Projects/step_2/Course_2/Visualizing Earnings Based On College Majors.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1. Introduction"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Pandas has many methods for quickly generating common plots from data in DataFrames. Like pyplot, the plotting functionality in pandas is a wrapper for matplotlib. This means we can customize the plots when necessary by accessing the underlying Figure, Axes, and other matplotlib objects.\n",
    "\n",
    "* In this  project, we'll explore how using the pandas plotting functionality along with the Jupyter notebook interface allows us to explore data quickly using visualizations.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* We'll be working with a dataset on the job outcomes of students who graduated from college between 2010 and 2012. The original data on job outcomes was released by [American Community Survey](https://www.census.gov/programs-surveys/acs/), which conducts surveys and aggregates the data. FiveThirtyEight cleaned the dataset and released it on their [Github repo](https://github.com/fivethirtyeight/data/tree/master/college-majors)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    ". Here are some of the columns in the dataset:\n",
    "\n",
    "* Rank - Rank by median earnings (the dataset is ordered by this column).\n",
    "* Major_code - Major code.\n",
    "* Major - Major description.\n",
    "* Major_category - Category of major.\n",
    "* Total - Total number of people with major.\n",
    "* Sample_size - Sample size (unweighted) of full-time.\n",
    "* Men - Male graduates.\n",
    "* Women - Female graduates.\n",
    "* ShareWomen - Women as share of total.\n",
    "* Employed - Number employed.\n",
    "* Median - Median salary of full-time, year-round workers.\n",
    "* Low_wage_jobs - Number in low-wage service jobs.\n",
    "* Full_time - Number employed 35 hours or more.\n",
    "* Part_time - Number employed less than 35 hours."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using visualizations, we can start to explore questions from the dataset like:\n",
    "\n",
    "* Do students in more popular majors make more money?\n",
    "  * Using scatter plots\n",
    "* How many majors are predominantly male? Predominantly female?\n",
    "  * Using histograms\n",
    "* Which category of majors have the most students?\n",
    "  * Using bar plots"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## TODO:\n",
    "* Let's setup the environment by importing the libraries we need and running the necessary Jupyter magic so that plots are displayed inline.\n",
    "\n",
    "  * Import pandas and matplotlib into the environment.\n",
    "  * Run the Jupyter magic %matplotlib inline so that plots are displayed inline.\n",
    "  * Read the dataset into a DataFrame and start exploring the data.\n",
    "\n",
    "* Read recent-grads.csv into pandas and assign the resulting DataFrame to recent_grads.\n",
    "  * Use DataFrame.iloc[] to return the first row formatted as a table.\n",
    "  * Use DataFrame.head() and DataFrame.tail() to become familiar with how the data is structured.\n",
    "  * Use DataFrame.describe() to generate summary statistics for all of the numeric columns.\n",
    "* Drop rows with missing values. Matplotlib expects that columns of values we pass in have matching lengths and missing values will cause matplotlib to throw errors.\n",
    "\n",
    "  * Look up the number of rows in recent_grads and assign the value to raw_data_count.\n",
    "  * Use DataFrame.dropna() to drop rows containing missing values and assign the resulting DataFrame back to recent_grads.\n",
    "  * Look up the number of rows in recent_grads now and assign the value to cleaned_data_count. If you compare cleaned_data_count and raw_data_count, you'll notice that only one row contained missing values and was dropped."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Rank                                        1\n",
       "Major_code                               2419\n",
       "Major                   PETROLEUM ENGINEERING\n",
       "Total                                    2339\n",
       "Men                                      2057\n",
       "Women                                     282\n",
       "Major_category                    Engineering\n",
       "ShareWomen                           0.120564\n",
       "Sample_size                                36\n",
       "Employed                                 1976\n",
       "Full_time                                1849\n",
       "Part_time                                 270\n",
       "Full_time_year_round                     1207\n",
       "Unemployed                                 37\n",
       "Unemployment_rate                   0.0183805\n",
       "Median                                 110000\n",
