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PyPI · #1149 most downloaded on PyPI
Fast numerical expression evaluator for NumPy
Last release 2 months ago
18 Jul 2026
Release timing varies
gaps range from 2 weeks to 9 months
Nearly every release is documented
notes for 58 of 59 stable releases
1 version withdrawn
withdrawn after publishing
18 years old
63 releases · first in 2009
Due to an oversight, uint32 types were not properly supported. That has been solved. Fixes #19.
Due to an oversight, uint32 types were not properly supported. That has been solved. Fixes #19.
Function abs for computing the absolute value added. However, it does not strictly follow NumPy conventions. See README.txt or website docs for more info on this. Thanks to Pauli Virtanen for the patch. Fixes #20.
A new type called internally float has been implemented so as to be able to work natively with single-precision floating points. This prevents the sil
A new type called internally float has been implemented so as to be able to work natively with single-precision floating points. This prevents the silent upcast to double types that was taking place in previous versions, so allowing both an improved performance and an optimal usage of memory for the single-precision computations. However, the casting rules for floating point types slightly differs from those of NumPy. See:
or the README.txt file for more info on this issue.
Support for Python 2.6 added.
When linking with the MKL, added a '-rpath' option to the link step so that the paths to MKL libraries are automatically included into the runtime library search path of the final package (i.e. the user won't need to update its LD_LIBRARY_PATH or LD_RUN_PATH environment variables anymore). Fixes #16.
One column per quarter.
Support for Intel's VML (Vector Math Library) added, normally included in Intel's MKL (Math Kernel Library). In addition, when the VML support is on,
Support for Intel's VML (Vector Math Library) added, normally included in Intel's MKL (Math Kernel Library). In addition, when the VML support is on, several processors can be used in parallel (see the new set_vml_num_threads() function). With that, the computations of transcendental functions can be accelerated quite a few. For example, typical speed-ups when using one single core for contiguous arrays are 3x with peaks of 7.5x (for the pow() function). When using 2 cores the speed-ups are around 4x and 14x respectively. Closes #9.
Some new VML-related functions have been added:
set_vml_accuracy_mode(mode): Set the accuracy for VML operations.
set_vml_num_threads(nthreads): Suggests a maximum number of threads to be used in VML operations.
get_vml_version(): Get the VML/MKL library version.
See the README.txt for more info about them.
In order to easily allow the detection of the MKL, the setup.py has been updated to use the numpy.distutils. So, if you are already used to link NumPy/SciPy with MKL, then you will find that giving VML support to numexpr works almost the same.
A new print_versions() function has been made available. This allows to quickly print the versions on which numexpr is based on. Very handy for issue reporting purposes.
The numexpr.numexpr compiler function has been renamed to numexpr.NumExpr in order to avoid name collisions with the name of the package (!). This function is mainly for internal use, so you should not need to upgrade your existing numexpr scripts.
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