SuperstoreSalesAnalysis_JupyterNotebook.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "45d7708e",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Importing necessary libraries:\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from datetime import datetime\n",
    "\n",
    "#Supressing warnings:\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "b3b26419",
   "metadata": {},
   "outputs": [],
   "source": [
    "#reading data from csv file:\n",
    "\n",
    "sales_df = pd.read_csv('superstore_final_dataset.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "c037a3aa",
   "metadata": {
    "scrolled": false
   },
   "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 Donnel</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 Donnel</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 Donnel   Consumer  United States  Fort Lauderdale     Florida   \n",
       "4    Sean O Donnel   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": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#checking the head of the data:\n",
    "\n",
    "sales_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "d78e133a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(9800, 18)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Checking the shape of the data:\n",
    "\n",
    "sales_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "26e89653",
   "metadata": {
    "scrolled": false
   },
   "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": [
    "#Checking the summary of the data:\n",
    "\n",
    "sales_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "1b6ae3a4",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Changing the data type of 'Order_Date' and 'Ship_Date' to datetime type:\n",
    "\n",
    "sales_df['Order_Date'] = pd.to_datetime(sales_df['Order_Date'],format = \"%d/%m/%Y\")\n",
    "sales_df['Ship_Date'] = pd.to_datetime(sales_df['Ship_Date'], format = \"%d/%m/%Y\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "915cd817",
   "metadata": {},
   "source": [
    "### Date column has been correctly parsed and formatted."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "4d6fd6c2",
   "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   datetime64[ns]\n",
      " 3   Ship_Date      9800 non-null   datetime64[ns]\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: datetime64[ns](2), float64(2), int64(1), object(13)\n",
      "memory usage: 1.3+ MB\n"
     ]
    }
   ],
   "source": [
    "#Checking if the datatype is properly changed:\n",
    "\n",
    "sales_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "78082c67",
   "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": [
    "#Checking the null values:\n",
    "\n",
    "sales_df.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a78a8976",
   "metadata": {},
   "source": [
    "### Only the Postal_Code has null values. Let's drop these rows as they are less in numbers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "44e1566b",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Dropping rows with null values:\n",
    "\n",
    "sales_df = sales_df[~(sales_df['Postal_Code'].isnull())]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "cf47e70a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(9789, 18)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Checking the data after dropping rows:\n",
    "\n",
    "sales_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "77ed7312",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Row_ID                    int64\n",
       "Order_ID                 object\n",
       "Order_Date       datetime64[ns]\n",
       "Ship_Date        datetime64[ns]\n",
       "Ship_Mode                object\n",
       "Customer_ID              object\n",
       "Customer_Name            object\n",
       "Segment                  object\n",
       "Country                  object\n",
       "City                     object\n",
       "State                    object\n",
       "Postal_Code             float64\n",
       "Region                   object\n",
       "Product_ID               object\n",
       "Category                 object\n",
       "Sub_Category             object\n",
       "Product_Name             object\n",
       "Sales                   float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Checking the dtypes:\n",
    "\n",
    "sales_df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "85670a85",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Converting the dtype of Postal_Code from float64 to int64:\n",
    "\n",
