intro-to-pandas-1-Data-Structures.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### AI-INVASION KADUNA DAY3\n",
    "\n",
    "### BASIC INTRODUCTION  PANDAS BY RISING ODEGUA\n",
    "\n",
    "\n",
    "[Pandas](http://pandas.pydata.org/) is an open source [Python](http://www.python.org/) library for data analysis. Python has always been great for prepping and munging data, but historically it has not been great for analysis - you'd usually end up using [R](http://www.r-project.org/) or loading it into a database and using SQL (or worse, Excel). pandas makes Python great for analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Structures\n",
    "pandas introduces two new data structures to Python - [Series](http://pandas.pydata.org/pandas-docs/dev/dsintro.html#series) and [DataFrame](http://pandas.pydata.org/pandas-docs/dev/dsintro.html#dataframe), both of which are built on top of [NumPy](http://www.numpy.org/) (this means it's fast)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Series"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A Series is a one-dimensional object similar to an array, list, or column in a table. It will assign a labeled index to each item in the Series. By default, each item will receive an index label from 0 to N, where N is the length of the Series minus one."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       1\n",
       "1       2\n",
       "2     one\n",
       "3      20\n",
       "4    30.7\n",
       "dtype: object"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# create a Series with an arbitrary list\n",
    "s = pd.Series([1, 2.0, \"one\", 20, 30.7])\n",
    "s"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Alternatively, you can specify an index to use when creating the Series."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a                7\n",
       "b          Nigeria\n",
       "c             3.14\n",
       "d      -1789710578\n",
       "e    Happy Eating!\n",
       "dtype: object"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s = pd.Series([7, ' Nigeria', 3.14, -1789710578, 'Happy Eating!'], index=[\"a\", \"b\", \"c\", \"d\", \"e\"])\n",
    "s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Happy Eating!'"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s['e']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The Series constructor can convert a dictonary as well, using the keys of the dictionary as its index."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Chicago          1000.0\n",
       "New York         1300.0\n",
       "Portland          900.0\n",
       "San Francisco    1100.0\n",
       "Austin            450.0\n",
       "Boston              NaN\n",
       "dtype: float64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d = {'Chicago': 1000, \n",
    "     'New York': 1300,\n",
    "     'Portland': 900,\n",
    "     'San Francisco': 1100,\n",
    "     'Austin': 450,\n",
    "     'Boston': None}\n",
    "\n",
    "cities = pd.Series(d)\n",
    "cities"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can use the index to select specific items from the Series ..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "450.0"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cities['Austin']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Chicago     1000.0\n",
       "Portland     900.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cities[['Chicago', 'Portland']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Or you can use boolean indexing for selection."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "New York         1300.0\n",
       "San Francisco    1100.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "large_cities = cities[cities > 1000]\n",
    "large_cities"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "That last one might be a little weird, so let's make it more clear - `cities < 1000` returns a Series of True/False values, which we then pass to our Series `cities`, returning the corresponding True items."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Chicago          False\n",
      "New York         False\n",
      "Portland          True\n",
      "San Francisco    False\n",
      "Austin            True\n",
      "Boston           False\n",
      "dtype: bool\n",
      "\n",
      "\n",
      "Portland    900.0\n",
      "Austin      450.0\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "less_than_1000 = cities < 1000\n",
    "print(less_than_1000)\n",
    "print('\\n')\n",
    "print(cities[less_than_1000])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can also change the values in a Series on the fly."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Old value: 1000.0\n"
     ]
    }
   ],
   "source": [
    "# changing based on the index\n",
    "print('Old value:', cities['Chicago'])\n",
    "cities['Chicago'] = 1500"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Chicago          1500.0\n",
       "New York         1300.0\n",
       "Portland          900.0\n",
       "San Francisco    1100.0\n",
       "Austin            450.0\n",
       "Boston              NaN\n",
       "dtype: float64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cities"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Chicago          1400.0\n",
       "New York         1300.0\n",
       "Portland          900.0\n",
       "San Francisco    1100.0\n",
       "Austin            450.0\n",
       "Boston              NaN\n",
       "dtype: float64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cities"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Portland    900.0\n",
      "Austin      450.0\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# changing values using boolean logic\n",
    "print(cities[cities < 1000])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "cities[cities < 1000] = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Chicago          1500.0\n",
       "New York         1300.0\n",
       "Portland            0.0\n",
       "San Francisco    1100.0\n",
       "Austin              0.0\n",
       "Boston              NaN\n",
       "dtype: float64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cities"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What if you aren't sure whether an item is in the Series?  You can check using idiomatic Python."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'Boston' in cities"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "False\n",
      "True\n"
     ]
    }
   ],
   "source": [
    "print('Seattle' in cities)\n",
    "print('San Francisco' in cities)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Mathematical operations can be done using scalars and functions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Chicago          1500.0\n",
       "New York         1300.0\n",
       "Portland            0.0\n",
       "San Francisco    1100.0\n",
       "Austin              0.0\n",
       "Boston              NaN\n",
       "dtype: float64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cities"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "combined_pop = cities['Chicago'] + cities['New York']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2800.0"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combined_pop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Chicago          750.0\n",
       "New York         650.0\n",
       "Portland           0.0\n",
       "San Francisco    550.0\n",
       "Austin             0.0\n",
       "Boston             NaN\n",
       "dtype: float64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# divide city values by 3\n",
    "cities/2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3900.0"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# square city values\n",
    "np.sum(cities)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can add two Series together, which returns a union of the two Series with the addition occurring on the shared index values.  Values on either Series that did not have a shared index will produce a NULL/NaN (not a number)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Chicago          1500.0\n",
       "New York         1300.0\n",
       "Portland            0.0\n",
       "San Francisco    1100.0\n",
       "Austin              0.0\n",
       "Boston              NaN\n",
       "dtype: float64"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cities"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Chicago           True\n",
       "New York          True\n",
       "Portland          True\n",
       "San Francisco     True\n",
       "Austin            True\n",
       "Boston           False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# returns a boolean series indicating which values aren't NULL\n",
    "cities.notnull()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Chicago          False\n",
      "New York         False\n",
      "Portland         False\n",
      "San Francisco    False\n",
      "Austin           False\n",
      "Boston            True\n",
      "dtype: bool\n",
      "\n",
      "\n",
      "Boston   NaN\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# use boolean logic to grab the NULL cities\n",
    "print(cities.isnull())\n",
    "print('\\n')\n",
    "print(cities[cities.isnull()])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## DataFrame\n",
    "\n",
    "A DataFrame is a tablular data structure comprised of rows and columns, akin to a spreadsheet, database table, or R's data.frame object. You can also think of a DataFrame as a group of Series objects that share an index (the column names).\n",
    "\n",
    "For the rest of the tutorial, we'll be primarily working with DataFrames."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Reading Data\n",
    "\n",
    "To create a DataFrame out of common Python data structures, we can pass a dictionary of lists to the DataFrame constructor.\n",
    "\n",
    "Using the `columns` parameter allows us to tell the constructor how we'd like the columns ordered. By default, the DataFrame constructor will order the columns alphabetically (though this isn't the case when reading from a file - more on that next)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "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>year</th>\n",
       "      <th>team</th>\n",
       "      <th>wins</th>\n",
       "      <th>losses</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2010</td>\n",
       "      <td>Bears</td>\n",
       "      <td>11</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2011</td>\n",
       "      <td>Bears</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2012</td>\n",
       "      <td>Bears</td>\n",
       "      <td>10</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2011</td>\n",
       "      <td>Packers</td>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2012</td>\n",
       "      <td>Packers</td>\n",
       "      <td>11</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2010</td>\n",
       "      <td>Lions</td>\n",
       "      <td>6</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2011</td>\n",
       "      <td>Lions</td>\n",
       "      <td>10</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2012</td>\n",
