Pandas_visualizations.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# Exploring Data Visualizations in Pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# import pandas and matplotlib\n",
    "%matplotlib inline\n",
    "import pandas as pd\n",
    "from matplotlib import pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Rank                                        1\n",
       "Major_code                               2419\n",
       "Major                   PETROLEUM ENGINEERING\n",
       "Total                                    2339\n",
       "Men                                      2057\n",
       "Women                                     282\n",
       "Major_category                    Engineering\n",
       "ShareWomen                           0.120564\n",
       "Sample_size                                36\n",
       "Employed                                 1976\n",
       "Full_time                                1849\n",
       "Part_time                                 270\n",
       "Full_time_year_round                     1207\n",
       "Unemployed                                 37\n",
       "Unemployment_rate                   0.0183805\n",
       "Median                                 110000\n",
       "P25th                                   95000\n",
       "P75th                                  125000\n",
       "College_jobs                             1534\n",
       "Non_college_jobs                          364\n",
       "Low_wage_jobs                             193\n",
       "Name: 0, dtype: object"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# read dataset into a dataframe and take a look\n",
    "# at the data\n",
    "recent_grads = pd.read_csv(\"recent-grads.csv\")\n",
    "recent_grads.iloc[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "<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>2339.0</td>\n",
       "      <td>2057.0</td>\n",
       "      <td>282.0</td>\n",
       "      <td>Engineering</td>\n",
       "      <td>0.120564</td>\n",
       "      <td>36</td>\n",
       "      <td>1976</td>\n",
       "      <td>...</td>\n",
       "      <td>270</td>\n",
       "      <td>1207</td>\n",
       "      <td>37</td>\n",
       "      <td>0.018381</td>\n",
       "      <td>110000</td>\n",
       "      <td>95000</td>\n",
       "      <td>125000</td>\n",
       "      <td>1534</td>\n",
       "      <td>364</td>\n",
       "      <td>193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2416</td>\n",
       "      <td>MINING AND MINERAL ENGINEERING</td>\n",
       "      <td>756.0</td>\n",
       "      <td>679.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>Engineering</td>\n",
       "      <td>0.101852</td>\n",
       "      <td>7</td>\n",
       "      <td>640</td>\n",
       "      <td>...</td>\n",
       "      <td>170</td>\n",
       "      <td>388</td>\n",
       "      <td>85</td>\n",
       "      <td>0.117241</td>\n",
       "      <td>75000</td>\n",
       "      <td>55000</td>\n",
       "      <td>90000</td>\n",
       "      <td>350</td>\n",
       "      <td>257</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>2415</td>\n",
       "      <td>METALLURGICAL ENGINEERING</td>\n",
       "      <td>856.0</td>\n",
       "      <td>725.0</td>\n",
       "      <td>131.0</td>\n",
       "      <td>Engineering</td>\n",
       "      <td>0.153037</td>\n",
       "      <td>3</td>\n",
       "      <td>648</td>\n",
       "      <td>...</td>\n",
       "      <td>133</td>\n",
       "      <td>340</td>\n",
       "      <td>16</td>\n",
       "      <td>0.024096</td>\n",
       "      <td>73000</td>\n",
       "      <td>50000</td>\n",
       "      <td>105000</td>\n",
       "      <td>456</td>\n",
       "      <td>176</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>2417</td>\n",
       "      <td>NAVAL ARCHITECTURE AND MARINE ENGINEERING</td>\n",
       "      <td>1258.0</td>\n",
       "      <td>1123.0</td>\n",
       "      <td>135.0</td>\n",
       "      <td>Engineering</td>\n",
       "      <td>0.107313</td>\n",
       "      <td>16</td>\n",
       "      <td>758</td>\n",
       "      <td>...</td>\n",
       "      <td>150</td>\n",
       "      <td>692</td>\n",
       "      <td>40</td>\n",
       "      <td>0.050125</td>\n",
       "      <td>70000</td>\n",
       "      <td>43000</td>\n",
       "      <td>80000</td>\n",
       "      <td>529</td>\n",
       "      <td>102</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>2405</td>\n",
       "      <td>CHEMICAL ENGINEERING</td>\n",
       "      <td>32260.0</td>\n",
       "      <td>21239.0</td>\n",
       "      <td>11021.0</td>\n",
       "      <td>Engineering</td>\n",
       "      <td>0.341631</td>\n",
       "      <td>289</td>\n",
       "      <td>25694</td>\n",
       "      <td>...</td>\n",
       "      <td>5180</td>\n",
       "      <td>16697</td>\n",
       "      <td>1672</td>\n",
       "      <td>0.061098</td>\n",
       "      <td>65000</td>\n",
       "      <td>50000</td>\n",
       "      <td>75000</td>\n",
       "      <td>18314</td>\n",
       "      <td>4440</td>\n",
       "      <td>972</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Rank  Major_code                                      Major    Total  \\\n",
       "0     1        2419                      PETROLEUM ENGINEERING   2339.0   \n",
       "1     2        2416             MINING AND MINERAL ENGINEERING    756.0   \n",
