Visualizing Earnings Based On College Majors.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# Guided Project: Visualizing Earnings Based On College Majors"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We'll be working with a dataset on the job outcomes of students who graduated from college between 2010 and 2012. The original data on job outcomes was released by [American Community Survey](https://www.census.gov/programs-surveys/acs/), which conducts surveys and aggregates the data. FiveThirtyEight cleaned the dataset and released it on their [Github repo](https://github.com/fivethirtyeight/data/tree/master/college-majors).\n",
    "\n",
    "Each row in the dataset represents a different major in college and contains information on gender diversity, employment rates, median salaries, and more. Here are some of the columns in the dataset:\n",
    "\n",
    "**Rank** - Rank by median earnings (the dataset is ordered by this column).\n",
    "\n",
    "**Major_code** - Major code.\n",
    "\n",
    "**Major** - Major description.\n",
    "\n",
    "**Major_category** - Category of major.\n",
    "\n",
    "**Total** - Total number of people with major.\n",
    "\n",
    "**Sample_size** - Sample size (unweighted) of full-time.\n",
    "\n",
    "**Men** - Male graduates.\n",
    "\n",
    "**Women** - Female graduates.\n",
    "\n",
    "**ShareWomen** - Women as share of total.\n",
    "\n",
    "**Employed** - Number employed.\n",
    "\n",
    "**Median** - Median salary of full-time, year-round \n",
    "workers.\n",
    "\n",
    "**Low_wage_jobs** - Number in low-wage service jobs.\n",
    "\n",
    "**Full_time** - Number employed 35 hours or more.\n",
    "\n",
    "**Part_time** - Number employed less than 35 hours."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Importing Libraries\n",
    "import pandas as pd\n",
    "import matplotlib\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Reading the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Rank                                        1\n",
       "Major_code                               2419\n",
       "Major                   PETROLEUM ENGINEERING\n",
       "Total                                    2339\n",
       "Men                                      2057\n",
       "Women                                     282\n",
       "Major_category                    Engineering\n",
       "ShareWomen                           0.120564\n",
       "Sample_size                                36\n",
       "Employed                                 1976\n",
       "Full_time                                1849\n",
       "Part_time                                 270\n",
       "Full_time_year_round                     1207\n",
       "Unemployed                                 37\n",
       "Unemployment_rate                   0.0183805\n",
       "Median                                 110000\n",
       "P25th                                   95000\n",
       "P75th                                  125000\n",
       "College_jobs                             1534\n",
       "Non_college_jobs                          364\n",
       "Low_wage_jobs                             193\n",
       "Name: 0, dtype: object"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "recent_grads = pd.read_csv(\"recent-grads.csv\")\n",
    "recent_grads.iloc[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Rank</th>\n",
       "      <th>Major_code</th>\n",
       "      <th>Major</th>\n",
       "      <th>Total</th>\n",
       "      <th>Men</th>\n",
       "      <th>Women</th>\n",
       "      <th>Major_category</th>\n",
       "      <th>ShareWomen</th>\n",
       "      <th>Sample_size</th>\n",
       "      <th>Employed</th>\n",
       "      <th>...</th>\n",
       "      <th>Part_time</th>\n",
       "      <th>Full_time_year_round</th>\n",
       "      <th>Unemployed</th>\n",
       "      <th>Unemployment_rate</th>\n",
       "      <th>Median</th>\n",
       "      <th>P25th</th>\n",
       "      <th>P75th</th>\n",
       "      <th>College_jobs</th>\n",
       "      <th>Non_college_jobs</th>\n",
       "      <th>Low_wage_jobs</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2419</td>\n",
       "      <td>PETROLEUM ENGINEERING</td>\n",
       "      <td>2339.0</td>\n",
       "      <td>2057.0</td>\n",
       "      <td>282.0</td>\n",
       "      <td>Engineering</td>\n",
       "      <td>0.120564</td>\n",
       "      <td>36</td>\n",
       "      <td>1976</td>\n",
       "      <td>...</td>\n",
       "      <td>270</td>\n",
       "      <td>1207</td>\n",
       "      <td>37</td>\n",
       "      <td>0.018381</td>\n",
       "      <td>110000</td>\n",
       "      <td>95000</td>\n",
       "      <td>125000</td>\n",
       "      <td>1534</td>\n",
       "      <td>364</td>\n",
       "      <td>193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2416</td>\n",
       "      <td>MINING AND MINERAL ENGINEERING</td>\n",
       "      <td>756.0</td>\n",
       "      <td>679.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>Engineering</td>\n",
       "      <td>0.101852</td>\n",
       "      <td>7</td>\n",
       "      <td>640</td>\n",
       "      <td>...</td>\n",
       "      <td>170</td>\n",
       "      <td>388</td>\n",
       "      <td>85</td>\n",
       "      <td>0.117241</td>\n",
       "      <td>75000</td>\n",
       "      <td>55000</td>\n",
       "      <td>90000</td>\n",
       "      <td>350</td>\n",
       "      <td>257</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>2415</td>\n",
       "      <td>METALLURGICAL ENGINEERING</td>\n",
       "      <td>856.0</td>\n",
       "      <td>725.0</td>\n",
       "      <td>131.0</td>\n",
       "      <td>Engineering</td>\n",
       "      <td>0.153037</td>\n",
       "      <td>3</td>\n",
       "      <td>648</td>\n",
       "      <td>...</td>\n",
       "      <td>133</td>\n",
       "      <td>340</td>\n",
       "      <td>16</td>\n",
       "      <td>0.024096</td>\n",
       "      <td>73000</td>\n",
       "      <td>50000</td>\n",
       "      <td>105000</td>\n",
       "      <td>456</td>\n",
       "      <td>176</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>2417</td>\n",
       "      <td>NAVAL ARCHITECTURE AND MARINE ENGINEERING</td>\n",
       "      <td>1258.0</td>\n",
       "      <td>1123.0</td>\n",
       "      <td>135.0</td>\n",
       "      <td>Engineering</td>\n",
       "      <td>0.107313</td>\n",
       "      <td>16</td>\n",
       "      <td>758</td>\n",
       "      <td>...</td>\n",
       "      <td>150</td>\n",
       "      <td>692</td>\n",
       "      <td>40</td>\n",
       "      <td>0.050125</td>\n",