       "      <td>Lions</td>\n",
       "      <td>4</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   year     team  wins  losses\n",
       "0  2010    Bears    11       5\n",
       "1  2011    Bears     8       8\n",
       "2  2012    Bears    10       6\n",
       "3  2011  Packers    15       1\n",
       "4  2012  Packers    11       5\n",
       "5  2010    Lions     6      10\n",
       "6  2011    Lions    10       6\n",
       "7  2012    Lions     4      12"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = {'year': [2010, 2011, 2012, 2011, 2012, 2010, 2011, 2012],\n",
    "        'team': ['Bears', 'Bears', 'Bears', 'Packers', 'Packers', 'Lions', 'Lions', 'Lions'],\n",
    "        'wins': [11, 8, 10, 15, 11, 6, 10, 4],\n",
    "        'losses': [5, 8, 6, 1, 5, 10, 6, 12]}\n",
    "\n",
    "\n",
    "football = pd.DataFrame(data, columns=['year', 'team', 'wins', 'losses'])\n",
    "football"
   ]
  },
  {
   "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>year</th>\n",
       "      <th>team</th>\n",
       "      <th>wins</th>\n",
       "      <th>losses</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2011</td>\n",
       "      <td>Packers</td>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   year     team  wins  losses\n",
       "3  2011  Packers    15       1"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "football[football['losses'] < 5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Much more often, you'll have a dataset you want to read into a DataFrame. Let's go through several common ways of doing so."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**CSV**\n",
    "\n",
    "Reading a CSV is as simple as calling the *read_csv* function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "supermarket_data = pd.read_csv('train.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "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>Product_Identifier</th>\n",
       "      <th>Supermarket_Identifier</th>\n",
       "      <th>Product_Supermarket_Identifier</th>\n",
       "      <th>Product_Weight</th>\n",
       "      <th>Product_Fat_Content</th>\n",
       "      <th>Product_Shelf_Visibility</th>\n",
       "      <th>Product_Type</th>\n",
       "      <th>Product_Price</th>\n",
       "      <th>Supermarket_Opening_Year</th>\n",
       "      <th>Supermarket _Size</th>\n",
       "      <th>Supermarket_Location_Type</th>\n",
       "      <th>Supermarket_Type</th>\n",
       "      <th>Product_Supermarket_Sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>DRA12</td>\n",
       "      <td>CHUKWUDI010</td>\n",
       "      <td>DRA12_CHUKWUDI010</td>\n",
       "      <td>11.6</td>\n",
       "      <td>Low Fat</td>\n",
       "      <td>0.068535</td>\n",
       "      <td>Soft Drinks</td>\n",
       "      <td>357.54</td>\n",
       "      <td>2005</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Cluster 3</td>\n",
       "      <td>Grocery Store</td>\n",
       "      <td>709.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>DRA12</td>\n",
       "      <td>CHUKWUDI013</td>\n",
       "      <td>DRA12_CHUKWUDI013</td>\n",
       "      <td>11.6</td>\n",
       "      <td>Low Fat</td>\n",
       "      <td>0.040912</td>\n",
       "      <td>Soft Drinks</td>\n",
       "      <td>355.79</td>\n",
       "      <td>1994</td>\n",
       "      <td>High</td>\n",
       "      <td>Cluster 3</td>\n",
       "      <td>Supermarket Type1</td>\n",
       "      <td>6381.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>DRA12</td>\n",
       "      <td>CHUKWUDI017</td>\n",
       "      <td>DRA12_CHUKWUDI017</td>\n",
       "      <td>11.6</td>\n",
       "      <td>Low Fat</td>\n",
       "      <td>0.041178</td>\n",
       "      <td>Soft Drinks</td>\n",
       "      <td>350.79</td>\n",
       "      <td>2014</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Cluster 2</td>\n",
       "      <td>Supermarket Type1</td>\n",
       "      <td>6381.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>DRA12</td>\n",
       "      <td>CHUKWUDI018</td>\n",
       "      <td>DRA12_CHUKWUDI018</td>\n",
       "      <td>11.6</td>\n",
       "      <td>Low Fat</td>\n",
       "      <td>0.041113</td>\n",
       "      <td>Soft Drinks</td>\n",
       "      <td>355.04</td>\n",
       "      <td>2016</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Cluster 3</td>\n",
       "      <td>Supermarket Type2</td>\n",
       "      <td>2127.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>DRA12</td>\n",
       "      <td>CHUKWUDI035</td>\n",
       "      <td>DRA12_CHUKWUDI035</td>\n",
       "      <td>11.6</td>\n",
       "      <td>Ultra Low fat</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>Soft Drinks</td>\n",
       "      <td>354.79</td>\n",
       "      <td>2011</td>\n",
       "      <td>Small</td>\n",
       "      <td>Cluster 2</td>\n",
       "      <td>Supermarket Type1</td>\n",
       "      <td>2481.77</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Product_Identifier Supermarket_Identifier Product_Supermarket_Identifier  \\\n",
       "0              DRA12            CHUKWUDI010              DRA12_CHUKWUDI010   \n",
       "1              DRA12            CHUKWUDI013              DRA12_CHUKWUDI013   \n",
       "2              DRA12            CHUKWUDI017              DRA12_CHUKWUDI017   \n",
       "3              DRA12            CHUKWUDI018              DRA12_CHUKWUDI018   \n",
       "4              DRA12            CHUKWUDI035              DRA12_CHUKWUDI035   \n",
       "\n",
       "   Product_Weight Product_Fat_Content  Product_Shelf_Visibility Product_Type  \\\n",
       "0            11.6             Low Fat                  0.068535  Soft Drinks   \n",
       "1            11.6             Low Fat                  0.040912  Soft Drinks   \n",
       "2            11.6             Low Fat                  0.041178  Soft Drinks   \n",
       "3            11.6             Low Fat                  0.041113  Soft Drinks   \n",
       "4            11.6       Ultra Low fat                  0.000000  Soft Drinks   \n",
       "\n",
       "   Product_Price  Supermarket_Opening_Year Supermarket _Size  \\\n",
       "0         357.54                      2005               NaN   \n",
       "1         355.79                      1994              High   \n",
       "2         350.79                      2014               NaN   \n",
       "3         355.04                      2016            Medium   \n",
       "4         354.79                      2011             Small   \n",
       "\n",
       "  Supermarket_Location_Type   Supermarket_Type  Product_Supermarket_Sales  \n",
       "0                 Cluster 3      Grocery Store                     709.08  \n",
       "1                 Cluster 3  Supermarket Type1                    6381.69  \n",
       "2                 Cluster 2  Supermarket Type1                    6381.69  \n",
       "3                 Cluster 3  Supermarket Type2                    2127.23  \n",
       "4                 Cluster 2  Supermarket Type1                    2481.77  "
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "supermarket_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "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>Product_Identifier</th>\n",
       "      <th>Supermarket_Identifier</th>\n",
       "      <th>Product_Supermarket_Identifier</th>\n",
       "      <th>Product_Weight</th>\n",
       "      <th>Product_Fat_Content</th>\n",
       "      <th>Product_Shelf_Visibility</th>\n",
       "      <th>Product_Type</th>\n",
       "      <th>Product_Price</th>\n",
       "      <th>Supermarket_Opening_Year</th>\n",
       "      <th>Supermarket _Size</th>\n",
       "      <th>Supermarket_Location_Type</th>\n",
       "      <th>Supermarket_Type</th>\n",
       "      <th>Product_Supermarket_Sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>DRC27</td>\n",
       "      <td>CHUKWUDI017</td>\n",
       "      <td>DRC27_CHUKWUDI017</td>\n",
       "      <td>13.800</td>\n",
       "      <td>Low Fat</td>\n",
       "      <td>0.058431</td>\n",
       "      <td>Dairy</td>\n",
       "      <td>609.20</td>\n",
       "      <td>2014</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Cluster 2</td>\n",
       "      <td>Supermarket Type1</td>\n",
       "      <td>4913.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>DRC27</td>\n",
       "      <td>CHUKWUDI035</td>\n",
       "      <td>DRC27_CHUKWUDI035</td>\n",
       "      <td>13.800</td>\n",
       "      <td>Low Fat</td>\n",
       "      <td>0.058091</td>\n",
       "      <td>Dairy</td>\n",
       "      <td>612.95</td>\n",
       "      <td>2011</td>\n",
       "      <td>Small</td>\n",
       "      <td>Cluster 2</td>\n",
       "      <td>Supermarket Type1</td>\n",
       "      <td>14126.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>DRC27</td>\n",
       "      <td>CHUKWUDI045</td>\n",
       "      <td>DRC27_CHUKWUDI045</td>\n",
       "      <td>13.800</td>\n",
       "      <td>Low Fat</td>\n",
       "      <td>0.058220</td>\n",
       "      <td>Dairy</td>\n",
       "      <td>618.70</td>\n",
       "      <td>2009</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Cluster 2</td>\n",
       "      <td>Supermarket Type1</td>\n",
       "      <td>3685.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>159</th>\n",
       "      <td>DRF37</td>\n",
       "      <td>CHUKWUDI017</td>\n",
       "      <td>DRF37_CHUKWUDI017</td>\n",
       "      <td>17.250</td>\n",
       "      <td>Low Fat</td>\n",
       "      <td>0.084810</td>\n",
       "      <td>Soft Drinks</td>\n",
       "      <td>652.98</td>\n",
       "      <td>2014</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Cluster 2</td>\n",
       "      <td>Supermarket Type1</td>\n",
       "      <td>7889.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>160</th>\n",
       "      <td>DRF37</td>\n",
       "      <td>CHUKWUDI019</td>\n",
       "      <td>DRF37_CHUKWUDI019</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Low Fat</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>Soft Drinks</td>\n",
       "      <td>653.23</td>\n",
       "      <td>1992</td>\n",
       "      <td>Small</td>\n",
       "      <td>Cluster 1</td>\n",
       "      <td>Grocery Store</td>\n",
       "      <td>1972.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>167</th>\n",
       "      <td>DRF60</td>\n",
       "      <td>CHUKWUDI045</td>\n",
       "      <td>DRF60_CHUKWUDI045</td>\n",
       "      <td>10.800</td>\n",
       "      <td>Low Fat</td>\n",
       "      <td>0.052174</td>\n",
       "      <td>Soft Drinks</td>\n",
       "      <td>600.89</td>\n",
       "      <td>2009</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Cluster 2</td>\n",
       "      <td>Supermarket Type1</td>\n",
       "      <td>10726.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>208</th>\n",
       "      <td>DRG49</td>\n",
       "      <td>CHUKWUDI017</td>\n",
       "      <td>DRG49_CHUKWUDI017</td>\n",
       "      <td>7.810</td>\n",
       "      <td>Low Fat</td>\n",
       "      <td>0.067837</td>\n",
       "      <td>Soft Drinks</td>\n",
       "      <td>615.37</td>\n",
