Pandas.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Pandas Session"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import python libraries\n",
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Read csv file\n",
    "df = pd.read_csv(\"http://rcs.bu.edu/examples/python/data_analysis/Salaries.csv\")"
   ]
  },
  {
   "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",
       "    .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>rank</th>\n",
       "      <th>discipline</th>\n",
       "      <th>phd</th>\n",
       "      <th>service</th>\n",
       "      <th>sex</th>\n",
       "      <th>salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Prof</td>\n",
       "      <td>B</td>\n",
       "      <td>56</td>\n",
       "      <td>49</td>\n",
       "      <td>Male</td>\n",
       "      <td>186960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Prof</td>\n",
       "      <td>A</td>\n",
       "      <td>12</td>\n",
       "      <td>6</td>\n",
       "      <td>Male</td>\n",
       "      <td>93000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Prof</td>\n",
       "      <td>A</td>\n",
       "      <td>23</td>\n",
       "      <td>20</td>\n",
       "      <td>Male</td>\n",
       "      <td>110515</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Prof</td>\n",
       "      <td>A</td>\n",
       "      <td>40</td>\n",
       "      <td>31</td>\n",
       "      <td>Male</td>\n",
       "      <td>131205</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Prof</td>\n",
       "      <td>B</td>\n",
       "      <td>20</td>\n",
       "      <td>18</td>\n",
       "      <td>Male</td>\n",
       "      <td>104800</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>Prof</td>\n",
       "      <td>B</td>\n",
       "      <td>18</td>\n",
       "      <td>10</td>\n",
       "      <td>Female</td>\n",
       "      <td>105450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>AssocProf</td>\n",
       "      <td>B</td>\n",
       "      <td>19</td>\n",
       "      <td>6</td>\n",
       "      <td>Female</td>\n",
       "      <td>104542</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>Prof</td>\n",
       "      <td>B</td>\n",
       "      <td>17</td>\n",
       "      <td>17</td>\n",
       "      <td>Female</td>\n",
       "      <td>124312</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>Prof</td>\n",
       "      <td>A</td>\n",
       "      <td>28</td>\n",
       "      <td>14</td>\n",
       "      <td>Female</td>\n",
       "      <td>109954</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>Prof</td>\n",
       "      <td>A</td>\n",
       "      <td>23</td>\n",
       "      <td>15</td>\n",
       "      <td>Female</td>\n",
       "      <td>109646</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>78 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         rank discipline  phd  service     sex  salary\n",
       "0        Prof          B   56       49    Male  186960\n",
       "1        Prof          A   12        6    Male   93000\n",
       "2        Prof          A   23       20    Male  110515\n",
       "3        Prof          A   40       31    Male  131205\n",
       "4        Prof          B   20       18    Male  104800\n",
       "..        ...        ...  ...      ...     ...     ...\n",
       "73       Prof          B   18       10  Female  105450\n",
       "74  AssocProf          B   19        6  Female  104542\n",
       "75       Prof          B   17       17  Female  124312\n",
       "76       Prof          A   28       14  Female  109954\n",
       "77       Prof          A   23       15  Female  109646\n",
       "\n",
       "[78 rows x 6 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Attributes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>rank</th>\n",
       "      <th>discipline</th>\n",
       "      <th>phd</th>\n",
       "      <th>service</th>\n",
       "      <th>sex</th>\n",
       "      <th>salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Prof</td>\n",
       "      <td>B</td>\n",
       "      <td>56</td>\n",
       "      <td>49</td>\n",
       "      <td>Male</td>\n",
       "      <td>186960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Prof</td>\n",
       "      <td>A</td>\n",
       "      <td>12</td>\n",
       "      <td>6</td>\n",
       "      <td>Male</td>\n",
       "      <td>93000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Prof</td>\n",
       "      <td>A</td>\n",
       "      <td>23</td>\n",
       "      <td>20</td>\n",
       "      <td>Male</td>\n",
       "      <td>110515</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Prof</td>\n",
       "      <td>A</td>\n",
       "      <td>40</td>\n",
       "      <td>31</td>\n",
       "      <td>Male</td>\n",
       "      <td>131205</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Prof</td>\n",
       "      <td>B</td>\n",
       "      <td>20</td>\n",
       "      <td>18</td>\n",
       "      <td>Male</td>\n",
       "      <td>104800</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   rank discipline  phd  service   sex  salary\n",
       "0  Prof          B   56       49  Male  186960\n",
       "1  Prof          A   12        6  Male   93000\n",
       "2  Prof          A   23       20  Male  110515\n",
       "3  Prof          A   40       31  Male  131205\n",
       "4  Prof          B   20       18  Male  104800"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# list first 5 records\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>rank</th>\n",
       "      <th>discipline</th>\n",
       "      <th>phd</th>\n",
       "      <th>service</th>\n",
       "      <th>sex</th>\n",
       "      <th>salary</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Prof</td>\n",
       "      <td>B</td>\n",
       "      <td>56</td>\n",
       "      <td>49</td>\n",
       "      <td>Male</td>\n",
       "      <td>186960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Prof</td>\n",
       "      <td>A</td>\n",
       "      <td>12</td>\n",
       "      <td>6</td>\n",
       "      <td>Male</td>\n",
       "      <td>93000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Prof</td>\n",
       "      <td>A</td>\n",
       "      <td>23</td>\n",
       "      <td>20</td>\n",
       "      <td>Male</td>\n",
       "      <td>110515</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Prof</td>\n",
       "      <td>A</td>\n",
       "      <td>40</td>\n",
       "      <td>31</td>\n",
       "      <td>Male</td>\n",
       "      <td>131205</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Prof</td>\n",
       "      <td>B</td>\n",
       "      <td>20</td>\n",
       "      <td>18</td>\n",
       "      <td>Male</td>\n",
       "      <td>104800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Prof</td>\n",
       "      <td>A</td>\n",
       "      <td>20</td>\n",
       "      <td>20</td>\n",
       "      <td>Male</td>\n",
       "      <td>122400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>AssocProf</td>\n",
       "      <td>A</td>\n",
       "      <td>20</td>\n",
       "      <td>17</td>\n",
       "      <td>Male</td>\n",
       "      <td>81285</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Prof</td>\n",
       "      <td>A</td>\n",
       "      <td>18</td>\n",
       "      <td>18</td>\n",
       "      <td>Male</td>\n",
       "      <td>126300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Prof</td>\n",
       "      <td>A</td>\n",
       "      <td>29</td>\n",
       "      <td>19</td>\n",
       "      <td>Male</td>\n",
       "      <td>94350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Prof</td>\n",
       "      <td>A</td>\n",
       "      <td>51</td>\n",
       "      <td>51</td>\n",
       "      <td>Male</td>\n",
       "      <td>57800</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        rank discipline  phd  service   sex  salary\n",
       "0       Prof          B   56       49  Male  186960\n",
       "1       Prof          A   12        6  Male   93000\n",
       "2       Prof          A   23       20  Male  110515\n",
       "3       Prof          A   40       31  Male  131205\n",
       "4       Prof          B   20       18  Male  104800\n",
       "5       Prof          A   20       20  Male  122400\n",
       "6  AssocProf          A   20       17  Male   81285\n",
       "7       Prof          A   18       18  Male  126300\n",
       "8       Prof          A   29       19  Male   94350\n",
       "9       Prof          A   51       51  Male   57800"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# List of toop 10 records\n",
    "df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>rank</th>\n",
       "      <th>discipline</th>\n",
       "      <th>phd</th>\n",
       "      <th>service</th>\n",
       "      <th>sex</th>\n",
       "      <th>salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Prof</td>\n",
       "      <td>B</td>\n",
       "      <td>56</td>\n",
       "      <td>49</td>\n",
       "      <td>Male</td>\n",
       "      <td>186960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Prof</td>\n",
       "      <td>A</td>\n",
       "      <td>12</td>\n",
       "      <td>6</td>\n",
       "      <td>Male</td>\n",
       "      <td>93000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Prof</td>\n",
       "      <td>A</td>\n",
       "      <td>23</td>\n",
       "      <td>20</td>\n",
       "      <td>Male</td>\n",
       "      <td>110515</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Prof</td>\n",
       "      <td>A</td>\n",
       "      <td>40</td>\n",
       "      <td>31</td>\n",
       "      <td>Male</td>\n",
       "      <td>131205</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Prof</td>\n",
       "      <td>B</td>\n",
       "      <td>20</td>\n",
       "      <td>18</td>\n",
       "      <td>Male</td>\n",
       "      <td>104800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Prof</td>\n",
       "      <td>A</td>\n",
       "      <td>20</td>\n",
       "      <td>20</td>\n",
       "      <td>Male</td>\n",
       "      <td>122400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>AssocProf</td>\n",
       "      <td>A</td>\n",
       "      <td>20</td>\n",
       "      <td>17</td>\n",
       "      <td>Male</td>\n",
       "      <td>81285</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Prof</td>\n",
       "      <td>A</td>\n",
       "      <td>18</td>\n",
       "      <td>18</td>\n",
       "      <td>Male</td>\n",
       "      <td>126300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Prof</td>\n",
       "      <td>A</td>\n",
       "      <td>29</td>\n",
       "      <td>19</td>\n",
       "      <td>Male</td>\n",
       "      <td>94350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Prof</td>\n",
       "      <td>A</td>\n",
       "      <td>51</td>\n",
       "      <td>51</td>\n",
       "      <td>Male</td>\n",
       "      <td>57800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Prof</td>\n",
       "      <td>B</td>\n",
       "      <td>39</td>\n",
       "      <td>33</td>\n",
       "      <td>Male</td>\n",
       "      <td>128250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Prof</td>\n",
       "      <td>B</td>\n",
       "      <td>23</td>\n",
       "      <td>23</td>\n",
