Adidas_sales_analysis (3) (2).ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "cell_id": "934640eefc0d4640832ba76aab193612",
    "deepnote_cell_type": "markdown"
   },
   "source": [
    "## 1. Importing necessary libraries and dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "cell_id": "6596298375e0423dac21f878ceb3a51e",
    "deepnote_cell_type": "code",
    "deepnote_table_loading": false,
    "deepnote_table_state": {
     "filters": [],
     "pageIndex": 0,
     "pageSize": 5,
     "sortBy": []
    },
    "deepnote_to_be_reexecuted": false,
    "execution_millis": 1614,
    "execution_start": 1703563163300,
    "source_hash": "381e7bac"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.deepnote.dataframe.v3+json": {
       "column_count": 13,
       "columns": [
        {
         "dtype": "object",
         "name": "Retailer",
         "stats": {
          "categories": [
           {
            "count": 10,
            "name": "Foot Locker"
           }
          ],
          "nan_count": 0,
          "unique_count": 1
         }
        },
        {
         "dtype": "int64",
         "name": "Retailer_ID",
         "stats": {
          "histogram": [
           {
            "bin_end": 1185731.6,
            "bin_start": 1185731.5,
            "count": 0
           },
           {
            "bin_end": 1185731.7,
            "bin_start": 1185731.6,
            "count": 0
           },
           {
            "bin_end": 1185731.8,
            "bin_start": 1185731.7,
            "count": 0
           },
           {
            "bin_end": 1185731.9,
            "bin_start": 1185731.8,
            "count": 0
           },
           {
            "bin_end": 1185732,
            "bin_start": 1185731.9,
            "count": 0
           },
           {
            "bin_end": 1185732.1,
            "bin_start": 1185732,
            "count": 10
           },
           {
            "bin_end": 1185732.2,
            "bin_start": 1185732.1,
            "count": 0
           },
           {
            "bin_end": 1185732.3,
            "bin_start": 1185732.2,
            "count": 0
           },
           {
            "bin_end": 1185732.4,
            "bin_start": 1185732.3,
            "count": 0
           },
           {
            "bin_end": 1185732.5,
            "bin_start": 1185732.4,
            "count": 0
           }
          ],
          "max": "1185732",
          "min": "1185732",
          "nan_count": 0,
          "unique_count": 1
         }
        },
        {
         "dtype": "object",
         "name": "Invoice_Date",
         "stats": {
          "categories": [
           {
            "count": 1,
            "name": "01-01-2020"
           },
           {
            "count": 1,
            "name": "02-01-2020"
           },
           {
            "count": 8,
            "name": "8 others"
           }
          ],
          "nan_count": 0,
          "unique_count": 10
         }
        },
        {
         "dtype": "object",
         "name": "Region",
         "stats": {
          "categories": [
           {
            "count": 10,
            "name": "Northeast"
           }
          ],
          "nan_count": 0,
          "unique_count": 1
         }
        },
        {
         "dtype": "object",
         "name": "State",
         "stats": {
          "categories": [
           {
            "count": 10,
            "name": "New York"
           }
          ],
          "nan_count": 0,
          "unique_count": 1
         }
        },
        {
         "dtype": "object",
         "name": "City",
         "stats": {
          "categories": [
           {
            "count": 10,
            "name": "New York"
           }
          ],
          "nan_count": 0,
          "unique_count": 1
         }
        },
        {
         "dtype": "object",
         "name": "Product",
         "stats": {
          "categories": [
           {
            "count": 2,
            "name": "Men's Street Footwear"
           },
           {
            "count": 2,
            "name": "Men's Athletic Footwear"
           },
           {
            "count": 6,
            "name": "4 others"
           }
          ],
          "nan_count": 0,
          "unique_count": 6
         }
        },
        {
         "dtype": "object",
         "name": "Price_per_Unit",
         "stats": {
          "categories": [
           {
            "count": 5,
            "name": "$50.00 "
           },
           {
            "count": 2,
            "name": "$40.00 "
           },
           {
            "count": 3,
            "name": "2 others"
           }
          ],
          "nan_count": 0,
          "unique_count": 4
         }
        },
        {
         "dtype": "object",
         "name": "Units_Sold",
         "stats": {
          "categories": [
           {
            "count": 3,
            "name": "1,000"
           },
           {
            "count": 2,
            "name": "900"
           },
           {
            "count": 5,
            "name": "5 others"
           }
          ],
          "nan_count": 0,
          "unique_count": 7
         }
        },
        {
         "dtype": "object",
         "name": "Total_Sales",
         "stats": {
          "categories": [
           {
            "count": 2,
            "name": "$5,00,000 "
           },
           {
            "count": 1,
            "name": "$6,00,000 "
           },
           {
            "count": 7,
            "name": "7 others"
           }
          ],
          "nan_count": 0,
          "unique_count": 9
         }
        },
        {
         "dtype": "object",
         "name": "Operating_Profit",
         "stats": {
          "categories": [
           {
            "count": 1,
            "name": "$3,00,000 "
           },
           {
            "count": 1,
            "name": "$1,50,000 "
           },
           {
            "count": 8,
            "name": "8 others"
           }
          ],
          "nan_count": 0,
          "unique_count": 10
         }
        },
        {
         "dtype": "object",
         "name": "Operating_Margin",
         "stats": {
          "categories": [
           {
            "count": 4,
            "name": "35%"
           },
           {
            "count": 3,
            "name": "30%"
           },
           {
            "count": 3,
            "name": "2 others"
           }
          ],
          "nan_count": 0,
          "unique_count": 4
         }
        },
        {
         "dtype": "object",
         "name": "Sales_Method",
         "stats": {
          "categories": [
           {
            "count": 7,
            "name": "In-store"
           },
           {
            "count": 3,
            "name": "Outlet"
           }
          ],
          "nan_count": 0,
          "unique_count": 2
         }
        },
        {
         "dtype": "int64",
         "name": "_deepnote_index_column"
        }
       ],
       "row_count": 10,
       "rows": [
        {
         "City": "New York",
         "Invoice_Date": "01-01-2020",
         "Operating_Margin": "50%",
         "Operating_Profit": "$3,00,000 ",
         "Price_per_Unit": "$50.00 ",
         "Product": "Men's Street Footwear",
         "Region": "Northeast",
         "Retailer": "Foot Locker",
         "Retailer_ID": 1185732,
         "Sales_Method": "In-store",
         "State": "New York",
         "Total_Sales": "$6,00,000 ",
         "Units_Sold": "1,200",
         "_deepnote_index_column": 0
        },
        {
         "City": "New York",
         "Invoice_Date": "02-01-2020",
         "Operating_Margin": "30%",
         "Operating_Profit": "$1,50,000 ",
         "Price_per_Unit": "$50.00 ",
         "Product": "Men's Athletic Footwear",
         "Region": "Northeast",
         "Retailer": "Foot Locker",
         "Retailer_ID": 1185732,
         "Sales_Method": "In-store",
         "State": "New York",
         "Total_Sales": "$5,00,000 ",
         "Units_Sold": "1,000",
         "_deepnote_index_column": 1
        },
        {
         "City": "New York",
         "Invoice_Date": "03-01-2020",
         "Operating_Margin": "35%",
         "Operating_Profit": "$1,40,000 ",
         "Price_per_Unit": "$40.00 ",
         "Product": "Women's Street Footwear",
         "Region": "Northeast",
         "Retailer": "Foot Locker",
         "Retailer_ID": 1185732,
         "Sales_Method": "In-store",
         "State": "New York",
         "Total_Sales": "$4,00,000 ",
         "Units_Sold": "1,000",
         "_deepnote_index_column": 2
        },
        {
         "City": "New York",
         "Invoice_Date": "04-01-2020",
         "Operating_Margin": "35%",
         "Operating_Profit": "$1,33,875 ",
         "Price_per_Unit": "$45.00 ",
         "Product": "Women's Athletic Footwear",
         "Region": "Northeast",
         "Retailer": "Foot Locker",
         "Retailer_ID": 1185732,
         "Sales_Method": "In-store",
         "State": "New York",
         "Total_Sales": "$3,82,500 ",
         "Units_Sold": "850",
         "_deepnote_index_column": 3
        },
        {
         "City": "New York",
         "Invoice_Date": "05-01-2020",
         "Operating_Margin": "30%",
         "Operating_Profit": "$1,62,000 ",
         "Price_per_Unit": "$60.00 ",
         "Product": "Men's Apparel",
         "Region": "Northeast",
         "Retailer": "Foot Locker",
         "Retailer_ID": 1185732,
         "Sales_Method": "In-store",
         "State": "New York",
         "Total_Sales": "$5,40,000 ",
         "Units_Sold": "900",
         "_deepnote_index_column": 4
        }
       ]
      },
      "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>Retailer</th>\n",
       "      <th>Retailer_ID</th>\n",
       "      <th>Invoice_Date</th>\n",
       "      <th>Region</th>\n",
       "      <th>State</th>\n",
       "      <th>City</th>\n",
       "      <th>Product</th>\n",
       "      <th>Price_per_Unit</th>\n",
       "      <th>Units_Sold</th>\n",
       "      <th>Total_Sales</th>\n",
       "      <th>Operating_Profit</th>\n",
       "      <th>Operating_Margin</th>\n",
       "      <th>Sales_Method</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Foot Locker</td>\n",
       "      <td>1185732</td>\n",
       "      <td>01-01-2020</td>\n",
       "      <td>Northeast</td>\n",
       "      <td>New York</td>\n",
       "      <td>New York</td>\n",
       "      <td>Men's Street Footwear</td>\n",
       "      <td>$50.00</td>\n",
       "      <td>1,200</td>\n",
       "      <td>$6,00,000</td>\n",
       "      <td>$3,00,000</td>\n",
       "      <td>50%</td>\n",
       "      <td>In-store</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Foot Locker</td>\n",
       "      <td>1185732</td>\n",
       "      <td>02-01-2020</td>\n",
       "      <td>Northeast</td>\n",
       "      <td>New York</td>\n",
       "      <td>New York</td>\n",
       "      <td>Men's Athletic Footwear</td>\n",
       "      <td>$50.00</td>\n",
       "      <td>1,000</td>\n",
       "      <td>$5,00,000</td>\n",
       "      <td>$1,50,000</td>\n",
       "      <td>30%</td>\n",
       "      <td>In-store</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Foot Locker</td>\n",
       "      <td>1185732</td>\n",
       "      <td>03-01-2020</td>\n",
       "      <td>Northeast</td>\n",
       "      <td>New York</td>\n",
       "      <td>New York</td>\n",
       "      <td>Women's Street Footwear</td>\n",
       "      <td>$40.00</td>\n",
       "      <td>1,000</td>\n",
       "      <td>$4,00,000</td>\n",
       "      <td>$1,40,000</td>\n",
       "      <td>35%</td>\n",
       "      <td>In-store</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Foot Locker</td>\n",
       "      <td>1185732</td>\n",
       "      <td>04-01-2020</td>\n",
       "      <td>Northeast</td>\n",
       "      <td>New York</td>\n",
       "      <td>New York</td>\n",
       "      <td>Women's Athletic Footwear</td>\n",
       "      <td>$45.00</td>\n",
       "      <td>850</td>\n",
       "      <td>$3,82,500</td>\n",
       "      <td>$1,33,875</td>\n",
       "      <td>35%</td>\n",
       "      <td>In-store</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Foot Locker</td>\n",
       "      <td>1185732</td>\n",
       "      <td>05-01-2020</td>\n",
       "      <td>Northeast</td>\n",
       "      <td>New York</td>\n",
       "      <td>New York</td>\n",
       "      <td>Men's Apparel</td>\n",
       "      <td>$60.00</td>\n",
       "      <td>900</td>\n",
       "      <td>$5,40,000</td>\n",
       "      <td>$1,62,000</td>\n",
       "      <td>30%</td>\n",
       "      <td>In-store</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Foot Locker</td>\n",
       "      <td>1185732</td>\n",
       "      <td>06-01-2020</td>\n",
       "      <td>Northeast</td>\n",
       "      <td>New York</td>\n",
       "      <td>New York</td>\n",
       "      <td>Women's Apparel</td>\n",
       "      <td>$50.00</td>\n",
       "      <td>1,000</td>\n",
       "      <td>$5,00,000</td>\n",
       "      <td>$1,25,000</td>\n",
       "      <td>25%</td>\n",
       "      <td>In-store</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Foot Locker</td>\n",
       "      <td>1185732</td>\n",
       "      <td>07-01-2020</td>\n",
       "      <td>Northeast</td>\n",
       "      <td>New York</td>\n",
       "      <td>New York</td>\n",
       "      <td>Men's Street Footwear</td>\n",
       "      <td>$50.00</td>\n",
       "      <td>1,250</td>\n",
       "      <td>$6,25,000</td>\n",
       "      <td>$3,12,500</td>\n",
       "      <td>50%</td>\n",
       "      <td>In-store</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Foot Locker</td>\n",
       "      <td>1185732</td>\n",
       "      <td>08-01-2020</td>\n",
       "      <td>Northeast</td>\n",
       "      <td>New York</td>\n",
       "      <td>New York</td>\n",
       "      <td>Men's Athletic Footwear</td>\n",
       "      <td>$50.00</td>\n",
       "      <td>900</td>\n",
       "      <td>$4,50,000</td>\n",
       "      <td>$1,35,000</td>\n",
       "      <td>30%</td>\n",
       "      <td>Outlet</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Foot Locker</td>\n",
       "      <td>1185732</td>\n",
       "      <td>21-01-2020</td>\n",
       "      <td>Northeast</td>\n",
       "      <td>New York</td>\n",
       "      <td>New York</td>\n",
       "      <td>Women's Street Footwear</td>\n",
       "      <td>$40.00</td>\n",
       "      <td>950</td>\n",
       "      <td>$3,80,000</td>\n",
       "      <td>$1,33,000</td>\n",
       "      <td>35%</td>\n",
       "      <td>Outlet</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Foot Locker</td>\n",
       "      <td>1185732</td>\n",
       "      <td>22-01-2020</td>\n",
       "      <td>Northeast</td>\n",
       "      <td>New York</td>\n",
       "      <td>New York</td>\n",
       "      <td>Women's Athletic Footwear</td>\n",
       "      <td>$45.00</td>\n",
       "      <td>825</td>\n",
       "      <td>$3,71,250</td>\n",
       "      <td>$1,29,938</td>\n",
       "      <td>35%</td>\n",
       "      <td>Outlet</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Retailer  Retailer_ID Invoice_Date     Region     State      City  \\\n",
       "0  Foot Locker      1185732   01-01-2020  Northeast  New York  New York   \n",
       "1  Foot Locker      1185732   02-01-2020  Northeast  New York  New York   \n",
       "2  Foot Locker      1185732   03-01-2020  Northeast  New York  New York   \n",
       "3  Foot Locker      1185732   04-01-2020  Northeast  New York  New York   \n",
       "4  Foot Locker      1185732   05-01-2020  Northeast  New York  New York   \n",
       "5  Foot Locker      1185732   06-01-2020  Northeast  New York  New York   \n",
       "6  Foot Locker      1185732   07-01-2020  Northeast  New York  New York   \n",
       "7  Foot Locker      1185732   08-01-2020  Northeast  New York  New York   \n",
       "8  Foot Locker      1185732   21-01-2020  Northeast  New York  New York   \n",
       "9  Foot Locker      1185732   22-01-2020  Northeast  New York  New York   \n",
       "\n",
       "                     Product Price_per_Unit Units_Sold Total_Sales  \\\n",
       "0      Men's Street Footwear        $50.00       1,200  $6,00,000    \n",
       "1    Men's Athletic Footwear        $50.00       1,000  $5,00,000    \n",
       "2    Women's Street Footwear        $40.00       1,000  $4,00,000    \n",
       "3  Women's Athletic Footwear        $45.00         850  $3,82,500    \n",
       "4              Men's Apparel        $60.00         900  $5,40,000    \n",
       "5            Women's Apparel        $50.00       1,000  $5,00,000    \n",
       "6      Men's Street Footwear        $50.00       1,250  $6,25,000    \n",
       "7    Men's Athletic Footwear        $50.00         900  $4,50,000    \n",
       "8    Women's Street Footwear        $40.00         950  $3,80,000    \n",
       "9  Women's Athletic Footwear        $45.00         825  $3,71,250    \n",
       "\n",
       "  Operating_Profit Operating_Margin Sales_Method  \n",
       "0       $3,00,000               50%     In-store  \n",
       "1       $1,50,000               30%     In-store  \n",
       "2       $1,40,000               35%     In-store  \n",
