Further improve the workaround for pandas dtypes

Author: seisman

pygmt/clib/conversion.py

@@ -162,14 +162,14 @@ def _to_numpy(data: Any) -> np.ndarray:
         "date64[ms][pyarrow]": np.datetime64,
     }
 
-    # pandas nullable dtypes and pyarrow types were converted to np.object_ dtype
-    # before, and are converted to suitable numpy dtypes since pandas 2.2.
-    # Refer to the following link for details:
-    # https://pandas.pydata.org/docs/whatsnew/v2.2.0.html#to-numpy-for-numpy-nullable-and-arrow-types-converts-to-suitable-numpy-dtype
+    # pandas numeric dtypes were converted to np.object_ dtype prior pandas 2.2, and are
+    # converted to suitable numpy dtypes since pandas 2.2. Refer to the following link
+    # for details: https://pandas.pydata.org/docs/whatsnew/v2.2.0.html#to-numpy-for-numpy-nullable-and-arrow-types-converts-to-suitable-numpy-dtype
     # Here are the workarounds for pandas < 2.2.
     # Following SPEC 0, pandas 2.1 should be dropped in 2025 Q3, so it's likely we can
     # remove the workaround in PyGMT v0.17.0.
     if Version(pd.__version__) < Version("2.2"):
+        # Specify mapping from pandas nullable dtypes to suitable numpy dtypes
         dtypes.update(
             {
                 "Int8": np.int8,
@@ -184,9 +184,10 @@ def _to_numpy(data: Any) -> np.ndarray:
                 "Float64": np.float64,
             }
         )
-        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.
+        # For pandas.Index/pandas.Series, pandas/pyarrow integer dtypes with missing
+        # values should be cast to NumPy float dtypes and NaN is used as missing value
+        # indicator.
+        if getattr(data, "hasnans", False):  # pandas.Index/pandas.Series has 'hasnans'
             dtype = np.float64 if data.dtype.kind in "iu" else data.dtype.numpy_dtype
             data = data.to_numpy(na_value=np.nan).astype(dtype=dtype)