Project_Notebook.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Analytics Assesment: Mini-Project\n",
    "\n",
    "**Name: Suyash Phatak<br>\n",
    "PRN: 19070122125**\n",
    "<hr>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Identifying and importing essential libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib import style\n",
    "%matplotlib inline\n",
    "style.use(\"ggplot\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__________________\n",
    "\n",
    "## Data loading and overview"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing dataset \n",
    "df = pd.read_excel ('superstore.xls')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Row ID        Order ID Order Date  Ship Date       Ship Mode Customer ID  \\\n",
      "0       1  CA-2016-152156 2016-11-08 2016-11-11    Second Class    CG-12520   \n",
      "1       2  CA-2016-152156 2016-11-08 2016-11-11    Second Class    CG-12520   \n",
      "2       3  CA-2016-138688 2016-06-12 2016-06-16    Second Class    DV-13045   \n",
      "3       4  US-2015-108966 2015-10-11 2015-10-18  Standard Class    SO-20335   \n",
      "4       5  US-2015-108966 2015-10-11 2015-10-18  Standard Class    SO-20335   \n",
      "\n",
      "     Customer Name    Segment        Country             City  ...  \\\n",
      "0      Claire Gute   Consumer  United States        Henderson  ...   \n",
      "1      Claire Gute   Consumer  United States        Henderson  ...   \n",
      "2  Darrin Van Huff  Corporate  United States      Los Angeles  ...   \n",
      "3   Sean O'Donnell   Consumer  United States  Fort Lauderdale  ...   \n",
      "4   Sean O'Donnell   Consumer  United States  Fort Lauderdale  ...   \n",
      "\n",
      "  Postal Code  Region       Product ID         Category Sub-Category  \\\n",
      "0       42420   South  FUR-BO-10001798        Furniture    Bookcases   \n",
      "1       42420   South  FUR-CH-10000454        Furniture       Chairs   \n",
      "2       90036    West  OFF-LA-10000240  Office Supplies       Labels   \n",
      "3       33311   South  FUR-TA-10000577        Furniture       Tables   \n",
      "4       33311   South  OFF-ST-10000760  Office Supplies      Storage   \n",
      "\n",
      "                                        Product Name     Sales  Quantity  \\\n",
      "0                  Bush Somerset Collection Bookcase  261.9600         2   \n",
      "1  Hon Deluxe Fabric Upholstered Stacking Chairs,...  731.9400         3   \n",
      "2  Self-Adhesive Address Labels for Typewriters b...   14.6200         2   \n",
      "3      Bretford CR4500 Series Slim Rectangular Table  957.5775         5   \n",
      "4                     Eldon Fold 'N Roll Cart System   22.3680         2   \n",
      "\n",
      "   Discount    Profit  \n",
      "0      0.00   41.9136  \n",
      "1      0.00  219.5820  \n",
      "2      0.00    6.8714  \n",
      "3      0.45 -383.0310  \n",
      "4      0.20    2.5164  \n",
      "\n",
      "[5 rows x 21 columns]\n"
     ]
    }
   ],
   "source": [
    "# Printing first 5 records\n",
    "print(df.head(5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Row ID', 'Order ID', 'Order Date', 'Ship Date', 'Ship Mode',\n",
       "       'Customer ID', 'Customer Name', 'Segment', 'Country', 'City', 'State',\n",
       "       'Postal Code', 'Region', 'Product ID', 'Category', 'Sub-Category',\n",
       "       'Product Name', 'Sales', 'Quantity', 'Discount', 'Profit'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Printing the columns contained in data\n",
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count     9994.000000\n",
      "mean       229.858001\n",
      "std        623.245101\n",
      "min          0.444000\n",
      "25%         17.280000\n",
      "50%         54.490000\n",
      "75%        209.940000\n",
      "max      22638.480000\n",
      "Name: Sales, dtype: float64\n",
      "\n",
      "\n",
      "count    9994.000000\n",
      "mean        3.789574\n",
      "std         2.225110\n",
      "min         1.000000\n",
      "25%         2.000000\n",
      "50%         3.000000\n",
      "75%         5.000000\n",
      "max        14.000000\n",
      "Name: Quantity, dtype: float64\n",
      "\n",
      "\n",
      "count    9994.000000\n",
      "mean        0.156203\n",
      "std         0.206452\n",
      "min         0.000000\n",
      "25%         0.000000\n",
      "50%         0.200000\n",
      "75%         0.200000\n",
      "max         0.800000\n",
      "Name: Discount, dtype: float64\n",
      "\n",
      "\n",
      "count    9994.000000\n",
      "mean       28.656896\n",
      "std       234.260108\n",
      "min     -6599.978000\n",
      "25%         1.728750\n",
      "50%         8.666500\n",
      "75%        29.364000\n",
      "max      8399.976000\n",
      "Name: Profit, dtype: float64\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Printing standard deviation, count, mean, max, min\n",
    "column_list = list(df.columns)\n",
    "for col in column_list[17:]:\n",
    "    print(df[col].describe())\n",
    "    print(\"\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Row ID           0\n",
       "Order ID         0\n",
       "Order Date       0\n",
       "Ship Date        0\n",
       "Ship Mode        0\n",
       "Customer ID      0\n",
       "Customer Name    0\n",
       "Segment          0\n",
       "Country          0\n",
       "City             0\n",
       "State            0\n",
       "Postal Code      0\n",
       "Region           0\n",
       "Product ID       0\n",
       "Category         0\n",
       "Sub-Category     0\n",
       "Product Name     0\n",
       "Sales            0\n",
       "Quantity         0\n",
       "Discount         0\n",
       "Profit           0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Finding Missing Values\n",
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "_____________________\n",
    "\n",
    "## Calculating per unit price"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Converting datetime field into pandas time object\n",
    "import datetime as dt\n",
    "df['Order Date'] = pd.to_datetime(df['Order Date'])\n",
    "df['Ship Date'] = pd.to_datetime(df['Ship Date'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calculating unit per price and adding a column in dataset\n",
    "price_per_unit = df[\"Sales\"] / df[\"Quantity\"]\n",
    "df[\"Price/Unit\"] = price_per_unit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Row ID</th>\n",
       "      <th>Order ID</th>\n",
       "      <th>Order Date</th>\n",
       "      <th>Ship Date</th>\n",
       "      <th>Ship Mode</th>\n",
       "      <th>Customer ID</th>\n",
       "      <th>Customer Name</th>\n",
       "      <th>Segment</th>\n",
       "      <th>Country</th>\n",
       "      <th>City</th>\n",
       "      <th>...</th>\n",
       "      <th>Region</th>\n",
       "      <th>Product ID</th>\n",
       "      <th>Category</th>\n",
       "      <th>Sub-Category</th>\n",
       "      <th>Product Name</th>\n",
       "      <th>Sales</th>\n",
       "      <th>Quantity</th>\n",
       "      <th>Discount</th>\n",
       "      <th>Profit</th>\n",
       "      <th>Price/Unit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>CA-2016-152156</td>\n",
       "      <td>2016-11-08</td>\n",
       "      <td>2016-11-11</td>\n",
       "      <td>Second Class</td>\n",
       "      <td>CG-12520</td>\n",
       "      <td>Claire Gute</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>United States</td>\n",
       "      <td>Henderson</td>\n",
       "      <td>...</td>\n",
       "      <td>South</td>\n",
       "      <td>FUR-BO-10001798</td>\n",
       "      <td>Furniture</td>\n",
       "      <td>Bookcases</td>\n",
       "      <td>Bush Somerset Collection Bookcase</td>\n",
       "      <td>261.9600</td>\n",
       "      <td>2</td>\n",
       "      <td>0.00</td>\n",
       "      <td>41.9136</td>\n",
       "      <td>130.9800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>CA-2016-152156</td>\n",
       "      <td>2016-11-08</td>\n",
       "      <td>2016-11-11</td>\n",
       "      <td>Second Class</td>\n",
       "      <td>CG-12520</td>\n",
       "      <td>Claire Gute</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>United States</td>\n",
       "      <td>Henderson</td>\n",
       "      <td>...</td>\n",
       "      <td>South</td>\n",
       "      <td>FUR-CH-10000454</td>\n",
       "      <td>Furniture</td>\n",
       "      <td>Chairs</td>\n",
       "      <td>Hon Deluxe Fabric Upholstered Stacking Chairs,...</td>\n",
       "      <td>731.9400</td>\n",
       "      <td>3</td>\n",
       "      <td>0.00</td>\n",
       "      <td>219.5820</td>\n",
       "      <td>243.9800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>CA-2016-138688</td>\n",
       "      <td>2016-06-12</td>\n",
       "      <td>2016-06-16</td>\n",
       "      <td>Second Class</td>\n",
       "      <td>DV-13045</td>\n",
       "      <td>Darrin Van Huff</td>\n",
       "      <td>Corporate</td>\n",
