Diwaly_Sales_Analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "3a2f2cee-5baf-42de-bf83-51014633d6e6",
   "metadata": {},
   "source": [
    "# **Diwaly_Sales_Analysis project**\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "3f15fcde-b4b2-4cd5-93a1-55d6cb84e746",
   "metadata": {},
   "outputs": [],
   "source": [
    "# library's for data visualizations\n",
    "import numpy as pd\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt # visualizing Data\n",
    "%matplotlib inline\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "26cbab94-79b7-49ae-a31d-6c4eae1b3f4b",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('Diwali Sales Data.csv',encoding = 'unicode_escape')\n",
    "# we used encoding to avoide error."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "4de476da-472b-4a1a-a361-f840b1afb1db",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(11251, 15)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape # provide the numbers of rows and colums in the table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "144ca24c-c7c2-4533-8e6b-b794dc7f0639",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>User_ID</th>\n",
       "      <th>Cust_name</th>\n",
       "      <th>Product_ID</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age Group</th>\n",
       "      <th>Age</th>\n",
       "      <th>Marital_Status</th>\n",
       "      <th>State</th>\n",
       "      <th>Zone</th>\n",
       "      <th>Occupation</th>\n",
       "      <th>Product_Category</th>\n",
       "      <th>Orders</th>\n",
       "      <th>Amount</th>\n",
       "      <th>Status</th>\n",
       "      <th>unnamed1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1002903</td>\n",
       "      <td>Sanskriti</td>\n",
       "      <td>P00125942</td>\n",
       "      <td>F</td>\n",
       "      <td>26-35</td>\n",
       "      <td>28</td>\n",
       "      <td>0</td>\n",
       "      <td>Maharashtra</td>\n",
       "      <td>Western</td>\n",
       "      <td>Healthcare</td>\n",
       "      <td>Auto</td>\n",
       "      <td>1</td>\n",
       "      <td>23952.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1000732</td>\n",
       "      <td>Kartik</td>\n",
       "      <td>P00110942</td>\n",
       "      <td>F</td>\n",
       "      <td>26-35</td>\n",
       "      <td>35</td>\n",
       "      <td>1</td>\n",
       "      <td>Andhra Pradesh</td>\n",
       "      <td>Southern</td>\n",
       "      <td>Govt</td>\n",
       "      <td>Auto</td>\n",
       "      <td>3</td>\n",
       "      <td>23934.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1001990</td>\n",
       "      <td>Bindu</td>\n",
       "      <td>P00118542</td>\n",
       "      <td>F</td>\n",
       "      <td>26-35</td>\n",
       "      <td>35</td>\n",
       "      <td>1</td>\n",
       "      <td>Uttar Pradesh</td>\n",
       "      <td>Central</td>\n",
       "      <td>Automobile</td>\n",
       "      <td>Auto</td>\n",
       "      <td>3</td>\n",
       "      <td>23924.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1001425</td>\n",
       "      <td>Sudevi</td>\n",
       "      <td>P00237842</td>\n",
       "      <td>M</td>\n",
       "      <td>0-17</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>Karnataka</td>\n",
       "      <td>Southern</td>\n",
       "      <td>Construction</td>\n",
       "      <td>Auto</td>\n",
       "      <td>2</td>\n",
       "      <td>23912.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1000588</td>\n",
       "      <td>Joni</td>\n",
       "      <td>P00057942</td>\n",
       "      <td>M</td>\n",
       "      <td>26-35</td>\n",
       "      <td>28</td>\n",
       "      <td>1</td>\n",
       "      <td>Gujarat</td>\n",
       "      <td>Western</td>\n",
       "      <td>Food Processing</td>\n",
       "      <td>Auto</td>\n",
       "      <td>2</td>\n",
       "      <td>23877.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "   User_ID  Cust_name Product_ID Gender Age Group  Age  Marital_Status  \\\n",
       "0  1002903  Sanskriti  P00125942      F     26-35   28               0   \n",
       "1  1000732     Kartik  P00110942      F     26-35   35               1   \n",
       "2  1001990      Bindu  P00118542      F     26-35   35               1   \n",
       "3  1001425     Sudevi  P00237842      M      0-17   16               0   \n",
       "4  1000588       Joni  P00057942      M     26-35   28               1   \n",
       "\n",
       "            State      Zone       Occupation Product_Category  Orders  \\\n",
       "0     Maharashtra   Western       Healthcare             Auto       1   \n",
       "1  Andhra Pradesh  Southern             Govt             Auto       3   \n",
       "2   Uttar Pradesh   Central       Automobile             Auto       3   \n",
       "3       Karnataka  Southern     Construction             Auto       2   \n",
       "4         Gujarat   Western  Food Processing             Auto       2   \n",
       "\n",
       "    Amount  Status  unnamed1  \n",
       "0  23952.0     NaN       NaN  \n",
       "1  23934.0     NaN       NaN  \n",
       "2  23924.0     NaN       NaN  \n",
       "3  23912.0     NaN       NaN  \n",
       "4  23877.0     NaN       NaN  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head() # it use to give the top values of csv file to see weather file is uploded correct or not "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "8bc7d173-78a1-474c-b9e3-f0a88434ed98",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 11251 entries, 0 to 11250\n",
      "Data columns (total 15 columns):\n",
      " #   Column            Non-Null Count  Dtype  \n",
      "---  ------            --------------  -----  \n",
      " 0   User_ID           11251 non-null  int64  \n",
      " 1   Cust_name         11251 non-null  object \n",
      " 2   Product_ID        11251 non-null  object \n",
      " 3   Gender            11251 non-null  object \n",
      " 4   Age Group         11251 non-null  object \n",
      " 5   Age               11251 non-null  int64  \n",
      " 6   Marital_Status    11251 non-null  int64  \n",
      " 7   State             11251 non-null  object \n",
      " 8   Zone              11251 non-null  object \n",
      " 9   Occupation        11251 non-null  object \n",
      " 10  Product_Category  11251 non-null  object \n",
      " 11  Orders            11251 non-null  int64  \n",
      " 12  Amount            11239 non-null  float64\n",
      " 13  Status            0 non-null      float64\n",
      " 14  unnamed1          0 non-null      float64\n",
      "dtypes: float64(3), int64(4), object(8)\n",
      "memory usage: 1.3+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info() # provide the information about the csv file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "1dabfe05-4e6b-4b99-9794-273b87f157c6",
   "metadata": {},
   "outputs": [],
   "source": [
    "#to drop the unrelated / Blank coloum \n",
    "df.drop([\"Status\",\"unnamed1\"], axis=1 ,inplace = True)\n",
    "# axis means hole row where we are going to perform this \n",
    "# inplace means whatever we did in above line it will save that"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "7723d8b8-d400-463c-8fa4-950c9b057ed1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 11251 entries, 0 to 11250\n",
      "Data columns (total 13 columns):\n",
      " #   Column            Non-Null Count  Dtype  \n",
      "---  ------            --------------  -----  \n",
      " 0   User_ID           11251 non-null  int64  \n",
      " 1   Cust_name         11251 non-null  object \n",
