Ultimately for spatial functions both PostGIS and Shapely/Geopandas are using GEOS as the base library, so neither are 'a lot faster' for analysis at a fundamen

Ultimately for spatial functions both PostGIS and Shapely/Geopandas are using GEOS as the base library, so neither are 'a lot faster' for analysis at a fundamental level. Lots of queries to a database brings some speed loss but this isn't necessary for the vast majority of use cases. OTOH PostGIS is in many cases much more finely tuned than shapely/geopandas for speed, scale and parallelisation (it is a much more highly maintained library) so can actually be faster. Functionality is a different thing. PostGIS has a lot more baked in functionality than shapely. But shapely/geopandas exposes the depth of python and its libraries which allow much more extensive customisation of how to work with data. Lots of tradeoffs and overlapping use cases - I just wanted to add some depth to this discussion.