ggplot_area_plot.ipynb

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  {
   "cells": [
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "# Draw a area plot 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": 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",
      "        'year': [1945, 1956, 1964, 1969, 1971, 1981, 1982, 1992, 1999, 2004]}\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",
        "      <th>year</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",
        "      <td> 1945</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",
        "      <td> 1956</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",
        "      <td> 1964</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",
        "      <td> 1969</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",
        "      <td> 1971</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",
        "      <td> 1981</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",
        "      <td> 1982</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",
        "      <td> 1992</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",
        "      <td> 1999</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",
        "      <td> 2004</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 6,
       "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  year  \n",
        "0                41                14  1945  \n",
        "1               214              1424  1956  \n",
        "2               131               131  1964  \n",
        "3                12                12  1969  \n",
        "4               123                34  1971  \n",
        "5               124               124  1981  \n",
        "6               264              1124  1982  \n",
        "7               311              1431  1992  \n",
        "8               132               122  1999  \n",
        "9               623              2563  2004  "
       ]
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Area plot \n",
      "ggplot(df, aes(x='year', ymin='0', ymax='deaths_defender + 1000')) + \\\n",
      "    geom_area()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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4JJMv6Oe9UbfLmFHBlqLc5wkAKBGCJ+CQgVhOneGk22VcEsETAFAqBE/AIUcHkyrTmaIp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\nJElSQ/wNsA1cngSFs5AAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x104aec1d0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 10,
       "text": [
        "<ggplot: (279009581)>"
       ]
      }
     ],
     "prompt_number": 10
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ggplot(df, aes(x='year', y='deaths_defender', ymin='0', ymax='deaths_defender')) + \\\n",
      "    geom_area(color='#333333')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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Qrnbv3p1SS0yx2gAAIF5ivmGrtLRUr776qjIyftd3L730Ug0ODloSDEhnR44c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\nzSsAAABsg+YVAAAAtkHzCgAAANv4P4WkqBJ0wmVZAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10a1597d0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 24,
       "text": [
        "<ggplot: (280248497)>"
       ]
      }
     ],
     "prompt_number": 24
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ggplot(df, aes(x='year', y='deaths_defender', ymin='0', ymax='deaths_defender')) + \\\n",
      "    geom_area(color='#333333') + \\\n",
      "    geom_point(color='coral')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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Wrl2L1atX4+DBg/6rSAcOHMB3v/tdrFu3Dl1dXaiqqgrl2yBSHKPRKNt42EuPQUREFCoh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\n+7c7AAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10b3eee50>"
       ]
      },
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 32,
       "text": [
        "<ggplot: (280228749)>"
       ]
      }
     ],
     "prompt_number": 32
    }
   ],
   "metadata": {}
  }
 ]
}