       "      <td>Male</td>\n",
       "      <td>134778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>AsstProf</td>\n",
       "      <td>B</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Male</td>\n",
       "      <td>88000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Prof</td>\n",
       "      <td>B</td>\n",
       "      <td>35</td>\n",
       "      <td>33</td>\n",
       "      <td>Male</td>\n",
       "      <td>162200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Prof</td>\n",
       "      <td>B</td>\n",
       "      <td>25</td>\n",
       "      <td>19</td>\n",
       "      <td>Male</td>\n",
       "      <td>153750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Prof</td>\n",
       "      <td>B</td>\n",
       "      <td>17</td>\n",
       "      <td>3</td>\n",
       "      <td>Male</td>\n",
       "      <td>150480</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>AsstProf</td>\n",
       "      <td>B</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>Male</td>\n",
       "      <td>75044</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>AsstProf</td>\n",
       "      <td>B</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>Male</td>\n",
       "      <td>92000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Prof</td>\n",
       "      <td>A</td>\n",
       "      <td>19</td>\n",
       "      <td>7</td>\n",
       "      <td>Male</td>\n",
       "      <td>107300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Prof</td>\n",
       "      <td>A</td>\n",
       "      <td>29</td>\n",
       "      <td>27</td>\n",
       "      <td>Male</td>\n",
       "      <td>150500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         rank discipline  phd  service   sex  salary\n",
       "0        Prof          B   56       49  Male  186960\n",
       "1        Prof          A   12        6  Male   93000\n",
       "2        Prof          A   23       20  Male  110515\n",
       "3        Prof          A   40       31  Male  131205\n",
       "4        Prof          B   20       18  Male  104800\n",
       "5        Prof          A   20       20  Male  122400\n",
       "6   AssocProf          A   20       17  Male   81285\n",
       "7        Prof          A   18       18  Male  126300\n",
       "8        Prof          A   29       19  Male   94350\n",
       "9        Prof          A   51       51  Male   57800\n",
       "10       Prof          B   39       33  Male  128250\n",
       "11       Prof          B   23       23  Male  134778\n",
       "12   AsstProf          B    1        0  Male   88000\n",
       "13       Prof          B   35       33  Male  162200\n",
       "14       Prof          B   25       19  Male  153750\n",
       "15       Prof          B   17        3  Male  150480\n",
       "16   AsstProf          B    8        3  Male   75044\n",
       "17   AsstProf          B    4        0  Male   92000\n",
       "18       Prof          A   19        7  Male  107300\n",
       "19       Prof          A   29       27  Male  150500"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "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>rank</th>\n",
       "      <th>discipline</th>\n",
       "      <th>phd</th>\n",
       "      <th>service</th>\n",
       "      <th>sex</th>\n",
       "      <th>salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Prof</td>\n",
       "      <td>B</td>\n",
       "      <td>56</td>\n",
       "      <td>49</td>\n",
       "      <td>Male</td>\n",
       "      <td>186960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Prof</td>\n",
       "      <td>A</td>\n",
       "      <td>12</td>\n",
       "      <td>6</td>\n",
       "      <td>Male</td>\n",
       "      <td>93000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Prof</td>\n",
       "      <td>A</td>\n",
       "      <td>23</td>\n",
       "      <td>20</td>\n",
       "      <td>Male</td>\n",
       "      <td>110515</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Prof</td>\n",
       "      <td>A</td>\n",
       "      <td>40</td>\n",
       "      <td>31</td>\n",
       "      <td>Male</td>\n",
       "      <td>131205</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Prof</td>\n",
       "      <td>B</td>\n",
       "      <td>20</td>\n",
       "      <td>18</td>\n",
       "      <td>Male</td>\n",
       "      <td>104800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Prof</td>\n",
       "      <td>A</td>\n",
       "      <td>20</td>\n",
       "      <td>20</td>\n",
       "      <td>Male</td>\n",
       "      <td>122400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>AssocProf</td>\n",
       "      <td>A</td>\n",
       "      <td>20</td>\n",
       "      <td>17</td>\n",
       "      <td>Male</td>\n",
       "      <td>81285</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Prof</td>\n",
       "      <td>A</td>\n",
       "      <td>18</td>\n",
       "      <td>18</td>\n",
       "      <td>Male</td>\n",
       "      <td>126300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Prof</td>\n",
       "      <td>A</td>\n",
       "      <td>29</td>\n",
       "      <td>19</td>\n",
       "      <td>Male</td>\n",
       "      <td>94350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Prof</td>\n",
       "      <td>A</td>\n",
       "      <td>51</td>\n",
       "      <td>51</td>\n",
       "      <td>Male</td>\n",
       "      <td>57800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Prof</td>\n",
       "      <td>B</td>\n",
       "      <td>39</td>\n",
       "      <td>33</td>\n",
       "      <td>Male</td>\n",
       "      <td>128250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Prof</td>\n",
       "      <td>B</td>\n",
       "      <td>23</td>\n",
       "      <td>23</td>\n",
       "      <td>Male</td>\n",
       "      <td>134778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>AsstProf</td>\n",
       "      <td>B</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Male</td>\n",
       "      <td>88000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Prof</td>\n",
       "      <td>B</td>\n",
       "      <td>35</td>\n",
       "      <td>33</td>\n",
       "      <td>Male</td>\n",
       "      <td>162200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Prof</td>\n",
       "      <td>B</td>\n",
       "      <td>25</td>\n",
       "      <td>19</td>\n",
       "      <td>Male</td>\n",
       "      <td>153750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Prof</td>\n",
       "      <td>B</td>\n",
       "      <td>17</td>\n",
       "      <td>3</td>\n",
       "      <td>Male</td>\n",
       "      <td>150480</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>AsstProf</td>\n",
       "      <td>B</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>Male</td>\n",
       "      <td>75044</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>AsstProf</td>\n",
       "      <td>B</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>Male</td>\n",
       "      <td>92000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Prof</td>\n",
       "      <td>A</td>\n",
       "      <td>19</td>\n",
       "      <td>7</td>\n",
       "      <td>Male</td>\n",
       "      <td>107300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Prof</td>\n",
       "      <td>A</td>\n",
       "      <td>29</td>\n",
       "      <td>27</td>\n",
       "      <td>Male</td>\n",
       "      <td>150500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>AsstProf</td>\n",
       "      <td>B</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>Male</td>\n",
       "      <td>92000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Prof</td>\n",
       "      <td>A</td>\n",
       "      <td>33</td>\n",
       "      <td>30</td>\n",
       "      <td>Male</td>\n",
       "      <td>103106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>AsstProf</td>\n",
       "      <td>A</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>Male</td>\n",
       "      <td>73000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>AsstProf</td>\n",
       "      <td>A</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>Male</td>\n",
       "      <td>85000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Prof</td>\n",
       "      <td>A</td>\n",
       "      <td>30</td>\n",
       "      <td>23</td>\n",
       "      <td>Male</td>\n",
       "      <td>91100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Prof</td>\n",
       "      <td>B</td>\n",
       "      <td>35</td>\n",
       "      <td>31</td>\n",
       "      <td>Male</td>\n",
       "      <td>99418</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Prof</td>\n",
       "      <td>A</td>\n",
       "      <td>38</td>\n",
       "      <td>19</td>\n",
       "      <td>Male</td>\n",
       "      <td>148750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Prof</td>\n",
       "      <td>A</td>\n",
       "      <td>45</td>\n",
       "      <td>43</td>\n",
       "      <td>Male</td>\n",
       "      <td>155865</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>AsstProf</td>\n",
       "      <td>B</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>Male</td>\n",
       "      <td>91300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Prof</td>\n",
       "      <td>B</td>\n",
       "      <td>21</td>\n",
       "      <td>20</td>\n",
       "      <td>Male</td>\n",
       "      <td>123683</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>AssocProf</td>\n",
       "      <td>B</td>\n",
       "      <td>9</td>\n",
       "      <td>7</td>\n",
       "      <td>Male</td>\n",
       "      <td>107008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>Prof</td>\n",
       "      <td>B</td>\n",
       "      <td>22</td>\n",
       "      <td>21</td>\n",
       "      <td>Male</td>\n",
       "      <td>155750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>Prof</td>\n",
       "      <td>A</td>\n",
       "      <td>27</td>\n",
       "      <td>19</td>\n",
       "      <td>Male</td>\n",
       "      <td>103275</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>Prof</td>\n",
       "      <td>B</td>\n",
       "      <td>18</td>\n",
       "      <td>18</td>\n",
       "      <td>Male</td>\n",
       "      <td>120000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>AssocProf</td>\n",
       "      <td>B</td>\n",
       "      <td>12</td>\n",
       "      <td>8</td>\n",
       "      <td>Male</td>\n",
       "      <td>119800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>Prof</td>\n",
       "      <td>B</td>\n",
       "      <td>28</td>\n",
       "      <td>23</td>\n",
       "      <td>Male</td>\n",
       "      <td>126933</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>Prof</td>\n",
       "      <td>B</td>\n",
       "      <td>45</td>\n",
       "      <td>45</td>\n",
       "      <td>Male</td>\n",
       "      <td>146856</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>Prof</td>\n",
       "      <td>A</td>\n",
       "      <td>20</td>\n",
       "      <td>8</td>\n",
       "      <td>Male</td>\n",
       "      <td>102000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>AsstProf</td>\n",
       "      <td>B</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>Male</td>\n",
       "      <td>91000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>Prof</td>\n",
