Data analysis (Pandas CaseStudy  7).ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "21bdff55",
   "metadata": {},
   "source": [
    "# Data Analysis on Super Market"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "b681f202",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as m\n",
    "import seaborn as sb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "67c1029d",
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset = pd.read_csv(\"Stores.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "17b9efa0",
   "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>Store ID</th>\n",
       "      <th>Store_Area</th>\n",
       "      <th>Items_Available</th>\n",
       "      <th>Daily_Customer_Count</th>\n",
       "      <th>Store_Sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1659</td>\n",
       "      <td>1961</td>\n",
       "      <td>530</td>\n",
       "      <td>66490</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1461</td>\n",
       "      <td>1752</td>\n",
       "      <td>210</td>\n",
       "      <td>39820</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1340</td>\n",
       "      <td>1609</td>\n",
       "      <td>720</td>\n",
       "      <td>54010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1451</td>\n",
       "      <td>1748</td>\n",
       "      <td>620</td>\n",
       "      <td>53730</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>1770</td>\n",
       "      <td>2111</td>\n",
       "      <td>450</td>\n",
       "      <td>46620</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Store ID   Store_Area  Items_Available  Daily_Customer_Count  Store_Sales\n",
       "0          1        1659             1961                   530        66490\n",
       "1          2        1461             1752                   210        39820\n",
       "2          3        1340             1609                   720        54010\n",
       "3          4        1451             1748                   620        53730\n",
       "4          5        1770             2111                   450        46620"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fcaf9de6",
   "metadata": {},
   "source": [
    "### No. of Rows & Columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "659e2d8d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Numbers of Rows:  896\n",
      "Numbers of Columns:  5\n"
     ]
    }
   ],
   "source": [
    "dataset.shape\n",
    "print(\"Numbers of Rows: \",dataset.shape[0])\n",
    "print(\"Numbers of Columns: \",dataset.shape[1])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "775a5315",
   "metadata": {},
   "source": [
    "### Check Missing Values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "6ee8e956",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Store ID                0\n",
       "Store_Area              0\n",
       "Items_Available         0\n",
       "Daily_Customer_Count    0\n",
       "Store_Sales             0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.isnull().sum() # there is no missing value"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "29fa11e9",
   "metadata": {},
   "source": [
    "### Data types of Attributes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "1fdd7d44",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Store ID                int64\n",
       "Store_Area              int64\n",
       "Items_Available         int64\n",
       "Daily_Customer_Count    int64\n",
       "Store_Sales             int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.dtypes # All attributes are int"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3b35998b",
   "metadata": {},
   "source": [
    "### Change the name of an Attribute"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "2a62250e",
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset.rename(columns = {\"Store ID \": \"store_id\"}, inplace = True )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "9440e3f7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['store_id', 'Store_Area', 'Items_Available', 'Daily_Customer_Count',\n",
       "       'Store_Sales'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7deac6c0",
   "metadata": {},
   "source": [
    "### Count of Unique values of all Attributes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "189f8376",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "store_id                896\n",
       "Store_Area              583\n",
       "Items_Available         616\n",
       "Daily_Customer_Count    130\n",
       "Store_Sales             816\n",
       "dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "6461bab2",
   "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>Store_Area</th>\n",
       "      <th>Items_Available</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>775</td>\n",
       "      <td>932</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>780</td>\n",
       "      <td>951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>854</td>\n",
       "      <td>1018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>869</td>\n",
       "      <td>1050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>891</td>\n",
       "      <td>1073</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>578</th>\n",
       "      <td>2063</td>\n",
       "      <td>2493</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>579</th>\n",
       "      <td>2067</td>\n",
       "      <td>2492</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>580</th>\n",
       "      <td>2169</td>\n",
       "      <td>2617</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>581</th>\n",
       "      <td>2214</td>\n",
       "      <td>2647</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>582</th>\n",
       "      <td>2229</td>\n",
       "      <td>2667</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>583 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Store_Area  Items_Available\n",
       "0           775              932\n",
       "1           780              951\n",
