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  {
   "cells": [
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "# Instructional Set - Plotting with matplotlib and pandas"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "_Sources include: [Research Computing](http://nbviewer.ipython.org/github/ResearchComputing/Meetup-Fall-2013/blob/master/python/lecture_17_matplotlib.ipynb), [Harvard CS109](http://nbviewer.ipython.org/github/cs109/content/blob/master/lec_03_statistical_graphs.ipynb),[Chris Fonnesbeck](http://nbviewer.ipython.org/urls/gist.github.com/fonnesbeck/5850463/raw/a29d9ffb863bfab09ff6c1fc853e1d5bf69fe3e4/3.+Plotting+and+Visualization.ipynb) and the [MatPlotLib documentation](http://nbviewer.ipython.org/github/ResearchComputing/Meetup-Fall-2013/blob/master/python/lecture_17_matplotlib.ipynb)_"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## What we will cover: \n",
      "\n",
      "* Plotting with Pandas\n",
      "  * Line Plots\n",
      "  * Bar Plots\n",
      "  * Histograms\n",
      "  * Subplots\n",
      "  \n",
      "* Plotting with MatPlotLib\n",
      "  * Syntax - basic plots and subplots\n",
      "  * Scatter Plots\n"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "##Built-in Plotting Functionality with Pandas"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "* Pandas plotting is built with Matplotlib\n",
      "* Pandas dataframes already include row and column label and grouping, so allow for more concise plotting statements."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%pylab inline"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Populating the interactive namespace from numpy and matplotlib\n"
       ]
      }
     ],
     "prompt_number": 9
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from pandas import *"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Subplotting example\n",
      "a = randn(50).cumsum()\n",
      "b = randn(50).cumsum()\n",
      "c = randn(50).cumsum()\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df=DataFrame({'a':a,'b':b,'c':c})"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df.plot(subplots=True)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df.plot()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Adding options\n",
      "#Generate a random graph \n",
      "a = randn(50).cumsum()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df = DataFrame(a) # convert it to a dataframe"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df.plot(title='Random Plot',kind='line')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Pandas data frames have a built in plot function with defaults:\n",
      "df = DataFrame(np.random.randn(10, 4).cumsum(0),\n",
      "columns=['A', 'B', 'C', 'D'],\n",
      "index=np.arange(0, 100, 10))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df.plot(title='Plot automatically finds Indexes and Places Labels')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "ename": "NameError",
       "evalue": "name 'df' is not defined",
       "output_type": "pyerr",
       "traceback": [
        "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
        "\u001b[0;32m<ipython-input-2-e4b4c775e666>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtitle\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'Plot automatically finds Indexes and Places Labels'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
        "\u001b[0;31mNameError\u001b[0m: name 'df' is not defined"
       ]
      }
     ],
     "prompt_number": 2
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "###Bar plots"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "titanic = read_csv(\"data/titanic.csv\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 47
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "titanic"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "SurvivedByClass=titanic.groupby('pclass').survived.sum()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "SurvivedByClass"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "SurvivedByClass.plot(kind='bar',title='Titanic Survivors by Class')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Combine multiple groups and plot\n",
      "titanic.groupby(['sex','pclass']).survived.sum().plot(kind='barh',title='Titanic Survivors by Class and Gender')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Plot CrossTabs\n",
      "death_counts = crosstab([titanic.pclass, titanic.sex], titanic.survived.astype(bool))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "death_counts.plot(kind='bar', stacked=True, title='Passenger Outcomes by Gender and Class', color=['white','blue'], grid=False)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "###Histograms and Density Plots"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Histogram of a particular column\n",
      "titanic.fare.hist(grid=False,bins=25,range=(0,100))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "ename": "NameError",
       "evalue": "name 'titanic' is not defined",
       "output_type": "pyerr",
       "traceback": [
        "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
        "\u001b[0;32m<ipython-input-3-4273bb896e58>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# Histogram of a particular column\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mtitanic\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfare\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgrid\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mFalse\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mbins\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m25\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mrange\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m100\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