       "      <td>B</td>\n",
       "      <td>18</td>\n",
       "      <td>18</td>\n",
       "      <td>Female</td>\n",
       "      <td>129000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>Prof</td>\n",
       "      <td>A</td>\n",
       "      <td>39</td>\n",
       "      <td>36</td>\n",
       "      <td>Female</td>\n",
       "      <td>137000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>AssocProf</td>\n",
       "      <td>A</td>\n",
       "      <td>13</td>\n",
       "      <td>8</td>\n",
       "      <td>Female</td>\n",
       "      <td>74830</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>AsstProf</td>\n",
       "      <td>B</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>Female</td>\n",
       "      <td>80225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>AsstProf</td>\n",
       "      <td>B</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>Female</td>\n",
       "      <td>77000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>Prof</td>\n",
       "      <td>B</td>\n",
       "      <td>23</td>\n",
       "      <td>19</td>\n",
       "      <td>Female</td>\n",
       "      <td>151768</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>Prof</td>\n",
       "      <td>B</td>\n",
       "      <td>25</td>\n",
       "      <td>25</td>\n",
       "      <td>Female</td>\n",
       "      <td>140096</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>AsstProf</td>\n",
       "      <td>B</td>\n",
       "      <td>11</td>\n",
       "      <td>3</td>\n",
       "      <td>Female</td>\n",
       "      <td>74692</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>AssocProf</td>\n",
       "      <td>B</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>Female</td>\n",
       "      <td>103613</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>Prof</td>\n",
       "      <td>B</td>\n",
       "      <td>17</td>\n",
       "      <td>17</td>\n",
       "      <td>Female</td>\n",
       "      <td>111512</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>Prof</td>\n",
       "      <td>B</td>\n",
       "      <td>17</td>\n",
       "      <td>18</td>\n",
       "      <td>Female</td>\n",
       "      <td>122960</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         rank discipline  phd  service     sex  salary\n",
       "0        Prof          B   56       49    Male  186960\n",
       "1        Prof          A   12        6    Male   93000\n",
       "2        Prof          A   23       20    Male  110515\n",
       "3        Prof          A   40       31    Male  131205\n",
       "4        Prof          B   20       18    Male  104800\n",
       "5        Prof          A   20       20    Male  122400\n",
       "6   AssocProf          A   20       17    Male   81285\n",
       "7        Prof          A   18       18    Male  126300\n",
       "8        Prof          A   29       19    Male   94350\n",
       "9        Prof          A   51       51    Male   57800\n",
       "10       Prof          B   39       33    Male  128250\n",
       "11       Prof          B   23       23    Male  134778\n",
       "12   AsstProf          B    1        0    Male   88000\n",
       "13       Prof          B   35       33    Male  162200\n",
       "14       Prof          B   25       19    Male  153750\n",
       "15       Prof          B   17        3    Male  150480\n",
       "16   AsstProf          B    8        3    Male   75044\n",
       "17   AsstProf          B    4        0    Male   92000\n",
       "18       Prof          A   19        7    Male  107300\n",
       "19       Prof          A   29       27    Male  150500\n",
       "20   AsstProf          B    4        4    Male   92000\n",
       "21       Prof          A   33       30    Male  103106\n",
       "22   AsstProf          A    4        2    Male   73000\n",
       "23   AsstProf          A    2        0    Male   85000\n",
       "24       Prof          A   30       23    Male   91100\n",
       "25       Prof          B   35       31    Male   99418\n",
       "26       Prof          A   38       19    Male  148750\n",
       "27       Prof          A   45       43    Male  155865\n",
       "28   AsstProf          B    7        2    Male   91300\n",
       "29       Prof          B   21       20    Male  123683\n",
       "30  AssocProf          B    9        7    Male  107008\n",
       "31       Prof          B   22       21    Male  155750\n",
       "32       Prof          A   27       19    Male  103275\n",
       "33       Prof          B   18       18    Male  120000\n",
       "34  AssocProf          B   12        8    Male  119800\n",
       "35       Prof          B   28       23    Male  126933\n",
       "36       Prof          B   45       45    Male  146856\n",
       "37       Prof          A   20        8    Male  102000\n",
       "38   AsstProf          B    4        3    Male   91000\n",
       "39       Prof          B   18       18  Female  129000\n",
       "40       Prof          A   39       36  Female  137000\n",
       "41  AssocProf          A   13        8  Female   74830\n",
       "42   AsstProf          B    4        2  Female   80225\n",
       "43   AsstProf          B    5        0  Female   77000\n",
       "44       Prof          B   23       19  Female  151768\n",
       "45       Prof          B   25       25  Female  140096\n",
       "46   AsstProf          B   11        3  Female   74692\n",
       "47  AssocProf          B   11       11  Female  103613\n",
       "48       Prof          B   17       17  Female  111512\n",
       "49       Prof          B   17       18  Female  122960"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "rank          object\n",
       "discipline    object\n",
       "phd            int64\n",
       "service        int64\n",
       "sex           object\n",
       "salary         int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check types for all columns\n",
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['rank', 'discipline', 'phd', 'service', 'sex', 'salary'], dtype='object')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Columns\n",
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[RangeIndex(start=0, stop=78, step=1),\n",
       " Index(['rank', 'discipline', 'phd', 'service', 'sex', 'salary'], dtype='object')]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.axes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(78, 6)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.ndim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "468"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([['Prof', 'B', 56, 49, 'Male', 186960],\n",
       "       ['Prof', 'A', 12, 6, 'Male', 93000],\n",
       "       ['Prof', 'A', 23, 20, 'Male', 110515],\n",
       "       ['Prof', 'A', 40, 31, 'Male', 131205],\n",
       "       ['Prof', 'B', 20, 18, 'Male', 104800],\n",
       "       ['Prof', 'A', 20, 20, 'Male', 122400],\n",
       "       ['AssocProf', 'A', 20, 17, 'Male', 81285],\n",
       "       ['Prof', 'A', 18, 18, 'Male', 126300],\n",
       "       ['Prof', 'A', 29, 19, 'Male', 94350],\n",
       "       ['Prof', 'A', 51, 51, 'Male', 57800],\n",
       "       ['Prof', 'B', 39, 33, 'Male', 128250],\n",
       "       ['Prof', 'B', 23, 23, 'Male', 134778],\n",
       "       ['AsstProf', 'B', 1, 0, 'Male', 88000],\n",
       "       ['Prof', 'B', 35, 33, 'Male', 162200],\n",
       "       ['Prof', 'B', 25, 19, 'Male', 153750],\n",
       "       ['Prof', 'B', 17, 3, 'Male', 150480],\n",
       "       ['AsstProf', 'B', 8, 3, 'Male', 75044],\n",
       "       ['AsstProf', 'B', 4, 0, 'Male', 92000],\n",
       "       ['Prof', 'A', 19, 7, 'Male', 107300],\n",
       "       ['Prof', 'A', 29, 27, 'Male', 150500],\n",
       "       ['AsstProf', 'B', 4, 4, 'Male', 92000],\n",
       "       ['Prof', 'A', 33, 30, 'Male', 103106],\n",
       "       ['AsstProf', 'A', 4, 2, 'Male', 73000],\n",
       "       ['AsstProf', 'A', 2, 0, 'Male', 85000],\n",
       "       ['Prof', 'A', 30, 23, 'Male', 91100],\n",
       "       ['Prof', 'B', 35, 31, 'Male', 99418],\n",
       "       ['Prof', 'A', 38, 19, 'Male', 148750],\n",
       "       ['Prof', 'A', 45, 43, 'Male', 155865],\n",
       "       ['AsstProf', 'B', 7, 2, 'Male', 91300],\n",
       "       ['Prof', 'B', 21, 20, 'Male', 123683],\n",
       "       ['AssocProf', 'B', 9, 7, 'Male', 107008],\n",
       "       ['Prof', 'B', 22, 21, 'Male', 155750],\n",
       "       ['Prof', 'A', 27, 19, 'Male', 103275],\n",
       "       ['Prof', 'B', 18, 18, 'Male', 120000],\n",
       "       ['AssocProf', 'B', 12, 8, 'Male', 119800],\n",
       "       ['Prof', 'B', 28, 23, 'Male', 126933],\n",
       "       ['Prof', 'B', 45, 45, 'Male', 146856],\n",
       "       ['Prof', 'A', 20, 8, 'Male', 102000],\n",
       "       ['AsstProf', 'B', 4, 3, 'Male', 91000],\n",
       "       ['Prof', 'B', 18, 18, 'Female', 129000],\n",
       "       ['Prof', 'A', 39, 36, 'Female', 137000],\n",
       "       ['AssocProf', 'A', 13, 8, 'Female', 74830],\n",
       "       ['AsstProf', 'B', 4, 2, 'Female', 80225],\n",
       "       ['AsstProf', 'B', 5, 0, 'Female', 77000],\n",
       "       ['Prof', 'B', 23, 19, 'Female', 151768],\n",
       "       ['Prof', 'B', 25, 25, 'Female', 140096],\n",
       "       ['AsstProf', 'B', 11, 3, 'Female', 74692],\n",
       "       ['AssocProf', 'B', 11, 11, 'Female', 103613],\n",
       "       ['Prof', 'B', 17, 17, 'Female', 111512],\n",
       "       ['Prof', 'B', 17, 18, 'Female', 122960],\n",
       "       ['AsstProf', 'B', 10, 5, 'Female', 97032],\n",
       "       ['Prof', 'B', 20, 14, 'Female', 127512],\n",
       "       ['Prof', 'A', 12, 0, 'Female', 105000],\n",
       "       ['AsstProf', 'A', 5, 3, 'Female', 73500],\n",
       "       ['AssocProf', 'A', 25, 22, 'Female', 62884],\n",
       "       ['AsstProf', 'A', 2, 0, 'Female', 72500],\n",
       "       ['AssocProf', 'A', 10, 8, 'Female', 77500],\n",
       "       ['AsstProf', 'A', 3, 1, 'Female', 72500],\n",
       "       ['Prof', 'B', 36, 26, 'Female', 144651],\n",
       "       ['AssocProf', 'B', 12, 10, 'Female', 103994],\n",
       "       ['AsstProf', 'B', 3, 3, 'Female', 92000],\n",
       "       ['AssocProf', 'B', 13, 10, 'Female', 103750],\n",
       "       ['AssocProf', 'B', 14, 7, 'Female', 109650],\n",
       "       ['Prof', 'A', 29, 27, 'Female', 91000],\n",
       "       ['AssocProf', 'A', 26, 24, 'Female', 73300],\n",
       "       ['Prof', 'A', 36, 19, 'Female', 117555],\n",
       "       ['AsstProf', 'A', 7, 6, 'Female', 63100],\n",
       "       ['Prof', 'A', 17, 11, 'Female', 90450],\n",
       "       ['AsstProf', 'A', 4, 2, 'Female', 77500],\n",
       "       ['Prof', 'A', 28, 7, 'Female', 116450],\n",
       "       ['AsstProf', 'A', 8, 3, 'Female', 78500],\n",
       "       ['AssocProf', 'B', 12, 9, 'Female', 71065],\n",
       "       ['Prof', 'B', 24, 15, 'Female', 161101],\n",
       "       ['Prof', 'B', 18, 10, 'Female', 105450],\n",
       "       ['AssocProf', 'B', 19, 6, 'Female', 104542],\n",
       "       ['Prof', 'B', 17, 17, 'Female', 124312],\n",
       "       ['Prof', 'A', 28, 14, 'Female', 109954],\n",
       "       ['Prof', 'A', 23, 15, 'Female', 109646]], dtype=object)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Methods"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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>rank</th>\n",
