formatting with black pandas

Author: CloseChoice

pandas/core/generic.py

@@ -6895,12 +6895,17 @@ def interpolate(
                 "column to a numeric dtype."
             )
 
-
-        if method in ['backfill', 'bfill', 'pad', 'ffill']:
-            return self.fillna(method=method, axis=axis, inplace=inplace, limit=limit, downcast=downcast)
+        if method in ["backfill", "bfill", "pad", "ffill"]:
+            return self.fillna(
+                method=method,
+                axis=axis,
+                inplace=inplace,
+                limit=limit,
+                downcast=downcast,
+            )
         # create/use the index
 
-        if axis==0:
+        if axis == 0:
             df = self
         else:
             df = self.T

pandas/tests/frame/methods/test_interpolate.py

@@ -202,7 +202,7 @@ def test_interp_leading_nans(self, check_scipy):
             result = df.interpolate(method="polynomial", order=1)
             tm.assert_frame_equal(result, expected)
 
-    @pytest.mark.parametrize('axis', [0, 1])
+    @pytest.mark.parametrize("axis", [0, 1])
     def test_interp_raise_on_only_mixed(self, axis):
         df = DataFrame(
             {
@@ -214,7 +214,7 @@ def test_interp_raise_on_only_mixed(self, axis):
             }
         )
         with pytest.raises(TypeError):
-            df.astype('object').interpolate(axis=axis)
+            df.astype("object").interpolate(axis=axis)
 
     def test_interp_raise_on_all_object_dtype(self):
         # GH 22985
@@ -286,7 +286,6 @@ def test_interp_time_inplace_axis(self, axis):
         expected.interpolate(axis=0, method="time", inplace=True)
         tm.assert_frame_equal(result, expected)
 
-
     @pytest.mark.parametrize("axis", [0, 1])
     def test_interp_ffill(self, axis):
         # GH 33956
@@ -325,6 +324,6 @@ def test_interp_pad(self, axis):
                 "C": [3.0, 6.0, 9.0, np.nan, np.nan, 30.0],
             }
         )
-        expected = df.fillna(method='pad', axis=axis)
+        expected = df.fillna(method="pad", axis=axis)
         result = df.interpolate(method="pad", axis=axis)
         tm.assert_frame_equal(result, expected)