Fix performance regression in Series.asof by avoiding pre-computing nulls and returning value by indexing the underlying ndarray.

Author: laudney

pandas/core/generic.py

@@ -3735,10 +3735,10 @@ def asof(self, where, subset=None):
         if not self.index.is_monotonic:
             raise ValueError("asof requires a sorted index")
 
-        if isinstance(self, ABCSeries):
+        is_series = isinstance(self, ABCSeries)
+        if is_series:
             if subset is not None:
                 raise ValueError("subset is not valid for Series")
-            nulls = self.isnull()
         elif self.ndim > 2:
             raise NotImplementedError("asof is not implemented "
                                       "for {type}".format(type(self)))
@@ -3747,7 +3747,6 @@ def asof(self, where, subset=None):
                 subset = self.columns
             if not is_list_like(subset):
                 subset = [subset]
-            nulls = self[subset].isnull().any(1)
 
         if not is_list_like(where):
             start = self.index[0]
@@ -3761,13 +3760,21 @@ def asof(self, where, subset=None):
             loc = self.index.searchsorted(where, side='right')
             if loc > 0:
                 loc -= 1
-            while nulls[loc] and loc > 0:
+
+            if is_series:
+                values = self._values
+                while loc > 0 and isnull(values[loc]):
+                    loc -= 1
+                return values[loc]
+
+            while loc > 0 and self.loc[loc, subset].isnull().any():
                 loc -= 1
             return self.iloc[loc]
 
         if not isinstance(where, Index):
             where = Index(where)
 
+        nulls = self.isnull() if is_series else self[subset].isnull().any(1)
         locs = self.index.asof_locs(where, ~(nulls.values))
 
         # mask the missing