ggplot_scatterplot.ipynb

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  {
   "cells": [
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "# Scatterplots in ggplot\n",
      "\n",
      "This snippit was written by [Chris R. Albon](http://www.chrisralbon.com/) and is part of his collection of [well-documented Python snippits](/~https://github.com/chrisalbon/code_py). All code is written in Python 3 in iPython notebook and offered under the [Creative Commons Attribution-ShareAlike 4.0 International License](http://creativecommons.org/licenses/by-sa/4.0/)."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Import required modules\n",
      "from ggplot import *\n",
      "%matplotlib inline\n",
      "import pandas as pd"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Create a dataset of battle data\n",
      "data = {'battle': ['Battle of Two Forks', 'Battle of Cochise', 'Battle of Bells', 'Battle of the Beach', 'Battle of Flatlander', 'Battle of Middorin', 'Battle of Massai', 'Battle of Monogrop', 'Battle of ', 'Battle of Sticks'], \n",
      "        'season': ['winter', 'fall', 'fall', 'fall', 'spring', 'winter', 'summer', 'winter', 'summer', 'summer'],\n",
      "        'terrain': ['mountains', 'mountains', 'mountains', 'beach', 'beach', 'plains', 'plains', 'city', 'city', 'city'],\n",
      "        'weather': ['rain', 'rain', 'cloudy', 'sunny', 'rain', 'rain', 'sunny', 'cloudy', 'rain', 'sunny'],\n",
      "        'victor': ['attacker', 'defender', 'attacker', 'defender', 'attacker', 'defender', 'attacker', 'defender', 'attacker', 'defender'],\n",
      "        'deaths_attacker': [425, 242, 323, 223, 783, 436, 324, 3321, 262, 843],\n",
      "        'deaths_defender': [423, 264, 1231, 23, 23, 42, 124, 631, 232, 213],\n",
      "        'wounded_attacker': [41, 214, 131, 12, 123, 124, 264, 311, 132, 623],\n",
      "        'wounded_defender': [14, 1424, 131, 12, 34, 124, 1124, 1431, 122, 2563],\n",
      "        'soldiers_attacker': [2532, 6346, 3341, 6732, 12563, 2356, 253, 5277, 2732, 6278],\n",
      "        'soldiers_defender': [37235, 2523, 2133, 1245, 2671, 7832, 2622, 3331, 2522, 26773]}\n",
      "df = pd.DataFrame(data)\n",
      "df"
     ],
     "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></th>\n",
        "      <th>battle</th>\n",
        "      <th>deaths_attacker</th>\n",
        "      <th>deaths_defender</th>\n",
        "      <th>season</th>\n",
        "      <th>soldiers_attacker</th>\n",
        "      <th>soldiers_defender</th>\n",
        "      <th>terrain</th>\n",
        "      <th>victor</th>\n",
        "      <th>weather</th>\n",
        "      <th>wounded_attacker</th>\n",
        "      <th>wounded_defender</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td>  Battle of Two Forks</td>\n",
        "      <td>  425</td>\n",
        "      <td>  423</td>\n",
        "      <td> winter</td>\n",
        "      <td>  2532</td>\n",
        "      <td> 37235</td>\n",
        "      <td> mountains</td>\n",
        "      <td> attacker</td>\n",
        "      <td>   rain</td>\n",
        "      <td>  41</td>\n",
        "      <td>   14</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>    Battle of Cochise</td>\n",
        "      <td>  242</td>\n",
        "      <td>  264</td>\n",
        "      <td>   fall</td>\n",
        "      <td>  6346</td>\n",
        "      <td>  2523</td>\n",
        "      <td> mountains</td>\n",
        "      <td> defender</td>\n",
        "      <td>   rain</td>\n",
        "      <td> 214</td>\n",
        "      <td> 1424</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>      Battle of Bells</td>\n",
        "      <td>  323</td>\n",
        "      <td> 1231</td>\n",
        "      <td>   fall</td>\n",
        "      <td>  3341</td>\n",
        "      <td>  2133</td>\n",
        "      <td> mountains</td>\n",
        "      <td> attacker</td>\n",
        "      <td> cloudy</td>\n",
        "      <td> 131</td>\n",
        "      <td>  131</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td>  Battle of the Beach</td>\n",
        "      <td>  223</td>\n",
        "      <td>   23</td>\n",
        "      <td>   fall</td>\n",
        "      <td>  6732</td>\n",
        "      <td>  1245</td>\n",
        "      <td>     beach</td>\n",
        "      <td> defender</td>\n",
        "      <td>  sunny</td>\n",
        "      <td>  12</td>\n",
        "      <td>   12</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td> Battle of Flatlander</td>\n",
        "      <td>  783</td>\n",
        "      <td>   23</td>\n",
        "      <td> spring</td>\n",
        "      <td> 12563</td>\n",
        "      <td>  2671</td>\n",
        "      <td>     beach</td>\n",
