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   "cells": [
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "#INTRODUCTION TO PYTHON FOR DATA MINING\n",
      "\n",
      "Python is a great language for data mining. It has a lot of great libraries for exploring, modeling, and visualizing data. To get started I would recommend downloading the [Anaconda Package](http://continuum.io/downloads). It comes with most of the libraries you will need and provides and IDE and package manager.\n",
      "\n",
      "I do most of my work from the command line, but Anaconda comes with a launcher app that can be found in the ~/anaconda directory. To get the launcher to work with a Mac, you need to do the following:\n",
      "1. Go to your terminal (hit command-space_bar and then type terminal)\n",
      "2. Type conda install -f launcher\n",
      "3. After that runs, type conda install -f node-webkit\n",
      "\n",
      "Now you can open the launcher and see:\n",
      "1. [glueviz](https://github.com/glue-viz/glue) - This lets you link multiple plots across files\n",
      "2. [Ipython Notebook](http://ipython.org/notebook.html) - A great way to display and work on your data mining projects\n",
      "3. [Ipython qtconsole](http://ipython.org/ipython-doc/2/interactive/qtconsole.html) - Basically an Ipython terminal for coding\n",
      "3. [Spyder](https://pythonhosted.org/spyder/) - An IDE for Ipython\n",
      "\n",
      "#IPython vs Python\n",
      "\n",
      "Ipython is what makes Python interactive. Meaning that you can type some code, get some results, and then type some more code. This is very useful for exploring data because you don't always know what you are looking for and it can be annoying to have to run your entire program every time you make changes.\n",
      "\n",
      "#Libraries You Should Know About\n",
      "\n",
      "1. [Pandas](http://pandas.pydata.org/) - Provides R like data structures and a high level API to work with data\n",
      "2. [Numpy](http://www.numpy.org/) - Provides fast numerical computing such as arrays and linear algebra\n",
      "3. [Scipy](http://www.scipy.org/) - For scientific computing such as drawing from distributions\n",
      "4. [Matplotlib](http://matplotlib.org/) - For plotting\n",
      "    1. [Seaborn](http://stanford.edu/~mwaskom/software/seaborn/) - To make your plots look better\n",
      "5. [Scikit-Learn](http://scikit-learn.org/stable/) - For machine learning; great documentation and tutorials\n",
      "6. [Statsmodels](http://statsmodels.sourceforge.net/) - For more traditional statistics\n",
      "\n",
      "###Getting Seaborn\n",
      "\n",
      "In the terminal type pip install seaborn\n",
      "\n",
      "#An Example\n",
      "### Read in Data\n",
      "\n",
      "I will use pandas to read in some data from the web and quickly remove the NA rows."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import matplotlib.pyplot as plt\n",
      "import numpy as np\n",
      "from sklearn import datasets, linear_model\n",
      "import pandas as pd\n",
      "from pandas import DataFrame, Series\n",
      "from __future__ import division\n",
      "import seaborn as sns\n",
      "from sklearn.cross_validation import train_test_split\n",
      "sns.set(style='ticks', palette='Set2')\n",
      "%matplotlib inline\n",
      "\n",
      "data = pd.read_csv(\"http://archive.ics.uci.edu/ml/machine-learning-databases/auto-mpg/auto-mpg.data-original\",\n",
      "                   delim_whitespace = True, header=None,\n",
      "                   names = ['mpg', 'cylinders', 'displacement', 'horsepower', 'weight', 'acceleration',\n",
      "                            'model', 'origin', 'car_name'])\n",
      "print(data.shape)\n",
      "data = data.dropna()\n",
      "data.head()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "(406, 9)\n"
       ]
      },
      {
       "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>mpg</th>\n",
        "      <th>cylinders</th>\n",
        "      <th>displacement</th>\n",
        "      <th>horsepower</th>\n",
        "      <th>weight</th>\n",
        "      <th>acceleration</th>\n",
        "      <th>model</th>\n",
        "      <th>origin</th>\n",
        "      <th>car_name</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td> 18</td>\n",
        "      <td> 8</td>\n",
        "      <td> 307</td>\n",
        "      <td> 130</td>\n",
        "      <td> 3504</td>\n",
        "      <td> 12.0</td>\n",
        "      <td> 70</td>\n",
        "      <td> 1</td>\n",
        "      <td> chevrolet chevelle malibu</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td> 15</td>\n",
        "      <td> 8</td>\n",
        "      <td> 350</td>\n",
        "      <td> 165</td>\n",
        "      <td> 3693</td>\n",
        "      <td> 11.5</td>\n",
        "      <td> 70</td>\n",
        "      <td> 1</td>\n",
        "      <td>         buick skylark 320</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td> 18</td>\n",
        "      <td> 8</td>\n",
        "      <td> 318</td>\n",
        "      <td> 150</td>\n",
        "      <td> 3436</td>\n",
        "      <td> 11.0</td>\n",
        "      <td> 70</td>\n",
        "      <td> 1</td>\n",
