Sales_analysis_project.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "b8685f34",
   "metadata": {},
   "outputs": [],
   "source": [
    "#import all the necessary libraries:\n",
    "\n",
    "import numpy as np #for numeric manipulation \n",
    "import pandas as pd #for data manipulation \n",
    "import matplotlib.pyplot as plt #for visualizing data\n",
    "%matplotlib inline \n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "0023d5cc",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Data processing:\n",
    "df =pd.read_csv('Sales Data.csv',encoding= 'unicode_escape') #to avoid encoding error, we use 'unicode_escape'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "b599d55d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(11251, 15)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "05c987cf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 11251 entries, 0 to 11250\n",
      "Data columns (total 15 columns):\n",
      " #   Column            Non-Null Count  Dtype  \n",
      "---  ------            --------------  -----  \n",
      " 0   User_ID           11251 non-null  int64  \n",
      " 1   Cust_name         11251 non-null  object \n",
      " 2   Product_ID        11251 non-null  object \n",
      " 3   Gender            11251 non-null  object \n",
      " 4   Age Group         11251 non-null  object \n",
      " 5   Age               11251 non-null  int64  \n",
      " 6   Marital_Status    11251 non-null  int64  \n",
      " 7   State             11251 non-null  object \n",
      " 8   Zone              11251 non-null  object \n",
      " 9   Occupation        11251 non-null  object \n",
      " 10  Product_Category  11251 non-null  object \n",
      " 11  Orders            11251 non-null  int64  \n",
      " 12  Amount            11239 non-null  float64\n",
      " 13  Status            0 non-null      float64\n",
      " 14  unnamed1          0 non-null      float64\n",
      "dtypes: float64(3), int64(4), object(8)\n",
      "memory usage: 1.3  MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "2dfee5bd",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1002903</td>\n",
       "      <td>Sanskriti</td>\n",
       "      <td>P00128632</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",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <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",
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       "      <td>1</td>\n",
       "      <td>Andhra Pradesh</td>\n",
       "      <td>Southern</td>\n",
       "      <td>Govt</td>\n",
       "      <td>Auto</td>\n",
       "      <td>3</td>\n",
       "      <td>23934.0</td>\n",
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       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
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       "      <td>Bindu</td>\n",
       "      <td>P00118542</td>\n",
       "      <td>F</td>\n",
       "      <td>26-35</td>\n",
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       "      <td>Uttar Pradesh</td>\n",
       "      <td>Central</td>\n",
       "      <td>Automobile</td>\n",
       "      <td>Auto</td>\n",
       "      <td>3</td>\n",
       "      <td>23924.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1001425</td>\n",
       "      <td>Sudevi</td>\n",
       "      <td>P00237842</td>\n",
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       "      <td>0-17</td>\n",
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       "      <td>Karnataka</td>\n",
       "      <td>Southern</td>\n",
       "      <td>Construction</td>\n",
       "      <td>Auto</td>\n",
       "      <td>2</td>\n",
       "      <td>23912.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1000588</td>\n",
       "      <td>Joni</td>\n",
       "      <td>P00057942</td>\n",
       "      <td>M</td>\n",
       "      <td>26-35</td>\n",
       "      <td>28</td>\n",
       "      <td>1</td>\n",
       "      <td>Gujarat</td>\n",
       "      <td>Western</td>\n",
       "      <td>Food Processing</td>\n",
       "      <td>Auto</td>\n",
       "      <td>2</td>\n",
       "      <td>23877.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
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      ],
      "text/plain": [
       "   User_ID  Cust_name Product_ID Gender Age Group  Age  Marital_Status   \n",
       "0  1002903  Sanskriti  P00128632      F     26-35   28               0  \\\n",
       "1  1000732     Kartik  P00110942      F     26-35   35               1   \n",
       "2  1001990      Bindu  P00118542      F     26-35   35               1   \n",
       "3  1001425     Sudevi  P00237842      M      0-17   16               0   \n",
       "4  1000588       Joni  P00057942      M     26-35   28               1   \n",
       "\n",
       "            State      Zone       Occupation Product_Category  Orders   \n",
       "0     Maharashtra   Western       Healthcare             Auto       1  \\\n",
       "1  Andhra Pradesh  Southern             Govt             Auto       3   \n",
       "2   Uttar Pradesh   Central       Automobile             Auto       3   \n",
       "3       Karnataka  Southern     Construction             Auto       2   \n",
       "4         Gujarat   Western  Food Processing             Auto       2   \n",
       "\n",
       "    Amount  Status  unnamed1  \n",
       "0  23952.0     NaN       NaN  \n",
       "1  23934.0     NaN       NaN  \n",
       "2  23924.0     NaN       NaN  \n",
       "3  23912.0     NaN       NaN  \n",
       "4  23877.0     NaN       NaN  "