       "P25th                                   95000\n",
       "P75th                                  125000\n",
       "College_jobs                             1534\n",
       "Non_college_jobs                          364\n",
       "Low_wage_jobs                             193\n",
       "Name: 0, dtype: object"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "recent_grads=pd.read_csv('recent-grads.csv')\n",
    "recent_grads.iloc[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Rank</th>\n",
       "      <th>Major_code</th>\n",
       "      <th>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  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": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "recent_grads.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Rank</th>\n",
       "      <th>Major_code</th>\n",
       "      <th>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>8409.0</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",
       "    </tr>\n",
       "    <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",
       "    </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  8409.0  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  Unemployment_rate  Median  \\\n",
       "168       2190                  3602         304           0.046320   26000   \n",
       "169        572                  1211         148           0.065112   25000   \n",
       "170        648                  1293         368           0.149048   25000   \n",
       "171        965                  2738         214           0.053621   23400   \n",
       "172        237                   410          87           0.104946   22000   \n",
       "\n",
       "     P25th  P75th  College_jobs  Non_college_jobs  Low_wage_jobs  \n",
       "168  20000  39000          2771              2947            743  \n",
       "169  24000  34000          1488               615             82  \n",
       "170  25000  40000           986               870            622  \n",
       "171  19200  26000          2403              1245            308  \n",
       "172  20000  22000           288               338            192  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "recent_grads.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Rank</th>\n",
       "      <th>Major_code</th>\n",
       "      <th>Total</th>\n",
       "      <th>Men</th>\n",
       "      <th>Women</th>\n",
       "      <th>ShareWomen</th>\n",
       "      <th>Sample_size</th>\n",
       "      <th>Employed</th>\n",
       "      <th>Full_time</th>\n",
       "      <th>Part_time</th>\n",
       "      <th>Full_time_year_round</th>\n",
       "      <th>Unemployed</th>\n",
       "      <th>Unemployment_rate</th>\n",
       "      <th>Median</th>\n",
       "      <th>P25th</th>\n",
       "      <th>P75th</th>\n",
       "      <th>College_jobs</th>\n",
       "      <th>Non_college_jobs</th>\n",
       "      <th>Low_wage_jobs</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>173.000000</td>\n",
       "      <td>173.000000</td>\n",
       "      <td>172.000000</td>\n",
       "      <td>172.000000</td>\n",
       "      <td>172.000000</td>\n",
       "      <td>172.000000</td>\n",
       "      <td>173.000000</td>\n",
       "      <td>173.000000</td>\n",
       "      <td>173.000000</td>\n",
       "      <td>173.000000</td>\n",
       "      <td>173.000000</td>\n",
       "      <td>173.000000</td>\n",
       "      <td>173.000000</td>\n",
       "      <td>173.000000</td>\n",
       "      <td>173.000000</td>\n",
       "      <td>173.000000</td>\n",
       "      <td>173.000000</td>\n",
       "      <td>173.000000</td>\n",
       "      <td>173.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>87.000000</td>\n",
       "      <td>3879.815029</td>\n",
       "      <td>39370.081395</td>\n",
       "      <td>16723.406977</td>\n",
       "      <td>22646.674419</td>\n",
       "      <td>0.522223</td>\n",
       "      <td>356.080925</td>\n",
       "      <td>31192.763006</td>\n",
       "      <td>26029.306358</td>\n",
       "      <td>8832.398844</td>\n",
       "      <td>19694.427746</td>\n",
       "      <td>2416.329480</td>\n",
       "      <td>0.068191</td>\n",
       "      <td>40151.445087</td>\n",
       "      <td>29501.445087</td>\n",
       "      <td>51494.219653</td>\n",
       "      <td>12322.635838</td>\n",
       "      <td>13284.497110</td>\n",
       "      <td>3859.017341</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>50.084928</td>\n",
       "      <td>1687.753140</td>\n",
       "      <td>63483.491009</td>\n",
       "      <td>28122.433474</td>\n",
       "      <td>41057.330740</td>\n",
       "      <td>0.231205</td>\n",
       "      <td>618.361022</td>\n",
       "      <td>50675.002241</td>\n",
       "      <td>42869.655092</td>\n",