       "      <td>2014</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Cluster 2</td>\n",
       "      <td>Supermarket Type1</td>\n",
       "      <td>13439.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>209</th>\n",
       "      <td>DRG49</td>\n",
       "      <td>CHUKWUDI027</td>\n",
       "      <td>DRG49_CHUKWUDI027</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Low Fat</td>\n",
       "      <td>0.067129</td>\n",
       "      <td>Soft Drinks</td>\n",
       "      <td>607.12</td>\n",
       "      <td>1992</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Cluster 3</td>\n",
       "      <td>Supermarket Type3</td>\n",
       "      <td>8552.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>210</th>\n",
       "      <td>DRG49</td>\n",
       "      <td>CHUKWUDI045</td>\n",
       "      <td>DRG49_CHUKWUDI045</td>\n",
       "      <td>7.810</td>\n",
       "      <td>Low Fat</td>\n",
       "      <td>0.067592</td>\n",
       "      <td>Soft Drinks</td>\n",
       "      <td>615.12</td>\n",
       "      <td>2009</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Cluster 2</td>\n",
       "      <td>Supermarket Type1</td>\n",
       "      <td>17715.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>343</th>\n",
       "      <td>DRK23</td>\n",
       "      <td>CHUKWUDI017</td>\n",
       "      <td>DRK23_CHUKWUDI017</td>\n",
       "      <td>8.395</td>\n",
       "      <td>Low Fat</td>\n",
       "      <td>0.072384</td>\n",
       "      <td>Hard Drinks</td>\n",
       "      <td>628.76</td>\n",
       "      <td>2014</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Cluster 2</td>\n",
       "      <td>Supermarket Type1</td>\n",
       "      <td>17710.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>344</th>\n",
       "      <td>DRK23</td>\n",
       "      <td>CHUKWUDI027</td>\n",
       "      <td>DRK23_CHUKWUDI027</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Low Fat</td>\n",
       "      <td>0.071628</td>\n",
       "      <td>Hard Drinks</td>\n",
       "      <td>629.76</td>\n",
       "      <td>1992</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Cluster 3</td>\n",
       "      <td>Supermarket Type3</td>\n",
       "      <td>10752.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>448</th>\n",
       "      <td>DRP47</td>\n",
       "      <td>CHUKWUDI017</td>\n",
       "      <td>DRP47_CHUKWUDI017</td>\n",
       "      <td>15.750</td>\n",
       "      <td>Low Fat</td>\n",
       "      <td>0.141399</td>\n",
       "      <td>Hard Drinks</td>\n",
       "      <td>626.35</td>\n",
       "      <td>2014</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Cluster 2</td>\n",
       "      <td>Supermarket Type1</td>\n",
       "      <td>6939.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>474</th>\n",
       "      <td>FDA04</td>\n",
       "      <td>CHUKWUDI017</td>\n",
       "      <td>FDA04_CHUKWUDI017</td>\n",
       "      <td>11.300</td>\n",
       "      <td>Normal Fat</td>\n",
       "      <td>0.067107</td>\n",
       "      <td>Frozen Foods</td>\n",
       "      <td>645.49</td>\n",
       "      <td>2014</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Cluster 2</td>\n",
       "      <td>Supermarket Type1</td>\n",
       "      <td>11007.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>475</th>\n",
       "      <td>FDA04</td>\n",
       "      <td>CHUKWUDI027</td>\n",
       "      <td>FDA04_CHUKWUDI027</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Normal Fat</td>\n",
       "      <td>0.066407</td>\n",
       "      <td>Frozen Foods</td>\n",
       "      <td>649.49</td>\n",
       "      <td>1992</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Cluster 3</td>\n",
       "      <td>Supermarket Type3</td>\n",
       "      <td>14244.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>476</th>\n",
       "      <td>FDA04</td>\n",
       "      <td>CHUKWUDI045</td>\n",
       "      <td>FDA04_CHUKWUDI045</td>\n",
       "      <td>11.300</td>\n",
       "      <td>Normal Fat</td>\n",
       "      <td>0.066865</td>\n",
       "      <td>Frozen Foods</td>\n",
       "      <td>650.49</td>\n",
       "      <td>2009</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Cluster 2</td>\n",
       "      <td>Supermarket Type1</td>\n",
       "      <td>7122.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>501</th>\n",
       "      <td>FDA15</td>\n",
       "      <td>CHUKWUDI017</td>\n",
       "      <td>FDA15_CHUKWUDI017</td>\n",
       "      <td>9.300</td>\n",
       "      <td>Ultra Low fat</td>\n",
       "      <td>0.016113</td>\n",
       "      <td>Dairy</td>\n",
       "      <td>622.02</td>\n",
       "      <td>2014</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Cluster 2</td>\n",
       "      <td>Supermarket Type1</td>\n",
       "      <td>14940.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>502</th>\n",
       "      <td>FDA15</td>\n",
       "      <td>CHUKWUDI027</td>\n",
       "      <td>FDA15_CHUKWUDI027</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Low Fat</td>\n",
       "      <td>0.015945</td>\n",
       "      <td>Dairy</td>\n",
       "      <td>623.77</td>\n",
       "      <td>1992</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Cluster 3</td>\n",
       "      <td>Supermarket Type3</td>\n",
       "      <td>16185.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>503</th>\n",
       "      <td>FDA15</td>\n",
       "      <td>CHUKWUDI035</td>\n",
       "      <td>FDA15_CHUKWUDI035</td>\n",
       "      <td>9.300</td>\n",
       "      <td>Low Fat</td>\n",
       "      <td>0.016019</td>\n",
       "      <td>Dairy</td>\n",
       "      <td>621.27</td>\n",
       "      <td>2011</td>\n",
       "      <td>Small</td>\n",
       "      <td>Cluster 2</td>\n",
       "      <td>Supermarket Type1</td>\n",
       "      <td>16185.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>504</th>\n",
       "      <td>FDA15</td>\n",
       "      <td>CHUKWUDI045</td>\n",
       "      <td>FDA15_CHUKWUDI045</td>\n",
       "      <td>9.300</td>\n",
       "      <td>Low Fat</td>\n",
       "      <td>0.016055</td>\n",
       "      <td>Dairy</td>\n",
       "      <td>625.52</td>\n",
       "      <td>2009</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Cluster 2</td>\n",
       "      <td>Supermarket Type1</td>\n",
       "      <td>14940.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>532</th>\n",
       "      <td>FDA27</td>\n",
       "      <td>CHUKWUDI017</td>\n",
       "      <td>FDA27_CHUKWUDI017</td>\n",
       "      <td>20.350</td>\n",
       "      <td>Normal Fat</td>\n",
       "      <td>0.031103</td>\n",
       "      <td>Dairy</td>\n",
       "      <td>643.17</td>\n",
       "      <td>2014</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Cluster 2</td>\n",
       "      <td>Supermarket Type1</td>\n",
       "      <td>5113.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>533</th>\n",
       "      <td>FDA27</td>\n",
       "      <td>CHUKWUDI045</td>\n",
       "      <td>FDA27_CHUKWUDI045</td>\n",
       "      <td>20.350</td>\n",
       "      <td>Normal Fat</td>\n",
       "      <td>0.030990</td>\n",
       "      <td>Dairy</td>\n",
       "      <td>640.92</td>\n",
       "      <td>2009</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Cluster 2</td>\n",
       "      <td>Supermarket Type1</td>\n",
       "      <td>2556.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>564</th>\n",
       "      <td>FDA38</td>\n",
       "      <td>CHUKWUDI019</td>\n",
       "      <td>FDA38_CHUKWUDI019</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Low Fat</td>\n",
       "      <td>0.044612</td>\n",
       "      <td>Dairy</td>\n",
       "      <td>602.88</td>\n",
       "      <td>1992</td>\n",
       "      <td>Small</td>\n",
       "      <td>Cluster 1</td>\n",
       "      <td>Grocery Store</td>\n",
       "      <td>1201.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>611</th>\n",
       "      <td>FDB03</td>\n",
       "      <td>CHUKWUDI035</td>\n",
       "      <td>FDB03_CHUKWUDI035</td>\n",
       "      <td>17.750</td>\n",
       "      <td>Normal Fat</td>\n",
       "      <td>0.156802</td>\n",
       "      <td>Dairy</td>\n",
       "      <td>605.38</td>\n",
       "      <td>2011</td>\n",
       "      <td>Small</td>\n",
       "      <td>Cluster 2</td>\n",
       "      <td>Supermarket Type1</td>\n",
       "      <td>10815.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>613</th>\n",
       "      <td>FDB05</td>\n",
       "      <td>CHUKWUDI019</td>\n",
       "      <td>FDB05_CHUKWUDI019</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Low Fat</td>\n",
       "      <td>0.145670</td>\n",
       "      <td>Frozen Foods</td>\n",
       "      <td>618.19</td>\n",
       "      <td>1992</td>\n",
       "      <td>Small</td>\n",
       "      <td>Cluster 1</td>\n",
       "      <td>Grocery Store</td>\n",
       "      <td>1857.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>614</th>\n",
       "      <td>FDB05</td>\n",
       "      <td>CHUKWUDI027</td>\n",
       "      <td>FDB05_CHUKWUDI027</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Low Fat</td>\n",
       "      <td>0.082795</td>\n",
       "      <td>Frozen Foods</td>\n",
       "      <td>614.69</td>\n",
       "      <td>1992</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Cluster 3</td>\n",
       "      <td>Supermarket Type3</td>\n",
       "      <td>11764.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>636</th>\n",
       "      <td>FDB15</td>\n",
       "      <td>CHUKWUDI017</td>\n",
       "      <td>FDB15_CHUKWUDI017</td>\n",
       "      <td>10.895</td>\n",
       "      <td>Low Fat</td>\n",
       "      <td>0.137585</td>\n",
       "      <td>Dairy</td>\n",
       "      <td>655.64</td>\n",
       "      <td>2014</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Cluster 2</td>\n",
       "      <td>Supermarket Type1</td>\n",
       "      <td>9887.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>645</th>\n",
       "      <td>FDB21</td>\n",
       "      <td>CHUKWUDI017</td>\n",
       "      <td>FDB21_CHUKWUDI017</td>\n",
       "      <td>7.475</td>\n",
       "      <td>Low Fat</td>\n",
       "      <td>0.149361</td>\n",
       "      <td>Fruits and Vegetables</td>\n",
       "      <td>608.71</td>\n",
       "      <td>2014</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Cluster 2</td>\n",
       "      <td>Supermarket Type1</td>\n",
       "      <td>4229.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>646</th>\n",
       "      <td>FDB21</td>\n",
       "      <td>CHUKWUDI027</td>\n",
       "      <td>FDB21_CHUKWUDI027</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Low Fat</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>Fruits and Vegetables</td>\n",
       "      <td>607.46</td>\n",
       "      <td>1992</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Cluster 3</td>\n",
       "      <td>Supermarket Type3</td>\n",
       "      <td>16917.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>647</th>\n",
       "      <td>FDB21</td>\n",
       "      <td>CHUKWUDI035</td>\n",
       "      <td>FDB21_CHUKWUDI035</td>\n",
       "      <td>7.475</td>\n",
