pandas-exercise-solution.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Load needed libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Load data\n",
    "\n",
    "Load data from [this](http://vincentarelbundock.github.io/Rdatasets/csv/carData/States.csv) address, [here](http://vincentarelbundock.github.io/Rdatasets/doc/carData/States.html) is data description.\n",
    "\n",
    "Assign dataset to `education` variable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\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>state</th>\n",
       "      <th>region</th>\n",
       "      <th>pop</th>\n",
       "      <th>SATV</th>\n",
       "      <th>SATM</th>\n",
       "      <th>percent</th>\n",
       "      <th>dollars</th>\n",
       "      <th>pay</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AL</td>\n",
       "      <td>ESC</td>\n",
       "      <td>4041</td>\n",
       "      <td>470</td>\n",
       "      <td>514</td>\n",
       "      <td>8</td>\n",
       "      <td>3.648</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AK</td>\n",
       "      <td>PAC</td>\n",
       "      <td>550</td>\n",
       "      <td>438</td>\n",
       "      <td>476</td>\n",
       "      <td>42</td>\n",
       "      <td>7.887</td>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AZ</td>\n",
       "      <td>MTN</td>\n",
       "      <td>3665</td>\n",
       "      <td>445</td>\n",
       "      <td>497</td>\n",
       "      <td>25</td>\n",
       "      <td>4.231</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AR</td>\n",
       "      <td>WSC</td>\n",
       "      <td>2351</td>\n",
       "      <td>470</td>\n",
       "      <td>511</td>\n",
       "      <td>6</td>\n",
       "      <td>3.334</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>CA</td>\n",
       "      <td>PAC</td>\n",
       "      <td>29760</td>\n",
       "      <td>419</td>\n",
       "      <td>484</td>\n",
       "      <td>45</td>\n",
       "      <td>4.826</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  state region    pop  SATV  SATM  percent  dollars  pay\n",
       "0    AL    ESC   4041   470   514        8    3.648   27\n",
       "1    AK    PAC    550   438   476       42    7.887   43\n",
       "2    AZ    MTN   3665   445   497       25    4.231   30\n",
       "3    AR    WSC   2351   470   511        6    3.334   23\n",
       "4    CA    PAC  29760   419   484       45    4.826   39"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "education = pd.read_csv('http://vincentarelbundock.github.io/Rdatasets/csv/carData/States.csv')\n",
    "education.rename({'Unnamed: 0': 'state'}, axis='columns', inplace=True)\n",
    "education.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1. How many row and columns are in data?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "51"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# num_rows\n",
    "education.shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# num_columns\n",
    "education.shape[1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2. See the first 10 entries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>state</th>\n",
       "      <th>region</th>\n",
       "      <th>pop</th>\n",
       "      <th>SATV</th>\n",
       "      <th>SATM</th>\n",
       "      <th>percent</th>\n",
       "      <th>dollars</th>\n",
       "      <th>pay</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AL</td>\n",
       "      <td>ESC</td>\n",
       "      <td>4041</td>\n",
       "      <td>470</td>\n",
       "      <td>514</td>\n",
       "      <td>8</td>\n",
       "      <td>3.648</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AK</td>\n",
       "      <td>PAC</td>\n",
       "      <td>550</td>\n",
       "      <td>438</td>\n",
       "      <td>476</td>\n",
       "      <td>42</td>\n",
       "      <td>7.887</td>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AZ</td>\n",
       "      <td>MTN</td>\n",
       "      <td>3665</td>\n",
       "      <td>445</td>\n",
       "      <td>497</td>\n",
       "      <td>25</td>\n",
       "      <td>4.231</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AR</td>\n",
       "      <td>WSC</td>\n",
       "      <td>2351</td>\n",
       "      <td>470</td>\n",
       "      <td>511</td>\n",
       "      <td>6</td>\n",
       "      <td>3.334</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>CA</td>\n",
       "      <td>PAC</td>\n",
       "      <td>29760</td>\n",
       "      <td>419</td>\n",
       "      <td>484</td>\n",
       "      <td>45</td>\n",
       "      <td>4.826</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>CO</td>\n",
       "      <td>MTN</td>\n",
       "      <td>3294</td>\n",
       "      <td>456</td>\n",
       "      <td>513</td>\n",
       "      <td>28</td>\n",
