PERF: change Series indexing on multi-indexes to use a fast path (GH5567)

doc/source/release.rst

@@ -81,6 +81,7 @@ Improvements to existing features
 - Allow multi-index slicers (:issue:`6134`, :issue:`4036`, :issue:`3057`, :issue:`2598`, :issue:`5641`)
 - improve performance of slice indexing on Series with string keys (:issue:`6341`)
 - implement joining a single-level indexed DataFrame on a matching column of a multi-indexed DataFrame (:issue:`3662`)
+- Performance improvement in indexing into a multi-indexed Series (:issue:`5567`)
 
 .. _release.bug_fixes-0.14.0:
 

pandas/core/common.py

@@ -1557,6 +1557,16 @@ def _maybe_box(indexer, values, obj, key):
     # return the value
     return values
 
+def _maybe_box_datetimelike(value):
+    # turn a datetime like into a Timestamp/timedelta as needed
+
+    if isinstance(value, np.datetime64):
+        value = tslib.Timestamp(value)
+    elif isinstance(value, np.timedelta64):
+        pass
+
+    return value
+
 _values_from_object = lib.values_from_object
 
 def _possibly_convert_objects(values, convert_dates=True,

pandas/core/frame.py

@@ -1588,7 +1588,7 @@ def _ixs(self, i, axis=0, copy=False):
                     result = self.reindex(i, takeable=True)
                     copy=True
                 else:
-                    new_values, copy = self._data.fast_2d_xs(i, copy=copy)
+                    new_values, copy = self._data.fast_xs(i, copy=copy)
                     result = Series(new_values, index=self.columns,
                                     name=self.index[i], dtype=new_values.dtype)
                 result._set_is_copy(self, copy=copy)

pandas/core/generic.py

@@ -21,8 +21,8 @@
 from pandas import compat, _np_version_under1p7
 from pandas.compat import map, zip, lrange, string_types, isidentifier
 from pandas.core.common import (isnull, notnull, is_list_like,
-                                _values_from_object, _maybe_promote, ABCSeries,
-                                SettingWithCopyError, SettingWithCopyWarning)
+                                _values_from_object, _maybe_promote, _maybe_box_datetimelike,
+                                ABCSeries, SettingWithCopyError, SettingWithCopyWarning)
 import pandas.core.nanops as nanops
 from pandas.util.decorators import Appender, Substitution
 from pandas.core import config
@@ -1304,7 +1304,12 @@ def xs(self, key, axis=0, level=None, copy=True, drop_level=True):
 
         if np.isscalar(loc):
             from pandas import Series
-            new_values, copy = self._data.fast_2d_xs(loc, copy=copy)
+            new_values, copy = self._data.fast_xs(loc, copy=copy)
+
+            # may need to box a datelike-scalar
+            if not is_list_like(new_values):
+                return _maybe_box_datetimelike(new_values)
+
             result = Series(new_values, index=self.columns,
                             name=self.index[loc])
 

pandas/core/indexing.py

@@ -70,9 +70,15 @@ def __getitem__(self, key):
             return self._getitem_axis(key, axis=0)
 
     def _get_label(self, label, axis=0):
-        # ueber-hack
         if self.ndim == 1:
-            return self.obj[label]
+            # for perf reasons we want to try _xs first
+            # as its basically direct indexing
+            # but will fail when the index is not present
+            # see GH5667
+            try:
+                return self.obj._xs(label, axis=axis, copy=False)
+            except:
+                return self.obj[label]
         elif (isinstance(label, tuple) and
                 isinstance(label[axis], slice)):
             raise IndexingError('no slices here, handle elsewhere')

pandas/core/internals.py

@@ -2829,7 +2829,7 @@ def xs(self, key, axis=1, copy=True, takeable=False):
 
         return self.__class__(new_blocks, new_axes)
 
-    def fast_2d_xs(self, loc, copy=False):
+    def fast_xs(self, loc, copy=False):
         """
         get a cross sectional for a given location in the
         items ; handle dups
@@ -3757,6 +3757,12 @@ def _consolidate_check(self):
     def _consolidate_inplace(self):
         pass
 
+    def fast_xs(self, loc, copy=False):
+        """
+        fast path for getting a cross-section
+        """
+        result = self._block.values[loc]
+        return result, False
 
 def construction_error(tot_items, block_shape, axes, e=None):
     """ raise a helpful message about our construction """

pandas/core/series.py

@@ -427,26 +427,6 @@ def _unpickle_series_compat(self, state):
     def axes(self):
         return [self.index]
 
-    def _maybe_box(self, values):
-        """ genericically box the values """
-
-        if isinstance(values, self.__class__):
-            return values
-        elif not hasattr(values, '__iter__'):
-            v = lib.infer_dtype([values])
-            if v == 'datetime':
-                return lib.Timestamp(v)
-            return values
-
-        v = lib.infer_dtype(values)
-        if v == 'datetime':
-            return lib.map_infer(values, lib.Timestamp)
-
-        if isinstance(values, np.ndarray):
-            return self.__class__(values)
-
-        return values
-
     def _ixs(self, i, axis=0):
         """
         Return the i-th value or values in the Series by location

vb_suite/indexing.py

@@ -91,6 +91,25 @@
                                      name='indexing_frame_get_value',
                                      start_date=datetime(2011, 11, 12))
 
+setup = common_setup + """
+mi = MultiIndex.from_tuples([(x,y) for x in range(1000) for y in range(1000)])
+s =  Series(np.random.randn(1000000), index=mi)
+"""
+
+series_xs_mi_ix = Benchmark("s.ix[999]", setup,
+                            name='series_xs_mi_ix',
+                            start_date=datetime(2013, 1, 1))
+
+setup = common_setup + """
+mi = MultiIndex.from_tuples([(x,y) for x in range(1000) for y in range(1000)])
+s =  Series(np.random.randn(1000000), index=mi)
+df = DataFrame(s)
+"""
+
+frame_xs_mi_ix = Benchmark("df.ix[999]", setup,
+                           name='frame_xs_mi_ix',
+                           start_date=datetime(2013, 1, 1))
+
 #----------------------------------------------------------------------
 # Boolean DataFrame row selection
 

vb_suite/join_merge.py

@@ -220,5 +220,5 @@ def sample(values, k):
 """
 
 join_non_unique_equal = Benchmark('fracofday * temp[fracofday.index]', setup,
-                                   start_date=datetime(2013 1, 1))
+                                   start_date=datetime(2013, 1, 1))