pep8, removed args, kwargs

Author: mroeschke

asv_bench/benchmarks/groupby.py

@@ -344,26 +344,6 @@ def time_groupby_dt_timegrouper_size(self):
         self.df.groupby(TimeGrouper(key='dates', freq='M')).size()
 
 
-#----------------------------------------------------------------------
-# groupby.cummin and groupby.cummax # GH 15048
-
-class groupby_cummax_cummin(object):
-    goal_time = 0.2
-
-    def setup(self):
-        np.random.seed(1234)
-        self.G = 1000
-        self.N = 10000
-        self.df = pd.DataFrame({'A': np.random.randint(0, self.G, size=self.N),
-                                'B': np.random.randn(self.N)})
-
-    def time_cummin(self):
-        self.df.groupby('A').cummin()
-
-    def time_cummax(self):
-        self.df.groupby('A').cummax()
-
-
 #----------------------------------------------------------------------
 # groupby with a variable value for ngroups
 

pandas/core/groupby.py

@@ -75,7 +75,7 @@
     'last', 'first',
     'head', 'tail', 'median',
     'mean', 'sum', 'min', 'max',
-    'cumsum', 'cumprod', 'cumcount',
+    'cumcount',
     'resample',
     'describe',
     'rank', 'quantile',
@@ -1418,19 +1418,17 @@ def cumsum(self, axis=0, *args, **kwargs):
 
     @Substitution(name='groupby')
     @Appender(_doc_template)
-    def cummin(self, axis=0, *args, **kwargs):
+    def cummin(self, axis=0):
         """Cumulative min for each group"""
-        nv.validate_groupby_func('cummin', args, kwargs)
         if axis != 0:
             return self.apply(lambda x: np.minimum.accumulate(x, axis))
 
         return self._cython_transform('cummin')
 
     @Substitution(name='groupby')
     @Appender(_doc_template)
-    def cummax(self, axis=0, *args, **kwargs):
+    def cummax(self, axis=0):
         """Cumulative max for each group"""
-        nv.validate_groupby_func('cummax', args, kwargs)
         if axis != 0:
             return self.apply(lambda x: np.maximum.accumulate(x, axis))
 

pandas/src/algos_groupby_helper.pxi.in

@@ -700,6 +700,7 @@ def group_cummin(numeric[:, :] out,
                     accum[lab, j] = min_val
                     out[i, j] = accum[lab, j]
 
+
 @cython.boundscheck(False)
 @cython.wraparound(False)
 def group_cummax(numeric[:, :] out,