Today I learned another way of parsing geo data formats in-memory, using geopandas. As part of a running django web service, I wanted to request and parse a WFS respose, which comes back in GML format. The solution to this is: gpd.GeoDataFrame.from_features(fiona.BytesCollection(bytes_str)) This was pointed out by sal in this stackoverflow answer. Since I could not find it in the documentation, either in fiona and not in geopandas. I want to try now to add this there.

If you want to know how to extract the individual layers programmatically from a GeoDatabase-ArcGIS archive, then this is for you. Geo data is often times provided in Geodatabase format. This format provides a container for several layers of data, which then can be rendered using software like ArcGIS or QGIS, which have already plugins to process this file format. But if you work on a lower level, you maybe need to process these files in their format programmatically.

Web-Scraping setzt oft voraus, dass ein eigener Rechner verfügbar ist, welcher bestenfalls 24h online 🌐 ist und die Abfragen für das Scrapen absenden kann. Wenn wir das vermeiden wollen, beöntigen wir entsprechende Infrakstruktur. Simon Willison beschreibt in seinem Blogpost git-history ein Python Tool, welches aus einer Historie an git commits zu einer Datei, eine Zeitreihe erstellt. Diese Serie von git commits basieren auf einer speziellen Art zu scrapen. Dieses Pattern vereinfacht Web-Scraping, indem als Infrastruktur kein eigener Rechner genutzt werden muss, sondern Github actions.