       "      <td>United States</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>...</td>\n",
       "      <td>West</td>\n",
       "      <td>OFF-LA-10000240</td>\n",
       "      <td>Office Supplies</td>\n",
       "      <td>Labels</td>\n",
       "      <td>Self-Adhesive Address Labels for Typewriters b...</td>\n",
       "      <td>14.6200</td>\n",
       "      <td>2</td>\n",
       "      <td>0.00</td>\n",
       "      <td>6.8714</td>\n",
       "      <td>7.3100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>US-2015-108966</td>\n",
       "      <td>2015-10-11</td>\n",
       "      <td>2015-10-18</td>\n",
       "      <td>Standard Class</td>\n",
       "      <td>SO-20335</td>\n",
       "      <td>Sean O'Donnell</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>United States</td>\n",
       "      <td>Fort Lauderdale</td>\n",
       "      <td>...</td>\n",
       "      <td>South</td>\n",
       "      <td>FUR-TA-10000577</td>\n",
       "      <td>Furniture</td>\n",
       "      <td>Tables</td>\n",
       "      <td>Bretford CR4500 Series Slim Rectangular Table</td>\n",
       "      <td>957.5775</td>\n",
       "      <td>5</td>\n",
       "      <td>0.45</td>\n",
       "      <td>-383.0310</td>\n",
       "      <td>191.5155</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>US-2015-108966</td>\n",
       "      <td>2015-10-11</td>\n",
       "      <td>2015-10-18</td>\n",
       "      <td>Standard Class</td>\n",
       "      <td>SO-20335</td>\n",
       "      <td>Sean O'Donnell</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>United States</td>\n",
       "      <td>Fort Lauderdale</td>\n",
       "      <td>...</td>\n",
       "      <td>South</td>\n",
       "      <td>OFF-ST-10000760</td>\n",
       "      <td>Office Supplies</td>\n",
       "      <td>Storage</td>\n",
       "      <td>Eldon Fold 'N Roll Cart System</td>\n",
       "      <td>22.3680</td>\n",
       "      <td>2</td>\n",
       "      <td>0.20</td>\n",
       "      <td>2.5164</td>\n",
       "      <td>11.1840</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Row ID        Order ID Order Date  Ship Date       Ship Mode Customer ID  \\\n",
       "0       1  CA-2016-152156 2016-11-08 2016-11-11    Second Class    CG-12520   \n",
       "1       2  CA-2016-152156 2016-11-08 2016-11-11    Second Class    CG-12520   \n",
       "2       3  CA-2016-138688 2016-06-12 2016-06-16    Second Class    DV-13045   \n",
       "3       4  US-2015-108966 2015-10-11 2015-10-18  Standard Class    SO-20335   \n",
       "4       5  US-2015-108966 2015-10-11 2015-10-18  Standard Class    SO-20335   \n",
       "\n",
       "     Customer Name    Segment        Country             City  ... Region  \\\n",
       "0      Claire Gute   Consumer  United States        Henderson  ...  South   \n",
       "1      Claire Gute   Consumer  United States        Henderson  ...  South   \n",
       "2  Darrin Van Huff  Corporate  United States      Los Angeles  ...   West   \n",
       "3   Sean O'Donnell   Consumer  United States  Fort Lauderdale  ...  South   \n",
       "4   Sean O'Donnell   Consumer  United States  Fort Lauderdale  ...  South   \n",
       "\n",
       "        Product ID         Category Sub-Category  \\\n",
       "0  FUR-BO-10001798        Furniture    Bookcases   \n",
       "1  FUR-CH-10000454        Furniture       Chairs   \n",
       "2  OFF-LA-10000240  Office Supplies       Labels   \n",
       "3  FUR-TA-10000577        Furniture       Tables   \n",
       "4  OFF-ST-10000760  Office Supplies      Storage   \n",
       "\n",
       "                                        Product Name     Sales Quantity  \\\n",
       "0                  Bush Somerset Collection Bookcase  261.9600        2   \n",
       "1  Hon Deluxe Fabric Upholstered Stacking Chairs,...  731.9400        3   \n",
       "2  Self-Adhesive Address Labels for Typewriters b...   14.6200        2   \n",
       "3      Bretford CR4500 Series Slim Rectangular Table  957.5775        5   \n",
       "4                     Eldon Fold 'N Roll Cart System   22.3680        2   \n",
       "\n",
       "   Discount    Profit  Price/Unit  \n",
       "0      0.00   41.9136    130.9800  \n",
       "1      0.00  219.5820    243.9800  \n",
       "2      0.00    6.8714      7.3100  \n",
       "3      0.45 -383.0310    191.5155  \n",
       "4      0.20    2.5164     11.1840  \n",
       "\n",
       "[5 rows x 22 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "____________________\n",
    "\n",
    "## Finding out the monthly revenue and analyze the findings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creating column with year and month\n",
    "df['month_year'] = df['Order Date'].dt.to_period('M')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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>Sales</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>month_year</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2014 : April</th>\n",
       "      <td>28295.3450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014 : August</th>\n",
       "      <td>27909.4685</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014 : December</th>\n",
       "      <td>69545.6205</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014 : February</th>\n",
       "      <td>4519.8920</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014 : January</th>\n",
       "      <td>14236.8950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014 : July</th>\n",
       "      <td>33946.3930</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014 : June</th>\n",
       "      <td>34595.1276</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014 : March</th>\n",
       "      <td>55691.0090</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014 : May</th>\n",
       "      <td>23648.2870</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014 : November</th>\n",
       "      <td>78628.7167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014 : October</th>\n",
       "      <td>31453.3930</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014 : September</th>\n",
       "      <td>81777.3508</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015 : April</th>\n",
       "      <td>34195.2085</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015 : August</th>\n",
       "      <td>36898.3322</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015 : December</th>\n",
       "      <td>74919.5212</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015 : February</th>\n",
       "      <td>11951.4110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015 : January</th>\n",
       "      <td>18174.0756</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015 : July</th>\n",
       "      <td>28765.3250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015 : June</th>\n",
       "      <td>24797.2920</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015 : March</th>\n",
       "      <td>38726.2520</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015 : May</th>\n",
       "      <td>30131.6865</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015 : November</th>\n",
       "      <td>75972.5635</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015 : October</th>\n",
       "      <td>31404.9235</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015 : September</th>\n",
       "      <td>64595.9180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016 : April</th>\n",
       "      <td>38750.0390</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016 : August</th>\n",
       "      <td>31115.3743</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016 : December</th>\n",
       "      <td>96999.0430</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016 : February</th>\n",
       "      <td>22978.8150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016 : January</th>\n",
       "      <td>18542.4910</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016 : July</th>\n",
       "      <td>39261.9630</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016 : June</th>\n",
       "      <td>40344.5340</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016 : March</th>\n",
       "      <td>51715.8750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016 : May</th>\n",
       "      <td>56987.7280</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016 : November</th>\n",
       "      <td>79411.9658</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016 : October</th>\n",
       "      <td>59687.7450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016 : September</th>\n",
       "      <td>73410.0249</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017 : April</th>\n",
       "      <td>36521.5361</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017 : August</th>\n",
       "      <td>63120.8880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017 : December</th>\n",
       "      <td>83829.3188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017 : February</th>\n",
       "      <td>20301.1334</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017 : January</th>\n",
