Exploratory Data Analysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "a8c51b20",
   "metadata": {},
   "outputs": [],
   "source": [
    "####Data Source : (https://www.kaggle.com/rohitsahoo/sales-forecasting)\n",
    "\n",
    "#OBJECTIVES TO FIND \n",
    "\n",
    "##What is the overall trend of the sales?\n",
    "##What are the top 10 products by sales?\n",
    "##What are the most selling products?\n",
    "##What is the most preferred Ship Mode?\n",
    "##Which are the most profitable category and Sub-category?\n",
    "\n",
    "\n",
    "\n",
    "#Libraries\n",
    "    \n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import seaborn as sns\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "import os\n",
    "import time\n",
    "from itertools import product\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "from sklearn.metrics import mean_absolute_error\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.model_selection import KFold\n",
    "from sklearn import base"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "10f1f1c4",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv('C:/Users/kruna/Downloads/train.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "a02df259",
   "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>Row ID</th>\n",
       "      <th>Order ID</th>\n",
       "      <th>Order Date</th>\n",
       "      <th>Ship Date</th>\n",
       "      <th>Ship Mode</th>\n",
       "      <th>Customer ID</th>\n",
       "      <th>Customer Name</th>\n",
       "      <th>Segment</th>\n",
       "      <th>Country</th>\n",
       "      <th>City</th>\n",
       "      <th>State</th>\n",
       "      <th>Postal Code</th>\n",
       "      <th>Region</th>\n",
       "      <th>Product ID</th>\n",
       "      <th>Category</th>\n",
       "      <th>Sub-Category</th>\n",
       "      <th>Product Name</th>\n",
       "      <th>Sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>CA-2017-152156</td>\n",
       "      <td>08/11/2017</td>\n",
       "      <td>11/11/2017</td>\n",
       "      <td>Second Class</td>\n",
       "      <td>CG-12520</td>\n",
       "      <td>Claire Gute</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>United States</td>\n",
       "      <td>Henderson</td>\n",
       "      <td>Kentucky</td>\n",
       "      <td>42420.0</td>\n",
       "      <td>South</td>\n",
       "      <td>FUR-BO-10001798</td>\n",
       "      <td>Furniture</td>\n",
       "      <td>Bookcases</td>\n",
       "      <td>Bush Somerset Collection Bookcase</td>\n",
       "      <td>261.9600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>CA-2017-152156</td>\n",
       "      <td>08/11/2017</td>\n",
       "      <td>11/11/2017</td>\n",
       "      <td>Second Class</td>\n",
       "      <td>CG-12520</td>\n",
       "      <td>Claire Gute</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>United States</td>\n",
       "      <td>Henderson</td>\n",
       "      <td>Kentucky</td>\n",
       "      <td>42420.0</td>\n",
       "      <td>South</td>\n",
       "      <td>FUR-CH-10000454</td>\n",
       "      <td>Furniture</td>\n",
       "      <td>Chairs</td>\n",
       "      <td>Hon Deluxe Fabric Upholstered Stacking Chairs,...</td>\n",
       "      <td>731.9400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>CA-2017-138688</td>\n",
       "      <td>12/06/2017</td>\n",
       "      <td>16/06/2017</td>\n",
       "      <td>Second Class</td>\n",
       "      <td>DV-13045</td>\n",
       "      <td>Darrin Van Huff</td>\n",
       "      <td>Corporate</td>\n",
       "      <td>United States</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>California</td>\n",
       "      <td>90036.0</td>\n",
       "      <td>West</td>\n",
       "      <td>OFF-LA-10000240</td>\n",
       "      <td>Office Supplies</td>\n",
       "      <td>Labels</td>\n",
       "      <td>Self-Adhesive Address Labels for Typewriters b...</td>\n",
       "      <td>14.6200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>US-2016-108966</td>\n",
       "      <td>11/10/2016</td>\n",
       "      <td>18/10/2016</td>\n",
       "      <td>Standard Class</td>\n",
       "      <td>SO-20335</td>\n",
       "      <td>Sean O'Donnell</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>United States</td>\n",
       "      <td>Fort Lauderdale</td>\n",
       "      <td>Florida</td>\n",
       "      <td>33311.0</td>\n",
       "      <td>South</td>\n",
       "      <td>FUR-TA-10000577</td>\n",
       "      <td>Furniture</td>\n",
       "      <td>Tables</td>\n",
       "      <td>Bretford CR4500 Series Slim Rectangular Table</td>\n",
       "      <td>957.5775</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>US-2016-108966</td>\n",
       "      <td>11/10/2016</td>\n",
       "      <td>18/10/2016</td>\n",
       "      <td>Standard Class</td>\n",
       "      <td>SO-20335</td>\n",
       "      <td>Sean O'Donnell</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>United States</td>\n",
       "      <td>Fort Lauderdale</td>\n",
       "      <td>Florida</td>\n",
       "      <td>33311.0</td>\n",
       "      <td>South</td>\n",
       "      <td>OFF-ST-10000760</td>\n",
       "      <td>Office Supplies</td>\n",
       "      <td>Storage</td>\n",
       "      <td>Eldon Fold 'N Roll Cart System</td>\n",
       "      <td>22.3680</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Row ID        Order ID  Order Date   Ship Date       Ship Mode Customer ID  \\\n",
       "0       1  CA-2017-152156  08/11/2017  11/11/2017    Second Class    CG-12520   \n",
       "1       2  CA-2017-152156  08/11/2017  11/11/2017    Second Class    CG-12520   \n",
       "2       3  CA-2017-138688  12/06/2017  16/06/2017    Second Class    DV-13045   \n",
       "3       4  US-2016-108966  11/10/2016  18/10/2016  Standard Class    SO-20335   \n",
       "4       5  US-2016-108966  11/10/2016  18/10/2016  Standard Class    SO-20335   \n",
       "\n",
       "     Customer Name    Segment        Country             City       State  \\\n",
       "0      Claire Gute   Consumer  United States        Henderson    Kentucky   \n",
       "1      Claire Gute   Consumer  United States        Henderson    Kentucky   \n",
       "2  Darrin Van Huff  Corporate  United States      Los Angeles  California   \n",
       "3   Sean O'Donnell   Consumer  United States  Fort Lauderdale     Florida   \n",
       "4   Sean O'Donnell   Consumer  United States  Fort Lauderdale     Florida   \n",
       "\n",
       "   Postal Code Region       Product ID         Category Sub-Category  \\\n",
       "0      42420.0  South  FUR-BO-10001798        Furniture    Bookcases   \n",
       "1      42420.0  South  FUR-CH-10000454        Furniture       Chairs   \n",
       "2      90036.0   West  OFF-LA-10000240  Office Supplies       Labels   \n",
       "3      33311.0  South  FUR-TA-10000577        Furniture       Tables   \n",
       "4      33311.0  South  OFF-ST-10000760  Office Supplies      Storage   \n",
       "\n",
       "                                        Product Name     Sales  \n",
       "0                  Bush Somerset Collection Bookcase  261.9600  \n",
       "1  Hon Deluxe Fabric Upholstered Stacking Chairs,...  731.9400  \n",
       "2  Self-Adhesive Address Labels for Typewriters b...   14.6200  \n",
       "3      Bretford CR4500 Series Slim Rectangular Table  957.5775  \n",
       "4                     Eldon Fold 'N Roll Cart System   22.3680  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "bfdae0fb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(9800, 18)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "671d2504",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 9800 entries, 0 to 9799\n",
      "Data columns (total 18 columns):\n",
      " #   Column         Non-Null Count  Dtype  \n",
      "---  ------         --------------  -----  \n",
      " 0   Row ID         9800 non-null   int64  \n",
      " 1   Order ID       9800 non-null   object \n",
      " 2   Order Date     9800 non-null   object \n",
      " 3   Ship Date      9800 non-null   object \n",
      " 4   Ship Mode      9800 non-null   object \n",
