A Rough & Ready 2016 Medal Notebook View

Medals_2016_mpl.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Olympics 2016\n",
    "-------------\n",
    "This notebook should track the progress of the 2016 Olympics using Wikipedea as a source of information."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import matplotlib\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import requests\n",
    "from bs4 import BeautifulSoup\n",
    "import datetime"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Get the data!\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "07:44 Fri 12 Aug 2016 Result: 200\n"
     ]
    }
   ],
   "source": [
    "r = requests.get(\"https://en.wikipedia.org/wiki/2016_Summer_Olympics_medal_table\")\n",
    "last_update = datetime.datetime.now()\n",
    "last_update = last_update.strftime('%H:%M %a %d %b %Y')\n",
    "print last_update, \"Result:\", r.status_code"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Parse the data\n",
    "----"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Great Britain GBR 4 6 6 16\n"
     ]
    }
   ],
   "source": [
    "soup = BeautifulSoup(r.text, 'html.parser')\n",
    "t = soup.find_all('table')\n",
    "Medals = []\n",
    "# Note that this depends on the current page layout - at the moment the table we are after is second\n",
    "for row in t[1].findAll('tr')[1:-1]: # Skip first (header) & last (totals) rows\n",
    "    vals = row.text.split('\\n')  # Split the text value on line breaks \n",
    "    if len(vals) > 6:\n",
    "        try:\n",
    "            start = 1  # Most lines start on line 2\n",
    "            if len(vals) == 8: # Some on line 3\n",
    "                start = 2\n",
    "            country, gold, silver, bronze, total = vals[start:-1] # Also skip the spare at the end\n",
    "            country, code = country.split(u'\\xa0')[1:]  # For some reason the countries are u'\\xa0Name\\xa0(code)'\n",
    "            Medals.append([country, code[1:-1],  # Country and code without brackets\n",
    "                           int(gold), int(silver), int(bronze), int(total) # Medals\n",
    "                          ])\n",
    "        except ValueError, e:  # Just in case!\n",
    "            print \"Problem\", vals, country, e\n",
    "try:\n",
    "    countries = [x[0] for x in Medals]\n",
    "    codes = [x[1] for x in Medals]\n",
    "    gold = [x[2] for x in Medals]\n",
    "    silver = [x[3] for x in Medals]\n",
    "    bronze = [x[4] for x in Medals]\n",
    "    total = [x[5] for x in Medals]\n",
    "except IndexError, e:  # May not need this any more\n",
    "    print x, e\n",
    "# Just so I can keep track of team GB\n",
    "gb = codes.index(u'GBR')\n",
    "print countries[gb], codes[gb], gold[gb], silver[gb], bronze[gb], total[gb]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Actually Plot The Data\n",
    "----"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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+ycckmZsAWeqev2TZ9h9y9L55RZL/rKrnV9Vt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\nQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x91c1df0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "matplotlib.rcParams['figure.figsize'] = (20.0, 10.0)\n",
    "ind = range(len(codes))    # the x locations for the groups\n",
    "width = 1.0       # the width of the bars: can also be len(x) sequence\n",
    "\n",
    "bm = plt.bar(ind, bronze, width=width, color='#BA8651')\n",
    "sm = plt.bar(ind, silver, width=width, color='#C0C0C0', bottom=bronze)\n",
    "gb = [bronze[i]+silver[i] for i in ind]\n",
    "gm = plt.bar(ind, gold, width=width, color='#FFD700', bottom=gb)\n",
    "\n",
    "plt.ylabel('Medals')\n",
    "plt.title('2016 Olympic Medals last update %s' % last_update)\n",
    "plt.xticks([i+width/2. for i in ind], codes)\n",
    "plt.xticks(rotation=90)\n",
    "plt.legend((bm[0], sm[0], gm[0]), ('Bronze', 'Silver', 'Gold'))\n",
    "plt.tight_layout(h_pad=2.0)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}