refactorings

Author: SaturnFromTitan

pandas/core/series.py

@@ -300,7 +300,6 @@ def __init__(
                 if not is_list_like(data):
                     data = [data]
                 index = ibase.default_index(len(data))
-
             elif is_list_like(data):
 
                 # a scalar numpy array is list-like but doesn't

pandas/tests/extension/base/missing.py

@@ -19,8 +19,7 @@ def test_isna(self, data_missing):
 
         # GH 21189
         result = pd.Series(data_missing).drop([0, 1]).isna()
-        index = pd.RangeIndex(0)
-        expected = pd.Series([], dtype=bool, index=index)
+        expected = pd.Series([], dtype=bool, index=pd.RangeIndex(0))
         self.assert_series_equal(result, expected)
 
     def test_dropna_array(self, data_missing):

pandas/tests/extension/test_sparse.py

@@ -185,8 +185,7 @@ def test_isna(self, data_missing):
 
         # GH 21189
         result = pd.Series(data_missing).drop([0, 1]).isna()
-        index = pd.RangeIndex(0)
-        expected = pd.Series([], dtype=expected_dtype, index=index)
+        expected = pd.Series([], dtype=expected_dtype, index=pd.RangeIndex(0))
         self.assert_series_equal(result, expected)
 
     def test_fillna_limit_pad(self, data_missing):

pandas/tests/groupby/test_filters.py

@@ -97,8 +97,7 @@ def test_filter_out_all_groups_in_df():
     df = pd.DataFrame({"a": [1, 1, 2], "b": [1, 2, 0]})
     res = df.groupby("a")
     res = res.filter(lambda x: x["b"].sum() > 5, dropna=True)
-    expected = pd.DataFrame({"a": [], "b": []}, dtype="int64")
-    expected.index = pd.RangeIndex(0)
+    expected = pd.DataFrame({"a": [], "b": []}, dtype="int64", index=pd.RangeIndex(0))
     tm.assert_frame_equal(expected, res)
 
 

pandas/tests/reshape/test_concat.py

@@ -743,17 +743,10 @@ def test_concat_categorical_empty(self):
         s2 = pd.Series([], dtype="object")
 
         # different dtype => not-category
-        result = pd.concat([s1, s2], ignore_index=True)
-        tm.assert_series_equal(result, s2)
-
-        result = s1.append(s2, ignore_index=True)
-        tm.assert_series_equal(result, s2)
-
-        result = pd.concat([s1, s2], ignore_index=True)
-        tm.assert_series_equal(result, s2)
-
-        result = s2.append(s1, ignore_index=True)
-        tm.assert_series_equal(result, s2)
+        tm.assert_series_equal(pd.concat([s1, s2], ignore_index=True), s2)
+        tm.assert_series_equal(s1.append(s2, ignore_index=True), s2)
+        tm.assert_series_equal(pd.concat([s2, s1], ignore_index=True), s2)
+        tm.assert_series_equal(s2.append(s1, ignore_index=True), s2)
 
         s1 = pd.Series([], dtype="category")
         s2 = pd.Series([np.nan, np.nan])
@@ -2206,10 +2199,11 @@ def test_concat_empty_series_timelike(self, tz, values):
             {
                 0: pd.Series([pd.NaT] * len(values), dtype="M8[ns]").dt.tz_localize(tz),
                 1: values,
-            }
+            },
+            index=pd.Index(list(range(len(values))), dtype="object"),
         )
         result = concat([first, second], axis=1)
-        tm.assert_frame_equal(result, expected, check_index_type=False)
+        tm.assert_frame_equal(result, expected)
 
     def test_default_index(self):
         # is_series and ignore_index

pandas/tests/series/test_missing.py

@@ -1151,8 +1151,7 @@ def test_interpolate_corners(self, kwargs):
         s = Series([np.nan, np.nan])
         tm.assert_series_equal(s.interpolate(**kwargs), s)
 
-        index = pd.RangeIndex(0)
-        s = Series([], dtype=object, index=index).interpolate()
+        s = Series([], dtype=object, index=pd.RangeIndex(0)).interpolate()
         tm.assert_series_equal(s.interpolate(**kwargs), s)
 
     def test_interpolate_index_values(self):