      " 2   Product_ID        11251 non-null  object \n",
      " 3   Gender            11251 non-null  object \n",
      " 4   Age Group         11251 non-null  object \n",
      " 5   Age               11251 non-null  int64  \n",
      " 6   Marital_Status    11251 non-null  int64  \n",
      " 7   State             11251 non-null  object \n",
      " 8   Zone              11251 non-null  object \n",
      " 9   Occupation        11251 non-null  object \n",
      " 10  Product_Category  11251 non-null  object \n",
      " 11  Orders            11251 non-null  int64  \n",
      " 12  Amount            11239 non-null  float64\n",
      "dtypes: float64(1), int64(4), object(8)\n",
      "memory usage: 1.1+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info() # updated information "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "3251dd28-8eb4-4c71-a467-c23f73704d63",
   "metadata": {},
   "outputs": [
    {
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      ],
      "text/plain": [
       "       User_ID  Cust_name  Product_ID  Gender  Age Group    Age  \\\n",
       "0        False      False       False   False      False  False   \n",
       "1        False      False       False   False      False  False   \n",
       "2        False      False       False   False      False  False   \n",
       "3        False      False       False   False      False  False   \n",
       "4        False      False       False   False      False  False   \n",
       "...        ...        ...         ...     ...        ...    ...   \n",
       "11246    False      False       False   False      False  False   \n",
       "11247    False      False       False   False      False  False   \n",
       "11248    False      False       False   False      False  False   \n",
       "11249    False      False       False   False      False  False   \n",
       "11250    False      False       False   False      False  False   \n",
       "\n",
       "       Marital_Status  State   Zone  Occupation  Product_Category  Orders  \\\n",
       "0               False  False  False       False             False   False   \n",
       "1               False  False  False       False             False   False   \n",
       "2               False  False  False       False             False   False   \n",
       "3               False  False  False       False             False   False   \n",
       "4               False  False  False       False             False   False   \n",
       "...               ...    ...    ...         ...               ...     ...   \n",
       "11246           False  False  False       False             False   False   \n",
       "11247           False  False  False       False             False   False   \n",
       "11248           False  False  False       False             False   False   \n",
       "11249           False  False  False       False             False   False   \n",
       "11250           False  False  False       False             False   False   \n",
       "\n",
       "       Amount  \n",
       "0       False  \n",
       "1       False  \n",
       "2       False  \n",
       "3       False  \n",
       "4       False  \n",
       "...       ...  \n",
       "11246   False  \n",
       "11247   False  \n",
       "11248   False  \n",
       "11249   False  \n",
       "11250   False  \n",
       "\n",
       "[11251 rows x 13 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.isnull(df)# will tell that whereever ther is false that is not null "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "a3ad8caf-7ce2-4520-a8fc-70793eaed163",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "User_ID              0\n",
       "Cust_name            0\n",
       "Product_ID           0\n",
       "Gender               0\n",
       "Age Group            0\n",
       "Age                  0\n",
       "Marital_Status       0\n",
       "State                0\n",
       "Zone                 0\n",
       "Occupation           0\n",
       "Product_Category     0\n",
       "Orders               0\n",
       "Amount              12\n",
       "dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.isnull(df).sum() # here it will tell u that sum of null values is that perticular column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "5e745992-b402-4210-8da0-bc6b7410db9d",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.dropna(inplace=True) # to remove the null values from the table we us dropna"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "f866c32e-a091-49a6-a5c1-f00cce8a6217",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(11239, 13)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape # updated values"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a0e21efc-33b1-40e5-b31d-9df03b904994",
   "metadata": {},
   "source": [
    "## Both are same\n",
    "df_test.dropna(inplace=True)\n",
    "\n",
    "df_test=df_test.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "8f5127d0-8023-417d-a965-635724483746",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Sai</td>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Gopal</td>\n",
       "      <td>29.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>yash</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Harsh</td>\n",
       "      <td>19.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Name   Age\n",
       "0    Sai  11.0\n",
       "1  Gopal  29.0\n",
       "2   yash   NaN\n",
       "3  Harsh  19.0"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# initialize list of lists\n",
    "data_set =[['Sai',11],['Gopal',29],['yash',],['Harsh',19]] # here we just assign the values \n",
    "\n",
    "# creat the pandas DataFrame using list \n",
    "df_test = pd.DataFrame(data_set,columns=['Name','Age']) # this how we use to create the dataframe \n",
    "\n",
    "df_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "18cc5253-3374-432d-8bda-ed61d5781742",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Sai</td>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Gopal</td>\n",
       "      <td>29.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Harsh</td>\n",
       "      <td>19.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Name   Age\n",
       "0    Sai  11.0\n",
       "1  Gopal  29.0\n",
       "3  Harsh  19.0"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test.dropna() # here we got the output which we want that delet null "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "d90957de-6d44-4ad3-9b49-c2bfd2dd0bf8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Sai</td>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Gopal</td>\n",
       "      <td>29.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>yash</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Harsh</td>\n",
       "      <td>19.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Name   Age\n",
       "0    Sai  11.0\n",
       "1  Gopal  29.0\n",
       "2   yash   NaN\n",
       "3  Harsh  19.0"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test # but aganin here we got that null again because we did use inplace , it worke as save the thing which we did "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "78005cce-1d4e-4221-b074-b649b7faeaf4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# To change data type \n",
    "df['Amount'] = df['Amount'].astype('int')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "b7fbd8fd-4c03-450c-92e8-bf9ad5ba34d9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('int32')"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# to cheak the datatype\n",