        "      <td>        plymouth satellite</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td> 16</td>\n",
        "      <td> 8</td>\n",
        "      <td> 304</td>\n",
        "      <td> 150</td>\n",
        "      <td> 3433</td>\n",
        "      <td> 12.0</td>\n",
        "      <td> 70</td>\n",
        "      <td> 1</td>\n",
        "      <td>             amc rebel sst</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td> 17</td>\n",
        "      <td> 8</td>\n",
        "      <td> 302</td>\n",
        "      <td> 140</td>\n",
        "      <td> 3449</td>\n",
        "      <td> 10.5</td>\n",
        "      <td> 70</td>\n",
        "      <td> 1</td>\n",
        "      <td>               ford torino</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 2,
       "text": [
        "   mpg  cylinders  displacement  horsepower  weight  acceleration  model  \\\n",
        "0   18          8           307         130    3504          12.0     70   \n",
        "1   15          8           350         165    3693          11.5     70   \n",
        "2   18          8           318         150    3436          11.0     70   \n",
        "3   16          8           304         150    3433          12.0     70   \n",
        "4   17          8           302         140    3449          10.5     70   \n",
        "\n",
        "   origin                   car_name  \n",
        "0       1  chevrolet chevelle malibu  \n",
        "1       1          buick skylark 320  \n",
        "2       1         plymouth satellite  \n",
        "3       1              amc rebel sst  \n",
        "4       1                ford torino  "
       ]
      }
     ],
     "prompt_number": 2
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "###Scikit-Learn\n",
      "\n",
      "Here is a quick intro to modeling with scikit-learn. Basically you split the data into test and training. Then choose a model, fit the train data, and predict of the test data. Scikit-learn has great documentation, so check out their page."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "indep_vars = ['cylinders', 'displacement', 'horsepower', 'weight', 'acceleration']\n",
      "dep_vars = ['mpg']\n",
      "indep_data = data[indep_vars]\n",
      "dep_data = data[dep_vars]\n",
      "indep_train, indep_test, dep_train, dep_test = train_test_split(indep_data, dep_data, test_size=0.33, random_state=42)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "regr = linear_model.LinearRegression()\n",
      "regr.fit(indep_train, dep_train)\n",
      "print('Coefficients: {0}'.format(zip(indep_vars,np.squeeze(regr.coef_))))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Coefficients: [('cylinders', -0.26226222120889731), ('displacement', -0.0019562996653683384), ('horsepower', -0.059849640079272119), ('weight', -0.0049401742792735473), ('acceleration', 0.0040334885752014889)]\n"
       ]
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "regr_predict = regr.predict(indep_test)\n",
      "print(\"Residual sum of squares: %.2f\"\n",
      "      % np.mean((regr_predict - dep_test) ** 2))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Residual sum of squares: 19.71\n"
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "###Tables\n",
      "\n",
      "Some examples of how to use pandas to create summary statistics and tables"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "data.groupby(['cylinders']).mpg.describe()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 6,
       "text": [
        "cylinders       \n",
        "3          count      4.000000\n",
        "           mean      20.550000\n",
        "           std        2.564501\n",
        "           min       18.000000\n",
        "           25%       18.750000\n",
        "           50%       20.250000\n",
        "           75%       22.050000\n",
        "           max       23.700000\n",
        "4          count    199.000000\n",
        "           mean      29.283920\n",
        "           std        5.670546\n",
        "           min       18.000000\n",
        "           25%       25.000000\n",
        "           50%       28.400000\n",
        "           75%       32.950000\n",
        "           max       46.600000\n",
        "5          count      3.000000\n",
        "           mean      27.366667\n",
        "           std        8.228204\n",
        "           min       20.300000\n",
        "           25%       22.850000\n",
        "           50%       25.400000\n",
        "           75%       30.900000\n",
        "           max       36.400000\n",
        "6          count     83.000000\n",
        "           mean      19.973494\n",
        "           std        3.828809\n",
        "           min       15.000000\n",
        "           25%       18.000000\n",
        "           50%       19.000000\n",
        "           75%       21.000000\n",
        "           max       38.000000\n",
        "8          count    103.000000\n",
        "           mean      14.963107\n",
        "           std        2.836284\n",
        "           min        9.000000\n",
        "           25%       13.000000\n",
        "           50%       14.000000\n",
        "           75%       16.000000\n",