       "      <th>discipline</th>\n",
       "      <th>phd</th>\n",
       "      <th>service</th>\n",
       "      <th>sex</th>\n",
       "      <th>salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Prof</td>\n",
       "      <td>B</td>\n",
       "      <td>56</td>\n",
       "      <td>49</td>\n",
       "      <td>Male</td>\n",
       "      <td>186960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Prof</td>\n",
       "      <td>A</td>\n",
       "      <td>12</td>\n",
       "      <td>6</td>\n",
       "      <td>Male</td>\n",
       "      <td>93000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Prof</td>\n",
       "      <td>A</td>\n",
       "      <td>23</td>\n",
       "      <td>20</td>\n",
       "      <td>Male</td>\n",
       "      <td>110515</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Prof</td>\n",
       "      <td>A</td>\n",
       "      <td>40</td>\n",
       "      <td>31</td>\n",
       "      <td>Male</td>\n",
       "      <td>131205</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Prof</td>\n",
       "      <td>B</td>\n",
       "      <td>20</td>\n",
       "      <td>18</td>\n",
       "      <td>Male</td>\n",
       "      <td>104800</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   rank discipline  phd  service   sex  salary\n",
       "0  Prof          B   56       49  Male  186960\n",
       "1  Prof          A   12        6  Male   93000\n",
       "2  Prof          A   23       20  Male  110515\n",
       "3  Prof          A   40       31  Male  131205\n",
       "4  Prof          B   20       18  Male  104800"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>rank</th>\n",
       "      <th>discipline</th>\n",
       "      <th>phd</th>\n",
       "      <th>service</th>\n",
       "      <th>sex</th>\n",
       "      <th>salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>Prof</td>\n",
       "      <td>B</td>\n",
       "      <td>18</td>\n",
       "      <td>10</td>\n",
       "      <td>Female</td>\n",
       "      <td>105450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>AssocProf</td>\n",
       "      <td>B</td>\n",
       "      <td>19</td>\n",
       "      <td>6</td>\n",
       "      <td>Female</td>\n",
       "      <td>104542</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>Prof</td>\n",
       "      <td>B</td>\n",
       "      <td>17</td>\n",
       "      <td>17</td>\n",
       "      <td>Female</td>\n",
       "      <td>124312</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>Prof</td>\n",
       "      <td>A</td>\n",
       "      <td>28</td>\n",
       "      <td>14</td>\n",
       "      <td>Female</td>\n",
       "      <td>109954</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>Prof</td>\n",
       "      <td>A</td>\n",
       "      <td>23</td>\n",
       "      <td>15</td>\n",
       "      <td>Female</td>\n",
       "      <td>109646</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         rank discipline  phd  service     sex  salary\n",
       "73       Prof          B   18       10  Female  105450\n",
       "74  AssocProf          B   19        6  Female  104542\n",
       "75       Prof          B   17       17  Female  124312\n",
       "76       Prof          A   28       14  Female  109954\n",
       "77       Prof          A   23       15  Female  109646"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>phd</th>\n",
       "      <th>service</th>\n",
       "      <th>salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>78.000000</td>\n",
       "      <td>78.000000</td>\n",
       "      <td>78.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>19.705128</td>\n",
       "      <td>15.051282</td>\n",
       "      <td>108023.782051</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>12.498425</td>\n",
       "      <td>12.139768</td>\n",
       "      <td>28293.661022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>57800.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>10.250000</td>\n",
       "      <td>5.250000</td>\n",
       "      <td>88612.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>18.500000</td>\n",
       "      <td>14.500000</td>\n",
       "      <td>104671.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>27.750000</td>\n",
       "      <td>20.750000</td>\n",
       "      <td>126774.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>56.000000</td>\n",
       "      <td>51.000000</td>\n",
       "      <td>186960.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             phd    service         salary\n",
       "count  78.000000  78.000000      78.000000\n",
       "mean   19.705128  15.051282  108023.782051\n",
       "std    12.498425  12.139768   28293.661022\n",
       "min     1.000000   0.000000   57800.000000\n",
       "25%    10.250000   5.250000   88612.500000\n",
       "50%    18.500000  14.500000  104671.000000\n",
       "75%    27.750000  20.750000  126774.750000\n",
       "max    56.000000  51.000000  186960.000000"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe() # Summary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "phd           12.498425\n",
       "service       12.139768\n",
       "salary     28293.661022\n",
       "dtype: float64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.std() # Standard deviation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_sub=df.head(50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "phd            21.52\n",
       "service        17.60\n",
       "salary     113789.14\n",
       "dtype: float64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_sub.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "phd            21.52\n",
       "service        17.60\n",
       "salary     113789.14\n",
       "dtype: float64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(50).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     186960\n",
       "1      93000\n",
       "2     110515\n",
       "3     131205\n",
       "4     104800\n",
       "       ...  \n",
       "73    105450\n",
       "74    104542\n",
       "75    124312\n",
       "76    109954\n",
       "77    109646\n",
       "Name: salary, Length: 78, dtype: int64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['salary']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     186960\n",
       "1      93000\n",
       "2     110515\n",
       "3     131205\n",
       "4     104800\n",
       "       ...  \n",
       "73    105450\n",
       "74    104542\n",
       "75    124312\n",
       "76    109954\n",
       "77    109646\n",
       "Name: salary, Length: 78, dtype: int64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.salary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     56\n",
       "1     12\n",
       "2     23\n",
       "3     40\n",
       "4     20\n",
       "      ..\n",
       "73    18\n",
       "74    19\n",
       "75    17\n",
       "76    28\n",
       "77    23\n",
       "Name: phd, Length: 78, dtype: int64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.phd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    78.000000\n",
       "mean     19.705128\n",
       "std      12.498425\n",
       "min       1.000000\n",
       "25%      10.250000\n",
       "50%      18.500000\n",
       "75%      27.750000\n",
       "max      56.000000\n",
       "Name: phd, dtype: float64"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.phd.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(78,)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.phd.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "19.705128205128204"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.phd.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>phd</th>\n",
       "      <th>service</th>\n",
       "      <th>salary</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rank</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>AssocProf</th>\n",
       "      <td>15.076923</td>\n",
       "      <td>11.307692</td>\n",
       "      <td>91786.230769</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AsstProf</th>\n",
       "      <td>5.052632</td>\n",
       "      <td>2.210526</td>\n",
       "      <td>81362.789474</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Prof</th>\n",
       "      <td>27.065217</td>\n",
       "      <td>21.413043</td>\n",
       "      <td>123624.804348</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 phd    service         salary\n",
       "rank                                          \n",
       "AssocProf  15.076923  11.307692   91786.230769\n",
       "AsstProf    5.052632   2.210526   81362.789474\n",
       "Prof       27.065217  21.413043  123624.804348"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('rank').mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Groupby"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<pandas.core.groupby.generic.DataFrameGroupBy object at 0x000001FACBA92670>"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('rank')[['salary']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>salary</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rank</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>AssocProf</th>\n",
       "      <td>91786.230769</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AsstProf</th>\n",
       "      <td>81362.789474</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Prof</th>\n",
       "      <td>123624.804348</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  salary\n",
       "rank                    \n",
       "AssocProf   91786.230769\n",
       "AsstProf    81362.789474\n",
       "Prof       123624.804348"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('rank')[['salary']].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Filtering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      True\n",
       "1     False\n",
       "2     False\n",
       "3      True\n",
       "4     False\n",
       "      ...  \n",
       "73    False\n",
       "74    False\n",
       "75     True\n",
       "76    False\n",
       "77    False\n",
       "Name: salary, Length: 78, dtype: bool"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.salary>120000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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>rank</th>\n",
       "      <th>discipline</th>\n",
       "      <th>phd</th>\n",
       "      <th>service</th>\n",
       "      <th>sex</th>\n",
       "      <th>salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Prof</td>\n",
       "      <td>B</td>\n",
       "      <td>56</td>\n",
       "      <td>49</td>\n",
       "      <td>Male</td>\n",
       "      <td>186960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Prof</td>\n",
       "      <td>A</td>\n",
       "      <td>40</td>\n",
       "      <td>31</td>\n",
       "      <td>Male</td>\n",
       "      <td>131205</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Prof</td>\n",
       "      <td>A</td>\n",
       "      <td>20</td>\n",
       "      <td>20</td>\n",
       "      <td>Male</td>\n",
       "      <td>122400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Prof</td>\n",
       "      <td>A</td>\n",
       "      <td>18</td>\n",
       "      <td>18</td>\n",
       "      <td>Male</td>\n",
