Pure Python Mode

In some cases, it’s desirable to speed up Python code without losing the ability to run it with the Python interpreter. While pure Python scripts can be compiled with Cython, it usually results only in a speed gain of about 20%-50%.

To go beyond that, Cython provides language constructs to add static typing and cythonic functionalities to a Python module to make it run much faster when compiled, while still allowing it to be interpreted. This is accomplished via an augmenting .pxd file, via Python type PEP-484 type annotations (following PEP 484 and PEP 526), and/or via special functions and decorators available after importing the magic cython module. All three ways can be combined at need, although projects would commonly decide on a specific way to keep the static type information easy to manage.

Although it is not typically recommended over writing straight Cython code in a .pyx file, there are legitimate reasons to do this - easier testing and debugging, collaboration with pure Python developers, etc. In pure mode, you are more or less restricted to code that can be expressed (or at least emulated) in Python, plus static type declarations. Anything beyond that can only be done in .pyx files with extended language syntax, because it depends on features of the Cython compiler.

Augmenting .pxd

Using an augmenting .pxd allows to let the original .py file completely untouched. On the other hand, one needs to maintain both the .pxd and the .py to keep them in sync.

While declarations in a .pyx file must correspond exactly with those of a .pxd file with the same name (and any contradiction results in a compile time error, see pxd files), the untyped definitions in a .py file can be overridden and augmented with static types by the more specific ones present in a .pxd.

If a .pxd file is found with the same name as the .py file being compiled, it will be searched for cdef classes and cdef/cpdef functions and methods. The compiler will then convert the corresponding classes/functions/methods in the .py file to be of the declared type. Thus if one has a file A.py:

def myfunction(x, y=2):
    a = x - y
    return a + x * y

def _helper(a):
    return a + 1

class A:
    def __init__(self, b=0):
        self.a = 3
        self.b = b

    def foo(self, x):
        print(x + _helper(1.0))

and adds A.pxd:

cpdef int myfunction(int x, int y=*)
cdef double _helper(double a)

cdef class A:
    cdef public int a, b
    cpdef foo(self, double x)

then Cython will compile the A.py as if it had been written as follows:

cpdef int myfunction(int x, int y=2):
    a = x - y
    return a + x * y

cdef double _helper(double a):
    return a + 1

cdef class A:
    cdef public int a, b
    def __init__(self, b=0):
        self.a = 3
        self.b = b

    cpdef foo(self, double x):
        print(x + _helper(1.0))

Notice how in order to provide the Python wrappers to the definitions in the .pxd, that is, to be accessible from Python,

  • Python visible function signatures must be declared as cpdef (with default arguments replaced by a * to avoid repetition):

    cpdef int myfunction(int x, int y=*)
  • C function signatures of internal functions can be declared as cdef:

    cdef double _helper(double a)
  • cdef classes (extension types) are declared as cdef class;

  • cdef class attributes must be declared as cdef public if read/write Python access is needed, cdef readonly for read-only Python access, or plain cdef for internal C level attributes;

  • cdef class methods must be declared as cpdef for Python visible methods or cdef for internal C methods.

In the example above, the type of the local variable a in myfunction() is not fixed and will thus be a Python object. To statically type it, one can use Cython’s @cython.locals decorator (see Magic Attributes, and Magic Attributes within the .pxd).

Normal Python (def) functions cannot be declared in .pxd files. It is therefore currently impossible to override the types of plain Python functions in .pxd files, e.g. to override types of their local variables. In most cases, declaring them as cpdef will work as expected.

Magic Attributes

Special decorators are available from the magic cython module that can be used to add static typing within the Python file, while being ignored by the interpreter.

This option adds the cython module dependency to the original code, but does not require to maintain a supplementary .pxd file. Cython provides a fake version of this module as Cython.Shadow, which is available as cython.py when Cython is installed, but can be copied to be used by other modules when Cython is not installed.

“Compiled” switch

  • compiled is a special variable which is set to True when the compiler runs, and False in the interpreter. Thus, the code

    import cython
    if cython.compiled:
        print("Yep, I'm compiled.")
        print("Just a lowly interpreted script.")

    will behave differently depending on whether or not the code is executed as a compiled extension (.so/.pyd) module or a plain .py file.

