <td>e481f51cbdc54678b7cc49136f2d6af7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>boleto</td>\n",
       "      <td>141.46</td>\n",
       "      <td>BA</td>\n",
       "      <td>53cdb2fc8bc7dce0b6741e2150273451</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>credit_card</td>\n",
       "      <td>179.12</td>\n",
       "      <td>GO</td>\n",
       "      <td>47770eb9100c2d0c44946d9cf07ec65d</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>100734</th>\n",
       "      <td>credit_card</td>\n",
       "      <td>85.08</td>\n",
       "      <td>SP</td>\n",
       "      <td>9c5dedf39a927c1b2549525ed64a053c</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100735</th>\n",
       "      <td>credit_card</td>\n",
       "      <td>195.00</td>\n",
       "      <td>SP</td>\n",
       "      <td>63943bddc261676b46f01ca7ac2f7bd8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100736</th>\n",
       "      <td>credit_card</td>\n",
       "      <td>100.00</td>\n",
       "      <td>BA</td>\n",
       "      <td>83c1379a015df1e13d02aae0204711ab</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100737</th>\n",
       "      <td>credit_card</td>\n",
       "      <td>100.00</td>\n",
       "      <td>RJ</td>\n",
       "      <td>11c177c8e97725db2631073c19f07b62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100738</th>\n",
       "      <td>debit_card</td>\n",
       "      <td>86.86</td>\n",
       "      <td>PR</td>\n",
       "      <td>66dea50a8b16d9b4dee7af250b4be1a5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100739 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       payment_type  payment_value customer_state  \\\n",
       "0       credit_card          18.12             SP   \n",
       "1           voucher           2.00             SP   \n",
       "2           voucher          18.59             SP   \n",
       "3            boleto         141.46             BA   \n",
       "4       credit_card         179.12             GO   \n",
       "...             ...            ...            ...   \n",
       "100734  credit_card          85.08             SP   \n",
       "100735  credit_card         195.00             SP   \n",
       "100736  credit_card         100.00             BA   \n",
       "100737  credit_card         100.00             RJ   \n",
       "100738   debit_card          86.86             PR   \n",
       "\n",
       "                                order_id  \n",
       "0       e481f51cbdc54678b7cc49136f2d6af7  \n",
       "1       e481f51cbdc54678b7cc49136f2d6af7  \n",
       "2       e481f51cbdc54678b7cc49136f2d6af7  \n",
       "3       53cdb2fc8bc7dce0b6741e2150273451  \n",
       "4       47770eb9100c2d0c44946d9cf07ec65d  \n",
       "...                                  ...  \n",
       "100734  9c5dedf39a927c1b2549525ed64a053c  \n",
       "100735  63943bddc261676b46f01ca7ac2f7bd8  \n",
       "100736  83c1379a015df1e13d02aae0204711ab  \n",
       "100737  11c177c8e97725db2631073c19f07b62  \n",
       "100738  66dea50a8b16d9b4dee7af250b4be1a5  \n",
       "\n",
       "[100739 rows x 4 columns]"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#aggregation payment type\n",
    "count_payment = order_payments['payment_type'].value_counts().reset_index().sort_values(by='payment_type', ascending=False)\n",
    "\n",
    "#aggregation value payment each order_id\n",
    "sum_value_payment = order_payments.groupby('payment_type')['payment_value'].sum().reset_index().sort_values(by='payment_value', ascending=False)\n",
    "\n",
    "#select columns\n",
    "order_customer_data = order_customer_data[['payment_type','payment_value','customer_state','order_id']]\n",
    "order_customer_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "07939b1a",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "#T_77049_row27_col0, #T_77049_row27_col1, #T_77049_row27_col2, #T_77049_row27_col3, #T_77049_row27_col4 {\n",
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       "</style>\n",
       "<table id=\"T_77049\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"index_name level0\" >payment_type</th>\n",
       "      <th id=\"T_77049_level0_col0\" class=\"col_heading level0 col0\" >boleto</th>\n",
       "      <th id=\"T_77049_level0_col1\" class=\"col_heading level0 col1\" >credit_card</th>\n",
       "      <th id=\"T_77049_level0_col2\" class=\"col_heading level0 col2\" >debit_card</th>\n",
       "      <th id=\"T_77049_level0_col3\" class=\"col_heading level0 col3\" >voucher</th>\n",
       "      <th id=\"T_77049_level0_col4\" class=\"col_heading level0 col4\" >All</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th class=\"index_name level0\" >customer_state</th>\n",
       "      <th class=\"blank col0\" >&nbsp;</th>\n",
       "      <th class=\"blank col1\" >&nbsp;</th>\n",
       "      <th class=\"blank col2\" >&nbsp;</th>\n",
       "      <th class=\"blank col3\" >&nbsp;</th>\n",
       "      <th class=\"blank col4\" >&nbsp;</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_77049_level0_row0\" class=\"row_heading level0 row0\" >AC</th>\n",
       "      <td id=\"T_77049_row0_col0\" class=\"data row0 col0\" >1670.420000</td>\n",
       "      <td id=\"T_77049_row0_col1\" class=\"data row0 col1\" >7209.810000</td>\n",
       "      <td id=\"T_77049_row0_col2\" class=\"data row0 col2\" >349.620000</td>\n",
       "      <td id=\"T_77049_row0_col3\" class=\"data row0 col3\" >339.430000</td>\n",
       "      <td id=\"T_77049_row0_col4\" class=\"data row0 col4\" >9569.280000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_77049_level0_row1\" class=\"row_heading level0 row1\" >AL</th>\n",
       "      <td id=\"T_77049_row1_col0\" class=\"data row1 col0\" >7639.090000</td>\n",
       "      <td id=\"T_77049_row1_col1\" class=\"data row1 col1\" >38042.090000</td>\n",
       "      <td id=\"T_77049_row1_col2\" class=\"data row1 col2\" >305.360000</td>\n",
