Plot velocity from Redmine

Velocity.ipynb

{
 "metadata": {
  "name": "",
  "signature": "sha256:062e97e14fde0a6e7b0510040e95a71e833a609de6bca5ba837751161029b4d6"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%matplotlib inline\n",
      "\n",
      "import pandas\n",
      "import matplotlib.pyplot as plt\n",
      "\n",
      "# load the data from Redmine\n",
      "key = 'XXX'\n",
      "data = pandas.read_csv('https://redmine/projects/XXX/issues.csv?query_id=XXX&key={key}'.format(key=key))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Plot the velocity\n",
      "\n",
      "velocity = data[data['Target version'] != 'Backlog']\\\n",
      "    .groupby('Target version', as_index=True)['Story Points'].sum()\n",
      "avg_velocity = pandas.rolling_mean(velocity, window=5, min_periods=1)\n",
      "\n",
      "ax = velocity.plot(kind='bar', color='steelblue', label=\"Velocity\",\n",
      "                   legend=True, title=\"Velocity\", figsize=(12,8))\n",
      "avg_velocity.plot(ax=ax, color='r', style='.-', label=\"Average velocity (window=5)\",\n",
      "                  legend=True)\n",
      "ax.xaxis.grid(False)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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JUgAGakmSJCkAA7UkSZIUgIFakiRJCsBALUmSJAVgoJYkSZICMFBLkiRJARio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\nIiIiIiJiZP0PxHmP0sCYx+EAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f76e8b0a8d0>"
       ]
      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}