Homework 1. Which of two things is larger?

Due: Thursday, September 19, 11:59 PM

Useful libraries for this assignment

numpy, for arrays
pandas, for data frames
matplotlib, for plotting
requests, for downloading web content
pattern, for parsing html and xml pages
fnmatch (optional), for Unix-style string matching



In [38]:

    
# special IPython command to prepare the notebook for matplotlib
%matplotlib inline 

from fnmatch import fnmatch

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import requests
import urllib2
from pattern import web
from bs4 import BeautifulSoup as bs

# set some nicer defaults for matplotlib
from matplotlib import rcParams

#these colors come from colorbrewer2.org. Each is an RGB triplet
dark2_colors = [(0.10588235294117647, 0.6196078431372549, 0.4666666666666667),
                (0.8509803921568627, 0.37254901960784315, 0.00784313725490196),
                (0.4588235294117647, 0.4392156862745098, 0.7019607843137254),
                (0.9058823529411765, 0.1607843137254902, 0.5411764705882353),
                (0.4, 0.6509803921568628, 0.11764705882352941),
                (0.9019607843137255, 0.6705882352941176, 0.00784313725490196),
                (0.6509803921568628, 0.4627450980392157, 0.11372549019607843),
                (0.4, 0.4, 0.4)]

rcParams['figure.figsize'] = (10, 6)
rcParams['figure.dpi'] = 150
rcParams['axes.color_cycle'] = dark2_colors
rcParams['lines.linewidth'] = 2
rcParams['axes.grid'] = True
rcParams['axes.facecolor'] = '#eeeeee'
rcParams['font.size'] = 14
rcParams['patch.edgecolor'] = 'none'

Introduction

This was the XKCD comic after the 2012 Presidential election:

The comic refers to the fact that Nate Silver's statistical model (which is based mostly on combining information from pre-election polls) correctly predicted the outcome of the 2012 presidential race in all 50 states.

Polling data isn't a perfect predictor for the future, and some polls are more accurate than others. This means that election forecastors must consider prediction uncertainty when building models.

In this first assignment, you will perform a simple analysis of polling data about the upcoming Governor races. The assignment has three main parts:

First you will build some tools to download historical polling data from the web, and parse it into a more convenient format.

Next you will use these tools to aggregate and visualize several past Governor races

Finally you will run a bootstrap analysis to estimate the probable outcome of current Governor races, given the level of precision of historical polls.

Part 1: Collect and Clean

The Real Clear Politics website archives many political polls. In addition, they combine related polls to form an "RCP average" estimate of public opinion over time. For example, the chart on this page shows historical polling data for the Obama-Romney presidential race. The chart is an average of the polling data table below the chart.

The data used to generate plots like this are stored as XML pages, with URLs like:

http://charts.realclearpolitics.com/charts/[id].xml

Here, [id] is a unique integer, found at the end of the URL of the page that displays the graph. The id for the Obama-Romney race is 1171:

http://charts.realclearpolitics.com/charts/1171.xml

Opening this page in Google Chrome or Firefox will show you the XML content in an easy-to-read format. Notice that XML tags are nested inside each other, hierarchically (the jargony term for this is the "Document Object Model", or "DOM"). The first step of webscraping is almost always exploring the HTML/XML source in a browser, and getting a sense of this hierarchy.

Problem 0

The above XML page includes 5 distinct tags (one, for example, is chart). List these tags, and depict how they nest inside each other using an indented list. For example:

Page
- Section
  - Paragraph
- Conclusion

Your Answer Here

chart
- series
- graphs
  - graph 1
  - graph 2

Problem 1

We want to download and work with poll data like this. Like most programming tasks, we will break this into many smaller, easier pieces

Fill in the code for the get_poll_xml function, that finds and downloads an XML page discussed above

Hint

requests.get("http://www.google.com").text downloads the text from Google's homepage



In [2]:

    
"""
Function
--------
get_poll_xml

Given a poll_id, return the XML data as a text string

Inputs
------
poll_id : int
    The ID of the poll to fetch

Returns
-------
xml : str
    The text of the XML page for that poll_id

Example
-------
>>> get_poll_xml(1044)
u'<?xml version="1.0" encoding="UTF-8"?><chart><series><value xid=\'0\'>1/27/2009</value>
...etc...
"""    
#your code here    
def get_poll_xml(poll_id):
    text = requests.get("http://charts.realclearpolitics.com/charts/%s.xml" %poll_id).text
    return text
get_poll_xml(1044)









    Out[2]:





