{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# Introduction to the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\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",
       "      <th>0</th>\n",
       "      <td> 1</td>\n",
       "      <td> 2419</td>\n",
       "      <td>                     PETROLEUM ENGINEERING</td>\n",
       "      <td>  2339</td>\n",
       "      <td>  2057</td>\n",
       "      <td>   282</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</td>\n",
       "      <td>   679</td>\n",
       "      <td>    77</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</td>\n",
       "      <td>   725</td>\n",
       "      <td>   131</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</td>\n",
       "      <td>  1123</td>\n",
       "      <td>   135</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</td>\n",
       "      <td> 21239</td>\n",
       "      <td> 11021</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    Men  \\\n",
       "0     1        2419                      PETROLEUM ENGINEERING   2339   2057   \n",
       "1     2        2416             MINING AND MINERAL ENGINEERING    756    679   \n",
       "2     3        2415                  METALLURGICAL ENGINEERING    856    725   \n",
       "3     4        2417  NAVAL ARCHITECTURE AND MARINE ENGINEERING   1258   1123   \n",
       "4     5        2405                       CHEMICAL ENGINEERING  32260  21239   \n",
       "\n",
       "   Women Major_category  ShareWomen  Sample_size  Employed      ...        \\\n",
       "0    282    Engineering    0.120564           36      1976      ...         \n",
       "1     77    Engineering    0.101852            7       640      ...         \n",
       "2    131    Engineering    0.153037            3       648      ...         \n",
       "3    135    Engineering    0.107313           16       758      ...         \n",
       "4  11021    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": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "recent_grads = pd.read_csv(\"recent-grads.csv\")\n",
    "recent_grads.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\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",
       "      <th>168</th>\n",
       "      <td> 169</td>\n",
       "      <td> 3609</td>\n",
       "      <td>                ZOOLOGY</td>\n",
       "      <td> 8409</td>\n",
       "      <td> 3050</td>\n",
       "      <td> 5359</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",
       "      <td> 304</td>\n",
       "      <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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>169</th>\n",
       "      <td> 170</td>\n",
       "      <td> 5201</td>\n",
       "      <td> EDUCATIONAL PSYCHOLOGY</td>\n",
       "      <td> 2854</td>\n",
       "      <td>  522</td>\n",
       "      <td> 2332</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>170</th>\n",
       "      <td> 171</td>\n",
       "      <td> 5202</td>\n",
       "      <td>    CLINICAL PSYCHOLOGY</td>\n",
       "      <td> 2838</td>\n",
       "      <td>  568</td>\n",
       "      <td> 2270</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</td>\n",
       "      <td>  931</td>\n",
       "      <td> 3695</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>172</th>\n",
       "      <td> 173</td>\n",
       "      <td> 3501</td>\n",
       "      <td>        LIBRARY SCIENCE</td>\n",
       "      <td> 1098</td>\n",
       "      <td>  134</td>\n",
       "      <td>  964</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",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Rank  Major_code                   Major  Total   Men  Women  \\\n",
       "168   169        3609                 ZOOLOGY   8409  3050   5359   \n",
       "169   170        5201  EDUCATIONAL PSYCHOLOGY   2854   522   2332   \n",
       "170   171        5202     CLINICAL PSYCHOLOGY   2838   568   2270   \n",
       "171   172        5203   COUNSELING PSYCHOLOGY   4626   931   3695   \n",
       "172   173        3501         LIBRARY SCIENCE   1098   134    964   \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": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "recent_grads.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\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",