       "      <td>70000</td>\n",
       "      <td>43000</td>\n",
       "      <td>80000</td>\n",
       "      <td>529</td>\n",
       "      <td>102</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>2405</td>\n",
       "      <td>CHEMICAL ENGINEERING</td>\n",
       "      <td>32260.0</td>\n",
       "      <td>21239.0</td>\n",
       "      <td>11021.0</td>\n",
       "      <td>Engineering</td>\n",
       "      <td>0.341631</td>\n",
       "      <td>289</td>\n",
       "      <td>25694</td>\n",
       "      <td>...</td>\n",
       "      <td>5180</td>\n",
       "      <td>16697</td>\n",
       "      <td>1672</td>\n",
       "      <td>0.061098</td>\n",
       "      <td>65000</td>\n",
       "      <td>50000</td>\n",
       "      <td>75000</td>\n",
       "      <td>18314</td>\n",
       "      <td>4440</td>\n",
       "      <td>972</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Rank  Major_code                                      Major    Total  \\\n",
       "0     1        2419                      PETROLEUM ENGINEERING   2339.0   \n",
       "1     2        2416             MINING AND MINERAL ENGINEERING    756.0   \n",
       "2     3        2415                  METALLURGICAL ENGINEERING    856.0   \n",
       "3     4        2417  NAVAL ARCHITECTURE AND MARINE ENGINEERING   1258.0   \n",
       "4     5        2405                       CHEMICAL ENGINEERING  32260.0   \n",
       "\n",
       "       Men    Women Major_category  ShareWomen  Sample_size  Employed  \\\n",
       "0   2057.0    282.0    Engineering    0.120564           36      1976   \n",
       "1    679.0     77.0    Engineering    0.101852            7       640   \n",
       "2    725.0    131.0    Engineering    0.153037            3       648   \n",
       "3   1123.0    135.0    Engineering    0.107313           16       758   \n",
       "4  21239.0  11021.0    Engineering    0.341631          289     25694   \n",
       "\n",
       "       ...        Part_time  Full_time_year_round  Unemployed  \\\n",
       "0      ...              270                  1207          37   \n",
       "1      ...              170                   388          85   \n",
       "2      ...              133                   340          16   \n",
       "3      ...              150                   692          40   \n",
       "4      ...             5180                 16697        1672   \n",
       "\n",
       "   Unemployment_rate  Median  P25th   P75th  College_jobs  Non_college_jobs  \\\n",
       "0           0.018381  110000  95000  125000          1534               364   \n",
       "1           0.117241   75000  55000   90000           350               257   \n",
       "2           0.024096   73000  50000  105000           456               176   \n",
       "3           0.050125   70000  43000   80000           529               102   \n",
       "4           0.061098   65000  50000   75000         18314              4440   \n",
       "\n",
       "   Low_wage_jobs  \n",
       "0            193  \n",
       "1             50  \n",
       "2              0  \n",
       "3              0  \n",
       "4            972  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "recent_grads.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Rank</th>\n",
       "      <th>Major_code</th>\n",
       "      <th>Major</th>\n",
       "      <th>Total</th>\n",
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       "      <th>P25th</th>\n",
       "      <th>P75th</th>\n",
       "      <th>College_jobs</th>\n",
       "      <th>Non_college_jobs</th>\n",
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       "      <th>168</th>\n",
       "      <td>169</td>\n",
       "      <td>3609</td>\n",
       "      <td>ZOOLOGY</td>\n",
       "      <td>8409.0</td>\n",
       "      <td>3050.0</td>\n",
       "      <td>5359.0</td>\n",
       "      <td>Biology &amp; Life Science</td>\n",
       "      <td>0.637293</td>\n",
       "      <td>47</td>\n",
       "      <td>6259</td>\n",
       "      <td>...</td>\n",
       "      <td>2190</td>\n",
       "      <td>3602</td>\n",
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       "      <td>0.046320</td>\n",
       "      <td>26000</td>\n",
       "      <td>20000</td>\n",
       "      <td>39000</td>\n",
       "      <td>2771</td>\n",
       "      <td>2947</td>\n",
       "      <td>743</td>\n",
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       "      <th>169</th>\n",
       "      <td>170</td>\n",
       "      <td>5201</td>\n",
       "      <td>EDUCATIONAL PSYCHOLOGY</td>\n",
       "      <td>2854.0</td>\n",
       "      <td>522.0</td>\n",
       "      <td>2332.0</td>\n",
       "      <td>Psychology &amp; Social Work</td>\n",
       "      <td>0.817099</td>\n",
       "      <td>7</td>\n",
       "      <td>2125</td>\n",
       "      <td>...</td>\n",
       "      <td>572</td>\n",
       "      <td>1211</td>\n",
       "      <td>148</td>\n",
       "      <td>0.065112</td>\n",
       "      <td>25000</td>\n",
       "      <td>24000</td>\n",
       "      <td>34000</td>\n",
       "      <td>1488</td>\n",
       "      <td>615</td>\n",
       "      <td>82</td>\n",
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       "      <th>170</th>\n",
       "      <td>171</td>\n",
       "      <td>5202</td>\n",
       "      <td>CLINICAL PSYCHOLOGY</td>\n",
       "      <td>2838.0</td>\n",
       "      <td>568.0</td>\n",
       "      <td>2270.0</td>\n",
       "      <td>Psychology &amp; Social Work</td>\n",
       "      <td>0.799859</td>\n",
       "      <td>13</td>\n",
       "      <td>2101</td>\n",
       "      <td>...</td>\n",
       "      <td>648</td>\n",
       "      <td>1293</td>\n",
       "      <td>368</td>\n",
       "      <td>0.149048</td>\n",
       "      <td>25000</td>\n",
       "      <td>25000</td>\n",
       "      <td>40000</td>\n",
       "      <td>986</td>\n",
       "      <td>870</td>\n",
       "      <td>622</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>171</th>\n",
       "      <td>172</td>\n",
       "      <td>5203</td>\n",
       "      <td>COUNSELING PSYCHOLOGY</td>\n",
       "      <td>4626.0</td>\n",
       "      <td>931.0</td>\n",
       "      <td>3695.0</td>\n",
       "      <td>Psychology &amp; Social Work</td>\n",
       "      <td>0.798746</td>\n",
       "      <td>21</td>\n",
       "      <td>3777</td>\n",
       "      <td>...</td>\n",
       "      <td>965</td>\n",
       "      <td>2738</td>\n",
       "      <td>214</td>\n",
       "      <td>0.053621</td>\n",
       "      <td>23400</td>\n",
       "      <td>19200</td>\n",
       "      <td>26000</td>\n",
       "      <td>2403</td>\n",
