Authors: Elizabeth Adeoye Viviana Yamell Mejia Dalton Schmidt
Objectives: 1.) Use Income Data for 2016 & 2020 to compare each demographic's median income data and show the economic impact of the 2020 Pandemic. 2.) Compare the voter turnout data for the General Presidential Election Data to the income data analysis.
Tools: 1.) Pandas 2.) NumbPy 3.) MatPlotLib 4.) Jupyter NoteBook
Function: This program generates data graphics for median income & voter turnout data from the years 2016 and 2020, for comparison. This comparison is both broad as well as specific to the demographic metrics of ethnicity, age, and household type. We imported CSVs that we manually generated however they were composed of data originally collected from: https://www.census.gov/library/publications/2021/demo/p60-273.html https://www.census.gov/data/tables/time-series/demo/voting-and-registration/p20-580.html https://www.census.gov/library/publications/2017/demo/p60-259.html https://www.census.gov/data/tables/time-series/demo/voting-and-registration/p20-585.html https://www.kaggle.com/datasets/imoore/2020-us-general-election-turnout-rates After importing the CSVs we built dataframes to reference in our graphics. We built seperate dataframes for each year 2016 & 2020, in addition we seperated the voter and income data into smaller dataframes based on sub categorical breakdown. Before we could generate our graphics we needed to merge our datasets. We did so by merging left and reformating the columns to reflect which date they represented. Once we had all of our dataframes formated and merged properly we could have our script generate our data graphics. The graphics this program generates are formatted as double bar graphs, pie charts, and scatter plots. These formats were chosen to allow to boths years data to be compared on the same figure.