pydata/pandas — pull request #3154 by jreback (2013-03-24 02:47:06+00:00)
Title: ENH: added numexpr support for where operations

Added support for numexpr to do where operations, so this will have an
effect on most boolean indexing operations themselves
(inital commit would speedup ops like ``df>0``)

this PR is in effect speeding up ``np.where``

``df[(df>0)&(df2>0)]`` (100k x 100 columns)

``no_ne`` is no numexpr at all
``st`` is single threaded

This operation is restricted to int64/float64 dtypes
(others would be upcasted, which we could deal with, but
not now)

note: *the above operation could (and should) be much faster
if done as a single operation, but for now this is basically
4 calls to numexpr (3 boolean, then the where), but that's
for another day*

```
----------------------------------------------------------------
Test name | target[ms] | base[ms] | ratio
----------------------------------------------------------------
frame_multi_and 128.5169 385.2890 0.3336
frame_multi_and_st 215.7741 417.0649 0.5174
frame_multi_and_no_ne 397.3501 401.0870 0.9907
```