00 .Exploratory+Data+Analysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'C:\\\\Users\\\\Mohan\\\\Desktop\\\\Remote Internship-2020\\\\Python codes'"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import os\n",
    "os.getcwd()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'C:\\\\Users\\\\Mohan\\\\Desktop\\\\Remote Internship-2020\\\\Data Sets'"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "os.chdir(\"C:/Users/Mohan/Desktop/Remote Internship-2020/Data Sets\")\n",
    "os.getcwd()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Sales Executive</th>\n",
       "      <th>Name</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Location</th>\n",
       "      <th>Sales in Rs</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Mahesh</td>\n",
       "      <td>Male</td>\n",
       "      <td>25</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Suresh</td>\n",
       "      <td>Male</td>\n",
       "      <td>22</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>Vijay</td>\n",
       "      <td>Male</td>\n",
       "      <td>20</td>\n",
       "      <td>Bangalore</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>Ramesh</td>\n",
       "      <td>Male</td>\n",
       "      <td>27</td>\n",
       "      <td>Bangalore</td>\n",
       "      <td>77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>Jagadish</td>\n",
       "      <td>Male</td>\n",
       "      <td>28</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>Harish</td>\n",
       "      <td>Male</td>\n",
       "      <td>24</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>Karan</td>\n",
       "      <td>Male</td>\n",
       "      <td>24</td>\n",
       "      <td>Bangalore</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>Raju</td>\n",
       "      <td>Male</td>\n",
       "      <td>23</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "      <td>Maya</td>\n",
       "      <td>Female</td>\n",
       "      <td>24</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>Rani</td>\n",
       "      <td>Female</td>\n",
       "      <td>30</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>11</td>\n",
       "      <td>Lakshmi</td>\n",
       "      <td>Female</td>\n",
       "      <td>19</td>\n",
       "      <td>Bangalore</td>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>12</td>\n",
       "      <td>Roopa</td>\n",
       "      <td>Female</td>\n",
       "      <td>24</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12</td>\n",
       "      <td>13</td>\n",
       "      <td>Sravya</td>\n",
       "      <td>Female</td>\n",
       "      <td>26</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13</td>\n",
       "      <td>14</td>\n",
       "      <td>Sruthi</td>\n",
       "      <td>Female</td>\n",
       "      <td>26</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>14</td>\n",
       "      <td>15</td>\n",
       "      <td>Swetha</td>\n",
       "      <td>Female</td>\n",
       "      <td>21</td>\n",
       "      <td>Bangalore</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>15</td>\n",
       "      <td>16</td>\n",
       "      <td>Aishwarya</td>\n",
       "      <td>Female</td>\n",
       "      <td>24</td>\n",
       "      <td>Bangalore</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16</td>\n",
       "      <td>17</td>\n",
       "      <td>Madhavi</td>\n",
       "      <td>Female</td>\n",
       "      <td>29</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>17</td>\n",
       "      <td>18</td>\n",
       "      <td>Rajini</td>\n",
       "      <td>Female</td>\n",
       "      <td>27</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>18</td>\n",
       "      <td>19</td>\n",
       "      <td>Haritha</td>\n",
       "      <td>Female</td>\n",
       "      <td>24</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>19</td>\n",
       "      <td>20</td>\n",
       "      <td>Swathi</td>\n",
       "      <td>Female</td>\n",
       "      <td>25</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>68</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Sales Executive       Name  Gender  Age   Location  Sales in Rs\n",
       "0                 1     Mahesh    Male   25  Hyderabad           50\n",
       "1                 2     Suresh    Male   22  Hyderabad           75\n",
       "2                 3      Vijay    Male   20  Bangalore           11\n",
       "3                 4     Ramesh    Male   27  Bangalore           77\n",
       "4                 5   Jagadish    Male   28     Mumbai           45\n",
       "5                 6     Harish    Male   24  Hyderabad           52\n",
       "6                 7      Karan    Male   24  Bangalore           26\n",
       "7                 8       Raju    Male   23     Mumbai           24\n",
       "8                 9       Maya  Female   24     Mumbai           28\n",
       "9                10       Rani  Female   30     Mumbai           31\n",
       "10               11    Lakshmi  Female   19  Bangalore           36\n",
       "11               12      Roopa  Female   24  Hyderabad           72\n",
       "12               13     Sravya  Female   26  Hyderabad           69\n",
       "13               14     Sruthi  Female   26  Hyderabad           51\n",
       "14               15     Swetha  Female   21  Bangalore           34\n",
       "15               16  Aishwarya  Female   24  Bangalore           40\n",
       "16               17    Madhavi  Female   29     Mumbai           18\n",
       "17               18     Rajini  Female   27     Mumbai           35\n",
       "18               19    Haritha  Female   24  Hyderabad           29\n",
       "19               20     Swathi  Female   25  Hyderabad           68"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "data1=pd.read_csv(\"sales.csv\")\n",
    "data1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data1=pd.read_csv(\"sales.csv\",nrows=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Sales Executive</th>\n",
       "      <th>Name</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Location</th>\n",
       "      <th>Sales in Rs</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Mahesh</td>\n",
       "      <td>Male</td>\n",
       "      <td>25</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Suresh</td>\n",
       "      <td>Male</td>\n",
       "      <td>22</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Vijay</td>\n",
       "      <td>Male</td>\n",
       "      <td>20</td>\n",
       "      <td>Bangalore</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Sales Executive    Name Gender  Age   Location  Sales in Rs\n",
       "0                1  Mahesh   Male   25  Hyderabad           50\n",
       "1                2  Suresh   Male   22  Hyderabad           75\n",
       "2                3   Vijay   Male   20  Bangalore           11"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Sales Executive</th>\n",
       "      <th>Age</th>\n",
       "      <th>Sales in Rs</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>count</td>\n",
       "      <td>20.00000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>20.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>mean</td>\n",
       "      <td>10.50000</td>\n",
       "      <td>24.600000</td>\n",
       "      <td>43.550000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>std</td>\n",
       "      <td>5.91608</td>\n",
       "      <td>2.835861</td>\n",
       "      <td>19.974918</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>min</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>11.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25%</td>\n",
       "      <td>5.75000</td>\n",
       "      <td>23.750000</td>\n",