      " 5   Customer ID    9800 non-null   object \n",
      " 6   Customer Name  9800 non-null   object \n",
      " 7   Segment        9800 non-null   object \n",
      " 8   Country        9800 non-null   object \n",
      " 9   City           9800 non-null   object \n",
      " 10  State          9800 non-null   object \n",
      " 11  Postal Code    9789 non-null   float64\n",
      " 12  Region         9800 non-null   object \n",
      " 13  Product ID     9800 non-null   object \n",
      " 14  Category       9800 non-null   object \n",
      " 15  Sub-Category   9800 non-null   object \n",
      " 16  Product Name   9800 non-null   object \n",
      " 17  Sales          9800 non-null   float64\n",
      "dtypes: float64(2), int64(1), object(15)\n",
      "memory usage: 1.3+ MB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "b46a1491",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Row ID           9800\n",
       "Order ID         4922\n",
       "Order Date       1230\n",
       "Ship Date        1326\n",
       "Ship Mode           4\n",
       "Customer ID       793\n",
       "Customer Name     793\n",
       "Segment             3\n",
       "Country             1\n",
       "City              529\n",
       "State              49\n",
       "Postal Code       626\n",
       "Region              4\n",
       "Product ID       1861\n",
       "Category            3\n",
       "Sub-Category       17\n",
       "Product Name     1849\n",
       "Sales            5757\n",
       "dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Find the unique values\n",
    "data.nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "41d1960b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Row ID            0\n",
       "Order ID          0\n",
       "Order Date        0\n",
       "Ship Date         0\n",
       "Ship Mode         0\n",
       "Customer ID       0\n",
       "Customer Name     0\n",
       "Segment           0\n",
       "Country           0\n",
       "City              0\n",
       "State             0\n",
       "Postal Code      11\n",
       "Region            0\n",
       "Product ID        0\n",
       "Category          0\n",
       "Sub-Category      0\n",
       "Product Name      0\n",
       "Sales             0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Find total sum of null values\n",
    "data.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "fce1afaa",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Remove NA/null values \n",
    "data = data.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "1a4020f0",
   "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>Row ID</th>\n",
       "      <th>Order ID</th>\n",
       "      <th>Order Date</th>\n",
       "      <th>Ship Date</th>\n",
       "      <th>Ship Mode</th>\n",
       "      <th>Customer ID</th>\n",
       "      <th>Customer Name</th>\n",
       "      <th>Segment</th>\n",
       "      <th>Country</th>\n",
       "      <th>City</th>\n",
       "      <th>State</th>\n",
       "      <th>Postal Code</th>\n",
       "      <th>Region</th>\n",
       "      <th>Product ID</th>\n",
       "      <th>Category</th>\n",
       "      <th>Sub-Category</th>\n",
       "      <th>Product Name</th>\n",
       "      <th>Sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>CA-2017-152156</td>\n",
       "      <td>08/11/2017</td>\n",
       "      <td>11/11/2017</td>\n",
       "      <td>Second Class</td>\n",
       "      <td>CG-12520</td>\n",
       "      <td>Claire Gute</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>United States</td>\n",
       "      <td>Henderson</td>\n",
       "      <td>Kentucky</td>\n",
       "      <td>42420.0</td>\n",
       "      <td>South</td>\n",
       "      <td>FUR-BO-10001798</td>\n",
       "      <td>Furniture</td>\n",
       "      <td>Bookcases</td>\n",
       "      <td>Bush Somerset Collection Bookcase</td>\n",
       "      <td>261.9600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>CA-2017-152156</td>\n",
       "      <td>08/11/2017</td>\n",
       "      <td>11/11/2017</td>\n",
       "      <td>Second Class</td>\n",
       "      <td>CG-12520</td>\n",
       "      <td>Claire Gute</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>United States</td>\n",
       "      <td>Henderson</td>\n",
       "      <td>Kentucky</td>\n",
       "      <td>42420.0</td>\n",
       "      <td>South</td>\n",
       "      <td>FUR-CH-10000454</td>\n",
       "      <td>Furniture</td>\n",
       "      <td>Chairs</td>\n",
       "      <td>Hon Deluxe Fabric Upholstered Stacking Chairs,...</td>\n",
       "      <td>731.9400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>CA-2017-138688</td>\n",
       "      <td>12/06/2017</td>\n",
       "      <td>16/06/2017</td>\n",
       "      <td>Second Class</td>\n",
       "      <td>DV-13045</td>\n",
       "      <td>Darrin Van Huff</td>\n",
       "      <td>Corporate</td>\n",
       "      <td>United States</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>California</td>\n",
       "      <td>90036.0</td>\n",
       "      <td>West</td>\n",
       "      <td>OFF-LA-10000240</td>\n",
       "      <td>Office Supplies</td>\n",
       "      <td>Labels</td>\n",
       "      <td>Self-Adhesive Address Labels for Typewriters b...</td>\n",
       "      <td>14.6200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>US-2016-108966</td>\n",
       "      <td>11/10/2016</td>\n",
       "      <td>18/10/2016</td>\n",
       "      <td>Standard Class</td>\n",
       "      <td>SO-20335</td>\n",
       "      <td>Sean O'Donnell</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>United States</td>\n",
       "      <td>Fort Lauderdale</td>\n",
       "      <td>Florida</td>\n",
       "      <td>33311.0</td>\n",
       "      <td>South</td>\n",
       "      <td>FUR-TA-10000577</td>\n",
       "      <td>Furniture</td>\n",
       "      <td>Tables</td>\n",
       "      <td>Bretford CR4500 Series Slim Rectangular Table</td>\n",
       "      <td>957.5775</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>US-2016-108966</td>\n",
       "      <td>11/10/2016</td>\n",
       "      <td>18/10/2016</td>\n",
       "      <td>Standard Class</td>\n",
       "      <td>SO-20335</td>\n",
       "      <td>Sean O'Donnell</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>United States</td>\n",
       "      <td>Fort Lauderdale</td>\n",
       "      <td>Florida</td>\n",
       "      <td>33311.0</td>\n",
       "      <td>South</td>\n",
       "      <td>OFF-ST-10000760</td>\n",
       "      <td>Office Supplies</td>\n",
       "      <td>Storage</td>\n",
       "      <td>Eldon Fold 'N Roll Cart System</td>\n",
       "      <td>22.3680</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Row ID        Order ID  Order Date   Ship Date       Ship Mode Customer ID  \\\n",
       "0       1  CA-2017-152156  08/11/2017  11/11/2017    Second Class    CG-12520   \n",
       "1       2  CA-2017-152156  08/11/2017  11/11/2017    Second Class    CG-12520   \n",
       "2       3  CA-2017-138688  12/06/2017  16/06/2017    Second Class    DV-13045   \n",
       "3       4  US-2016-108966  11/10/2016  18/10/2016  Standard Class    SO-20335   \n",
       "4       5  US-2016-108966  11/10/2016  18/10/2016  Standard Class    SO-20335   \n",
       "\n",
       "     Customer Name    Segment        Country             City       State  \\\n",
       "0      Claire Gute   Consumer  United States        Henderson    Kentucky   \n",
       "1      Claire Gute   Consumer  United States        Henderson    Kentucky   \n",
       "2  Darrin Van Huff  Corporate  United States      Los Angeles  California   \n",
       "3   Sean O'Donnell   Consumer  United States  Fort Lauderdale     Florida   \n",
       "4   Sean O'Donnell   Consumer  United States  Fort Lauderdale     Florida   \n",
       "\n",
       "   Postal Code Region       Product ID         Category Sub-Category  \\\n",
       "0      42420.0  South  FUR-BO-10001798        Furniture    Bookcases   \n",
       "1      42420.0  South  FUR-CH-10000454        Furniture       Chairs   \n",
       "2      90036.0   West  OFF-LA-10000240  Office Supplies       Labels   \n",
