The Beginner’s Guide to Choropleth Maps in Python

The Beginner’s Guide to Choropleth Maps in Python

Choropleth map is one of the most effective methods to visualize geographic data. The most popular methods to construct such maps are sophisticated software like QGIS, ArcGIS, and so on. However, these types of visualizations are usually a small part of my entire workflow. Hence, I don’t like to switch to specialized software to render these maps. In this article, I will explore how to make these maps using Python. using GeoPandas and Matplotlib

What is a Choropleth Map?

A choropleth map is a map of a geographic area, in which different regions are represented by a color or pattern based on an aggregated attribute of that particular subregion. For example, you could map the world countries based on population density. The more dense a country is, the darker shade of red it will get. In this way, we can easily identify the densest countries at a glance.

Choropleth map is one of the most effective methods to visualize geographic data. The most popular methods to construct such maps are sophisticated software like QGIS, ArcGIS, and so on. However, these types of visualizations are usually a small part of my entire workflow. Hence, I don’t like to switch to specialized software to render these maps.

In this article, I will explore how to make these maps using Python.

What do we need?

We will need a couple of things to accomplish our goal.

  1. Shapefiles: Shapefiles are data structures that contain information about different geographic regions. They contain the geometric representation of the regions, which we will need to map them. Besides, the shapefiles optionally contain some additional metadata like name of regions, regional hierarchies, and so on. There are many sources where you can find these shapefiles, for example,  Humdata and  GADM. In Figure 1, I have provided a screenshot showing how a shapefile looks like after being loaded by GeoPandas.

  2. Libraries:  Matplotlib and  Geopandas

    Image for post

Figure 1. Screenshot of a shapefile loaded by GeoPandas

Without further ado, let’s begin by loading the data and creating a simple map.

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