       "3       $1,33,875               35%     In-store  \n",
       "4       $1,62,000               30%     In-store  \n",
       "5       $1,25,000               25%     In-store  \n",
       "6       $3,12,500               50%     In-store  \n",
       "7       $1,35,000               30%       Outlet  \n",
       "8       $1,33,000               35%       Outlet  \n",
       "9       $1,29,938               35%       Outlet  "
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "df = pd.read_csv('Adidas US Sales Datasets.csv')\n",
    "df.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "cell_id": "d8fd4c3889c84bd6a6dcbee8c57214c8",
    "deepnote_cell_type": "markdown"
   },
   "source": [
    "## 2. Data inspection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "cell_id": "d4f350dbe6e34934a731aec8094a4cee",
    "deepnote_cell_type": "code",
    "deepnote_to_be_reexecuted": false,
    "execution_millis": 31,
    "execution_start": 1703563164925,
    "source_hash": "d1cb0f11"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(9648, 13)"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape\n",
    "#There are 9648 rows and 13 columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "cell_id": "a35707c3161141f9896148b38037021f",
    "deepnote_cell_type": "code",
    "deepnote_to_be_reexecuted": false,
    "execution_millis": 36,
    "execution_start": 1703563164926,
    "source_hash": "8a4111dc"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Retailer', 'Retailer_ID', 'Invoice_Date', 'Region', 'State', 'City',\n",
       "       'Product', 'Price_per_Unit', 'Units_Sold', 'Total_Sales',\n",
       "       'Operating_Profit', 'Operating_Margin', 'Sales_Method'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns\n",
    "#This gives us the list of columns in the  dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "cell_id": "0c5292fe7634419c8c0ff0256b1a5ccf",
    "deepnote_cell_type": "code",
    "deepnote_to_be_reexecuted": false,
    "execution_millis": 41,
    "execution_start": 1703563164940,
    "source_hash": "de1e323c"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 9648 entries, 0 to 9647\n",
      "Data columns (total 13 columns):\n",
      " #   Column            Non-Null Count  Dtype \n",
      "---  ------            --------------  ----- \n",
      " 0   Retailer          9648 non-null   object\n",
      " 1   Retailer_ID       9648 non-null   int64 \n",
      " 2   Invoice_Date      9648 non-null   object\n",
      " 3   Region            9648 non-null   object\n",
      " 4   State             9648 non-null   object\n",
      " 5   City              9648 non-null   object\n",
      " 6   Product           9648 non-null   object\n",
      " 7   Price_per_Unit    9648 non-null   object\n",
      " 8   Units_Sold        9648 non-null   object\n",
      " 9   Total_Sales       9648 non-null   object\n",
      " 10  Operating_Profit  9648 non-null   object\n",
      " 11  Operating_Margin  9648 non-null   object\n",
      " 12  Sales_Method      9648 non-null   object\n",
      "dtypes: int64(1), object(12)\n",
      "memory usage: 980.0+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "cell_id": "6f594251a10b4dcea46c09b6db871f21",
    "deepnote_cell_type": "markdown"
   },
   "source": [
    "## 3. Data cleaning (Data preprocessing)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "cell_id": "7566bb6ec5c64fcc8562d61d2619b6d2",
    "deepnote_cell_type": "code",
    "deepnote_to_be_reexecuted": false,
    "execution_millis": 110,
    "execution_start": 1703563164963,
    "source_hash": "a453868"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_66/4034482655.py:2: FutureWarning: The default value of regex will change from True to False in a future version. In addition, single character regular expressions will*not* be treated as literal strings when regex=True.\n",
      "  df['Price_per_Unit'] = df['Price_per_Unit'].astype(str).str.replace('$', '').str.replace(',', '').astype(float)\n",
      "/tmp/ipykernel_66/4034482655.py:3: FutureWarning: The default value of regex will change from True to False in a future version. In addition, single character regular expressions will*not* be treated as literal strings when regex=True.\n",
      "  df['Total_Sales'] = df['Total_Sales'].astype(str).str.replace('$', '').str.replace(',', '').astype(float)\n",
      "/tmp/ipykernel_66/4034482655.py:4: FutureWarning: The default value of regex will change from True to False in a future version. In addition, single character regular expressions will*not* be treated as literal strings when regex=True.\n",
      "  df['Operating_Profit'] = df['Operating_Profit'].astype(str).str.replace('$', '').str.replace(',', '').astype(float)\n"
     ]
    },
    {
     "data": {
      "application/vnd.deepnote.dataframe.v3+json": {
       "column_count": 13,
       "columns": [
        {
         "dtype": "object",
         "name": "Retailer",
         "stats": {
          "categories": [
           {
            "count": 5,
            "name": "Foot Locker"
           }
          ],
          "nan_count": 0,
          "unique_count": 1
         }
        },
        {
         "dtype": "int64",
         "name": "Retailer_ID",
         "stats": {
          "histogram": [
           {
            "bin_end": 1185731.6,
            "bin_start": 1185731.5,
            "count": 0
           },
           {
            "bin_end": 1185731.7,
            "bin_start": 1185731.6,
            "count": 0
           },
           {
            "bin_end": 1185731.8,
            "bin_start": 1185731.7,
            "count": 0
           },
           {
            "bin_end": 1185731.9,
            "bin_start": 1185731.8,
            "count": 0
           },
           {
            "bin_end": 1185732,
            "bin_start": 1185731.9,
            "count": 0
           },
           {
            "bin_end": 1185732.1,
            "bin_start": 1185732,
            "count": 5
           },
           {
            "bin_end": 1185732.2,
            "bin_start": 1185732.1,
            "count": 0
           },
           {
            "bin_end": 1185732.3,
            "bin_start": 1185732.2,
            "count": 0
           },
           {
            "bin_end": 1185732.4,
            "bin_start": 1185732.3,
            "count": 0
           },
           {
            "bin_end": 1185732.5,
            "bin_start": 1185732.4,
            "count": 0
           }
          ],
          "max": "1185732",
          "min": "1185732",
          "nan_count": 0,
          "unique_count": 1
         }
        },
        {
         "dtype": "object",
         "name": "Invoice_Date",
         "stats": {
          "categories": [
           {
            "count": 1,
            "name": "01-01-2020"
           },
           {
            "count": 1,
            "name": "02-01-2020"
           },
           {
            "count": 3,
            "name": "3 others"
           }
          ],
          "nan_count": 0,
          "unique_count": 5
         }
        },
        {
         "dtype": "object",
         "name": "Region",
         "stats": {
          "categories": [
           {
            "count": 5,
            "name": "Northeast"
           }
          ],
          "nan_count": 0,
          "unique_count": 1
         }
        },
        {
         "dtype": "object",
         "name": "State",
         "stats": {
          "categories": [
           {
            "count": 5,
            "name": "New York"
           }
          ],
          "nan_count": 0,
          "unique_count": 1
         }
        },
        {
         "dtype": "object",
         "name": "City",
         "stats": {
          "categories": [
           {
            "count": 5,
            "name": "New York"
           }
          ],
          "nan_count": 0,
          "unique_count": 1
         }
        },
        {
         "dtype": "object",
         "name": "Product",
         "stats": {
          "categories": [
           {
            "count": 1,
            "name": "Men's Street Footwear"
           },
           {
            "count": 1,
            "name": "Men's Athletic Footwear"
           },
           {
            "count": 3,
            "name": "3 others"
           }
          ],
          "nan_count": 0,
          "unique_count": 5
         }
        },
        {
         "dtype": "float64",
         "name": "Price_per_Unit",
         "stats": {
          "histogram": [
           {
            "bin_end": 42,
            "bin_start": 40,
            "count": 1
           },
           {
            "bin_end": 44,
            "bin_start": 42,
            "count": 0
           },
           {
            "bin_end": 46,
            "bin_start": 44,
            "count": 1
           },
           {
            "bin_end": 48,
            "bin_start": 46,
            "count": 0
           },
           {
            "bin_end": 50,
            "bin_start": 48,
            "count": 0
           },
           {
            "bin_end": 52,
            "bin_start": 50,
            "count": 2
           },
           {
            "bin_end": 54,
            "bin_start": 52,
            "count": 0
           },
           {
            "bin_end": 56,
            "bin_start": 54,
            "count": 0
           },
           {
            "bin_end": 58,
            "bin_start": 56,
            "count": 0
           },
           {
            "bin_end": 60,
            "bin_start": 58,
            "count": 1
           }
          ],
          "max": "60.0",
          "min": "40.0",
          "nan_count": 0,
          "unique_count": 4
         }
        },
        {
         "dtype": "float64",
         "name": "Units_Sold",
         "stats": {
          "histogram": [
           {
            "bin_end": 885,
            "bin_start": 850,
            "count": 1
           },
           {
            "bin_end": 920,
            "bin_start": 885,
            "count": 1
           },
           {
            "bin_end": 955,
            "bin_start": 920,
            "count": 0
           },
           {
            "bin_end": 990,
            "bin_start": 955,
            "count": 0
           },
           {
            "bin_end": 1025,
            "bin_start": 990,
            "count": 2
           },
           {
            "bin_end": 1060,
            "bin_start": 1025,
            "count": 0
           },
           {
            "bin_end": 1095,
            "bin_start": 1060,
            "count": 0
           },
           {
            "bin_end": 1130,
            "bin_start": 1095,
            "count": 0
           },
           {
            "bin_end": 1165,
            "bin_start": 1130,
            "count": 0
           },
           {
            "bin_end": 1200,
            "bin_start": 1165,
            "count": 1
           }
          ],
          "max": "1200.0",
          "min": "850.0",
          "nan_count": 0,
          "unique_count": 4
         }
        },
        {
         "dtype": "float64",
         "name": "Total_Sales",
         "stats": {
          "histogram": [
           {
            "bin_end": 404250,
            "bin_start": 382500,
            "count": 2
           },
           {
            "bin_end": 426000,
            "bin_start": 404250,
            "count": 0
           },
           {
            "bin_end": 447750,
            "bin_start": 426000,
            "count": 0
           },
           {
            "bin_end": 469500,
            "bin_start": 447750,
            "count": 0
           },
           {
            "bin_end": 491250,
            "bin_start": 469500,
            "count": 0
           },
           {
            "bin_end": 513000,
            "bin_start": 491250,
            "count": 1
           },
           {
            "bin_end": 534750,
            "bin_start": 513000,
            "count": 0
           },
           {
            "bin_end": 556500,
            "bin_start": 534750,
            "count": 1
           },
           {
            "bin_end": 578250,
            "bin_start": 556500,
            "count": 0
           },
           {
            "bin_end": 600000,
            "bin_start": 578250,
            "count": 1
           }
          ],
          "max": "600000.0",
          "min": "382500.0",
          "nan_count": 0,
          "unique_count": 5
         }
        },
        {
         "dtype": "float64",
         "name": "Operating_Profit",
         "stats": {
          "histogram": [
           {
            "bin_end": 150487.5,
            "bin_start": 133875,
            "count": 3
           },
           {
            "bin_end": 167100,
            "bin_start": 150487.5,
            "count": 1
           },
           {
            "bin_end": 183712.5,
            "bin_start": 167100,
            "count": 0
           },
           {
            "bin_end": 200325,
            "bin_start": 183712.5,
            "count": 0
           },
           {
            "bin_end": 216937.5,
            "bin_start": 200325,
            "count": 0
           },
           {
            "bin_end": 233550,
            "bin_start": 216937.5,
            "count": 0
           },
           {
            "bin_end": 250162.5,
            "bin_start": 233550,
            "count": 0
           },
           {
            "bin_end": 266775,
            "bin_start": 250162.5,
            "count": 0
           },
           {
            "bin_end": 283387.5,
            "bin_start": 266775,
            "count": 0
           },
           {
            "bin_end": 300000,
            "bin_start": 283387.5,
            "count": 1
           }
          ],
          "max": "300000.0",
          "min": "133875.0",
          "nan_count": 0,
          "unique_count": 5
         }
        },
        {
         "dtype": "float64",
         "name": "Operating_Margin",
         "stats": {
          "histogram": [
           {
            "bin_end": 32,
            "bin_start": 30,
            "count": 2
           },
           {
            "bin_end": 34,
            "bin_start": 32,
            "count": 0
           },
           {
            "bin_end": 36,
            "bin_start": 34,
            "count": 2
           },
           {
            "bin_end": 38,
            "bin_start": 36,
            "count": 0
           },
           {
            "bin_end": 40,
            "bin_start": 38,
            "count": 0
           },
           {
            "bin_end": 42,
            "bin_start": 40,
            "count": 0
           },
           {
            "bin_end": 44,
            "bin_start": 42,
            "count": 0
           },
           {
            "bin_end": 46,
            "bin_start": 44,
            "count": 0
           },
           {
            "bin_end": 48,
            "bin_start": 46,
            "count": 0
           },
           {
            "bin_end": 50,
            "bin_start": 48,
            "count": 1
           }
          ],
          "max": "50.0",
          "min": "30.0",
          "nan_count": 0,
          "unique_count": 3
         }
        },
        {
         "dtype": "object",
         "name": "Sales_Method",
         "stats": {
          "categories": [
           {
            "count": 5,
            "name": "In-store"
           }
          ],
          "nan_count": 0,
          "unique_count": 1
         }
        },
        {
         "dtype": "int64",
         "name": "_deepnote_index_column"
        }
       ],
       "row_count": 5,
       "rows": [
        {
         "City": "New York",
         "Invoice_Date": "01-01-2020",
         "Operating_Margin": 50,
         "Operating_Profit": 300000,
         "Price_per_Unit": 50,
         "Product": "Men's Street Footwear",
         "Region": "Northeast",
         "Retailer": "Foot Locker",
         "Retailer_ID": 1185732,
         "Sales_Method": "In-store",
         "State": "New York",
         "Total_Sales": 600000,
         "Units_Sold": 1200,
         "_deepnote_index_column": 0
        },
        {
         "City": "New York",
         "Invoice_Date": "02-01-2020",
         "Operating_Margin": 30,
         "Operating_Profit": 150000,
         "Price_per_Unit": 50,
         "Product": "Men's Athletic Footwear",
         "Region": "Northeast",
         "Retailer": "Foot Locker",
         "Retailer_ID": 1185732,
         "Sales_Method": "In-store",
         "State": "New York",
         "Total_Sales": 500000,
         "Units_Sold": 1000,
         "_deepnote_index_column": 1
        },
        {
         "City": "New York",
         "Invoice_Date": "03-01-2020",
         "Operating_Margin": 35,
         "Operating_Profit": 140000,
         "Price_per_Unit": 40,
         "Product": "Women's Street Footwear",
         "Region": "Northeast",
         "Retailer": "Foot Locker",
         "Retailer_ID": 1185732,
         "Sales_Method": "In-store",
         "State": "New York",
         "Total_Sales": 400000,
         "Units_Sold": 1000,
         "_deepnote_index_column": 2
        },
        {
         "City": "New York",
         "Invoice_Date": "04-01-2020",
         "Operating_Margin": 35,
         "Operating_Profit": 133875,
         "Price_per_Unit": 45,
         "Product": "Women's Athletic Footwear",
         "Region": "Northeast",
         "Retailer": "Foot Locker",
