{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# Introduction to the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "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": {
    "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": {
    "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": {},
   "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": {},
   "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": "iVBORw0KGgoAAAANSUhEUgAAAYEAAAEWCAYAAACAOivfAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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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.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": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAfgAAAHfCAYAAAC8gcrxAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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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": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAfoAAAGKCAYAAADkN4OIAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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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": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x10ae25358>"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAW8AAAHPCAYAAACcIFv6AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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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": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAW8AAAH4CAYAAACFX2zvAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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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": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAW8AAAH4CAYAAACFX2zvAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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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": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
