Marketing+and+Retail+Analysis+%E2%80%93+Project+2++%28Grocery+Store%29+-Yaresh+Vijayasundaram.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "c57ee331",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from pylab import rcParams\n",
    "\n",
    "import scipy.stats as stats "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4fb70449",
   "metadata": {},
   "source": [
    "# Problem Statement:\n",
    "\n",
    "A grocery store shared the transactional data with you. Your job is to conduct a thorough analysis of Point of Sale (POS) data, identify the most commonly occurring sets of items in the customer orders, and provide recommendations through which a grocery store can increase its revenue by popular combo offers & discounts for customers."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c57574f5",
   "metadata": {},
   "source": [
    "# PART B: Exploratory Analysis --> Exploratory Analysis of data & an executive summary (in PPT) of your top findings, supported by graphs. --> Are there trends across months/years/quarters/days etc. that you are able to notice?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "e5ee1dba",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"/Users/yareshvijayasundaram/Downloads/dataset_group.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "b25d4c9f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Order_id</th>\n",
       "      <th>Product</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>01-01-2018</td>\n",
       "      <td>1</td>\n",
       "      <td>yogurt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>01-01-2018</td>\n",
       "      <td>1</td>\n",
       "      <td>pork</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>01-01-2018</td>\n",
       "      <td>1</td>\n",
       "      <td>sandwich bags</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>01-01-2018</td>\n",
       "      <td>1</td>\n",
       "      <td>lunch meat</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>01-01-2018</td>\n",
       "      <td>1</td>\n",
       "      <td>all- purpose</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Date  Order_id        Product\n",
       "0  01-01-2018         1         yogurt\n",
       "1  01-01-2018         1           pork\n",
       "2  01-01-2018         1  sandwich bags\n",
       "3  01-01-2018         1     lunch meat\n",
       "4  01-01-2018         1   all- purpose"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "7fa2a015",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Order_id</th>\n",
       "      <th>Product</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>20636</th>\n",
       "      <td>25-02-2020</td>\n",
       "      <td>1138</td>\n",
       "      <td>soda</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20637</th>\n",
       "      <td>25-02-2020</td>\n",
       "      <td>1138</td>\n",
       "      <td>paper towels</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20638</th>\n",
       "      <td>26-02-2020</td>\n",
       "      <td>1139</td>\n",
       "      <td>soda</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20639</th>\n",
       "      <td>26-02-2020</td>\n",
       "      <td>1139</td>\n",
       "      <td>laundry detergent</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20640</th>\n",
       "      <td>26-02-2020</td>\n",
       "      <td>1139</td>\n",
       "      <td>shampoo</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Date  Order_id            Product\n",
       "20636  25-02-2020      1138               soda\n",
       "20637  25-02-2020      1138       paper towels\n",
       "20638  26-02-2020      1139               soda\n",
       "20639  26-02-2020      1139  laundry detergent\n",
       "20640  26-02-2020      1139            shampoo"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "2cd29cb1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 20641 entries, 0 to 20640\n",
      "Data columns (total 3 columns):\n",
      " #   Column    Non-Null Count  Dtype \n",
      "---  ------    --------------  ----- \n",
      " 0   Date      20641 non-null  object\n",
      " 1   Order_id  20641 non-null  int64 \n",
      " 2   Product   20641 non-null  object\n",
      "dtypes: int64(1), object(2)\n",
      "memory usage: 483.9+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "17d54026",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(20641, 3)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "02e15558",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Date        0\n",
       "Order_id    0\n",
       "Product     0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "95026555",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4730"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.duplicated().sum() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "d59f0b72",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Date        0\n",
       "Order_id    0\n",
       "Product     0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "781705b9",
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "'<=' not supported between instances of 'int' and 'numpy.str_'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "Input \u001b[0;32mIn [13]\u001b[0m, in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mdf\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdescribe\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mall\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\u001b[38;5;241m.\u001b[39mT\n",
