{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Matplotlib\n",
    "\n",
    "In th first part of this tutorial, we will work with a data set in \"mtcars.csv\" that contains information on 31 cars.  We will create a collection of scatterplots showing the relationship between horsepower (hp) and miles per gallon (mpg), displacement(disp) and quarter mile time (qsec).  Also, in each of these plots we will distinguish between automatic (am = 0) and manual (am=1) cars. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>car_name</th>\n",
       "      <th>mpg</th>\n",
       "      <th>cyl</th>\n",
       "      <th>disp</th>\n",
       "      <th>hp</th>\n",
       "      <th>drat</th>\n",
       "      <th>wt</th>\n",
       "      <th>qsec</th>\n",
       "      <th>vs</th>\n",
       "      <th>am</th>\n",
       "      <th>gear</th>\n",
       "      <th>carb</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Lincoln Continental</td>\n",
       "      <td>10.4</td>\n",
       "      <td>8</td>\n",
       "      <td>460.0</td>\n",
       "      <td>215</td>\n",
       "      <td>3.00</td>\n",
       "      <td>5.424</td>\n",
       "      <td>17.82</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Dodge Challenger</td>\n",
       "      <td>15.5</td>\n",
       "      <td>8</td>\n",
       "      <td>318.0</td>\n",
       "      <td>150</td>\n",
       "      <td>2.76</td>\n",
       "      <td>3.520</td>\n",
       "      <td>16.87</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Toyota Corona</td>\n",
       "      <td>21.5</td>\n",
       "      <td>4</td>\n",
       "      <td>120.1</td>\n",
       "      <td>97</td>\n",
       "      <td>3.70</td>\n",
       "      <td>2.465</td>\n",
       "      <td>20.01</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Pontiac Firebird</td>\n",
       "      <td>19.2</td>\n",
       "      <td>8</td>\n",
       "      <td>400.0</td>\n",
       "      <td>175</td>\n",
       "      <td>3.08</td>\n",
       "      <td>3.845</td>\n",
       "      <td>17.05</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Chrysler Imperial</td>\n",
       "      <td>14.7</td>\n",
       "      <td>8</td>\n",
       "      <td>440.0</td>\n",
       "      <td>230</td>\n",
       "      <td>3.23</td>\n",
       "      <td>5.345</td>\n",
       "      <td>17.42</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Cadillac Fleetwood</td>\n",
       "      <td>10.4</td>\n",
       "      <td>8</td>\n",
       "      <td>472.0</td>\n",
       "      <td>205</td>\n",
       "      <td>2.93</td>\n",
       "      <td>5.250</td>\n",
       "      <td>17.98</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Merc 450SLC</td>\n",
       "      <td>15.2</td>\n",
       "      <td>8</td>\n",
       "      <td>275.8</td>\n",
       "      <td>180</td>\n",
       "      <td>3.07</td>\n",
       "      <td>3.780</td>\n",
       "      <td>18.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Merc 450SL</td>\n",
       "      <td>17.3</td>\n",
       "      <td>8</td>\n",
       "      <td>275.8</td>\n",
       "      <td>180</td>\n",
       "      <td>3.07</td>\n",
       "      <td>3.730</td>\n",
       "      <td>17.60</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>AMC Javelin</td>\n",
       "      <td>15.2</td>\n",
       "      <td>8</td>\n",
       "      <td>304.0</td>\n",
       "      <td>150</td>\n",
       "      <td>3.15</td>\n",
       "      <td>3.435</td>\n",
       "      <td>17.30</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Merc 280C</td>\n",
       "      <td>17.8</td>\n",
       "      <td>6</td>\n",
       "      <td>167.6</td>\n",
       "      <td>123</td>\n",
       "      <td>3.92</td>\n",
       "      <td>3.440</td>\n",
       "      <td>18.90</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Merc 450SE</td>\n",
       "      <td>16.4</td>\n",
       "      <td>8</td>\n",
       "      <td>275.8</td>\n",
       "      <td>180</td>\n",
       "      <td>3.07</td>\n",
       "      <td>4.070</td>\n",
       "      <td>17.40</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Merc 230</td>\n",
       "      <td>22.8</td>\n",
       "      <td>4</td>\n",
       "      <td>140.8</td>\n",