Static typing

  • cython.declare declares a typed variable in the current scope, which can be used in place of the cdef type var [= value] construct. This has two forms, the first as an assignment (useful as it creates a declaration in interpreted mode as well):

    import cython
    x = cython.declare(cython.int)              # cdef int x
    y = cython.declare(cython.double, 0.57721)  # cdef double y = 0.57721

    and the second mode as a simple function call:

    import cython
    cython.declare(x=cython.int, y=cython.double)  # cdef int x; cdef double y

    It can also be used to define extension type private, readonly and public attributes:

    import cython
    class A:
        cython.declare(a=cython.int, b=cython.int)
        c = cython.declare(cython.int, visibility='public')
        d = cython.declare(cython.int)  # private by default.
        e = cython.declare(cython.int, visibility='readonly')
        def __init__(self, a, b, c, d=5, e=3):
            self.a = a
            self.b = b
            self.c = c
            self.d = d
            self.e = e
  • @cython.locals is a decorator that is used to specify the types of local variables in the function body (including the arguments):

    import cython
    @cython.locals(a=cython.long, b=cython.long, n=cython.longlong)
    def foo(a, b, x, y):
        n = a * b
        # ...
  • @cython.returns(<type>) specifies the function’s return type.

  • @cython.exceptval(value=None, *, check=False) specifies the function’s exception return value and exception check semantics as follows:

    @exceptval(-1)               # cdef int func() except -1:
    @exceptval(-1, check=False)  # cdef int func() except -1:
    @exceptval(check=True)       # cdef int func() except *:
    @exceptval(-1, check=True)   # cdef int func() except? -1:
    @exceptval(check=False)      # no exception checking/propagation

    If exception propagation is disabled, any Python exceptions that are raised inside of the function will be printed and ignored.

C types

There are numerous types built into the Cython module. It provides all the standard C types, namely char, short, int, long, longlong as well as their unsigned versions uchar, ushort, uint, ulong, ulonglong. The special bint type is used for C boolean values and Py_ssize_t for (signed) sizes of Python containers.

For each type, there are pointer types p_int, pp_int, etc., up to three levels deep in interpreted mode, and infinitely deep in compiled mode. Further pointer types can be constructed with cython.pointer(cython.int), and arrays as cython.int[10]. A limited attempt is made to emulate these more complex types, but only so much can be done from the Python language.

The Python types int, long and bool are interpreted as C int, long and bint respectively. Also, the Python builtin types list, dict, tuple, etc. may be used, as well as any user defined types.

Typed C-tuples can be declared as a tuple of C types.

Extension types and cdef functions

  • The class decorator @cython.cclass creates a cdef class.

  • The function/method decorator @cython.cfunc creates a cdef function.

  • @cython.ccall creates a cpdef function, i.e. one that Cython code can call at the C level.

  • @cython.locals declares local variables (see above). It can also be used to declare types for arguments, i.e. the local variables that are used in the signature.

  • @cython.inline is the equivalent of the C inline modifier.

  • @cython.final terminates the inheritance chain by preventing a type from being used as a base class, or a method from being overridden in subtypes. This enables certain optimisations such as inlined method calls.

Here is an example of a cdef function:

@cython.locals(a=cython.int, b=cython.int)
def c_compare(a,b):
    return a == b

Managing the Global Interpreter Lock

  • cython.nogil can be used as a context manager or as a decorator to replace the nogil keyword:

    with cython.nogil:
        # code block with the GIL released
    def func_released_gil() -> cython.int:
        # function that can be run with the GIL released

    Note that the two uses differ: the context manager releases the GIL while the decorator marks that a function can be run without the GIL. See <cython_and_gil> for more details.

  • cython.gil can be used as a context manager to replace the gil keyword:

    with cython.gil:
        # code block with the GIL acquired


    Cython currently does not support the @cython.with_gil decorator.

Both directives accept an optional boolean parameter for conditionally releasing or acquiring the GIL. The condition must be constant (at compile time):

with cython.nogil(False):
    # code block with the GIL not released

def func_released_gil() -> cython.int:
    # function with the GIL released

with cython.gil(False):
    # code block with the GIL not acquired

with cython.gil(True):
    # code block with the GIL acquired

A common use case for conditionally acquiring and releasing the GIL are fused types that allow different GIL handling depending on the specific type (see Conditional Acquiring / Releasing the GIL).