      "File \u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/pandas/core/generic.py:10225\u001b[0m, in \u001b[0;36mNDFrame.describe\u001b[0;34m(self, percentiles, include, exclude, datetime_is_numeric)\u001b[0m\n\u001b[1;32m   9976\u001b[0m \u001b[38;5;129m@final\u001b[39m\n\u001b[1;32m   9977\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mdescribe\u001b[39m(\n\u001b[1;32m   9978\u001b[0m     \u001b[38;5;28mself\u001b[39m: NDFrameT,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m   9982\u001b[0m     datetime_is_numeric\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m,\n\u001b[1;32m   9983\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m NDFrameT:\n\u001b[1;32m   9984\u001b[0m     \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m   9985\u001b[0m \u001b[38;5;124;03m    Generate descriptive statistics.\u001b[39;00m\n\u001b[1;32m   9986\u001b[0m \n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m  10223\u001b[0m \u001b[38;5;124;03m    max            NaN      3.0\u001b[39;00m\n\u001b[1;32m  10224\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[0;32m> 10225\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mdescribe_ndframe\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m  10226\u001b[0m \u001b[43m        \u001b[49m\u001b[43mobj\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m  10227\u001b[0m \u001b[43m        \u001b[49m\u001b[43minclude\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43minclude\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m  10228\u001b[0m \u001b[43m        \u001b[49m\u001b[43mexclude\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mexclude\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m  10229\u001b[0m \u001b[43m        \u001b[49m\u001b[43mdatetime_is_numeric\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdatetime_is_numeric\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m  10230\u001b[0m \u001b[43m        \u001b[49m\u001b[43mpercentiles\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mpercentiles\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m  10231\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/pandas/core/describe.py:78\u001b[0m, in \u001b[0;36mdescribe_ndframe\u001b[0;34m(obj, include, exclude, datetime_is_numeric, percentiles)\u001b[0m\n\u001b[1;32m     47\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mdescribe_ndframe\u001b[39m(\n\u001b[1;32m     48\u001b[0m     \u001b[38;5;241m*\u001b[39m,\n\u001b[1;32m     49\u001b[0m     obj: NDFrameT,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m     53\u001b[0m     percentiles: Sequence[\u001b[38;5;28mfloat\u001b[39m] \u001b[38;5;241m|\u001b[39m np\u001b[38;5;241m.\u001b[39mndarray \u001b[38;5;241m|\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[1;32m     54\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m NDFrameT:\n\u001b[1;32m     55\u001b[0m     \u001b[38;5;124;03m\"\"\"Describe series or dataframe.\u001b[39;00m\n\u001b[1;32m     56\u001b[0m \n\u001b[1;32m     57\u001b[0m \u001b[38;5;124;03m    Called from pandas.core.generic.NDFrame.describe()\u001b[39;00m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m     76\u001b[0m \u001b[38;5;124;03m    Dataframe or series description.\u001b[39;00m\n\u001b[1;32m     77\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[0;32m---> 78\u001b[0m     percentiles \u001b[38;5;241m=\u001b[39m \u001b[43mrefine_percentiles\u001b[49m\u001b[43m(\u001b[49m\u001b[43mpercentiles\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     80\u001b[0m     describer: NDFrameDescriberAbstract\n\u001b[1;32m     81\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m obj\u001b[38;5;241m.\u001b[39mndim \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m1\u001b[39m:\n",
      "File \u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/pandas/core/describe.py:408\u001b[0m, in \u001b[0;36mrefine_percentiles\u001b[0;34m(percentiles)\u001b[0m\n\u001b[1;32m    405\u001b[0m percentiles \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlist\u001b[39m(percentiles)\n\u001b[1;32m    407\u001b[0m \u001b[38;5;66;03m# get them all to be in [0, 1]\u001b[39;00m\n\u001b[0;32m--> 408\u001b[0m \u001b[43mvalidate_percentile\u001b[49m\u001b[43m(\u001b[49m\u001b[43mpercentiles\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    410\u001b[0m \u001b[38;5;66;03m# median should always be included\u001b[39;00m\n\u001b[1;32m    411\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;241m0.5\u001b[39m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m percentiles:\n",
      "File \u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/pandas/util/_validators.py:422\u001b[0m, in \u001b[0;36mvalidate_percentile\u001b[0;34m(q)\u001b[0m\n\u001b[1;32m    420\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(msg\u001b[38;5;241m.\u001b[39mformat(q_arr \u001b[38;5;241m/\u001b[39m \u001b[38;5;241m100.0\u001b[39m))\n\u001b[1;32m    421\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 422\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;43mall\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;241;43m<\u001b[39;49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mqs\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m<\u001b[39;49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mfor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mqs\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01min\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mq_arr\u001b[49m\u001b[43m)\u001b[49m:\n\u001b[1;32m    423\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(msg\u001b[38;5;241m.\u001b[39mformat(q_arr \u001b[38;5;241m/\u001b[39m \u001b[38;5;241m100.0\u001b[39m))\n\u001b[1;32m    424\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m q_arr\n",
      "File \u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/pandas/util/_validators.py:422\u001b[0m, in \u001b[0;36m<genexpr>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m    420\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(msg\u001b[38;5;241m.\u001b[39mformat(q_arr \u001b[38;5;241m/\u001b[39m \u001b[38;5;241m100.0\u001b[39m))\n\u001b[1;32m    421\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 422\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mall\u001b[39m(\u001b[38;5;241m0\u001b[39m \u001b[38;5;241m<\u001b[39m\u001b[38;5;241m=\u001b[39m qs \u001b[38;5;241m<\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m qs \u001b[38;5;129;01min\u001b[39;00m q_arr):\n\u001b[1;32m    423\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(msg\u001b[38;5;241m.\u001b[39mformat(q_arr \u001b[38;5;241m/\u001b[39m \u001b[38;5;241m100.0\u001b[39m))\n\u001b[1;32m    424\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m q_arr\n",
      "\u001b[0;31mTypeError\u001b[0m: '<=' not supported between instances of 'int' and 'numpy.str_'"
     ]
    }
   ],
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