       "3      33311.0  South  FUR-TA-10000577        Furniture       Tables   \n",
       "4      33311.0  South  OFF-ST-10000760  Office Supplies      Storage   \n",
       "\n",
       "                                        Product Name     Sales  \n",
       "0                  Bush Somerset Collection Bookcase  261.9600  \n",
       "1  Hon Deluxe Fabric Upholstered Stacking Chairs,...  731.9400  \n",
       "2  Self-Adhesive Address Labels for Typewriters b...   14.6200  \n",
       "3      Bretford CR4500 Series Slim Rectangular Table  957.5775  \n",
       "4                     Eldon Fold 'N Roll Cart System   22.3680  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "b24e6e80",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Row ID           0\n",
       "Order ID         0\n",
       "Order Date       0\n",
       "Ship Date        0\n",
       "Ship Mode        0\n",
       "Customer ID      0\n",
       "Customer Name    0\n",
       "Segment          0\n",
       "Country          0\n",
       "City             0\n",
       "State            0\n",
       "Postal Code      0\n",
       "Region           0\n",
       "Product ID       0\n",
       "Category         0\n",
       "Sub-Category     0\n",
       "Product Name     0\n",
       "Sales            0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "d6e1cfb3",
   "metadata": {},
   "outputs": [],
   "source": [
    "###Multi-table analysis of the data to help summarise the data fast.\n",
    "\n",
    "from IPython.core.display import HTML\n",
    "\n",
    "def multi_table(table_list):\n",
    "    ''' Accepts a list of IpyTable objects and returns a table which contains each IpyTable in a cell\n",
    "    '''\n",
    "    return HTML(\n",
    "        '<table><tr style=\"background-color:white;\">' + \n",
    "        ''.join(['<td>' + table._repr_html_() + '</td>' for table in table_list]) +\n",
    "        '</tr></table>')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "2e9d16ab",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_nunique = {var: pd.DataFrame(data[var].value_counts()) \n",
    "              for var in {'Order ID', 'Order Date', 'Ship Date', 'Ship Mode',\n",
    "       'Customer ID', 'Customer Name', 'Segment', 'Country', 'City', 'State',\n",
    "       'Postal Code', 'Region', 'Product ID', 'Category', 'Sub-Category',\n",
    "       'Product Name', 'Sales'}}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "3f7bacff",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table><tr style=\"background-color:white;\"><td><div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Order ID</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>CA-2018-100111</th>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CA-2018-157987</th>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CA-2017-165330</th>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>US-2017-108504</th>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CA-2017-105732</th>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>US-2015-161613</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CA-2018-101728</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CA-2017-112942</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CA-2018-126438</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CA-2017-147123</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4916 rows × 1 columns</p>\n",
       "</div></td><td><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 Date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>05/09/2017</th>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10/11/2017</th>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>02/12/2018</th>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>01/12/2018</th>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>09/12/2018</th>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25/02/2017</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25/10/2017</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11/09/2015</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16/09/2015</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>09/05/2016</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1229 rows × 1 columns</p>\n",
       "</div></td><td><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>Ship Date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>26/09/2018</th>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>06/12/2018</th>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16/12/2016</th>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>06/09/2018</th>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12/12/2018</th>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30/07/2016</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26/01/2017</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17/01/2015</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17/08/2018</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13/05/2016</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1326 rows × 1 columns</p>\n",
       "</div></td><td><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>Ship Mode</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Standard Class</th>\n",
       "      <td>5849</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Second Class</th>\n",
       "      <td>1901</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>First Class</th>\n",
       "      <td>1501</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Same Day</th>\n",
       "      <td>538</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div></td><td><div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Customer ID</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>WB-21850</th>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MA-17560</th>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PP-18955</th>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>JL-15835</th>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CK-12205</th>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AO-10810</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>JR-15700</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CJ-11875</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SC-20845</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RE-19405</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>793 rows × 1 columns</p>\n",
       "</div></td><td><div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Customer Name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>William Brown</th>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Matt Abelman</th>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Paul Prost</th>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>John Lee</th>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chloris Kastensmidt</th>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Anthony O'Donnell</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jocasta Rupert</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Carl Jackson</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sung Chung</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ricardo Emerson</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>793 rows × 1 columns</p>\n",
       "</div></td><td><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>Segment</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Consumer</th>\n",
       "      <td>5096</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Corporate</th>\n",
       "      <td>2948</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Home Office</th>\n",
       "      <td>1745</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div></td><td><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>Country</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>United States</th>\n",
       "      <td>9789</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div></td><td><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>City</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>New York City</th>\n",
       "      <td>891</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Los Angeles</th>\n",
