

#ANACONDA CONTEXTLIB2 PYTHON 3 INSTALL CODE#
To compile the C code generated by the cython compiler, a C compiler is needed. To use Cython two things are needed.The Cython package itself, which contains the cython source-to-source compiler and Cython interfaces to several C and Python libraries (for example numpy).

The shared object (.so) file can be imported and used from Python, so now we can run the test.py : $ python test.py An easier way is to let distutils handle this: $ lsīuild hello.c hello.pyx setup.py test.py This can be done by, using cython hello.pyx to translate the code to C and then compile it using gcc. # define an extension that will be cythonized and compiledĮxt = Extension(name="hello", sources=) Test.py # Import the extension module hello. """This is a cpdef function that can be called from Python.""" This is a cdef function that can be called from within setup.py is used to compile the Cython code.test.py is a Python script that uses the hello extension.A common approach is to create an extension module which is then imported in a Python program. Hello WorldĪ Cython pyx file needs to be translated to C code ( cythonized) and compiled before it can be used from Python. This allows to retain Python syntax for the bulk of the code and apply the speedup where it is most needed.

How do I use it to speed up my code?Ī common use case, when trying to speed up a program using Cython, is to profile the code and move the computationally expensive parts to compiled Cython modules. Hence, Cython especially shines for mathematic problems in which the types are clear. In the Cython-generated C code, the types are already know and only one function call to is made. For example when adding two integers, Python performs a type check for each variable, finds an add function that satisfies the found types, and calls that function. The main performance gain Cython can reach in contrast to pure Python stems from bypassing the CPython API. This allows to create extensions that can be imported from Python or executables. How does it work?Ĭython code is compiled using the cython source-to-source compiler to create C or C++ code, which in turn can be compiled using a C compiler. This allows to reach C-level performance while still using a Python-like syntax. The Cython programming language enriches Python by C-like static typing, the ability to directly call C functions, and several other features.