       "2     3        2415                  METALLURGICAL ENGINEERING    856.0   \n",
       "3     4        2417  NAVAL ARCHITECTURE AND MARINE ENGINEERING   1258.0   \n",
       "4     5        2405                       CHEMICAL ENGINEERING  32260.0   \n",
       "\n",
       "       Men    Women Major_category  ShareWomen  Sample_size  Employed  \\\n",
       "0   2057.0    282.0    Engineering    0.120564           36      1976   \n",
       "1    679.0     77.0    Engineering    0.101852            7       640   \n",
       "2    725.0    131.0    Engineering    0.153037            3       648   \n",
       "3   1123.0    135.0    Engineering    0.107313           16       758   \n",
       "4  21239.0  11021.0    Engineering    0.341631          289     25694   \n",
       "\n",
       "       ...        Part_time  Full_time_year_round  Unemployed  \\\n",
       "0      ...              270                  1207          37   \n",
       "1      ...              170                   388          85   \n",
       "2      ...              133                   340          16   \n",
       "3      ...              150                   692          40   \n",
       "4      ...             5180                 16697        1672   \n",
       "\n",
       "   Unemployment_rate  Median  P25th   P75th  College_jobs  Non_college_jobs  \\\n",
       "0           0.018381  110000  95000  125000          1534               364   \n",
       "1           0.117241   75000  55000   90000           350               257   \n",
       "2           0.024096   73000  50000  105000           456               176   \n",
       "3           0.050125   70000  43000   80000           529               102   \n",
       "4           0.061098   65000  50000   75000         18314              4440   \n",
       "\n",
       "   Low_wage_jobs  \n",
       "0            193  \n",
       "1             50  \n",
       "2              0  \n",
       "3              0  \n",
       "4            972  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# top five majors by median earnings\n",
    "# guess I should've majored in engineering...\n",
    "recent_grads.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Rank</th>\n",
       "      <th>Major_code</th>\n",
       "      <th>Major</th>\n",
       "      <th>Total</th>\n",
       "      <th>Men</th>\n",
       "      <th>Women</th>\n",
       "      <th>Major_category</th>\n",
       "      <th>ShareWomen</th>\n",
       "      <th>Sample_size</th>\n",
       "      <th>Employed</th>\n",
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       "      <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",
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       "    <tr>\n",
       "      <th>168</th>\n",
       "      <td>169</td>\n",
       "      <td>3609</td>\n",
       "      <td>ZOOLOGY</td>\n",
       "      <td>8409.0</td>\n",
       "      <td>3050.0</td>\n",
       "      <td>5359.0</td>\n",
       "      <td>Biology &amp; Life Science</td>\n",
       "      <td>0.637293</td>\n",
       "      <td>47</td>\n",
       "      <td>6259</td>\n",
       "      <td>...</td>\n",
       "      <td>2190</td>\n",
       "      <td>3602</td>\n",
       "      <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",
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       "    <tr>\n",
       "      <th>169</th>\n",
       "      <td>170</td>\n",
       "      <td>5201</td>\n",
       "      <td>EDUCATIONAL PSYCHOLOGY</td>\n",
       "      <td>2854.0</td>\n",
       "      <td>522.0</td>\n",
       "      <td>2332.0</td>\n",
       "      <td>Psychology &amp; Social Work</td>\n",
       "      <td>0.817099</td>\n",
       "      <td>7</td>\n",
       "      <td>2125</td>\n",
       "      <td>...</td>\n",
       "      <td>572</td>\n",
       "      <td>1211</td>\n",
       "      <td>148</td>\n",
       "      <td>0.065112</td>\n",
       "      <td>25000</td>\n",
       "      <td>24000</td>\n",
       "      <td>34000</td>\n",
       "      <td>1488</td>\n",
       "      <td>615</td>\n",
       "      <td>82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>170</th>\n",
       "      <td>171</td>\n",
       "      <td>5202</td>\n",
       "      <td>CLINICAL PSYCHOLOGY</td>\n",
       "      <td>2838.0</td>\n",
       "      <td>568.0</td>\n",
       "      <td>2270.0</td>\n",
       "      <td>Psychology &amp; Social Work</td>\n",
       "      <td>0.799859</td>\n",
       "      <td>13</td>\n",
       "      <td>2101</td>\n",
       "      <td>...</td>\n",
       "      <td>648</td>\n",
       "      <td>1293</td>\n",
       "      <td>368</td>\n",
       "      <td>0.149048</td>\n",
       "      <td>25000</td>\n",
       "      <td>25000</td>\n",
       "      <td>40000</td>\n",
       "      <td>986</td>\n",
       "      <td>870</td>\n",
       "      <td>622</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>171</th>\n",
       "      <td>172</td>\n",
       "      <td>5203</td>\n",
       "      <td>COUNSELING PSYCHOLOGY</td>\n",
       "      <td>4626.0</td>\n",
       "      <td>931.0</td>\n",
       "      <td>3695.0</td>\n",
       "      <td>Psychology &amp; Social Work</td>\n",
       "      <td>0.798746</td>\n",
       "      <td>21</td>\n",
       "      <td>3777</td>\n",
       "      <td>...</td>\n",
       "      <td>965</td>\n",
       "      <td>2738</td>\n",
       "      <td>214</td>\n",
       "      <td>0.053621</td>\n",
       "      <td>23400</td>\n",