       "      <td>28.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50%</td>\n",
       "      <td>10.50000</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>38.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>75%</td>\n",
       "      <td>15.25000</td>\n",
       "      <td>26.250000</td>\n",
       "      <td>56.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>max</td>\n",
       "      <td>20.00000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>77.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Sales Executive        Age  Sales in Rs\n",
       "count         20.00000  20.000000    20.000000\n",
       "mean          10.50000  24.600000    43.550000\n",
       "std            5.91608   2.835861    19.974918\n",
       "min            1.00000  19.000000    11.000000\n",
       "25%            5.75000  23.750000    28.750000\n",
       "50%           10.50000  24.000000    38.000000\n",
       "75%           15.25000  26.250000    56.000000\n",
       "max           20.00000  30.000000    77.000000"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data1.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Sales Executive</th>\n",
       "      <th>Age</th>\n",
       "      <th>Sales in Rs</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>Sales Executive</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>2.947368</td>\n",
       "      <td>-13.868421</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Age</td>\n",
       "      <td>2.947368</td>\n",
       "      <td>8.042105</td>\n",
       "      <td>6.705263</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Sales in Rs</td>\n",
       "      <td>-13.868421</td>\n",
       "      <td>6.705263</td>\n",
       "      <td>398.997368</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Sales Executive       Age  Sales in Rs\n",
       "Sales Executive        35.000000  2.947368   -13.868421\n",
       "Age                     2.947368  8.042105     6.705263\n",
       "Sales in Rs           -13.868421  6.705263   398.997368"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data1.cov()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Sales Executive</th>\n",
       "      <th>Age</th>\n",
       "      <th>Sales in Rs</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>Sales Executive</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.175677</td>\n",
       "      <td>-0.117357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Age</td>\n",
       "      <td>0.175677</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.118371</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Sales in Rs</td>\n",
       "      <td>-0.117357</td>\n",
       "      <td>0.118371</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Sales Executive       Age  Sales in Rs\n",
       "Sales Executive         1.000000  0.175677    -0.117357\n",
       "Age                     0.175677  1.000000     0.118371\n",
       "Sales in Rs            -0.117357  0.118371     1.000000"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data1.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Sales Executive</th>\n",
       "      <th>Name</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Location</th>\n",
       "      <th>Sales in Rs</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Mahesh</td>\n",
       "      <td>Male</td>\n",
       "      <td>25</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Suresh</td>\n",
       "      <td>Male</td>\n",
       "      <td>22</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Vijay</td>\n",
       "      <td>Male</td>\n",
       "      <td>20</td>\n",
       "      <td>Bangalore</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Ramesh</td>\n",
       "      <td>Male</td>\n",
       "      <td>27</td>\n",
       "      <td>Bangalore</td>\n",
       "      <td>77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Jagadish</td>\n",
       "      <td>Male</td>\n",
       "      <td>28</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Sales Executive      Name Gender  Age   Location  Sales in Rs\n",
       "0                1    Mahesh   Male   25  Hyderabad           50\n",
       "1                2    Suresh   Male   22  Hyderabad           75\n",
       "2                3     Vijay   Male   20  Bangalore           11\n",
       "3                4    Ramesh   Male   27  Bangalore           77\n",
       "4                5  Jagadish   Male   28     Mumbai           45"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Sales Executive</th>\n",
       "      <th>Name</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Location</th>\n",
       "      <th>Sales ( in Rs)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Mahesh</td>\n",
       "      <td>Male</td>\n",
       "      <td>25</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Suresh</td>\n",
       "      <td>Male</td>\n",
       "      <td>22</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Sales Executive    Name Gender  Age   Location  Sales ( in Rs)\n",
       "0                1  Mahesh   Male   25  Hyderabad              50\n",
       "1                2  Suresh   Male   22  Hyderabad              75"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data1.head(n=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Sales Executive</th>\n",
       "      <th>Name</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Location</th>\n",
       "      <th>Sales in Rs</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>16</td>\n",
       "      <td>Aishwarya</td>\n",
       "      <td>Female</td>\n",
       "      <td>24</td>\n",
       "      <td>Bangalore</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>17</td>\n",
       "      <td>Madhavi</td>\n",
       "      <td>Female</td>\n",
       "      <td>29</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>18</td>\n",
       "      <td>Rajini</td>\n",
       "      <td>Female</td>\n",
       "      <td>27</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>19</td>\n",
       "      <td>Haritha</td>\n",
       "      <td>Female</td>\n",
       "      <td>24</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>20</td>\n",
       "      <td>Swathi</td>\n",
       "      <td>Female</td>\n",
       "      <td>25</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>68</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Sales Executive       Name  Gender  Age   Location  Sales in Rs\n",
       "15               16  Aishwarya  Female   24  Bangalore           40\n",
       "16               17    Madhavi  Female   29     Mumbai           18\n",
       "17               18     Rajini  Female   27     Mumbai           35\n",
       "18               19    Haritha  Female   24  Hyderabad           29\n",
       "19               20     Swathi  Female   25  Hyderabad           68"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data1.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Sales Executive', 'Name', 'Gender', 'Age', 'Location', 'Sales in Rs'], dtype='object')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data1.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Sales Executive     int64\n",
       "Name               object\n",
       "Gender             object\n",
       "Age                 int64\n",
       "Location           object\n",
       "Sales in Rs         int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data1.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([25, 22, 20, 27, 28, 24, 23, 30, 19, 26, 21, 29], dtype=int64)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data1['Age'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     Hyderabad\n",
       "1     Hyderabad\n",
       "2     Bangalore\n",