         "Retailer_ID": 1185732,
         "Sales_Method": "In-store",
         "State": "New York",
         "Total_Sales": 382500,
         "Units_Sold": 850,
         "_deepnote_index_column": 3
        },
        {
         "City": "New York",
         "Invoice_Date": "05-01-2020",
         "Operating_Margin": 30,
         "Operating_Profit": 162000,
         "Price_per_Unit": 60,
         "Product": "Men's Apparel",
         "Region": "Northeast",
         "Retailer": "Foot Locker",
         "Retailer_ID": 1185732,
         "Sales_Method": "In-store",
         "State": "New York",
         "Total_Sales": 540000,
         "Units_Sold": 900,
         "_deepnote_index_column": 4
        }
       ]
      },
      "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>Retailer</th>\n",
       "      <th>Retailer_ID</th>\n",
       "      <th>Invoice_Date</th>\n",
       "      <th>Region</th>\n",
       "      <th>State</th>\n",
       "      <th>City</th>\n",
       "      <th>Product</th>\n",
       "      <th>Price_per_Unit</th>\n",
       "      <th>Units_Sold</th>\n",
       "      <th>Total_Sales</th>\n",
       "      <th>Operating_Profit</th>\n",
       "      <th>Operating_Margin</th>\n",
       "      <th>Sales_Method</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Foot Locker</td>\n",
       "      <td>1185732</td>\n",
       "      <td>01-01-2020</td>\n",
       "      <td>Northeast</td>\n",
       "      <td>New York</td>\n",
       "      <td>New York</td>\n",
       "      <td>Men's Street Footwear</td>\n",
       "      <td>50.0</td>\n",
       "      <td>1200.0</td>\n",
       "      <td>600000.0</td>\n",
       "      <td>300000.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>In-store</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Foot Locker</td>\n",
       "      <td>1185732</td>\n",
       "      <td>02-01-2020</td>\n",
       "      <td>Northeast</td>\n",
       "      <td>New York</td>\n",
       "      <td>New York</td>\n",
       "      <td>Men's Athletic Footwear</td>\n",
       "      <td>50.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>500000.0</td>\n",
       "      <td>150000.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>In-store</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Foot Locker</td>\n",
       "      <td>1185732</td>\n",
       "      <td>03-01-2020</td>\n",
       "      <td>Northeast</td>\n",
       "      <td>New York</td>\n",
       "      <td>New York</td>\n",
       "      <td>Women's Street Footwear</td>\n",
       "      <td>40.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>400000.0</td>\n",
       "      <td>140000.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>In-store</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Foot Locker</td>\n",
       "      <td>1185732</td>\n",
       "      <td>04-01-2020</td>\n",
       "      <td>Northeast</td>\n",
       "      <td>New York</td>\n",
       "      <td>New York</td>\n",
       "      <td>Women's Athletic Footwear</td>\n",
       "      <td>45.0</td>\n",
       "      <td>850.0</td>\n",
       "      <td>382500.0</td>\n",
       "      <td>133875.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>In-store</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Foot Locker</td>\n",
       "      <td>1185732</td>\n",
       "      <td>05-01-2020</td>\n",
       "      <td>Northeast</td>\n",
       "      <td>New York</td>\n",
       "      <td>New York</td>\n",
       "      <td>Men's Apparel</td>\n",
       "      <td>60.0</td>\n",
       "      <td>900.0</td>\n",
       "      <td>540000.0</td>\n",
       "      <td>162000.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>In-store</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Retailer  Retailer_ID Invoice_Date     Region     State      City  \\\n",
       "0  Foot Locker      1185732   01-01-2020  Northeast  New York  New York   \n",
       "1  Foot Locker      1185732   02-01-2020  Northeast  New York  New York   \n",
       "2  Foot Locker      1185732   03-01-2020  Northeast  New York  New York   \n",
       "3  Foot Locker      1185732   04-01-2020  Northeast  New York  New York   \n",
       "4  Foot Locker      1185732   05-01-2020  Northeast  New York  New York   \n",
       "\n",
       "                     Product  Price_per_Unit  Units_Sold  Total_Sales  \\\n",
       "0      Men's Street Footwear            50.0      1200.0     600000.0   \n",
       "1    Men's Athletic Footwear            50.0      1000.0     500000.0   \n",
       "2    Women's Street Footwear            40.0      1000.0     400000.0   \n",
       "3  Women's Athletic Footwear            45.0       850.0     382500.0   \n",
       "4              Men's Apparel            60.0       900.0     540000.0   \n",
       "\n",
       "   Operating_Profit  Operating_Margin Sales_Method  \n",
       "0          300000.0              50.0     In-store  \n",
       "1          150000.0              30.0     In-store  \n",
       "2          140000.0              35.0     In-store  \n",
       "3          133875.0              35.0     In-store  \n",
       "4          162000.0              30.0     In-store  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Ensure that the columns are of type string before trying to use string methods on them\n",
    "df['Price_per_Unit'] = df['Price_per_Unit'].astype(str).str.replace('$', '').str.replace(',', '').astype(float)\n",
    "df['Total_Sales'] = df['Total_Sales'].astype(str).str.replace('$', '').str.replace(',', '').astype(float)\n",
    "df['Operating_Profit'] = df['Operating_Profit'].astype(str).str.replace('$', '').str.replace(',', '').astype(float)\n",
    "df['Units_Sold'] = df['Units_Sold'].astype(str).str.replace(',', '').astype(float)\n",
    "df['Operating_Margin'] = df['Operating_Margin'].astype(str).str.replace('%', '').astype(float)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "cell_id": "b04c2b643444491695ed7f3b6217d91c",
    "deepnote_cell_type": "code",
    "deepnote_to_be_reexecuted": false,
    "execution_millis": 33,
    "execution_start": 1703563165068,
    "source_hash": "b4cb6b07"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.deepnote.dataframe.v3+json": {
       "column_count": 13,
       "columns": [
        {
         "dtype": "object",
         "name": "Retailer",
         "stats": {
          "categories": [
           {
            "count": 3,
            "name": "Foot Locker"
           }
          ],
          "nan_count": 0,
          "unique_count": 1
         }
        },
        {
         "dtype": "int64",
         "name": "Retailer_ID",
         "stats": {
          "histogram": [
           {
            "bin_end": 1185731.6,
            "bin_start": 1185731.5,
            "count": 0
           },
           {
            "bin_end": 1185731.7,
            "bin_start": 1185731.6,
            "count": 0
           },
           {
            "bin_end": 1185731.8,
            "bin_start": 1185731.7,
            "count": 0
           },
           {
            "bin_end": 1185731.9,
            "bin_start": 1185731.8,
            "count": 0
           },
           {
            "bin_end": 1185732,
            "bin_start": 1185731.9,
            "count": 0
           },
           {
            "bin_end": 1185732.1,
            "bin_start": 1185732,
            "count": 3
           },
           {
            "bin_end": 1185732.2,
            "bin_start": 1185732.1,
            "count": 0
           },
           {
            "bin_end": 1185732.3,
            "bin_start": 1185732.2,
            "count": 0
           },
           {
            "bin_end": 1185732.4,
            "bin_start": 1185732.3,
            "count": 0
           },
           {
            "bin_end": 1185732.5,
            "bin_start": 1185732.4,
            "count": 0
           }
          ],
          "max": "1185732",
          "min": "1185732",
          "nan_count": 0,
          "unique_count": 1
         }
        },
        {
         "dtype": "object",
         "name": "Invoice_Date",
         "stats": {
          "categories": [
           {
            "count": 1,
            "name": "01-01-2020"
           },
           {
            "count": 1,
            "name": "02-01-2020"
           },
           {
            "count": 1,
            "name": "03-01-2020"
           }
          ],
          "nan_count": 0,
          "unique_count": 3
         }
        },
        {
         "dtype": "object",
         "name": "Region",
         "stats": {
          "categories": [
           {
            "count": 3,
            "name": "Northeast"
           }
          ],
          "nan_count": 0,
          "unique_count": 1
         }
        },
        {
         "dtype": "object",
         "name": "State",
         "stats": {
          "categories": [
           {
            "count": 3,
            "name": "New York"
           }
          ],
          "nan_count": 0,
          "unique_count": 1
         }
        },
        {
         "dtype": "object",
         "name": "City",
         "stats": {
          "categories": [
           {
            "count": 3,
            "name": "New York"
           }
          ],
          "nan_count": 0,
          "unique_count": 1
         }
        },
        {
         "dtype": "object",
         "name": "Product",
         "stats": {
          "categories": [
           {
            "count": 1,
            "name": "Men's Street Footwear"
           },
           {
            "count": 1,
            "name": "Men's Athletic Footwear"
           },
           {
            "count": 1,
            "name": "Women's Street Footwear"
           }
          ],
          "nan_count": 0,
          "unique_count": 3
         }
        },
        {
         "dtype": "float64",
         "name": "Price_per_Unit",
         "stats": {
          "histogram": [
           {
            "bin_end": 41,
            "bin_start": 40,
            "count": 1
           },
           {
            "bin_end": 42,
            "bin_start": 41,
            "count": 0
           },
           {
            "bin_end": 43,
            "bin_start": 42,
            "count": 0
           },
           {
            "bin_end": 44,
            "bin_start": 43,
            "count": 0
           },
           {
            "bin_end": 45,
            "bin_start": 44,
            "count": 0
           },
           {
            "bin_end": 46,
            "bin_start": 45,
            "count": 0
           },
           {
            "bin_end": 47,
            "bin_start": 46,
            "count": 0
           },
           {
            "bin_end": 48,
            "bin_start": 47,
            "count": 0
           },
           {
            "bin_end": 49,
            "bin_start": 48,
            "count": 0
           },
           {
            "bin_end": 50,
            "bin_start": 49,
            "count": 2
           }
          ],
          "max": "50.0",
          "min": "40.0",
          "nan_count": 0,
          "unique_count": 2
         }
        },
        {
         "dtype": "float64",
         "name": "Units_Sold",
         "stats": {
          "histogram": [
           {
            "bin_end": 1020,
            "bin_start": 1000,
            "count": 2
           },
           {
            "bin_end": 1040,
            "bin_start": 1020,
            "count": 0
           },
           {
            "bin_end": 1060,
            "bin_start": 1040,
            "count": 0
           },
           {
            "bin_end": 1080,
            "bin_start": 1060,
            "count": 0
           },
           {
            "bin_end": 1100,
            "bin_start": 1080,
            "count": 0
           },
           {
            "bin_end": 1120,
            "bin_start": 1100,
            "count": 0
           },
           {
            "bin_end": 1140,
            "bin_start": 1120,
            "count": 0
           },
           {
            "bin_end": 1160,
            "bin_start": 1140,
            "count": 0
           },
           {
            "bin_end": 1180,
            "bin_start": 1160,
            "count": 0
           },
           {
            "bin_end": 1200,
            "bin_start": 1180,
            "count": 1
           }
          ],
          "max": "1200.0",
          "min": "1000.0",
          "nan_count": 0,
          "unique_count": 2
         }
        },
        {
         "dtype": "float64",
         "name": "Total_Sales",
         "stats": {
          "histogram": [
           {
            "bin_end": 42000,
            "bin_start": 40000,
            "count": 1
           },
           {
            "bin_end": 44000,
            "bin_start": 42000,
            "count": 0
           },
           {
            "bin_end": 46000,
            "bin_start": 44000,
            "count": 0
           },
           {
            "bin_end": 48000,
            "bin_start": 46000,
            "count": 0
           },
           {
            "bin_end": 50000,
            "bin_start": 48000,
            "count": 0
           },
           {
            "bin_end": 52000,
            "bin_start": 50000,
            "count": 1
           },
           {
            "bin_end": 54000,
            "bin_start": 52000,
            "count": 0
           },
           {
            "bin_end": 56000,
            "bin_start": 54000,
            "count": 0
           },
           {
            "bin_end": 58000,
            "bin_start": 56000,
            "count": 0
           },
           {
            "bin_end": 60000,
            "bin_start": 58000,
            "count": 1
           }
          ],
          "max": "60000.0",
          "min": "40000.0",
          "nan_count": 0,
          "unique_count": 3
         }
        },
        {
         "dtype": "float64",
         "name": "Operating_Profit",
         "stats": {
          "histogram": [
           {
            "bin_end": 15600,
            "bin_start": 14000,
            "count": 2
           },
           {
            "bin_end": 17200,
            "bin_start": 15600,
            "count": 0
           },
           {
            "bin_end": 18800,
            "bin_start": 17200,
            "count": 0
           },
           {
            "bin_end": 20400,
            "bin_start": 18800,
            "count": 0
           },
           {
            "bin_end": 22000,
            "bin_start": 20400,
            "count": 0
           },
           {
            "bin_end": 23600,
            "bin_start": 22000,
            "count": 0
           },
           {
            "bin_end": 25200,
            "bin_start": 23600,
            "count": 0
           },
           {
            "bin_end": 26800,
            "bin_start": 25200,
            "count": 0
           },
           {
            "bin_end": 28400,
            "bin_start": 26800,
            "count": 0
           },
           {
            "bin_end": 30000,
            "bin_start": 28400,
            "count": 1
           }
          ],
          "max": "30000.0",
          "min": "14000.0",
          "nan_count": 0,
          "unique_count": 3
         }
        },
        {
         "dtype": "float64",
         "name": "Operating_Margin",
         "stats": {
          "histogram": [
           {
            "bin_end": 32,
            "bin_start": 30,
            "count": 1
           },
           {
            "bin_end": 34,
            "bin_start": 32,
            "count": 0
           },
           {
            "bin_end": 36,
            "bin_start": 34,
            "count": 1
           },
           {
            "bin_end": 38,
            "bin_start": 36,
            "count": 0
           },
           {
            "bin_end": 40,
            "bin_start": 38,
            "count": 0
           },
           {
            "bin_end": 42,
            "bin_start": 40,
            "count": 0
           },
           {
            "bin_end": 44,
            "bin_start": 42,
            "count": 0
           },
           {
            "bin_end": 46,
            "bin_start": 44,
            "count": 0
           },
           {
            "bin_end": 48,
            "bin_start": 46,
            "count": 0
           },
           {
            "bin_end": 50,
            "bin_start": 48,
            "count": 1
           }
          ],
          "max": "50.0",
          "min": "30.0",
          "nan_count": 0,
          "unique_count": 3
         }
        },
        {
         "dtype": "object",
         "name": "Sales_Method",
         "stats": {
          "categories": [
           {
            "count": 3,
            "name": "In-store"
           }
          ],
          "nan_count": 0,
          "unique_count": 1
         }
        },
        {
         "dtype": "int64",
         "name": "_deepnote_index_column"
        }
       ],
       "row_count": 3,
       "rows": [
        {
         "City": "New York",
         "Invoice_Date": "01-01-2020",
         "Operating_Margin": 50,
         "Operating_Profit": 30000,
         "Price_per_Unit": 50,
         "Product": "Men's Street Footwear",
         "Region": "Northeast",
         "Retailer": "Foot Locker",
         "Retailer_ID": 1185732,
         "Sales_Method": "In-store",
         "State": "New York",
         "Total_Sales": 60000,
