Documentation cleanup

pandas/_libs/groupby_helper.pxi.in

@@ -452,8 +452,29 @@ def group_rank_{{name}}(ndarray[float64_t, ndim=2] out,
                         ndarray[int64_t] labels,
                         bint is_datetimelike, object ties_method,
                         bint ascending, bint pct, object na_option):
-    """
-    Only transforms on axis=0
+    """Provides the rank of values within each group
+
+    Parameters
+    ----------
+    out : array of float64_t values which this method will write its results to
+    values : array of {{c_type}} values to be ranked
+    labels : array containing unique label for each group, with its ordering
+        matching up to the corresponding record in `values`
+    is_datetimelike : bool
+        unused in this method but provided for call compatability with other
+        Cython transformations
+    ties_method :  {'keep', 'top', 'bottom'}
+        * keep: leave NA values where they are
+        * top: smallest rank if ascending
+        * bottom: smallest rank if descending
+    ascending : boolean
+        False for ranks by high (1) to low (N)
+    pct : boolean
+        Compute percentage rank of data within each group
+
+    Notes
+    -----
+    This method modifies the `out` parameter rather than returning an object
     """
     cdef:
         TiebreakEnumType tiebreak

pandas/core/groupby.py

@@ -1776,7 +1776,29 @@ def cumcount(self, ascending=True):
     @Appender(_doc_template)
     def rank(self, method='average', ascending=True, na_option='keep',
              pct=False, axis=0):
-        """Rank within each group"""
+        """Provides the rank of values within each group
+
+        Parameters
+        ----------
+        method : {'average', 'min', 'max', 'first', 'dense'}, efault 'average'
+            * average: average rank of group
+            * min: lowest rank in group
+            * max: highest rank in group
+            * first: ranks assigned in order they appear in the array
+            * dense: like 'min', but rank always increases by 1 between groups
+        method :  {'keep', 'top', 'bottom'}, default 'keep'
+            * keep: leave NA values where they are
+            * top: smallest rank if ascending
+            * bottom: smallest rank if descending
+        ascending : boolean, default True
+            False for ranks by high (1) to low (N)
+        pct : boolean, default False
+            Compute percentage rank of data within each group
+
+        Returns
+        -----
+        DataFrame with ranking of values within each group
+        """
         return self._cython_transform('rank', numeric_only=False,
                                       ties_method=method, ascending=ascending,
                                       na_option=na_option, pct=pct, axis=axis)
@@ -2198,11 +2220,12 @@ def get_group_levels(self):
             'cummax': 'group_cummax',
             'rank': {
                 'name': 'group_rank',
-                'f': lambda func, a, b, c, d, **kwargs: func(a, b, c, d,
-                        kwargs.get('ties_method', 'average'),
-                        kwargs.get('ascending', True),
-                        kwargs.get('pct', False),
-                        kwargs.get('na_option', 'keep')
+                'f': lambda func, a, b, c, d, **kwargs: func(
+                    a, b, c, d,
+                    kwargs.get('ties_method', 'average'),
+                    kwargs.get('ascending', True),
+                    kwargs.get('pct', False),
+                    kwargs.get('na_option', 'keep')
                 )
             }
         }