# 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
from pattern import web
# 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'
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
- Section
- chart
- series
- value
- graphs
- graph
- value
- graph
- series
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
"""
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):
url = "http://charts.realclearpolitics.com/charts/%i.xml" % int(poll_id)
return requests.get(url).text
Here are some other functions we'll use later. plot_colors contains hints about parsing XML data.
# "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
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_datetimeto convert strings into dates
"""
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 = {n.attributes['xid']: str(n.content) for n in dates.by_tag('value')}
keys = dates.keys()
result['date'] = pd.to_datetime([dates[k] for k in keys])
for graph in dom.by_tag('graph'):
name = graph.attributes['title']
data = {n.attributes['xid']: float(n.content)
if n.content else np.nan for n in graph.by_tag('value')}
result[name] = [data[k] for k in keys]
result = pd.DataFrame(result)
result = result.sort(columns=['date'])
return result
The output from rcp_poll_data is much more useful for analysis. For example, we can plot with it:
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
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.
"""
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)
def find_governor_races(html):
dom = web.Element(html)
links = [a.attributes.get('href', '') for a in dom.by_tag('a')]
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"
"""
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
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.
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!
id = id_from_url(url)
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='--')
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.
page = requests.get('http://www.realclearpolitics.com/epolls/2010/governor/2010_elections_governor_map.html').text.encode('ascii', 'ignore')
for race in find_governor_races(page):
plot_race(race)
plt.show()