         "Units_Sold": 1200,
         "_deepnote_index_column": 0
        },
        {
         "City": "New York",
         "Invoice_Date": "02-01-2020",
         "Operating_Margin": 30,
         "Operating_Profit": 15000,
         "Price_per_Unit": 50,
         "Product": "Men's Athletic Footwear",
         "Region": "Northeast",
         "Retailer": "Foot Locker",
         "Retailer_ID": 1185732,
         "Sales_Method": "In-store",
         "State": "New York",
         "Total_Sales": 50000,
         "Units_Sold": 1000,
         "_deepnote_index_column": 1
        },
        {
         "City": "New York",
         "Invoice_Date": "03-01-2020",
         "Operating_Margin": 35,
         "Operating_Profit": 14000,
         "Price_per_Unit": 40,
         "Product": "Women's Street Footwear",
         "Region": "Northeast",
         "Retailer": "Foot Locker",
         "Retailer_ID": 1185732,
         "Sales_Method": "In-store",
         "State": "New York",
         "Total_Sales": 40000,
         "Units_Sold": 1000,
         "_deepnote_index_column": 2
        }
       ]
      },
      "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>Retailer</th>\n",
       "      <th>Retailer_ID</th>\n",
       "      <th>Invoice_Date</th>\n",
       "      <th>Region</th>\n",
       "      <th>State</th>\n",
       "      <th>City</th>\n",
       "      <th>Product</th>\n",
       "      <th>Price_per_Unit</th>\n",
       "      <th>Units_Sold</th>\n",
       "      <th>Total_Sales</th>\n",
       "      <th>Operating_Profit</th>\n",
       "      <th>Operating_Margin</th>\n",
       "      <th>Sales_Method</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Foot Locker</td>\n",
       "      <td>1185732</td>\n",
       "      <td>01-01-2020</td>\n",
       "      <td>Northeast</td>\n",
       "      <td>New York</td>\n",
       "      <td>New York</td>\n",
       "      <td>Men's Street Footwear</td>\n",
       "      <td>50.0</td>\n",
       "      <td>1200.0</td>\n",
       "      <td>60000.0</td>\n",
       "      <td>30000.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>In-store</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Foot Locker</td>\n",
       "      <td>1185732</td>\n",
       "      <td>02-01-2020</td>\n",
       "      <td>Northeast</td>\n",
       "      <td>New York</td>\n",
       "      <td>New York</td>\n",
       "      <td>Men's Athletic Footwear</td>\n",
       "      <td>50.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>50000.0</td>\n",
       "      <td>15000.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>In-store</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Foot Locker</td>\n",
       "      <td>1185732</td>\n",
       "      <td>03-01-2020</td>\n",
       "      <td>Northeast</td>\n",
       "      <td>New York</td>\n",
       "      <td>New York</td>\n",
       "      <td>Women's Street Footwear</td>\n",
       "      <td>40.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>40000.0</td>\n",
       "      <td>14000.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>In-store</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Retailer  Retailer_ID Invoice_Date     Region     State      City  \\\n",
       "0  Foot Locker      1185732   01-01-2020  Northeast  New York  New York   \n",
       "1  Foot Locker      1185732   02-01-2020  Northeast  New York  New York   \n",
       "2  Foot Locker      1185732   03-01-2020  Northeast  New York  New York   \n",
       "\n",
       "                   Product  Price_per_Unit  Units_Sold  Total_Sales  \\\n",
       "0    Men's Street Footwear            50.0      1200.0      60000.0   \n",
       "1  Men's Athletic Footwear            50.0      1000.0      50000.0   \n",
       "2  Women's Street Footwear            40.0      1000.0      40000.0   \n",
       "\n",
       "   Operating_Profit  Operating_Margin Sales_Method  \n",
       "0           30000.0              50.0     In-store  \n",
       "1           15000.0              30.0     In-store  \n",
       "2           14000.0              35.0     In-store  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_clean = df\n",
    "df_clean['Total_Sales'] = df_clean['Price_per_Unit'] * df_clean['Units_Sold']\n",
    "df_clean['Operating_Profit'] = df_clean['Total_Sales'] * df_clean['Operating_Margin']/100\n",
    "df_clean.head(3)\n",
    "#The columns are now corrected and stored in a new dataframe called 'df_clean'\n",
    "#The total sales and operating profit doesn't  match the actual value. That is why we have created another data frame."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "cell_id": "d2d08d87829e47799c128d6535b5d45a",
    "deepnote_cell_type": "code",
    "deepnote_to_be_reexecuted": false,
    "execution_millis": 17,
    "execution_start": 1703563165140,
    "source_hash": "9460f21c"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.deepnote.dataframe.v3+json": {
       "column_count": 13,
       "columns": [
        {
         "dtype": "object",
         "name": "Retailer",
         "stats": {
          "categories": [
           {
            "count": 3,
            "name": "foot locker"
           }
          ],
          "nan_count": 0,
          "unique_count": 1
         }
        },
        {
         "dtype": "int64",
         "name": "Retailer_ID",
         "stats": {
          "histogram": [
           {
            "bin_end": 1185731.6,
            "bin_start": 1185731.5,
            "count": 0
           },
           {
            "bin_end": 1185731.7,
            "bin_start": 1185731.6,
            "count": 0
           },
           {
            "bin_end": 1185731.8,
            "bin_start": 1185731.7,
            "count": 0
           },
           {
            "bin_end": 1185731.9,
            "bin_start": 1185731.8,
            "count": 0
           },
           {
            "bin_end": 1185732,
            "bin_start": 1185731.9,
            "count": 0
           },
           {
            "bin_end": 1185732.1,
            "bin_start": 1185732,
            "count": 3
           },
           {
            "bin_end": 1185732.2,
            "bin_start": 1185732.1,
            "count": 0
           },
           {
            "bin_end": 1185732.3,
            "bin_start": 1185732.2,
            "count": 0
           },
           {
            "bin_end": 1185732.4,
            "bin_start": 1185732.3,
            "count": 0
           },
           {
            "bin_end": 1185732.5,
            "bin_start": 1185732.4,
            "count": 0
           }
          ],
          "max": "1185732",
          "min": "1185732",
          "nan_count": 0,
          "unique_count": 1
         }
        },
        {
         "dtype": "object",
         "name": "Invoice_Date",
         "stats": {
          "categories": [
           {
            "count": 1,
            "name": "01-01-2020"
           },
           {
            "count": 1,
            "name": "02-01-2020"
           },
           {
            "count": 1,
            "name": "03-01-2020"
           }
          ],
          "nan_count": 0,
          "unique_count": 3
         }
        },
        {
         "dtype": "object",
         "name": "Region",
         "stats": {
          "categories": [
           {
            "count": 3,
            "name": "northeast"
           }
          ],
          "nan_count": 0,
          "unique_count": 1
         }
        },
        {
         "dtype": "object",
         "name": "State",
         "stats": {
          "categories": [
           {
            "count": 3,
            "name": "new york"
           }
          ],
          "nan_count": 0,
          "unique_count": 1
         }
        },
        {
         "dtype": "object",
         "name": "City",
         "stats": {
          "categories": [
           {
            "count": 3,
            "name": "new york"
           }
          ],
          "nan_count": 0,
          "unique_count": 1
         }
        },
        {
         "dtype": "object",
         "name": "Product",
         "stats": {
          "categories": [
           {
            "count": 1,
            "name": "men's street footwear"
           },
           {
            "count": 1,
            "name": "men's athletic footwear"
           },
           {
            "count": 1,
            "name": "women's street footwear"
           }
          ],
          "nan_count": 0,
          "unique_count": 3
         }
        },
        {
         "dtype": "float64",
         "name": "Price_per_Unit",
         "stats": {
          "histogram": [
           {
            "bin_end": 41,
            "bin_start": 40,
            "count": 1
           },
           {
            "bin_end": 42,
            "bin_start": 41,
            "count": 0
           },
           {
            "bin_end": 43,
            "bin_start": 42,
            "count": 0
           },
           {
            "bin_end": 44,
            "bin_start": 43,
            "count": 0
           },
           {
            "bin_end": 45,
            "bin_start": 44,
            "count": 0
           },
           {
            "bin_end": 46,
            "bin_start": 45,
            "count": 0
           },
           {
            "bin_end": 47,
            "bin_start": 46,
            "count": 0
           },
           {
            "bin_end": 48,
            "bin_start": 47,
            "count": 0
           },
           {
            "bin_end": 49,
            "bin_start": 48,
            "count": 0
           },
           {
            "bin_end": 50,
            "bin_start": 49,
            "count": 2
           }
          ],
          "max": "50.0",
          "min": "40.0",
          "nan_count": 0,
          "unique_count": 2
         }
        },
        {
         "dtype": "float64",
         "name": "Units_Sold",
         "stats": {
          "histogram": [
           {
            "bin_end": 1020,
            "bin_start": 1000,
            "count": 2
           },
           {
            "bin_end": 1040,
            "bin_start": 1020,
            "count": 0
           },
           {
            "bin_end": 1060,
            "bin_start": 1040,
            "count": 0
           },
           {
            "bin_end": 1080,
            "bin_start": 1060,
            "count": 0
           },
           {
            "bin_end": 1100,
            "bin_start": 1080,
            "count": 0
           },
           {
            "bin_end": 1120,
            "bin_start": 1100,
            "count": 0
           },
           {
            "bin_end": 1140,
            "bin_start": 1120,
            "count": 0
           },
           {
            "bin_end": 1160,
            "bin_start": 1140,
            "count": 0
           },
           {
            "bin_end": 1180,
            "bin_start": 1160,
            "count": 0
           },
           {
            "bin_end": 1200,
            "bin_start": 1180,
            "count": 1
           }
          ],
          "max": "1200.0",
          "min": "1000.0",
          "nan_count": 0,
          "unique_count": 2
         }
        },
        {
         "dtype": "float64",
         "name": "Total_Sales",
         "stats": {
          "histogram": [
           {
            "bin_end": 42000,
            "bin_start": 40000,
            "count": 1
           },
           {
            "bin_end": 44000,
            "bin_start": 42000,
            "count": 0
           },
           {
            "bin_end": 46000,
            "bin_start": 44000,
            "count": 0
           },
           {
            "bin_end": 48000,
            "bin_start": 46000,
            "count": 0
           },
           {
            "bin_end": 50000,
            "bin_start": 48000,
            "count": 0
           },
           {
            "bin_end": 52000,
            "bin_start": 50000,
            "count": 1
           },
           {
            "bin_end": 54000,
            "bin_start": 52000,
            "count": 0
           },
           {
            "bin_end": 56000,
            "bin_start": 54000,
            "count": 0
           },
           {
            "bin_end": 58000,
            "bin_start": 56000,
            "count": 0
           },
           {
            "bin_end": 60000,
            "bin_start": 58000,
            "count": 1
           }
          ],
          "max": "60000.0",
          "min": "40000.0",
          "nan_count": 0,
          "unique_count": 3
         }
        },
        {
         "dtype": "float64",
         "name": "Operating_Profit",
         "stats": {
          "histogram": [
           {
            "bin_end": 15600,
            "bin_start": 14000,
            "count": 2
           },
           {
            "bin_end": 17200,
            "bin_start": 15600,
            "count": 0
           },
           {
            "bin_end": 18800,
            "bin_start": 17200,
            "count": 0
           },
           {
            "bin_end": 20400,
            "bin_start": 18800,
            "count": 0
           },
           {
            "bin_end": 22000,
            "bin_start": 20400,
            "count": 0
           },
           {
            "bin_end": 23600,
            "bin_start": 22000,
            "count": 0
           },
           {
            "bin_end": 25200,
            "bin_start": 23600,
            "count": 0
           },
           {
            "bin_end": 26800,
            "bin_start": 25200,
            "count": 0
           },
           {
            "bin_end": 28400,
            "bin_start": 26800,
            "count": 0
           },
           {
            "bin_end": 30000,
            "bin_start": 28400,
            "count": 1
           }
          ],
          "max": "30000.0",
          "min": "14000.0",
          "nan_count": 0,
          "unique_count": 3
         }
        },
        {
         "dtype": "float64",
         "name": "Operating_Margin",
         "stats": {
          "histogram": [
           {
            "bin_end": 32,
            "bin_start": 30,
            "count": 1
           },
           {
            "bin_end": 34,
            "bin_start": 32,
            "count": 0
           },
           {
            "bin_end": 36,
            "bin_start": 34,
            "count": 1
           },
           {
            "bin_end": 38,
            "bin_start": 36,
            "count": 0
           },
           {
            "bin_end": 40,
            "bin_start": 38,
            "count": 0
           },
           {
            "bin_end": 42,
            "bin_start": 40,
            "count": 0
           },
           {
            "bin_end": 44,
            "bin_start": 42,
            "count": 0
           },
           {
            "bin_end": 46,
            "bin_start": 44,
            "count": 0
           },
           {
            "bin_end": 48,
            "bin_start": 46,
            "count": 0
           },
           {
            "bin_end": 50,
            "bin_start": 48,
            "count": 1
           }
          ],
          "max": "50.0",
          "min": "30.0",
          "nan_count": 0,
          "unique_count": 3
         }
        },
        {
         "dtype": "object",
         "name": "Sales_Method",
         "stats": {
          "categories": [
           {
            "count": 3,
            "name": "in-store"
           }
          ],
          "nan_count": 0,
          "unique_count": 1
         }
        },
        {
         "dtype": "int64",
         "name": "_deepnote_index_column"
        }
       ],
       "row_count": 3,
       "rows": [
        {
         "City": "new york",
         "Invoice_Date": "01-01-2020",
         "Operating_Margin": 50,
         "Operating_Profit": 30000,
         "Price_per_Unit": 50,
         "Product": "men's street footwear",
         "Region": "northeast",
         "Retailer": "foot locker",
         "Retailer_ID": 1185732,
         "Sales_Method": "in-store",
         "State": "new york",
         "Total_Sales": 60000,
         "Units_Sold": 1200,
         "_deepnote_index_column": 0
        },
        {
         "City": "new york",
         "Invoice_Date": "02-01-2020",
         "Operating_Margin": 30,
         "Operating_Profit": 15000,
         "Price_per_Unit": 50,
         "Product": "men's athletic footwear",
         "Region": "northeast",
         "Retailer": "foot locker",
         "Retailer_ID": 1185732,
         "Sales_Method": "in-store",
         "State": "new york",
         "Total_Sales": 50000,
         "Units_Sold": 1000,
         "_deepnote_index_column": 1
        },
        {
         "City": "new york",
         "Invoice_Date": "03-01-2020",
         "Operating_Margin": 35,
         "Operating_Profit": 14000,
         "Price_per_Unit": 40,
         "Product": "women's street footwear",
         "Region": "northeast",
         "Retailer": "foot locker",
         "Retailer_ID": 1185732,
         "Sales_Method": "in-store",
         "State": "new york",
         "Total_Sales": 40000,
         "Units_Sold": 1000,
         "_deepnote_index_column": 2
        }
       ]
      },
      "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>Retailer</th>\n",
       "      <th>Retailer_ID</th>\n",
       "      <th>Invoice_Date</th>\n",
       "      <th>Region</th>\n",
       "      <th>State</th>\n",