       "      <td>19200</td>\n",
       "      <td>26000</td>\n",
       "      <td>2403</td>\n",
       "      <td>1245</td>\n",
       "      <td>308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>172</th>\n",
       "      <td>173</td>\n",
       "      <td>3501</td>\n",
       "      <td>LIBRARY SCIENCE</td>\n",
       "      <td>1098.0</td>\n",
       "      <td>134.0</td>\n",
       "      <td>964.0</td>\n",
       "      <td>Education</td>\n",
       "      <td>0.877960</td>\n",
       "      <td>2</td>\n",
       "      <td>742</td>\n",
       "      <td>...</td>\n",
       "      <td>237</td>\n",
       "      <td>410</td>\n",
       "      <td>87</td>\n",
       "      <td>0.104946</td>\n",
       "      <td>22000</td>\n",
       "      <td>20000</td>\n",
       "      <td>22000</td>\n",
       "      <td>288</td>\n",
       "      <td>338</td>\n",
       "      <td>192</td>\n",
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       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Rank  Major_code                   Major   Total     Men   Women  \\\n",
       "168   169        3609                 ZOOLOGY  8409.0  3050.0  5359.0   \n",
       "169   170        5201  EDUCATIONAL PSYCHOLOGY  2854.0   522.0  2332.0   \n",
       "170   171        5202     CLINICAL PSYCHOLOGY  2838.0   568.0  2270.0   \n",
       "171   172        5203   COUNSELING PSYCHOLOGY  4626.0   931.0  3695.0   \n",
       "172   173        3501         LIBRARY SCIENCE  1098.0   134.0   964.0   \n",
       "\n",
       "               Major_category  ShareWomen  Sample_size  Employed  \\\n",
       "168    Biology & Life Science    0.637293           47      6259   \n",
       "169  Psychology & Social Work    0.817099            7      2125   \n",
       "170  Psychology & Social Work    0.799859           13      2101   \n",
       "171  Psychology & Social Work    0.798746           21      3777   \n",
       "172                 Education    0.877960            2       742   \n",
       "\n",
       "         ...        Part_time  Full_time_year_round  Unemployed  \\\n",
       "168      ...             2190                  3602         304   \n",
       "169      ...              572                  1211         148   \n",
       "170      ...              648                  1293         368   \n",
       "171      ...              965                  2738         214   \n",
       "172      ...              237                   410          87   \n",
       "\n",
       "     Unemployment_rate  Median  P25th  P75th  College_jobs  Non_college_jobs  \\\n",
       "168           0.046320   26000  20000  39000          2771              2947   \n",
       "169           0.065112   25000  24000  34000          1488               615   \n",
       "170           0.149048   25000  25000  40000           986               870   \n",
       "171           0.053621   23400  19200  26000          2403              1245   \n",
       "172           0.104946   22000  20000  22000           288               338   \n",
       "\n",
       "     Low_wage_jobs  \n",
       "168            743  \n",
       "169             82  \n",
       "170            622  \n",
       "171            308  \n",
       "172            192  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# bottom five majors by median earnings\n",
    "recent_grads.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>Rank</th>\n",
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       "      <th>Men</th>\n",
       "      <th>Women</th>\n",
       "      <th>ShareWomen</th>\n",
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       "      <th>P25th</th>\n",
       "      <th>P75th</th>\n",
       "      <th>College_jobs</th>\n",
       "      <th>Non_college_jobs</th>\n",
       "      <th>Low_wage_jobs</th>\n",
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       "      <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": [
    "# summary statistics\n",
    "recent_grads.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "173"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# drop rows with missing values\n",
    "raw_data_count = recent_grads.shape[0]\n",
    "raw_data_count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "172"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "recent_grads = recent_grads.dropna()\n",
    "cleaned_data_count = recent_grads.shape[0]\n",
    "cleaned_data_count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f15c91e5d68>"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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0adMAmDp1KmlpaVRWVnLs2DGOHDnCiBEjCAgIwNvbm8zMTESEt956y26f1atX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\nUsoE/j9vn0YdM7FojwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f15c9f0fa20>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# explore the data using some scatter plots\n",
    "# this plot shows that the more populat majors do\n",
    "# not appear to make more money, however these\n",
    "# conclusions could be limited due to the small\n",
    "# sample size\n",
    "recent_grads.plot(x='Sample_size', y='Median', kind='scatter', title='Median vs. Sample_size', figsize=(5,5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f15c91fddd8>"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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gwYP01ltvkY+PD7399tvC12uvvUaRkZEmK8TcpBjI+oGWT8Bz1eEbSLa2zkKI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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f15c9176da0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# this plot shows that (outside of the scatter\n",