       "      <td>14648.179473</td>\n",
       "      <td>33160.941514</td>\n",
       "      <td>4112.803148</td>\n",
       "      <td>0.030331</td>\n",
       "      <td>11470.181802</td>\n",
       "      <td>9166.005235</td>\n",
       "      <td>14906.279740</td>\n",
       "      <td>21299.868863</td>\n",
       "      <td>23789.655363</td>\n",
       "      <td>6944.998579</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1100.000000</td>\n",
       "      <td>124.000000</td>\n",
       "      <td>119.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>111.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>111.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>22000.000000</td>\n",
       "      <td>18500.000000</td>\n",
       "      <td>22000.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>44.000000</td>\n",
       "      <td>2403.000000</td>\n",
       "      <td>4549.750000</td>\n",
       "      <td>2177.500000</td>\n",
       "      <td>1778.250000</td>\n",
       "      <td>0.336026</td>\n",
       "      <td>39.000000</td>\n",
       "      <td>3608.000000</td>\n",
       "      <td>3154.000000</td>\n",
       "      <td>1030.000000</td>\n",
       "      <td>2453.000000</td>\n",
       "      <td>304.000000</td>\n",
       "      <td>0.050306</td>\n",
       "      <td>33000.000000</td>\n",
       "      <td>24000.000000</td>\n",
       "      <td>42000.000000</td>\n",
       "      <td>1675.000000</td>\n",
       "      <td>1591.000000</td>\n",
       "      <td>340.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>87.000000</td>\n",
       "      <td>3608.000000</td>\n",
       "      <td>15104.000000</td>\n",
       "      <td>5434.000000</td>\n",
       "      <td>8386.500000</td>\n",
       "      <td>0.534024</td>\n",
       "      <td>130.000000</td>\n",
       "      <td>11797.000000</td>\n",
       "      <td>10048.000000</td>\n",
       "      <td>3299.000000</td>\n",
       "      <td>7413.000000</td>\n",
       "      <td>893.000000</td>\n",
       "      <td>0.067961</td>\n",
       "      <td>36000.000000</td>\n",
       "      <td>27000.000000</td>\n",
       "      <td>47000.000000</td>\n",
       "      <td>4390.000000</td>\n",
       "      <td>4595.000000</td>\n",
       "      <td>1231.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>130.000000</td>\n",
       "      <td>5503.000000</td>\n",
       "      <td>38909.750000</td>\n",
       "      <td>14631.000000</td>\n",
       "      <td>22553.750000</td>\n",
       "      <td>0.703299</td>\n",
       "      <td>338.000000</td>\n",
       "      <td>31433.000000</td>\n",
       "      <td>25147.000000</td>\n",
       "      <td>9948.000000</td>\n",
       "      <td>16891.000000</td>\n",
       "      <td>2393.000000</td>\n",
       "      <td>0.087557</td>\n",
       "      <td>45000.000000</td>\n",
       "      <td>33000.000000</td>\n",
       "      <td>60000.000000</td>\n",
       "      <td>14444.000000</td>\n",
       "      <td>11783.000000</td>\n",
       "      <td>3466.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>173.000000</td>\n",
       "      <td>6403.000000</td>\n",
       "      <td>393735.000000</td>\n",
       "      <td>173809.000000</td>\n",
       "      <td>307087.000000</td>\n",
       "      <td>0.968954</td>\n",
       "      <td>4212.000000</td>\n",
       "      <td>307933.000000</td>\n",
       "      <td>251540.000000</td>\n",
       "      <td>115172.000000</td>\n",
       "      <td>199897.000000</td>\n",
       "      <td>28169.000000</td>\n",
       "      <td>0.177226</td>\n",
       "      <td>110000.000000</td>\n",
       "      <td>95000.000000</td>\n",
       "      <td>125000.000000</td>\n",
       "      <td>151643.000000</td>\n",
       "      <td>148395.000000</td>\n",
       "      <td>48207.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Rank   Major_code          Total            Men          Women  \\\n",
       "count  173.000000   173.000000     172.000000     172.000000     172.000000   \n",
       "mean    87.000000  3879.815029   39370.081395   16723.406977   22646.674419   \n",
       "std     50.084928  1687.753140   63483.491009   28122.433474   41057.330740   \n",
       "min      1.000000  1100.000000     124.000000     119.000000       0.000000   \n",
       "25%     44.000000  2403.000000    4549.750000    2177.500000    1778.250000   \n",
       "50%     87.000000  3608.000000   15104.000000    5434.000000    8386.500000   \n",
       "75%    130.000000  5503.000000   38909.750000   14631.000000   22553.750000   \n",
       "max    173.000000  6403.000000  393735.000000  173809.000000  307087.000000   \n",
       "\n",
       "       ShareWomen  Sample_size       Employed      Full_time      Part_time  \\\n",
       "count  172.000000   173.000000     173.000000     173.000000     173.000000   \n",
