Project 4 --- Visualizing Earnings Based On College Majors (Matplotlib).ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Introduction\n",
    "We'll work with a dataset of college majors and their post-graduate employment information. We'll explore the data with the pandas plotting functionality in Jupyter.\n",
    "\n",
    "### Goals\n",
    "We might explore some questions like:\n",
    "- Do students in more popular majors make more money?\n",
    "   - Using scatter plots\n",
    "- How many majors are predominantly male? Predominantly female?\n",
    "   - Using histograms\n",
    "- Which category of majors have the most students?\n",
    "   - Using bar plots\n",
    "\n",
    "### Given: Data Set\n",
    "The dataset was originally compiled by the American Community Survey, and Github user FiveThirtyEight cleaned it and released it to their [Github](https://github.com/fivethirtyeight/data/tree/master/college-majors).\n",
    "\n",
    "Here are the column names and descriptions:\n",
    "\n",
    "|Header|Description|\n",
    "|:-----|:----|\n",
    "|`Rank`|Rank by median earnings |\n",
    "|`Major_code`|Major code, FO1DP in ACS PUMS|\n",
    "|`Major`|Major description|\n",
    "|`Major_category`|Category of major from Carnevale et al|\n",
    "|`Total`|Total number of people with major|\n",
    "|`Sample_size`|Sample size (unweighted) of full-time, year-round ONLY (used for earnings)|\n",
    "|`Men`|Male graduates|\n",
    "|`Women`|Female graduates|\n",
    "|`ShareWomen`|Women as share of total|\n",
    "|`Employed`|Number employed (ESR == 1 or 2)|\n",
    "|`Full_time`|Employed 35 hours or more|\n",
    "|`Part_time`|Employed less than 35 hours|\n",
    "|`Full_time_year_round`|Employed at least 50 weeks (WKW == 1) and at least 35 hours (WKHP >= 35)|\n",
    "|`Unemployed`|Number unemployed (ESR == 3)|\n",
    "|`Unemployment_rate`|Unemployed / (Unemployed + Employed)|\n",
    "|`Median`|Median earnings of full-time, year-round workers|\n",
    "|`P25th`|25th percentile of earnings|\n",
    "|`P75th`|75th percentile of earnings|\n",
    "|`College_jobs`|Number with job requiring a college degree|\n",
    "|`Non_college_jobs`|Number with job not requiring a college degree|\n",
    "|`Low_wage_jobs`|Number in low-wage service jobs|"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "--------\n",
    "## Step 1: Open the dataset\n",
    "\n",
    "We'll open our data set now and make it ready to be used for analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "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\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Rank</th>\n",
       "      <th>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",
       "      <td>0</td>\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",
       "      <td>1</td>\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",
       "      <td>2</td>\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",
       "      <td>3</td>\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",
       "      <td>4</td>\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  Unemployment_rate  Median  \\\n",
       "0        270                  1207          37           0.018381  110000   \n",
       "1        170                   388          85           0.117241   75000   \n",
       "2        133                   340          16           0.024096   73000   \n",
       "3        150                   692          40           0.050125   70000   \n",
       "4       5180                 16697        1672           0.061098   65000   \n",
       "\n",
       "   P25th   P75th  College_jobs  Non_college_jobs  Low_wage_jobs  \n",
       "0  95000  125000          1534               364            193  \n",
       "1  55000   90000           350               257             50  \n",
       "2  50000  105000           456               176              0  \n",
       "3  43000   80000           529               102              0  \n",
       "4  50000   75000         18314              4440            972  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#global imports\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from pandas.plotting import scatter_matrix\n",
    "%matplotlib inline\n",
    "recent_grads=pd.read_csv(\"recent-grads.csv\")\n",
    "print(recent_grads.iloc[0])\n",
    "recent_grads.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "----------------\n",
    "## Step 2: Explore the data\n",
    "Let's print out a few lines of the datasets to see what they look like:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Rank</th>\n",
       "      <th>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",
       "      <td>count</td>\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",
       "      <td>mean</td>\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",
       "      <td>std</td>\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",
       "      <td>min</td>\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",
       "      <td>25%</td>\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",
       "      <td>50%</td>\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",
       "      <td>75%</td>\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",
       "      <td>max</td>\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": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "recent_grads.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We'll delete any rows with missing values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "173\n",
      "172\n"
     ]
    }
   ],
   "source": [
    "raw_data_count=recent_grads.count()[0]\n",
    "recent_grads=recent_grads.dropna()\n",
    "cleaned_data_count=recent_grads.count()[0]\n",
    "\n",
    "print(raw_data_count)\n",
    "print(cleaned_data_count)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "----------------\n",
    "## Step 3: Plot data we're interested in\n",
    "Let's explore some questions, like:\n",
    "- Do students in more popular majors make more money?\n",
    "- Do students that majored in subjects that were majority female make more money?\n",
