Diwali_Sales_Analysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "3ad6b08e-5c9e-48ce-876f-eff9d0ffbc0f",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np  # for mathematical and arrays works\n",
    "import pandas as pd # for using any formats files to work in data frame\n",
    "import matplotlib.pyplot as mat  # for charts and visualizing data\n",
    "%matplotlib inline\n",
    "import seaborn as sb # for charts and visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "3e7bbb7b-1cbb-4fea-95d0-23322e327d32",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('Diwali Sales Data.csv', encoding='unicode_escape')\n",
    "# to avoid encoding error, use 'unicode_escape'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "cf655b65-d899-4e2b-bc2e-fcfa5a0628e2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(11251, 15)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape #rows and column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "6be0decb-52bd-46e0-b60c-3ce130847e21",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <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>23952.00</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>23934.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <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>23924.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
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       "      <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>23912.00</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>23877.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</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>Himachal Pradesh</td>\n",
       "      <td>Northern</td>\n",
       "      <td>Food Processing</td>\n",
       "      <td>Auto</td>\n",
       "      <td>1</td>\n",
       "      <td>23877.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1001132</td>\n",
       "      <td>Balk</td>\n",
       "      <td>P00018042</td>\n",
       "      <td>F</td>\n",
       "      <td>18-25</td>\n",
       "      <td>25</td>\n",
       "      <td>1</td>\n",
       "      <td>Uttar Pradesh</td>\n",
       "      <td>Central</td>\n",
       "      <td>Lawyer</td>\n",
       "      <td>Auto</td>\n",
       "      <td>4</td>\n",
       "      <td>23841.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1002092</td>\n",
       "      <td>Shivangi</td>\n",
       "      <td>P00273442</td>\n",
       "      <td>F</td>\n",
       "      <td>55+</td>\n",
       "      <td>61</td>\n",
       "      <td>0</td>\n",
       "      <td>Maharashtra</td>\n",
       "      <td>Western</td>\n",
       "      <td>IT Sector</td>\n",
       "      <td>Auto</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1003224</td>\n",
       "      <td>Kushal</td>\n",
       "      <td>P00205642</td>\n",
       "      <td>M</td>\n",
       "      <td>26-35</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>Uttar Pradesh</td>\n",
       "      <td>Central</td>\n",
       "      <td>Govt</td>\n",
       "      <td>Auto</td>\n",
       "      <td>2</td>\n",
       "      <td>23809.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1003650</td>\n",
       "      <td>Ginny</td>\n",
       "      <td>P00031142</td>\n",
       "      <td>F</td>\n",
       "      <td>26-35</td>\n",
       "      <td>26</td>\n",
       "      <td>1</td>\n",
       "      <td>Andhra Pradesh</td>\n",
       "      <td>Southern</td>\n",
       "      <td>Media</td>\n",
       "      <td>Auto</td>\n",
       "      <td>4</td>\n",
       "      <td>23799.99</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\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",
       "5  1000588       Joni  P00057942      M     26-35   28               1   \n",
       "6  1001132       Balk  P00018042      F     18-25   25               1   \n",
       "7  1002092   Shivangi  P00273442      F       55+   61               0   \n",
       "8  1003224     Kushal  P00205642      M     26-35   35               0   \n",
       "9  1003650      Ginny  P00031142      F     26-35   26               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",
       "5  Himachal Pradesh  Northern  Food Processing             Auto       1   \n",
       "6     Uttar Pradesh   Central           Lawyer             Auto       4   \n",
       "7       Maharashtra   Western        IT Sector             Auto       1   \n",
       "8     Uttar Pradesh   Central             Govt             Auto       2   \n",
       "9    Andhra Pradesh  Southern            Media             Auto       4   \n",
       "\n",
       "     Amount  Status  unnamed1  \n",
       "0  23952.00     NaN       NaN  \n",
       "1  23934.00     NaN       NaN  \n",
       "2  23924.00     NaN       NaN  \n",
       "3  23912.00     NaN       NaN  \n",
       "4  23877.00     NaN       NaN  \n",
       "5  23877.00     NaN       NaN  \n",
       "6  23841.00     NaN       NaN  \n",
       "7       NaN     NaN       NaN  \n",
       "8  23809.00     NaN       NaN  \n",
       "9  23799.99     NaN       NaN  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "63ccdd32-32d1-4b24-9c5b-14c6d707ac6a",
   "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": [
    "#Start with cleaning process:\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "44f1366d-39c6-48e1-8659-55986b9c0fd9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# if coulumn is no need then kindly drop unrelated/blank columns, axis means full rows, inplace means save and update \n",
    "df.drop(['Status','unnamed1'],axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "f87cf7b4-7cf6-42a3-b131-0650b89eacd4",
   "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()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "378e7ddb-46c3-4ab4-87a3-eaf54ccbe42d",
   "metadata": {},
   "outputs": [
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11249</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11250</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</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  \\\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",
       "[11239 rows x 13 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#null checking process:\n",
    "pd.isnull(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "2e77b76c-c0d1-4a53-95c9-264be0793b8b",
   "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": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.isnull(df).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "dd5a5897-b4c8-46f7-8ac7-e62e9fababca",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(11239, 13)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "0a4150c2-a561-437b-885f-f7cfb79719d5",
   "metadata": {},
   "outputs": [],
   "source": [
    "#drop null values(na)\n",
    "df.dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "9f4f8b8e-5bcc-4e1b-8a07-c1be3a86a577",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(11239, 13)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "7b49fa0d-7111-4f0b-875a-a6bc0e767116",
   "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": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.isnull(df).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "93ac59cc-101f-43f1-8a6e-57926423d174",
   "metadata": {},
   "outputs": [],
   "source": [
    "#change the data type of amount from float to int\n",
    "df['Amount'] = df['Amount'].astype('int')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "8d9c3e8e-4b8b-4dfc-a833-8dcc4c3d20be",
   "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()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "fc1bd4b2-c3e3-457f-9feb-bc0055d5eb80",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('int32')"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#check particular column data type:\n",