       "mean     0.522223   356.080925   31192.763006   26029.306358    8832.398844   \n",
       "std      0.231205   618.361022   50675.002241   42869.655092   14648.179473   \n",
       "min      0.000000     2.000000       0.000000     111.000000       0.000000   \n",
       "25%      0.336026    39.000000    3608.000000    3154.000000    1030.000000   \n",
       "50%      0.534024   130.000000   11797.000000   10048.000000    3299.000000   \n",
       "75%      0.703299   338.000000   31433.000000   25147.000000    9948.000000   \n",
       "max      0.968954  4212.000000  307933.000000  251540.000000  115172.000000   \n",
       "\n",
       "       Full_time_year_round    Unemployed  Unemployment_rate         Median  \\\n",
       "count            173.000000    173.000000         173.000000     173.000000   \n",
       "mean           19694.427746   2416.329480           0.068191   40151.445087   \n",
       "std            33160.941514   4112.803148           0.030331   11470.181802   \n",
       "min              111.000000      0.000000           0.000000   22000.000000   \n",
       "25%             2453.000000    304.000000           0.050306   33000.000000   \n",
       "50%             7413.000000    893.000000           0.067961   36000.000000   \n",
       "75%            16891.000000   2393.000000           0.087557   45000.000000   \n",
       "max           199897.000000  28169.000000           0.177226  110000.000000   \n",
       "\n",
       "              P25th          P75th   College_jobs  Non_college_jobs  \\\n",
       "count    173.000000     173.000000     173.000000        173.000000   \n",
       "mean   29501.445087   51494.219653   12322.635838      13284.497110   \n",
       "std     9166.005235   14906.279740   21299.868863      23789.655363   \n",
       "min    18500.000000   22000.000000       0.000000          0.000000   \n",
       "25%    24000.000000   42000.000000    1675.000000       1591.000000   \n",
       "50%    27000.000000   47000.000000    4390.000000       4595.000000   \n",
       "75%    33000.000000   60000.000000   14444.000000      11783.000000   \n",
       "max    95000.000000  125000.000000  151643.000000     148395.000000   \n",
       "\n",
       "       Low_wage_jobs  \n",
       "count     173.000000  \n",
       "mean     3859.017341  \n",
       "std      6944.998579  \n",
       "min         0.000000  \n",
       "25%       340.000000  \n",
       "50%      1231.000000  \n",
       "75%      3466.000000  \n",
       "max     48207.000000  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "recent_grads.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "173"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raw_data_count=recent_grads.shape[0]\n",
    "raw_data_count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "recent_grads=recent_grads.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "172"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cleaned_data_count=recent_grads.shape[0]\n",
    "cleaned_data_count"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2. Pandas, Scatter Plots"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Most of the plotting functionality in pandas is contained within the `DataFrame.plot()` method. When we call this method, we specify the data we want plotted as well as the type of plot. We use the `kind parameter` to specify the type of plot we want. We use x and y to specify the data we want on each axis. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`recent_grads.plot(x='Sample_size', y='Employed', kind='scatter', title='Employed vs. Sample_size', figsize=(5,10))`\n",
    "\n",
    "We can access the underlying matplotlib Axes object by assigning the return value to a variable:\n",
    "\n",
    "`ax = recent_grads.plot(x='Sample_size', y='Employed', kind='scatter')\n",
    "ax.set_title('Employed vs. Sample_size')`\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## TODO:\n",
    "* Generate scatter plots in separate jupyter notebook cells to explore the following relations:\n",
    "  * Sample_size and Median\n",
    "  * Sample_size and Unemployment_rate\n",
    "  * Full_time and Median\n",
    "  * ShareWomen and Unemployment_rate\n",
    "  * Men and Median\n",
    "  * Women and Median\n",
    "* Use the plots to explore the following questions:\n",
    "  * Do students in more popular majors make more money?\n",
    "  * Do students that majored in subjects that were majority female make more money?\n",