       "      <td id=\"T_77049_row1_col3\" class=\"data row1 col3\" >924.810000</td>\n",
       "      <td id=\"T_77049_row1_col4\" class=\"data row1 col4\" >46911.350000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_77049_level0_row2\" class=\"row_heading level0 row2\" >AM</th>\n",
       "      <td id=\"T_77049_row2_col0\" class=\"data row2 col0\" >2355.680000</td>\n",
       "      <td id=\"T_77049_row2_col1\" class=\"data row2 col1\" >13615.950000</td>\n",
       "      <td id=\"T_77049_row2_col2\" class=\"data row2 col2\" >152.020000</td>\n",
       "      <td id=\"T_77049_row2_col3\" class=\"data row2 col3\" >210.820000</td>\n",
       "      <td id=\"T_77049_row2_col4\" class=\"data row2 col4\" >16334.470000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_77049_level0_row3\" class=\"row_heading level0 row3\" >AP</th>\n",
       "      <td id=\"T_77049_row3_col0\" class=\"data row3 col0\" >2258.270000</td>\n",
       "      <td id=\"T_77049_row3_col1\" class=\"data row3 col1\" >5907.120000</td>\n",
       "      <td id=\"T_77049_row3_col2\" class=\"data row3 col2\" >0.000000</td>\n",
       "      <td id=\"T_77049_row3_col3\" class=\"data row3 col3\" >237.420000</td>\n",
       "      <td id=\"T_77049_row3_col4\" class=\"data row3 col4\" >8402.810000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_77049_level0_row4\" class=\"row_heading level0 row4\" >BA</th>\n",
       "      <td id=\"T_77049_row4_col0\" class=\"data row4 col0\" >60304.690000</td>\n",
       "      <td id=\"T_77049_row4_col1\" class=\"data row4 col1\" >281004.170000</td>\n",
       "      <td id=\"T_77049_row4_col2\" class=\"data row4 col2\" >5685.130000</td>\n",
       "      <td id=\"T_77049_row4_col3\" class=\"data row4 col3\" >13247.980000</td>\n",
       "      <td id=\"T_77049_row4_col4\" class=\"data row4 col4\" >360241.970000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_77049_level0_row5\" class=\"row_heading level0 row5\" >CE</th>\n",
       "      <td id=\"T_77049_row5_col0\" class=\"data row5 col0\" >21364.150000</td>\n",
       "      <td id=\"T_77049_row5_col1\" class=\"data row5 col1\" >117408.650000</td>\n",
       "      <td id=\"T_77049_row5_col2\" class=\"data row5 col2\" >2551.030000</td>\n",
       "      <td id=\"T_77049_row5_col3\" class=\"data row5 col3\" >5083.120000</td>\n",
       "      <td id=\"T_77049_row5_col4\" class=\"data row5 col4\" >146406.950000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_77049_level0_row6\" class=\"row_heading level0 row6\" >DF</th>\n",
       "      <td id=\"T_77049_row6_col0\" class=\"data row6 col0\" >36727.270000</td>\n",
       "      <td id=\"T_77049_row6_col1\" class=\"data row6 col1\" >178742.060000</td>\n",
       "      <td id=\"T_77049_row6_col2\" class=\"data row6 col2\" >1661.410000</td>\n",
       "      <td id=\"T_77049_row6_col3\" class=\"data row6 col3\" >5210.590000</td>\n",
       "      <td id=\"T_77049_row6_col4\" class=\"data row6 col4\" >222341.330000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_77049_level0_row7\" class=\"row_heading level0 row7\" >ES</th>\n",
       "      <td id=\"T_77049_row7_col0\" class=\"data row7 col0\" >38390.970000</td>\n",
       "      <td id=\"T_77049_row7_col1\" class=\"data row7 col1\" >167485.070000</td>\n",
       "      <td id=\"T_77049_row7_col2\" class=\"data row7 col2\" >2305.420000</td>\n",
       "      <td id=\"T_77049_row7_col3\" class=\"data row7 col3\" >6416.690000</td>\n",
       "      <td id=\"T_77049_row7_col4\" class=\"data row7 col4\" >214598.150000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_77049_level0_row8\" class=\"row_heading level0 row8\" >GO</th>\n",
       "      <td id=\"T_77049_row8_col0\" class=\"data row8 col0\" >45171.260000</td>\n",
       "      <td id=\"T_77049_row8_col1\" class=\"data row8 col1\" >158382.300000</td>\n",
       "      <td id=\"T_77049_row8_col2\" class=\"data row8 col2\" >2527.790000</td>\n",
       "      <td id=\"T_77049_row8_col3\" class=\"data row8 col3\" >5506.870000</td>\n",
       "      <td id=\"T_77049_row8_col4\" class=\"data row8 col4\" >211588.220000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_77049_level0_row9\" class=\"row_heading level0 row9\" >MA</th>\n",
       "      <td id=\"T_77049_row9_col0\" class=\"data row9 col0\" >22393.480000</td>\n",
       "      <td id=\"T_77049_row9_col1\" class=\"data row9 col1\" >59930.970000</td>\n",
       "      <td id=\"T_77049_row9_col2\" class=\"data row9 col2\" >409.360000</td>\n",
       "      <td id=\"T_77049_row9_col3\" class=\"data row9 col3\" >1722.660000</td>\n",
       "      <td id=\"T_77049_row9_col4\" class=\"data row9 col4\" >84456.470000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_77049_level0_row10\" class=\"row_heading level0 row10\" >MG</th>\n",
       "      <td id=\"T_77049_row10_col0\" class=\"data row10 col0\" >228044.440000</td>\n",
       "      <td id=\"T_77049_row10_col1\" class=\"data row10 col1\" >937582.220000</td>\n",
       "      <td id=\"T_77049_row10_col2\" class=\"data row10 col2\" >14138.990000</td>\n",
       "      <td id=\"T_77049_row10_col3\" class=\"data row10 col3\" >32845.460000</td>\n",
       "      <td id=\"T_77049_row10_col4\" class=\"data row10 col4\" >1212611.110000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_77049_level0_row11\" class=\"row_heading level0 row11\" >MS</th>\n",
       "      <td id=\"T_77049_row11_col0\" class=\"data row11 col0\" >18582.760000</td>\n",
       "      <td id=\"T_77049_row11_col1\" class=\"data row11 col1\" >53851.810000</td>\n",
       "      <td id=\"T_77049_row11_col2\" class=\"data row11 col2\" >1138.350000</td>\n",
       "      <td id=\"T_77049_row11_col3\" class=\"data row11 col3\" >1878.730000</td>\n",
       "      <td id=\"T_77049_row11_col4\" class=\"data row11 col4\" >75451.650000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_77049_level0_row12\" class=\"row_heading level0 row12\" >MT</th>\n",
       "      <td id=\"T_77049_row12_col0\" class=\"data row12 col0\" >24896.150000</td>\n",
