The data used for this project describes the top trending YouTube videos, which I found on Kaggle. The folder that contained the data had both CSV and JSON files, 1 each for 8 different countries. I used the data for the US only, since the CSV files each have over 40,000 lines. I read the CSV files in Pandas and in cleaning the data, I dropped a few columns that were not necessary for the analysis. I converted the JSON into a CSV in order to populate the columns after merging the tables together in Pandas. Some of the columns were also renamed in order to understand it more clearly. The new Excel report has the 40,000+ lines but with the new data populated in each of the rows- the JSON file was smaller and when converted only contained 32 rows. The jupyter notebooks also show the number of videos from each of the categories, which came from the JSON file.