       "      <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": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "recent_grads.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "173\n",
      "172\n"
     ]
    }
   ],
   "source": [
    "print(recent_grads.shape[0])\n",
    "print(recent_grads.dropna().shape[0])\n",
    "\n",
    "recent_grads = recent_grads.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x10a871c88>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x10abc8dd8>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x10ab8a5f8>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x10ac514e0>]], dtype=object)"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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pzmpkKhcL8TRamVqmdu7uXsBNN60gmVTa26eOqSNcyvWUTqdZvTpFX18/HR2z2bLlcb75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\nYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10aa05e80>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Scatter plot that Pandas generates\n",
    "from pandas.tools.plotting import scatter_matrix\n",
    "scatter_matrix(recent_grads[[\"ShareWomen\",\"Unemployment_rate\"]])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Scatter matrix, step 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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q9pkZ7h5tgpnQbbnbqdXrXdh/7NhRYDUjI2c1FWl+00038Qd/8Me4vwzYxODg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\nYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10ac5f470>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# First, we need to create a Figure instance and create 4 axes, corresponding to each subplot.\n",
    "fig_rg = plt.figure(figsize=(8,8))\n",
    "ax1_rg = fig_rg.add_subplot(2,2,1)\n",
    "ax2_rg = fig_rg.add_subplot(2,2,2)\n",
    "ax3_rg = fig_rg.add_subplot(2,2,3)\n",
    "ax4_rg = fig_rg.add_subplot(2,2,4)\n",
    "\n",
    "# Top left plot\n",
    "ax1_rg.hist(recent_grads[\"ShareWomen\"])\n",
    "\n",
    "# Top right plot\n",
    "ax2_rg.scatter(recent_grads[\"Unemployment_rate\"], recent_grads[\"ShareWomen\"])\n",
    "\n",
    "# Bottom left plot\n",
    "ax3_rg.scatter(recent_grads[\"ShareWomen\"], recent_grads[\"Unemployment_rate\"])\n",
    "\n",
    "# Bottom right plot\n",
    "ax4_rg.hist(recent_grads[\"Unemployment_rate\"])\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Scatter matrix, steps 2 and 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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3+XXAVcBHgSMS5tFS6OPPD7c4z88///z5dM0112RWkeX4IZatqoN866DdM5KU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\n5v8DK0hz59iElyMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10aa37080>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# First, we need to create a Figure instance and create 4 axes, corresponding to each subplot.\n",
    "fig_rg = plt.figure(figsize=(8,6))\n",
    "ax1_rg = fig_rg.add_subplot(2,2,1)\n",
    "ax2_rg = fig_rg.add_subplot(2,2,2)\n",
    "ax3_rg = fig_rg.add_subplot(2,2,3)\n",
    "ax4_rg = fig_rg.add_subplot(2,2,4)\n",
    "\n",
    "# Top left plot\n",
    "ax1_rg.hist(recent_grads[\"ShareWomen\"])\n",
    "# Top right plot\n",
    "ax2_rg.scatter(recent_grads[\"Unemployment_rate\"], recent_grads[\"ShareWomen\"])\n",
    "# Bottom left plot\n",
    "ax3_rg.scatter(recent_grads[\"ShareWomen\"], recent_grads[\"Unemployment_rate\"])\n",
    "# Bottom right plot\n",
    "ax4_rg.hist(recent_grads[\"Unemployment_rate\"])\n",
    "\n",
    "# Top left plot\n",
    "ax1_rg.set_ylabel(\"ShareWomen\")\n",
    "ax1_rg.get_xaxis().tick_top()\n",
    "ax1_rg.get_xaxis().set_visible(False)\n",
    "ax1_rg.get_yaxis().tick_left()\n",
    "ax1_rg.set_ylim(0,30)\n",
    "ax1_rg.set_yticklabels([0, 5, 10, 15, 20, 25, 30])\n",
    "\n",
    "# Top right plot\n",
    "ax2_rg.get_yaxis().set_visible(False)\n",
    "ax2_rg.get_xaxis().set_visible(False)\n",
    "ax2_rg.set_xlim(0.0, 0.20)\n",
    "\n",
    "# Bottom left plot\n",
    "ax3_rg.set_xlabel(\"ShareWomen\")\n",
    "ax3_rg.set_ylabel(\"Unemployment_rate\")\n",
    "ax3_rg.get_xaxis().tick_bottom()\n",
