PyBank:
For this analysis, I was tasked with creating a Python script to analyze the financial records of a fictional company using the company's budget data. The dataset is composed of two columns: "Date" and "Profit/Losses".
I created a Python script that analyzes the records and calculated each of the following values:
The total number of months included in the dataset
The net total amount of "Profit/Losses" over the entire period
The changes in "Profit/Losses" over the entire period, and then the average of those changes
The greatest increase in profits (date and amount) over the entire period
The greatest decrease in profits (date and amount) over the entire period
PyPoll:
For this analysis, I was tasked with helping a small, rural town modernize its vote-counting process.
I was given a set of poll data called election_data.csv. The dataset is composed of three columns: "Voter ID", "County", and "Candidate". I created a Python script that analyzes the votes and calculates each of the following values:
The total number of votes cast
A complete list of candidates who received votes
The percentage of votes each candidate won
The total number of votes each candidate won
The winner of the election based on popular vote