Loading back a dumped GeoDataFrame leads to: `GeoDataFrame does not support multiple columns using the geometry column name 'geometry'.`

Question

Let the following file be the result of a GeoDataFrame dump to a CSV file: gdf.to_csv('/path/to/gdf.csv')

$ cat '/path/to/gdf.csv'

myid,geometry
332,"MULTIPOLYGON Z (((0 0 0, 0 0 1, 0 1 1, 0 0 0)))"
220,"MULTIPOLYGON Z (((1 1 1, 1 1 2, 1 2 2, 1 1 1)))"

When I try to load it back, things surprisingly go wrong:

import geopandas as gpd

gdf = gpd.read_file('/path/to/gdf.csv')

Traceback (most recent call last):

  File "/tmp/ipykernel_369762/4150368047.py", line 1, in <cell line: 1>
    gpd.read_file('/path/to/gdf.csv')

  File "/usr/local/lib/python3.10/dist-packages/geopandas/io/file.py", line 259, in _read_file
    return _read_file_fiona(

  File "/usr/local/lib/python3.10/dist-packages/geopandas/io/file.py", line 360, in _read_file_fiona
    df = GeoDataFrame.from_features(

  File "/usr/local/lib/python3.10/dist-packages/geopandas/geodataframe.py", line 643, in from_features
    return cls(rows, columns=columns, crs=crs)

  File "/usr/local/lib/python3.10/dist-packages/geopandas/geodataframe.py", line 159, in __init__
    raise ValueError(

ValueError: GeoDataFrame does not support multiple columns using the geometry column name 'geometry'.

while if I sed -i 's/geometry/geometry2/g' /path/to/gdf.csv and then open the same file again:

gpd.read_file('/path/to/gdf.csv')

>: 
  myid                                geometry2 geometry
0  332  MULTIPOLYGON Z (((0 0 0, 0 0 1, 0 1 ...     None
1  220  MULTIPOLYGON Z (((1 1 1, 1 1 2, 1 2 ...     None

So, for the moment, I have to load it this way:

import pandas as pd
import geopandas as gpd

gpd.GeoDataFrame(pd.read_csv('/path/to/gdf.csv'))

>: myid                                geometry2
0   332  MULTIPOLYGON Z (((0 0 0, 0 0 1, 0 1 ...
1   220  MULTIPOLYGON Z (((1 1 1, 1 1 2, 1 2 ...

Also, by carefully reading the doc of the read_file() method, one can notice the ignore_geometry option (which is a little counter intuitive imho):

gdf = gpd.read_file('/path/to/gdf.csv', ignore_geometry=True)

type(gdf)
>: pandas.core.frame.DataFrame

This forces me to do kind of a casting-back-to-GeoDataFrame operation:

gdf = gpd.GeoDataFrame(gpd.read_file('/path/to/gdf.csv', ignore_geometry=True))

type(gdf)
>: geopandas.geodataframe.GeoDataFrame

which sounds to be a bit of an "overstatement" to me, because I must tell it first to ignore the geometry, and then set it back as a geometry the step after...

I'm therefore wondering if there is a more natural/intuitive way of loading a CSV file dumped from a GeoDataFrame back into a GeoDataFrame?

My GeoPandas' version is '0.12.2'.

Answer 1

From @gene's answer to a similar question:

When you open a csv file, with GeoPandas it automatically adds a geometry field

As you already have a field called "geometry", GeoPandas raises an exception. GeoPandas doesn't read the WKT strings in the "geometry" field as geometry.

However, if you look at the GDAL/OGR CSV driver documentation, if a field is named "WKT" it will be read as geometry:

When reading a field named “WKT” is assumed to contain WKT geometry, but also is treated as a regular field.

Approaches:

1. One option is to write out the CSV using "WKT" as the geometry column name and then it can be read straight back in:

   gdf = gdf.rename_geometry('WKT')
   gdf.to_csv('/path/to/gdf.csv', index=False)
   
   gdf = gpd.read_file('/path/to/gdf.csv')
   

2. Alternatively, the CSV driver also supports the open option GEOM_POSSIBLE_NAMES which lets you specify a different field name to look for geometry in.

   gdf = gpd.read_file('/path/to/gdf.csv', GEOM_POSSIBLE_NAMES="geometry", KEEP_GEOM_COLUMNS="NO") 
   

   The KEEP_GEOM_COLUMNS option is also required otherwise GDAL/OGR will return a "geometry" column as well as the geometry and you still get the original ValueError: GeoDataFrame does not support multiple columns using the geometry column name 'geometry'.

3. A final option is to read the CSV in as a Pandas DataFrame then manually create the geometry from the WKT and convert to a GeoDataFrame:

   import geopandas as gpd
   
   df = gpd.read_file('/path/to/gdf.csv', ignore_geometry=True)
   
   # Create geometry objects from WKT strings
   df['geometry'] = gpd.GeoSeries.from_wkt(df['geometry'])
   
   # Convert to GDF
   gdf = gpd.GeoDataFrame(df)