Update sparse array minmax method 8

Author: taytzehao

pandas/core/arrays/sparse/array.py

@@ -1391,10 +1391,9 @@ def mean(self, axis=0, *args, **kwargs):
         else:
             nsparse = self.sp_index.ngaps
             return (sp_sum + self.fill_value * nsparse) / (ct + nsparse)
-            
-            
-    def max(self, axis=0, *args, **kwargs):
 
+
+    def max(self, axis=0, *args, **kwargs):
         nv.validate_max(args, kwargs)
 
         if self.sp_index.ngaps > 0 and np.all(self._valid_sp_values < 0):
@@ -1404,7 +1403,6 @@ def max(self, axis=0, *args, **kwargs):
 
 
     def min(self, axis=0, *args, **kwargs):
-
         nv.validate_min(args, kwargs)
 
         if self.sp_index.ngaps > 0 and np.all(self._valid_sp_values > 0):

pandas/tests/arrays/sparse/test_array.py

@@ -1311,16 +1311,16 @@ def test_dropna(fill_value):
     df = pd.DataFrame({"a": [0, 1], "b": arr})
     expected_df = pd.DataFrame({"a": [1], "b": exp}, index=pd.Int64Index([1]))
     tm.assert_equal(df.dropna(), expected_df)
-    
-    
+
+
 def test_maxmin():
     data = np.arange(10).astype(float)
     max_out = SparseArray(data).max()
     min_out = SparseArray(data).min()
     assert max_out == 9
     assert min_out == 0
 
-    data = data*(-1)
+    data = data * (-1)
     max_out = SparseArray(data).max()
     min_out = SparseArray(data).min()
     assert max_out == 0