       "      <td>4.809</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>CN</td>\n",
       "      <td>NE</td>\n",
       "      <td>3287</td>\n",
       "      <td>430</td>\n",
       "      <td>471</td>\n",
       "      <td>74</td>\n",
       "      <td>7.914</td>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>DE</td>\n",
       "      <td>SA</td>\n",
       "      <td>666</td>\n",
       "      <td>433</td>\n",
       "      <td>470</td>\n",
       "      <td>58</td>\n",
       "      <td>6.016</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>DC</td>\n",
       "      <td>SA</td>\n",
       "      <td>607</td>\n",
       "      <td>409</td>\n",
       "      <td>441</td>\n",
       "      <td>68</td>\n",
       "      <td>8.210</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>FL</td>\n",
       "      <td>SA</td>\n",
       "      <td>12938</td>\n",
       "      <td>418</td>\n",
       "      <td>466</td>\n",
       "      <td>44</td>\n",
       "      <td>5.154</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  state region    pop  SATV  SATM  percent  dollars  pay\n",
       "0    AL    ESC   4041   470   514        8    3.648   27\n",
       "1    AK    PAC    550   438   476       42    7.887   43\n",
       "2    AZ    MTN   3665   445   497       25    4.231   30\n",
       "3    AR    WSC   2351   470   511        6    3.334   23\n",
       "4    CA    PAC  29760   419   484       45    4.826   39\n",
       "5    CO    MTN   3294   456   513       28    4.809   31\n",
       "6    CN     NE   3287   430   471       74    7.914   43\n",
       "7    DE     SA    666   433   470       58    6.016   35\n",
       "8    DC     SA    607   409   441       68    8.210   39\n",
       "9    FL     SA  12938   418   466       44    5.154   30"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "education.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3. List top 5 states with the highest average teacher's salary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>state</th>\n",
       "      <th>region</th>\n",
       "      <th>pop</th>\n",
       "      <th>SATV</th>\n",
       "      <th>SATM</th>\n",
       "      <th>percent</th>\n",
       "      <th>dollars</th>\n",
       "      <th>pay</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AK</td>\n",
       "      <td>PAC</td>\n",
       "      <td>550</td>\n",
       "      <td>438</td>\n",
       "      <td>476</td>\n",
       "      <td>42</td>\n",
       "      <td>7.887</td>\n",
       "      <td>43</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>CN</td>\n",
       "      <td>NE</td>\n",
       "      <td>3287</td>\n",
       "      <td>430</td>\n",
       "      <td>471</td>\n",
       "      <td>74</td>\n",
       "      <td>7.914</td>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>NY</td>\n",
       "      <td>MA</td>\n",
       "      <td>17990</td>\n",
       "      <td>412</td>\n",
       "      <td>470</td>\n",
       "      <td>70</td>\n",
       "      <td>8.500</td>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>CA</td>\n",
       "      <td>PAC</td>\n",
       "      <td>29760</td>\n",
       "      <td>419</td>\n",
       "      <td>484</td>\n",
       "      <td>45</td>\n",
       "      <td>4.826</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>DC</td>\n",
       "      <td>SA</td>\n",
       "      <td>607</td>\n",
       "      <td>409</td>\n",
       "      <td>441</td>\n",
       "      <td>68</td>\n",
       "      <td>8.210</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   state region    pop  SATV  SATM  percent  dollars  pay\n",
       "1     AK    PAC    550   438   476       42    7.887   43\n",
       "6     CN     NE   3287   430   471       74    7.914   43\n",
       "32    NY     MA  17990   412   470       70    8.500   42\n",
       "4     CA    PAC  29760   419   484       45    4.826   39\n",
       "8     DC     SA    607   409   441       68    8.210   39"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "education.sort_values(by='pay', ascending=False)[:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 4. Are there any missing values?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "state      False\n",
       "region     False\n",
       "pop        False\n",
       "SATV       False\n",
       "SATM       False\n",
       "percent    False\n",
       "dollars    False\n",
       "pay        False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "education.isnull().any()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 5. Show descriptive statistics for all columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>state</th>\n",
       "      <th>region</th>\n",
       "      <th>pop</th>\n",
       "      <th>SATV</th>\n",
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       "      <th>count</th>\n",