       "      <td id=\"T_77049_row12_col1\" class=\"data row12 col1\" >72011.980000</td>\n",
       "      <td id=\"T_77049_row12_col2\" class=\"data row12 col2\" >445.260000</td>\n",
       "      <td id=\"T_77049_row12_col3\" class=\"data row12 col3\" >1884.790000</td>\n",
       "      <td id=\"T_77049_row12_col4\" class=\"data row12 col4\" >99238.180000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_77049_level0_row13\" class=\"row_heading level0 row13\" >PA</th>\n",
       "      <td id=\"T_77049_row13_col0\" class=\"data row13 col0\" >21532.850000</td>\n",
       "      <td id=\"T_77049_row13_col1\" class=\"data row13 col1\" >82830.490000</td>\n",
       "      <td id=\"T_77049_row13_col2\" class=\"data row13 col2\" >1644.860000</td>\n",
       "      <td id=\"T_77049_row13_col3\" class=\"data row13 col3\" >3125.190000</td>\n",
       "      <td id=\"T_77049_row13_col4\" class=\"data row13 col4\" >109133.390000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_77049_level0_row14\" class=\"row_heading level0 row14\" >PB</th>\n",
       "      <td id=\"T_77049_row14_col0\" class=\"data row14 col0\" >11296.100000</td>\n",
       "      <td id=\"T_77049_row14_col1\" class=\"data row14 col1\" >48614.560000</td>\n",
       "      <td id=\"T_77049_row14_col2\" class=\"data row14 col2\" >1611.120000</td>\n",
       "      <td id=\"T_77049_row14_col3\" class=\"data row14 col3\" >2564.590000</td>\n",
       "      <td id=\"T_77049_row14_col4\" class=\"data row14 col4\" >64086.370000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_77049_level0_row15\" class=\"row_heading level0 row15\" >PE</th>\n",
       "      <td id=\"T_77049_row15_col0\" class=\"data row15 col0\" >28007.630000</td>\n",
       "      <td id=\"T_77049_row15_col1\" class=\"data row15 col1\" >151692.110000</td>\n",
       "      <td id=\"T_77049_row15_col2\" class=\"data row15 col2\" >1885.320000</td>\n",
       "      <td id=\"T_77049_row15_col3\" class=\"data row15 col3\" >5764.750000</td>\n",
       "      <td id=\"T_77049_row15_col4\" class=\"data row15 col4\" >187349.810000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_77049_level0_row16\" class=\"row_heading level0 row16\" >PI</th>\n",
       "      <td id=\"T_77049_row16_col0\" class=\"data row16 col0\" >10227.140000</td>\n",
       "      <td id=\"T_77049_row16_col1\" class=\"data row16 col1\" >45108.140000</td>\n",
       "      <td id=\"T_77049_row16_col2\" class=\"data row16 col2\" >969.990000</td>\n",
       "      <td id=\"T_77049_row16_col3\" class=\"data row16 col3\" >2005.440000</td>\n",
       "      <td id=\"T_77049_row16_col4\" class=\"data row16 col4\" >58310.710000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_77049_level0_row17\" class=\"row_heading level0 row17\" >PR</th>\n",
       "      <td id=\"T_77049_row17_col0\" class=\"data row17 col0\" >105133.870000</td>\n",
       "      <td id=\"T_77049_row17_col1\" class=\"data row17 col1\" >382068.460000</td>\n",
       "      <td id=\"T_77049_row17_col2\" class=\"data row17 col2\" >6286.990000</td>\n",
       "      <td id=\"T_77049_row17_col3\" class=\"data row17 col3\" >14688.120000</td>\n",
       "      <td id=\"T_77049_row17_col4\" class=\"data row17 col4\" >508177.440000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_77049_level0_row18\" class=\"row_heading level0 row18\" >RJ</th>\n",
       "      <td id=\"T_77049_row18_col0\" class=\"data row18 col0\" >212723.670000</td>\n",
       "      <td id=\"T_77049_row18_col1\" class=\"data row18 col1\" >1066816.390000</td>\n",
       "      <td id=\"T_77049_row18_col2\" class=\"data row18 col2\" >19422.200000</td>\n",
       "      <td id=\"T_77049_row18_col3\" class=\"data row18 col3\" >44898.340000</td>\n",
       "      <td id=\"T_77049_row18_col4\" class=\"data row18 col4\" >1343860.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_77049_level0_row19\" class=\"row_heading level0 row19\" >RN</th>\n",
       "      <td id=\"T_77049_row19_col0\" class=\"data row19 col0\" >8985.920000</td>\n",
       "      <td id=\"T_77049_row19_col1\" class=\"data row19 col1\" >45407.750000</td>\n",
       "      <td id=\"T_77049_row19_col2\" class=\"data row19 col2\" >826.830000</td>\n",
       "      <td id=\"T_77049_row19_col3\" class=\"data row19 col3\" >2742.660000</td>\n",
       "      <td id=\"T_77049_row19_col4\" class=\"data row19 col4\" >57963.160000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_77049_level0_row20\" class=\"row_heading level0 row20\" >RO</th>\n",
       "      <td id=\"T_77049_row20_col0\" class=\"data row20 col0\" >7429.170000</td>\n",
       "      <td id=\"T_77049_row20_col1\" class=\"data row20 col1\" >21163.470000</td>\n",
       "      <td id=\"T_77049_row20_col2\" class=\"data row20 col2\" >296.650000</td>\n",
       "      <td id=\"T_77049_row20_col3\" class=\"data row20 col3\" >685.970000</td>\n",
       "      <td id=\"T_77049_row20_col4\" class=\"data row20 col4\" >29575.260000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_77049_level0_row21\" class=\"row_heading level0 row21\" >RR</th>\n",
       "      <td id=\"T_77049_row21_col0\" class=\"data row21 col0\" >959.510000</td>\n",
       "      <td id=\"T_77049_row21_col1\" class=\"data row21 col1\" >3508.200000</td>\n",
       "      <td id=\"T_77049_row21_col2\" class=\"data row21 col2\" >0.000000</td>\n",
       "      <td id=\"T_77049_row21_col3\" class=\"data row21 col3\" >0.000000</td>\n",
       "      <td id=\"T_77049_row21_col4\" class=\"data row21 col4\" >4467.710000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_77049_level0_row22\" class=\"row_heading level0 row22\" >RS</th>\n",
       "      <td id=\"T_77049_row22_col0\" class=\"data row22 col0\" >134151.130000</td>\n",
       "      <td id=\"T_77049_row22_col1\" class=\"data row22 col1\" >412216.390000</td>\n",
       "      <td id=\"T_77049_row22_col2\" class=\"data row22 col2\" >7646.000000</td>\n",