The special cython.cimports package name gives access to cimports in code that uses Python syntax. Note that this does not mean that C libraries become available to Python code. It only means that you can tell Cython what cimports you want to use, without requiring special syntax. Running such code in plain Python will fail.

from cython.cimports.libc import math

def use_libc_math():
    return math.ceil(5.5)

Since such code must necessarily refer to the non-existing cython.cimports ‘package’, the plain cimport form cimport cython.cimports... is not available. You must use the form from cython.cimports....

Further Cython functions and declarations

  • address is used in place of the & operator:

    cython.declare(x=cython.int, x_ptr=cython.p_int)
    x_ptr = cython.address(x)
  • sizeof emulates the sizeof operator. It can take both types and expressions.

  • typeof returns a string representation of the argument’s type for debugging purposes. It can take expressions.

  • struct can be used to create struct types.:

    MyStruct = cython.struct(x=cython.int, y=cython.int, data=cython.double)
    a = cython.declare(MyStruct)

    is equivalent to the code:

    cdef struct MyStruct:
        int x
        int y
        double data
    cdef MyStruct a
  • union creates union types with exactly the same syntax as struct.

  • typedef defines a type under a given name:

    T = cython.typedef(cython.p_int)   # ctypedef int* T
  • cast will (unsafely) reinterpret an expression type. cython.cast(T, t) is equivalent to <T>t. The first attribute must be a type, the second is the expression to cast. Specifying the optional keyword argument typecheck=True has the semantics of <T?>t.

    t1 = cython.cast(T, t)
    t2 = cython.cast(T, t, typecheck=True)
  • fused_type creates a new type definition that refers to the multiple types. The following example declares a new type called my_fused_type which can be either an int or a double.:

    my_fused_type = cython.fused_type(cython.int, cython.float)

Magic Attributes within the .pxd

The special cython module can also be imported and used within the augmenting .pxd file. For example, the following Python file dostuff.py:

def dostuff(n):
    t = 0
    for i in range(n):
        t += i
    return t

can be augmented with the following .pxd file dostuff.pxd:

import cython

@cython.locals(t=cython.int, i=cython.int)
cpdef int dostuff(int n)

The cython.declare() function can be used to specify types for global variables in the augmenting .pxd file.

PEP-484 type annotations

Python type hints can be used to declare argument types, as shown in the following example:

import cython

def func(foo: dict, bar: cython.int) -> tuple:
    foo["hello world"] = 3 + bar
    return foo, 5

Note the use of cython.int rather than int - Cython does not translate an int annotation to a C integer by default since the behaviour can be quite different with respect to overflow and division.

Annotations on global variables are currently ignored. This is because we expect annotation-typed code to be in majority written for Python, and global type annotations would turn the Python variable into an internal C variable, thus removing it from the module dict. To declare global variables as typed C variables, use @cython.declare().

Annotations can be combined with the @cython.exceptval() decorator for non-Python return types:

import cython

def func(x: cython.int) -> cython.int:
    if x < 0:
        raise ValueError("need integer >= 0")
    return x + 1

Note that the default exception handling behaviour when returning C numeric types is to check for -1, and if that was returned, check Python’s error indicator for an exception. This means, if no @exceptval decorator is provided, and the return type is a numeric type, then the default with type annotations is @exceptval(-1, check=True), in order to make sure that exceptions are correctly and efficiently reported to the caller. Exception propagation can be disabled explicitly with @exceptval(check=False), in which case any Python exceptions raised inside of the function will be printed and ignored.

Since version 0.27, Cython also supports the variable annotations defined in PEP 526. This allows to declare types of variables in a Python 3.6 compatible way as follows:

import cython

def func():
    # Cython types are evaluated as for cdef declarations
    x: cython.int               # cdef int x
    y: cython.double = 0.57721  # cdef double y = 0.57721
    z: cython.float = 0.57721   # cdef float z  = 0.57721

    # Python types shadow Cython types for compatibility reasons
    a: float = 0.54321          # cdef double a = 0.54321
    b: int = 5                  # cdef object b = 5
    c: long = 6                 # cdef object c = 6

class A:
    a: cython.int
    b: cython.int

    def __init__(self, b=0):
        self.a = 3
        self.b = b

There is currently no way to express the visibility of object attributes.