       "2           854             1018\n",
       "3           869             1050\n",
       "4           891             1073\n",
       "..          ...              ...\n",
       "578        2063             2493\n",
       "579        2067             2492\n",
       "580        2169             2617\n",
       "581        2214             2647\n",
       "582        2229             2667\n",
       "\n",
       "[583 rows x 2 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d1 = dataset.groupby('Store_Area')['Items_Available'].max().reset_index()\n",
    "d1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "25601a1d",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Store_Area', ylabel='Items_Available'>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "m.figure(figsize=(15,4))\n",
    "sb.barplot(x='Store_Area',y='Items_Available',data=d1) "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "67edc9f3",
   "metadata": {},
   "source": [
    "So by this barplot , one can make out that if the area is large more no. of items are also more"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a3f33a76",
   "metadata": {},
   "source": [
    "### Among all the store which store cover more area & how much does it cover"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "e631dd43",
   "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>store_id</th>\n",
       "      <th>Store_Area</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>466</th>\n",
       "      <td>467</td>\n",
       "      <td>2229</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     store_id  Store_Area\n",
       "466       467        2229"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset[['store_id','Store_Area']][dataset['Store_Area'] == dataset['Store_Area'].max()]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "22a7e35d",
   "metadata": {},
   "source": [
    "Store id no. 467 cover the largest area among all stores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "2d58d305",
   "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>Items_Available</th>\n",
       "      <th>Daily_Customer_Count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>932</td>\n",
       "      <td>1090</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>951</td>\n",
       "      <td>790</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1018</td>\n",
       "      <td>660</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1050</td>\n",
       "      <td>850</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1059</td>\n",
       "      <td>870</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>611</th>\n",
       "      <td>2492</td>\n",
       "      <td>790</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>612</th>\n",
       "      <td>2493</td>\n",
       "      <td>810</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>613</th>\n",
       "      <td>2617</td>\n",
       "      <td>600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>614</th>\n",
       "      <td>2647</td>\n",
       "      <td>740</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>615</th>\n",
       "      <td>2667</td>\n",
       "      <td>660</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>616 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Items_Available  Daily_Customer_Count\n",
       "0                932                  1090\n",
       "1                951                   790\n",
       "2               1018                   660\n",
       "3               1050                   850\n",
       "4               1059                   870\n",
       "..               ...                   ...\n",
       "611             2492                   790\n",
       "612             2493                   810\n",
       "613             2617                   600\n",
       "614             2647                   740\n",
       "615             2667                   660\n",
       "\n",
       "[616 rows x 2 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d2 = dataset.groupby('Items_Available')['Daily_Customer_Count'].max().reset_index()\n",
    "d2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "eb79357d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Items_Available', ylabel='Daily_Customer_Count'>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x144 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "m.figure(figsize=(15,2))\n",
    "sb.barplot(x='Items_Available',y='Daily_Customer_Count',data=d2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b9b2a6a9",
   "metadata": {},
   "source": [
    "There is no correlation between Available items in a store and the count of a Customers"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b54b813b",
   "metadata": {},
   "source": [
    "### Which Store has more no. of Daily Customers & what's area of that Store"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "f4c6ce82",
   "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>Daily_Customer_Count</th>\n",
       "      <th>Store_Area</th>\n",
       "      <th>Items_Available</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>848</th>\n",
       "      <td>1560</td>\n",
       "      <td>919</td>\n",
       "      <td>1099</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Daily_Customer_Count  Store_Area  Items_Available\n",
       "848                  1560         919             1099"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d3 = dataset[['Daily_Customer_Count','Store_Area','Items_Available']][dataset['Daily_Customer_Count'] == dataset['Daily_Customer_Count'].max()]\n",
    "d3"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e8103de8",
   "metadata": {},
   "source": [
    "### connection between \"Daily_Customer_Count\" & \"Store_Sales\" Attributes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "483fd2a9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['store_id', 'Store_Area', 'Items_Available', 'Daily_Customer_Count',\n",
       "       'Store_Sales'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "758455ec",