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "0e60608b",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Cust_name</th>\n",
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       "      <th>unnamed1</th>\n",
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       "      <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",
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       "      <th>11247</th>\n",
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       "      <td>Reichenbach</td>\n",
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       "      <td>Haryana</td>\n",
       "      <td>Northern</td>\n",
       "      <td>Healthcare</td>\n",
       "      <td>Veterinary</td>\n",
       "      <td>3</td>\n",
       "      <td>367.0</td>\n",
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       "      <td>NaN</td>\n",
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       "      <th>11248</th>\n",
       "      <td>1001209</td>\n",
       "      <td>Oshin</td>\n",
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       "      <td>36-45</td>\n",
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       "      <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",
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       "      <td>1004023</td>\n",
       "      <td>Noonan</td>\n",
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       "      <td>M</td>\n",
       "      <td>36-45</td>\n",
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       "      <td>Karnataka</td>\n",
       "      <td>Southern</td>\n",
       "      <td>Agriculture</td>\n",
       "      <td>Office</td>\n",
       "      <td>3</td>\n",
       "      <td>206.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <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>188.0</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",
       "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  P00086342      M     36-45   37               0   \n",
       "11250  1002744      Brumley  P00281742      F     18-25   19               0   \n",
       "\n",
       "                State      Zone   Occupation Product_Category  Orders  Amount   \n",
       "11246     Maharashtra   Western     Chemical           Office       4   370.0  \\\n",
       "11247         Haryana  Northern   Healthcare       Veterinary       3   367.0   \n",
       "11248  Madhya Pradesh   Central      Textile           Office       4   213.0   \n",
       "11249       Karnataka  Southern  Agriculture           Office       3   206.0   \n",
       "11250     Maharashtra   Western   Healthcare           Office       3   188.0   \n",
       "\n",
       "       Status  unnamed1  \n",
       "11246     NaN       NaN  \n",
       "11247     NaN       NaN  \n",
       "11248     NaN       NaN  \n",
       "11249     NaN       NaN  \n",
       "11250     NaN       NaN  "
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "a2d6b83b",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Drop unrelated/blank columns\n",
    "df.drop(['Status','unnamed1'],axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "bae943a3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "User_ID              0\n",
       "Cust_name            0\n",
       "Product_ID           0\n",
       "Gender               0\n",
       "Age Group            0\n",
       "Age                  0\n",
       "Marital_Status       0\n",
       "State                0\n",
       "Zone                 0\n",
       "Occupation           0\n",
       "Product_Category     0\n",
       "Orders               0\n",
       "Amount              12\n",
       "dtype: int64"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#check for null values and sum count of elements\n",
    "pd.isnull(df).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "2251018a",
   "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": [
    "#we will notice the changes in df.info()\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "3870a281",
   "metadata": {
    "scrolled": false
   },
   "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",
       "      <td>False</td>\n",
       "      <td>False</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>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>11247</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>11248</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>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>11251 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",
       "[11251 rows x 13 columns]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Cleaning all data\n",
    "pd.isnull(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "5c3c4488",
   "metadata": {},
   "outputs": [],
   "source": [
    "#get rid of NaN values and affect these changes to the original df\n",
    "df.dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "a096088d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(11239, 13)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#We can notice that the df dimensions decreased cause inplace=True\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "80fca629",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Maxim</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Ismail</td>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Kevin</td>\n",
       "      <td>17.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Julie</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Name   Age\n",
       "0   Maxim   NaN\n",
       "1  Ismail  11.0\n",
       "2   Kevin  17.0\n",
       "3   Julie   7.0"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Create a data for testing the model\n",