    "df['Amount'].dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "7efb7020-0505-44e4-b08f-3b0ae3524f3f",
   "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": [
    "# check all columns names:\n",
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "ddd79fc2-734c-4603-8d49-42dde29c8a4e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\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>Marraige_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  Marraige_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": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#if we want to rename column name:\n",
    "df.rename(columns={'Marital_Status':'Marraige_Status'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "325fdc05-0729-4222-b6d0-64b8e057a70a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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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": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#describe() method return description of the data in Mathemattically in the DataFrame(i.e. count, mean, std, etc )\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "420d84e8-cfbf-439a-a0da-98031f128fac",
   "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": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#use describe() for specific columns\n",
    "df[['Age', 'Orders', 'Amount']].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "4e6b4818-f3b1-40b4-93aa-ecf2867a3cc0",
   "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": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#after completion of Data cleaning we will start with EDA(Exploratory Data Analysis)\n",
    "#Gender\n",
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "a48f9729-b824-4ed2-ab65-5ca9877a1c7e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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WKDs7W0VFRXK5XBo9erRqamqsmoyMDOXm5ionJ0c7d+5UbW2tUlNT1djYaNWkp6erpKREeXl5ysvLU0lJiTweT3tOFwAAdFAO0zTNQJ38l7/8pV5//XW99tprLe43TVNut1sZGRlavHixpC9Xg5xOp5YtW6ZZs2bJ5/OpR48e2rBhg+644w5J0smTJxUXF6ctW7Zo7NixOnTokAYMGKDCwkIlJydLkgoLC5WSkqLDhw+rX79+39hrdXW1DMOQz+dTVFTUJfoJNJe08IU2OzbQWRU/cXegWwDQSV3o53dAV4hefvllDR48WBMnTlRsbKxuvPFGPfvss9b+0tJSeb1ejRkzxhoLCwvTsGHDtGvXLklScXGxzpw541fjdrs1cOBAq2b37t0yDMMKQ5I0ZMgQGYZh1Zyrvr5e1dXVfhsAALg8BTQQffjhh1q9erUSEhK0detW3XvvvZo3b55eeOHLVRKv1ytJcjqdfu9zOp3WPq/Xq9DQUHXr1u28NbGxsc3OHxsba9WcKysry7reyDAMxcXFXdxkAQBAhxXQQNTU1KSbbrpJmZmZuvHGGzVr1izNmDFDq1ev9qtzOBx+r03TbDZ2rnNrWqo/33GWLFkin89nbceOHbvQaQEAgE4moIGoZ8+eGjBggN9Y//79dfToUUmSy+WSpGarOOXl5daqkcvlUkNDgyorK89bc+rUqWbnr6ioaLb6dFZYWJiioqL8NgAAcHkKaCD6wQ9+oCNHjviNvfvuu7rmmmskSfHx8XK5XMrPz7f2NzQ0qKCgQEOHDpUkJSUlKSQkxK+mrKxMBw4csGpSUlLk8/m0d+9eq2bPnj3y+XxWDQAAsK/gQJ78gQce0NChQ5WZmalJkyZp7969euaZZ/TMM89I+vJrroyMDGVmZiohIUEJCQnKzMxUly5dlJ6eLkkyDEPTp0/X/PnzFR0dre7du2vBggVKTEzUqFGjJH256jRu3DjNmDFDa9askSTNnDlTqampF3SHGQAAuLwFNBB9//vfV25urpYsWaJHH31U8fHxWrVqlSZPnmzVLFq0SHV1dZo9e7YqKyuVnJysbdu2KTIy0qpZuXKlgoODNWnSJNXV1WnkyJFav369goKCrJpNmzZp3rx51t1oaWlpys7Obr/JAgCADiugzyHqTHgOERA4PIcIQGt1iucQAQAAdAQEIgAAYHsEIgAAYHsEIgAAYHsEIgAAYHsEIgAAYHsEIgAAYHsEIgAAYHsEIgAAYHsEIgAAYHsEIgAAYHsEIgAAYHsEIgAAYHsEIgAAYHsEIgAAYHsEIgAAYHsEIgAAYHsEIgAAYHsEIgAAYHsEIgAAYHsEIgAAYHsEIgAAYHsEIgAAYHsEIgAAYHsEIgAAYHsEIgAAYHsEIgAAYHsEIgAAYHsEIgAAYHsEIgAAYHsEIgAAYHsEIgAAYHsEIgAAYHsEIgAAYHsBDURLly6Vw+Hw21wul7XfNE0tXbpUbrdb4eHhGj58uA4ePOh3jPr6es2dO1cxMTGKiIhQWlqajh8/7ldTWVkpj8cjwzBkGIY8Ho+qqqraY4oAAKATCPgK0fXXX6+ysjJr279/v7Vv+fLlWrFihbKzs1VUVCSXy6XRo0erpqbGqsnIyFBubq5ycnK0c+dO1dbWKjU1VY2NjVZNenq6SkpKlJeXp7y8PJWUlMjj8bTrPAEAQMcVHPAGgoP9VoXOMk1Tq1at0sMPP6zbb79dkvT888/L6XTqxRdf1KxZs+Tz+bR27Vpt2LBBo0aNkiRt3LhRcXFx2r59u8aOHatDhw4pLy9PhYWFSk5OliQ9++yzSklJ0ZEjR9SvX7/2mywAAOiQAr5C9N5778ntdis+Pl533nmnPvzwQ0lSaWmpvF6vxowZY9WGhYVp2LBh2rVrlySpuLhYZ86c8atxu90aOHCgVbN7924ZhmGFIUkaMmSIDMOwalpSX1+v6upqvw0AAFyeAhqIkpOT9cILL2jr1q169tln5fV6NXToUH3yySfyer2SJKfT6fcep9Np7fN6vQoNDVW3bt3OWxMbG9vs3LGxsVZNS7KysqxrjgzDUFxc3EXNFQAAdFwBDUTjx4/XT3/6UyUmJmrUqFH629/+JunLr8bOcjgcfu8xTbPZ2LnOrWmp/puOs2TJEvl8Pms7duzYBc0JAAB0PgH/yuyrIiIilJiYqPfee8+6rujcVZzy8nJr1cjlcqmhoUGVlZXnrTl16lSzc1VUVDRbffqqsLAwRUVF+W0AAODy1KECUX19vQ4dOqSePXsqPj5eLpdL+fn51v6GhgYVFBRo6NChkqSkpCSFhIT41ZSVlenAgQNWTUpKinw+n/bu3WvV7NmzRz6fz6oBAAD2FtC7zBYsWKDbbrtNvXv3Vnl5uR577DFVV1drypQpcjgcysjIUGZmphISEpSQkKDMzEx16dJF6enpkiTDMDR9+nTNnz9f0dHR6t69uxYsWGB9BSdJ/fv317hx4zRjxgytWbNGkjRz5kylpqZyhxkAAJAU4EB0/Phx3XXXXfrXv/6lHj16aMiQISosLNQ111wjSVq0aJHq6uo0e/ZsVVZWKjk5Wdu2bVNkZKR1jJUrVyo4OFiTJk1SXV2dRo4cqfXr1ysoKMiq2bRpk+bNm2fdjZaWlqbs7Oz2nSwAAOiwHKZpmoFuojOorq6WYRjy+Xxtej1R0sIX2uzYQGdV/MTdgW4BQCd1oZ/fHeoaIgAAgEAgEAEAANsjEAEAANsjEAEAANsjEAEAANsjEAEAANsjEAEAANsjEAEAANsjEAEAANsjEAEAANsjEAEAANsjEAEAANsjEAEAANsjEAEAANsjEAEAANsjEAEAANtrVSAaMWKEqqqqmo1XV1drxIgRF9sTAABAu2pVINqxY4caGhqajZ8+fVqvvfbaRTcFAADQnoK/TfHbb79t/fudd96R1+u1Xjc2NiovL09XX331pesOAACgHXyrQPS9731PDodDDoejxa/GwsPD9cc//vGSNQcAANAevlUgKi0tlWma6tOnj/bu3asePXpY+0JDQxUbG6ugoKBL3iQAAEBb+laB6JprrpEkNTU1tUkzAAAAgfCtAtFXvfvuu9qxY4fKy8ubBaRf//rXF90YAABAe2lVIHr22Wf1i1/8QjExMXK5XHI4HNY+h8NBIAIAAJ1KqwLRY489pt/+9rdavHjxpe4HAACg3bXqOUSVlZWaOHHipe4FAAAgIFoViCZOnKht27Zd6l4AAAAColVfmfXt21f/7//9PxUWFioxMVEhISF+++fNm3dJmgMAAGgPrQpEzzzzjLp27aqCggIVFBT47XM4HAQiAADQqbQqEJWWll7qPgAAAAKmVdcQAQAAXE5atUI0bdq08+5/7rnnWtUMAABAILQqEFVWVvq9PnPmjA4cOKCqqqoW/+grAABAR9aqQJSbm9tsrKmpSbNnz1afPn0uuikAAID2dMmuIbriiiv0wAMPaOXKla16f1ZWlhwOhzIyMqwx0zS1dOlSud1uhYeHa/jw4Tp48KDf++rr6zV37lzFxMQoIiJCaWlpOn78uF9NZWWlPB6PDMOQYRjyeDyqqqpqVZ8AAODyc0kvqv7ggw/0xRdffOv3FRUV6ZlnntGgQYP8xpcvX64VK1YoOztbRUVFcrlcGj16tGpqaqyajIwM5ebmKicnRzt37lRtba1SU1PV2Nho1aSnp6ukpER5eXnKy8tTSUmJPB5P6ycKAAAuK636yuzBBx/0e22apsrKyvS3v/1NU6ZM+VbHqq2t1eTJk/Xss8/qscce8zvmqlWr9PDDD+v222+XJD3//PNyOp168cUXNWvWLPl8Pq1du1YbNmzQqFGjJEkbN25UXFyctm/frrFjx+rQoUPKy8tTYWGhkpOTJX35x2lTUlJ05MgR9evXrzU/AgAAcBlp1QrRm2++6be9/fbbkqQnn3xSq1at+lbHuu+++zRhwgQr0JxVWloqr9erMWPGWGNhYWEaNmyYdu3aJUkqLi7WmTNn/GrcbrcGDhxo1ezevVuGYVhhSJKGDBkiwzCsmpbU19erurrabwMAAJenVq0Q/fOf/7wkJ8/JydEbb7yhoqKiZvu8Xq8kyel0+o07nU59/PHHVk1oaKi6devWrObs+71er2JjY5sdPzY21qppSVZWln7zm998uwkBAIBO6aKuIaqoqNDOnTv1+uuvq6Ki4lu999ixY7r//vu1ceNGXXnllV9b53A4/F6bptls7Fzn1rRU/03HWbJkiXw+n7UdO3bsvOcEAACdV6sC0WeffaZp06apZ8+euuWWW/TDH/5Qbrdb06dP1+eff35BxyguLlZ5ebmSkpIUHBys4OBgFRQU6A9/+IOCg4OtlaFzV3HKy8utfS6XSw0NDc2ei3RuzalTp5qdv6Kiotnq01eFhYUpKirKbwMAAJenVgWiBx98UAUFBfqf//kfVVVVqaqqSn/9619VUFCg+fPnX9AxRo4cqf3796ukpMTaBg8erMmTJ6ukpER9+vSRy+VSfn6+9Z6GhgYVFBRo6NChkqSkpCSFhIT41ZSVlenAgQNWTUpKinw+n/bu3WvV7NmzRz6fz6oBAAD21qpriP7yl7/ov//7vzV8+HBr7Ec/+pHCw8M1adIkrV69+huPERkZqYEDB/qNRUREKDo62hrPyMhQZmamEhISlJCQoMzMTHXp0kXp6emSJMMwNH36dM2fP1/R0dHq3r27FixYoMTEROsi7f79+2vcuHGaMWOG1qxZI0maOXOmUlNTucMMAABIamUg+vzzz1v8uik2NvaCvzK7EIsWLVJdXZ1mz56tyspKJScna9u2bYqMjLRqVq5cqeDgYE2aNEl1dXUaOXKk1q9fr6CgIKtm06ZNmjdvnnU3WlpamrKzsy9ZnwAAoHNzmKZpfts3jRw5UtHR0XrhhResC6Lr6uo0ZcoUffrpp9q+ffslbzTQqqurZRiGfD5fm15PlLTwhTY7NtBZFT9xd6BbANBJXejnd6tWiFatWqXx48erV69euuGGG+RwOFRSUqKwsDBt27at1U0DAAAEQqsCUWJiot577z1t3LhRhw8flmmauvPOOzV58mSFh4df6h4BAADaVKsCUVZWlpxOp2bMmOE3/txzz6miokKLFy++JM0BAAC0h1bddr9mzRp997vfbTZ+/fXX6+mnn77opgAAANpTqwKR1+tVz549m4336NFDZWVlF90UAABAe2pVIIqLi9Prr7/ebPz111+X2+2+6KYAAADaU6uuIbrnnnuUkZGhM2fOaMSIEZKkV199VYsWLbrgJ1UDAAB0FK1aIVq0aJGmT5+u2bNnq0+fPurTp4/mzp2refPmacmSJZe6RwAALKtXr9agQYOsvzOZkpKiv//97y3Wzpo1Sw6HQ6tWrfIbr6+v19y5cxUTE6OIiAilpaXp+PHj1v4dO3bI4XC0uBUVFbXl9BAgrQpEDodDy5YtU0VFhQoLC/XWW2/p008/1a9//etL3R8AAH569eqlxx9/XPv27dO+ffs0YsQI/fjHP9bBgwf96l566SXt2bOnxUs5MjIylJubq5ycHO3cuVO1tbVKTU1VY2OjJGno0KEqKyvz2+655x5de+21Gjx4cLvME+2rVV+ZndW1a1d9//vfv1S9AADwjW677Ta/17/97W+1evVqFRYW6vrrr5cknThxQnPmzNHWrVs1YcIEv3qfz6e1a9dqw4YN1t+93Lhxo+Li4rR9+3aNHTtWoaGhcrlc1nvOnDmjl19+WXPmzJHD4WjjGSIQWrVCBABAR9DY2KicnBx99tlnSklJkSQ1NTXJ4/Fo4cKFVkD6quLiYp05c8b6+5aS5Ha7NXDgQO3atavF87z88sv617/+palTp7bJPBB4F7VCBABAIOzfv18pKSk6ffq0unbtqtzcXA0YMECStGzZMgUHB2vevHktvtfr9So0NFTdunXzG3c6nfJ6vS2+Z+3atRo7dqzi4uIu7UTQYRCIAACdTr9+/VRSUqKqqir95S9/0ZQpU1RQUKC6ujr9/ve/1xtvvPGtv9oyTbPF9xw/flxbt27Vf/7nf16q9tEBEYgAAJ1OaGio+vbtK0kaPHiwioqK9Pvf/179+/dXeXm5evfubdU2NjZq/vz5WrVqlT766CO5XC41NDSosrLSb5WovLxcQ4cObXaudevWKTo6WmlpaW0/MQQM1xABADo90zRVX18vj8ejt99+WyUlJdbmdru1cOFCbd26VZKUlJSkkJAQ5efnW+8vKyvTgQMHmgUi0zS1bt063X333QoJCWnXOaF9sUIEAOhUHnroIY0fP15xcXGqqalRTk6OduzYoby8PEVHRys6OtqvPiQkRC6XS/369ZMkGYah6dOna/78+YqOjlb37t21YMECJSYmWnednfWPf/xDpaWlmj59ervND4FBIAIAdCqnTp2Sx+NRWVmZDMPQoEGDlJeXp9GjR1/wMVauXKng4GBNmjRJdXV1GjlypNavX6+goCC/urVr12ro0KHq37//pZ4GOhiHaZpmoJvoDKqrq2UYhnw+n6KiotrsPEkLX2izYwOdVfETdwe6BQCd1IV+fnMNEQAAsD0CEQAAsD2uIQKAdnL00cRAtwB0OL1/vT/QLUhihQgAAIBABAAAQCACAAC2RyACAAC2RyACAAC2RyACAAC2RyACAAC2RyACAAC2RyACAAC2RyACAAC2RyACAAC2RyACAAC2RyACAAC2F9BAtHr1ag0aNEhRUVGKiopSSkqK/v73v1v7TdPU0qVL5Xa7FR4eruHDh+vgwYN+x6ivr9fcuXMVExOjiIgIpaWl6fjx4341lZWV8ng8MgxDhmHI4/GoqqqqPaYIAAA6gYAGol69eunxxx/Xvn37tG/fPo0YMUI//vGPrdCzfPlyrVixQtnZ2SoqKpLL5dLo0aNVU1NjHSMjI0O5ubnKycnRzp07VVtbq9TUVDU2Nlo16enpKikpUV5envLy8lRSUiKPx9Pu8wUAAB2TwzRNM9BNfFX37t31xBNPaNq0aXK73crIyNDixYslfbka5HQ6tWzZMs2aNUs+n089evTQhg0bdMcdd0iSTp48qbi4OG3ZskVjx47VoUOHNGDAABUWFio5OVmSVFhYqJSUFB0+fFj9+vW7oL6qq6tlGIZ8Pp+ioqLaZvKSkha+0GbHBjqr4ifuDnQLl8TRRxMD3QLQ4fT+9f42Pf6Ffn53mGuIGhsblZOTo88++0wpKSkqLS2V1+vVmDFjrJqwsDANGzZMu3btkiQVFxfrzJkzfjVut1sDBw60anbv3i3DMKwwJElDhgyRYRhWTUvq6+tVXV3ttwEAgMtTwAPR/v371bVrV4WFhenee+9Vbm6uBgwYIK/XK0lyOp1+9U6n09rn9XoVGhqqbt26nbcmNja22XljY2OtmpZkZWVZ1xwZhqG4uLiLmicAAOi4Ah6I+vXrp5KSEhUWFuoXv/iFpkyZonfeecfa73A4/OpN02w2dq5za1qq/6bjLFmyRD6fz9qOHTt2oVMCAACdTMADUWhoqPr27avBgwcrKytLN9xwg37/+9/L5XJJUrNVnPLycmvVyOVyqaGhQZWVleetOXXqVLPzVlRUNFt9+qqwsDDr7rezGwAAuDwFPBCdyzRN1dfXKz4+Xi6XS/n5+da+hoYGFRQUaOjQoZKkpKQkhYSE+NWUlZXpwIEDVk1KSop8Pp/27t1r1ezZs0c+n8+qAQAA9hYcyJM/9NBDGj9+vOLi4lRTU6OcnBzt2LFDeXl5cjgcysjIUGZmphISEpSQkKDMzEx16dJF6enpkiTDMDR9+nTNnz9f0dHR6t69uxYsWKDExESNGjVKktS/f3+NGzdOM2bM0Jo1ayRJM2fOVGpq6gXfYQYAAC5vAQ1Ep06dksfjUVlZmQzD0KBBg5SXl6fRo0dLkhYtWqS6ujrNnj1blZWVSk5O1rZt2xQZGWkdY+XKlQoODtakSZNUV1enkSNHav369QoKCrJqNm3apHnz5ll3o6WlpSk7O7t9JwsAADqsDvccoo6K5xABgcNziIDLF88hAgAA6CAIRAAAwPYIRAAAwPYIRAAAwPYIRAAAwPYIRAAAwPYIRAAAwPYIRAAAwPYIRAAAwPYIRAAAwPYIRAAAwPYIRAAAwPYIRAAAwPYIRAAAwPYIRAAAwPYIRAAAwPYIRAAAwPYIRAAAwPYIRAAAwPYIRAAAwPYIRAAAwPYIRAAAwPYIRAAAwPYIRAAAwPYIRAAAwPYIRAAAwPYIRAAAwPYIRAAAwPYIRAAAwPYIRAAAwPYIRAAAwPYIRAAAwPYIRAAAwPYIRAAAwPYCGoiysrL0/e9/X5GRkYqNjdVPfvITHTlyxK/GNE0tXbpUbrdb4eHhGj58uA4ePOhXU19fr7lz5yomJkYRERFKS0vT8ePH/WoqKyvl8XhkGIYMw5DH41FVVVVbTxEAAHQCAQ1EBQUFuu+++1RYWKj8/Hx98cUXGjNmjD777DOrZvny5VqxYoWys7NVVFQkl8ul0aNHq6amxqrJyMhQbm6ucnJytHPnTtXW1io1NVWNjY1WTXp6ukpKSpSXl6e8vDyVlJTI4/G063wBAEDH5DBN0wx0E2dVVFQoNjZWBQUFuuWWW2SaptxutzIyMrR48WJJX64GOZ1OLVu2TLNmzZLP51OPHj20YcMG3XHHHZKkkydPKi4uTlu2bNHYsWN16NAhDRgwQIWFhUpOTpYkFRYWKiUlRYcPH1a/fv2+sbfq6moZhiGfz6eoqKg2+xkkLXyhzY4NdFbFT9wd6BYuiaOPJga6BaDD6f3r/W16/Av9/O5Q1xD5fD5JUvfu3SVJpaWl8nq9GjNmjFUTFhamYcOGadeuXZKk4uJinTlzxq/G7XZr4MCBVs3u3btlGIYVhiRpyJAhMgzDqjlXfX29qqur/TYAAHB56jCByDRNPfjgg7r55ps1cOBASZLX65UkOZ1Ov1qn02nt83q9Cg0NVbdu3c5bExsb2+ycsbGxVs25srKyrOuNDMNQXFzcxU0QAAB0WB0mEM2ZM0dvv/22/vSnPzXb53A4/F6bptls7Fzn1rRUf77jLFmyRD6fz9qOHTt2IdMAAACdUIcIRHPnztXLL7+sf/7zn+rVq5c17nK5JKnZKk55ebm1auRyudTQ0KDKysrz1pw6darZeSsqKpqtPp0VFhamqKgovw0AAFyeAhqITNPUnDlztHnzZv3jH/9QfHy83/74+Hi5XC7l5+dbYw0NDSooKNDQoUMlSUlJSQoJCfGrKSsr04EDB6yalJQU+Xw+7d2716rZs2ePfD6fVQMAAOwrOJAnv++++/Tiiy/qr3/9qyIjI62VIMMwFB4eLofDoYyMDGVmZiohIUEJCQnKzMxUly5dlJ6ebtVOnz5d8+fPV3R0tLp3764FCxYoMTFRo0aNkiT1799f48aN04wZM7RmzRpJ0syZM5WamnpBd5gBAIDLW0AD0erVqyVJw4cP9xtft26dpk6dKklatGiR6urqNHv2bFVWVio5OVnbtm1TZGSkVb9y5UoFBwdr0qRJqqur08iRI7V+/XoFBQVZNZs2bdK8efOsu9HS0tKUnZ3dthMEAACdQod6DlFHxnOIgMDhOUTA5YvnEAEAAHQQBCIAAGB7BCIAAGB7BCIAAGB7BCIAAGB7BCIAAGB7BCIAAGB7BCIAAGB7BCIAAGB7BCIAAGB7BCIAAGB7BCIAAGB7BCIAAGB7BCIAAGB7BCIAAGB7BCIAAGB7BCIAAGB7BCIAAGB7BCIAAGB7BCIAAGB7BCIAAGB7BCIAAGB7BCIAAGB7BCIAAGB7BCIAAGB7BCIAAGB7BCIAAGB7BCIAAGB7BCIAAGB7BCIAAGB7BCIAAGB7BCIAAGB7BCIAAGB7BCIAAGB7AQ1E//u//6vbbrtNbrdbDodDL730kt9+0zS1dOlSud1uhYeHa/jw4Tp48KBfTX19vebOnauYmBhFREQoLS1Nx48f96uprKyUx+ORYRgyDEMej0dVVVVtPDsAANBZBDQQffbZZ7rhhhuUnZ3d4v7ly5drxYoVys7OVlFRkVwul0aPHq2amhqrJiMjQ7m5ucrJydHOnTtVW1ur1NRUNTY2WjXp6ekqKSlRXl6e8vLyVFJSIo/H0+bzAwAAnUNwIE8+fvx4jR8/vsV9pmlq1apVevjhh3X77bdLkp5//nk5nU69+OKLmjVrlnw+n9auXasNGzZo1KhRkqSNGzcqLi5O27dv19ixY3Xo0CHl5eWpsLBQycnJkqRnn31WKSkpOnLkiPr169c+kwUAAB1Wh72GqLS0VF6vV2PGjLHGwsLCNGzYMO3atUuSVFxcrDNnzvjVuN1uDRw40KrZvXu3DMOwwpAkDRkyRIZhWDUtqa+vV3V1td8GAAAuTx02EHm9XkmS0+n0G3c6ndY+r9er0NBQdevW7bw1sbGxzY4fGxtr1bQkKyvLuubIMAzFxcVd1HwAAEDH1WED0VkOh8PvtWmazcbOdW5NS/XfdJwlS5bI5/NZ27Fjx75l5wAAoLPosIHI5XJJUrNVnPLycmvVyOVyqaGhQZWVleetOXXqVLPjV1RUNFt9+qqwsDBFRUX5bQAA4PLUYQNRfHy8XC6X8vPzrbGGhgYVFBRo6NChkqSkpCSFhIT41ZSVlenAgQNWTUpKinw+n/bu3WvV7NmzRz6fz6oBAAD2FtC7zGpra/X+++9br0tLS1VSUqLu3burd+/eysjIUGZmphISEpSQkKDMzEx16dJF6enpkiTDMDR9+nTNnz9f0dHR6t69uxYsWKDExETrrrP+/ftr3LhxmjFjhtasWSNJmjlzplJTU7nDDAAASApwINq3b59uvfVW6/WDDz4oSZoyZYrWr1+vRYsWqa6uTrNnz1ZlZaWSk5O1bds2RUZGWu9ZuXKlgoODNWnSJNXV1WnkyJFav369goKCrJpNmzZp3rx51t1oaWlpX/vsIwAAYD8O0zTNQDfRGVRXV8swDPl8vja9nihp4Qttdmygsyp+4u5At3BJHH00MdAtAB1O71/vb9PjX+jnd4e9hggAAKC9EIgAAIDtEYgAAIDtEYgAAIDtEYgAAIDtEYgAAIDtEYgAAIDtEYgAAIDtEYgAAIDtEYgAAIDtEYgAAIDtEYgAAIDtEYgAAIDtEYgAAIDtEYgAAIDtEYgAAIDtEYgAAIDtEYgAAIDtEYgAAIDtEYgAAIDtEYgAAIDtEYgAAIDtEYgAAIDtEYgAAIDtEYgAAIDtEYgAAIDtEYgAAIDtEYgAAIDtEYgAAIDtEYgAAIDtEYgAAIDtEYgAAIDtEYgAAIDtEYgAAIDt2SoQPfXUU4qPj9eVV16ppKQkvfbaa4FuCQAAdAC2CUR//vOflZGRoYcfflhvvvmmfvjDH2r8+PE6evRooFsDAAABZptAtGLFCk2fPl333HOP+vfvr1WrVikuLk6rV68OdGsAACDAggPdQHtoaGhQcXGxfvnLX/qNjxkzRrt27WrxPfX19aqvr7de+3w+SVJ1dXXbNSqpsb6uTY8PdEZt/XvXXmpONwa6BaDDaevf77PHN03zvHW2CET/+te/1NjYKKfT6TfudDrl9XpbfE9WVpZ+85vfNBuPi4trkx4BfD3jj/cGugUAbSXLaJfT1NTUyDC+/ly2CERnORwOv9emaTYbO2vJkiV68MEHrddNTU369NNPFR0d/bXvweWjurpacXFxOnbsmKKiogLdDoBLiN9vezFNUzU1NXK73eets0UgiomJUVBQULPVoPLy8marRmeFhYUpLCzMb+yqq65qqxbRQUVFRfEfTOAyxe+3fZxvZegsW1xUHRoaqqSkJOXn5/uN5+fna+jQoQHqCgAAdBS2WCGSpAcffFAej0eDBw9WSkqKnnnmGR09elT33su1CQAA2J1tAtEdd9yhTz75RI8++qjKyso0cOBAbdmyRddcc02gW0MHFBYWpkceeaTZ16YAOj9+v9ESh/lN96EBAABc5mxxDREAAMD5EIgAAIDtEYgAAIDtEYgAAIDtEYiAr5g6daocDkez7f333w90awBa6ezvdUuPWZk9e7YcDoemTp3a/o2hQyEQAecYN26cysrK/Lb4+PhAtwXgIsTFxSknJ0d1df/3B7RPnz6tP/3pT+rdu3cAO0NHQSACzhEWFiaXy+W3BQUFBbotABfhpptuUu/evbV582ZrbPPmzYqLi9ONN94YwM7QURCIAAC28POf/1zr1q2zXj/33HOaNm1aADtCR0IgAs7xyiuvqGvXrtY2ceLEQLcE4BLweDzauXOnPvroI3388cd6/fXX9bOf/SzQbaGDsM2f7gAu1K233qrVq1dbryMiIgLYDYBLJSYmRhMmTNDzzz8v0zQ1YcIExcTEBLotdBAEIuAcERER6tu3b6DbANAGpk2bpjlz5kiS/v3f/z3A3aAjIRABAGxj3LhxamhokCSNHTs2wN2gIyEQAQBsIygoSIcOHbL+DZxFIAIA2EpUVFSgW0AH5DBN0wx0EwAAAIHEbfcAAMD2CEQAAMD2CEQAAMD2CEQAAMD2CEQAAMD2CEQAAMD2CEQAAMD2CEQAAMD2CEQAcAGGDx+ujIyMQLcBoI0QiAB0Gl6vV/fff7/69u2rK6+8Uk6nUzfffLOefvppff7554FuD0Anxt8yA9ApfPjhh/rBD36gq666SpmZmUpMTNQXX3yhd999V88995zcbrfS0tIC3ebXamxslMPh0BVX8P+hQEfEbyaATmH27NkKDg7Wvn37NGnSJPXv31+JiYn66U9/qr/97W+67bbbJEk+n08zZ85UbGysoqKiNGLECL311lvWcZYuXarvfe972rBhg6699loZhqE777xTNTU1Vs1nn32mu+++W127dlXPnj315JNPNuunoaFBixYt0tVXX62IiAglJydrx44d1v7169frqquu0iuvvKIBAwYoLCxMH3/8cdv9gABcFAIRgA7vk08+0bZt23TfffcpIiKixRqHwyHTNDVhwgR5vV5t2bJFxcXFuummmzRy5Eh9+umnVu0HH3ygl156Sa+88opeeeUVFRQU6PHHH7f2L1y4UP/85z+Vm5urbdu2aceOHSouLvY7389//nO9/vrrysnJ0dtvv62JEydq3Lhxeu+996yazz//XFlZWfqP//gPHTx4ULGxsZf4JwPgkjEBoIMrLCw0JZmbN2/2G4+OjjYjIiLMiIgIc9GiRearr75qRkVFmadPn/aru+6668w1a9aYpmmajzzyiNmlSxezurra2r9w4UIzOTnZNE3TrKmpMUNDQ82cnBxr/yeffGKGh4eb999/v2mapvn++++bDofDPHHihN95Ro4caS5ZssQ0TdNct26dKcksKSm5ND8EAG2Ka4gAdBoOh8Pv9d69e9XU1KTJkyervr5excXFqq2tVXR0tF9dXV2dPvjgA+v1tddeq8jISOt1z549VV5eLunL1aOGhgalpKRY+7t3765+/fpZr9944w2ZpqnvfOc7fuepr6/3O3doaKgGDRp0ETMG0F4IRAA6vL59+8rhcOjw4cN+43369JEkhYeHS5KamprUs2dPv2t5zrrqqqusf4eEhPjtczgcampqkiSZpvmN/TQ1NSkoKEjFxcUKCgry29e1a1fr3+Hh4c1CHICOiUAEoMOLjo7W6NGjlZ2drblz537tdUQ33XSTvF6vgoODde2117bqXH379lVISIgKCwvVu3dvSVJlZaXeffddDRs2TJJ04403qrGxUeXl5frhD3/YqvMA6Fi4qBpAp/DUU0/piy++0ODBg/XnP/9Zhw4d0pEjR7Rx40YdPnxYQUFBGjVqlFJSUvSTn/xEW7du1UcffaRdu3bpV7/6lfbt23dB5+nataumT5+uhQsX6tVXX9WBAwc0depUv9vlv/Od72jy5Mm6++67tXnzZpWWlqqoqEjLli3Tli1b2upHAKANsUIEoFO47rrr9OabbyozM1NLlizR8ePHFRYWpgEDBmjBggWaPXu2HA6HtmzZoocffljTpk1TRUWFXC6XbrnlFjmdzgs+1xNPPKHa2lqlpaUpMjJS8+fPl8/n86tZt26dHnvsMc2fP18nTpxQdHS0UlJS9KMf/ehSTx1AO3CYF/KFOQAAwGWMr8wAAIDtEYgAAIDtEYgAAIDtEYgAAIDtEYgAAIDtEYgAAIDtEYgAAIDtEYgAAIDtEYgAAIDtEYgAAIDtEYgAAIDt/X8gBm1BYaoDQQAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sb.countplot(x='Gender', data = df)\n",