        "           max       26.600000\n",
        "dtype: float64"
       ]
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "pivot_table = data.pivot_table(index='cylinders', columns='acceleration', values='mpg', aggfunc=np.mean)\n",
      "pivot_table.head()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "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>acceleration</th>\n",
        "      <th>8.0</th>\n",
        "      <th>8.5</th>\n",
        "      <th>9.0</th>\n",
        "      <th>9.5</th>\n",
        "      <th>10.0</th>\n",
        "      <th>10.5</th>\n",
        "      <th>11.0</th>\n",
        "      <th>11.1</th>\n",
        "      <th>11.2</th>\n",
        "      <th>11.3</th>\n",
        "      <th>...</th>\n",
        "      <th>21.5</th>\n",
        "      <th>21.7</th>\n",
        "      <th>21.8</th>\n",
        "      <th>21.9</th>\n",
        "      <th>22.1</th>\n",
        "      <th>22.2</th>\n",
        "      <th>23.5</th>\n",
        "      <th>23.7</th>\n",
        "      <th>24.6</th>\n",
        "      <th>24.8</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>cylinders</th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td>NaN</td>\n",
        "      <td>  NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>  NaN</td>\n",
        "      <td>  NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>       NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>  NaN</td>\n",
        "      <td>  NaN</td>\n",
        "      <td>...</td>\n",
        "      <td>  NaN</td>\n",
        "      <td>  NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>  NaN</td>\n",
        "      <td>  NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>  NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>  NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td>NaN</td>\n",
        "      <td>  NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>  NaN</td>\n",
        "      <td>  NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>       NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>  NaN</td>\n",
        "      <td>  NaN</td>\n",
        "      <td>...</td>\n",
        "      <td> 43.1</td>\n",
        "      <td> 44.3</td>\n",
        "      <td> 30</td>\n",
        "      <td> 19</td>\n",
        "      <td> 24.5</td>\n",
        "      <td> 29.0</td>\n",
        "      <td> 23</td>\n",
        "      <td> 43.4</td>\n",
        "      <td> 44</td>\n",
        "      <td> 27.2</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5</th>\n",
        "      <td>NaN</td>\n",
        "      <td>  NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>  NaN</td>\n",
        "      <td>  NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>       NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>  NaN</td>\n",
        "      <td>  NaN</td>\n",
        "      <td>...</td>\n",
        "      <td>  NaN</td>\n",
        "      <td>  NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>  NaN</td>\n",
        "      <td>  NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>  NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>  NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>6</th>\n",
        "      <td>NaN</td>\n",
        "      <td>  NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>  NaN</td>\n",
        "      <td>  NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>       NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>  NaN</td>\n",
        "      <td> 28.8</td>\n",
        "      <td>...</td>\n",
        "      <td>  NaN</td>\n",
        "      <td>  NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>  NaN</td>\n",
        "      <td>  NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>  NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>  NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>8</th>\n",
        "      <td> 14</td>\n",
        "      <td> 14.5</td>\n",
        "      <td> 14</td>\n",
        "      <td> 15.5</td>\n",
        "      <td> 14.5</td>\n",
        "      <td> 17</td>\n",
        "      <td> 13.285714</td>\n",
        "      <td> 16</td>\n",
        "      <td> 18.1</td>\n",
        "      <td>  NaN</td>\n",
        "      <td>...</td>\n",
        "      <td>  NaN</td>\n",
        "      <td>  NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>  NaN</td>\n",
        "      <td> 23.9</td>\n",
        "      <td>NaN</td>\n",
        "      <td>  NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>  NaN</td>\n",
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        "  </tbody>\n",
        "</table>\n",
        "<p>5 rows \u00d7 95 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 7,
       "text": [
        "acceleration  8.0   8.5   9.0   9.5   10.0  10.5       11.0  11.1  11.2  11.3  \\\n",
        "cylinders                                                                       \n",
        "3              NaN   NaN   NaN   NaN   NaN   NaN        NaN   NaN   NaN   NaN   \n",
        "4              NaN   NaN   NaN   NaN   NaN   NaN        NaN   NaN   NaN   NaN   \n",
        "5              NaN   NaN   NaN   NaN   NaN   NaN        NaN   NaN   NaN   NaN   \n",
        "6              NaN   NaN   NaN   NaN   NaN   NaN        NaN   NaN   NaN  28.8   \n",
