Okay, so today I decided to mess around with some data about women’s tennis. I’ve always had this idea in my head, you know, assumptions about the game, and I wanted to see if they held any water.
Getting Started
First, I needed some data. I dug around online, and found a few datasets with match statistics. I grabbed one that looked pretty comprehensive – it had stuff like player rankings, match results, and even some stats on things like aces and double faults. I am so excited!
Cleaning Up the Mess
Of course, the data wasn’t perfect. There were missing values, weird formats, that kind of thing. So I spent a good chunk of time cleaning it up. I used a Python and pandas, that always my favorite tools.
- Checked for missing data and decided how to handle it (sometimes I deleted rows, sometimes I filled them in with averages).
- Made sure all the dates and numbers were in the right format.
- Got rid of any columns that I didn’t think I’d need.
Putting in Some Analysis
I have learned all data. Now time to test out some of the analysis!
I calculated some simple stuff like win percentages for different players and average number of aces per match. I also looked at how rankings correlated with match outcomes.
Seeing the Results
Finally I can have some output of the result, although just a start.
I generated some basic charts and graphs. I used Python and matplotlib for it. It’s pretty cool to actually start testing some assumptions I used to have, finally get my hand dirty to prove I am right or wrong from the data!
