GeoDataFrame conversion to polars with from_pandas fails with ArrowTypeError: Did not pass numpy.dtype object

I try to convert a GeoDataFrame to a polars DataFrame with from_pandas. I receive an ArrowTypeError: Did not pass numpy.dtype object exception.

Expected outcome would be a polars DataFrame with the geometry column being typed as pl.Object.

I'm aware of https://github.com/geopolars/geopolars (alpha) and https://github.com/pola-rs/polars/issues/1830 and would be OK with the shapely objects just being represented as pl.Object for now.

Here is a minimal example to demonstrate the problem:

## Minimal example displaying the issueimport geopandas as gpdprint("geopandas version: ", gpd.__version__)import geodatasetsprint("geodatasets version: ", geodatasets.__version__)import polars as plprint("polars version: ", pl.__version__)gdf = gpd.GeoDataFrame.from_file(geodatasets.get_path("nybb"))print("\nOriginal GeoDataFrame")print(gdf.dtypes)print(gdf.head())print("\nGeoDataFrame to Polars without geometry")print(pl.from_pandas(gdf.drop("geometry", axis=1)).head())try:    print("\nGeoDataFrame to Polars naiive")     print(pl.from_pandas(gdf).head())except Exception as e:    print(e)try:    print("\nGeoDataFrame to Polars with schema override")     print(pl.from_pandas(gdf, schema_overrides={"geometry": pl.Object}).head())except Exception as e:    print(e)# again to print stack tracepl.from_pandas(gdf).head()

Output

geopandas version:  0.14.4geodatasets version:  2023.12.0polars version:  0.20.23Original GeoDataFrameBoroCode         int64BoroName        objectShape_Leng     float64Shape_Area     float64geometry      geometrydtype: object   BoroCode       BoroName     Shape_Leng    Shape_Area  \0         5  Staten Island  330470.010332  1.623820e+09   1         4         Queens  896344.047763  3.045213e+09   2         3       Brooklyn  741080.523166  1.937479e+09   3         1      Manhattan  359299.096471  6.364715e+08   4         2          Bronx  464392.991824  1.186925e+09                                               geometry  0  MULTIPOLYGON (((970217.022 145643.332, 970227....  1  MULTIPOLYGON (((1029606.077 156073.814, 102957...  2  MULTIPOLYGON (((1021176.479 151374.797, 102100...  3  MULTIPOLYGON (((981219.056 188655.316, 980940....  4  MULTIPOLYGON (((1012821.806 229228.265, 101278...  GeoDataFrame to Polars without geometryshape: (5, 4)┌──────────┬───────────────┬───────────────┬────────────┐│ BoroCode ┆ BoroName      ┆ Shape_Leng    ┆ Shape_Area ││ ---      ┆ ---           ┆ ---           ┆ ---        ││ i64      ┆ str           ┆ f64           ┆ f64        │╞══════════╪═══════════════╪═══════════════╪════════════╡│ 5        ┆ Staten Island ┆ 330470.010332 ┆ 1.6238e9   ││ 4        ┆ Queens        ┆ 896344.047763 ┆ 3.0452e9   ││ 3        ┆ Brooklyn      ┆ 741080.523166 ┆ 1.9375e9   ││ 1        ┆ Manhattan     ┆ 359299.096471 ┆ 6.3647e8   ││ 2        ┆ Bronx         ┆ 464392.991824 ┆ 1.1869e9   │└──────────┴───────────────┴───────────────┴────────────┘GeoDataFrame to Polars naiiveDid not pass numpy.dtype objectGeoDataFrame to Polars with schema overrideDid not pass numpy.dtype object

Stack trace (is the same with and without schema_overrides)

---------------------------------------------------------------------------ArrowTypeError                            Traceback (most recent call last)Cell In[59], line 27     24     print(e)     26 # again to print stack trace---> 27 pl.from_pandas(gdf).head()File c:\Users\...\polars\convert.py:571, in from_pandas(data, schema_overrides, rechunk, nan_to_null, include_index)    568     return wrap_s(pandas_to_pyseries("", data, nan_to_null=nan_to_null))    569 elif isinstance(data, pd.DataFrame):    570     return wrap_df(--> 571         pandas_to_pydf(    572             data,    573             schema_overrides=schema_overrides,    574             rechunk=rechunk,    575             nan_to_null=nan_to_null,    576             include_index=include_index,    577         )    578     )    579 else:    580     msg = f"expected pandas DataFrame or Series, got {type(data).__name__!r}"File c:\Users\...\polars\_utils\construction\dataframe.py:1032, in pandas_to_pydf(data, schema, schema_overrides, strict, rechunk, nan_to_null, include_index)   1025         arrow_dict[str(idxcol)] = plc.pandas_series_to_arrow(   1026             data.index.get_level_values(idxcol),   1027             nan_to_null=nan_to_null,   1028             length=length,   1029         )   1031 for col in data.columns:-> 1032     arrow_dict[str(col)] = plc.pandas_series_to_arrow(   1033         data[col], nan_to_null=nan_to_null, length=length   1034     )   1036 arrow_table = pa.table(arrow_dict)   1037 return arrow_to_pydf(   1038     arrow_table,   1039     schema=schema,   (...)   1042     rechunk=rechunk,   1043 )File c:\Users\...\polars\_utils\construction\other.py:97, in pandas_series_to_arrow(values, length, nan_to_null)     95     return pa.array(values, from_pandas=nan_to_null)     96 elif dtype:---> 97     return pa.array(values, from_pandas=nan_to_null)     98 else:     99     # Pandas Series is actually a Pandas DataFrame when the original DataFrame    100     # contains duplicated columns and a duplicated column is requested with df["a"].    101     msg = "duplicate column names found: "File c:\Users\...\pyarrow\array.pxi:323, in pyarrow.lib.array()File c:\Users\...\pyarrow\array.pxi:79, in pyarrow.lib._ndarray_to_array()File c:\Users\...\pyarrow\array.pxi:67, in pyarrow.lib._ndarray_to_type()File c:\Users\...\pyarrow\error.pxi:123, in pyarrow.lib.check_status()ArrowTypeError: Did not pass numpy.dtype object