        "\u001b[0;31mNameError\u001b[0m: name 'titanic' is not defined"
       ]
      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Density plot -- Estimate the 'true' distribution\n",
      "titanic.fare.dropna().plot(kind='kde', xlim=(0,100))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "titanic.fare.hist(bins=25, normed=True, color='lightseagreen')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "titanic.fare.plot(kind='kde', xlim=(0,600), style='r--')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "titanic.boxplot(column='fare',by='pclass',grid=False)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Combine pandas plots by running them together -- if pandas thinks they can be overlapped, it will\n",
      "titanic.fare.hist(bins=25, normed=True, color='lightseagreen')\n",
      "titanic.fare.plot(kind='kde', xlim=(0,100), style='r--')\n",
      "titanic.boxplot(column='fare',by='pclass',grid=False)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Matplotlib"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "### Figures and Subplots"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import matplotlib.pyplot as plt"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "x = np.arange(50)\n",
      "y = np.random.randn(50)\n",
      "plt.scatter(x,y)\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%pylab inline"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#Single Plot\n",
      "a = randn(50).cumsum()\n",
      "\n",
      "plt.plot(a, color='red')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": true,
     "input": [
      "fig = figure()\n",
      "\n",
      "# Two rows, one column, first plot\n",
      "ax1 = fig.add_subplot(2,1,1)\n",
      "ax1.plot(a, color='red')\n",
      "\n",
      "#Two rows, one column, second plot\n",
      "ax2 = fig.add_subplot(2,1,2)\n",
      "ax2.scatter(np.arange(50), randn(50))\n",
      "\n",
      "# Exercise: Try 1 row, two columns\n",
      "# Exercise: Try 1 row, one column"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "*fig* is the :class:`matplotlib.figure.Figure` object\n",
      "    \n",
      "*ax* can be either a single axis object or an array of axis\n",
      "objects if more than one subplot was created.  The dimensions\n",
      "of the resulting array can be controlled with the squeeze\n",
      "keyword, see above.\n",
      "    "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fig, ax = plt.subplots(2,3)\n",
      "\n",
      "ax[0,1].plot(randn(50), color='green', linestyle='-')\n",
      "ax[1,2].scatter(np.arange(50), randn(50), color='red')\n",
      "ax[1,0].hist(randn(1000))\n",
      "plt.show() #Similar to print()\n",
      "\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "\n",
      "# Exercise: Swap the top row and bottom row plots\n",
      "# Excecise: Change the line style to dotted and add circles as markers \n",
      "# Exercise: change the bins to 100 for the histogram"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "###Ticks, Labels and Legends"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fig = plt.figure(); ax = fig.add_subplot(1, 1, 1)\n",
      "ax.plot(randn(1000).cumsum(), 'k', label='one') \n",
      "ax.plot(randn(1000).cumsum(), 'k--', label='two')\n",
      "ax.plot(randn(1000).cumsum(), 'k.', label='three')\n",
      "ax.set_title('Three random lines')\n",
      "ax.legend(loc='best')\n",
      "plt.show()\n",
      "\n",
      "#Exercise: Change the color and line style of each line using the minimum amount of characters\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Save the file\n",
      "plt.savefig('ThreeRandomLines.pdf')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "<matplotlib.figure.Figure at 0x10494e990>"
       ]
      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%pylab inline"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Populating the interactive namespace from numpy and matplotlib\n"
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import pandas as pd"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 6
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "### ScatterPlots from Data Frames"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Combine pandas with matplotlib for scatterplots\n",
      "baseball = pd.read_csv(\"data/baseball.csv\")\n",
      "#Offensive Statistics from MLB -- 2008.  Glossary: http://www.baseballprospectus.com/glossary/index.php?context=all&category=true\n",
      "\n",
      "baseball.head()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<pre>\n",
        "&lt;class 'pandas.core.frame.DataFrame'&gt;\n",
        "Int64Index: 5 entries, 0 to 4\n",
        "Data columns (total 23 columns):\n",
        "id        5  non-null values\n",
        "player    5  non-null values\n",
        "year      5  non-null values\n",
        "stint     5  non-null values\n",
        "team      5  non-null values\n",
        "lg        5  non-null values\n",
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        "Int64Index: 5 entries, 0 to 4\n",
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        "team      5  non-null values\n",
        "lg        5  non-null values\n",
        "g         5  non-null values\n",
        "ab        5  non-null values\n",
        "r         5  non-null values\n",
        "h         5  non-null values\n",
        "X2b       5  non-null values\n",
        "X3b       5  non-null values\n",
        "hr        5  non-null values\n",
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        "bb        5  non-null values\n",
        "so        5  non-null values\n",
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     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fig = plt.figure()\n",