    "- Is there any link between the number of full-time employees and median salary?\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x106e82810>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAncAAAFOCAYAAADkVzuaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8QZhcZAAAgAElEQVR4nO3de3xddZnv8c9371waGmhLKBxpuXkKavGUCuHi1HEEHUSE4hyq4g28Mnp01BmRog4DyDhHLiMOyugweAFviNRziqAyjMBRFJAgbaGA0lGhaRFKeqGBNk2yn/PHXjvsJDvJbpJ9ycr3/Xrllezfuv3WXrubh9+znt9SRGBmZmZm6ZCpdQfMzMzMbPI4uDMzMzNLEQd3ZmZmZini4M7MzMwsRRzcmZmZmaWIgzszMzOzFHFwZ2Z1S1JIWpD8/VVJ59e6T1ONpDslvb9C+/Y1MatDDbXugJlNfZL+COwP7B8RzxS1rwKOAA6JiD9O5BgR8cGJbF9tkt4HfBKYBzwPdABnRMT2mnZsEk21a2I2XXjkzswmyx+AtxVeSPofQEvtulM7kv4C+CfgbRGxJ/Ay4Iba9srMpgsHd2Y2Wb4FnFn0+izguuIVJDVLulzSE5KeStJ6LUXLPynpSUkbJb13yLbflPSPyd9zJN0saZOkLcnf84vWvVPSxZJ+KWm7pP+QtE+pTkt6RNIpRa8bJD0j6UhJMyR9W1KXpK2S7pO0XxnvxdHA3RHxAEBEbI6IawujdpLeKOkBSc9KWi/pwqLjH5yko9+TLNsi6YOSjpa0JunHl4vWf3dynl+StE3So5JeO1LHJL03Oectkm6VdNBoJ6K8KyQ9nex/jaSXJ8uKr8mPJHUX/eQkvTtZ9lJJt0naLOm3kt5SxntoZuPk4M7MJss9wF6SXiYpC7wV+PaQdS4BDgMWAwvIpyz/AUDSScA5wF8ChwKvG+VYGeAbwEHAgcAO4MtD1nk78B5gX6Ap2Xcp36NoxBF4PfBMRPyGfIA6CzgAaAM+mBxrLPcCr5d0kaQlkpqHLH+OfCA8G3gj8CFJbxqyzrHk34e3Al8EPkP+PTkceEsyOli87u+BfYALgB9K2ntop5JjfBr4n8Bc4BfJ+Y/mRODV5K/b7KQ/XUNXiohTI6I1IlqBZcCfgJ9JmgncBnyX/LV4G/Cvkg4f47hmNk4O7sxsMhVG7/4SeBTYUFggScAHgL9NRrK2k09dnpGs8hbgGxHxUEQ8B1w40kEioisiVkTE88l+Pgf8xZDVvhERv4uIHeRTootH2N13gaWS9khevz1pA+glH9QtiIj+iLg/Ip4d602IiF+QD6COBG4BuiR9IQl6iYg7I+LBiMhFxBryAdbQ/l8cETsj4j/IB4Pfi4inI2ID+aDsFUXrPg18MSJ6I+L7wG/JB41D/TXwvyPikYjoI//+Lx5j9K4X2BN4KaBk2ydHWlnSYeRHbN8aEeuBU4A/RsQ3IqIvCZpXkA8AzawCHNyZ2WT6Fvng6N0MScmSHynaA7g/SS1uBX6atEO+IGN90fqPj3QQSXtI+jdJj0t6Fvg5MLsQPCX+VPT380BrqX1FxDrgEeDUJMBbygvB3beAW4Hrk1TxpZIaR+rXkP3+JCJOBfYGTiP/nrw/6f+xku5I0srbyI8IDk0bP1X0944Sr4vPZ0NERNHrx8m/n0MdBPxL0fu/GRD5EdSRzuN28qOiVwFPSbpa0l6l1pU0C1gJnJ8EuIVjHls4ZnLcdwD/baRjmtnEOLgzs0kTEY+TL6w4GfjhkMXPkA9KDo+I2cnPrCSNB/Ak+fRnwYGjHOoTwEuAYyNiL/JpQ8gHKuNRSM2eBjycBHwkI2EXRcRC4M/Ij0KdOfJuhktG534G3A68PGn+LnATcEBEzAK+OoG+A8xLRkYLDgQ2llhvPfDXRe//7IhoiYhfjXEOV0bEUeRTwoeRrwIeRFKG/HndERH/NuSY/2/IMVsj4kO7eY5mViYHd2Y22d4HnJCkVgdERA74d+AKSfsCSJon6fXJKjcA75a0MBlBu2CUY+xJPlDcmtxbNtq65bie/L1lH+KFUTskHS/pfyQjgs+ST1H2j7UzSadJOkP5wg9JOoZ82vWeov5vjoidybK3T7D/+wIfldQo6c3kq3N/XGK9rwKfKtzvJmlWsv5o53J0MtLYSD49vJPS78HngJnAx4a03wwcJuldSf8ak32+bLfO0MzK5uDOzCZVRPxXRHSMsHg5sA64J0mn/if5ETgi4ifkCwduT9a5fZTDfJH8NCvPkA+YfjrBPj8J3E1+dO77RYv+G3Aj+cDuEeD/kRSJKF/p+9URdrmF/P2FjyXbfhu4LCK+kyz/X8BnJW0nX1Ay0WlS7iVffPEM+SBrWUSUKnr4P+SLWq5P3v+HgDeMse+9yAflW8ine7uAy0us9zbgOGBLUcXsO5J7Ik8kf2/lRvLp8kuAoUUmZjZJNPg2DTMzm0qS6UbeHxGvqnVfzKw+eOTOzMzMLEX8+DEzs2lO0p8DPym1rKjgxcymCKdlzczMzFLEaVkzMzOzFHFwZ2ZmZpYivucusc8++8TBBx9c626YmZmZjen+++9/JiLmllrm4C5x8MEH09Ex0tRcZmZmZvVD0oiPaHRa1szMzCxFHNyZmZmZpYiDOzMzM7MUcXBnZmZmliIO7szMzMxSxMGdmZmZWYo4uDMzMzNLEQd3NdLV3cPq9Vvp6u6pdVfMzMwsRTyJcQ2sXLWB5SvW0JjJ0JvLcenpi1i6eF6tu2VmZmYp4JG7Kuvq7mH5ijXs7M2xvaePnb05zl2xxiN4ZmZmNikc3FVZ55YdNGYGv+2NmQydW3bUqEdmZmaWJg7uqmz+nBZ6c7lBbb25HPPntNSoR2ZmZpYmDu6qrK21mUtPX8SMxgx7NjcwozHDpacvoq21udZdMzMzsxRwQUUNLF08jyUL9qFzyw7mz2lxYGdmZmaTxsFdjbS1NjuoMzMzs0nntKyZmZlZiji4MzMzM0sRB3dmZmZmKeLgzszMzCxFHNyZmZmZpYiDOzMzM7MUcXBnZmZmliIO7szMzMxSxMGdmZmZWYo4uDMzMzNLEQd3ZmZmZini4M7MzMwsRRzcmZmZmaWIgzszMzOzFHFwZ2ZmZpYiDu7MzMzMUsTBnZmZmVmKOLgzMzMzSxEHd2ZmZmYp4uDOzMzMLEUqFtxJ+rqkpyU9VNS2t6TbJD2W/J6TtEvSlZLWSVoj6ciibc5K1n9M0llF7UdJejDZ5kpJGu0YZmZmZtNBJUfuvgmcNKTtPOBnEXEo8LPkNcAbgEOTn7OBr0A+UAMuAI4FjgEuKArWvpKsW9jupDGOYWZmZpZ6FQvuIuLnwOYhzacB1yZ/Xwu8qaj9usi7B5gt6UXA64HbImJzRGwBbgNOSpbtFRF3R0QA1w3ZV6ljmJmZmaVete+52y8ingRIfu+btM8D1het15m0jdbeWaJ9tGOYmZmZpV69FFSoRFuMo333DiqdLalDUsemTZt2d3MzMzOzulPt4O6pJKVK8vvppL0TOKBovfnAxjHa55doH+0Yw0TE1RHRHhHtc+fOHfdJmZmZmdWLagd3NwGFitezgJVF7WcmVbPHAduSlOqtwImS5iSFFCcCtybLtks6LqmSPXPIvkodw8zMzCz1Giq1Y0nfA14D7COpk3zV6+eBGyS9D3gCeHOy+o+Bk4F1wPPAewAiYrOki4H7kvU+GxGFIo0Pka/IbQF+kvwwyjHMzMzMUk/5YlNrb2+Pjo6OWnfDzMzMbEyS7o+I9lLL6qWgwszMzMwmgYM7MzMzsxRxcGdmZmaWIg7uzMzMzFLEwZ2ZmZlZiji4MzMzM0sRB3dmZmZmKeLgzszMzCxFHNyZmZmZpYiDOzMzM7MUcXBnZmZmliIO7szMzMxSxMGdmZmZWYo4uDMzMzNLEQd3ZmZmZini4M7MzMwsRRzcmZmZmaWIgzszMzOzFHFwZ2ZmZpYiDu7MzMzMUsTBnZmZmVmKOLgzMzMzSxEHd2ZmZmYp4uDOzMzMLEUc3JmZmZmliIM7MzMzsxRxcGdmZmaWIg7uzMzMzFLEwZ2ZmZlZiji4MzMzM0sRB3dmZmZmKeLgzszMzCxFHNyZmZmZpYiDOzMzM7MUcXBnZmZmliIO7szMzMxSxMGdmZmZWYo4uDMzMzNLEQd3ZmZmZini4M7MzMwsRRzcmZmZmaWIgzszMzOzFHFwZ2ZmZpYiDu7MzMzMUsTBnZmZmVmKOLgzMzMzSxEHd2ZmZmYpUpPgTtLfSlor6SFJ35M0Q9Ihku6V9Jik70tqStZtTl6vS5YfXLSfTyXtv5X0+qL2k5K2dZLOq/4ZmpmZmdVG1YM7SfOAjwLtEfFyIAucAVwCXBERhwJbgPclm7wP2BIRC4ArkvWQtDDZ7nDgJOBfJWUlZYGrgDcAC4G3JeuamZmZpV6t0rINQIukBmAP4EngBODGZPm1wJuSv09LXpMsf60kJe3XR0RPRPwBWAcck/ysi4jfR8Qu4PpkXTMzM7PUq3pwFxEbgMuBJ8gHdduA+4GtEdGXrNYJzEv+ngesT7btS9ZvK24fss1I7WZmZmapV4u07BzyI2mHAPsDM8mnUIeKwiYjLNvd9lJ9OVtSh6SOTZs2jdV1MzMzs7pXi7Ts64A/RMSmiOgFfgj8GTA7SdMCzAc2Jn93AgcAJMtnAZuL24dsM1L7MBFxdUS0R0T73LlzJ+PczMzMzGqqFsHdE8BxkvZI7p17LfAwcAewLFnnLGBl8vdNyWuS5bdHRCTtZyTVtIcAhwK/Bu4DDk2qb5vIF13cVIXzMjMzM6u5hrFXmVwRca+kG4HfAH3AA8DVwC3A9ZL+MWn7WrLJ14BvSVpHfsTujGQ/ayXdQD4w7AM+HBH9AJI+AtxKvhL36xGxtlrnZ2ZmZlZLyg+CWXt7e3R0dNS6G2ZmZmZjknR/RLSXWuYnVJiZmZmliIM7MzMzsxRxcGdmZmaWIg7uzMzMzFLEwZ2ZmZlZiji4MzMzM0sRB3dV1NXdw+r1W+nq7ql1V8zMzCylqj6J8XS1ctUGlq9YQ2MmQ28ux6WnL2Lp4nm17paZmZmljEfuqqCru4flK9awszfH9p4+dvbmOHfFGo/gmZmZ2aRzcFcFnVt20JgZ/FY3ZjJ0btlRox6ZmZlZWjm4q4L5c1rozeUGtfXmcsyf01KjHpmZmVlaObirgrbWZs5/40KasmJmU5YZjRkuPX0Rba3Nte6amZmZpYyDuypYuWoDF9/yME0NGXpzwfmnLHQxhZmZmVWEg7sKKy6m6O7pZ1dfjotvftjFFGZmZlYRDu4qzMUUZmZmVk0O7irMxRRmZmZWTQ7uKqyttZlLT1/EjMYMezY3uJjCzMzMKspPqKiCpYvnsWTBPnRu2cH8OS0O7MzMzKxiygruJM0FPgAcXLxNRLy3Mt1Kn7bWZgd1ZmZmVnHljtytBH4B/CfQX7numJmZmdlElBvc7RERyyvak2miq7vH6VkzMzOrmHKDu5slnRwRP65ob1Ju5aoNLF+xhsZMht5cjktPX+TJjM3MzGxSlVst+zHyAd4OSc9K2i7p2Up2LG2KJzPe3tPHzt4c565Y48mMzczMbFKVNXIXEXtWuiNpV5jMeCcvzHlXmMzY6VkzMzObLGVPhSJpDnAoMKPQFhE/r0Sn0siTGZuZmVk1lJWWlfR+4OfArcBFye8LK9et9PFkxmZmZlYN5Y7cfQw4GrgnIo6X9FLyQZ7tBk9mbGZmZpVWbnC3MyJ2SkJSc0Q8KuklFe1ZSnkyYzMzM6ukcoO7Tkmzgf8L3CZpC7Cxct0yMzMzs/Eot1r2r5I/L5R0BzAL+GnFemVmZmZm4zJqcCdpr4h4VtLeRc0PJr9bgc0V65mZmZmZ7baxRu6+C5wC3A8EoKJlAby4Qv0yMzMzs3EYNbiLiFOS34dUpztmZmZmNhFjpWWPHG15RPxmcrtjZmZmZhMxVlr2n5PfM4B2YDX51Owi4F7gVZXrWjp1dffUfJ67euiDmZmZVcZYadnjASRdD5wdEQ8mr18OnFP57qXLylUbWL5iDY2ZDL25HJeevoili+dNuz6YmZlZ5ZT1+DHgpYXADiAiHgIWV6ZL6dTV3cPyFWvY2Ztje08fO3tznLtiDV3dPdOqD2ZmZlZZ5QZ3j0i6RtJrJP2FpH8HHqlkx9Kmc8sOGjOD3+7GTIbOLTumVR/MzMysssp9QsV7gA+Rf8YswM+Br1SkRyk1f04LvbncoLbeXI75c1qmVR/MzMysssoauYuIncBXgfMi4q8i4oqkzcrU1trMpacvYkZjhj2bG5jRmOHS0xdVtaChHvpgZmZmlaWIGHslaSlwGdAUEYdIWgx8NiKWVrqD1dLe3h4dHR0VP049VKrWQx/MzMxs/CTdHxHtpZaVm5a9ADgGuBMgIlZJOngyOjfdtLU21zygqoc+mJmZWWWUW1DRFxHbKtqTaaCru4fV67cOqk4t1WZmZmY2XuWO3D0k6e1AVtKhwEeBX1WuW+lTan65AM85Z2ZmZpOq3Hvu9gA+A5xI/gkVtwIXp6moopL33HV197DkktvZ2ftCpWpzQwYIevpeeP9nNGb45fITnDI1MzOzUU34nruIeJ58cPeZyezYdFGYX24nLwR32YwgBPQPtBXmnHNwZ2ZmZuM1anAn6abRlqepWraSSs0v158LYPCoqeecMzMzs4kaq6DilcB84BfA5cA/D/kZF0mzJd0o6VFJj0h6paS9Jd0m6bHk95xkXUm6UtI6SWskHVm0n7OS9R+TdFZR+1GSHky2uVKSxtvXyVBqfrnLli3ismVHeM45MzMzm1Sj3nMnKQv8JfA2YBFwC/C9iFg7oYNK1wK/iIhrJDUBewCfBjZHxOclnQfMiYjlkk4G/gY4GTgW+JeIOFbS3kAH0E5+COx+4KiI2CLp1+SfpnEP8GPgyoj4yWh9qsY8d4X55WY2ZXluV//AKF1hzrniv0cK8mo1R121jzvV5+Kb6v03M7P6Nu577iKiH/gp8FNJzeSDvDslfTYivjTOzuwFvBp4d3KMXcAuSacBr0lWu5b8nHrLgdOA6yIfhd6TjPq9KFn3tojYnOz3NuAkSXcCe0XE3Un7dcCbgFGDu2poa23mrnXPlKyQLVVNO7Rytpx1KqHax63VeU6Wqd5/MzOb2sac505Ss6T/CXwb+DBwJfDDCRzzxcAm4BuSHpB0jaSZwH4R8SRA8nvfZP15wPqi7TuTttHaO0u011xXdw/LV6xhZ2+O7T197OzNce6KNax7anvJ9qHz4Y21TjX7XKnj1uo8J8tU77+ZmU19owZ3Sfr0V8CRwEURcXREXBwRGyZwzIZkf1+JiFcAzwHnjdaNEm0xjvbhO5bOltQhqWPTpk2j93oSFKpmizVmMqxav7Vke+eWHWNuW7xOJVT7uLU6z8ky1ftvZmZT31gjd+8CDiN//9qvJD2b/GyX9Ow4j9kJdEbEvcnrG8kHe08l6VaS308XrX9A0fbzgY1jtM8v0T5MRFwdEe0R0T537txxnk75SlXN9uZyLD5gdsn24srZkbatdHVttY9bq/OcLFO9/2ZmNvWNGtxFRCYi9kx+9ir62TMi9hrPASPiT8B6SS9Jml4LPAzcBBQqXs8CViZ/3wScmVTNHgdsS9K2twInSpqTVNaeCNyaLNsu6bikSvbMon3VVHHV7MymLE1Zcf4bF7Jgvz2HVdMOrZwtVXFbjeraah+3Vuc5WaZ6/83MbOor6wkVk35QaTFwDdAE/B54D/lA8wbgQOAJ4M0RsTkJ0L4MnAQ8D7wnIjqS/byXfJUtwOci4htJezvwTaCFfCHF38QYJ1qNatmC79zzOBfd/DBNWdGXi4Eb7supsHS17NQw1ftvZmb1bbRq2ZoEd/WoWsFdqUeR+bFjZmZmtjtGC+7GrJa1yVXqhvusxB2PPj2uisqu7h5Wr9/qakwzMzMDHNxVXakb7p/b1c+FP1rLkktu56ZV5Rcir1y1gSWX3M47r7l3t7c1MzOzdHJwV2VDiyoKunv6d2tONM+nZmZmZqU4uKuBpYvn8cvlJ3DR0sNpbc4OWlbunGi1nE/NqWAzM7P65eCuRtpamzn+pfvSlxtc0FLunGi1mk/NqWAzM7P65uCuhiYyJ1ot5lNzKtjMzKz+NdS6A9Pd0sXzWLJgn3HNiTaRbcejkAreyQsjhoVUsKdxMTMzqw8O7upAW2vzuIOjiWy7u/xoLTMzs/rntKyVzY/WMjMzq38euauyru4e1m7cBojD999rygVG1U4Fm5mZ2e5xcFdFK1dt4JwfrKa3P18h25CBL7xlMUsXz6txz3ZPNVPBZmZmtnuclq2Sru4ezr1xzUBgB9CXg0/euNrVpjYmzy1oZmbl8shdlXRu2UE2o2HtWbna1Ea3ctUGlq9YQ2MmQ28ux6WnL5pyo71mZlY9HrmrkvlzWugfMmExQH+42tRG5rkFzcxsdzm4q5K21mYuW7aIxuwLo3dZwWXLjig5auc0nEFtHzNnZmZTk9OyVbR08Ty27+zjoh+tJZvJkIvhI3ngNJy9wHMLmpnZ7vLIXRV1dfdw8S0Ps6s/2NHbT0/f8BSb03BWzHMLmpnZ7vLIXRWV8/iuSj7iq6u7x/PTTUGeW9DMzHaHg7sqKifFVqk0nFO9U5vnFjQzs3I5LVtF5aTYKpGGc6rXzMxs+vDIXRV1dfdwUNtMvv3eY3ho47PMaMgwe48m1j21ned29Q+k3IrTcDObsjy3q5+u7p6BAG9306ulUr0ZxNqNz/Lqw+ZW7HxH4xSxmZlZZTi4q5JCWhRgZ29u2PLmrFBGA+nSttZm7lr3zLBUasBup1dLpXqf7+3nA9d1cNmy6qdnnSI2MzOrHMUI03FMN+3t7dHR0VGRfXd197DkkttLBnVDzWjM8MvlJwAM26a5IQMEPX0xbP2xRr9uWrWBT964etC2u7P9ZCn1XlS7D2ZmZlOdpPsjor3UMt9zVwWlJqIdSaEyttQ22YzIanwT2i5dPI9/P7OdPZqy49p+snhSXjMzs8pyWrYKSqVFR1JcGTt0m/zjy2LE9cdy+P6zhk2cXO0JcT0pr5mZWWV55K4KiitgZzSWfsubsxpUGVuqavayZYu4bNkR466krYcJceuhD2ZmZmnme+4SlbznrqBQITqzKcvGbTt5dkcve7U0sv+sGYOqZUttU7xsopWm9VCpWg99MLPx8b9fs9ob7Z47B3eJagR3ZmZTnavdzeqDCyrMprGu7h5Wr9/qSavr2FS5Rp4Q3WxqcEGFWYp5lKX+TaVrVMlnX5vZ5PHInVlKeZSl/k21a+Rqd7OpwcFdjZRKw0wkNTNV0jpWPZ5TsP5NtWvkanezqcFp2RoolYYZz2PFRttfvaZ1rHo8ylL/puI1Kn72tatlzeqTR+6qrFQa5pM3ruHcG1ePKzUz1dI6Vj0eZal/U/UatbU2c8QBs+u+n2bTlUfuqqQwL9S2Hb3DbkjOCEQG6B9oK3WTcvE8eYV58Tq37CArDTpWpW9wHu8cV54bq/o8ylJZk/GZ9jUys8nm4K4KitOmu/pz7OobnIbZ0ZsjOzg+G5aaKewjckFPfww86WLpEfvz3K7+Ubet1LnsTgrYqePaKTzxxCbXZH6mfY3MbDI5LVthQ9OmPX05Sk0bncmI5gaVTM0U76OnP7/1zt4cO3tz3NDROWxf579xYUX+QzHeFLBTx5Y2/kybWT3zyF2FlZoXqpSGjPi3dx3FrJamYamZcvcBMLM5y8vnzZpwv0sZ7xxXnhvL0safaTOrZw7uKqxUNVwpfbng8P1nlfwPQ7n7AOjPRcVSsuOt7JuKFYFmo/Fn2szqmdOyFVZcDTezKTvieheeeviI/8dfvI/m5Oa8GY0ZZjRmOPOVB1at0m68lX1TtSLQbCT+TJtZPVNEqTvApp/29vbo6Oio2P67unu449GnufBHa+nuGVwA8aHXvJjlJ72srH0MrZZta22uehWqq2XN8vyZNrNakXR/RLSXWua0bJW0tTZz/Ev35e9XPjSovbkhw/tf9eKy91HqPyDVrrQb7/FcEWhp48+0mdUjp2Wr7MOvWTCoKvayZYsGRt/8+DAzMzObKI/cVUnxnFggzn71i3n7sQfS1trsOeDMzMxs0njkrgpKzXV31Z3r2PLcLn7+u6fH/egxMzMzs6E8clcFpR4R1t+f4w1X/oLGbIaevsFFLZ4vy8zMzMbLwV0VPLRhW4lHhAEEvf39w9b3fFlmZmY2XjVLy0rKSnpA0s3J60Mk3SvpMUnfl9SUtDcnr9clyw8u2senkvbfSnp9UftJSds6SedV+9yKdXX3cPEtD5e17h6NWc+XZWZmZhNSy5G7jwGPAHslry8BroiI6yV9FXgf8JXk95aIWCDpjGS9t0paCJwBHA7sD/ynpMOSfV0F/CXQCdwn6aaIKC/CmmTlPjqsuSHDpcsWsVdLA/vPamH1+q0jzmM32txaxcsKxy/Mizd0frzReP4us3Twv2Wz6acmwZ2k+cAbgc8BfydJwAnA25NVrgUuJB/cnZb8DXAj8OVk/dOA6yOiB/iDpHXAMcl66yLi98mxrk/WrUlwN39OCzt6+8Zc76C2Fs65cTUAO3tzNGeFMuItR83nhvs7Bypp39I+nxs6OktW1hZX3e7o7UMS2YzY2ZsjK+iP/JMtgFErcl29a5YO/rdsNj3VKi37ReBcGBjOagO2RkQhCuoECt9A84D1AMnybcn6A+1DthmpvYY05hq/e+o5dvbm2Jm/GY+e/mBnb47r7nliUCXtdXc/UbKydmhFbl8OepN9QD6wAwaOMVJF7tD9uHrXbGryv2Wz6avqwZ2kU4CnI+L+4uYSq8YYy3a3vVRfzpbUIalj06ZNo/R6/Dq37KAhM3ZwN16FytpC+nd3txtq7cZnyQx5C0da18zqV6nvBP9bNpseapGWXQIslXQyMIP8PXdfBGZLakhG5+YDG5P1O4EDgE5JDcAsYHNRe0HxNiO1DxIRVwNXQ/7ZshM/teHmz2kpHVlOkuLK2t7c6Pf1jbRdwcpVGzj3xjX09OXGXNfM6tv8OS3DvhP8b9lseqj6yF1EfCoi5kfEweQLIm6PiHcAdwDLktXOAlYmf9+UvCZZfntERHM3l6YAABchSURBVNJ+RlJNewhwKPBr4D7g0KT6tik5xk1VOLWS2lqbuWzZIrJjjN79+YI2ZjRmBu6Ja86KGY0ZznzlgcxozAw8rmzo60JlbVtrM5eevmhgWUMGGpN9AGSTwxeOMbQit5DCGRrYNTfI1btmU9DQ7wRX4ptNH8rHSTU6uPQa4JyIOEXSi4Hrgb2BB4B3RkSPpBnAt4BXkB+xO6OoWOIzwHuBPuDjEfGTpP1k8qOBWeDrEfG5sfrS3t4eHR0dk32KA7q6e7j7v7p4vOs5vvizx+jtf+F9b8qKa85qZ/9ZLSWrWkerlgXGXFZOtezq9Vt55zX3sr3nheKPPZqyfPWdR/Lqw/at2PtitedqynSrt+tbb/0xm0zV/HxLuj8i2ksuq2VwV08qHdwVlAqiIB9I5SIGqtnK+YB8557HuehHa2nMZugv2nY8urp7WHLJ7QMFGJAf5fvl8hP8BZxirqa0avLnzdKs2p/v0YI7P1u2irq6e9i2Yxe7SjyV4vld/QPVbN+553GWXHI777zmXpZccjs3rdowbP3v3PM4n/m/D7GrP3iuaNvxVsI5hTP9uJrSqsmfN0uzevt8+/FjVVIc0ecCGjLQlM3yfO/gQC8rcdHND7OrLzcw8fG5K9awZME+A4FWV3cPF908fNq+rDShZ9IuXTyPJQv2ccpkmig1wbafa2yV4s+bpVm9fb49clcFQyP63v4gm8k/kaK5YfAl6O3P0ZQdfSqSzi07hq1T2HailXBtrc0cccBsf9lOA66mtGry583SrN4+3w7uqqDUfFMNGbFXSyMfOX4BzQ1iz+YGmhvE2485kOd6Bo/m7ejtG/QBmT+nhb7c8HslLzj18BGDsq7uHlav3+oUiA1wKt6qyZ83S7N6+3y7oCJRyYKKru4ejv2n/2TILCNkBXs0NbCrP8drX7ovt679E/0lLkdjVtzzqdcO+pDctGoD565YQzYjevuDC05dyDuOPWjgeMWpVd/EbKNx9aJVkz9vlmb1Ui3re+6qJP843MGRW38wUDX744f+NOK2TdnhefuR7o8bGsidf8pCLr754fxjx0a4h8+mt8I8iWbV4M+bpVm9fL6dlq2CiT6CbGdvP+s3Pz/wDNlCerWttZmZTVnuePRp1j21nXVPbeecH6waVK1zwcqHyA1J4U70EUS1TPFO9rGnQrp6KvSx0vweTIzfP7PpxSN3VfDQhm3s6C3/0WBD9Qd85HsPkM0IEbQ0NtCby3H0QXP4xbquUbfNp4IHB3cTucmzlineyT72VEhXT4U+Vprfg4nx+2c2/XjkrsK6unu4+Jbh05aMR38u6MsxMCo3VmBXSlOWcd/kWct5fCb72PU2J1EpU6GPleb3YGL8/plNTw7uKqxUpexo3rR4/4HnwVbCp09+2Yj/1z5W6qbUuUw0xVuuyT727uxvoimt8W5fy/e7Xvg9mBi/f2bTk9OyFVZq7pvRnLroRfx07cjFFRP1qgVzS7aXk7qp5Tw+k33scvc30ZTWRLavt3mTasHvwcT4/TObnjxyV2Ftrc2c/8aFZa3bmBUf/t4DvKV9PjMaMzRN4Oo0ZODPF7QNajvzlQeyYL89h61bbuqmlvP4TPaxy9nfRFNaE92+0u/3VLjJvt7mjppq/P6ZTU8euauCl8+bRWtzlu6e4c+ULdbbH/T2B9+99wn++tUvZo+mBr50xzp2llmMMbM5yydPPIx9WmcAwV4tjXzstYfyx67nWXzA7JKBHezeY1Mm+oiyUnMAlTsv0GQ/Hm2s/Y33cTKF89m2o3fCj6Op1CPhptJN9n4s3sT4/TObfhzcVcFIT5QYSV8Orrrz97t9nN6+HLv6go9//4GBCZMbs+Kf33zEiIFdoX+7k7oZ7zw+pQKKgN0KMiZ7DqHR9jeelFbxOe7qz9E/CSmxyT7n4hHFqTL3Yb3MHTVV+f0zm16clq2C4tTIzKbspO5bQHPynFlJ/NNPHh30JIze/uCTN46eCiykjhuz0NKQoblBXHr6IoBJS9uVSlGe84PVw+blq6dKvt1NaXV193DujasHzqenL4ekgcfL1UtKrNyb7CuZtp0KKWEzs6nKI3dVUpwaeWjjNi6++WEaMxl29PYRiP7dGNkr1pgVQf7pFz1Dn2+WyGY0aipw5aoNXPijtfT2Qy85soKOxzdz7iSm7UqlOHeVeNba7qYtK213UlrfufcJevoGn9OMhixXveMVzGppqpuUWDkjkpVM206llLCZ2VTkkbsqamtt5ogDZvOOYw/il8tP4Kp3HEk2kxl3YAfQkMnQkB396Rf9uRgxFZgfbVpDb1Gg1R9w3d1PTOqIWrlVw/VYyVe4bmPdZ3fVHeuGte/q7+fw/WeNuX01jTUiWcm50TzvWvp4FNas/njkrkbaWpuZ1dJIUzYz4ohbKdkM9Bet3h85iJGDu8as+IdTFg6k3EoVDWTLeDRacdpuPDdmFwKKc1esIYN4vnd4cUlTVnWRthyPzi07Sl7Ljxx/aF2ez2gjkuMtJClHJfdt1edRWLP65OCuhkqNZjU3ZDjnxMO45Ke/HVaE0ZQVF5x6OBff8vCgL1OAv7th1aAiive/6hBe+d/bWL95x7D1i798589pKWvksDeX46EN23jr1XeP+4u8EFCs3fgsH7iuY1Ag1NSQ4cd/86pRCz/q2UjX8u3HHlijHo1tpJvsKzk3muddS4+pWJhjNl04LVtDpdJjly1bxAde/d/5wluOoLEo3dqQgcvffATvOC6f0v32+4/ll8tPGAiYskU3yPf2B9/41R/Zf1YLF9/y8KgpsLbWZi5btmjYsc585YGD+nX+GxeOua9yz/nVh83lsmWDz/vyZYsGArupmOYZ6VpOxf/IVXJuNM+7lh5++oVZ/fLIXY0tWbAPly87gme6e3jVgn1YsN+edHX3cFDbTH7y0T9n47YdgNh/1gw2btvJz3/39MA9XAWlUoKNmQyr1m8tKwX2wojaNkAcvv9etLU2c+ZxB7Nq/VYWHzCb53b1T2jOt6Gpv5HSglM5zZOm+cQqeS5pep+mM4/CmtUvB3c1tHLVBs75weqBYoaGDLz9mAO54f7OYXPBvfebvx42d10h6BnpS3bxAbPZ0ds3qH1Hb1/JL9/8iNq+g/pWHGSdf8rCCc35VipQG5oWTEOaJ03ziVXyXNL0Pk1XxffRFv8b93U1qz0HdzVSqkq1LwfX3fMEwEBw88kb1xCRKzl3XSHoGelLds7MJqT8NCkF+ddj921okHXxzQ8PpGbL+SIfT6Dmm+3NphaPwprVJwd3NdDV3cMdjz5NGUWqZAR9ucEBGrwwdx3kg6IlC/bhl8tPGPQlu3r9VmY0ZOntf2H0bkZDdiBYGillOlKQ9fJ5s4YdYyTjCdSc5jGbejwKa1Z/HNxVWSFV2ZARO8p4ZuxI6/TnYszq1dGCpdFSpqNtV+4X+XgCNad5zMzMJs7VslVUnKrs7hk+z1txlepojykrzF1XTiVsqcpEYNSJZCejonG8+1i6eN6wamAzMzMrn0fuqqhUqnJmc5ZPnngYL56756Aq1ZtWb+Rrd/2e53a9sG5LY5blJx3GqUfMKzvtuWTBPlz9rqMoroJdPUIV7dqNzzKrpZH5c1om5V6a8e7DaR4zM7Pxc3BXRaVSlf254FUL5vLcrvxIXnHatjiwAwiCU4+YNxD4jPf5oKX6saO3jw9c10FTdvC6Ew2yHKiZmZlVl9OyVVQqVfmWo+Zzypfv4p3X3Mufff52PnHDqmFp25lN2UEp1dXrtwKM+/mgQ/vR3CAk0dP3wrrn/GA1657aXv03yczMzCbEI3dVVpyqnNmU5ZQv3zVoupChZjZnuejUwzn+pfty17pnWHLJ7YNG4kaqXh0rbVvcj207dvHh7zwwqKp2V39w8pfu4vJlU2cSYTMzM/PIXU20tTZzRNFTH0bTnwuOf2l+cuFSI3EARxwwe1jqs5xq1UI/Dt9/1rB1AXb1je8RY2ZmZlY7Du5qaP6cFnb1D66abcyK5gYNS7Xu7nMcd6datbBuU3b4xHt+VqSZmdnU4rRsDd217hlyRXMTN2Tgn998RMkK0/HMG7c71apLF89j4Yv24uQv3cWuosdheBJhMzOzqcUjdzVSKHgofvxYNpMZeDzX0FTreOeNK7WvkSzYb08uXzax+e3MzMystjxyV0Vd3T2s3fgs+UeJaVjBQ1N29MdzVeM5jn5WpJmZ2dTm4K5KVq7awCduWEUh49mYFRGDnxdbTgq0GvPGeW46MzOzqcvBXRV0dfdw7o2rKbqVjd7+SIonoCmb9XNUzczMbFI4uKuCzi07yCoDDK2MzfDVdx7JrJYmp0DNzMxsUji4q4L5c1roj+HzyPXngsP3n+WgzszMzCaNq2WroK21mcuWHUFD0bvdkIHLljkNa2ZmZpPLI3dVsnTxPLbv7OOiHz1MNsOg+e3MzMzMJotH7qqkq7uHi295mF39OXb05ujxo73MzMysAhzcVcnuPj7MzMzMbDwc3FXJzKYsPX2Dq2X9aC8zMzObbL7nrgpWrtrA8hVryGQE/UFzVigjz2tnZmZmk87BXYUVniG7s/eFqVBC4paPvIoF++1Zw56ZmZlZGjktW2Gl7rVrzmZ4blf/CFuYmZmZjV/VgztJB0i6Q9IjktZK+ljSvrek2yQ9lvyek7RL0pWS1klaI+nIon2dlaz/mKSzitqPkvRgss2VklTt8yyYP6eF3tzgCYx9r52ZmZlVSi1G7vqAT0TEy4DjgA9LWgicB/wsIg4Ffpa8BngDcGjyczbwFcgHg8AFwLHAMcAFhYAwWefsou1OqsJ5ldTW2sylpy9iRmOGPZsbmNGY8b12ZmZmVjFVv+cuIp4Enkz+3i7pEWAecBrwmmS1a4E7geVJ+3UREcA9kmZLelGy7m0RsRlA0m3ASZLuBPaKiLuT9uuANwE/qcb5lbJ08TyWLNiHzi07/AxZMzMzq6iaFlRIOhh4BXAvsF8S+BERT0raN1ltHrC+aLPOpG209s4S7TXV1trsoM7MzMwqrmYFFZJagRXAxyPi2dFWLdEW42gv1YezJXVI6ti0adNYXTYzMzOrezUJ7iQ1kg/svhMRP0yan0rSrSS/n07aO4EDijafD2wco31+ifZhIuLqiGiPiPa5c+dO7KTMzMzM6kAtqmUFfA14JCK+ULToJqBQ8XoWsLKo/cykavY4YFuSvr0VOFHSnKSQ4kTg1mTZdknHJcc6s2hfZmZmZqlWi3vulgDvAh6UtCpp+zTweeAGSe8DngDenCz7MXAysA54HngPQERslnQxcF+y3mcLxRXAh4BvAi3kCylqVkxhZmZmVk3KF6Fae3t7dHR01LobZmZmZmOSdH9EtJda5idUmJmZmaWIgzszMzOzFHFaNiFpE/B4hQ+zD/BMhY9htePrm36+xunm65t+abrGB0VEyak+HNxVkaSOkfLjNvX5+qafr3G6+fqm33S5xk7LmpmZmaWIgzszMzOzFHFwV11X17oDVlG+vunna5xuvr7pNy2use+5MzMzM0sRj9yZmZmZpYiDuyqQdJKk30paJ+m8WvfHyifp65KelvRQUdvekm6T9Fjye07SLklXJtd5jaQji7Y5K1n/MUlnlTqWVZ+kAyTdIekRSWslfSxp9zVOCUkzJP1a0urkGl+UtB8i6d7ken1fUlPS3py8XpcsP7hoX59K2n8r6fW1OSMrRVJW0gOSbk5eT+vr6+CuwiRlgauANwALgbdJWljbXtlu+CZw0pC284CfRcShwM+S15C/xocmP2cDX4F8oABcABwLHANcUAgWrOb6gE9ExMuA44APJ/8+fY3Towc4ISKOABYDJ0k6DrgEuCK5xluA9yXrvw/YEhELgCuS9Ug+F2cAh5P/TvjX5Pvd6sPHgEeKXk/r6+vgrvKOAdZFxO8jYhdwPXBajftkZYqInwObhzSfBlyb/H0t8Kai9usi7x5gtqQXAa8HbouIzRGxBbiN4QGj1UBEPBkRv0n+3k7+Pw7z8DVOjeRadScvG5OfAE4Abkzah17jwrW/EXitJCXt10dET0T8AVhH/vvdakzSfOCNwDXJazHNr6+Du8qbB6wvet2ZtNnUtV9EPAn54ADYN2kf6Vr7MzAFJOmZVwD34mucKknKbhXwNPnA+7+ArRHRl6xSfL0GrmWyfBvQhq9xPfsicC6QS163Mc2vr4O7ylOJNpcop9NI19qfgTonqRVYAXw8Ip4dbdUSbb7GdS4i+iNiMTCf/GjMy0qtlvz2NZ5CJJ0CPB0R9xc3l1h1Wl1fB3eV1wkcUPR6PrCxRn2xyfFUkooj+f100j7StfZnoI5JaiQf2H0nIn6YNPsap1BEbAXuJH9/5WxJDcmi4us1cC2T5bPI35rha1yflgBLJf2R/G1PJ5AfyZvW19fBXeXdBxyaVO40kb9h86Ya98km5iagUA15FrCyqP3MpKLyOGBbktK7FThR0pzkJvsTkzarseRem68Bj0TEF4oW+RqnhKS5kmYnf7cAryN/b+UdwLJktaHXuHDtlwG3R35C2JuAM5Jqy0PIF9X8ujpnYSOJiE9FxPyIOJj8f19vj4h3MM2vb8PYq9hERESfpI+Q/6LPAl+PiLU17paVSdL3gNcA+0jqJF8R+XngBknvA54A3pys/mPgZPI34j4PvAcgIjZLuph8oA/w2YgYWqRhtbEEeBfwYHJPFsCn8TVOkxcB1yaVjxnghoi4WdLDwPWS/hF4gHyQT/L7W5LWkR/ROQMgItZKugF4mHyV9Ycjor/K52LlW840vr5+QoWZmZlZijgta2ZmZpYiDu7MzMzMUsTBnZmZmVmKOLgzMzMzSxEHd2ZmZmYp4uDOzMzMLEUc3JnZtCHpM5LWSlojaZWkYyt4rDsltU/Svj4o6czJ2JeZpZ8nMTazaUHSK4FTgCMjokfSPkBTjbtVloj4aq37YGZTh0fuzGy6eBHwTET0AETEMxGxUdI/SLpP0kOSrk4eSVYYebtC0s8lPSLpaEk/lPRYMus9kg6W9Kika5PRwBsl7TH0wJJOlHS3pN9I+oGk1pE6Kenzkh5O9nd50nahpHMk7Z+MOBZ++iUdlDxia0VyHvdJWlKRd9DMpgQHd2Y2XfwHcICk30n6V0l/kbR/OSKOjoiXAy3kR/cKdkXEq4Gvkn825YeBlwPvltSWrPMS4OqIWAQ8C/yv4oMmI4R/D7wuIo4EOoC/K9VBSXsDfwUcnuzvH4uXR8TGiFgcEYuBfwdWRMTjwL8AV0TE0cDpwDW7/e6YWWo4LWtm00JEdEs6Cvhz4Hjg+5LOA7ZLOhfYA9gbWAv8KNnspuT3g8DaiHgSQNLvgQOArcD6iPhlst63gY8Clxcd+jhgIfDLZFCwCbh7hG4+C+wErpF0C3BzqZWSkbn3J+cC8DpgYbJ/gL0k7RkR20d9U8wslRzcmdm0kTwI/E7gTkkPAn8NLALaI2K9pAuBGUWb9CS/c0V/F14Xvj+HPqB76GsBt0XE28roX5+kY4DXkn+g+UeAEwbtTHoR+YefL42I7qQ5A7wyInaMdQwzSz+nZc1sWpD0EkmHFjUtBn6b/P1Mch/csnHs+sCkWAPgbcBdQ5bfAyyRtCDpxx6SDhuhj63ArIj4MfDxpI/FyxuBG4DlEfG7okX/QT4QLKw3aDszm148cmdm00Ur8CVJs4E+YB1wNvnU6oPAH4H7xrHfR4CzJP0b8BjwleKFEbFJ0ruB70lqTpr/Hvgdw+0JrJQ0g/yI398OWf5nwNHARZIuStpOJp8KvkrSGvLf6z8HPjiOczGzFFDE0AyCmZmVQ9LBwM1JMYaZWV1wWtbMzMwsRTxyZ2ZWA5L+D3DIkOblEXFrLfpjZunh4M7MzMwsRZyWNTMzM0sRB3dmZmZmKeLgzszMzCxFHNyZmZmZpYiDOzMzM7MU+f9W99MoaLMAhgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "recent_grads.plot(x='Sample_size', y='Median', kind='scatter', title='Median vs. Sample_size', figsize=(10,5))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> Do students in more popular majors make more money?\n",