    "df['Amount'].dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "8f38206d-9ac8-4eb2-8996-96576f48db85",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 11239 entries, 0 to 11250\n",
      "Data columns (total 13 columns):\n",
      " #   Column            Non-Null Count  Dtype \n",
      "---  ------            --------------  ----- \n",
      " 0   User_ID           11239 non-null  int64 \n",
      " 1   Cust_name         11239 non-null  object\n",
      " 2   Product_ID        11239 non-null  object\n",
      " 3   Gender            11239 non-null  object\n",
      " 4   Age Group         11239 non-null  object\n",
      " 5   Age               11239 non-null  int64 \n",
      " 6   Marital_Status    11239 non-null  int64 \n",
      " 7   State             11239 non-null  object\n",
      " 8   Zone              11239 non-null  object\n",
      " 9   Occupation        11239 non-null  object\n",
      " 10  Product_Category  11239 non-null  object\n",
      " 11  Orders            11239 non-null  int64 \n",
      " 12  Amount            11239 non-null  int32 \n",
      "dtypes: int32(1), int64(4), object(8)\n",
      "memory usage: 1.2+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info() # to cheak the updated values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "64797a75-e2aa-4367-be79-b574c5c76c9c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['User_ID', 'Cust_name', 'Product_ID', 'Gender', 'Age Group', 'Age',\n",
       "       'Marital_Status', 'State', 'Zone', 'Occupation', 'Product_Category',\n",
       "       'Orders', 'Amount'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# To cheak the colums we can use \n",
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "5b9a3fa4-45c6-4b66-901f-48af0a7f2287",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>User_ID</th>\n",
       "      <th>Cust_name</th>\n",
       "      <th>Product_ID</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age Group</th>\n",
       "      <th>Age</th>\n",
       "      <th>Single/Engaged</th>\n",
       "      <th>State</th>\n",
       "      <th>Zone</th>\n",
       "      <th>Occupation</th>\n",
       "      <th>Product_Category</th>\n",
       "      <th>Orders</th>\n",
       "      <th>Amount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1002903</td>\n",
       "      <td>Sanskriti</td>\n",
       "      <td>P00125942</td>\n",
       "      <td>F</td>\n",
       "      <td>26-35</td>\n",
       "      <td>28</td>\n",
       "      <td>0</td>\n",
       "      <td>Maharashtra</td>\n",
       "      <td>Western</td>\n",
       "      <td>Healthcare</td>\n",
       "      <td>Auto</td>\n",
       "      <td>1</td>\n",
       "      <td>23952</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1000732</td>\n",
       "      <td>Kartik</td>\n",
       "      <td>P00110942</td>\n",
       "      <td>F</td>\n",
       "      <td>26-35</td>\n",
       "      <td>35</td>\n",
       "      <td>1</td>\n",
       "      <td>Andhra Pradesh</td>\n",
       "      <td>Southern</td>\n",
       "      <td>Govt</td>\n",
       "      <td>Auto</td>\n",
       "      <td>3</td>\n",
       "      <td>23934</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1001990</td>\n",
       "      <td>Bindu</td>\n",
       "      <td>P00118542</td>\n",
       "      <td>F</td>\n",
       "      <td>26-35</td>\n",
       "      <td>35</td>\n",
       "      <td>1</td>\n",
       "      <td>Uttar Pradesh</td>\n",
       "      <td>Central</td>\n",
       "      <td>Automobile</td>\n",
       "      <td>Auto</td>\n",
       "      <td>3</td>\n",
       "      <td>23924</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1001425</td>\n",
       "      <td>Sudevi</td>\n",
       "      <td>P00237842</td>\n",
       "      <td>M</td>\n",
       "      <td>0-17</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>Karnataka</td>\n",
       "      <td>Southern</td>\n",
       "      <td>Construction</td>\n",
       "      <td>Auto</td>\n",
       "      <td>2</td>\n",
       "      <td>23912</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1000588</td>\n",
       "      <td>Joni</td>\n",
       "      <td>P00057942</td>\n",
       "      <td>M</td>\n",
       "      <td>26-35</td>\n",
       "      <td>28</td>\n",
       "      <td>1</td>\n",
       "      <td>Gujarat</td>\n",
       "      <td>Western</td>\n",
       "      <td>Food Processing</td>\n",
       "      <td>Auto</td>\n",
       "      <td>2</td>\n",
       "      <td>23877</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11246</th>\n",
       "      <td>1000695</td>\n",
       "      <td>Manning</td>\n",
       "      <td>P00296942</td>\n",
       "      <td>M</td>\n",
       "      <td>18-25</td>\n",
       "      <td>19</td>\n",
       "      <td>1</td>\n",
       "      <td>Maharashtra</td>\n",
       "      <td>Western</td>\n",
       "      <td>Chemical</td>\n",
       "      <td>Office</td>\n",
       "      <td>4</td>\n",
       "      <td>370</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11247</th>\n",
       "      <td>1004089</td>\n",
       "      <td>Reichenbach</td>\n",
       "      <td>P00171342</td>\n",
       "      <td>M</td>\n",
       "      <td>26-35</td>\n",
       "      <td>33</td>\n",
       "      <td>0</td>\n",
       "      <td>Haryana</td>\n",
       "      <td>Northern</td>\n",
       "      <td>Healthcare</td>\n",
       "      <td>Veterinary</td>\n",
       "      <td>3</td>\n",
       "      <td>367</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11248</th>\n",
       "      <td>1001209</td>\n",
       "      <td>Oshin</td>\n",
       "      <td>P00201342</td>\n",
       "      <td>F</td>\n",
       "      <td>36-45</td>\n",
       "      <td>40</td>\n",
       "      <td>0</td>\n",
       "      <td>Madhya Pradesh</td>\n",
       "      <td>Central</td>\n",
       "      <td>Textile</td>\n",
       "      <td>Office</td>\n",
       "      <td>4</td>\n",
       "      <td>213</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11249</th>\n",
       "      <td>1004023</td>\n",
       "      <td>Noonan</td>\n",
       "      <td>P00059442</td>\n",
       "      <td>M</td>\n",
       "      <td>36-45</td>\n",
       "      <td>37</td>\n",
       "      <td>0</td>\n",
       "      <td>Karnataka</td>\n",
       "      <td>Southern</td>\n",
       "      <td>Agriculture</td>\n",
       "      <td>Office</td>\n",
       "      <td>3</td>\n",
       "      <td>206</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11250</th>\n",
       "      <td>1002744</td>\n",
       "      <td>Brumley</td>\n",
       "      <td>P00281742</td>\n",
       "      <td>F</td>\n",
       "      <td>18-25</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "      <td>Maharashtra</td>\n",
       "      <td>Western</td>\n",
       "      <td>Healthcare</td>\n",
       "      <td>Office</td>\n",
       "      <td>3</td>\n",
       "      <td>188</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>11239 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       User_ID    Cust_name Product_ID Gender Age Group  Age  Single/Engaged  \\\n",
       "0      1002903    Sanskriti  P00125942      F     26-35   28               0   \n",
       "1      1000732       Kartik  P00110942      F     26-35   35               1   \n",
       "2      1001990        Bindu  P00118542      F     26-35   35               1   \n",
       "3      1001425       Sudevi  P00237842      M      0-17   16               0   \n",
       "4      1000588         Joni  P00057942      M     26-35   28               1   \n",
