Chapter 10. Cython, NumPy, and Typed Memoryviews
All problems in computer science can be solved by another level of indirection, except, of course, for the problem of too many indirections.
â D. Wheeler
Two great qualities of Cython are its breadth and maturity: it compiles nearly all Python code (and whatever it cannot handle is typically straightforward to address); it brings the power and control of Câs type system to Python; and it integrates with external C and C++ code with ease. The task for this chapter is to round out Cythonâs capabilities and cover Cythonâs array featuresâwhich include support for NumPy arraysâin depth.
We have seen how well Cython supports built-in containers like list
,
tuple
, dict
, and set
. These container objects are very easy to use,
can contain any type of Python object, and are highly optimized for object
lookup, assignment, and retrieval. The way the list
type implements
storage and retrieval is very different from dict
, but from an
implementation perspective, containers all have one thing in common: they all store
references to Python objects. If we have a Python list of one million
int
s, every element in that list, at the C level, is a pointer to a
boxed-up PyObject
. Converting such a list to a C array of C int
s is
expensive, requiring us to iterate through the list and convert each PyObject
to a C int
, all the while doing proper error checking.
For homogeneous containers (e.g., a list
containing nothing but float
s), we can do much ...
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