       "      <td>1245</td>\n",
       "      <td>308</td>\n",
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       "    <tr>\n",
       "      <th>172</th>\n",
       "      <td>173</td>\n",
       "      <td>3501</td>\n",
       "      <td>LIBRARY SCIENCE</td>\n",
       "      <td>1098.0</td>\n",
       "      <td>134.0</td>\n",
       "      <td>964.0</td>\n",
       "      <td>Education</td>\n",
       "      <td>0.877960</td>\n",
       "      <td>2</td>\n",
       "      <td>742</td>\n",
       "      <td>...</td>\n",
       "      <td>237</td>\n",
       "      <td>410</td>\n",
       "      <td>87</td>\n",
       "      <td>0.104946</td>\n",
       "      <td>22000</td>\n",
       "      <td>20000</td>\n",
       "      <td>22000</td>\n",
       "      <td>288</td>\n",
       "      <td>338</td>\n",
       "      <td>192</td>\n",
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       "     Rank  Major_code                   Major   Total     Men   Women  \\\n",
       "168   169        3609                 ZOOLOGY  8409.0  3050.0  5359.0   \n",
       "169   170        5201  EDUCATIONAL PSYCHOLOGY  2854.0   522.0  2332.0   \n",
       "170   171        5202     CLINICAL PSYCHOLOGY  2838.0   568.0  2270.0   \n",
       "171   172        5203   COUNSELING PSYCHOLOGY  4626.0   931.0  3695.0   \n",
       "172   173        3501         LIBRARY SCIENCE  1098.0   134.0   964.0   \n",
       "\n",
       "               Major_category  ShareWomen  Sample_size  Employed  \\\n",
       "168    Biology & Life Science    0.637293           47      6259   \n",
       "169  Psychology & Social Work    0.817099            7      2125   \n",
       "170  Psychology & Social Work    0.799859           13      2101   \n",
       "171  Psychology & Social Work    0.798746           21      3777   \n",
       "172                 Education    0.877960            2       742   \n",
       "\n",
       "         ...        Part_time  Full_time_year_round  Unemployed  \\\n",
       "168      ...             2190                  3602         304   \n",
       "169      ...              572                  1211         148   \n",
       "170      ...              648                  1293         368   \n",
       "171      ...              965                  2738         214   \n",
       "172      ...              237                   410          87   \n",
       "\n",
       "     Unemployment_rate  Median  P25th  P75th  College_jobs  Non_college_jobs  \\\n",
       "168           0.046320   26000  20000  39000          2771              2947   \n",
       "169           0.065112   25000  24000  34000          1488               615   \n",
       "170           0.149048   25000  25000  40000           986               870   \n",
       "171           0.053621   23400  19200  26000          2403              1245   \n",
       "172           0.104946   22000  20000  22000           288               338   \n",
       "\n",
       "     Low_wage_jobs  \n",
       "168            743  \n",
       "169             82  \n",
       "170            622  \n",
       "171            308  \n",
       "172            192  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "recent_grads.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Rank</th>\n",
       "      <th>Major_code</th>\n",
       "      <th>Total</th>\n",
       "      <th>Men</th>\n",
       "      <th>Women</th>\n",
       "      <th>ShareWomen</th>\n",
       "      <th>Sample_size</th>\n",
       "      <th>Employed</th>\n",
       "      <th>Full_time</th>\n",
       "      <th>Part_time</th>\n",
       "      <th>Full_time_year_round</th>\n",
       "      <th>Unemployed</th>\n",
       "      <th>Unemployment_rate</th>\n",
       "      <th>Median</th>\n",
       "      <th>P25th</th>\n",
       "      <th>P75th</th>\n",
       "      <th>College_jobs</th>\n",
       "      <th>Non_college_jobs</th>\n",
       "      <th>Low_wage_jobs</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>173.000000</td>\n",
       "      <td>173.000000</td>\n",
       "      <td>172.000000</td>\n",
       "      <td>172.000000</td>\n",
       "      <td>172.000000</td>\n",
       "      <td>172.000000</td>\n",
       "      <td>173.000000</td>\n",
       "      <td>173.000000</td>\n",
       "      <td>173.000000</td>\n",
       "      <td>173.000000</td>\n",
       "      <td>173.000000</td>\n",
       "      <td>173.000000</td>\n",
       "      <td>173.000000</td>\n",
       "      <td>173.000000</td>\n",
       "      <td>173.000000</td>\n",
       "      <td>173.000000</td>\n",
       "      <td>173.000000</td>\n",
       "      <td>173.000000</td>\n",
       "      <td>173.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>87.000000</td>\n",
       "      <td>3879.815029</td>\n",
       "      <td>39370.081395</td>\n",
       "      <td>16723.406977</td>\n",
       "      <td>22646.674419</td>\n",
       "      <td>0.522223</td>\n",
       "      <td>356.080925</td>\n",
       "      <td>31192.763006</td>\n",
       "      <td>26029.306358</td>\n",
       "      <td>8832.398844</td>\n",
       "      <td>19694.427746</td>\n",
       "      <td>2416.329480</td>\n",
       "      <td>0.068191</td>\n",
       "      <td>40151.445087</td>\n",
       "      <td>29501.445087</td>\n",
       "      <td>51494.219653</td>\n",
       "      <td>12322.635838</td>\n",
       "      <td>13284.497110</td>\n",
       "      <td>3859.017341</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>50.084928</td>\n",
       "      <td>1687.753140</td>\n",
       "      <td>63483.491009</td>\n",
       "      <td>28122.433474</td>\n",
       "      <td>41057.330740</td>\n",
       "      <td>0.231205</td>\n",
       "      <td>618.361022</td>\n",
       "      <td>50675.002241</td>\n",
       "      <td>42869.655092</td>\n",
       "      <td>14648.179473</td>\n",
       "      <td>33160.941514</td>\n",
       "      <td>4112.803148</td>\n",
       "      <td>0.030331</td>\n",
       "      <td>11470.181802</td>\n",
       "      <td>9166.005235</td>\n",
       "      <td>14906.279740</td>\n",
       "      <td>21299.868863</td>\n",
       "      <td>23789.655363</td>\n",
       "      <td>6944.998579</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1100.000000</td>\n",
       "      <td>124.000000</td>\n",
       "      <td>119.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>111.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>111.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>22000.000000</td>\n",
       "      <td>18500.000000</td>\n",
       "      <td>22000.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>44.000000</td>\n",