    "\n",
    "It would seem that majors with fewer graduates make more money. The range for low-student majors varies from a little over \\\\$20k to over \\\\$100k, however, due to the large number of low-student majors. As major sizes grow, salaries seem to hover between \\\\$60k and \\\\$30k."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1185a6c50>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "recent_grads.plot(x='Sample_size', y='Unemployment_rate', kind='scatter', title='Unemployment Rate vs. Sample_size', figsize=(10,5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1186fd2d0>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "recent_grads.plot(x='Full_time', y='Median', kind='scatter', title='Full-time vs. Median', figsize=(10,5))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> Is there any link between the number of full-time employees and median salary?\n",
    "\n",
    "It would seem the trend here is very similar to the trend between number of students in the major vs median salary. Essentially, there is a massive range in salary for those who are working full-time, and many post-graduate students working full-time have a salary between \\\\$35k and \\\\$42k."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x118755050>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "recent_grads.plot(x='ShareWomen', y='Median', kind='scatter', title='Share of Women vs. Median Salary', figsize=(10,5))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> Do students that majored in subjects that were majority female make more money?\n",
    "\n",
    "In majors where the majority of share of students were women, salaries are skewed towards the range of \\\\$65k to a little over \\\\$20k. In majors were women didn't comprise the majority, the range was much bigger, from \\\\$30k to over \\\\$100k."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1188902d0>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "recent_grads.plot(x='Men', y='Median', kind='scatter', title='Men vs. Median', figsize=(10,5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1189d2d10>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "recent_grads.plot(x='Women', y='Median', kind='scatter', title='Women vs. Median', figsize=(10,5))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's keep going! To figure out the distribution of values for each column, we'll use a histogram plot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x118b04790>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "recent_grads['Sample_size'].hist(bins=25, range=(0,5000))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x118d04f90>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "recent_grads['Median'].hist(bins=50, range=(15000, 120000))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x118e8d2d0>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXQAAAD4CAYAAAD8Zh1EAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8QZhcZAAATu0lEQVR4nO3df4zkd13H8efbnoWWrddiYdNc0W0NNMGeAjdiEcVdftTjh+APEtsUuQJmIwoWLdFrGsV/jPVHNRqIcMqlRGoXKCjYBqGpXRoNVO9K6baU0hMvcke5s1QOF6pw8vaP+V6dbnd29vtjZ/Y+Ph/J5mY+8/3xms/Mvm72uzPfjcxEknTy+45JB5AkdcNCl6RCWOiSVAgLXZIKYaFLUiG2jHNnZ599ds7MzIxzl4/x9a9/nSc96UkT2/96mLEbZuyGGbvTJuf+/fsfysynjFwwM8f2tWPHjpyk2267baL7Xw8zdsOM3TBjd9rkBPblOjrWQy6SVAgLXZIKYaFLUiEsdEkqhIUuSYWw0CWpECMLPSL2RsTRiLhnxfibI+L+iLg3In5/4yJKktZjPa/QrwN2Dg5ExBzwKuAHMvP7gT/sPpokqY6RhZ6ZtwMPrxh+I3BNZv53tczRDcgmSaohch1/4CIiZoCbMvPC6vpdwIfpv3L/L+CtmfnPQ9adB+YBpqendywsLDQKunT4WK3lt2/b+rix5eVlpqamGu1/XMzYDTN2w4zdaZNzbm5uf2b2Ri3X9FwuW4CzgIuAHwLeHxHn5yr/O2TmHmAPQK/Xy9nZ2UY7vHz3zbWWP3jZ4/ezuLhI0/2Pixm7YcZumLE748jZ9F0uh4APVacZ+Cfg28DZ3cWSJNXVtND/BnghQEQ8AzgVeKirUJKk+kYecomIG4BZ4OyIOAS8DdgL7K3eyvhNYNdqh1skSeMzstAz89IhN72m4yySpBb8pKgkFcJCl6RCWOiSVAgLXZIKYaFLUiEsdEkqhIUuSYWw0CWpEBa6JBXCQpekQljoklQIC12SCmGhS1IhLHRJKoSFLkmFsNAlqRAjCz0i9kbE0eqvE6287a0RkRHh3xOVpAlbzyv064CdKwcj4mnAS4B/6ziTJKmBkYWembcDD69y0x8Dvw74t0QlaRNodAw9Il4JHM7Mz3ScR5LUUGSOfoEdETPATZl5YUScDtwGXJyZxyLiINDLzIeGrDsPzANMT0/vWFhYaBR06fCxWstv37b1cWPLy8tMTU012v+4mLEbZuyGGbvTJufc3Nz+zOyNWq5JoW8HbgW+Ud18LvAl4LmZ+eW1ttPr9XLfvn0j97eamd0311r+4DUvf9zY4uIis7OzjfY/Lmbshhm7YcbutMkZEesq9C11N5yZS8BTB3Z0kDVeoUuSxmM9b1u8AfgkcEFEHIqIN2x8LElSXSNfoWfmpSNun+ksjSSpMT8pKkmFsNAlqRAWuiQVwkKXpEJY6JJUCAtdkgphoUtSISx0SSqEhS5JhbDQJakQFrokFcJCl6RCWOiSVAgLXZIKYaFLUiEsdEkqhIUuSYVYz5+g2xsRRyPinoGxP4iIz0XE3RHx1xFx5sbGlCSNsp5X6NcBO1eM3QJcmJk/AHweuKrjXJKkmkYWembeDjy8YuzjmXm8uvop4NwNyCZJqiEyc/RCETPATZl54Sq3/S3wvsx875B154F5gOnp6R0LCwuNgi4dPtZovUHTp8GRR4bfvn3b1tb7aGt5eZmpqalJx1iTGbthxm6cDBmhXc65ubn9mdkbtdyWRluvRMTVwHHg+mHLZOYeYA9Ar9fL2dnZRvu6fPfNjdYbdOX241y7NPwuH7xstvU+2lpcXKTpHI2LGbthxm6cDBlhPDkbF3pE7AJeAbwo1/MyX5K0oRoVekTsBH4D+PHM/Ea3kSRJTaznbYs3AJ8ELoiIQxHxBuDtwBnALRFxV0S8c4NzSpJGGPkKPTMvXWX43RuQRZLUgp8UlaRCWOiSVAgLXZIKYaFLUiEsdEkqhIUuSYWw0CWpEBa6JBXCQpekQljoklQIC12SCmGhS1IhLHRJKoSFLkmFsNAlqRAWuiQVwkKXpEKs50/Q7Y2IoxFxz8DYkyPiloh4oPr3rI2NKUkaZT2v0K8Ddq4Y2w3cmplPB26trkuSJmhkoWfm7cDDK4ZfBbynuvwe4Kc6ziVJqikyc/RCETPATZl5YXX9q5l55sDt/5GZqx52iYh5YB5genp6x8LCQqOgS4ePNVpv0PRpcOSR4bdv37a19T7aWl5eZmpqatIx1mTGbpixGydDRmiXc25ubn9m9kYtt6XR1mvIzD3AHoBer5ezs7ONtnP57ptbZ7ly+3GuXRp+lw9eNtt6H20tLi7SdI7GxYzdMGM3ToaMMJ6cTd/lciQizgGo/j3aXSRJUhNNC/0jwK7q8i7gw93EkSQ1tZ63Ld4AfBK4ICIORcQbgGuAl0TEA8BLquuSpAkaeQw9My8dctOLOs4iSWrBT4pKUiEsdEkqhIUuSYWw0CWpEBa6JBXCQpekQljoklQIC12SCmGhS1IhLHRJKoSFLkmFsNAlqRAWuiQVwkKXpEJY6JJUCAtdkgphoUtSIVoVekT8akTcGxH3RMQNEfHEroJJkuppXOgRsQ34FaCXmRcCpwCXdBVMklRP20MuW4DTImILcDrwpfaRJElNRGY2XzniCuB3gEeAj2fmZassMw/MA0xPT+9YWFhotK+lw8ca5zxh+jQ48sjw27dv29p6H2tZz30YzLjReZpaXl5mampq0jHWZMZumLE7bXLOzc3tz8zeqOUaF3pEnAV8EPg54KvAB4AbM/O9w9bp9Xq5b9++Rvub2X1zo/UGXbn9ONcubRl6+8FrXt56H2tZz30YzLjReZpaXFxkdnZ20jHWZMZumLE7bXJGxLoKvc0hlxcD/5qZ/56Z3wI+BPxIi+1JklpoU+j/BlwUEadHRAAvAu7rJpYkqa7GhZ6ZdwA3AncCS9W29nSUS5JU0/ADyuuQmW8D3tZRFklSC35SVJIKYaFLUiEsdEkqhIUuSYWw0CWpEBa6JBXCQpekQrR6H3pp6p4vZrOea0XS/0++QpekQljoklQIC12SCmGhS1IhLHRJKoSFLkmFsNAlqRAWuiQVwkKXpEK0KvSIODMiboyIz0XEfRHxvK6CSZLqafvR/z8B/i4zXx0RpwKnd5BJktRA40KPiO8CXgBcDpCZ3wS+2U0sSVJdkZnNVox4FrAH+Czwg8B+4IrM/PqK5eaBeYDp6ekdCwsLjfa3dPhYo/UGTZ8GRx5pvZlHbd+2tdby