       "      <td>43971.3740</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017 : July</th>\n",
       "      <td>45264.4160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017 : June</th>\n",
       "      <td>52981.7257</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017 : March</th>\n",
       "      <td>58872.3528</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017 : May</th>\n",
       "      <td>44261.1102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017 : November</th>\n",
       "      <td>118447.8250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017 : October</th>\n",
       "      <td>77776.9232</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017 : September</th>\n",
       "      <td>87866.6520</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        Sales\n",
       "month_year                   \n",
       "2014 : April       28295.3450\n",
       "2014 : August      27909.4685\n",
       "2014 : December    69545.6205\n",
       "2014 : February     4519.8920\n",
       "2014 : January     14236.8950\n",
       "2014 : July        33946.3930\n",
       "2014 : June        34595.1276\n",
       "2014 : March       55691.0090\n",
       "2014 : May         23648.2870\n",
       "2014 : November    78628.7167\n",
       "2014 : October     31453.3930\n",
       "2014 : September   81777.3508\n",
       "2015 : April       34195.2085\n",
       "2015 : August      36898.3322\n",
       "2015 : December    74919.5212\n",
       "2015 : February    11951.4110\n",
       "2015 : January     18174.0756\n",
       "2015 : July        28765.3250\n",
       "2015 : June        24797.2920\n",
       "2015 : March       38726.2520\n",
       "2015 : May         30131.6865\n",
       "2015 : November    75972.5635\n",
       "2015 : October     31404.9235\n",
       "2015 : September   64595.9180\n",
       "2016 : April       38750.0390\n",
       "2016 : August      31115.3743\n",
       "2016 : December    96999.0430\n",
       "2016 : February    22978.8150\n",
       "2016 : January     18542.4910\n",
       "2016 : July        39261.9630\n",
       "2016 : June        40344.5340\n",
       "2016 : March       51715.8750\n",
       "2016 : May         56987.7280\n",
       "2016 : November    79411.9658\n",
       "2016 : October     59687.7450\n",
       "2016 : September   73410.0249\n",
       "2017 : April       36521.5361\n",
       "2017 : August      63120.8880\n",
       "2017 : December    83829.3188\n",
       "2017 : February    20301.1334\n",
       "2017 : January     43971.3740\n",
       "2017 : July        45264.4160\n",
       "2017 : June        52981.7257\n",
       "2017 : March       58872.3528\n",
       "2017 : May         44261.1102\n",
       "2017 : November   118447.8250\n",
       "2017 : October     77776.9232\n",
       "2017 : September   87866.6520"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Creating new dataframe add adding specific columns \n",
    "monthly_revenue = pd.DataFrame(df.groupby(df['month_year'].dt.strftime('%Y : %B'))['Sales'].sum())\n",
    "monthly_revenue"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting the dataset\n",
    "plt.title('Monthly Revenue')\n",
    "plt.xlabel('Month Name')\n",
    "plt.ylabel('Sales')\n",
    "plt.plot(monthly_revenue, 'r')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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>Sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>48.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>47858.351256</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>25195.890700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>4519.892000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>29790.096125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>39803.248500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>65833.343625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>118447.825000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Sales\n",
       "count      48.000000\n",
       "mean    47858.351256\n",
       "std     25195.890700\n",
       "min      4519.892000\n",
       "25%     29790.096125\n",
       "50%     39803.248500\n",
       "75%     65833.343625\n",
       "max    118447.825000"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Describing the dataset\n",
    "mothly_revenue.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__________________________________\n",
    "\n",
    "## Finding out the yearly revenue and analyze the findings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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>Sales</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>month_year</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2014</th>\n",
       "      <td>484247.4981</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015</th>\n",
       "      <td>470532.5090</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016</th>\n",
       "      <td>609205.5980</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017</th>\n",
       "      <td>733215.2552</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Sales\n",
       "month_year             \n",
       "2014        484247.4981\n",
       "2015        470532.5090\n",
       "2016        609205.5980\n",
       "2017        733215.2552"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Creating new dataframe yearly\n",
    "yearly_revenue = pd.DataFrame(df.groupby(df['month_year'].dt.strftime('%Y'))['Sales'].sum())\n",
    "yearly_revenue"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting the dataframe\n",
    "plt.title('Yearly Revenue')\n",
    "plt.xlabel('Year')\n",
    "plt.ylabel('Sales')\n",
    "plt.plot(yearly_revenue, 'g')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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>Sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>574300.215075</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>122949.318005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>470532.509000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>480818.750825</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>546726.548050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>640208.012300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>733215.255200</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Sales\n",
       "count       4.000000\n",
       "mean   574300.215075\n",
       "std    122949.318005\n",
       "min    470532.509000\n",
       "25%    480818.750825\n",
       "50%    546726.548050\n",
       "75%    640208.012300\n",
       "max    733215.255200"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Describing the dataset\n",
    "yearly_revenue.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "_______________________________\n",
    "\n",
    "## Finding out the monthly growth rate and analyse the findings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Sales</th>\n",
       "      <th>Monthly Growth Rate</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>month_year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2014 : April</th>\n",
       "      <td>28295.3450</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014 : August</th>\n",
       "      <td>27909.4685</td>\n",
       "      <td>-1.363746</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014 : December</th>\n",
       "      <td>69545.6205</td>\n",
       "      <td>149.182891</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014 : February</th>\n",
       "      <td>4519.8920</td>\n",
       "      <td>-93.500824</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014 : January</th>\n",
       "      <td>14236.8950</td>\n",
       "      <td>214.983079</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014 : July</th>\n",
       "      <td>33946.3930</td>\n",
       "      <td>138.439583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014 : June</th>\n",
       "      <td>34595.1276</td>\n",
       "      <td>1.911056</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014 : March</th>\n",
       "      <td>55691.0090</td>\n",
       "      <td>60.979343</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014 : May</th>\n",
       "      <td>23648.2870</td>\n",
       "      <td>-57.536616</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014 : November</th>\n",
       "      <td>78628.7167</td>\n",
       "      <td>232.492230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014 : October</th>\n",
       "      <td>31453.3930</td>\n",
       "      <td>-59.997576</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014 : September</th>\n",
       "      <td>81777.3508</td>\n",
       "      <td>159.995323</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015 : April</th>\n",
       "      <td>34195.2085</td>\n",
       "      <td>-58.184989</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015 : August</th>\n",
       "      <td>36898.3322</td>\n",
       "      <td>7.904978</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015 : December</th>\n",