       "      <td>2403.000000</td>\n",
       "      <td>4549.750000</td>\n",
       "      <td>2177.500000</td>\n",
       "      <td>1778.250000</td>\n",
       "      <td>0.336026</td>\n",
       "      <td>39.000000</td>\n",
       "      <td>3608.000000</td>\n",
       "      <td>3154.000000</td>\n",
       "      <td>1030.000000</td>\n",
       "      <td>2453.000000</td>\n",
       "      <td>304.000000</td>\n",
       "      <td>0.050306</td>\n",
       "      <td>33000.000000</td>\n",
       "      <td>24000.000000</td>\n",
       "      <td>42000.000000</td>\n",
       "      <td>1675.000000</td>\n",
       "      <td>1591.000000</td>\n",
       "      <td>340.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>87.000000</td>\n",
       "      <td>3608.000000</td>\n",
       "      <td>15104.000000</td>\n",
       "      <td>5434.000000</td>\n",
       "      <td>8386.500000</td>\n",
       "      <td>0.534024</td>\n",
       "      <td>130.000000</td>\n",
       "      <td>11797.000000</td>\n",
       "      <td>10048.000000</td>\n",
       "      <td>3299.000000</td>\n",
       "      <td>7413.000000</td>\n",
       "      <td>893.000000</td>\n",
       "      <td>0.067961</td>\n",
       "      <td>36000.000000</td>\n",
       "      <td>27000.000000</td>\n",
       "      <td>47000.000000</td>\n",
       "      <td>4390.000000</td>\n",
       "      <td>4595.000000</td>\n",
       "      <td>1231.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>130.000000</td>\n",
       "      <td>5503.000000</td>\n",
       "      <td>38909.750000</td>\n",
       "      <td>14631.000000</td>\n",
       "      <td>22553.750000</td>\n",
       "      <td>0.703299</td>\n",
       "      <td>338.000000</td>\n",
       "      <td>31433.000000</td>\n",
       "      <td>25147.000000</td>\n",
       "      <td>9948.000000</td>\n",
       "      <td>16891.000000</td>\n",
       "      <td>2393.000000</td>\n",
       "      <td>0.087557</td>\n",
       "      <td>45000.000000</td>\n",
       "      <td>33000.000000</td>\n",
       "      <td>60000.000000</td>\n",
       "      <td>14444.000000</td>\n",
       "      <td>11783.000000</td>\n",
       "      <td>3466.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>173.000000</td>\n",
       "      <td>6403.000000</td>\n",
       "      <td>393735.000000</td>\n",
       "      <td>173809.000000</td>\n",
       "      <td>307087.000000</td>\n",
       "      <td>0.968954</td>\n",
       "      <td>4212.000000</td>\n",
       "      <td>307933.000000</td>\n",
       "      <td>251540.000000</td>\n",
       "      <td>115172.000000</td>\n",
       "      <td>199897.000000</td>\n",
       "      <td>28169.000000</td>\n",
       "      <td>0.177226</td>\n",
       "      <td>110000.000000</td>\n",
       "      <td>95000.000000</td>\n",
       "      <td>125000.000000</td>\n",
       "      <td>151643.000000</td>\n",
       "      <td>148395.000000</td>\n",
       "      <td>48207.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Rank   Major_code          Total            Men          Women  \\\n",
       "count  173.000000   173.000000     172.000000     172.000000     172.000000   \n",
       "mean    87.000000  3879.815029   39370.081395   16723.406977   22646.674419   \n",
       "std     50.084928  1687.753140   63483.491009   28122.433474   41057.330740   \n",
       "min      1.000000  1100.000000     124.000000     119.000000       0.000000   \n",
       "25%     44.000000  2403.000000    4549.750000    2177.500000    1778.250000   \n",
       "50%     87.000000  3608.000000   15104.000000    5434.000000    8386.500000   \n",
       "75%    130.000000  5503.000000   38909.750000   14631.000000   22553.750000   \n",
       "max    173.000000  6403.000000  393735.000000  173809.000000  307087.000000   \n",
       "\n",
       "       ShareWomen  Sample_size       Employed      Full_time      Part_time  \\\n",
       "count  172.000000   173.000000     173.000000     173.000000     173.000000   \n",
       "mean     0.522223   356.080925   31192.763006   26029.306358    8832.398844   \n",
       "std      0.231205   618.361022   50675.002241   42869.655092   14648.179473   \n",
       "min      0.000000     2.000000       0.000000     111.000000       0.000000   \n",
       "25%      0.336026    39.000000    3608.000000    3154.000000    1030.000000   \n",
       "50%      0.534024   130.000000   11797.000000   10048.000000    3299.000000   \n",
       "75%      0.703299   338.000000   31433.000000   25147.000000    9948.000000   \n",
       "max      0.968954  4212.000000  307933.000000  251540.000000  115172.000000   \n",
       "\n",
       "       Full_time_year_round    Unemployed  Unemployment_rate         Median  \\\n",
       "count            173.000000    173.000000         173.000000     173.000000   \n",
       "mean           19694.427746   2416.329480           0.068191   40151.445087   \n",
       "std            33160.941514   4112.803148           0.030331   11470.181802   \n",
       "min              111.000000      0.000000           0.000000   22000.000000   \n",
       "25%             2453.000000    304.000000           0.050306   33000.000000   \n",
       "50%             7413.000000    893.000000           0.067961   36000.000000   \n",
       "75%            16891.000000   2393.000000           0.087557   45000.000000   \n",
       "max           199897.000000  28169.000000           0.177226  110000.000000   \n",
       "\n",
       "              P25th          P75th   College_jobs  Non_college_jobs  \\\n",
       "count    173.000000     173.000000     173.000000        173.000000   \n",
       "mean   29501.445087   51494.219653   12322.635838      13284.497110   \n",
       "std     9166.005235   14906.279740   21299.868863      23789.655363   \n",
       "min    18500.000000   22000.000000       0.000000          0.000000   \n",
       "25%    24000.000000   42000.000000    1675.000000       1591.000000   \n",
       "50%    27000.000000   47000.000000    4390.000000       4595.000000   \n",
       "75%    33000.000000   60000.000000   14444.000000      11783.000000   \n",
       "max    95000.000000  125000.000000  151643.000000     148395.000000   \n",
       "\n",
       "       Low_wage_jobs  \n",
       "count     173.000000  \n",
       "mean     3859.017341  \n",
       "std      6944.998579  \n",
       "min         0.000000  \n",
       "25%       340.000000  \n",
       "50%      1231.000000  \n",
       "75%      3466.000000  \n",
       "max     48207.000000  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "recent_grads.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "173\n"
     ]
    }
   ],
   "source": [