       "      <td id=\"T_77049_row22_col3\" class=\"data row22 col3\" >15615.990000</td>\n",
       "      <td id=\"T_77049_row22_col4\" class=\"data row22 col4\" >569629.510000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_77049_level0_row23\" class=\"row_heading level0 row23\" >SC</th>\n",
       "      <td id=\"T_77049_row23_col0\" class=\"data row23 col0\" >81506.480000</td>\n",
       "      <td id=\"T_77049_row23_col1\" class=\"data row23 col1\" >286966.000000</td>\n",
       "      <td id=\"T_77049_row23_col2\" class=\"data row23 col2\" >4660.460000</td>\n",
       "      <td id=\"T_77049_row23_col3\" class=\"data row23 col3\" >9826.740000</td>\n",
       "      <td id=\"T_77049_row23_col4\" class=\"data row23 col4\" >382959.680000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_77049_level0_row24\" class=\"row_heading level0 row24\" >SE</th>\n",
       "      <td id=\"T_77049_row24_col0\" class=\"data row24 col0\" >8439.520000</td>\n",
       "      <td id=\"T_77049_row24_col1\" class=\"data row24 col1\" >29533.380000</td>\n",
       "      <td id=\"T_77049_row24_col2\" class=\"data row24 col2\" >654.980000</td>\n",
       "      <td id=\"T_77049_row24_col3\" class=\"data row24 col3\" >1297.230000</td>\n",
       "      <td id=\"T_77049_row24_col4\" class=\"data row24 col4\" >39925.110000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_77049_level0_row25\" class=\"row_heading level0 row25\" >SP</th>\n",
       "      <td id=\"T_77049_row25_col0\" class=\"data row25 col0\" >733957.290000</td>\n",
       "      <td id=\"T_77049_row25_col1\" class=\"data row25 col1\" >3049039.160000</td>\n",
       "      <td id=\"T_77049_row25_col2\" class=\"data row25 col2\" >64712.530000</td>\n",
       "      <td id=\"T_77049_row25_col3\" class=\"data row25 col3\" >116590.010000</td>\n",
       "      <td id=\"T_77049_row25_col4\" class=\"data row25 col4\" >3964298.990000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_77049_level0_row26\" class=\"row_heading level0 row26\" >TO</th>\n",
       "      <td id=\"T_77049_row26_col0\" class=\"data row26 col0\" >8754.570000</td>\n",
       "      <td id=\"T_77049_row26_col1\" class=\"data row26 col1\" >21993.670000</td>\n",
       "      <td id=\"T_77049_row26_col2\" class=\"data row26 col2\" >465.740000</td>\n",
       "      <td id=\"T_77049_row26_col3\" class=\"data row26 col3\" >1442.070000</td>\n",
       "      <td id=\"T_77049_row26_col4\" class=\"data row26 col4\" >32656.050000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_77049_level0_row27\" class=\"row_heading level0 row27\" >All</th>\n",
       "      <td id=\"T_77049_row27_col0\" class=\"data row27 col0\" >1882903.480000</td>\n",
       "      <td id=\"T_77049_row27_col1\" class=\"data row27 col1\" >7738132.370000</td>\n",
       "      <td id=\"T_77049_row27_col2\" class=\"data row27 col2\" >142753.410000</td>\n",
       "      <td id=\"T_77049_row27_col3\" class=\"data row27 col3\" >296756.470000</td>\n",
       "      <td id=\"T_77049_row27_col4\" class=\"data row27 col4\" >10060545.730000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x2f04e0065b0>"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# create pivot table\n",
    "heatmap = order_customer_data.pivot_table(values='payment_value', index=['customer_state'], columns=['payment_type'], aggfunc='sum', fill_value=0, margins=True) #fill_value mengisi nilai NaN, margins untuk menjumlahkan total\n",
    "heatmap.style.background_gradient(cmap='Blues')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1f10273a",
   "metadata": {},
   "source": [
    "### Location State Customer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "328e3f45",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_db279_row0_col1 {\n",
       "  background-color: #08306b;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_db279_row1_col1 {\n",
       "  background-color: #b5d4e9;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_db279_row2_col1 {\n",
       "  background-color: #bdd7ec;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_db279_row3_col1 {\n",
       "  background-color: #ddeaf7;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_db279_row4_col1 {\n",
       "  background-color: #dfecf7;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_db279_row5_col1 {\n",
       "  background-color: #e6f0f9;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_db279_row6_col1 {\n",
       "  background-color: #e7f1fa;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_db279_row7_col1, #T_db279_row8_col1, #T_db279_row9_col1 {\n",
       "  background-color: #eef5fc;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_db279_row10_col1 {\n",
       "  background-color: #f0f6fd;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_db279_row11_col1 {\n",
       "  background-color: #f2f7fd;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_db279_row12_col1, #T_db279_row13_col1 {\n",
       "  background-color: #f3f8fe;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_db279_row14_col1, #T_db279_row15_col1 {\n",
       "  background-color: #f4f9fe;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_db279_row16_col1 {\n",
       "  background-color: #f5f9fe;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_db279_row17_col1, #T_db279_row18_col1, #T_db279_row19_col1 {\n",
       "  background-color: #f5fafe;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_db279_row20_col1, #T_db279_row21_col1, #T_db279_row22_col1 {\n",
       "  background-color: #f6faff;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_db279_row23_col1, #T_db279_row24_col1, #T_db279_row25_col1, #T_db279_row26_col1 {\n",
       "  background-color: #f7fbff;\n",
       "  color: #000000;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_db279\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_db279_level0_col0\" class=\"col_heading level0 col0\" >customer_state</th>\n",