    "sales_df['Postal_Code'] = sales_df['Postal_Code'].astype('int64')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "1c91e9ee",
   "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>Postal_Code</th>\n",
       "      <th>Sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>9789.000000</td>\n",
       "      <td>9789.000000</td>\n",
       "      <td>9789.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>4896.705588</td>\n",
       "      <td>55273.322403</td>\n",
       "      <td>230.116193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2827.486899</td>\n",
       "      <td>32041.223413</td>\n",
       "      <td>625.302079</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1040.000000</td>\n",
       "      <td>0.444000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2449.000000</td>\n",
       "      <td>23223.000000</td>\n",
       "      <td>17.248000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>4896.000000</td>\n",
       "      <td>58103.000000</td>\n",
       "      <td>54.384000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>7344.000000</td>\n",
       "      <td>90008.000000</td>\n",
       "      <td>210.392000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>9800.000000</td>\n",
       "      <td>99301.000000</td>\n",
       "      <td>22638.480000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Row_ID   Postal_Code         Sales\n",
       "count  9789.000000   9789.000000   9789.000000\n",
       "mean   4896.705588  55273.322403    230.116193\n",
       "std    2827.486899  32041.223413    625.302079\n",
       "min       1.000000   1040.000000      0.444000\n",
       "25%    2449.000000  23223.000000     17.248000\n",
       "50%    4896.000000  58103.000000     54.384000\n",
       "75%    7344.000000  90008.000000    210.392000\n",
       "max    9800.000000  99301.000000  22638.480000"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Checking the statistical summary of the data:\n",
    "\n",
    "sales_df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "443b41a5",
   "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>3405</th>\n",
       "      <td>3406</td>\n",
       "      <td>US-2015-150119</td>\n",
       "      <td>2015-04-23</td>\n",
       "      <td>2015-04-27</td>\n",
       "      <td>Standard Class</td>\n",
       "      <td>LB-16795</td>\n",
       "      <td>Laurel Beltran</td>\n",
       "      <td>Home Office</td>\n",
       "      <td>United States</td>\n",
       "      <td>Columbus</td>\n",
       "      <td>Ohio</td>\n",
       "      <td>43229</td>\n",
       "      <td>East</td>\n",
       "      <td>FUR-CH-10002965</td>\n",
       "      <td>Furniture</td>\n",
       "      <td>Chairs</td>\n",
       "      <td>Global Leather Highback Executive Chair with P...</td>\n",
       "      <td>281.372</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3406</th>\n",
       "      <td>3407</td>\n",
       "      <td>US-2015-150119</td>\n",
       "      <td>2015-04-23</td>\n",
       "      <td>2015-04-27</td>\n",
       "      <td>Standard Class</td>\n",
       "      <td>LB-16795</td>\n",
       "      <td>Laurel Beltran</td>\n",
       "      <td>Home Office</td>\n",
       "      <td>United States</td>\n",
       "      <td>Columbus</td>\n",
       "      <td>Ohio</td>\n",
       "      <td>43229</td>\n",
       "      <td>East</td>\n",
       "      <td>FUR-CH-10002965</td>\n",
       "      <td>Furniture</td>\n",
       "      <td>Chairs</td>\n",
       "      <td>Global Leather Highback Executive Chair with P...</td>\n",
       "      <td>281.372</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Row_ID        Order_ID Order_Date  Ship_Date       Ship_Mode  \\\n",
       "3405    3406  US-2015-150119 2015-04-23 2015-04-27  Standard Class   \n",
       "3406    3407  US-2015-150119 2015-04-23 2015-04-27  Standard Class   \n",
       "\n",
       "     Customer_ID   Customer_Name      Segment        Country      City State  \\\n",
       "3405    LB-16795  Laurel Beltran  Home Office  United States  Columbus  Ohio   \n",
       "3406    LB-16795  Laurel Beltran  Home Office  United States  Columbus  Ohio   \n",
       "\n",
       "      Postal_Code Region       Product_ID   Category Sub_Category  \\\n",
       "3405        43229   East  FUR-CH-10002965  Furniture       Chairs   \n",
       "3406        43229   East  FUR-CH-10002965  Furniture       Chairs   \n",
       "\n",
       "                                           Product_Name    Sales  \n",
       "3405  Global Leather Highback Executive Chair with P...  281.372  \n",
       "3406  Global Leather Highback Executive Chair with P...  281.372  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#checking the duplicate records:\n",
