The Colorado Board of Elections requested help in completing an election audit of a recent local congressional election.
This election analysis audit calculates the total number of votes cast in the congressional election. The task also required a complete list of candidates who received votes. Calculate the total number of votes received by candidates. The percentage of votes each candidate won. And, Determining the winning candidate for the election based on popular vote. This analysis used python to calculate the result in the provided CSV data.
Data Source: election_results.csv
Software: Python 3.8.3, Visual Studio Code 1.46.0
The election analysis show that:
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There were 369,711 votes cast in the election.
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The candidates were:
- Charles Casper Stockham
- Diana DeGette
- Raymon Anthony Doane
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The candidate results were:
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Charles Casper Stockham received 23.0% of the vote and 85,213 number of votes.
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Diana DeGette received 73.8% of the vote and 272,892 number of votes.
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Raymon Anthony Doane received 3.1% of the vote and 11,606 number of votes.
The winner of the election was:
Diana DeGette, who received 272,892 number of votes, 73.8% of the total votes cast in the election.
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The script used to gather all the data points for this analysis was written in python using VSC. The method employed for this analysis should be considered by The Election Commission Board as a standard practice moving forward. The written code is highly versatile and efficient (less than) 200 lines of code enable the following functions:
1. Calculate the total number of votes cast.
2. Get a complete list of candidates who received votes.
3. Calculate the total number of votes each candidate received.
4. Calculate the percentage of votes each candidate won.
5. Determine the winner of the election based on popular vote.
6. Calculate the voter turnout for each county.
7. Calculate the percentage of votes each county contributed to the election.
8. Determine which county had the largest turnout.
The python code is versatile in nature. The script assigns values structure to the given data. So, new data can be input into the code. The python code is also efficient because the code doesn't have to be modified heavily to calculate results from a new dataset. The efficiencies of the code are inherent because the actual structure of the code mimics the human language. The code requires fewer lines to execute a function.