       "      <td>126300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Prof</td>\n",
       "      <td>B</td>\n",
       "      <td>39</td>\n",
       "      <td>33</td>\n",
       "      <td>Male</td>\n",
       "      <td>128250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Prof</td>\n",
       "      <td>B</td>\n",
       "      <td>23</td>\n",
       "      <td>23</td>\n",
       "      <td>Male</td>\n",
       "      <td>134778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Prof</td>\n",
       "      <td>B</td>\n",
       "      <td>35</td>\n",
       "      <td>33</td>\n",
       "      <td>Male</td>\n",
       "      <td>162200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Prof</td>\n",
       "      <td>B</td>\n",
       "      <td>25</td>\n",
       "      <td>19</td>\n",
       "      <td>Male</td>\n",
       "      <td>153750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Prof</td>\n",
       "      <td>B</td>\n",
       "      <td>17</td>\n",
       "      <td>3</td>\n",
       "      <td>Male</td>\n",
       "      <td>150480</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Prof</td>\n",
       "      <td>A</td>\n",
       "      <td>29</td>\n",
       "      <td>27</td>\n",
       "      <td>Male</td>\n",
       "      <td>150500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Prof</td>\n",
       "      <td>A</td>\n",
       "      <td>38</td>\n",
       "      <td>19</td>\n",
       "      <td>Male</td>\n",
       "      <td>148750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Prof</td>\n",
       "      <td>A</td>\n",
       "      <td>45</td>\n",
       "      <td>43</td>\n",
       "      <td>Male</td>\n",
       "      <td>155865</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Prof</td>\n",
       "      <td>B</td>\n",
       "      <td>21</td>\n",
       "      <td>20</td>\n",
       "      <td>Male</td>\n",
       "      <td>123683</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>Prof</td>\n",
       "      <td>B</td>\n",
       "      <td>22</td>\n",
       "      <td>21</td>\n",
       "      <td>Male</td>\n",
       "      <td>155750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>Prof</td>\n",
       "      <td>B</td>\n",
       "      <td>28</td>\n",
       "      <td>23</td>\n",
       "      <td>Male</td>\n",
       "      <td>126933</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>Prof</td>\n",
       "      <td>B</td>\n",
       "      <td>45</td>\n",
       "      <td>45</td>\n",
       "      <td>Male</td>\n",
       "      <td>146856</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>Prof</td>\n",
       "      <td>B</td>\n",
       "      <td>18</td>\n",
       "      <td>18</td>\n",
       "      <td>Female</td>\n",
       "      <td>129000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>Prof</td>\n",
       "      <td>A</td>\n",
       "      <td>39</td>\n",
       "      <td>36</td>\n",
       "      <td>Female</td>\n",
       "      <td>137000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>Prof</td>\n",
       "      <td>B</td>\n",
       "      <td>23</td>\n",
       "      <td>19</td>\n",
       "      <td>Female</td>\n",
       "      <td>151768</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>Prof</td>\n",
       "      <td>B</td>\n",
       "      <td>25</td>\n",
       "      <td>25</td>\n",
       "      <td>Female</td>\n",
       "      <td>140096</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>Prof</td>\n",
       "      <td>B</td>\n",
       "      <td>17</td>\n",
       "      <td>18</td>\n",
       "      <td>Female</td>\n",
       "      <td>122960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>Prof</td>\n",
       "      <td>B</td>\n",
       "      <td>20</td>\n",
       "      <td>14</td>\n",
       "      <td>Female</td>\n",
       "      <td>127512</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>Prof</td>\n",
       "      <td>B</td>\n",
       "      <td>36</td>\n",
       "      <td>26</td>\n",
       "      <td>Female</td>\n",
       "      <td>144651</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>Prof</td>\n",
       "      <td>B</td>\n",
       "      <td>24</td>\n",
       "      <td>15</td>\n",
       "      <td>Female</td>\n",
       "      <td>161101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>Prof</td>\n",
       "      <td>B</td>\n",
       "      <td>17</td>\n",
       "      <td>17</td>\n",
       "      <td>Female</td>\n",
       "      <td>124312</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    rank discipline  phd  service     sex  salary\n",
       "0   Prof          B   56       49    Male  186960\n",
       "3   Prof          A   40       31    Male  131205\n",
       "5   Prof          A   20       20    Male  122400\n",
       "7   Prof          A   18       18    Male  126300\n",
       "10  Prof          B   39       33    Male  128250\n",
       "11  Prof          B   23       23    Male  134778\n",
       "13  Prof          B   35       33    Male  162200\n",
       "14  Prof          B   25       19    Male  153750\n",
       "15  Prof          B   17        3    Male  150480\n",
       "19  Prof          A   29       27    Male  150500\n",
       "26  Prof          A   38       19    Male  148750\n",
       "27  Prof          A   45       43    Male  155865\n",
       "29  Prof          B   21       20    Male  123683\n",
       "31  Prof          B   22       21    Male  155750\n",
       "35  Prof          B   28       23    Male  126933\n",
       "36  Prof          B   45       45    Male  146856\n",
       "39  Prof          B   18       18  Female  129000\n",
       "40  Prof          A   39       36  Female  137000\n",
       "44  Prof          B   23       19  Female  151768\n",
       "45  Prof          B   25       25  Female  140096\n",
       "49  Prof          B   17       18  Female  122960\n",
       "51  Prof          B   20       14  Female  127512\n",
       "58  Prof          B   36       26  Female  144651\n",
       "72  Prof          B   24       15  Female  161101\n",
       "75  Prof          B   17       17  Female  124312"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df.salary>120000]  # df[previous step]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>rank</th>\n",
       "      <th>discipline</th>\n",
       "      <th>phd</th>\n",
       "      <th>service</th>\n",
       "      <th>sex</th>\n",
       "      <th>salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Prof</td>\n",
       "      <td>B</td>\n",
       "      <td>56</td>\n",
       "      <td>49</td>\n",
       "      <td>Male</td>\n",
       "      <td>186960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Prof</td>\n",
       "      <td>B</td>\n",
       "      <td>20</td>\n",
       "      <td>18</td>\n",
       "      <td>Male</td>\n",
       "      <td>104800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Prof</td>\n",
       "      <td>B</td>\n",
       "      <td>39</td>\n",
       "      <td>33</td>\n",
       "      <td>Male</td>\n",
       "      <td>128250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Prof</td>\n",
       "      <td>B</td>\n",
       "      <td>23</td>\n",
       "      <td>23</td>\n",
       "      <td>Male</td>\n",
       "      <td>134778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>AsstProf</td>\n",
       "      <td>B</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Male</td>\n",
       "      <td>88000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Prof</td>\n",
       "      <td>B</td>\n",
       "      <td>35</td>\n",
       "      <td>33</td>\n",
       "      <td>Male</td>\n",
       "      <td>162200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Prof</td>\n",
       "      <td>B</td>\n",
       "      <td>25</td>\n",
       "      <td>19</td>\n",
       "      <td>Male</td>\n",
       "      <td>153750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Prof</td>\n",
       "      <td>B</td>\n",
       "      <td>17</td>\n",
       "      <td>3</td>\n",
       "      <td>Male</td>\n",
       "      <td>150480</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>AsstProf</td>\n",
       "      <td>B</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>Male</td>\n",
       "      <td>75044</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>AsstProf</td>\n",
       "      <td>B</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>Male</td>\n",
       "      <td>92000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>AsstProf</td>\n",
       "      <td>B</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>Male</td>\n",
       "      <td>92000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Prof</td>\n",
       "      <td>B</td>\n",
       "      <td>35</td>\n",
       "      <td>31</td>\n",
       "      <td>Male</td>\n",
       "      <td>99418</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>AsstProf</td>\n",
       "      <td>B</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>Male</td>\n",
       "      <td>91300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Prof</td>\n",
       "      <td>B</td>\n",
       "      <td>21</td>\n",
       "      <td>20</td>\n",
       "      <td>Male</td>\n",
       "      <td>123683</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>AssocProf</td>\n",
       "      <td>B</td>\n",
       "      <td>9</td>\n",
       "      <td>7</td>\n",
       "      <td>Male</td>\n",
       "      <td>107008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>Prof</td>\n",
       "      <td>B</td>\n",
       "      <td>22</td>\n",
       "      <td>21</td>\n",
       "      <td>Male</td>\n",
       "      <td>155750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>Prof</td>\n",
       "      <td>B</td>\n",
       "      <td>18</td>\n",
       "      <td>18</td>\n",
       "      <td>Male</td>\n",
       "      <td>120000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>AssocProf</td>\n",
       "      <td>B</td>\n",
       "      <td>12</td>\n",
       "      <td>8</td>\n",
       "      <td>Male</td>\n",
       "      <td>119800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>Prof</td>\n",
       "      <td>B</td>\n",
       "      <td>28</td>\n",
       "      <td>23</td>\n",
       "      <td>Male</td>\n",
       "      <td>126933</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>Prof</td>\n",
       "      <td>B</td>\n",
       "      <td>45</td>\n",
       "      <td>45</td>\n",
       "      <td>Male</td>\n",
       "      <td>146856</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>AsstProf</td>\n",
       "      <td>B</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>Male</td>\n",
       "      <td>91000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>Prof</td>\n",
       "      <td>B</td>\n",
       "      <td>18</td>\n",
       "      <td>18</td>\n",
       "      <td>Female</td>\n",
       "      <td>129000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>AsstProf</td>\n",
       "      <td>B</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>Female</td>\n",
       "      <td>80225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>AsstProf</td>\n",
       "      <td>B</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>Female</td>\n",
       "      <td>77000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>Prof</td>\n",