       "3     Bangalore\n",
       "4        Mumbai\n",
       "5     Hyderabad\n",
       "6     Bangalore\n",
       "7        Mumbai\n",
       "8        Mumbai\n",
       "9        Mumbai\n",
       "10    Bangalore\n",
       "11    Hyderabad\n",
       "12    Hyderabad\n",
       "13    Hyderabad\n",
       "14    Bangalore\n",
       "15    Bangalore\n",
       "16       Mumbai\n",
       "17       Mumbai\n",
       "18    Hyderabad\n",
       "19    Hyderabad\n",
       "Name: Location, dtype: object"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data1['Location']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Sales Executive</th>\n",
       "      <th>Name</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Location</th>\n",
       "      <th>Sales ( in Rs)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Mahesh</td>\n",
       "      <td>Male</td>\n",
       "      <td>25</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Suresh</td>\n",
       "      <td>Male</td>\n",
       "      <td>22</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Vijay</td>\n",
       "      <td>Male</td>\n",
       "      <td>20</td>\n",
       "      <td>Bangalore</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Sales Executive    Name Gender  Age   Location  Sales ( in Rs)\n",
       "0                1  Mahesh   Male   25  Hyderabad              50\n",
       "1                2  Suresh   Male   22  Hyderabad              75\n",
       "2                3   Vijay   Male   20  Bangalore              11"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data1[0:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "mean_sales=data1['Sales in Rs'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "43.55"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_sales"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(20, 6)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data1.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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>Age</th>\n",
       "      <th>Sales Executive</th>\n",
       "      <th>Sales in Rs</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Location</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Bangalore</th>\n",
       "      <td>22.500000</td>\n",
       "      <td>9.333333</td>\n",
       "      <td>37.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hyderabad</th>\n",
       "      <td>24.500000</td>\n",
       "      <td>10.875000</td>\n",
       "      <td>58.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mumbai</th>\n",
       "      <td>26.833333</td>\n",
       "      <td>11.166667</td>\n",
       "      <td>30.166667</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Age  Sales Executive  Sales in Rs\n",
       "Location                                          \n",
       "Bangalore  22.500000         9.333333    37.333333\n",
       "Hyderabad  24.500000        10.875000    58.250000\n",
       "Mumbai     26.833333        11.166667    30.166667"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data1.pivot_table(data1,index=['Location'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 20 entries, 0 to 19\n",
      "Data columns (total 6 columns):\n",
      "Sales Executive    20 non-null int64\n",
      "Name               20 non-null object\n",
      "Gender             20 non-null object\n",
      "Age                20 non-null int64\n",
      "Location           20 non-null object\n",
      "Sales in Rs        20 non-null int64\n",
      "dtypes: int64(3), object(3)\n",
      "memory usage: 1.0+ KB\n"
     ]
    }
   ],
   "source": [
    "data1.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Female    12\n",
       "Male       8\n",
       "Name: Gender, dtype: int64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data1['Gender'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Hyderabad    8\n",
       "Bangalore    6\n",
       "Mumbai       6\n",
       "Name: Location, dtype: int64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data1['Location'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "male_data=data1[data1['Gender'].str.contains('Male')]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Sales Executive</th>\n",
       "      <th>Name</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Location</th>\n",
       "      <th>Sales in Rs</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Mahesh</td>\n",
       "      <td>Male</td>\n",
       "      <td>25</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Suresh</td>\n",
       "      <td>Male</td>\n",
       "      <td>22</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>Vijay</td>\n",
       "      <td>Male</td>\n",
       "      <td>20</td>\n",
       "      <td>Bangalore</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>Ramesh</td>\n",
       "      <td>Male</td>\n",
       "      <td>27</td>\n",
       "      <td>Bangalore</td>\n",
       "      <td>77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>Jagadish</td>\n",
       "      <td>Male</td>\n",
       "      <td>28</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>Harish</td>\n",
       "      <td>Male</td>\n",
       "      <td>24</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>Karan</td>\n",
       "      <td>Male</td>\n",
       "      <td>24</td>\n",
       "      <td>Bangalore</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>Raju</td>\n",
       "      <td>Male</td>\n",
       "      <td>23</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Sales Executive      Name Gender  Age   Location  Sales in Rs\n",
       "0                1    Mahesh   Male   25  Hyderabad           50\n",
       "1                2    Suresh   Male   22  Hyderabad           75\n",
       "2                3     Vijay   Male   20  Bangalore           11\n",
       "3                4    Ramesh   Male   27  Bangalore           77\n",
       "4                5  Jagadish   Male   28     Mumbai           45\n",
       "5                6    Harish   Male   24  Hyderabad           52\n",
       "6                7     Karan   Male   24  Bangalore           26\n",
       "7                8      Raju   Male   23     Mumbai           24"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "male_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "45.0"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "male_data['Sales in Rs'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "female_data=data1[data1['Gender'].str.contains('Female')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Sales Executive</th>\n",
       "      <th>Name</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Location</th>\n",
       "      <th>Sales in Rs</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "      <td>Maya</td>\n",
       "      <td>Female</td>\n",
       "      <td>24</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>Rani</td>\n",
       "      <td>Female</td>\n",
       "      <td>30</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>11</td>\n",
       "      <td>Lakshmi</td>\n",
       "      <td>Female</td>\n",
       "      <td>19</td>\n",
       "      <td>Bangalore</td>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>12</td>\n",
       "      <td>Roopa</td>\n",
       "      <td>Female</td>\n",
       "      <td>24</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12</td>\n",
       "      <td>13</td>\n",
       "      <td>Sravya</td>\n",
       "      <td>Female</td>\n",
       "      <td>26</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13</td>\n",
       "      <td>14</td>\n",
       "      <td>Sruthi</td>\n",
       "      <td>Female</td>\n",
       "      <td>26</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>14</td>\n",