u'<?xml version="1.0" encoding="UTF-8"?><chart><series><value xid=\'0\'>1/27/2009</value><value xid=\'1\'>1/28/2009</value><value xid=\'2\'>1/29/2009</value><value xid=\'3\'>1/30/2009</value><value xid=\'4\'>1/31/2009</value><value xid=\'5\'>2/01/2009</value><value xid=\'6\'>2/02/2009</value><value xid=\'7\'>2/03/2009</value><value xid=\'8\'>2/04/2009</value><value xid=\'9\'>2/05/2009</value><value xid=\'10\'>2/06/2009</value><value xid=\'11\'>2/07/2009</value><value xid=\'12\'>2/08/2009</value><value xid=\'13\'>2/09/2009</value><value xid=\'14\'>2/10/2009</value><value xid=\'15\'>2/11/2009</value><value xid=\'16\'>2/12/2009</value><value xid=\'17\'>2/13/2009</value><value xid=\'18\'>2/14/2009</value><value xid=\'19\'>2/15/2009</value><value xid=\'20\'>2/16/2009</value><value xid=\'21\'>2/17/2009</value><value xid=\'22\'>2/18/2009</value><value xid=\'23\'>2/19/2009</value><value xid=\'24\'>2/20/2009</value><value xid=\'25\'>2/21/2009</value><value xid=\'26\'>2/22/2009</value><value xid=\'27\'>2/23/2009</value><value xid=\'28\'>2/24/2009</value><value xid=\'29\'>2/25/2009</value><value xid=\'30\'>2/26/2009</value><value xid=\'31\'>2/27/2009</value><value xid=\'32\'>2/28/2009</value><value xid=\'33\'>3/01/2009</value><value xid=\'34\'>3/02/2009</value><value xid=\'35\'>3/03/2009</value><value xid=\'36\'>3/04/2009</value><value xid=\'37\'>3/05/2009</value><value xid=\'38\'>3/06/2009</value><value xid=\'39\'>3/07/2009</value><value xid=\'40\'>3/08/2009</value><value xid=\'41\'>3/09/2009</value><value xid=\'42\'>3/10/2009</value><value xid=\'43\'>3/11/2009</value><value xid=\'44\'>3/12/2009</value><value xid=\'45\'>3/13/2009</value><value xid=\'46\'>3/14/2009</value><value xid=\'47\'>3/15/2009</value><value xid=\'48\'>3/16/2009</value><value xid=\'49\'>3/17/2009</value><value xid=\'50\'>3/18/2009</value><value xid=\'51\'>3/19/2009</value><value xid=\'52\'>3/20/2009</value><value xid=\'53\'>3/21/2009</value><value xid=\'54\'>3/22/2009</value><value xid=\'55\'>3/23/2009</value><value xid=\'56\'>3/24/2009</value><value xid=\'57\'>3/25/2009</value><value xid=\'58\'>3/26/2009</value><value xid=\'59\'>3/27/2009</value><value xid=\'60\'>3/28/2009</value><value xid=\'61\'>3/29/2009</value><value xid=\'62\'>3/30/2009</value><value xid=\'63\'>3/31/2009</value><value xid=\'64\'>4/01/2009</value><value xid=\'65\'>4/02/2009</value><value xid=\'66\'>4/03/2009</value><value xid=\'67\'>4/04/2009</value><value xid=\'68\'>4/05/2009</value><value xid=\'69\'>4/06/2009</value><value xid=\'70\'>4/07/2009</value><value xid=\'71\'>4/08/2009</value><value xid=\'72\'>4/09/2009</value><value xid=\'73\'>4/10/2009</value><value xid=\'74\'>4/11/2009</value><value xid=\'75\'>4/12/2009</value><value xid=\'76\'>4/13/2009</value><value xid=\'77\'>4/14/2009</value><value xid=\'78\'>4/15/2009</value><value xid=\'79\'>4/16/2009</value><value xid=\'80\'>4/17/2009</value><value xid=\'81\'>4/18/2009</value><value xid=\'82\'>4/19/2009</value><value 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xid=\'1668\'>50.3</value><value xid=\'1669\'>50.2</value><value xid=\'1670\'>50.2</value><value xid=\'1671\'>50.8</value><value xid=\'1672\'>50.7</value><value xid=\'1673\'>50.5</value><value xid=\'1674\'>50.6</value><value xid=\'1675\'>50.0</value><value xid=\'1676\'>50.4</value><value xid=\'1677\'>50.2</value><value xid=\'1678\'>50.2</value><value xid=\'1679\'>50.4</value><value xid=\'1680\'>50.4</value><value xid=\'1681\'>50.8</value><value xid=\'1682\'>50.8</value><value xid=\'1683\'>50.8</value><value xid=\'1684\'>50.8</value><value xid=\'1685\'>51.0</value><value xid=\'1686\'>50.9</value><value xid=\'1687\'>51.1</value><value xid=\'1688\'>51.0</value><value xid=\'1689\'>51.1</value><value xid=\'1690\'>51.0</value><value xid=\'1691\'>50.9</value><value xid=\'1692\'>51.0</value><value xid=\'1693\'>50.6</value><value xid=\'1694\'>50.6</value><value xid=\'1695\'>50.6</value><value xid=\'1696\'>50.6</value><value xid=\'1697\'>50.6</value><value xid=\'1698\'>50.5</value><value xid=\'1699\'>50.5</value><value xid=\'1700\'>50.7</value><value xid=\'1701\'>50.5</value><value xid=\'1702\'>50.5</value><value xid=\'1703\'>50.8</value><value xid=\'1704\'>51.2</value><value xid=\'1705\'>51.3</value><value xid=\'1706\'>51.0</value><value xid=\'1707\'>51.0</value><value xid=\'1708\'>50.6</value><value xid=\'1709\'>50.6</value><value xid=\'1710\'>50.9</value><value xid=\'1711\'>50.7</value><value xid=\'1712\'>50.9</value><value xid=\'1713\'>50.3</value><value xid=\'1714\'>50.3</value><value xid=\'1715\'>49.8</value><value xid=\'1716\'>50.4</value><value xid=\'1717\'>50.1</value><value xid=\'1718\'>50.1</value><value xid=\'1719\'>50.3</value><value xid=\'1720\'>50.4</value><value xid=\'1721\'>50.6</value><value xid=\'1722\'>50.3</value><value xid=\'1723\'>51.4</value><value xid=\'1724\'>51.3</value><value xid=\'1725\'>51.1</value><value xid=\'1726\'>51.2</value><value xid=\'1727\'>51.0</value><value xid=\'1728\'>51.3</value><value xid=\'1729\'>51.0</value><value xid=\'1730\'>50.8</value><value xid=\'1731\'>50.5</value><value xid=\'1732\'>50.8</value><value xid=\'1733\'>50.8</value><value xid=\'1734\'>50.8</value><value xid=\'1735\'>50.9</value><value xid=\'1736\'>50.9</value><value xid=\'1737\'>51.6</value><value xid=\'1738\'>51.5</value><value xid=\'1739\'>51.6</value><value xid=\'1740\'>51.7</value><value xid=\'1741\'>51.9</value><value xid=\'1742\'>51.9</value><value xid=\'1743\'>51.9</value><value xid=\'1744\'>52.2</value><value xid=\'1745\'>52.0</value><value xid=\'1746\'>52.4</value><value xid=\'1747\'>52.9</value><value xid=\'1748\'>52.9</value><value xid=\'1749\'>53.1</value><value xid=\'1750\'>53.3</value><value xid=\'1751\'>53.4</value><value xid=\'1752\'>54.3</value><value xid=\'1753\'>54.2</value><value xid=\'1754\'>54.2</value><value xid=\'1755\'>54.2</value><value xid=\'1756\'>54.4</value><value xid=\'1757\'>54.9</value><value xid=\'1758\'>55.3</value><value xid=\'1759\'>55.4</value><value xid=\'1760\'>55.4</value><value xid=\'1761\'>55.1</value><value xid=\'1762\'>55.0</value><value xid=\'1763\'>55.0</value><value xid=\'1764\'>55.0</value><value xid=\'1765\'>55.5</value><value xid=\'1766\'>55.5</value><value xid=\'1767\'>55.6</value><value xid=\'1768\'>55.5</value><value xid=\'1769\'>55.6</value><value xid=\'1770\'>55.6</value><value xid=\'1771\'>55.9</value><value xid=\'1772\'>55.5</value><value xid=\'1773\'>55.3</value><value xid=\'1774\'>55.4</value><value xid=\'1775\'>55.3</value><value xid=\'1776\'>55.0</value><value xid=\'1777\'>54.2</value><value xid=\'1778\'>53.4</value><value xid=\'1779\'>53.3</value><value xid=\'1780\'>53.1</value><value xid=\'1781\'>53.1</value><value xid=\'1782\'>53.2</value><value xid=\'1783\'>53.4</value><value xid=\'1784\'>53.3</value><value xid=\'1785\'>53.4</value><value xid=\'1786\'>53.5</value><value xid=\'1787\'>53.6</value><value xid=\'1788\'>53.9</value><value xid=\'1789\'>53.9</value><value xid=\'1790\'>54.1</value><value xid=\'1791\'>54.0</value><value xid=\'1792\'>53.8</value><value xid=\'1793\'>53.8</value><value xid=\'1794\'>53.9</value><value xid=\'1795\'>53.7</value><value xid=\'1796\'>53.6</value><value xid=\'1797\'>53.3</value><value xid=\'1798\'>53.4</value><value xid=\'1799\'>53.4</value><value xid=\'1800\'>53.4</value><value xid=\'1801\'>53.4</value><value xid=\'1802\'>53.6</value><value xid=\'1803\'>53.8</value><value xid=\'1804\'>53.8</value><value xid=\'1805\'>53.7</value><value xid=\'1806\'>53.7</value><value xid=\'1807\'>53.6</value><value xid=\'1808\'>53.1</value><value xid=\'1809\'>53.1</value><value xid=\'1810\'>53.3</value><value xid=\'1811\'>53.4</value><value xid=\'1812\'>53.4</value><value xid=\'1813\'>53.6</value><value xid=\'1814\'>53.3</value><value xid=\'1815\'>53.4</value><value xid=\'1816\'>52.8</value><value xid=\'1817\'>53.0</value><value xid=\'1818\'>53.2</value><value xid=\'1819\'>52.2</value><value xid=\'1820\'>51.8</value><value xid=\'1821\'>51.9</value><value xid=\'1822\'>51.6</value><value xid=\'1823\'>51.7</value><value xid=\'1824\'></value><value xid=\'1825\'></value><value xid=\'1826\'></value><value xid=\'1827\'></value><value xid=\'1828\'></value><value xid=\'1829\'></value><value xid=\'1830\'></value></graph></graphs></chart>'