    "  * Is there any link between the number of full-time employees and median salary?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x17828739da0>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "recent_grads.plot(x='Sample_size',y='Median',kind='scatter')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x17828739128>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "recent_grads.plot(x='Sample_size',y='Unemployment_rate',kind='scatter')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x17828ab2828>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "recent_grads.plot(x='Full_time',y='Median',kind='scatter')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x17828a98668>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "recent_grads.plot(x='ShareWomen',y='Unemployment_rate',kind='scatter')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x17828af9cc0>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "recent_grads.plot(x='Men',y='Median',kind='scatter')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x17828b629b0>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZsAAAEKCAYAAADEovgeAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvnQurowAAIABJREFUeJzt3Xt4XNV57/HvO6OLjWXHQnZcY9kxiUyoSWyXqGDiNCeBhICbmvTBEJK2cFIaTnMSQnpCuSTnFHLpecKl4YQ0h4QSWmgpxEBP7SZQSiCEQIAgB9lgCKAQwLKpMfIFC+zRZd7zx15jj6SRNJqZrbno93meeTSzZu3Za89I82rt9e61zN0RERGJU6LcDRARkdqnYCMiIrFTsBERkdgp2IiISOwUbEREJHYKNiIiEjsFGxERiZ2CjYiIxE7BRkREYldX7gZUijlz5vjixYvL3QwRkaqycePG19x97nj1FGyCxYsX09HRUe5miIhUFTN7KZ96Oo0mIiKxU7AREZHYKdiIiEjsFGxERCR2CjYiIhI7BZsi9fSm2LR1Dz29qXI3RUSkYin1uQjrO7dx8Z2bqU8k6E+nufL0ZaxZsaDczRIRqTjq2RSopzfFxXdu5kB/mn2pAQ70p7nozs3q4YiI5KBgU6Du3fupTwx9++oTCbp37y9Ti0REKpeCTYFam6fTn04PKetPp2ltnl6mFomIVC4FmwK1NDVy5enLmFafYGZjHdPqE1x5+jJamhrL3TQRkYqjBIEirFmxgFVtc+jevZ/W5ukKNCIio1CwKVJLU6OCjIjIOHQaTUREYqdgIyIisVOwERGR2CnYiIhI7BRsREQkdgo2IiISOwUbERGJnYKNiIjETsFGRERip2AjIiKxU7AREZHYKdiIiEjsFGxERCR2CjYiIhK72IKNmd1oZq+a2VNZZYeb2b1m9nz42RzKzcyuNbMuM9tsZsdmbXNOqP+8mZ2TVf4eM3sybHOtmdlY+xARkfKJs2fzD8Apw8ouAe5z9yXAfeExwKnAknA7D7gOosABXAYcDxwHXJYVPK4LdTPbnTLOPkREpExiCzbu/iCwa1jxacBN4f5NwMeyym/2yKPAbDObD3wEuNfdd7n7buBe4JTw3Cx3f8TdHbh52Gvl2oeIiJTJZI/ZzHP3VwDCz7eG8gXA1qx63aFsrPLuHOVj7UNERMqkUhIELEeZF1A+sZ2anWdmHWbWsXPnzoluLiIieZrsYLMjnAIj/Hw1lHcDC7PqtQLbxylvzVE+1j5GcPfr3b3d3dvnzp1b8EGJiMjYJjvYbAAyGWXnAOuzys8OWWkrgb3hFNg9wMlm1hwSA04G7gnP7TOzlSEL7exhr5VrHyIiUiZ1cb2wmd0KfACYY2bdRFll3wDWmdm5wMvAGaH6XcBqoAt4E/gUgLvvMrOvAY+Hel9190zSwWeIMt6mA3eHG2PsQ0REysSiZC5pb2/3jo6OcjdDRKSqmNlGd28fr16lJAiIiEgNU7AREZHYKdiIiEjsFGxERCR2CjYiIhI7BRsREYmdgo2IiMROwUZERGKnYCMiIrFTsBERkdgp2IiISOwUbEREJHYKNiIiEjsFGxERiZ2CjYiIxE7BRkREYqdgIyIisVOwERGR2CnYiIhI7BRsREQkdgo2IiISOwUbERGJnYKNiIjETsFGRERip2AjIiKxU7AREZHYKdiIiEjsFGxERCR2CjYiIhI7BRsREYmdgo2IiMROwUZERGKnYCMiIrFTsBERkdgp2IiISOwUbEREJHZlCTZm9hdmtsXMnjKzW81smpkdaWaPmdnzZvYDM2sIdRvD467w/OKs17k0lD9rZh/JKj8llHWZ2SWTf4QiIpJt0oONmS0APg+0u/u7gCRwFnAFcI27LwF2A+eGTc4Fdrt7G3BNqIeZLQ3bHQOcAvxfM0uaWRL4DnAqsBT4RKgrIiJlUq7TaHXAdDOrAw4DXgFOBO4Iz98EfCzcPy08Jjx/kplZKL/N3VPu/hugCzgu3Lrc/QV37wNuC3VFRKRMJj3YuPs24GrgZaIgsxfYCOxx94FQrRtYEO4vALaGbQdC/Zbs8mHbjFYuIiJlUo7TaM1EPY0jgSOAGUSnvIbzzCajPDfR8lxtOc/MOsysY+fOneM1XUREClSO02gfAn7j7jvdvR/4F+C9wOxwWg2gFdge7ncDCwHC828BdmWXD9tmtPIR3P16d2939/a5c+eW4thERCSHcgSbl4GVZnZYGHs5CXga+AmwNtQ5B1gf7m8IjwnP3+/uHsrPCtlqRwJLgF8AjwNLQnZbA1ESwYZJOC4RERlF3fhVSsvdHzOzO4BfAgPAE8D1wI+A28zs66Hs+2GT7wP/aGZdRD2as8LrbDGzdUSBagD4rLsPApjZ54B7iDLdbnT3LZN1fCIiMpJFnQRpb2/3jo6OcjdDRKSqmNlGd28fr55mEBARkdgp2IiISOwUbEREJHYKNkXq6U2xaeseenpT5W6KiEjFmvRstFqyvnMbF9+5mfpEgv50mitPX8aaFZqsQERkOPVsCtTTm+LiOzdzoD/NvtQAB/rTXHTnZvVwRERyULApUPfu/dQnhr599YkE3bv3l6lFIiKVS8GmQK3N0+lPp4eU9afTtDZPL1OLREQql4JNgVqaGjnzPa1Dys5sb6WlqbFMLRIRqVwKNgXq6U2xbmP3kLJ1Hd0asxERyUHBpkAasxERyV9eqc9mNhf4NLA4ext3/9N4mlX5NGYjIpK/fHs264nWkfkx0ezMmduU1dLUyJWnL2NafYKZjXVMq09w5enLNGYjIpJDvhd1HubuF8fakiq0ZsUCVrXNoXv3flqbpyvQiIiMIt+ezQ/NbHWsLRERkZqVb8/mAuBLZpYC+gED3N1nxdayKqDpakRE8pNXz8bdZ7p7wt2nu/us8HhKBxpNVyMikr+8J+I0s2ZgCTAtU+buD8bRqGqQSX0+wKGMtEzqs8ZuRESGyjf1+c+ITqW1Ap3ASuAR4MT4mlbZlPosIpK/fBMELgB+F3jJ3T8I/A6wM7ZWVQGlPouI5C/f02gH3P2AmWFmje7+KzN7Z6wtqwJKfRYRyU++wabbzGYD/wrca2a7ge3xNat6tDQ1KsiIiIwjr2Dj7n8Y7l5uZj8hmk3g32NrlYiI1JQxg42ZzXL3183s8KziJ8PPJmBXbC0TEZGaMV7P5p+BjwIbASe6mDPDgbfH1C4REakhYwYbd/9o+Hnk5DSn+vT0pvJOEJhIXRGRWjLeabRjx3re3X9Z2uZUl4lMV6OpbURkKhvvNNrfhJ/TgHZgE9GptGXAY8D74mtaZcueriYzi8BFd25mVducEb2WidQVEalFY17U6e4fDBdxvgQc6+7t7v4eoos6uyajgZVqIit1alVPEZnq8p1B4Gh3z2Sh4e5PASviaVJ1mMh0NZraRkSmunyDzTNmdoOZfcDM/ouZ/R3wTJwNq3QTma5GU9uIyFRn7j5+JbNpwGeA94eiB4Hr3P1AjG2bVO3t7d7R0THh7ZSNJiJTmZltdPf28erlO4PAATP7LnCXuz9bdOtqSCZobNm+FzCOOWLWqIFEU9uIyFSV7xIDa4CrgAbgSDNbAXzV3dfE2bhqsL5zGxfevon+waiHWJeAb565QmnNIiJZ8h2zuQw4DtgD4O6dwOKY2lQ1enpTXHTH5oOBBmAgDX95xyat2CkikiXfYDPg7ntjbUkV6t69n2TCRpQnTWnNIiLZ8g02T5nZJ4GkmS0xs28DPy90p2Y228zuMLNfmdkzZnaCmR1uZvea2fPhZ3Ooa2Z2rZl1mdnm7FkNzOycUP95Mzsnq/w9ZvZk2OZaMxsZEUqgtXk6g+mRCRaDrrRmEZFs+Qab84FjgBRwK/A68IUi9vst4N/d/WhgOVEa9SXAfe6+BLgvPAY4FVgSbucB1wGEmagvA44nOsV3WSZAhTrnZW13ShFtHVVLUyNXrV1GXda7mDS4au1yWpoa6elN8eBzr/Lgczvp6U3R05ti09Y9Q06x5SoTEak1+WajvQl8OdyKYmaziFKo/2t47T6gz8xOAz4Qqt0EPABcDJwG3OxRjvajoVc0P9S91913hde9FzjFzB4AZrn7I6H8ZuBjwN3Ftj0Xj/YR7h0yPHEgYdFten3dwbnRHDRfmohMCeNNxLlhrOcLzEZ7O7AT+HszW060fMEFwDx3fyW87itm9tZQfwGwNWv77lA2Vnl3jvKSy5UgMOhw4