       "...        ...          ...        ...    ...       ...  ...             ...   \n",
       "11246  1000695      Manning  P00296942      M     18-25   19               1   \n",
       "11247  1004089  Reichenbach  P00171342      M     26-35   33               0   \n",
       "11248  1001209        Oshin  P00201342      F     36-45   40               0   \n",
       "11249  1004023       Noonan  P00059442      M     36-45   37               0   \n",
       "11250  1002744      Brumley  P00281742      F     18-25   19               0   \n",
       "\n",
       "                State      Zone       Occupation Product_Category  Orders  \\\n",
       "0         Maharashtra   Western       Healthcare             Auto       1   \n",
       "1      Andhra Pradesh  Southern             Govt             Auto       3   \n",
       "2       Uttar Pradesh   Central       Automobile             Auto       3   \n",
       "3           Karnataka  Southern     Construction             Auto       2   \n",
       "4             Gujarat   Western  Food Processing             Auto       2   \n",
       "...               ...       ...              ...              ...     ...   \n",
       "11246     Maharashtra   Western         Chemical           Office       4   \n",
       "11247         Haryana  Northern       Healthcare       Veterinary       3   \n",
       "11248  Madhya Pradesh   Central          Textile           Office       4   \n",
       "11249       Karnataka  Southern      Agriculture           Office       3   \n",
       "11250     Maharashtra   Western       Healthcare           Office       3   \n",
       "\n",
       "       Amount  \n",
       "0       23952  \n",
       "1       23934  \n",
       "2       23924  \n",
       "3       23912  \n",
       "4       23877  \n",
       "...       ...  \n",
       "11246     370  \n",
       "11247     367  \n",
       "11248     213  \n",
       "11249     206  \n",
       "11250     188  \n",
       "\n",
       "[11239 rows x 13 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# to rename  the column \n",
    "df.rename(columns ={'Marital_Status':\"Single/Engaged\"})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "9496f3d1-e415-49c4-9e76-37d3f8a12344",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>User_ID</th>\n",
       "      <th>Age</th>\n",
       "      <th>Marital_Status</th>\n",
       "      <th>Orders</th>\n",
       "      <th>Amount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1.123900e+04</td>\n",
       "      <td>11239.000000</td>\n",
       "      <td>11239.000000</td>\n",
       "      <td>11239.000000</td>\n",
       "      <td>11239.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.003004e+06</td>\n",
       "      <td>35.410357</td>\n",
       "      <td>0.420055</td>\n",
       "      <td>2.489634</td>\n",
       "      <td>9453.610553</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.716039e+03</td>\n",
       "      <td>12.753866</td>\n",
       "      <td>0.493589</td>\n",
       "      <td>1.114967</td>\n",
       "      <td>5222.355168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000001e+06</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>188.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.001492e+06</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>5443.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.003064e+06</td>\n",
       "      <td>33.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>8109.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.004426e+06</td>\n",
       "      <td>43.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>12675.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.006040e+06</td>\n",
       "      <td>92.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>23952.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            User_ID           Age  Marital_Status        Orders        Amount\n",
       "count  1.123900e+04  11239.000000    11239.000000  11239.000000  11239.000000\n",
       "mean   1.003004e+06     35.410357        0.420055      2.489634   9453.610553\n",
       "std    1.716039e+03     12.753866        0.493589      1.114967   5222.355168\n",
       "min    1.000001e+06     12.000000        0.000000      1.000000    188.000000\n",
       "25%    1.001492e+06     27.000000        0.000000      2.000000   5443.000000\n",
       "50%    1.003064e+06     33.000000        0.000000      2.000000   8109.000000\n",
       "75%    1.004426e+06     43.000000        1.000000      3.000000  12675.000000\n",
       "max    1.006040e+06     92.000000        1.000000      4.000000  23952.000000"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# describe() method will provide us the description of data in the DataFrame (i.e. Count,Mean,Std,Min,Max,etc\n",
    "df.describe() # it will give descripthon for numeric values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "aa9058fd-3b3a-4e07-acd9-af83dfe0b939",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Orders</th>\n",
       "      <th>Amount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>11239.000000</td>\n",
       "      <td>11239.000000</td>\n",
       "      <td>11239.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>35.410357</td>\n",
       "      <td>2.489634</td>\n",
       "      <td>9453.610553</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>12.753866</td>\n",
       "      <td>1.114967</td>\n",
       "      <td>5222.355168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>12.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>188.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>27.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>5443.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>33.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>8109.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>43.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>12675.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>92.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>23952.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Age        Orders        Amount\n",
       "count  11239.000000  11239.000000  11239.000000\n",
       "mean      35.410357      2.489634   9453.610553\n",
       "std       12.753866      1.114967   5222.355168\n",
       "min       12.000000      1.000000    188.000000\n",
       "25%       27.000000      2.000000   5443.000000\n",
       "50%       33.000000      2.000000   8109.000000\n",
       "75%       43.000000      3.000000  12675.000000\n",
       "max       92.000000      4.000000  23952.000000"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# If we want for perticular column we us \n",
    "df[['Age','Orders','Amount']].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9a88f19e-190f-45c1-882b-e63f90fd2142",
   "metadata": {},
   "source": [
    "# **Exploratory Data Analysis**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4f711f99-4443-430b-a646-131581f08cbd",
   "metadata": {},
   "source": [
    "### Gender"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "af6e5896-f05a-40f8-8f51-a9239cbb245a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['User_ID', 'Cust_name', 'Product_ID', 'Gender', 'Age Group', 'Age',\n",
       "       'Marital_Status', 'State', 'Zone', 'Occupation', 'Product_Category',\n",
       "       'Orders', 'Amount'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "3a006220-08ea-4dd3-a10d-f5c84aa835e7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "A = sns.countplot( x = 'Gender', data = df) \n",
    "# here  dota is taken from csv we assign it as df (we can us only this to get the data )\n",