       "      <td>15</td>\n",
       "      <td>Swetha</td>\n",
       "      <td>Female</td>\n",
       "      <td>21</td>\n",
       "      <td>Bangalore</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>15</td>\n",
       "      <td>16</td>\n",
       "      <td>Aishwarya</td>\n",
       "      <td>Female</td>\n",
       "      <td>24</td>\n",
       "      <td>Bangalore</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16</td>\n",
       "      <td>17</td>\n",
       "      <td>Madhavi</td>\n",
       "      <td>Female</td>\n",
       "      <td>29</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>17</td>\n",
       "      <td>18</td>\n",
       "      <td>Rajini</td>\n",
       "      <td>Female</td>\n",
       "      <td>27</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>18</td>\n",
       "      <td>19</td>\n",
       "      <td>Haritha</td>\n",
       "      <td>Female</td>\n",
       "      <td>24</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>19</td>\n",
       "      <td>20</td>\n",
       "      <td>Swathi</td>\n",
       "      <td>Female</td>\n",
       "      <td>25</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>68</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Sales Executive       Name  Gender  Age   Location  Sales in Rs\n",
       "8                 9       Maya  Female   24     Mumbai           28\n",
       "9                10       Rani  Female   30     Mumbai           31\n",
       "10               11    Lakshmi  Female   19  Bangalore           36\n",
       "11               12      Roopa  Female   24  Hyderabad           72\n",
       "12               13     Sravya  Female   26  Hyderabad           69\n",
       "13               14     Sruthi  Female   26  Hyderabad           51\n",
       "14               15     Swetha  Female   21  Bangalore           34\n",
       "15               16  Aishwarya  Female   24  Bangalore           40\n",
       "16               17    Madhavi  Female   29     Mumbai           18\n",
       "17               18     Rajini  Female   27     Mumbai           35\n",
       "18               19    Haritha  Female   24  Hyderabad           29\n",
       "19               20     Swathi  Female   25  Hyderabad           68"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "female_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "42.583333333333336"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "female_data['Sales in Rs'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Sales Executive</th>\n",
       "      <th>Name</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Location</th>\n",
       "      <th>Sales in Rs</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>12</td>\n",
       "      <td>Roopa</td>\n",
       "      <td>Female</td>\n",
       "      <td>24</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>13</td>\n",
       "      <td>Sravya</td>\n",
       "      <td>Female</td>\n",
       "      <td>26</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>14</td>\n",
       "      <td>Sruthi</td>\n",
       "      <td>Female</td>\n",
       "      <td>26</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>19</td>\n",
       "      <td>Haritha</td>\n",
       "      <td>Female</td>\n",
       "      <td>24</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>20</td>\n",
       "      <td>Swathi</td>\n",
       "      <td>Female</td>\n",
       "      <td>25</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>68</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Sales Executive     Name  Gender  Age   Location  Sales in Rs\n",
       "11               12    Roopa  Female   24  Hyderabad           72\n",
       "12               13   Sravya  Female   26  Hyderabad           69\n",
       "13               14   Sruthi  Female   26  Hyderabad           51\n",
       "18               19  Haritha  Female   24  Hyderabad           29\n",
       "19               20   Swathi  Female   25  Hyderabad           68"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data1[data1['Gender'].str.contains('Female') & data1['Location'].str.contains('Hyderabad')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Sales Executive</th>\n",
       "      <th>Name</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Location</th>\n",
       "      <th>Sales in Rs</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Mahesh</td>\n",
       "      <td>Male</td>\n",
       "      <td>25</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Suresh</td>\n",
       "      <td>Male</td>\n",
       "      <td>22</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>Harish</td>\n",
       "      <td>Male</td>\n",
       "      <td>24</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>52</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Sales Executive    Name Gender  Age   Location  Sales in Rs\n",
       "0                1  Mahesh   Male   25  Hyderabad           50\n",
       "1                2  Suresh   Male   22  Hyderabad           75\n",
       "5                6  Harish   Male   24  Hyderabad           52"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data1[data1['Gender'].str.contains('Male') & data1['Location'].str.contains('Hyderabad')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x16a279b4c88>"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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4doWlrtsar8u/V3OuU7ZfJ+kcjdgnzq3R25OSfql4fKWkrw+7tkGwff7KlVS2t0p6i5rz8PdJurYYNpBM2TB3Jtr+eUn/LGlRzbMTSfo9NefMDkn6YUn/KelXI+I7lRTZozV6+zNJr5T0X8WyByLit4ZfYX/a9RcR/7hqzDFJjYgYtTBrd+w+L+lWSZdKekHSTRHxhUqK7NEavT0r6WY1p3f+V9JvR8RCJUX2wfZPqfnHwk1qnvQeiog/tP2jan5u/2slfVnSu1b+KNzzvjZKUAPAqNowUx8AMKoIagBIjqAGgOQIagBIjqAGgOQIagBIjqAGgOT+HyiYH9qGn3HIAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data1['Age'].hist(bins=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Sales Executive', 'Name', 'Gender', 'Age', 'Location', 'Sales in Rs'], dtype='object')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data1.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x19a74bfb1d0>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x19a74c0a550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data1['Sales in Rs'].hist(bins=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x16a2803c548>"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data1.boxplot(column='Sales in Rs')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x243005eb588>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2430059f208>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data1.boxplot(column='Sales in Rs',by='Age')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x216b0cd3ef0>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x216b0cba1d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data1.boxplot(column='Sales in Rs',by = 'Location')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x243007d0cf8>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x24300768be0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data1.boxplot(column='Sales in Rs',by='Gender')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "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>Gender</th>\n",
       "      <th>Female</th>\n",
       "      <th>Male</th>\n",
       "      <th>All</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sales in Rs</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>All</th>\n",
       "      <td>12</td>\n",
       "      <td>8</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Gender       Female  Male  All\n",
       "Sales in Rs                   \n",
       "11                0     1    1\n",
       "18                1     0    1\n",
       "24                0     1    1\n",