    "for bars in ax.containers:\n",
    "    ax.bar_label(bars)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "86fa2438-b5eb-4242-8312-2921ac76c04d",
   "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": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(['Gender'], as_index=False)['Amount'].sum().sort_values(by='Amount',ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "85b3f985-361d-48e3-b206-255c1ad0f170",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Gender', ylabel='Amount'>"
      ]
     },
     "execution_count": 30,
     "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": [
    "sales_gen = df.groupby(['Gender'], as_index=False)['Amount'].sum().sort_values(by='Amount',ascending=False)\n",
    "\n",
    "sb.barplot(x='Gender', y='Amount', data=sales_gen)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "42daccf9-de1c-4678-91dd-dc1ea5efe647",
   "metadata": {},
   "outputs": [],
   "source": [
    "#From the above graphs we can see that most of the buyers are females and even the purchasing power of females are greater than men\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "83e85872-7ecf-4c8c-91de-9e7fbe6a78ff",
   "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": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#AGE:\n",
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "68de94ce-c7f3-4d75-8029-ebb03a1eadaf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sb.countplot(data=df, x='Age Group', hue='Gender')\n",
    "for bars in ax.containers:\n",
    "    ax.bar_label(bars)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "cfd88de1-5f79-4647-baed-7bbff0b54fa8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Age Group', ylabel='Amount'>"
      ]
     },
     "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": [
    "#total amount vs total age\n",
    "sales_age = df.groupby(['Age Group'], as_index=False)['Amount'].sum().sort_values(by='Amount',ascending=False)\n",
    "\n",
    "sb.barplot(x='Age Group', y='Amount', data=sales_age)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "515d0bfc-24e5-44fa-ba86-82fd49c3270f",
   "metadata": {},
   "outputs": [],
   "source": [
    "#From the above graph we can notice that most of the buyers are of age group between 26-35 females"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "98d05741-c082-4a04-8c25-5b83fd638b03",
   "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": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#State\n",
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "7db6f1ed-169d-4e7e-858a-203530dd4d7a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='State', ylabel='Orders'>"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#total number of orders state wise:\n",
    "sales_state = df.groupby(['State'], as_index=False)['Orders'].sum().sort_values(by='Orders',ascending=False)\n",
    "\n",
    "sb.set(rc={'figure.figsize':(25,5)})\n",
    "\n",
    "sb.barplot(x='State', y='Orders', data=sales_state)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "2acb0167-7e97-4b16-adfb-565ecfeef779",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='State', ylabel='Amount'>"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 2500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#total amount spended state wise:\n",
    "sales_state = df.groupby(['State'], as_index=False)['Amount'].sum().sort_values(by='Amount',ascending=False)\n",
    "\n",
    "sb.set(rc={'figure.figsize':(25,5)})\n",
    "\n",
    "sb.barplot(x='State', y='Amount', data=sales_state)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5b173b65-6203-472e-9ee9-1839c33fab18",
   "metadata": {},
   "outputs": [],
   "source": [
    "# from the above graphs we can observed total number of sales is equal total number of amount spended (purchasing power) both are equal to each other first position state wise as UP and second as Maharashtra and third as karnataka"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "27e32399-10e2-435f-9d29-7c9226365f4c",
   "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": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Marital Status:\n",
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "2bde5381-8477-4e0c-a0b8-150c1fdb27d0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sb.countplot(data = df, x='Marital_Status')\n",
    "\n",
    "for bars in ax.containers:\n",
    "    ax.bar_label(bars)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "42bfca65-0c1a-4d0a-ad2f-61198ac0a185",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Marital_Status', ylabel='Amount'>"
      ]
     },
     "execution_count": 57,
     "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": [
    "#total number of amounts Marital wise:\n",
    "sales_marital = df.groupby(['Marital_Status','Gender'], as_index=False)['Amount'].sum().sort_values(by='Amount',ascending=False)\n",
    "\n",
    "sb.set(rc={'figure.figsize':(6,5)})\n",
    "\n",
    "sb.barplot(x='Marital_Status', y='Amount',hue='Gender', data=sales_marital)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "57f65893-5e18-41d8-8178-d496fc428df9",
   "metadata": {},
   "outputs": [],
   "source": [
    "#0=married, 1=singles, from the abpve graph we can see that most of the buyers are married women and they have high purchasing power.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "ac189547-9d35-4f22-99f9-095b73a26942",
   "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": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Occupation wise:\n",
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "8f6a157b-dab2-4efe-b857-469764df47ae",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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EcXFxODo60rx5c5o0acK4ceO4e/eu8r6dnR1eXl58//33NG3alMaNGzNlyhSePXv21udBk+srv3SlpKTg4eGBmZkZTZs2Zf78+W/9DC5aNGOMh2r0a7t27fDw8KBLly40b96cEydOaJTm27dvM3r0aJo0aULz5s1xcnJSC3eSX76xbds2unbtSqNGjWjdujXu7u5K2IK0tDR8fHywsLDA0NCQzp07s27dOuW7mc+r6ll7+PBhunXrhqGhIV27duXgwYNq58DPz49WrVphbGzM2LFjcXd3/+Ah0FJSUpg/fz7t27fH0NCQ5s2bM3HiRB49ekR6ejpmZmZqobRWrVqFgYEB58+fV14bP348zs7OjBo1KlsnW3x8PAYGBly5cgXIeIb17t0bIyMjOnTowIIFC9RCRRgYGODn54elpSXm5uZq9/d/hZGREVWrVmX37t1qr+/cuRNra2u1mTJnz55l4MCBGBkZ0bZtW9zc3EhKSlLe//vvv5kyZQpNmzbFzMyMlStXqm0zaziZ3377jVGjRtG8eXMMDQ3p0KEDq1atejc/9COSuSPs6dOnzJo1CwsLCxo2bIi5uTmzZs1SQmNp8jzILC0tjQkTJmBhYcHNmzezhT3RJE+4ePEiAwcOxNjYGCsrK8LDw/nyyy8lBFom2traFC6cUZXNq0yUmJiodAIMHjxYyfs1qb98yHAWH6tChQrx7NmzbNdq7dq1iYyMpEWLFspr+eUfz58/Z+7cubRq1QpTU1MGDhxITExMrudUk/JYTnXSj1le9aKpU6dmmzXxxx9/0KBBA44fP/5G+UVO7QhZv/9vlPs/JfkdjxcvXjBt2jTMzc1p1KgRPXv2ZO/evcr7dnZ2eHh4MHnyZExMTGjTpg3Lli1TGpIzh/CZOnUqW7duJTo6Wjkn+eVtWameeTndXz179sTFxUXt80eOHMHQ0JCHDx/+q8ftn8qvrSC/8rvquC5fvpzmzZvTq1cvbt26lS182Zo1a+jUqRNGRkZ06dKF7du3A7mHOsstzFNO7Tc5hc3KnCfllMa0tDT+/PNPnJycaNq0Kc2bN8fR0ZGbN2/+84NagOWVj+zZswcDAwP27NmjfN7Z2RlLS0tiY2OzXef5hcXKr4z+qXiT9h7ICFlqb2+PkZERrVq1YunSpWqf16SuuGPHDgYPHqx85sCBAxw4cIBOnTphYmLCsGHDlGdN1vP0T9urPzTplCnAXr9+zfDhw7l58yZLly5l48aNmJiYYGNjQ2xsLJDRmLJ06VKcnZ3Zs2cPixYt4saNG3h6embbnqmpqfJw37Rpk9qUv/Xr1zN//ny2bNmClpaW0kEA4OHhwdq1a5k0aRIRERFYWFgwatQorl+/zsWLFxk9ejQdO3YkPDycTZs2Ub58eSZNmqR2o4WFhTF48GDWrVuHhYUFGzZswMvLi