Differences between Cython and Pyrex

Warning

Both Cython and Pyrex are moving targets. It has come to the point that an explicit list of all the differences between the two projects would be laborious to list and track, but hopefully this high-level list gives an idea of the differences that are present. It should be noted that both projects make an effort at mutual compatibility, but Cython’s goal is to be as close to and complete as Python as reasonable.

Python 3 Support

Cython creates .c files that can be built and used with both Python 2.x and Python 3.x. In fact, compiling your module with Cython may very well be an easy way to port code to Python 3.

Cython also supports various syntax additions that came with Python 3.0 and later major Python releases. If they do not conflict with existing Python 2.x syntax or semantics, they are usually just accepted by the compiler. Everything else depends on the compiler directive language_level=3 (see compiler directives).

List/Set/Dict Comprehensions

Cython supports the different comprehensions defined by Python 3 for lists, sets and dicts:

[expr(x) for x in A]             # list
{expr(x) for x in A}             # set
{key(x) : value(x) for x in A}   # dict

Looping is optimized if A is a list, tuple or dict. You can use the for … from syntax, too, but it is generally preferred to use the usual for … in range(...) syntax with a C run variable (e.g. cdef int i).

Note

see Automatic range conversion

Note that Cython also supports set literals starting from Python 2.4.

Keyword-only arguments

Python functions can have keyword-only arguments listed after the * parameter and before the ** parameter if any, e.g.:

def f(a, b, *args, c, d = 42, e, **kwds):
    ...

Here c, d and e cannot be passed as position arguments and must be passed as keyword arguments. Furthermore, c and e are required keyword arguments, since they do not have a default value.

If the parameter name after the * is omitted, the function will not accept any extra positional arguments, e.g.:

def g(a, b, *, c, d):
    ...

takes exactly two positional parameters and has two required keyword parameters.

Conditional expressions “x if b else y”

Conditional expressions as described in https://www.python.org/dev/peps/pep-0308/:

X if C else Y

Only one of X and Y is evaluated (depending on the value of C).

cdef inline

Module level functions can now be declared inline, with the inline keyword passed on to the C compiler. These can be as fast as macros.:

cdef inline int something_fast(int a, int b):
    return a*a + b

Note that class-level cdef functions are handled via a virtual function table, so the compiler won’t be able to inline them in almost all cases.

Assignment on declaration (e.g. “cdef int spam = 5”)

In Pyrex, one must write:

cdef int i, j, k
i = 2
j = 5
k = 7

Now, with cython, one can write:

cdef int i = 2, j = 5, k = 7

The expression on the right hand side can be arbitrarily complicated, e.g.:

cdef int n = python_call(foo(x,y), a + b + c) - 32

‘by’ expression in for loop (e.g. “for i from 0 <= i < 10 by 2”)

for i from 0 <= i < 10 by 2:
    print i

yields:

0
2
4
6
8

Note

Usage of this syntax is discouraged as it is redundant with the normal Python for loop. See Automatic range conversion.

Boolean int type (e.g. it acts like a c int, but coerces to/from python as a boolean)

In C, ints are used for truth values. In python, any object can be used as a truth value (using the __nonzero__() method), but the canonical choices are the two boolean objects True and False. The bint (for “boolean int”) type is compiled to a C int, but coerces to and from Python as booleans. The return type of comparisons and several builtins is a bint as well. This reduces the need for wrapping things in bool(). For example, one can write:

def is_equal(x):
    return x == y

which would return 1 or 0 in Pyrex, but returns True or False in Cython. One can declare variables and return values for functions to be of the bint type. For example:

cdef int i = x
cdef bint b = x

The first conversion would happen via x.__int__() whereas the second would happen via x.__bool__() (a.k.a. __nonzero__()), with appropriate optimisations for known builtin types.