    "raw_data_count = recent_grads.shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Deleting all rows with missing values\n",
    "recent_grads = recent_grads.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "172\n"
     ]
    }
   ],
   "source": [
    "cleaned_data_count = recent_grads.shape[0]\n",
    "print(cleaned_data_count)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f806ce86710>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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KSkhISCA0NJQRI0Zw+vRp87Zly5YRHBxMWFgYGzduNKfv2rWLqKgoQkJCmD9/vjm9qqqK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\nF7LURKJXcu3W1h7zpJSZRUN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lojJ15DLa2tqye/duSktLGTFiBOnp6VZRvn/96194enoS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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f806ced9198>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Scatter plot for 'Sample_size' and 'Median'\n",
    "recent_grads.plot(x='Sample_size', \n",
    "                  y='Median', \n",
    "                  kind='scatter', \n",
    "                  title='Sample_size vs. Median')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualizing the data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Part 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f806ce7b860>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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e1G/00yNznEsNAE6cOIG4uLm4eXMugJcAuAAowN3lCu7Oi1ZUVISDBw9CKpWi\ne/fu8PT0RGlpaaPf+g8cOIBx4/6J27d/AqB6zt4+EgcPJjc6GaghstrUE4yql1uoP8Foe8ysY8zU\nGWV5gqSkJHTu3Bljx46FRCIRtjs7O+ukIPpmrgFHoVCgW7ceqK5WQhVkKgDMgmp2AZWOHcMQH98L\nO3fugmowaB6efXYWPvhgXbPn1XZCT0PONM1BhTHDMkrA6dGjR6MFuXLlik4Kom/mH3B8oQoyCqjG\n39wNFBLJQFRUVKJuqwfoh6yskwgKCmry3C21KNSv395nmuYgxljTjLIeztWrV3Xygky35HI5pNKe\nKCnJw91O81cBqEfU5+Ohhx7EDz/kou7oesADMpms2YCjTWd7c5357eHizesAMWY4Wrdw7ty5gzVr\n1iA3NxcbN27E5cuXcenSJTz22GP6LqNOmHMLp3t3f5SXR0O1+JongGsAhgB4E8BtSCSjUVFRhda2\ncLR9fUO0cPTRCjGH1hlj+maU9XBmzpwJa2tr/PzzzwBU08q88cYbOikEa7uDBw+hpkYJIBuADYAn\nAOwFcBTAXwAegETigwkTRkHV6vEH0A/PPjvrnoMNoDmBqK1tCCSSh7F27XKdXrD1NbuzunV2v8+r\npp7YlCcwZXqnbTpb7969iUhz3RtdTG1jKK2oarvR+AwDzgRsrE2LDiPAicRiWyosLKSjR4/Sm2++\nSUePHtV5WTZs2EgSiSPZ2ek2LVmfMx+Y8qwKhmLK0xgx06DLa6fWZ+rfvz/duXOHIiIiiIgoOzub\noqKidFYQfTPHgNP4AmzBBDhoXETFYjtasOA1srFxJDu7EJJI7GnDho06K4c+L9wtzSJwr3Qxfqe9\nLubGAZdpwygB58CBAzRw4EBycXGhKVOmkJeXFx0+fFhnBdE3cww4jbdwJAT0IEBGQGHtoM0OtXOo\n2RNgS3VXANUFfQYFQ1wU7yVgtOcWgr6DOTMPRgk4RERFRUW0Z88e2r17NykUCp0VwhDMMeAQNfyG\nPnLk2NqZBMIIcKwNMnUDklNtIDpDEolju7g1ZaqzCLT3FkJ7Lz8zDF1eO1u14md+fj5qampQWVmJ\nH3/8EV999ZWuu5RYK9VdgO3kyaM4ePAIVNlomQD+DdUsAXXTob2hWg1UNx3k6uyxtWuX622J6vqL\nzJlK2nJ7TzpobMVYc15anBmf1uNwnnzySZw9exYPPPAALCxUcUokEmHcuHF6KxzTjnoBtgMHDsDC\nou6y0nGSN5+kAAAgAElEQVRQzTpQd1JPOVRB5yxKS3/DqVOZDaaq0TYFuf4YlrVrlyMyMlwvAyjV\ndTQlLU0a2h7wxKbMoLRtCgUFBemsWWUMrahqu5SSkko2No5Uf/mBu5N1htbeXrMmIKQ2m21Fg1so\n9fskNmzY2Gj/Bt+OUTHV232M6Your51an2nGjBl0/vx5nb2woZlzwMnKyiKJxLH24p9a23fjQzY2\nTpSSklqbsmxPHTr0rJdQoNlJ3NRCbnZ2IQ0uptzhfJcpZqmZYplY+6TLa6fWfThPPPEE+vXrh4CA\nAISGhiIkJAShoaEtH8j0avv2LxAS0gcVFZ1w91YaAFRBqazB77//jnHjxiAvLxtff/0RpNKbACRQ\n9e2ko6Lid9ja2gIAkpM3oazMGZp9Pn4oKdmMsrLDSEycJwwO1LydBLTH20m64urqiqioKJO5HaWv\ngbKM3TNtI5OPjw/t2rWLrly5QnK5XPhpL1pR1XajsLCw9jaaQ22r5nDtrbK6LZQOZGPjKCwzvWTJ\nu2Rj40g2Nj0IkJJUGiLcOlOdy6mRgaQNW0NEfDvJFPGtTqZrurx2ap004OrqilGjRukv8rFWk8vl\nsLTsAqAKQCGAEQC6Q9UyUUC1VEFPlJcvxIwZs2BlJa7NqgKqq4sAZNROunkWzz8/CNbWnigvfx1A\nbO15LgFIgqo11LAFwx3OpqexVmp7mkzV3N3vM5NrHXAiIiIwZcoUjBw5UmM9HM5SMx5vb29UVxdA\nFXAyAIgB9AfwHoAVUE3kmQ1AhqqqalRVHa0NMClQTex596KkyjS7AiAIwEUA38PKajbE4hUQi1OF\n5QnM4Y/EXP/oFQoF3n13JVTLiLffzDlzxTOTt2LyzrKyMkgkEhw4cAC7d+/G7t27sWfPHn2WjbXA\n1dUV8+c/C6AbADcApVAFkiSo1sNZCEAK4DuoLkIXao+Mg2pV0Lv9L9XVuVi3blXtmIzhkEqfw+ef\nb252/Isu+goMPXGkKfdv3Ot7IZfLIZH0BPARVK3USAD98dpr/zKrwNoeKRQKJCbOQ1nZYdy8ebJB\nn+h9Q9t7b0VFRTq7j2cMrahqu5KWllY7nY1TbbpzRwJcCciq7ddpfJYB1RQ3zgREENCBlix5l4ju\nZjdlZWU1m+Wki74CQ08LY8r9G7p4LzTrV0jANrKx0c1sEuzetOesTl1eO7U+k6+vL40fP56+++47\nUiqVOiuAoZhrwMnKyqodZ7OiNoCE1T4WE+Cj8QsO+FCHDv519m/8oqTNxS8tLY06dgwQEgoa+wNq\nLjXXGBd/U/2j1+V7wYkcpsmUv+y0xCgBR6lU0oEDB2jy5Mnk4+NDCxcupEuXLumsIPpmrgFHJpOR\njU1QI9lpUqo/a7RE4khpaWm0YcPGJi9K2vxhqC9qqglBnWrH/mju11LQMsbF31T/6HX9XvAYHNPU\nXr8MGCXg1HXo0CHq1q0bOTg40MCBA+nnn3/WWYH0xVwDTmFhIVlb2xLgp9HaUD1+tzbo+BDQgSwt\nOwq/5E1dlBq7+NnZhbcwOPRu6nVT+9S/sBvr4m+Kf/SmGgiZ7rXHLwNGCThFRUX0f//3f9S7d2+K\nj4+nL7/8kqqqqujEiRPk7e2tswLpi7kGnJSUVBKL7WuDyt3WhqqF83VtP842oe/G2tqh2V/2pmYb\nUC9l0FhA6tgxlNLS0oRzaPuN3VgXf1P8ozfFQMgYkZECjp+fH7399tuUl5fX4Lnly5frrED6Yo4B\np6nWBmBHgA2pkgn8GvTjqIND3Qtv3f9v2LCR7s6/pjnnmq5bL7q8+JtiIGmN9l5+Zp6M1odDRFRS\nUkIlJSU6K4ChmGPAaXrFzzQCDpOlZQeqP5kn0IHS0tI0+ljEYjuytnYQ+luWLHmX7OxCqKk517T5\nNm7ob+zteSE0xkyZUQLOuXPnKDw8nLp3706enp4UGRlJ586d01lB9M0cA05Tt79sbAKF6WpUs0U7\n1rZWHMnKqiN98cUXtdPYqNNnnRq0Ru4+33QrJi0tjdLS0ppNnTbEN/b7pQ/EkC0gbm0xNaMEnP79\n+9OhQ4eEx4cPH6b+/fvrrCD6Zo4Bh4jq3P4KqQ0s/yKJxJ6ysrKIiCgxcRapliRwJ8CarKzsqEOH\n0NqWz8baVkzD/pYlS94lqdSZbG2DSSKxb7AcdVMtCmNcqEw13VmXDNmC49Yiq8soASc0NFSrbabK\nXAOOKi3ai1Rr3ajWubG2dhPSn5tfblpKwOpGWzjqvhyJxJHs7CIaBJXGWhTqdGtDX6jMvYVjyPqZ\n+3vJWs8oAWfMmDH09ttv09WrV+nq1au0ZMkSGjNmjM4Kom/mGnDuDvzU7KeRSOxrs9fU27eRatxM\n3f6e0NrEgg51WklSWrBgYbMXnqZSpyUS+3u+UDXVQmqp5WTOWV6GbMHdD61F1jq6vHZqPZfali1b\noFAoMG7cOIwbNw4KhQJbtmzRzfw6rM1KS0shlfpCcw2bAFRUvISqqs7QXG76D9SdPw24BsAHwNe1\n//4LQBfExsZALpfXziztBuAEADdh1uHG1sKprJTD2toLjc1SrK2m5jnTZv6zhIRJzc771p4Zcu0h\nXueI6ZXOQpeJM9eqNp444EyqudSkVHcMjqovRzPdWfXvYeFfsdheSH8Wi+1qb7dFEuBEYrFtg5kE\n1C0K9e20trZwmmpRZWVl8S0eMmwLzpxbi6z1dHntbHF5gpEjR0IkEjX5/LfffqvD8Mday9XVFa+9\n9i8sWhQNoBNUM0avhGrGYEA1e/QsWFoCy5cvg52dHZ5//iUAbqioSKo9Jh6qNW/isXTpO5DL5bC1\ntYVIZAEgHepp7kWigcLrNrYWjr29PRITYyEWe7V6OQN1i0q1fAKgbiHJZLJGt99v67sYcu0hXueI\n6UuLAeell14yRDnYPaiuroZq+QE7AMUAnoXqo82AOljU1PTHokVLsGXLBuTlZUMul+PIkZ/wxhuL\nYWXlherqP/D449Pw5pvvwtraG+Xl2bC01LxFZmPTU+NC7+rqqnExupcLleatnLvruPTt27fR7ffj\nLZ7677e5vBa7j7SmOVRRUUFnzpyhs2fPUkVFhc6aWfv27aOAgADy8/NrctaC5557jnx9fSksLIxO\nnTolbPfy8qLQ0FAKDw+nqKioJl+jlVVtN1RzqWlO0qmaaSC4kQSBjxodT6NejkDz1tXhBskI+r6V\n1dStHFW2nD3Z2gbzLR7GDEyX106tz7Rnzx7y8PCgQYMG0cCBA8nT05P27t17zwWoqakhHx8fksvl\nVFlZSWFhYXThwgWNffbu3Uvx8fFERJSRkUHR0dHCcz169KDi4uIWX8dcA45MJiNb2/B6wcWvNvus\n/pQ3jiSReAvZXnUHbjaWnWRj400SiaNB7+XXz0ZTByE7uwiSSBwbjAdijOmXUQJOQEAAXb58WXic\nnZ1NAQEB91yA48eP04gRI4THy5Yta9DKmTNnDqWm3r3YBQYG0vXr14mIyNvbW6vF4cw14DSdNLCo\ntoXScFLPlStX1yYEdCDAl8Rie1q5cnWTnfbGGnHOY0IYMz5dXju1Tou2s7ODr6+v8Lhnz56ws7O7\n51t6+fn58PT0FB57eHggPz+/2X3c3d2FfUQiEeLi4hAVFYVNmzbdc3naG1dXVyQmTgXQD4AfVEsL\nrwfwNoAuAG4DuARgEoBQSCQ98Prrb6GqygrAcQCXUVX1E15++Q1MmTKudonpSEilsdi8eT2CgoIQ\nFRVllPv5d1Oz255qzRgzHS0mDaj16dMH8fHxmDhxIkQiEXbu3ImoqCh89dVXAIBx48bprZDNOXbs\nGNzc3KBQKBAXF4egoCA89NBDRimLMSgUCmzevBWqrLRnAOwBEAMgHRYWN6BUKgEUQJWFdhZK5TWI\nxV1QWekAzbE7Pti6dQdOn85AaWmpVp3+CoVCr5lMTSUS3I8JA4yZA60DTnl5Obp06YIjR44AUH2z\nLisrw+7duyESidoccNzd3ZGbmys8vnbtGtzd3Rvsk5eX1+g+bm5uQnnGjh0LmUzWZMBJSkoS/h8T\nE4OYmJg2ldmUqL7tOwJ4AUAFgBEAHACUQKnsBtVgz/4A/AHkoKamCpaWf0KVzXb3Qg78AbG4O0pL\nSxEVFdXi627f/gUSE+fB2loVFDZvXq/zwZaurq7YvHl9m1Ot9UHfQZYxY0tPT0d6erp+Tq6zm3Nt\nVF1dLSQNVFRUUFhYmDDxpNp3330nJA0cP35cSBq4ffu2sFRCaWkpDRgwQGMhsLpMoKp6cXdqm6c0\npqdRDepU9+k4kWrJgkICfGjBgoW1s0h3qO3jcaC6a960xNB9K6Yyc7G5TWppKu8rM226vHZqfaYr\nV67Q/PnzaezYsTRy5EjhRxf27dtH/v7+5OvrS8uWLSMiog0bNlBycrKwzzPPPEM+Pj4UGhpKJ0+e\nFMoUFhZG4eHhFBwcLBzbGHMNOKrJO4NINTln/cQB9ZLTPqRabvruejiFhYW0YMFrZG1t2+p04/tx\nvi1zS2Awt+DJ9EeX105R7QlbFBYWhsTERISEhMDC4m6uwaBBg/TS8tI1kUgELavarigUCnh6+qKi\nwhvAmTrPhAH4GIAEqj6dcgDWsLSsxrlzJxAUFCQc39pbRAqFAl5egSgrOwz1LTmpNBY5ORfN9jbT\niRMnEBc3FzdvnhS22dtH4uDBZK1uQZqS+/HzY22n02untpGpuUGV7UErqtru3F2GoP7SA4G1LZ1U\nUs0UbU1SaQ9h7rN7uZ1yv823ZU4tnPuxhcraTpfXTq1bOFu3bkV2djaGDx8OiUQibI+MjNRN5NMz\nc23hqK1atQYvv/wGVLM+X4MqgeBjqGaJLoAqbToFwBgA7wFIgp1dIKqrczQ6/FvT4rnfOtDViRJ1\nExja46zU3MJhraHLa6fWAWfBggX4z3/+A19fX+GWmkgkwqFDh3RSEH0z94Bz4sQJDBmSiJKSzQC8\nARwC8CQ6dvRFdXUuLCycUFZ2BYACQCCAhhebgwcP6T3zrL0zlyBrLsGT6Z9RAo6vry+ysrJgbW2t\nkxc2NHMPOA2/tabD2nok/vOfzQgJCUHv3g/VPlcBYBaATOFYe/tI7Ny5HGPGJPC33vuIuQRPpl+6\nvHZqPdNAcHAw/v77b528KNM99ZgVqTQWNjY9AcTD0rIHnnjiaWRmnsXmzethYzMIHTpMBvAb6i+w\npeIOzcGg3XhUvxlzdXU12iwS7P6k9cDPv//+G4GBgYiKitLow+H1cExHQsIkhIeHIiJiAICM2jVk\nziIxMRbPPz8XACAS2UEsLoRINBA2Nj2F2ymenp4oK8tG3cGgZWW/w9bW1ngVYoyZFa0DzuLFi/VZ\nDnYP6t4aKS0thY1NT1RU3G2plJU5Yfnyz6FaM2chgCDY2AzCzp3LERERAVdXV5w4cQJSaVeUlcUC\n8AKQAxubLigtLTVWtRhjZkbrgDNo0CDk5OTg8uXLGDp0KO7cuYOamhp9lo1pQd35KxJ5oKYmF++8\ns6jB/GOqaWwuQZWtFgvgIqyte8DJyUm4naKan+wmgC8BdARwGyLRP3jeMsaY7mibP71x40bq06cP\n9ezZk4iIfvvtNxo8eLDO8rP1rRVVbTcaX3xNSk8+OYukUmfq2DG0dvqa1DpjLiII2NboGJL7bWwN\nY6xlurx2ap2lFh4eDplMhujoaJw+fRoAEBISgnPnzukxHOqOOWapHThwAMOHPwPgcp2tvrC2voHM\nTBny8vIwevQklJcfwd3WTn/Y2Fhjy5YNjabBcuYSY6wuXV47tb6lJpFINFKiq6urIRKJdFIIdi/+\ngObtswJYWXVFaWkphg0bhi1bNmjMtvzaa69jzpxZTQYTXsueMaYvrerDWbp0KcrKyvD9999j/fr1\nGDlypD7LxloQEREBsdgCVVUDoVpsrQCAEjU1RULfS0LCJAwdOphbLYwxo9N6HM7y5cvh6uqKkJAQ\nJCcnIz4+Hu+8844+y8Za4OrqijlzEgFUAlACqAbgCKUSSE7eBIVCIezH4y0YY8amdR9Oe2eOfTiN\nzYmlykL7EsCjzfbVMMaYNowy08CxY8cQFxcHf39/9OzZEz169EDPnj11UgjWNnK5HFZWXtCcHcAL\nqrTmAJSX/xuJifOElg5jjBmT1n04iYmJWLt2LXr37g1LS0t9lolpydvbG5WVV6GZNJAD4Hbtv3EQ\ni70gl8v5dhpjzOi0DjgODg545JFH9FkW1kqurq5Yt+49zJ3bD0A3qJIGagA8BmAzgAJUVeXw4E3G\nmEnQOuDExsbi5Zdfxrhx49rlejjmjQC8BKAnAGsA8bC1fQc1NX9g8+b13LphjJkErZMGYmNjVQfU\njr0hIl4Px8juLi/tANVtNG8AclhadsDmze8gPj6egw1j7J4YdODnmjVrAACPPfaY8OKurq546KGH\n0KNHD50UgrWNXC4HUUcARQAyoO7HqanpB19fXw42jDGT0mKWWklJCUpKSlBaWorS0lKUlJTg119/\nxSOPPILU1FRDlJE1wdbWFpWVRQA8oJmp5o7s7GxhP4VCgRMnTnC2GmPMqNo8Dqe4uBhDhw7FqVOn\ndF0mvTDHW2onTpzAww8/joqKa6jbwgH6ISvrJIKCgoTZpHnZaMZYWxhlHE59zs7OZncBb2+8vb1h\nYfEngLEA+gHwB9APIpESmZlnoVAokJg4D2Vlh3Hz5kmUlR3mcTmMMaNpc8A5fPgwnJycdFkW1kp3\nl5XeD8AFQD6Ad0AkQ2LiPJw+fRpWVu4AKgAoAIQK43IYY8zQWkwaCAkJaTArdHFxMbp164bPP/9c\nbwVj2klImIROnZwwbtw/cfv2SQCqRAGx2AuHDqWjpCQbwCwAeQBe5XE5jDGjabEPJycnR/MAkQid\nOnVCx44d9VowXTPHPhy1xuZUs7EZBJHIot48a/2wYcM6zJkzy6jlZYy1HwZNi/by8tLJCzH9Ud9a\n01z35mWsWvUlysruZq/Z2QUgMjLcqGVljN2/eLZoM6BepdPW1halpaXCLbP6rR6pNBY5ORd5fA5j\nTGsmkaXGTMP27V/AyysQcXFz0bv3Q8jOviKs2qlKKIiFvX0kpNJYnuaGMWZU3MJpxxrru6nfilG3\nfni1T8ZYWxi0D4eZLrlcDmtrb41+mvrLEahbO4wxZmx8S60dU62HI4cqAw0AznLaM2PMZHHAacfq\n9tPY2UVAIhmEtWuXc4uGMWaSuA/HDCQnb8Lzz78Ea2svVFfn83xpjDGd0eW1kwNOO6dN4gBjjLUV\np0UzgTpxoO7yBDxfGmPMFJlEwNm/fz8CAwPh7++PFStWNLrPP//5T/j5+SE8PByZmZmtOtacceIA\nY6zdICOrqakhHx8fksvlVFlZSWFhYXThwgWNffbu3Uvx8fFERJSRkUHR0dFaH6tmAlXVm3nzniWR\nyIpsbLqTVOpMKSmpxi4SY8xM6PLaafRxODKZDH5+fsKcbZMnT8auXbsQGBgo7LNr1y5Mnz4dABAd\nHY2bN2/ixo0buHr1aovHmjtPz564du06gB4oL78GNzcXThhgjJkko99Sy8/Ph6enp/DYw8MD+fn5\nWu2jzbHmbNu2bbXBJgPAbwAyUFBQhG3bthm5ZIwx1pDRWzhtQW3MmEhKShL+HxMTg5iYGN0UyEh2\n7NgBwAN1EwYAd+zYsQOPP/648QrGGGu30tPTkZ6erpdzGz3guLu7Izc3V3h87do1uLu7N9gnLy+v\nwT6VlZUtHltX3YBjDiZOnIhvv50FVcKAes2bfEycmGTUcjHG2q/6X8YXL16ss3Mb/ZZaVFQUsrOz\nkZOTg8rKSqSmpmLUqFEa+4waNUpYXTQjIwOOjo7o0qWLVseas8cffxyenl0A9APgB6AfPD27cOuG\nMWaSjN7CsbS0xIcffohhw4ZBqVQiMTERQUFBSE5OhkgkwuzZsxEfH4+9e/fC19cXHTt2xCeffNLs\nsfeT3Nyr2LZtG3bs2IGJE5M42DDGTBbPNMAYY6xJPNMAY4yxdocDDmOMMYPggMMYY8wgOOAwxhgz\nCA44jDHGDIIDDmOMMYPggMMYY8wgOOAwxhgzCA44jDHGDIIDDmOMMYPggMMYY8wgOOAwxhgzCA44\njDHGDIIDDmOMMYPggMMYY8wgOOAwxhgzCA44jDHGDIIDDmOMMYPggMMYY8wgOOAwxhgzCA44jDHG\nDIIDDmOMMYPggMMYY8wgOOAwxhgzCA44jDHGDIIDDmOMMYPggMMYY8wgOOAwxhgzCA44jDHGDIID\nDmOMMYPggMMYY8wgOOAwxhgzCA44jDHGDIIDDmOMMYPggMMYY8wgjBpw/vrrLwwbNgwBAQEYPnw4\nbt682eh++/fvR2BgIPz9/bFixQph++LFi+Hh4YHIyEhERkZi//79hio6Y4yxVjJqwFm+fDmGDh2K\nS5cuYfDgwVi2bFmDfZRKJZ599lmkpaXh/Pnz2L59Oy5evCg8/+KLL+LUqVM4deoURowYYcjim5T0\n9HRjF0FvzLluANevvTP3+umSUQPOrl27MGPGDADAjBkz8M033zTYRyaTwc/PD15eXhCLxZg8eTJ2\n7dolPE9EBiuvKTPnX3pzrhvA9WvvzL1+umTUgFNYWIguXboAALp27YrCwsIG++Tn58PT01N47OHh\ngfz8fOHxhx9+iPDwcDz11FNN3pJjjDFmfHoPOHFxcQgNDRV+QkJCEBoaim+//bbBviKRqFXnnjdv\nHq5cuYLMzEx07doVL774oq6KzRhjTNfIiAIDA+n69etERFRQUECBgYEN9jl+/DgNHz5ceLxs2TJa\nvnx5g/3kcjmFhIQ0+VoA+Id/+Id/+KcNP7piBSMaNWoUPv30U7z66qv47LPPMHr06Ab7REVFITs7\nGzk5OXBzc0Nqaiq2b98OALh+/Tq6du0KAPjqq68QHBzc5GsR9/UwxphRiciIV+Li4mJMnDgReXl5\n8PLywo4dO+Do6IiCggLMmjULe/bsAaBKi37++eehVCqRmJiIBQsWAACmT5+OzMxMWFhYwNvbG8nJ\nyUKfEGOMMdNi1IDDGGPs/mEWMw3897//RXBwMCwtLXHq1CmN55YtWwY/Pz8EBQXhwIEDwvZTp04h\nNDQU/v7+eOGFF4TtlZWVmDx5Mvz8/NC/f3/k5uYarB5t0dSgWFOXmJiILl26IDQ0VNjW3EDg1n6O\nxnTt2jUMHjwYDzzwAEJCQvD+++8DMJ/6VVRUIDo6GhEREXjggQfw2muvATCf+qkplUpERkZi1KhR\nAMyrft7e3ggLC0NERAT69u0LwED101lvkBFdvHiRfvvtN4qNjaWTJ08K27Oysig8PJyqqqro6tWr\n5OPjQ0qlkoiI+vbtSzKZjIiIHnnkEdq/fz8REa1fv56efvppIiJKTU2lSZMmGbg22qupqSEfHx+S\ny+VUWVlJYWFhdOHCBWMXSys//fQTnT59WiPR45VXXqEVK1YQEdHy5cvp1VdfJSKi8+fPt/pzNKaC\nggI6ffo0ERGVlJSQv78/XbhwwWzqR0R0+/ZtIiKqrq6m6OhoOnr0qFnVj4hozZo19Pjjj9PIkSOJ\nyHx+P4mIevToQcXFxRrbDFE/swg4ajExMRoBp35G24gRIygjI4MKCgooKChI2L59+3aaO3cuEREN\nHz6cMjIyiEj1x+Ti4mKg0rfe8ePHacSIEcLjpjL4TFX9zMKAgACNrMWAgAAiatvnaEpGjx5N33//\nvVnW7/bt2xQVFUXnz583q/rl5eXR0KFD6fDhw0LAMaf6eXt7U1FRkcY2Q9TPLG6pNaX+oFF3d3fk\n5+cjPz8fHh4ewva6g0nrHmNpaQlHR0cUFxcbtuBaamlQbHvT1EDgtnyOpkIulyMzMxP9+vXDjRs3\nzKZ+SqUSERER6Nq1K2JiYtCrVy+zqt/8+fOxcuVKjbGB5lQ/kUiEuLg4REVF4eOPPwZgmPoZNS26\nNeLi4nDjxg3hMRFBJBLh3XffxciRI/X2usQ5FUbT2oHApqa0tBTjx4/HunXrYGtr26A+7bl+FhYW\nOH36NG7duoXhw4cjPT3dbOr33XffoUuXLggPD2922pr2Wj8AOHbsGNzc3KBQKIR+G0N8fu0m4Hz/\n/fetPsbd3R15eXnC42vXrsHd3b3J7XWP6datG2pqanDr1i04OzvfewX0wN3dXSOpoW492qMuXboI\n37KuX7+Ozp07A2jb52hs1dXVGD9+PKZNmyaMLzOn+qnZ29sjPj4ev/76q9nU79ixY/j222+xd+9e\nlJWVoaSkBNOmTUPXrl3Non4A4ObmBgBwdXXFmDFjIJPJDPL5md0ttbotklGjRiE1NRWVlZW4evUq\nsrOz0bdvX3Tt2hUODg6QyWQgInz++efCRWHUqFH47LPPAAA7d+7E4MGDjVIPbdQdFFtZWYnU1FQh\no6Y9IFUfovBYPRAYgMZA4LZ8jsb25JNPolevXnj++eeFbeZSv6KiIiGDqaysDN9//z0iIiLMpn5L\nly5Fbm4urly5gtTUVAwePBj/+c9/MHLkSLOo3507d1BaWgoAuH37Ng4cOICQkBDDfH731PNkIr7+\n+mvy8PAgGxsb6tq1q0ZH+tKlS8nHx4cCAwMpLS1N2P7rr79ScHAw+fr60j//+U9he3l5OU2YMIF8\nfX0pOjqarl69asiqtNq+ffvI39+ffH19admyZcYujtYSEhLIzc2NrK2tydPTk7Zs2ULFxcU0ZMgQ\n8vf3p7i4OPrrr7+E/Vv7ORrT0aNHycLCgsLCwig8PJwiIiJo37599Oeff5pF/c6ePUsREREUHh5O\noaGhtHLlSiIis6lfXenp6ULSgLnU78qVK8LvZnBwsHDdMET9eOAnY4wxgzC7W2qMMcZMEwccxhhj\nBsEBhzHGmEFwwGGMMWYQHHAYY4wZBAccxhhjBsEBhzHGmEFwwGGsjnfffRfBwcEICwtDZGQkTpw4\nobfXio2NbbB+U1slJydj69atOjkXY/rSbuZSY0zfMjIysHfvXmRmZsLKygrFxcWorKw0drG0MmfO\nHMj1T9QAAAQjSURBVGMXgbEWcQuHsVoFBQVwcXGBlZXqe5izszO6du2KJUuWIDo6GqGhoZg7d66w\nf2xsLF588UVERUWhV69eOHHiBMaNG4eAgAAsWrQIAJCTk4OgoCBMnToVvXr1wsSJE1FeXt7gtb//\n/nsMGDAAffr0waRJk3Dnzp0my7lgwQIEBwcjPDwcr7zyCgBg8eLFWLNmDQoKChAREYHIyEhERETA\nysoKeXl5KCoqwvjx4xEdHY3o6Gj8/PPPunzrGNOOrufpYay9Ki0tpfDwcAoICKB58+bRkSNHiIg0\n5pSaNm0a7dmzh4hUC/4tWLCAiIjWrVtHbm5udOPGDaqoqCAPDw8qLi4muVxOIpGIjh8/TkRETz75\nJK1evVo4/uTJk1RUVEQDBw6kO3fuEBHRihUr6O233260jH/++aewMBYR0c2bN4mIKCkpSTiv2r//\n/W+aPHkyERFNmTKFjh07RkREubm5GgtnMWYofEuNsVodO3bEqVOn8NNPP+HQoUOYPHkyli9fDltb\nW7z33nu4c+cO/vrrLwQHB+PRRx8FAGF27pCQEISEhAhTuvv4+CAvLw8ODg7o3r07+vXrBwCYOnUq\nPvjgA7z44ovC62ZkZCArKwsPPvggiAhVVVXo379/o2V0cHCAVCrFU089hUcffRSPPfZYo/sdO3YM\nH3/8MY4dOwYAOHjwIC5cuCDMzl1aWoo7d+6gQ4cOOnjnGNMOBxzG6hCJRBg4cCAGDhyIkJAQJCcn\n49y5czh58iS6deuGxYsXa9wSk0gkAFQLkqn/rz5PdXV1k69RFxFh2LBh2LZtW4vls7S0hEwmww8/\n/ICdO3fiww8/xA8//KCxT0FBAWbNmoXdu3dDKpUKr/HLL79ALBZr90Ywpgfch8NYrd9++w3Z2dnC\n48zMTAQGBgJQ9eeUlpbiv//9b6vPm5ubi19++QUAkJKSgocffljj+X79+uHYsWP4/fffAajWK7l8\n+XKj57p9+zb+/vtvjBgxAmvWrMHZs2c1nq+ursbEiROxYsUK+Pj4CNuHDRuGdevWCY/PnDnT6now\ndq+4hcNYrdLSUjz33HO4efMmrKys4Ovri40bN8LBwQHBwcFwc3ND3759hf2bW4K37nMBAQH497//\njZkzZ+KBBx4QEg/U+7i4uODTTz9FQkICKioqIBKJ8M4778DPz6/BeUtKSjB69GihlbV27VqN53/+\n+WecPHkSb731Ft58802IRCLs3bsX69atwzPPPIOwsDDU1NRg4MCBWL9+fdvfLMbagNfDYUyPcnJy\n8Nhjj+HcuXPGLgpjRse31BjTs+ZaQozdT7iFw5iJGjduHORyOQBVp79IJMKKFSsQFxdn3IIx1kYc\ncBhjjBkE31JjjDFmEBxwGGOMGQQHHMYYYwbBAYcxxphBcMBhjDFmEP8PsxvHDV7pJNMAAAAASUVO\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f80ba0df0f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Scatter plot for 'Sample_size' and 'Unemployment_rate'\n",
    "recent_grads.plot(x='Sample_size', \n",
    "                  y='Unemployment_rate', \n",
    "                  kind='scatter', \n",
    "                  title='Sample_size vs. Unemployment_rate')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f806ab3b710>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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/gE8++ZTdv/uBE9YSqcdta1K+vr4ICgpCWloaEhISumTNRx55xOIBWpq91qSA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\nEakWkxQREanW/wM+nRdVo3UMBwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f806ceb1d68>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Scatter plot for 'Full_time' and 'Median'\n",
    "recent_grads.plot(x='Full_time', \n",
    "                  y='Median', \n",
    "                  kind='scatter', \n",
    "                  title='Full_time vs. Median')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f806a86fb00>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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5cHnjjTfo73//O50+fZrOnz8v/DkLDi6Oz5FLLsbutAsLC9u0swAetHDhQlky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\nY4zJ7v8DrOaPGOiakSUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f806a87d160>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Scatter plot for 'ShareWomen' and 'Unemployment_rate'\n",
    "recent_grads.plot(x='ShareWomen', \n",
    "                  y='Unemployment_rate', \n",
    "                  kind='scatter', \n",
    "                  title='ShareWomen vs. Unemployment_rate')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f806a7de198>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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fl5KSkt5adNHsenALBoTWJcltbAINS8e7CTBEABuJiJin7DN48HABfJRta9eu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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f806a798780>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Scatter plot for 'Men' and 'Median'\n",
    "recent_grads.plot(x='Men', \n",
    "                  y='Median', \n",
    "                  kind='scatter', \n",
    "                  title='Men vs. Median')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f806a7bb748>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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F7U66AKJS6lrSrprUwIEDWbt2LfX19Rw+fJhHHnmEkSNHdnbeFFBQUNAYoHZg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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f806a7bdac8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Scatter plot for 'Women' and 'Median'\n",
    "recent_grads.plot(x='Women', \n",
    "                  y='Median', \n",
    "                  kind='scatter', \n",
    "                  title='Women vs. Median')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Part 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f806a6009b0>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f806a64aa90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Histogram for 'Sample_size\n",
    "recent_grads[\"Sample_size\"].hist(bins=15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f806a4e50b8>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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xxReRlpYWWQAAIDc3N7JqlZaWIi8vz5BAI9z9/yZl0KwOiEriY8Umfdikn9QuM/S5HXT4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\nAYn7YNAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f806a4d3780>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Histogram for 'Median'\n",
    "recent_grads['Median'].hist(bins=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f806a383518>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f806a393748>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Histogram for 'Employed'\n",
    "recent_grads[\"Employed\"].hist(bins=15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f806a16e748>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f806a150ef0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Histogram for 'Full_time'\n",
    "recent_grads[\"Full_time\"].hist(bins=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f806a0291d0>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f806a02ccf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Histogram for 'ShareWomen'\n",
