UCD Data Analytics Bootcamp Pandas Challenge
For the Heroes of Pymoli Pandas Challenge, I used the Pandas Python library in Jupyter Notebook to assess the spending trends of users. I created a variety of DataFrames analyzing trends based on demographics, revenue, most popular items, and more. I used Binning applications to assess spending trends from different age ranges, and grouped by gender and individual user trends to add to my final assessment.
I noticed a few observable trends from this analysis. The first trend I noticed is that males dominate spending. Males spend nearly $2000 on items while females only spend $361 and non-disclosed genders spend $50. The next trend that stood out was that age groups follow a pretty normal distribution, with 15-19- and 20–24-year-old users spending the most on items. The third trend I noticed was that purchases were gennerally low, with the most popular item purchased only 13 times.