LINT fixup

Author: WillAyd

asv_bench/benchmarks/groupby.py

@@ -314,8 +314,9 @@ class GroupByMethods(object):
               ['all', 'any', 'bfill', 'count', 'cumcount', 'cummax', 'cummin',
                'cumprod', 'cumsum', 'describe', 'ffill', 'first', 'head',
                'last', 'mad', 'max', 'min', 'median', 'mean', 'nunique',
-               'pct_change', 'prod', 'quantile', 'rank', 'sem', 'shift', 'size',
-               'skew', 'std', 'sum', 'tail', 'unique', 'value_counts', 'var'],
+               'pct_change', 'prod', 'quantile', 'rank', 'sem', 'shift',
+               'size', 'skew', 'std', 'sum', 'tail', 'unique', 'value_counts',
+               'var'],
               ['direct', 'transformation']]
 
     def setup(self, dtype, method, application):

pandas/_libs/groupby.pyx

@@ -448,7 +448,6 @@ def group_quantile(ndarray[float64_t] out,
             grp_sz = counts[i]
             non_na_sz = non_na_counts[i]
 
-            # If we have a group of all NA data we need to bail out
             if non_na_sz == 0:
                 out[i] = NaN
             else:
@@ -473,7 +472,7 @@ def group_quantile(ndarray[float64_t] out,
                     elif interp == INTERPOLATION_MIDPOINT:
                         out[i] = (val + next_val) / 2.0
                     elif interp == INTERPOLATION_NEAREST:
-                        if frac > .5 or (frac == .5 and q > .5):  # Always safe?
+                        if frac > .5 or (frac == .5 and q > .5):  # Always OK?
                             out[i] = next_val
                         else:
                             out[i] = val