       "      <td>95</td>\n",
       "      <td>3.92</td>\n",
       "      <td>3.150</td>\n",
       "      <td>22.90</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Merc 240D</td>\n",
       "      <td>24.4</td>\n",
       "      <td>4</td>\n",
       "      <td>146.7</td>\n",
       "      <td>62</td>\n",
       "      <td>3.69</td>\n",
       "      <td>3.190</td>\n",
       "      <td>20.00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Duster 360</td>\n",
       "      <td>14.3</td>\n",
       "      <td>8</td>\n",
       "      <td>360.0</td>\n",
       "      <td>245</td>\n",
       "      <td>3.21</td>\n",
       "      <td>3.570</td>\n",
       "      <td>15.84</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Valiant</td>\n",
       "      <td>18.1</td>\n",
       "      <td>6</td>\n",
       "      <td>225.0</td>\n",
       "      <td>105</td>\n",
       "      <td>2.76</td>\n",
       "      <td>3.460</td>\n",
       "      <td>20.22</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Hornet Sportabout</td>\n",
       "      <td>18.7</td>\n",
       "      <td>8</td>\n",
       "      <td>360.0</td>\n",
       "      <td>175</td>\n",
       "      <td>3.15</td>\n",
       "      <td>3.440</td>\n",
       "      <td>17.02</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Hornet 4 Drive</td>\n",
       "      <td>21.4</td>\n",
       "      <td>6</td>\n",
       "      <td>258.0</td>\n",
       "      <td>110</td>\n",
       "      <td>3.08</td>\n",
       "      <td>3.215</td>\n",
       "      <td>19.44</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Merc 280</td>\n",
       "      <td>19.2</td>\n",
       "      <td>6</td>\n",
       "      <td>167.6</td>\n",
       "      <td>123</td>\n",
       "      <td>3.92</td>\n",
       "      <td>3.440</td>\n",
       "      <td>18.30</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Camaro Z28</td>\n",
       "      <td>13.3</td>\n",
       "      <td>8</td>\n",
       "      <td>350.0</td>\n",
       "      <td>245</td>\n",
       "      <td>3.73</td>\n",
       "      <td>3.840</td>\n",
       "      <td>15.41</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Lotus Europa</td>\n",
       "      <td>30.4</td>\n",
       "      <td>4</td>\n",
       "      <td>95.1</td>\n",
       "      <td>113</td>\n",
       "      <td>3.77</td>\n",
       "      <td>1.513</td>\n",
       "      <td>16.90</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Porsche 914-2</td>\n",
       "      <td>26.0</td>\n",
       "      <td>4</td>\n",
       "      <td>120.3</td>\n",
       "      <td>91</td>\n",
       "      <td>4.43</td>\n",
       "      <td>2.140</td>\n",
       "      <td>16.70</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Fiat X1-9</td>\n",
       "      <td>27.3</td>\n",
       "      <td>4</td>\n",
       "      <td>79.0</td>\n",
       "      <td>66</td>\n",
       "      <td>4.08</td>\n",
       "      <td>1.935</td>\n",
       "      <td>18.90</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Ford Pantera L</td>\n",
       "      <td>15.8</td>\n",
       "      <td>8</td>\n",
       "      <td>351.0</td>\n",
       "      <td>264</td>\n",
       "      <td>4.22</td>\n",
       "      <td>3.170</td>\n",
       "      <td>14.50</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Ferrari Dino</td>\n",
       "      <td>19.7</td>\n",
       "      <td>6</td>\n",
       "      <td>145.0</td>\n",
       "      <td>175</td>\n",
       "      <td>3.62</td>\n",
       "      <td>2.770</td>\n",
       "      <td>15.50</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Mazda RX4</td>\n",
       "      <td>21.0</td>\n",
       "      <td>6</td>\n",
       "      <td>160.0</td>\n",
       "      <td>110</td>\n",
       "      <td>3.90</td>\n",
       "      <td>2.620</td>\n",
       "      <td>16.46</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Honda Civic</td>\n",
       "      <td>30.4</td>\n",
       "      <td>4</td>\n",
       "      <td>75.7</td>\n",
       "      <td>52</td>\n",
       "      <td>4.93</td>\n",
       "      <td>1.615</td>\n",
       "      <td>18.52</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Fiat 128</td>\n",