    "\n",
    "duplicate = sales_df[sales_df.iloc[:,1:].duplicated(keep=False)]\n",
    "duplicate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "8c5f338d",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Dropping the duplicate records:\n",
    "\n",
    "sales_df = sales_df.iloc[:,1:].drop_duplicates()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "2728c2c5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(9788, 17)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sales_df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cb0c7b78",
   "metadata": {},
   "source": [
    "### There was one duplicated record."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "e549a2bd",
   "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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CA-2017-152156</td>\n",
       "      <td>2017-11-08</td>\n",
       "      <td>2017-11-11</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</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>CA-2017-152156</td>\n",
       "      <td>2017-11-08</td>\n",
       "      <td>2017-11-11</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</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>CA-2017-138688</td>\n",
       "      <td>2017-06-12</td>\n",
       "      <td>2017-06-16</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</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>US-2016-108966</td>\n",
       "      <td>2016-10-11</td>\n",
       "      <td>2016-10-18</td>\n",
       "      <td>Standard Class</td>\n",
       "      <td>SO-20335</td>\n",
       "      <td>Sean O Donnel</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>United States</td>\n",
       "      <td>Fort Lauderdale</td>\n",
       "      <td>Florida</td>\n",
       "      <td>33311</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>US-2016-108966</td>\n",
       "      <td>2016-10-11</td>\n",
       "      <td>2016-10-18</td>\n",
       "      <td>Standard Class</td>\n",
       "      <td>SO-20335</td>\n",
       "      <td>Sean O Donnel</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>United States</td>\n",
       "      <td>Fort Lauderdale</td>\n",
       "      <td>Florida</td>\n",
       "      <td>33311</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": [
       "         Order_ID Order_Date  Ship_Date       Ship_Mode Customer_ID  \\\n",
       "0  CA-2017-152156 2017-11-08 2017-11-11    Second Class    CG-12520   \n",
       "1  CA-2017-152156 2017-11-08 2017-11-11    Second Class    CG-12520   \n",
       "2  CA-2017-138688 2017-06-12 2017-06-16    Second Class    DV-13045   \n",
       "3  US-2016-108966 2016-10-11 2016-10-18  Standard Class    SO-20335   \n",
       "4  US-2016-108966 2016-10-11 2016-10-18  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 Donnel   Consumer  United States  Fort Lauderdale     Florida   \n",
       "4    Sean O Donnel   Consumer  United States  Fort Lauderdale     Florida   \n",
       "\n",
       "   Postal_Code Region       Product_ID         Category Sub_Category  \\\n",
       "0        42420  South  FUR-BO-10001798        Furniture    Bookcases   \n",
       "1        42420  South  FUR-CH-10000454        Furniture       Chairs   \n",
       "2        90036   West  OFF-LA-10000240  Office Supplies       Labels   \n",
       "3        33311  South  FUR-TA-10000577        Furniture       Tables   \n",
       "4        33311  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": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Checking the head of the data:\n",
    "\n",
    "sales_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "e1f88bbd",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Order_ID\n",
      "\n",
      "CA-2018-100111    14\n",
      "CA-2018-157987    12\n",
      "CA-2017-165330    11\n",
      "US-2017-108504    11\n",
      "US-2016-126977    10\n",
      "                  ..\n",
      "US-2015-161613     1\n",
      "CA-2018-101728     1\n",
      "CA-2017-112942     1\n",
      "CA-2018-126438     1\n",
      "CA-2017-147123     1\n",
      "Name: Order_ID, Length: 4916, dtype: int64\n",
      "\n",
      "Order_Date\n",
      "\n",
      "2017-09-05    38\n",
      "2017-11-10    35\n",
      "2018-12-02    34\n",
      "2018-12-01    34\n",
      "2018-12-09    33\n",
      "              ..\n",
      "2017-02-25     1\n",
      "2017-10-25     1\n",
      "2015-09-11     1\n",
      "2015-09-16     1\n",
      "2016-05-09     1\n",
      "Name: Order_Date, Length: 1229, dtype: int64\n",
      "\n",
