Alright, let’s dive into my recent experience with a bit of political data wrangling: Miller-Meeks vs. Bohannan. It was a real rollercoaster, lemme tell ya.
It all started when I was looking at some election results, and I stumbled upon the super tight race between Mariannette Miller-Meeks and Christina Bohannan. I’m talking razor-thin margins! I got curious and wanted to dig deeper, see if I could visualize the data and get a better understanding of what went down.
First thing I did was hunt down the data. That’s always the first step, right? I scoured the internet, state election websites, news outlets, anywhere I could think of. Finally, I pieced together a decent dataset from a few different sources. It wasn’t perfect, mind you. Had to do some serious cleaning and formatting in Excel. You know, removing those pesky commas from numbers, standardizing county names, the usual headache.
Next, I fired up Python. Gotta love Pandas for this kind of stuff. I loaded the CSV into a DataFrame and started exploring. I wanted to see the vote totals for each candidate in each county. Simple stuff, using `groupby()` and `sum()`. Nothing too fancy.
Then came the fun part: visualization. I’m a big fan of Matplotlib for quick and dirty charts. I whipped up a bar chart showing the vote difference between Miller-Meeks and Bohannan in each county. Counties where Miller-Meeks won were colored blue, and counties where Bohannan won were red. This instantly gave me a sense of where each candidate had their strongholds.
But a bar chart only tells you so much. I really wanted to see this data on a map. So, I decided to use GeoPandas. This required a bit more effort. I needed a shapefile for Iowa counties. Found one online (after a bit of searching, of course). Then I had to merge my election data with the shapefile, using the county names as the key. This took a few tries to get right, dealing with encoding issues and slight variations in county names.
Finally, I had a GeoDataFrame with both the geographic data and the election results. Now I could create a choropleth map, coloring each county based on the vote difference. The deeper the blue, the bigger Miller-Meeks’ win. The deeper the red, the bigger Bohannan’s. It was awesome to see the spatial patterns emerge! You could clearly see the rural areas leaning towards Miller-Meeks, and the more urban areas favoring Bohannan.
To make the map even more informative, I added some annotations. I used Matplotlib to overlay text labels showing the vote totals for each candidate in a few key counties. This helped to highlight the areas that had the biggest impact on the overall outcome.
Of course, no project is without its hiccups. I spent way too long wrestling with coordinate systems in GeoPandas. Let’s just say I learned a lot about projections and transformations. And debugging the merge operation between my data and the shapefile was a real pain. I swear, sometimes the slightest typo can throw everything off!
In the end, I had a pretty cool map that visualized the Miller-Meeks vs. Bohannan election results. It wasn’t just a bunch of numbers; it was a story told visually. And that’s what I love about data analysis!
So, to recap, here’s the steps I took:
- Data Acquisition: Sourced the election data from various websites.
- Data Cleaning: Cleaned and formatted the data in Excel.
- Data Analysis: Used Pandas to analyze vote totals by county.
- Visualization: Created bar charts and a choropleth map using Matplotlib and GeoPandas.
- Annotation: Added text labels to highlight key counties.
Would I do anything differently next time? Probably. I’d try to find a cleaner, more comprehensive dataset to start with. And maybe explore some interactive mapping tools like Folium to make the map even more engaging. But overall, I’m pretty happy with how it turned out. It was a fun and insightful project!