       "      <td>32.4</td>\n",
       "      <td>4</td>\n",
       "      <td>78.7</td>\n",
       "      <td>66</td>\n",
       "      <td>4.08</td>\n",
       "      <td>2.200</td>\n",
       "      <td>19.47</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Maserati Bora</td>\n",
       "      <td>15.0</td>\n",
       "      <td>8</td>\n",
       "      <td>301.0</td>\n",
       "      <td>335</td>\n",
       "      <td>3.54</td>\n",
       "      <td>3.570</td>\n",
       "      <td>14.60</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Datsun 710</td>\n",
       "      <td>22.8</td>\n",
       "      <td>4</td>\n",
       "      <td>108.0</td>\n",
       "      <td>93</td>\n",
       "      <td>3.85</td>\n",
       "      <td>2.320</td>\n",
       "      <td>18.61</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Mazda RX4 Wag</td>\n",
       "      <td>21.0</td>\n",
       "      <td>6</td>\n",
       "      <td>160.0</td>\n",
       "      <td>110</td>\n",
       "      <td>3.90</td>\n",
       "      <td>2.875</td>\n",
       "      <td>17.02</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>Toyota Corolla</td>\n",
       "      <td>33.9</td>\n",
       "      <td>4</td>\n",
       "      <td>71.1</td>\n",
       "      <td>65</td>\n",
       "      <td>4.22</td>\n",
       "      <td>1.835</td>\n",
       "      <td>19.90</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>Volvo 142E</td>\n",
       "      <td>21.4</td>\n",
       "      <td>4</td>\n",
       "      <td>121.0</td>\n",
       "      <td>109</td>\n",
       "      <td>4.11</td>\n",
       "      <td>2.780</td>\n",
       "      <td>18.60</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               car_name   mpg  cyl   disp   hp  drat     wt   qsec  vs  am  \\\n",
       "0   Lincoln Continental  10.4    8  460.0  215  3.00  5.424  17.82   0   0   \n",
       "1      Dodge Challenger  15.5    8  318.0  150  2.76  3.520  16.87   0   0   \n",
       "2         Toyota Corona  21.5    4  120.1   97  3.70  2.465  20.01   1   0   \n",
       "3      Pontiac Firebird  19.2    8  400.0  175  3.08  3.845  17.05   0   0   \n",
       "4     Chrysler Imperial  14.7    8  440.0  230  3.23  5.345  17.42   0   0   \n",
       "5    Cadillac Fleetwood  10.4    8  472.0  205  2.93  5.250  17.98   0   0   \n",
       "6           Merc 450SLC  15.2    8  275.8  180  3.07  3.780  18.00   0   0   \n",
       "7            Merc 450SL  17.3    8  275.8  180  3.07  3.730  17.60   0   0   \n",
       "8           AMC Javelin  15.2    8  304.0  150  3.15  3.435  17.30   0   0   \n",
       "9             Merc 280C  17.8    6  167.6  123  3.92  3.440  18.90   1   0   \n",
       "10           Merc 450SE  16.4    8  275.8  180  3.07  4.070  17.40   0   0   \n",
       "11             Merc 230  22.8    4  140.8   95  3.92  3.150  22.90   1   0   \n",
       "12            Merc 240D  24.4    4  146.7   62  3.69  3.190  20.00   1   0   \n",
       "13           Duster 360  14.3    8  360.0  245  3.21  3.570  15.84   0   0   \n",
       "14              Valiant  18.1    6  225.0  105  2.76  3.460  20.22   1   0   \n",
       "15    Hornet Sportabout  18.7    8  360.0  175  3.15  3.440  17.02   0   0   \n",
       "16       Hornet 4 Drive  21.4    6  258.0  110  3.08  3.215  19.44   1   0   \n",
       "17             Merc 280  19.2    6  167.6  123  3.92  3.440  18.30   1   0   \n",
       "18           Camaro Z28  13.3    8  350.0  245  3.73  3.840  15.41   0   0   \n",
       "19         Lotus Europa  30.4    4   95.1  113  3.77  1.513  16.90   1   1   \n",
       "20        Porsche 914-2  26.0    4  120.3   91  4.43  2.140  16.70   0   1   \n",
       "21            Fiat X1-9  27.3    4   79.0   66  4.08  1.935  18.90   1   1   \n",
       "22       Ford Pantera L  15.8    8  351.0  264  4.22  3.170  14.50   0   1   \n",
       "23         Ferrari Dino  19.7    6  145.0  175  3.62  2.770  15.50   0   1   \n",
       "24            Mazda RX4  21.0    6  160.0  110  3.90  2.620  16.46   0   1   \n",
       "25          Honda Civic  30.4    4   75.7   52  4.93  1.615  18.52   1   1   \n",
       "26             Fiat 128  32.4    4   78.7   66  4.08  2.200  19.47   1   1   \n",
