By Kurt W. Smith
Build software program that mixes Python’s expressivity with the functionality and keep watch over of C (and C++). It’s attainable with Cython, the compiler and hybrid programming language utilized by foundational applications corresponding to NumPy, and well-known in initiatives together with Pandas, h5py, and scikits-learn. during this functional advisor, you’ll how one can 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 services, with pattern code and in-depth perform workouts. If you’re simply beginning with Cython, or are looking to cross deeper, you’ll find out how 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 positive factors to spice up your numeric-heavy Python
- Create new kinds with Cython—and see how briskly object-oriented programming in Python can be
- Effectively arrange 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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Extra resources for Cython - A guide for Python programmers
Given Python’s design and flexibility, the Python interpreter always has to determine the low-level operation in a completely general way, because a variable can have any type at any time. 1 For example, consider what happens when the Python runtime evaluates a + b: 1. The interpreter inspects the Python object referred to by a for its type, which re‐ quires at least one pointer lookup at the C level. 2. The interpreter asks the type for an implementation of the addition method, which may require one or more additional pointer lookups and internal function calls.
Another good alternative is to use the Windows SDK C/C++ compil‐ er. These compilers are the only reliable options for compiling 64-bit extensions. Another option on Windows for 32-bit extensions is to use MinGW. It is not as reliable as the Microsoft-provided compilers, but will likely work for simple ex‐ amples. The MinGW compiler is distributed via several prepackaged Python soft‐ ware distributions, mentioned in the next section. Installing Cython Likely the easiest way to acquire Cython is via a packaged software distribution, such as these popular options: • The Sage Mathematics software system • Enthought’s Canopy The Cython Compilation Pipeline | 13 • Anaconda, from Continuum Analytics • The GPL-licensed and Windows-centric Python(x,y) Being prepackaged, these options are likely to lag one or two releases behind the most up-to-date version of Cython.
The assign‐ ment b = a allows both a and b to access the same list object created on the first line in the preceding example. Modifying the list via a = 42 modifies the same list refer‐ enced by b, so the assertion holds true. The assignment a = 13 leaves b referring to the original list object, while a is now referring to a Python integer object. This reassignment to a changes a’s type, which is perfectly valid Python code. To statically type variables in Cython, we use the cdef keyword with a type and the variable name.
Cython - A guide for Python programmers by Kurt W. Smith