ANN: NumExpr 2.8.6 Released
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Robert McLeod
Sep 12, 2023, 5:56:24 PM9/12/23
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Hi everyone,
NumExpr 2.8.6 is a release to deal with issues related to downstream `pandas`
where the sanitization blacklist was hitting private variables used in their
evaluate. In addition the sanitization was hitting on scientific notation.
For those who do not wish to have sanitization on by default, it can be changed
by setting an environment variable, `NUMEXPR_SANITIZE=0`.
If you use `pandas` in your packages it is advisable you pin
`numexpr >= 2.8.6`
in your requirements.
Project documentation is available at:
http://numexpr.readthedocs.io/
Changes from 2.8.5 to 2.8.6
---------------------------
* The sanitization can be turned off by default by setting an environment variable,
`set NUMEXPR_SANITIZE=0`
* Improved behavior of the blacklist to avoid triggering on private variables
and scientific notation numbers.
What's Numexpr?
---------------
Numexpr is a fast numerical expression evaluator for NumPy. With it,
expressions that operate on arrays (like "3*a+4*b") are accelerated
and use less memory than doing the same calculation in Python.
It has multi-threaded capabilities, as well as support for Intel's
MKL (Math Kernel Library), which allows an extremely fast evaluation
of transcendental functions (sin, cos, tan, exp, log...) while
squeezing the last drop of performance out of your multi-core
processors. Look here for a some benchmarks of numexpr using MKL:
https://github.com/pydata/numexpr/wiki/NumexprMKL
Its only dependency is NumPy (MKL is optional), so it works well as an
easy-to-deploy, easy-to-use, computational engine for projects that
don't want to adopt other solutions requiring more heavy dependencies.
Where I can find Numexpr?
-------------------------
The project is hosted at GitHub in:
https://github.com/pydata/numexpr
You can get the packages from PyPI as well (but not for RC releases):
http://pypi.python.org/pypi/numexpr
Documentation is hosted at:
http://numexpr.readthedocs.io/en/latest/
Share your experience
---------------------
Let us know of any bugs, suggestions, gripes, kudos, etc. you may
have.
Enjoy data!
--
NumExpr 2.8.6 is a release to deal with issues related to downstream `pandas`
where the sanitization blacklist was hitting private variables used in their
evaluate. In addition the sanitization was hitting on scientific notation.
For those who do not wish to have sanitization on by default, it can be changed
by setting an environment variable, `NUMEXPR_SANITIZE=0`.
If you use `pandas` in your packages it is advisable you pin
`numexpr >= 2.8.6`
in your requirements.
Project documentation is available at:
http://numexpr.readthedocs.io/
Changes from 2.8.5 to 2.8.6
---------------------------
* The sanitization can be turned off by default by setting an environment variable,
`set NUMEXPR_SANITIZE=0`
* Improved behavior of the blacklist to avoid triggering on private variables
and scientific notation numbers.
What's Numexpr?
---------------
Numexpr is a fast numerical expression evaluator for NumPy. With it,
expressions that operate on arrays (like "3*a+4*b") are accelerated
and use less memory than doing the same calculation in Python.
It has multi-threaded capabilities, as well as support for Intel's
MKL (Math Kernel Library), which allows an extremely fast evaluation
of transcendental functions (sin, cos, tan, exp, log...) while
squeezing the last drop of performance out of your multi-core
processors. Look here for a some benchmarks of numexpr using MKL:
https://github.com/pydata/numexpr/wiki/NumexprMKL
Its only dependency is NumPy (MKL is optional), so it works well as an
easy-to-deploy, easy-to-use, computational engine for projects that
don't want to adopt other solutions requiring more heavy dependencies.
Where I can find Numexpr?
-------------------------
The project is hosted at GitHub in:
https://github.com/pydata/numexpr
You can get the packages from PyPI as well (but not for RC releases):
http://pypi.python.org/pypi/numexpr
Documentation is hosted at:
http://numexpr.readthedocs.io/en/latest/
Share your experience
---------------------
Let us know of any bugs, suggestions, gripes, kudos, etc. you may
have.
Enjoy data!
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