       "27        Maserati Bora  15.0    8  301.0  335  3.54  3.570  14.60   0   1   \n",
       "28           Datsun 710  22.8    4  108.0   93  3.85  2.320  18.61   1   1   \n",
       "29        Mazda RX4 Wag  21.0    6  160.0  110  3.90  2.875  17.02   0   1   \n",
       "30       Toyota Corolla  33.9    4   71.1   65  4.22  1.835  19.90   1   1   \n",
       "31           Volvo 142E  21.4    4  121.0  109  4.11  2.780  18.60   1   1   \n",
       "\n",
       "    gear  carb  \n",
       "0      3     4  \n",
       "1      3     2  \n",
       "2      3     1  \n",
       "3      3     2  \n",
       "4      3     4  \n",
       "5      3     4  \n",
       "6      3     3  \n",
       "7      3     3  \n",
       "8      3     2  \n",
       "9      4     4  \n",
       "10     3     3  \n",
       "11     4     2  \n",
       "12     4     2  \n",
       "13     3     4  \n",
       "14     3     1  \n",
       "15     3     2  \n",
       "16     3     1  \n",
       "17     4     4  \n",
       "18     3     4  \n",
       "19     5     2  \n",
       "20     5     2  \n",
       "21     4     1  \n",
       "22     5     4  \n",
       "23     5     6  \n",
       "24     4     4  \n",
       "25     4     2  \n",
       "26     4     1  \n",
       "27     5     8  \n",
       "28     4     1  \n",
       "29     4     4  \n",
       "30     4     1  \n",
       "31     4     2  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "\n",
    "#Read in data frame - Note the new style \"bmh\"\n",
    "plt.style.use(\"ggplot\")\n",
    "df_cars = pd.read_csv(\"Data/mtcars.csv\")\n",
    "#I am sorting so I can slice out the automatic and manual calls.\n",
    "df_cars_sorted = df_cars.sort_values(by=\"am\").reset_index(drop = True)\n",
    "df_cars_sorted"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#Get automatic (am=0) versus manual (am=1) cars.  We will see a faster way to do this using SQL.\n",
    "df_cars_auto = df_cars_sorted.loc[0:18,:]\n",
    "df_cars_man = df_cars_sorted.loc[19:,:]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's first create a 1x3 figure on which we will place the three plots.  Here are the key matplotlib terms.:\n",
    "\n",
    "- subplots(nrows, ncols, figsize).  This creates your figure and axes.  The layout of your figure is determined by what you specify for nrows and ncols.  You will get a grid of axes.  The size of this grid is determined by what you specify for figsize, which takes a list where the first entry specifies the horizontal length and the second entry specifies the vertical length.\n",
    "- fig.suptitle(): You can use this method of the figure to add an overarching title to the plot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x114dd8e10>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ww/vaDTWcyYm9SXJHf6I32cPp72uJ/uQU9CbnuBz608W+p+v8O3KX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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x114dd8e48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Create figure and get the axes\n",
    "fig , (ax0,ax1,ax2) = plt.subplots(nrows=1,ncols=3, figsize = (15,5))\n",
    "\n",
    "#Add title for entire figure\n",
    "fig.suptitle(\"Understanding the Effect of Horspower on other Features\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "On the first axis I will plot hp versus miles per gallon. Recall the axis method set():\n",
    "\n",
    "- ax.set(title, xlabel, ylabel):  You can use these arguments given to the set method to add titles and axes lables to a given axis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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bbrpJCQkJGjlypH799VcP1QIAAACuQAKYlSUlJkp9+0o7d9peExNJAuEW+/b5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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x114dd8e48>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Plot hp versus mpg for auto\n",
    "df_cars_auto.plot(kind=\"scatter\", x = \"hp\", y = \"mpg\", color = 'b', label = \"auto\",  ax = ax0 )\n",
    "\n",
    "#Plot hp versus mpg for manual\n",
    "df_cars_man.plot(kind=\"scatter\", x = \"hp\", y = \"mpg\", color = 'r', marker = \"x\", label = \"manual\", ax = ax0 )\n",
    "\n",