       "      <td>74919.5212</td>\n",
       "      <td>103.043110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015 : February</th>\n",
       "      <td>11951.4110</td>\n",
       "      <td>-84.047668</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015 : January</th>\n",
       "      <td>18174.0756</td>\n",
       "      <td>52.066359</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015 : July</th>\n",
       "      <td>28765.3250</td>\n",
       "      <td>58.276688</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015 : June</th>\n",
       "      <td>24797.2920</td>\n",
       "      <td>-13.794501</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015 : March</th>\n",
       "      <td>38726.2520</td>\n",
       "      <td>56.171295</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015 : May</th>\n",
       "      <td>30131.6865</td>\n",
       "      <td>-22.193125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015 : November</th>\n",
       "      <td>75972.5635</td>\n",
       "      <td>152.135119</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015 : October</th>\n",
       "      <td>31404.9235</td>\n",
       "      <td>-58.662809</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015 : September</th>\n",
       "      <td>64595.9180</td>\n",
       "      <td>105.687232</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016 : April</th>\n",
       "      <td>38750.0390</td>\n",
       "      <td>-40.011629</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016 : August</th>\n",
       "      <td>31115.3743</td>\n",
       "      <td>-19.702341</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016 : December</th>\n",
       "      <td>96999.0430</td>\n",
       "      <td>211.739920</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016 : February</th>\n",
       "      <td>22978.8150</td>\n",
       "      <td>-76.310266</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016 : January</th>\n",
       "      <td>18542.4910</td>\n",
       "      <td>-19.306148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016 : July</th>\n",
       "      <td>39261.9630</td>\n",
       "      <td>111.740499</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016 : June</th>\n",
       "      <td>40344.5340</td>\n",
       "      <td>2.757302</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016 : March</th>\n",
       "      <td>51715.8750</td>\n",
       "      <td>28.185580</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016 : May</th>\n",
       "      <td>56987.7280</td>\n",
       "      <td>10.193878</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016 : November</th>\n",
       "      <td>79411.9658</td>\n",
       "      <td>39.349240</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016 : October</th>\n",
       "      <td>59687.7450</td>\n",
       "      <td>-24.837845</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016 : September</th>\n",
       "      <td>73410.0249</td>\n",
       "      <td>22.990113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017 : April</th>\n",
       "      <td>36521.5361</td>\n",
       "      <td>-50.249934</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017 : August</th>\n",
       "      <td>63120.8880</td>\n",
       "      <td>72.831964</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017 : December</th>\n",
       "      <td>83829.3188</td>\n",
       "      <td>32.807572</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017 : February</th>\n",
       "      <td>20301.1334</td>\n",
       "      <td>-75.782777</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017 : January</th>\n",
       "      <td>43971.3740</td>\n",
       "      <td>116.595661</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017 : July</th>\n",
       "      <td>45264.4160</td>\n",
       "      <td>2.940645</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017 : June</th>\n",
       "      <td>52981.7257</td>\n",
       "      <td>17.049396</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017 : March</th>\n",
       "      <td>58872.3528</td>\n",
       "      <td>11.118224</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017 : May</th>\n",
       "      <td>44261.1102</td>\n",
       "      <td>-24.818513</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017 : November</th>\n",
       "      <td>118447.8250</td>\n",
       "      <td>167.611509</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017 : October</th>\n",
       "      <td>77776.9232</td>\n",
       "      <td>-34.336554</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017 : September</th>\n",
       "      <td>87866.6520</td>\n",
       "      <td>12.972651</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        Sales  Monthly Growth Rate\n",
       "month_year                                        \n",
       "2014 : April       28295.3450                  NaN\n",
       "2014 : August      27909.4685            -1.363746\n",
       "2014 : December    69545.6205           149.182891\n",
       "2014 : February     4519.8920           -93.500824\n",
       "2014 : January     14236.8950           214.983079\n",
       "2014 : July        33946.3930           138.439583\n",
       "2014 : June        34595.1276             1.911056\n",
       "2014 : March       55691.0090            60.979343\n",
       "2014 : May         23648.2870           -57.536616\n",
       "2014 : November    78628.7167           232.492230\n",
       "2014 : October     31453.3930           -59.997576\n",
       "2014 : September   81777.3508           159.995323\n",
       "2015 : April       34195.2085           -58.184989\n",
       "2015 : August      36898.3322             7.904978\n",
       "2015 : December    74919.5212           103.043110\n",
       "2015 : February    11951.4110           -84.047668\n",
       "2015 : January     18174.0756            52.066359\n",
       "2015 : July        28765.3250            58.276688\n",
       "2015 : June        24797.2920           -13.794501\n",
       "2015 : March       38726.2520            56.171295\n",
       "2015 : May         30131.6865           -22.193125\n",
       "2015 : November    75972.5635           152.135119\n",
       "2015 : October     31404.9235           -58.662809\n",
       "2015 : September   64595.9180           105.687232\n",
       "2016 : April       38750.0390           -40.011629\n",
       "2016 : August      31115.3743           -19.702341\n",
       "2016 : December    96999.0430           211.739920\n",
       "2016 : February    22978.8150           -76.310266\n",
       "2016 : January     18542.4910           -19.306148\n",
       "2016 : July        39261.9630           111.740499\n",
       "2016 : June        40344.5340             2.757302\n",
       "2016 : March       51715.8750            28.185580\n",
       "2016 : May         56987.7280            10.193878\n",
       "2016 : November    79411.9658            39.349240\n",
       "2016 : October     59687.7450           -24.837845\n",
       "2016 : September   73410.0249            22.990113\n",
       "2017 : April       36521.5361           -50.249934\n",
       "2017 : August      63120.8880            72.831964\n",
       "2017 : December    83829.3188            32.807572\n",
       "2017 : February    20301.1334           -75.782777\n",
       "2017 : January     43971.3740           116.595661\n",
       "2017 : July        45264.4160             2.940645\n",
       "2017 : June        52981.7257            17.049396\n",
       "2017 : March       58872.3528            11.118224\n",
       "2017 : May         44261.1102           -24.818513\n",
       "2017 : November   118447.8250           167.611509\n",
       "2017 : October     77776.9232           -34.336554\n",
       "2017 : September   87866.6520            12.972651"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Finding monthly growth rate with the formula\n",
    "mothly_revenue[\"Monthly Growth Rate\"]=(mothly_revenue['Sales']-mothly_revenue['Sales'].shift())/mothly_revenue['Sales'].shift()*100\n",
    "mothly_revenue"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "232.4922295640271\n"
     ]
    }
   ],
   "source": [
    "# Finding Maximum of the dataset\n",
    "print(np.max(mothly_revenue['Monthly Growth Rate']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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>Sales</th>\n",
       "      <th>Monthly Growth Rate</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>month_year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2014 : November</th>\n",
       "      <td>78628.7167</td>\n",
       "      <td>232.49223</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      Sales  Monthly Growth Rate\n",
       "month_year                                      \n",
       "2014 : November  78628.7167            232.49223"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Finding the coorosponding month\n",
    "mothly_revenue.loc[mothly_revenue['Monthly Growth Rate'] == 232.4922295640271]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x225d011b1f0>,\n",