      "Ship_Date\n",
      "\n",
      "2018-09-26    34\n",
      "2018-12-06    32\n",
      "2016-12-16    31\n",
      "2018-09-06    30\n",
      "2018-12-12    30\n",
      "              ..\n",
      "2016-07-30     1\n",
      "2017-01-26     1\n",
      "2015-01-17     1\n",
      "2018-08-17     1\n",
      "2016-05-13     1\n",
      "Name: Ship_Date, Length: 1326, dtype: int64\n",
      "\n",
      "Ship_Mode\n",
      "\n",
      "Standard Class    5848\n",
      "Second Class      1901\n",
      "First Class       1501\n",
      "Same Day           538\n",
      "Name: Ship_Mode, dtype: int64\n",
      "\n",
      "Customer_ID\n",
      "\n",
      "WB-21850    35\n",
      "PP-18955    34\n",
      "MA-17560    34\n",
      "JL-15835    33\n",
      "JD-15895    32\n",
      "            ..\n",
      "AO-10810     1\n",
      "JR-15700     1\n",
      "CJ-11875     1\n",
      "SC-20845     1\n",
      "RE-19405     1\n",
      "Name: Customer_ID, Length: 793, dtype: int64\n",
      "\n",
      "Customer_Name\n",
      "\n",
      "William Brown       35\n",
      "Paul Prost          34\n",
      "Matt Abelman        34\n",
      "John Lee            33\n",
      "Jonathan Doherty    32\n",
      "                    ..\n",
      "Anthony O Donnel     1\n",
      "Jocasta Rupert       1\n",
      "Carl Jackson         1\n",
      "Sung Chung           1\n",
      "Ricardo Emerson      1\n",
      "Name: Customer_Name, Length: 793, dtype: int64\n",
      "\n",
      "Segment\n",
      "\n",
      "Consumer       5096\n",
      "Corporate      2948\n",
      "Home Office    1744\n",
      "Name: Segment, dtype: int64\n",
      "\n",
      "Country\n",
      "\n",
      "United States    9788\n",
      "Name: Country, dtype: int64\n",
      "\n",
      "City\n",
      "\n",
      "New York City    891\n",
      "Los Angeles      728\n",
      "Philadelphia     532\n",
      "San Francisco    500\n",
      "Seattle          426\n",
      "                ... \n",
      "San Mateo          1\n",
      "Cheyenne           1\n",
      "Conway             1\n",
      "Melbourne          1\n",
      "Springdale         1\n",
      "Name: City, Length: 529, dtype: int64\n",
      "\n",
      "State\n",
      "\n",
      "California              1946\n",
      "New York                1097\n",
      "Texas                    973\n",
      "Pennsylvania             582\n",
      "Washington               504\n",
      "Illinois                 483\n",
      "Ohio                     453\n",
      "Florida                  373\n",
      "Michigan                 253\n",
      "North Carolina           247\n",
      "Virginia                 224\n",
      "Arizona                  223\n",
      "Tennessee                183\n",
      "Colorado                 179\n",
      "Georgia                  177\n",
      "Kentucky                 137\n",
      "Indiana                  135\n",
      "Massachusetts            135\n",
      "Oregon                   122\n",
      "New Jersey               122\n",
      "Maryland                 105\n",
      "Wisconsin                105\n",
      "Delaware                  93\n",
      "Minnesota                 89\n",
      "Connecticut               82\n",
      "Missouri                  66\n",
      "Oklahoma                  66\n",
      "Alabama                   61\n",
      "Arkansas                  60\n",
      "Rhode Island              55\n",
      "Mississippi               53\n",
      "Utah                      53\n",
      "South Carolina            42\n",
      "Louisiana                 41\n",
      "Nevada                    39\n",
      "Nebraska                  38\n",
      "New Mexico                37\n",
      "New Hampshire             27\n",
      "Iowa                      26\n",
      "Kansas                    24\n",
      "Idaho                     21\n",
      "Montana                   15\n",
      "South Dakota              12\n",
      "District of Columbia      10\n",
      "Maine                      8\n",
      "North Dakota               7\n",
      "West Virginia              4\n",
      "Wyoming                    1\n",
      "Name: State, dtype: int64\n",
      "\n",
      "Postal_Code\n",
      "\n",
      "10035    253\n",
      "10024    225\n",
      "10009    220\n",
      "94122    195\n",
      "10011    193\n",
      "        ... \n",
      "32935      1\n",
      "76248      1\n",
      "32127      1\n",
      "32503      1\n",
      "72762      1\n",
      "Name: Postal_Code, Length: 626, dtype: int64\n",
      "\n",
      "Region\n",
      "\n",
      "West       3140\n",
