Colorado Board of Elections in Election Audit
Overview
The goal of this exercise is to generate a vote counting report to certify the US congressional race in Colorado. The scenario is that this election audit of the tabulated results for U.S Congressional precinct was requested by Colorado Board of Elections. The task is to report the total number of votes cast and total number of votes and its percentage for each candidate, and the winner of the election based on the popular votes. Another aim is to build a successful code that can be used to audit future congressional district and possibly also senatorial district and local elections. The data provided to be audited contains votes from three primary voting methods, mail-in ballots, punch cards and direct recording electronic (DRE counting machine).
In this analysis Python was utilized to extract and summarize voting data from dataset of ~400,000 ballots. The data consists of a number for the ballot ID and a name for the county and candidate. All this was analyzed and desired results were reached (Figure 01)
The Results of Election-Audi
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The total votes casted in the congressional election:
369,711 -
Voting results by county are follow:
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Jefferson: 10.5% (38,855)
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Denver: 82.8% (306,055)
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Arapahoe: 6.7% (24,801)
Which makes Denver the county with the highest voter turnout.
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Voting results by candidate are as follow:
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Charles Casper Stockham: 23.0% (85,213)
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Diana DeGette: 73.8% (272,892)
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Raymon Anthony Doane: 3.1% (11,606)
The winner of the election was Diana DeGette with a total vote of 272,892, what makes 73.8% of the total votes.
Figure 01: Snipped after printing the python code to VS Code terminal.
Election-Audit Summary
As mentioned, Python code was used to define the variables for the candidates and counties depending on the available data. This code could be modified to include other variables if given more categories of interest to work with such as voting methods, voters ages, education, income, etc. It could be also be modified to used for other similar campaign and data set in another state or counties by redefining the variables.