       "      <td>Low Fat</td>\n",
       "      <td>0.148493</td>\n",
       "      <td>Fruits and Vegetables</td>\n",
       "      <td>601.71</td>\n",
       "      <td>2011</td>\n",
       "      <td>Small</td>\n",
       "      <td>Cluster 2</td>\n",
       "      <td>Supermarket Type1</td>\n",
       "      <td>4229.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>648</th>\n",
       "      <td>FDB21</td>\n",
       "      <td>CHUKWUDI045</td>\n",
       "      <td>FDB21_CHUKWUDI045</td>\n",
       "      <td>7.475</td>\n",
       "      <td>Low Fat</td>\n",
       "      <td>0.148822</td>\n",
       "      <td>Fruits and Vegetables</td>\n",
       "      <td>603.21</td>\n",
       "      <td>2009</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Cluster 2</td>\n",
       "      <td>Supermarket Type1</td>\n",
       "      <td>13292.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4423</th>\n",
       "      <td>NCM05</td>\n",
       "      <td>CHUKWUDI019</td>\n",
       "      <td>NCM05_CHUKWUDI019</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Low Fat</td>\n",
       "      <td>0.104784</td>\n",
       "      <td>Health and Hygiene</td>\n",
       "      <td>665.06</td>\n",
       "      <td>1992</td>\n",
       "      <td>Small</td>\n",
       "      <td>Cluster 1</td>\n",
       "      <td>Grocery Store</td>\n",
       "      <td>1321.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4527</th>\n",
       "      <td>NCN55</td>\n",
       "      <td>CHUKWUDI035</td>\n",
       "      <td>NCN55_CHUKWUDI035</td>\n",
       "      <td>14.600</td>\n",
       "      <td>Ultra Low fat</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>Others</td>\n",
       "      <td>605.88</td>\n",
       "      <td>2011</td>\n",
       "      <td>Small</td>\n",
       "      <td>Cluster 2</td>\n",
       "      <td>Supermarket Type1</td>\n",
       "      <td>13219.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4642</th>\n",
       "      <td>NCQ06</td>\n",
       "      <td>CHUKWUDI017</td>\n",
       "      <td>NCQ06_CHUKWUDI017</td>\n",
       "      <td>13.000</td>\n",
       "      <td>Low Fat</td>\n",
       "      <td>0.042061</td>\n",
       "      <td>Household</td>\n",
       "      <td>635.25</td>\n",
       "      <td>2014</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Cluster 2</td>\n",
       "      <td>Supermarket Type1</td>\n",
       "      <td>8287.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4643</th>\n",
       "      <td>NCQ06</td>\n",
       "      <td>CHUKWUDI019</td>\n",
       "      <td>NCQ06_CHUKWUDI019</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Low Fat</td>\n",
       "      <td>0.073229</td>\n",
       "      <td>Household</td>\n",
       "      <td>635.25</td>\n",
       "      <td>1992</td>\n",
       "      <td>Small</td>\n",
       "      <td>Cluster 1</td>\n",
       "      <td>Grocery Store</td>\n",
       "      <td>637.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4644</th>\n",
       "      <td>NCQ06</td>\n",
       "      <td>CHUKWUDI027</td>\n",
       "      <td>NCQ06_CHUKWUDI027</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Low Fat</td>\n",
       "      <td>0.041622</td>\n",
       "      <td>Household</td>\n",
       "      <td>634.00</td>\n",
       "      <td>1992</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Cluster 3</td>\n",
       "      <td>Supermarket Type3</td>\n",
       "      <td>16575.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4645</th>\n",
       "      <td>NCQ06</td>\n",
       "      <td>CHUKWUDI035</td>\n",
       "      <td>NCQ06_CHUKWUDI035</td>\n",
       "      <td>13.000</td>\n",
       "      <td>Low Fat</td>\n",
       "      <td>0.041817</td>\n",
       "      <td>Household</td>\n",
       "      <td>636.25</td>\n",
       "      <td>2011</td>\n",
       "      <td>Small</td>\n",
       "      <td>Cluster 2</td>\n",
       "      <td>Supermarket Type1</td>\n",
       "      <td>12112.57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4646</th>\n",
       "      <td>NCQ06</td>\n",
       "      <td>CHUKWUDI045</td>\n",
       "      <td>NCQ06_CHUKWUDI045</td>\n",
       "      <td>13.000</td>\n",
       "      <td>Low Fat</td>\n",
       "      <td>0.041909</td>\n",
       "      <td>Household</td>\n",
       "      <td>637.50</td>\n",
       "      <td>2009</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Cluster 2</td>\n",
       "      <td>Supermarket Type1</td>\n",
       "      <td>14662.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4652</th>\n",
       "      <td>NCQ29</td>\n",
       "      <td>CHUKWUDI017</td>\n",
       "      <td>NCQ29_CHUKWUDI017</td>\n",
       "      <td>12.000</td>\n",
       "      <td>Low Fat</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>Health and Hygiene</td>\n",
       "      <td>655.82</td>\n",
       "      <td>2014</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Cluster 2</td>\n",
       "      <td>Supermarket Type1</td>\n",
       "      <td>3904.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4653</th>\n",
       "      <td>NCQ29</td>\n",
       "      <td>CHUKWUDI019</td>\n",
       "      <td>NCQ29_CHUKWUDI019</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Low Fat</td>\n",
       "      <td>0.182494</td>\n",
       "      <td>Health and Hygiene</td>\n",
       "      <td>647.07</td>\n",
       "      <td>1992</td>\n",
       "      <td>Small</td>\n",
       "      <td>Cluster 1</td>\n",
       "      <td>Grocery Store</td>\n",
       "      <td>2603.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4696</th>\n",
       "      <td>NCR38</td>\n",
       "      <td>CHUKWUDI017</td>\n",
       "      <td>NCR38_CHUKWUDI017</td>\n",
       "      <td>17.250</td>\n",
       "      <td>Low Fat</td>\n",
       "      <td>0.114161</td>\n",
       "      <td>Household</td>\n",
       "      <td>632.93</td>\n",
       "      <td>2014</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Cluster 2</td>\n",
       "      <td>Supermarket Type1</td>\n",
       "      <td>9437.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4697</th>\n",
       "      <td>NCR38</td>\n",
       "      <td>CHUKWUDI019</td>\n",
       "      <td>NCR38_CHUKWUDI019</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Low Fat</td>\n",
       "      <td>0.198756</td>\n",
       "      <td>Household</td>\n",
       "      <td>626.93</td>\n",
       "      <td>1992</td>\n",
       "      <td>Small</td>\n",
       "      <td>Cluster 1</td>\n",
       "      <td>Grocery Store</td>\n",
       "      <td>629.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4698</th>\n",
       "      <td>NCR38</td>\n",
       "      <td>CHUKWUDI035</td>\n",
       "      <td>NCR38_CHUKWUDI035</td>\n",
       "      <td>17.250</td>\n",
       "      <td>Low Fat</td>\n",
       "      <td>0.113497</td>\n",
       "      <td>Household</td>\n",
       "      <td>633.68</td>\n",
       "      <td>2011</td>\n",
       "      <td>Small</td>\n",
       "      <td>Cluster 2</td>\n",
       "      <td>Supermarket Type1</td>\n",
       "      <td>1887.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4699</th>\n",
       "      <td>NCR38</td>\n",
       "      <td>CHUKWUDI045</td>\n",
       "      <td>NCR38_CHUKWUDI045</td>\n",
       "      <td>17.250</td>\n",
       "      <td>Low Fat</td>\n",
       "      <td>0.113749</td>\n",
       "      <td>Household</td>\n",
       "      <td>628.43</td>\n",
       "      <td>2009</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Cluster 2</td>\n",
       "      <td>Supermarket Type1</td>\n",
       "      <td>16987.89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4717</th>\n",
       "      <td>NCS06</td>\n",
       "      <td>CHUKWUDI019</td>\n",
       "      <td>NCS06_CHUKWUDI019</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Low Fat</td>\n",
       "      <td>0.055567</td>\n",
       "      <td>Household</td>\n",
       "      <td>658.98</td>\n",
       "      <td>1992</td>\n",
       "      <td>Small</td>\n",
       "      <td>Cluster 1</td>\n",
       "      <td>Grocery Store</td>\n",
       "      <td>657.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4718</th>\n",
       "      <td>NCS06</td>\n",
       "      <td>CHUKWUDI027</td>\n",
       "      <td>NCS06_CHUKWUDI027</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Low Fat</td>\n",
       "      <td>0.031583</td>\n",
       "      <td>Household</td>\n",
       "      <td>652.48</td>\n",
       "      <td>1992</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Cluster 3</td>\n",
       "      <td>Supermarket Type3</td>\n",
       "      <td>17751.89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4725</th>\n",
       "      <td>NCS29</td>\n",
       "      <td>CHUKWUDI017</td>\n",
       "      <td>NCS29_CHUKWUDI017</td>\n",
       "      <td>9.000</td>\n",
       "      <td>Low Fat</td>\n",
       "      <td>0.069939</td>\n",
       "      <td>Health and Hygiene</td>\n",
       "      <td>662.22</td>\n",
       "      <td>2014</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Cluster 2</td>\n",
       "      <td>Supermarket Type1</td>\n",
       "      <td>7287.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4726</th>\n",
       "      <td>NCS29</td>\n",
       "      <td>CHUKWUDI019</td>\n",
       "      <td>NCS29_CHUKWUDI019</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Low Fat</td>\n",
       "      <td>0.121765</td>\n",
       "      <td>Health and Hygiene</td>\n",
       "      <td>660.47</td>\n",
       "      <td>1992</td>\n",
       "      <td>Small</td>\n",
       "      <td>Cluster 1</td>\n",
       "      <td>Grocery Store</td>\n",
       "      <td>662.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4754</th>\n",
       "      <td>NCT05</td>\n",
       "      <td>CHUKWUDI045</td>\n",
       "      <td>NCT05_CHUKWUDI045</td>\n",
       "      <td>10.895</td>\n",
       "      <td>Low Fat</td>\n",
       "      <td>0.020994</td>\n",
       "      <td>Health and Hygiene</td>\n",
       "      <td>639.17</td>\n",
       "      <td>2009</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Cluster 2</td>\n",
       "      <td>Supermarket Type1</td>\n",
       "      <td>10865.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4868</th>\n",
       "      <td>NCW30</td>\n",
       "      <td>CHUKWUDI017</td>\n",
       "      <td>NCW30_CHUKWUDI017</td>\n",
       "      <td>5.210</td>\n",
       "      <td>Low Fat</td>\n",
       "      <td>0.011072</td>\n",
       "      <td>Household</td>\n",
       "      <td>644.74</td>\n",
       "      <td>2014</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Cluster 2</td>\n",
       "      <td>Supermarket Type1</td>\n",
       "      <td>11007.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4869</th>\n",
       "      <td>NCW30</td>\n",
       "      <td>CHUKWUDI035</td>\n",
       "      <td>NCW30_CHUKWUDI035</td>\n",
       "      <td>5.210</td>\n",
       "      <td>Low Fat</td>\n",
       "      <td>0.011008</td>\n",
       "      <td>Household</td>\n",
       "      <td>648.99</td>\n",
       "      <td>2011</td>\n",
       "      <td>Small</td>\n",
       "      <td>Cluster 2</td>\n",
       "      <td>Supermarket Type1</td>\n",