       "      <th id=\"T_db279_level0_col1\" class=\"col_heading level0 col1\" >order_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_db279_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "      <td id=\"T_db279_row0_col0\" class=\"data row0 col0\" >SP</td>\n",
       "      <td id=\"T_db279_row0_col1\" class=\"data row0 col1\" >40489</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_db279_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "      <td id=\"T_db279_row1_col0\" class=\"data row1 col0\" >RJ</td>\n",
       "      <td id=\"T_db279_row1_col1\" class=\"data row1 col1\" >12351</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_db279_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "      <td id=\"T_db279_row2_col0\" class=\"data row2 col0\" >MG</td>\n",
       "      <td id=\"T_db279_row2_col1\" class=\"data row2 col1\" >11352</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_db279_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
       "      <td id=\"T_db279_row3_col0\" class=\"data row3 col0\" >RS</td>\n",
       "      <td id=\"T_db279_row3_col1\" class=\"data row3 col1\" >5342</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_db279_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
       "      <td id=\"T_db279_row4_col0\" class=\"data row4 col0\" >PR</td>\n",
       "      <td id=\"T_db279_row4_col1\" class=\"data row4 col1\" >4923</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_db279_level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
       "      <td id=\"T_db279_row5_col0\" class=\"data row5 col0\" >SC</td>\n",
       "      <td id=\"T_db279_row5_col1\" class=\"data row5 col1\" >3547</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_db279_level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
       "      <td id=\"T_db279_row6_col0\" class=\"data row6 col0\" >BA</td>\n",
       "      <td id=\"T_db279_row6_col1\" class=\"data row6 col1\" >3256</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_db279_level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
       "      <td id=\"T_db279_row7_col0\" class=\"data row7 col0\" >DF</td>\n",
       "      <td id=\"T_db279_row7_col1\" class=\"data row7 col1\" >2080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_db279_level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
       "      <td id=\"T_db279_row8_col0\" class=\"data row8 col0\" >ES</td>\n",
       "      <td id=\"T_db279_row8_col1\" class=\"data row8 col1\" >1995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_db279_level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
       "      <td id=\"T_db279_row9_col0\" class=\"data row9 col0\" >GO</td>\n",
       "      <td id=\"T_db279_row9_col1\" class=\"data row9 col1\" >1957</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_db279_level0_row10\" class=\"row_heading level0 row10\" >10</th>\n",
       "      <td id=\"T_db279_row10_col0\" class=\"data row10 col0\" >PE</td>\n",
       "      <td id=\"T_db279_row10_col1\" class=\"data row10 col1\" >1593</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_db279_level0_row11\" class=\"row_heading level0 row11\" >11</th>\n",
       "      <td id=\"T_db279_row11_col0\" class=\"data row11 col0\" >CE</td>\n",
       "      <td id=\"T_db279_row11_col1\" class=\"data row11 col1\" >1278</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_db279_level0_row12\" class=\"row_heading level0 row12\" >12</th>\n",
       "      <td id=\"T_db279_row12_col0\" class=\"data row12 col0\" >PA</td>\n",
       "      <td id=\"T_db279_row12_col1\" class=\"data row12 col1\" >946</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_db279_level0_row13\" class=\"row_heading level0 row13\" >13</th>\n",
       "      <td id=\"T_db279_row13_col0\" class=\"data row13 col0\" >MT</td>\n",
       "      <td id=\"T_db279_row13_col1\" class=\"data row13 col1\" >886</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_db279_level0_row14\" class=\"row_heading level0 row14\" >14</th>\n",
       "      <td id=\"T_db279_row14_col0\" class=\"data row14 col0\" >MA</td>\n",
       "      <td id=\"T_db279_row14_col1\" class=\"data row14 col1\" >716</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_db279_level0_row15\" class=\"row_heading level0 row15\" >15</th>\n",
       "      <td id=\"T_db279_row15_col0\" class=\"data row15 col0\" >MS</td>\n",
       "      <td id=\"T_db279_row15_col1\" class=\"data row15 col1\" >701</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_db279_level0_row16\" class=\"row_heading level0 row16\" >16</th>\n",
       "      <td id=\"T_db279_row16_col0\" class=\"data row16 col0\" >PB</td>\n",
       "      <td id=\"T_db279_row16_col1\" class=\"data row16 col1\" >517</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_db279_level0_row17\" class=\"row_heading level0 row17\" >17</th>\n",
       "      <td id=\"T_db279_row17_col0\" class=\"data row17 col0\" >PI</td>\n",
       "      <td id=\"T_db279_row17_col1\" class=\"data row17 col1\" >476</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_db279_level0_row18\" class=\"row_heading level0 row18\" >18</th>\n",
       "      <td id=\"T_db279_row18_col0\" class=\"data row18 col0\" >RN</td>\n",
       "      <td id=\"T_db279_row18_col1\" class=\"data row18 col1\" >474</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_db279_level0_row19\" class=\"row_heading level0 row19\" >19</th>\n",
       "      <td id=\"T_db279_row19_col0\" class=\"data row19 col0\" >AL</td>\n",
       "      <td id=\"T_db279_row19_col1\" class=\"data row19 col1\" >397</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_db279_level0_row20\" class=\"row_heading level0 row20\" >20</th>\n",
       "      <td id=\"T_db279_row20_col0\" class=\"data row20 col0\" >SE</td>\n",
       "      <td id=\"T_db279_row20_col1\" class=\"data row20 col1\" >335</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_db279_level0_row21\" class=\"row_heading level0 row21\" >21</th>\n",
       "      <td id=\"T_db279_row21_col0\" class=\"data row21 col0\" >TO</td>\n",