      "ax = fig.add_subplot(1,1,1) # one row, one column, first plot\n",
      "ax.set_title(\"Hits vs. At Bats\")\n",
      "ax.set_xlabel(\"At Bats\")\n",
      "ax.set_ylabel(\"Hits\")\n",
      "ax.scatter(baseball.ab, baseball.h) \n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
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       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 10,
       "text": [
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fkY1hfP311zzwwAO8//77F10aeP7UlCOfI6zAEBG5fkU2hnF+nKJgXpxLA0NE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\nERG7KDBERMQu/w+3wmhgx2aTLAAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x106222310>"
       ]
      }
     ],
     "prompt_number": 10
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fig = plt.figure()\n",
      "ax = fig.add_subplot(1,1,1) # one row, one column, first plot\n",
      "ax.set_title(\"At Bats vs. Hits.  Size = Home Runs\")\n",
      "ax.set_xlabel(\"At Bats\")\n",
      "ax.set_ylabel(\"Hits\")\n",
      "plt.scatter(baseball.ab, baseball.h, s=baseball.hr*10, alpha=0.5)\n",
      "xlim(0, 700); ylim(0, 200)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 15,
       "text": [
        "(0, 200)"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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9dy8eHh4d8A1dmy7l3CmJQojryPHjJ3jjjXUEBc1tNqy31Wrm6NG/YTSeo2/f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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x106324c10>"
       ]
      }
     ],
     "prompt_number": 15
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "baseball"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "xkcd()\n",
      "#plt.scatter(baseball.ab, baseball.h, c=baseball.hr, s=40, cmap='hot')\n",
      "fig = plt.figure()\n",
      "ax = fig.add_subplot(1,1,1) # one row, one column, first plot\n",
      "ax.set_title(\"At Bats vs. Hits, Size and Color = Home Runs\")\n",
      "ax.set_xlabel(\"At Bats\")\n",
      "ax.set_ylabel(\"Hits\")\n",
      "plt.scatter(baseball.ab, baseball.h, c=baseball.hr,cmap='hot')\n",
      "xlim(0, 700); ylim(0, 200)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 15,
       "text": [
        "(0, 200)"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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6WiPqNr3FjRGVR67JDHxcspVyzmDQtMgiIhVZsy7d2Jj2IftXLMTs60+DJ54n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\nYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x109e53350>"
       ]
      }
     ],
     "prompt_number": 15
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Other Packages"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "### Seaborn"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#With Titanic Data\n",
      "import seaborn as sns\n",
      "sns.set(style=\"darkgrid\")\n",
      "\n",
      "df = sns.load_dataset(\"titanic\")\n",
      "\n",
      "pal = dict(male=\"#6495ED\", female=\"#F08080\")\n",
      "g = sns.lmplot(\"age\", \"survived\", col=\"sex\", hue=\"sex\", data=df,\n",
      "               palette=pal, y_jitter=.02, logistic=True)\n",
      "g.set(xlim=(0, 80), ylim=(-.05, 1.05))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 13,
       "text": [
        "<seaborn.axisgrid.FacetGrid at 0x109c37b90>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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r6zk/13hwNwVraobtbcwS2/OGF2L9R0gmIPGOisEo3dFGmM9WhrnHxmoEn0nE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\nQogMUiQLIYQQQgiRQYpkIYQQQgghMvx/bWDYL8XdAYAAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10265d310>"
       ]
      }
     ],
     "prompt_number": 13
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Seaborn with Anscombe Data\n",
      "import seaborn as sns\n",
      "sns.set(style=\"ticks\")\n",
      "\n",
      "df = sns.load_dataset(\"anscombe\")\n",
      "sns.lmplot(\"x\", \"y\", col=\"dataset\", hue=\"dataset\", data=df,\n",
      "           col_wrap=2, ci=None, palette=\"muted\", size=4,\n",
      "           scatter_kws={\"s\": 50, \"alpha\": 1})"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
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      "#### Other packages\n",
      "\n",
      "vincent: wraps vega and is built on d3 -- javascript capabilities\n",
      "bokeh: Interactive plots (javascript)\n",
      "ggplot: (similar to R ggplot)\n",
      "\n",
      "\n",
      "\n"
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      "## Practice\n",
      "\n",
      "If one of your group has a datset for your independent assignment:\n",
      "\n",
      "Create a plot of some features of the dataset, include title, axes labels\n",
      "* Include titles and axes labels\n",
      "* Include multiple variables, either on different or one one chart.\n",
      "* Include data subsets\n",
      "\n",
      "Or: Using Titanic, Baseball, or one of these [example datasets](http://vincentarelbundock.github.io/Rdatasets/datasets.html), ask a question that can be answered through visualization, and create the graph that provides the answer.\n",
      "* Include titles and axes labels\n",
      "* Include multiple variables, either on different or one one chart.\n",
      "* Include data subsets\n",
      "\n",
      "At the end of the period, we will go over two of the works in progress, and discuss ways of creating or improving the plots\n",
      "\n",
      "\n",
      "BONUS: Load Anscombe's Quartet and Create a Grid with a Scatter and Line Graph of Each\n",
      "[Anscombe's Quartet Data](http://vincentarelbundock.github.io/Rdatasets/csv/datasets/anscombe.csv)\n",
      "\n"
     ]
    }
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