boxplot_fliers-mpl1.3.ipynb

{
 "metadata": {
  "name": "",
  "signature": "sha256:0ed925221518c78a4807db2feca735fd5cc723262d9e236ab378df2a0cd37a4d"
 },
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 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Messing with boxplot flier attributes - Matplotlib 1.3"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import matplotlib\n",
      "print('mpl version: {}'.format(matplotlib.__version__))\n",
      "import matplotlib.pyplot as plt\n",
      "%matplotlib inline"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "mpl version: 1.3.1\n"
       ]
      }
     ],
     "prompt_number": 9
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "This works in mpl1.3 but not 1.4"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def makeItGreen(boxpl):\n",
      "  for key in boxpl:\n",
      "    for obj in boxpl[key]:\n",
      "      plt.setp(obj, color=\"green\")\n",
      "\n",
      "x = [0.15, 0.11, 0.06, 0.06, 0.12, 0.56, -0.56]\n",
      "fig, ax = plt.subplots()\n",
      "bp = ax.boxplot(x, sym=\"o\")\n",
      "makeItGreen(bp)\n",
      "fliers = bp['fliers']\n",
      "for n, f in enumerate(fliers):\n",
      "    f.set_color('firebrick')\n",
      "    print('Flier set: {} at {}; color = {}'.format(n, f.get_xydata(), f.get_color()))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Flier set: 0 at [[ 1.    0.56]]; color = firebrick\n",
        "Flier set: 1 at [[ 1.   -0.56]]; color = firebrick\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAXcAAAEACAYAAABI5zaHAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nkiRJkiRJkiRJkiSpT/wPntVuTNQGgRcAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x74de4e0>"
       ]
      }
     ],
     "prompt_number": 12
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}