🔎 Input format and package overview ⭐
A map is basically a set of polygons displayed on a 2 dimensional canvas. There are two problems you need to solve when building one:
Anatomy of a matplotlib chart: all the basic vocabulary you need to know to understand the documentation properly
- Where to find polygon coordinates. It can come from:
- a shape file or a geoJson file. That's 2 different ways to store the information. The
geopandaspackage is the best way to read those file formats with Python. - a python library like
geopandasorbasemapproviding the information for common location like the US. - google map or open street map.
- a shape file or a geoJson file. That's 2 different ways to store the information. The
- How to plot those polygons?
- Once you get a
geodataframethanks to thegeopandaspackage,geoplotis your best choice to build a static map - If you need an interactive map from a
geodataframe,plotlyis a good option. - If you want to use google map style maps,
foliumis the way to go.
- Once you get a
GeoPlot & Cartopy
GeoPlot is an extension of Cartopy and Matplotlib to create maps with minimal efforts.
When creating maps, there are a large number of possible projections. The article below explains how to modify them.
Change projection in maps
Folium for google maps style maps
Folium is a wrapper of the leaflet.js javascript library. It allows to make all the data wrangling with python, and benefit the power of javascript for interactive data visualization.
The map below has been created with folium... with 1 line of code only! 😍
Folium allows different types of background tiles. Here is an example with a tile called Cartodb Dark Matter:
It is also possible to add markers with associated popups on top of the map:
Background map with Basemap
Seaborn is another great alternative to build an area chart with python. The below examples show how to start basic, apply usual customization, and use the small multiple technique for when you have several groups to compare.
