ipynb/boxplots.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[Sebastian Raschka](http://www.sebastianraschka.com)\n",
    "\n",
    "[back](https://github.com/rasbt/matplotlib-gallery) to the `matplotlib-gallery` at [https://github.com/rasbt/matplotlib-gallery](https://github.com/rasbt/matplotlib-gallery)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "%load_ext watermark"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Last updated: 26/08/2014 \n",
      "\n",
      "CPython 3.4.1\n",
      "IPython 2.1.0\n",
      "\n",
      "matplotlib 1.4.0\n",
      "numpy 1.8.2\n"
     ]
    }
   ],
   "source": [
    "%watermark -u -v -d -p matplotlib,numpy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font size=\"1.5em\">[More info](http://nbviewer.ipython.org/github/rasbt/python_reference/blob/master/ipython_magic/watermark.ipynb) about the `%watermark` extension</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>\n",
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Boxplots in matplotlib"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Sections"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- [Simple Boxplot](#Simple-Boxplot)\n",
    "\n",
    "- [Black and white Boxplot](#Black-and-white-Boxplot)\n",
    "\n",
    "- [Horizontal Boxplot](#Horizontal-Boxplot)\n",
    "\n",
    "- [Filled and cylindrical boxplots](#Filled-and-cylindrical-boxplots)\n",
    "\n",
    "- [Boxplots with custom fill colors](#Boxplots-with-custom-fill-colors)\n",
    "\n",
    "- [Violin plots](#Violin-plots)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>\n",
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>\n",
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](../images/intro_boxplot.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>\n",
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Simple Boxplot"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[[back to top](#Sections)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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aE4kfXx8/1pxI/Pj6+LHm+jTKrf1Wr16trl27Kj09XSkpKRo1apSWLFliZ5cAAMQtW6G7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\nA0M1HY/LcIccAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10b33fa58>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "all_data = [np.random.normal(0, std, 100) for std in range(1, 4)]\n",
    "\n",
    "fig = plt.figure(figsize=(8,6))\n",
    "\n",
    "plt.boxplot(all_data, \n",
    "            notch=False, # box instead of notch shape \n",
    "            sym='rs',    # red squares for outliers\n",
    "            vert=True)   # vertical box aligmnent\n",
    "\n",
    "plt.xticks([y+1 for y in range(len(all_data))], ['x1', 'x2', 'x3'])\n",
    "plt.xlabel('measurement x')\n",
    "t = plt.title('Box plot')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>\n",
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Black and white Boxplot"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[[back to top](#Sections)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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HXdYAgEZheR44uqwBALAcgQwAgAUIZAAALEAgAwBgAQIZANAozGUdHHRZAwDQRELWZV1V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\nAv8PtdnZMfb1oPEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x108651978>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "all_data = [np.random.normal(0, std, 100) for std in range(1, 4)]\n",
    "\n",
    "fig = plt.figure(figsize=(8,6))\n",
    "\n",
    "bplot = plt.boxplot(all_data, \n",
    "            notch=False, # box instead of notch shape \n",
    "            sym='rs',    # red squares for outliers\n",
    "            vert=True)   # vertical box aligmnent\n",
    "\n",
    "plt.xticks([y+1 for y in range(len(all_data))], ['x1', 'x2', 'x3'])\n",
    "plt.xlabel('measurement x')\n",
    "\n",
    "for components in bplot.keys():\n",
    "    for line in bplot[components]:\n",
    "        line.set_color('black')     # black lines\n",
    "\n",
    "t = plt.title('Black and white box plot')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>\n",
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Horizontal Boxplot"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[[back to top](#Sections)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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oXJxcBQAAQ3gt7Q0bNlx23caNG/0SBgAAlM/r37QTExO1detWr9c5Goq/aQMAQowv3Vfu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\nAAxBaQMAYAhKGwAAQ/wvpSHCqDPpO1UAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10b305470>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "all_data = [np.random.normal(0, std, 100) for std in range(1, 4)]\n",
    "\n",
    "fig = plt.figure(figsize=(8,6))\n",
