Alright folks, gather ’round! Today I’m gonna walk you through my little experiment inspired by, well, let’s just say a certain invisible man… John Cena, yeah that’s the guy!
So, the idea popped into my head – what if I could somehow track and visualize all of John Cena’s appearances? Not just wrestling matches, but like, everything. Movies, TV shows, guest spots, the whole shebang. Seemed like a fun little project, right?
First things first, I needed data. And LOTS of it. I started by hitting up the usual suspects: IMDb, Wikipedia, wrestling databases… you name it. Basically, I spent a solid afternoon just copy-pasting stuff into a giant spreadsheet. It was tedious, I ain’t gonna lie.
Data Cleaning Nightmare
Oh man, cleaning the data was a beast. Dates were all over the place, some entries were incomplete, some were straight-up wrong. I had to manually go through each entry and make sure everything was consistent. Ugh!
- Standardized all the date formats (YYYY-MM-DD, baby!)
- Filled in missing information where I could find reliable sources.
- Deleted duplicates. There were a surprising number of them.
Next up, visualization. I wanted something interactive, so I decided to use Python with a couple of libraries I’m familiar with, Pandas for data manipulation and Matplotlib for plotting. I wanted a timeline, obviously.
Coding Time!
The coding part was actually kinda fun. I loaded the cleaned data into a Pandas DataFrame. Then I did some grouping to count the number of appearances per year.
Then came the plotting. I chose a simple line graph to show the trend of his appearances over time. Added some labels, a title, and boom! A basic visualization.
The initial results were… underwhelming. The graph was cluttered and hard to read. So I tweaked it a bit.
- I used a rolling average to smooth out the line and make the overall trend clearer.
- Added annotations to highlight some key events, like his debut and major movie releases.
- Played around with the colors and fonts to make it look more visually appealing.
After some refining, the visualization started to look pretty decent. You could clearly see the peaks and valleys in John Cena’s career. It’s pretty cool to see how much of his content creation is out there.
Lessons Learned
This project was more fun than I thought it would be. I was reminded that data cleaning is the most boring but most important part of any data project. I also got some good practice with data visualization, and even learned some new tricks with Matplotlib.
