Converted kwargs to positional arguments in Cython layer

pandas/_libs/groupby.pyx

@@ -126,7 +126,8 @@ def group_last_object(ndarray[object, ndim=2] out,
 def group_rank_object(ndarray[float64_t, ndim=2] out,
                       ndarray[object, ndim=2] values,
                       ndarray[int64_t] labels,
-                      bint is_datetimelike, **kwargs):
+                      bint is_datetimelike, object ties_method,
+                      bint ascending, bint pct, object na_option):
     """
     Only transforms on axis=0
     """
@@ -137,18 +138,16 @@ def group_rank_object(ndarray[float64_t, ndim=2] out,
         int64_t grp_na_count=0
         ndarray[int64_t] _as
         ndarray[object] _values
-        bint pct, ascending, keep_na
+        bint keep_na
 
-    tiebreak = tiebreakers[kwargs['ties_method']]
-    ascending = kwargs['ascending']
-    pct = kwargs['pct']
-    keep_na = kwargs['na_option'] == 'keep'
+    tiebreak = tiebreakers[ties_method]
+    keep_na = na_option == 'keep'
     N, K = (<object> values).shape
 
     masked_vals = np.array(values[:, 0], copy=True)
     mask = missing.isnaobj(masked_vals)
 
-    if ascending ^ (kwargs['na_option'] == 'top'):
+    if ascending ^ (na_option == 'top'):
         nan_fill_val = np.inf
         order = (masked_vals, mask, labels)
     else:

pandas/_libs/groupby_helper.pxi.in

@@ -450,7 +450,8 @@ def group_nth_{{name}}(ndarray[{{dest_type2}}, ndim=2] out,
 def group_rank_{{name}}(ndarray[float64_t, ndim=2] out,
                         ndarray[{{c_type}}, ndim=2] values,
                         ndarray[int64_t] labels,
-                        bint is_datetimelike, **kwargs):
+                        bint is_datetimelike, object ties_method,
+                        bint ascending, bint pct, object na_option):
     """
     Only transforms on axis=0
     """
@@ -462,13 +463,11 @@ def group_rank_{{name}}(ndarray[float64_t, ndim=2] out,
         ndarray[int64_t] _as
         ndarray[{{c_type}}] masked_vals
         ndarray[uint8_t] mask
-        bint pct, ascending, keep_na
+        bint keep_na
         {{c_type}} nan_fill_val
 
-    tiebreak = tiebreakers[kwargs['ties_method']]
-    ascending = kwargs['ascending']
-    pct = kwargs['pct']
-    keep_na = kwargs['na_option'] == 'keep'
+    tiebreak = tiebreakers[ties_method]
+    keep_na = na_option == 'keep'
     N, K = (<object> values).shape
 
     masked_vals = np.array(values[:, 0], copy=True)
@@ -478,7 +477,7 @@ def group_rank_{{name}}(ndarray[float64_t, ndim=2] out,
     mask = np.isnan(masked_vals).astype(np.uint8)
     {{endif}}
 
-    if ascending ^ (kwargs['na_option'] == 'top'):
+    if ascending ^ (na_option == 'top'):
         {{if name == 'int64'}}
         nan_fill_val = np.iinfo(np.int64).max
         {{else}}

pandas/core/groupby.py

@@ -2171,6 +2171,12 @@ def get_group_levels(self):
     # ------------------------------------------------------------
     # Aggregation functions
 
+    def _group_rank_wrapper(func, *args, **kwargs):
+        return func(*args, kwargs.get('ties_method', 'average'),
+                    kwargs.get('ascending', True),
+                    kwargs.get('pct', False),
+                    kwargs.get('na_option', 'keep'))
+
     _cython_functions = {
         'aggregate': {
             'add': 'group_add',
@@ -2195,7 +2201,10 @@ def get_group_levels(self):
             'cumsum': 'group_cumsum',
             'cummin': 'group_cummin',
             'cummax': 'group_cummax',
-            'rank': 'group_rank',
+            'rank': {
+                'name': 'group_rank',
+                'f': _group_rank_wrapper
+            }
         }
     }
 
@@ -2424,7 +2433,7 @@ def _transform(self, result, values, comp_ids, transform_func,
 
                 chunk = chunk.squeeze()
                 transform_func(result[:, :, i], values,
-                               comp_ids, is_datetimelike)
+                               comp_ids, is_datetimelike, **kwargs)
         else:
             transform_func(result, values, comp_ids, is_datetimelike, **kwargs)