       "      <th>City</th>\n",
       "      <th>Product</th>\n",
       "      <th>Price_per_Unit</th>\n",
       "      <th>Units_Sold</th>\n",
       "      <th>Total_Sales</th>\n",
       "      <th>Operating_Profit</th>\n",
       "      <th>Operating_Margin</th>\n",
       "      <th>Sales_Method</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>foot locker</td>\n",
       "      <td>1185732</td>\n",
       "      <td>01-01-2020</td>\n",
       "      <td>northeast</td>\n",
       "      <td>new york</td>\n",
       "      <td>new york</td>\n",
       "      <td>men's street footwear</td>\n",
       "      <td>50.0</td>\n",
       "      <td>1200.0</td>\n",
       "      <td>60000.0</td>\n",
       "      <td>30000.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>in-store</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>foot locker</td>\n",
       "      <td>1185732</td>\n",
       "      <td>02-01-2020</td>\n",
       "      <td>northeast</td>\n",
       "      <td>new york</td>\n",
       "      <td>new york</td>\n",
       "      <td>men's athletic footwear</td>\n",
       "      <td>50.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>50000.0</td>\n",
       "      <td>15000.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>in-store</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>foot locker</td>\n",
       "      <td>1185732</td>\n",
       "      <td>03-01-2020</td>\n",
       "      <td>northeast</td>\n",
       "      <td>new york</td>\n",
       "      <td>new york</td>\n",
       "      <td>women's street footwear</td>\n",
       "      <td>40.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>40000.0</td>\n",
       "      <td>14000.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>in-store</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Retailer  Retailer_ID Invoice_Date     Region     State      City  \\\n",
       "0  foot locker      1185732   01-01-2020  northeast  new york  new york   \n",
       "1  foot locker      1185732   02-01-2020  northeast  new york  new york   \n",
       "2  foot locker      1185732   03-01-2020  northeast  new york  new york   \n",
       "\n",
       "                   Product  Price_per_Unit  Units_Sold  Total_Sales  \\\n",
       "0    men's street footwear            50.0      1200.0      60000.0   \n",
       "1  men's athletic footwear            50.0      1000.0      50000.0   \n",
       "2  women's street footwear            40.0      1000.0      40000.0   \n",
       "\n",
       "   Operating_Profit  Operating_Margin Sales_Method  \n",
       "0           30000.0              50.0     in-store  \n",
       "1           15000.0              30.0     in-store  \n",
       "2           14000.0              35.0     in-store  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for column in df_clean.columns:\n",
    "    if df_clean[column].dtype == 'O':\n",
    "        df_clean[column] = df_clean[column].str.lower()\n",
    "df_clean.head(3)\n",
    "#Converting all strings to lower case to maintain consistency."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "cell_id": "b18348fc863f4d76a6b4e6ddf3df29be",
    "deepnote_cell_type": "code",
    "deepnote_to_be_reexecuted": false,
    "execution_millis": 17,
    "execution_start": 1703563165187,
    "source_hash": "6842616d"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 9648 entries, 0 to 9647\n",
      "Data columns (total 13 columns):\n",
      " #   Column            Non-Null Count  Dtype         \n",
      "---  ------            --------------  -----         \n",
      " 0   Retailer          9648 non-null   object        \n",
      " 1   Retailer_ID       9648 non-null   int64         \n",
      " 2   Invoice_Date      9648 non-null   datetime64[ns]\n",
      " 3   Region            9648 non-null   object        \n",
      " 4   State             9648 non-null   object        \n",
      " 5   City              9648 non-null   object        \n",
      " 6   Product           9648 non-null   object        \n",
      " 7   Price_per_Unit    9648 non-null   float64       \n",
      " 8   Units_Sold        9648 non-null   float64       \n",
      " 9   Total_Sales       9648 non-null   float64       \n",
      " 10  Operating_Profit  9648 non-null   float64       \n",
      " 11  Operating_Margin  9648 non-null   float64       \n",
      " 12  Sales_Method      9648 non-null   object        \n",
      "dtypes: datetime64[ns](1), float64(5), int64(1), object(6)\n",
      "memory usage: 980.0+ KB\n"
     ]
    }
   ],
   "source": [
    "df_clean['Invoice_Date'] = pd.to_datetime(df_clean['Invoice_Date'], format=\"%d-%m-%Y\")\n",
    "#Converting Invoice Date to datetime format\n",
    "df_clean.info()\n",
    "#The new dataframe has all the columns of proper data types"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "cell_id": "d5b8c92031d44b87bde4e0f5e376b866",
    "deepnote_cell_type": "code",
    "deepnote_to_be_reexecuted": false,
    "execution_millis": 24,
    "execution_start": 1703563165200,
    "source_hash": "f6ccc18a"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(9648, 13)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_clean.drop_duplicates(inplace = True)\n",
    "df_clean.shape\n",
    "#There are No Duplicate Rows in the Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "cell_id": "b1a0d214fc7843738dd48c3648d2ff8b",
    "deepnote_cell_type": "code",
    "deepnote_to_be_reexecuted": false,
    "execution_millis": 54,
    "execution_start": 1703563165271,
    "source_hash": "4050c17a"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "City                1\n",
       "State               1\n",
       "Retailer            1\n",
       "Price_per_Unit      1\n",
       "Region              1\n",
       "Retailer_ID         1\n",
       "Operating_Profit    1\n",
       "Total_Sales         1\n",
       "Invoice_Date        1\n",
       "Product             1\n",
       "Units_Sold          1\n",
       "Operating_Margin    1\n",
       "Sales_Method        1\n",
       "dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_clean.columns.value_counts()\n",
    "#There are No Duplicate Columns in the Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "cell_id": "81ac4b9143f249f4b4c036b83c73dac3",
    "deepnote_cell_type": "code",
    "deepnote_to_be_reexecuted": false,
    "execution_millis": 54,
    "execution_start": 1703563165272,
    "source_hash": "3405ff62"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Retailer            0\n",
       "Retailer_ID         0\n",
       "Invoice_Date        0\n",
       "Region              0\n",
       "State               0\n",
       "City                0\n",
       "Product             0\n",
       "Price_per_Unit      0\n",
       "Units_Sold          0\n",
       "Total_Sales         0\n",
       "Operating_Profit    0\n",
       "Operating_Margin    0\n",
       "Sales_Method        0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_clean.isnull().sum()\n",
    "#There are no null values in the data set"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "cell_id": "5330f46ccc7e46aca0e29ebeb632b6f6",
    "deepnote_cell_type": "markdown"
   },
   "source": [
    "## 4. Exploratory Data Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "cell_id": "c28f7f86ff71477b817e63c98cd6dfea",
    "deepnote_cell_type": "code",
    "deepnote_to_be_reexecuted": false,
    "execution_millis": 49,
    "execution_start": 1703563165275,
    "source_hash": "e315bb47"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.deepnote.dataframe.v3+json": {
       "column_count": 6,
       "columns": [
        {
         "dtype": "float64",
         "name": "Retailer_ID",
         "stats": {
          "histogram": [
           {
            "bin_end": 128466.3,
            "bin_start": 9648,
            "count": 2
           },
           {
            "bin_end": 247284.6,
            "bin_start": 128466.3,
            "count": 0
           },
           {
            "bin_end": 366102.9,
            "bin_start": 247284.6,
            "count": 0
           },
           {
            "bin_end": 484921.2,
            "bin_start": 366102.9,
            "count": 0
           },
           {
            "bin_end": 603739.5,
            "bin_start": 484921.2,
            "count": 0
           },
           {
            "bin_end": 722557.8,
            "bin_start": 603739.5,
            "count": 0
           },
           {
            "bin_end": 841376.1,
            "bin_start": 722557.8,
            "count": 0
           },
           {
            "bin_end": 960194.4,
            "bin_start": 841376.1,
            "count": 0
           },
           {
            "bin_end": 1079012.7,
            "bin_start": 960194.4,
            "count": 0
           },
           {
            "bin_end": 1197831,
            "bin_start": 1079012.7,
            "count": 6
           }
          ],
          "max": "1197831.0",
          "min": "9648.0",
          "nan_count": 0,
          "unique_count": 6
         }
        },
        {
         "dtype": "float64",
         "name": "Price_per_Unit",
         "stats": {
          "histogram": [
           {
            "bin_end": 971.1,
            "bin_start": 7,
            "count": 7
           },
           {
            "bin_end": 1935.2,
            "bin_start": 971.1,
            "count": 0
           },
           {
            "bin_end": 2899.3,
            "bin_start": 1935.2,
            "count": 0
           },
           {
            "bin_end": 3863.4,
            "bin_start": 2899.3,
            "count": 0
           },
           {
            "bin_end": 4827.5,
            "bin_start": 3863.4,
            "count": 0
           },
           {
            "bin_end": 5791.6,
            "bin_start": 4827.5,
            "count": 0
           },
           {
            "bin_end": 6755.7,
            "bin_start": 5791.6,
            "count": 0
           },
           {
            "bin_end": 7719.8,
            "bin_start": 6755.7,
            "count": 0
           },
           {
            "bin_end": 8683.9,
            "bin_start": 7719.8,
            "count": 0
           },
           {
            "bin_end": 9648,
            "bin_start": 8683.9,
            "count": 1
           }
          ],
          "max": "9648.0",
          "min": "7.0",
          "nan_count": 0,
          "unique_count": 8
         }
        },
        {
         "dtype": "float64",
         "name": "Units_Sold",
         "stats": {
          "histogram": [
           {
            "bin_end": 964.8,
            "bin_start": 0,
            "count": 6
           },
           {
            "bin_end": 1929.6,
            "bin_start": 964.8,
            "count": 1
           },
           {
            "bin_end": 2894.3999999999996,
            "bin_start": 1929.6,
            "count": 0
           },
           {
            "bin_end": 3859.2,
            "bin_start": 2894.3999999999996,
            "count": 0
           },
           {
            "bin_end": 4824,
            "bin_start": 3859.2,
            "count": 0
           },
           {
            "bin_end": 5788.799999999999,
            "bin_start": 4824,
            "count": 0
           },
           {
            "bin_end": 6753.599999999999,
            "bin_start": 5788.799999999999,
            "count": 0
           },
           {
            "bin_end": 7718.4,
            "bin_start": 6753.599999999999,
            "count": 0
           },
           {
            "bin_end": 8683.199999999999,
            "bin_start": 7718.4,
            "count": 0
           },
           {
            "bin_end": 9648,
            "bin_start": 8683.199999999999,
            "count": 1
           }
          ],
          "max": "9648.0",
          "min": "0.0",
          "nan_count": 0,
          "unique_count": 8
         }
        },
        {
         "dtype": "float64",
         "name": "Total_Sales",
         "stats": {
          "histogram": [
           {
            "bin_end": 8250,
            "bin_start": 0,
            "count": 3
           },
           {
            "bin_end": 16500,
            "bin_start": 8250,
            "count": 4
           },
           {
            "bin_end": 24750,
            "bin_start": 16500,
            "count": 0
           },
           {
            "bin_end": 33000,
            "bin_start": 24750,
            "count": 0
           },
           {
            "bin_end": 41250,
            "bin_start": 33000,
            "count": 0
           },
           {
            "bin_end": 49500,
            "bin_start": 41250,
            "count": 0
           },
           {
            "bin_end": 57750,
            "bin_start": 49500,
            "count": 0
           },
           {
            "bin_end": 66000,
            "bin_start": 57750,
            "count": 0
           },
           {
            "bin_end": 74250,
            "bin_start": 66000,
            "count": 0
           },
           {
            "bin_end": 82500,
            "bin_start": 74250,
            "count": 1
           }
          ],
          "max": "82500.0",
          "min": "0.0",
          "nan_count": 0,
          "unique_count": 8
         }
        },
        {
         "dtype": "float64",
         "name": "Operating_Profit",
         "stats": {
          "histogram": [
           {
            "bin_end": 3900,
            "bin_start": 0,
            "count": 3
           },
           {
            "bin_end": 7800,
            "bin_start": 3900,
            "count": 3
           },
           {
            "bin_end": 11700,
            "bin_start": 7800,
            "count": 1
           },
           {
            "bin_end": 15600,
            "bin_start": 11700,
            "count": 0
           },
           {
            "bin_end": 19500,
            "bin_start": 15600,
            "count": 0
           },
           {
            "bin_end": 23400,
            "bin_start": 19500,
            "count": 0
           },
           {
            "bin_end": 27300,
            "bin_start": 23400,
            "count": 0
           },
           {
            "bin_end": 31200,
            "bin_start": 27300,
            "count": 0
           },
           {
            "bin_end": 35100,
            "bin_start": 31200,
            "count": 0
           },
           {
            "bin_end": 39000,
            "bin_start": 35100,
            "count": 1
           }
          ],
          "max": "39000.0",
          "min": "0.0",
          "nan_count": 0,
          "unique_count": 8
         }
        },
        {
         "dtype": "float64",
         "name": "Operating_Margin",
         "stats": {
          "histogram": [
           {
            "bin_end": 973.5477676654596,
            "bin_start": 9.719741850510681,
            "count": 7
           },
           {
            "bin_end": 1937.3757934804084,
            "bin_start": 973.5477676654596,
            "count": 0
           },
           {
            "bin_end": 2901.2038192953573,
            "bin_start": 1937.3757934804084,
            "count": 0
           },
           {
            "bin_end": 3865.031845110306,
            "bin_start": 2901.2038192953573,
            "count": 0
           },
           {
            "bin_end": 4828.859870925256,
            "bin_start": 3865.031845110306,
            "count": 0
           },
           {
            "bin_end": 5792.687896740204,
            "bin_start": 4828.859870925256,
            "count": 0
           },
           {
            "bin_end": 6756.515922555153,
            "bin_start": 5792.687896740204,
            "count": 0
           },
           {
            "bin_end": 7720.343948370102,
            "bin_start": 6756.515922555153,
            "count": 0
           },
           {
            "bin_end": 8684.171974185052,
            "bin_start": 7720.343948370102,
            "count": 0
           },
           {
            "bin_end": 9648,
            "bin_start": 8684.171974185052,
            "count": 1
           }
          ],
          "max": "9648.0",
          "min": "9.719741850510681",
          "nan_count": 0,
          "unique_count": 8
         }
        },
        {
         "dtype": "object",
         "name": "_deepnote_index_column"
        }
       ],
       "row_count": 8,
       "rows": [
        {
         "Operating_Margin": 9648,
         "Operating_Profit": 9648,
         "Price_per_Unit": 9648,
         "Retailer_ID": 9648,
         "Total_Sales": 9648,
         "Units_Sold": 9648,
         "_deepnote_index_column": "count"
        },
        {
         "Operating_Margin": 42.29912935323383,
         "Operating_Profit": 4894.793501243781,
         "Price_per_Unit": 45.21662520729685,
         "Retailer_ID": 1173849.7235696518,
         "Total_Sales": 12455.083955223881,
         "Units_Sold": 256.9300373134328,
         "_deepnote_index_column": "mean"
        },
        {
         "Operating_Margin": 9.719741850510681,
         "Operating_Profit": 4866.464372066413,
         "Price_per_Unit": 14.705397125008332,
         "Retailer_ID": 26360.37730933121,
         "Total_Sales": 12716.392111457199,
         "Units_Sold": 214.25203007420507,
         "_deepnote_index_column": "std"
        },
        {
         "Operating_Margin": 10,
