standage/StackedBarTest.ipynb

## StackedBarTest.ipynb
{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import pandas\n",
    "import seaborn\n",
    "seaborn.set_style(\"darkgrid\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/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>Species</th>\n",
       "      <th>HC</th>\n",
       "      <th>C</th>\n",
       "      <th>O</th>\n",
       "      <th>N</th>\n",
       "      <th>X</th>\n",
       "      <th>I</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A</td>\n",
       "      <td>7004673</td>\n",
       "      <td>40636259</td>\n",
       "      <td>5933867</td>\n",
       "      <td>7871672</td>\n",
       "      <td>101213488</td>\n",
       "      <td>87516590</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>B</td>\n",
       "      <td>11679275</td>\n",
       "      <td>86957825</td>\n",
       "      <td>3927280</td>\n",
       "      <td>1887982</td>\n",
       "      <td>58583974</td>\n",
       "      <td>85554162</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>H</td>\n",
       "      <td>12994799</td>\n",
       "      <td>90421224</td>\n",
       "      <td>17788009</td>\n",
       "      <td>2695192</td>\n",
       "      <td>38564850</td>\n",
       "      <td>131243367</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>N</td>\n",
       "      <td>14826134</td>\n",
       "      <td>25111368</td>\n",
       "      <td>38717031</td>\n",
       "      <td>2269404</td>\n",
       "      <td>62922830</td>\n",
       "      <td>151686815</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>P</td>\n",
       "      <td>9108590</td>\n",
       "      <td>21837182</td>\n",
       "      <td>25450161</td>\n",
       "      <td>263955</td>\n",
       "      <td>7662034</td>\n",
       "      <td>143704298</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>S</td>\n",
       "      <td>13493735</td>\n",
       "      <td>91118758</td>\n",
       "      <td>28831421</td>\n",
       "      <td>2769097</td>\n",
       "      <td>56709525</td>\n",
       "      <td>201086981</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Species        HC         C         O        N          X          I\n",
       "0       A   7004673  40636259   5933867  7871672  101213488   87516590\n",
       "1       B  11679275  86957825   3927280  1887982   58583974   85554162\n",
       "2       H  12994799  90421224  17788009  2695192   38564850  131243367\n",
       "3       N  14826134  25111368  38717031  2269404   62922830  151686815\n",
       "4       P   9108590  21837182  25450161   263955    7662034  143704298\n",
       "5       S  13493735  91118758  28831421  2769097   56709525  201086981"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pandas.read_table('bk.tsv')\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "species = ['A', 'B', 'H', 'S', 'P', 'N']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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US5M8Ksm5ST45ap/datmy5XnyRe+diS89JY8YybCrXvzil+foo38xV111Zd7xjvNz5513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\nktRyFoaSJEmS1HL/Bd4S782bz7ptAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10b39d650>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot = data.plot(kind='bar', stacked=True, figsize=(15, 10), x='Species', fontsize=16, rot=0)\n",
    "_ = plot.legend(loc=2, prop={'size': 16})\n",
    "_ = plot.xaxis.label.set_text('')\n",
    "_ = plot.yaxis.label.set_text('Size')\n",
    "_ = plot.yaxis.label.set_fontsize(18)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}