Make a duplicate fixture for pandas dtypes including pyarrow types (#…

…2941)

* Make a duplicate fixture for pandas dtypes including pyarrow types

Explicitly copying the numpy dtypes fixture in test_clib_virtualfiles.py to a
new dtypes_pandas fixture that includes pyarrow types, so that modifications
to the original numpy dtypes fixture won't affect other tests.

* Let test_virtualfile_from_vectors_transpose use dtypes fixture

No need to recreate a new dtypes list.

pygmt/tests/test_clib_virtualfiles.py

@@ -34,6 +34,19 @@ def fixture_dtypes():
  return "int8 int16 int32 int64 uint8 uint16 uint32 uint64 float32 float64".split()
 
 
+@pytest.fixture(scope="module", name="dtypes_pandas")
+def fixture_dtypes_pandas(dtypes):
+ """
+ List of supported pandas dtypes.
+ """
+ dtypes_pandas = dtypes.copy()
+
+ if find_spec("pyarrow") is not None:
+ dtypes_pandas.extend([f"{dtype}[pyarrow]" for dtype in dtypes_pandas])
+
+ return tuple(dtypes_pandas)
+
+
 def test_virtual_file(dtypes):
  """
  Test passing in data via a virtual file with a Dataset.
@@ -248,11 +261,10 @@ def test_virtualfile_from_vectors_two_string_or_object_columns(dtype):
  assert output == expected
 
 
-def test_virtualfile_from_vectors_transpose():
+def test_virtualfile_from_vectors_transpose(dtypes):
  """
  Test transforming matrix columns to virtual file dataset.
  """
- dtypes = "float32 float64 int32 int64 uint32 uint64".split()
  shape = (7, 5)
  for dtype in dtypes:
  data = np.arange(shape[0] * shape[1], dtype=dtype).reshape(shape)
@@ -315,16 +327,14 @@ def test_virtualfile_from_matrix_slice(dtypes):
  assert output == expected
 
 
-def test_virtualfile_from_vectors_pandas(dtypes):
+def test_virtualfile_from_vectors_pandas(dtypes_pandas):
  """
  Pass vectors to a dataset using pandas.Series, checking both numpy and pyarrow
  dtypes.
  """
  size = 13
- if find_spec("pyarrow") is not None:
- dtypes.extend([f"{dtype}[pyarrow]" for dtype in dtypes])
 
- for dtype in dtypes:
+ for dtype in dtypes_pandas:
  data = pd.DataFrame(
  data={
  "x": np.arange(size),