Refactor the workaround for pandas<2.2

Author: seisman

pygmt/clib/conversion.py

@@ -162,37 +162,28 @@ def _to_numpy(data: Any) -> np.ndarray:
         "date64[ms][pyarrow]": np.datetime64,
     }
 
+    # The expected numpy dtype for the result numpy array, but can be None.
+    dtype = dtypes.get(str(getattr(data, "dtype", None)))
+
+    # 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.
+    #
     # 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,
-                "Int16": np.int16,
-                "Int32": np.int32,
-                "Int64": np.int64,
-                "UInt8": np.uint8,
-                "UInt16": np.uint16,
-                "UInt32": np.uint32,
-                "UInt64": np.uint64,
-                "Float32": np.float32,
-                "Float64": np.float64,
-            }
-        )
-        # 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)
-
-    vec_dtype = str(getattr(data, "dtype", ""))
-    array = np.ascontiguousarray(data, dtype=dtypes.get(vec_dtype))
+    if (
+        Version(pd.__version__) < Version("2.2")
+        and hasattr(data, "dtype")
+        and hasattr(data.dtype, "numpy_dtype")
+    ):  # pandas.Series/pandas.Index with pandas nullable dtypes.
+        dtype = data.dtype.numpy_dtype
+        if data.hasnans:
+            if data.dtype.kind in "iu":
+                # Integers with missing values are converted to float64
+                dtype = np.float64
+            data = data.to_numpy(na_value=np.nan)
+
+    array = np.ascontiguousarray(data, dtype=dtype)
     return array