    "recent_grads[\"ShareWomen\"].hist(bins=25)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f8069ee09b0>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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+eeI6TzzxhH3vvffaDQ0N9vvvv5/2trrMNFd/f79t27Z98uRJe9asWfbSpUvt\nSCRiNzY22m+//baRTFOPVZLOxZ9rJsuy7AceeMBeunSp/d3vftf+7LPPjGf61a9+ZdfW1tr19fX2\npk2b7KtXrxYkUywWs6uqquzbb7/drqystBcuXGhfunQp5W11yTaXyXWe7lglFXqdp8tkap2ny5TN\nOueboYiIShh/kTcRUQljkyciKmFs8kREJYxNnoiohLHJExGVMDZ5IqISxiZPRFTC2OSJiErY/wfI\nl40RHuhhRgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8069ebb9e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Histogram for 'Unemployment_rate'\n",
    "recent_grads[\"Unemployment_rate\"].hist(bins=15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f8069d6f400>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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AAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8069d88cf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Histogram for 'Men'\n",
    "recent_grads[\"Men\"].hist(bins=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f8069cd00f0>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8069c2b080>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Histogram for 'Women'\n",
    "recent_grads[\"Women\"].hist(bins=20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Scatter Matrix Plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from pandas.plotting import scatter_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7f8068d66710>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f8068d54f28>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7f8068d23940>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f8068cdd4a8>]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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75s6VB48pUyS2hoaO6//CCzKXID5edhbs2lU2KoqIaLtbYFmZ9FhER19cxj//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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8068d7d3c8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "scatter_matrix(recent_grads[['Sample_size','Median']], \n",
    "              figsize=(8,6))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7f8068872128>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f80687e56a0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f80687b50b8>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7f8068e59588>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f8068788208>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f8068743438>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7f806870fba8>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f8068757cc0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f8068697828>]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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E3o09sbs2RYcDqa6uxsqVK/HNN99Ao9FAr9fjiSeeQI8ePRx+zmKxYP78+Xj11VfRp08f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\nu53m78Nd3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Oc//5neeecdIiLasWMHzZ8/X/ostm7desfPMD4+XspNRHT9+nVpCZqcnBx6\n9tlniYho9+7dtHTpUul1a9asoY8++oiIiDQaDT3++ONSNlvHt0p7uMLCQhw/fhylpaWwt7dHXV0d\n9Hq9tWPdk5deesnaERhjd9DS0oIBAwZgypQpJsfnzJkj1G4v1tLerUmZTAaZTIbo6GgAgK+vL6qr\nqwEAJ06cwIULF6Tllerr61FVVYVevXrBz89PWobJw8ND+tzlcrnJouKzZs2SXjN8+HB88803Jtc/\ne/YsDh06BAAICQlBXV0dGhoakJCQgJkzZ2L58uXYuXMnEhISpHOMy9DI5XIMHjwYo0aNAgB4enpC\nrVbjxx9/RGVlJZ566ikQEW7dugWlUimdHxUVJbU1Ozu7Q59jTEyM9LVGo8ELL7yAqqoqyGQyNDc3\nt3nOiRMncPToUWkEXa/Xo6amBk888USHri0i7rj1cL/88gsGDhwoVXj1798fAJCSkoJjx47hxo0b\nUCqVSE9PB9D6Q6xQKHDmzBk0Njbiww8/xD/+8Q9UVFRg1qxZSElJQXV1NcLCwuDr64uSkhKMHj0a\nmZmZeOihh0yuffLkSaxfvx56vR7Dhw/Hrl272t3HMCkpCceOHYO9vT1CQ0PxxhtvSJN4Y2NjMW3a\nNMhkMhARLly4gEuXLqF3795YsGABfvzxRwCttw9v/0XBGLOsvn37trvbCwMGDBiAuro6k2N1dXX4\ny1/+AgBwdHQEAPzhD3+QOiBEhH/961+YPHmyyXkFBQXS64HW4hrjYzs7O5MOzO0dRiK669w2+v/K\n70ceeQR//OMfcerUKahUKmRlZUmvuf1av8/R3NwMOzs7hIaG4qOPPmrzGm219V45OTlJX69duxYT\nJ07EoUOHUF1dfcdiok8++QQjRozo0LVsAc9x6+FCQ0NRU1ODkSNHYvHixVIl4tKlS3H+/HmUl5ej\nqanJZB6Eo6MjVCoVFixYgBkzZiA9PR0XLlzA7t27ce3aNQDAt99+iyVLlqCyshLOzs7YsWOHyXVr\na2uRmpqK3NxcFBUVwdfXF1u3bm0zY11dHT799FNcvHgRpaWleO2110yeHzx4ML7++muUlJRg/vz5\niImJwdChQ7F8+XKsWLEC58+fx8GDB5GYmNiVHx1j7C6efPJJZGdnIy8vT/p36tQp+Pj4WDuaEJyc\nnDBkyBCcOnUKQOvvui+++AJPP/201FkyMj6eMmUKduzYIXVuqqqqpLli9+rAgQMgIvzwww+4dOmS\n2ShTYGCgVPWbn58Pd3d39O3bFwDw4osvIi4uDrNmzWq3w/f77ADg7++Pc+fO4YcffgAANDU1oaqq\n6o45nZ2dUV9f36G21dfX4+GHHwYA7Nq1q933mjJlCt5++23pcWlpaYeuIzLuuPVwTk5OKCkpQUZG\nBtzd3TF79mxkZmYiLy8P/v7+8PLywqlTp0yqEY0Ti+VyOeRyOQYNGgQHBwcMHz5cGt0aNmyY9Jd2\nXFwczp49a3LdwsJCadhcoVAgMzMTNTU1bWZ0dXVF7969kZiYiOzs7DZ3jwCAc+fO4YMPPsDOnTsB\nADk5OViyZAkUCgUiIyPR0NDQ4V9wjLHOs5XdXqwpMzMTKSkpUCgUeOaZZ5CcnIzHHnvMrFNkfJyY\nmIhRo0bBx8cHcrkcCxYsgMFgMHvfO42iDRs2DOPGjcP06dPx3nvvwcHBweT55ORkFBcXw9vbG2vW\nrMGHH34oPRcZGYnGxkbEx8e3e63bHxu/HjhwIHbv3o3Y2Fip6OHbb7+9Y9aIiAhkZ2ffsTjh9+eu\nXLkSSUlJ8PX1RUtLi3Q8JCQElZWVUnHC2rVrcevWLXh5eUEul2PdunXtfVy2x1qT65h1HDx4kCZP\nnkx/+tOf6Oeffyai1gmiGzZsICLT4oLbJ7Pe/pxaraZHH31UOp6Xl0fR0dEmrzl69CjNmTPnnnPp\n9Xr6/PPPad68eTRx4kQpl3Hi6uXLl+nJJ5+k77//XjrH3d2d9Hp9Jz4FxhjrmeLj4+mTTz7p9Pkq\nlYqCgoK6MBHrajzi1sN99913+P7776XHpaWlGDlyJIDW+W4NDQ04ePBgh9+3pqYG58+fBwBkZWWZ\nrdvUkWHzxsZGaDQahIWF4Z///KfZ/Jjm5mbMmjULW7ZswfDhw6XjoaGhJstflJWVdbgdjDHWk9zP\nWm1btmxBTEwMNm/e3IWJWFfj4oQerqGhAUuXLoVWq4W9vT08PDyQkZEBV1dXjB49GoMHD8a4ceOk\n19/ph/7255544gm88847SEhIgKenJxYsWGDymtuHzXU6HWQyGVJTU9ucKHr9+nXMmDEDN2/eBNBa\nZHC7L7/8EsXFxVi/fj3WrVsHmUyG48ePY/v27Vi8eDG8vb1hMBgQFBRkNteOMcYeJMapJJ2xatUq\nrFq1qgvT3LvXX38dBw4ckIrQZDIZYmJipN1D2G94r1LWYW1t48IYY4wxy+NbpaxTRNs6hbGOsrOz\nwwsvvCA9NhgMcHd37/Cq/yEhISgpKQEAhIeHd7hKjjHGOoJvlbIOe/TRRzu9TlN0dDTUajWA39YX\n2rJli9maRYxZmpOTEy5evAidTgdHR0ecPHkSQ4cOva/3PHbsWBelY4yxtvGIG+tWhw4dQklJCUpK\nSqS12bjTxqxl2rRp0hqG+/btQ2xsrPTc7/c6PHLkCADg5s2biI2NhaenJ6Kjo6W5mUDrnozGBVej\noqLg5+cHuVyODz74QHqNs7MzXnvtNYwZMwZKpRK//vprdzSVMdZDcMeNMfZAkslkmD17Nvbt2wed\nTofy8nKMHz9een7Tpk2YNGkSCgsLkZeXh5UrV+LGjRt499134eTkhIqKCmzYsAFFRUUm72m0a9cu\nqFQqqFQqbN++XVq8urGxEUqlEqWlpQgMDJQ2EWeMsXvBHTfG2ANr9OjRUKvV2LdvH6ZPn26yIvyJ\nEyewefNmKBQKBAcHS3sdnj59GnFxcQBaF6n29vaWzrn9/G3btmHMmDHw9/fHTz/9JC2H4+joiGnT\npgFo3bfROHWAMcbuBc9xY4w90CIjI7Fy5Urk5+fj6tWr0nEiuqe9DtsqzC8oKEBeXh7Onz8PR0dH\nhISESLdUe/XqJb2uM/s2MsYebDzixhh7IBk7XPPmzcP69evh6elp8nx7ex0GBQVJG2lfvHixzUId\nrVYLNzc3ODo64ptvvkFhYaHZdRljrDO448YYeyAZ56M9/PDDWLJkidnz7e11uHDhQjQ0NMDT0xPJ\nyckYO3as2XuGhYXh1q1b8PT0xJo1axAQEGD2GsYY6wxegJcxxhhjzEbwiBtjjDHGmI3gjhtjjDHG\nmI3gjhtjjDHGmI3gjhtjjDHGmI3gjhtjjDHGmI3gjhtjjDHGmI3gjhtjjDHGmI3gjhtjjDHGmI34\nP4DcItHweIUkAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f80689d95f8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "scatter_matrix(recent_grads[['Sample_size','Median','Unemployment_rate']], \n",
    "              figsize=(10,10))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Bar Plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "recent_grads['ShareWomen'][:10]"
   ]
  }
 ],
 "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": "3.4.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}