e2dmCWGlKc9uu1LDQBREgEYqQHNlyYitW+8ns0JRF/otxJNvFmKsY864FjgfHd/zMy+xaFTZrnk2ufwtXXyKR/5wmbnEZ1uY9GiRWO1OafREgSMxLhvVNISI1qamS9NwUZEas14Yza/BXwJeBfROMuHgdfc/afu/tMC99kNdLv7Y+HxHUTBZ0c4PUb4+WpW/YVZ27cC28cpb81RPoK7Xx8mF22fO3fuhA9ktAQBJ507umUZ9PSIbTVfmojUqvFmfR50939393OAlUQzPT9gZucXukN3/09gq5m9MxSdBDwNbAAyGWXnAOvD/Q3A2SErbSWwN5xuuwc42cyaQ2LAycA94bl9ZrYyZKGdnfVaJdXS1MjH21uHlBlw9RkruGrtMuqTh7ouCYsu+MzMjXbV2uVctVbzpYnI1DBugoCZNQK/D3yC6ELOa4F/KXK/5wO3mFkD8ALwKaLAt87MzgVeBs4Ide8CVhMFujdDXdx9l5l9DXg81PtqJlmAaB63fwCmEyUGxJIc0NObYt3G7iFldUlj6fxZtM2bydL5s3ioaydzmqZxwjtaAEbMjbaqbY7mSxORmjfmRJxmdhPRKbS7gdvC0gI1qZCJODdt3cMf3/DYwUH/jIa6BGe1t7JuY7cyzUSkppVqIs4/Ad4AjgI+n3VtpAHu7rOKamWVy7VODUDfQJqbH30ZQJlmIiKMP2aTcPeZ4TYr6zZzqgcaOLROTUNy/CS9XCtz6oJOEZkq8rqoU0a3ZsUCls6fxepvP0TfwMheTsbwTLP1ndt0QaeITBn5TlcjY2ibN5Orh2WWnX3ColEzzXp6U1x852YO9KfZlxrgQH+ai+7crB6OiNQs9WxKZM2KBSMyyy446aicmWbdu/dTn0gcHM8BXdApIrVNwaZIPb2pIat0Ll84++Bzo63MmSuxQBd0ikgtU7ApQqGrdGYSCy4aNmajXo2I1CoFmwKNtUpnPinOuU675btfXQQqItVGwaZA463SmU8gGO0022iUwSYi1UrZaAWa7FU6lcEmItVMwaZAmVU6h0y2CXzug0ti2V8mgy1brgtFRUQqkYJNEdasWMDlf3AMDUmjIWmkgb+9/3lWXXE/Gzq3lXRfymATkWqmYFOEnt4UX/vR0/QNOn0hUSA16BzoT3Ph7Zvo2rGvZPvKZLBpSQIRqUZKEChCroszM/oGndXffoir15ZuEL/QDDYRkXJTz6YIrc3T6RscHPX5voGRg/jFTr7Z0tTI8oWzFWhEpKqoZ1OEh7peo29g7AWgs6ehUeqyiExV6tkUqKc3xV/evpmxQw30DQ7S2jxdqcsiMqUp2BQoSjkeL9REqdAtTY1KXRaRKU3BpkBRyvHYi6Y1JI1PHr/oYH2lLovIVKVgU6CWpkauPmMZOWasAaJJOa8+Y/nBgXylLks106qyUixzH/9U0FTQ3t7uHR0dE96upzfFvVv+k46XdjNrWh1HzZvJEc2HccwRs3IGEk2kKdVGiS0yFjPb6O7t49ZTsIkUGmxqQb4BUIFy6unpTbHqivs50H/oFPC0+gQPX3yifgcEyD/YKPV5isv3v1b9dzs1aVVZKRWN2ZRAT2+KB5/byYPPvXrwnHY1nOPONx1badtTlxJbpFTUsynS+s5tfHFdJwPh77E+aXziuIWs6+iu+F5Avv+16r/bqUurykqpKNgUIVqtc9PBQAPQP+jc/MjLAAe/nC+8vZNdb6R4X9tcmmc0lHTco5hxlHz/a63W/241xlQampNPSkHBpgjdu/eTHjkH5wh9g3D5vz0DPEMyYRxWnyxJj6fYcZR8/2utxv9uNcZUWhNdVVZkOGWjBYVko3Xt2MeHrnmw4H0Wk9VTyiyhWstGUwaVyORRNtokeKNvkATkWGAgkjQYHCOWFzPuUcpxlHz/a62W/241xiRSeZSNVoQZDclRAw3AJacePeb2xYx7VOs4ymTQeyNSeRRsivBG3yCNdbnfwjPbF/Dp97+Ds09YNKQ8mbCSTFej6W9Gp/dGpPJozCYoZMwm19gAwAUntvEXJ7/z4OOuHfvo3LqHFQtnV1Q2Wq3TeyMSP43ZTIKWpkb+1+8v5Sv/toW6pDGQhsv+YCl/dPzbhtRrmzfzYJBpngHLF84uaRv0RZqb3huRyqFgU4T1ndv42o+epqEuQd+g5ww0mXpKwxWRqUxjNgXKnsKlNzVI30Cay9Y/RdeOfaPW01QvIjJVKdgUKNfKmwNp+PA1D7Khc9uY9bRCp4hMNWULNmaWNLMnzOyH4fGRZvaYmT1vZj8ws4ZQ3hged4XnF2e9xqWh/Fkz+0hW+SmhrMvMLomj/a3N00kNDIwod+AvftDJDzdto6c3xYyGJKmBwSF1+gYHaW2eTteOfdzRsZWuHfuGTNxZDZN4VjK9fyKVp5xjNhcAzwCzwuMrgGvc/TYz+y5wLnBd+Lnb3dvM7KxQ7+NmthQ4CzgGOAL4sZkdFV7rO8CHgW7gcTPb4O5Pl7LxLU2NvO3wGTy/840Rzw06fO7WThIGCYO6ZGLI1Z1phy/c9gQ/6+o5WGZAU2Md+/sHMDOm1ZVmSpupRuNjIpWpLD0bM2sFfh+4ITw24ETgjlDlJuBj4f5p4THh+ZNC/dOA29w95e6/AbqA48Kty91fcPc+4LZQt6S6duzLGWiypT06tTY8Nbp/0IcEGoh6RPtSAwyko+c1vjNxGh8TqVzlOo32f4CLODTTSwuwx90z56W6gcy/owuArQDh+b2h/sHyYduMVj6CmZ1nZh1m1rFz584JHUDn1j0Tql+ofMZ3dNooovExkco16afRzOyjwKvuvtHMPpApzlHVx3lutPJcATTnlavufj1wPUQXdY7R7BFWlPBambGMN82KThsdomlqRCpXOXo2q4A1ZvYi0SmuE4l6OrPNLBP8WoHt4X43sBAgPP8WYFd2+bBtRisvqbZ5M0dMRTOcEU3GeVh9gmQiup+ZPuX32lpG1J3ZWEddIlqALZ9pVqbCaaOJ9Noma5qacvUk1YOVajbpPRt3vxS4FCD0bC509z8ys9uBtUQB6BxgfdhkQ3j8SHj+fnd3M9sA/LOZfZMoQWAJ8Aui7+0lZnYksI0oieCTcRzLBScdxQ8e7yaVtXpafdK45szlbNn+Ojc+/CK482Z/+uAcaue9/+188vhFtDQ15pzGZkZDku179wPGMUfMGvOLstZnNy6k1xb3Ql/l6kmqByvVrpJmELgYuM3Mvg48AXw/lH8f+Ecz6yLq0ZwF4O5bzGwd8DQwAHzW3QcBzOxzwD1AErjR3bfE0eDu3fth2NxyCWDW9Ab+/ucvDglCmfvfeaCLTx4f9Yja5s2kbd7Mg3Ue6nptQl8otXzaKLvXlgmmF925mVVtc8YNIHFNU1NMm6pxvyKlVNaLOt39AXf/aLj/grsf5+5t7n6Gu6dC+YHwuC08/0LW9n/t7u9w93e6+91Z5Xe5+1Hhub+Oq/0zGpKkhi1Ykxp0Xt/fN2KgOmO0AetCTonV8uzGlTjYX642VeJ7ITJRldSzqTrb9x7IWf7K3gMjLuTM6BscZO/+Pnp6U0OCwpbtr5MYlvOQzymxWl0fvhJ7beVqUyW+FyITpelqipI7ge2v7/oV6XB6bXjK3EAaPnvLE6y64n5uefQlNm3dwy2PvsSnb+7gzf6hASrfL5SWpkaWL5xdM4EGKrPXVq42VeJ7ITJRWs8mKGQ9m47f9LD2e48Wtd8ZDUne6BvZC2qsM65au3zKDwJX4po05WpTJb4XIlrPZhK82PNm0a+RK9Ac1pDkytPfzcLDZxw83ZbPF02uOuNtF+cXWE9vii3b95JPZt1oKnFNmlK0qZD3vRLfC5F8KdgU4cHnJzbrQL76Bgb54u2baUhGWWlnvqeVdRu7x8xSy5Ua6zBmdluc6bTrO7dx4e2b6A8JFHUJ+OaZK6Z8Tw2UxixTk06jBRM9jda1Yx8fuubBkrejIQmOHfySzmVafYKHLz5xSO9l+PLUjXUG2JD06+ztcm0z/HXHM9p/5z29Kd77jfuH7DvTpp9fclJF/3ce96mqUrzvIpUk39NoShAoUBxzoyUMvrT6t5lWlxyz3vC01+7d+/H00OCUTkMykTu7LbNNMem06zu3seqK+/njGx5j1RX3j1jDZ/i+AZJW2em6Yx1TqUxmGrNmHJBKotNoBYpjbrS0w8Lmw0akuQ73Zt8A/Vmp1bmu9+lPOzY4erpsMem0411k2No8ncH0yJ7ZoI/++uUe/J6sCycnK41Zp+qk0qhnU6C2eTNZ/e55JX/d3W/2j0hzPfuERdRlfVKDDmu/9yh/tf5JIEoyqBv2SdYl4PwTl4yaLltMOu14/523NDVy1dpl1CcP9W7qEnDV2uU5X38yehTjmawex2SkMU+FOfOk+qhnU4SvnfZu7ntm54ixiWKsWDibtnkzWdU2h0d+3cNrvQd41xFv4dbHtjL8up6bH3mZs1cuZkZDkuFNGEjDqe/6LT55/KJRewyZC0K3bH8dcI454i15tbG1eTp9g2NfE3TotcfORquUqVji6HGM1lsr5kLcfHqAtT5nnlQnBZsiZP6Dv+jOzXjaR5zKGk/ColNnGb/X1nJwrrRv/fg5bn705XFfo3PrHpbMm8m0+sSIQec3+gZpmzd2uuxE52PLbJPd7roEOf87b2lq5P1HvXXM16qUL8ZMj+OiYe9FoW0Y7zRWIWnM+Z4a04wDUokUbIq0ZsUCls6fxanX/mzC2w4f1nj8pd309KbY/UZfXoEGODhjdC7jfbkU0qvIbJOdLZdMJFjVNiev9uZqY6V8MZZq6p84emsTec1SB06pXZM5VqpgUwJv9A1Gf9SDuedDy6UuAXXJob2RBMaW7a/z6uu551wbbvW759E8o6HgL5dCehW5tmlIFt4TqbQvxlJcOBlHb22ir1mrc+ZJ6Ux2EomCTQm0Nk9n0PMft2lIJjCDgWHZYm/2D/Lpmzv44oePGnN7A+qTCX72XA+rrrj/4C/JRL9cCulVxNETqbUvxjjeo0JeUzMOyGjKMVaqbLQSiMZulg/JCEsm4Mz2BTQkoTGZoM6gLlx70jeYJjWQxsyoH/YJpAbSfPPHz3Fm+9D/MDIreTbWGXVJo29wZKbRRCfkbGlqZM2y+UPK1iyfT/fu/aNmLsWVTVVLk4nG8R5pMk4ppXIsW6EZBIJCJuIcrqc3xQ0/e4HvP/QbGuoS7O9P57zeJMOILrwcGFZnZmMd//RnxzOjITliJc+9+/v47C1PsC81MKL+8gle+3PLoy/x5X99akR5U2OSgbSP2a0u93Ux1SCO90jvu5RCKWey0ESckyjzBTCjIcnf//xF+gZ9RGpwLg4jAg0cOj3S0tQ4ZCXPzDQzpThF09Ob4is/fDrnc72pqO1jdat1imZ8cbxHet+lFMoxVqpgU6TsQbbUYBorsqfYkLQxP/RS/ZJ0795PQ9LoGxi9jq7NEKldkz1WqmBThFyDbMVoqEtw1/nvG9KbGb6/7t37WdU2h4cvPnHUSTDzmda/tXl6zl5VNl2bIVLbJrOnrGBThNHSgPsG8w88dQmYXl93sIcyWqDJJ01xItP6Z/eQkololumPrTiCDZu2V0QKsojUFgWbIuRKRzWLToX1DbnoEb7y0aX8zw0jx0hu+/RK6uuS4y6KNl6aYk9viovuGHqx5UAa/vKOTaOOu+TqRl98ytEagBaRklPqcxFypaNetXYZV5+xnGn1CRrDRJT1yQRfv/tX/F5by5Dtzz5hEe1Htoyb8psrTTFzAWh2nUKm9R+eclxLKcgiUjnUsynSaINsS+fPYvW3HwL8YHrhIy/00JBMkLAoE639bYfntY9cPajMBaBXrY1OpxUyrb+IyGRRz6YEhvcGenpTdG7dQ/2wnsZAOrqg88BAdFFnvtO+Z3pQ0eqbh2S/xkSn9RcRmUzq2ZRYZiC/LmG80Tf2tTYTSS1es2IBsw+r58//6Ze8mfW62a+R77T+IiKTTcGmRLp27OOhrp3877ueYXiMidabSZN2hgzgTzS1+Jgj3kLah6/IOfQ18pnWX0RksinYlMBf/euToy4JMKMxyVf+4Bg+ePRbebjrtaIuxqy0GZJFRPKlYFOkrh37xlx7ZjDtfPDotw45zVVManGtzZAsIlODgk2ROrfuyVnemExgOVawLMUVu5ofS0SqjYJNkVaMMtPy5WuWcvIxv6WgICKCUp+L1jZvJmefsGhIWX3S+MoPn+bhrtfK1CoRkcqiYFMCXz3t3dzx31YevMalf9CHLGomIjLVKdiUQE9vihd73qQhObkr34mIVAuN2RRprIs4NUW/iEhEwaYI2bMxZ5vRkGTQXdfAiIgECjZFyLWeTfZFnAo0IiKRSR+zMbOFZvYTM3vGzLaY2QWh/HAzu9fMng8/m0O5mdm1ZtZlZpvN7Nis1zon1H/ezM7JKn+PmT0ZtrnWzEbOvV8CuWZjzr6IU0REIuVIEBgAvujuvw2sBD5rZkuBS4D73H0JcF94DHAqsCTczgOugyg4AZcBxwPHAZdlAlSoc17WdqfEcSC51rPRqTMRkZEm/TSau78CvBLu7zOzZ4AFwGnAB0K1m4AHgItD+c3u7sCjZjbbzOaHuve6+y4AM7sXOMXMHgBmufsjofxm4GPA3XEcj6aPEREZX1nHbMxsMfA7wGPAvBCIcPdXzCwzdfECYGvWZt2hbKzy7hzlufZ/HlEPiEWLFuWqkhdNHyMiMrayXWdjZk3AncAX3P31sarmKPMCykcWul/v7u3u3j537tzxmiwiIgUqS7Axs3qiQHOLu/9LKN4RTo8Rfr4ayruBhVmbtwLbxylvzVEuIiJlUo5sNAO+Dzzj7t/MemoDkMkoOwdYn1V+dshKWwnsDafb7gFONrPmkBhwMnBPeG6fma0M+zo767VERKQMyjF+zLnhAAAGy0lEQVRmswr4E+BJM+sMZV8CvgGsM7NzgZeBM8JzdwGrgS7gTeBTAO6+y8y+Bjwe6n01kywAfAb4B2A6UWJALMkBIiKSH3PPOZwx5bS3t3tHR0e5myEiUlXMbKO7t49bT8EmYmY7gZcK3HwOUAvrCdTCcdTCMUBtHEctHAPUxnHEeQxvc/dxM6wUbErAzDryieyVrhaOoxaOAWrjOGrhGKA2jqMSjkFLDIiISOwUbEREJHYKNqVxfbkbUCK1cBy1cAxQG8dRC8cAtXEcZT8GjdmIiEjs1LMREZHYKdgUycxOMbNnw9o5l4y/RfzM7MWwnk+nmXWEsopeL8jMbjSzV83sqayy2Ns82j5KfByXm9m28Hl0mtnqrOcuDW161sw+klWe8/fKzI40s8dCe39gZg2hvDE87grPLy7iGMq25lQpP48xjqNqPg8zm2ZmvzCzTeEYvlLofkt1bAVzd90KvAFJ4NfA24EGYBOwtALa9SIwZ1jZlcAl4f4lwBXh/mqiGRaMaH2hx0L54cAL4WdzuN8cnvsFcELY5m7g1BK0+f3AscBTk9nm0fZR4uO4HLgwR92l4XemETgy/C4lx/q9AtYBZ4X73wU+E+7/d+C74f5ZwA+KOIb5wLHh/kzgudDWqvo8xjiOqvk8wvvTFO7XE82Qv3Ki+y3lsRX8eRT7JTGVb+GP5Z6sx5cCl1ZAu15kZLB5Fpgf7s8Hng33vwd8Yng94BPA97LKvxfK5gO/yiofUq/Idi9m6Jd07G0ebR8lPo7Lyf3lNuT3hWi+vxNG+70KXzyvAXXDf/8y24b7daGelehzWQ98uFo/jxzHUZWfB3AY8EuiBSMntN9SHluhN51GK85oa+qUmwP/YWYbLVqzB4atFwTEvl5QCUxGm0fbR6l9LpxiujHr1NBEj6MF2OPuA8PKh7xWeH5vqF8UG2PNKaro8xh2HFBFn4eZJS2aR/JV4F6inshE91vKYyuIgk1x8l47Z5KtcvdjiZbU/qyZvX+MurGtFxSjamvzdcA7gBVEq9T+TSgv5XGU/BitAtacKoUcx1FVn4e7D7r7CqLlUo4DfruA/Zb9M1KwKc5oa+qUlbtvDz9fBf4f0S9oNa4XNBltHm0fJePuO8IXRhr4O6LPo5DjeA2YbWZ1w8qHvFZ4/i3ALgpk5VtzqqSfR67jqMbPI7R7D/AA0ZjNRPdbymMriIJNcR4HloSsjQaiAbkN5WyQmc0ws5mZ+0Tr/DxFda4XNBltHm0fJZP58gz+kOjzyOz7rJBBdCSwhGjgPOfvlUcnz38CrM3R3uzjWAvcH+oX0t5yrjlVss9jtOOops/DzOaa2exwfzrwIeCZAvZbymMrTKkG36bqjSgT5zmi86hfroD2vJ0oo2QTsCXTJqJzsPcBz4efh4dyA74T2v8k0J71Wn9KtI5QF/CprPJ2oj/QXwN/SwkGooFbiU5p9BP9t3XuZLR5tH2U+Dj+MbRzM9Ef/fys+l8ObXqWrKy+0X6vwuf7i3B8twONoXxaeNwVnn97EcfwPqJTJpuBznBbXW2fxxjHUTWfB7AMeCK09Sngrwrdb6mOrdCbZhAQEZHY6TSaiIjETsFGRERip2AjIiKxU7AREZHYKdiIiEjsFGxEYmJm15jZF7Ie32NmN2Q9/hsz+x/laZ3I5FKwEYnPz4H3AphZApgDHJP1/HuBh8vQLpFJp2AjEp+HCcGGKMg8RXTlfLOZNRLNcdVpZleZ2VMWre/ycQAz+4CZ/dTM1pnZc2b2DTP7I4vWNnnSzN4R6s01szvN7PFwWxXKLw+TTD5gZi+Y2ecn//BFDqkbv4qIFMLdt5vZgJktIgo6jxDNnHsC0Wy8m4GPEk0IuZyo5/O4mT0YXmI5UUDaRbQWzA3ufpxFi4CdD3wB+BZwjbs/FPZzD4cmajwa+CDRWi7Pmtl17t4f93GL5KJgIxKvTO/mvcA3iYLNe4mCzc+JplS51d0HiSah/Cnwu8DrwOMepts3s18D/xFe80miIALRXFlL7dBiqbMyc+MBP3L3FJAys1eBeQyd2l9k0ijYiMQrM27zbqLTaFuBLxIFkxuBk8bYNpV1P531OM2hv90E0WJZ+7M3DMEne/tB9PcuZaQxG5F4PUx0qmyXR9Pa7wJmE51KewR4EPi4RQtkzSVaVvoXE3j9/wA+l3lgZitK1nKRElKwEYnXk0RjMY8OK9vr7q8RrTe0mWiW7vuBi9z9Pyfw+p8H2i1adfJp4M9L02yR0tKszyIiEjv1bEREJHYKNiIiEjsFGxERiZ2CjYiIxE7BRkREYqdgIyIisVOwERGR2CnYiIhI7P4/7hhZOScmrv8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "recent_grads.plot(x='Women',y='Median',kind='scatter')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3. Pandas, Histograms"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To explore the distribution of values in a column, we can select it from the DataFrame, call Series.plot(), and set the kind parameter to hist:\n",
    "\n",
    "`recent_grads['Sample_size'].plot(kind='hist')`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## TODO:\n",