         "Operating_Profit": 0,
         "Price_per_Unit": 7,
         "Retailer_ID": 1128299,
         "Total_Sales": 0,
         "Units_Sold": 0,
         "_deepnote_index_column": "min"
        },
        {
         "Operating_Margin": 35,
         "Operating_Profit": 1753.44,
         "Price_per_Unit": 35,
         "Retailer_ID": 1185732,
         "Total_Sales": 4065.25,
         "Units_Sold": 106,
         "_deepnote_index_column": "25%"
        },
        {
         "Operating_Margin": 41,
         "Operating_Profit": 3262.98,
         "Price_per_Unit": 45,
         "Retailer_ID": 1185732,
         "Total_Sales": 7803.5,
         "Units_Sold": 176,
         "_deepnote_index_column": "50%"
        },
        {
         "Operating_Margin": 49,
         "Operating_Profit": 6192.36,
         "Price_per_Unit": 55,
         "Retailer_ID": 1185732,
         "Total_Sales": 15864.5,
         "Units_Sold": 350,
         "_deepnote_index_column": "75%"
        },
        {
         "Operating_Margin": 80,
         "Operating_Profit": 39000,
         "Price_per_Unit": 110,
         "Retailer_ID": 1197831,
         "Total_Sales": 82500,
         "Units_Sold": 1275,
         "_deepnote_index_column": "max"
        }
       ]
      },
      "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>Retailer_ID</th>\n",
       "      <th>Price_per_Unit</th>\n",
       "      <th>Units_Sold</th>\n",
       "      <th>Total_Sales</th>\n",
       "      <th>Operating_Profit</th>\n",
       "      <th>Operating_Margin</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>9.648000e+03</td>\n",
       "      <td>9648.000000</td>\n",
       "      <td>9648.000000</td>\n",
       "      <td>9648.000000</td>\n",
       "      <td>9648.000000</td>\n",
       "      <td>9648.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.173850e+06</td>\n",
       "      <td>45.216625</td>\n",
       "      <td>256.930037</td>\n",
       "      <td>12455.083955</td>\n",
       "      <td>4894.793501</td>\n",
       "      <td>42.299129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.636038e+04</td>\n",
       "      <td>14.705397</td>\n",
       "      <td>214.252030</td>\n",
       "      <td>12716.392111</td>\n",
       "      <td>4866.464372</td>\n",
       "      <td>9.719742</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.128299e+06</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>10.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.185732e+06</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>106.000000</td>\n",
       "      <td>4065.250000</td>\n",
       "      <td>1753.440000</td>\n",
       "      <td>35.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.185732e+06</td>\n",
       "      <td>45.000000</td>\n",
       "      <td>176.000000</td>\n",
       "      <td>7803.500000</td>\n",
       "      <td>3262.980000</td>\n",
       "      <td>41.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.185732e+06</td>\n",
       "      <td>55.000000</td>\n",
       "      <td>350.000000</td>\n",
       "      <td>15864.500000</td>\n",
       "      <td>6192.360000</td>\n",
       "      <td>49.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.197831e+06</td>\n",
       "      <td>110.000000</td>\n",
       "      <td>1275.000000</td>\n",
       "      <td>82500.000000</td>\n",
       "      <td>39000.000000</td>\n",
       "      <td>80.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Retailer_ID  Price_per_Unit   Units_Sold   Total_Sales  \\\n",
       "count  9.648000e+03     9648.000000  9648.000000   9648.000000   \n",
       "mean   1.173850e+06       45.216625   256.930037  12455.083955   \n",
       "std    2.636038e+04       14.705397   214.252030  12716.392111   \n",
       "min    1.128299e+06        7.000000     0.000000      0.000000   \n",
       "25%    1.185732e+06       35.000000   106.000000   4065.250000   \n",
       "50%    1.185732e+06       45.000000   176.000000   7803.500000   \n",
       "75%    1.185732e+06       55.000000   350.000000  15864.500000   \n",
       "max    1.197831e+06      110.000000  1275.000000  82500.000000   \n",
       "\n",
       "       Operating_Profit  Operating_Margin  \n",
       "count       9648.000000       9648.000000  \n",
       "mean        4894.793501         42.299129  \n",
       "std         4866.464372          9.719742  \n",
       "min            0.000000         10.000000  \n",
       "25%         1753.440000         35.000000  \n",
       "50%         3262.980000         41.000000  \n",
       "75%         6192.360000         49.000000  \n",
       "max        39000.000000         80.000000  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_clean.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "cell_id": "38aa99be26ad4559b25b47aaa820b2df",
    "deepnote_cell_type": "code",
    "deepnote_to_be_reexecuted": false,
    "execution_millis": 32,
    "execution_start": 1703563165293,
    "source_hash": "64d1e116"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "range of  Price_per_Unit  :  103.0\n",
      "range of  Units_Sold  :  1275.0\n",
      "range of  Total_Sales  :  82500.0\n",
      "range of  Operating_Profit  :  39000.0\n",
      "range of  Operating_Margin  :  70.0\n"
     ]
    }
   ],
   "source": [
    "range = ['Price_per_Unit','Units_Sold', 'Total_Sales', 'Operating_Profit', 'Operating_Margin']\n",
    "for r in range:\n",
    "    print(\"range of \", r , \" : \", df_clean[r].max() - df_clean[r].min())\n",
    "#The range of numerical columns are as follows"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "cell_id": "9b280d16777145bfab128437a4de9f42",
    "deepnote_cell_type": "text-cell-h3",
    "formattedRanges": []
   },
   "source": [
    "### Sales Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "cell_id": "3b9d919cb1de4c7e8c574438090a8f43",
    "deepnote_cell_type": "code",
    "deepnote_to_be_reexecuted": false,
    "execution_millis": 26,
    "execution_start": 1703563165300,
    "source_hash": "1f821005"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"men's street footwear\""
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_clean['Product'].value_counts().idxmax()\n",
    "# The most listed product is 'men's athletic footwear'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "cell_id": "6cc25a277567496c81b1c586e820c141",
    "deepnote_cell_type": "code",
    "deepnote_to_be_reexecuted": false,
    "execution_millis": 285,
    "execution_start": 1703563165308,
    "source_hash": "b771293b"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 640,
       "width": 691
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "product_sales = df_clean.groupby('Product')['Total_Sales'].sum()\n",
    "\n",
    "plt.figure(figsize=(8, 5))\n",
    "product_sales.plot(kind='bar', color='skyblue')\n",
    "plt.xlabel('Product')\n",
    "plt.ylabel('Total Sales')\n",
    "plt.title('Total Sales per Product')\n",
    "plt.show()\n",
    "# Men's Street Footwear' has the highest total sales"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "cell_id": "86924cb65e8a4638a9cb6a8405035b45",
    "deepnote_cell_type": "code",
    "deepnote_to_be_reexecuted": false,
    "execution_millis": 376,
    "execution_start": 1703563165589,
    "source_hash": "a80b0e10"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1400x700 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 588,
       "width": 1156
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.dates as mdates\n",
    "\n",
    "df_clean['Year'] = df_clean['Invoice_Date'].dt.year\n",
    "df_clean['Month'] = df_clean['Invoice_Date'].dt.month\n",
    "monthly_data = df_clean.groupby(['Year', 'Month']).agg({\n",
    "    'Total_Sales': 'sum',\n",
    "    'Operating_Profit': 'sum'\n",
    "}).reset_index()\n",
    "monthly_data['Date'] = pd.to_datetime(monthly_data[['Year', 'Month']].assign(day=1))\n",
    "\n",
    "plt.figure(figsize=(14, 7))\n",
    "\n",
    "plt.plot(monthly_data['Date'], monthly_data['Total_Sales'], label='Total Sales')\n",
    "plt.plot(monthly_data['Date'], monthly_data['Operating_Profit'], label='Operating Profit')\n",
    "\n",
    "plt.gca().xaxis.set_major_formatter(mdates.DateFormatter('%Y-%m'))\n",
    "plt.gca().xaxis.set_major_locator(mdates.MonthLocator())\n",
    "plt.gcf().autofmt_xdate() # for the x date angle\n",
    "\n",
    "plt.title(\"Total Sales and Operating Profit over Time\")\n",
    "plt.xlabel('Time')\n",
    "plt.ylabel('Amount')\n",
    "plt.legend()\n",
    "\n",
    "plt.show()\n",
    "#The below graph shows trend of total sales and operating profit over time\n",
    "#There is a gradual increase in total sales and operating profit over time.\n",
    "#During dec 2020 to jan 2021, there is a sudden increase in total sales and operating profit but the point to note is that the extent of increase of profit is lesser than that of sales."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "cell_id": "931fe7607d2c42eaa0df8bb8be38e51e",
    "deepnote_cell_type": "code",
    "deepnote_to_be_reexecuted": false,
    "execution_millis": 19,
    "execution_start": 1703563165985,
    "source_hash": "b7e35a00"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total sales:  Sales_Method\n",
      "in-store    35664375.0\n",
      "online      44965657.0\n",
      "outlet      39536618.0\n",
      "Name: Total_Sales, dtype: float64\n",
      "Total profit:  Sales_Method\n",
      "in-store    12759128.75\n",
      "online      19552537.72\n",
      "outlet      14913301.23\n",
      "Name: Operating_Profit, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "print(\"Total sales: \", df_clean.groupby(['Sales_Method']).Total_Sales.sum())\n",
    "print(\"Total profit: \", df_clean.groupby(['Sales_Method']).Operating_Profit.sum())\n",
    "# Out of different sales methods, 'online' has the highest total sales and profit whereas, 'in-store' has the lowest total sales and profit."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "cell_id": "54c51402718e4761acd5b0aa27e1c145",
    "deepnote_cell_type": "code",
    "deepnote_to_be_reexecuted": false,
    "execution_millis": 29,
    "execution_start": 1703563165986,
    "source_hash": "f191034d"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Product\n",
       "men's apparel                190.960772\n",
       "men's athletic footwear      270.513043\n",
       "men's street footwear        368.521739\n",
       "women's apparel              269.792910\n",
       "women's athletic footwear    197.531756\n",
       "women's street footwear      243.948383\n",
       "Name: Units_Sold, dtype: float64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_clean.groupby(['Product'])['Units_Sold'].mean()\n",
    "#The below lists the average number of units sold per product per day. That is, the average transaction per day.\n",
    "# men's street footwear has the highest transaction per day whereas, men's apparel has the least."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "cell_id": "37c725d06c274f4eaa1730ba920c336f",
    "deepnote_cell_type": "code",
    "deepnote_to_be_reexecuted": false,
    "execution_millis": 16,
    "execution_start": 1703563165987,
    "source_hash": "b4cbdf7a"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Sales_Method\n",
       "in-store    203.410458\n",
       "online      176.269773\n",
       "outlet      232.193750\n",
       "Name: Units_Sold, dtype: float64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_clean.groupby(['Sales_Method'])['Units_Sold'].std()\n",
    "#The belows lists the amount of units sold variation for each sales method\n",
    "#It is clear that 'outlet' has the highest variation which means that the number of units sold every day differs a lot, while 'online' has the lowest variation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "cell_id": "1a18189caf3847bc92c3e1a4c9658a66",
    "deepnote_cell_type": "code",
    "deepnote_to_be_reexecuted": false,
    "execution_millis": 28,
    "execution_start": 1703563165989,
    "source_hash": "c4de092f"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Product\n",
       "men's apparel                10783.845485\n",
       "men's athletic footwear      12707.987489\n",
       "men's street footwear        14978.931461\n",
       "women's apparel              14199.454793\n",
       "women's athletic footwear     9630.473858\n",
       "women's street footwear      11196.643353\n",
       "Name: Total_Sales, dtype: float64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_clean.groupby(['Product'])['Total_Sales'].std()\n",
    "#These values represent the spread or variability of 'Total_Sales' within each product category.\n",
    "# A higher standard deviation indicates greater variability in sales within that product category."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "cell_id": "6b1f00ab1d9f41a7b94481ef43076744",
    "deepnote_cell_type": "code",
    "deepnote_to_be_reexecuted": false,
    "execution_millis": 285,
    "execution_start": 1703563166005,
    "source_hash": "487f10bd"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1000x800 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 832,
       "width": 846
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "product_sales_profit = df_clean.groupby('Product')[['Total_Sales', 'Operating_Profit']].sum()\n",
    "\n",
    "plt.figure(figsize=[10,8])\n",
    "plt.bar(product_sales_profit.index, product_sales_profit['Total_Sales'], color='blue')\n",
    "plt.bar(product_sales_profit.index, product_sales_profit['Operating_Profit'], bottom=product_sales_profit['Total_Sales'], color='red')\n",
    "\n",
    "plt.title(\"Total Sales and Operating Profit by Product\", fontsize=18)\n",
    "plt.xlabel(\"Product\")\n",
    "plt.ylabel(\"Amount\")\n",
    "plt.legend([\"Operating Profit\", \"Total Sales\"])\n",
    "plt.xticks(rotation=45)\n",
    "plt.show()\n",
    "#This visualization shows operating profit and total salesby product. \n",
    "#It is clear that men's street footwear has the highest total sales and operating profit."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "cell_id": "d3f8780b7bc2461599cd69f420265b7e",
    "deepnote_cell_type": "text-cell-h3",
    "formattedRanges": []
   },
   "source": [
    "### Profitability Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "cell_id": "d5f9a6d8d7f74ac0ab974e8ac9897f89",
    "deepnote_cell_type": "code",
    "deepnote_to_be_reexecuted": false,
    "execution_millis": 18,
    "execution_start": 1703563166284,
    "source_hash": "b05ab260"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Product\n",
       "men's apparel                2679.415\n",
       "men's athletic footwear      3293.760\n",
       "men's street footwear        5201.500\n",
       "women's apparel              4004.200\n",
       "women's athletic footwear    2357.100\n",
       "women's street footwear      2703.000\n",
       "Name: Operating_Profit, dtype: float64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_clean.groupby(['Product'])['Operating_Profit'].median()\n",
    "#Considering median, there is more profit in men's street footwear and least in women's athletic footwear."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "cell_id": "b5cd611ed2674b5783ee3919952705d6",
    "deepnote_cell_type": "code",
    "deepnote_to_be_reexecuted": false,
    "execution_millis": 17,
    "execution_start": 1703563166290,
    "source_hash": "bc9894cc"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Product\n",
       "men's apparel                40.0\n",
       "men's athletic footwear      40.0\n",
       "men's street footwear        45.0\n",
       "women's apparel              44.0\n",
       "women's athletic footwear    41.0\n",
       "women's street footwear      40.0\n",
       "Name: Operating_Margin, dtype: float64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_clean.groupby(['Product'])['Operating_Margin'].median()\n",
    "#men's street footwear has the highest operating margin . And the general operating margin is 40%."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "cell_id": "1c63aea2b11d4a8cbd15dbac1c58b231",
    "deepnote_cell_type": "code",
    "deepnote_to_be_reexecuted": false,
    "execution_millis": 430,
    "execution_start": 1703563166301,
    "source_hash": "f52a1958"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1400x700 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 588,
       "width": 1156
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.dates as mdates\n",
    "\n",
    "df_clean['Year'] = df_clean['Invoice_Date'].dt.year\n",
    "df_clean['Month'] = df_clean['Invoice_Date'].dt.month\n",
    "monthly_data = df_clean.groupby(['Year', 'Month']).agg({\n",
    "    'Operating_Margin': 'median'\n",
    "}).reset_index()\n",
    "monthly_data['Date'] = pd.to_datetime(monthly_data[['Year', 'Month']].assign(day=1))\n",
    "\n",
    "plt.figure(figsize=(14, 7))\n",
    "plt.plot(monthly_data['Date'], monthly_data['Operating_Margin'], label='Operating Margin')\n",
    "\n",
    "plt.gca().xaxis.set_major_formatter(mdates.DateFormatter('%Y-%m'))\n",
    "plt.gca().xaxis.set_major_locator(mdates.MonthLocator())\n",
    "plt.gcf().autofmt_xdate() # for the x date angle\n",
    "\n",
    "plt.title(\"Median Operating Margin over Time\")\n",
    "plt.xlabel('Time')\n",
    "plt.ylabel('Amount')\n",