Here are some other functions we'll use later. plot_colors contains hints about parsing XML data.



In [37]:

    
# "r"egular "e"xpressions is kind of a mini-language to
# do pattern matching on text
import re

def _strip(s):
    """This function removes non-letter characters from a word
    
    for example _strip('Hi there!') == 'Hi there'
    """
    return re.sub(r'[\W_]+', '', s)


def plot_colors(xml):
    """
    Given an XML document like the link above, returns a python dictionary
    that maps a graph title to a graph color.
    
    Both the title and color are parsed from attributes of the <graph> tag:
    <graph title="the title", color="#ff0000"> -> {'the title': '#ff0000'}
    
    These colors are in "hex string" format. This page explains them:
    http://coding.smashingmagazine.com/2012/10/04/the-code-side-of-color/
    
    Example
    -------
    >>> plot_colors(get_poll_xml(1044))
    {u'Approve': u'#000000', u'Disapprove': u'#FF0000'}
    """
    dom = web.Element(xml)
    result = {}
    for graph in dom.by_tag('graph'):
        title = _strip(graph.attributes['title'])
        result[title] = graph.attributes['color']
    return result

_strip('Hi there!')
plot_colors(get_poll_xml(1044))









    Out[37]:





{u'Approve': u'#000000', u'Disapprove': u'#FF0000'}

Problem 2

Even though get_poll_xml pulls data from the web into Python, it does so as a block of text. This still isn't very useful. Use the web module in pattern to parse this text, and extract data into a pandas DataFrame.

Hints

You might want create python lists for each column in the XML. Then, to turn these lists into a DataFrame, run

pd.DataFrame({'column_label_1': list_1, 'column_label_2':list_2, ...})

use the pandas function pd.to_datetime to convert strings into dates



In [36]:

    
"""
    Function
    ---------
    rcp_poll_data

    Extract poll information from an XML string, and convert to a DataFrame

    Parameters
    ----------
    xml : str
        A string, containing the XML data from a page like 
        get_poll_xml(1044)
        
    Returns
    -------
    A pandas DataFrame with the following columns:
        date: The date for each entry
        title_n: The data value for the gid=n graph (take the column name from the `title` tag)
        
    This DataFrame should be sorted by date
        
    Example
    -------
    Consider the following simple xml page:
    
    <chart>
    <series>
    <value xid="0">1/27/2009</value>
    <value xid="1">1/28/2009</value>
    </series>
    <graphs>
    <graph gid="1" color="#000000" balloon_color="#000000" title="Approve">
    <value xid="0">63.3</value>
    <value xid="1">63.3</value>
    </graph>
    <graph gid="2" color="#FF0000" balloon_color="#FF0000" title="Disapprove">
    <value xid="0">20.0</value>
    <value xid="1">20.0</value>
    </graph>
    </graphs>
    </chart>
    