    "ax3_rg.get_yaxis().tick_left()\n",
    "ax3_rg.set_ylim(0.00, 0.20)\n",
    "ax3_rg.set_yticklabels([0.00, 0.05, 0.10, 0.15])\n",
    "ax3_rg.set_xlim(0.0, 1.0)\n",
    "ax3_rg.set_xticklabels([0.0, 0.2, 0.4, 0.6, 0.8], rotation=90)\n",
    "\n",
    "# Bottom right plot\n",
    "ax4_rg.set_xlabel(\"Unemployment_rate\")\n",
    "ax4_rg.get_yaxis().set_visible(False)\n",
    "ax4_rg.get_xaxis().tick_bottom()\n",
    "ax4_rg.set_xlim(0.0, 0.20)\n",
    "ax4_rg.set_xticklabels([0.00, 0.05, 0.10, 0.15, 0.20], rotation=90)\n",
    "\n",
    "# Remove space between sub plots.\n",
    "plt.subplots_adjust(wspace=0, hspace=0)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Grouped bar plots, part 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x10ae25358>"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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LUBTFW9JaklZLbQ+SdLKk0VnmKiXpHUlvl/l4X9LSrPMVSPqqpG2Sx5J0maS3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\nJN1I+eK9E7CJmeWund6Lt3MfnaTVCN2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8A0mLgGspByW/xvYLK0eKiFnkCcsWSXpvx/VV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\njKD/A3/O76i0O12hAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10acbc6d8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Pandas generated grouped bar plot\n",
    "recent_grads[\"ShareMen\"] = recent_grads[\"Men\"] / recent_grads[\"Total\"]\n",
    "arts = recent_grads[recent_grads[\"Major_category\"] == \"Arts\"]\n",
    "arts.set_index(\"Major\", inplace=True)\n",
    "arts[[\"ShareMen\", \"ShareWomen\"]].plot(kind=\"bar\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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vGJjohqTHA6+x3YmJbpK+YPvJbccxbjM9zht4qO2bBo7fAvi+7W3biWx+Sd5r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\ngGUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10bc41e48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "recent_grads[\"ShareMen\"] = recent_grads[\"Men\"] / recent_grads[\"Total\"]\n",
    "\n",
    "# Create figure instance and subplot\n",
    "fig_rg2 = plt.figure(figsize=(6,5))\n",
    "ax1_rg2 = fig_rg2.add_subplot(1,1,1)\n",
    "\n",
    "major_names = arts[\"Major\"].tolist()\n",
    "locs = np.arange(len(major_names))\n",
    "width = 0.35\n",
    "\n",
    "men_proportions = arts[\"ShareMen\"].tolist()\n",
    "bar_1 = ax1_rg2.bar(locs, men_proportions, width)\n",
    "ax1_rg2.set_xticklabels(major_names, rotation=90)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Grouped bar plots, part 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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7+JndzTese/kq24sydDVfZbtn+Zb857O7+SrbPcuXzfPZzXzRns8iQ1fzpfx8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\n5ZqzNRyWAtoAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10b6ca978>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## Part 1\n",
    "\n",
    "import numpy as np\n",
    "recent_grads[\"ShareMen\"] = recent_grads[\"Men\"] / recent_grads[\"Total\"]\n",
    "\n",
    "fig_rg2 = plt.figure(figsize=(6,5))\n",
    "ax1_rg2 = fig_rg2.add_subplot(1,1,1)\n",
    "\n",
    "arts = recent_grads[recent_grads[\"Major_category\"] == \"Arts\"]\n",
    "major_names = arts[\"Major\"].tolist()\n",
    "locs = np.arange(len(major_names))\n",
    "width = 0.35\n",
    "\n",
    "# List representation of these columns\n",
    "men_proportions = arts[\"ShareMen\"].tolist()\n",
    "women_proportions = arts[\"ShareWomen\"].tolist()\n",
    "\n",
    "# Generate both the bars\n",
    "bar_1 = ax1_rg2.bar(locs, men_proportions, width)\n",
    "ax1_rg2.set_xticklabels(major_names, rotation=90)\n",
    "\n",
    "## Part 2\n",
    "\n",
    "# Each value offset by `0.35`\n",
    "offset_locs = locs + width\n",
    "\n",
    "# Set of bars for `ShareWomen`\n",
    "bar_2 = ax1_rg2.bar(offset_locs, women_proportions, width, color=\"green\")\n",
    "\n",
    "# Align x labels with bars better\n",
    "ax1_rg2.set_xticks(offset_locs)\n",
    "\n",
    "# Create a legend\n",
    "plt.legend((bar_1, bar_2), (\"ShareMen\", \"ShareWomen\"), loc=\"upper left\")\n",
    "\n",
    "# Display the background grid\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}