    "for bars in A.containers: # this is used to give the lable for the data\n",
    "    A.bar_label(bars)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "982c1c5f-7141-4638-9808-8458a51dcfe7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Gender</th>\n",
       "      <th>Amount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>F</td>\n",
       "      <td>74335853</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>M</td>\n",
       "      <td>31913276</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Gender    Amount\n",
       "0      F  74335853\n",
       "1      M  31913276"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Total Amount Vs Gender in table\n",
    "df.groupby(['Gender'], as_index = False) ['Amount'].sum().sort_values(by = 'Amount',ascending =False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "6f54edee-7c13-494d-8b7b-f1bcdfcd9955",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Gender', ylabel='Amount'>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Total Amount Vs Gender in bar chart\n",
    "sales_gen = df.groupby(['Gender'], as_index=False)['Amount'].sum().sort_values(by='Amount', ascending=False)\n",
    "sns.barplot(x='Gender', y='Amount', data=sales_gen)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bcecef76-ea02-4e77-9b88-6ff2e057e4d9",
   "metadata": {},
   "source": [
    "* From above graphs we can see that most of the buyers are **Females** and the purchasing power of **Females are Greater then Man**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fff79ac3-a970-47db-86f3-162300e87d27",
   "metadata": {},
   "source": [
    "###  Age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "21db5734-d5d7-4657-8102-54c80b3f361a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['User_ID', 'Cust_name', 'Product_ID', 'Gender', 'Age Group', 'Age',\n",
       "       'Marital_Status', 'State', 'Zone', 'Occupation', 'Product_Category',\n",
       "       'Orders', 'Amount'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "6884fe00-b032-4590-adc8-22ca68620816",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Age Group', ylabel='count'>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(data = df, x = 'Age Group') \n",
    "# this will give us the rough idea that which age group  had done more shoping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "cb5e4ef6-b62a-46fb-9272-f4e98584c54a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Age Group', ylabel='count'>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(data = df, x = 'Age Group', hue = 'Gender') \n",
    "# here we can see it all age group are split in gender for this we us hue "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "37258829-6af7-4445-ade6-52c2dda3c2b8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create the count plot\n",
    "A = sns.countplot(data=df, x='Age Group', hue='Gender')\n",
    "\n",
    "# Add labels to the bars\n",
    "for bars in A.containers:\n",
    "    A.bar_label(bars)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "90bf74cd-c366-43a0-96d5-b37f770e8b0e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Age Group', ylabel='Amount'>"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Total Amount Vs Age Group\n",
    "sales_age = df.groupby(['Age Group'], as_index=False)['Amount'].sum().sort_values(by='Amount', ascending=False)\n",
    "\n",
    "sns.barplot(x = 'Age Group', y = 'Amount', data = sales_age)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "667da8a9-9e0a-451b-8482-5a12e38d4e6b",
   "metadata": {},
   "source": [
    "* From above graphs we can see that most of the buyers are of **Age Group Between 26-35 yrs Females** "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4ed827b4-217d-4149-a892-53b52c396863",
   "metadata": {},
   "source": [
    "### State"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "b8a82fff-8290-49c8-a16d-30629bade452",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['User_ID', 'Cust_name', 'Product_ID', 'Gender', 'Age Group', 'Age',\n",
       "       'Marital_Status', 'State', 'Zone', 'Occupation', 'Product_Category',\n",
       "       'Orders', 'Amount'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "f8194500-2235-4b7c-b27a-e40e81ee2fba",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='State', ylabel='Orders'>"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Total numbers of orders from top 10 states\n",
    "sales_state = df.groupby(['State'], as_index=False)['Orders'].sum().sort_values(by='Orders', ascending=False).head(10)\n",
    "sns.set(rc={'figure.figsize': (15, 5)})  # here we gave the chart size we can change according to our use\n",
    "sns.barplot(x='State', y='Orders', data=sales_state)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "b8e172e6-3987-4b56-a22a-4f02d59ee09e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='State', ylabel='Amount'>"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Total Amount/Sales from top 10 states\n",
    "sales_state = df.groupby(['State'], as_index=False)['Amount'].sum().sort_values(by='Amount', ascending=False).head(10)\n",
    "sns.set(rc={'figure.figsize': (15, 5)}) # here we gave the chart size we can change according to our use\n",
    "sns.barplot(x='State', y='Amount', data=sales_state)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4f7f8382-0049-483a-9eb9-3d7817ec5631",
   "metadata": {},
   "source": [
    "*  From above  graph we can see that Most of the **orders & total sale/amount** are from **Uttar pradesh**, **Maharashtra**, **Karnatak** respectively*"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2486c20b-a425-4c89-95da-64d7c71bc01f",
   "metadata": {},
   "source": [
    "### Marital Status"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "ce4a11e3-66d4-4693-9a37-c50a26b96141",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>User_ID</th>\n",
       "      <th>Cust_name</th>\n",
       "      <th>Product_ID</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age Group</th>\n",
       "      <th>Age</th>\n",
       "      <th>Marital_Status</th>\n",
       "      <th>State</th>\n",
       "      <th>Zone</th>\n",
       "      <th>Occupation</th>\n",
       "      <th>Product_Category</th>\n",
       "      <th>Orders</th>\n",
       "      <th>Amount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1002903</td>\n",
       "      <td>Sanskriti</td>\n",
       "      <td>P00125942</td>\n",
       "      <td>F</td>\n",
       "      <td>26-35</td>\n",
       "      <td>28</td>\n",
       "      <td>0</td>\n",
       "      <td>Maharashtra</td>\n",
       "      <td>Western</td>\n",
       "      <td>Healthcare</td>\n",
       "      <td>Auto</td>\n",
       "      <td>1</td>\n",
       "      <td>23952</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1000732</td>\n",
       "      <td>Kartik</td>\n",
       "      <td>P00110942</td>\n",
       "      <td>F</td>\n",
       "      <td>26-35</td>\n",
       "      <td>35</td>\n",
       "      <td>1</td>\n",
       "      <td>Andhra Pradesh</td>\n",
       "      <td>Southern</td>\n",
       "      <td>Govt</td>\n",
       "      <td>Auto</td>\n",
       "      <td>3</td>\n",
       "      <td>23934</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1001990</td>\n",
       "      <td>Bindu</td>\n",
       "      <td>P00118542</td>\n",
       "      <td>F</td>\n",
       "      <td>26-35</td>\n",
       "      <td>35</td>\n",
       "      <td>1</td>\n",
       "      <td>Uttar Pradesh</td>\n",
       "      <td>Central</td>\n",
       "      <td>Automobile</td>\n",
       "      <td>Auto</td>\n",