        "      <td> attacker</td>\n",
        "      <td>   rain</td>\n",
        "      <td> 123</td>\n",
        "      <td>   34</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5</th>\n",
        "      <td>   Battle of Middorin</td>\n",
        "      <td>  436</td>\n",
        "      <td>   42</td>\n",
        "      <td> winter</td>\n",
        "      <td>  2356</td>\n",
        "      <td>  7832</td>\n",
        "      <td>    plains</td>\n",
        "      <td> defender</td>\n",
        "      <td>   rain</td>\n",
        "      <td> 124</td>\n",
        "      <td>  124</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>6</th>\n",
        "      <td>     Battle of Massai</td>\n",
        "      <td>  324</td>\n",
        "      <td>  124</td>\n",
        "      <td> summer</td>\n",
        "      <td>   253</td>\n",
        "      <td>  2622</td>\n",
        "      <td>    plains</td>\n",
        "      <td> attacker</td>\n",
        "      <td>  sunny</td>\n",
        "      <td> 264</td>\n",
        "      <td> 1124</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>7</th>\n",
        "      <td>   Battle of Monogrop</td>\n",
        "      <td> 3321</td>\n",
        "      <td>  631</td>\n",
        "      <td> winter</td>\n",
        "      <td>  5277</td>\n",
        "      <td>  3331</td>\n",
        "      <td>      city</td>\n",
        "      <td> defender</td>\n",
        "      <td> cloudy</td>\n",
        "      <td> 311</td>\n",
        "      <td> 1431</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>8</th>\n",
        "      <td>           Battle of </td>\n",
        "      <td>  262</td>\n",
        "      <td>  232</td>\n",
        "      <td> summer</td>\n",
        "      <td>  2732</td>\n",
        "      <td>  2522</td>\n",
        "      <td>      city</td>\n",
        "      <td> attacker</td>\n",
        "      <td>   rain</td>\n",
        "      <td> 132</td>\n",
        "      <td>  122</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9</th>\n",
        "      <td>     Battle of Sticks</td>\n",
        "      <td>  843</td>\n",
        "      <td>  213</td>\n",
        "      <td> summer</td>\n",
        "      <td>  6278</td>\n",
        "      <td> 26773</td>\n",
        "      <td>      city</td>\n",
        "      <td> defender</td>\n",
        "      <td>  sunny</td>\n",
        "      <td> 623</td>\n",
        "      <td> 2563</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 5,
       "text": [
        "                 battle  deaths_attacker  deaths_defender  season  \\\n",
        "0   Battle of Two Forks              425              423  winter   \n",
        "1     Battle of Cochise              242              264    fall   \n",
        "2       Battle of Bells              323             1231    fall   \n",
        "3   Battle of the Beach              223               23    fall   \n",
        "4  Battle of Flatlander              783               23  spring   \n",
        "5    Battle of Middorin              436               42  winter   \n",
        "6      Battle of Massai              324              124  summer   \n",
        "7    Battle of Monogrop             3321              631  winter   \n",
        "8            Battle of               262              232  summer   \n",
        "9      Battle of Sticks              843              213  summer   \n",
        "\n",
        "   soldiers_attacker  soldiers_defender    terrain    victor weather  \\\n",
        "0               2532              37235  mountains  attacker    rain   \n",
        "1               6346               2523  mountains  defender    rain   \n",
        "2               3341               2133  mountains  attacker  cloudy   \n",
        "3               6732               1245      beach  defender   sunny   \n",
        "4              12563               2671      beach  attacker    rain   \n",
        "5               2356               7832     plains  defender    rain   \n",
        "6                253               2622     plains  attacker   sunny   \n",
        "7               5277               3331       city  defender  cloudy   \n",
        "8               2732               2522       city  attacker    rain   \n",
        "9               6278              26773       city  defender   sunny   \n",
        "\n",
        "   wounded_attacker  wounded_defender  \n",
        "0                41                14  \n",
        "1               214              1424  \n",
        "2               131               131  \n",
        "3                12                12  \n",
        "4               123                34  \n",
        "5               124               124  \n",
