PERF: perf enhancements in indexing/query/eval

Author: jreback

pandas/computation/align.py

@@ -151,7 +151,8 @@ def _align_core(terms):
                     f = partial(ti.reindex_axis, reindexer, axis=axis,
                                 copy=False)
 
-                if pd.lib.is_bool_array(ti.values):
+                # need  to fill if we have a bool dtype/array
+                if isinstance(ti, (np.ndarray, pd.Series)) and ti.dtype == object and pd.lib.is_bool_array(ti.values):
                     r = f(fill_value=True)
                 else:
                     r = f()

pandas/computation/expr.py

@@ -183,6 +183,8 @@ def update(self, level=None):
             while sl >= 0:
                 frame = frame.f_back
                 sl -= 1
+                if frame is None:
+                    break
                 frames.append(frame)
             for f in frames[::-1]:
                 self.locals.update(f.f_locals)

pandas/core/frame.py

@@ -3061,9 +3061,9 @@ def combine_first(self, other):
         Examples
         --------
         a's values prioritized, use values from b to fill holes:
-        
+
         >>> a.combine_first(b)
-            
+
 
         Returns
         -------
@@ -3623,7 +3623,7 @@ def join(self, other, on=None, how='left', lsuffix='', rsuffix='',
         how : {'left', 'right', 'outer', 'inner'}
             How to handle indexes of the two objects. Default: 'left'
             for joining on index, None otherwise
-            
+
             * left: use calling frame's index
             * right: use input frame's index
             * outer: form union of indexes

pandas/core/generic.py

@@ -1092,7 +1092,7 @@ def take(self, indices, axis=0, convert=True):
             new_data = self._data.reindex_axis(new_items, indexer=indices,
                                                axis=0)
         else:
-            new_data = self._data.take(indices, axis=baxis)
+            new_data = self._data.take(indices, axis=baxis, verify=convert)
         return self._constructor(new_data)\
                    ._setitem_copy(True)\
                    .__finalize__(self)

pandas/core/indexing.py

@@ -978,7 +978,7 @@ def _get_slice_axis(self, slice_obj, axis=0):
         if isinstance(indexer, slice):
             return self._slice(indexer, axis=axis, typ='iloc')
         else:
-            return self.obj.take(indexer, axis=axis)
+            return self.obj.take(indexer, axis=axis, convert=False)
 
 
 class _IXIndexer(_NDFrameIndexer):
@@ -1038,7 +1038,7 @@ def _get_slice_axis(self, slice_obj, axis=0):
         if isinstance(indexer, slice):
             return self._slice(indexer, axis=axis, typ='iloc')
         else:
-            return self.obj.take(indexer, axis=axis)
+            return self.obj.take(indexer, axis=axis, convert=False)
 
 
 class _LocIndexer(_LocationIndexer):
@@ -1195,7 +1195,7 @@ def _get_slice_axis(self, slice_obj, axis=0):
             return self._slice(slice_obj, axis=axis, raise_on_error=True,
                                typ='iloc')
         else:
-            return self.obj.take(slice_obj, axis=axis)
+            return self.obj.take(slice_obj, axis=axis, convert=False)
 
     def _getitem_axis(self, key, axis=0):
 

pandas/core/internals.py

@@ -3247,10 +3247,9 @@ def take(self, indexer, new_index=None, axis=1, verify=True):
 
         if verify:
             indexer = _maybe_convert_indices(indexer, n)
-
-        if ((indexer == -1) | (indexer >= n)).any():
-            raise Exception('Indices must be nonzero and less than '
-                            'the axis length')
+            if ((indexer == -1) | (indexer >= n)).any():
+                raise Exception('Indices must be nonzero and less than '
+                                'the axis length')
 
         new_axes = list(self.axes)
         if new_index is None:

vb_suite/eval.py

@@ -139,3 +139,14 @@
 
 query_datetime_index = Benchmark("df.query('index < ts')",
                                  index_setup, start_date=datetime(2013, 9, 27))
+
+setup = setup + """
+N = 1000000
+df = DataFrame({'a': np.random.randn(N)})
+min_val = df['a'].min()
+max_val = df['a'].max()
+"""
+
+query_with_boolean_selection = Benchmark("df.query('(a >= min_val) & (a <= max_val)')",
+                                         index_setup, start_date=datetime(2013, 9, 27))
+