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Senate Vote Scraper and Graph Builder
import json
from itertools import combinations
import requests
import networkx as nx
def get_senate_vote(vote):
# Year can be replaced to fetch votes from different years (e.g., 2013). 1989 is used
# as an example.
url = "{}/data.json".format(vote)
page = requests.get(url).text
data = json.loads(page)
return data
except ValueError:
raise Exception("Not a valid vote number.")
def get_all_votes():
vote_num = 1
vote_dicts = []
while True:
vote_dict = get_senate_vote(vote_num)
vote_num += 1
except Exception:
return vote_dicts
def vote_graph(data):
graph = nx.Graph()
# Set to contain all senator display names - these will be our nodes
all_senators = set()
# List to contain roll_call dicts, one for each vote
roll_calls = []
for vote in data:
# Dict with keys for each vote class; values are lists of senator display names
roll_call = {}
for key, value in vote['votes'].iteritems():
senators = []
for senator in value:
if senator == 'VP':
roll_call[key] = senators
# Add any new senators to the set
# All combinations of 2 senator display names
all_senator_pairs = combinations(all_senators, 2)
common_votes = {}
for pair in all_senator_pairs:
common_votes[pair] = 0
for vote in roll_calls:
yea_pairs = combinations(vote['Yea'], 2)
for pair in yea_pairs:
common_votes[pair] += 1
except KeyError:
# Flip senator names so we can find the pair in the common_votes db
common_votes[(pair[1],pair[0])] += 1
nay_pairs = combinations(vote['Nay'], 2)
for pair in nay_pairs:
common_votes[pair] += 1
except KeyError:
common_votes[(pair[1],pair[0])] += 1
for senator in all_senators:
party = senator.split()[1][1]
# Use color names that Graphviz understands
if party == 'D':
graph.add_node(senator, color='blue')
elif party == 'R':
graph.add_node(senator, color='red')
graph.add_node(senator, color='black')
for pair, votes in common_votes.iteritems():
# Don't draw an edge for senators with 0 votes in common
if votes == 0:
graph.add_edge(pair[0], pair[1], weight=votes, difference=1.0/votes)
return graph
vote_data = get_all_votes()
votes = vote_graph(vote_data)
nx.write_gexf(votes, 'votes-101-1989.gexf')
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