       "      <td>B</td>\n",
       "      <td>23</td>\n",
       "      <td>19</td>\n",
       "      <td>Female</td>\n",
       "      <td>151768</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>Prof</td>\n",
       "      <td>B</td>\n",
       "      <td>25</td>\n",
       "      <td>25</td>\n",
       "      <td>Female</td>\n",
       "      <td>140096</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>AsstProf</td>\n",
       "      <td>B</td>\n",
       "      <td>11</td>\n",
       "      <td>3</td>\n",
       "      <td>Female</td>\n",
       "      <td>74692</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>AssocProf</td>\n",
       "      <td>B</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>Female</td>\n",
       "      <td>103613</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>Prof</td>\n",
       "      <td>B</td>\n",
       "      <td>17</td>\n",
       "      <td>17</td>\n",
       "      <td>Female</td>\n",
       "      <td>111512</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>Prof</td>\n",
       "      <td>B</td>\n",
       "      <td>17</td>\n",
       "      <td>18</td>\n",
       "      <td>Female</td>\n",
       "      <td>122960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>AsstProf</td>\n",
       "      <td>B</td>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
       "      <td>Female</td>\n",
       "      <td>97032</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>Prof</td>\n",
       "      <td>B</td>\n",
       "      <td>20</td>\n",
       "      <td>14</td>\n",
       "      <td>Female</td>\n",
       "      <td>127512</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>Prof</td>\n",
       "      <td>B</td>\n",
       "      <td>36</td>\n",
       "      <td>26</td>\n",
       "      <td>Female</td>\n",
       "      <td>144651</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>AssocProf</td>\n",
       "      <td>B</td>\n",
       "      <td>12</td>\n",
       "      <td>10</td>\n",
       "      <td>Female</td>\n",
       "      <td>103994</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>AsstProf</td>\n",
       "      <td>B</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>Female</td>\n",
       "      <td>92000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>AssocProf</td>\n",
       "      <td>B</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>Female</td>\n",
       "      <td>103750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>AssocProf</td>\n",
       "      <td>B</td>\n",
       "      <td>14</td>\n",
       "      <td>7</td>\n",
       "      <td>Female</td>\n",
       "      <td>109650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>AssocProf</td>\n",
       "      <td>B</td>\n",
       "      <td>12</td>\n",
       "      <td>9</td>\n",
       "      <td>Female</td>\n",
       "      <td>71065</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>Prof</td>\n",
       "      <td>B</td>\n",
       "      <td>24</td>\n",
       "      <td>15</td>\n",
       "      <td>Female</td>\n",
       "      <td>161101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>Prof</td>\n",
       "      <td>B</td>\n",
       "      <td>18</td>\n",
       "      <td>10</td>\n",
       "      <td>Female</td>\n",
       "      <td>105450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>AssocProf</td>\n",
       "      <td>B</td>\n",
       "      <td>19</td>\n",
       "      <td>6</td>\n",
       "      <td>Female</td>\n",
       "      <td>104542</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>Prof</td>\n",
       "      <td>B</td>\n",
       "      <td>17</td>\n",
       "      <td>17</td>\n",
       "      <td>Female</td>\n",
       "      <td>124312</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         rank discipline  phd  service     sex  salary\n",
       "0        Prof          B   56       49    Male  186960\n",
       "4        Prof          B   20       18    Male  104800\n",
       "10       Prof          B   39       33    Male  128250\n",
       "11       Prof          B   23       23    Male  134778\n",
       "12   AsstProf          B    1        0    Male   88000\n",
       "13       Prof          B   35       33    Male  162200\n",
       "14       Prof          B   25       19    Male  153750\n",
       "15       Prof          B   17        3    Male  150480\n",
       "16   AsstProf          B    8        3    Male   75044\n",
       "17   AsstProf          B    4        0    Male   92000\n",
       "20   AsstProf          B    4        4    Male   92000\n",
       "25       Prof          B   35       31    Male   99418\n",
       "28   AsstProf          B    7        2    Male   91300\n",
       "29       Prof          B   21       20    Male  123683\n",
       "30  AssocProf          B    9        7    Male  107008\n",
       "31       Prof          B   22       21    Male  155750\n",
       "33       Prof          B   18       18    Male  120000\n",
       "34  AssocProf          B   12        8    Male  119800\n",
       "35       Prof          B   28       23    Male  126933\n",
       "36       Prof          B   45       45    Male  146856\n",
       "38   AsstProf          B    4        3    Male   91000\n",
       "39       Prof          B   18       18  Female  129000\n",
       "42   AsstProf          B    4        2  Female   80225\n",
       "43   AsstProf          B    5        0  Female   77000\n",
       "44       Prof          B   23       19  Female  151768\n",
       "45       Prof          B   25       25  Female  140096\n",
       "46   AsstProf          B   11        3  Female   74692\n",
       "47  AssocProf          B   11       11  Female  103613\n",
       "48       Prof          B   17       17  Female  111512\n",
       "49       Prof          B   17       18  Female  122960\n",
       "50   AsstProf          B   10        5  Female   97032\n",
       "51       Prof          B   20       14  Female  127512\n",
       "58       Prof          B   36       26  Female  144651\n",
       "59  AssocProf          B   12       10  Female  103994\n",
       "60   AsstProf          B    3        3  Female   92000\n",
       "61  AssocProf          B   13       10  Female  103750\n",
       "62  AssocProf          B   14        7  Female  109650\n",
       "71  AssocProf          B   12        9  Female   71065\n",
       "72       Prof          B   24       15  Female  161101\n",
       "73       Prof          B   18       10  Female  105450\n",
       "74  AssocProf          B   19        6  Female  104542\n",
       "75       Prof          B   17       17  Female  124312"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df.discipline=='B']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "scrolled": true
   },
   "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",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>rank</th>\n",
       "      <th>discipline</th>\n",
       "      <th>phd</th>\n",
       "      <th>service</th>\n",
       "      <th>sex</th>\n",
       "      <th>salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Prof</td>\n",
       "      <td>B</td>\n",
       "      <td>56</td>\n",
       "      <td>49</td>\n",
       "      <td>Male</td>\n",
       "      <td>186960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Prof</td>\n",
       "      <td>B</td>\n",
       "      <td>39</td>\n",
       "      <td>33</td>\n",
       "      <td>Male</td>\n",
       "      <td>128250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Prof</td>\n",
       "      <td>B</td>\n",
       "      <td>23</td>\n",
       "      <td>23</td>\n",
       "      <td>Male</td>\n",
       "      <td>134778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Prof</td>\n",
       "      <td>B</td>\n",
       "      <td>35</td>\n",
       "      <td>33</td>\n",
       "      <td>Male</td>\n",
       "      <td>162200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Prof</td>\n",
       "      <td>B</td>\n",
       "      <td>25</td>\n",
       "      <td>19</td>\n",
       "      <td>Male</td>\n",
       "      <td>153750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Prof</td>\n",
       "      <td>B</td>\n",
       "      <td>17</td>\n",
       "      <td>3</td>\n",
       "      <td>Male</td>\n",
       "      <td>150480</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Prof</td>\n",
       "      <td>B</td>\n",
       "      <td>21</td>\n",
       "      <td>20</td>\n",
       "      <td>Male</td>\n",
       "      <td>123683</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>Prof</td>\n",
       "      <td>B</td>\n",
       "      <td>22</td>\n",
       "      <td>21</td>\n",
       "      <td>Male</td>\n",
       "      <td>155750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>Prof</td>\n",
       "      <td>B</td>\n",
       "      <td>28</td>\n",
       "      <td>23</td>\n",
       "      <td>Male</td>\n",
       "      <td>126933</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>Prof</td>\n",
       "      <td>B</td>\n",
       "      <td>45</td>\n",
       "      <td>45</td>\n",
       "      <td>Male</td>\n",
       "      <td>146856</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>Prof</td>\n",
       "      <td>B</td>\n",
       "      <td>18</td>\n",
       "      <td>18</td>\n",
       "      <td>Female</td>\n",
       "      <td>129000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>Prof</td>\n",
       "      <td>B</td>\n",
       "      <td>23</td>\n",
       "      <td>19</td>\n",
       "      <td>Female</td>\n",
       "      <td>151768</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>Prof</td>\n",
       "      <td>B</td>\n",
       "      <td>25</td>\n",
       "      <td>25</td>\n",
       "      <td>Female</td>\n",
       "      <td>140096</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>Prof</td>\n",
       "      <td>B</td>\n",
       "      <td>17</td>\n",
       "      <td>18</td>\n",
       "      <td>Female</td>\n",
       "      <td>122960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>Prof</td>\n",
       "      <td>B</td>\n",
       "      <td>20</td>\n",
       "      <td>14</td>\n",
       "      <td>Female</td>\n",
       "      <td>127512</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>Prof</td>\n",
       "      <td>B</td>\n",
       "      <td>36</td>\n",
       "      <td>26</td>\n",
       "      <td>Female</td>\n",
       "      <td>144651</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>Prof</td>\n",
       "      <td>B</td>\n",
       "      <td>24</td>\n",
       "      <td>15</td>\n",
       "      <td>Female</td>\n",
       "      <td>161101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>Prof</td>\n",
       "      <td>B</td>\n",
       "      <td>17</td>\n",
       "      <td>17</td>\n",
       "      <td>Female</td>\n",
       "      <td>124312</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    rank discipline  phd  service     sex  salary\n",
       "0   Prof          B   56       49    Male  186960\n",
       "10  Prof          B   39       33    Male  128250\n",
       "11  Prof          B   23       23    Male  134778\n",
       "13  Prof          B   35       33    Male  162200\n",
       "14  Prof          B   25       19    Male  153750\n",
       "15  Prof          B   17        3    Male  150480\n",