       "      <td>51</td>\n",
       "      <td>51</td>\n",
       "      <td>51.000000</td>\n",
       "      <td>51.000000</td>\n",
       "      <td>51.000000</td>\n",
       "      <td>51.000000</td>\n",
       "      <td>51.000000</td>\n",
       "      <td>51.000000</td>\n",
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       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>51</td>\n",
       "      <td>9</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>WY</td>\n",
       "      <td>SA</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4876.647059</td>\n",
       "      <td>448.156863</td>\n",
       "      <td>497.392157</td>\n",
       "      <td>33.745098</td>\n",
       "      <td>5.175490</td>\n",
       "      <td>30.941176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5439.202691</td>\n",
       "      <td>30.821014</td>\n",
       "      <td>34.568817</td>\n",
       "      <td>24.073922</td>\n",
       "      <td>1.376166</td>\n",
       "      <td>5.308151</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>454.000000</td>\n",
       "      <td>397.000000</td>\n",
       "      <td>437.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>2.993000</td>\n",
       "      <td>22.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1215.000000</td>\n",
       "      <td>422.500000</td>\n",
       "      <td>470.000000</td>\n",
       "      <td>11.500000</td>\n",
       "      <td>4.354000</td>\n",
       "      <td>27.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3294.000000</td>\n",
       "      <td>443.000000</td>\n",
       "      <td>490.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>5.045000</td>\n",
       "      <td>30.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5780.000000</td>\n",
       "      <td>474.500000</td>\n",
       "      <td>522.500000</td>\n",
       "      <td>57.500000</td>\n",
       "      <td>5.689500</td>\n",
       "      <td>33.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>29760.000000</td>\n",
       "      <td>511.000000</td>\n",
       "      <td>577.000000</td>\n",
       "      <td>74.000000</td>\n",
       "      <td>9.159000</td>\n",
       "      <td>43.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       state region           pop        SATV        SATM    percent  \\\n",
       "count     51     51     51.000000   51.000000   51.000000  51.000000   \n",
       "unique    51      9           NaN         NaN         NaN        NaN   \n",
       "top       WY     SA           NaN         NaN         NaN        NaN   \n",
       "freq       1      9           NaN         NaN         NaN        NaN   \n",
       "mean     NaN    NaN   4876.647059  448.156863  497.392157  33.745098   \n",
       "std      NaN    NaN   5439.202691   30.821014   34.568817  24.073922   \n",
       "min      NaN    NaN    454.000000  397.000000  437.000000   4.000000   \n",
       "25%      NaN    NaN   1215.000000  422.500000  470.000000  11.500000   \n",
       "50%      NaN    NaN   3294.000000  443.000000  490.000000  25.000000   \n",
       "75%      NaN    NaN   5780.000000  474.500000  522.500000  57.500000   \n",
       "max      NaN    NaN  29760.000000  511.000000  577.000000  74.000000   \n",
       "\n",
       "          dollars        pay  \n",
       "count   51.000000  51.000000  \n",
       "unique        NaN        NaN  \n",
       "top           NaN        NaN  \n",
       "freq          NaN        NaN  \n",
       "mean     5.175490  30.941176  \n",
       "std      1.376166   5.308151  \n",
       "min      2.993000  22.000000  \n",
       "25%      4.354000  27.500000  \n",
       "50%      5.045000  30.000000  \n",
       "75%      5.689500  33.500000  \n",
       "max      9.159000  43.000000  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "education.describe(include='all')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 6. Show 10 states with the lowest percentage of graduating high-school students who took the SAT exam."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>state</th>\n",
       "      <th>region</th>\n",
       "      <th>pop</th>\n",
       "      <th>SATV</th>\n",
       "      <th>SATM</th>\n",
       "      <th>percent</th>\n",
       "      <th>dollars</th>\n",
       "      <th>pay</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>MS</td>\n",
       "      <td>ESC</td>\n",
       "      <td>2573</td>\n",
       "      <td>477</td>\n",
       "      <td>519</td>\n",
       "      <td>4</td>\n",
       "      <td>3.322</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>UT</td>\n",
       "      <td>MTN</td>\n",
       "      <td>1723</td>\n",
       "      <td>492</td>\n",
       "      <td>539</td>\n",
       "      <td>5</td>\n",
       "      <td>2.993</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>SD</td>\n",