       "      <td>728</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Philadelphia</th>\n",
       "      <td>532</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>San Francisco</th>\n",
       "      <td>500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Seattle</th>\n",
       "      <td>426</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>San Mateo</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cheyenne</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Conway</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Melbourne</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Springdale</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>529 rows × 1 columns</p>\n",
       "</div></td><td><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>State</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>California</th>\n",
       "      <td>1946</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>New York</th>\n",
       "      <td>1097</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Texas</th>\n",
       "      <td>973</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pennsylvania</th>\n",
       "      <td>582</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Washington</th>\n",
       "      <td>504</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Illinois</th>\n",
       "      <td>483</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ohio</th>\n",
       "      <td>454</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Florida</th>\n",
       "      <td>373</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Michigan</th>\n",
       "      <td>253</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>North Carolina</th>\n",
       "      <td>247</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Virginia</th>\n",
       "      <td>224</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Arizona</th>\n",
       "      <td>223</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tennessee</th>\n",
       "      <td>183</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Colorado</th>\n",
       "      <td>179</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Georgia</th>\n",
       "      <td>177</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kentucky</th>\n",
       "      <td>137</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Indiana</th>\n",
       "      <td>135</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Massachusetts</th>\n",
       "      <td>135</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Oregon</th>\n",
       "      <td>122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>New Jersey</th>\n",
       "      <td>122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Maryland</th>\n",
       "      <td>105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wisconsin</th>\n",
       "      <td>105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Delaware</th>\n",
       "      <td>93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Minnesota</th>\n",
       "      <td>89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Connecticut</th>\n",
       "      <td>82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Missouri</th>\n",
       "      <td>66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Oklahoma</th>\n",
       "      <td>66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Alabama</th>\n",
       "      <td>61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Arkansas</th>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rhode Island</th>\n",
       "      <td>55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mississippi</th>\n",
       "      <td>53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Utah</th>\n",
       "      <td>53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>South Carolina</th>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Louisiana</th>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nevada</th>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nebraska</th>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>New Mexico</th>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>New Hampshire</th>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Iowa</th>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kansas</th>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Idaho</th>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Montana</th>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>South Dakota</th>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>District of Columbia</th>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Maine</th>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>North Dakota</th>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>West Virginia</th>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wyoming</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div></td><td><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>Postal Code</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>10035.0</th>\n",
       "      <td>253</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10024.0</th>\n",
       "      <td>225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10009.0</th>\n",
       "      <td>220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94122.0</th>\n",
       "      <td>195</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10011.0</th>\n",
       "      <td>193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32935.0</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76248.0</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32127.0</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32503.0</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72762.0</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>626 rows × 1 columns</p>\n",
       "</div></td><td><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>Region</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>West</th>\n",
       "      <td>3140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>East</th>\n",
       "      <td>2774</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Central</th>\n",
       "      <td>2277</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>South</th>\n",
       "      <td>1598</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div></td><td><div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Product ID</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>OFF-PA-10001970</th>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TEC-AC-10003832</th>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FUR-FU-10004270</th>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TEC-AC-10002049</th>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TEC-AC-10003628</th>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OFF-ST-10001034</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TEC-PH-10001468</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OFF-AR-10004974</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TEC-AC-10003133</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TEC-PH-10002645</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1860 rows × 1 columns</p>\n",
       "</div></td><td><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>Category</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Office Supplies</th>\n",
       "      <td>5903</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Furniture</th>\n",
       "      <td>2076</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Technology</th>\n",
       "      <td>1810</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div></td><td><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>Sub-Category</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Binders</th>\n",
       "      <td>1492</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Paper</th>\n",
       "      <td>1336</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Furnishings</th>\n",
       "      <td>931</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Phones</th>\n",
       "      <td>875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Storage</th>\n",
       "      <td>831</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Art</th>\n",