       "26                0     1    1\n",
       "28                1     0    1\n",
       "29                1     0    1\n",
       "31                1     0    1\n",
       "34                1     0    1\n",
       "35                1     0    1\n",
       "36                1     0    1\n",
       "40                1     0    1\n",
       "45                0     1    1\n",
       "50                0     1    1\n",
       "51                1     0    1\n",
       "52                0     1    1\n",
       "68                1     0    1\n",
       "69                1     0    1\n",
       "72                1     0    1\n",
       "75                0     1    1\n",
       "77                0     1    1\n",
       "All              12     8   20"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(data1['Sales in Rs'],data1['Gender'],margins=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([<matplotlib.axes._subplots.AxesSubplot object at 0x00000243007F66A0>,\n",
       "       <matplotlib.axes._subplots.AxesSubplot object at 0x0000024301880AC8>],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2430181ec88>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data1.hist(column = \"Sales in Rs\", by = \"Gender\", bins = 10)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Plotly Viuslizations\n",
    "#Plotly is an open-source and browser-based graphing library which facilitates interactive plotting.\n",
    "#Plotly provides interactive visualizations using a wrapper called 'cufflinks'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting cufflinks\n",
      "  Downloading https://files.pythonhosted.org/packages/1a/18/4d32edaaf31ba4af9745dac676c4a28c48d3fc539000c29e855bd8db3b86/cufflinks-0.17.3.tar.gz (81kB)\n",
      "Collecting plotly\n",
      "  Downloading https://files.pythonhosted.org/packages/15/90/918bccb0ca60dc6d126d921e2c67126d75949f5da777e6b18c51fb12603d/plotly-4.6.0-py2.py3-none-any.whl (7.1MB)\n",
      "Requirement already satisfied: numpy>=1.9.2 in c:\\users\\mohan\\anacond3\\lib\\site-packages (from cufflinks) (1.16.5)\n",
      "Requirement already satisfied: pandas>=0.19.2 in c:\\users\\mohan\\anacond3\\lib\\site-packages (from cufflinks) (0.25.1)\n",
      "Requirement already satisfied: six>=1.9.0 in c:\\users\\mohan\\anacond3\\lib\\site-packages (from cufflinks) (1.12.0)\n",
      "Collecting colorlover>=0.2.1 (from cufflinks)\n",
      "  Downloading https://files.pythonhosted.org/packages/9a/53/f696e4480b1d1de3b1523991dea71cf417c8b19fe70c704da164f3f90972/colorlover-0.3.0-py3-none-any.whl\n",
      "Requirement already satisfied: setuptools>=34.4.1 in c:\\users\\mohan\\anacond3\\lib\\site-packages (from cufflinks) (41.4.0)\n",
      "Requirement already satisfied: ipython>=5.3.0 in c:\\users\\mohan\\anacond3\\lib\\site-packages (from cufflinks) (7.8.0)\n",
      "Requirement already satisfied: ipywidgets>=7.0.0 in c:\\users\\mohan\\anacond3\\lib\\site-packages (from cufflinks) (7.5.1)\n",
      "Collecting retrying>=1.3.3 (from plotly)\n",
      "  Downloading https://files.pythonhosted.org/packages/44/ef/beae4b4ef80902f22e3af073397f079c96969c69b2c7d52a57ea9ae61c9d/retrying-1.3.3.tar.gz\n",
      "Requirement already satisfied: pytz>=2017.2 in c:\\users\\mohan\\anacond3\\lib\\site-packages (from pandas>=0.19.2->cufflinks) (2019.3)\n",
      "Requirement already satisfied: python-dateutil>=2.6.1 in c:\\users\\mohan\\anacond3\\lib\\site-packages (from pandas>=0.19.2->cufflinks) (2.8.0)\n",
      "Requirement already satisfied: decorator in c:\\users\\mohan\\anacond3\\lib\\site-packages (from ipython>=5.3.0->cufflinks) (4.4.0)\n",
      "Requirement already satisfied: jedi>=0.10 in c:\\users\\mohan\\anacond3\\lib\\site-packages (from ipython>=5.3.0->cufflinks) (0.15.1)\n",
      "Requirement already satisfied: prompt-toolkit<2.1.0,>=2.0.0 in c:\\users\\mohan\\anacond3\\lib\\site-packages (from ipython>=5.3.0->cufflinks) (2.0.10)\n",
      "Requirement already satisfied: pickleshare in c:\\users\\mohan\\anacond3\\lib\\site-packages (from ipython>=5.3.0->cufflinks) (0.7.5)\n",
      "Requirement already satisfied: backcall in c:\\users\\mohan\\anacond3\\lib\\site-packages (from ipython>=5.3.0->cufflinks) (0.1.0)\n",
      "Requirement already satisfied: colorama; sys_platform == \"win32\" in c:\\users\\mohan\\anacond3\\lib\\site-packages (from ipython>=5.3.0->cufflinks) (0.4.1)\n",
      "Requirement already satisfied: pygments in c:\\users\\mohan\\anacond3\\lib\\site-packages (from ipython>=5.3.0->cufflinks) (2.4.2)\n",
      "Requirement already satisfied: traitlets>=4.2 in c:\\users\\mohan\\anacond3\\lib\\site-packages (from ipython>=5.3.0->cufflinks) (4.3.3)\n",
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      "Requirement already satisfied: ipython-genutils in c:\\users\\mohan\\anacond3\\lib\\site-packages (from traitlets>=4.2->ipython>=5.3.0->cufflinks) (0.2.0)\n",
      "Requirement already satisfied: notebook>=4.4.1 in c:\\users\\mohan\\anacond3\\lib\\site-packages (from widgetsnbextension~=3.5.0->ipywidgets>=7.0.0->cufflinks) (6.0.1)\n",
      "Requirement already satisfied: jupyter-client in c:\\users\\mohan\\anacond3\\lib\\site-packages (from ipykernel>=4.5.1->ipywidgets>=7.0.0->cufflinks) (5.3.3)\n",
      "Requirement already satisfied: tornado>=4.2 in c:\\users\\mohan\\anacond3\\lib\\site-packages (from ipykernel>=4.5.1->ipywidgets>=7.0.0->cufflinks) (6.0.3)\n",
      "Requirement already satisfied: jsonschema!=2.5.0,>=2.4 in c:\\users\\mohan\\anacond3\\lib\\site-packages (from nbformat>=4.2.0->ipywidgets>=7.0.0->cufflinks) (3.0.2)\n",
      "Requirement already satisfied: jupyter-core in c:\\users\\mohan\\anacond3\\lib\\site-packages (from nbformat>=4.2.0->ipywidgets>=7.0.0->cufflinks) (4.5.0)\n",
      "Requirement already satisfied: terminado>=0.8.1 in c:\\users\\mohan\\anacond3\\lib\\site-packages (from notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets>=7.0.0->cufflinks) (0.8.2)\n",
      "Requirement already satisfied: pyzmq>=17 in c:\\users\\mohan\\anacond3\\lib\\site-packages (from notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets>=7.0.0->cufflinks) (18.1.0)\n",
      "Requirement already satisfied: Send2Trash in c:\\users\\mohan\\anacond3\\lib\\site-packages (from notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets>=7.0.0->cufflinks) (1.5.0)\n",
      "Requirement already satisfied: prometheus-client in c:\\users\\mohan\\anacond3\\lib\\site-packages (from notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets>=7.0.0->cufflinks) (0.7.1)\n",
      "Requirement already satisfied: jinja2 in c:\\users\\mohan\\anacond3\\lib\\site-packages (from notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets>=7.0.0->cufflinks) (2.10.3)\n",
      "Requirement already satisfied: nbconvert in c:\\users\\mohan\\anacond3\\lib\\site-packages (from notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets>=7.0.0->cufflinks) (5.6.0)\n",
      "Requirement already satisfied: pywin32>=1.0; sys_platform == \"win32\" in c:\\users\\mohan\\anacond3\\lib\\site-packages (from jupyter-client->ipykernel>=4.5.1->ipywidgets>=7.0.0->cufflinks) (223)\n",
      "Requirement already satisfied: pyrsistent>=0.14.0 in c:\\users\\mohan\\anacond3\\lib\\site-packages (from jsonschema!=2.5.0,>=2.4->nbformat>=4.2.0->ipywidgets>=7.0.0->cufflinks) (0.15.4)\n",
      "Requirement already satisfied: attrs>=17.4.0 in c:\\users\\mohan\\anacond3\\lib\\site-packages (from jsonschema!=2.5.0,>=2.4->nbformat>=4.2.0->ipywidgets>=7.0.0->cufflinks) (19.2.0)\n",
      "Requirement already satisfied: MarkupSafe>=0.23 in c:\\users\\mohan\\anacond3\\lib\\site-packages (from jinja2->notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets>=7.0.0->cufflinks) (1.1.1)\n",