       "      <td>WNC</td>\n",
       "      <td>696</td>\n",
       "      <td>506</td>\n",
       "      <td>555</td>\n",
       "      <td>5</td>\n",
       "      <td>3.730</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>IA</td>\n",
       "      <td>WNC</td>\n",
       "      <td>2777</td>\n",
       "      <td>511</td>\n",
       "      <td>577</td>\n",
       "      <td>5</td>\n",
       "      <td>4.839</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AR</td>\n",
       "      <td>WSC</td>\n",
       "      <td>2351</td>\n",
       "      <td>470</td>\n",
       "      <td>511</td>\n",
       "      <td>6</td>\n",
       "      <td>3.334</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>ND</td>\n",
       "      <td>WNC</td>\n",
       "      <td>639</td>\n",
       "      <td>505</td>\n",
       "      <td>564</td>\n",
       "      <td>6</td>\n",
       "      <td>3.685</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AL</td>\n",
       "      <td>ESC</td>\n",
       "      <td>4041</td>\n",
       "      <td>470</td>\n",
       "      <td>514</td>\n",
       "      <td>8</td>\n",
       "      <td>3.648</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>OK</td>\n",
       "      <td>WSC</td>\n",
       "      <td>3146</td>\n",
       "      <td>478</td>\n",
       "      <td>523</td>\n",
       "      <td>9</td>\n",
       "      <td>3.742</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>LA</td>\n",
       "      <td>WSC</td>\n",
       "      <td>4220</td>\n",
       "      <td>476</td>\n",
       "      <td>517</td>\n",
       "      <td>9</td>\n",
       "      <td>4.012</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>NE</td>\n",
       "      <td>WNC</td>\n",
       "      <td>1578</td>\n",
       "      <td>484</td>\n",
       "      <td>546</td>\n",
       "      <td>10</td>\n",
       "      <td>4.381</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   state region   pop  SATV  SATM  percent  dollars  pay\n",
       "24    MS    ESC  2573   477   519        4    3.322   24\n",
       "44    UT    MTN  1723   492   539        5    2.993   25\n",
       "41    SD    WNC   696   506   555        5    3.730   22\n",
       "15    IA    WNC  2777   511   577        5    4.839   28\n",
       "3     AR    WSC  2351   470   511        6    3.334   23\n",
       "34    ND    WNC   639   505   564        6    3.685   23\n",
       "0     AL    ESC  4041   470   514        8    3.648   27\n",
       "36    OK    WSC  3146   478   523        9    3.742   24\n",
       "18    LA    WSC  4220   476   517        9    4.012   26\n",
       "27    NE    WNC  1578   484   546       10    4.381   26"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "education.sort_values(by='percent')[:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 7. Filter states with population more or equal than 2000000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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>state</th>\n",
       "      <th>region</th>\n",
       "      <th>pop</th>\n",
       "      <th>SATV</th>\n",
       "      <th>SATM</th>\n",
       "      <th>percent</th>\n",
       "      <th>dollars</th>\n",
       "      <th>pay</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AL</td>\n",
       "      <td>ESC</td>\n",
       "      <td>4041</td>\n",
       "      <td>470</td>\n",
       "      <td>514</td>\n",
       "      <td>8</td>\n",
       "      <td>3.648</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AZ</td>\n",
       "      <td>MTN</td>\n",
       "      <td>3665</td>\n",
       "      <td>445</td>\n",
       "      <td>497</td>\n",
       "      <td>25</td>\n",
       "      <td>4.231</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AR</td>\n",
       "      <td>WSC</td>\n",
       "      <td>2351</td>\n",
       "      <td>470</td>\n",
       "      <td>511</td>\n",
       "      <td>6</td>\n",
       "      <td>3.334</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>CA</td>\n",
       "      <td>PAC</td>\n",
       "      <td>29760</td>\n",
       "      <td>419</td>\n",
       "      <td>484</td>\n",
       "      <td>45</td>\n",
       "      <td>4.826</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>CO</td>\n",
       "      <td>MTN</td>\n",
       "      <td>3294</td>\n",
       "      <td>456</td>\n",
       "      <td>513</td>\n",
       "      <td>28</td>\n",
       "      <td>4.809</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  state region    pop  SATV  SATM  percent  dollars  pay\n",
       "0    AL    ESC   4041   470   514        8    3.648   27\n",
       "2    AZ    MTN   3665   445   497       25    4.231   30\n",
       "3    AR    WSC   2351   470   511        6    3.334   23\n",
       "4    CA    PAC  29760   419   484       45    4.826   39\n",