    "#First create 2D array that represents our data (rows)\n",
    "data_test=[['Maxim',],['Ismail',11],['Kevin',17],['Julie',7]]\n",
    "#take the rows and create a df from it by adding colums\n",
    "df_test=pd.DataFrame(data_test,columns=['Name','Age'])\n",
    "df_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "adfe67ba",
   "metadata": {},
   "outputs": [],
   "source": [
    "#There are easier to create Testing sets from our origin dataset \n",
    "#By creating copy and droping columns/rows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "f3f0bb57",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Ismail</td>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Kevin</td>\n",
       "      <td>17.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Julie</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Name   Age\n",
       "1  Ismail  11.0\n",
       "2   Kevin  17.0\n",
       "3   Julie   7.0"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Cleaning Data by getting rid of unconsistencies and redondances\n",
    "df_test.dropna() #its gonna the drop NaN in the df "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "17ae101e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Maxim</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Ismail</td>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Kevin</td>\n",
       "      <td>17.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Julie</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Name   Age\n",
       "0   Maxim   NaN\n",
       "1  Ismail  11.0\n",
       "2   Kevin  17.0\n",
       "3   Julie   7.0"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#WARNING: The original df doesnt change:\n",
    "df_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "5bda382d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Maxim</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Ismail</td>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Kevin</td>\n",
       "      <td>17.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Julie</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Name   Age\n",
       "0   Maxim   NaN\n",
       "1  Ismail  11.0\n",
       "2   Kevin  17.0\n",
       "3   Julie   7.0"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#We wanted to drop NaN values using dropna but the changes didnt get affected to the original array\n",
    "df_test.dropna()\n",
    "df_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "42e77fac",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Ismail</td>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Kevin</td>\n",
       "      <td>17.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Julie</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Name   Age\n",
       "1  Ismail  11.0\n",
       "2   Kevin  17.0\n",
       "3   Julie   7.0"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Drop NaN values using dropna and in order to affect it to the original array we use args inplace=True\n",
    "df_test.dropna(inplace=True)\n",
    "df_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "ed3b4366",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Convert data type of a column\n",
    "df['Amount']=df['Amount'].astype('int')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "10b973cf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('int32')"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Amount'].dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "2cbfc36d",
   "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": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "bb0d4e15",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>User_ID</th>\n",
       "      <th>Cust_name</th>\n",
       "      <th>Product_ID</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age Group</th>\n",
       "      <th>Age</th>\n",
       "      <th>Marital_Status</th>\n",
       "      <th>State</th>\n",
       "      <th>Zone</th>\n",
       "      <th>Occupation</th>\n",
       "      <th>Product_Category</th>\n",
       "      <th>Orders</th>\n",
       "      <th>Amount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1002903</td>\n",
       "      <td>Sanskriti</td>\n",
       "      <td>P00128632</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>P00086342</td>\n",
       "      <td>M</td>\n",
       "      <td>36-45</td>\n",
       "      <td>37</td>\n",
       "      <td>0</td>\n",
       "      <td>Karnataka</td>\n",
       "      <td>Southern</td>\n",
       "      <td>Agriculture</td>\n",
       "      <td>Office</td>\n",
       "      <td>3</td>\n",
       "      <td>206</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11250</th>\n",
       "      <td>1002744</td>\n",
       "      <td>Brumley</td>\n",
       "      <td>P00281742</td>\n",
       "      <td>F</td>\n",
       "      <td>18-25</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "      <td>Maharashtra</td>\n",
       "      <td>Western</td>\n",
       "      <td>Healthcare</td>\n",
       "      <td>Office</td>\n",
       "      <td>3</td>\n",
       "      <td>188</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>11239 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       User_ID    Cust_name Product_ID Gender Age Group  Age  Marital_Status   \n",
       "0      1002903    Sanskriti  P00128632      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  P00086342      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": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Change the name of a column\n",