    "# for the majors with low statistics), the \n",
    "# unemployment rate is roughly constant\n",
    "recent_grads.plot(x='Sample_size', y='Unemployment_rate', kind='scatter', title='Unemployment rate vs. Sample_size', figsize=(5,5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f15c92a7780>"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ZoqOjxWg0Sm5ubrtt7OVDQET9lLOyRWffWb+l0+nuuJZORNQeZ2ULr9gkItIwhjgRkYYx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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f15c94841d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# this shows that the median salary seems to\n",
    "# decrease with the number of full time\n",
    "# employees\n",
    "recent_grads.plot(x='Full_time', y='Median', kind='scatter', title='Median vs. Full Time', figsize=(5,5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f15c95f2898>"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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wpeGipivblpaleXkvr3gCtAQsazrmZU2PI8jNzafNdmzdU8FcYFrynqjpS5EZ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\nDmDGGFMIBzBjjCmEA5gxxhTCAcwYYwr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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f15c9198400>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# this plot shows that there is not really any\n",
    "# correlation between the unemployment rate for\n",
    "# a major and the share of women in that major\n",
    "recent_grads.plot(x='ShareWomen', y='Unemployment_rate', kind='scatter', title='Unemployment Rate vs. Share of Women', figsize=(5,5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f15c94a50b8>"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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PP/xQ+vfvL3/84x97ZuYWB3Abb4HqbNqUJ+7uPgJECKATFxedvPPOuxbbcPIm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\npGIMcSIiFfv/OL7sncNOAZMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f15c9405ac8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# this plot shows that the median salary is\n",
    "# fairly constant/slightly decreases with the \n",
    "# number of male graduates\n",
    "recent_grads.plot(x='Men', y='Median', kind='scatter', title='Median vs. Men', figsize=(5,5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f15c9226e48>"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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2YfU28cLCQvz8/ADw9/ensLAQgPz8fIKCgoz1AgICyM/PJz8/n8DAQGN5YGAg+fn5V23j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\ndaUhbiX2EOIN/8O/U9s88lp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zcPm3a528vDwCAgKaXH7lNtXV1Zw7dw4fHx8CAgIa3Iio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\ngf0/t9CZcGPsB2UAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f15c9470f60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# the plot is similar for female graduates\n",
    "recent_grads.plot(x='Women', y='Median', kind='scatter', title='Median vs. Women', figsize=(5,5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7f15c90cacf8>"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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uKSLHjx/n7rvv5rnnnqNv377G/TxemM+k45ecnMyuXbuoq6tjy5YteL3euBy/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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f15c911c400>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# explore the data using histograms\n",
    "# from this plot we can see the small sample size \n",
    "# problem\n",
    "recent_grads[\"Sample_size\"].hist(bins=30,range=(0,3000))\n",
    "plt.title(\"Sample Size\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7f15c8b8e7f0>"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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sDXqf393AmQDJgjMBIiLyOYMuAoreAV4oegd0QtE7wAtF74AOZNxTZpN6snZp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\nXASIiAyMiwARkYFxESAiMrD/B6aEBODPYggxAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f15c8bf55c0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# from this plot we can see that typical salaries\n",
    "# are around $30K to $40K for college graduates\n",
    "recent_grads['Median'].hist()\n",
    "plt.title('Median Salary')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7f15c8b20c18>"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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sMSGEEHl5eWLJkiV+OS9/+PDDD0VpaamIjY1Vl/kjd7W1teLaa68V//vf/4TX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\ngLEIEBEFMBYBIqIAxiJARBTA/h85xnh7HTUyEgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f15c8b4c588>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# employment numbers look small, but not when\n",
    "# compared to the sample size\n",
    "recent_grads['Employed'].hist(bins=20,range=(0,200000))\n",
    "plt.title('Number employed')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7f15c8a4dbe0>"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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wSICIiBTHIqAxN56LTe5hLuXFfGoTiwARkRdjT8DD22BPgIhcwZ4AEREpjkVA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\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f15c8ac4828>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# same goes for this full time employment \n",