    "\n",
    "plt.boxplot(all_data, \n",
    "            notch=False,  # box instead of notch shape \n",
    "            sym='rs',     # red squares for outliers\n",
    "            vert=False)   # horizontal box aligmnent\n",
    "\n",
    "plt.yticks([y+1 for y in range(len(all_data))], ['x1', 'x2', 'x3'])\n",
    "plt.ylabel('measurement x')\n",
    "t = plt.title('Horizontal Box plot')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>\n",
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Filled and cylindrical boxplots"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[[back to top](#Sections)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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s1AUACDPVej/koUOHavXq1ZKkf//73yooKDgnjAEAwNkcn2U9fvx4jR8/Xp06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\nBQhkAAAs8P8AMi93naGnVY4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10643a978>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "all_data = [np.random.normal(0, std, 100) for std in range(1, 4)]\n",
    "\n",
    "fig = plt.figure(figsize=(8,6))\n",
    "\n",
    "plt.boxplot(all_data, \n",
    "            notch=True,  # notch shape \n",
    "            sym='bs',     # blue squares for outliers\n",
    "            vert=True,   # vertical box aligmnent\n",
    "            patch_artist=True)   # fill with color\n",
    "\n",
    "plt.xticks([y+1 for y in range(len(all_data))], ['x1', 'x2', 'x3'])\n",
    "plt.xlabel('measurement x')\n",
    "t = plt.title('Box plot')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>\n",
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Boxplots with custom fill colors"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[[back to top](#Sections)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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oIyPjhO3CPCgWABBgx08ox9XwrFZXp5d79eqlDRs2qLi4WIcOHdJrr72mwYMHuxkSAIDA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\nYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x106adc0b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "all_data = [np.random.normal(0, std, 100) for std in range(1, 4)]\n",
    "\n",
    "fig = plt.figure(figsize=(8,6))\n",
    "\n",
    "bplot = plt.boxplot(all_data, \n",
    "            notch=False,  # notch shape \n",
    "            vert=True,   # vertical box aligmnent\n",
    "            patch_artist=True)   # fill with color\n",
    "\n",
    "colors = ['pink', 'lightblue', 'lightgreen']\n",
    "for patch, color in zip(bplot['boxes'], colors):\n",
    "    patch.set_facecolor(color)\n",
    "\n",
    "plt.xticks([y+1 for y in range(len(all_data))], ['x1', 'x2', 'x3'])\n",
    "plt.xlabel('measurement x')\n",
    "t = plt.title('Box plot')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>\n",
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Violin plots"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[[back to top](#Sections)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Violin plots are closely related to Tukey's (1977) box plots but add useful information such as the distribution of the sample data (density trace).   \n",
    "\n",
    "Violin plots were added in [matplotlib 1.4](http://matplotlib.org/1.4.0/users/whats_new.html)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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UDIqKiIgmBRNpMkWtVobT6TA7DG3t7m7C7+9+oxEMepFKbaDRaIwoKr0opbC6ehEzM72V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\nIy1JkiS1YCMtSZIktWAjLUmSJLXwP88ntGKTGjetAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x105ea24e0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "fig, axes = plt.subplots(nrows=1,ncols=2, figsize=(12,5))\n",
    "\n",
    "all_data = [np.random.normal(0, std, 100) for std in range(6, 10)]\n",
    "\n",
    "#fig = plt.figure(figsize=(8,6))\n",
    "\n",
    "axes[0].violinplot(all_data,\n",
    "               showmeans=False,\n",
    "               showmedians=True \n",
    "               )\n",
    "axes[0].set_title('violin plot')\n",
    "\n",
    "axes[1].boxplot(all_data,\n",
    "               )\n",
    "axes[1].set_title('box plot')\n",
    "\n",
    "# adding horizontal grid lines \n",
    "for ax in axes:\n",
    "    ax.yaxis.grid(True) \n",
    "    ax.set_xticks([y+1 for y in range(len(all_data))], )\n",
    "    ax.set_xlabel('xlabel')\n",
    "    ax.set_ylabel('ylabel')\n",
    "\n",
    "plt.setp(axes, xticks=[y+1 for y in range(len(all_data))], \n",
    "        xticklabels=['x1', 'x2', 'x3', 'x4'],\n",
    "        )    \n",
    "    \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "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": "3.4.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}