    "plt.legend()\n",
    "\n",
    "plt.show()\n",
    "#The below graph shows trend of median operating margin over time.\n",
    "#It is clear that the median operating margin is constantly changing over time and was the least in june 2020.\n",
    "#The highest median operating margin was in sept 2020"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "cell_id": "7a4cd4b543a647978fc8ee20251e03cf",
    "deepnote_cell_type": "code",
    "deepnote_to_be_reexecuted": false,
    "execution_millis": 27,
    "execution_start": 1703563166730,
    "source_hash": "2167aa56"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "223569.0"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Filter the dataframe for June 2020 and sales method as online\n",
    "filtered_data = df_clean[(df_clean['Year'] == 2020) & (df_clean['Month'] == 6) & (df_clean['Sales_Method'] == 'online')]\n",
    "\n",
    "# Calculate total sales\n",
    "total_sales = filtered_data['Total_Sales'].sum()\n",
    "total_sales\n",
    "#The total sales that happened via online in June 2020 is 223,569 units."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "cell_id": "8116cb904c4e4eb3a5672fdc8e2b615e",
    "deepnote_cell_type": "code",
    "deepnote_to_be_reexecuted": false,
    "execution_millis": 23,
    "execution_start": 1703563166763,
    "source_hash": "92158bda"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "456214.0"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "filtered_data = df_clean[(df_clean['Year'] == 2020) & (df_clean['Month'] == 9) & (df_clean['Sales_Method'] == 'online')]\n",
    "\n",
    "# Calculate total sales\n",
    "total_sales = filtered_data['Total_Sales'].sum()\n",
    "total_sales"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "cell_id": "c91972cd40e54433b1b63ed27ace2290",
    "deepnote_cell_type": "code",
    "deepnote_to_be_reexecuted": false,
    "execution_millis": 21,
    "execution_start": 1703563166775,
    "source_hash": "fc5cb923"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "42.078631683997514"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate total sales for June 2020\n",
    "total_sales_June2020 = df_clean[(df_clean['Year'] == 2020) & (df_clean['Month'] == 6)]['Total_Sales'].sum()\n",
    "\n",
    "# Calculate percentage of online sales for June 2020\n",
    "percentage_online_sales_June2020 = (total_sales / total_sales_June2020) * 100\n",
    "percentage_online_sales_June2020\n",
    "#The percentage of sales via online in June 2020 is approximately 20.62%."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "cell_id": "cff7efe834c34d46a91591f415ac3824",
    "deepnote_cell_type": "code",
    "deepnote_to_be_reexecuted": false,
    "execution_millis": 22,
    "execution_start": 1703563166778,
    "source_hash": "ed7f9fbb"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "19.268348928025084"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total_sales_June2020 = df_clean[(df_clean['Year'] == 2020) & (df_clean['Month'] == 9)]['Total_Sales'].sum()\n",
    "\n",
    "# Calculate percentage of online sales for Sept 2020\n",
    "percentage_online_sales_June2020 = (total_sales / total_sales_June2020) * 100\n",
    "percentage_online_sales_June2020"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "cell_id": "e9f5888fa4e6400080321f9a9da8dbb9",
    "deepnote_cell_type": "code",
    "deepnote_to_be_reexecuted": false,
    "execution_millis": 20,
    "execution_start": 1703563166787,
    "source_hash": "a7e323e3"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Kurtosis :  7.181164174449948\n",
      "Skewness :  2.334590765903441\n"
     ]
    }
   ],
   "source": [
    "print(\"Kurtosis : \", df_clean['Operating_Profit'].kurt())\n",
    "print(\"Skewness : \", df_clean['Operating_Profit'].skew())\n",
    "#The positive kurtosis suggests that the distribution has heavier tails and a sharper peak compared to a normal distribution.\n",
    "#The positive skewness suggests that the distribution is right-skewed, meaning it is stretched more to the right."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "cell_id": "1cf38f6c1b80464b9083afa5dd7ae8d4",
    "deepnote_cell_type": "code",
    "deepnote_to_be_reexecuted": false,
    "execution_millis": 18,
    "execution_start": 1703563166799,
    "source_hash": "8d0a158c"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Sales_Method\n",
       "in-store    0.363159\n",
       "online      0.251876\n",
       "outlet      0.148255\n",
       "Name: Operating_Margin, dtype: float64"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_clean.groupby(['Sales_Method'])['Operating_Margin'].skew()\n",
    "#The operating margin for each sales method is right skewed or positively skewed. And in-store has the highest skewness whereas, outlet has the lowest skewness.\n",
    "#This means that the right tail is longer and fatter than the left tail."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "cell_id": "9364965c77174e72b39053327c0e96e4",
    "deepnote_cell_type": "text-cell-h3",
    "formattedRanges": []
   },
   "source": [
    "### Regional Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "cell_id": "8118625e3b9a4e4fad7222a3286c3414",
    "deepnote_cell_type": "code",
    "deepnote_to_be_reexecuted": false,
    "execution_millis": 14,
    "execution_start": 1703563166802,
    "source_hash": "e34ca6c1"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Region wise:  Region\n",
      "midwest      16674434.0\n",
      "northeast    25078267.0\n",
      "south        20603356.0\n",
      "southeast    21374436.0\n",
      "west         36436157.0\n",
      "Name: Total_Sales, dtype: float64\n",
      "State wise:  new york\n",
      "City wise:  new york\n"
     ]
    }
   ],
   "source": [
    "print(\"Region wise: \",df_clean.groupby(['Region'])['Total_Sales'].sum())\n",
    "print(\"State wise: \", df_clean.groupby(['State'])['Total_Sales'].sum().idxmax())\n",
    "print(\"City wise: \", df_clean.groupby(['City'])['Total_Sales'].sum().idxmax())\n",
    "#In general the western region has highest sales. The State with highest sales is New York."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "cell_id": "b987ce705d094c6f91c2778177c77392",
    "deepnote_cell_type": "code",
    "deepnote_to_be_reexecuted": false,
    "execution_millis": 53,
    "execution_start": 1703563166813,
    "source_hash": "f1127427"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('west', \"men's athletic footwear\")\n",
      "('california', \"men's apparel\")\n",
      "('portland', \"men's apparel\")\n"
     ]
    }
   ],
   "source": [
    "print(df_clean.groupby(['Region'])['Product'].value_counts().idxmax())\n",
    "print(df_clean.groupby(['State'])['Product'].value_counts().idxmax())\n",
    "print(df_clean.groupby(['City'])['Product'].value_counts().idxmax())\n",
    "#The following gives the overall most popular Product in each of region, state and city division.\n",
    "#men's apparel is the most popular , city and state wise but on a bigger picture, men's athletic footwear is the most popular product in western region.\n",
    "#Note: Here popularity is taken as the number of times a particular product is listed and not number of products sold."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "cell_id": "8d26cc88a6324c86b86e519f49dbefe0",
    "deepnote_cell_type": "code",
    "deepnote_to_be_reexecuted": false,
    "execution_millis": 15,
    "execution_start": 1703563166862,
    "source_hash": "f5dbdcdc"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Region wise:  ('west', \"men's street footwear\")\n",
      "State wise:  ('new york', \"men's street footwear\")\n",
      "City wise:  ('charleston', \"men's street footwear\")\n"
     ]
    }
   ],
   "source": [
    "print(\"Region wise: \", df_clean.groupby(['Region', 'Product'])['Units_Sold'].sum().idxmax())\n",
    "print(\"State wise: \", df_clean.groupby(['State', 'Product'])['Units_Sold'].sum().idxmax())\n",
    "print(\"City wise: \", df_clean.groupby(['City', 'Product'])['Units_Sold'].sum().idxmax())\n",
    "#specific regional, state, city preferences for certain products are listed below. In each of them, men's street footwear is more preferred."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "cell_id": "6c677f0ca3bf43baafd257e24fed696c",
    "deepnote_cell_type": "code",
    "deepnote_to_be_reexecuted": false,
    "execution_millis": 15,
    "execution_start": 1703563166869,
    "source_hash": "7b13d26d"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Region     Sales_Method\n",
       "midwest    in-store        2316565.00\n",
       "           online          3133263.98\n",
       "           outlet          1410116.25\n",
       "northeast  in-store        4254420.00\n",
       "           online          2246831.65\n",
       "           outlet          3231522.25\n",
       "south      in-store         134800.00\n",
       "           online          4149888.22\n",
       "           outlet          4936917.10\n",
       "southeast  in-store        2558256.25\n",
       "           online          5080401.63\n",
       "           outlet           754401.32\n",
       "west       in-store        3495087.50\n",
       "           online          4942152.24\n",
       "           outlet          4580344.31\n",
       "Name: Operating_Profit, dtype: float64"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_clean.groupby(['Region', 'Sales_Method'])['Operating_Profit'].sum()\n",
    "#This tells us the best sales method for each region for max profit."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "cell_id": "426ced8c1814487889a52eb73d9f00b6",
    "deepnote_cell_type": "text-cell-h3",
    "formattedRanges": []
   },
   "source": [
    "### Retailer Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "cell_id": "e69baf6e394d458d8d3ebdd396c1a91c",
    "deepnote_cell_type": "code",
    "deepnote_to_be_reexecuted": false,
    "execution_millis": 267,
    "execution_start": 1703563166881,
    "source_hash": "a88b6006"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 800x800 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 658,
       "width": 661
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sales_by_location = df_clean.groupby('Retailer')['Total_Sales'].sum()\n",
    "\n",
    "# Plotting a pie chart for total sales by retailer\n",
    "plt.figure(figsize=(8, 8))\n",
    "sales_by_location.plot.pie(autopct='%1.1f%%', startangle=90, colors=['skyblue', 'lightgreen', 'lightcoral'])\n",
    "plt.title('Total Sales by Retailer')\n",
    "plt.ylabel('')\n",
    "plt.show()\n",
    "#The total sales is highest by the retailer - west gear followed by foot locker. \n",
    "#Even though online mode has the highest sales, amazon has the least total sales."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "cell_id": "73171230b66546e995c07151db40c1cc",
    "deepnote_cell_type": "code",
    "deepnote_to_be_reexecuted": false,
    "execution_millis": 139,
    "execution_start": 1703563167149,
    "source_hash": "84473510"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 800x800 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 658,
       "width": 668
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sales_by_location = df_clean.groupby('Retailer')['Operating_Profit'].sum()\n",
    "\n",
    "# Plotting a pie chart for operating profit by retailer\n",
    "plt.figure(figsize=(8, 8))\n",
    "sales_by_location.plot.pie(autopct='%1.1f%%', startangle=90, colors=['skyblue', 'lightgreen', 'lightcoral'])\n",
    "plt.title('Total Operating Profit by Retailer')\n",
    "plt.ylabel('')\n",
    "plt.show()\n",
    "#The operating profit is highest by the retailer - west gear followed by foot locker. \n",
    "#Even though online mode has the highest operating profit, amazon has the least operating profit."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "cell_id": "435b98ca924f438090ed1ab8b2322d5b",
    "deepnote_cell_type": "code",
    "deepnote_to_be_reexecuted": false,
    "execution_millis": 22,
    "execution_start": 1703563167350,
    "source_hash": "17057790"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Retailer       Sales_Method\n",
       "amazon         in-store         7083.972458\n",
       "               online           3773.055296\n",
       "               outlet           3818.121821\n",
       "foot locker    in-store         6256.570156\n",
       "               online           3717.388738\n",
       "               outlet           4189.243405\n",
       "kohl's         in-store         7359.071181\n",
       "               online           3831.638194\n",
       "               outlet           6179.129774\n",
       "sports direct  in-store         7035.712457\n",
       "               online           4542.114372\n",
       "               outlet           5457.985430\n",
       "walmart        in-store        13325.337838\n",
       "               online           4781.102743\n",
       "               outlet           6753.334784\n",
       "west gear      in-store         7868.115165\n",
       "               online           3859.311032\n",
       "               outlet           4260.573448\n",
       "Name: Operating_Profit, dtype: float64"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_clean.groupby(['Retailer', 'Sales_Method'])['Operating_Profit'].mean()\n",
    "# The below lists the average operating profit by each retailer.\n",
    "#Even though west gear had the highest total operating profit, walmart outperformed them in terms of average operational profits.\n",
    "# Even though online mode had the overall highest operating profit, considering each retailer's sales method, in-store had the highest operating profit."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "cell_id": "9a3667ee80d54826b026bdb72660a3f2",
    "deepnote_cell_type": "code",
    "deepnote_to_be_reexecuted": false,
    "execution_millis": 48,
    "execution_start": 1703563167367,
    "source_hash": "2ce8509d"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Retailer       Product                  \n",
       "amazon         men's athletic footwear      159\n",
       "               men's street footwear        159\n",
       "               women's apparel              159\n",
       "               women's athletic footwear    158\n",
       "               men's apparel                157\n",
       "               women's street footwear      157\n",
       "foot locker    men's street footwear        449\n",
       "               men's athletic footwear      442\n",
       "               women's athletic footwear    442\n",
       "               women's street footwear      438\n",
       "               men's apparel                433\n",
       "               women's apparel              433\n",
       "kohl's         men's athletic footwear      172\n",
       "               men's street footwear        172\n",
       "               women's apparel              172\n",
       "               women's street footwear      172\n",
       "               men's apparel                171\n",
       "               women's athletic footwear    171\n",
       "sports direct  women's street footwear      342\n",
       "               women's apparel              341\n",
       "               men's apparel                339\n",
       "               women's athletic footwear    338\n",
       "               men's athletic footwear      337\n",
       "               men's street footwear        335\n",
       "walmart        men's apparel                113\n",
       "               women's apparel              107\n",
       "               men's athletic footwear      104\n",
       "               women's athletic footwear    102\n",
       "               men's street footwear        101\n",
       "               women's street footwear       99\n",
       "west gear      women's street footwear      400\n",
       "               men's athletic footwear      396\n",
       "               women's apparel              396\n",
       "               women's athletic footwear    395\n",
       "               men's street footwear        394\n",
       "               men's apparel                393\n",
       "Name: Product, dtype: int64"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_clean.groupby('Retailer')['Product'].value_counts()\n",
    "#The below lists the most popular products sold by each retailer. This gives us an idea about how each retailer is making more profits through their products."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "cell_id": "8e696a1cad6a47eca5eed3bbaee224c9",
    "deepnote_cell_type": "text-cell-h3",
    "formattedRanges": []
   },
   "source": [
    "### Pricing Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "cell_id": "54c2e3d863104055a5f1b1e8fca14bd7",
    "deepnote_cell_type": "code",
    "deepnote_to_be_reexecuted": false,
    "execution_millis": 29,
    "execution_start": 1703563167395,
    "source_hash": "792518b"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.deepnote.dataframe.v3+json": {
       "column_count": 5,
       "columns": [
        {
         "dtype": "float64",
         "name": "Price_per_Unit",
         "stats": {
          "histogram": [
           {
            "bin_end": -0.023737834642533567,
