Data Visualization

During my job search I have applied for lots of positions online, usually in a web form that is part of a company’s application tracking system. These systems send an automatic email when you apply. And typically at some point, whether it’s automatically through some algorithm or when a human presses a button, I’ll get a form email notifying me that I am no longer being considered for the position. I have put all these emails into their own folder within gmail. I wanted to make a word cloud (https://en.wikipedia.org/wiki/Tag_cloud) of the words in these emails. There are three high-level steps to create this: ...

For some reason I wanted to know if there was any trend in first names of MLB draftees. If you ask me, the first names of MLB draftees & players have some kind of perceived stereotype, and I wanted to see if that was true. For example, it seems like a lot of JJs are drafted. First Step: Get (Extract) the data From googling around and just looking at data on the internet for many years, I knew there was an MLB Stats-API. I had been a member of the mlbdata subreddit for many years. I knew there was a draft endpoint from looking around at the Python wrapper for MLB Stats API Created by Todd Roberts. You can look at what the results of an API call to the draft endpoint looks like for a specific year. For this project, I wanted the useName value in the person object to analyze, as the firstName field has Christopher’s while I wanted Chris’s. At the beginning I wasn’t sure what I would do with this data, so for now I wanted to just loop through every year of MLB drafts and throw the data into a csv. I included some other fields in my final dataframe, in case I wanted to use them. The blank dataframe I created (with pandas as pd) looked like this: ...