      "East       2773\n",
      "Central    2277\n",
      "South      1598\n",
      "Name: Region, dtype: int64\n",
      "\n",
      "Product_ID\n",
      "\n",
      "OFF-PA-10001970    18\n",
      "TEC-AC-10003832    18\n",
      "FUR-FU-10004270    16\n",
      "TEC-AC-10002049    15\n",
      "TEC-AC-10003628    15\n",
      "                   ..\n",
      "OFF-ST-10001034     1\n",
      "TEC-PH-10001468     1\n",
      "OFF-AR-10004974     1\n",
      "TEC-AC-10003133     1\n",
      "TEC-PH-10002645     1\n",
      "Name: Product_ID, Length: 1860, dtype: int64\n",
      "\n",
      "Category\n",
      "\n",
      "Office Supplies    5903\n",
      "Furniture          2075\n",
      "Technology         1810\n",
      "Name: Category, dtype: int64\n",
      "\n",
      "Sub_Category\n",
      "\n",
      "Binders        1492\n",
      "Paper          1336\n",
      "Furnishings     931\n",
      "Phones          875\n",
      "Storage         831\n",
      "Art             784\n",
      "Accessories     754\n",
      "Chairs          605\n",
      "Appliances      458\n",
      "Labels          357\n",
      "Tables          314\n",
      "Envelopes       247\n",
      "Bookcases       225\n",
      "Fasteners       214\n",
      "Supplies        184\n",
      "Machines        115\n",
      "Copiers          66\n",
      "Name: Sub_Category, dtype: int64\n",
      "\n",
      "Product_Name\n",
      "\n",
      "Staple envelope                                                      47\n",
      "Staples                                                              46\n",
      "Easy-staple paper                                                    44\n",
      "Avery Non-Stick Binders                                              20\n",
      "Staples in misc. colors                                              18\n",
      "                                                                     ..\n",
      "Acco Glide Clips                                                      1\n",
      "Wasp CCD Handheld Bar Code Reader                                     1\n",
      "Computer Printout Paper with Letter-Trim Fine Perforations            1\n",
      "Universal Ultra Bright White Copier/Laser Paper, 8 1/2 x 11, Ream     1\n",
      "LG G2                                                                 1\n",
      "Name: Product_Name, Length: 1848, dtype: int64\n",
      "\n",
      "Sales\n",
      "\n",
      "12.960      55\n",
      "19.440      39\n",
      "15.552      39\n",
      "10.368      35\n",
      "25.920      34\n",
      "            ..\n",
      "2504.740     1\n",
      "27.264       1\n",
      "56.650       1\n",
      "58.720       1\n",
      "10.384       1\n",
      "Name: Sales, Length: 5750, dtype: int64\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#list of all column names:\n",
    "\n",
    "cols = list(sales_df.columns)\n",
    "\n",
    "#Checking values count of each column:\n",
    "\n",
    "for col in cols:\n",
    "    print(col)\n",
    "    print('')\n",
    "    print(sales_df[col].value_counts())\n",
    "    print('')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5bf1556e",
   "metadata": {},
   "source": [
    "### It seems there are no irrelevant values present in the dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "8c99591f",
   "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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [Order_ID, Order_Date, Ship_Date, Ship_Mode, Customer_ID, Customer_Name, Segment, Country, City, State, Postal_Code, Region, Product_ID, Category, Sub_Category, Product_Name, Sales]\n",
       "Index: []"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Checking For each entry in dataset if ship date >= order date:\n",
    "\n",
    "sales_df[sales_df['Ship_Date'] < sales_df['Order_Date']]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "31078fcb",
   "metadata": {},
   "source": [
    "### There are no records which have Ship date before the Order date."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "48ef52f5",
   "metadata": {},
   "outputs": [],
   "source": [
    "sales_df.reset_index(drop = True,inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "e761989e",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Exporting the cleaned dataset to csv:\n",
    "\n",
    "sales_df.to_csv('superstore_sales_cleaned.csv',index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5e650067",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "09435fab",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.9.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}