TST: follow-up to Test nested pandas array #24993

pandas/compat/numpy/__init__.py

@@ -12,7 +12,7 @@
 _np_version_under1p13 = _nlv < LooseVersion('1.13')
 _np_version_under1p14 = _nlv < LooseVersion('1.14')
 _np_version_under1p15 = _nlv < LooseVersion('1.15')
-
+_np_version_under1p16 = _nlv < LooseVersion('1.16')
 
 if _nlv < '1.12':
     raise ImportError('this version of pandas is incompatible with '
@@ -64,5 +64,6 @@ def np_array_datetime64_compat(arr, *args, **kwargs):
 __all__ = ['np',
            '_np_version_under1p13',
            '_np_version_under1p14',
-           '_np_version_under1p15'
+           '_np_version_under1p15',
+           '_np_version_under1p16'
            ]

pandas/tests/extension/base/groupby.py

@@ -55,19 +55,14 @@ def test_groupby_extension_transform(self, data_for_grouping):
 
         self.assert_series_equal(result, expected)
 
-    @pytest.mark.parametrize('op', [
-        lambda x: 1,
-        lambda x: [1] * len(x),
-        lambda x: pd.Series([1] * len(x)),
-        lambda x: x,
-    ], ids=['scalar', 'list', 'series', 'object'])
-    def test_groupby_extension_apply(self, data_for_grouping, op):
+    def test_groupby_extension_apply(
+            self, data_for_grouping, groupby_apply_op):
         df = pd.DataFrame({"A": [1, 1, 2, 2, 3, 3, 1, 4],
                            "B": data_for_grouping})
-        df.groupby("B").apply(op)
-        df.groupby("B").A.apply(op)
-        df.groupby("A").apply(op)
-        df.groupby("A").B.apply(op)
+        df.groupby("B").apply(groupby_apply_op)
+        df.groupby("B").A.apply(groupby_apply_op)
+        df.groupby("A").apply(groupby_apply_op)
+        df.groupby("A").B.apply(groupby_apply_op)
 
     def test_in_numeric_groupby(self, data_for_grouping):
         df = pd.DataFrame({"A": [1, 1, 2, 2, 3, 3, 1, 4],

pandas/tests/extension/base/methods.py

@@ -240,7 +240,6 @@ def test_shift_fill_value(self, data):
         expected = data.take([2, 3, 0, 0])
         self.assert_extension_array_equal(result, expected)
 
-    @pytest.mark.parametrize("as_frame", [True, False])
     def test_hash_pandas_object_works(self, data, as_frame):
         # https://github.com/pandas-dev/pandas/issues/23066
         data = pd.Series(data)
@@ -250,7 +249,6 @@ def test_hash_pandas_object_works(self, data, as_frame):
         b = pd.util.hash_pandas_object(data)
         self.assert_equal(a, b)
 
-    @pytest.mark.parametrize("as_series", [True, False])
     def test_searchsorted(self, data_for_sorting, as_series):
         b, c, a = data_for_sorting
         arr = type(data_for_sorting)._from_sequence([a, b, c])
@@ -275,7 +273,6 @@ def test_searchsorted(self, data_for_sorting, as_series):
         sorter = np.array([1, 2, 0])
         assert data_for_sorting.searchsorted(a, sorter=sorter) == 0
 
-    @pytest.mark.parametrize("as_frame", [True, False])
     def test_where_series(self, data, na_value, as_frame):
         assert data[0] != data[1]
         cls = type(data)
@@ -309,8 +306,6 @@ def test_where_series(self, data, na_value, as_frame):
             expected = expected.to_frame(name='a')
         self.assert_equal(result, expected)
 
-    @pytest.mark.parametrize("use_numpy", [True, False])
-    @pytest.mark.parametrize("as_series", [True, False])
     @pytest.mark.parametrize("repeats", [0, 1, 2, [1, 2, 3]])
     def test_repeat(self, data, repeats, as_series, use_numpy):
         arr = type(data)._from_sequence(data[:3], dtype=data.dtype)
@@ -327,7 +322,6 @@ def test_repeat(self, data, repeats, as_series, use_numpy):
 
         self.assert_equal(result, expected)
 
-    @pytest.mark.parametrize("use_numpy", [True, False])
     @pytest.mark.parametrize('repeats, kwargs, error, msg', [
         (2, dict(axis=1), ValueError, "'axis"),
         (-1, dict(), ValueError, "negative"),

pandas/tests/extension/base/missing.py

@@ -1,5 +1,4 @@
 import numpy as np
-import pytest
 
 import pandas as pd
 import pandas.util.testing as tm
@@ -89,14 +88,13 @@ def test_fillna_series(self, data_missing):
         result = ser.fillna(ser)
         self.assert_series_equal(result, ser)
 
-    @pytest.mark.parametrize('method', ['ffill', 'bfill'])
-    def test_fillna_series_method(self, data_missing, method):
+    def test_fillna_series_method(self, data_missing, fillna_method):
         fill_value = data_missing[1]
 
-        if method == 'ffill':
+        if fillna_method == 'ffill':
             data_missing = data_missing[::-1]
 
-        result = pd.Series(data_missing).fillna(method=method)
+        result = pd.Series(data_missing).fillna(method=fillna_method)
         expected = pd.Series(data_missing._from_sequence(
             [fill_value, fill_value], dtype=data_missing.dtype))
 

pandas/tests/extension/base/setitem.py

@@ -24,7 +24,6 @@ def test_setitem_sequence(self, data, box_in_series):
         assert data[0] == original[1]
         assert data[1] == original[0]
 
-    @pytest.mark.parametrize('as_array', [True, False])
     def test_setitem_sequence_mismatched_length_raises(self, data, as_array):
         ser = pd.Series(data)
         original = ser.copy()

pandas/tests/extension/conftest.py

@@ -2,6 +2,8 @@
 
 import pytest
 
+from pandas import Series
+
 
 @pytest.fixture
 def dtype():
@@ -108,3 +110,39 @@ def data_for_grouping():
 def box_in_series(request):
     """Whether to box the data in a Series"""
     return request.param
+
+
+@pytest.fixture(params=[
+    lambda x: 1,
+    lambda x: [1] * len(x),
+    lambda x: Series([1] * len(x)),
+    lambda x: x,
+], ids=['scalar', 'list', 'series', 'object'])
+def groupby_apply_op(request):
+    """functions to test groupby.apply()"""
+    return request.param
+
+
+@pytest.fixture(params=[True, False])
+def as_frame(request):
+    return request.param
+
+
+@pytest.fixture(params=[True, False])
+def as_series(request):
+    return request.param
+
+
+@pytest.fixture(params=[True, False])
+def use_numpy(request):
+    return request.param
+
+
+@pytest.fixture(params=['ffill', 'bfill'])
+def fillna_method(request):
+    return request.param
+
+
+@pytest.fixture(params=[True, False])
+def as_array(request):
+    return request.param