       " <matplotlib.lines.Line2D at 0x225d011b100>]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting the graph\n",
    "plt.title('Monthly Growth Rate')\n",
    "plt.xlabel('Month')\n",
    "plt.ylabel('Growth Rate')\n",
    "plt.plot(mothly_revenue)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "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>Sales</th>\n",
       "      <th>Monthly Growth Rate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>48.000000</td>\n",
       "      <td>47.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>47858.351256</td>\n",
       "      <td>32.755629</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>25195.890700</td>\n",
       "      <td>85.070021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>4519.892000</td>\n",
       "      <td>-93.500824</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>29790.096125</td>\n",
       "      <td>-24.828179</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>39803.248500</td>\n",
       "      <td>11.118224</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>65833.343625</td>\n",
       "      <td>87.937537</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>118447.825000</td>\n",
       "      <td>232.492230</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Sales  Monthly Growth Rate\n",
       "count      48.000000            47.000000\n",
       "mean    47858.351256            32.755629\n",
       "std     25195.890700            85.070021\n",
       "min      4519.892000           -93.500824\n",
       "25%     29790.096125           -24.828179\n",
       "50%     39803.248500            11.118224\n",
       "75%     65833.343625            87.937537\n",
       "max    118447.825000           232.492230"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Describing the dataset \n",
    "mothly_revenue.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "_______________________\n",
    "\n",
    "## Finding out the most and least sold product id"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "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>Quantity</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Product ID</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>FUR-BO-10000112</th>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FUR-BO-10000330</th>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FUR-BO-10000362</th>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FUR-BO-10000468</th>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FUR-BO-10000711</th>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TEC-PH-10004912</th>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TEC-PH-10004922</th>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TEC-PH-10004924</th>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TEC-PH-10004959</th>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TEC-PH-10004977</th>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1862 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Quantity\n",
       "Product ID               \n",
       "FUR-BO-10000112         9\n",
       "FUR-BO-10000330        10\n",
       "FUR-BO-10000362        14\n",
       "FUR-BO-10000468        21\n",
       "FUR-BO-10000711        12\n",
       "...                   ...\n",
       "TEC-PH-10004912        11\n",
       "TEC-PH-10004922        19\n",
       "TEC-PH-10004924         8\n",
       "TEC-PH-10004959         4\n",
       "TEC-PH-10004977        32\n",
       "\n",
       "[1862 rows x 1 columns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Creating a new dataset\n",
    "product_id_df = pd.DataFrame(df.groupby(df[\"Product ID\"])[\"Quantity\"].sum())\n",
    "product_id_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "75\n"
     ]
    }
   ],
   "source": [
    "# Finding the most value\n",
    "print(np.max(product_id_df[\"Quantity\"]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "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>Quantity</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Product ID</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>TEC-AC-10003832</th>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Quantity\n",
       "Product ID               \n",
       "TEC-AC-10003832        75"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Finding the corrosponding value\n",
    "product_id_df.loc[product_id_df['Quantity'] == 75]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__________________________\n",
    "\n",
    "## Finding out the customer who bought most and least from us in terms of quantity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "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>Quantity</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Customer Name</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Aaron Bergman</th>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Aaron Hawkins</th>\n",
       "      <td>54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Aaron Smayling</th>\n",
       "      <td>48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Adam Bellavance</th>\n",
       "      <td>56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Adam Hart</th>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Xylona Preis</th>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yana Sorensen</th>\n",
       "      <td>58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yoseph Carroll</th>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Zuschuss Carroll</th>\n",
       "      <td>105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Zuschuss Donatelli</th>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>793 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                    Quantity\n",
       "Customer Name               \n",
       "Aaron Bergman             13\n",
       "Aaron Hawkins             54\n",
       "Aaron Smayling            48\n",
       "Adam Bellavance           56\n",
       "Adam Hart                 75\n",
       "...                      ...\n",
       "Xylona Preis             100\n",
       "Yana Sorensen             58\n",
       "Yoseph Carroll            31\n",
       "Zuschuss Carroll         105\n",
       "Zuschuss Donatelli        32\n",
       "\n",
       "[793 rows x 1 columns]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Creating a dataset\n",
    "customer_name_df = pd.DataFrame(df.groupby(df[\"Customer Name\"])[\"Quantity\"].sum())\n",
    "customer_name_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "150\n"
     ]
    }
   ],
   "source": [
    "# Finding the max value\n",
    "print(np.max(customer_name_df[\"Quantity\"]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "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>Quantity</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Customer Name</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Jonathan Doherty</th>\n",
       "      <td>150</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Quantity\n",
       "Customer Name             \n",
       "Jonathan Doherty       150"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "customer_name_df.loc[customer_name_df['Quantity'] == 150]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "________________________________\n",
    "\n",
    "## Finding out the customer who bought most and least from us in terms of value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "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>Sales</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Customer Name</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Aaron Bergman</th>\n",
       "      <td>886.156</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Aaron Hawkins</th>\n",
       "      <td>1744.700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Aaron Smayling</th>\n",
       "      <td>3050.692</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Adam Bellavance</th>\n",
       "      <td>7755.620</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Adam Hart</th>\n",
       "      <td>3250.337</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Xylona Preis</th>\n",
       "      <td>2374.658</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yana Sorensen</th>\n",
       "      <td>6720.444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yoseph Carroll</th>\n",
       "      <td>5454.350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Zuschuss Carroll</th>\n",
       "      <td>8025.707</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Zuschuss Donatelli</th>\n",
       "      <td>1493.944</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>793 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                       Sales\n",
       "Customer Name               \n",
       "Aaron Bergman        886.156\n",
       "Aaron Hawkins       1744.700\n",
       "Aaron Smayling      3050.692\n",
       "Adam Bellavance     7755.620\n",
       "Adam Hart           3250.337\n",
       "...                      ...\n",
       "Xylona Preis        2374.658\n",