       "5    CO    MTN   3294   456   513       28    4.809   31"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "education_sub = education[education['pop'] >= 2000]\n",
    "education_sub.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 8. How many states did you get fro the Question 7?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "33"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(education_sub)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 9. Using data from the Question 7., how many of them have average teacher's salary more or equal than 30000$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "19"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(education_sub[education_sub.pay >= 30])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 10. Show min, max, median and average percentage of graduating high-school students in the state who took the SAT exam in each US region"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "education.percent.min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "74"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "education.percent.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "25.0"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "education.percent.median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "33.745098039215684"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "education.percent.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    51.000000\n",
       "mean     33.745098\n",
       "std      24.073922\n",
       "min       4.000000\n",
       "25%      11.500000\n",
       "50%      25.000000\n",
       "75%      57.500000\n",
       "max      74.000000\n",
       "Name: percent, dtype: float64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "education.percent.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 11. BONUS: rename `region` column using following dictionary\n",
    "```python\n",
    "new_names = {\n",
    "'ENC': 'East North Central', 'ESC' : 'East South Central', 'MA' : 'Mid-Atlantic', 'MTN' : 'Mountain',\n",
    "'NE' : 'New England', 'PAC' : 'Pacific', 'SA' : 'South Atlantic', 'WNC' : 'West North Central', \n",
    "'WSC' : 'West South Central'\n",
    "}\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_names = {\n",
    "'ENC': 'East North Central', 'ESC' : 'East South Central', 'MA' : 'Mid-Atlantic', 'MTN' : 'Mountain',\n",
    "'NE' : 'New England', 'PAC' : 'Pacific', 'SA' : 'South Atlantic', 'WNC' : 'West North Central', \n",
    "'WSC' : 'West South Central'\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>state</th>\n",
       "      <th>region</th>\n",
       "      <th>pop</th>\n",
       "      <th>SATV</th>\n",
       "      <th>SATM</th>\n",
       "      <th>percent</th>\n",
       "      <th>dollars</th>\n",
       "      <th>pay</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AL</td>\n",
       "      <td>East South Central</td>\n",
       "      <td>4041</td>\n",
       "      <td>470</td>\n",
       "      <td>514</td>\n",
       "      <td>8</td>\n",
       "      <td>3.648</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AK</td>\n",
       "      <td>Pacific</td>\n",
       "      <td>550</td>\n",
       "      <td>438</td>\n",
       "      <td>476</td>\n",
       "      <td>42</td>\n",
       "      <td>7.887</td>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AZ</td>\n",
       "      <td>Mountain</td>\n",
       "      <td>3665</td>\n",
       "      <td>445</td>\n",
       "      <td>497</td>\n",
       "      <td>25</td>\n",
       "      <td>4.231</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AR</td>\n",
       "      <td>West South Central</td>\n",
       "      <td>2351</td>\n",
       "      <td>470</td>\n",
       "      <td>511</td>\n",
       "      <td>6</td>\n",
       "      <td>3.334</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>CA</td>\n",
       "      <td>Pacific</td>\n",
       "      <td>29760</td>\n",
       "      <td>419</td>\n",
       "      <td>484</td>\n",
       "      <td>45</td>\n",
       "      <td>4.826</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  state              region    pop  SATV  SATM  percent  dollars  pay\n",
       "0    AL  East South Central   4041   470   514        8    3.648   27\n",
       "1    AK             Pacific    550   438   476       42    7.887   43\n",
       "2    AZ            Mountain   3665   445   497       25    4.231   30\n",
       "3    AR  West South Central   2351   470   511        6    3.334   23\n",
       "4    CA             Pacific  29760   419   484       45    4.826   39"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "education['region'] = education['region'].map(new_names)\n",
    "education.head()"
   ]
  },
  {
   "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",
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