CLN: consolidate arrow roundtrip tests for nullable dtypes in base masked tests (#34308)

Author: jorisvandenbossche

pandas/tests/arrays/boolean/test_construction.py

@@ -1,8 +1,6 @@
 import numpy as np
 import pytest
 
-import pandas.util._test_decorators as td
-
 import pandas as pd
 import pandas._testing as tm
 from pandas.arrays import BooleanArray
@@ -346,31 +344,3 @@ def test_to_numpy_copy():
 # mask = pd.array([True, False, True, None], dtype="boolean")
 # with pytest.raises(IndexError):
 # result = arr[mask]
-
-
-@td.skip_if_no("pyarrow", min_version="0.15.0")
-def test_arrow_array(data):
- # protocol added in 0.15.0
- import pyarrow as pa
-
- arr = pa.array(data)
-
- # TODO use to_numpy(na_value=None) here
- data_object = np.array(data, dtype=object)
- data_object[data.isna()] = None
- expected = pa.array(data_object, type=pa.bool_(), from_pandas=True)
- assert arr.equals(expected)
-
-
-@td.skip_if_no("pyarrow", min_version="0.15.1.dev")
-def test_arrow_roundtrip():
- # roundtrip possible from arrow 1.0.0
- import pyarrow as pa
-
- data = pd.array([True, False, None], dtype="boolean")
- df = pd.DataFrame({"a": data})
- table = pa.table(df)
- assert table.field("a").type == "bool"
- result = table.to_pandas()
- assert isinstance(result["a"].dtype, pd.BooleanDtype)
- tm.assert_frame_equal(result, df)

pandas/tests/arrays/integer/test_construction.py

@@ -1,8 +1,6 @@
 import numpy as np
 import pytest
 
-import pandas.util._test_decorators as td
-
 import pandas as pd
 import pandas._testing as tm
 from pandas.api.types import is_integer
@@ -199,40 +197,3 @@ def test_to_integer_array(values, to_dtype, result_dtype):
  assert result.dtype == result_dtype()
  expected = integer_array(values, dtype=result_dtype())
  tm.assert_extension_array_equal(result, expected)
-
-
-@td.skip_if_no("pyarrow", min_version="0.15.0")
-def test_arrow_array(data):
- # protocol added in 0.15.0
- import pyarrow as pa
-
- arr = pa.array(data)
- expected = np.array(data, dtype=object)
- expected[data.isna()] = None
- expected = pa.array(expected, type=data.dtype.name.lower(), from_pandas=True)
- assert arr.equals(expected)
-
-
-@td.skip_if_no("pyarrow", min_version="0.16.0")
-def test_arrow_roundtrip(data):
- # roundtrip possible from arrow 0.16.0
- import pyarrow as pa
-
- df = pd.DataFrame({"a": data})
- table = pa.table(df)
- assert table.field("a").type == str(data.dtype.numpy_dtype)
- result = table.to_pandas()
- tm.assert_frame_equal(result, df)
-
-
-@td.skip_if_no("pyarrow", min_version="0.16.0")
-def test_arrow_from_arrow_uint():
- # https://github.com/pandas-dev/pandas/issues/31896
- # possible mismatch in types
- import pyarrow as pa
-
- dtype = pd.UInt32Dtype()
- result = dtype.__from_arrow__(pa.array([1, 2, 3, 4, None], type="int64"))
- expected = pd.array([1, 2, 3, 4, None], dtype="UInt32")
-
- tm.assert_extension_array_equal(result, expected)

pandas/tests/arrays/masked/__init__.py

@@ -0,0 +1,53 @@
+import pytest
+
+import pandas.util._test_decorators as td
+
+import pandas as pd
+import pandas._testing as tm
+
+arrays = [pd.array([1, 2, 3, None], dtype=dtype) for dtype in tm.ALL_EA_INT_DTYPES]
+arrays += [pd.array([True, False, True, None], dtype="boolean")]
+
+
+@pytest.fixture(params=arrays, ids=[a.dtype.name for a in arrays])
+def data(request):
+ return request.param
+
+
+@td.skip_if_no("pyarrow", min_version="0.15.0")
+def test_arrow_array(data):
+ # protocol added in 0.15.0
+ import pyarrow as pa
+
+ arr = pa.array(data)
+ expected = pa.array(
+ data.to_numpy(object, na_value=None),
+ type=pa.from_numpy_dtype(data.dtype.numpy_dtype),
+ )
+ assert arr.equals(expected)
+
+
+@td.skip_if_no("pyarrow", min_version="0.16.0")
+def test_arrow_roundtrip(data):
+ # roundtrip possible from arrow 0.16.0
+ import pyarrow as pa
+
+ df = pd.DataFrame({"a": data})
+ table = pa.table(df)
+ assert table.field("a").type == str(data.dtype.numpy_dtype)
+ result = table.to_pandas()
+ assert result["a"].dtype == data.dtype
+ tm.assert_frame_equal(result, df)
+
+
+@td.skip_if_no("pyarrow", min_version="0.16.0")
+def test_arrow_from_arrow_uint():
+ # https://github.com/pandas-dev/pandas/issues/31896
+ # possible mismatch in types
+ import pyarrow as pa
+
+ dtype = pd.UInt32Dtype()
+ result = dtype.__from_arrow__(pa.array([1, 2, 3, 4, None], type="int64"))
+ expected = pd.array([1, 2, 3, 4, None], dtype="UInt32")
+
+ tm.assert_extension_array_equal(result, expected)