    "#Change axis labels and title\n",
    "ax0.set(title = \"hp vs. mpg\", xlabel=\"horspower\", ylabel=\"miles per gallon\")\n",
    "\n",
    "#Get rid of grid lines\n",
    "ax0.grid(False)\n",
    "\n",
    "#Change the background color\n",
    "ax0.set_facecolor(\"white\")\n",
    "\n",
    "\n",
    "fig"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There were  a couple of new inputs that I used when creating the plot:\n",
    "\n",
    "- color: changes color of marks.\n",
    "- marker: style of the mark.\n",
    "- label: identification for the legend.\n",
    "\n",
    "I also introduced some new axes method:\n",
    "\n",
    "- ax.grid(False): Removes the horizontal an vertical lines that create a grid on top of the plot.\n",
    "- ax.set_facecolor(\"color\"): Changes background color of the plot.\n",
    "\n",
    "Also, note that I can plot two different plot on the same axes simply by specifying the same axes with the ax input when I create the plots.\n",
    "\n",
    "Now, let's create the other two plots on the remaining two axes.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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YW+Uwtsp50rbJaAmgXC7H0qVLce/ePSxbtgw3b95E7969MWjQIADA9u3bsXnz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      "text/plain": [
       "<matplotlib.figure.Figure at 0x114dd8e48>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Plot hp versus displacement for auto\n",
    "df_cars_auto.plot(kind=\"scatter\", x = \"hp\", y = \"disp\", color = 'b', label = \"auto\",  ax = ax1 )\n",
    "\n",
    "#Plot hp versus mpg for manual\n",
    "df_cars_man.plot(kind=\"scatter\", x = \"hp\", y = \"disp\", color = 'r', marker = \"x\", label = \"manual\", ax = ax1)\n",
    "\n",
    "#Change axis labels and title\n",
    "ax1.set(title = \"hp vs. disp\", xlabel=\"horspower\", ylabel=\"dispersion (cu. in.)\")\n",
    "\n",
    "#Plot hp versus quater mile time for auto\n",
    "df_cars_auto.plot(kind=\"scatter\", x = \"hp\", y = \"qsec\", color = 'b', label = \"auto\",  ax = ax2, )\n",
    "\n",
    "#Plot hp versus mpg for manual\n",
    "df_cars_man.plot(kind=\"scatter\", x = \"hp\", y = \"qsec\", color = 'r', marker = \"x\", label = \"manual\", ax = ax2)\n",
    "\n",
    "#Change axis labels and title\n",
    "ax2.set(title = \"hp vs. qsec\", xlabel=\"horspower\", ylabel=\"1/4 mile time (sec)\")\n",
    "\n",
    "fig"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We'll finish by adding two horizontal lines to the first plot denoting the respective average mpg for automatic and manual cars respectively."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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YW+Uwtsp50rbJaAmgXC7H0qVLce/ePSxbtgw3b95E7969MWjQIADA9u3bsXnz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      "text/plain": [
       "<matplotlib.figure.Figure at 0x114dd8e48>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Compute the two means\n",
    "mean_mpg_auto = df_cars_auto.mpg.mean()\n",
    "mean_mpg_man = df_cars_man.mpg.mean()\n",
    "\n",
    "#Draw the two lines\n",
    "ax0.axhline(mean_mpg_auto, color = \"b\", linestyle = \"--\")\n",
    "ax0.axhline(mean_mpg_man, color = \"r\", linestyle = \"--\")\n",
    "\n",
    "fig"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can save the figure as follows.  The bbox_inches = \"tight\" will eliminate the white space around the saved image."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Saving the figure to png\n",
    "fig.savefig(\"carplots.png\", bbox_inches = \"tight\")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
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    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.0"
  },
  "toc": {
   "colors": {
    "hover_highlight": "#DAA520",
    "navigate_num": "#000000",
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    "running_highlight": "#FF0000",
    "selected_highlight": "#FFD700",
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   "moveMenuLeft": true,
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   "navigate_menu": true,
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 },
 "nbformat": 4,
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}