      "Requirement already satisfied: mistune<2,>=0.8.1 in c:\\users\\mohan\\anacond3\\lib\\site-packages (from nbconvert->notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets>=7.0.0->cufflinks) (0.8.4)\n",
      "Requirement already satisfied: pandocfilters>=1.4.1 in c:\\users\\mohan\\anacond3\\lib\\site-packages (from nbconvert->notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets>=7.0.0->cufflinks) (1.4.2)\n",
      "Requirement already satisfied: bleach in c:\\users\\mohan\\anacond3\\lib\\site-packages (from nbconvert->notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets>=7.0.0->cufflinks) (3.1.0)\n",
      "Requirement already satisfied: entrypoints>=0.2.2 in c:\\users\\mohan\\anacond3\\lib\\site-packages (from nbconvert->notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets>=7.0.0->cufflinks) (0.3)\n",
      "Requirement already satisfied: testpath in c:\\users\\mohan\\anacond3\\lib\\site-packages (from nbconvert->notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets>=7.0.0->cufflinks) (0.4.2)\n",
      "Requirement already satisfied: defusedxml in c:\\users\\mohan\\anacond3\\lib\\site-packages (from nbconvert->notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets>=7.0.0->cufflinks) (0.6.0)\n",
      "Requirement already satisfied: webencodings in c:\\users\\mohan\\anacond3\\lib\\site-packages (from bleach->nbconvert->notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets>=7.0.0->cufflinks) (0.5.1)\n",
      "Building wheels for collected packages: cufflinks, retrying\n",
      "  Building wheel for cufflinks (setup.py): started\n",
      "  Building wheel for cufflinks (setup.py): finished with status 'done'\n",
      "  Created wheel for cufflinks: filename=cufflinks-0.17.3-cp37-none-any.whl size=68730 sha256=766b1610f044350ffe9c57ace77c86c06946f58bb5d22e85568a9260629f934d\n",
      "  Stored in directory: C:\\Users\\Mohan\\AppData\\Local\\pip\\Cache\\wheels\\7d\\ba\\8d\\38b672c3e40d8bd22dd60b8e6e29965b43f2b4be4d064e44d5\n",
      "  Building wheel for retrying (setup.py): started\n",
      "  Building wheel for retrying (setup.py): finished with status 'done'\n",
      "  Created wheel for retrying: filename=retrying-1.3.3-cp37-none-any.whl size=11435 sha256=78d4f94110c97dfb3da3ed2b9edebd2654372d18ec34b97b6520624abcb6e768\n",
      "  Stored in directory: C:\\Users\\Mohan\\AppData\\Local\\pip\\Cache\\wheels\\d7\\a9\\33\\acc7b709e2a35caa7d4cae442f6fe6fbf2c43f80823d46460c\n",
      "Successfully built cufflinks retrying\n",
      "Installing collected packages: retrying, plotly, colorlover, cufflinks\n",
      "Successfully installed colorlover-0.3.0 cufflinks-0.17.3 plotly-4.6.0 retrying-1.3.3\n"
     ]
    }
   ],
   "source": [
    "#Install plotly package\n",
    "!pip install cufflinks plotly"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting chart-studio\n",
      "  Downloading https://files.pythonhosted.org/packages/ca/ce/330794a6b6ca4b9182c38fc69dd2a9cbff60fd49421cb8648ee5fee352dc/chart_studio-1.1.0-py3-none-any.whl (64kB)\n",
      "Requirement already satisfied: six in c:\\users\\mohan\\anacond3\\lib\\site-packages (from chart-studio) (1.12.0)\n",
      "Requirement already satisfied: retrying>=1.3.3 in c:\\users\\mohan\\anacond3\\lib\\site-packages (from chart-studio) (1.3.3)\n",
      "Requirement already satisfied: requests in c:\\users\\mohan\\anacond3\\lib\\site-packages (from chart-studio) (2.22.0)\n",
      "Requirement already satisfied: plotly in c:\\users\\mohan\\anacond3\\lib\\site-packages (from chart-studio) (4.6.0)\n",
      "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in c:\\users\\mohan\\anacond3\\lib\\site-packages (from requests->chart-studio) (1.24.2)\n",
      "Requirement already satisfied: certifi>=2017.4.17 in c:\\users\\mohan\\anacond3\\lib\\site-packages (from requests->chart-studio) (2019.9.11)\n",
      "Requirement already satisfied: idna<2.9,>=2.5 in c:\\users\\mohan\\anacond3\\lib\\site-packages (from requests->chart-studio) (2.8)\n",
      "Requirement already satisfied: chardet<3.1.0,>=3.0.2 in c:\\users\\mohan\\anacond3\\lib\\site-packages (from requests->chart-studio) (3.0.4)\n",
      "Installing collected packages: chart-studio\n",
      "Successfully installed chart-studio-1.1.0\n"
     ]
    }
   ],
   "source": [
    "!pip install chart-studio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Standard plotly imports\n",
    "import chart_studio.plotly as py\n",
    "import plotly.graph_objs as go\n",
    "from plotly.offline import iplot, init_notebook_mode"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "        <script type=\"text/javascript\">\n",
       "        window.PlotlyConfig = {MathJaxConfig: 'local'};\n",
       "        if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}\n",
       "        if (typeof require !== 'undefined') {\n",
       "        require.undef(\"plotly\");\n",
       "        requirejs.config({\n",
       "            paths: {\n",
       "                'plotly': ['https://cdn.plot.ly/plotly-latest.min']\n",
       "            }\n",
       "        });\n",
       "        require(['plotly'], function(Plotly) {\n",
       "            window._Plotly = Plotly;\n",
       "        });\n",
       "        }\n",
       "        </script>\n",
       "        "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "        <script type=\"text/javascript\">\n",
       "        window.PlotlyConfig = {MathJaxConfig: 'local'};\n",
       "        if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}\n",
       "        if (typeof require !== 'undefined') {\n",
       "        require.undef(\"plotly\");\n",
       "        requirejs.config({\n",
       "            paths: {\n",
       "                'plotly': ['https://cdn.plot.ly/plotly-latest.min']\n",
       "            }\n",
       "        });\n",
       "        require(['plotly'], function(Plotly) {\n",
       "            window._Plotly = Plotly;\n",
       "        });\n",
       "        }\n",
       "        </script>\n",
       "        "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Using plotly + cufflinks in offline mode\n",
    "import cufflinks\n",
    "cufflinks.go_offline(connected=True)\n",
    "init_notebook_mode(connected=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
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   "source": [
    "#Single Variable Distributions\n",
    "#Plot histogram on 'Age' variable\n",
    "data1['Age'].iplot(kind='hist', xTitle='Age',\n",
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   ],
   "source": [
    "#Plot histogram on 'Sales in Rs' variable\n",
    "data1['Sales in Rs'].iplot(kind='hist', xTitle='Sales in Rs',\n",
    "                  yTitle='count', title='Sales Distribution')"
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   "source": [
    "#Combine the power of Pandas with plotly + cufflinks\n",
    "#Plot a boxplot of Sales by Age\n",
    "data1.pivot(columns='Age', values='Sales in Rs').iplot(\n",
    "        kind='box',xTitle='Age',\n",
    "        yTitle='Sales in Rs',\n",
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   "source": [
    "#Plot a boxplot of Sales by Location\n",
    "data1.pivot(columns='Location', values='Sales in Rs').iplot(\n",
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    "        yTitle='Sales in Rs',\n",
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    "#Plot a boxplot of Sales by Gender\n",
    "#Plot a boxplot of Sales by Location\n",
    "data1.pivot(columns='Gender', values='Sales in Rs').iplot(\n",
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   ],
   "source": [
    "#Plot a Bivariate scatter plot\n",
    "data1.iplot(\n",
    "    x='Age',\n",
    "    y='Sales in Rs',\n",
    "    # Specify the category\n",
    "    categories='Location',\n",