    Given this string, rcp_poll_data should return
    result = pd.DataFrame({'date': pd.to_datetime(['1/27/2009', '1/28/2009']), 
                           'Approve': [63.3, 63.3], 'Disapprove': [20.0, 20.0]})
"""
#your code here
def rcp_poll_data(xml):
    dom = web.Element(xml)
    
    result = {}
    dates = dom.by_tag('series')[0]  
    dates = {date.attributes['xid']: str(date.content) for date in dates.by_tag('value')}
    
    keys = dates.keys();
    result['date'] = pd.to_datetime([dates[k] for k in keys])
    
    #retrieve graph attributes
    for graph in dom.by_tag('graph'):
        name = graph.attributes['title']  
        data = {x.attributes['xid'] : float(x.content) if x.content else np.nan for x in graph.by_tag('value')}
        result[name] = [data[k] for k in keys]
        
    result = pd.DataFrame(result);
    result = result.sort_values(by=['date'])
    return result                     

rcp_poll_data(get_poll_xml(1044))









    Out[36]:






  
    
      
      Approve
      Disapprove
      date
    
  
  
    
      464
      63.3
      20.0
      2009-01-27
    
    
      558
      63.3
      20.0
      2009-01-28
    
    
      653
      63.5
      19.3
      2009-01-29
    
    
      714
      63.5
      19.3
      2009-01-30
    
    
      806
      61.8
      19.4
      2009-01-31
    
    
      1306
      61.8
      19.4
      2009-02-01
    
    
      960
      61.8
      19.4
      2009-02-02
    
    
      1051
      61.8
      19.4
      2009-02-03
    
    
      1459
      61.8
      19.4
      2009-02-04
    
    
      1220
      61.8
      19.4
      2009-02-05
    
    
      656
      61.6
      21.4
      2009-02-06
    
    
      655
      61.6
      21.4
      2009-02-07
    
    
      658
      61.6
      21.4
      2009-02-08
    
    
      657
      65.4
      22.6
      2009-02-09
    
    
      660
      65.4
      22.6
      2009-02-10
    
    
      659
      64.2
      23.3
      2009-02-11
    
    
      662
      64.2
      23.3
      2009-02-12
    
    
      661
      64.2
      23.3
      2009-02-13
    
    
      664
      64.8
      25.4
      2009-02-14
    
    
      663
      65.5
      25.5
      2009-02-15
    
    
      1043
      65.5
      25.5
      2009-02-16
    
    
      1044
      65.5
      25.5
      2009-02-17
    
    
      1045
      65.1
      25.9
      2009-02-18
    
    
      1046
      62.3
      28.5
      2009-02-19
    
    
      1039
      63.2
      28.6
      2009-02-20
    
    
      1040
      63.2
      28.6
      2009-02-21
    
    
      1041
      63.2
      28.6
      2009-02-22
    
    
      1042
      63.0
      27.8
      2009-02-23
    
    
      1047
      62.5
      27.8
      2009-02-24
    
    
      1048
      62.5
      27.8
      2009-02-25
    
    
      ...
      ...
      ...
      ...
    
    
      455
      42.6
      53.4
      2014-01-02
    
    
      454
      42.4
      53.6
      2014-01-03
    
    
      453
      42.4
      53.8
      2014-01-04
    
    
      452
      42.3
      53.8
      2014-01-05
    
    
      451
      42.4
      53.7
      2014-01-06
    
    
      450
      42.8
      53.7
      2014-01-07
    
    
      449
      42.4
      53.6
      2014-01-08
    
    
      458
      42.1
      53.1
      2014-01-09
    
    
      457
      42.0
      53.1
      2014-01-10
    
    
      338
      42.0
      53.3
      2014-01-11
    
    
      339
      42.1
      53.4
      2014-01-12
    
    
      340
      42.0
      53.4
      2014-01-13
    
    
      341
      41.9
      53.6
      2014-01-14
    
    
      334
      42.1
      53.3
      2014-01-15
    
    
      335
      42.3
      53.4
      2014-01-16
    
    
      336
      42.4
      52.8
      2014-01-17
    
    
      337
      42.2
      53.0
      2014-01-18
    
    
      342
      42.0
      53.2
      2014-01-19
    
    
      343
      42.5
      52.2
      2014-01-20
    
    
      691
      42.8
      51.8
      2014-01-21
    
    
      690
      42.8
      51.9
      2014-01-22
    
    
      693
      43.2
      51.6
      2014-01-23
    
    
      692
      43.2
      51.7
      2014-01-24
    
    
      687
      NaN
      NaN
      2014-01-25
    
    
      686
      NaN
      NaN
      2014-01-26
    
    
      689
      NaN
      NaN
      2014-01-27
    
    
      688
      NaN
      NaN
      2014-01-28
    
    
      685
      NaN
      NaN
      2014-01-29
    
    
      684
      NaN
      NaN
      2014-01-30
    
    
      569
      NaN
      NaN
      2014-01-31
    
  

1831 rows × 3 columns



In [5]:

    
result['date'] = pd.to_datetime









    



---------------------------------------------------------------------------
NameError                                 Traceback (most recent call last)
<ipython-input-5-bf656feffff7> in <module>()
----> 1 result['date'] = pd.to_datetime

NameError: name 'result' is not defined

The output from rcp_poll_data is much more useful for analysis. For example, we can plot with it:



In [35]:

    
def poll_plot(poll_id):
    """
    Make a plot of an RCP Poll over time
    
    Parameters
    ----------
    poll_id : int
        An RCP poll identifier
    """

    # hey, you wrote two of these functions. Thanks for that!
    xml = get_poll_xml(poll_id)
    data = rcp_poll_data(xml)
    colors = plot_colors(xml)

    #remove characters like apostrophes
    data = data.rename(columns = {c: _strip(c) for c in data.columns})

    #normalize poll numbers so they add to 100%    
    norm = data[colors.keys()].sum(axis=1) / 100    
    for c in colors.keys():
        data[c] /= norm
    
    for label, color in colors.items():
        plt.plot(data.date, data[label], color=color, label=label)        
        
    plt.xticks(rotation=70)
    plt.legend(loc='best')
    plt.xlabel("Date")
    plt.ylabel("Normalized Poll Percentage")

If you've done everything right so far, the following code should reproduce the graph on this page



In [ ]:

    
poll_plot(1044)
plt.title("Obama Job Approval")

Part 2: Aggregate and Visualize

Problem 3

Unfortunately, these data don't have any error bars. If a candidate leads by 10% in the RCP average, is she a shoo-in to win? Or is this number too close to call? Does a 10% poll lead mean more 1 day before a race than it does 1 week before? Without error estimates, these questions are impossible to answer.