       "      <td>784</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Accessories</th>\n",
       "      <td>754</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chairs</th>\n",
       "      <td>606</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Appliances</th>\n",
       "      <td>458</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Labels</th>\n",
       "      <td>357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tables</th>\n",
       "      <td>314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Envelopes</th>\n",
       "      <td>247</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bookcases</th>\n",
       "      <td>225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fasteners</th>\n",
       "      <td>214</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Supplies</th>\n",
       "      <td>184</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Machines</th>\n",
       "      <td>115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Copiers</th>\n",
       "      <td>66</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div></td><td><div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Product Name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Staple envelope</th>\n",
       "      <td>47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Staples</th>\n",
       "      <td>46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Easy-staple paper</th>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Avery Non-Stick Binders</th>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Staples in misc. colors</th>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Park Ridge Embossed Executive Business Envelopes</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Canon imageCLASS MF7460 Monochrome Digital Laser Multifunction Copier</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Belkin 8 Outlet SurgeMaster II Gold Surge Protector with Phone Protection</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Boston 1900 Electric Pencil Sharpener</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LG G2</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1848 rows × 1 columns</p>\n",
       "</div></td><td><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>Sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>12.960</th>\n",
       "      <td>55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19.440</th>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15.552</th>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10.368</th>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25.920</th>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27.264</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56.650</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58.720</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16.496</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10.384</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5750 rows × 1 columns</p>\n",
       "</div></td></tr></table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Summarise the data \n",
    "multi_table([data_nunique['Order ID'],data_nunique['Order Date'],data_nunique['Ship Date'],\n",
    "            data_nunique['Ship Mode'],data_nunique['Customer ID'],data_nunique['Customer Name'],data_nunique['Segment'],\n",
    "            data_nunique['Country'],data_nunique['City'],data_nunique['State'],data_nunique['Postal Code'],data_nunique['Region'],\n",
    "            data_nunique['Product ID'],data_nunique['Category'],data_nunique['Sub-Category'],data_nunique['Product Name'],data_nunique['Sales']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "b9d990b9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Order ID</th>\n",
       "      <th>Order Date</th>\n",
       "      <th>Ship Date</th>\n",
       "      <th>Ship Mode</th>\n",
       "      <th>Customer ID</th>\n",
       "      <th>Customer Name</th>\n",
       "      <th>Segment</th>\n",
       "      <th>Country</th>\n",
       "      <th>City</th>\n",
       "      <th>State</th>\n",
       "      <th>Postal Code</th>\n",
       "      <th>Region</th>\n",
       "      <th>Product ID</th>\n",
       "      <th>Category</th>\n",
       "      <th>Sub-Category</th>\n",
       "      <th>Product Name</th>\n",
       "      <th>Sales</th>\n",
       "      <th>year_month</th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CA-2017-152156</td>\n",
       "      <td>2017-08-11</td>\n",
       "      <td>11/11/2017</td>\n",
       "      <td>Second Class</td>\n",
       "      <td>CG-12520</td>\n",
       "      <td>Claire Gute</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>United States</td>\n",
       "      <td>Henderson</td>\n",
       "      <td>Kentucky</td>\n",
       "      <td>42420.0</td>\n",
       "      <td>South</td>\n",
       "      <td>FUR-BO-10001798</td>\n",
       "      <td>Furniture</td>\n",
       "      <td>Bookcases</td>\n",
       "      <td>Bush Somerset Collection Bookcase</td>\n",
       "      <td>261.9600</td>\n",
       "      <td>2017-08</td>\n",
       "      <td>2017</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>CA-2017-152156</td>\n",
       "      <td>2017-08-11</td>\n",
       "      <td>11/11/2017</td>\n",
       "      <td>Second Class</td>\n",
       "      <td>CG-12520</td>\n",
       "      <td>Claire Gute</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>United States</td>\n",
       "      <td>Henderson</td>\n",
       "      <td>Kentucky</td>\n",
       "      <td>42420.0</td>\n",
       "      <td>South</td>\n",
       "      <td>FUR-CH-10000454</td>\n",
       "      <td>Furniture</td>\n",
       "      <td>Chairs</td>\n",
       "      <td>Hon Deluxe Fabric Upholstered Stacking Chairs,...</td>\n",
       "      <td>731.9400</td>\n",
       "      <td>2017-08</td>\n",
       "      <td>2017</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>CA-2017-138688</td>\n",
       "      <td>2017-12-06</td>\n",
       "      <td>16/06/2017</td>\n",
       "      <td>Second Class</td>\n",
       "      <td>DV-13045</td>\n",
       "      <td>Darrin Van Huff</td>\n",
       "      <td>Corporate</td>\n",
       "      <td>United States</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>California</td>\n",
       "      <td>90036.0</td>\n",
       "      <td>West</td>\n",
       "      <td>OFF-LA-10000240</td>\n",
       "      <td>Office Supplies</td>\n",
       "      <td>Labels</td>\n",
       "      <td>Self-Adhesive Address Labels for Typewriters b...</td>\n",
       "      <td>14.6200</td>\n",
       "      <td>2017-12</td>\n",
       "      <td>2017</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>US-2016-108966</td>\n",
       "      <td>2016-11-10</td>\n",
       "      <td>18/10/2016</td>\n",
       "      <td>Standard Class</td>\n",
       "      <td>SO-20335</td>\n",
       "      <td>Sean O'Donnell</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>United States</td>\n",
       "      <td>Fort Lauderdale</td>\n",
       "      <td>Florida</td>\n",
       "      <td>33311.0</td>\n",
       "      <td>South</td>\n",
       "      <td>FUR-TA-10000577</td>\n",
       "      <td>Furniture</td>\n",
       "      <td>Tables</td>\n",
       "      <td>Bretford CR4500 Series Slim Rectangular Table</td>\n",
       "      <td>957.5775</td>\n",
       "      <td>2016-11</td>\n",
       "      <td>2016</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>US-2016-108966</td>\n",
       "      <td>2016-11-10</td>\n",
       "      <td>18/10/2016</td>\n",
       "      <td>Standard Class</td>\n",
       "      <td>SO-20335</td>\n",
       "      <td>Sean O'Donnell</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>United States</td>\n",
       "      <td>Fort Lauderdale</td>\n",
       "      <td>Florida</td>\n",
       "      <td>33311.0</td>\n",
       "      <td>South</td>\n",
       "      <td>OFF-ST-10000760</td>\n",
       "      <td>Office Supplies</td>\n",
       "      <td>Storage</td>\n",
       "      <td>Eldon Fold 'N Roll Cart System</td>\n",
       "      <td>22.3680</td>\n",
       "      <td>2016-11</td>\n",