       "      <td id=\"T_db279_row21_col1\" class=\"data row21 col1\" >274</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_db279_level0_row22\" class=\"row_heading level0 row22\" >22</th>\n",
       "      <td id=\"T_db279_row22_col0\" class=\"data row22 col0\" >RO</td>\n",
       "      <td id=\"T_db279_row22_col1\" class=\"data row22 col1\" >243</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_db279_level0_row23\" class=\"row_heading level0 row23\" >23</th>\n",
       "      <td id=\"T_db279_row23_col0\" class=\"data row23 col0\" >AM</td>\n",
       "      <td id=\"T_db279_row23_col1\" class=\"data row23 col1\" >145</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_db279_level0_row24\" class=\"row_heading level0 row24\" >24</th>\n",
       "      <td id=\"T_db279_row24_col0\" class=\"data row24 col0\" >AC</td>\n",
       "      <td id=\"T_db279_row24_col1\" class=\"data row24 col1\" >80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_db279_level0_row25\" class=\"row_heading level0 row25\" >25</th>\n",
       "      <td id=\"T_db279_row25_col0\" class=\"data row25 col0\" >AP</td>\n",
       "      <td id=\"T_db279_row25_col1\" class=\"data row25 col1\" >67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_db279_level0_row26\" class=\"row_heading level0 row26\" >26</th>\n",
       "      <td id=\"T_db279_row26_col0\" class=\"data row26 col0\" >RR</td>\n",
       "      <td id=\"T_db279_row26_col1\" class=\"data row26 col1\" >41</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x2f04e00b220>"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#join table\n",
    "customer_state = pd.merge(order_customer, order, on =  'customer_id')\n",
    "#aggregation order_id based on customer_state\n",
    "customer_state= customer_state.groupby('customer_state').aggregate({'order_id':'count'}).rename(columns={'order_id':'order_count'}).sort_values(by='order_count',ascending=False).reset_index()\n",
    "#heatmaps\n",
    "customer_state.style.background_gradient(cmap='Blues')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "65b63909",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_840ec_row0_col1 {\n",
       "  background-color: #f5f9fe;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_840ec_row1_col1, #T_840ec_row11_col1 {\n",
       "  background-color: #e7f0fa;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_840ec_row2_col1 {\n",
       "  background-color: #eff6fc;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_840ec_row3_col1, #T_840ec_row21_col1 {\n",
       "  background-color: #f7fbff;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_840ec_row4_col1, #T_840ec_row17_col1 {\n",
       "  background-color: #7fb9da;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_840ec_row5_col1 {\n",
       "  background-color: #d5e5f4;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_840ec_row6_col1 {\n",
       "  background-color: #f5fafe;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_840ec_row7_col1 {\n",
       "  background-color: #e3eef8;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_840ec_row8_col1 {\n",
       "  background-color: #c2d9ee;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_840ec_row9_col1 {\n",
       "  background-color: #d0e1f2;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_840ec_row10_col1 {\n",
       "  background-color: #08306b;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_840ec_row12_col1, #T_840ec_row19_col1 {\n",
       "  background-color: #dbe9f6;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_840ec_row13_col1 {\n",
       "  background-color: #dae8f6;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_840ec_row14_col1 {\n",
       "  background-color: #d6e5f4;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_840ec_row15_col1 {\n",
       "  background-color: #d4e4f4;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_840ec_row16_col1 {\n",
       "  background-color: #d3e3f3;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_840ec_row18_col1 {\n",
       "  background-color: #d7e6f5;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_840ec_row20_col1 {\n",
       "  background-color: #eef5fc;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_840ec_row22_col1 {\n",
       "  background-color: #74b3d8;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_840ec_row23_col1 {\n",
       "  background-color: #bad6eb;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_840ec_row24_col1 {\n",
       "  background-color: #ecf4fb;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_840ec_row25_col1 {\n",
       "  background-color: #2575b7;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_840ec_row26_col1 {\n",
       "  background-color: #e1edf8;\n",
       "  color: #000000;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_840ec\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_840ec_level0_col0\" class=\"col_heading level0 col0\" >customer_state</th>\n",
       "      <th id=\"T_840ec_level0_col1\" class=\"col_heading level0 col1\" >gelocation_city_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_840ec_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "      <td id=\"T_840ec_row0_col0\" class=\"data row0 col0\" >AC</td>\n",
       "      <td id=\"T_840ec_row0_col1\" class=\"data row0 col1\" >34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_840ec_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "      <td id=\"T_840ec_row1_col0\" class=\"data row1 col0\" >AL</td>\n",
       "      <td id=\"T_840ec_row1_col1\" class=\"data row1 col1\" >130</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_840ec_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "      <td id=\"T_840ec_row2_col0\" class=\"data row2 col0\" >AM</td>\n",
       "      <td id=\"T_840ec_row2_col1\" class=\"data row2 col1\" >74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_840ec_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
