Diwali_Sales_Analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "d7a94ce8",
   "metadata": {},
   "source": [
    "#                              Python Diwali Sales Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "0979edbb",
   "metadata": {},
   "outputs": [],
   "source": [
    "# import python libraries\n",
    "\n",
    "import numpy as np               # helps with working with arrays and mathematical calculations\n",
    "import pandas as pd              # helps in working with dataframes (means tables or 2-D data) \n",
    "import matplotlib.pyplot as plt  # helps in visualizing data\n",
    "%matplotlib inline\n",
    "import seaborn as sns            # for better visualization charts"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "64d69fa2",
   "metadata": {},
   "source": [
    "Why matplotlib inline is Used? <br>\n",
    "The line magic command %matplotlib inline enables the drawing of matplotlib figures in the IPython environment. Once this command is executed in any cell, the matplotlib plots will appear directly below the cell in which the plot function was called for the rest of the session."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f6d7d01e",
   "metadata": {},
   "source": [
    "What is the difference between matplotlib and matplotlib inline? <br>\n",
    "\n",
    "\n",
    "Matplotlib plots are displayed in a separate window or as image files by default. However, when you use the \"inline\" setting, the plots are embedded directly in the Jupyter Notebook or IPython shell output. This line tells Jupyter to display Matplotlib plots directly in the output of the notebook."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "7fbfb436",
   "metadata": {},
   "outputs": [],
   "source": [
    "# import csv file\n",
    "\n",
    "df = pd.read_csv(r'Diwali Sales Data.csv', encoding= 'unicode_escape') # we use 'r' to avoid or escpae special character"
   ]
  },
  {
   "cell_type": "raw",
   "id": "21eb9854",
   "metadata": {},
   "source": [
    "#if getting encoding error then use below line (error: 'utf-8' codec can't decode byte 0xa0 in position 0: invalid start byte)\n",
    "# df = pd.read_csv(filename, encoding= 'unicode_escape')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "662c471c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "    }\n",
       "\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",
       "      <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",
       "</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",
       "            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": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "b8db4a5b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(11251, 15)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "0814c45b",
   "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()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "29afa95b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# drop unrelated/blank columns\n",
    "\n",
    "df.drop(['Status', 'unnamed1'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "5cee58eb",
   "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": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#check for null values\n",
    "\n",
    "pd.isnull(df).sum() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "65de74e3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# drop null values\n",
    "\n",
    "df.dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "2eb4b144",
   "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              0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check if null value is drop or not.\n",
    "\n",
    "df.isnull().sum() # yes it's dropped as 'amount' is now 0, so rows with null values are dropped."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "8a09b7fb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(11239, 13)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# another way to confirm is\n",
    "\n",
    "df.shape  # from 11251 to 11239, Show that 12 rows have been removed."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9b1416af",
   "metadata": {},
   "source": [
    "**Note**:<br>\n",
    "\n",
    "Significance of **inplace = True** <br>\n",
    "\n",
    "*Below two statement does the same thing. It persists the changes made*\n",
    "\n",
    "* df_test.dropna(inplace = True)\n",
    "\n",
    "* df_test = df_test.dropna()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5e025a6c",
   "metadata": {},
   "source": [
    "*Back to original dataset*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "14e4ff48",
   "metadata": {},
   "outputs": [],
   "source": [
    "# change data type\n",
    "\n",
    "df['Amount'] = df['Amount'].astype('int')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "aa6752dc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('int32')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checking if it is changed or not\n",
    "\n",
    "df['Amount'].dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "5f723a99",
   "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": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# to get all the columns\n",
    "\n",
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "a85bb3c3",
   "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>Shaadi</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  Shaadi  \\\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": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# rename column\n",
    "\n",
    "df.rename(columns= {'Marital_Status':'Shaadi'}) # it won't get save as we've not use inplace = True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "a2484863",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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>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": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# describe() method returns description of the data in the DataFrame (i.e. count, mean, std, etc) [For numnerical columns]\n",
    "\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "2c380030",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<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": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# use describe() for specific columns\n",
    "\n",
    "df[['Age', 'Orders', 'Amount']].describe()  # but only works for numerical columns, if a categorical column name is given, \n",
    "                                            # it won't show it's stats, for that we've to use include parameter"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cc649558",
   "metadata": {},
   "source": [
    "# Exploratory Data Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "504b419c",
   "metadata": {},
   "source": [
