By Kurt W. Smith

Build software program that mixes Python’s expressivity with the functionality and regulate of C (and C++). It’s attainable with Cython, the compiler and hybrid programming language utilized by foundational applications resembling NumPy, and well-liked in tasks together with Pandas, h5py, and scikits-learn. during this sensible advisor, you’ll the way to use Cython to enhance Python’s performance—up to 3000x— and to wrap C and C++ libraries in Python with ease.

Author Kurt Smith takes you thru Cython’s features, with pattern code and in-depth perform workouts. If you’re simply beginning with Cython, or are looking to move deeper, you’ll learn the way this language is an important a part of any performance-oriented Python programmer’s arsenal.

  • Use Cython’s static typing to hurry up Python code
  • Gain hands-on adventure utilizing Cython good points to spice up your numeric-heavy Python
  • Create new kinds with Cython—and see how briskly object-oriented programming in Python can be
  • Effectively set up Cython code into separate modules and programs with out sacrificing performance
  • Use Cython to offer Pythonic interfaces to C and C++ libraries
  • Optimize code with Cython’s runtime and compile-time profiling tools
  • Use Cython’s prange functionality to parallelize loops transparently with OpenMP

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Additional info for Cython - A guide for Python programmers

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For example: cdef cdef cdef cdef list particles, modified_particles dict names_from_particles str pname set unique_particles The variables in this example are full Python objects. Under the hood, Cython declares them as C pointers to some built-in Python struct type. initialize names_from_particles... keys()) Dynamic variables can be initialized from statically declared Python types: other_particles = particles del other_particles[0] Here, deleting the 0th element via other_particles will delete the 0th element of particles as well, since they are referring to the same list.

When we are adding, subtracting, or multiplying scalars, the operations have Python semantics (including automatic Python long coercion for large values) when the operands are dynamically typed Python objects. , the result may overflow for limited-precision integer types) when the operands are statically typed C variables. , computing the remainder) deserve special mention. C and Python have markedly different behavior when computing the modulus with signed integer operands: C rounds toward zero, while Python rounds toward infinity.

We can pass optional arguments to the %%cython magic command. pyx file --cplus Instructs cython to generate C++ source -a, --annotate Instructs cython to output an annotated source file (see Chapter 9) -f, --force Forces cython to regenerate C or C++ source The second set of options allows us to control the second pipeline stage: 20 | Chapter 2: Compiling and Running Cython Code -I, --include Adds extra directories to search for file inclusions and cimports -c, --compile-args Allows inclusion of extra C compiler arguments --link-args Allows inclusion of extra link arguments -L Adds extra library search directories -l Adds extra library names to link against There are other Cython magic commands that are loaded by %load_ext cythonmagic: the %%cython_inline command and the %%cython_pyximport command.

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