To get a sense of how accurate the RCP polls are, you will gather data from many previous Governor races, where the outcome is known.

This url has links to many governer races.

http://www.realclearpolitics.com/epolls/2010/governor/2010_elections_governor_map.html

Notice that each link to a governor race has the following URL pattern:

http://www.realclearpolitics.com/epolls/[YEAR]/governor/[STATE]/[TITLE]-[ID].html

Write a function that scans html for links to URLs like this

Hint The fnmatch function is useful for simple string matching tasks.



In [31]:

    
"""
    Function
    --------
    find_governor_races

    Find and return links to RCP races on a page like
    http://www.realclearpolitics.com/epolls/2010/governor/2010_elections_governor_map.html
    
    Parameters
    ----------
    html : str
        The HTML content of a page to scan
        
    Returns
    -------
    A list of urls for Governer race pages
    
    Example
    -------
    For a page like
    
    <html>
    <body>
    <a href="http://www.realclearpolitics.com/epolls/2010/governor/ma/massachusetts_governor_baker_vs_patrick_vs_cahill-1154.html"></a>
    <a href="http://www.realclearpolitics.com/epolls/2010/governor/ca/california_governor_whitman_vs_brown-1113.html"></a>
    </body>
    </html>
    
    find_governor_races would return
    ['http://www.realclearpolitics.com/epolls/2010/governor/ma/massachusetts_governor_baker_vs_patrick_vs_cahill-1154.html',
     'http://www.realclearpolitics.com/epolls/2010/governor/ca/california_governor_whitman_vs_brown-1113.html']
"""
#your code here
def is_gov_race(l):
    """return True if a URL refers to a Governor race"""    
    pattern = 'http://www.realclearpolitics.com/epolls/????/governor/??/*-*.html'
    return fnmatch(l, pattern)

url1 = 'http://www.realclearpolitics.com/epolls/2010/governor/2010_elections_governor_map.html'

def find_governor_races(html):
    #tree = bs(get_html(url1))
    
    tree = bs(urllib2.urlopen(html).read())
    links =[]
  
    selects = tree.find_all(class_ ="search_by_race")
    for select in selects:
        for option in select.find_all('option'):
            links.append(str(option.get('value')))
      
    links = [l for l in links if is_gov_race(l)]
    #eliminate duplicates!
    links = list(set(links))
    return  links

Problem 4

At this point, you have functions to find a collection of governor races, download historical polling data from each one, parse them into a numerical DataFrame, and plot this data.

The main question we have about these data are how accurately they predict election outcomes. To answer this question, we need to grab the election outcome data.

Write a function that looks up and returns the election result on a page like this one.

Remember to look at the HTML source!

You can do this by selection view->developer->view source in Chrome, or Tools -> web developer -> page source in Firefox. Altenatively, you can right-click on a part of the page, and select "inspect element"



In [34]:

    
"""
    Function
    --------
    race_result

    Return the actual voting results on a race page
    
    Parameters
    ----------
    url : string
        The website to search through
        
    Returns
    -------
    A dictionary whose keys are candidate names,
    and whose values is the percentage of votes they received.
    
    If necessary, normalize these numbers so that they add up to 100%.
    
    Example
    --------
    >>> url = 'http://www.realclearpolitics.com/epolls/2010/governor/ca/california_governor_whitman_vs_brown-1113.html'
    >>> race_result(url)
    {'Brown': 56.0126582278481, 'Whitman': 43.9873417721519}
"""
#your code here
url = 'http://www.realclearpolitics.com/epolls/2010/governor/ca/california_governor_whitman_vs_brown-1113.html'
""""
def race_result(url):
    
    result = {}
    soup = bs(urllib2.urlopen(url).read())
    rows = [row for row in soup.find("table", class_ ="data").find_all("tr")]
    name_columns = [col.get_text() for col in rows[0].find_all("th")]
    res_columns = [col.get_text() for col in rows[1].find_all("td")]
    
    res1 = float(res_columns[3])
    res2 = float(res_columns[4])
    sum =  res1 + res2
    tot = sum / 100                                  
    result[name_columns[3]] = res1 / tot
    result[name_columns[4]] = res2 / tot
    
    return result
race_result(url)   
"""
def race_result(url):
    
    dom = web.Element(requests.get(url).text)
    
    table = dom.by_tag('div#polling-data-rcp')[0]
    result_data = table.by_tag('tr.final')[0]
    td = result_data.by_tag('td')

    results = [float(t.content) for t in td[3:-1]]
    tot = sum(results) / 100
    
    #get table headers
    headers = table.by_tag('th')
    labels = [str(t.content).split('(')[0].strip() for t in headers[3:-1]]
    
    return {l:r / tot for l, r in zip(labels, results)}

Here are some more utility functions that take advantage of what you've done so far.