       "      <td>7122.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4910</th>\n",
       "      <td>NCX30</td>\n",
       "      <td>CHUKWUDI017</td>\n",
       "      <td>NCX30_CHUKWUDI017</td>\n",
       "      <td>16.700</td>\n",
       "      <td>Low Fat</td>\n",
       "      <td>0.026771</td>\n",
       "      <td>Household</td>\n",
       "      <td>615.94</td>\n",
       "      <td>2014</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Cluster 2</td>\n",
       "      <td>Supermarket Type1</td>\n",
       "      <td>4953.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4911</th>\n",
       "      <td>NCX30</td>\n",
       "      <td>CHUKWUDI019</td>\n",
       "      <td>NCX30_CHUKWUDI019</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Ultra Low fat</td>\n",
       "      <td>0.046609</td>\n",
       "      <td>Household</td>\n",
       "      <td>621.69</td>\n",
       "      <td>1992</td>\n",
       "      <td>Small</td>\n",
       "      <td>Cluster 1</td>\n",
       "      <td>Grocery Store</td>\n",
       "      <td>1238.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4912</th>\n",
       "      <td>NCX30</td>\n",
       "      <td>CHUKWUDI027</td>\n",
       "      <td>NCX30_CHUKWUDI027</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Low Fat</td>\n",
       "      <td>0.026492</td>\n",
       "      <td>Household</td>\n",
       "      <td>618.19</td>\n",
       "      <td>1992</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Cluster 3</td>\n",
       "      <td>Supermarket Type3</td>\n",
       "      <td>15479.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4959</th>\n",
       "      <td>NCZ06</td>\n",
       "      <td>CHUKWUDI017</td>\n",
       "      <td>NCZ06_CHUKWUDI017</td>\n",
       "      <td>19.600</td>\n",
       "      <td>Low Fat</td>\n",
       "      <td>0.094694</td>\n",
       "      <td>Household</td>\n",
       "      <td>637.42</td>\n",
       "      <td>2014</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Cluster 2</td>\n",
       "      <td>Supermarket Type1</td>\n",
       "      <td>6341.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4960</th>\n",
       "      <td>NCZ06</td>\n",
       "      <td>CHUKWUDI019</td>\n",
       "      <td>NCZ06_CHUKWUDI019</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Low Fat</td>\n",
       "      <td>0.164865</td>\n",
       "      <td>Household</td>\n",
       "      <td>638.17</td>\n",
       "      <td>1992</td>\n",
       "      <td>Small</td>\n",
       "      <td>Cluster 1</td>\n",
       "      <td>Grocery Store</td>\n",
       "      <td>1902.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4961</th>\n",
       "      <td>NCZ06</td>\n",
       "      <td>CHUKWUDI027</td>\n",
       "      <td>NCZ06_CHUKWUDI027</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Low Fat</td>\n",
       "      <td>0.093706</td>\n",
       "      <td>Household</td>\n",
       "      <td>634.67</td>\n",
       "      <td>1992</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Cluster 3</td>\n",
       "      <td>Supermarket Type3</td>\n",
       "      <td>8244.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4963</th>\n",
       "      <td>NCZ18</td>\n",
       "      <td>CHUKWUDI017</td>\n",
       "      <td>NCZ18_CHUKWUDI017</td>\n",
       "      <td>7.825</td>\n",
       "      <td>Low Fat</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>Household</td>\n",
       "      <td>633.67</td>\n",
       "      <td>2014</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Cluster 2</td>\n",
       "      <td>Supermarket Type1</td>\n",
       "      <td>17122.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4964</th>\n",
       "      <td>NCZ18</td>\n",
       "      <td>CHUKWUDI019</td>\n",
       "      <td>NCZ18_CHUKWUDI019</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Low Fat</td>\n",
       "      <td>0.325781</td>\n",
       "      <td>Household</td>\n",
       "      <td>631.92</td>\n",
       "      <td>1992</td>\n",
       "      <td>Small</td>\n",
       "      <td>Cluster 1</td>\n",
       "      <td>Grocery Store</td>\n",
       "      <td>1902.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4965</th>\n",
       "      <td>NCZ18</td>\n",
       "      <td>CHUKWUDI035</td>\n",
       "      <td>NCZ18_CHUKWUDI035</td>\n",
       "      <td>7.825</td>\n",
       "      <td>Low Fat</td>\n",
       "      <td>0.186033</td>\n",
       "      <td>Household</td>\n",
       "      <td>632.67</td>\n",
       "      <td>2011</td>\n",
       "      <td>Small</td>\n",
       "      <td>Cluster 2</td>\n",
       "      <td>Supermarket Type1</td>\n",
       "      <td>15220.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4966</th>\n",
       "      <td>NCZ18</td>\n",
       "      <td>CHUKWUDI045</td>\n",
       "      <td>NCZ18_CHUKWUDI045</td>\n",
       "      <td>7.825</td>\n",
       "      <td>Low Fat</td>\n",
       "      <td>0.186445</td>\n",
       "      <td>Household</td>\n",
       "      <td>638.92</td>\n",
       "      <td>2009</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Cluster 2</td>\n",
       "      <td>Supermarket Type1</td>\n",
       "      <td>3805.05</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>270 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Product_Identifier Supermarket_Identifier Product_Supermarket_Identifier  \\\n",
       "44                DRC27            CHUKWUDI017              DRC27_CHUKWUDI017   \n",
       "45                DRC27            CHUKWUDI035              DRC27_CHUKWUDI035   \n",
       "46                DRC27            CHUKWUDI045              DRC27_CHUKWUDI045   \n",
       "159               DRF37            CHUKWUDI017              DRF37_CHUKWUDI017   \n",
       "160               DRF37            CHUKWUDI019              DRF37_CHUKWUDI019   \n",
       "167               DRF60            CHUKWUDI045              DRF60_CHUKWUDI045   \n",
       "208               DRG49            CHUKWUDI017              DRG49_CHUKWUDI017   \n",
       "209               DRG49            CHUKWUDI027              DRG49_CHUKWUDI027   \n",
       "210               DRG49            CHUKWUDI045              DRG49_CHUKWUDI045   \n",
       "343               DRK23            CHUKWUDI017              DRK23_CHUKWUDI017   \n",
       "344               DRK23            CHUKWUDI027              DRK23_CHUKWUDI027   \n",
       "448               DRP47            CHUKWUDI017              DRP47_CHUKWUDI017   \n",
       "474               FDA04            CHUKWUDI017              FDA04_CHUKWUDI017   \n",
       "475               FDA04            CHUKWUDI027              FDA04_CHUKWUDI027   \n",
       "476               FDA04            CHUKWUDI045              FDA04_CHUKWUDI045   \n",
       "501               FDA15            CHUKWUDI017              FDA15_CHUKWUDI017   \n",
       "502               FDA15            CHUKWUDI027              FDA15_CHUKWUDI027   \n",
       "503               FDA15            CHUKWUDI035              FDA15_CHUKWUDI035   \n",
       "504               FDA15            CHUKWUDI045              FDA15_CHUKWUDI045   \n",
       "532               FDA27            CHUKWUDI017              FDA27_CHUKWUDI017   \n",
       "533               FDA27            CHUKWUDI045              FDA27_CHUKWUDI045   \n",
       "564               FDA38            CHUKWUDI019              FDA38_CHUKWUDI019   \n",
       "611               FDB03            CHUKWUDI035              FDB03_CHUKWUDI035   \n",
       "613               FDB05            CHUKWUDI019              FDB05_CHUKWUDI019   \n",
       "614               FDB05            CHUKWUDI027              FDB05_CHUKWUDI027   \n",
       "636               FDB15            CHUKWUDI017              FDB15_CHUKWUDI017   \n",
       "645               FDB21            CHUKWUDI017              FDB21_CHUKWUDI017   \n",
       "646               FDB21            CHUKWUDI027              FDB21_CHUKWUDI027   \n",
       "647               FDB21            CHUKWUDI035              FDB21_CHUKWUDI035   \n",
       "648               FDB21            CHUKWUDI045              FDB21_CHUKWUDI045   \n",
       "...                 ...                    ...                            ...   \n",
       "4423              NCM05            CHUKWUDI019              NCM05_CHUKWUDI019   \n",
       "4527              NCN55            CHUKWUDI035              NCN55_CHUKWUDI035   \n",
       "4642              NCQ06            CHUKWUDI017              NCQ06_CHUKWUDI017   \n",
       "4643              NCQ06            CHUKWUDI019              NCQ06_CHUKWUDI019   \n",
       "4644              NCQ06            CHUKWUDI027              NCQ06_CHUKWUDI027   \n",
       "4645              NCQ06            CHUKWUDI035              NCQ06_CHUKWUDI035   \n",
       "4646              NCQ06            CHUKWUDI045              NCQ06_CHUKWUDI045   \n",
       "4652              NCQ29            CHUKWUDI017              NCQ29_CHUKWUDI017   \n",
       "4653              NCQ29            CHUKWUDI019              NCQ29_CHUKWUDI019   \n",
       "4696              NCR38            CHUKWUDI017              NCR38_CHUKWUDI017   \n",
       "4697              NCR38            CHUKWUDI019              NCR38_CHUKWUDI019   \n",
       "4698              NCR38            CHUKWUDI035              NCR38_CHUKWUDI035   \n",
       "4699              NCR38            CHUKWUDI045              NCR38_CHUKWUDI045   \n",
       "4717              NCS06            CHUKWUDI019              NCS06_CHUKWUDI019   \n",
       "4718              NCS06            CHUKWUDI027              NCS06_CHUKWUDI027   \n",
       "4725              NCS29            CHUKWUDI017              NCS29_CHUKWUDI017   \n",
       "4726              NCS29            CHUKWUDI019              NCS29_CHUKWUDI019   \n",
       "4754              NCT05            CHUKWUDI045              NCT05_CHUKWUDI045   \n",
       "4868              NCW30            CHUKWUDI017              NCW30_CHUKWUDI017   \n",
       "4869              NCW30            CHUKWUDI035              NCW30_CHUKWUDI035   \n",
       "4910              NCX30            CHUKWUDI017              NCX30_CHUKWUDI017   \n",
       "4911              NCX30            CHUKWUDI019              NCX30_CHUKWUDI019   \n",
       "4912              NCX30            CHUKWUDI027              NCX30_CHUKWUDI027   \n",
       "4959              NCZ06            CHUKWUDI017              NCZ06_CHUKWUDI017   \n",
       "4960              NCZ06            CHUKWUDI019              NCZ06_CHUKWUDI019   \n",
       "4961              NCZ06            CHUKWUDI027              NCZ06_CHUKWUDI027   \n",
       "4963              NCZ18            CHUKWUDI017              NCZ18_CHUKWUDI017   \n",
       "4964              NCZ18            CHUKWUDI019              NCZ18_CHUKWUDI019   \n",
       "4965              NCZ18            CHUKWUDI035              NCZ18_CHUKWUDI035   \n",
       "4966              NCZ18            CHUKWUDI045              NCZ18_CHUKWUDI045   \n",
       "\n",
       "      Product_Weight Product_Fat_Content  Product_Shelf_Visibility  \\\n",
       "44            13.800             Low Fat                  0.058431   \n",
       "45            13.800             Low Fat                  0.058091   \n",
