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  "signature": "sha256:1e0613ccda1cb137d68bcfd73073f46346e6525719e487f509bdce5265bc51e7"
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  {
   "cells": [
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "# Saving ggplot plots as vector\n",
      "\n",
      "- **Author:** [Chris Albon](http://www.chrisalbon.com/), [@ChrisAlbon](https://twitter.com/chrisalbon)\n",
      "- **Date:** -\n",
      "- **Repo:** [Python 3 code snippets for data science](https://github.com/chrisalbon/code_py)\n",
      "- **Note:**"
     ]
    },
    {
     "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": 1
    },
    {
     "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": 2,
       "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": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# scatterplot of attacker deaths vs defender deaths, with the color of the point determined by weather\n",
      "plot = ggplot(aes(x='deaths_attacker', y='deaths_defender', colour='weather'), data=df) + \\\n",
      "    geom_point()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plot"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
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C0ByPvj3JdXZccwMAAJyveHieisWRGwAA4DCUGwAA4CiUGwAA4CiUGwAA4CiUGwAA4CiU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\nyVieC2DiaukAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10b2fe310>"
       ]
      },
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 8,
       "text": [
        "<ggplot: (280192593)>"
       ]
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Save the ggplot object \"plot\" as \"example_plot.pdf\"\n",
      "ggsave(plot, \"example_plot.pdf\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "Saving 11.0 x 8.0 in image.\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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vnTt3qq2tLbbdMAzt3bs3JcMBAAD0V7+uuSkuLtbDDz+snJycVM4EAABwyhIu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\nZjQaNX//+9+b48aNM3t6ekzTNM1169a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       "text": [
        "<matplotlib.figure.Figure at 0x10a7d2bd0>"
       ]
      }
     ],
     "prompt_number": 17
    }
   ],
   "metadata": {}
  }
 ]
}