Add tests for pandas.Series with NA

Author: seisman

pygmt/tests/test_clib_to_numpy.py

@@ -171,3 +171,35 @@ def test_to_numpy_pandas_series_pandas_dtypes_numeric(dtype, supported):
     result = _to_numpy(series)
     _check_result(result, supported)
     npt.assert_array_equal(result, series)
+
+
+@pytest.mark.parametrize(
+    ("dtype", "supported"),
+    [
+        pytest.param(pd.Int8Dtype(), True, id="Int8"),
+        pytest.param(pd.Int16Dtype(), True, id="Int16"),
+        pytest.param(pd.Int32Dtype(), True, id="Int32"),
+        pytest.param(pd.Int64Dtype(), True, id="Int64"),
+        pytest.param(pd.UInt8Dtype(), True, id="UInt8"),
+        pytest.param(pd.UInt16Dtype(), True, id="UInt16"),
+        pytest.param(pd.UInt32Dtype(), True, id="UInt32"),
+        pytest.param(pd.UInt64Dtype(), True, id="UInt64"),
+        pytest.param(pd.Float32Dtype(), True, id="Float32"),
+        pytest.param(pd.Float64Dtype(), True, id="Float64"),
+    ],
+)
+def test_to_numpy_pandas_series_pandas_dtypes_numeric_with_na(dtype, supported):
+    """
+    Test the _to_numpy function with pandas.Series of pandas numeric dtypes and NA.
+    """
+    series = pd.Series([1, pd.NA, 3], dtype=dtype)
+    assert series.dtype == dtype
+    result = _to_numpy(series)
+    _check_result(result, supported)
+    # Integer dtypes with missing values are cast to NumPy float64 dtype, and Float
+    # dtypes with missing values are cast to NumPy float32/float64 dtype.
+    # np.NaN is used as missing value indicator.
+    expected_dtype = np.float64 if series.dtype.kind in "iu" else series.dtype.type
+    npt.assert_array_equal(
+        result, np.array([1.0, np.nan, 3.0], dtype=expected_dtype), strict=True
+    )