    "# histogram\n",
    "recent_grads['Full_time'].hist(bins=20,range=(0,200000))\n",
    "plt.title('Number employed full time')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7f15c8974ac8>"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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BO3UiIvICh7qhhOoAGhGqA2hEqA6gEaE6QMDjUCciMhHu1A2hfo/HHqiur0OG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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f15c8d387f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# this histogram shows that most majors have more\n",
    "# females compared to males\n",
    "recent_grads['ShareWomen'].hist()\n",
    "plt.title('Women as share of total')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7f15c8971fd0>"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Y2cWm0ARrugjnf/jo+S6nTXAR/l3O/0BWpIQ95Pfs2YMbb7wRKSkpGDhwIBYt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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f15c89ee3c8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# the unemployment rate looks reasonably\n",
    "# normally distributed, with a mean around 0.7\n",
    "recent_grads['Unemployment_rate'].hist()\n",
    "plt.title('Unemployment rate')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7f15c8881320>"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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FwE+qr0uqnE/lbADzkW9YBIiIwhh7AkREBsSeABER6Y5FwE+qr0uqnE/lbADz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\njP0/HuyyGai/pr0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f15c88cb2e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# most majors contain only less than 10K male\n",
    "# graduates\n",
    "recent_grads['Men'].hist(bins=20,range=(0,120000))\n",
    "plt.title('Number of male graduates')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7f15c87ba5f8>"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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wID4+HhcvXlSiXiIi6kQdmgnY7XaEhobi+++/x/r16xEYGIjly5dj9erVsNlsSE1NbXkC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\nRG6MTYCIyI39fxBu9v0zQWfzAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f15c88aa278>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# similarly most majors have less than 20K \n",
    "# female graduates\n",
    "recent_grads['Women'].hist(bins=30,range=(0,200000))\n",
    "plt.title('Number of women graduates')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7f15c878b550>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f15c872ba90>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7f15c8674d68>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f15c86b2630>]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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KSiIiIoL4+HgKzy4ZlpKSQnh4ODExMeTk5ABgNpsxmUyYTCYOHDhgu0ZMTAw3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\nhBCiUSRxCCGEaBRJHEIIIRrl/wG3p1wOfIxohAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f15c874dbe0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# try making scatterplot matrices\n",
    "from pandas.plotting import scatter_matrix\n",
    "# this plot shows that there is a lot of scatter\n",
    "# in the median salary for small sample sizes\n",
    "# and the median salary is somewhat constant\n",
    "# around about $40K for larger sample sizes\n",
    "scatter_matrix(recent_grads[[\"Sample_size\",\"Median\"]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7f15c86ef780>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f15c854cf60>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f15c851b898>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7f15c84d8470>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f15c84a0860>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f15c8462080>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7f15c842ad30>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f15c8469b70>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f15c83351d0>]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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UEIILgchInQB54glO1snJ/A6zsigIevYsPwJOc72zsriwUKn4vWnMjklJvK72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\nAAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f15c863ea20>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# this plot shows that there is not much\n",
    "# correlation between the median salary for a\n",
    "# major and the unemployment rate.  We also see\n",
    "# a lot of scatter in the unemployment rate for\n",
    "# the majors with a small sample size, but for\n",
    "# larger sample sizes the unemployment rate\n",
    "# flattens around 0.7\n",
    "scatter_matrix(recent_grads[['Sample_size','Median','Unemployment_rate']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7f15c819e0b8>"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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JnDHGNBwncsYY03CcyBljTMNxImeMMQ3HiZwxxjQcJ3LGGNNwnMgZ0yAWFhaQ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\niZwQQt5xlMgJIeQd93/y2xAWZi0yWwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f15c820f1d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# explore the data using bar plots\n",
    "# the top ten majors by median salary are male-\n",
    "# dominated\n",
    "# hurray for women in astrophysics though!\n",
    "recent_grads[:10].plot.bar(x='Major',y='ShareWomen')\n",