       "      <td>3</td>\n",
       "      <td>23924</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1001425</td>\n",
       "      <td>Sudevi</td>\n",
       "      <td>P00237842</td>\n",
       "      <td>M</td>\n",
       "      <td>0-17</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>Karnataka</td>\n",
       "      <td>Southern</td>\n",
       "      <td>Construction</td>\n",
       "      <td>Auto</td>\n",
       "      <td>2</td>\n",
       "      <td>23912</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1000588</td>\n",
       "      <td>Joni</td>\n",
       "      <td>P00057942</td>\n",
       "      <td>M</td>\n",
       "      <td>26-35</td>\n",
       "      <td>28</td>\n",
       "      <td>1</td>\n",
       "      <td>Gujarat</td>\n",
       "      <td>Western</td>\n",
       "      <td>Food Processing</td>\n",
       "      <td>Auto</td>\n",
       "      <td>2</td>\n",
       "      <td>23877</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11246</th>\n",
       "      <td>1000695</td>\n",
       "      <td>Manning</td>\n",
       "      <td>P00296942</td>\n",
       "      <td>M</td>\n",
       "      <td>18-25</td>\n",
       "      <td>19</td>\n",
       "      <td>1</td>\n",
       "      <td>Maharashtra</td>\n",
       "      <td>Western</td>\n",
       "      <td>Chemical</td>\n",
       "      <td>Office</td>\n",
       "      <td>4</td>\n",
       "      <td>370</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11247</th>\n",
       "      <td>1004089</td>\n",
       "      <td>Reichenbach</td>\n",
       "      <td>P00171342</td>\n",
       "      <td>M</td>\n",
       "      <td>26-35</td>\n",
       "      <td>33</td>\n",
       "      <td>0</td>\n",
       "      <td>Haryana</td>\n",
       "      <td>Northern</td>\n",
       "      <td>Healthcare</td>\n",
       "      <td>Veterinary</td>\n",
       "      <td>3</td>\n",
       "      <td>367</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11248</th>\n",
       "      <td>1001209</td>\n",
       "      <td>Oshin</td>\n",
       "      <td>P00201342</td>\n",
       "      <td>F</td>\n",
       "      <td>36-45</td>\n",
       "      <td>40</td>\n",
       "      <td>0</td>\n",
       "      <td>Madhya Pradesh</td>\n",
       "      <td>Central</td>\n",
       "      <td>Textile</td>\n",
       "      <td>Office</td>\n",
       "      <td>4</td>\n",
       "      <td>213</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11249</th>\n",
       "      <td>1004023</td>\n",
       "      <td>Noonan</td>\n",
       "      <td>P00059442</td>\n",
       "      <td>M</td>\n",
       "      <td>36-45</td>\n",
       "      <td>37</td>\n",
       "      <td>0</td>\n",
       "      <td>Karnataka</td>\n",
       "      <td>Southern</td>\n",
       "      <td>Agriculture</td>\n",
       "      <td>Office</td>\n",
       "      <td>3</td>\n",
       "      <td>206</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11250</th>\n",
       "      <td>1002744</td>\n",
       "      <td>Brumley</td>\n",
       "      <td>P00281742</td>\n",
       "      <td>F</td>\n",
       "      <td>18-25</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "      <td>Maharashtra</td>\n",
       "      <td>Western</td>\n",
       "      <td>Healthcare</td>\n",
       "      <td>Office</td>\n",
       "      <td>3</td>\n",
       "      <td>188</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>11239 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       User_ID    Cust_name Product_ID Gender Age Group  Age  Marital_Status  \\\n",
       "0      1002903    Sanskriti  P00125942      F     26-35   28               0   \n",
       "1      1000732       Kartik  P00110942      F     26-35   35               1   \n",
       "2      1001990        Bindu  P00118542      F     26-35   35               1   \n",
       "3      1001425       Sudevi  P00237842      M      0-17   16               0   \n",
       "4      1000588         Joni  P00057942      M     26-35   28               1   \n",
       "...        ...          ...        ...    ...       ...  ...             ...   \n",
       "11246  1000695      Manning  P00296942      M     18-25   19               1   \n",
       "11247  1004089  Reichenbach  P00171342      M     26-35   33               0   \n",
       "11248  1001209        Oshin  P00201342      F     36-45   40               0   \n",
       "11249  1004023       Noonan  P00059442      M     36-45   37               0   \n",
       "11250  1002744      Brumley  P00281742      F     18-25   19               0   \n",
       "\n",
       "                State      Zone       Occupation Product_Category  Orders  \\\n",
       "0         Maharashtra   Western       Healthcare             Auto       1   \n",
       "1      Andhra Pradesh  Southern             Govt             Auto       3   \n",
       "2       Uttar Pradesh   Central       Automobile             Auto       3   \n",
       "3           Karnataka  Southern     Construction             Auto       2   \n",
       "4             Gujarat   Western  Food Processing             Auto       2   \n",
       "...               ...       ...              ...              ...     ...   \n",
       "11246     Maharashtra   Western         Chemical           Office       4   \n",
       "11247         Haryana  Northern       Healthcare       Veterinary       3   \n",
       "11248  Madhya Pradesh   Central          Textile           Office       4   \n",
       "11249       Karnataka  Southern      Agriculture           Office       3   \n",
       "11250     Maharashtra   Western       Healthcare           Office       3   \n",
       "\n",
       "       Amount  \n",
       "0       23952  \n",
       "1       23934  \n",
       "2       23924  \n",
       "3       23912  \n",
       "4       23877  \n",
       "...       ...  \n",
       "11246     370  \n",
       "11247     367  \n",
       "11248     213  \n",
       "11249     206  \n",
       "11250     188  \n",
       "\n",
       "[11239 rows x 13 columns]"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# to rename  the column \n",
    "df.rename(columns ={'Single/Engaged':'Marital_Status'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "83d583c9-7cf1-4e28-8bb2-a4c64743c8d6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['User_ID', 'Cust_name', 'Product_ID', 'Gender', 'Age Group', 'Age',\n",
       "       'Marital_Status', 'State', 'Zone', 'Occupation', 'Product_Category',\n",
       "       'Orders', 'Amount'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "b1d3db85-fd68-4782-93b6-21956289468c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create the count plot\n",
    "A = sns.countplot(data=df, x='Marital_Status')\n",
    "sns.set(rc={'figure.figsize': (7,4)})\n",
    "# here we gave the chart size we can change according to our use\n",
    "# Add labels to the bars\n",
    "for bars in A.containers:\n",
    "    A.bar_label(bars)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "faed64e9-5672-4619-aec9-8da80c9d6bfd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Marital_Status', ylabel='Amount'>"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Marital_status Vs Amount/Gender\n",
    "sales_status = df.groupby(['Marital_Status','Gender'], as_index=False)['Amount'].sum().sort_values(by='Amount', ascending=False)\n",
    "sns.set(rc={'figure.figsize': (6, 5)})  # here we gave the chart size we can change according to our use\n",
    "sns.barplot(x='Marital_Status', y='Amount', hue ='Gender', data=sales_status)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "65888a1e-dfc7-439a-9f06-811b465a65bb",