    "    xTitle='Age',\n",
    "    yTitle='Sales in Rs',\n",
    "    title='Sales by Age vs Location')"
   ]
  },
  {
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   "execution_count": 24,
   "metadata": {},
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       "                        [{\"dimensions\": [{\"axis\": {\"matches\": true}, \"label\": \"Sales Executive\", \"values\": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20]}, {\"axis\": {\"matches\": true}, \"label\": \"Name\", \"values\": [\"Mahesh\", \"Suresh\", \"Vijay\", \"Ramesh\", \"Jagadish\", \"Harish\", \"Karan\", \"Raju\", \"Maya\", \"Rani\", \"Lakshmi\", \"Roopa\", \"Sravya\", \"Sruthi\", \"Swetha\", \"Aishwarya\", \"Madhavi\", \"Rajini\", \"Haritha\", \"Swathi\"]}, {\"axis\": {\"matches\": true}, \"label\": \"Gender\", \"values\": [\"Male\", \"Male\", \"Male\", \"Male\", \"Male\", \"Male\", \"Male\", \"Male\", \"Female\", \"Female\", \"Female\", \"Female\", \"Female\", \"Female\", \"Female\", \"Female\", \"Female\", \"Female\", \"Female\", \"Female\"]}, {\"axis\": {\"matches\": true}, \"label\": \"Age\", \"values\": [25, 22, 20, 27, 28, 24, 24, 23, 24, 30, 19, 24, 26, 26, 21, 24, 29, 27, 24, 25]}, {\"axis\": {\"matches\": true}, \"label\": \"Location\", \"values\": [\"Hyderabad\", \"Hyderabad\", \"Bangalore\", \"Bangalore\", \"Mumbai\", \"Hyderabad\", \"Bangalore\", \"Mumbai\", \"Mumbai\", \"Mumbai\", \"Bangalore\", \"Hyderabad\", \"Hyderabad\", \"Hyderabad\", \"Bangalore\", \"Bangalore\", \"Mumbai\", \"Mumbai\", \"Hyderabad\", \"Hyderabad\"]}, {\"axis\": {\"matches\": true}, \"label\": \"Sales in Rs\", \"values\": [50, 75, 11, 77, 45, 52, 26, 24, 28, 31, 36, 72, 69, 51, 34, 40, 18, 35, 29, 68]}], \"hovertemplate\": \"%{xaxis.title.text}=%{x}<br>%{yaxis.title.text}=%{y}<extra></extra>\", \"legendgroup\": \"\", \"marker\": {\"color\": \"#636efa\", \"symbol\": \"circle\"}, \"name\": \"\", \"showlegend\": false, \"type\": \"splom\"}],\n",
       "                        {\"dragmode\": \"select\", \"legend\": {\"tracegroupgap\": 0}, \"margin\": {\"t\": 60}, \"template\": {\"data\": {\"bar\": [{\"error_x\": {\"color\": \"#2a3f5f\"}, \"error_y\": {\"color\": \"#2a3f5f\"}, \"marker\": {\"line\": {\"color\": \"#E5ECF6\", \"width\": 0.5}}, \"type\": \"bar\"}], \"barpolar\": [{\"marker\": {\"line\": {\"color\": \"#E5ECF6\", \"width\": 0.5}}, \"type\": \"barpolar\"}], \"carpet\": [{\"aaxis\": {\"endlinecolor\": \"#2a3f5f\", \"gridcolor\": \"white\", \"linecolor\": \"white\", \"minorgridcolor\": \"white\", \"startlinecolor\": \"#2a3f5f\"}, \"baxis\": {\"endlinecolor\": \"#2a3f5f\", \"gridcolor\": \"white\", \"linecolor\": \"white\", \"minorgridcolor\": \"white\", \"startlinecolor\": \"#2a3f5f\"}, \"type\": \"carpet\"}], \"choropleth\": [{\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}, \"type\": \"choropleth\"}], \"contour\": [{\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}, \"colorscale\": [[0.0, \"#0d0887\"], 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\"white\", \"linecolor\": \"white\", \"ticks\": \"\"}, \"bgcolor\": \"#E5ECF6\", \"radialaxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}}, \"scene\": {\"xaxis\": {\"backgroundcolor\": \"#E5ECF6\", \"gridcolor\": \"white\", \"gridwidth\": 2, \"linecolor\": \"white\", \"showbackground\": true, \"ticks\": \"\", \"zerolinecolor\": \"white\"}, \"yaxis\": {\"backgroundcolor\": \"#E5ECF6\", \"gridcolor\": \"white\", \"gridwidth\": 2, \"linecolor\": \"white\", \"showbackground\": true, \"ticks\": \"\", \"zerolinecolor\": \"white\"}, \"zaxis\": {\"backgroundcolor\": \"#E5ECF6\", \"gridcolor\": \"white\", \"gridwidth\": 2, \"linecolor\": \"white\", \"showbackground\": true, \"ticks\": \"\", \"zerolinecolor\": \"white\"}}, \"shapedefaults\": {\"line\": {\"color\": \"#2a3f5f\"}}, \"ternary\": {\"aaxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}, \"baxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}, \"bgcolor\": \"#E5ECF6\", \"caxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}}, \"title\": {\"x\": 0.05}, \"xaxis\": {\"automargin\": true, \"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"title\": {\"standoff\": 15}, \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}, \"yaxis\": {\"automargin\": true, \"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"title\": {\"standoff\": 15}, \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}}}},\n",
       "                        {\"responsive\": true}\n",
       "                    ).then(function(){\n",
       "                            \n",
       "var gd = document.getElementById('abd5cbb2-0836-4e2e-9ddf-c5c41440a1d7');\n",
       "var x = new MutationObserver(function (mutations, observer) {{\n",
       "        var display = window.getComputedStyle(gd).display;\n",
       "        if (!display || display === 'none') {{\n",
       "            console.log([gd, 'removed!']);\n",
       "            Plotly.purge(gd);\n",
       "            observer.disconnect();\n",
       "        }}\n",
       "}});\n",
       "\n",
       "// Listen for the removal of the full notebook cells\n",
       "var notebookContainer = gd.closest('#notebook-container');\n",
       "if (notebookContainer) {{\n",
       "    x.observe(notebookContainer, {childList: true});\n",
       "}}\n",
       "\n",
       "// Listen for the clearing of the current output cell\n",
       "var outputEl = gd.closest('.output');\n",
       "if (outputEl) {{\n",
       "    x.observe(outputEl, {childList: true});\n",
       "}}\n",
       "\n",
       "                        })\n",
       "                };\n",
       "                });\n",
       "            </script>\n",
       "        </div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plot a Scatteplot matrix(splom)\n",
    "import plotly.express as px\n",
    "fig = px.scatter_matrix(data1)\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "config": {
        "plotlyServerURL": "https://plot.ly"
       },
       "data": [
        {
         "dimensions": [
          {
           "axis": {
            "matches": true
           },
           "label": "Gender",
           "values": [
            "Male",
            "Male",
            "Male",
            "Female",
            "Female",
            "Female",
            "Female",
            "Female"
           ]
          },
          {
           "axis": {
            "matches": true
           },
           "label": "Age",
           "values": [
            25,
            22,
            24,
            24,
            26,
            26,
            24,
            25
           ]
          },
          {
           "axis": {
            "matches": true
           },
           "label": "Location",
           "values": [
            "Hyderabad",
            "Hyderabad",
            "Hyderabad",
            "Hyderabad",
            "Hyderabad",
            "Hyderabad",
            "Hyderabad",
            "Hyderabad"
           ]
          },
          {
           "axis": {
            "matches": true
           },
           "label": "Sales in Rs",
           "values": [
            50,
            75,
            52,
            72,
            69,
            51,
            29,
            68
           ]
          }
         ],
         "hovertemplate": "Location=Hyderabad<br>%{xaxis.title.text}=%{x}<br>%{yaxis.title.text}=%{y}<extra></extra>",
         "legendgroup": "Hyderabad",
         "marker": {
          "color": "#636efa",
          "symbol": "circle"
         },
         "name": "Hyderabad",
         "showlegend": true,
         "type": "splom"
        },
        {
         "dimensions": [
          {
           "axis": {
            "matches": true
           },
           "label": "Gender",
           "values": [