67kPgxnrbn9clpeXmZqamnSMNZmxG2bsTpucc3Nz+zOzN2q5NoXeAz4FPD8z74iIPwG+lpm/OWydXq+X+/bta7S/umdCXM2V249z7VJ3J5ise7bF9dyHwYyb9WyOi4uLzM7OTjrGmszYDTN2p03OiFhXobf5pegh4FBm3lFdvxF4TovtSZJaaFzomfll4IsRcUE19CL6h18kSRPQ9vjDm4Hrq3e4fAF4XftIkqQmWhV6Zt4FjDyuI0naeH5SVJIKYaFLUiEsdEkqhIUuSYWw0CWpEBa6JBXCQpekQljoklQIC12SCmGhS1IhLHRJKoSFLkmFsNAlqRAWuiQVwkKXpEJY6JJUCAtdkgrRutAj4pSI+HRE3NRFIElSM128Qr8CuK+D7UiSWmhV6BFxLvBy4C+6iSNJaioys/nKETcCvwucAbw1M1+xyjLzwDzA9PT0joWFhUb7Wjp8rHHOE6ZPgyOPtN7MhhrMuH3b1smGGWJ5eZmpqalJx1iTGbtxsmas2xfj+F5rM5dzc3P7M7M3arktjbYORMQrgKOZuT8iZoctl5l7gD0AvV4vZ2eHLrqmy3ff3Gi9QVduP861S43v8lgMZjx42exkwwyxuLhI08dxXMzYjZM1Y92+GMf32jjmss0hl+cDr4yIg8AC8MKIeG8nqSRJtTUu9My8KjPPzcwZ4BLg7zPzNZ0lkyTV4vvQJakQnRxQzsxFYLGLbUmSmvEVuiQVwkKXpEJY6JJUCAtdkgphoUtSISx0SSqEhS5JhdjcJzZRLTN1z19xzcs3KImkSfAVuiQVwkKXpEJY6JJUCAtdkgphoUtSISx0SSqEhS5JhbDQJakQFrokFaJxoUfE0yLitoi4LyLujYgrugwmSaqnzUf/jwNXZuadEXEGsD8ibsnMz3aUTZJUQ+NX6Jn5YGbeWV3+T+A+YFtXwSRJ9URmtt9IxAxwO3BhZn5txW3zwDzA9PT0joWFhUb7WDp8rF1IYPo0OPJI681sqHFm3L5ta6P1lpeXmZqa6jhNM8OeF13NY9M5Wo/NNI+DBud0PfO4kXO0HqvNY92+GMd9aPN4z83N7c/M3qjlWhd6REwBnwB+JzM/tNayvV4v9+3b12g/dc8kuJortx/n2qXNfYLJcWZserbFxcVFZmdnuw3T0LDnRVfzuJFnpNxM8zhocE7XM4+TPmvnavO4Gc882ubxjoh1FXqrd7lExHcCHwSuH1XmkqSN1eZdLgG8G7gvM/+ou0iSpCbavEJ/PvDzwAsj4q7q62Ud5ZIk1dT4IGNm/gMQHWaRJLXgJ0UlqRAWuiQVwkKXpEJY6JJUCAtdkgphoUtSISx0SSrE5j6xiTadmd03c+X241zewbl1TgZdnENomBPzWPc8IhuZaVw227lWNluepnyFLkmFsNAlqRAWuiQVwkKXpEJY6JJUCAtdkgphoUtSISx0SSqEhS5JhWj7R6J3RsT9EXEgInZ3FUqSVF+bPxJ9CvAO4KXAM4FLI+KZXQWTJNXT5hX6c4EDmfmFzPwmsAC8qptYkqS6IjObrRjxamBnZv5Cdf3ngR/OzDetWG4emK+uXgDc3zxua2cDD01w/+thxm6YsRtm7E6bnN+bmU8ZtVCbsy3GKmOP+98hM/cAe1rspzMRsS8ze5POsRYzdsOM3TBjd8aRs80hl0PA0waunwt8qV0cSVJTbQr9n4GnR8R5EXEqcAnwkW5iSZLqanzIJTOPR8SbgI8BpwB7M/PezpJtjE1x6GcEM3bDjN0wY3c2PGfjX4pKkjYXPykqSYWw0CWpFJl5Un3Rf2fNbcB9wL3AFdX4bwOHgbuqr5cNrHMVcID+e+B/YmB8ZzV2ANg9MH4ecAfwAPA+4NQGOQ8CS1WWfdXYk4Fbqu3eApxVjQfwp1WOu4HnDGxnV7X8A8CugfEd1fYPVOtGzXwXDMzVXcDXgLdMeh6BvcBR4J6BsQ2ft2H7qJHxD4DPVTn+GjizGp8BHhmYz3c2zbLW/a2Rc8MfX+AJ1fUD1e0zNTO+byDfQeCuSc0lw/tmUz0nH91W3aKa9BdwzolJAs4APk//1AO/Dbx1leWfCXymepKdB/wL/V/inlJdPh84tVrmmdU67wcuqS6/E3hjg5wHgbNXjP3+iW8IYDfwe9XllwEfrZ4MFwF3DDygX6j+Pau6fOKJ80/A86p1Pgq8tMWcngJ8GfjeSc8j8ALgOTz2G3zD523YPmpkvBjYUl3+vYGMM4PLrdhOrSzD7m/NnBv++AK/RFW29N/99r46GVfcfi3wW5OaS4b3zaZ6Tj6at2kJbJYv4MPAS9Z4ol4FXDVw/WPV5D0P+NjK5apJfYj/++Z8zHI1ch3k8YV+P3DOwBPl/uryu4BLVy4HXAq8a2D8XdXYOcDnBsYfs1yDrBcD/1hdnvg8suIbdxzzNmwf68244rafBq5fa7kmWYbd35pzueGP74l1q8tbquWG/gS5xhwF8EXg6ZthLqvlTvTNpntOZubJfQw9ImaAZ9P/sQ7gTRFxd0TsjYizqrFt9J8UJxyqxoaNfzfw1cw8vmK8rgQ+HhH7q9MfAExn5oMA1b9PbZhxW3V55XhTlwA3DFzfTPMI45m3Yfto4vX0X2mdcF5EfDoiPhERPzaQvW6WYferro1+fB9dp7r9WLV8XT8GHMnMBwbGJjaXK/pmUz4nT9pCj4gp4IPAWzLza8CfAd8HPAt4kP6PajD8FAV1x+t6fmY+h/7ZKH85Il6wxrKTykj1obBXAh+ohjbbPK5l02WKiKuB48D11dCDwPdk5rOBXwP+KiK+q2GWLvKP4/Htap4v5bEvNCY2l6v0Td3tjuU5eVIWekR8J/3JvT4zPwSQmUcy838y89vAn9M/GyQMP0XBsPGHgDMjYsuK8Voy80vVv0fp/5LsucCRiDinug/n0P9lUJOMh6rLK8ebeClwZ2YeqfJuqnmsjGPehu1j3SJiF/AK4LKsfk7OzP/OzK9Ul/fTPx79jIZZWp9uY0yP76PrVLdvBR6uk7Na72fo/4L0RPaJzOVqfdNgu2N5Tp50hR4RAbwbuC8z/2hg/JyBxX4auKe6/BHgkoh4QkScBzyd/i8hVj11QfWNeBvw6mr9XfSPm9XJ+KSIOOPEZfrHqO+psuxaZbsfAV4bfRcBx6ofsT4GXBwRZ1U/Gl9M/zjlg8B/RsRF1Xy8tm7GAY95FbSZ5nHAOOZt2D7WJSJ2Ar8BvDIzvzEw/pTqbwcQEefTn7cvNMwy7P7WyTmOx3cw/6uBvz/xH1wNL6Z/bPnRwxGTmMthfdNgu+N5To46yL7ZvoAfpf8jyd0MvPUK+Ev6b/25u5qIcwbWuZr+/+b3M/BukGq9z1e3XT0wfj79J/MB+ocinlAz4/n03w3wGfpvdbq6Gv9u4Fb6b0O6FXhy/t8vf95R5VgCegPben2V4wDwuoHxHv1vxn8B3k7Nty1W2zgd+AqwdWBsovNI/z+XB4Fv0X/18oZxzNuwfdTIeID+MdLHvKUO+NnqOfAZ4E7gJ5tmWev+1si54Y8v8MTq+oHq9vPrZKzGrwN+ccWyY59LhvfNpnpOnvjyo/+SVIiT7pCLJGl1FrokFcJCl6RCWOiSVAgLXZIKYaFLUiEsdEkqxP8C8LQgVlmHAiMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "recent_grads['Employed'].hist(bins=25, range=(15000, 200000))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x118e94610>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "recent_grads['Full_time'].hist(bins=25, range=(15000, 180000))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x119140a10>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "recent_grads['ShareWomen'].hist(bins=10, range=(0,1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1191dcd90>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "recent_grads['Unemployment_rate'].hist(bins=25, range=(0,.2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1193ab590>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "recent_grads['Men'].hist(bins=25, range=(0,175000))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1194ec250>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "recent_grads['Women'].hist(bins=25, range=(0,200000))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, let's explore the relationships in the data using a scatter matrix plot. A scatter matrix plot combines both scatter plots and histograms into one grid of plots and allows us to explore potential relationships and distributions simultaneously."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x119660110>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x119678550>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x1195aed50>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x1196ff590>]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x720 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "scatter_matrix(recent_grads[['Sample_size', 'Median']], figsize=(10,10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x119817a50>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x119bbce10>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x119bf0ad0>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x119c2ee50>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x119c62b10>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x119ca2e90>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x119cdab50>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x119d19390>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x119d19ed0>]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x720 with 9 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "scatter_matrix(recent_grads[['Sample_size', 'Median', 'Unemployment_rate']], figsize=(10,10))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Comparison of percents in share of women (First ten rows/Last ten