In [3]:

    
def id_from_url(url):
    """Given a URL, look up the RCP identifier number"""
    return url.split('-')[-1].split('.html')[0]


def plot_race(url):
    """Make a plot summarizing a senate race
    
    Overplots the actual race results as dashed horizontal lines
    """
    #hey, thanks again for these functions!
    print url
    id = id_from_url(url)
    print id
    xml = get_poll_xml(id)    
    colors = plot_colors(xml)

    if len(colors) == 0:
        return
    
    #really, you shouldn't have
    result = race_result(url)
    
    poll_plot(id)
    plt.xlabel("Date")
    plt.ylabel("Polling Percentage")
    for r in result:
        plt.axhline(result[r], color=colors[_strip(r)], alpha=0.6, ls='--')

plot_race('http://www.realclearpolitics.com/epolls/2010/governor/ca/california_governor_whitman_vs_brown-1113.html')









    



http://www.realclearpolitics.com/epolls/2010/governor/ca/california_governor_whitman_vs_brown-1113.html
1113






    



---------------------------------------------------------------------------
NameError                                 Traceback (most recent call last)
<ipython-input-3-3cb72da66a90> in <module>()
     28         plt.axhline(result[r], color=colors[_strip(r)], alpha=0.6, ls='--')
     29 
---> 30 plot_race('http://www.realclearpolitics.com/epolls/2010/governor/ca/california_governor_whitman_vs_brown-1113.html')

<ipython-input-3-3cb72da66a90> in plot_race(url)
     13     id = id_from_url(url)
     14     print id
---> 15     xml = get_poll_xml(id)
     16     colors = plot_colors(xml)
     17 

NameError: global name 'get_poll_xml' is not defined

Now that this is done, we can easily visualize many historical Governer races. The solid line plots the poll history, the dotted line reports the actual result.

If this code block fails, you probably have a bug in one of your functions.



In [33]:

    
#page = requests.get('http://www.realclearpolitics.com/epolls/2010/governor/2010_elections_governor_map.html').text.encode('ascii', 'ignore')
page = 'http://www.realclearpolitics.com/epolls/2010/governor/2010_elections_governor_map.html'
for race in find_governor_races(page):
    plot_race(race)
    plt.show()









    



/Users/apple/anaconda/envs/python2/lib/python2.7/site-packages/bs4/__init__.py:166: UserWarning: No parser was explicitly specified, so I'm using the best available HTML parser for this system ("lxml"). This usually isn't a problem, but if you run this code on another system, or in a different virtual environment, it may use a different parser and behave differently.

To get rid of this warning, change this:

 BeautifulSoup([your markup])

to this:

 BeautifulSoup([your markup], "lxml")

  markup_type=markup_type))






    



---------------------------------------------------------------------------
NameError                                 Traceback (most recent call last)
<ipython-input-33-887da342b444> in <module>()
      2 page = 'http://www.realclearpolitics.com/epolls/2010/governor/2010_elections_governor_map.html'
      3 for race in find_governor_races(page):
----> 4     plot_race(race)
      5     plt.show()

<ipython-input-29-eda9602b49d7> in plot_race(url)
     20     result = race_result(url)
     21 
---> 22     poll_plot(id)
     23     plt.xlabel("Date")
     24     plt.ylabel("Polling Percentage")

NameError: global name 'poll_plot' is not defined

Briefly summarize these graphs -- how accurate is the typical poll a day before the election? How often does a prediction one month before the election mispredict the actual winner?

Your summary here Generally, these polls seem to be within about 5% of the election outcome. The accuracy is higher shortly before the election. (within 2% or so). Only for about 20% of the time, a poll one month before the election mispredict the actual winner.

Part 3: Analysis

Problem 5

You are (finally!) in a position to do some quantitative analysis.

We have provided an error_data function that builds upon the functions you have written. It computes a new DataFrame with information about polling errors.

Use error_data, find_governer_races, and pd.concat to construct a Data Frame summarizing the forecast errors from all the Governor races

Hint

It's best to set ignore_index=True in pd.concat



In [28]:

    
def party_from_color(color):
    if color in ['#0000CC', '#3B5998']:
        return 'democrat'
    if color in ['#FF0000', '#D30015']:
        return 'republican'
    return 'other'


def error_data(url):
    """
    Given a Governor race URL, download the poll data and race result,
    and construct a DataFrame with the following columns:
    
    candidate: Name of the candidate
    forecast_length: Number of days before the election
    percentage: The percent of poll votes a candidate has.
                Normalized to that the canddidate percentages add to 100%
    error: Difference between percentage and actual race reulst
    party: Political party of the candidate
    
    The data are resampled as necessary, to provide one data point per day
    """
    
    id = id_from_url(url)
    xml = get_poll_xml(id)
    
    colors = plot_colors(xml)
    if len(colors) == 0:
        return pd.DataFrame()
    
    df = rcp_poll_data(xml)
    result = race_result(url)
    
    #remove non-letter characters from columns
    df = df.rename(columns={c: _strip(c) for c in df.columns})
    for k, v in result.items():
        result[_strip(k)] = v 
    
    candidates = [c for c in df.columns if c is not 'date']
   
    #turn into a timeseries...
    df.index = df.date
    
    #...so that we can resample at regular, daily intervals
    df = df.resample('D').mean()
    df = df.dropna()
    
    #compute forecast length in days
    #(assuming that last forecast happens on the day of the election, for simplicity)
    forecast_length = (df.index.max() - df.index).values
                  
    forecast_length = forecast_length / np.timedelta64(1, 'D')  # convert to number of days
   
    errors = {}
    normalized = {}
    poll_lead = {}
    
    for c in candidates:
        #turn raw percentage into percentage of poll votes
        corr = df[c].values / df[candidates].sum(axis=1).values * 100.
        err = corr - result[_strip(c)]
        
        normalized[c] = corr
        errors[c] = err
        
    n = forecast_length.size
    
    result = {}
    result['percentage'] = np.hstack(normalized[c] for c in candidates)
    result['error'] = np.hstack(errors[c] for c in candidates)
    result['candidate'] = np.hstack(np.repeat(c, n) for c in candidates)
    result['party'] = np.hstack(np.repeat(party_from_color(colors[_strip(c)]), n) for c in candidates)
    result['forecast_length'] = np.hstack(forecast_length for _ in candidates)
    
    result = pd.DataFrame(result)
    return result

#url = 'http://www.realclearpolitics.com/epolls/2010/governor/ca/california_governor_whitman_vs_brown-1113.html'
#print error_data(url)



In [26]:

    
"""
function
---------
all_error_data

Calls error_data on all races from find_governer_races(page),
and concatenates into a single DataFrame

Parameters
-----------
None

Examples
--------
df = all_error_data()
"""
#your code here
def all_error_data():
    frames = [error_data(url) for url in find_governor_races(page)]
    result = pd.concat(frames,ignore_index=True)
    return result



