Use data.dtype.numpy_dtype

Author: seisman

pygmt/clib/conversion.py

@@ -187,7 +187,7 @@ def _to_numpy(data: Any) -> np.ndarray:
         if hasattr(data, "isna") and data.isna().any():
             # Integer dtypes with missing values are cast to NumPy float dtypes and NaN
             # is used as missing value indicator.
-            dtype = np.float64 if data.dtype.kind in "iu" else data.dtype.type
+            dtype = np.float64 if data.dtype.kind in "iu" else data.dtype.numpy_dtype
             data = data.to_numpy(dtype=dtype, na_value=np.nan)
 
     vec_dtype = str(getattr(data, "dtype", ""))

pygmt/tests/test_clib_to_numpy.py

@@ -199,7 +199,9 @@ def test_to_numpy_pandas_series_pandas_dtypes_numeric_with_na(dtype, 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
+    expected_dtype = (
+        np.float64 if series.dtype.kind in "iu" else series.dtype.numpy_dtype
+    )
     npt.assert_array_equal(
         result, np.array([1.0, np.nan, 3.0], dtype=expected_dtype), strict=True
     )