    "### Gender"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "4bbc5b3f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.countplot(x = 'Gender', data = df)  # but values is not coming on the bars, so to get values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "84c7918d",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plotting a bar chart for Gender with data labels\n",
    "\n",
    "ax = sns.countplot(x = 'Gender', data = df)\n",
    "\n",
    "for bars in ax.containers:\n",
    "    ax.bar_label(bars)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "66874313",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<function matplotlib.pyplot.show(close=None, block=None)>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# another way to display labels without using for loop\n",
    "\n",
    "ax = sns.countplot(x = 'Gender', data = df)\n",
    "\n",
    "ax.bar_label(ax.containers[0])\n",
    "plt.show"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "da54e69f",
   "metadata": {},
   "source": [
    "**Insight:**\n",
    "\n",
    "* Female have purchased more than Male."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "42002e0c",
   "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": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(['Gender'], as_index=False)['Amount'].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "9087f70c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Gender\n",
       "F    74335853\n",
       "M    31913276\n",
       "Name: Amount, dtype: int32"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(['Gender'])['Amount'].sum()  # it will make or treat 'Gender' column as index, with as_index = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "61b948a6",
   "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": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# to sort the values, based on a particular column in asc or desc order\n",
    "\n",
    "df.groupby(['Gender'], as_index=False)['Amount'].sum().sort_values(by='Amount', ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "c5fd4566",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Gender', ylabel='Amount'>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plotting a bar chart for gender vs total amount\n",
    "\n",
    "sales_gen = df.groupby(['Gender'], as_index=False)['Amount'].sum().sort_values(by='Amount', ascending=False)\n",
    "\n",
    "sns.barplot(x = 'Gender', y = 'Amount', data = sales_gen)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "c02cc695",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Sales by Gender with data labels\n",
    "\n",
    "ax = sns.barplot(x = 'Gender', y = 'Amount' ,data = sales_gen)\n",
    "\n",
    "for bar in ax.containers:\n",
    "    ax.bar_label(bar)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1e108cec",
   "metadata": {},
   "source": [
    "*From above graphs we can see that most of the buyers are females and even the purchasing power of females are greater than men.*"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dcc927f9",
   "metadata": {},
   "source": [
    "### Age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "5ebdb9b2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# countplot for Age Group with data labels\n",
    "\n",
    "ax = sns.countplot(data = df, x = 'Age Group')\n",
    "\n",
    "for bar in ax.containers:\n",
    "    ax.bar_label(bar)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ad77871e",
   "metadata": {},
   "source": [
    "**Insigths**\n",
    "\n",
    "* *(26-35) age group people do the max shopping. After that (36-45) age group comes next.* \n",
    "\n",
    "* *But we want this shopping data to be divided among gender. For this we will use 'hue' parameter.*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "88563dec",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# countplot with 'hue' and data labels\n",
    "\n",
    "ax = sns.countplot(data = df, x = 'Age Group', hue = 'Gender')\n",
    "\n",
    "for bars in ax.containers:\n",
    "    ax.bar_label(bars)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "6efe419b",
   "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 Group</th>\n",
       "      <th>Amount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>26-35</td>\n",
       "      <td>42613442</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>36-45</td>\n",
       "      <td>22144994</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>18-25</td>\n",
       "      <td>17240732</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>46-50</td>\n",
       "      <td>9207844</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>51-55</td>\n",
       "      <td>8261477</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>55+</td>\n",
       "      <td>4080987</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0-17</td>\n",
       "      <td>2699653</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Age Group    Amount\n",
       "2     26-35  42613442\n",
       "3     36-45  22144994\n",
       "1     18-25  17240732\n",
       "4     46-50   9207844\n",
       "5     51-55   8261477\n",
       "6       55+   4080987\n",
       "0      0-17   2699653"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Total Amount vs Age Group\n",
    "\n",
    "df.groupby(['Age Group'], as_index = False)['Amount'].sum().sort_values(by = 'Amount', ascending = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "bfbe3937",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Age Group', ylabel='Amount'>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plotting Age Group vs Total Amount\n",
    "\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": "abaa6c07",
   "metadata": {},
   "source": [
    "*From above graphs we can see that most of the buyers are from age group 26-35 yrs female.*"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "83a49098",
   "metadata": {},
   "source": [
    "### States"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "dfac4f2c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='State', ylabel='Orders'>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# total number of orders from top 10 states\n",
    "\n",
    "sales_state = df.groupby(['State'], as_index=False)['Orders'].sum().sort_values(by='Orders', ascending=False).head(10)\n",
    "\n",
    "sns.barplot(data = sales_state, x = 'State', y = 'Orders')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d69f2315",
   "metadata": {},
   "source": [
    "*Clearly default size is not enough to accomodate overlapping states name. So, we have to manually gives appropriate size.*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "7c704a6e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='State', ylabel='Orders'>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1600x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Setting size for barplot in seaborn\n",
    "\n",
