Alright, so I was messing around with some data the other day, trying to see if I could pull out some interesting insights. The whole thing started ’cause I was watching this old documentary about Dennis Rodman – you know, the basketball player, the one with all the crazy hair and piercings.
Anyway, I was thinking about him and his dad, Philander Rodman Jr., who apparently bounced pretty early and ended up in the Philippines. And I thought, “Hey, that’s a kinda unique name, ‘Philander.’ I wonder if there’s anything interesting I could dig up using that.” So, yeah, that’s how it kicked off.
First thing I did was hit up the usual data sources. I started with public record stuff – you know, census data, voter registration lists, that kinda stuff. I figured if I could find other people named Philander, maybe I could see if there were any patterns or interesting connections.
It was kinda slow going at first. “Philander” isn’t exactly a common name. I found a few here and there, mostly older guys. Not a whole lot of juice there, y’know? Then I thought, “Okay, maybe I need to broaden my search.” So, I started looking for variations – spellings, nicknames, stuff like that.
That’s when I hit something interesting. I found a few records where “Philander” was used as a middle name. And then I started cross-referencing those records with other databases – genealogical websites, obituaries, that kind of thing.
BOOM! I started seeing some connections. Turns out there’s a whole cluster of Philanders (or people with Philander somewhere in their name) in a particular region of the South. And they all seemed to be related somehow.
- I pulled out all the related records I could find
- I cleaned them up a bit.
I used a little bit of Python – nothing fancy, just some basic data wrangling. The goal was to create a network graph, where each person was a node and the connections were family relationships.
Next, I loaded everything into Gephi, a graph visualization tool. It’s pretty cool – you can use it to see patterns and relationships in complex networks.
After playing around with the layout a bit, I started to see some interesting clusters and central figures. It turns out there was one particular Philander in the late 1800s who seemed to be the linchpin of the whole family. He had a bunch of kids, and they all went on to have their own families, spreading the “Philander” name around.
I looked into his background, and it turns out he was a pretty prominent figure in his community – a landowner, a businessman, the whole deal. So, it seems like the name “Philander” was a point of pride for this family, something they wanted to pass down through the generations.
Then I cross-referenced my network graph with historical records. It gave me a much better understanding of how the family had evolved over time.
I won’t bore you with all the details, but basically, I was able to trace the family’s movements, their occupations, and even some of their personal stories. It was like piecing together a giant jigsaw puzzle. It was actually pretty cool to see how one guy’s name could lead to all of that.
Now, I’m not saying I discovered anything earth-shattering here. It was just a fun little side project. I guess the takeaway is that even the most obscure data can be surprisingly revealing if you’re willing to dig deep enough. And hey, you never know what you might find when you start poking around in the past.
So, yeah, that’s the story of how I spent a few days tracking down the descendants of Philander Rodman Jr. Just another day in the life of a data nerd, I guess.