       "      <td id=\"T_840ec_row3_col0\" class=\"data row3 col0\" >AP</td>\n",
       "      <td id=\"T_840ec_row3_col1\" class=\"data row3 col1\" >17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_840ec_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
       "      <td id=\"T_840ec_row4_col0\" class=\"data row4 col0\" >BA</td>\n",
       "      <td id=\"T_840ec_row4_col1\" class=\"data row4 col1\" >652</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_840ec_level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
       "      <td id=\"T_840ec_row5_col0\" class=\"data row5 col0\" >CE</td>\n",
       "      <td id=\"T_840ec_row5_col1\" class=\"data row5 col1\" >260</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_840ec_level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
       "      <td id=\"T_840ec_row6_col0\" class=\"data row6 col0\" >DF</td>\n",
       "      <td id=\"T_840ec_row6_col1\" class=\"data row6 col1\" >28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_840ec_level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
       "      <td id=\"T_840ec_row7_col0\" class=\"data row7 col0\" >ES</td>\n",
       "      <td id=\"T_840ec_row7_col1\" class=\"data row7 col1\" >160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_840ec_level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
       "      <td id=\"T_840ec_row8_col0\" class=\"data row8 col0\" >GO</td>\n",
       "      <td id=\"T_840ec_row8_col1\" class=\"data row8 col1\" >384</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_840ec_level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
       "      <td id=\"T_840ec_row9_col0\" class=\"data row9 col0\" >MA</td>\n",
       "      <td id=\"T_840ec_row9_col1\" class=\"data row9 col1\" >299</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_840ec_level0_row10\" class=\"row_heading level0 row10\" >10</th>\n",
       "      <td id=\"T_840ec_row10_col0\" class=\"data row10 col0\" >MG</td>\n",
       "      <td id=\"T_840ec_row10_col1\" class=\"data row10 col1\" >1426</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_840ec_level0_row11\" class=\"row_heading level0 row11\" >11</th>\n",
       "      <td id=\"T_840ec_row11_col0\" class=\"data row11 col0\" >MS</td>\n",
       "      <td id=\"T_840ec_row11_col1\" class=\"data row11 col1\" >133</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_840ec_level0_row12\" class=\"row_heading level0 row12\" >12</th>\n",
       "      <td id=\"T_840ec_row12_col0\" class=\"data row12 col0\" >MT</td>\n",
       "      <td id=\"T_840ec_row12_col1\" class=\"data row12 col1\" >213</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_840ec_level0_row13\" class=\"row_heading level0 row13\" >13</th>\n",
       "      <td id=\"T_840ec_row13_col0\" class=\"data row13 col0\" >PA</td>\n",
       "      <td id=\"T_840ec_row13_col1\" class=\"data row13 col1\" >219</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_840ec_level0_row14\" class=\"row_heading level0 row14\" >14</th>\n",
       "      <td id=\"T_840ec_row14_col0\" class=\"data row14 col0\" >PB</td>\n",
       "      <td id=\"T_840ec_row14_col1\" class=\"data row14 col1\" >254</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_840ec_level0_row15\" class=\"row_heading level0 row15\" >15</th>\n",
       "      <td id=\"T_840ec_row15_col0\" class=\"data row15 col0\" >PE</td>\n",
       "      <td id=\"T_840ec_row15_col1\" class=\"data row15 col1\" >267</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_840ec_level0_row16\" class=\"row_heading level0 row16\" >16</th>\n",
       "      <td id=\"T_840ec_row16_col0\" class=\"data row16 col0\" >PI</td>\n",
       "      <td id=\"T_840ec_row16_col1\" class=\"data row16 col1\" >278</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_840ec_level0_row17\" class=\"row_heading level0 row17\" >17</th>\n",
       "      <td id=\"T_840ec_row17_col0\" class=\"data row17 col0\" >PR</td>\n",
       "      <td id=\"T_840ec_row17_col1\" class=\"data row17 col1\" >651</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_840ec_level0_row18\" class=\"row_heading level0 row18\" >18</th>\n",
       "      <td id=\"T_840ec_row18_col0\" class=\"data row18 col0\" >RJ</td>\n",
       "      <td id=\"T_840ec_row18_col1\" class=\"data row18 col1\" >245</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_840ec_level0_row19\" class=\"row_heading level0 row19\" >19</th>\n",
       "      <td id=\"T_840ec_row19_col0\" class=\"data row19 col0\" >RN</td>\n",
       "      <td id=\"T_840ec_row19_col1\" class=\"data row19 col1\" >214</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_840ec_level0_row20\" class=\"row_heading level0 row20\" >20</th>\n",
       "      <td id=\"T_840ec_row20_col0\" class=\"data row20 col0\" >RO</td>\n",
       "      <td id=\"T_840ec_row20_col1\" class=\"data row20 col1\" >83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_840ec_level0_row21\" class=\"row_heading level0 row21\" >21</th>\n",
       "      <td id=\"T_840ec_row21_col0\" class=\"data row21 col0\" >RR</td>\n",
       "      <td id=\"T_840ec_row21_col1\" class=\"data row21 col1\" >14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_840ec_level0_row22\" class=\"row_heading level0 row22\" >22</th>\n",
       "      <td id=\"T_840ec_row22_col0\" class=\"data row22 col0\" >RS</td>\n",
       "      <td id=\"T_840ec_row22_col1\" class=\"data row22 col1\" >691</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_840ec_level0_row23\" class=\"row_heading level0 row23\" >23</th>\n",
       "      <td id=\"T_840ec_row23_col0\" class=\"data row23 col0\" >SC</td>\n",
       "      <td id=\"T_840ec_row23_col1\" class=\"data row23 col1\" >420</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_840ec_level0_row24\" class=\"row_heading level0 row24\" >24</th>\n",
       "      <td id=\"T_840ec_row24_col0\" class=\"data row24 col0\" >SE</td>\n",