In [39]:

    
errors = all_error_data()
print errors









    



     candidate      error  forecast_length       party  percentage
0       Caprio  16.284671             32.0    democrat   41.095890
1       Caprio  16.284671             31.0    democrat   41.095890
2       Caprio  16.284671             30.0    democrat   41.095890
3       Caprio  16.284671             29.0    democrat   41.095890
4       Caprio  17.082426             28.0    democrat   41.893645
5       Caprio  17.246073             27.0    democrat   42.057292
6       Caprio  17.246073             26.0    democrat   42.057292
7       Caprio  17.246073             25.0    democrat   42.057292
8       Caprio  17.246073             24.0    democrat   42.057292
9       Caprio  17.246073             23.0    democrat   42.057292
10      Caprio  17.246073             22.0    democrat   42.057292
11      Caprio  17.246073             21.0    democrat   42.057292
12      Caprio  15.017212             20.0    democrat   39.828431
13      Caprio  15.017212             19.0    democrat   39.828431
14      Caprio  15.017212             18.0    democrat   39.828431
15      Caprio  15.017212             17.0    democrat   39.828431
16      Caprio  15.017212             16.0    democrat   39.828431
17      Caprio  15.017212             15.0    democrat   39.828431
18      Caprio  15.017212             14.0    democrat   39.828431
19      Caprio  15.017212             13.0    democrat   39.828431
20      Caprio  15.017212             12.0    democrat   39.828431
21      Caprio  14.070920             11.0    democrat   38.882139
22      Caprio  14.070920             10.0    democrat   38.882139
23      Caprio  14.070920              9.0    democrat   38.882139
24      Caprio  14.070920              8.0    democrat   38.882139
25      Caprio  14.070920              7.0    democrat   38.882139
26      Caprio  14.070920              6.0    democrat   38.882139
27      Caprio  10.859138              5.0    democrat   35.670357
28      Caprio   5.453453              4.0    democrat   30.264672
29      Caprio   5.453453              3.0    democrat   30.264672
...        ...        ...              ...         ...         ...
9598   Parnell  -0.225529             29.0  republican   60.371179
9599   Parnell  -0.225529             28.0  republican   60.371179
9600   Parnell  -0.225529             27.0  republican   60.371179
9601   Parnell  -0.225529             26.0  republican   60.371179
9602   Parnell  -0.225529             25.0  republican   60.371179
9603   Parnell  -0.225529             24.0  republican   60.371179
9604   Parnell  -0.225529             23.0  republican   60.371179
9605   Parnell  -0.225529             22.0  republican   60.371179
9606   Parnell  -1.901056             21.0  republican   58.695652
9607   Parnell  -1.901056             20.0  republican   58.695652
9608   Parnell  -1.901056             19.0  republican   58.695652
9609   Parnell  -1.901056             18.0  republican   58.695652
9610   Parnell  -1.901056             17.0  republican   58.695652
9611   Parnell  -3.284880             16.0  republican   57.311828
9612   Parnell  -3.284880             15.0  republican   57.311828
9613   Parnell  -3.284880             14.0  republican   57.311828
9614   Parnell  -2.086070             13.0  republican   58.510638
9615   Parnell  -2.086070             12.0  republican   58.510638
9616   Parnell  -2.086070             11.0  republican   58.510638
9617   Parnell  -2.086070             10.0  republican   58.510638
9618   Parnell  -2.086070              9.0  republican   58.510638
9619   Parnell  -2.086070              8.0  republican   58.510638
9620   Parnell  -2.086070              7.0  republican   58.510638
9621   Parnell  -2.086070              6.0  republican   58.510638
9622   Parnell  -2.086070              5.0  republican   58.510638
9623   Parnell  -2.086070              4.0  republican   58.510638
9624   Parnell  -1.515511              3.0  republican   59.081197
9625   Parnell  -1.515511              2.0  republican   59.081197
9626   Parnell  -2.395650              1.0  republican   58.201058
9627   Parnell  -2.395650              0.0  republican   58.201058

[9628 rows x 5 columns]

Here's a histogram of the error of every polling measurement in the data



In [40]:

    
errors.error.hist(bins=50)
plt.xlabel("Polling Error")
plt.ylabel('N')









    Out[40]:





<matplotlib.text.Text at 0x118427d10>

Problem 6

Compute the standard deviation of the polling errors. How much uncertainty is there in the typical RCP poll?



In [41]:

    
#your code here
errors.error.std()









    Out[41]:





5.271742498957164

Problem 7

Repeat this calculation for the data where errors.forecast_length < 7 (i.e. the polls within a week of an election). How much more/less accurate are they? How about the data where errors.forecast_length > 30?

Comment on this in 1 or 2 sentences. Does this make sense?



In [42]:

    
#your code here
print "Standard deviation < 7 days: %0.2f" % errors[errors.forecast_length < 7].error.std()
print "Standard deviation > 30 days: %0.2f" % errors[errors.forecast_length < 30].error.std()









    



Standard deviation < 7 days: 3.23
Standard deviation > 30 days: 4.95

The above result is in line with the conclusion from our visual inspection before. The polls shortly before the election(< 7 days) is more accurate than than polls more than a month before the election

Problem 8

Bootstrap resampling is a general purpose way to use empirical data like the errors DataFrame to estimate uncertainties. For example, consider the Viriginia Governor Race. If we wanted to estimate how likey it is that McAuliffe will win given the current RCP data, the approch would be:

Pick a large number N of experiments to run (say N=1000).
For each experiment, randomly select a value from errors.error. We are assuming that these numbers represent a reasonable error distribution for the current poll data.
Assume that the error on McAullife's current polling score is given by this number (and, by extension, the error on Cuccinelli's poll score is the opposite). Calculate who actually wins the election in this simulation.
Repeat N times, and calculate the percentage of simulations where either candidate wins.

Bootstrapping isn't foolproof: it makes the assumption that the previous Governor race errors are representative of the Virginia race, and it does a bad job at estimating very rare events (with only ~30 races in the errors DataFrame, it would be hard to accurately predict probabilities for 1-in-a-million scenarios). Nevertheless, it's a versatile technique.

