By this short introduction using geospatial data in Python I combine three different types of data sources which can be implemented in one map. For this purpose I start with reading a .csv with random adresses in order to request geo coordinates from Google using its API and creating a new dataframe.

I continue reading a zip folder into python with data from Natural Earth and transform my first dataframe into a geo dataframe with the characteristics of geometry. It´s possible as well to construct a geodataframe manuelly by geopandas. Reading then geo spatial data from GeoJSON allows me to gain more exactly Polygons of the German districts for plotting them with previous geo dataframes into a unique map.

In a 2nd jupyter notebook I apply Agglomerative and K-Means Clustering for the gdp per capita data by manipulating the Natural Earth data sheet.

In a following project I plan to start with SVM algorithms on geo data.

view file Using Geo Data in Python

view file Agglomerative and Kmeans Clustering

Enjoy Reading and contact for questions anytime!