       "      <td>2016</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9795</th>\n",
       "      <td>CA-2017-125920</td>\n",
       "      <td>2017-05-21</td>\n",
       "      <td>28/05/2017</td>\n",
       "      <td>Standard Class</td>\n",
       "      <td>SH-19975</td>\n",
       "      <td>Sally Hughsby</td>\n",
       "      <td>Corporate</td>\n",
       "      <td>United States</td>\n",
       "      <td>Chicago</td>\n",
       "      <td>Illinois</td>\n",
       "      <td>60610.0</td>\n",
       "      <td>Central</td>\n",
       "      <td>OFF-BI-10003429</td>\n",
       "      <td>Office Supplies</td>\n",
       "      <td>Binders</td>\n",
       "      <td>Cardinal HOLDit! Binder Insert Strips,Extra St...</td>\n",
       "      <td>3.7980</td>\n",
       "      <td>2017-05</td>\n",
       "      <td>2017</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9796</th>\n",
       "      <td>CA-2016-128608</td>\n",
       "      <td>2016-12-01</td>\n",
       "      <td>17/01/2016</td>\n",
       "      <td>Standard Class</td>\n",
       "      <td>CS-12490</td>\n",
       "      <td>Cindy Schnelling</td>\n",
       "      <td>Corporate</td>\n",
       "      <td>United States</td>\n",
       "      <td>Toledo</td>\n",
       "      <td>Ohio</td>\n",
       "      <td>43615.0</td>\n",
       "      <td>East</td>\n",
       "      <td>OFF-AR-10001374</td>\n",
       "      <td>Office Supplies</td>\n",
       "      <td>Art</td>\n",
       "      <td>BIC Brite Liner Highlighters, Chisel Tip</td>\n",
       "      <td>10.3680</td>\n",
       "      <td>2016-12</td>\n",
       "      <td>2016</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9797</th>\n",
       "      <td>CA-2016-128608</td>\n",
       "      <td>2016-12-01</td>\n",
       "      <td>17/01/2016</td>\n",
       "      <td>Standard Class</td>\n",
       "      <td>CS-12490</td>\n",
       "      <td>Cindy Schnelling</td>\n",
       "      <td>Corporate</td>\n",
       "      <td>United States</td>\n",
       "      <td>Toledo</td>\n",
       "      <td>Ohio</td>\n",
       "      <td>43615.0</td>\n",
       "      <td>East</td>\n",
       "      <td>TEC-PH-10004977</td>\n",
       "      <td>Technology</td>\n",
       "      <td>Phones</td>\n",
       "      <td>GE 30524EE4</td>\n",
       "      <td>235.1880</td>\n",
       "      <td>2016-12</td>\n",
       "      <td>2016</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9798</th>\n",
       "      <td>CA-2016-128608</td>\n",
       "      <td>2016-12-01</td>\n",
       "      <td>17/01/2016</td>\n",
       "      <td>Standard Class</td>\n",
       "      <td>CS-12490</td>\n",
       "      <td>Cindy Schnelling</td>\n",
       "      <td>Corporate</td>\n",
       "      <td>United States</td>\n",
       "      <td>Toledo</td>\n",
       "      <td>Ohio</td>\n",
       "      <td>43615.0</td>\n",
       "      <td>East</td>\n",
       "      <td>TEC-PH-10000912</td>\n",
       "      <td>Technology</td>\n",
       "      <td>Phones</td>\n",
       "      <td>Anker 24W Portable Micro USB Car Charger</td>\n",
       "      <td>26.3760</td>\n",
       "      <td>2016-12</td>\n",
       "      <td>2016</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9799</th>\n",
       "      <td>CA-2016-128608</td>\n",
       "      <td>2016-12-01</td>\n",
       "      <td>17/01/2016</td>\n",
       "      <td>Standard Class</td>\n",
       "      <td>CS-12490</td>\n",
       "      <td>Cindy Schnelling</td>\n",
       "      <td>Corporate</td>\n",
       "      <td>United States</td>\n",
       "      <td>Toledo</td>\n",
       "      <td>Ohio</td>\n",
       "      <td>43615.0</td>\n",
       "      <td>East</td>\n",
       "      <td>TEC-AC-10000487</td>\n",
       "      <td>Technology</td>\n",
       "      <td>Accessories</td>\n",
       "      <td>SanDisk Cruzer 4 GB USB Flash Drive</td>\n",
       "      <td>10.3840</td>\n",
       "      <td>2016-12</td>\n",
       "      <td>2016</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>9789 rows × 20 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Order ID Order Date   Ship Date       Ship Mode Customer ID  \\\n",
       "0     CA-2017-152156 2017-08-11  11/11/2017    Second Class    CG-12520   \n",
       "1     CA-2017-152156 2017-08-11  11/11/2017    Second Class    CG-12520   \n",
       "2     CA-2017-138688 2017-12-06  16/06/2017    Second Class    DV-13045   \n",
       "3     US-2016-108966 2016-11-10  18/10/2016  Standard Class    SO-20335   \n",
       "4     US-2016-108966 2016-11-10  18/10/2016  Standard Class    SO-20335   \n",
       "...              ...        ...         ...             ...         ...   \n",
       "9795  CA-2017-125920 2017-05-21  28/05/2017  Standard Class    SH-19975   \n",
       "9796  CA-2016-128608 2016-12-01  17/01/2016  Standard Class    CS-12490   \n",
       "9797  CA-2016-128608 2016-12-01  17/01/2016  Standard Class    CS-12490   \n",
       "9798  CA-2016-128608 2016-12-01  17/01/2016  Standard Class    CS-12490   \n",
       "9799  CA-2016-128608 2016-12-01  17/01/2016  Standard Class    CS-12490   \n",
       "\n",
       "         Customer Name    Segment        Country             City       State  \\\n",
       "0          Claire Gute   Consumer  United States        Henderson    Kentucky   \n",
       "1          Claire Gute   Consumer  United States        Henderson    Kentucky   \n",
       "2      Darrin Van Huff  Corporate  United States      Los Angeles  California   \n",
       "3       Sean O'Donnell   Consumer  United States  Fort Lauderdale     Florida   \n",
       "4       Sean O'Donnell   Consumer  United States  Fort Lauderdale     Florida   \n",
       "...                ...        ...            ...              ...         ...   \n",
       "9795     Sally Hughsby  Corporate  United States          Chicago    Illinois   \n",
       "9796  Cindy Schnelling  Corporate  United States           Toledo        Ohio   \n",
       "9797  Cindy Schnelling  Corporate  United States           Toledo        Ohio   \n",
       "9798  Cindy Schnelling  Corporate  United States           Toledo        Ohio   \n",
       "9799  Cindy Schnelling  Corporate  United States           Toledo        Ohio   \n",
       "\n",
       "      Postal Code   Region       Product ID         Category Sub-Category  \\\n",
       "0         42420.0    South  FUR-BO-10001798        Furniture    Bookcases   \n",
       "1         42420.0    South  FUR-CH-10000454        Furniture       Chairs   \n",
       "2         90036.0     West  OFF-LA-10000240  Office Supplies       Labels   \n",
       "3         33311.0    South  FUR-TA-10000577        Furniture       Tables   \n",
       "4         33311.0    South  OFF-ST-10000760  Office Supplies      Storage   \n",
       "...           ...      ...              ...              ...          ...   \n",
       "9795      60610.0  Central  OFF-BI-10003429  Office Supplies      Binders   \n",
       "9796      43615.0     East  OFF-AR-10001374  Office Supplies          Art   \n",
       "9797      43615.0     East  TEC-PH-10004977       Technology       Phones   \n",
       "9798      43615.0     East  TEC-PH-10000912       Technology       Phones   \n",
       "9799      43615.0     East  TEC-AC-10000487       Technology  Accessories   \n",
       "\n",
       "                                           Product Name     Sales year_month  \\\n",
       "0                     Bush Somerset Collection Bookcase  261.9600    2017-08   \n",
       "1     Hon Deluxe Fabric Upholstered Stacking Chairs,...  731.9400    2017-08   \n",
       "2     Self-Adhesive Address Labels for Typewriters b...   14.6200    2017-12   \n",
       "3         Bretford CR4500 Series Slim Rectangular Table  957.5775    2016-11   \n",
       "4                        Eldon Fold 'N Roll Cart System   22.3680    2016-11   \n",
       "...                                                 ...       ...        ...   \n",
       "9795  Cardinal HOLDit! Binder Insert Strips,Extra St...    3.7980    2017-05   \n",
       "9796           BIC Brite Liner Highlighters, Chisel Tip   10.3680    2016-12   \n",
       "9797                                        GE 30524EE4  235.1880    2016-12   \n",
