BUG: crs is lost after calling GeoDataFrame.apply() method

crs is null on GeoDataFrame.apply(...) result. the following unit test fails because result.crs is None:

    def test_apply_function_to_rows():
        gdf = GeoDataFrame(
            data={'foo': ['Bar']},
            geometry=[Point(45.5089192, -73.5565212)],
            crs={'init': 'epsg:4326'}
        )

        def _lower_foo(row):
            row['foo'] = row['foo'].lower()
            return row

        result = gdf.apply(_lower_foo, axis=1, reduce=False)

        assert_geodataframe_equal(
            result,
            GeoDataFrame(
                data={'foo': ['bar']},
                geometry=[Point(45.5089192, -73.5565212)],
                crs={'init': 'epsg:4326'}
            )
        )

running with the environment below:

$ pip list
Package         Version    
--------------- -----------
atomicwrites    1.2.1      
attrs           18.2.0     
Click           7.0        
click-plugins   1.0.4      
cligj           0.5.0      
Fiona           1.8.4      
geopandas       0.4.0      
more-itertools  5.0.0      
munch           2.3.2      
numpy           1.16.0     
pandas          0.23.4     
pip             19.0.1     
pluggy          0.8.1      
py              1.7.0      
pyproj          1.9.6      
pytest          4.1.1      
python-dateutil 2.7.5      
pytz            2018.9     
setuptools      40.6.3     
Shapely         1.6.4.post2
six             1.12.0     

[martinfleis]

I honestly thought we fixed this in #1478 but that apparently fixes only GeoSeries.apply not GeoDataFrame.apply. We should do the similar wrapper for GeoDataFrame as well then.

[jorisvandenbossche]

The original issue about CRS getting lost is fixed in #1848, so closing this issue.

There is one more aspect that @JessicaS11 mentioned, about what object is being passed to the applied function (when applying it to each column, I assume), whether it is a Series or GeoSeries.

In [1]: df = geopandas.read_file(geopandas.datasets.get_path("naturalearth_lowres"))

In [2]: def func(col):
   ...:     print(type(col))
   ...:     return col
   ...: 

In [3]: df.apply(func)
<class 'pandas.core.series.Series'>
<class 'pandas.core.series.Series'>
<class 'pandas.core.series.Series'>
<class 'pandas.core.series.Series'>
<class 'pandas.core.series.Series'>
<class 'pandas.core.series.Series'>
Out[3]: 
       pop_est      continent                      name iso_a3  gdp_md_est                                           geometry
0       920938        Oceania                      Fiji    FJI      8374.0  MULTIPOLYGON (((180.00000 -16.06713, 180.00000...
1     53950935         Africa                  Tanzania    TZA    150600.0  POLYGON ((33.90371 -0.95000, 34.07262 -1.05982...
2       603253         Africa                 W. Sahara    ESH       906.5  POLYGON ((-8.66559 27.65643, -8.66512 27.58948...
3     35623680  North America                    Canada    CAN   1674000.0  MULTIPOLYGON (((-122.84000 49.00000, -122.9742...
4    326625791  North America  United States of America    USA  18560000.0  MULTIPOLYGON (((-122.84000 49.00000, -120.0000...
..         ...            ...                       ...    ...         ...                                                ...
172    7111024         Europe                    Serbia    SRB    101800.0  POLYGON ((18.82982 45.90887, 18.82984 45.90888...
173     642550         Europe                Montenegro    MNE     10610.0  POLYGON ((20.07070 42.58863, 19.80161 42.50009...
174    1895250         Europe                    Kosovo    -99     18490.0  POLYGON ((20.59025 41.85541, 20.52295 42.21787...
175    1218208  North America       Trinidad and Tobago    TTO     43570.0  POLYGON ((-61.68000 10.76000, -61.10500 10.890...
176   13026129         Africa                  S. Sudan    SSD     20880.0  POLYGON ((30.83385 3.50917, 29.95350 4.17370, ...

[177 rows x 6 columns]

So the last column is also passed as a Series and not as GeoSeries.

I am not sure that is easy to fix (override) on the GeoPandas side, though. Should take a look at how this is implemented in pandas (eg it might not be using _constructor properly).