{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "# Subplots: Apple & Microsoft stock"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>Adj Close</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td> 2015-07-01</td>\n",
       "      <td> 126.900002</td>\n",
       "      <td> 126.940002</td>\n",
       "      <td> 125.989998</td>\n",
       "      <td> 126.599998</td>\n",
       "      <td> 30128600</td>\n",
       "      <td> 126.599998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td> 2015-06-30</td>\n",
       "      <td> 125.570000</td>\n",
       "      <td> 126.120003</td>\n",
       "      <td> 124.860001</td>\n",
       "      <td> 125.430000</td>\n",
       "      <td> 43849800</td>\n",
       "      <td> 125.430000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td> 2015-06-29</td>\n",
       "      <td> 125.459999</td>\n",
       "      <td> 126.470001</td>\n",
       "      <td> 124.480003</td>\n",
       "      <td> 124.529999</td>\n",
       "      <td> 48911400</td>\n",
       "      <td> 124.529999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td> 2015-06-26</td>\n",
       "      <td> 127.669998</td>\n",
       "      <td> 127.989998</td>\n",
       "      <td> 126.510002</td>\n",
       "      <td> 126.750000</td>\n",
       "      <td> 42111000</td>\n",
       "      <td> 126.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td> 2015-06-25</td>\n",
       "      <td> 128.860001</td>\n",
       "      <td> 129.199997</td>\n",
       "      <td> 127.500000</td>\n",
       "      <td> 127.500000</td>\n",
       "      <td> 31816700</td>\n",
       "      <td> 127.500000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Date        Open        High         Low       Close    Volume  \\\n",
       "0  2015-07-01  126.900002  126.940002  125.989998  126.599998  30128600   \n",
       "1  2015-06-30  125.570000  126.120003  124.860001  125.430000  43849800   \n",
       "2  2015-06-29  125.459999  126.470001  124.480003  124.529999  48911400   \n",
       "3  2015-06-26  127.669998  127.989998  126.510002  126.750000  42111000   \n",
       "4  2015-06-25  128.860001  129.199997  127.500000  127.500000  31816700   \n",
       "\n",
       "    Adj Close  \n",
       "0  126.599998  \n",
       "1  125.430000  \n",
       "2  124.529999  \n",
       "3  126.750000  \n",
       "4  127.500000  "
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "apple =  pd.read_csv(\"AAPL_historical_stock.csv\")\n",
    "msft = pd.read_csv(\"MSFT_historical_stock.csv\")\n",
    "\n",
    "apple.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>Adj Close</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td> 2015-07-01</td>\n",
       "      <td> 44.459999</td>\n",
       "      <td> 45.230000</td>\n",
       "      <td> 44.099998</td>\n",
       "      <td> 44.450001</td>\n",
       "      <td> 28337200</td>\n",
       "      <td> 44.450001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td> 2015-06-30</td>\n",
       "      <td> 44.709999</td>\n",
       "      <td> 44.720001</td>\n",
       "      <td> 43.939999</td>\n",
       "      <td> 44.150002</td>\n",
       "      <td> 35214100</td>\n",
       "      <td> 44.150002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td> 2015-06-29</td>\n",
       "      <td> 45.040001</td>\n",
       "      <td> 45.230000</td>\n",
       "      <td> 44.360001</td>\n",
       "      <td> 44.369999</td>\n",
       "      <td> 33899600</td>\n",
       "      <td> 44.369999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td> 2015-06-26</td>\n",
       "      <td> 45.650002</td>\n",
       "      <td> 46.279999</td>\n",
       "      <td> 45.029999</td>\n",
       "      <td> 45.259998</td>\n",
       "      <td> 45610900</td>\n",
       "      <td> 45.259998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td> 2015-06-25</td>\n",
       "      <td> 46.029999</td>\n",
       "      <td> 46.060001</td>\n",
       "      <td> 45.500000</td>\n",
       "      <td> 45.650002</td>\n",
       "      <td> 20569500</td>\n",
       "      <td> 45.650002</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Date       Open       High        Low      Close    Volume  Adj Close\n",
       "0  2015-07-01  44.459999  45.230000  44.099998  44.450001  28337200  44.450001\n",
       "1  2015-06-30  44.709999  44.720001  43.939999  44.150002  35214100  44.150002\n",
       "2  2015-06-29  45.040001  45.230000  44.360001  44.369999  33899600  44.369999\n",
       "3  2015-06-26  45.650002  46.279999  45.029999  45.259998  45610900  45.259998\n",
       "4  2015-06-25  46.029999  46.060001  45.500000  45.650002  20569500  45.650002"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "msft.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# Convert Date columns to datetime64 dtype.\n",
