Add tests for pandas.Series with NumPy numeric dtypes

pygmt/tests/test_clib_to_numpy.py

@@ -4,6 +4,7 @@
 
 import numpy as np
 import numpy.testing as npt
+import pandas as pd
 import pytest
 from pygmt.clib.conversion import _to_numpy
 from pygmt.clib.session import DTYPES
@@ -21,7 +22,7 @@ def _check_result(result, supported):
 
 
 ########################################################################################
-# Test the _to_numpy function with NumPy dtypes.
+# Test the _to_numpy function with NumPy arrays.
 #
 # There are 24 fundamental dtypes in NumPy. Not all of them are supported by PyGMT.
 # Reference: https://numpy.org/doc/2.1/reference/arrays.scalars.html
@@ -80,3 +81,47 @@ def test_to_numpy_ndarray_numpy_dtypes_numeric(dtype, supported):
     result = _to_numpy(array)
     _check_result(result, supported)
     npt.assert_array_equal(result, array)
+
+
+########################################################################################
+# Test the _to_numpy function with pandas.Series.
+#
+# In pandas, dtype can be specified by
+#
+# 1. NumPy dtypes (see above)
+# 2. pandas dtypes
+# 3. PyArrow dtypes
+#
+# Reference: https://pandas.pydata.org/docs/reference/arrays.html
+########################################################################################
+@pytest.mark.parametrize(
+    ("dtype", "supported"),
+    [
+        (np.int8, True),
+        (np.int16, True),
+        (np.int32, True),
+        (np.int64, True),
+        (np.longlong, True),
+        (np.uint8, True),
+        (np.uint16, True),
+        (np.uint32, True),
+        (np.uint64, True),
+        (np.ulonglong, True),
+        (np.float16, False),
+        (np.float32, True),
+        (np.float64, True),
+        (np.longdouble, False),
+        (np.complex64, False),
+        (np.complex128, False),
+        (np.clongdouble, False),
+    ],
+)
+def test_to_numpy_pandas_series_numpy_dtypes_numeric(dtype, supported):
+    """
+    Test the _to_numpy function with pandas.Series of NumPy numeric dtypes.
+    """
+    series = pd.Series([1, 2, 3], dtype=dtype)
+    assert series.dtype == dtype
+    result = _to_numpy(series)
+    _check_result(result, supported)
+    npt.assert_array_equal(result, series)