       "29  Prof          B   21       20    Male  123683\n",
       "31  Prof          B   22       21    Male  155750\n",
       "35  Prof          B   28       23    Male  126933\n",
       "36  Prof          B   45       45    Male  146856\n",
       "39  Prof          B   18       18  Female  129000\n",
       "44  Prof          B   23       19  Female  151768\n",
       "45  Prof          B   25       25  Female  140096\n",
       "49  Prof          B   17       18  Female  122960\n",
       "51  Prof          B   20       14  Female  127512\n",
       "58  Prof          B   36       26  Female  144651\n",
       "72  Prof          B   24       15  Female  161101\n",
       "75  Prof          B   17       17  Female  124312"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[(df.salary>120000) & (df.discipline=='B')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     186960\n",
       "1      93000\n",
       "2     110515\n",
       "3     131205\n",
       "4     104800\n",
       "       ...  \n",
       "73    105450\n",
       "74    104542\n",
       "75    124312\n",
       "76    109954\n",
       "77    109646\n",
       "Name: salary, Length: 78, dtype: int64"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['salary']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>rank</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
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       "      <td>110515</td>\n",
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       "      <th>3</th>\n",
       "      <td>131205</td>\n",
       "      <td>Prof</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>104800</td>\n",
       "      <td>Prof</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>105450</td>\n",
       "      <td>Prof</td>\n",
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       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>104542</td>\n",
       "      <td>AssocProf</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>124312</td>\n",
       "      <td>Prof</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>109954</td>\n",
       "      <td>Prof</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>109646</td>\n",
       "      <td>Prof</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>78 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    salary       rank\n",
       "0   186960       Prof\n",
       "1    93000       Prof\n",
       "2   110515       Prof\n",
       "3   131205       Prof\n",
       "4   104800       Prof\n",
       "..     ...        ...\n",
       "73  105450       Prof\n",
       "74  104542  AssocProf\n",
       "75  124312       Prof\n",
       "76  109954       Prof\n",
       "77  109646       Prof\n",
       "\n",
       "[78 rows x 2 columns]"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[['salary','rank']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
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       "      <th>discipline</th>\n",
       "      <th>phd</th>\n",
       "      <th>service</th>\n",
       "      <th>sex</th>\n",
       "      <th>salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Prof</td>\n",
       "      <td>B</td>\n",
       "      <td>56</td>\n",
       "      <td>49</td>\n",
       "      <td>Male</td>\n",
       "      <td>186960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Prof</td>\n",
       "      <td>A</td>\n",
       "      <td>12</td>\n",
       "      <td>6</td>\n",
       "      <td>Male</td>\n",
       "      <td>93000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Prof</td>\n",
       "      <td>A</td>\n",
       "      <td>23</td>\n",
       "      <td>20</td>\n",
       "      <td>Male</td>\n",
       "      <td>110515</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Prof</td>\n",
       "      <td>A</td>\n",
       "      <td>40</td>\n",
       "      <td>31</td>\n",
       "      <td>Male</td>\n",
       "      <td>131205</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Prof</td>\n",
       "      <td>B</td>\n",
       "      <td>20</td>\n",
       "      <td>18</td>\n",
       "      <td>Male</td>\n",
       "      <td>104800</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   rank discipline  phd  service   sex  salary\n",
       "0  Prof          B   56       49  Male  186960\n",
       "1  Prof          A   12        6  Male   93000\n",
       "2  Prof          A   23       20  Male  110515\n",
       "3  Prof          A   40       31  Male  131205\n",
       "4  Prof          B   20       18  Male  104800"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "      <td>49</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A</td>\n",
       "      <td>12</td>\n",
       "      <td>6</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A</td>\n",
       "      <td>23</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A</td>\n",
       "      <td>40</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  discipline  phd  service\n",
       "0          B   56       49\n",
       "1          A   12        6\n",
       "2          A   23       20\n",
       "3          A   40       31"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[0:4,1:4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "      <th>service</th>\n",
       "      <th>sex</th>\n",
       "      <th>salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>56</td>\n",
       "      <td>49</td>\n",
       "      <td>Male</td>\n",
       "      <td>186960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>12</td>\n",
       "      <td>6</td>\n",
       "      <td>Male</td>\n",
       "      <td>93000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>23</td>\n",
       "      <td>20</td>\n",
       "      <td>Male</td>\n",
       "      <td>110515</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>40</td>\n",
       "      <td>31</td>\n",
       "      <td>Male</td>\n",
       "      <td>131205</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>20</td>\n",
       "      <td>18</td>\n",
       "      <td>Male</td>\n",
       "      <td>104800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>18</td>\n",
       "      <td>10</td>\n",
       "      <td>Female</td>\n",
       "      <td>105450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>19</td>\n",
       "      <td>6</td>\n",
       "      <td>Female</td>\n",
       "      <td>104542</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>17</td>\n",
       "      <td>17</td>\n",
       "      <td>Female</td>\n",
       "      <td>124312</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>28</td>\n",
       "      <td>14</td>\n",
       "      <td>Female</td>\n",
       "      <td>109954</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>23</td>\n",
       "      <td>15</td>\n",
       "      <td>Female</td>\n",
       "      <td>109646</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>78 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    phd  service     sex  salary\n",
       "0    56       49    Male  186960\n",
       "1    12        6    Male   93000\n",
       "2    23       20    Male  110515\n",
       "3    40       31    Male  131205\n",
       "4    20       18    Male  104800\n",
       "..  ...      ...     ...     ...\n",
       "73   18       10  Female  105450\n",
       "74   19        6  Female  104542\n",
       "75   17       17  Female  124312\n",
       "76   28       14  Female  109954\n",
       "77   23       15  Female  109646\n",
       "\n",
       "[78 rows x 4 columns]"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[:,2:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>discipline</th>\n",
       "      <th>phd</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>B</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A</td>\n",
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       "      <th>2</th>\n",
       "      <td>A</td>\n",
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       "      <th>3</th>\n",
       "      <td>A</td>\n",
       "      <td>40</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>B</td>\n",
       "      <td>20</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>A</td>\n",
       "      <td>20</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>A</td>\n",
       "      <td>20</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>A</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>A</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>A</td>\n",
       "      <td>51</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  discipline  phd\n",
       "0          B   56\n",
       "1          A   12\n",
       "2          A   23\n",
       "3          A   40\n",
       "4          B   20\n",
       "5          A   20\n",
       "6          A   20\n",
       "7          A   18\n",
       "8          A   29\n",
       "9          A   51"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[0:10,1:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        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>phd</th>\n",
       "      <th>salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>57800.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>56.000000</td>\n",
       "      <td>186960.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>19.705128</td>\n",
       "      <td>108023.782051</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            phd         salary\n",
       "min    1.000000   57800.000000\n",
       "max   56.000000  186960.000000\n",
       "mean  19.705128  108023.782051"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[['phd','salary']].agg(['min','max','mean'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 8 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#install and then import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "x = [-3, 5, 7]\n",
    "y = [10, 2, 5]\n",
    "y\n",
    "fig = plt.figure(figsize=(15,3))\n",
    "\n",
    "plt.plot(x, y)\n",
    "plt.xlim(-4, 10)\n",
    "plt.ylim(0, 12)\n",
    "plt.xlabel('X Axis')\n",
    "plt.ylabel('Y axis')\n",
    "plt.title('Line Plot')\n",
    "plt.suptitle('Sales Comaprison', size=20, y=1.03)\n",
    "fig.get_size_inches()\n",
    "fig.set_size_inches(14, 4)\n",
    "fig\n",
    "fig, axs = plt.subplots(nrows=1, ncols=1)\n",