       "Yana Sorensen       6720.444\n",
       "Yoseph Carroll      5454.350\n",
       "Zuschuss Carroll    8025.707\n",
       "Zuschuss Donatelli  1493.944\n",
       "\n",
       "[793 rows x 1 columns]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Creating dataset\n",
    "customer_by_value = pd.DataFrame(df.groupby(df[\"Customer Name\"])[\"Sales\"].sum())\n",
    "customer_by_value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "25043.05\n"
     ]
    }
   ],
   "source": [
    "# Finding max value\n",
    "print(np.max(customer_by_value[\"Sales\"]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "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>Sales</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Customer Name</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Sean Miller</th>\n",
       "      <td>25043.05</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Sales\n",
       "Customer Name          \n",
       "Sean Miller    25043.05"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Finding the value corrosponding to above value\n",
    "customer_by_value.loc[customer_by_value['Sales'] == 25043.05]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "________________\n",
    "\n",
    "## Finding out the majority and minority customer cities on basis of "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Number of customers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "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>Customer Count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>City</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Aberdeen</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Abilene</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Akron</th>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Albuquerque</th>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Alexandria</th>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Woonsocket</th>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yonkers</th>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>York</th>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yucaipa</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yuma</th>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>531 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             Customer Count\n",
       "City                       \n",
       "Aberdeen                  1\n",
       "Abilene                   1\n",
       "Akron                    21\n",
       "Albuquerque              14\n",
       "Alexandria               16\n",
       "...                     ...\n",
       "Woonsocket                4\n",
       "Yonkers                  15\n",
       "York                      5\n",
       "Yucaipa                   1\n",
       "Yuma                      4\n",
       "\n",
       "[531 rows x 1 columns]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Creating the dataframe by counting the customer\n",
    "city_customer_group=df.groupby(\"City\").count()['Customer Name']\n",
    "city_customer_group = pd.DataFrame(city_customer_group)\n",
    "city_customer_group['Customer Count'] = city_customer_group['Customer Name']\n",
    "city_customer_group.drop('Customer Name',  axis='columns', inplace=True)\n",
    "city_customer_group"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "915\n"
     ]
    }
   ],
   "source": [
    "# Finding max value\n",
    "print(np.max(city_customer_group[\"Customer Count\"]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "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>Customer Count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>City</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>New York City</th>\n",
       "      <td>915</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Customer Count\n",
       "City                         \n",
       "New York City             915"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Finding the value corrosponding to above value\n",
    "city_customer_group.loc[city_customer_group['Customer Count'] == 915]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n"
     ]
    }
   ],
   "source": [
    "# Finding max value\n",
    "print(np.min(city_customer_group[\"Customer Count\"]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "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",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Customer Count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>City</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Aberdeen</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Abilene</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Antioch</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Arlington Heights</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Atlantic City</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Vacaville</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Waterloo</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Waukesha</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Whittier</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yucaipa</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>70 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Customer Count\n",
       "City                             \n",
       "Aberdeen                        1\n",
       "Abilene                         1\n",
       "Antioch                         1\n",
       "Arlington Heights               1\n",
       "Atlantic City                   1\n",
       "...                           ...\n",
       "Vacaville                       1\n",
       "Waterloo                        1\n",
       "Waukesha                        1\n",
       "Whittier                        1\n",
       "Yucaipa                         1\n",
       "\n",
       "[70 rows x 1 columns]"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Finding the value corrosponding to above value\n",
    "city_customer_group.loc[city_customer_group['Customer Count'] == 1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Sales "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "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>Sales</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>City</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Aberdeen</th>\n",
       "      <td>25.500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Abilene</th>\n",
       "      <td>1.392</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Akron</th>\n",
       "      <td>2729.986</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Albuquerque</th>\n",
       "      <td>2220.160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Alexandria</th>\n",
       "      <td>5519.570</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Woonsocket</th>\n",
       "      <td>195.550</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yonkers</th>\n",
       "      <td>7657.666</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>York</th>\n",
       "      <td>817.978</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yucaipa</th>\n",
       "      <td>50.800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yuma</th>\n",
       "      <td>840.865</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>531 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                Sales\n",
       "City                 \n",
       "Aberdeen       25.500\n",
       "Abilene         1.392\n",
       "Akron        2729.986\n",
       "Albuquerque  2220.160\n",
       "Alexandria   5519.570\n",
       "...               ...\n",
       "Woonsocket    195.550\n",
       "Yonkers      7657.666\n",
       "York          817.978\n",
       "Yucaipa        50.800\n",
       "Yuma          840.865\n",
       "\n",
       "[531 rows x 1 columns]"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Creating dataframe\n",
    "city_by_sale = pd.DataFrame(df.groupby(df[\"City\"])[\"Sales\"].sum())\n",
    "city_by_sale"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "256368.161\n"
     ]
    }
   ],
   "source": [
    "# Finding max value\n",
    "print(np.max(city_by_sale[\"Sales\"]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "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>Sales</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>City</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>New York City</th>\n",
       "      <td>256368.161</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    Sales\n",
       "City                     \n",
       "New York City  256368.161"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Finding the value corrosponding to above value\n",
    "city_by_sale.loc[city_by_sale['Sales'] == 256368.161]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.3919999999999997\n"
     ]
    }
   ],
   "source": [
    "# Finding min value\n",
    "print(np.min(city_by_sale[\"Sales\"]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "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>Sales</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>City</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Abilene</th>\n",