    "apple[\"Date\"] = pd.to_datetime(apple[\"Date\"])\n",
    "msft[\"Date\"] = pd.to_datetime(msft[\"Date\"])\n",
    "\n",
    "apple.set_index(\"Date\", inplace=True)\n",
    "msft.set_index(\"Date\", inplace=True)\n",
    "apple.sort_index(inplace=True)\n",
    "msft.sort_index(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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WAABDFCsAgCGKFQDAEMUKAGCIYgUAMESxAgAYolgBAAxRrAAAhihWAABDFCsA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\nBQAw5P8DVAW3d75lTJEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10e6bd438>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a Figure instance, and Axes objects in a 2 row by 1 column layout.\n",
    "fig = plt.figure(figsize=(10,10))\n",
    "ax1 = fig.add_subplot(2,2,1)\n",
    "ax2 = fig.add_subplot(2,2,2)\n",
    "ax3 = fig.add_subplot(2,2,3)\n",
    "ax4 = fig.add_subplot(2,2,4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# Function to plot intervals.\n",
    "def plot_interval(start_date, end_date):\n",
    "    # Use to_datetime() to try to convert string values to datetime values.\n",
    "    start_datetime = pd.to_datetime(start_date)\n",
    "    end_datetime = pd.to_datetime(end_date)\n",
    "    \n",
    "    # Since these DataFrames have a DateTime index, you can use slicing for selection / filtering.\n",
    "    apple_interval = apple[start_datetime:end_datetime]\n",
    "    msft_interval = msft[start_datetime:end_datetime]\n",
    "    \n",
    "    # Plot the low and high prices on top 2 plots.\n",
    "    ax1.plot(apple_interval.index, apple_interval[\"Low\"])\n",
    "    ax1.plot(apple_interval.index, apple_interval[\"High\"])\n",
    "    ax1.set_title(\"Apple: High and Low\")\n",
    "    ax2.plot(msft_interval.index, msft_interval[\"Low\"])\n",
    "    ax2.plot(msft_interval.index, msft_interval[\"High\"])\n",
    "    ax2.set_title(\"Microsoft: High and Low\")\n",
    "    \n",
    "    # Plot trading volumes on bottom 2 plots.\n",
    "    ax3.plot(apple_interval.index, apple_interval[\"Volume\"])\n",
    "    ax3.set_title(\"Apple: Trading Volume\")\n",
    "    ax4.plot(msft_interval.index, msft_interval[\"Volume\"])\n",
    "    ax4.set_title(\"Microsoft: Trading Volume\")\n",
    "    fig.autofmt_xdate()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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ScVJH9h3fxzd3f0P+HPn9DsmYdBEREcHM2TOZuXEm6/eu59DMQ9bHzBgTsoKm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+OO0UM7YkTP2Fm/Pua6Pf10zJs87riesZMSN9wQeXPvuw/237/o2mxh4sR4f517btE16RmqeSgT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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10e6bd438>"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# IPhone Launch Week - June 29, 07\n",
    "plot_interval(\"6/25/2007\", \"7/1/2007\")\n",
    "fig"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# Subplots: recreating pair plots using Matplotlib only"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Rank</th>\n",
       "      <th>Major_code</th>\n",
       "      <th>Major</th>\n",
       "      <th>Major_category</th>\n",
       "      <th>Total</th>\n",
       "      <th>Sample_size</th>\n",
       "      <th>Men</th>\n",
       "      <th>Women</th>\n",
       "      <th>ShareWomen</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> Engineering</td>\n",
       "      <td>  2339</td>\n",
       "      <td>  36</td>\n",
       "      <td>  2057</td>\n",
       "      <td>   282</td>\n",
       "      <td> 0.120564</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> Engineering</td>\n",
       "      <td>   756</td>\n",
       "      <td>   7</td>\n",
       "      <td>   679</td>\n",
       "      <td>    77</td>\n",
       "      <td> 0.101852</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> Engineering</td>\n",
       "      <td>   856</td>\n",
       "      <td>   3</td>\n",
       "      <td>   725</td>\n",
       "      <td>   131</td>\n",
       "      <td> 0.153037</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> Engineering</td>\n",
       "      <td>  1258</td>\n",
       "      <td>  16</td>\n",
       "      <td>  1123</td>\n",
       "      <td>   135</td>\n",
       "      <td> 0.107313</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> Engineering</td>\n",
       "      <td> 32260</td>\n",
       "      <td> 289</td>\n",
       "      <td> 21239</td>\n",
       "      <td> 11021</td>\n",
       "      <td> 0.341631</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 Major_category  \\\n",
       "0     1        2419                      PETROLEUM ENGINEERING    Engineering   \n",
       "1     2        2416             MINING AND MINERAL ENGINEERING    Engineering   \n",
       "2     3        2415                  METALLURGICAL ENGINEERING    Engineering   \n",
