Package: numexpr
Author:David M. Cooke, Francesc Alted, and others.Maintainer:Francesc AltedContact:faltet@gmail.comURL:https://github.com/pydata/numexprDocumentation:http://numexpr.readthedocs.io/en/latest/Travis CI:GitHub Actions:PyPi:DOI:readthedocs:What is 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.In addition, its multi-threaded capabilities can make use of all your
cores – which generally results in substantial performance scaling compared
to NumPy.Last but not least, numexpr can make use of Intel’s VML (Vector Math
Library, normally integrated in its Math Kernel Library, or MKL).
This allows further acceleration of transcendent expressions.How NumExpr achieves high performanceThe main reason why NumExpr achieves better performance than NumPy is
that it avoids allocating memory for intermediate results. This
results in better cache utilization and reduces memory access in
general. Due to this, NumExpr works best with large arrays.NumExpr parses expressions into its own op-codes that are then used by
an integrated computing virtual machine. The array operands are split
into small chunks that easily fit in the cache of the CPU and passed
to the virtual machine. The virtual machine then applies the
operations on each chunk. It’s worth noting that all temporaries and
constants in the expression are also chunked. Chunks are distributed among
the available cores of the CPU, resulting in highly parallelized code
execution.The result is that NumExpr can get the most of your machine computing
capabilities for array-wise computations. Common speed-ups with regard
to NumPy are usually between 0.95x (for very simple expressions like'a + 1') and 4x (for relatively complex ones like'a*b-4.1*a > 2.5*b'),
although much higher speed-ups can be achieved for some functions  and complex
math operations (up to 15x in some cases).NumExpr performs best on matrices that are too large to fit in L1 CPU cache.
In order to get a better idea on the different speed-ups that can be achieved
on your platform, run the provided benchmarks.InstallationFrom wheelsNumExpr is available for install viapipfor a wide range of platforms and
Python versions (which may be browsed at:https://pypi.org/project/numexpr/#files).
Installation can be performed as:pip install numexprIf you are using the Anaconda or Miniconda distribution of Python you may prefer
to use thecondapackage manager in this case:conda install numexprFrom SourceOn most *nix systems your compilers will already be present. However if you
are using a virtual environment with a substantially newer version of Python than
your system Python you may be prompted to install a new version ofgccorclang.For Windows, you will need to install the Microsoft Visual C++ Build Tools
(which are free) first. The version depends on which version of Python you have
installed:https://wiki.python.org/moin/WindowsCompilersFor Python 3.6+ simply installing the latest version of MSVC build tools should
be sufficient. Note that wheels found via pip do not include MKL support. Wheels
available viacondawill have MKL, if the MKL backend is used for NumPy.Seerequirements.txtfor the required version of NumPy.NumExpr is built in the standard Python way:python setup.py build installYou can testnumexprwith:python -c "import numexpr; numexpr.test()"Do not test NumExpr in the source directory or you will generate import errors.Enable Intel® MKL supportNumExpr includes support for Intel’s MKL library. This may provide better
performance on Intel architectures, mainly when evaluating transcendental
functions (trigonometrical, exponential, …).If you have Intel’s MKL, copy thesite.cfg.examplethat comes with the
distribution tosite.cfgand edit the latter file to provide correct paths to
the MKL libraries in your system.  After doing this, you can proceed with the
usual building instructions listed above.Pay attention to the messages during the building process in order to know
whether MKL has been detected or not.  Finally, you can check the speed-ups on
your machine by running thebench/vml_timing.pyscript (you can play with
different parameters to theset_vml_accuracy_mode()andset_vml_num_threads()functions in the script so as to see how it would affect performance).Usage>>> import numpy as np
>>> import numexpr as ne

>>> a = np.arange(1e6)   # Choose large arrays for better speedups
>>> b = np.arange(1e6)

>>> ne.evaluate("a + 1")   # a simple expression
array([  1.00000000e+00,   2.00000000e+00,   3.00000000e+00, ...,
         9.99998000e+05,   9.99999000e+05,   1.00000000e+06])

>>> ne.evaluate('a*b-4.1*a > 2.5*b')   # a more complex one
array([False, False, False, ...,  True,  True,  True], dtype=bool)

>>> ne.evaluate("sin(a) + arcsinh(a/b)")   # you can also use functions
array([        NaN,  1.72284457,  1.79067101, ...,  1.09567006,
        0.17523598, -0.09597844])

>>> s = np.array([b'abba', b'abbb', b'abbcdef'])
>>> ne.evaluate("b'abba' == s")   # string arrays are supported too
array([ True, False, False], dtype=bool)DocumentationPlease see the official documentation atnumexpr.readthedocs.io.
Included is a user guide, benchmark results, and the reference API.AuthorsPlease seeAUTHORS.txt.LicenseNumExpr is distributed under theMITlicense.