suntong/Pandas, Bar Chart With Error Bars.ipynb

## Pandas, Bar Chart With Error Bars.ipynb
{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Pandas, Bar Chart With Error Bars\n",
    "http://pandas.pydata.org/pandas-docs/stable/visualization.html#visualization-errorbars\n",
    "\n",
    "New in version 0.14.\n",
    "\n",
    "Plotting with error bars is now supported in the DataFrame.plot() and Series.plot()\n",
    "\n",
    "Horizontal and vertical errorbars can be supplied to the xerr and yerr keyword arguments to plot(). The error values can be specified using a variety of formats.\n",
    "\n",
    "- As a DataFrame or dict of errors with column names matching the columns attribute of the plotting DataFrame or matching the name attribute of the Series\n",
    "- As a str indicating which of the columns of plotting DataFrame contain the error values\n",
    "- As raw values (list, tuple, or np.ndarray). Must be the same length as the plotting DataFrame/Series\n",
    "\n",
    "Asymmetrical error bars are also supported, however raw error values must be provided in this case. For a M length Series, a Mx2 array should be provided indicating lower and upper (or left and right) errors. For a MxN DataFrame, asymmetrical errors should be in a Mx2xN array.\n",
    "\n",
    "Here is an example of one way to easily plot group means with standard deviations from the raw data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             data1  data2\n",
      "letter word              \n",
      "a      bar     3.5    6.0\n",
      "       foo     2.5    5.5\n",
      "b      bar     2.5    5.5\n",
      "       foo     3.0    4.5\n",
      "                data1     data2\n",
      "letter word                    \n",
      "a      bar   0.707107  1.414214\n",
      "       foo   0.707107  0.707107\n",
      "b      bar   0.707107  0.707107\n",
      "       foo   1.414214  0.707107\n"
     ]
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "\n",
    "# Generate the data\n",
    "ix3 = pd.MultiIndex.from_arrays([['a', 'a', 'a', 'a', 'b', 'b', 'b', 'b'], ['foo', 'foo', 'bar', 'bar', 'foo', 'foo', 'bar', 'bar']], names=['letter', 'word'])\n",
    "\n",
    "df3 = pd.DataFrame({'data1': [3, 2, 4, 3, 2, 4, 3, 2], 'data2': [6, 5, 7, 5, 4, 5, 6, 5]}, index=ix3)\n",
    "\n",
    "# Group by index labels and take the means and standard deviations for each group\n",
    "gp3 = df3.groupby(level=('letter', 'word'))\n",
    "\n",
    "means = gp3.mean()\n",
    "errors = gp3.std()\n",
    "\n",
    "print(means)\n",
    "print(errors)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x153f036b38>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x153f23e8d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot\n",
    "fig, ax = plt.subplots()\n",
    "\n",
    "means.plot(yerr=errors, ax=ax, kind='bar')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x15424695f8>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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h2dNXImJjsqoqxHMwlpfnYKyW1fUcjKTFwGMU70D5RCOEi5mZpZX3KLKxwEnZ4xPARmBR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\n3JXRhuQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x154246ccc0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot\n",
    "fig, ax = plt.subplots()\n",
    "\n",
    "means.plot(yerr=errors, ax=ax, kind='barh')\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
}