    "df.rename(columns={'Mariage_Status':'Marital_Status'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "bca43220",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>User_ID</th>\n",
       "      <th>Age</th>\n",
       "      <th>Marital_Status</th>\n",
       "      <th>Orders</th>\n",
       "      <th>Amount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1.123900e 04</td>\n",
       "      <td>11239.000000</td>\n",
       "      <td>11239.000000</td>\n",
       "      <td>11239.000000</td>\n",
       "      <td>11239.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.003004e 06</td>\n",
       "      <td>35.410357</td>\n",
       "      <td>0.420055</td>\n",
       "      <td>2.489634</td>\n",
       "      <td>9453.610553</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.716039e 03</td>\n",
       "      <td>12.753866</td>\n",
       "      <td>0.493589</td>\n",
       "      <td>1.114967</td>\n",
       "      <td>5222.355168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000001e 06</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>188.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.001492e 06</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>5443.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.003064e 06</td>\n",
       "      <td>33.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>8109.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.004426e 06</td>\n",
       "      <td>43.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>12675.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.006040e 06</td>\n",
       "      <td>92.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>23952.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            User_ID           Age  Marital_Status        Orders        Amount\n",
       "count  1.123900e 04  11239.000000    11239.000000  11239.000000  11239.000000\n",
       "mean   1.003004e 06     35.410357        0.420055      2.489634   9453.610553\n",
       "std    1.716039e 03     12.753866        0.493589      1.114967   5222.355168\n",
       "min    1.000001e 06     12.000000        0.000000      1.000000    188.000000\n",
       "25%    1.001492e 06     27.000000        0.000000      2.000000   5443.000000\n",
       "50%    1.003064e 06     33.000000        0.000000      2.000000   8109.000000\n",
       "75%    1.004426e 06     43.000000        1.000000      3.000000  12675.000000\n",
       "max    1.006040e 06     92.000000        1.000000      4.000000  23952.000000"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Have a description of the data frame (Like statistics descriptive measures)\n",
    "df.describe()\n",
    "#We can also use it for a specific columns not all df df[].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "934210d9",
   "metadata": {},
   "source": [
    "# Data Manipulation & Visualization"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "377c3fa8",
   "metadata": {},
   "source": [
    "## Gender"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "e121ac04",
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Visualize the proportions of Gender in df using seaborn\n",
    "ax = sns.countplot(x='Gender',data=df)\n",
    "for bars in ax.containers:\n",
    "    ax.bar_label(bars)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "947f2676",
   "metadata": {},
   "outputs": [],
   "source": [
    "#First we are grouping the dataframe by Gender and then we sum the Amount and sort it\n",
    "new_df=df.groupby(['Gender'], as_index=False)['Amount'].sum().sort_values(by='Amount', ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "3ed90c55",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Gender', ylabel='Amount'>"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Lets draw a basic graph:\n",
    "sns.barplot(x='Gender',y='Amount',data= new_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "706eb02e",
   "metadata": {},
   "source": [
    "From the graph we can see the most of the buyers are females and even the purchasing power of females are greater than men."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "47b4de62",
   "metadata": {},
   "source": [
    "## Age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "32a182f9",
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Compare the number of females and males in each group ages\n",
    "ax =sns.countplot(data=df,x='Age Group',hue='Gender')\n",
    "for bars in ax.containers:\n",
    "    ax.bar_label(bars)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f597a77f",
   "metadata": {},
   "source": [
    "We can notice that in all age groups #females > #males"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "fcda35fb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Age Group', ylabel='Amount'>"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Total Amount vs Age Group:\n",
    "sales_age =df.groupby(['Age Group'],as_index=False)['Amount'].sum().sort_values(by=\"Amount\",ascending=False)\n",
    "#Visualize the result in a bar plot\n",
    "sns.barplot(x='Age Group',y='Amount',data=sales_age)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "49b1f9f5",
   "metadata": {},
   "source": [
    "From the graphs above we can see the most of the buyers are in the gae range of 26-35 yrs females "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f96ee55f",
   "metadata": {},
   "source": [
    "## State"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "4caf5f44",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='State', ylabel='Orders'>"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
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      "text/plain": [