        "8               14  14.5    14  15.5  14.5    17  13.285714    16  18.1   NaN   \n",
        "\n",
        "acceleration  ...   21.5  21.7  21.8  21.9  22.1  22.2  23.5  23.7  24.6  24.8  \n",
        "cylinders     ...                                                               \n",
        "3             ...    NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n",
        "4             ...   43.1  44.3    30    19  24.5  29.0    23  43.4    44  27.2  \n",
        "5             ...    NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n",
        "6             ...    NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n",
        "8             ...    NaN   NaN   NaN   NaN   NaN  23.9   NaN   NaN   NaN   NaN  \n",
        "\n",
        "[5 rows x 95 columns]"
       ]
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "###Plotting\n",
      "\n",
      "Some examples creating some useful plots using matplotlib and seaborn. The despine function removes chart junk."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "p = plt.hist(data.mpg)\n",
      "plt.title(\"MPG\")\n",
      "p\n",
      "sns.despine()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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2M5gkaf5aPZP/faA/M18D/BWwtX2RJEnt0mrJ/xbwJYDM/Crw8rYlkiS1Tatvhvpl4Md1\n2yci4rTMnJzD13gWwKOPPtpihNYc/p9HOPzI97sy1tFDR5h4zrP5yZGjXRnvsbFHeProccdzPMfr\nwXjH/+8QP1j5A5YuXdrxsS6++OJB4OHMnGj03FZL/sfAQN32KQs+IoaBLbMdu+SSS1qMIEkLy9vv\nvLdbQ40C5wJjjZ7YasnfB2wAPh8RrwK+eaonZ+YwMFy/LyLOAJ4EzgNOtJijE6YnbyExU/MWYi4z\nNcdMzRsFHm7miX1TU1Nz/uoR0cfPrq4BuDwzH2zh60xlZt+cA3SQmZqzEDPBwsxlpuaYqXlzydXS\nmXxmTgF/1sprJUnd45uhJKlglrwkFazXJf/eHo8/GzM1ZyFmgoWZy0zNMVPzms7V0g9eJUnPDL0+\nk5ckdZAlL0kFs+QlqWCWvCQVzJKXpIK1eu+aeYmIrwOPVZvfy8w/7kWOKssrgQ9m5kURcR4wAkwC\n+4FN1bt7e5npfOAe4KHq8C2Z+bku51kC3Ab8BnAG8D7gW/Rwrk6S6WHgn4HpW2z0Yq6eBWwHhoAp\n4EpqH6wzQu/marZM/fR4rqpszwO+BlxMbX5G6P2fv/pMZ9H776mf60vgA8xhnrpe8hFxJkBmXtTt\nsWfJci3wFuBYtetGYHNm7o2IW4CNwN09zrQKuDEzb+xmjhkuAcYz89KIeA7wDWAfvZ2r2TK9F9ja\n47l6IzCZmRdGxBrg/dX+Xs7VzEx/Q+3EoadzVf1F/THgONDHwvjzNzPTKno4T7P1ZUR8kTnMUy+W\na14KLI2IL0fEruqstVe+A7yJ2v9MgAsyc2/1eCewbgFkWgX8XkTsiYhbI+KXepDp88D11ePTgKfp\n/VzNlqnnc5WZXwDeUW0OAoeBVb2cq1kyHWEBzBVwA3ALcLDa7vX31KyZ6O08zezLVzHHeepFyR8H\nbsjM11P7Z+Md1WfGdl1m3gXU33S//q5ux4Bl3U00a6avAn+RmWuo/VNt1vvydzjT8cw8FhED1Mr1\nPfz8907X52qWTO8G7qfHc1VlOxERI8A24A4WxvfVzEw9nauIuIzav8Smb8DeR4/naZZM0PvvqV/o\nyxnHG85TL8r1QaqgmfkQcAhY0YMcs6n/4JMBamc8vfZPmbmvenw3cH4vQkTESuDfgE9m5p0sgLma\nkekzLJC5AsjMy4AAbgXOrDvUs++rukzbgXt7PFeXA78TEbuBlwGfAJbXHe/FPM2WaWeP52m2vjyn\n7njDeepFyV9O9cHfEfF8ah8lePCUr+iefdWaJcB6YO+pntwlX4qIV1SPLwYe6HaAiDgHuBe4NjNH\nqt09nauTZFoIc3VpRFxXbT5B7QNxHujxXM3MNAnc1cu5ysw1mbm2Wmv+T+Ct1P7/9WyeZsn0NuDu\nHn9PzezLAeDeucxTL66u+Thwe0RMB7t8jp8N2wnTP5m+BtgeEf3AAWBH7yL9NNOVwM0R8TS1vwz/\ntAdZNlP7J+H1ETG9Dn4V8JEeztVsma4G/rbHc7UDGImIPcASavP0bXr7fTVbpu/T+++relMsrD9/\n8LMrkXo5T7/Ql9TO5pueJ29QJkkF881QklQwS16SCmbJS1LBLHlJKpglL0kFs+QlqWCWvCQVzJKX\npIL9PyqC/emQkRGAAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10caf8f90>"
       ]
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "sns.lmplot(\"mpg\", \"weight\", data);"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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8kVSM93fdzNpAiBeG+qtuftmiD9ATauVzN9xXdf0z2UybzYabEF/BUkZszK1Q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\nHIebJ83moXm39pTvPB9v44e7Xk7eTdHd834fDi3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/TMpY8H4E22+21jYZY2K4QfFr9P6O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\nTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10dca4d10>"
       ]
      }
     ],
     "prompt_number": 9
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "sns.lmplot(\"mpg\", \"weight\", data, order=2);"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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e/JiwQhS7I8r7wlWiJRDGdVz/jplzbN6cvEawSqxwRyjKntZeDnX1Yzk2aSuH\nQqlVPJ5OMJ5OcH/3Vj6z7xE+s+8RsUgFYRmoJ1njN/EWyP41EAB+2jCM/97oiQlLR1EUFEUp+eW+\nOXGV18au0BwI+bWGobTu8I3j5/ufnh86zxfPvMgXz7zI8bHLK2aJFqzvApI4Itxu1BMd8SRwCHjd\nNM0pwzB+CHgb+D8aOjPBZzHuiALt4ShRK0jKzhHPpQH4h8s/oK+plSc33+HHAxfXHS7Ui9DxUqQt\nx0ZXNYzWnpLmoCvtEpD4ZeF2pp6W93bZ+1CFMaGBLNYdUbAeVVUlpAXY0ewlaqTtHH998Y2SVkfl\n9SIK5S9bghF+ctd9fGbfIzzWtwfLsf1tq0HhswvC7UY9lvBfA38JdBiG8b8DnwD+oqGzEpYF23VQ\nFNje3ImiKLwzM8a15DRfPPMCrgsu7g3rt0K9iEJ23ZHBU8xZWVL5oj6P9e0RQRSEZaIeEX4v8M94\nqcP9wP9jmuY/N3RWQgmLdUeMpxM8N3SOuXwLo9fGLvMTO+9jLDXLTC7N1blp2oIRYnoIF3y3Q6V6\nEYXohNZghGhepAv7VLpu8bGCICxMPSL8X/Ey1g7gLcwlDcMYN03zlYbOTPCpN1mjwGRmjtlc2q8/\nnLAyhDSdj20/yP889wouLvFsioCi0RII8+M7D1WNB57MzPkiXasS260ayys3DmG1qSdj7hXgFcMw\nfg94CvgvwP8FBGseKCw7N1PbN6hpbG/u44c27+VbV8/gAlPZJI927q5Y1AduCGvSyuLidXyuFJ1w\nq8by3qo3DuH2op4QtT8wDOMt4JvAHrw6vz2Nnphwg4XCtAoxvAUKReALBFSNjlCUaCDE4xv3sK9t\nAwCW6zCSmiWRTc87V7GwtuRdEU/tOHjbCFWlG0etxU5BaBT1uCNa8cT6NHAGOGua5oIVzYTaLPYx\neKGWRFBqzYW0AHbeHRHSAv7+HeEoH912D1848wITmTlOTg3x7SGTH+6/k28PnfXPZbSW3mf1vJBX\nmnu1TDp51BeEhanHHfFTAPnSle8HnjUMo8k0zU2NntztylIfg+t1AwAlC2nlboxNsTY+1L+fv3jn\nNSzX4ZuDp4npIV4fu4KS78xsxkfZ29rrxwcXhLV87oXrld8k1vqj/mqkYAtCJRYUYcMw9uL1h3sf\nXl3gV4FnGzyv25ZG+E8L8bsFsV1IYL559QyvjV4mpoeYzqVwcHl28CTNeoiQfsNqfrRvN4/27fbf\nn4uP+Of0rtrpAAAgAElEQVS0HJvvXDvLsdEBP6GjELp2q/iIJQlEWAvUGyf8LF5/uO+bpimJGmuE\nWqFr1QSmIJCqqhINhMg6Nkk7S