       "9798           Anker 24W Portable Micro USB Car Charger   26.3760    2016-12   \n",
       "9799                SanDisk Cruzer 4 GB USB Flash Drive   10.3840    2016-12   \n",
       "\n",
       "      year  month  \n",
       "0     2017      8  \n",
       "1     2017      8  \n",
       "2     2017     12  \n",
       "3     2016     11  \n",
       "4     2016     11  \n",
       "...    ...    ...  \n",
       "9795  2017      5  \n",
       "9796  2016     12  \n",
       "9797  2016     12  \n",
       "9798  2016     12  \n",
       "9799  2016     12  \n",
       "\n",
       "[9789 rows x 20 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "MONTHS = ['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun', 'Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec']\n",
    "LINEWIDTH=2\n",
    "ALPHA=.6\n",
    "\n",
    "data = data[['Order ID', 'Order Date', 'Ship Date', 'Ship Mode',\n",
    "       'Customer ID', 'Customer Name', 'Segment', 'Country', 'City', 'State',\n",
    "       'Postal Code', 'Region', 'Product ID', 'Category', 'Sub-Category',\n",
    "       'Product Name', 'Sales']].copy()\n",
    "\n",
    "# Extract the year and the month from the date column into indepedent columns\n",
    "data['Order Date']  = pd.to_datetime(data['Order Date'])\n",
    "data['year_month']  = data['Order Date'].apply(lambda x : x.strftime('%Y-%m'))\n",
    "data['year']  = data['Order Date'].dt.year\n",
    "data['month'] = data['Order Date'].dt.month\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "4f122400",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Investigating the trend of the sale and Grouping the data to get monthly sales.\n",
    "data_trend = data.groupby(['year','month']).sum()['Sales'].reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "6747553b",
   "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>year</th>\n",
       "      <th>month</th>\n",
       "      <th>Sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2015</td>\n",
       "      <td>1</td>\n",
       "      <td>28828.254</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2015</td>\n",
       "      <td>2</td>\n",
       "      <td>12588.484</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2015</td>\n",
       "      <td>3</td>\n",
       "      <td>54027.692</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2015</td>\n",
       "      <td>4</td>\n",
       "      <td>24710.016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2015</td>\n",
       "      <td>5</td>\n",
       "      <td>29520.490</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   year  month      Sales\n",
       "0  2015      1  28828.254\n",
       "1  2015      2  12588.484\n",
       "2  2015      3  54027.692\n",
       "3  2015      4  24710.016\n",
       "4  2015      5  29520.490"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_trend.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "7f67b998",
   "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>year</th>\n",
       "      <th>month</th>\n",
       "      <th>Sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2015</td>\n",
       "      <td>1</td>\n",
       "      <td>28828.2540</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2015</td>\n",
       "      <td>2</td>\n",
       "      <td>12588.4840</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2015</td>\n",
       "      <td>3</td>\n",
       "      <td>54027.6920</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2015</td>\n",
       "      <td>4</td>\n",
       "      <td>24710.0160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2015</td>\n",
       "      <td>5</td>\n",
       "      <td>29520.4900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2015</td>\n",
       "      <td>6</td>\n",
       "      <td>29181.3346</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2015</td>\n",
       "      <td>7</td>\n",
       "      <td>35194.5580</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2015</td>\n",
       "      <td>8</td>\n",
       "      <td>37349.2655</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2015</td>\n",
       "      <td>9</td>\n",
       "      <td>65956.3998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2015</td>\n",
       "      <td>10</td>\n",
       "      <td>34561.9470</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2015</td>\n",
       "      <td>11</td>\n",
       "      <td>64369.4565</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2015</td>\n",
       "      <td>12</td>\n",
       "      <td>63568.3107</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    year  month       Sales\n",
       "0   2015      1  28828.2540\n",
       "1   2015      2  12588.4840\n",
       "2   2015      3  54027.6920\n",
       "3   2015      4  24710.0160\n",
       "4   2015      5  29520.4900\n",
       "5   2015      6  29181.3346\n",
       "6   2015      7  35194.5580\n",
       "7   2015      8  37349.2655\n",
       "8   2015      9  65956.3998\n",
       "9   2015     10  34561.9470\n",
       "10  2015     11  64369.4565\n",
       "11  2015     12  63568.3107"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_trend[data_trend.year==2015]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "7debbf9a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1152x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "##Sales Per Month for Each Year Since 2015 - 2018\n",
    "\n",
    "plt.figure(figsize=(16,6))\n",
    "# Plot the sales of the year 2013\n",
    "plt.plot(MONTHS, data_trend[data_trend.year==2015].Sales, '-o', color='steelblue', linewidth=LINEWIDTH, alpha=ALPHA,label='2015')\n",
    "plt.plot(MONTHS, data_trend[data_trend.year==2016].Sales, '-o', color='seagreen', linewidth=LINEWIDTH, alpha=ALPHA,label='2016')\n",
    "plt.plot(MONTHS, data_trend[data_trend.year==2017].Sales, '-o', color='pink', linewidth=LINEWIDTH, alpha=ALPHA,label='2017')\n",
    "plt.plot(MONTHS, data_trend[data_trend.year==2018].Sales, '-o', color='blue', linewidth=LINEWIDTH, alpha=ALPHA,label='2018')\n",
    "\n",
    "ax = plt.gca()\n",
    "ax.set_title('Sales per month')\n",
    "ax.set_ylabel('Sales in dollars')\n",
    "ax.grid(axis='y', color='gray', alpha=.2)\n",
    "\n",
    "for spine in plt.gca().spines.values():\n",
    "    spine.set_visible(False)\n",
    "plt.legend(loc=2, title='Legend')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "b7c353cb",
   "metadata": {},
   "outputs": [],
   "source": [
    "##Top 10 products getting higher Sales.\n",
    "Product_Sales=pd.DataFrame(data.groupby('Product Name').sum()['Sales'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "4bfb5d2f",
   "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>Sales</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Product Name</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>\"While you Were Out\" Message Book, One Form per Page</th>\n",
       "      <td>25.228</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>#10 Gummed Flap White Envelopes, 100/Box</th>\n",
       "      <td>41.300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>#10 Self-Seal White Envelopes</th>\n",
       "      <td>108.682</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>#10 White Business Envelopes,4 1/8 x 9 1/2</th>\n",
       "      <td>379.214</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>#10- 4 1/8\" x 9 1/2\" Recycled Envelopes</th>\n",
       "      <td>286.672</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                      Sales\n",
       "Product Name                                               \n",
       "\"While you Were Out\" Message Book, One Form per...   25.228\n",
       "#10 Gummed Flap White Envelopes, 100/Box             41.300\n",
       "#10 Self-Seal White Envelopes                       108.682\n",
       "#10 White Business Envelopes,4 1/8 x 9 1/2          379.214\n",
       "#10- 4 1/8\" x 9 1/2\" Recycled Envelopes             286.672"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Product_Sales.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "f97c9369",
   "metadata": {},
   "outputs": [],
   "source": [