       "      <td>1.392</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Sales\n",
       "City          \n",
       "Abilene  1.392"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Finding the value corrosponding to above value\n",
    "city_by_sale.loc[city_by_sale['Sales'] == 1.3919999999999997]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Number of Quantity Sold"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "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>Quantity</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>City</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Aberdeen</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Abilene</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Akron</th>\n",
       "      <td>65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Albuquerque</th>\n",
       "      <td>65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Alexandria</th>\n",
       "      <td>84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Woonsocket</th>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yonkers</th>\n",
       "      <td>57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>York</th>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yucaipa</th>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yuma</th>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>531 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             Quantity\n",
       "City                 \n",
       "Aberdeen            3\n",
       "Abilene             2\n",
       "Akron              65\n",
       "Albuquerque        65\n",
       "Alexandria         84\n",
       "...               ...\n",
       "Woonsocket         15\n",
       "Yonkers            57\n",
       "York               19\n",
       "Yucaipa             5\n",
       "Yuma               22\n",
       "\n",
       "[531 rows x 1 columns]"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Creating dataframe\n",
    "city_by_quantity = pd.DataFrame(df.groupby(df[\"City\"])[\"Quantity\"].sum())\n",
    "city_by_quantity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3417\n"
     ]
    }
   ],
   "source": [
    "# Finding max value\n",
    "print(np.max(city_by_quantity[\"Quantity\"]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "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>Quantity</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>City</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>New York City</th>\n",
       "      <td>3417</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Quantity\n",
       "City                   \n",
       "New York City      3417"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Finding the value corrosponding to above value\n",
    "city_by_quantity.loc[city_by_quantity['Quantity'] == 3417]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n"
     ]
    }
   ],
   "source": [
    "# Finding min value\n",
    "print(np.min(city_by_quantity[\"Quantity\"]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "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>Quantity</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>City</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Elyria</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Iowa City</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jupiter</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lindenhurst</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Littleton</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Port Orange</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Quantity\n",
       "City                 \n",
       "Elyria              1\n",
       "Iowa City           1\n",
       "Jupiter             1\n",
       "Lindenhurst         1\n",
       "Littleton           1\n",
       "Port Orange         1"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Finding the value corrosponding to above value\n",
    "city_by_quantity.loc[city_by_quantity['Quantity'] == 1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "________________________\n",
    "\n",
    "## Find out the most and least sold product category from the store"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Quantity based"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "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>Quantity</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Category</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Furniture</th>\n",
       "      <td>8028</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Office Supplies</th>\n",
       "      <td>22906</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Technology</th>\n",
       "      <td>6939</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Quantity\n",
       "Category                 \n",
       "Furniture            8028\n",
       "Office Supplies     22906\n",
       "Technology           6939"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Creating the dataframe\n",
    "product_category_by_quantity = pd.DataFrame(df.groupby(df[\"Category\"])[\"Quantity\"].sum())\n",
    "product_category_by_quantity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "22906\n"
     ]
    }
   ],
   "source": [
    "# Finding min value\n",
    "print(np.max(product_category_by_quantity[\"Quantity\"]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "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>Quantity</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Category</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Office Supplies</th>\n",
       "      <td>22906</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Quantity\n",
       "Category                 \n",
       "Office Supplies     22906"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Finding the value corrosponding to above value\n",
    "product_category_by_quantity.loc[product_category_by_quantity['Quantity'] == 22906]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6939\n"
     ]
    }
   ],
   "source": [
    "# Finding min value\n",
    "print(np.min(product_category_by_quantity[\"Quantity\"]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "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>Quantity</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Category</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Technology</th>\n",
       "      <td>6939</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Quantity\n",
       "Category            \n",
       "Technology      6939"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Finding the value corrosponding to above value\n",
    "product_category_by_quantity.loc[product_category_by_quantity['Quantity'] == 6939]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Value based"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "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>Sales</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Category</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Furniture</th>\n",
       "      <td>741999.7953</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Office Supplies</th>\n",
       "      <td>719047.0320</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Technology</th>\n",
       "      <td>836154.0330</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       Sales\n",
       "Category                    \n",
       "Furniture        741999.7953\n",
       "Office Supplies  719047.0320\n",
       "Technology       836154.0330"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Creating Dataframe\n",
    "product_category_by_value = pd.DataFrame(df.groupby(df[\"Category\"])[\"Sales\"].sum())\n",
    "product_category_by_value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "836154.0329999966\n"
     ]
    }
   ],
   "source": [
    "# Finding max value\n",
    "print(np.max(product_category_by_value[\"Sales\"]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "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>Sales</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Category</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Technology</th>\n",
       "      <td>836154.033</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Sales\n",
       "Category              \n",
       "Technology  836154.033"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Finding the value corrosponding to above value\n",
    "product_category_by_value.loc[product_category_by_value['Sales'] == 836154.0329999966]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "719047.0320000029\n"
     ]
    }
   ],
   "source": [
    "# Finding min value\n",
    "print(np.min(product_category_by_value[\"Sales\"]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "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>Sales</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Category</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Office Supplies</th>\n",
       "      <td>719047.032</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      Sales\n",
       "Category                   \n",