       "46            13.800             Low Fat                  0.058220   \n",
       "159           17.250             Low Fat                  0.084810   \n",
       "160              NaN             Low Fat                  0.000000   \n",
       "167           10.800             Low Fat                  0.052174   \n",
       "208            7.810             Low Fat                  0.067837   \n",
       "209              NaN             Low Fat                  0.067129   \n",
       "210            7.810             Low Fat                  0.067592   \n",
       "343            8.395             Low Fat                  0.072384   \n",
       "344              NaN             Low Fat                  0.071628   \n",
       "448           15.750             Low Fat                  0.141399   \n",
       "474           11.300          Normal Fat                  0.067107   \n",
       "475              NaN          Normal Fat                  0.066407   \n",
       "476           11.300          Normal Fat                  0.066865   \n",
       "501            9.300       Ultra Low fat                  0.016113   \n",
       "502              NaN             Low Fat                  0.015945   \n",
       "503            9.300             Low Fat                  0.016019   \n",
       "504            9.300             Low Fat                  0.016055   \n",
       "532           20.350          Normal Fat                  0.031103   \n",
       "533           20.350          Normal Fat                  0.030990   \n",
       "564              NaN             Low Fat                  0.044612   \n",
       "611           17.750          Normal Fat                  0.156802   \n",
       "613              NaN             Low Fat                  0.145670   \n",
       "614              NaN             Low Fat                  0.082795   \n",
       "636           10.895             Low Fat                  0.137585   \n",
       "645            7.475             Low Fat                  0.149361   \n",
       "646              NaN             Low Fat                  0.000000   \n",
       "647            7.475             Low Fat                  0.148493   \n",
       "648            7.475             Low Fat                  0.148822   \n",
       "...              ...                 ...                       ...   \n",
       "4423             NaN             Low Fat                  0.104784   \n",
       "4527          14.600       Ultra Low fat                  0.000000   \n",
       "4642          13.000             Low Fat                  0.042061   \n",
       "4643             NaN             Low Fat                  0.073229   \n",
       "4644             NaN             Low Fat                  0.041622   \n",
       "4645          13.000             Low Fat                  0.041817   \n",
       "4646          13.000             Low Fat                  0.041909   \n",
       "4652          12.000             Low Fat                  0.000000   \n",
       "4653             NaN             Low Fat                  0.182494   \n",
       "4696          17.250             Low Fat                  0.114161   \n",
       "4697             NaN             Low Fat                  0.198756   \n",
       "4698          17.250             Low Fat                  0.113497   \n",
       "4699          17.250             Low Fat                  0.113749   \n",
       "4717             NaN             Low Fat                  0.055567   \n",
       "4718             NaN             Low Fat                  0.031583   \n",
       "4725           9.000             Low Fat                  0.069939   \n",
       "4726             NaN             Low Fat                  0.121765   \n",
       "4754          10.895             Low Fat                  0.020994   \n",
       "4868           5.210             Low Fat                  0.011072   \n",
       "4869           5.210             Low Fat                  0.011008   \n",
       "4910          16.700             Low Fat                  0.026771   \n",
       "4911             NaN       Ultra Low fat                  0.046609   \n",
       "4912             NaN             Low Fat                  0.026492   \n",
       "4959          19.600             Low Fat                  0.094694   \n",
       "4960             NaN             Low Fat                  0.164865   \n",
       "4961             NaN             Low Fat                  0.093706   \n",
       "4963           7.825             Low Fat                  0.000000   \n",
       "4964             NaN             Low Fat                  0.325781   \n",
       "4965           7.825             Low Fat                  0.186033   \n",
       "4966           7.825             Low Fat                  0.186445   \n",
       "\n",
       "               Product_Type  Product_Price  Supermarket_Opening_Year  \\\n",
       "44                    Dairy         609.20                      2014   \n",
       "45                    Dairy         612.95                      2011   \n",
       "46                    Dairy         618.70                      2009   \n",
       "159             Soft Drinks         652.98                      2014   \n",
       "160             Soft Drinks         653.23                      1992   \n",
       "167             Soft Drinks         600.89                      2009   \n",
       "208             Soft Drinks         615.37                      2014   \n",
       "209             Soft Drinks         607.12                      1992   \n",
       "210             Soft Drinks         615.12                      2009   \n",
       "343             Hard Drinks         628.76                      2014   \n",
       "344             Hard Drinks         629.76                      1992   \n",
       "448             Hard Drinks         626.35                      2014   \n",
       "474            Frozen Foods         645.49                      2014   \n",
       "475            Frozen Foods         649.49                      1992   \n",
       "476            Frozen Foods         650.49                      2009   \n",
       "501                   Dairy         622.02                      2014   \n",
       "502                   Dairy         623.77                      1992   \n",
       "503                   Dairy         621.27                      2011   \n",
       "504                   Dairy         625.52                      2009   \n",
       "532                   Dairy         643.17                      2014   \n",
       "533                   Dairy         640.92                      2009   \n",
       "564                   Dairy         602.88                      1992   \n",
       "611                   Dairy         605.38                      2011   \n",
       "613            Frozen Foods         618.19                      1992   \n",
       "614            Frozen Foods         614.69                      1992   \n",
       "636                   Dairy         655.64                      2014   \n",
       "645   Fruits and Vegetables         608.71                      2014   \n",
       "646   Fruits and Vegetables         607.46                      1992   \n",
       "647   Fruits and Vegetables         601.71                      2011   \n",
       "648   Fruits and Vegetables         603.21                      2009   \n",
       "...                     ...            ...                       ...   \n",
       "4423     Health and Hygiene         665.06                      1992   \n",
       "4527                 Others         605.88                      2011   \n",
       "4642              Household         635.25                      2014   \n",
       "4643              Household         635.25                      1992   \n",
       "4644              Household         634.00                      1992   \n",
       "4645              Household         636.25                      2011   \n",
       "4646              Household         637.50                      2009   \n",
       "4652     Health and Hygiene         655.82                      2014   \n",
       "4653     Health and Hygiene         647.07                      1992   \n",
       "4696              Household         632.93                      2014   \n",
       "4697              Household         626.93                      1992   \n",
       "4698              Household         633.68                      2011   \n",
       "4699              Household         628.43                      2009   \n",
       "4717              Household         658.98                      1992   \n",
       "4718              Household         652.48                      1992   \n",
       "4725     Health and Hygiene         662.22                      2014   \n",
       "4726     Health and Hygiene         660.47                      1992   \n",
       "4754     Health and Hygiene         639.17                      2009   \n",
       "4868              Household         644.74                      2014   \n",
       "4869              Household         648.99                      2011   \n",
       "4910              Household         615.94                      2014   \n",
       "4911              Household         621.69                      1992   \n",
       "4912              Household         618.19                      1992   \n",
       "4959              Household         637.42                      2014   \n",
       "4960              Household         638.17                      1992   \n",
       "4961              Household         634.67                      1992   \n",
       "4963              Household         633.67                      2014   \n",
       "4964              Household         631.92                      1992   \n",
       "4965              Household         632.67                      2011   \n",
       "4966              Household         638.92                      2009   \n",
       "\n",
       "     Supermarket _Size Supermarket_Location_Type   Supermarket_Type  \\\n",
       "44                 NaN                 Cluster 2  Supermarket Type1   \n",
       "45               Small                 Cluster 2  Supermarket Type1   \n",
       "46                 NaN                 Cluster 2  Supermarket Type1   \n",
       "159                NaN                 Cluster 2  Supermarket Type1   \n",
       "160              Small                 Cluster 1      Grocery Store   \n",
       "167                NaN                 Cluster 2  Supermarket Type1   \n",
       "208                NaN                 Cluster 2  Supermarket Type1   \n",
       "209             Medium                 Cluster 3  Supermarket Type3   \n",
       "210                NaN                 Cluster 2  Supermarket Type1   \n",
       "343                NaN                 Cluster 2  Supermarket Type1   \n",
       "344             Medium                 Cluster 3  Supermarket Type3   \n",
       "448                NaN                 Cluster 2  Supermarket Type1   \n",
       "474                NaN                 Cluster 2  Supermarket Type1   \n",
       "475             Medium                 Cluster 3  Supermarket Type3   \n",