    "Sorted_Prod=Product_Sales.sort_values('Sales',ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "8132fb74",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Top 10 products by sales'}, xlabel='Product Name'>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 936x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "Sorted_Prod[:10].plot(kind='bar',figsize=(13,6),color='blue',title = 'Top 10 products by sales')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "de6cc237",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\kruna\\anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Segment', ylabel='count'>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Most Performaing Segments\n",
    "\n",
    "sns.countplot(data['Segment'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "b3d70c7c",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\kruna\\anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Ship Mode', ylabel='count'>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Most prefered Shipmode\n",
    "sns.countplot(data['Ship Mode'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "5820837f",
   "metadata": {},
   "outputs": [],
   "source": [
    "##Most Sold products per State\n",
    "STATE=pd.DataFrame(data.groupby(['State','Product Name']).sum()['Sales'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "832cf488",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>Sales</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>State</th>\n",
       "      <th>Product Name</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">Alabama</th>\n",
       "      <th>1.7 Cubic Foot Compact \"Cube\" Office Refrigerators</th>\n",
       "      <td>208.160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14-7/8 x 11 Blue Bar Computer Printout Paper</th>\n",
       "      <td>96.080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AT&amp;T 17929 Lendline Telephone</th>\n",
       "      <td>90.480</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Acme Stainless Steel Office Snips</th>\n",
       "      <td>7.270</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Acrylic Self-Standing Desk Frames</th>\n",
       "      <td>21.360</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">Wisconsin</th>\n",
       "      <th>Xerox 1917</th>\n",
       "      <td>195.640</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Xerox 1940</th>\n",
       "      <td>219.840</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Xerox 205</th>\n",
       "      <td>51.840</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Xerox 217</th>\n",
       "      <td>45.360</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wyoming</th>\n",
       "      <th>Global Troy Executive Leather Low-Back Tilter</th>\n",
       "      <td>1603.136</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8066 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                 Sales\n",
       "State     Product Name                                                \n",
       "Alabama   1.7 Cubic Foot Compact \"Cube\" Office Refrigerators   208.160\n",
       "          14-7/8 x 11 Blue Bar Computer Printout Paper          96.080\n",
       "          AT&T 17929 Lendline Telephone                         90.480\n",
       "          Acme Stainless Steel Office Snips                      7.270\n",
       "          Acrylic Self-Standing Desk Frames                     21.360\n",
       "...                                                                ...\n",
       "Wisconsin Xerox 1917                                           195.640\n",
       "          Xerox 1940                                           219.840\n",
       "          Xerox 205                                             51.840\n",
       "          Xerox 217                                             45.360\n",
       "Wyoming   Global Troy Executive Leather Low-Back Tilter       1603.136\n",
       "\n",
       "[8066 rows x 1 columns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "STATE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "aa9b2b9d",
   "metadata": {},
   "outputs": [],
   "source": [
    "Sorted_STATE=STATE.sort_values('Sales',ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "id": "6308b566",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='State,Product Name'>"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 936x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "Sorted_STATE[:10].plot(kind='bar',figsize=(13,6),color='seagreen')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "cb6a6c6b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Most Profitable Categories and Sub-categories'}, xlabel='Category,Sub-Category'>"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 936x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Most Profitable Categories and Sub-categories\n",
    "\n",
    "Categ_and_Sub=pd.DataFrame(data.groupby(['Category','Sub-Category']).sum()['Sales'])\n",
    "Sorted_Categs=Categ_and_Sub.sort_values('Sales', ascending=False)\n",
    "Sorted_Categs.plot(kind='bar',figsize=(13,6),color='purple',title ='Most Profitable Categories and Sub-categories')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "id": "7b88ef42",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>Sales</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Category</th>\n",
       "      <th>Sub-Category</th>\n",
       "      <th>State</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">Furniture</th>\n",
       "      <th rowspan=\"5\" valign=\"top\">Bookcases</th>\n",
       "      <th>Arizona</th>\n",
       "      <td>519.2130</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Arkansas</th>\n",
       "      <td>638.8200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>California</th>\n",
       "      <td>26875.1895</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Colorado</th>\n",
       "      <td>1583.5230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Connecticut</th>\n",
       "      <td>1682.7400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">Technology</th>\n",
       "      <th rowspan=\"5\" valign=\"top\">Phones</th>\n",
       "      <th>Texas</th>\n",
       "      <td>28589.5680</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Utah</th>\n",
       "      <td>615.2240</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Virginia</th>\n",
       "      <td>10649.5000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Washington</th>\n",
       "      <td>9101.5680</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wisconsin</th>\n",
       "      <td>3705.1300</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>645 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                          Sales\n",
       "Category   Sub-Category State                  \n",
       "Furniture  Bookcases    Arizona        519.2130\n",
       "                        Arkansas       638.8200\n",
       "                        California   26875.1895\n",
       "                        Colorado      1583.5230\n",
       "                        Connecticut   1682.7400\n",
       "...                                         ...\n",
       "Technology Phones       Texas        28589.5680\n",
       "                        Utah           615.2240\n",
       "                        Virginia     10649.5000\n",
       "                        Washington    9101.5680\n",
       "                        Wisconsin     3705.1300\n",
       "\n",
       "[645 rows x 1 columns]"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Most Profitable Categories and Sub-categories per State\n",
    "Categ_and_Sub2=pd.DataFrame(data.groupby(['Category','Sub-Category','State']).sum()['Sales'])\n",
    "Categ_and_Sub2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "id": "91065916",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Category,Sub-Category,State'>"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 936x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "Sorted_Categs2=Categ_and_Sub2.sort_values('Sales', ascending=False)\n",
    "Sorted_Categs2=Sorted_Categs2[:10]\n",
    "Sorted_Categs2.plot(kind='bar',figsize=(13,6),color='thistle')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}