       "3     4        2417  NAVAL ARCHITECTURE AND MARINE ENGINEERING    Engineering   \n",
       "4     5        2405                       CHEMICAL ENGINEERING    Engineering   \n",
       "\n",
       "   Total  Sample_size    Men  Women  ShareWomen  Employed      ...        \\\n",
       "0   2339           36   2057    282    0.120564      1976      ...         \n",
       "1    756            7    679     77    0.101852       640      ...         \n",
       "2    856            3    725    131    0.153037       648      ...         \n",
       "3   1258           16   1123    135    0.107313       758      ...         \n",
       "4  32260          289  21239  11021    0.341631     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": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "recent_grads = pd.read_csv(\"recent-grads.csv\")\n",
    "recent_grads.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# Drop any rows with `NaN` in place.\n",
    "recent_grads.dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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khxc6rPbBwRVLDmFpRusORe06EyWfZFtJ5ptDTh5oYcLVtDL4Svy3XkDvLLjODKe5GoQo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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x111013860>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig_rg = plt.figure(figsize=(7,7))\n",
    "ax1_rg = fig_rg.add_subplot(2,2,1)\n",
    "ax2_rg = fig_rg.add_subplot(2,2,2)\n",
    "ax3_rg = fig_rg.add_subplot(2,2,3)\n",
    "ax4_rg = fig_rg.add_subplot(2,2,4)\n",
    "\n",
    "# Top left plot\n",
    "ax1_rg.hist(recent_grads[\"ShareWomen\"])\n",
    "ax1_rg.set_ylabel(\"ShareWomen\")\n",
    "ax1_rg.get_xaxis().tick_top()\n",
    "ax1_rg.get_xaxis().set_visible(False)\n",
    "ax1_rg.get_yaxis().tick_left()\n",
    "\n",
    "# Top right plot\n",
    "ax2_rg.scatter(recent_grads[\"Unemployment_rate\"], recent_grads[\"ShareWomen\"])\n",
    "ax2_rg.get_yaxis().set_visible(False)\n",
    "ax2_rg.get_xaxis().set_visible(False)\n",
    "\n",
    "# Bottom left plot\n",
    "ax3_rg.scatter(recent_grads[\"ShareWomen\"], recent_grads[\"Unemployment_rate\"])\n",
    "ax3_rg.set_xlim(0.0, 1.0)\n",
    "ax3_rg.set_xlabel(\"ShareWomen\")\n",
    "ax3_rg.set_ylabel(\"Unemployment_rate\")\n",
    "ax3_rg.get_xaxis().tick_bottom()\n",
    "ax3_rg.get_yaxis().tick_left()\n",
    "\n",
    "# Bottom right plot\n",
    "ax4_rg.hist(recent_grads[\"Unemployment_rate\"])\n",
    "ax4_rg.set_xlabel(\"Unemployment_rate\")\n",
    "ax4_rg.get_yaxis().set_visible(False)\n",
    "ax4_rg.get_xaxis().tick_bottom()\n",
    "ax4_rg.set_xlim(0.0, 0.20)\n",
    "\n",
    "# Remove space between sub plots.\n",
    "plt.subplots_adjust(wspace=0, hspace=0)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Grouped bar plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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4+oqjR/p6q45ZNdLXi9mbmJhgYmJiy/6yZct6XjcwvG1vlnQycBEwDzjL9mpJJ9XnVwD/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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x110e1c7f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "recent_grads[\"ShareMen\"] = recent_grads[\"Men\"] / recent_grads[\"Total\"]\n",
    "\n",
    "fig_rg2 = plt.figure(figsize=(6,5))\n",
    "ax1_rg2 = fig_rg2.add_subplot(1,1,1)\n",
    "\n",
    "def grouped_bar_plots(majors_list):\n",
    "    n = len(majors_list)\n",
    "    locs = np.arange(n)\n",
    "    specific_rows = recent_grads[recent_grads[\"Major\"].isin(majors_list)]\n",
    "    width = 0.35\n",
    "    men_proportions = specific_rows[\"ShareMen\"].tolist()\n",
    "    women_proportions = specific_rows[\"ShareWomen\"].tolist()\n",
    "    \n",
    "    # Plot both bar plots.\n",
    "    ax1_rg2.bar(locs, men_proportions, width=width, color='lightblue')\n",
    "    ax1_rg2.bar(locs + width, women_proportions, width=width, color=\"limegreen\")\n",
    "    \n",
    "    # X ticks and labels\n",
    "    ax1_rg2.set_xticks(locs + width)\n",
    "    ax1_rg2.set_xticklabels(majors_list, rotation=90)\n",
    "    \n",
    "    \n",
    "majors = [\"PETROLEUM ENGINEERING\", \"ACTUARIAL SCIENCE\", \"COMPUTER ENGINEERING\", \"MATERIALS SCIENCE\"]\n",
    "grouped_bar_plots(majors)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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VA3UkTdfZcnAB1ETPkL5cXLQ8RbSpG5a2Dx887m5cHEL5+Pd1yhhedaNTm1rV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\n74iIHvr/iAz2Os2DuY4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1107d3f60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig_rg2 = plt.figure(figsize=(6,5))\n",
    "ax1_rg2 = fig_rg2.add_subplot(1,1,1)\n",
    "\n",
    "majors_2 = recent_grads[recent_grads[\"Major_category\"] == \"Arts\"][\"Major\"].tolist()\n",
    "grouped_bar_plots(majors_2)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}