rows)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11a951610>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXQAAAD1CAYAAABA+A6aAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8QZhcZAAAPA0lEQVR4nO3dfYxdeV3H8fdnW0pYdkWTnSD0gVmhqFWQh7FLxLCoC3ZdbX1Yky5RwbA2RprFoIYiZE3WaFY0Ev/oH1R3CT5AYTcSB7dSBFwSVKCzUHfplrKllu2kgkWQDSyyFL7+cW/JzeydmdP2zp3bX96vpOk95/zm/D6duf3MmXPvOZOqQpJ06btstQNIkkbDQpekRljoktQIC12SGmGhS1IjLHRJasTa1Zr4qquuqunp6dWaXpIuSffdd98Xq2pq2LZVK/Tp6Wnm5uZWa3pJuiQl+dxi2zzlIkmNsNAlqREWuiQ1wkKXpEZY6JLUCAtdkhphoUtSIyx0SWrEql1YJKkN03vuueh9nLz9hhEkkUfoktQIC12SGmGhS1IjLHRJaoSFLkmNsNAlqREWuiQ1wkKXpEZY6JLUiE6FnmRbkmNJjifZM2T7q5KcSXK4/+fm0UeVJC1l2Uv/k6wB9gIvA+aBQ0lmq+rBBUPfVVW7VyCjJKmDLkfoW4HjVXWiqh4D9gM7VjaWJOl8dSn09cCpgeX5/rqFfinJ/UnuTrJxJOkkSZ11KfQMWVcLlt8LTFfVc4EPAG8fuqNkV5K5JHNnzpw5v6SSpCV1KfR5YPCIewNwenBAVf1PVX2jv/iXwAuH7aiq9lXVTFXNTE1NXUheSdIiuhT6IWBzkquTrAN2ArODA5I8bWBxO3B0dBElSV0s+y6XqjqbZDdwEFgD3FlVR5LcBsxV1SxwS5LtwFngS8CrVjCzJGmITr+xqKoOAAcWrLt14PEbgDeMNpok6Xx4pagkNcJCl6RGWOiS1AgLXZIaYaFLUiMsdElqhIUuSY2w0CWpERa6JDXCQpekRljoktQIC12SGmGhS1IjLHRJaoSFLkmNsNAlqREWuiQ1wkKXpEZ0+hV0kibP9J57LnofJ2+/YQRJNCk8QpekRljoktQIC12SGmGhS1IjLHRJaoSFLkmNsNAlqREWuiQ1wkKXpEZY6JLUCAtdkhrRqdCTbEtyLMnxJHuWGHdjkkoyM7qIkqQuli30JGuAvcD1wBbgpiRbhoy7ErgF+NioQ0qSltflCH0rcLyqTlTVY8B+YMeQcX8IvBn4vxHmkyR11KXQ1wOnBpbn++u+I8nzgY1V9Y8jzCZJOg9dCj1D1tV3NiaXAW8BfmfZHSW7kswlmTtz5kz3lJKkZXUp9Hlg48DyBuD0wPKVwA8D9yY5CbwImB32wmhV7auqmaqamZqauvDUkqTH6VLoh4DNSa5Osg7YCcye21hVX6mqq6pquqqmgY8C26tqbkUSS5KGWrbQq+ossBs4CBwF3l1VR5LclmT7SgeUJHXT6XeKVtUB4MCCdbcuMvalFx9LknS+vFJUkhphoUtSIyx0SWqEhS5JjbDQJakRFrokNcJCl6RGWOiS1AgLXZIaYaFLUiMsdElqhIUuSY2w0CWpERa6JDXCQpekRljoktQIC12SGtHpNxZJk2J6zz0XvY+Tt98wgiTS5PEIXZIaYaFLUiMsdElqhIUuSY2w0CWpERa6JDXCQpekRljoktQIC12SGmGhS1IjLHRJaoSFLkmN6FToSbYlOZbkeJI9Q7b/ZpIHkhxO8pEkW0YfVZK0lGULPckaYC9wPbAFuGlIYb+jqp5TVc8D3gz8+ciTSpKW1OUIfStwvKpOVNVjwH5gx+CAqnpkYPHJQI0uoiSpiy73Q18PnBpYngeuWTgoyWuA1wHrgJ8ctqMku4BdAJs2bTrfrJKkJXQ5Qs+QdY87Aq+qvVX1TOD1wJuG7aiq9lXVTFXNTE1NnV9SSdKSuhT6PLBxYHkDcHqJ8fuBn7+YUJKk89el0A8Bm5NcnWQdsBOYHRyQZPPA4g3AQ6OLKEnqYtlz6FV1Nslu4CCwBrizqo4kuQ2Yq6pZYHeS64BvAl8GXrmSoSVJj9fpl0RX1QHgwIJ1tw48fu2Ic0mSzpNXikpSIyx0SWqEhS5JjbDQJakRFrokNcJCl6RGWOiS1AgLXZIaYaFLUiMsdElqhIUuSY2w0CWpERa6JDXCQpekRljoktQIC12SGmGhS1IjLHRJaoSFLkmNsNAlqREWuiQ1wkKXpEZY6JLUCAtdkhphoUtSIyx0SWqEhS5JjbDQJakRFrokNaJToSfZluRYkuNJ9gzZ/rokDya5P8kHkzxj9FElSUtZttCTrAH2AtcDW4CbkmxZMOyTwExVPRe4G3jzqINKkpbW5Qh9K3C8qk5U1WPAfmDH4ICq+peqerS/+FFgw2hjSpKW06XQ1wOnBpbn++sW82rgny4mlCTp/K3tMCZD1tXQgcmvADPAtYts3wXsAti0aVPHiJKkLroU+jywcWB5A3B64aAk1wFvBK6tqm8M21FV7QP2AczMzAz9piBJl6rpPfdc9D5O3n7DBX9sl1Muh4DNSa5Osg7YCcwODkjyfOCtwPaq+u8LTiNJumDLFnpVnQV2AweBo8C7q+pIktuSbO8P+1PgCuCuJIeTzC6yO0nSCulyyoWqOgAcWLDu1oHH1404lyTpPHmlqCQ1wkKXpEZY6JLUCAtdkhphoUtSIyx0SWqEhS5JjbDQJakRFrokNcJCl6RGWOiS1AgLXZIaYaFLUiM63W1Rq2u1b5ov6dLgEbokNcJCl6RGeMpFugCeBtMk8ghdkhphoUtSIyx0SWqEhS5JjbDQJakRFrokNcJCl6RGWOiS1AgLXZIaMbFXinolnqSu7Isej9AlqREWuiQ1wkKXpEZ0KvQk25IcS3I8yZ4h21+S5BNJzia5cfQxJUnLWfZF0SRrgL3Ay4B54FCS2ap6cGDYw8CrgN9diZCaDL7wJE22Lu9y2Qocr6oTAEn2AzuA7xR6VZ3sb/v2CmSUJHXQpdDXA6cGlueBa1YmzuTxqFTSpaLLOfQMWVcXMlmSXUnmksydOXPmQnYhSVpEl0KfBzYOLG8ATl/IZFW1r6pmqmpmamrqQnYhSVpEl0I/BGxOcnWSdcBOYHZlY0mSzteyhV5VZ4HdwEHgKPDuqjqS5LYk2wGS/GiSeeCXgbcmObKSoSVJj9fpXi5VdQA4sGDdrQOPD9E7FSNJWiVeKSpJjbDQJakRFrokNcJCl6RGWOiS1AgLXZIaYaFLUiMsdElqhIUuSY2w0CWpERa6JDXCQpekRljoktQIC12SGmGhS1IjLHRJaoSFLkmNsNAlqREWuiQ1wkKXpEZY6JLUCAtdkhphoUtSIyx0SWqEhS5JjbDQJakRFrokNcJCl6RGWOiS1AgLXZIa0anQk2xLcizJ8SR7hmx/YpJ39bd/LMn0qINKkpa2bKEnWQPsBa4HtgA3JdmyYNirgS9X1bOAtwB/MuqgkqSldTlC3wocr6oTVfUYsB/YsWDMDuDt/cd3Az+VJKOLKUlaTqpq6QHJjcC2qrq5v/yrwDVVtXtgzKf6Y+b7y5/tj/nign3tAnb1F78fOHaR+a8CvrjsqJU1CRlgMnJMQgaYjByTkAEmI8ckZIDJyDGKDM+oqqlhG9Z2+OBhR9oLvwt0GUNV7QP2dZizkyRzVTUzqv1dqhkmJcckZJiUHJOQYVJyTEKGScmx0hm6nHKZBzYOLG8ATi82Jsla4CnAl0YRUJLUTZdCPwRsTnJ1knXATmB2wZhZ4JX9xzcCH6rlzuVIkkZq2VMuVXU2yW7gILAGuLOqjiS5DZirqlngDuBvkhynd2S+cyVDDxjZ6ZuLMAkZYDJyTEIGmIwck5ABJiPHJGSAycixohmWfVFUknRp8EpRSWqEhS5JjbDQJakRXd6HPhGS/AC9K1LX03uP+2lgtqqOrmqwVdL/fKwHPlZVXx1Yv62q3jemDFuBqqpD/dtBbAM+XVUHxjH/Ipn+uqp+bbXm72f4cXpXWH+qqt4/pjmvAY5W1SNJngTsAV4APAj8cVV9ZUw5bgHeU1WnxjHfIhnOvRvvdFV9IMkrgB8DjgL7quqbY8zyTOAX6L2t+yzwEPDOlfp6XBIviiZ5PXATvdsOzPdXb6D3RdtfVbevVrZzkvx6Vb1tTHPdAryG3hP0ecBrq+of+ts+UVUvGEOGP6B3f5+1wD8D1wD3AtcBB6vqj8aQYeHbZwP8BPAhgKravtIZ+jk+XlVb+49/g97X5j3Ay4H3juP5meQI8CP9d6XtAx6lfxuO/vpfXOkM/RxfAb4GfBZ4J3BXVZ0Zx9wDGf6O3vPycuB/gSuAv6f3uUhVvXKJDx9ljluAnwM+DPwMcBj4Mr2C/62qunfkk1bVxP8BPgM8Ycj6dcBDq52vn+XhMc71AHBF//E0MEev1AE+OcYMa+j9p3kE+K7++icB948pwyeAvwVeClzb//u/+o+vHePX45MDjw8BU/3HTwYeGFOGo4OflwXbDo/zc0HvVO7L6b2d+QzwPnrXqVw5pgz39/9eC3wBWNNfzriem/35HhiY+3Lg3v7jTSv1//RSOeXybeDpwOcWrH9af9tYJLl/sU3AU8eVg96T5KsAVXUyyUuBu5M8g+G3YVgJZ6vqW8CjST5bVY/083w9ybi+JjPAa4E3Ar9XVYeTfL2qPjym+c+5LMn30CuyVP+ItKq+luTsmDJ8auCnxP9IMlNVc0meDYztFAO9U3DfBt4PvD/JE+j9JHcT8GfA0HuQjNhl/dMuT6ZXpOeuXH8i8IQxzD9oLfCt/txXAlTVw/3Py4pMdin4beCDSR4Czp2b2wQ8C9i96EeN3lOBn6b3Y9OgAP82xhyfT/K8qjoMUFVfTfKzwJ3Ac8aU4bEkl1fVo8ALz61M8hTG9E22XxxvSXJX/+8vsDrP6acA99F7HlSS762qzye5gvF9g70Z+Iskb6J386d/T3KK3v+Xm8eUARb8e6t3vnoWmO2f2x+HO4BP0/sJ8o3AXUlOAC+id9p2XP4KOJTko8BL6N9WPMkUK3RrlEviHDpAksvovdC0nt6TZh441D9KHFeGO4C3VdVHhmx7R1W9Ykw5NtA7Qv78kG0vrqp/HUOGJ1bVN4asvwp4WlU9sNIZhsx9A/Diqvr9cc89TJLLgadW1X+Occ4rge+j941tvqq+MK65+/M/u6o+M845F8nxdICqOp3ku+m9tvNwVX18zDl+CPhBei+Qf3rF57tUCl2StDTfhy5JjbDQJakRFrokNcJCl6RGWOiS1Ij/B433lPoL/DBKAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "recent_grads.head(10)['ShareWomen'].plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11a98add0>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXQAAAECCAYAAADuGCyPAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8QZhcZAAARKUlEQVR4nO3dfZBddX3H8feHxPisWLP2gSQEa6imjB1tBDpaxYIKtRLtYCWd0kot8aHYmYrOxOqg4rRDfaydYisj1ocWUmodm2padCy2HVswC4o1MGljRLKiQ0S0Mz4h+u0f90TWm032hty9e/aX92tmh/Pwyz0fdpLPPfu755xNVSFJWvqOWewAkqTxsNAlqREWuiQ1wkKXpEZY6JLUCAtdkhqxfL4BSd4D/BpwR1WdNMf+AO8AfhX4NvDCqrpxvtdduXJlrV279rADS9LR7IYbbvhaVU3NtW/eQgfeC/wF8P6D7D8LWNd9nQL8ZfffQ1q7di3T09MjHF6StF+SLx1s37xTLlX178DXDzFkI/D+GrgOODbJTx9+TEnSkRjHHPpxwN5Z6zPdNknSBI2j0DPHtjmfJ5Bkc5LpJNP79u0bw6ElSfuNo9BngNWz1lcBt881sKour6oNVbVhamrOOX1J0n00jkLfBvx2Bk4FvllVXxnD60qSDsMoly1eBZwGrEwyA7wOuB9AVf0VsJ3BJYu7GVy2eP5ChZUkHdy8hV5Vm+bZX8Dvjy2RJOk+8U5RSWqEhS5JjRjlTtFFsXbLR4/4NW699NnN5JCk+XiGLkmNsNAlqREWuiQ1wkKXpEZY6JLUCAtdkhphoUtSIyx0SWqEhS5JjbDQJakRFrokNcJCl6RGWOiS1AgLXZIaYaFLUiMsdElqhIUuSY2w0CWpEb39FXSStNQs9q+s9AxdkhphoUtSIyx0SWqEhS5JjbDQJakRFrokNcJCl6RGWOiS1AgLXZIaYaFLUiMsdElqhIUuSY0Y6eFcSc4E3gEsA95dVZcO7V8DvA84thuzpaq2jzmrpFkW+0FQ6p95z9CTLAMuA84C1gObkqwfGvZa4OqqegJwLvDOcQeVJB3aKFMuJwO7q2pPVd0NbAU2Do0p4GHd8sOB28cXUZI0ilGmXI4D9s5anwFOGRrzeuBjSV4OPBg4YyzpJEkjG6XQM8e2GlrfBLy3qt6a5JeADyQ5qap++GMvlGwGNgOsWbPmvuSV1DN9mMvvQ4Y+GGXKZQZYPWt9FQdOqbwIuBqgqv4LeACwcviFquryqtpQVRumpqbuW2JJ0pxGKfQdwLokJyRZweBDz21DY24DTgdI8jgGhb5vnEElSYc2b6FX1T3AhcA1wC0MrmbZmeSSJGd3wy4CLkhyE3AV8MKqGp6WkSQtoJGuQ++uKd8+tO3iWcs3A08ebzRJ0uHwTlFJaoSFLkmNsNAlqREWuiQ1wkKXpEZY6JLUCAtdkhphoUtSIyx0SWqEhS5JjbDQJakRFrokNcJCl6RGWOiS1AgLXZIaYaFLUiMsdElqhIUuSY0Y6VfQaXGt3fLRI36NWy999hiSSOozz9AlqREWuiQ1wkKXpEZY6JLUCD8U1cj8cFbqN8/QJakRFrokNcJCl6RGWOiS1AgLXZIa4VUu0n3gFT/qI8/QJakRnqFrSfHMWDo4z9AlqREWuiQ1YqRCT3Jmkl1JdifZcpAxv5Hk5iQ7k1w53piSpPnMO4eeZBlwGfAMYAbYkWRbVd08a8w64NXAk6vqriSPWqjAkqS5jXKGfjKwu6r2VNXdwFZg49CYC4DLquougKq6Y7wxJUnzGaXQjwP2zlqf6bbNdiJwYpJPJbkuyZlzvVCSzUmmk0zv27fvviWWJM1plELPHNtqaH05sA44DdgEvDvJsQf8oarLq2pDVW2Ympo63KySpEMYpdBngNWz1lcBt88x5h+r6vtV9UVgF4OClyRNyCiFvgNYl+SEJCuAc4FtQ2M+DDwdIMlKBlMwe8YZVJJ0aPMWelXdA1wIXAPcAlxdVTuTXJLk7G7YNcCdSW4GrgVeVVV3LlRoSdKBRrr1v6q2A9uHtl08a7mAV3RfkqRF4J2iktQIC12SGmGhS1IjLHRJaoSFLkmNsNAlqREWuiQ1wkKXpEZY6JLUCAtdkhphoUtSIyx0SWqEhS5JjbDQJakRFrokNcJCl6RGWOiS1AgLXZIaYaFLUiMsdElqhIUuSY2w0CWpERa6JDXCQpekRljoktQIC12SGmGhS1IjLHRJaoSFLkmNsNAlqREWuiQ1wkKXpEZY6JLUiJEKPcmZSXYl2Z1kyyHGnZOkkmwYX0RJ0ijmLfQky4DLgLOA9cCmJOvnGPdQ4A+A68cdUpI0v1HO0E8GdlfVnqq6G9gKbJxj3BuBNwHfHWM+SdKIRin044C9s9Znum0/kuQJwOqq+sgYs0mSDsMohZ45ttWPdibHAG8HLpr3hZLNSaaTTO/bt2/0lJKkeY1S6DPA6lnrq4DbZ60/FDgJ+GSSW4FTgW1zfTBaVZdX1Yaq2jA1NXXfU0uSDjBKoe8A1iU5IckK4Fxg2/6dVfXNqlpZVWurai1wHXB2VU0vSGJJ0pzmLfSquge4ELgGuAW4uqp2JrkkydkLHVCSNJrlowyqqu3A9qFtFx9k7GlHHkuSdLi8U1SSGmGhS1IjLHRJaoSFLkmNsNAlqREWuiQ1wkKXpEZY6JLUCAtdkhphoUtSIyx0SWqEhS5JjbDQJakRFrokNcJCl6RGWOiS1AgLXZIaYaFLUiMsdElqhIUuSY2w0CWpERa6JDXCQpekRljoktQIC12SGmGhS1IjLHRJaoSFLkmNsNAlqREWuiQ1wkKXpEZY6JLUCAtdkhphoUtSI0Yq9CRnJtmVZHeSLXPsf0WSm5N8Lsknkhw//qiSpEOZt9CTLAMuA84C1gObkqwfGvYZYENVPR74IPCmcQeVJB3aKGfoJwO7q2pPVd0NbAU2zh5QVddW1be71euAVeONKUmazyiFfhywd9b6TLftYF4E/PORhJIkHb7lI4zJHNtqzoHJbwEbgKcdZP9mYDPAmjVrRowoSRrFKGfoM8DqWeurgNuHByU5A3gNcHZVfW+uF6qqy6tqQ1VtmJqaui95JUkHMUqh7wDWJTkhyQrgXGDb7AFJngC8i0GZ3zH+mJKk+cxb6FV1D3AhcA1wC3B1Ve1MckmSs7thbwYeAvx9ks8m2XaQl5MkLZBR5tCpqu3A9qFtF89aPmPMuSRJh8k7RSWpERa6JDXCQpekRljoktQIC12SGmGhS1IjLHRJaoSFLkmNsNAlqREWuiQ1wkKXpEZY6JLUCAtdkhphoUtSIyx0SWqEhS5JjbDQJakRFrokNcJCl6RGWOiS1AgLXZIaYaFLUiMsdElqhIUuSY2w0CWpERa6JDXCQpekRljoktQIC12SGmGhS1IjLHRJaoSFLkmNsNAlqREWuiQ1YqRCT3Jmkl1JdifZMsf++yf5u27/9UnWjjuoJOnQ5i30JMuAy4CzgPXApiTrh4a9CLirqh4DvB3403EHlSQd2ihn6CcDu6tqT1XdDWwFNg6N2Qi8r1v+IHB6kowvpiRpPqMU+nHA3lnrM922OcdU1T3AN4FHjiOgJGk0qapDD0ieDzyrqn6vWz8POLmqXj5rzM5uzEy3/oVuzJ1Dr7UZ2Nyt/hyw6wjzrwS+doSvcaT6kAH6kaMPGaAfOfqQAfqRow8ZoB85xpHh+KqammvH8hH+8Aywetb6KuD2g4yZSbIceDjw9eEXqqrLgctHSTyKJNNVtWFcr7dUM/QlRx8y9CVHHzL0JUcfMvQlx0JnGGXKZQewLskJSVYA5wLbhsZsA36nWz4H+Nea79RfkjRW856hV9U9SS4ErgGWAe+pqp1JLgGmq2obcAXwgSS7GZyZn7uQoSVJBxplyoWq2g5sH9p28azl7wLPH2+0kYxt+uYI9CED9CNHHzJAP3L0IQP0I0cfMkA/cixohnk/FJUkLQ3e+i9JjbDQJakRFrokNcJCl6RGLKlCT3JMkmO65RVJnpjkJxY508sW8/hdhod034tjJ3jMFbOf15Pk6UkuSnLWpDJ0x338JI93MEnW7P/+J1mb5JwkJy1Slg1JnpfkOUkeuwjHT5JTkvx6l+OUPj3badLfkyT3m2PbygU51lK5yiXJc4F3AT8EXgL8EfAt4ETgpVX1TxPI8IrhTcCrgT8BqKq3LXSGLsc7q+pl3fJTgCuBLwCPAV7cXWa60BluAk6rqruSvAp4HoNLW5/G4P6EVy90hi7HD4AvAlcBV1XVzZM47lCGLcCLge8BbwFeCXwKOBW4YoJ/L54GvBX4BvCLXYZHAN8HzquqvYf44+PK8EzgncD/Al/uNq9i8HfzZVX1sYXOMJ8kt1XVmgkc5+nAB4D7A58BNlfVrd2+G6vqieM+5kjXoffE64BfAB4I3AQ8qap2JTke+AdgwQsdeAOD0trJoMxhcLPVQydw7NlOnbX8RuC5VXVjkkcDVzN0z8ACWVZVd3XLLwB+uaq+k+RS4EYGb3ST8DngPGATsC3JtxiU+9b9/3gm4DwGj5Z+EHAr8Oiq2pfkwcD1wEQKHfgz4JndsU8A3lZVT07yDAY3/z1zAhneAZwx/L3v8mwHHjeBDCT584PtAib1k+ybGDzjameSc4CPJzmvqq7j3v4YqyU15VJVX62qLwK3VdWubtuXmNz/x88zKPAHA2+uqjcweA78G7rlxfCwqroRoKr2dPkm4f9mTSl8DXhAt7ycyf69qqr6fFW9pnse/wXAo4D/SPKfE8rwg6r6DoMz4+8Ad3bBvjWh4++3rKr2dcu3Acd3OT7OgU9IXSjLGTzbadiXgQOmHhbQ+cDngRuGvqaBuyeUYUVV7QSoqg8CzwXel+R5wIJMjSylM3SSHFNVPwR+d9a2ZcCKSRy/qm4DzkmykcG77dsncdw5PDbJ5xi8y69N8ohu6uMYJveP5iXA33ZTL3cA00n+DXg83RTUhPzYmU5VfRr4dJKLgKdOKMONSa5k8Eb/CQb/aP8F+BVgklNA00mu6DJsBD4JkORBTO6N/j3AjiRbufex26sZPA7kigllgMEzqD5fVQe8qSd5/YQyfD/JT1XVVwG6M/XTgY8AP7sQB1xKc+hPAv67e8zA7O1rgadU1d9MOM+DGEzBnFJVkyqO/cc+fmjTV6rq7u6DlqdW1YcmlGMZgx/jT+TeM7Nrquobkzh+l+E3q+rKSR3vIBmWM3j0RTH4BS+nMJgCug24bFJn6t2HbxcwmP65icFzl36Q5IHAo7qfZieR43EM3lCOY/CGOwNsm+TnG93FEt+tqm9P6phzZDgD2FdVNw1tfzhwYVX98diPuVQKfS5JHjn8zPWjMUNfcvQhQ19y9CFDn3JoMpbMHHqSS/df6tNdlrUHuD7Jl7pP9xcrw3WTzHCIHH34Xkw0Q19y+PfixzLcmOS13Qf0i2ZWjgWZ2uhthqpaEl8Mplv2L1/L4CoXGPy4P320ZOhLjj5k6EuOPmToSw4Gl5C+hcF006eBPwR+ZlLfgz7lWIwMS+YMHbhfN1cJ8MCq2gFQVf/D4DrPoyVDX3L0IUNfcvQhQ19y3FVVr6zBdd4XAesYfGh8bQa/gnJS+pBj4hmWzBx6kpcDzwEuZXD1wrHAh4DTGVz3e97RkKEvOfqQoS85+pChLzkyxw0z3YfnzwBeUFXnL3SGvuRYjAxLptABkpwGvJR7r6rYC3yYwaf59xwtGfqSow8Z+pKjDxn6kCPJ1qpa9N9Y1occi5FhSRX6wSQ5v6r++mjP0JccfcjQlxx9yNCXHH3I0JccC5WhlUKfyLMZ+p6hLzn6kKEvOfqQoS85+pChLzkWKsOSuVM0gzsj59wF/OTRkqEvOfqQoS85+pChLzn6kKEvORYjw5IpdAbfgGcBdw1tDzCpZ3b0IUNfcvQhQ19y9CFDX3L0IUNfckw8w1Iq9I8AD6mqzw7vSPLJoyhDX3L0IUNfcvQhQ19y9CFDX3JMPEMTc+iSpCV0678k6dAsdElqhIUuSY2w0CWpERa6JDXi/wEUeKB1Csq04QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "recent_grads.tail(10)['ShareWomen'].plot(kind='bar')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Comparison of unemployment rates (First ten rows/Last ten rows)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11bd3ce50>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "recent_grads.head(10)['Unemployment_rate'].plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11bc05d90>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "recent_grads.tail(10)['Unemployment_rate'].plot(kind='bar')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "----------------\n",
    "## Step 4: Next Steps\n",
    "Our analysis only skimmed the surface of what problems we could find answers for in this dataset using visual analysis tools.\n",
    "\n",
    "Here are some interesting questions that we can come back to solve:\n",
    "- Use a grouped bar plot to compare the number of men with the number of women in each category of majors.\n",
    "- Use a box plot to explore the distributions of median salaries and unemployment rate.\n",
    "- Use a hexagonal bin plot to visualize the columns that had dense scatter plots from earlier in the project."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}