Week 3 Challenge
1. PyPoll Election Audit
In this project, we were given the task to find the results of an election by counting the votes in a large dataset. We were also looking for the following data:
- Total number of votes.
- List of candidates who received votes.
- Number of votes per candidate and their percentage.
- List of counties that had vote turnouts.
- County with the largest amount of vote turnouts.
We designed an algorithm that would do the work for us by feeding it a csv file and storing the results in a text file.
1.1. Resources
Python version: Python 3.7.13 VS Code version: 1.69.2.
Dataset: election_results.csv.
1.2. Election Audit Results
We found out that the there were 369,711 votes registered, from which 82.8% came from Denver, the rest came from Jefferson (10.5%) and Arapahoe (6.7%).
The winner of the election was Diana DeGetter with 73.8% of the vote. These are the rest of the results:
Candidate
Votes
Percentage
Raymond Anthony Doane
11,606
3.1%
Charles Casper Stockham
85,212
23.0%
Diana DeGette
272,892
73.8%
The following data was stored to the election_analysis.txt file.
cat election_analysis.txt
Election Results
-------------------------
Total Votes: 369,711
-------------------------County Votes:
Jefferson: 10.5% (38,855)
Denver: 82.8% (306,055)
Arapahoe: 6.7% (24,801)-------------------------
Largest County Turnout: Denver
-------------------------
Charles Casper Stockham: 23.0% (85,213)
Diana DeGette: 73.8% (272,892)
Raymon Anthony Doane: 3.1% (11,606)
-------------------------
Winner: Diana DeGette
Winning Vote Count: 272,892
Winning Percentage: 73.8%
-------------------------
1.3. Summary of the Python Problem
The following is a partial description of how to solve the problem using Python.
Importing the libraries and loading the data.
import csv
from pathlib import Pathresources = Path.cwd() / "resources"
file_to_load = resources / "election_results.csv"
file_to_save = Path.cwd() / "election_analysis.txt"
Initializing variables.
candidate_list = []
county_list = []
candidate_votes = {}
county_votes = {}
The most important part of the code was that we were able to check if the candidate name existed already in the dictionary, and if it didn’t we would add it to the list only once and move on into just incrementing the vote count.
This is a reduced example of the process.
with open(file_to_load) as election_data:
reader = csv.reader(election_data)
header = next(reader)
for row in reader:
candidate_name = row[2]
county_name = row[1]
# Check for candidate
if candidate_name not in candidate_list:
candidate_list.append(candidate_name)
candidate_votes[candidate_name] = 0
candidate_votes[candidate_name] += 1
# Check for county
if county_name not in county_list:
county_list.append(county_name)
county_votes[county_name] = 0
county_votes[county_name] += 1print(candidate_votes)
print(county_votes)
{'Charles Casper Stockham': 85213, 'Diana DeGette': 272892, 'Raymon Anthony Doane': 11606}
{'Jefferson': 38855, 'Denver': 306055, 'Arapahoe': 24801}
Then after that storing the results was a simple process as we can do whatever we want with the data stored in the dictionaries.
For example, printing the total votes or percentage of values from any key. As well as making sure that the totals match.
total_votes_candidate = sum(candidate_votes.values())
total_votes_county = sum(county_votes.values())
assert total_votes_candidate == total_votes_county
print(f"Total Votes: {total_votes_candidate}")
print("Percentage for Diana DeGette:")
print(f"{100 * candidate_votes['Diana DeGette'] / total_votes_candidate:.2f}%")
Total Votes: 369711
Percentage for Diana DeGette:
73.81%
1.4. Closing Thoughts
Python does a great job on removing a lot of obstacles in between us as the users and the computer, as it’s really easy to generate working code starting from a solid pseudocode / algorithm. Even if it’s not the fastest in processing the data, it’s definitely the tool that can get the job done in the least amount of time once you learn it.