       "      <td id=\"T_840ec_row24_col1\" class=\"data row24 col1\" >96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_840ec_level0_row25\" class=\"row_heading level0 row25\" >25</th>\n",
       "      <td id=\"T_840ec_row25_col0\" class=\"data row25 col0\" >SP</td>\n",
       "      <td id=\"T_840ec_row25_col1\" class=\"data row25 col1\" >1048</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_840ec_level0_row26\" class=\"row_heading level0 row26\" >26</th>\n",
       "      <td id=\"T_840ec_row26_col0\" class=\"data row26 col0\" >TO</td>\n",
       "      <td id=\"T_840ec_row26_col1\" class=\"data row26 col1\" >173</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x2f04dfb71c0>"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "geoplocation_city = geolocation.groupby('geolocation_state')['geolocation_city'].nunique().reset_index()\n",
    "geoplocation_city.rename(columns={'geolocation_state': 'customer_state','geolocation_city':'gelocation_city_count'}, inplace=True)\n",
    "geoplocation_city.sort_values(by = 'gelocation_city_count', ascending= False)\n",
    "geoplocation_city.style.background_gradient(cmap='Blues')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "658332c3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "### Visualising top 10 most bought product categories from customer_state:\n",
    "\n",
    "ax = sns.barplot(x='order_count',y='customer_state',data=customer_state[:10],palette=\"YlGnBu_r\")\n",
    "ax.grid(False)\n",
    "plt.xlabel(\"Total Number of orders\")\n",
    "plt.ylabel(\"customer_state\")\n",
    "plt.title(\"Most bought from customer state\")\n",
    "\n",
    "#get total from order_count\n",
    "total = customer_state['order_count'].sum()\n",
    "\n",
    "ax.get_xaxis().set_major_formatter(matplotlib.ticker.FuncFormatter(lambda x, p: format(int(x), ',')))\n",
    "\n",
    "#percantage\n",
    "for c in ax.containers:\n",
    "        labels = [f\" {(value.get_width() / total):.1%}\" for value in c]\n",
    "        ax.bar_label(c, labels=labels, label_type=\"edge\",fontsize='smaller', fontname='Sans',color='grey')    \n",
    "\n",
    "for spine in ['top', 'right','bottom']:\n",
    "    ax.spines[spine].set_visible(False)\n",
    "    \n",
    "ax.set_title(\n",
    "        \"Top 10 Customer state with the most number of order \",\n",
    "        fontdict={\"fontweight\": \"bold\"},\n",
    "    )\n",
    "\n",
    "plt.savefig(\n",
    "        Path(\".\") / \"plots\" / f\"state_with_most_order.png\",\n",
    "        bbox_inches=\"tight\",\n",
    "        dpi=200,\n",
    "    )\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "4fc8b48f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "### Visualising top 5 lowest bought product categories from order:\n",
    "ax = sns.barplot(x='order_count',y='customer_state',data=customer_state[22:],palette=\"YlOrRd\")\n",
    "ax.grid(False)\n",
    "plt.xlabel(\"Total Number of orders\")\n",
    "plt.ylabel(\"customer_state\")\n",
    "plt.title(\"Most bought from customer state\")\n",
    "plt.xticks(rotation='horizontal')\n",
    "total = customer_state['order_count'].sum()\n",
    "ax.grid(False)\n",
    "#percantage\n",
    "for c in ax.containers:\n",
    "        labels = [f\" {(value.get_width() / total):.1%}\" for value in c]\n",
    "        ax.bar_label(c, labels=labels, label_type=\"edge\",fontsize='smaller', fontname='Sans',color='grey')    \n",
    "\n",
    "for spine in ['top', 'right','bottom']:\n",
    "    ax.spines[spine].set_visible(False)\n",
    "    \n",
    "ax.set_title(\n",
    "        \"Top 5 Customer state with the lowest number of order\",\n",
    "        fontdict={\"fontweight\": \"bold\"},\n",
    "        color=\"red\"\n",
    "    )\n",
    "plt.savefig(\n",
    "        Path(\".\") / \"plots\" / f\"state_with_lowest_order.png\",\n",
    "        bbox_inches=\"tight\",\n",
    "        dpi=200,\n",
    "    )\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5bc42383",
   "metadata": {},
   "source": [
    "#### Correlation between the number of cities in each state with the total transactions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "37899658",
   "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_count</th>\n",
       "      <th>gelocation_city_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>order_count</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.656747</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gelocation_city_count</th>\n",
       "      <td>0.656747</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       order_count  gelocation_city_count\n",
       "order_count               1.000000               0.656747\n",
       "gelocation_city_count     0.656747               1.000000"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "customer_state_order = pd.merge(customer_state,geoplocation_city, on='customer_state')\n",
    "customer_state_order[['order_count','gelocation_city_count']].corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "820ae783",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1100x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "corr = customer_state_order.corr()\n",
    "# corr.style.background_gradient(cmap='Greens')\n",
    "plt.figure(figsize=(11,8))\n",
    "sns.heatmap(corr, cmap=\"Blues\",annot=True)\n",
    "plt.savefig(\n",
    "        Path(\".\") / \"plots\" / f\"correlation_state_order.png\",\n",
    "        bbox_inches=\"tight\",\n",
    "        dpi=200,\n",
    "    )\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3a8b337c",
   "metadata": {},
   "source": [
    "It was found that correlation 1 is a correlation with the variable itself while the correlation value of 0.66 indicates the correlation between variables."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e04a7bad",
   "metadata": {},
   "source": [
    "# Read More on [Medium](https://medium.com/@nicodemusnaisau/ecommerce-analytics-df427308bcd)"
   ]
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