Pandas, Grouped Horizontal Bar Chart.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n"
   ]
  },
  {
   "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": 6,
   "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>0.126642</td>\n",
       "      <td>0.076797</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>bar</td>\n",
       "      <td>one</td>\n",
       "      <td>1.890603</td>\n",
       "      <td>-1.091927</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>foo</td>\n",
       "      <td>two</td>\n",
       "      <td>0.165926</td>\n",
       "      <td>0.372946</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>bar</td>\n",
       "      <td>three</td>\n",
       "      <td>1.957743</td>\n",
       "      <td>-1.225335</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>foo</td>\n",
       "      <td>two</td>\n",
       "      <td>-1.058914</td>\n",
       "      <td>-1.918918</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>bar</td>\n",
       "      <td>two</td>\n",
       "      <td>-0.689587</td>\n",
       "      <td>-0.920019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>foo</td>\n",
       "      <td>one</td>\n",
       "      <td>0.523851</td>\n",
       "      <td>0.059292</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>bar</td>\n",
       "      <td>three</td>\n",
       "      <td>1.181483</td>\n",
       "      <td>2.060387</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     A      B         C         D\n",
       "0  foo    one  0.126642  0.076797\n",
       "1  bar    one  1.890603 -1.091927\n",
       "2  foo    two  0.165926  0.372946\n",
       "3  bar  three  1.957743 -1.225335\n",
       "4  foo    two -1.058914 -1.918918\n",
       "5  bar    two -0.689587 -0.920019\n",
       "6  foo    one  0.523851  0.059292\n",
       "7  bar  three  1.181483  2.060387"
      ]
     },
     "execution_count": 6,
     "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": "markdown",
   "metadata": {},
   "source": [
    "## Fixed version\n",
    "http://stackoverflow.com/questions/34076177/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x71e2be2be0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = df.plot(kind='barh')\n",
    "ax.invert_yaxis()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x71e2cc8fd0>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x71e2b9c208>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Wrong, the chart is upside-down\n",
    "df.plot(kind='barh')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Programs\\Anaconda3\\lib\\site-packages\\ipykernel\\__main__.py:2: FutureWarning: sort(columns=....) is deprecated, use sort_values(by=.....)\n",
      "  from ipykernel import kernelapp as app\n"
     ]
    },
    {
     "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>0.126642</td>\n",
       "      <td>0.076797</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>foo</td>\n",
       "      <td>one</td>\n",
       "      <td>0.523851</td>\n",
       "      <td>0.059292</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>foo</td>\n",
       "      <td>two</td>\n",
       "      <td>0.165926</td>\n",
       "      <td>0.372946</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>foo</td>\n",
       "      <td>two</td>\n",
       "      <td>-1.058914</td>\n",
       "      <td>-1.918918</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>bar</td>\n",
       "      <td>one</td>\n",
       "      <td>1.890603</td>\n",
       "      <td>-1.091927</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>bar</td>\n",
       "      <td>three</td>\n",
       "      <td>1.957743</td>\n",
       "      <td>-1.225335</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>bar</td>\n",
       "      <td>three</td>\n",
       "      <td>1.181483</td>\n",
       "      <td>2.060387</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>bar</td>\n",
       "      <td>two</td>\n",
       "      <td>-0.689587</td>\n",
       "      <td>-0.920019</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     A      B         C         D\n",
       "0  foo    one  0.126642  0.076797\n",
       "6  foo    one  0.523851  0.059292\n",
       "2  foo    two  0.165926  0.372946\n",
       "4  foo    two -1.058914 -1.918918\n",
       "1  bar    one  1.890603 -1.091927\n",
       "3  bar  three  1.957743 -1.225335\n",
       "7  bar  three  1.181483  2.060387\n",
       "5  bar    two -0.689587 -0.920019"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Sort dataframe by multiple columns\n",
    "df = df.sort(['A','B'],ascending=[0,1])\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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>0.126642</td>\n",
       "      <td>0.076797</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>foo</td>\n",
       "      <td>one</td>\n",
       "      <td>0.523851</td>\n",
       "      <td>0.059292</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>foo</td>\n",
       "      <td>two</td>\n",
       "      <td>0.165926</td>\n",
       "      <td>0.372946</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>foo</td>\n",
       "      <td>two</td>\n",
       "      <td>-1.058914</td>\n",
       "      <td>-1.918918</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>bar</td>\n",
       "      <td>one</td>\n",
       "      <td>1.890603</td>\n",
       "      <td>-1.091927</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>bar</td>\n",
       "      <td>three</td>\n",
       "      <td>1.957743</td>\n",
       "      <td>-1.225335</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>bar</td>\n",
       "      <td>three</td>\n",
       "      <td>1.181483</td>\n",
       "      <td>2.060387</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>bar</td>\n",
       "      <td>two</td>\n",
       "      <td>-0.689587</td>\n",
       "      <td>-0.920019</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     A      B         C         D\n",
       "0  foo    one  0.126642  0.076797\n",
       "6  foo    one  0.523851  0.059292\n",
       "2  foo    two  0.165926  0.372946\n",
       "4  foo    two -1.058914 -1.918918\n",
       "1  bar    one  1.890603 -1.091927\n",
       "3  bar  three  1.957743 -1.225335\n",
       "7  bar  three  1.181483  2.060387\n",
       "5  bar    two -0.689587 -0.920019"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.set_index(['A', 'B'])\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x57b0464f28>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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lSpKKaLkGHxGXAFcAk41ZfcAvgCcb03uBpVOethi4DrimMcY9EfHPmflwGUVLklprGfCZ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\nSUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x57b0454fd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot(kind='bar')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Although the above has set `df.set_index(['A', 'B'])`, but when doing `plot`, The labels for \"A\" and \"B\" are still not yet. \n",
    "\n",
    "To properly showing two levels of labeling, \"A\" as the first level and \"B\" as the secondary one, Ref\n",
    "\n",
    "<a name=\"hierarchically_labeling\"/>\n",
    "**Pandas, hierarchically labeling bar plot**  \n",
    "http://stackoverflow.com/questions/34097154/pandas-hierarchically-labeling-bar-plot/34098738#34098738\n",
    "\n",
    "Demo follows:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x57b06a5b38>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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k3GJmxwKvMLO3Ans3s/HgumjinX0qsJRoZz/W3T+ecaztFCFj7Fh3PwbAzBYC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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x57b0695c88>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.set_index(['A', 'B']).plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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2KbNrVpLqgIMjYrGks4CDgNskHQ8cAxwPvAc8FREXStoZuBvYCagFZkbEE71U/4/A/pK+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\nLPecrMzMLPecrMzMLPecrMzMLPf+P2iW4gJR0B5sAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x57b1e0eb00>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = df.set_index(['A', 'B']).plot(kind='barh')\n",
    "ax.invert_yaxis()"
   ]
  },
  {
   "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
}