Disabling annotations

To avoid conflicts with other kinds of annotation usages, Cython’s use of annotations to specify types can be disabled with the annotation_typing compiler directive. From Cython 3 you can use this as a decorator or a with statement, as shown in the following example:

import cython

def function_without_typing(a: int, b: int) -> int:
    """Cython is ignoring annotations in this function"""
    c: int = a + b
    return c * a

class NotAnnotatedClass:
    """Cython is ignoring annotatons in this class except annotated_method"""
    d: dict

    def __init__(self, dictionary: dict):
        self.d = dictionary

    def annotated_method(self, key: str, a: cython.int, b: cython.int):
        prefixed_key: str = 'prefix_' + key
        self.d[prefixed_key] = a + b

def annotated_function(a: cython.int, b: cython.int):
    s: cython.int = a + b
    with cython.annotation_typing(False):
        # Cython is ignoring annotations within this code block
        c: list = []
    return c

typing Module

Support for the full range of annotations described by PEP-484 is not yet complete. Cython 3 currently understands the following features from the typing module:

  • Optional[tp], which is interpreted as tp or None;

  • typed containers such as List[str], which is interpreted as list. The hint that the elements are of type str is currently ignored;

  • Tuple[...], which is converted into a Cython C-tuple where possible and a regular Python tuple otherwise.

  • ClassVar[...], which is understood in the context of cdef class or @cython.cclass.

Some of the unsupported features are likely to remain unsupported since these type hints are not relevant for the compilation to efficient C code. In other cases, however, where the generated C code could benefit from these type hints but does not currently, help is welcome to improve the type analysis in Cython.

Reference table

The following reference table documents how type annotations are currently interpreted. Cython 0.29 behaviour is only shown where it differs from Cython 3.0 behaviour. The current limitations will likely be lifted at some point.

Annotation typing rules


Cython 0.29

Cython 3.0


Any Python object

Exact Python int (language_level=3 only)


C double

Builtin type e.g. dict, list

Exact type (no subclasses), not None

Extension type defined in Cython

Specified type or a subclasses, not None

cython.int, cython.long, etc.

Equivalent C numeric type


Not supported

Specified type (which must be a Python object), allows None

typing.List[any_type] (and similar)

Not supported

Exact list, with the element type ignored currently


Not supported

Python-object class variable (when used in a class definition)

Tips and Tricks

Calling C functions

Normally, it isn’t possible to call C functions in pure Python mode as there is no general way to support it in normal (uncompiled) Python. However, in cases where an equivalent Python function exists, this can be achieved by combining C function coercion with a conditional import as follows:

# mymodule.pxd

# declare a C function as "cpdef" to export it to the module
cdef extern from "math.h":
    cpdef double sin(double x)
# mymodule.py

import cython

# override with Python import if not in compiled code
if not cython.compiled:
    from math import sin

# calls sin() from math.h when compiled with Cython and math.sin() in Python

Note that the “sin” function will show up in the module namespace of “mymodule” here (i.e. there will be a mymodule.sin() function). You can mark it as an internal name according to Python conventions by renaming it to “_sin” in the .pxd file as follows:

cdef extern from "math.h":
    cpdef double _sin "sin" (double x)

You would then also change the Python import to from math import sin as _sin to make the names match again.

Using C arrays for fixed size lists

C arrays can automatically coerce to Python lists or tuples. This can be exploited to replace fixed size Python lists in Python code by C arrays when compiled. An example:

import cython

@cython.locals(counts=cython.int[10], digit=cython.int)
def count_digits(digits):
    >>> digits = '01112222333334445667788899'
    >>> count_digits(map(int, digits))
    [1, 3, 4, 5, 3, 1, 2, 2, 3, 2]
    counts = [0] * 10
    for digit in digits:
        assert 0 <= digit <= 9
        counts[digit] += 1
    return counts

In normal Python, this will use a Python list to collect the counts, whereas Cython will generate C code that uses a C array of C ints.