9GjRxMZGcmECRNYvnw5vr6+7+hofjiJiYncvXuXli1b5voZPT09tLS0SEpKwsbGhj///JPFixezfv16dHV1GTRoEHfv3qVZs2ZUq1aN8PBw5bv37t3jxIkT9OrVi4CAAExNTbG2tmbz5s3v4+e9d5s3b0ZXV5c2bdrQvn17tLS01BpWNfl+lSpVcHBwICAggLS0NBwcHDh9+jReXl5s3bqV+vXrM2TIELUO0NOnT3P69GlWrVrFggUL2LNnD926daNBgwZs2bKFNm3aMHPmTB49egSAp6cnixYtYsyYMURERDB48GBmz57NmjVr1NITFBTEjBkz2Lx5M9ra2tjY2PDixYt8f4eLiws///wzPj4+bN26FWtraxwdHd/5Wjm3b9/m8ePHmJiY5Ph+pUqVMDIyAtDouJ47d45Tp06xbNkyVq5cyd27d3FzcwPg4cOHjBkzhj59+rBz504CAwM5deoU3t7eAEqDy/fff68W2iQ8PJxVq1axcOFCSpcune9vSkxMxM7OjnLlyrFhwwYCAwM5efIkM2fOVMLeVKlShaNHj6qNcoeMac/9+/dHS0uLVatW8dNPP/HXX38xaNAgtULamjVrqFChAps2bWLu3Lns3LkzW2Pqm8jv+tIkXap0eHp6sm7dOu7evftW68L8+eefzJ49W7kvVdatW8f06dNZsWIFjRs3zjfNf//9N7a2tjx//pyVK1eycuVK7ty5w9ixYwHyzTeuXLnC9OnTGTt2LHv27MHDw4Pt27cra0eEhoaye/du/Pz82LNnD4MGDcLV1TXP3+zj48O0adMICwtDX1+fSZMmKQ2nvr6+bNiwgZkzZxIWFkalSpWy3d8fgre3Nzt27MDd3Z09e/bg5eXFsWPHWLx4MYULF8bCwoJjx44pnz9+/DiFChXixIkTQEbB9tixY1hZWdGnTx+io6PVGrW2bdtGw4YNqV+/PkeOHGH8+PH069ePHTt2MGvWLHbt2oWzs7NamjZs2IC/vz8//vgjtWvXfj8HooCxtrZW65RJSUlh//79aqM3r1y5wpAhQzA3Nyc8PBxfX18uXbqEg4OD0ggzYcIEYmJiWLJkCcHBwRw8eJA7d+7kuM8XL15gb2+Prq4uoaGhREZGYm1tjYeHB5cvX363P7gAi4mJISIigv79+wMZYUNiYmLw9/dnz549uLi4EBYWxoYNG9S+l9fzQCU9PZ3Jkydz4cIFQkJCqFmzZo5pyCtP+PPPP/n222/R09Njy5YtzJw5Ez8/Pwmv+P+9evWKQ4cOsX37dnr06AHkXSb6/PPPlXUWAgICcHBw0Lj+It5c165dqVq1Kt9++y09evRg3rx57N+/n6SkJOrWravMvtEk/3BycuLgwYN4eHiwbds2atWqxdChQylevHi2c6ppeSxrnfRjl1e9qFevXsTExJCQkKB8Pjw8nMqVK9O8eXO17eSXX+TVjgBoXJ/Kq9z/KdHkeCxcuJCrV6+ybNkydu7cSZs2bXByclJrEA4NDUVHR4ctW7bg5OTEjz/+yPLly7Ptb9q0aVhbWythfUDzvC2rnJ6ZvXv3Zs+ePWodOtu3b8fS0rLAhRDKr61A0/L7oUOH2LBhAx4eHsoAAJWgoCB8fX0ZOnQoO3bsYODAgbi4uKiVrzX1T9pvMqcxOTkZOzs70tLSCAkJYc2aNZQtW5ZvvvnmX12/uCDJLx/p1KkTPXr0YM6cOTx58oTIyEgiIyPx8fHBwMAg23WeF03K6J8KTdt7VOEOQ0JC6NGjB5GRkdja2vLDDz8o685oWlecO3cuAwcOZMeOHdStW5fvvvuOxYsX4+Pjw5IlS4iJicnx2Qf/Tnv1hyThyz6giIgItV5blZSUFBo3bsyJEyc4d+4cx48fVzK7iRMncvbsWVavXo2npyfVq1fH09NT6VXX09PD2to6x4XCtLS0lDiY5cqVU5tu7OHhocQLHTp0KGPGjOGvv/5CW1ubjRs3Mn36dKXwNX78eNLT03n27BlaWlpMnz5dLfzD4MGDcXBw4K+//lIaMBs0aMDXX3+tfGbRokWMHDmSbt26ARmhJJKSknBzc2P8+PFoa2u//YEtYFSjvLMWWBwdHdVGUFStWpWBAwfy6NEjwsLClM/7+vrSvn171q5di7OzM7169WLr1q04OTkBsGPHDipXrkyLFi0oXLgwxYoVo3jx4gWugPRvePXqFREREVhaWiqjXC0sLNi+fTvfffedRiFgypUrp4Rg+Oyzzzh8+DCXLl0iIiKCevXqARkLL1+4cIGgoCAWLFgAZDS0zJ07l9KlS1OnTh0aNGhAsWLFlDUChgwZwsaNG0lISKBYsWKsW7eOqVOnKtd9zZo1uX37NkuWLGHQoEFKeqZPn67EY/b29sbCwoIdO3bkGQ84ISGBHTt2sHnzZho1agRkzJa6cuUKQUFB73StHNUslfxi6h49elSj4/rq1Su8vb2VKfN2dnb4+PgAGQ1TKSkpVK1aFT09PfT09FiyZInSMKWatqqa1qxKm62t7RvFP964cSNlypTB09OTYsWKARkFg+joaEqUKIGurm6u09ZDQ0PR1dXF19dXmSXi7+9Pu3btCA8Px9bWFoA6deowceJEAGrVqkVkZCRnz57NNU055Q+mpqYEBweTlJSU7/WVX7q6d+9OWFiYMoIOMvIBTUZhL126lODgYCDj/KWkpFCnTh0WLFhA1apVlc9ZWFgondGapHnnzp38/fff+Pn5KdeDu7s727dvJzk5Od98IzExkUKFClGtWjWqVq1K1apVCQoKomTJkgDcunULXV1d9PX1qVixIoMGDaJ27drUqlUr1986YcIEzMzMlH/36NGDa9euUb9+fUJDQ3FxcaFjx44AzJgxg3PnzuV7/N61Ro0a0bFjR5o1awZklAtatWqlzLi0tLRkypQppKSkULhwYaKjo+nQoQMnT57E0dGRM2fOkJycTOvWrdHW1qZChQpEREQwcuRI0tPTCQ8PZ9iwYQAsWbKEvn37YmNjA0D16tVxc3Pj22+/JTExkWrVqgHQo0cP5Vn1X2VtbU1QUJASwuzYsWOULVuWL7/8UvlMUFAQZmZmjBo1Csi4R1SznqKjo6lYsSJHjx5l5cqVyjpa8+fPx9LSMsd9vnjxgsGDB2Nra6vcB2PGjGHp0qVcvXqVBg0avONfXTBkfmalpqaSmpqKsbGxUq41NzenadOm1K9fH4Bq1aoREhKi3DMquT0PTE1NgYxygouLC+fPnyckJAQ9Pb1c05RXnrBhwwZKly6Nu7s7xYoVo27dusyYMYP//e9//+JR+XhkzQ9fvnxJ1apVGTp0KI6OjhqViVRl4jJlylCiRAmKFCmiUf1FvLnPPvuMsLAwVq9ezd69e5VBFsWLF2fEiBGMHj0ayD//SE1N5dChQ6xYsUIpJ6vW5Hz69Gm2c7po0SKNymNZ66Qfs/zqRc2aNUNfX5+IiAjGjBkDZNxPPXr0yNbIrEl+kVs7Avw75f6PSX7tOJocj1u3blGyZEmqV69OqVKlGD9+PE2bNlWrY9WuXRtXV1cKFSpEnTp1iIuLY/Xq1QwfPlxtv6VKlaJ48eIUK1ZMqa9omrdlVaRIkWz3V/fu3fHx8WH//v1069aNpKQk9u/fr5zXgiK/ewLQuPzu4OCgDKzIOnNi5cqVDB48mG+++QaAgQMH8vLly7caPPHZZ5+9dftN5jRu2rSJR48eMX/+fKU+6+7uzsmTJ9m4caMyyO1Tokk9ZObMmXTv3p1p06Zx8uRJRo0apZShs17necmvjJ61o/tjpml7j4qNjY2yvsyoUaMIDg7m4sWLmJmZaVxX7NWrF506dQJgwIABHDhwACcnJ2Wwr7m5OdeuXcu276SkpH+lvfpDkk6ZD6hdu3ZMmjQp2+u+vr48fvyYS5cuAWSLu5uSkkJycrKyjQsXLuDv709CQgJxcXH89ttvVK5c+Y3SkrlRSjWy/OXLl9y9e5fU1NRsvaSqDgHIuFmXL1/OjRs3uHnzpjL6MnOmVKNGDeXfDx8+5I8//mDhwoUEBgYqr6enp5OcnExiYiJ16tR5o/QXZGXLlgUypjBn5ubmpow2WbNmDQcOHODatWvUrFlTLUPW1tbGyMhIKUD17NmTwMBAzp49S+PGjZWRglkL15+iw4cPc//+fbXRWV26dGHfvn1ERkbSt2/fN97mtWvXKFWqlFJghozQC02bNuXnn39WXitfvrzarAsdHR21h7iqIzE5OZn4+HhSU1Np0qSJ2r6aNm2qjNzL/JpK6dKlqVmzZo4ZTmaqmXJZwwqlpqZqNDPkn1Bdm1mv56w0Pa4VKlRQi2FcqlQpUlNTgYyKc7du3XB0dOTzzz+nZcuWtG3bNt/QfJmfN5q4evUqDRs2VAqwAF999RVfffVVvt+9du0ahoaGaovWli9fnlq1aqlVerI+00qVKsXTp09z3W5O+YOqAqzJ9ZVfum7cuEFqaqpaQ7m2trZGjbQDBgxQpvgXLlyYzz77LMf1WDKfB03SfPXqVWrWrKl2PXzxxRdMmjRJo3xDFbamT58+1KxZk5YtW2JlZYWhoSGQUWHav38/bdq0wdDQEHNzc6ytrSlfvnyuvzXzrA5VI0VqaipxcXG8fPkyW97YpEkTJazXh9KjRw+OHz/ODz/8wM2bN4mLiyM+Pl551rRq1Yq0tDTOnDmDlpYWxYsXZ8CAAYwaNYqUlBQOHTqEmZmZUjnp3r0727dvZ+TIkZw4cYIHDx4oHWOxsbHExMSohW5UjRaLi4tTCtpvek9+igwNDdHX12f37t3Y29uzc+dO5TiqxMbGkpCQoDTyZxYXF6fMxMx831aoUAF9ff0c91muXDlsbW3ZuXMnV65cISEhQSmjfYrhYnOT+Zn16tUrbt68iZ+fH7a2tmzZsgVbW1sOHDjA9u3buXXrFteuXeP27dvZZrnk9jxQ2bVrF6mpqdSuXTvfWNd55QmxsbHZ8qTMZYX/GlV+mJ6ezoULF5g3bx4tW7bE0dGRokWLvlWZSNXAnF/9RbydMmXKMHbsWMaOHctff/3FiRMnlBmTZcuWxdbWNt/8QzVrPHM+q6WlpYRQytpIqml57FPKjzSpF/Xs2VPplLl8+TLXrl3D398/27b+aX7xb5T7Pyb5teNocjyGDx+Oo6MjZmZmmJqaYm5uTteuXdXK1M2aNVMLcWpiYsLy5cuV8kBeNM3bNPHZZ5/Rrl07tm3bRrdu3di1axelSpVSOkwLivzuifr162tcfs/tOD18+JB79+5hbGys9vrQoUOB7M+mdylzGmNjY0lKSlIGZakkJydnC33/qdCkHlKyZEnc3d0ZMmQIDRs2fOsBLvmV0T+lThlN23tUsg5wLF26tNJerWldMfM2VG0emes32traOc5sUbVp/NP26g9JOmU+oBIlSuRYMCxRogSPHz8mPT2dkiVL5hh7UlXgXL58OQEBAfTu3ZtmzZphZ2dHVFRUjjNl8qKaepbZ69ev1SqEOTl16hQODg5YWFjQtGlTunbtyosXL5RRUCqZR9OoCnYuLi45hvQqCL2V/ybVqOzo6Gi1MCWZO85UvdCvX79WK3ippKWlKWs2VKtWjWbNmhEREUGpUqW4cuVKgRul8q6o7oVx48Zle2/9+vVKp0zWKaR5FfZzO+bp6enKMQdyvBdy6whT7T/rdlXXfubtZr330tLS1CqUeW1/7dq12UZ1vOvOOX19fSpUqMC5c+eyhS4AuHnzJrNnz6Zx48YaHdf8fuv8+fMZPXo0R44c4ZdffmHixIk0btyY1atX5/qdnBYdzeuaKFq0aI5p1URe92zmaya/35lVbvmDap+Q9/WlabqyynxuclOmTBmNGjUynwdN0pzXedAk39DS0mL16tXExsZy9OhRjh49yvr16+nZsyfz5s2jZs2a7N27l+joaI4dO0ZUVBRLlixh3rx59OrVK8f95nTeXr9+rRyngjhd3dXVlZ07d9KzZ0/atm3L//73P4KCgpTQBSVKlKBZs2YcO3aM4sWL06JFC5o2bcrr16+5cOEChw8fZsiQIcr2+vTpQ1BQEBcvXiQ8PBwrKyulQSU9PZ1hw4blePwyN0rLQsAZVCHMbG1tiYqKUsImqKSnp/P111/j6OiY7bvlypVTwmJkbSDL7b598OAB33zzDWXLlsXKygozMzMaNWr0SYTseRNZn1l16tShTJkyDBw4kF9++YXQ0FCuXr3K119/TadOnZg4cSIzZszItp3cngcqlSpV4ocffmDo0KH4+/vn2GCX17ZUihQp8p/qNMtP5vywVq1aVKlSBXt7e4oUKYKrq+tblYk0rb+IN7d8+XKqVauGtbU1kNEx0rVrV7p06UL//v05fPgwtra2+eYfqnjvmpbPNC33fEr5kSb1ol69ehEYGEhMTAy7du3C1NQ0xxnC/zS/0LQ+9abl4YIqv3YcTY6Hqakphw8f5tixYxw/fpzNmzcTEBDAihUrlFmZWfN31fMup7abrJ9zdHTUKG/TVJ8+fXB0dOTBgwfKrHtN6g3vU373xNy5cwHNyu+5RW5RXcP5PZsy7+NtOh41ac/InMb09HRq1arF4sWLs31OV1f3jff/MdC0HnLp0iWKFi3KjRs3uHPnDtWrV3+rfeVVRv+UaNreM2XKFCD3tmTQ/Bzl9CzRJP//t9qrP6RPf2j9R6xevXokJSWRkpJCjRo1lL/ly5cTFRUFwOLFixkzZgyurq70798fExMTbt68mWtG86YNjzVq1KBYsWJqsWAB+vbty4oVKwgKCqJ58+YEBgYqMRZ///13IPfMrnz58pQvX55bt26p/a5Lly59kp0LRYoUYfDgwWzbti3XEdSqY1avXj1u3LihNpMiOTmZixcvqoVj6t27N/v27SMiIiLXwvWn5uHDh8qi79u2bVP769u3L7/++iuXLl2iWLFi/P3332rfvXXrVq7bNTAw4OnTp9lmp5w5c+aNQmBlVrt2bYoWLZptrYrTp09TsWJFtamgFy9eVPuNN2/e5Isvvshz+6r37927p3YPhYWFsWXLlrdKs6YKFy5M3759CQsLyzE+7YoVKzh//jz169f/x8f1/PnzeHh4ULt2bYYMGcKyZcuUEFuZ75H8ZL0mkpKS1BalrFu3LrGxsWqjJfbt20ebNm148eJFns/NevXqERMTozZy48GDByQkJLyzGX+aXF/5patOnTpoa2tz5swZ5f1Xr169s1kemqS5bt263Lx5U+1cxcbG0rx5c5KTk/PNNw4fPkxgYCBffvklI0aMYPXq1YwbN46dO3cCKKFUzM3NmTx5MhEREZiZmSnvv4kaNWpQvHhxzp8/r/Z6TEzMG2/r3/To0SPWrVuHq6sr33//Pb1796ZBgwbEx8er5cnt2rXj2LFjnDhxghYtWqCtrY2pqSmbN28mPj5eLRxWnTp1MDU1ZefOncpCjSpffPEF8fHxaufkzz//xNvbO9taGyKjU+bChQts3rwZfX39bM+IL774gt9++03teKalpTFv3jx+//13JdRZ5tCHT58+zTWPi4iI4PHjx6xfv55Ro0bRoUMHJSRBQexQ/BAuXrzI4cOHlU6U7t27U716dW7duvXGx+irr77C2NiYSZMmERwc/NbPg/r163Pp0iW1BpgLFy681bY+RS1atMDe3p5169Zx5MgRjcpEWfPxt6m/CM1cuHCBRYsW8erVK7XXCxUqRIkSJZTZqfnlH6rnY+Y66KtXr2jbti2RkZHZzumHKI99SJrWi/T09GjWrBm7d+9m586duQ5C0SS/yKs8/C7qUx8zTY6Hv78/Z86cwcrKiunTp7Nnzx709fXVwqJlbYM5e/Ys1apVyzGsUObzExsb+4/ytpzOdatWrahYsSKbNm3izJkzauXBgkCTe+L58+f/uPxesmRJKlWqlO3cjBs3jrlz5yqNxJnXssqrLSInWeuu6enp+c7AqVevHnfv3qVUqVLKM1VPT4/58+dz6tSpN9r/x0KTesjVq1dZuHAhs2bNwtDQkMmTJyt1/jdpG82vjP4p0bS9J68wvSrvuq74Ltqr3zfplCnAWrduTYMGDZgwYQLHjx8nISEBLy8vtmzZohQuVXHJr1+/Tnx8PH5+fuzduzfXRYtUveRXrlzR6CbQ0dFh0KBBLFy4kKioKG7duoWfnx/Xr1/H0tKSzz//nKtXr3L69GkSExPZsmULCxcuBMg1DYUKFWLYsGGsWbOGNWvWcOvWLfbv34+bmxtaWlqfzAiazIYNG4alpSW2trYsWbKEK1eukJiYyIEDB3BwcGDLli20aNGCr7/+mtKlSysL+V65cgVnZ2eeP3+uLEgLGYuWPX/+nJCQkGyF6xIlSnDnzh3++OOP9/0z36nt27fz6tUrhg0bRr169dT+HB0dKVKkCOvWraNx48b88ssvHDhwgNu3b+Pv75+tQFyiRAlu3rzJgwcPMDc3x8DAgO+++46TJ08SFxeHm5sb165d49tvv32rtJYqVYpvvvkGf39/IiIiSEhIYO3atYSGhuLg4KBWAJg5cybHjx/n8uXLODk58fnnn+c4IiGzL774AktLS2bNmkVUVBS3b98mKCiIpUuX5hrG5t/k6OhIjRo1GDBgANu2bePWrVv8+uuvTJs2jS1btjBnzhzatm37j49ryZIlCQ0NxcfHh4SEBK5evUpkZCQ1a9ZUwgLq6uqqhfXJiampKRs2bODSpUtcu3aNyZMnq43GsLW15dGjR8yaNYu4uDhOnz6Nr68v5ubm6OjooKury5MnT5TpsZnZ2NiQlJTEpEmTuHLlCjExMYwfP56yZcuqzYz7N2lyfeWXLl1dXQYNGoS/vz979+4lLi6OWbNmvbOFIDVJ89dff02ZMmVwdnbmypUrXLx4EVdXV+rVq4eenl6++UbRokX58ccfWblyJbdv3+bXX3/l4MGDyjTzv/76i9mzZxMVFcWdO3c4cuQIsbGxOU5Dz4+Ojg52dnb4+/uzf/9+bty4ga+vb7ZK3ruUkJDAkSNH1P4uX75MqVKliIqKUu6ZGTNmcOnSJbU8uV27dly5coXz58/TokULAMzMzAgPD8fExCRb6KU+ffqwdu1atLS0aNWqlfL68OHD2bt3LwEBAdy4cYPjx4/j4uLC06dP8w3f9F/UoEEDatSowQ8//JDj88HBwYHLly8zc+ZMrl+/zoULF5g0aRI3btygZs2aVK9enc6dOzN79mx++eUX5XmWW3mrSpUqvHjxgl27dnH37l2OHj2qrGNSUBa3fB+eP3/O/fv3uX//Pvfu3eP06dN4eHhQqVIl+vXrR9GiRdm1a5fy3JgwYQL3799/62PUv39/GjdujIuLy1ttw9bWlr///psZM2YQFxfH8ePHmT17NvDmg6s+VePHj6dmzZrMmjWLqlWr5lsmUtV/rl27xt9///1W9RehmdGjR5OYmMjQoUM5evQod+7c4dy5c3h6enL+/HllLcb88o9atWrRsWNH3NzcOH78ODdu3GDmzJmkpKRgZmaW7Zx+iPLYh6RpvQgyBvOtX7+eR48e5VrH0CS/yKsd4V3Upz5mmhyPhIQEZs2axfHjx7lz5w67d+/m7t27auXS06dP4+/vz40bN9i8eTNr165V1vXLSldXl3v37nH79m0qVKjwj/K2rPcXZDTU9uzZkyVLlmBoaFjgOts0uSfCwsL+lfL7iBEjWLVqlVIPXrt2LVFRUbRv355KlSqhr6/PTz/9xPXr1/n111+ZMWNGnm1cWdtvGjduzM6dOzl16hQ3btzA1dU126DTrLp3706ZMmUYM2YM58+fJy4uDhcXFw4fPpzvgM+PVX75SEpKCs7OzjRr1oxvvvmGuXPncvnyZZYtWwbkfJ3nJr8y+qdGk/YeVSjfvLzruuK7aK9+3wrWfEOhpkiRIgQHB+Pj44OTkxMvXrygTp06BAQEKFNavb29mT17Nn369KFEiRIYGxvj5uaGq6ur2sJJKvXq1cPCwoIJEyYwceJEtZiuuZk4cSJFixbF1dWVp0+fYmBgwLJly6hTpw7jxo3jwYMHyjS+unXr4uHhgbOzMzExMbmOTHJwcEBbW5s1a9bg5eVF+fLl6d27t1rsv09J4cKFWbBgAbt27WLLli2sXr2ap0+fUqFCBZo2bUpISIiyfkVISAheXl5K+JgmTZqwbt06tcZ2HR0drK2tiYiIUMIDqAwYMIApU6bQvXt3jh8/nu/05o9FWFgYLVu2zPGa0tfXp0OHDkRGRnLkyBFu376Ns7MzhQoVokuXLgwZMkRtZLGdnR1eXl789ttvhIeH89NPP+Hl5cXYsWNJSUmhYcOGrFy5Mltsyjcxbdo0ypYty/z583nw4AE1atRg5syZyoKAKqNGjcLFxYWHDx/SvHlzVqxYoVHHpJ+fH35+fsyaNYsnT56gr6/PnDlz6NOnz1unWVM6OjqEhIQQHBzM8uXLuXv3Ltra2jRs2JBVq1YpsWz/6XGtW7cuAQEBBAYGEhoaSuHChWnRogXLly9XQpI4ODiwYsUK4uPjmTZtWo7bcXV1xc3NjQEDBlCuXDns7e15/vy58n7lypUJDg7G19eXXr16Ubp0abp06aJURjt27MjGjRvp3r07ISEhatvW19dnzZo1+Pr60r9/f7S0tDA3N8fHx+edru+T3/WlSbq+++47tLW1mT17Ns+ePcPa2jrf9XreZZp1dHQICgrC09MTGxsbtLS0aNeuHZMnTwbyzzfMzc1xd3cnODgYPz8/ihcvjoWFBVOnTgUyFqx99eoVc+bM4cGDB1SsWBFbW1tGjhz5Vr9n/PjxpKamMn36dF68eIGlpSVWVlZKDN13LSIigoiICLXXKleuzMKFC/H09FQ6uZo3b87EiRNZsmQJz58/R1dXl88//xwDAwOePHmiTOFv2bIlfn5+OV4D1tbWzJ07l549e6rlKZ07d8bPz4+lS5eydOlSypQpg6WlJc7Ozu/2x3/ErK2tWbx4cY4NYyYmJqxYsYKFCxfSu3dvdHR0aNGiBVOmTFHyBS8vL7y9vXFyciI9PZ3+/furzfzLrHPnzly6dAkvLy+SkpLQ09OjX79+REVFERMToyy6+akLDg4mODgYyCiPlS1bliZNmuDr60vlypXx9PQkICCAtWvXUrFiRdq2bcuQIUOIiop6q1F0hQoVYs6cOfTo0YPAwMBs+X5+ypcvz4oVK/Dw8KBHjx5UqVIFGxsbvL298w3T8F+hra3NnDlzGDx4sFIeyqtMVLZsWfr06YO3tzcJCQlvXX8R+WvQoAGbNm1i0aJFuLi48OjRI0qUKMFXX33F+vXrlcZBTfKPefPmKc+75ORkjI2NCQ4OVkLFZD6n06dP/yDlsQ9F03rR1KlT6dSpE7Nnz6Z9+/Y5rgEImuUXebUjFC1a9J3Upz5WmhwPNzc3vLy8cHZ25vHjx+jp6TFp0iR69OihbMfKyorffvuNHj16UKlSJaZOnZpr3t2zZ0/27dtHt27d2Ldv3z/K27I+M6dPnw5kdPAtWbKkwM2SgTdrK/in5fdBgwaRnJyMv78/9+/fp2bNmvj5+SkDnXx8fHB3d6dnz55UrVqVcePGKQ3BOcnafuPk5MSTJ08YPnw4Ojo69OvXjy5duuR53kqVKkVISAje3t4MGzaMtLQ0GjRoQFBQ0CfbKZNfPuLn50diYiJLliwBMmZVjBs3Dj8/P1q3bo2hoaHadd6hQ4dc96VJGf1Toml7T37eR13xXbRXv0+FXheUOTtCCCGEEB+Jffv20aRJE7U4wg4ODlSpUgUPD48PmLJ/X2JiIh06dGDXrl2f5GgwIQqS69ev8+TJE5o0aaK8dvbsWWxsbDh06NAnt/aiEEKI7Ozs7NDT08PT0/NDJ0Vx6tQphg8fzs8//5xrB19B918qvwshCj6ZKSOEEEII8YaCgoIIDQ1l8uTJlCxZkqioKE6cOKGMyP8U/P7778TExBAaGkrr1q2lQ0aI9+DPP/9kxIgRuLu789VXX3Hv3j3mzZtHs2bNpENGCCHEexcXF8e1a9dYsmQJvXr1+mg7ZOC/UX4XQnw8ZKaMEEIIIcQbSkxMxNPTk1OnTvHy5Uvq1q2Lo6NjnlPfPzaxsbEMHDiQmjVrEhgYqNGCjkKIfy40NJQ1a9aQmJhIqVKlaNeuHZMmTdIo7LAQQoiPX0GaKRMVFcV3332HkZERgYGBH3VIwP9C+V0I8fGQThkhhBBCCCGEEEIIIYQQQoj3oPCHToAQQgghhBBCCCGEEEIIIcR/gXTKCCGEEEIIIYQQQgghhBBCvAfSKSOEEEIIIYQQQgghhBBCCPEeSKeMEEIIIYQQQgghhBBCCCHEeyCdMkIIIYQQQgjxAb1+/fpDJ0EIIYQQQgjxnkinjBBCCCGEEKLAO3PmDGPHjsXc3JxGjRphZWXF9OnTiYuL+9BJ+0euX7+OjY2N2msGBgYEBAR8oBQJIYQQQggh3iXplBFCCCGEEEIUaMuWLWPgwIE8f/4cFxcXgoKCcHR0JDY2ll69ehEZGfmhk/jWdu3axblz59Re27BhA/369ftAKRJCCCGEEEK8S0U/dAKEEEIIIYQQIjcHDx5k/vz5jBo1ivHjxyuvN2vWjJ49e/Ldd98xdepU6tWrxxdffPEBU/rvMTEx+dBJEEIIIYQQQrwjMlNGCCGEEEIIUWAFBgZSq1Ytxo0bl+29YsWK4ebmRpEiRVi+fDkAqamp/Pjjj7Rv3x4jIyO6du3Kli1b1L4XGRlJ7969MTY2pm3btvj4+JCSkgJAQEAABgYG2faVOaRYYmIiBgYGREZG4ujoiLGxMRYWFgQEBJCenq585+XLl8yfP5+OHTtiaGhI48aNsbe35/Lly8q+AgMDs20/a/iye/fu4eLigoWFBUZGRvTt25eoqKhs6Vu7di3Tpk2jWbNmmJqaMm7cOB48ePBmB1wIIYQQQgjxTkmnjBBCCCGEEKJAevjwIRcvXsTS0pJChQrl+JmyZcvSsmVLpZNiypQpLFu2jL59+7J06VIsLCz4/vvv2bZtGwDr169n4sSJNGjQgMDAQEaOHEloaCiurq5vnD5XV1dKlixJQEAAPXv2ZNGiRXh7eyvvT548mc2bNzNixAiCg4OZOnUq165dw8nJidevX9OvXz/69u0L5B6y7MGDB/Tt25fo6GicnJwICAhAT0+P0aNHEx4ervZZPz8/0tPT+eGHH5g8eTKHDh3Cw8PjjX+XEEIIIYQQ4t2R8GVCCCGEEEKIAunOnTsAVKtWLc/P1ahRg6ioKM6cOUNkZCTTpk1j8ODBAJiZmXH37l1OnjxJ9+7dCQgIoEOHDri7uyvfT05OZuvWrcpsGU19+eWX+Pr6AtCmTRueP39OSEgIo0aNonjx4jx79owZM2bQpUsXICPk2rNnz/D09OT+/ftUqVKFKlWqALmHLPvpp594+PAhu3btQl9fHwALCwuGDBmCt7c33bp1o3DhjLF29erVY968ecp3Y2Ji2L179xv9JiGEEEIIIcS7JTNlhBBCCCGEEAXS69evgYwwZXkpUqQIAKdOnQKgQ4cOau8vWLCAefPmcePGDR48eED79u3V3h8yZAjbt29HS0vrjdLXvXt3tf936tSJ1NRUzp8/j5aWFkFBQXTp0oV79+5x6tQpNmzYwMG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      "text/plain": [
       "<Figure size 2000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sb.set(rc={'figure.figsize':(20,5)})\n",
    "ax=sb.countplot(data=df,x='Occupation')\n",
    "\n",
    "for bars in ax.containers:\n",
    "    ax.bar_label(bars)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "28e827b9-4a4d-4b46-8b64-c09305f8d567",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Occupation', ylabel='Amount'>"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 2500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#total number of amount occupation wise:\n",
    "sales_occupation = df.groupby(['Occupation'], as_index=False)['Amount'].sum().sort_values(by='Amount',ascending=False)\n",
    "\n",
    "sb.set(rc={'figure.figsize':(25,5)})\n",
    "\n",
    "sb.barplot(x='Occupation', y='Amount',data=sales_occupation)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "eb3e4f7d-7eca-41ef-96ef-91a7a263aff9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Occupation', ylabel='Orders'>"
      ]
     },
     "execution_count": 79,
     "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 number of Orders as per Occupation wise and gender:\n",
    "sales_occupation = df.groupby(['Occupation','Gender'], as_index=False)['Orders'].sum().sort_values(by='Orders',ascending=False)\n",
    "\n",
    "sb.set(rc={'figure.figsize':(20,5)})\n",
    "\n",
    "sb.barplot(x='Occupation', y='Orders',hue='Gender', data=sales_occupation)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "3c944203-6edb-4458-8316-ef6e5ad7507c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Occupation', ylabel='Amount'>"
      ]
     },
     "execution_count": 80,
     "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 number of Amount spents Occupation wise and gender:\n",
    "sales_occupation = df.groupby(['Occupation','Gender'], as_index=False)['Amount'].sum().sort_values(by='Amount',ascending=False)\n",
    "\n",
    "sb.set(rc={'figure.figsize':(20,5)})\n",
