Nullable pandas dtypes crash in categorical handling: "Cannot interpret 'BooleanDtype' as a data type" #281

A DataFrame column with a pandas nullable or pyarrow dtype raises TypeError when patsy treats it as categorical, even when the column has no missing values. DataFrame.convert_dtypes() and read_csv(dtype_backend=...) produce these dtypes.

Reproduce

import pandas as pd
import patsy

df = pd.DataFrame({"b": [True, False, True], "i": [1, 2, 1]}).convert_dtypes()
patsy.dmatrix("b", df)     # TypeError: Cannot interpret 'BooleanDtype' as a data type
patsy.dmatrix("C(i)", df)  # TypeError: Cannot interpret 'Int64Dtype()' as a data type

df = df.convert_dtypes(dtype_backend="pyarrow")
patsy.dmatrix("b", df)     # TypeError: Cannot interpret 'bool[pyarrow]' as a data type

Tested with patsy main (38a1ac5), numpy 2.5.4 and pandas 3.1.0. Numeric use of the same columns (dmatrix("i", df)) works.

Cause

CategoricalSniffer.sniff() and categorical_to_int() call safe_issubdtype(data.dtype, np.bool_) on the pandas Series. safe_issubdtype passes the pandas extension dtype to np.issubdtype, and numpy can't interpret it. Nullable and pyarrow dtypes expose the equivalent numpy dtype as numpy_dtype.

Columns that contain pd.NA are a separate gap: patsy doesn't treat pd.NA as missing in any dtype (string, Int64, boolean).