suntong/Pandas, Grouped Horizontal Bar Chart.ipynb

## Pandas, Grouped Horizontal Bar Chart.ipynb
{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Programs\\Anaconda3\\lib\\site-packages\\matplotlib\\__init__.py:872: UserWarning: axes.color_cycle is deprecated and replaced with axes.prop_cycle; please use the latter.\n",
      "  warnings.warn(self.msg_depr % (key, alt_key))\n"
     ]
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>person</th>\n",
       "      <th>score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AAA</td>\n",
       "      <td>-0.826664</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>BBB</td>\n",
       "      <td>-0.272566</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>CCC</td>\n",
       "      <td>-0.616078</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>DDD</td>\n",
       "      <td>-1.354531</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>EEE</td>\n",
       "      <td>0.871340</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>FFF</td>\n",
       "      <td>1.076733</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  person     score\n",
       "0    AAA -0.826664\n",
       "1    BBB -0.272566\n",
       "2    CCC -0.616078\n",
       "3    DDD -1.354531\n",
       "4    EEE  0.871340\n",
       "5    FFF  1.076733"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame({'score':np.random.randn(6),\n",
    "                   'person':[x*3 for x in list('ABCDEF')]})\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>foo</td>\n",
       "      <td>one</td>\n",
       "      <td>-1.386826</td>\n",
       "      <td>0.391962</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>bar</td>\n",
       "      <td>one</td>\n",
       "      <td>1.685887</td>\n",
       "      <td>-0.853601</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>foo</td>\n",
       "      <td>two</td>\n",
       "      <td>-1.170102</td>\n",
       "      <td>0.313107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>bar</td>\n",
       "      <td>three</td>\n",
       "      <td>-0.677186</td>\n",
       "      <td>-0.385979</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>foo</td>\n",
       "      <td>two</td>\n",
       "      <td>2.049869</td>\n",
       "      <td>-0.076735</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>bar</td>\n",
       "      <td>two</td>\n",
       "      <td>0.107279</td>\n",
       "      <td>1.608779</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>foo</td>\n",
       "      <td>one</td>\n",
       "      <td>-0.282100</td>\n",
       "      <td>0.634909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>bar</td>\n",
       "      <td>three</td>\n",
       "      <td>-0.663462</td>\n",
       "      <td>2.128385</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     A      B         C         D\n",
       "0  foo    one -1.386826  0.391962\n",
       "1  bar    one  1.685887 -0.853601\n",
       "2  foo    two -1.170102  0.313107\n",
       "3  bar  three -0.677186 -0.385979\n",
       "4  foo    two  2.049869 -0.076735\n",
       "5  bar    two  0.107279  1.608779\n",
       "6  foo    one -0.282100  0.634909\n",
       "7  bar  three -0.663462  2.128385"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame({'A' : ['foo', 'bar', 'foo', 'bar']*2,\n",
    "                   'B' : ['one', 'one', 'two', 'three',\n",
    "                         'two', 'two', 'one', 'three'],\n",
    "                  'C' : np.random.randn(8),\n",
    "                  'D' : np.random.randn(8)})\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.text.Text at 0x71e2acbc18>,\n",
       " <matplotlib.text.Text at 0x71e2ad80b8>,\n",
       " <matplotlib.text.Text at 0x71e2b562e8>,\n",
       " <matplotlib.text.Text at 0x71e2b56dd8>,\n",
       " <matplotlib.text.Text at 0x71e2b5c908>,\n",
       " <matplotlib.text.Text at 0x71e2b5e438>,\n",
       " <matplotlib.text.Text at 0x71e2b5ef28>,\n",
       " <matplotlib.text.Text at 0x71e2b64a58>]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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urlZ1dbUTQwEAkJC40hkAAAYgsAEAMAD3w5Y0MDCgU6dOhXVEYFZWjtzuyJ16BgBITAS2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\ngAH+H0yIliOASUsNAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x71e2aa0278>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = df.plot(kind='bar')\n",
    "ax.set_xticklabels(df.index.format(names=False))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x71e2b465f8>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x71e2aab3c8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot(kind='barh')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}