    "\n",
    "sb.barplot(x='Occupation', y='Amount',hue='Gender', data=sales_occupation)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "ee6223c5-8522-4b0d-bdee-d694986eb9d8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Occupation', ylabel='Amount'>"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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Ly8s2TmHevHnthvoYPXq0bQzc9957Tz169NA///wjb29vff311xo8eLDtov4HH3wgi8Wi27dvy8vLS4MHD7Ybfqldu3bq2LGj/vnnH9sO+v72mD59urp06aLXXntN0r2hnG7duqXhw4frgw8+kLe3t/EFm4ni4+O1du1a1alTx9ZLIiQkRKtXr9ZHH32kqlWrqnjx4lq7dq169Ogh6V67N23aNNnOICYmRu3atVPr1q2VK1cuSVKPHj00c+ZMHT16VOXKlXtg+0n3DsAOHTqktWvX6umnn5Z0b13av3+/5syZo08//dRW8/jx423d7Nu2basJEyZkyPJxpZkzZ2ru3LmS7t2NdPfuXQUFBdn+jmvWrKnKlSurbNmykqRixYpp4cKFtl4PiT788EPVqFHD9v9NmzbVsWPHFBwcLOlewDJw4ED98ccfWrhwoYoWLfrAmkqXLq0+ffpIkkqVKqX169dr3759ku6djAQEBGjUqFHKkSOHnnzySQ0ZMkT//ve/nbhUsp7EO+LuvxjetWtXuztQihQponfeeUeRkZFauXKlbfqJEyfq5Zdf1qJFixQaGqrmzZvr22+/Ve/evSVJ69at02OPPabq1avLbDYrR44c8vHxeeQuvieV1r7HkW3JmTNnlCtXLj3++OPy9/fXBx98oMqVK9uNhfvEE08oLCxMJpNJpUuX1vHjxzV//nx17tzZ7nv9/f3l4+OjHDly2Lp6O7p+3s/DwyPZcUOTJk00YcIEbd68Wa+99ppu3bqlzZs327aJj5KGDRtqzpw5tiHDtm/frsDAQD3zzDO2aebMmaMaNWqoW7dukqSSJUva7hDfvXu3ChQooG3btmnevHm2sb8nTZqkOnXqpPidjuy7srK09vOODMOWN29e23C8efLk0c8//+zQ/tpisWjkyJEKCAhQ6dKlVa5cOeXIkcP23J327dvr66+/1unTp5UjRw4tWbJEAwYMsB1vlSxZUmfPntWMGTPUpk0bWz2DBw+2HXOPHz9eISEhWrduXarPRDp9+rTWrVun5cuX67nnnpN0rxfgkSNHNGfOHLd51kzinb+OjsvdqlUr25j63bp109y5c3Xw4EHVqFEjzXOYYsWKSbp3Q02DBg0kSW+//bZ+/PFH9e7d23YBrmbNmjp27Fiy775165ZTjrGzg+eee06vvPKKqlatKune+U6tWrVs+4Q6deqof//+iouLk9ls1u7du1W/fn3t2rVLXbt21d69exUbG6vatWvL29tb+fPn19q1a9WlSxdZLBatWbNGnTp1kpT2uWtiuzdt2tS2LmRn9x9T3LlzR0WKFNF7772nrl27OrTtuH+/7eHhkaX+9lM6rgoODtbcuXN169atNLfNixcvTvX6RpMmTbRy5UpbD1/p3rm7Iz3dk54PxcfHKy4uTqVLl9ann36qIkWK2KYLCQmx3dDkSM0bNmzQzZs3FR4ebjunHDVqlFavXq3Y2Ng0z/UjIiJkMplUrFgxFSlSREWKFNGcOXNsxxFnzpyRn5+fihcvrgIFCqhNmzZ64oknVKpUqQf+1geds5UtW1aLFy/WwIED9corr0iShgwZot9///2B83J0X+KM8+9Lly4pLi5ORYoUUdGiRVW0aFHNmDHDdqNd4rFx4pBWibW1bt06Xc8e/Prrr5U7d26NHTtWOXLkkHQvvNu9e7dy5swpPz+/Bw67lNbfaOI1uNTOdbMqR/YJQ4cOVZMmTTRo0CDt2rVL3bp1sx1D3799S01ax+j336Sb1aR1TC3J4XW1Y8eOthtb7+8VNG/ePLVr105vvvmmJOmdd97RnTt3DN28midPHsPXB5LW+M033ygyMlKTJk2yrX+jRo3Srl279PXXX9sC6oxEyJIF5M6dWz179lTPnj31zz//aOfOnba7egIDA9W6dWsdPnxYBw4csBvOIDGFPX78uO3OuaR3CXh5edmG9rh/hTl27JjKly9v27hLUr58+VSqVCm7iy8lSpRw+u91trp166pv377JXp84caKuX7+uQ4cOSVKyZ9vExcUpNjbWNo/9+/drypQpOn36tI4fP66//vpLjz32WLpqSXrAkngnxJ07d3T+/HndvXs32V0ciRcopXt/B7NmzdLJkyd16tQp252rSTdiSdvj2rVrunjxoiZPnqxp06bZXrdYLIqNjVVERIRKly6drvpd5eeff9aVK1fsepU0atRImzZt0vr169WyZUs1a9bMFrL8+eefOnbsmKZMmZJsXnnz5lXr1q21YcMGHTlyRKdPn7YtS0e6gx87dkz+/v62Azzp3rB+lStX1q+//mp7LX/+/HbjGPv7++vu3btGfr5be/vtt23dSuPj43Xq1CmFh4erdevWWrFihVq3bq0ff/xRq1ev1pkzZ3Ts2DGdPXs2WS+UpHcnJh74J11e3333ne7evasnnngizbFA7/+79vf3V1RUlCTp8OHDevbZZ207XUkZ+nDDrCIwMFDSvaF1kho+fLitZ8KCBQv0448/6tixYypZsqTdAZC3t7cqVKhg2z80a9ZM06ZN0759+1SpUiXbnZb3h56PsrT2PY5sSzp37qyuXbuqRo0aCg4OVs2aNdW4cWO78YyrVq1qN0RVxYoVNWvWLNtd96lxdP10RJ48eVS3bl2tWrVKr732mr777jv5+/s/kg9uL1++vIoXL67vv/9eHTp00IYNG2wXOBIdPnxYp0+ftgXFSR0/ftzWPkkvHubPn1/FixdP8Tsfdt/l7hzZz6eXo/vrfPny2d2d6uvra3dxMfGGlNjYWJ04cUJ3797V888/b/ddlStXtt1xmvS1RAEBASpZsmSKIUBSiT2o7x9m6e7duw7dQZtZErf/9+8zHuT+C3YBAQG2Y+y0zmESL7YnnUfiDTBJ1xdvb+8U774/efKkU46xs4OmTZtqx44d+uSTT3Tq1CkdP35cJ06csP0t16pVSwkJCdq7d6+8vLzk4+Ojt99+W926dVNcXJx++ukn1ahRw3aBq0mTJlq9erW6dOminTt36urVq7ZtpaPtnhXONTND4jGFxWLR/v37NWbMGL3wwgvq2rWrPD09DW07Em8syyp/+ykdVyVuCxzZNqd1fSNxW5F0v+zt7e3QTQxJz4fMZrPy5MmT4rM2kv49O1Lz0aNHVbJkSbtzyqeeekp9+/Z16Fw/cei4119/XSVLltQLL7ygevXqqXz58pLuXRTdvHmzXnzxRZUvX141a9ZUw4YNlS9fvgf+1gedsx0/flx37txJtq19/vnnbUME3s/RfYkzzr/LlSun1157TV27dlXhwoX1wgsv6KWXXkpz6OL0boOOHj2a7FyzSpUqqlKlSpqfdfQaXGrnulmVI/uEXLlyadSoUWrfvr2effZZwzdJpnWMntVDlrSOqcuWLevwuvqgc8Jr167p8uXLCgoKsnv9vffek5T8+nJGSlrj4cOHdevWLdvNIoliY2OTDWWeUQhZ3NysWbNUrFgxNWzYUNK9jWzjxo3VqFEjvfXWW/r555/VunVrWSwWderUSc2bN082jwIFCtjGVk16ISY1Vqs1xWkTEhLsdhhZ4YGsOXPmTHHnmDNnTl2/fl0Wi0W5cuVKcWzAxB3crFmzNHXqVLVo0UJVq1ZV27ZttWXLlhR7sqQmcdiEpKxWq90yTcmePXvUsWNHhYSEqHLlymrcuLFiYmJsPZkSJW2PxIsuAwcOTHEIIHe7Oyk1iW3Tq1evZO8tXbpULVu2VPPmzTVt2jQdOHBA3333nYKDg1O8C+fq1at68803FRgYqHr16qlGjRp67rnnHB7u7kHrhsVisY29K8nu4OhRljt3brv1q3Tp0sqdO7feeecd/fe//9XixYt19OhR/etf/1KDBg3Up08fDRkyJNl8UlpeSbvrFixYUJ988onee+89TZkyJcWL16nNK5GHh8cjcUHS2RLvYNu9e7fdkEdJg+TEu8xS2z8krgPFihVT1apVtXbtWvn7++vIkSOPZI+G1KS173FkWxIcHKyff/5Z27dv144dO7R8+XJNnTpVs2fPtt1FmHS7I/3fepPS/ub+6bp27erQ+umo119/XV27dtXVq1dtd4XeX9+jInHIsNatW2vLli22YQoSWSwW/etf/1LXrl2TfTZv3ry2rvT3b48etLwedt/l7hzZz0vJH2ya2s0Lju6vUzoGe1AgnPj99883sR2Tzvf+dTAhISHNY4PE+S9atCjZnZjuFFIXL15c+fPn1++//55sWFVJOnXqlEaMGKH+/ftLevDxr6Q0z2ESpbRuOHJe46xj7OwgLCxMGzZsULNmzfTSSy/p3//+t+bMmWMbYiNnzpyqWrWqtm/fLh8fH1WvXl2VK1eW1WrV/v379fPPP6t9+/a2+b3++uuaM2eODh48qDVr1qhevXq2C6COtntWONfMDEmPKUqVKqVChQqpQ4cO8vDwUFhYmKFtR1b723/QcZXk2LbZ0esb93PkOOb+86EHSfr37EjNnp6eD9zOOXKu7+Xlpfnz5+vw4cPatm2btm3bpqVLl6pZs2YaM2aMSpYsqY0bN2r37t3avn27tmzZohkzZmjMmDEprpvSg8/ZEpdTeoZbcnRfUqlSJaecf0+aNEndu3fXL7/8ov/+97/q06ePKlWqpPnz5z/wMyltg1I7FkmtzdLi6N/oo3idwdF9wqFDh+Tp6amTJ0/q3Llzevzxxw19V2rH6FldWsfUI0eOlOTYuvqgkW8S/wbT+ltP+h1Gbjh25Lg/aY0Wi0WlSpXS559/nmw6Pz+/dH+/Ee5ztI4U7d+/X9OnT1d8fLzd6yaTSTlz5rTdZfDUU0/pxIkTKlGihO2/S5cuafz48bp9+7Yt7U46ZmV8fLxeeuklrV+/PtnK8fTTT+vAgQN2d4RdvXpVp0+fzjK9Hxz19NNP69atW4qLi7NbfrNmzdKWLVskSZ9//rl69OihsLAwvfXWW6pYsaJOnTr1wA1TenesJUqUUI4cOezaR5Jatmyp2bNna86cOapWrZqmTZtmGz/ywoULkh68ccyXL5/y5cunM2fO2P2uQ4cOZamLndeuXbM9pG7VqlV2/7Vs2VL/+9//dOjQIRUtWlRVq1bV999/rw0bNjzwwHDt2rW6fv26li5dqm7duql+/fq27sAPOthNqkyZMoqKikp2N+revXvT1ZU4Ozh48KB+/vlnWyjSpEkTPf744zpz5ky6xzutUqWKgoKC1LdvX82dO1cHDhwwVFPZsmV16NAhux30/v37Dc3rUeLh4aF27dpp1apVD7zbLHGb8/TTT+vkyZN2d2zHxsbq4MGDdutAixYttGnTJq1du/aBoWd25si2ZMqUKdq7d6/q1aunwYMH64cfflDx4sXthsu4f7+xb98+FStWLMWhF5Ju2w4fPvxQ62dK28latWqpQIEC+uabb7R37161aNEizflkVQ0bNtT+/fu1fPlyFS9ePNmx0VNPPaW//vrLbv+bkJCgMWPG6MKFC7ahxZIO7xAVFaUzZ86k+H2O7LuyKkf38zly5NDNmzftPvug5SVlzP76iSeekKenZ7Lx6n/77TcVKFDAbr07ePCg3W88deqUnnrqqVTnn/j+5cuX7f52Vq5cqRUrVhiqOSOYzWa1bNlSK1euTHGM69mzZ+uPP/5IdWjPRGmdwzysjDjGfhRFRkZqyZIlCgsL03/+8x+1aNFC5cqV04kTJ+yWQ926dbV9+3bt3LlT1atXl7e3t4KDg7V8+XKdOHHCbsjD0qVLKzg4WBs2bLA90DhRRrf7o6569erq0KGDlixZol9++cWhbcf9++1H6W/fkW1zWtc3SpcuLW9vb+3du9f2fnx8/AOPizOj5ieffFKnTp2y2/cdPnxY1apVU2xsbJrn+j///LOmTZumZ555Ru+//77mz5+vXr16acOGDZJkG5K+Zs2a6tevn9auXasaNWrY3k+PEiVKyMfHR3/88Yfd66mdszm6LylbtuxD78//+OMPjR49Wk888YTat2+vL774wjYcXNJzmrTcfyxy69YtXbt2zfbvJ598UocPH7brDbZp0ya9+OKLiomJSfU6Q3a6Bnc/R/YJR48e1eTJkzVs2DCVL19e/fr1sy3n9Fx/S+sYPStz5Jg6Ojo63evq/XLlyqWCBQsmO7bq1auXRo4caQsFkz5LKLVj9pTcv65ZLJY0e8g8/fTTOn/+vPz9/W1tW7RoUU2aNEl79uxJ1/cbRcji5rp3766IiAi999572rZtm86dO6fff/9dY8eO1R9//GEbL7pz587auHGjpk6dqpMnT2rHjh0aOHCgoqKiVKBAAZUqVUqvvPKKhg8frh07dujkyZMaOnSo4uLiVKNGDVuqd+zYMd28eVOtWrXSrVu31LdvXx05ckQHDhzQBx98oMDAQLu7nB8FtWvXVrly5fThhx9qx44dOn36tMaNG6cVK1bYdmaJY67//fffOnHihMLDw7Vx48YHPhQwcXkeOXLEoRMFX19ftWnTRpMnT9aWLVt05swZhYeH6++//1adOnVUuHBhHT16VL/99psiIiK0YsUKTZ48WZIeWIPJZFKnTp20YMECLViwQGfOnNHmzZs1fPhweXl5ZZk7IFavXq34+Hh16tRJTz/9tN1/Xbt2lYeHh+0hXi1atNDSpUsVGRmZ4t0wklSoUCHFxMTou+++0/nz57Vt2zbbmKaJyzK19qtZs6bKlCmjjz76SLt27dLx48c1fPhwHTt2TO+++25GLQa3FR0drStXrujKlSu6fPmyfvvtN40ePVoFCxbUG2+8IU9PT3333Xc6e/as/ve//+nDDz/UlStXDD9Q86233lKlSpU0cOBAQ/No3bq1bt68qSFDhuj48ePasWOHRowYISn94eijplOnTqpTp45at26tGTNm6MiRI4qIiNCPP/6ojh07asWKFapevbr+9a9/KSAgwPbQ7iNHjig0NFTR0dF66623bPNr0KCBoqOjtXDhwmShZ86cOXXu3DldvHgxs3+m23BkW3L69GkNGzZMO3bs0Llz5/T999/r/Pnzdt3bf/vtN02ZMkUnT57U8uXLtWjRItuY9/fz8/PT5cuXdfbsWeXPn/+h1s/7jxukeyfLzZo104wZM1S+fPlHOnguV66cSpQooU8++STF46KOHTvqzz//1NChQ/X3339r//796tu3r06ePKmSJUvq8ccf16uvvqoRI0bov//9r44dO6Z+/fo9cNk7su/Kqhzdz1eqVEn//e9/9eOPP+rs2bOaMmVKsgsuOXPm1KlTp3T16tUM2V/7+/vrzTff1JQpU7R27VqdPn1aixYt0uLFi9WxY0e7/cjQoUO1Y8cO/fnnn+rdu7cKFy78wGOTRE899ZTq1KmjYcOGacuWLTp79qzmzJmjmTNnPnAoOVfp2rWrSpQoobffflurVq3SmTNn9L///U+DBg3SihUr9PHHH9uGkklNWucwDysjjrGzutOnT+uXX36x++/PP/+Uv7+/tmzZotOnT+vo0aMaMmSIDh06ZLcc6tatqyNHjuiPP/5Q9erVJUk1atTQmjVrVLFixWRt9vrrr2vRokXy8vJSrVq1bK9ndLtnBx988IFKliypYcOGqUiRImluO+7fbz9Kf/uObJvTur7h5+enNm3aaMqUKdq4caOOHz+uYcOGZdjDkh2p+V//+pdy586t0NBQHTlyRAcPHlRYWJiefvppFS1aNM1zfU9PT3322WeaN2+e7Vhv69attuPIf/75RyNGjNCWLVt07tw5/fLLLzp8+HCKwyilxdfXV23bttWUKVO0efNmnTx5UhMnTkx2Ifd+juxLXnrppYfen+fKlUuLFy/WhAkTbNu49evXq2TJkrZhk/38/OyGdE1JcHCwli1bpkOHDtmO3ZL2pmndurUiIyM1bNgwHT9+XL/99psmTpyomjVrytfXV35+frpx44ZteLqkstM1uPultU+Ii4tTaGioqlatqjfffFMjR47Un3/+qS+++EJSyuclD5LWMXpW5sgx9cqVKw2tq/d7//339dVXX9nW20WLFmnLli16+eWXVbBgQRUvXlxffvml/v77b/3vf//TkCFDUr0Gef/1gUqVKmnDhg3as2ePTp48qbCwsDTbtkmTJsqdO7d69OihP/74Q8ePH9fAgQP1888/p3mjk7M8mmM4PELKlSunb775RtOnT9fAgQMVGRmpnDlzqkqVKlq6dKntD+XVV19VeHi4Zs6cqZkzZyp37tyqU6eOQkNDbfMaM2aMxo8fr969eys2NlZBQUGaO3eurUvc66+/rvHjx+v06dMaPHiwFixYoIkTJ+qtt96Sl5eXatasqQkTJrjVmNDO4OHhoblz52rChAnq3bu3YmJiVLp0aU2dOtU2HMv48eM1YsQIvf7668qZM6eCgoI0fPhwhYWF2T2cMdHTTz+tkJAQffjhh+rTp4/d2KAP0qdPH3l6eiosLExRUVEqU6aMvvjiC5UuXVq9evXS1atXbV0an3zySY0ePVqhoaE6cODAA+9s6Nixo7y9vbVgwQKNGzdO+fLlU4sWLezGoXZ3K1eu1AsvvJDibyxevLjq16+v9evXa8CAAWrQoIFGjBihl19+OcVxcKV768qhQ4c0btw43bp1S0WLFtUbb7yhLVu26MCBA2rVqlWq7efp6akvv/xS48aNU8+ePRUXF6dnn31W8+bNSzauZXYwd+5c24MezWazAgMD9fzzz2vixIl67LHHNHbsWE2dOlWLFi1SgQIF9NJLL6l9+/basmWLobvkTCaTPv74YzVt2lTTpk2zPWjNUfny5dPs2bM1evRoNW3aVIUKFVKrVq00fvz4NIcUedSZzWZ9+umn+u6777RixQrNnz9fUVFRyp8/vypXrqyFCxfaxhNeuHChxo0bZxsW5Pnnn9eSJUvsLgL6+vqqYcOGWrt2rW3Iy0Rvv/22+vfvryZNmmjHjh1pDm31KHJkWzJ8+HCNGzdOoaGhun79uooWLaq+ffuqadOmtvnUq1dPf/31l5o2baqCBQtqwIABtodG3q9Zs2batGmTXnvtNW3atOmh1s/AwMBkxw3SvbB7xowZj3QvlkQNGzbU559/nuKF84oVK2r27NmaPHmyWrRoIV9fX1WvXl39+/e3nWCMGzfOdlxmsVj01ltv2d0NmZQj+66sytH9/C+//KKzZ88qNDRUJpNJjRo1Uvv27e16A7Vt21bjxo3TX3/9pTVr1mTI/nrQoEEKDAzUpEmTdPXqVZUoUUJDhw5Ntj/q1q2bBg4cqGvXrqlatWqaPXu2Qze4hIeHKzw8XMOGDdONGzdUvHhxffzxx3r99dcN15wRfH19tXDhQs2dO1ezZs3S+fPn5e3trWeffVZfffVVsvGwH8SRc5iHlRHH2FnZ2rVrtXbtWrvXHnvsMU2ePFljx461XditVq2a+vTpoxkzZig6Olp+fn4qXLiwypQpoxs3btiGaXnhhRcUHh6e4rMNGjZsqJEjR6pZs2Z2+/rMaPdHnbe3tz7++GO1a9fOtt1Ibdtx/377UfvbT2vbXLx48TSvb3z00Ufy9vbWiBEjdPv2bTVs2DDNZ3ZkZM2+vr6aM2eOxo4dq1atWsnLy0t169ZVv379JKV9rl+zZk2NGjVKc+fOVXh4uHx8fBQSEqIBAwZIknr06KH4+Hh9/PHHunr1qgoUKKDWrVurS5cuhn7PBx98oLt372rw4MGKiYlRnTp1VK9ePdszuFLi6L7kYffnTz75pKZOnapp06Zp8eLFMpvNql69umbNmmUbUq9jx46aPXu2Tpw4oUGDBqU4n7CwMA0fPlxvv/228ubNqw4dOig6Otr2/mOPPaa5c+dq4sSJat68uQICAtSoUSPbjTGvvPKKvv76azVp0kQLFy60m7cjf6OPqrT2CeHh4YqIiNCMGTMk3es51atXL4WHh6t27doqX7683fatfv36D/wuR47Rs6r0HFOnd129X5s2bRQbG6spU6boypUrKlmypMLDw203YEyYMEGjRo1Ss2bNVKRIEfXq1csW5Kfk/usDvXv31o0bN9S5c2f5+vr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      "text/plain": [
       "<Figure size 2000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#total number of Amount spents Occupation wise :\n",
    "sales_occupation = df.groupby(['Occupation'], as_index=False)['Amount'].sum().sort_values(by='Amount',ascending=False)\n",
    "\n",
    "sb.set(rc={'figure.figsize':(20,5)})\n",
    "\n",
    "sb.barplot(x='Occupation', y='Amount', data=sales_occupation)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d7a60358-79d7-444f-9221-7b8bcfd0cb98",
   "metadata": {},
   "outputs": [],
   "source": [
    "#From the above the graph we can notice that the most purachasing power and most order are done by IT Sectors, Healthcare and Aviation of Women only."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "32d10be6-146d-4a1a-8196-ea75456284af",
   "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": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Analysis on Product Category:\n",
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "33ba3f14-1cac-4481-a187-5009b84feb58",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 2600x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sb.set(rc={'figure.figsize':(26,5)})\n",
    "ax=sb.countplot(data=df,x='Product_Category')\n",
    "\n",
    "for bars in ax.containers:\n",
    "    ax.bar_label(bars)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "7627e0aa-81ab-444e-9a0a-9eb47d5c6958",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Product_Category', ylabel='Orders'>"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 2000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#total number of Orders Product wise :\n",
    "sales_products = df.groupby(['Product_Category'], as_index=False)['Orders'].sum().sort_values(by='Orders',ascending=False).head(10)\n",
    "\n",
    "sb.set(rc={'figure.figsize':(20,5)})\n",
    "\n",
    "sb.barplot(x='Product_Category', y='Orders', data=sales_products)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "3c8e5505-5d90-4fa4-ab8a-cbfa357c00a3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Product_Category', ylabel='Orders'>"
      ]
     },
     "execution_count": 95,
     "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 number of Orders Product wise and Gender :\n",
    "sales_products = df.groupby(['Product_Category', 'Gender'], as_index=False)['Orders'].sum().sort_values(by='Orders',ascending=False).head(10)\n",
    "\n",
    "sb.set(rc={'figure.figsize':(20,5)})\n",
    "\n",
    "sb.barplot(x='Product_Category', y='Orders',hue='Gender', data=sales_products)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "18f19b7f-5c9d-4397-bd1a-e455183714ac",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Product_Category', ylabel='Amount'>"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 2000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#total number of amounts spent in Products wise :\n",
    "sales_products = df.groupby(['Product_Category'], as_index=False)['Amount'].sum().sort_values(by='Amount',ascending=False).head(10)\n",
    "\n",
    "sb.set(rc={'figure.figsize':(20,5)})\n",
    "\n",
    "sb.barplot(x='Product_Category', y='Amount', data=sales_products)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "e50ef24b-ffbc-4825-9126-f92c3365ff07",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Product_Category', ylabel='Amount'>"
      ]
     },
     "execution_count": 98,
     "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 number of Orders Product wise and Gender :\n",
    "sales_products = df.groupby(['Product_Category', 'Gender'], as_index=False)['Amount'].sum().sort_values(by='Amount',ascending=False).head(10)\n",
    "\n",
    "sb.set(rc={'figure.figsize':(20,5)})\n",
    "\n",
    "sb.barplot(x='Product_Category', y='Amount',hue='Gender', data=sales_products)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7cde3214-4f90-45ee-9158-2e3583e6bb1e",
   "metadata": {},
   "outputs": [],
   "source": [
    "#From the above graph we can see most of the sold products are from food, cloths and electronics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "dd65f4b1-df60-41e4-947b-76b35446f274",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Product_ID', ylabel='Orders'>"
      ]
     },
     "execution_count": 103,
     "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": [
    "#checking top selling products using product id:\n",
    "sales_products_id = df.groupby(['Product_ID'], as_index=False)['Orders'].sum().sort_values(by='Orders',ascending=False).head(10)\n",
    "\n",
    "sb.set(rc={'figure.figsize':(20,5)})\n",
    "\n",
    "sb.barplot(x='Product_ID', y='Orders', data=sales_products_id)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0081c777-06de-4a92-b0a8-2b0acc841a6f",
   "metadata": {},
   "outputs": [],
   "source": [
    "''' CONCULUSION:\n",
    "Married women aged 26-35 from Uttar Pradesh, Maharashtra, and Karnataka who work in the IT, healthcare, and aviation sectors are most likely to purchase\n",
    "products in the food, clothing, and electronics categories from your store.\n",
    "\n",
    "'''"
   ]
  }
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