    "fig, axs = plt.subplots(2, 4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Step by Step"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "#install and then import matplotlib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = [-3, 5, 7]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[-3, 5, 7]"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = [10, 2, 5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[10, 2, 5]"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.03, 'Sales Comaprison')"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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uJ0mSJEk6qj/X2ZMkSZIkVYnNniRJkiTVIJs9SZIkSapBNnuSJEmSVINs9iRJkiSpBtnsSZIkSVINstmTJEmSpBpksydJkiRJNchmT5IkSZJqkM2eJEmSJNUgmz1JkiRJqkE2e5IkSZJUg2z2JEmSJKkG2exJkiRJUg2y2ZMkSZKkGmSzJ0mSJEk1yGZPkiRJkmqQzZ4kSZIk1aB+a/Yi4pMRsTUi7q/YNjUi7oiIR8rnuv66viRJkiQNZ/05svcp4Nou294JfDszFwHfLl9LkiRJkvrYqP5648z8fkQs6LL5BuDF5a9vBu4E3tFfNQw17e3JVR+4k4UzJrBsfh3L59Wx5NwpjBszstqlSZIkSRpi+q3Z68HMzGwByMyWiJjR04ERcSNwI8C8efMGqLzq2neojeecN5U1Ta18a/1WAEaNCC6eM5nl8+pYPr94zJp8VpUrlSRJkjTYRWb235sXI3urM/OS8vXOzJxSsb81M096396KFSuyoaGh3+ocjHbsO8Q9G1tZ01Q81m3ayYHD7QDMmTKOZfPrWFE2fxfOmsiokWbtSJIkSbUqItZk5opTOWegR/a2RER9OapXD2wd4OsPGVPPHsPVF83k6otmAnD4SDsPNu8umr+Nrfz0iR2sWtcMwLjRI7ls7pRi5G9BHcvm1jF5/Ohqli9JkiSpyga62fsa8Frg78vnrw7w9Yes0SNHsHTuFJbOncLrOQ+A5p3PdI78rWlq5aPfe4wj3y1GahfNmMDy+XWdI4DnTT+biKjmR5AkSZI0gPptGmdE3EIRxjId2AL8BfBfwBeAecBG4FWZueNk7zUcp3Gejv2H2lj35C7WbmylYcMO1m7cya5nDgNQN350Z/Nn8IskSZI0tJzONM5+vWevr9jsnZ729uTx7Xs7R/4amlp5fNs+wOAXSZIkaSix2dNJnSz4paPxM/hFkiRJGjyGQkCLquxkwS8/eWIHXyuDX8aPGcnSc6ewYkEx/dPgF0mSJGnocGRPx2ne+QwNTa2sLUf/HmzZzZH2Y4NfOh4Gv0iSJEn9z2mc6hcdwS9rmnawpqm1x+CXFfOnsuTcyZw12uAXSZIkqS85jVP9YvyYUTzv2dN43rOnAccGvzRsKKZ/fmt9sWSiwS+SJEnS4ODInvpEZfBLQ1Mr657cycE2g18kSZKkvuDInqqma/DLobZ21rf0HPxy2dwpndM/DX6RJEmS+p4jexowT+18prjnr4fglxUL6lg2z+AXSZIkqSsDWjSk7D/Uxr1P7uxs/tY0tbL7QBtg8IskSZJUyWmcGlLGjxnF8589nec/ezpwNPilYUNr5/TP7oJfViwoRv9mTjL4RZIkSeqJI3sa1DqCXxrKkT+DXyRJkjQcObKnmmPwiyRJknR6HNnTkJaZNO860GPwy/kzJxTNn8EvkiRJGsIMaJGAfQfbWLep++CXqWeP6Wz8ls+vM/hFkiRJQ4LTOCXg7LHHB788tm1vZ+NXBL9sAY4Gv6youPfP4BdJkiTVAkf2NCzt2HeoGPnbaPCLJEmSBj9H9qRemnr2GK5ZPJNrFh8b/NJQ3vv34yee7jn4ZV4dk8cZ/CJJkqTBzZE9qRtdg18amnawvmWPwS+SJEmqiiET0BIRbwXeACRwH/DbmXmgp+Nt9jQYGPwiSZKkahkS0zgjYg7wJmBxZj4TEV8AXg18aqBrkU7FqQS/jB4ZXDx78jH3/hn8IkmSpIFUrXv2RgHjIuIwMB5orlId0mkbMSJYNHMii2ZO5NXPmQccH/zyn3c38YkfPAEUwS8rFtR1Tv80+EWSJEn9qVrTON8MvBd4Brg9M3+jm2NuBG4EmDdv3vKmpqaBLVLqA4fa2nmwZfcx9/5t2X0QODb4Zfn8Oi43+EWSJEk9GBL37EVEHfAl4NeAncD/A76Ymf/Z0znes6daURn8smbDDtZsbO0x+GXFgqksmDbe4BdJkiQNjXv2gGuAJzJzG0BEfBl4PtBjsyfViohgzpRxzJkyjlcsnQ0cDX5Zs6GY/nlrYwu3/ORJwOAXSZIknb5qNHsbgSsiYjzFNM6rAYftNGydKPilY90/g18kSZJ0qqp1z95fUUzjbAPuAd6QmQd7Ot5pnBrujgl+2dDKuk07OdjWDhj8IkmSNBwMiXv2TofNnnSs3gS/rJhfxzKDXyRJkmqCzZ40TGUmT+18prP5qwx+iYBFMyaU0z6nsnx+ncEvkiRJQ4zNnqRO+w62se7JnZ0Lvq9tamX3gTbA4BdJkqShZqikcUoaAGePHcXzF07n+QuPDX5paGrtHAE0+EWSJKl2ObInDWNP7z3IPRt3dqZ+Vga/nFs3rrPxM/hFkiSpuhzZk3RKpk0YyzWLZ3LN4pnA8cEvdz/+NF+9txmAs8eM5LJ5U1g+z+AXSZKkocCRPUk96i745cHm3bQnFcEvUztHAA1+kSRJ6h8GtEjqd70NflmxoI5L5xj8IkmS1Becximp33UX/PLotr1F83eS4JcV8+uYYfCLJEnSgHBkT1Kfe3rvQdZu3NnZ/Bn8IkmSdGb6ZRpnRLwK+GZm7omI9wDLgL/JzLWnX+qpsdmThrbK4Jc1TTto2NDK1j0HAYNfJEmSeqO/mr3GzFwSES8A/g54P/Cnmfnc0y/11NjsSbWla/BLQ1Mr61sMfpEkSepJf92zd6R8fjnw0cz8akT85akWJ0kdIoJz68Zzbt14brhsDnB88Mutjc3c8pONwNHglxULiubP4BdJkqST602z91REfAy4BviHiBgLeIONpD51usEvK8rRP4NfJEmSjtWbaZzjgWuB+zLzkYioBy7NzNsHokBwGqekQm+DX5bPr+OCmQa/SJKk2tGn9+xFxKTM3B0RU7vbn5k7TqPG02KzJ6k7h9raeaB5V9H8bWw1+EWSJNWsvr5n77PASmANkEBlOkICzzrlCiWpD40ZNYLL5xWNHBwb/NLx+Mh3H+0Mfjl/xkSWVYz+GfwiSZJqmevsSapplcEvDeUI4J4DbQBMO3vMMc2fwS+SJGmw6pc0zoj4ncz8RMXrkcB7MvOvTqNGSRpQJwt+WdPUyh0PHhv8sqKiATT4RZIkDVW9CWj5LDAF+B1gOvBJ4HuZ+fbTvmjEFODjwCUUU0Jfn5n/3dPxjuxJ6k+9CX5ZMb+498/gF0mSVA39sqh6+ca/BvwrsB94TWb+8PRK7Hy/m4G7MvPjETEGGJ+ZO3s63mZP0kAy+EWSJA02/dLsRcQi4GbgPuAi4EHgbZm5/zSLnASsA56Vvbxh0GZPUjVlJptan2HtxqNTP9e37Db4RZIkDZj+avYeAv4wM78dxd9e3kYx7fLi0yzyMuAmiqZxKUXa55szc1+X424EbgSYN2/e8qamptO5nCT1i47gl4aORd8NfpEkSf2ov5q9SZm5u8u2RZn5yGnUSESsAO4GrszMH0fEPwO7M/PPejrHkT1Jg11l8EvDhqL5e2J78W9Yo0cGl8yZzPJ5Br9IkqTT05/37F0CLAY6/3aSmZ8+5QqL95oF3J2ZC8rXLwTemZkv7+kcmz1JQ1Fl8Muaph2s27SLQwa/SJKk09BfSy/8BfBiimbv68DLgB8Ap9XsZebmiHgyIi7IzJ8BV1NM6ZSkmjJtwlhesngmL1k8Ezg++OW/H3uar97bDBwb/LJ8wVQumzvF4BdJknRGTtrsAb9CcW/dPZn52xExk2LZhDPxR8BnyiTOx4HfPsP3k6RBb8yoEVw+r0jwhO6DXz7y3UePC37pWPdvvsEvkiTpFPSm2XsmM9sjoq1M0twKPOtMLpqZ9wKnNAQpSbUmIpg7dTxzp47nhsvmALC3DH7paP5WNzZzy082Aga/SJKkU9ObZq+hXAT93yiSM/cCP+nXqiRpmJowdhRXLpzOlQunA0eDXxo2HE39vOPBLYDBL5Ik6cR6FdDSeXDEAmBSZjb2V0HdMaBFko7qCH5paNrB2qbWY4Jf5k4d19n8LZtfx4WzJjFyhFM/JUka6votjbPabPYkqWeVwS9rmlppaGpl256DQBH8cvm8us7pn5fPm8Kkswx+kSRpqLHZkyQdF/zSsKGVhzbvPib4ZfmCus4RQINfJEka/Pq02YuIrwN/kJkb+qC2M2KzJ0lnpmvwy9qNrew50AYcG/yyYn4dlxj8IknSoNPX6+x9Crg9Im4G/jEzD59JcZKk6uku+OWRrXuPaf4MfpEkqbaccBpnRJwN/DlwLfAfQHvHvsz8YL9XV3JkT5L63/a9B1nb1Mqaja0Gv0iqCUfakwBG+PuVakBfj+wBHAb2AWOBiVQ0e5Kk2jJ9wlheevEsXnrxLKAIfrm/eVfRADa18sPHnua/7m0GDH6RNHi1tydrN7ayal0zX79/Mx/61ct4waLp1S5Lqooem72IuBb4IPA1YFlm7h+wqiRJVTdm1AiWzatj2bw63vDCY4NfOtb9+8h3HukMfrlg5sSi+TP4RdIAy0waN+1i1bpmbr2vhZZdBxg7agRXXTiDiWf1ZllpqTadKKDlLuD3M/OBgS3peE7jlKTBqTL4paGplXuaWtlz8NjglxXl6J/BL5L6UmbyYMtuVje2cGtjCxt37Gf0yOBF58/g+qX1XH3RTCaMtdFT7XDpBUlSVXUNflnTtIMNTxcTQyqDX1YsKEYMDX6RdKoe2bKHVY0trG5s5vFt+xg5InjBwumsXFLPSy+exeRxTilXbbLZkyQNOr0Nflk+fyoXzJpo8Iuk42zYvo/Vjc2sWtfCz7bsYUTAFc+axsols7n2kllMPXtMtUuU+p3NniRp0DvYdoQHmnd3Br80NLWybc9BwOAXSUdtat3PrY0trGps5v6ndgPwcwvqWLlkNi+7dBYzJjozQMOLzZ4kacjpCH45OvWzlYc27zb4RRqGNu86wK33FVM079m4E4Clc6dw/ZJ6rru0ntlTxlW5Qql6bPYkSTWhI/ilYUMx/bMy+GX6hDEsq1jw3eAXaWjbvvcg37ivhVWNLfx0ww4yYXH9JFYurWflpbOZN218tUuUBoX+WGdPkqQBN2HsKK5cOJ0rFxZrY1UGvzQ07WBtUyu3P7gFOBr80pH6uWx+ndO7pEFu5/5DfPP+zaxubOFHj22nPWHRjAm89ZrzWbmknmedM6HaJUo1wZE9SdKQVBn8smZDK41PGfwiDWa7Dxzmjge2sLqxmbse2U5be7Jg2niuXzqblUtmc8GsidUuURrUnMYpSRq2DH6RBp/9h9r41vqtrF7XzJ0Pb+NQWztzpoxj5dJ6rl8ym4tnT/IeXKmXhlSzFxEjgQbgqcxceaJjbfYkSafK4BepOg4cPsKdP9vKqnUtfPuhLRw43M7MSWN5+aWzWbm0nsvnTvH/Nek0DLV79t4MrAcmVbEGSVKNigjmTh3P3KnjeeXlc4Ai+OXejTuL5m9jK6vubeazP94IGPwinYmDbUe46+HtrG5s5o4Ht7Dv0BGmTxjDq5bPZeWSen5uwVRGOJVaGnBVafYi4lzg5cB7gbdVowZJ0vAzYewoXrBoOi9YVAS/HGlPHt26l4amHaxpaj0m+GXMyBFcMmdSZ/Nn8It0rMNH2vnRY0+zel0ztz2wmd0H2pgyfjTXL53N9Utn89zzpjJq5IhqlykNa1WZxhkRXwT+DpgIvL27aZwRcSNwI8C8efOWNzU1DWyRkqRhqTP4pXx0DX5ZMX9q5/RPg1803BxpT378xNOsbmzhm/dvZse+Q0wcO4qXXDyT65fO5gULpzPaBk/qF0Pinr2IWAlcl5l/EBEvpodmr5L37EmSqqUy+KVhQxH8sn3v8cEvK+bXcZnBL6pB7e3J2o2trG5s4db7Wti25yDjx4zk6otmcv2Sen7+/HOc8iwNgKHS7P0d8FtAG3AWxT17X87M3+zpHJs9SdJgcSrBLysW1DFvqsEvGnoyk8ZNu1jd2MytjS007zrA2FEjuOrCGaxcMpurLpzBuDE2eNJAGhLN3jEXd2RPklQD9hw4zLond3UGv9zT1Mqeg22AwS8aOjKT9S17WN3YzOrGFjbu2M/okcGLzj+HlUtmc83imUwYW81sP2l4G2ppnJIk1YSJZ40+Lvjlka17Okf+DH7RYPbIlj2samxhdWMzj2/bx8gRwZULp/PGqxbyi4tnMXm8U5OlocpF1RtzamQAAA4QSURBVCVJGgDb9x7sbPwMflG1bdi+r3ME76HNe4iAK86bxsql9bzsknqmnj2m2iVK6mLITePsLZs9SVKt6Qh+WbOhaP4qg18mjB3FZXOndI7+GfyivrCpdT+3NrawurGF+57aBcCK+XWsXFLPdZfWM2OSI8zSYGazJ0nSENU1+KWhqZWfdRP8sqJsAA1+UW9s2X2AWxtbWNXYzD0bdwKw9NzJXL90NtddWs/sKeOqXKGk3rLZkySphvQ2+GXFgjounm3wiwrb9x7kG/dvZtW6Zn66YQeZsLh+EiuX1rPy0tnMmza+2iVKOg0GtEiSVEMMflFv7dx/iNse2MyqdS386LHttCcsnDGBt1x9PiuX1vPscyZUu0RJVeDIniRJQ9i2PQdZu7H74Jd5U8d3Nn4Gv9Se3QcOc8cDW1jd2Mxdj2ynrT1ZMG08K5fMZuXSei6YOdGpvlINcRqnJEnD3MG2I9z/1O7O5q9r8Mvl86Z0Tv80+GXo2X+ojW+t38rqdc3c+fA2DrW1M2fKOFYuqWflktlcMmeSDZ5Uo2z2JEnSMTqCXxqadpTTP3ceF/zSMfXT4JfB6cDhI9z5s62samzh2+u3cOBwOzMnjeW6S+u5fulsLp87xe9MGgZs9iRJ0klVBr80NO3g3o07DX4ZZA61tXPXI9tY3djC7Q9sZt+hI0w7ewwvu3QW1y+Zzc8tmMoIp+RKw4oBLZIk6aROFvyyxuCXqmg70s6PHnuaVeuaue2Bzew+0MbkcaO5fulsVi6ZzRXPmsqokSOqXaakIcSRPUmSdJzjgl827eLQEYNf+tqR9uQnT+xgVWMz37x/Mzv2HWLC2FG89OKZXL9kNlcunM6YUTZ4kpzGKUmS+onBL32nvT2558lWVq1r4db7Wti25yDjRo/kmsUzWbmknhedf45TZyUdx2ZPkiQNiMzkyR3PsGajwS+9kZk0btrF6sZmbm1soXnXAcaMGsFVF8xg5dJ6rrpwBuPHeHeNpJ7Z7EmSpKrpCH7pSP7sLvhlxYKi+RsOwS+ZyfqWPaxubGZ1Ywsbd+xn9Mjg5xedw8ql9Vxz0UwmOgIqqZcMaJEkSVXTU/BLw4by3r+NPQW/TGXZ/Ck1E/zy6NY9rFrXwqrGZh7fto+RI4LnP3sab7xqIb+4eBaTx9vgSRoYjuxJkqQB0xH80pH6eV8PwS8r5tdx/syhE/yyYfu+zhG8hzbvIQKee95Url86m2svnsW0CWOrXaKkIc5pnJIkaUipDH4ppn/u7DH45fJ5UwbVtMdNrfu5tbGF1Y0t3PfULgCWz6/j+iX1XHdpPTMm1cZIpaTBwWZPkiQNad0Fvzy0eTc5SIJftuw+UDZ4zazduBOApedOZuWS2Vy3pJ45U8YNWC2ShhebPUmSVHP2HDjMvU/u7Jz6ec/GneztDH4Zy/L5Uzqbv/4Iftm+9yDfuH8zq9c185MNO8iEi+onsXJJPSuX1DN/2tl9ej1J6o4BLZIkqeZMPGs0L1x0Di9cdA5QBL88vGUPa5qOBr/c9sDR4JdLz51c3PtXTv88Z+Kp3y+3c/8hbntgM6sbW/jRY09zpD1ZOGMCb756ESuXzGbhjAl9+hklqT8M+MheRMwFPg3MAtqBmzLzn090jiN7kiTpRLbtOVg0fxt7Dn7pePQU/LLnwGHueHALq9Y184NHt3P4SDJ/2niuXzKblUvruWDmxGG9VqCk6hoS0zgjoh6oz8y1ETERWAO8MjMf7Okcmz1JknQqOoJf1jQdvfevp+CXXc8cZtW6Zu58eBuH2tqZM2VcOUVzNpfMmWSDJ2lQGBLTODOzBWgpf70nItYDc4Aemz1JkqRTMXbUyM6RPDg++KVhQysf/s4jdPyb94yJY/mN585j5ZLZXD53CiOGyJIPknQiVQ1oiYgFwPeBSzJzd5d9NwI3AsybN295U1PTgNcnSZJqV0fwS0djOFTW9JM0PA2JaZydF46YAHwPeG9mfvlExzqNU5IkSdJwdjrN3oj+KuZEImI08CXgMydr9CRJkiRJp27Am70o7nL+BLA+Mz840NeXJEmSpOGgGiN7VwK/BVwVEfeWj+uqUIckSZIk1axqpHH+APAOaEmSJEnqR1W5Z0+SJEmS1L9s9iRJkiSpBtnsSZIkSVINstmTJEmSpBpksydJkiRJNchmT5IkSZJqkM2eJEmSJNUgmz1JkiRJqkE2e5IkSZJUg2z2JEmSJKkG2exJkiRJUg2y2ZMkSZKkGmSzJ0mSJEk1yGZPkiRJkmqQzZ4kSZIk1SCbPUmSJEmqQTZ7kiRJklSDbPYkSZIkqQZVpdmLiGsj4mcR8WhEvLMaNUiSJElSLRvwZi8iRgL/CrwMWAy8JiIWD3QdkiRJklTLqjGy9xzg0cx8PDMPAZ8DbqhCHZIkSZJUs0ZV4ZpzgCcrXm8Cntv1oIi4EbixfHkwIu4fgNpUHdOB7dUuQv3C77a2+f3WLr/b2ub3W7v8bmvbBad6QjWavehmWx63IfMm4CaAiGjIzBX9XZiqw++3dvnd1ja/39rld1vb/H5rl99tbYuIhlM9pxrTODcBcytenws0V6EOSZIkSapZ1Wj2fgosiojzImIM8Grga1WoQ5IkSZJq1oBP48zMtoh4I3AbMBL4ZGY+cJLTbur/ylRFfr+1y++2tvn91i6/29rm91u7/G5r2yl/v5F53O1ykiRJkqQhriqLqkuSJEmS+pfNniRJkiTVoCHX7EXE2yMiI2J6tWtR34iI90XEQxHRGBFfiYgp1a5JZy4iro2In0XEoxHxzmrXo74REXMj4rsRsT4iHoiIN1e7JvWtiBgZEfdExOpq16K+FRFTIuKL5Z+56yPiedWuSX0nIt5a/r58f0TcEhFnVbsmnb6I+GREbK1cazwipkbEHRHxSPlcd7L3GVLNXkTMBV4CbKx2LepTdwCXZOYS4GHgXVWuR2coIkYC/wq8DFgMvCYiFle3KvWRNuB/ZeZFwBXAH/rd1pw3A+urXYT6xT8D38zMC4Gl+D3XjIiYA7wJWJGZl1CEIL66ulXpDH0KuLbLtncC387MRcC3y9cnNKSaPeBDwJ/QzSLsGroy8/bMbCtf3k2x9qKGtucAj2bm45l5CPgccEOVa1IfyMyWzFxb/noPxV8W51S3KvWViDgXeDnw8WrXor4VEZOAnwc+AZCZhzJzZ3WrUh8bBYyLiFHAeFzHekjLzO8DO7psvgG4ufz1zcArT/Y+Q6bZi4hXAE9l5rpq16J+9XrgG9UuQmdsDvBkxetN2BDUnIhYAFwO/Li6lagP/RPFP6q2V7sQ9blnAduAfy+n6X48Is6udlHqG5n5FPB+itlvLcCuzLy9ulWpH8zMzBYo/vEVmHGyEwZVsxcR3yrnGXd93AC8G/jzateo03OS77bjmHdTTBH7TPUqVR+JbrY5Il9DImIC8CXgLZm5u9r16MxFxEpga2auqXYt6hejgGXARzPzcmAfvZgCpqGhvHfrBuA8YDZwdkT8ZnWr0mAw4Iuqn0hmXtPd9oi4lOI/3nURAcU0v7UR8ZzM3DyAJeo09fTddoiI1wIrgavTxR9rwSZgbsXrc3E6Sc2IiNEUjd5nMvPL1a5HfeZK4BURcR1wFjApIv4zM/0LY23YBGzKzI6R+C9is1dLrgGeyMxtABHxZeD5wH9WtSr1tS0RUZ+ZLRFRD2w92QmDamSvJ5l5X2bOyMwFmbmA4jesZTZ6tSEirgXeAbwiM/dXux71iZ8CiyLivIgYQ3GT+NeqXJP6QBT/4vYJYH1mfrDa9ajvZOa7MvPc8s/ZVwPfsdGrHeXfmZ6MiAvKTVcDD1axJPWtjcAVETG+/H36agzgqUVfA15b/vq1wFdPdsKgGtnTsPURYCxwRzlye3dm/n51S9KZyMy2iHgjcBtFItgnM/OBKpelvnEl8FvAfRFxb7ntTzPz61WsSVLv/BHwmfIf4R4HfrvK9aiPZOaPI+KLwFqKW2LuAW6qblU6ExFxC/BiYHpEbAL+Avh74AsR8TsUDf6rTvo+zpiTJEmSpNozJKZxSpIkSZJOjc2eJEmSJNUgmz1JkiRJqkE2e5IkSZJUg2z2JEmSJKkG2exJkoaFiJgbEU9ExNTydV35en4Px/9SRGREXNiL914RER/u65olSToTLr0gSRo2IuJPgIWZeWNEfAzYkJl/18OxXwDqgW9n5l8OYJmSJPUJR/YkScPJh4ArIuItwAuAD3R3UERMoFhA/neAV1ds/6WI+FYU6iPi4YiYFREvjojV5TEvioh7y8c9ETGx/z+WJEnHs9mTJA0bmXkY+GOKpu8tmXmoh0NfCXwzMx8GdkTEsvL8rwCbgT8E/g34i8zc3OXctwN/mJmXAS8Enun7TyJJ0snZ7EmShpuXAS3AJSc45jXA58pff6583eGPgHcBBzPzlm7O/SHwwYh4EzAlM9vOvGRJkk7dqGoXIEnSQImIy4CXAFcAP4iIz2VmS5djpgFXAZdERAIjgYyIP8niRvc5QDswMyJGZGZ75fmZ+fcRcStwHXB3RFyTmQ/1/6eTJOlYjuxJkoaFiAjgoxTTNzcC7wPe382hvwJ8OjPnZ+aCzJwLPAG8ICJGAf8O/DqwHnhbN9d5dmbel5n/ADQAJ03zlCSpP9jsSZKGi98FNmbmHeXr/wNcGBEv6nLca4CvdNn2JYoG70+BuzLzLopG7w0RcVGXY98SEfdHxDqK+/W+0ZcfQpKk3nLpBUmSJEmqQY7sSZIkSVINstmTJEmSpBpksydJkiRJNchmT5IkSZJqkM2eJEmSJNUgmz1JkiRJqkE2e5IkSZJUg/4/AW9WfZ2KqTMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1080x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(15,3))\n",
    "plt.plot(x, y)\n",
    "plt.xlim(-4, 10)\n",
    "plt.ylim(0, 12)\n",
    "plt.xlabel('X Axis')\n",
    "plt.ylabel('Y axis')\n",
    "plt.title('Line Plot')\n",
    "plt.suptitle('Sales Comaprison', size=20, y=1.03)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([15.,  3.])"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fig.get_size_inches()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x288 with 1 Axes>"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fig.set_size_inches(14, 4)\n",
    "fig"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "fig, axs = plt.subplots(nrows=1, ncols=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 8 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(2, 4)"
   ]
  },
  {
   "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": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}