    "plt.title('Share of women in the top ten majors by median salary')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7f15c8128400>"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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pUhgYGIgRP2OMsRckI6KG/Vd6kcJkMtRVXNlYrw0Jqe4y6tyCTNbgwZOxAA2O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\nN2vqAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f15c81974e0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# sigh...these majors are mostly women\n",
    "recent_grads[-10:].plot.bar(x='Major',y='ShareWomen')\n",
    "plt.title('Share of women in the bottom ten majors by median salary')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7f15c8035be0>"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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7d+/GDkfUsWNHmJubi6+WLVvC3NwcUVFRjR0aY08NvT66oqSkBH/88QfWr18P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\nQgghAKhBIIQQ8l//D0wwSUYI37chAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f15c8123048>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# surprisingly the unemployment rates are \n",
    "# sometimes quite high for the top ten majors\n",
    "# with highest median salary\n",
    "# The unemployment rate for petroleum engineering\n",
    "# and astrophysics/astronomy is quite low though!\n",
    "recent_grads[:10].plot.bar(x='Major',y='Unemployment_rate')\n",
    "plt.title('Unemployment rate in the top ten majors by median salary')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7f15c7fb8390>"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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RI0fi119/xaZNm2o0r3yYL8D06dOxcuVK8YLXnTt3sH///gbNa29vj5ycnBpHePVRqVRQ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\nCiUQQgghL4USCCGEkJfyf+EvihczXgCrAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f15c800ba58>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# unemployment rates for the 10 majors with the\n",
    "# lowest median salaries look a little higher\n",
    "# than the average 0.7\n",
    "recent_grads[-10:].plot.bar(x='Major',y='Unemployment_rate')\n",
    "plt.title('Unemployment rate in the bottom ten majors by median salary')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    172.000000\n",
       "mean       0.068024\n",
       "std        0.030340\n",
       "min        0.000000\n",
       "25%        0.050261\n",
       "50%        0.067544\n",
       "75%        0.087247\n",
       "max        0.177226\n",
       "Name: Unemployment_rate, dtype: float64"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "recent_grads['Unemployment_rate'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "collapsed": 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>Men</th>\n",
       "      <th>Women</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Major_category</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Agriculture &amp; Natural Resources</th>\n",
       "      <td>40357.0</td>\n",
       "      <td>35263.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Arts</th>\n",
       "      <td>134390.0</td>\n",
       "      <td>222740.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Biology &amp; Life Science</th>\n",
       "      <td>184919.0</td>\n",
       "      <td>268943.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Business</th>\n",
       "      <td>667852.0</td>\n",
       "      <td>634524.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Communications &amp; Journalism</th>\n",
       "      <td>131921.0</td>\n",
       "      <td>260680.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                      Men     Women\n",
       "Major_category                                     \n",
       "Agriculture & Natural Resources   40357.0   35263.0\n",
       "Arts                             134390.0  222740.0\n",
       "Biology & Life Science           184919.0  268943.0\n",
       "Business                         667852.0  634524.0\n",
       "Communications & Journalism      131921.0  260680.0"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# try a grouped bar plot to compare the number \n",
    "# of men with the number of women in each \n",
    "# category of majors\n",
    "category_counts=recent_grads.groupby(\"Major_category\")['Men','Women'].sum()\n",
    "category_counts.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "Unknown property columns",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0mTraceback (most recent call last)",
      "\u001b[1;32m<ipython-input-89-2f4bffb7d67c>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mcategory_counts\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbar\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'Men'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'Women'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m/dataquest/system/env/python3/lib/python3.4/site-packages/pandas/plotting/_core.py\u001b[0m in \u001b[0;36mbar\u001b[1;34m(self, x, y, **kwds)\u001b[0m\n\u001b[0;32m   2714\u001b[0m         \u001b[0maxes\u001b[0m \u001b[1;33m:\u001b[0m \u001b[0mmatplotlib\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mAxesSubplot\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0marray\u001b[0m \u001b[0mof\u001b[0m \u001b[0mthem\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2715\u001b[0m         \"\"\"\n\u001b[1;32m-> 2716\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkind\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'bar'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2717\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2718\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mbarh\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m/dataquest/system/env/python3/lib/python3.4/site-packages/pandas/plotting/_core.