        "6               264              1124  \n",
        "7               311              1431  \n",
        "8               132               122  \n",
        "9               623              2563  "
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# scatterplot of attacker deaths vs defender deaths, with the color of the point determined by weather\n",
      "ggplot(aes(x='deaths_attacker.notnull', y='deaths_defender.notfull', colour='weather'), data=df) + \\\n",
      "    geom_point()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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C0ByPvj3JdXZccwMAAJyveHieisWRGwAA4DCUGwAA4CiUGwAA4CiUGwAA4CiUGwAA4CiU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\nyVieC2DiaukAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10b43c190>"
       ]
      },
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 6,
       "text": [
        "<ggplot: (280245241)>"
       ]
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# scatterplot of attacker deaths vs defender deaths, with the size of the point determined by weather\n",
      "ggplot(df, aes(x='deaths_attacker', y='deaths_defender', size='soldiers_attacker')) + \\\n",
      "    geom_point()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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mfAAAAF0Sdrm59dZbdeWVV8rv9+v++++XJO3YseOkJ4UDAADEUtjl5qmnntI7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\nshSGGyIiIrIUhhsiIiKyFIYbIiIispSfm9FqdSa0NM4AAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10a8fbe10>"
       ]
      },
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 7,
       "text": [
        "<ggplot: (279512909)>"
       ]
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# scatterplot of attacker deaths vs defender deaths, with each dot at 50% opacity\n",
      "ggplot(df, aes(x='deaths_attacker', y='deaths_defender', )) + \\\n",
      "    geom_point(size = 300, alpha=0.5)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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g/AsXLtSiRYuGPCcuLhwOF3qEDyyydRb5Oot8nUO2zvqg5TtmzBjddNNNhR4j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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10a8e0690>"
       ]
      },
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 8,
       "text": [
        "<ggplot: (279503077)>"
       ]
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# scatterplot of attacker deaths vs defender deaths, with each dot given a black outline (by overlaying double plotted points)\n",
      "ggplot(df, aes(x='deaths_attacker', y='deaths_defender', )) + \\\n",
      "    geom_point(colour=\"black\", size = 300) +\\\n",
      "    geom_point(colour=\"pink\", size = 100)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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9ek2ZMkVlZWWaN2+ebr/9dp4KAwCAk97kyZOVk5OjlStXGnNPvDJXT7wyV2kp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\ngg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10a0b4cd0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 9,
       "text": [
        "<ggplot: (279482093)>"
       ]
      }
     ],
     "prompt_number": 9
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# scatterplot of attacker deaths vs defender deaths, with the size preset at 2000\n",
      "ggplot(df, aes(x='deaths_attacker', y='deaths_defender', )) + \\\n",
      "    geom_point(colour=\"#E86C38\", size=2000)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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SUAAAAGcy05Xme8MOJMWlO+HdCb7zne/o9ddf19q1a3XkyBGtXr1aVVVV6urq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\nFrFEREREZDssYomIiIjIdv4fBtC2hOrlmfQAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x103164450>"
       ]
      },
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 10,
       "text": [
        "<ggplot: (280301001)>"
       ]
      }
     ],
     "prompt_number": 10
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# scatterplot of attacker deaths vs defender deaths, with the size of the point determined by soldiers_defender\n",
      "ggplot(df, aes(x='deaths_attacker', y='deaths_defender', size='soldiers_defender')) + \\\n",
      "    geom_point(colour=\"#E86C38\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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ShgEAADhfCZebSCRiua2joyOpAwEAAJyPz7wU/PLLL5cknThxIv7zKXV1dZox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\nRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10b4db790>"
       ]
      },
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 14,
       "text": [
        "<ggplot: (279513521)>"
       ]
      }
     ],
     "prompt_number": 14
    }
   ],
   "metadata": {}
  }
 ]
}