       "476                NaN                 Cluster 2  Supermarket Type1   \n",
       "501                NaN                 Cluster 2  Supermarket Type1   \n",
       "502             Medium                 Cluster 3  Supermarket Type3   \n",
       "503              Small                 Cluster 2  Supermarket Type1   \n",
       "504                NaN                 Cluster 2  Supermarket Type1   \n",
       "532                NaN                 Cluster 2  Supermarket Type1   \n",
       "533                NaN                 Cluster 2  Supermarket Type1   \n",
       "564              Small                 Cluster 1      Grocery Store   \n",
       "611              Small                 Cluster 2  Supermarket Type1   \n",
       "613              Small                 Cluster 1      Grocery Store   \n",
       "614             Medium                 Cluster 3  Supermarket Type3   \n",
       "636                NaN                 Cluster 2  Supermarket Type1   \n",
       "645                NaN                 Cluster 2  Supermarket Type1   \n",
       "646             Medium                 Cluster 3  Supermarket Type3   \n",
       "647              Small                 Cluster 2  Supermarket Type1   \n",
       "648                NaN                 Cluster 2  Supermarket Type1   \n",
       "...                ...                       ...                ...   \n",
       "4423             Small                 Cluster 1      Grocery Store   \n",
       "4527             Small                 Cluster 2  Supermarket Type1   \n",
       "4642               NaN                 Cluster 2  Supermarket Type1   \n",
       "4643             Small                 Cluster 1      Grocery Store   \n",
       "4644            Medium                 Cluster 3  Supermarket Type3   \n",
       "4645             Small                 Cluster 2  Supermarket Type1   \n",
       "4646               NaN                 Cluster 2  Supermarket Type1   \n",
       "4652               NaN                 Cluster 2  Supermarket Type1   \n",
       "4653             Small                 Cluster 1      Grocery Store   \n",
       "4696               NaN                 Cluster 2  Supermarket Type1   \n",
       "4697             Small                 Cluster 1      Grocery Store   \n",
       "4698             Small                 Cluster 2  Supermarket Type1   \n",
       "4699               NaN                 Cluster 2  Supermarket Type1   \n",
       "4717             Small                 Cluster 1      Grocery Store   \n",
       "4718            Medium                 Cluster 3  Supermarket Type3   \n",
       "4725               NaN                 Cluster 2  Supermarket Type1   \n",
       "4726             Small                 Cluster 1      Grocery Store   \n",
       "4754               NaN                 Cluster 2  Supermarket Type1   \n",
       "4868               NaN                 Cluster 2  Supermarket Type1   \n",
       "4869             Small                 Cluster 2  Supermarket Type1   \n",
       "4910               NaN                 Cluster 2  Supermarket Type1   \n",
       "4911             Small                 Cluster 1      Grocery Store   \n",
       "4912            Medium                 Cluster 3  Supermarket Type3   \n",
       "4959               NaN                 Cluster 2  Supermarket Type1   \n",
       "4960             Small                 Cluster 1      Grocery Store   \n",
       "4961            Medium                 Cluster 3  Supermarket Type3   \n",
       "4963               NaN                 Cluster 2  Supermarket Type1   \n",
       "4964             Small                 Cluster 1      Grocery Store   \n",
       "4965             Small                 Cluster 2  Supermarket Type1   \n",
       "4966               NaN                 Cluster 2  Supermarket Type1   \n",
       "\n",
       "      Product_Supermarket_Sales  \n",
       "44                      4913.60  \n",
       "45                     14126.61  \n",
       "46                      3685.20  \n",
       "159                     7889.73  \n",
       "160                     1972.43  \n",
       "167                    10726.04  \n",
       "208                    13439.17  \n",
       "209                     8552.20  \n",
       "210                    17715.27  \n",
       "343                    17710.28  \n",
       "344                    10752.67  \n",
       "448                     6939.30  \n",
       "474                    11007.34  \n",
       "475                    14244.79  \n",
       "476                     7122.40  \n",
       "501                    14940.55  \n",
       "502                    16185.60  \n",
       "503                    16185.60  \n",
       "504                    14940.55  \n",
       "532                     5113.34  \n",
       "533                     2556.67  \n",
       "564                     1201.77  \n",
       "611                    10815.92  \n",
       "613                     1857.58  \n",
       "614                    11764.69  \n",
       "636                     9887.13  \n",
       "645                     4229.49  \n",
       "646                    16917.98  \n",
       "647                     4229.49  \n",
       "648                    13292.70  \n",
       "...                         ...  \n",
       "4423                    1321.61  \n",
       "4527                   13219.46  \n",
       "4642                    8287.55  \n",
       "4643                     637.50  \n",
       "4644                   16575.09  \n",
       "4645                   12112.57  \n",
       "4646                   14662.58  \n",
       "4652                    3904.92  \n",
       "4653                    2603.28  \n",
       "4696                    9437.72  \n",
       "4697                     629.18  \n",
       "4698                    1887.54  \n",
       "4699                   16987.89  \n",
       "4717                     657.48  \n",
       "4718                   17751.89  \n",
       "4725                    7287.18  \n",
       "4726                     662.47  \n",
       "4754                   10865.86  \n",
       "4868                   11007.34  \n",
       "4869                    7122.40  \n",
       "4910                    4953.55  \n",
       "4911                    1238.39  \n",
       "4912                   15479.85  \n",
       "4959                    6341.75  \n",
       "4960                    1902.52  \n",
       "4961                    8244.27  \n",
       "4963                   17122.71  \n",
       "4964                    1902.52  \n",
       "4965                   15220.19  \n",
       "4966                    3805.05  \n",
       "\n",
       "[270 rows x 13 columns]"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "supermarket_data[supermarket_data['Product_Price'] > 600]"
   ]
  },
  {
   "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>Product_Identifier</th>\n",
       "      <th>Supermarket_Identifier</th>\n",
       "      <th>Product_Supermarket_Identifier</th>\n",
       "      <th>Product_Weight</th>\n",
       "      <th>Product_Fat_Content</th>\n",
       "      <th>Product_Shelf_Visibility</th>\n",
       "      <th>Product_Type</th>\n",
       "      <th>Product_Price</th>\n",
       "      <th>Supermarket_Opening_Year</th>\n",
       "      <th>Supermarket _Size</th>\n",
       "      <th>Supermarket_Location_Type</th>\n",
       "      <th>Supermarket_Type</th>\n",
       "      <th>Product_Supermarket_Sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>DRA12</td>\n",
       "      <td>CHUKWUDI010</td>\n",
       "      <td>DRA12_CHUKWUDI010</td>\n",
       "      <td>11.6</td>\n",
       "      <td>Low Fat</td>\n",
       "      <td>0.068535</td>\n",
       "      <td>Soft Drinks</td>\n",
       "      <td>357.54</td>\n",
       "      <td>2005</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Cluster 3</td>\n",
       "      <td>Grocery Store</td>\n",
       "      <td>709.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>DRA12</td>\n",
       "      <td>CHUKWUDI013</td>\n",
       "      <td>DRA12_CHUKWUDI013</td>\n",
       "      <td>11.6</td>\n",
       "      <td>Low Fat</td>\n",
       "      <td>0.040912</td>\n",
       "      <td>Soft Drinks</td>\n",
       "      <td>355.79</td>\n",
       "      <td>1994</td>\n",
       "      <td>High</td>\n",
       "      <td>Cluster 3</td>\n",
       "      <td>Supermarket Type1</td>\n",
       "      <td>6381.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>DRA12</td>\n",
       "      <td>CHUKWUDI017</td>\n",
       "      <td>DRA12_CHUKWUDI017</td>\n",
       "      <td>11.6</td>\n",
       "      <td>Low Fat</td>\n",
       "      <td>0.041178</td>\n",
       "      <td>Soft Drinks</td>\n",
       "      <td>350.79</td>\n",
       "      <td>2014</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Cluster 2</td>\n",
       "      <td>Supermarket Type1</td>\n",
       "      <td>6381.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>DRA12</td>\n",
       "      <td>CHUKWUDI018</td>\n",
       "      <td>DRA12_CHUKWUDI018</td>\n",
       "      <td>11.6</td>\n",
       "      <td>Low Fat</td>\n",
       "      <td>0.041113</td>\n",
       "      <td>Soft Drinks</td>\n",
       "      <td>355.04</td>\n",
       "      <td>2016</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Cluster 3</td>\n",
       "      <td>Supermarket Type2</td>\n",
       "      <td>2127.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>DRA12</td>\n",
       "      <td>CHUKWUDI035</td>\n",
       "      <td>DRA12_CHUKWUDI035</td>\n",
       "      <td>11.6</td>\n",
       "      <td>Ultra Low fat</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>Soft Drinks</td>\n",
       "      <td>354.79</td>\n",
       "      <td>2011</td>\n",
       "      <td>Small</td>\n",
       "      <td>Cluster 2</td>\n",
       "      <td>Supermarket Type1</td>\n",
       "      <td>2481.77</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Product_Identifier Supermarket_Identifier Product_Supermarket_Identifier  \\\n",
       "0              DRA12            CHUKWUDI010              DRA12_CHUKWUDI010   \n",
       "1              DRA12            CHUKWUDI013              DRA12_CHUKWUDI013   \n",
       "2              DRA12            CHUKWUDI017              DRA12_CHUKWUDI017   \n",
       "3              DRA12            CHUKWUDI018              DRA12_CHUKWUDI018   \n",
       "4              DRA12            CHUKWUDI035              DRA12_CHUKWUDI035   \n",
       "\n",
       "   Product_Weight Product_Fat_Content  Product_Shelf_Visibility Product_Type  \\\n",
       "0            11.6             Low Fat                  0.068535  Soft Drinks   \n",
       "1            11.6             Low Fat                  0.040912  Soft Drinks   \n",
       "2            11.6             Low Fat                  0.041178  Soft Drinks   \n",
       "3            11.6             Low Fat                  0.041113  Soft Drinks   \n",
       "4            11.6       Ultra Low fat                  0.000000  Soft Drinks   \n",
       "\n",
       "   Product_Price  Supermarket_Opening_Year Supermarket _Size  \\\n",
       "0         357.54                      2005               NaN   \n",