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, x, y, kind, ax, subplots, sharex, sharey, layout, figsize, use_index, title, grid, legend, style, logx, logy, loglog, xticks, yticks, xlim, ylim, rot, fontsize, colormap, table, yerr, xerr, secondary_y, sort_columns, **kwds)\u001b[0m\n\u001b[0;32m   2675\u001b[0m                           \u001b[0mfontsize\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mfontsize\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcolormap\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mcolormap\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtable\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mtable\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2676\u001b[0m                           \u001b[0myerr\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0myerr\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mxerr\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mxerr\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msecondary_y\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0msecondary_y\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2677\u001b[1;33m                           sort_columns=sort_columns, **kwds)\n\u001b[0m\u001b[0;32m   2678\u001b[0m     \u001b[0m__call__\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__doc__\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mplot_frame\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__doc__\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2679\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m/dataquest/system/env/python3/lib/python3.4/site-packages/pandas/plotting/_core.py\u001b[0m in \u001b[0;36mplot_frame\u001b[1;34m(data, x, y, kind, ax, subplots, sharex, sharey, layout, figsize, use_index, title, grid, legend, style, logx, logy, loglog, xticks, yticks, xlim, ylim, rot, fontsize, colormap, table, yerr, xerr, secondary_y, sort_columns, **kwds)\u001b[0m\n\u001b[0;32m   1900\u001b[0m                  \u001b[0myerr\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0myerr\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mxerr\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mxerr\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1901\u001b[0m                  \u001b[0msecondary_y\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0msecondary_y\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msort_columns\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0msort_columns\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1902\u001b[1;33m                  **kwds)\n\u001b[0m\u001b[0;32m   1903\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1904\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m/dataquest/system/env/python3/lib/python3.4/site-packages/pandas/plotting/_core.py\u001b[0m in \u001b[0;36m_plot\u001b[1;34m(data, x, y, subplots, ax, kind, **kwds)\u001b[0m\n\u001b[0;32m   1727\u001b[0m         \u001b[0mplot_obj\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mklass\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msubplots\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0msubplots\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0max\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0max\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkind\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mkind\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1728\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1729\u001b[1;33m     \u001b[0mplot_obj\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgenerate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1730\u001b[0m     \u001b[0mplot_obj\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1731\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0mplot_obj\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mresult\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m/dataquest/system/env/python3/lib/python3.4/site-packages/pandas/plotting/_core.py\u001b[0m in \u001b[0;36mgenerate\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    250\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_compute_plot_data\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    251\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_setup_subplots\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 252\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_make_plot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    253\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_add_table\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    254\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_make_legend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m/dataquest/system/env/python3/lib/python3.4/site-packages/pandas/plotting/_core.py\u001b[0m in \u001b[0;36m_make_plot\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m   1255\u001b[0m                 rect = self._plot(ax, self.ax_pos + (i + 0.5) * w, y, w,\n\u001b[0;32m   1256\u001b[0m                                   \u001b[0mstart\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mstart\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlabel\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mlabel\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1257\u001b[1;33m                                   log=self.log, **kwds)\n\u001b[0m\u001b[0;32m   1258\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_add_legend_handle\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrect\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlabel\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1259\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m/dataquest/system/env/python3/lib/python3.4/site-packages/pandas/plotting/_core.py\u001b[0m in \u001b[0;36m_plot\u001b[1;34m(cls, ax, x, y, w, start, log, **kwds)\u001b[0m\n\u001b[0;32m   1199\u001b[0m     \u001b[1;33m@\u001b[0m\u001b[0mclassmethod\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1200\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_plot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcls\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0max\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mw\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mstart\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlog\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1201\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0max\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbar\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mw\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbottom\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mstart\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlog\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mlog\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1202\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1203\u001b[0m     \u001b[1;33m@\u001b[0m\u001b[0mproperty\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m/dataquest/system/env/python3/lib/python3.4/site-packages/matplotlib/__init__.py\u001b[0m in \u001b[0;36minner\u001b[1;34m(ax, *args, **kwargs)\u001b[0m\n\u001b[0;32m   1810\u001b[0m                     warnings.warn(msg % (label_namer, func.__name__),\n\u001b[0;32m   1811\u001b[0m                                   RuntimeWarning, stacklevel=2)\n\u001b[1;32m-> 1812\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0max\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1813\u001b[0m         \u001b[0mpre_doc\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0minner\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__doc__\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1814\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mpre_doc\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m/dataquest/system/env/python3/lib/python3.4/site-packages/matplotlib/axes/_axes.py\u001b[0m in \u001b[0;36mbar\u001b[1;34m(self, left, height, width, bottom, **kwargs)\u001b[0m\n\u001b[0;32m   2129\u001b[0m                 \u001b[0mlabel\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'_nolegend_'\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2130\u001b[0m                 )\n\u001b[1;32m-> 2131\u001b[1;33m             \u001b[0mr\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2132\u001b[0m             \u001b[0mr\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_path\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_interpolation_steps\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m100\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2133\u001b[0m             \u001b[1;31m#print r.get_label(), label, 'label' in kwargs\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m/dataquest/system/env/python3/lib/python3.4/site-packages/matplotlib/artist.py\u001b[0m in \u001b[0;36mupdate\u001b[1;34m(self, props)\u001b[0m\n\u001b[0;32m    854\u001b[0m                 \u001b[0mfunc\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'set_'\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0mk\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    855\u001b[0m                 \u001b[1;32mif\u001b[0m \u001b[0mfunc\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m \u001b[1;32mor\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0msix\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcallable\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfunc\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 856\u001b[1;33m                     \u001b[1;32mraise\u001b[0m \u001b[0mAttributeError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'Unknown property %s'\u001b[0m \u001b[1;33m%\u001b[0m \u001b[0mk\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    857\u001b[0m                 \u001b[0mfunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mv\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    858\u001b[0m             \u001b[0mchanged\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mTrue\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mAttributeError\u001b[0m: Unknown property columns"
     ]
    },
    {
     "data": {
      "image/png": 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vTw3KZC/+6ejRo9qwYcN4jDbuMtmLBw8e6PTp09q5c6fpT2Jlshe3b99Wf3+/ysrKVFpa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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f15c7f9f438>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "category_counts.plot.bar(columns=['Men','Women'])"
   ]
  },
  {
   "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": "3.4.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}