    "* Generate histograms in separate jupyter notebook cells to explore the distributions of the following columns:\n",
    "  * Sample_size\n",
    "  * Median\n",
    "  * Employed\n",
    "  * Full_time\n",
    "  * ShareWomen\n",
    "  * Unemployment_rate\n",
    "  * Men\n",
    "  * Women\n",
    "* We encourage you to experiment with different bin sizes and ranges when generating these histograms.\n",
    "* Use the plots to explore the following questions:\n",
    "  * What percent of majors are predominantly male? Predominantly female?\n",
    "  * What's the most common median salary range?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x17828b69cf8>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "recent_grads['Sample_size'].plot(kind='hist')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x17828b9b7f0>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "recent_grads['Median'].plot(kind='hist')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x17828c217f0>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "recent_grads['Median'].plot(kind='hist',bins=40)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x17828ceda20>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "recent_grads['Employed'].plot(kind='hist')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x17828db39b0>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "recent_grads['ShareWomen'].plot(kind='hist',bins=30)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 4. Pandas, Scatter Matrix Plot"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* In the last 2 steps, we created individual scatter plots to visualize potential relationships between columns and histograms to visualize the distributions of individual columns. **A scatter matrix plot combines both scatter plots and histograms into one grid of plots and allows us to explore potential relationships and distributions simultaneously**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Because scatter matrix plots are frequently used in the exploratory data analysis, pandas contains a function named `scatter_matrix()` that generates the plots for us. This function is part of the pandas.plotting module and needs to be imported separately."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## TODO:\n",
    "* Import scatter_matrix from the pandas.plotting module.\n",
    "* Create a 2 by 2 scatter matrix plot using the Sample_size and Median columns.\n",
    "* Create a 3 by 3 scatter matrix plot using the Sample_size, Median, and Unemployment_rate columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pandas.plotting import scatter_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x0000017828DE6080>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x0000017829EE0518>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x0000017829F08A90>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x0000017829F39048>]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x360 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "scatter_matrix(recent_grads[['Sample_size','Median']],figsize=(5,5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x0000017829FDFDA0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x000001782A025F98>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x000001782A054550>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x000001782A07AAC8>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x000001782A0AD080>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x000001782A0D55F8>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x000001782A0FCB38>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x000001782A12E0F0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x000001782A12E128>]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x360 with 9 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "scatter_matrix(recent_grads[['Sample_size','Median','Unemployment_rate']],figsize=(5,5))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 5. Pandas, Bar Plots"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* If we instead use the `DataFrame.plot.bar()` method, we can use the `x parameter to specify the labels` and the y parameter to specify the data for the bars"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## TODO:\n",
    "\n",
    "* Use bar plots to compare the percentages of women (ShareWomen) from the first ten rows and last ten rows of the recent_grads dataframe.\n",
    "* Use bar plots to compare the unemployment rate (Unemployment_rate) from the first ten rows and last ten rows of the recent_grads dataframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1782a2a8eb8>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "recent_grads[:10].plot.bar(x='Major', y='ShareWomen', legend=False)\n",
    "recent_grads[163:].plot.bar(x='Major', y='ShareWomen', legend=False)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}