       "<Figure size 1500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# total number of orders from top 10 states\n",
    "\n",
    "sales_state= df.groupby(['State'],as_index=False)['Orders'].sum().sort_values(by='Orders',ascending=False).head(10)\n",
    "#groupby ==> Split,Apply and then merge\n",
    "\n",
    "sns.set(rc={'figure.figsize':(15,5)}) #Set size of Seaborn module\n",
    "sns.barplot(data =sales_state , x='State',y='Orders')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "69f06bc0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='State', ylabel='Amount'>"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
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      "text/plain": [
       "<Figure size 1500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# total amount/sales from top 10 states\n",
    "\n",
    "sales_state = df.groupby(['State'], as_index=False)['Amount'].sum().sort_values(by='Amount', ascending=False).head(10)\n",
    "\n",
    "sns.set(rc={'figure.figsize':(15,5)}) #Set size of Seaborn module \n",
    "sns.barplot(data = sales_state, x = 'State',y= 'Amount')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7924e47e",
   "metadata": {},
   "source": [
    "## Marital Status"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "7669b9f2",
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 1500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.countplot(data = df, x = 'Marital_Status')\n",
    "\n",
    "sns.set(rc={'figure.figsize':(7,5)})#Set size of Seaborn module\n",
    "\n",
    "for bars in ax.containers: #give a label for each chart\n",
    "    ax.bar_label(bars)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "c7058e72",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Marital_Status', ylabel='Amount'>"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 600x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sales_state = df.groupby(['Marital_Status', 'Gender'], as_index=False)['Amount'].sum().sort_values(by='Amount', ascending=False)\n",
    "\n",
    "sns.set(rc={'figure.figsize':(6,5)})#Set size of Seaborn module\n",
    "\n",
    "sns.barplot(data = sales_state, x = 'Marital_Status',y= 'Amount', hue='Gender')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c862b2f9",
   "metadata": {},
   "source": [
    "From above graphs we can see that most of the buyers are married (women) and they have high purchasing power"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fd91f6cc",
   "metadata": {},
   "source": [
    "## Occupation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "b6a4c14e",
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 2000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(rc={'figure.figsize':(20,5)}) #Set size of Seaborn module\n",
    "\n",
    "ax = sns.countplot(data = df, x = 'Occupation')\n",
    "for bars in ax.containers: #give a label for each chart\n",
    "    ax.bar_label(bars)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "e7a9f470",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Occupation', ylabel='Amount'>"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
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      "text/plain": [
       "<Figure size 2000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sales_state = df.groupby(['Occupation'], as_index=False)['Amount'].sum().sort_values(by='Amount', ascending=False)\n",
    "\n",
    "sns.set(rc={'figure.figsize':(20,5)})\n",
    "sns.barplot(data = sales_state, x = 'Occupation',y= 'Amount')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2f4ae2a3",
   "metadata": {},
   "source": [
    "From above graphs we can see that most of the buyers are working in IT, Healthcare and Aviation sector"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0dfb549d",
   "metadata": {},
   "source": [
    "## Product Category"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "e7907756",
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 2000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(rc={'figure.figsize':(20,5)})#set the size of the module\n",
    "\n",
    "ax = sns.countplot(data = df, x = 'Product_Category')\n",
    "\n",
    "for bars in ax.containers:#give a label for each chart\n",
    "    ax.bar_label(bars)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "3c048908",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Product_Category', ylabel='Amount'>"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
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      "text/plain": [
       "<Figure size 2000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sales_state = df.groupby(['Product_Category'], as_index=False)['Amount'].sum().sort_values(by='Amount', ascending=False).head(10)\n",
    "\n",
    "sns.set(rc={'figure.figsize':(20,5)})\n",
    "sns.barplot(data = sales_state, x = 'Product_Category',y= 'Amount')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8becd9b8",
   "metadata": {},
   "source": [
    "From above graphs we can see that most of the sold products are from Food, Clothing and Electronics category"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "6c26f1db",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Product_Category'>"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 1200x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# top 10 most sold products (same thing as above)\n",
    "\n",
    "fig1, ax1 = plt.subplots(figsize=(12,7))\n",
    "df.groupby('Product_Category')['Orders'].sum().nlargest(10).sort_values(ascending=False).plot(kind='bar')\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "47422868",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}