9LKoisqqqIS0LQS4S7vwuxSmtihKirPXz/PqanrPNCzrWoI21pE\nxFdYbepxR9y1EhNZLyznY/BCoWsLnVdTVVqDETrUJq7OTTOTS7Ml1sFHt99DX7TNv0ZBgMFLAEnn\nuzmrikLayoGi+MWCClZv4TNajs3+9o0idoJQhXosYWGZWe7H4Gqha5UWxspvAg/2bmNf6wb+4PTz\nxHNpTk4Nsa25gw1NLaiKFzwTz6ZIWVkAQppOR6gJTVFJWlk/IaS8o/Ph/v0kc1lOTg1hxkc5Mnhq\nzfmFBWEtICK8SiyH+NayqmstjJXfBBzX5Sd33c+Xzr6E5Tp8/copesMtPNC7DSgtIq0pKrtbe7g4\nM05LMMID3d4+J6eGsBybB3q2+X7hs/GRknKYq+UXligNYS0jInyLU8mqrmdhrPi1qigcaO/jR7fe\nxd8MvImDy19cfA1dU4kFQrQEI4Q0779K2ra4ODMOeGFsH91xkCODpxr+OZfKWo/SEIR6qqgJa5zl\nqDCmaxrv7dvNQz1e16qkleWPz77Mn59/jbSVZSqTZDI9R8bO+SnMZnzUj5gojBWSHtZCHWBJyBBu\nBcQSvg1Z6uJfWAvwY9vv4VpymsuJSSzXYTydwHZtusLNAExnk34tiYWQEDBBWBgR4duUpQpg1rF4\nfONu/vTCcWzXIed6URGz2RTt4WjFcpe1BH81xVcSMoRbARHhdcq5fCZccfGeYv9pbyjGUHrG35Zy\nLLLJGYy2Xn5q9wPADYE93L+fnS1d88630PVWArHGhbWOiPBtSq0Fqd95+7tcmBnDdV22xjr4hXs+\nMM9/mnMdFEp7zdm4DM5NAfXVryi/HngJJT9/4IkF519PREO9UQ8ivsJaRhbmbkOKEywsxy5ZkDoX\nH+HCzBi262C5Du/MjvOn51+dd46AptGkBUrC0wBmc5mSaIiFFr8K1ytwYWbMt4qrcWTwlN9UtFrk\nRT37CMKtgIjwbcpMNuWXpJzJpkq2ua7rF3wHOB8fBSiJZnigZxs/1H8HncHovHOfmxopEdpK/efK\ny2sWruu65X2cS6knokGiHoTbCXFH3Ka4VV7vae1la6yDd2a9WN+QpvulLiv5T+/v3so/XHqTY+NX\n/HNM51LM5dJ0hWMcH7tM0vJqT0T0II/37eH42OUS98Sulm7M+Ai26xJUNd6ZGa/oGx5PJ5jMzC3j\ntyAIax+xhG9TWoMR2oJNtAWbaA1GSrb9wj0f4OENO+mJNNMTaakZNdAVjvEf9j3CIz070fNpzFnH\n5h8vn+RaYpo3xgdpCUboCseI6kF2tnTNs1IPb9lPRAv4x39v6Nw8y7XgXvjaxRPEyiIwyue2FmKQ\nBWG5EEv4NqRQXW04GQdgV3h+Y9BP7H5w3sJWrQW2TxgPclfXJr56/hgz+RoTYU3HdV0URakZNxzP\npsg6Nmq+VnHKyjKZmaua4Zewsjy142DVvncgUQ/C7YNYwrchxdXVusIxv7paOcWZdgv5WcfTCTZF\n2/hf9rybSN598dr4FcAlnS/uUxw3XOBQVz/bm7toKrJum/Sg36WjGrUEuNL8BeFWRSzh25ha1uli\nitoUW8gHOzfz8Z338+Xzr2C7DoNzXqerXc1dvuVcbKUWeKxvD8dGBwD8Ij8FJKlCWM+ICN+GLCRq\nldwOtfrEFVvIJyau8qm9D/Pejbv43tA5f/zC7Djn4iP+glulFkef3f+ov61A4WZwu7kXpHKbUC8i\nwrcpC3XWKFBcYa1eIVQVhc5g07zx10Yv+yJcTyW3av3q1iJLfXKQym3CQogI38YsxQqrFolQ3iVj\na0vnvGNfHRvgjvaN3JP3CZf3viumXKSfGzrn96tba8K1GFG9VfrrCWsHWZhbZywmvKuQcHG4fz97\n8xZuoUvGntZeesqOS9sWf3j2Rf7qndc4PnaZufyC4Ew2VfM6hd51BVYz+aI8yUQSQ4RGI5bwLcRy\n+RnrcTsUW39Gaw9mfLSkS8bOli7CepCecDPZXI64ncYFbNfl+esX6MzHJ0fzURHlroZyH3SkrD3S\narAcbgRZZBQWi4jwLcJSBKKWaC9UGKfY+js1dR2o7FYI6wF0VWU2mUZVVHKug+06TGaTdOAVi9dV\nzc+EK75u8c2gPMtuLRSAL7gRFiuq1bqdFL8XhAIiwrcAS/EzLtWqq5Q6rKsaPZEYg4kpQlpgXh1h\nXdUw2jYwmUkxnp7FyhcHGsskUIHOcIyvXTxRcS7F5TDXanTEUua2mCpzwvpGRPg2ZKmLQ8ViEdOD\nJPJ+2rSV5QcT1/zSl5XigQvhbMPJGf7swjEmM0lcQFFUJtIJepta/fZH1eaylgvAL3VuslAnLISI\n8C3AQgKxHI+61VKH49kUXznnlbpUFIUrc1Pz4oGL59kVjvETO+/l/z39Ai5guQ4Atl1fS6TyOd3s\n51oMa9kaF25fRIRvEaoJxGISLxbLQqnF1dgcbadJCzJn34h4mM6l6VI17ujYWNc5VusRfrnFVxbq\n1h+feeHP1S++5+NOvfuLCN9ClP/xLkfiRfG5K4lFVzjGrpbuks4Y9bQo6ozECGXTTGVTuLhkHIus\na3N68jpmfLSmsN5uj/BiYa8fZr3a3T3AcL3HiAjfxiz2D76aWPz8gSc4PjYAwP3d2+q67qGufr57\nzUTFxcGraTyWThDRArToIY6NDtzSwrpY1svnXM/MZtOFdZS6rWBosAgbhqEBfwTswfs7/BkgA3wZ\nb6Ingc+apukahvEp4NOABfyaaZrPGoYRAb4KdAOzwCdN0xxv5JxvJRrxqFvp+GLXwHh6ri7XwP3d\nWzk2OkBUD5JzHSbSCVwgZedI2zlUvCy5j+04dFOfqzxxQsRu+ZHwuoWZy2WYy2X8cq2LodGW8IcB\nxzTNRwzDeBT4b/nxz5mmedQwjC8AP2oYxivAzwH3AhHgRcMw/gX4WeAt0zR/1TCMnwB+CfhPDZ7z\nLUWjH3WL+9VB/a6BQphbQNPRXZfOcIzpTBLLdfyoiVOT13msL1HxXItJKIlnUyhASzAiIWDLjITX\nLUwyl2Emm16SAEOD05ZN0/wH4DP5t9uAKeBe0zSP5seOAO8H7gdeMk0zZ5rmDHABuAt4GPhGft9v\n5PcVymh0Xd14NsVYOsFYOkG8rF9dJY4MnuJrF08wZ2WZziSxXYeHe3fwU7sf8BuHWq7DZHaORC5T\n9Ty1PlfxzSGVb69U3tRUuDkkZXth0laO+E0IMKxA7QjTNG3DML4M/A7wZ1DSwHcWaAVagHiV8Zmy\nMWGFydg5v5lnxs7V3Lf4D1fB66JhOQ6KovDunu1sbmrz903bFn936U3imZR/bLU/8lrblmP/pR5z\nq7KePmujSNs5prLJmxJgWKGFOdM0/71hGL3AMSBctKkFmMYT2uai8eYK44WxqhiG8TTw+eWZtQCe\nWyHn2H6z0Jxjz2tNBPNdBoWiPF7rI9WvN6GoKh3BJqazKRxczsZH+MOzL7I91ok5c6Prc/Fj70Jh\neCHN+29cqMBWngJdzyP0enrsrvezSnhdddJ2jqlMEpWbE2BosCVsGMYnDMP4xfzbFGADr+X9wwCH\ngaN44vwewzBChmG0AvvwFu1eAj5Utm9VTNN82jRNpfgH2L68n2r94RS1qS9+fWTwFL9/6nl+/9Tz\nHBk8BZRWaXPxujmXJ2nEgmG6wlHfgrgwM8Zzw+fJ2jf8zgVxL3Y7lLsbCtXdmgNhmgNh9rb2cn/3\n1nmP0OfiIzWtvrX62N0Ia3Wxn/Vw/34+s+8RPrPvkdv6xrQY0tbyCTA03hJ+BviyYRjPAwHg54Gz\nwB8ZhhEETgPP5KMjfhd4Ae/G8DnTNDP5hbs/MQzjBbyoio83eL5CBVRFwc6Lb0E4x9MJnhs655eg\nfG7oXEmM8lwuwyujl8jYFjPZFI/17SmpNxHWgzzU2suZ6WEm8tb2eHqWznAUTVFLrO2ZbMq/TnGv\nuvF0grPxEV/kz8ZHONDZVzL3mWyKv7jw2pqsU1yLtWSZi/V7g2Quc9M+4HIaKsKmaaaAn6iw6bEK\n+34J+FKF43+8IZMT6qIjFCUWCJeIYEcoymRmjqSV9d0UyaIOyuPpBGZ8lPZQ1I+qKEQ6lEc9XJ6d\n4I/NlxlJeYV/RlOzNGlB/vqdN7i3ewv3d2/FLZpP8WuYXzi+IxQtKULvFm0rj+wodqWspcfuRiar\nrLXPeivhCfDSwtBqIckaQk26wjEer9KkU1UUMrYFeG6HSmnOlepFFP/Rb23u5DN7H+H/O/d9rs5N\ne7HETo6UneP1sSvsbOkqqUtcfL7nh86TyGVI2zkiepDH+/aUZAtOZub86m3lVLI010tW23r6rMvF\n3E2GodVCOmsIC3K4fz+f3f8on93/aMljcUgLoCsquqIS0gL+eHn3DqO1p+b5N8Xa+bTxMBsi3hqs\n47pMpOeYy2VpCYQ51NWPrmq+S6ErHOOZi2/w3PVzpO0cIS1AVA+WFI7vCsd890eBas1LC37RRof6\n1ctiup/czDXWwme9FWikAINYwkKdVPqDrWahgifcO1u6eHV0ADM+umC9iL+8+DqjqVn/vYvLdDbJ\n6cnrPNG3Z17JzEKhefBC6JoDoYrnXQ6rbzUyxsRaXRvMZtMklpgJVy8iwkJdlAvRQr7FI4OnODY6\nwHg6QZMepCUYqerbPD42wNn4yDx/r4PLX196gxkrzQc27aOpSGh1VSOiB0nlfdV3tvdVFatazUuL\n515JbFdzgUzEd3WZzaZIWNmGCjCICAt1UBBU8HzC1Yq6Fyh/3E9aWZqq9JA7MniKF69f8EPfFLzF\nt7s6NvH25DVc4MjgaaYyKX5s2920hZpKRDSqB9nfvpGP7ji4qM9UPvdKYnu7VXMT6mc6myRl5RYd\nhlYcwlkvIsJCTWqFokFta63cWq1UjP6N8UGCmu6LL0BY0/nI1gPsbe3lbwfexHIdXhm9xGhqlo9s\nuZN97RsX3cet0rbi4yqJrbA+mUrPkXasRQvweDrBn184vujriQgLNSmEohUoDkWrRrGl2hqM8ED3\nVh7LRy6UUwgxaw1GSOQytAYivKdvF1tiHfREmmkOhvnLd15jzspycXac3z99lLs7NvMjWw+wIXoj\ni72aJQvzm4hWcymUh7s1IpxLKpKtXVzXZTLtxawv1gXxzsw4f3XxddILpPVXQkRYqElHKFpizUby\nccILUc