   "metadata": {},
   "source": [
    "* From the above graph we can see that most of the buyers are **Married(Women)** and they have high Purchasing power"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4f2a8a03-8723-4f09-b2ba-d076c684f0d4",
   "metadata": {},
   "source": [
    "### Occupation "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "0a332f25-6d75-4144-b3f1-ee0d751b7dcf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2000x1500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create the count plot\n",
    "A = sns.countplot(data=df, x='Occupation')\n",
    "sns.set(rc={'figure.figsize': (20,15)})\n",
    "# here we gave the chart size we can change according to our use\n",
    "# Add labels to the bars\n",
    "for bars in A.containers:\n",
    "    A.bar_label(bars)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "18ebb126-0884-4d2d-8d28-a15509b9d2f3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Occupation', ylabel='Amount'>"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 2000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Occupation/ amount spent\n",
    "Occupation = df.groupby(['Occupation'], as_index=False)['Amount'].sum().sort_values(by='Amount', ascending=False).head(10)\n",
    "sns.set(rc={'figure.figsize': (20, 5)}) # here we gave the chart size we can change according to our use\n",
    "sns.barplot(x='Occupation', y='Amount', data=Occupation)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e6006b50-0f79-40ff-880f-0c54ae206da4",
   "metadata": {},
   "source": [
    "* From above graphs we can see that most of the buyers are working in **IT**, **Healthcare Sector**, and **Aviation Sector**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "36d1920a-9287-4422-af1e-a096d9fdd589",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['User_ID', 'Cust_name', 'Product_ID', 'Gender', 'Age Group', 'Age',\n",
       "       'Marital_Status', 'State', 'Zone', 'Occupation', 'Product_Category',\n",
       "       'Orders', 'Amount'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "78c2b026-8f37-4a2b-8a5e-d4a374b84975",
   "metadata": {},
   "source": [
    "### Product_Category"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "5d4dc84c-11a6-4f46-b8d4-95055a5658a8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 2000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create the count plot\n",
    "A = sns.countplot(data=df, x='Product_Category')\n",
    "sns.set(rc={'figure.figsize': (20,15)})\n",
    "# here we gave the chart size we can change according to our use\n",
    "# Add labels to the bars\n",
    "for bars in A.containers:\n",
    "    A.bar_label(bars)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "8ea0eca5-eb2b-454e-b65a-4fb567cec13f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Product_Category', ylabel='Amount'>"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Product_Category/ amount spent\n",
    "Product_Category = df.groupby(['Product_Category'], as_index=False)['Amount'].sum().sort_values(by='Amount', ascending=False).head(10)\n",
    "sns.set(rc={'figure.figsize': (20, 5)}) # here we gave the chart size we can change according to our use\n",
    "sns.barplot(x='Product_Category', y='Amount', data=Product_Category)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f6306a2e-2e3d-46d6-b6aa-2e2a0a06caf8",
   "metadata": {},
   "source": [
    "* From above graphs we can see that most of the Sold products are  **Food**, **Clothing**, and **Electronic Products**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "f294cdbf-9a9a-4eac-9b86-f67dcfeb7863",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Product_ID', ylabel='Orders'>"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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jnB2b7MtlrxU3aJKzY5N9uey1XRqaQ+uT7d1rdf72YpW3FSstLa22vLS0NFq1apWLkgAAAAAAgHqozocuHTp0iJ133jnmzp1btWzNmjXx8ssvR48ePXJYGQAAAAAAUJ/U+duLFRcXx8CBA2PChAnRokWL2HPPPeOaa66JVq1axdFHH53r8gAAAAAAgHqizocuEREjRoyITZs2xSWXXBLr16+Pnj17xowZM6K42L34AAAAAACA7ChIkiTJdREAAAAAAAB1XZ3/TBcAAAAAAIB8IHQBAAAAAABIgdAFAAAAAAAgBUIXAAAAAACAFAhdAAAAAAAAUiB0AQAAAAAASIHQBQAAAAAAIAVClzroiCOOiPbt21f9O+CAA6JHjx4xaNCgmD9/ftV2v/rVr+LII4+Mzp07xw9+8IP497//XW0/77zzTgwfPjy6d+8ehx56aFxzzTVRXl5etX79+vVx7bXXxhFHHBHdunWLE044IR577LFq+1i7dm1cdtll0atXr/j6178ep59+erz99ttV6zdu3BgHHnhgtXrbt28fEydOrNrmueeei0GDBsXXv/716N27d4wePTpWrVq1xdf+4YcfxmGHHRZTpkyptvzL7IOay1avfdaDDz4YRxxxxBbXVf78586dW215RUVFTJ48OXr37h1dunSJoUOHxpIlS6pt8/TTT8f3vve96NatW3z729+OX/7yl5/7uhcvXhzdunWL++67r9ryxx9/PAYMGBDdunWLI444Iq6++upYv3795+6Hmsunce3vf/97fO9734suXbrEt771rbjmmmuirKysav//OZ5V/uvQoUPVPm644YYtbrNp06bNXrtxLbvyaVx7880347TTTosePXrEYYcdFpdcckmsWbNmi/v4vD556aWX4uSTT47u3btH3759Y8qUKZ9bh17LrnzqtYjPn0M/6/PmP3Nofstmr21tH1sbT2ry3uCzbrrppmjfvv3nvnbjWnbVhXFt7ty5n/u32pFHHlm1nXEtv+Vbr1Xa0phUk3Hty9RhXMuufJpDP6u2818a+9Br20e+jWvbcn7t3//+dwwcODC6desWffr0iZ///OdV50z+U72eQxPqnMMPPzy56qqrktLS0qS0tDRZvnx58sorryTDhw9PunTpkrz33nvJfffdl3Tp0iWZPXt28tprryUXXnhhcvDBBycrVqxIkiRJysrKkn79+iXDhw9PXnnllWTOnDnJwQcfnEyaNKnqOKNHj0769u2bPPXUU8mbb76Z3HzzzUmHDh2Sf/zjH1Xb/PjHP06OPvro5JlnnkkWLFiQ/PCHP0yOPfbYpLy8PEmSJFm4cGHSrl27ZMGCBVX1lpaWJmvXrk2SJEneeOONpGvXrskVV1yRvP7668m8efOS7373u8mgQYO2+NrPOOOMpF27dsnkyZOrln3ZfVBz2eq1Sg8//HDSqVOn5PDDD99s3bvvvpscd9xxSbt27ar1YJIkyZQpU5JDDjkk+fOf/5wsWLAgGTp0aHL00UcnGzZsSJIkSZ5//vmkQ4cOyU9/+tNk0aJFyWOPPZZ885vfTKZOnbrZccrKypITTjghadeuXTJr1qyq5fPmzUsOOOCA5JZbbknefPPN5Mknn0z69OmT/OQnP9mm7zGfypdxbcGCBUmnTp2SSZMmJW+99Vby17/+NTnssMOSyy+/PEmSJNm0aVO1say0tDR56aWXkm7duiXXXXdd1XHOOuus5MILL9xs2y0xrmVXvoxrZWVlydFHH52cccYZyaJFi5J58+Yl/fr1S84+++wt1r2lPnnrrbeSLl26JGeccUaycOHCZO7cucm3v/3tZPTo0TXeh17bfvKl15Lki+fQSp83/5lD81+2em1r+6jJeLK19waf9cILLyQdO3ZM2rVr97mv3biWXXVhXNuwYcNmf3/99a9/TTp27Jj8+te/TpLEuFYX5FOvVfq8MWlr49qXqSNJjGvZli9z6GfVdv5LYx96bfvJp3FtW86vrVixIjn44IOTSy+9tGr+69WrV3LVVVdtdpz6PocKXeqgww8/fIsD67Jly5J27dolM2fOTPr165dcc801Ves2btyY9OnTJ7nllluSJEmS3/3ud8mBBx6YrF69umqbe+65J+nevXuyYcOGZN26dUmnTp2S2bNnVzvG4MGDkwsvvDBJkiT5xz/+kbRv3z5ZuHBh1frXXnst6du3b/L6668nSZIks2fPTr7+9a9/7mu57rrrkn79+iUVFRVVy+bNm5e0a9cueeutt6pte8899yTHHHNM8s1vfrPa6/8y++DLyUavJUmSfPTRR8n555+fdOzYMTn++OM3mxR+/etfJz179kz69++/xTdW3bp1S+66666qZatXr046d+6cPPTQQ0mSJMmZZ56ZnHjiidX2+eCDDyZdunSpqqHStddemwwaNGizSeH8889PhgwZUm3bBx54IOnYseNm++DLy5dx7bHHHksuvfTSauvHjRuXfOc73/nc2ocOHZr84Ac/qAqbkyRJ+vXrl9x+++1bfd3GtezLl3Ht5ZdfTtq1a1dtDp05c2bStWvXzWr7vD658sorkz59+lQbg+bPn5+0b98+effdd2u0D722/eRLr33RHPpZnzf/mUPzX7Z6bWv7qMl4srX3BpU+/vjjpF+/fsnJJ5/8uSeMjGvZV9fGtST59ITPsccem5x77rlVy4xr+S9feq3SF41JWxvXalLHZ5cb17IrX+bQStsy/6WxD722/eTLuLat59fmzJmTtGvXLvnoo4+qtrnyyiuT7373u5u9tvo+h7q92A6kqKgoIj69fc6bb74ZvXr1qrauR48eMW/evIiImD9/fnTq1ClKSkqqtunVq1esXbs2Fi5cGAUFBXHzzTdH7969NzvO6tWrIyLiL3/5S7Rr167a5Yr77bdfPPHEE/G1r30tIiJeeeWV2G+//T635uOPPz6uvvrqKCgo2GzdZy9fXLx4cUyYMCGuueaaKC4urtU+SE+avRbx6eWRq1evjt/+9rdx1FFHbXa8J554Ii688MKYNGnSZusWLlwYH3/8cbUaSkpKomPHjlU1LF68OHr06FHteR07doxPPvkk/vWvf1UtmzdvXtx7771x9dVXb3acoUOHxsiRIzdbvmnTpli7du0WvkukIdvj2hFHHBE/+9nPIiIiSZJ44YUXYs6cOfHNb35zi/X96U9/iqeffjrGjBkTmcynU+onn3wSb7311heOfRHGtXyT7XGtadOmUVBQEL/5zW+irKwsPvzww/jDH/4QXbt2rbbdF/XJ4sWLo3PnztWWd+zYMZIkqap1a/vQa9mXT3NopS+a/8yhdVeavbZixYqt7qMm48nW3htUGjduXLRr1y7++7//e4vrjWv5JR/HtUq/+tWv4r333ouLLrqoaplxre7Kdq9V+qIxaWvjWk3qiDCu5Ztsz6GVtmX+S2Mfei378mkOrcn5tWbNmkVExN133x3l5eXxzjvvxJNPPhldunSpti9zqM902WEsX748fvazn0Xjxo2jW7duERGxxx57VNtmt912i/feey8iIpYtWxatWrXabH1ExNKlS6Nhw4Zx2GGHVf0yRUS88MIL8Y9//CMOO+ywiPj0XvStW7eOu+66K4499tjo3bt3nHvuubF8+fKq57z66quxadOmOOWUU+LQQw+NE044IR588MGq9W3btt3sBNO0adNi1113rfp8hI0bN8b5558fp5xySnTq1Gmz116TfZCetHstIqJDhw4xbdq0OOCAA7Z4zKlTp8b3vve9LU78y5Yt22oNu+66a9XXld59992IiFixYkVERKxZsyZGjhwZl1xyyWb7ivj0jdhn+6msrCxuv/326NSpU7Ro