            "Male",
            "Male",
            "Male",
            "Female",
            "Female",
            "Female"
           ]
          },
          {
           "axis": {
            "matches": true
           },
           "label": "Age",
           "values": [
            20,
            27,
            24,
            19,
            21,
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           ]
          },
          {
           "axis": {
            "matches": true
           },
           "label": "Location",
           "values": [
            "Bangalore",
            "Bangalore",
            "Bangalore",
            "Bangalore",
            "Bangalore",
            "Bangalore"
           ]
          },
          {
           "axis": {
            "matches": true
           },
           "label": "Sales in Rs",
           "values": [
            11,
            77,
            26,
            36,
            34,
            40
           ]
          }
         ],
         "hovertemplate": "Location=Bangalore<br>%{xaxis.title.text}=%{x}<br>%{yaxis.title.text}=%{y}<extra></extra>",
         "legendgroup": "Bangalore",
         "marker": {
          "color": "#EF553B",
          "symbol": "circle"
         },
         "name": "Bangalore",
         "showlegend": true,
         "type": "splom"
        },
        {
         "dimensions": [
          {
           "axis": {
            "matches": true
           },
           "label": "Gender",
           "values": [
            "Male",
            "Male",
            "Female",
            "Female",
            "Female",
            "Female"
           ]
          },
          {
           "axis": {
            "matches": true
           },
           "label": "Age",
           "values": [
            28,
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            24,
            30,
            29,
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           ]
          },
          {
           "axis": {
            "matches": true
           },
           "label": "Location",
           "values": [
            "Mumbai",
            "Mumbai",
            "Mumbai",
            "Mumbai",
            "Mumbai",
            "Mumbai"
           ]
          },
          {
           "axis": {
            "matches": true
           },
           "label": "Sales in Rs",
           "values": [
            45,
            24,
            28,
            31,
            18,
            35
           ]
          }
         ],
         "hovertemplate": "Location=Mumbai<br>%{xaxis.title.text}=%{x}<br>%{yaxis.title.text}=%{y}<extra></extra>",
         "legendgroup": "Mumbai",
         "marker": {
          "color": "#00cc96",
          "symbol": "circle"
         },
         "name": "Mumbai",
         "showlegend": true,
         "type": "splom"
        }
       ],
       "layout": {
        "dragmode": "select",
        "legend": {
         "title": {
          "text": "Location"
         },
         "tracegroupgap": 0
        },
        "margin": {
         "t": 60
        },
        "template": {
         "data": {
          "bar": [
           {
            "error_x": {
             "color": "#2a3f5f"
            },
            "error_y": {
             "color": "#2a3f5f"
            },
            "marker": {
             "line": {
              "color": "#E5ECF6",
              "width": 0.5
             }
            },
            "type": "bar"
           }
          ],
          "barpolar": [
           {
            "marker": {
             "line": {
              "color": "#E5ECF6",
              "width": 0.5
             }
            },
            "type": "barpolar"
           }
          ],
          "carpet": [
           {
            "aaxis": {
             "endlinecolor": "#2a3f5f",
             "gridcolor": "white",
             "linecolor": "white",
             "minorgridcolor": "white",
             "startlinecolor": "#2a3f5f"
            },
            "baxis": {
             "endlinecolor": "#2a3f5f",
             "gridcolor": "white",
             "linecolor": "white",
             "minorgridcolor": "white",
             "startlinecolor": "#2a3f5f"
            },
            "type": "carpet"
           }
          ],
          "choropleth": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "type": "choropleth"
           }
          ],
          "contour": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
              0,
              "#0d0887"
             ],
             [
              0.1111111111111111,
              "#46039f"
             ],
             [
              0.2222222222222222,
              "#7201a8"
             ],
             [
              0.3333333333333333,
              "#9c179e"
             ],
             [
              0.4444444444444444,
              "#bd3786"
             ],
             [
              0.5555555555555556,
              "#d8576b"
             ],
             [
              0.6666666666666666,
              "#ed7953"
             ],
             [
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              "#fb9f3a"
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             [
              0.8888888888888888,
              "#fdca26"
             ],
             [
              1,
              "#f0f921"
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            ],
            "type": "contour"
           }
          ],
          "contourcarpet": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "type": "contourcarpet"
           }
          ],
          "heatmap": [
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             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
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              "#0d0887"
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              "#46039f"
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              "#fdca26"
             ],
             [
              1,
              "#f0f921"
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            ],
            "type": "heatmap"
           }
          ],
          "heatmapgl": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
              0,
              "#0d0887"
             ],
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              "#46039f"
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              "#ed7953"
             ],
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              "#fdca26"
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            "type": "heatmapgl"
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          ],
          "histogram": [
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            "marker": {
             "colorbar": {
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              "ticks": ""
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            },
            "type": "histogram"
           }
          ],
          "histogram2d": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
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              "#0d0887"
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     },
     "metadata": {},
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   ],
   "source": [
    "#Specify columns to be represented with the dimension argument\n",
    "fig = px.scatter_matrix(data1,\n",
    "    dimensions=[\"Gender\", \"Age\", \"Location\", \"Sales in Rs\"],\n",
    "    color=\"Location\")\n",
    "fig.show()"
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  },
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   "outputs": [],
   "source": []
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