clib.conversion._to_numpy: Add tests for pandas.Series with pandas string dtype

pygmt/clib/conversion.py

@@ -2,6 +2,7 @@
 Functions to convert data types into ctypes friendly formats.
 """
 
+import contextlib
 import ctypes as ctp
 import warnings
 from collections.abc import Sequence
@@ -160,7 +161,7 @@ def _to_numpy(data: Any) -> np.ndarray:
     dtypes: dict[str, type | str] = {
         # For string dtypes.
         "large_string": np.str_,  # pa.large_string and pa.large_utf8
-        "string": np.str_,  # pa.string and pa.utf8
+        "string": np.str_,  # pa.string, pa.utf8, pd.StringDtype
         "string_view": np.str_,  # pa.string_view
         # For datetime dtypes.
         "date32[day][pyarrow]": "datetime64[D]",
@@ -180,6 +181,11 @@ def _to_numpy(data: Any) -> np.ndarray:
     else:
         vec_dtype = str(getattr(data, "dtype", getattr(data, "type", "")))
         array = np.ascontiguousarray(data, dtype=dtypes.get(vec_dtype))
+
+    # Check if a np.object_ array can be converted to np.str_.
+    if array.dtype == np.object_:
+        with contextlib.suppress(TypeError, ValueError):
+            return np.ascontiguousarray(array, dtype=np.str_)
     return array
 
 

pygmt/clib/session.py

@@ -1475,7 +1475,7 @@ def virtualfile_from_vectors(
         # 2 columns contains coordinates like longitude, latitude, or datetime string
         # types.
         for col, array in enumerate(arrays[2:]):
-            if pd.api.types.is_string_dtype(array.dtype):
+            if np.issubdtype(array.dtype, np.str_):
                 columns = col + 2
                 break
 
@@ -1506,9 +1506,9 @@ def virtualfile_from_vectors(
                 strings = string_arrays[0]
             elif len(string_arrays) > 1:
                 strings = np.array(
-                    [" ".join(vals) for vals in zip(*string_arrays, strict=True)]
+                    [" ".join(vals) for vals in zip(*string_arrays, strict=True)],
+                    dtype=np.str_,
                 )
-            strings = np.asanyarray(a=strings, dtype=np.str_)
             self.put_strings(
                 dataset, family="GMT_IS_VECTOR|GMT_IS_DUPLICATE", strings=strings
             )

pygmt/tests/test_clib_to_numpy.py

@@ -11,6 +11,7 @@
 import pytest
 from packaging.version import Version
 from pygmt.clib.conversion import _to_numpy
+from pygmt.helpers.testing import skip_if_no
 
 try:
     import pyarrow as pa
@@ -174,6 +175,31 @@ def test_to_numpy_pandas_series_numpy_dtypes_numeric(dtype, expected_dtype):
     npt.assert_array_equal(result, series)
 
 
+@pytest.mark.parametrize(
+    "dtype",
+    [
+        None,
+        np.str_,
+        "U10",
+        "string[python]",
+        pytest.param("string[pyarrow]", marks=skip_if_no(package="pyarrow")),
+        pytest.param("string[pyarrow_numpy]", marks=skip_if_no(package="pyarrow")),
+    ],
+)
+def test_to_numpy_pandas_series_pandas_dtypes_string(dtype):
+    """
+    Test the _to_numpy function with pandas.Series of pandas string types.
+
+    In pandas, string arrays can be specified in multiple ways.
+
+    Reference: https://pandas.pydata.org/docs/reference/api/pandas.StringDtype.html
+    """
+    array = pd.Series(["abc", "defg", "12345"], dtype=dtype)
+    result = _to_numpy(array)
+    _check_result(result, np.str_)
+    npt.assert_array_equal(result, array)
+
+
 @pytest.mark.skipif(not _HAS_PYARROW, reason="pyarrow is not installed")
 @pytest.mark.parametrize(
     ("dtype", "expected_dtype"),