       "Office Supplies  719047.032"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Finding the value corrosponding to above value\n",
    "product_category_by_value.loc[product_category_by_value['Sales'] == 719047.0320000029]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "______________________________________\n",
    "\n",
    "## Find out the most and least sold product sub category from the store"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### By Quantity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "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>Quantity</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sub-Category</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Accessories</th>\n",
       "      <td>2976</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Appliances</th>\n",
       "      <td>1729</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Art</th>\n",
       "      <td>3000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Binders</th>\n",
       "      <td>5974</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bookcases</th>\n",
       "      <td>868</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chairs</th>\n",
       "      <td>2356</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Copiers</th>\n",
       "      <td>234</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Envelopes</th>\n",
       "      <td>906</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fasteners</th>\n",
       "      <td>914</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Furnishings</th>\n",
       "      <td>3563</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Labels</th>\n",
       "      <td>1400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Machines</th>\n",
       "      <td>440</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Paper</th>\n",
       "      <td>5178</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Phones</th>\n",
       "      <td>3289</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Storage</th>\n",
       "      <td>3158</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Supplies</th>\n",
       "      <td>647</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tables</th>\n",
       "      <td>1241</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Quantity\n",
       "Sub-Category          \n",
       "Accessories       2976\n",
       "Appliances        1729\n",
       "Art               3000\n",
       "Binders           5974\n",
       "Bookcases          868\n",
       "Chairs            2356\n",
       "Copiers            234\n",
       "Envelopes          906\n",
       "Fasteners          914\n",
       "Furnishings       3563\n",
       "Labels            1400\n",
       "Machines           440\n",
       "Paper             5178\n",
       "Phones            3289\n",
       "Storage           3158\n",
       "Supplies           647\n",
       "Tables            1241"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Creating Dataframe\n",
    "subcat_by_quantity = pd.DataFrame(df.groupby(df[\"Sub-Category\"])[\"Quantity\"].sum())\n",
    "subcat_by_quantity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5974\n"
     ]
    }
   ],
   "source": [
    "# Finding max value\n",
    "print(np.max(subcat_by_quantity[\"Quantity\"]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "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>Quantity</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sub-Category</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Binders</th>\n",
       "      <td>5974</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Quantity\n",
       "Sub-Category          \n",
       "Binders           5974"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Finding the value corrosponding to above value\n",
    "subcat_by_quantity.loc[subcat_by_quantity['Quantity'] == 5974]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "234\n"
     ]
    }
   ],
   "source": [
    "# Finding min value\n",
    "print(np.min(subcat_by_quantity[\"Quantity\"]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "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>Quantity</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sub-Category</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Copiers</th>\n",
       "      <td>234</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Quantity\n",
       "Sub-Category          \n",
       "Copiers            234"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Finding the value corrosponding to above value\n",
    "subcat_by_quantity.loc[subcat_by_quantity['Quantity'] == 234]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### By Value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "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>Sales</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sub-Category</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Accessories</th>\n",
       "      <td>167380.3180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Appliances</th>\n",
       "      <td>107532.1610</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Art</th>\n",
       "      <td>27118.7920</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Binders</th>\n",
       "      <td>203412.7330</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bookcases</th>\n",
       "      <td>114879.9963</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chairs</th>\n",
       "      <td>328449.1030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Copiers</th>\n",
       "      <td>149528.0300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Envelopes</th>\n",
       "      <td>16476.4020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fasteners</th>\n",
       "      <td>3024.2800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Furnishings</th>\n",
       "      <td>91705.1640</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Labels</th>\n",
       "      <td>12486.3120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Machines</th>\n",
       "      <td>189238.6310</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Paper</th>\n",
       "      <td>78479.2060</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Phones</th>\n",
       "      <td>330007.0540</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Storage</th>\n",
       "      <td>223843.6080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Supplies</th>\n",
       "      <td>46673.5380</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tables</th>\n",
       "      <td>206965.5320</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    Sales\n",
       "Sub-Category             \n",
       "Accessories   167380.3180\n",
       "Appliances    107532.1610\n",
       "Art            27118.7920\n",
       "Binders       203412.7330\n",
       "Bookcases     114879.9963\n",
       "Chairs        328449.1030\n",
       "Copiers       149528.0300\n",
       "Envelopes      16476.4020\n",
       "Fasteners       3024.2800\n",
       "Furnishings    91705.1640\n",
       "Labels         12486.3120\n",
       "Machines      189238.6310\n",
       "Paper          78479.2060\n",
       "Phones        330007.0540\n",
       "Storage       223843.6080\n",
       "Supplies       46673.5380\n",
       "Tables        206965.5320"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Creating Dataframe\n",
    "subcat_by_value = pd.DataFrame(df.groupby(df[\"Sub-Category\"])[\"Sales\"].sum())\n",
    "subcat_by_value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "330007.0540000001\n"
     ]
    }
   ],
   "source": [
    "# Finding max value\n",
    "print(np.max(subcat_by_value[\"Sales\"]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "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>Sales</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sub-Category</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Phones</th>\n",
       "      <td>330007.054</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Sales\n",
       "Sub-Category            \n",
       "Phones        330007.054"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Finding the value corrosponding to above value\n",
    "subcat_by_value.loc[subcat_by_value['Sales'] == 330007.0540000001]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3024.2799999999997\n"
     ]
    }
   ],
   "source": [
    "# Finding min value\n",
    "print(np.min(subcat_by_value[\"Sales\"]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "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>Sales</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sub-Category</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Fasteners</th>\n",
       "      <td>3024.28</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Sales\n",
       "Sub-Category         \n",
       "Fasteners     3024.28"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Finding the value corrosponding to above value\n",
    "subcat_by_value.loc[subcat_by_value['Sales'] == 3024.2799999999997]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr>"
   ]
  }
 ],
 "metadata": {
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