    "# plt.figure(figsize = (10,5))) # this is how we set size in pyplot\n",
    "\n",
    "sns.set(rc = {'figure.figsize':(16,5)})\n",
    "sns.barplot(data = sales_state, x = 'State',y = 'Orders')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "a66617bc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='State', ylabel='Amount'>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1600x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# total amount/sales from top 10 states\n",
    "\n",
    "sales_state = df.groupby(['State'], as_index=False)['Amount'].sum().sort_values(by='Amount', ascending=False).head(10)\n",
    "\n",
    "sns.set(rc={'figure.figsize':(16,5)})\n",
    "sns.barplot(data = sales_state, x = 'State',y= 'Amount')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "39d46e9e",
   "metadata": {},
   "source": [
    "*From above graphs we can see that most of the orders & total sales/amount are from Uttar Pradesh, Maharashtra and Karnataka respectively.*\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "89f865a5",
   "metadata": {},
   "source": [
    "### Marital Status"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "77e6635e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1600x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# total number of order vs  Marital status\n",
    "\n",
    "ax = sns.countplot(data = df, x = 'Marital_Status')\n",
    "\n",
    "sns.set(rc={'figure.figsize':(7,5)})\n",
    "for bars in ax.containers:\n",
    "    ax.bar_label(bars)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "2d8c7b27",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Marital_Status', ylabel='Amount'>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# total amount vs marital status by gender\n",
    "\n",
    "sales_state = df.groupby(['Marital_Status', 'Gender'], as_index=False)['Amount'].sum().sort_values(by='Amount', ascending=False)\n",
    "\n",
    "sns.set(rc={'figure.figsize':(6,5)})\n",
    "sns.barplot(data = sales_state, x = 'Marital_Status', y= 'Amount', hue='Gender')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "37afbb83",
   "metadata": {},
   "source": [
    "*From above graphs we can see that most of the buyers are married (women) and they have high purchasing power.*"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "230cfe66",
   "metadata": {},
   "source": [
    "### Occupation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "4deb41f4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 2000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Order by Occupation\n",
    "\n",
    "sns.set(rc={'figure.figsize':(20,5)})\n",
    "ax = sns.countplot(data = df, x = 'Occupation')\n",
    "\n",
    "for bars in ax.containers:\n",
    "    ax.bar_label(bars)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "4eb620f6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Occupation', ylabel='Amount'>"
      ]
     },
     "execution_count": 35,
     "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": [
    "# total Sales by Occupation\n",
    "\n",
    "sales_state = df.groupby(['Occupation'], as_index=False)['Amount'].sum().sort_values(by='Amount', ascending=False)\n",
    "\n",
    "sns.set(rc={'figure.figsize':(20,5)})\n",
    "sns.barplot(data = sales_state, x = 'Occupation',y= 'Amount')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "26c22532",
   "metadata": {},
   "source": [
    "*From above graphs we can see that most of the buyers are working in IT, Healthcare and Aviation sector.*"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "40518579",
   "metadata": {},
   "source": [
    "### Product Category"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "3e7e4d39",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 2400x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Orders by Product Category\n",
    "\n",
    "sns.set(rc={'figure.figsize':(24,5)})\n",
    "ax = sns.countplot(data = df, x = 'Product_Category')\n",
    "\n",
    "for bars in ax.containers:\n",
    "    ax.bar_label(bars)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0cd8880d",
   "metadata": {},
   "source": [
    "*Order wise Clothings & Apparel is more than Food.*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "99f5c6f3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Product_Category', ylabel='Amount'>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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4Ho83z5TL5ajVar/UEQAA8CqELAAAAAAAADkUn90AAAAAAADAfyRkAQAAAAAAyEHIAgAAAAAAkIOQBQAAAAAAIAchCwAAAAAAQA5CFgAAAAAAgByELAAAAAAAADkIWQAAAAAAAHL4AAONgAq/lsx/AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 2000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Amount by Product Category\n",
    "\n",
    "sales_state = df.groupby(['Product_Category'], as_index=False)['Amount'].sum().sort_values(by='Amount', ascending=False).head(10)\n",
    "\n",
    "sns.set(rc={'figure.figsize':(20,5)})\n",
    "sns.barplot(data = sales_state, x = 'Product_Category',y= 'Amount')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5be54973",
   "metadata": {},
   "source": [
    "*From above graphs we can see that most of the sold products are from Food, Clothing and Electronics category.*"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7fe1ce3e",
   "metadata": {},
   "source": [
    "*Amount wise Food sectore defeats Clothign & Apparel.*\n",
    "\n",
    "*That\\'s why we should always check order and amount both, before giving conclusion.*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "f33d23c7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Product_ID', ylabel='Orders'>"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 2000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# top 10 selling products\n",
    "\n",
    "sales_state = df.groupby(['Product_ID'], as_index=False)['Orders'].sum().sort_values(by='Orders', ascending=False).head(10)\n",
    "\n",
    "sns.set(rc={'figure.figsize':(20,5)})\n",
    "sns.barplot(data = sales_state, x = 'Product_ID',y= 'Orders')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "17d2bb43",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Product_ID'>"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# top 10 most sold products (same thing as above)\n",
    "\n",
    "fig1, ax1 = 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": "31f88f63",
   "metadata": {},
   "source": [
    "## Conclusion:\n",
    "\n",
    "### "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "56e52bca",
   "metadata": {},
   "source": [
    "*Married women age group 26-35 yrs from UP,  Maharastra and Karnataka working in IT, Healthcare and Aviation are more likely to buy products from Food, Clothing and Electronics category*"
   ]
  }
 ],
 "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": "3.11.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}