Use bootstrap resampling to estimate how likely it is that each candidate could win the following races.

Summarize your results in a paragraph. What conclusions do you draw from the bootstrap analysis, and what assumptions did you make in reaching this conclusion. What are some limitations of this analysis?



In [60]:

    
#your code here
def bootstrap_result(s1, s2, errors, nsample = 10000):
    """
    Given the current polling data for 2 candidates, return the
    bootstrap-estimate for the win probability of each candidate
    
    Parameters
    ----------
    s1 : float
       The current proportion of poll votes for candidate 1
    s2 : float
       The current proportio of poll votes for candidate 2
    errors : DataFrame
       The errors DataFrame
    nsample : int
       The number of bootstrap iteraionts. Default=1000
       
    Returns
    -------
    p1, p2
    The probability that each candidate will win, based on the bootstrap simulations
    """
    
    #normalize s1, s2
    tot = (s1 + s2)
    s1 = 100 * s1 / tot
    s2 = 100 * s2 / tot

    indices = np.random.randint(0, errors.shape[0], nsample)
    errors =  errors.error.irow(indices).values
    
    s1_actual = s1 - errors
    s2_actual = s2 + errors
    
   
    p1 = (s1_actual > s2_actual).mean()  
    p2 = 1 - p1
 
    return p1, p2


#Look up the data as of 10/03/2013
#virginia
nsample = 10000
mcauliffe, cuccinelli = 44.2, 39.3

pm, pc = bootstrap_result(mcauliffe, cuccinelli, errors, nsample=nsample)
print "Virginia Race"
print "-------------------------"
print "P(McAuliffe wins)  = %0.2f" % pm
print "P(Cuccinelli wins) = %0.2f" % pc

#new jersey
print "\n\n"
print "New Jersey Race"
print "-----------------------"
christie, buono = 57.2, 33
pc, pb = bootstrap_result(christie, buono, errors, nsample=nsample)
print "P(Christie wins) = %0.2f" % pc
print "P(Buono wins)    = %0.2f" % pb









    



Virginia Race
-------------------------
P(McAuliffe wins)  = 0.75
P(Cuccinelli wins) = 0.25



New Jersey Race
-----------------------
P(Christie wins) = 0.98
P(Buono wins)    = 0.02






    



/Users/apple/anaconda/envs/python2/lib/python2.7/site-packages/ipykernel/__main__.py:32: FutureWarning: irow(i) is deprecated. Please use .iloc[i] or .iat[i]

summary

Our bootstrap simulations assume that historical RCP poll accuracies are representative of the uncertainty in the actual polls. The Virginia race is fairly close. In 10,000 of these simulations for , McCauliffe won 74% of the time. The New Jersey race, on the other hand, is less close. In 10,000 simulations, a candidate enjoying a lead as big as Chris Christie wins about 98% of the time.

Parting Thoughts

For comparison, most of the predictions in Nate Silver's presidental forecast had confidences of >95%. This is more precise than what we can estimate from the RCP poll alone. His approach, however, is the same basic idea (albeit he used many more polls, and carefully calibrated each based on demographic and other information). Homework 2 will dive into some of his techniques further.

How to submit

To submit your homework, create a folder named lastname_firstinitial_hw0 and place this notebook file in the folder. If your notebook requires any additional data files to run (it shouldn't), add them to this directory as well. Compress the folder (please use .zip compression) and submit to the CS109 dropbox in the appropriate folder. If we cannot access your work because these directions are not followed correctly, we will not grade your work.

css tweaks in this cell

	Approve	Disapprove	date
464	63.3	20.0	2009-01-27
558	63.3	20.0	2009-01-28
653	63.5	19.3	2009-01-29
714	63.5	19.3	2009-01-30
806	61.8	19.4	2009-01-31
1306	61.8	19.4	2009-02-01
960	61.8	19.4	2009-02-02
1051	61.8	19.4	2009-02-03
1459	61.8	19.4	2009-02-04
1220	61.8	19.4	2009-02-05
656	61.6	21.4	2009-02-06
655	61.6	21.4	2009-02-07
658	61.6	21.4	2009-02-08
657	65.4	22.6	2009-02-09
660	65.4	22.6	2009-02-10
659	64.2	23.3	2009-02-11
662	64.2	23.3	2009-02-12
661	64.2	23.3	2009-02-13
664	64.8	25.4	2009-02-14
663	65.5	25.5	2009-02-15
1043	65.5	25.5	2009-02-16
1044	65.5	25.5	2009-02-17
1045	65.1	25.9	2009-02-18
1046	62.3	28.5	2009-02-19
1039	63.2	28.6	2009-02-20
1040	63.2	28.6	2009-02-21
1041	63.2	28.6	2009-02-22
1042	63.0	27.8	2009-02-23
1047	62.5	27.8	2009-02-24
1048	62.5	27.8	2009-02-25
...	...	...	...
455	42.6	53.4	2014-01-02
454	42.4	53.6	2014-01-03
453	42.4	53.8	2014-01-04
452	42.3	53.8	2014-01-05
451	42.4	53.7	2014-01-06
450	42.8	53.7	2014-01-07
449	42.4	53.6	2014-01-08
458	42.1	53.1	2014-01-09
457	42.0	53.1	2014-01-10
338	42.0	53.3	2014-01-11
339	42.1	53.4	2014-01-12
340	42.0	53.4	2014-01-13
341	41.9	53.6	2014-01-14
334	42.1	53.3	2014-01-15
335	42.3	53.4	2014-01-16
336	42.4	52.8	2014-01-17
337	42.2	53.0	2014-01-18
342	42.0	53.2	2014-01-19
343	42.5	52.2	2014-01-20
691	42.8	51.8	2014-01-21
690	42.8	51.9	2014-01-22
693	43.2	51.6	2014-01-23
692	43.2	51.7	2014-01-24
687	NaN	NaN	2014-01-25
686	NaN	NaN	2014-01-26
689	NaN	NaN	2014-01-27
688	NaN	NaN	2014-01-28
685	NaN	NaN	2014-01-29
684	NaN	NaN	2014-01-30
569	NaN	NaN	2014-01-31