Support `orient` keyword in to_json about geopandas HOT 3 OPEN

FWIW I was able to get a regular pandas dataframe with the following code:

pd_df = pd.DataFrame(dict(zip(df.columns, df.to_numpy().T)))

Then I serialize via existing pandas methods.

I no longer need geopandas to change the behavior of to_json, but I figure this code could be useful for others who stumble across this problem.

from geopandas.

Thanks @paddymul for opening this issue. I wouldn't say this is a bug per se, but a mismatch of expectations. In the geospatial world the standard json representation of features is GeoJson - which specifies a specific layout for storing features separate to geometry alongside storing spatial metadata like a CRS (coordinate reference system).

(For instance compare the output of gdf.to_json(indent=2) and pd.DataFrame(gdf.to_wkt()).to_json(indent=2) in your example).

In principle I think we would be open to supporting other orientations, so long as that does not lead to confusion about what is a generic interoperable geojson format and what is a pandas specific format. It'd be good for other maintainers to weight in, but my initial proposal to support this would be to introduce an orient="GeoJSON" which is the default value and support the other values via pandas. Geometry would be exported as WKT and we'd probably need to emit a warning about the loss of CRS metadata (and then perhaps a second stage would be to agree on a way to embed CRSs if there was an appetite for that.

from geopandas.

FWIW I was able to get a regular pandas dataframe with the following code:

pd_df = pd.DataFrame(dict(zip(df.columns, df.to_numpy().T)))

I haven't compared the performance, but I think a slightly clearer way to express this would be pd.DataFrame(gdf).astype({"geometry":"object"}) (or a generalisation of it if there are multiple geometry columns), but it is more verbose.

from geopandas.