/C0vGxy8xZWVJ5d8UTfQaP9u329w9rAe7p3Izjunzl/KvY+Qair09c4Z2ZMR7esJOHNuxg\nMjM3z5JN5rKcjY9gOTZzVpbWYMTfVu5S6ArHCKoa15NxFLzC9TfTgLSa0K6lBAyhFMd1GE8l8hX+\nFifAx8cu8+yVkzjzVjXqQ0RYqEmtOOF6jq1GwQVQHGFRLMAFgprOtuYO2oIR4tmU37NuOpfi29fO\ncHzsMrqqkrSytOSF1nJsTk4N+RZtysoSreKTBnjm4htcSUyC6xLSAySsrB+ytliqCa34l9culm0z\nnplbdBKy5Th8ffAkr41f8cce3bib56+fX9R5RISFBWl0uNRCDUBPTFwl59g4bmnDgoxjE8+maAmE\ncFwXy/Gaie5v38gPJq9h2w4hLVCS6lzJL10Id1PyySdWvkgRzHdl1PoeblZoxVWx8qTtHNPp5KKt\n30Quw19dfJ3LiUkAdEXlX227m7s6NokIC41hOYWhIDb1+FsLwhYLhAhrAeZyaRRFKbSRIeNYjGcs\nmrQgH9t+kN6mFv7s/DHG0wkc1yWk6XxwgZtIYQGxODX7axdP+JXgChb2c0PnODY6sKQ6FPWE9Imr\nYmWZzaZI5BYfgjaYmOKvLr7OTC4NQEsgzE/uvI9N0bYFjqyMiLBwUyzWeisXm8/se2TB48sL+Hx8\n131cmp3kG4OncPGiKpJ2luFUHMu1uTAzhqaoqLjYjsPOli4/DriSm8Fo7eHY2GXPHaHpjKUTvg85\naWUJqrr/umBVV/Mt1xLaahEdlXza4qpoHIUFuKy7uAU413V5bfwKXx886Re06o+282923ktzILzA\n0dURERaWzGKtt0qP6ztbuhZc6Csv4LMp2k5nOMZrY5eZKmqZ9MzFN7mvewu2Y6MqaskjZqW5FsYs\nx8Z1XbrzadPj6YTvQ1YUxXdNaKq6oOtkIddNJeu33OIu/86qnUtYPJZjM5GZA5dFhaBlbYt/uvI2\nb01e88ce6N7KBzfvR1dvrvqDiLCwJOrxf9Yq+A4wZ2XrKjPZGowQylujYd2rUbE52s67erZzfPQy\nM1aapJXFweXY2GWiepC0lUVxFfa09dIRivK18RuFfI6NDtAVjpbMP23niAVC6KrmW7uWY6NwY7Vc\nyY/pqlazHkY9gln8/emqhlt07oIFLS6K5WWp/t/R1Cx/dfF1xvL/n3VF5Ue23sU9nZuXZV4iwkJD\nqNYNI6YHuTAzhuu6aKpaM3QM8I8ZTnrdr3aFb4SPfXjrAR7s2cZ0NsWl2XH+6fLbZPMhaVE9yAM9\n2/jh/v1k8qIPXr+8lJXlny6/Tdq2/MiMSNHi3WN9e9jZ0sXVxBR/N/CWby85rsuHtuznyuwUZ+Mj\nC9bDqES1AuqtwQhP7ThIRyhatciQuCiWzlJqQLiuyxsTg3x98BS5/P+hzlCUn9hxLxuaWpZtbiLC\nwpKo5f+slYGWsLJ0hWNYjsN0Nulbf9UYTyf8YwrHF/t1FUWhPdTEhsh2esMtPHPpBKPpWeasLN8b\nOsdgYopP7H6Quzs28fr4FT8mOaQFiGdTJHMZFEVhV0s3P7X7AcCLiCgszKmK4qeiRvQgrcEIZ+Nn\n5322esSx+Ma0t7WXva29nI2P+N/fntbeBc8h7onF4fl/k2Rda1ECnLJy/NOVtzk5NeSP3dnex49s\nPUC4qGLgciAiLCyZpYauFcpSRpzqoWOVjimn3Np+vG8PTykH+cMzL5LLh7NdmBnjS2df4qkdh9gc\na+MfBn6ArmpYjo3jurSHouiq6kdbACVugpAWIKTq6KrKAz3b5vmvrbKee9UovjHNZFM8d/0cXeEY\n+9s3VswmrHSTW0rvvPVMzraZzC7e/zswO8HfDrzJdL6zuK6ofLD/Du7v2rpoV0Y9iAgLN0W1xadq\nVnJx14tCOnPhmGr1HQ519c9LFqlmbQdUFdd1UYFCVPHg3BRfOH2UH95yJ/vbN3JmahjwLNuCj7ka\n5W6C4s8Qz6ZQgK9dPFG3KBYW4QqY8VH/Oyin+CYH8MUzL877vNXEf71bzHO5DDO59KLE13Icvnfd\n5MXhd/zF4J5wM0/tOEhvZFHuh0UptYiw0BCqWcmH+/eTzGU5OTWEGR8lGgiVRCrA4qy88noP3ZEW\nosEwc9k0Ci5u3p2QsnM8c+kEB9r7+NCWO+kKRzk9PVyxnKXR2oMZv9H5udxNUKiVXFhUhIVFsfxm\nEqmRwVd+HFT3JVdiPS/oua5XhzptL64Az3Byhr8deJPh1Iw/9kD3Vp7cfAeBOn5P4JVeDXiREhOL\nmbOIsNAwqiVfFPvZCmFq1RahChZvJbErLPJBab2HQpq14zjc0b4RRVH43tA5HFzenhri0uwEH956\nJ/d2beGejs0ENG1eNMLe1l4/jbqSVdkRitYlosUUbkzPDZ0rEfl6rNV6iwmt5wW9rG0xlUkC9bsf\nbNfh6PULPH/9vF/7IaaH+LFtd7N7gY4wxTi4NAfCxAIhvviej1uLmbeIsLCiPDd0zhe1QrH3pVBr\nwa7YCu8IRZlKJ9nV2s3fD7zFSGqWhJXhL995nQPt13ly8x30BJoZS82WiNfZ+AiP9u2ualUutcJa\nVzjGx3YcWpK7QLptVGc2m2LOyqIswvodSsb5+4G3Sqzf/e0b+fCWA37UzEIUrN/O4OJvygVEhIUV\no9CFuVCVLWlleaB7m98LrpKgVRO7c/kKadUoFqnOSJSwrvMp42G+euE4AwnvafHtqSHemRnjyc13\nsDXWgeM4qEWB9wtlst2MKC5VRBc6bqk3h1vVh2zZNpPZJLbj1B39kHVsvjdk8vLIRd/326QH+fCW\nO7mzva/uaxdbvzeDiLDQMKr9YUf1oB9x8GjfbuCGnxWo6IOt1AVjIl/7VVfVEndEJaKBELO5NBnH\nojMU9SuyJe0cf3f5LbY3d7I51s7lxCQaCvf1bK2rZOdaFK3F3hxuRR+y67rM5tLMWVlUlLoF2IyP\n8OyVk37kA8Cd7Rv5UP+ddYupg0tQ1WgNRpZs/RYjIiw0hIWSNaDUj3tk8BQvD18E4KENO+YJQXkM\n8nQmSdbxXG8BVaur/KSuagRUDcdxaAtESLsWKSuH7Tpcmp3gSmKKe7v6ua97K9ubO2nOd3peilU5\nmZkriahoBLWs18VUbrvVfMjJXIaZXAZct27xnc6mODJ4kjPTI/5YSyDMh7ccYG/bwvHZBVygLRAh\nEqjPXVEPIsLCslNPskbhdUFIjgyeImtb/utaQmA5NqmiDgaF8pPlc4D54W6F7DvHcdjW3MnhLXfy\n7JWTXJwdx3Ydjo1dxpwe4cnNd3BH+0Ye3bBr0VZloR1URA/yeN+ehliWS7Veb1W3A3hxv9NZr1aI\nigJ1CHDWsXlp+B1eGL7g16JWgAd7tvNE3566Ey8cXG8NIxBe9lhhEWFhxSl/hLs0O+4LMHir3Jdm\nx6taeHe29/Hc9XOAZ5nYrkOTFiixqqsVVi++CWRdh5ZghH+3+wFOTl3nm1dPM5vLEM+l+etLb7B1\nrIMnN99Bf6yd1uDCVbLG0wmOjQ74ccApK8ux0YFFW5YLCeVSrddqTyfL3cJpuXFcl5lsymu8qSh1\nRT64rsvJqSG+de0s8SLXw+ZoGx/ZcoCNTa01jr6BjUNQ1egMRNG1m3c9VEJEWLgpaiVY1ErWKB6f\nzMyVpAeriuLXlKjER3ccJGlleXX0EigKYU0n6zr+XBYSqOKbQJMeoCMUJaQH2N3Sw9Hh83x/5BIO\nLpcTk/zh2Re5u2MTj23cQ09TMy2BMEGtcX82jfLP1hLutRp14eT9vsl8zd94zhPT9mBTzeMGZif4\n5tUzXEtO+2MxPcT7N+3lns7NdbkwXNf1UuIDTcvqeqiEiLCwZI4MnirJZCsWjFrJGuXje1p7MVp7\nOZ/3Fe9u6V6wjsLhLfu5ODsOLNyZA0oLyVdq1dQRivpFf3a1dPPq6IBf1+GtyWucnLrOA91beaR3\nJ+3hJloDkXmWUVc4xgM920rcEYtpB1WvhdsI63Utia+/6JYXX1VRODp8we+Asr99I+/dsGveccPJ\nGb49dJZz+RhsAE1ReVfPNh7duLtu14PrukQDQWINcD1UQkRYWBLj6QTfGzrnNwD93tC5uh+7K+3z\n8wee4Fxe9OopZFMQvGOjA14KdJHY1aq5EKsR//ndoXP5lGqHfW29PNiznW9dPc311Ay26/D90Uu8\nPn6Fd/Vs510922gLNdFcZhkXbjJLWZibzMyV+LZr3VwWa73eCm4H13VJ5DLMWRmUooiHqWzSF2CA\nU1PXOdDR51vEY6lZnrt+nreLkoDAK7jz/k1GXVEukPf7akGag+FFd9u4GUSEhSUxmZnzBRg8/2dx\nIZulPFbXI771UK3mguV4XTeatCCaolbMzAPQVZVz8VHu7d7C3rZe5qwMCSuL47pkHZujwxd4dXSA\nB3u28WCRGIfyYtwVji1a4ErC7lwbXVk47G6x11irbgfwerYl8hEP9Vqfo6lZnr9+npNTQyWF/7c3\nd/L+TXvpj7bXdR7HdQjpAVoC4WUJOVssIsLCkugIRWkq68tWsDgaEfZU7nuulc5cvl8xWccm63i+\nxYKPsRKKomC7Dq+PXSFpZVFwCal6PhwuQ8axODp8ge+PXuK+ri28q2c7XeEY0UCwpDZxvZ/tjfFB\nporD7rSFw+6WEumwlsTXdV3mrCxzuQxOIdysggC3B5vY377Rt4Y3R9s4cuWU7y4qsKmplfdv2svO\nlu66rl/oQRgLNDXUz78QIsLCkugKx3gsX6MBWLTvs3COeqjle67G8TFv//u7t/mP4cURGACW6/hR\nGJUe1wHSjoWqqjiOg+3YPLXjIJOZJC8MX2A2lyHn2Hx/9BKvjg5woKOPd/VuZ3O0nYimEw3Mf6yt\n1W0kXRR2l60Qdlf+nVSLAKl0/rWE4zokchnvBu56N7yFHv8f6d1JRAvwxsQgx8cul2zb1NTG4317\n2N3SXZcV7bguAU2jvcGLrPWy+jMQblmqPd7W8j8upS9dYaELvNoTBYu32jV++fg/+q1o/nHgB/zX\n+3+E+7u38vLwOzw7eKrk/LPZtP/6/u6tfs+7gouikGKtqioRPcjOlm7u0HQOdfbzzMAJzsdHsV0X\nB5e3Jq/x1uQ1tsY6eLB7G0ZbL9FAiKgeJKjpNetQFMLuCmKkKAp3tvdVFNNz8RG/6zN47Zp2tnTx\nzsz4gt/taop0xs4xl8uScbwKZwrKgkUfk1aWExNXOTY6wFQ2WbJtW6yT92zYya66xdchpAWIBUJr\nQnwLrJ2ZCLck1f6Yq3UWXqybYjIzV1J/N1nke650jeNjA74AA4ylExwfG+D+7m3sbd8wT4Q3xzy/\nYbUY2sfLrP2+fFvzjD1NPJumKxQj41jMWVm/Bc7lxCSXE5M0B0Ic7OxnR0snmqJybPQymqKgKMq8\nz/7RHQdxcUsiAD6646D/vRU+Y+GpYDyd8HvhzVlZvnz2FWwcvyBSpe92NdKTbddhLpfxMhNx0FAX\njPN1XZfBuSleG7/CyckhP8kCPM3e27aBR3p30h+r0+eLS1gL0KyHGhbrezOICAsNYzmsrY5Q1LdG\nwavDW221ezydKLFsK50rpOlk8m6JkKbTEYouGEPbFfaud3/3Nn+foOb5h7OOQ1DVCQd13tO3i9OT\nw5yZHsbBZTaX4ejwBY4OXyCgamiKQksggpbvBG0XiQvAx3Yc4rEyS7VYOAt1jnVVI6IHmbOy2K6D\nAsdyyYwAABYqSURBVMzkUn5WV6XFpcJnLLg4GpmebNk2KTtL2rbIOTaa4hVF0qjdlXgmm+KtyWuc\nGB9kPN/hukBI1TnU1c+7erbTHqodJwyekKNARAvSHAyhKjfXEbmRiAgLK8ZSwqQqWaOVBCqmB/0W\nRcULht3hWIl4RrQAjuP4rxfid97+rl/r4uXhi/z8gSf8ecX0IBfyDUh3Nnexs7mbvqY2ZrMprian\nKZbYnGOTA9L2LGEtgNHaQ9bKMZqaJajqhDSNcFHWH8x/cihYybqqEdWDqHjuFFVVcfGEx3YddLSK\n3+1MNlWykLqcZOwcacsi43i+bF94a4jfVDZJyspxPRnnB5PXGJidKIlyANgYaeH+7q0c6NjkR5/U\nwsFFU1TfDbQScb43i4iwUBfL5UtcSpjUQq6NQuhZVziGrmp0hmM81r6RWCDEE5v2lpyrJRjxBahg\nMRZuDt8f8QoIvbt3h18u88LMmJ/Jd2FmjHPxEfa09s5LgR5KzfCV869iOTZj6QQaCuDiAAFFI+fe\nWGRL2znemrzG2ekR9rT2sLdtAztbukjZOQKKSk9TC2FN96y5InRVY29rL8fGvNTokKajaxq5fL88\nVVHY3dLN4S13Vvxu3fwPRf+WU+n3XGksZ1ukbYusY5OzLVzlRiH1WsILXjGdZ6+c9NLVKyw+hrUA\nBzr6ONTZT19Ta0UhLfiHC7HCtusQ1oJEAwFCy9yIs9GICAsLsty+xKUIea1iPpZT+lg/k01xYuIq\nuqqRcey6CrGfmx5hIpXwXxeOyTm2L1hKBeUqNA1NWdkSy0tRFFQXXFzPF6lrtAUjJHIZhvLWc8ax\neHtqiLenhlDwqsGFtAB3tG/gyc13oKJgtPZwamoYBbi3ewsP9mzj5NSQ73aYziSxHAddVWnSg1yZ\nm54/ycL8AfLCXsk+rPR7PjJ4itfHruC4Lnd29PHeDbuwXE/0C2KrKLVLqeccm9PT17mSmOJKYpKR\n1Oy8fTRFYU9rDwfaN2G09dZsKVTInnNdlzvb+/hg/76KkSgLsVYiSUSEhZqs1VKH5WUx2/K1Xa28\naFaLH65kVZ+Lj2DGR/z2NmZ8xM/eq3X9YkEviKKORkjTsRwHRVXZ19LNT+66n2QuQ1MghOU4xLNJ\nTk5d5/TUdV+QXQoxzDavjg7wg4lrbG/uZFtzJ4/17aIzFKUjFOV8fBTLsfOf1SGiBQgqGgFNWzDR\nwAX/JlF8P3Fdl9HULK+NXvat5WOjA3SFo7wycsnf7weT17ijfQPtwSa0GoKXtS2uzk37C5QDsxP+\nd1tOSNUJ6wE+vus+NkYWLqozmZnj5OQQqqKgKSqnp69zd9cm9iyyQ8taqqEsIizckpS7A3RV46l8\nNMHXLp6oeWz5DSSeTfkuB/DiSOPZFK3BiFd/uKiwUDHFgl6cGr2juYuRpGft7WnrpSfSDJFmwHuM\njwaCdIZjPNy7g0Quw4mJQV4avkjGuRHHnLJznJ4e5vS01xk6kPdzZh0b23X8Rb4DnZtwXS+qwnJs\n9rVvxP7/27vzIDmq+4Dj3+6ZnZ1d7aEVK3QYHSChH8raKCBEbFABNiFEkIpTqYpThhCgYsCOk4Jy\nAlUhBIsEYicEEki5KIJCFLCDK1CYOHGESQmBEBSHgsIhi58Q4ta5SFrtvXN0/uie0exoZo/Zne7V\nzu/z12xPT79+M7O/ef36vd/LZjnY3x20Th0cBw4P9NIYT1AfBOqYE6Ozv4d0JoPnwKGBXtJedtil\nf2YMs9f60yl2dR+kc6CHrsF+Pu07woH+7rLdHS4OZ8ycQ8x1OdDfg+s4dLTNGzEA597/+mDsdZ0b\nw3GcfB/3Y7u2jnn8OEy9hoUFYTOiqZ5zoLD1lxvfO97zPbW5/bhRE6c2t9OebGJpy+xhSejLTa0u\nzBmx7p2XhuXUKBx7XBeL0xrcYNrX20XCjfGV+X6/9fZDe+lKDZD1/HZjuqArJOVlh60GAX4ATHVm\nOCk5g7b6RpoTSeY0NPNp7xFaEkka4/70bDy/L7xw1llH27z8KAOHYKzyrPnDnp+ZaGDRjDbe6/6M\nrJdlXmMrbx3aw5HBPjoHevlssIfu1OCI722dG8PBoT4W9/uwHZdLF/wSbYnG4/p1C2XI4uKScOM0\nxPzWsuM4zGIGK2cvzKcMza1aHXUgnQinuPN/sohIHfAwsAioB+4EdgDrgSzwNvBtVfVE5DrgeiAN\n3KmqPxORBuCHwGygG7haVTsrOI/FwPsbN27klFNOmXC9atVU6T8rNNIlZSWz8gpvzBUeq1xioVLl\n7+zazz++/Vx+n4yXpT3ZRH2sbtg5Fg89u2De6ezt6+KJ3a/nh1N5wPlzTuPwYB/vd3fyUc/hspf1\n5SRjdTTG66h34yRi8XxinIaYH9Q8PDzPYyCTZiCTIu1lyXpZulODDGXS4ywNWhNJFs6YxedmzGRR\n0yzmNrbw4v7do2ZAy3oeHh4JN04i5veNjzQaYmfXfh7btXXYj/ANy1eP6/OuYnfEuDqnqxmErwHO\nVNXviEgb8AawDbhHVTeLyAPAz4GXgWeAlUADsAU4B/gjoElV/1JEfhf4kqreVMF5LMaC8LQ1mT8O\n4zlW50BPPjFQzg3LVwNw9xv/Q196CC+YSTenoSUfLIq7TLqG+ukPulVOa25nd3cn8WAJpixwxZKV\nNCeSZD2PLfve461De0hlM8xKNjIjXs++vi4ODvSMO1hOVFO8ntZEkkODfdS5sWDpKJcrlq4q2bIt\nbvVm8cCDOtclXjBEbzxDyiYaSKvYsBhXEK5md8TjwBPBYxdIAWer6uZg2wbg14AM8KKqpoCUiOwC\nzgTOB/4m2Pdp4C+qeK7mBDWZ/0CTcaxcTo2X9u0mk82SIZsPwEeH+nls11bAn/mXcONBciDoTg3y\nysEPSATr4LUkGjinfQFtwUSRtvpGLl/4ec5pX0jay9BS10AWj+2H9/DspzuJx2J+eZ7HipM+Rzqb\noS89RG96iP50isFMmqFsmsFMmmzw45D1PBwYNiMN/CFmuTy+GS9LNpulMV7PkpZ2Vs9dQmuiIZ+b\ntzjPb7muhZa6JDHHJe661Ll+10Qi6NsdTblgOdGscFPlqq5qQVhVewFEpBk/IN8G/F3BLt1AK9AC\ndJXZfrRomzETNp68xeWM1vccd/2AMzPRQE96aFj/Lvg33npSg2S8bBCMB8l4Hl42i1vn8DunncV7\nRzvzre1cS++01mMZwv7hzY1+7go86hyXkxqa6Zg5j9Vzl+DhDcvJC8cHzAvmLuXwUB//tGMLvUEf\ntgucVN/I6S0n8+7RAxwe7CMZdA0cGOghEYsPS45+wdylfGHWfLJelpmJxvx4ZT/gxoi7bv6HpZKJ\nE6O1dqdKIJ2Iqt6YE5EFwJPAD1T1MRH524KnW4Aj+IG2uWB7c4ntuW2jlbcW+O7Ez9xMV/e99eyw\nFTxyM+AqsWZBR360QW5SSHGKzZ70UL4LYt07L/ldB0H/Z3uyib6gpZoJugUzePSlh+ga6i+7WCr4\nQ7Xe6+7EdV1c/GFmly/syM8OzHhZ0tks6WAiR+dAD78oSoy+YtZ8HMil0skPTzu95WTWLOzgjO45\nPKKv0JdJ0ZdJkXBc4o7fknWdYCw0Dk119UGXhDup04On2iiGaqlaEBaROfh9vX+oqpuCzdtE5EJV\nfR5YA2wEXgXuEpF6IAksx79p9yJwGfBasO9mRqGqa4G1ReexGHi/xO6mxuTHA3vDxwNX2iIuzNa2\nac9O/mrVb5bcL5frYjCTGpaeMua4tNXPIOEMcmioj/Lz2HzP7dmJBkv3LAwSCeU4RevyxRyXWMzN\n39za2+9fbBZOgpiVW9opOYOWbDI/6eOyRf6Mu8NDfWQdLz9JJYPfjTErOXruBjN21cxqcSt+F8Lt\nIrJJRDbhd0ncISIv4f8APKGq+4H7gRfwg/KtqjoIPAB0iMgLwDeAO6p4rqYGlBsPXIly2dpy3RQ5\nhYuZZj0vuESP4TpOfqZfW30DbnCl7jgOzXVJTm1uH3acM1rn5AMwwEe9R1jYNCv/90jD5zZ8vJ3H\nd2+jNz3E0aC+ufPKnW/cjZGM1x2XFzoWtH7jjjvqdOTJVu69nG6q2Sd8I3BjiacuKrHvOmBd0bZ+\n4GtVOTlTk8qNB55spW4YFWeDa6pLctWycwF/pERTXTKfXOfc2YuPS9UJHLeSxLXyJQ4F2cbKBeDC\nS/rWRAPpIDF94f7lbnAta50z4jjpMIYtTuUlmSaLTdYwNaM92cSvL+jgpX3+eODz5p5W8T/2qtmL\n+ekHb+Zbw8XZ2kqtkFycDS6XCAj8ADkjSCx04fzTSx6n1I3A8Z5/3I2VTAVa7jjlFmANc9rvdA2+\nORaETU1Zs6CDJS1+67e49Tjelt15c5fwwp5d+cejHadUq654lEW51TTKvX40kzHjsdT7NNVumE3F\nyURjZUHY1JRyLbhKll16vfNjZiTqAT8QLWlp563P9uS7DcY6pGrNgg56U4NsP7yXd7r2s+Hj7WXL\nryTIFHdrjLR46IloKiXjqcTUTTdvzCQr1YLrHOgpu308uob6eXTnqzy3d2f+5tdYj9M50JNfMaPU\n63aOIavbaNqTTbx28EMe3LGF+97axKM7X57QsabKDbPJ+OyiZi1hYypQeJmfzmZw8CdogD8bbqRl\nhnKvH4vClT2WTmBccy5Y7e8/ylAmzcGBbvb1d3PziksqOl4t3DALi7WETc0o14KrtGW3ZkEHNyxf\nzdeXnkNLkM+4oWDZoOLjbPh4Ow/u2MKDO7awoWDB0XLl51b2yMmt7DEWuRZ+oYF0iqHMsXSZH/Yc\nmnALO2pTqVVeKWsJm5pSrgVXacuuMIi/3vkxrYkGzp29iIvmLxtxvbixJJuvVLmVo5e1zubggJ/n\n2B1lNYxKyojKid4qtyBsak65f9SJ/ANPRiAoft1o43RLGSnYX7Xsi+zr7+bDnkM4YzzeeMuIyokY\nfHMsCBszSUYKBJUOFSs3TrdSN6+4ZFKPZybOgrAxIam0tTyeYDmWYD/R4DvVV1s50VgQNiZEYQSr\nMPpIT/R+2KnEgrAxIQprZlcYgdGC7+SwIGxMSMIcUXAiT+OtNRaEjQlBmCMKptLwMTM6m6xhzDQy\nHabx1hoLwsaEYDrM7DLVYd0RxoQkjBEFNnzsxGNB2JgQTZchambyWBA2Zhqy4HvisD5hY4yJkAVh\nY4yJkAVhY4yJkAVhY4yJkAVhY4yJkAVhY4yJkAVhY4yJkAVhY4yJkAVhY4yJkAVhY4yJkAVhY4yJ\nkAVhY4yJkAVhY4yJkAVhY4yJkAVhY4yJkAVhY4yJkAVhY4yJkAVhY4yJkAVhY4yJkAVhY4yJkAVh\nY4yJkAVhY4yJkAVhY4yJULzaBYjIrwDfV9Uvi8hSYD2QBd4Gvq2qnohcB1wPpIE7VfVnItIA/BCY\nDXQDV6tqZ7XP1xhjwlTVlrCI3AI8BNQHm+4FblXVCwAH+KqIzAX+GDgPuBT4nogkgG8BbwT7PgLc\nVs1zNcaYKFS7O2IX8Nv4ARfgbFXdHDzeAPwqsAp4UVVTqno0eM2ZwPnA08G+Twf7GmPMtFLV7ghV\nfVJEFhdscgoedwOtQAvQVWb70aJtlYgB7Nu3r8KXG2PM2F188cWLgU9UNT2W/aveJ1wkW/C4BTiC\nH2ibC7Y3l9ie2zYiEVkLfLfUc1deeeX4z9YYY8bvfeBU4IOx7Bx2EN4mIheq6vPAGmAj8Cpwl4jU\nA0lgOf5NuxeBy4DXgn03lz7kMaq6FlhbuC047gCwFMhMVkXGKfeh1FrZUZdfy3WPuvxar/snY93Z\n8TyviucCQXfEv6nqeSJyOv6NugTwC+C6YHTEN/BHR7jAXar6k2B0xL8C84BB4ApVPVDhOXiq6oy+\nZ3VEWb7VvTbrHnX5Vvexl131lrCqfoA/8gFVfRe4qMQ+64B1Rdv6ga9V+/yMMSZKNlnDGGMiZEHY\nGGMiVCtB+I4aLt/qbuXXWtlRlz+usqt+Y84YY0x5tdISNsaYKcmCsDHGRMiCsDHGRMiCsDHGRMiC\nsDHGRMiCsDHGRCjsBD6hE5HXOZYqc7eq/kEIZY66mkhIZZ8F/CfwbvD0A6r671Usuw54GFiEn8j/\nTmAHIdS/TNmfAP8F7Ax2q1r9RSSGnxdlGeAB38TPebKe6te9VNkJQqp7wXmcDPwvcDF+ndcT3ve+\nsOwZhFj34hgDfI9x1H1aB2ERSQKo6pdDLPMW4PeAnmBTbjWRzSLyAPBV4KmQyl4J3Kuq91ajvBKu\nBA6q6lUi0ga8AWwjnPqXKvsO4J6Q6v8bQFZVV4vIhcBfB9vDqHtx2Xfh//iGVffcj+CDQC9+3vAw\nv/fFZa8kpLqXijEi8lPGUffp3h2xAmgUkZ+LyMaglVhtY1lNJKyyVwKXi8jzIrJORJqqWDbA48Dt\nwWMXSBFe/UuVHVr9VfU/gBuCPxcDh4GVYdS9RNlHCP+zvxt4ANgb/B3m9/64sgmv7sUx5ouMs+7T\nPQj3Aner6qX4l2g/EpGq1llVn8RfsDSnMKVdD5WvEFJJ2a8Af6qqF+JfJpVMeD+J5feqao+INOMH\nxdsY/h2rWv1LlP3n+Lmqw6x/RkTWA/cBPyLcz7647NDqLiLX4F+FPBNscgip7iXKhnA/9+NiTNHz\no9Z9ugfhnQRvSpBG8zP8/MRhKlxNZEwrhEyin6jqtuDxU8BZ1S5QRBYAzwKPqOpjhFj/orJ/TAT1\nV9VrAMFPzZoseKrqn31B2Q8Bz4RY92uBS0RkE/DL+HnAZxc8X826lyp7Q4h1LxVj5hQ8P2rdp3sQ\nvha4B0BE5uMvqbR3xFdMvm1BPx2McYWQSfS0iKwKHl8MbK1mYSIyB3gGuEVV1webQ6l/mbJDq7+I\nXCUifxb82Y+/isvWkOpeXHYWeDKsuqvqhap6UdAv+n/A7+O/91Wve4myrwaeCvF7XxxjmoFnxlP3\naX1jDvhn4F9EJPcmXKuq2ZFeMIlyd0P/BHhIRHKriTwRYtnfBH4gIin8H5/rq1zurfiXXreLSK5/\n9kbg/hDqX6rsm4C/D6n+TwDrReR5oA6/3u8QzmdfquyPCPezL+QRzfc+V3aY3/vjYgx+a3jMdbcs\nasYYE6Hp3h1hjDFTmgVhY4yJkAVhY4yJkAVhY4yJkAVhY4yJkAVhY4yJkAVhY4yJkAVhY4yJ0HSf\nMWfMcUTkIvwEPwBL8Gc0dQG/hZ945jLgdWAjfj6CbuBKVf0weO39+ImSXgaWh5kq1Uw/1hI2tepc\n4BqgA/gWcEBVVwFvAl8H5uMnglkB/Bh/6nUceBS4QlXPBoY4NkXcmIpYEDa16m1V/VRV+4FO/FYv\nwIfATOBokIkN4BHgK8AXgP2q+naw/WGGp2w0ZtwsCJtaNVT0d3qEv93g7wzD/2csAJsJsyBsTGmz\nROTS4PG1wH/jr5fXJiKfD7ZfgXVHmAmyIGxqkcfowTMFXCUibwCXADepagp/Db9HRGQrcAp+/l5j\nKmapLI0pQUT6VbWhaJsDfB+4Q1X7ROQ7wDxVvTmSkzTTgrWEjSntuNZJsGz5IeA1EdkGrObYqsrG\nVMRawsYYEyFrCRtjTIQsCBtjTIQsCBtjTIQsCBtjTIQsCBtjTIT+H2l7R8eGWS8EAAAAAElFTkSu\nQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10dcf4310>"
       ]
      }
     ],
     "prompt_number": 10
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "sns.jointplot(\"mpg\", \"weight\", data, kind=\"reg\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 11,
       "text": [
        "<seaborn.axisgrid.JointGrid at 0x10f72c610>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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Hj5uHw8xWUialJ6UQQkwYEtyGsapyDp9cfCE+mwOAQ+EeNuzeREtk+F0HCgrd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\nuAkhhFhwJLgJIYRYcCS4CSGEWHD+P0sFm9JEgF7qAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10f72c650>"
       ]
      }
     ],
     "prompt_number": 11
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "sns.boxplot(data[['displacement', 'horsepower']])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 12,
       "text": [
        "<matplotlib.axes._subplots.AxesSubplot at 0x10fc9a4d0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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TzRgRPcDlo00755xzWlyWJE0LayNiZNvSjsHBwZa9QkR0sPuoG4ALMvOHE1h+FnAi8DAw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\nL0mFM+glqXAGvSQV7v8Br+w2PXWSlWMAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10fcfb650>"
       ]
      }
     ],
     "prompt_number": 12
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "sns.violinplot(data[['displacement', 'horsepower']])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 13,
       "text": [
        "<matplotlib.axes._subplots.AxesSubplot at 0x10fdebfd0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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+malTp8blQ72hNv/qa/if/Qd4r7KZRUXR34YeDIX48+FT+IIhmQ2sF0n+59HZ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\nAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10fe86fd0>"
       ]
      }
     ],
     "prompt_number": 13
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "g = sns.FacetGrid(data, col=\"cylinders\")\n",
      "g.map(plt.hist, \"mpg\");"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0x10ffc6750>"
       ]
      }
     ],
     "prompt_number": 14
    }
   ],
   "metadata": {}
  }
 ]
}