Okay, so check it, recently I was messing around with some data and thought, “Hey, let’s build a leaderboard!” I’m talking about a “Boeing Classic Leaderboard” type deal. Why? No real reason, just felt like a fun project. So, here’s how it all went down.
First things first, I needed data. Scraped some mock scores and player names from a few different sources online – nothing fancy, just enough to get the ball rolling. Threw it all into a CSV file, because who doesn’t love a good CSV?
Then, I fired up my Python environment (because Python’s my go-to for this kind of stuff). Loaded the CSV into a Pandas DataFrame. Pandas, man, that library is a lifesaver. Made it super easy to manipulate the data.
Next up: cleaning and organizing. The data was a mess, naturally. Had to deal with missing values (filled them with zeros for simplicity), inconsistent naming conventions (trimmed whitespace, made everything uppercase), and some weird data types (converted scores to integers). Basically, the usual data cleaning grind.
After cleaning, it was time to calculate the rankings. Used Pandas’ `sort_values()` function to sort the DataFrame by score in ascending order. Then, added a ‘Rank’ column using `rank(method=’dense’)`. ‘Dense’ method is key here – it assigns consecutive ranks without gaps, even if there are ties. Important for a proper leaderboard!
Now for the fun part: visualizing the leaderboard. Initially, I just printed the DataFrame to the console. Looked okay, but not exactly impressive. So, I decided to use Matplotlib to create a simple bar chart. Plotted the player names against their scores, added some labels and a title, and boom – a basic leaderboard visualization.
But I didn’t stop there. I wanted to make it interactive. So, I looked into Plotly. Plotly’s awesome because it lets you create interactive charts that you can easily embed in a webpage. Redid the leaderboard visualization using Plotly Express. Added hover info to show the player’s name, score, and rank. That made it much cooler!
Finally, I deployed it! Pushed the code to a Github repo. Used Streamlit to create a simple web app that displays the interactive Plotly leaderboard. Streamlit is super easy to use – just a few lines of code and you’ve got a working web app. Deployed it to Streamlit’s free hosting platform. Now, I’ve got a shareable link to my Boeing Classic Leaderboard!
Lessons learned? Data cleaning is always more time-consuming than you think. Pandas and Plotly are your best friends. And building a leaderboard is actually a pretty fun way to spend an afternoon.
- Data aquired & processed with Python
- Leaderboard ranked in Panda
- Visualized by Streamlit