   "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>Daily_Customer_Count</th>\n",
       "      <th>Store_Sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10</td>\n",
       "      <td>45480</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>70</td>\n",
       "      <td>63730</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>100</td>\n",
       "      <td>88270</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>120</td>\n",
       "      <td>72350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>130</td>\n",
       "      <td>58920</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125</th>\n",
       "      <td>1450</td>\n",
       "      <td>43640</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>126</th>\n",
       "      <td>1470</td>\n",
       "      <td>77120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>127</th>\n",
       "      <td>1490</td>\n",
       "      <td>35420</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128</th>\n",
       "      <td>1530</td>\n",
       "      <td>40350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129</th>\n",
       "      <td>1560</td>\n",
       "      <td>73810</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>130 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Daily_Customer_Count  Store_Sales\n",
       "0                      10        45480\n",
       "1                      70        63730\n",
       "2                     100        88270\n",
       "3                     120        72350\n",
       "4                     130        58920\n",
       "..                    ...          ...\n",
       "125                  1450        43640\n",
       "126                  1470        77120\n",
       "127                  1490        35420\n",
       "128                  1530        40350\n",
       "129                  1560        73810\n",
       "\n",
       "[130 rows x 2 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d4 = dataset.groupby('Daily_Customer_Count')['Store_Sales'].max().reset_index()\n",
    "d4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "d0f6b3c4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Daily_Customer_Count', ylabel='Store_Sales'>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sb.scatterplot(x='Daily_Customer_Count',y='Store_Sales',hue='Store_Sales',data=d4)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "df5d6dae",
   "metadata": {},
   "source": [
    "This plotting of graph implies that it's not like that if there are more no. daily customers the sales will be more"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ef737ef8",
   "metadata": {},
   "source": [
    "Now, let's make an analysis  between Store Area & Sales"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "92e98a10",
   "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>Store_Area</th>\n",
       "      <th>Store_Sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>775</td>\n",
       "      <td>42530</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>780</td>\n",
       "      <td>25600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>854</td>\n",
       "      <td>77740</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>869</td>\n",
       "      <td>52540</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>891</td>\n",
       "      <td>67370</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>578</th>\n",
       "      <td>2063</td>\n",
       "      <td>51480</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>579</th>\n",
       "      <td>2067</td>\n",
       "      <td>70230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>580</th>\n",
       "      <td>2169</td>\n",
       "      <td>67080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>581</th>\n",
       "      <td>2214</td>\n",
       "      <td>65900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>582</th>\n",
       "      <td>2229</td>\n",
       "      <td>87410</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>583 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Store_Area  Store_Sales\n",
       "0           775        42530\n",
       "1           780        25600\n",
       "2           854        77740\n",
       "3           869        52540\n",
       "4           891        67370\n",
       "..          ...          ...\n",
       "578        2063        51480\n",
       "579        2067        70230\n",
       "580        2169        67080\n",
       "581        2214        65900\n",
       "582        2229        87410\n",
       "\n",
       "[583 rows x 2 columns]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d5 = dataset.groupby('Store_Area')['Store_Sales'].max().reset_index()\n",
    "d5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "509a901a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Store_Area', ylabel='Store_Sales'>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sb.scatterplot(x='Store_Area',y='Store_Sales',hue='Store_Sales',data=d5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "245e8885",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Store_Area', ylabel='Store_Sales'>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sb.barplot(x='Store_Area',y='Store_Sales',data=d5)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9175143c",
   "metadata": {},
   "source": [
    "There is no correlation between Store_Area and Store_Sales as such"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6bf2872e",
   "metadata": {},
   "source": [
    "It keeps on vary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2d0ba993",
   "metadata": {},
   "outputs": [],
   "source": [
    "Analysis would have been much better than this if this dataset would have content more inform"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "990bc73c",
   "metadata": {},
   "source": [
    "So, These was all about data Analysis on the supermarket"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d2951027",
   "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": "3.9.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}