       "1         355.79                      1994              High   \n",
       "2         350.79                      2014               NaN   \n",
       "3         355.04                      2016            Medium   \n",
       "4         354.79                      2011             Small   \n",
       "\n",
       "  Supermarket_Location_Type   Supermarket_Type  Product_Supermarket_Sales  \n",
       "0                 Cluster 3      Grocery Store                     709.08  \n",
       "1                 Cluster 3  Supermarket Type1                    6381.69  \n",
       "2                 Cluster 2  Supermarket Type1                    6381.69  \n",
       "3                 Cluster 3  Supermarket Type2                    2127.23  \n",
       "4                 Cluster 2  Supermarket Type1                    2481.77  "
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "supermarket_data.head()"
   ]
  },
  {
   "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>Product_Weight</th>\n",
       "      <th>Product_Shelf_Visibility</th>\n",
       "      <th>Product_Price</th>\n",
       "      <th>Supermarket_Opening_Year</th>\n",
       "      <th>Product_Supermarket_Sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>4188.000000</td>\n",
       "      <td>4990.000000</td>\n",
       "      <td>4990.000000</td>\n",
       "      <td>4990.000000</td>\n",
       "      <td>4990.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>12.908838</td>\n",
       "      <td>0.066916</td>\n",
       "      <td>391.803796</td>\n",
       "      <td>2004.783567</td>\n",
       "      <td>6103.520164</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>4.703256</td>\n",
       "      <td>0.053058</td>\n",
       "      <td>119.378259</td>\n",
       "      <td>8.283151</td>\n",
       "      <td>4447.333835</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>4.555000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>78.730000</td>\n",
       "      <td>1992.000000</td>\n",
       "      <td>83.230000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>8.767500</td>\n",
       "      <td>0.027273</td>\n",
       "      <td>307.890000</td>\n",
       "      <td>1994.000000</td>\n",
       "      <td>2757.660000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>12.600000</td>\n",
       "      <td>0.053564</td>\n",
       "      <td>393.860000</td>\n",
       "      <td>2006.000000</td>\n",
       "      <td>5374.675000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>17.100000</td>\n",
       "      <td>0.095358</td>\n",
       "      <td>465.067500</td>\n",
       "      <td>2011.000000</td>\n",
       "      <td>8522.240000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>21.350000</td>\n",
       "      <td>0.328391</td>\n",
       "      <td>667.220000</td>\n",
       "      <td>2016.000000</td>\n",
       "      <td>32717.410000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Product_Weight  Product_Shelf_Visibility  Product_Price  \\\n",
       "count     4188.000000               4990.000000    4990.000000   \n",
       "mean        12.908838                  0.066916     391.803796   \n",
       "std          4.703256                  0.053058     119.378259   \n",
       "min          4.555000                  0.000000      78.730000   \n",
       "25%          8.767500                  0.027273     307.890000   \n",
       "50%         12.600000                  0.053564     393.860000   \n",
       "75%         17.100000                  0.095358     465.067500   \n",
       "max         21.350000                  0.328391     667.220000   \n",
       "\n",
       "       Supermarket_Opening_Year  Product_Supermarket_Sales  \n",
       "count               4990.000000                4990.000000  \n",
       "mean                2004.783567                6103.520164  \n",
       "std                    8.283151                4447.333835  \n",
       "min                 1992.000000                  83.230000  \n",
       "25%                 1994.000000                2757.660000  \n",
       "50%                 2006.000000                5374.675000  \n",
       "75%                 2011.000000                8522.240000  \n",
       "max                 2016.000000               32717.410000  "
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "supermarket_data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4990, 13)"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "supermarket_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "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>Product_Identifier</th>\n",
       "      <th>Supermarket_Identifier</th>\n",
       "      <th>Product_Supermarket_Identifier</th>\n",
       "      <th>Product_Weight</th>\n",
       "      <th>Product_Fat_Content</th>\n",
       "      <th>Product_Shelf_Visibility</th>\n",
       "      <th>Product_Type</th>\n",
       "      <th>Product_Price</th>\n",
       "      <th>Supermarket_Opening_Year</th>\n",
       "      <th>Supermarket _Size</th>\n",
       "      <th>Supermarket_Location_Type</th>\n",
       "      <th>Supermarket_Type</th>\n",
       "      <th>Product_Supermarket_Sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>DRA12</td>\n",
       "      <td>CHUKWUDI010</td>\n",
       "      <td>DRA12_CHUKWUDI010</td>\n",
       "      <td>11.6</td>\n",
       "      <td>Low Fat</td>\n",
       "      <td>0.068535</td>\n",
       "      <td>Soft Drinks</td>\n",
       "      <td>357.54</td>\n",
       "      <td>2005</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Cluster 3</td>\n",
       "      <td>Grocery Store</td>\n",
       "      <td>709.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>DRA12</td>\n",
       "      <td>CHUKWUDI013</td>\n",
       "      <td>DRA12_CHUKWUDI013</td>\n",
       "      <td>11.6</td>\n",
       "      <td>Low Fat</td>\n",
       "      <td>0.040912</td>\n",
       "      <td>Soft Drinks</td>\n",
       "      <td>355.79</td>\n",
       "      <td>1994</td>\n",
       "      <td>High</td>\n",
       "      <td>Cluster 3</td>\n",
       "      <td>Supermarket Type1</td>\n",
       "      <td>6381.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>DRA12</td>\n",
       "      <td>CHUKWUDI017</td>\n",
       "      <td>DRA12_CHUKWUDI017</td>\n",
       "      <td>11.6</td>\n",
       "      <td>Low Fat</td>\n",
       "      <td>0.041178</td>\n",
       "      <td>Soft Drinks</td>\n",
       "      <td>350.79</td>\n",
       "      <td>2014</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Cluster 2</td>\n",
       "      <td>Supermarket Type1</td>\n",
       "      <td>6381.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>DRA12</td>\n",
       "      <td>CHUKWUDI018</td>\n",
       "      <td>DRA12_CHUKWUDI018</td>\n",
       "      <td>11.6</td>\n",
       "      <td>Low Fat</td>\n",
       "      <td>0.041113</td>\n",
       "      <td>Soft Drinks</td>\n",
       "      <td>355.04</td>\n",
       "      <td>2016</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Cluster 3</td>\n",
       "      <td>Supermarket Type2</td>\n",
       "      <td>2127.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>DRA12</td>\n",
       "      <td>CHUKWUDI035</td>\n",
       "      <td>DRA12_CHUKWUDI035</td>\n",
       "      <td>11.6</td>\n",
       "      <td>Ultra Low fat</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>Soft Drinks</td>\n",
       "      <td>354.79</td>\n",
       "      <td>2011</td>\n",
       "      <td>Small</td>\n",
       "      <td>Cluster 2</td>\n",
       "      <td>Supermarket Type1</td>\n",
       "      <td>2481.77</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Product_Identifier Supermarket_Identifier Product_Supermarket_Identifier  \\\n",
       "0              DRA12            CHUKWUDI010              DRA12_CHUKWUDI010   \n",
       "1              DRA12            CHUKWUDI013              DRA12_CHUKWUDI013   \n",
       "2              DRA12            CHUKWUDI017              DRA12_CHUKWUDI017   \n",
       "3              DRA12            CHUKWUDI018              DRA12_CHUKWUDI018   \n",
       "4              DRA12            CHUKWUDI035              DRA12_CHUKWUDI035   \n",
       "\n",
       "   Product_Weight Product_Fat_Content  Product_Shelf_Visibility Product_Type  \\\n",
       "0            11.6             Low Fat                  0.068535  Soft Drinks   \n",
       "1            11.6             Low Fat                  0.040912  Soft Drinks   \n",
       "2            11.6             Low Fat                  0.041178  Soft Drinks   \n",
       "3            11.6             Low Fat                  0.041113  Soft Drinks   \n",
       "4            11.6       Ultra Low fat                  0.000000  Soft Drinks   \n",
       "\n",
       "   Product_Price  Supermarket_Opening_Year Supermarket _Size  \\\n",
       "0         357.54                      2005               NaN   \n",
       "1         355.79                      1994              High   \n",
       "2         350.79                      2014               NaN   \n",
       "3         355.04                      2016            Medium   \n",
       "4         354.79                      2011             Small   \n",
       "\n",
       "  Supermarket_Location_Type   Supermarket_Type  Product_Supermarket_Sales  \n",
       "0                 Cluster 3      Grocery Store                     709.08  \n",
       "1                 Cluster 3  Supermarket Type1                    6381.69  \n",
       "2                 Cluster 2  Supermarket Type1                    6381.69  \n",
       "3                 Cluster 3  Supermarket Type2                    2127.23  \n",
       "4                 Cluster 2  Supermarket Type1                    2481.77  "
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "supermarket_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "modern_supermarket = supermarket_data[(supermarket_data['Product_Price'] > 500) & (supermarket_data['Supermarket_Opening_Year'] > 2014)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(25, 13)"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "modern_supermarket.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Low Fat', 'Ultra Low fat', 'Normal Fat'], dtype=object)"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "supermarket_data['Product_Fat_Content'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Low Fat          3039\n",
       "Normal Fat       1773\n",
       "Ultra Low fat     178\n",
       "Name: Product_Fat_Content, dtype: int64"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "supermarket_data['Product_Fat_Content'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Grocery Store', 'Supermarket Type1', 'Supermarket Type2',\n",
       "       'Supermarket Type3'], dtype=object)"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "supermarket_data['Supermarket_Type'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Supermarket Type1    3304\n",
       "Grocery Store         724\n",
       "Supermarket Type2     500\n",
       "Supermarket Type3     462\n",
       "Name: Supermarket_Type, dtype: int64"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "supermarket_data['Supermarket_Type'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}