Making spatial pie chart using two columns in GeoPandas

Spatial Pie Charts with GeoPandas: Making Maps That Tell a Story

Ever wanted to show not just where something is, but what it’s made of, all on a single map? That’s where spatial pie charts come in. They’re like regular pie charts, but instead of sitting neatly in a report, they’re plopped right onto a map, showing the composition of different areas at a glance. Think of it: you could visualize land use across a city, election results by district, or even the makeup of different soil types across a region. And the best part? GeoPandas, a super-handy Python library, makes it all possible.

So, what is GeoPandas, anyway? Well, imagine Pandas, the data analysis workhorse, but with superpowers for dealing with maps and geographic data. It’s built on top of Pandas and plays well with other cool libraries like Shapely (for handling shapes) and Matplotlib (for making plots). The key is the GeoDataFrame, which is like a regular table but with an extra column that holds the geometry – the shapes that define your map’s regions.

Before we dive into the code, let’s talk about the ingredients you’ll need. First, you absolutely need that GeoDataFrame. It’s gotta have a column that tells GeoPandas where each region is – that’s the geometry column. Think of it as the map itself. Then, you’ll need at least two columns with the data you want to visualize in your pie charts. These are the slices of your pie. For example, if you’re mapping land use, you might have columns for “Residential,” “Commercial,” and “Parks.”

Alright, here’s the thing: GeoPandas doesn’t have a magic “make_spatial_pie_chart()” function, unfortunately. We’re gonna have to roll up our sleeves and build these pies ourselves. But don’t worry, it’s not as scary as it sounds!

First, we need to find the center of each region, the perfect spot to place our pie. Luckily, GeoPandas makes this easy:

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