            "bin_start": -0.1374864829361484,
            "count": 1
           },
           {
            "bin_end": 0.09001081365108127,
            "bin_start": -0.023737834642533567,
            "count": 0
           },
           {
            "bin_end": 0.20375946194469607,
            "bin_start": 0.09001081365108127,
            "count": 0
           },
           {
            "bin_end": 0.31750811023831094,
            "bin_start": 0.20375946194469607,
            "count": 1
           },
           {
            "bin_end": 0.4312567585319258,
            "bin_start": 0.31750811023831094,
            "count": 0
           },
           {
            "bin_end": 0.5450054068255406,
            "bin_start": 0.4312567585319258,
            "count": 2
           },
           {
            "bin_end": 0.6587540551191554,
            "bin_start": 0.5450054068255406,
            "count": 0
           },
           {
            "bin_end": 0.7725027034127703,
            "bin_start": 0.6587540551191554,
            "count": 0
           },
           {
            "bin_end": 0.886251351706385,
            "bin_start": 0.7725027034127703,
            "count": 0
           },
           {
            "bin_end": 1,
            "bin_start": 0.886251351706385,
            "count": 1
           }
          ],
          "max": "1.0",
          "min": "-0.1374864829361484",
          "nan_count": 0,
          "unique_count": 5
         }
        },
        {
         "dtype": "float64",
         "name": "Units_Sold",
         "stats": {
          "histogram": [
           {
            "bin_end": -0.17493131379816187,
            "bin_start": -0.3054792375535132,
            "count": 1
           },
           {
            "bin_end": -0.044383390042810555,
            "bin_start": -0.17493131379816187,
            "count": 0
           },
           {
            "bin_end": 0.08616453371254079,
            "bin_start": -0.044383390042810555,
            "count": 0
           },
           {
            "bin_end": 0.21671245746789208,
            "bin_start": 0.08616453371254079,
            "count": 0
           },
           {
            "bin_end": 0.3472603812232434,
            "bin_start": 0.21671245746789208,
            "count": 1
           },
           {
            "bin_end": 0.4778083049785948,
            "bin_start": 0.3472603812232434,
            "count": 0
           },
           {
            "bin_end": 0.6083562287339461,
            "bin_start": 0.4778083049785948,
            "count": 0
           },
           {
            "bin_end": 0.7389041524892974,
            "bin_start": 0.6083562287339461,
            "count": 0
           },
           {
            "bin_end": 0.8694520762446487,
            "bin_start": 0.7389041524892974,
            "count": 0
           },
           {
            "bin_end": 1,
            "bin_start": 0.8694520762446487,
            "count": 3
           }
          ],
          "max": "1.0",
          "min": "-0.3054792375535132",
          "nan_count": 0,
          "unique_count": 5
         }
        },
        {
         "dtype": "float64",
         "name": "Total_Sales",
         "stats": {
          "histogram": [
           {
            "bin_end": -0.17206588741686876,
            "bin_start": -0.3022954304631875,
            "count": 1
           },
           {
            "bin_end": -0.041836344370550005,
            "bin_start": -0.17206588741686876,
            "count": 0
           },
           {
            "bin_end": 0.08839319867576878,
            "bin_start": -0.041836344370550005,
            "count": 0
           },
           {
            "bin_end": 0.2186227417220875,
            "bin_start": 0.08839319867576878,
            "count": 0
           },
           {
            "bin_end": 0.34885228476840624,
            "bin_start": 0.2186227417220875,
            "count": 0
           },
           {
            "bin_end": 0.4790818278147251,
            "bin_start": 0.34885228476840624,
            "count": 0
           },
           {
            "bin_end": 0.6093113708610438,
            "bin_start": 0.4790818278147251,
            "count": 1
           },
           {
            "bin_end": 0.7395409139073625,
            "bin_start": 0.6093113708610438,
            "count": 0
           },
           {
            "bin_end": 0.8697704569536813,
            "bin_start": 0.7395409139073625,
            "count": 0
           },
           {
            "bin_end": 1,
            "bin_start": 0.8697704569536813,
            "count": 3
           }
          ],
          "max": "1.0",
          "min": "-0.3022954304631875",
          "nan_count": 0,
          "unique_count": 5
         }
        },
        {
         "dtype": "float64",
         "name": "Operating_Profit",
         "stats": {
          "histogram": [
           {
            "bin_end": 0.057258179977764515,
            "bin_start": -0.047490911135817215,
            "count": 1
           },
           {
            "bin_end": 0.16200727109134624,
            "bin_start": 0.057258179977764515,
            "count": 0
           },
           {
            "bin_end": 0.266756362204928,
            "bin_start": 0.16200727109134624,
            "count": 0
           },
           {
            "bin_end": 0.3715054533185097,
            "bin_start": 0.266756362204928,
            "count": 0
           },
           {
            "bin_end": 0.4762545444320914,
            "bin_start": 0.3715054533185097,
            "count": 0
           },
           {
            "bin_end": 0.5810036355456731,
            "bin_start": 0.4762545444320914,
            "count": 1
           },
           {
            "bin_end": 0.6857527266592549,
            "bin_start": 0.5810036355456731,
            "count": 0
           },
           {
            "bin_end": 0.7905018177728367,
            "bin_start": 0.6857527266592549,
            "count": 0
           },
           {
            "bin_end": 0.8952509088864185,
            "bin_start": 0.7905018177728367,
            "count": 1
           },
           {
            "bin_end": 1,
            "bin_start": 0.8952509088864185,
            "count": 2
           }
          ],
          "max": "1.0",
          "min": "-0.047490911135817215",
          "nan_count": 0,
          "unique_count": 5
         }
        },
        {
         "dtype": "float64",
         "name": "Operating_Margin",
         "stats": {
          "histogram": [
           {
            "bin_end": -0.17493131379816187,
            "bin_start": -0.3054792375535132,
            "count": 2
           },
           {
            "bin_end": -0.044383390042810555,
            "bin_start": -0.17493131379816187,
            "count": 2
           },
           {
            "bin_end": 0.08616453371254079,
            "bin_start": -0.044383390042810555,
            "count": 0
           },
           {
            "bin_end": 0.21671245746789208,
            "bin_start": 0.08616453371254079,
            "count": 0
           },
           {
            "bin_end": 0.3472603812232434,
            "bin_start": 0.21671245746789208,
            "count": 0
           },
           {
            "bin_end": 0.4778083049785948,
            "bin_start": 0.3472603812232434,
            "count": 0
           },
           {
            "bin_end": 0.6083562287339461,
            "bin_start": 0.4778083049785948,
            "count": 0
           },
           {
            "bin_end": 0.7389041524892974,
            "bin_start": 0.6083562287339461,
            "count": 0
           },
           {
            "bin_end": 0.8694520762446487,
            "bin_start": 0.7389041524892974,
            "count": 0
           },
           {
            "bin_end": 1,
            "bin_start": 0.8694520762446487,
            "count": 1
           }
          ],
          "max": "1.0",
          "min": "-0.3054792375535132",
          "nan_count": 0,
          "unique_count": 5
         }
        },
        {
         "dtype": "object",
         "name": "_deepnote_index_column"
        }
       ],
       "row_count": 5,
       "rows": [
        {
         "Operating_Margin": -0.1374864829361484,
         "Operating_Profit": 0.5036827423355905,
         "Price_per_Unit": 1,
         "Total_Sales": 0.5395466820338701,
         "Units_Sold": 0.2658685038122002,
         "_deepnote_index_column": "Price_per_Unit"
        },
        {
         "Operating_Margin": -0.3054792375535132,
         "Operating_Profit": 0.8719927612728451,
         "Price_per_Unit": 0.2658685038122002,
         "Total_Sales": 0.9193389329179877,
         "Units_Sold": 1,
         "_deepnote_index_column": "Units_Sold"
        },
        {
         "Operating_Margin": -0.3022954304631875,
         "Operating_Profit": 0.9353716555885865,
         "Price_per_Unit": 0.5395466820338701,
         "Total_Sales": 1,
         "Units_Sold": 0.9193389329179877,
         "_deepnote_index_column": "Total_Sales"
        },
        {
         "Operating_Margin": -0.047490911135817215,
         "Operating_Profit": 1,
         "Price_per_Unit": 0.5036827423355905,
         "Total_Sales": 0.9353716555885865,
         "Units_Sold": 0.8719927612728451,
         "_deepnote_index_column": "Operating_Profit"
        },
        {
         "Operating_Margin": 1,
         "Operating_Profit": -0.047490911135817215,
         "Price_per_Unit": -0.1374864829361484,
         "Total_Sales": -0.3022954304631875,
         "Units_Sold": -0.3054792375535132,
         "_deepnote_index_column": "Operating_Margin"
        }
       ]
      },
      "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>Price_per_Unit</th>\n",
       "      <th>Units_Sold</th>\n",
       "      <th>Total_Sales</th>\n",
       "      <th>Operating_Profit</th>\n",
       "      <th>Operating_Margin</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Price_per_Unit</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.265869</td>\n",
       "      <td>0.539547</td>\n",
       "      <td>0.503683</td>\n",
       "      <td>-0.137486</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Units_Sold</th>\n",
       "      <td>0.265869</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.919339</td>\n",
       "      <td>0.871993</td>\n",
       "      <td>-0.305479</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Total_Sales</th>\n",
       "      <td>0.539547</td>\n",
       "      <td>0.919339</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.935372</td>\n",
       "      <td>-0.302295</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Operating_Profit</th>\n",
       "      <td>0.503683</td>\n",
       "      <td>0.871993</td>\n",
       "      <td>0.935372</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.047491</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Operating_Margin</th>\n",
       "      <td>-0.137486</td>\n",
       "      <td>-0.305479</td>\n",
       "      <td>-0.302295</td>\n",
       "      <td>-0.047491</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Price_per_Unit  Units_Sold  Total_Sales  Operating_Profit  \\\n",
       "Price_per_Unit          1.000000    0.265869     0.539547          0.503683   \n",
       "Units_Sold              0.265869    1.000000     0.919339          0.871993   \n",
       "Total_Sales             0.539547    0.919339     1.000000          0.935372   \n",
       "Operating_Profit        0.503683    0.871993     0.935372          1.000000   \n",
       "Operating_Margin       -0.137486   -0.305479    -0.302295         -0.047491   \n",
       "\n",
       "                  Operating_Margin  \n",
       "Price_per_Unit           -0.137486  \n",
       "Units_Sold               -0.305479  \n",
       "Total_Sales              -0.302295  \n",
       "Operating_Profit         -0.047491  \n",
       "Operating_Margin          1.000000  "
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_clean[['Price_per_Unit','Units_Sold', 'Total_Sales', 'Operating_Profit', 'Operating_Margin']].corr()\n",
    "# If Price per unit is more, the total sales & operating profit will be more since they are positively correlated.\n",
    "# Whereas, price per unit and operating margin has negative weak correaltion.\n",
    "# Price per unit and units sold are positively weakly correlated. This wouldn't guarantee that more price per unit would mean more units sold to some extent.\n",
    "# units sold and operating profit have positive strong correlation which is by logic correct and same with units sold and total sales.\n",
    "# Interestingly, operating margin and units sold are negatively moderately correlated.\n",
    "# As operating margin and unit sold is negatively moderately correlated, so we can say that the most sales/more units would be sold due to excess money spent on ads and hence we could justify that maybe the sales are high but the profit margin is comparitively less."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "cell_id": "aeb954a055614231b935b616ea779314",
    "deepnote_cell_type": "code",
    "deepnote_to_be_reexecuted": false,
    "execution_millis": 278,
    "execution_start": 1703563167452,
    "source_hash": "5ec9fa5e"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1000x800 with 2 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 682,
       "width": 776
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "heatmap = df_clean[['Price_per_Unit','Units_Sold', 'Total_Sales', 'Operating_Profit', 'Operating_Margin']]\n",
    "plt.figure(figsize=(10, 8))\n",
    "sns.heatmap(heatmap.corr(), cmap='viridis', annot=True, fmt=\".2f\", linewidths=.5)\n",
    "plt.title('Heatmap')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "cell_id": "a623a5485b4e49d7bc6cfbbc238f9cdd",
    "deepnote_cell_type": "code",
    "deepnote_to_be_reexecuted": false,
    "execution_millis": 341,
    "execution_start": 1703563167731,
    "source_hash": "28e14492"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1000x800 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 827,
       "width": 850
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 8))\n",
    "sns.boxplot(x='Product', y='Price_per_Unit', data=df_clean)\n",
    "plt.title('Box Plot of Price Per Unit Grouped by Product')\n",
    "plt.xlabel('Product')\n",
    "plt.ylabel('Price Per Unit')\n",
    "plt.xticks(rotation=45)\n",
    "plt.show()\n",
    "#Generally, price per unit for each product has normal distribution but for \"men's athletic footwear\" it is slightly left skewed\n",
    "# and for \"women's athletic footwear\" it is slightly right skewed.\n",
    "\n",
    "#The median price per unit for \"men's apparel\" and \"women's apparel\" is the highest.\n",
    "#\"women's street footwear\" and \"women's athletic footwear\" has the lowest median price per unit.\n",
    "#There are many outliers towards the right side of the graph for each product."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "cell_id": "9eed351a214446cdab80f574659068bd",
    "deepnote_cell_type": "code",
    "deepnote_to_be_reexecuted": false,
    "execution_millis": 366,
    "execution_start": 1703563168073,
    "source_hash": "486b92d4"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1000x800 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 827,
       "width": 841
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 8))\n",
    "sns.boxplot(x='Product', y='Operating_Margin', data=df_clean)\n",
    "plt.title('Box Plot of Operating Margin Grouped by Product')\n",
    "plt.xlabel('Product')\n",
    "plt.ylabel('Operating Margin')\n",
    "plt.xticks(rotation=45)\n",
    "plt.show()\n",
    "#In general, the operating margin for each product has slight right skewness, \n",
    "# but for \"men's street footwear\" and \"women's apparel\" it is normal distributed."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "cell_id": "1be9373b49614a2eb8ee70ed4295cde6",
    "deepnote_cell_type": "text-cell-p",
    "formattedRanges": []
   },
   "source": [
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "cell_id": "4d50077b53f14297bd9822176f3a0aa7",
    "deepnote_cell_type": "code",
    "deepnote_to_be_reexecuted": false,
    "execution_millis": 420,
    "execution_start": 1703563459254,
    "source_hash": "f46515d1"
   },
   "outputs": [],
   "source": [
    "final_table = df_clean.groupby(['Region', 'State', 'City','Sales_Method','Product']).Operating_Profit.median()\n",
    "final_table = final_table.sort_values(ascending=False)\n",
    "final_table.to_csv('Final_Profit_dataset.csv')\n",
    "#This creates a new csv file which groups region, state, city, sales method and product based on median operating profits which helps the company make better decisions while investing to gain maximum profits."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "cell_id": "d776a468adfd4defb7fba06541e3e617",
    "deepnote_cell_type": "code",
    "deepnote_to_be_reexecuted": true,
    "source_hash": "b623e53d"
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "deepnote": {},
  "deepnote_execution_queue": [],
  "deepnote_notebook_id": "478635606ba0489b90efb95bf5412b0f",
  "deepnote_persisted_session": {
   "createdAt": "2023-12-26T03:45:35.800Z"
  },
  "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": "3.11.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}