0WKLdbNtcjGuVSovL4+uXbvG97///WjWrFmcddZZW6xx0qRJcdxxx1Xrjddeey0qKiri0UcfjX79+kXfvn1j5MiRUVpaWrWNcS2/5GJc++pXvxoXX3xxzJo1K7p06RKHHHJIrF69Oq677rqqbbbWJzUZ2/Rafsm3OTRi6/OfObRuSrvXavL3Vk3Gk629N4iImDNnTjz55JNV/wniPxnX8ks+jmuVNmzYEDfffHMMHjy46hgRxrW6Khe9FrH1MWlr41pN6jCu5ZdczKER2z7/pbEPvZZd+TaH1qRXe/ToEaeddlpMmjQpDjrooDjyyCNj1113jUsvvbRqe3Pop4pyXQC1c8stt8Rtt90WEZ8mgWVlZdG2bdu4/vrrq35x/jOxbtCgQWzYsCEiPk1QP5uMVq6PiKptPuuNN96IM888Mw488MD4wQ9+EBERa9eujZdeeilWrlwZY8eOjYiICRMmxMknnxyzZ8+OBg0axGuvvRZFRUUxYsSI2HXXXePPf/5zXHTRRbFx48Y48cQTNzvOVVddFU8++WRMnjw5dtppp4iImDx5cjRo0CCGDRtWo+/NlvZB7WW7176sTz755HNrqLx64YQTTohRo0bFAw88EMcee2wsXbq0qv7KD/saM2ZMdO3aNY477ritHnPTpk0xcuTIWLRoUfzqV7/a5tfAp/JhXKtUUVERv/zlL2PlypVx5ZVXxrBhw+Luu++uupolIuJvf/tbvPbaa3H99ddXe+5rr70WERG77LJLTJ48OT744IO47rrr4uSTT477778/GjVqZFzLsXwY18rKyuK1116Lfv36xUknnRQrV66Mn//853HeeefFbbfdFplMZqt90r9//xg4cGDceuutMXjw4Fi9enVcfvnlUVRUVDW26bXcyode25qtzX/m0Lphe/faF/299Xm9uKXxZGvvDZYvXx6XXnpp/PznP4/mzZtvcb/GtdyqC+NapQcffDA2bNgQgwYNqrbcuFY35EOv1WRM2tq4VpM6jGu5lQ9zaBrznzk0/+XDuPZFanJ+bc2aNfHmm2/GSSedFMcff3y8/fbbMX78+BgzZkyMHz8+IsyhlYQuddQPf/jDqj8eM5lMNGvWLHbZZZeIiHjxxRcjIqr+YKy0YcOGaNSoUURENGzYcIvrIyIaN25cbflzzz0XZ5xxRuy6665x6623Vv3y7bTTTrFhw4a48cYbo2nTphERccMNN0Tv3r3j8ccfj2OOOSYeffTRqKioqDruAQccEO+9917MmDGjWuiycePG+OlPfxr3339/XHbZZdGvX7+IiHjmmWfi7rvvjvvvvz8KCwu/8Hvyeftg22Sz12qjYcOGVTVUfv2fNRx//PGxbNmyGDt2bFx88cXRvHnzuPDCC+MnP/lJ7LLLLvHAAw/E/Pnz43e/+91Wj7d27do499xzY+7cuTF58uTNLqGk9vJhXKu00047xUEHHRQRn94G6vvf/348++yz0bNnz6ptHnjggejRo0e0bdu22nMHDBgQRx11VNW4GBGx//77R58+feKJJ56Ir3zlK8a1HMuHce3222+PZ555Jh555JGqPth3332jX79+8fjjj0dJSclW+6RHjx5x5ZVXxtVXXx0TJ06MJk2axIgRI+L111+PXXbZxRyaB/Kh175ITeY/c2jdsL177bN/b33ePip90XjyRe8NBgwYED/5yU/imGOOiW9961tbfJ3GtdzL93Htsx544IHo16/fZicfjWt1Q657LUmSrY5JEV88rp144olbrcO4lnu5nkNr0mtb65M09vFZem37yPW4tjU1Ob82YcKEWLNmTUyZMiUiIjp16hRNmzaNH//4xzF48OBYuHChOfT/E7rUUU2bNo3WrVtvcd1Xv/rViIgoLS2tdjKwtLS06jK0Vq1axauvvlrteZW3vtl9992rls2ZMyfOP//8OOigg+Kmm26qlqi2atUqdt9992onFr/yla9Es2bN4p133omI/01cP6t9+/bVfvnWrl0bZ511VsyfPz+uvfbaOPbYY6vW3X///bFu3bo4/vjjq5Z98sknVenw888/v9V9sG2y1Wu1VXmpYmlpaeyzzz7VjvHZyxVPO+20OPXUU+P999+Pr3zlK7F48eJIkiRat24dM2bMiBUrVkTfvn2r7fuyyy6LGTNmxMMPP1y1z2HDhsU777wT06ZNq3afS7ZdPoxrL774Yqxbty4OPvjgqmX7779/RES1Wydu2rQp/vznP8d55533ua/ls3bfffdo1qxZLFu2LP7yl78Y13IsH8a1Z599Njp27FjtDU/r1q2jefPm8eabb8brr79eoz454YQTon///lFaWhrNmzePTZs2xZVXXhmtW7c2h+aBfOi1LzJr1qwazX/m0Py3vXutJvuI2Pp48kXvDZYuXRp///vf47nnnosHHnggIj6dbyMiunXrFsOHD48lS5YY13Is38e1Sh9++GE8//zzMXz48C2uN67lv1z3Wk3GpNNPP32r5zy2Vsfdd99tXMuxXM+hacx/Dz30kDm0Dsj1uLY1NTm/9uyzz8bhhx9e7XmVYcnixYtr/P6iPsyhQpcdUIsWLaJNmzYxd+7cOOSQQyLi08F2/vz58aMf/SgiInr27BkPPPBArF27NnbeeeeIiHj66aejSZMmVb9Ijz/+eJx77rlx5JFHxoQJEzb7n+A9evSI++67L0pLS6vuIVhaWhorV66M1q1bx6pVq+Koo46KSy+9tNoHeP373/+uOolZVlYWw4cPj4ULF8b06dM3+yW74IIL4vTTT6+2bNCgQdGvX7+qdHhr+2D7SavXtkWHDh1i5513jrlz51ZNCmvWrImXX345Bg4cGBGffoDmq6++GmPHjq2aiB599NHYa6+9ok2bNjFhwoRYv359tf3269cvRowYEd/5znci4tMPWh88eHCsXbs27rrrrmjfvv02107NZWtce/DBB+Oxxx6LOXPmVJ0Mf+GFFyIiqn1A5qJFi2LNmjVbHG+uvfbaeOyxx+Lhhx+uukT4nXfeiZUrV8Z+++0X//3f/21cy2PZGtdatWoVzz33XCRJUtUny5cvj1WrVsW+++4b/fv332qf/OEPf4jZs2fHjTfeWDW2Pfzww1X3BD7ggAP0Wh7Lhzm0JvOfObTuS6PXiouLt7qPrY0nW3tvsPvuu8cf//jHas/54x//GBMmTIgHHnggmjZtGuXl5ca1PJYP41ql5557LgoKCqr9R5pKxrW6Lxu9VpMxqSbnPLZWh3Me+S0bc2ga8585tO7Lhzm0JufXWrVqFa+88kq151UGQfvuu6859DOELjuooUOHxrhx46J169Zx0EEHxa233hrr16+vuqXXUUcdFddff32ce+65ccEFF8Q777wTEydOjKFDh0ZxcXGsXr06Ro0aFZ06dYrRo0dX3bsv4tPb7jRr1iyOOeaYuPXWW+Occ86Jiy++OAoLC+PKK6+MNm3aRN++faO4uDgOPfTQuO6666JFixax9957xx//+MeYPXt23HLLLRHx6f0Mn3322bj22mujbdu28f7771cdp2nTptGyZcto2bJltddWVFRULR3e2j7+86Qq6drWXttWxcXFMXDgwJgwYUK0aNEi9txzz7jmmmuiVatWcfTRR0fEpyfLx40bFwceeGAceuih8de//jVuvvnm+PnPfx4Rn/8/Alq2bBl77rlnRESMHz8+3n777Zg+fXq0aNGiWp+1aNFiq5fnsu2yMa6ddNJJ8dvf/jbGjBkTQ4cOjbfffjvGjBkT3/72t6v9EbNw4cKqP57/03/913/FHXfcEZdffnkMGjQoPvjgg7jyyiuje/fu0bt37ygoKDCu5blsjGsDBw6M+++/Py699NIYMmRIfPTRRzF+/Pho37599OnTJ3baaaet9sn+++8fTz31VEydOjWOP/74eOmll+Lyyy+PM844I5o0aRJNmjTRa3ku13NoTeY/c+iOIY1e29o+tjaeNGvW7AvfGxQVFW32vz8rx7DPLjeu5bdcj2uVFi5cGHvvvfdmt7+LMK7tKLZ3r9V0TNraOY+t1eGcR/7b3nNoWvOfObTuy/UcWpPza0OGDIlTTz01rr/++jjhhBPi3XffjbFjx0afPn3igAMO+Nx918c5VOiyg/r+978fH330UVx//fWxatWqOPDAA+P222+PFi1aRMSnl/ZPnz49xo4dG9///vejadOm8aMf/SjOOOOMiIh46qmnYs2aNfHCCy9sdj/Igw8+OO68884oLi6OO+64I6666qr48Y9/HEmSxDe/+c249tprq37Zr7rqqpgyZUpceumlsWLFimjbtm1Mnjw5evfuHRERDz30UCRJEv/zP/+z2Wv4xS9+Ed/4xje2+lrT2Ae1t629loYRI0bEpk2b4pJLLon169dHz549Y8aMGVV9+I1vfCPGjRsXN998c1xxxRXRunXruOaaa+KYY46p0f4rKirikUceiY0bN8bgwYM3W//YY4/FXnvtldrrYcuyMa7tu+++MXPmzLjmmmvihBNOiCZNmsRxxx232W3E3n///WjatGnVFQqf1alTp5g+fXrVHyHFxcVx5JFHxqhRo7a4/ZYY13IrG+Nau3bt4s4774zrrrsufvCDH0SjRo3isMMOiwsvvLDGH1L5ta99LW644Ya49tpr45ZbbolWrVrF+eefHyeddFKN69BruZUPc+jWmEN3DGn02tb2UZPxZGvvDdJgXMutfBnXPvjgg2jWrNkW1xnXdgz50mtbG9fSqMO4llvZmEPzhV7LrXwY17Z2fu2www6LW265JW688caYOXNmNG/ePI4++ug455xzarT/+jSHFiRJkuS6CAAAAAAAgLouk+sCAAAAAAAAdgRCFwAAAAAAgBQIXQAAAAAAAFIgdAEAAAAAAEiB0AUAAAAAACAFQhcAAAAAAIAUCF0AAAAAAABSIHQBAABIUZIkuS4BAADIEaELAACQM4MGDYr27dtX+3fggQdG3759Y+zYsbF69ertctz77rsv2rdvH++8806q+33sscdi1KhRX/p5U6ZMifbt21c9njt37ha/L717947zzz8/Xn/99TTLBgAAUlKU6wIAAID6rWPHjnHZZZdVPd64cWO89NJLcd1118WCBQvi7rvvjoKCghxWWHN33HFHqvv76U9/Gp06dYqIiPXr18fbb78d06ZNixNPPDFmzpwZnTt3TvV4AADAthG6AAAAObXzzjtH165dqy3r2bNnfPzxxzF58uR44YUXNltfX+y3337VXnuvXr2iX79+0b9//xg1alQ89NBDUVhYmLsCAQCAatxeDAAAyEsHHnhgREQsXbo0Bg0aFBdccEGMGDEiunfvHqeddlpERHz00Ucxfvz4OOqoo+Kggw6K7373u/Hb3/622n4qKipi6tSp0bdv3+jSpUucccYZm9227D9v71Wpffv2MWXKlKrHH3/8cYwfPz6+9a1vRdeuXeOEE06Ixx9/PCI+vVXaM888E88880y0b98+5s6dm+r3o1LTpk3j1FNPjTfeeCOeeeaZ7XIMAACgdlzpAgAA5KXFixdHRMTee+8dERG///3v47/+67/ixhtvjPLy8li/fn386Ec/ig8++CDOPvvs2HvvveNPf/pTjB49Oj744IM4/fTTIyLimmuuiV/84hdx+umnR9euXePRRx+Na6+99kvXU1FREaeeemq8/vrrMWLEiGjbtm08+OCDcdZZZ8Xtt98el112WVx44YUREXHZZZfFfvvtl9J3YnO9e/eOiIhnn302DjnkkO12HAAA4MsRugAAADmVJEls2rSp6vHq1avjmWeeiZtuuim6du1adcVLJpOJyy+/PBo3bhwREXfddVe8+uqrcdddd8XXv/71iPg0jNi0aVNMnTo1fvjDH0Ymk4k777wzTj755Dj77LOrtlm+fHn85S9/+VJ1PvXUU/Hcc8/F1KlT48gjj4yIT2/3tWTJkvjHP/4R55xzTuy8884REdv9dmhf+cpXIiLi/fff367HAQAAvhyhCwAAkFPz5s2r+rD4SplMJg455JC4/PLLo6CgICIi9tprr6rAJSLimWeeiT333LMqcKl0/PHHx29/+9t44YUXoqCgIDZu3FgVklQ65phjvnToMn/+/Nhpp53i8MMPr1pWUFAQd99995faT5oqvzcAAEB+ELoAAAA51alTpxg7dmxEfBo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      "text/plain": [
       "<Figure size 2000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Product_ID/Orders\n",
    "Product = df.groupby(['Product_ID'], as_index=False)['Orders'].sum().sort_values(by='Orders', ascending=False).head(10)\n",
    "sns.set(rc={'figure.figsize': (20, 5)}) # here we gave the chart size we can change according to our use\n",
    "sns.barplot(x='Product_ID', y='Orders', data=Product)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "3a84a6c2-b0e2-4eb0-b32c-eee03e39fc8f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Product_ID'>"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# top 10 most Sold Products (Same things as above)\n",
    "\n",
    "fig1, A = plt.subplots(figsize = (12,7))\n",
    "df.groupby('Product_ID')['Orders'].sum().nlargest(10).sort_values(ascending=False).plot(kind = 'bar')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "64453528-d556-4a25-805b-328341d7d3a6",
   "metadata": {},
   "source": [
    "# CONCLUSION"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "02f291e0-c158-4faf-b0a7-da7b0db63c80",
   "metadata": {},
   "source": [
    "### *Marrid Women Age group 26-35 from UP, Maharashtra, Karnatak working in IT, Healthcare and Aviation sector are more likely to buy product from Food, Clothing and Electronics Category*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "84457a44-029e-444d-8112-29a2a9cd5842",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}