Executable class bodies

Including a working classmethod():

cdef class Blah:
    def some_method(self):
        print self
    some_method = classmethod(some_method)
    a = 2*3
    print "hi", a

cpdef functions

Cython adds a third function type on top of the usual def and cdef. If a function is declared cpdef it can be called from and overridden by both extension and normal python subclasses. You can essentially think of a cpdef method as a cdef method + some extras. (That’s how it’s implemented at least.) First, it creates a def method that does nothing but call the underlying cdef method (and does argument unpacking/coercion if needed). At the top of the cdef method a little bit of code is added to see if it’s overridden, similar to the following pseudocode:

if hasattr(type(self), '__dict__'):
    foo = self.foo
    if foo is not wrapper_foo:
        return foo(args)
[cdef method body]

To detect whether or not a type has a dictionary, it just checks the tp_dictoffset slot, which is NULL (by default) for extension types, but non- null for instance classes. If the dictionary exists, it does a single attribute lookup and can tell (by comparing pointers) whether or not the returned result is actually a new function. If, and only if, it is a new function, then the arguments packed into a tuple and the method called. This is all very fast. A flag is set so this lookup does not occur if one calls the method on the class directly, e.g.:

cdef class A:
    cpdef foo(self):
        pass

x = A()
x.foo()  # will check to see if overridden
A.foo(x) # will call A's implementation whether overridden or not

See Early Binding for Speed for explanation and usage tips.

Automatic range conversion

This will convert statements of the form for i in range(...) to for i from ... when i is any cdef’d integer type, and the direction (i.e. sign of step) can be determined.

Warning

This may change the semantics if the range causes assignment to i to overflow. Specifically, if this option is set, an error will be raised before the loop is entered, whereas without this option the loop will execute until a overflowing value is encountered. If this affects you, change Cython/Compiler/Options.py (eventually there will be a better way to set this).

More friendly type casting

In Pyrex, if one types <int>x where x is a Python object, one will get the memory address of x. Likewise, if one types <object>i where i is a C int, one will get an “object” at location i in memory. This leads to confusing results and segfaults.

In Cython <type>x will try and do a coercion (as would happen on assignment of x to a variable of type type) if exactly one of the types is a python object. It does not stop one from casting where there is no conversion (though it will emit a warning). If one really wants the address, cast to a void * first.

As in Pyrex <MyExtensionType>x will cast x to type MyExtensionType without any type checking. Cython supports the syntax <MyExtensionType?> to do the cast with type checking (i.e. it will throw an error if x is not a (subclass of) MyExtensionType.

Optional arguments in cdef/cpdef functions

Cython now supports optional arguments for cdef and cpdef functions.

The syntax in the .pyx file remains as in Python, but one declares such functions in the .pxd file by writing cdef foo(x=*). The number of arguments may increase on subclassing, but the argument types and order must remain the same. There is a slight performance penalty in some cases when a cdef/cpdef function without any optional is overridden with one that does have default argument values.

For example, one can have the .pxd file:

cdef class A:
    cdef foo(self)

cdef class B(A):
    cdef foo(self, x=*)

cdef class C(B):
    cpdef foo(self, x=*, int k=*)

with corresponding .pyx file:

from __future__ import print_function

cdef class A:
    cdef foo(self):
        print("A")

cdef class B(A):
    cdef foo(self, x=None):
        print("B", x)

cdef class C(B):
    cpdef foo(self, x=True, int k=3):
        print("C", x, k)

Note

this also demonstrates how cpdef functions can override cdef functions.

Function pointers in structs

Functions declared in struct are automatically converted to function pointers for convenience.

C++ Exception handling

cdef functions can now be declared as:

cdef int foo(...) except +
cdef int foo(...) except +TypeError
cdef int foo(...) except +python_error_raising_function

in which case a Python exception will be raised when a C++ error is caught. See Using C++ in Cython for more details.

Synonyms

cdef import from means the same thing as cdef extern from

Source code encoding

Cython supports PEP 3120 and PEP 263, i.e. you can start your Cython source file with an encoding comment and generally write your source code in UTF-8. This impacts the encoding of byte strings and the conversion of unicode string literals like u'abcd' to unicode objects.

Automatic typecheck

Rather than introducing a new keyword typecheck as explained in the Pyrex docs, Cython emits a (non-spoofable and faster) typecheck whenever isinstance() is used with an extension type as the second parameter.

From __future__ directives

Cython supports several from __future__ import ... directives, namely absolute_import, unicode_literals, print_function and division.

With statements are always enabled.

Pure Python mode

Cython has support for compiling .py files, and accepting type annotations using decorators and other valid Python syntax. This allows the same source to be interpreted as straight Python, or compiled for optimized results. See Pure Python Mode for more details.