Language Basics

Note

This page uses two different syntax variants:

  • Cython specific cdef syntax, which was designed to make type declarations concise and easily readable from a C/C++ perspective.

  • Pure Python syntax which allows static Cython type declarations in pure Python code, following PEP-484 type hints and PEP 526 variable annotations.

    To make use of C data types in Python syntax, you need to import the special cython module in the Python module that you want to compile, e.g.

    import cython
    

Declaring Data Types

As a dynamic language, Python encourages a programming style of considering classes and objects in terms of their methods and attributes, more than where they fit into the class hierarchy.

This can make Python a very relaxed and comfortable language for rapid development, but with a price - the ‘red tape’ of managing data types is dumped onto the interpreter. At run time, the interpreter does a lot of work searching namespaces, fetching attributes and parsing argument and keyword tuples. This run-time ‘late binding’ is a major cause of Python’s relative slowness compared to ‘early binding’ languages such as C++.

However with Cython it is possible to gain significant speed-ups through the use of ‘early binding’ programming techniques.

Note

Typing is not a necessity

Providing static typing to parameters and variables is convenience to speed up your code, but it is not a necessity. Optimize where and when needed. In fact, typing can slow down your code in the case where the typing does not allow optimizations but where Cython still needs to check that the type of some object matches the declared type.

C variable and type definitions

C variables can be declared by

  • using the Cython specific cdef statement,

  • using PEP-484/526 type annotations with C data types or

  • using the function cython.declare().

The cdef statement and declare() can define function-local and module-level variables as well as attributes in classes, but type annotations only affect local variables and attributes and are ignored at the module level. This is because type annotations are not Cython specific, so Cython keeps the variables in the module dict (as Python values) instead of making them module internal C variables. Use declare() in Python code to explicitly define global C variables.

a_global_variable = declare(cython.int)

def func():
    i: cython.int
    j: cython.int
    k: cython.int
    f: cython.float
    g: cython.int[42]
    h: cython.p_float

Moreover, C struct, union and enum are supported:

Grail = cython.struct(
    age=cython.int,
    volume=cython.float)

Food = cython.union(
    spam=cython.p_char,
    eggs=cython.p_float)

Note

Currently, Pure Python mode does not support enums. (GitHub issue #4252)

Note

There is also support for giving names to types using the ctypedef statement or the cython.typedef() function, e.g.

ULong = cython.typedef(cython.ulong)

IntPtr = cython.typedef(cython.p_int)

You can create a C function by declaring it with cdef or by decorating a Python function with @cfunc:

@cython.cfunc
def eggs(l: cython.ulong, f: cython.float) -> cython.int:
    ...

You can read more about them in Python functions vs. C functions.

Classes can be declared as Extension Types. Those will have a behavior very close to python classes, but are faster because they use a struct internally to store attributes. They are declared with the cdef keyword or the @cclass class decorator.

Here is a simple example:

from __future__ import print_function

@cython.cclass
class Shrubbery:
    width: cython.int
    height: cython.int

    def __init__(self, w, h):
        self.width = w
        self.height = h

    def describe(self):
        print("This shrubbery is", self.width,
              "by", self.height, "cubits.")

You can read more about them in Extension Types.

Types

The Cython language uses the normal C syntax for C types, including pointers. It provides all the standard C types, namely char, short, int, long, long long as well as their unsigned versions, e.g. unsigned int (cython.uint in Python code). The special bint type is used for C boolean values (int with 0/non-0 values for False/True) and Py_ssize_t for (signed) sizes of Python containers.

Pointer types are constructed as in C when using Cython syntax, by appending a * to the base type they point to, e.g. int** for a pointer to a pointer to a C int. In Pure python mode, simple pointer types use a naming scheme with “p”s instead, separated from the type name with an underscore, e.g. cython.pp_int for a pointer to a pointer to a C int. Further pointer types can be constructed with the cython.pointer() function, e.g. cython.pointer(cython.int).

Arrays use the normal C array syntax, e.g. int[10], and the size must be known at compile time for stack allocated arrays. Cython doesn’t support variable length arrays from C99. Note that Cython uses array access for pointer dereferencing, as *x is not valid Python syntax, whereas x[0] is.

Also, the Python types list, dict, tuple, etc. may be used for static typing, as well as any user defined Extension Types. For example

def main():
    foo: list = []

This requires an exact match of the class, it does not allow subclasses. This allows Cython to optimize code by accessing internals of the builtin class, which is the main reason for declaring builtin types in the first place.

For declared builtin types, Cython uses internally a C variable of type PyObject*.

Note

The Python types int, long, and float are not available for static typing in .pyx files and instead interpreted as C int, long, and float respectively, as statically typing variables with these Python types has zero advantages. On the other hand, annotating in Pure Python with int, long, and float Python types will be interpreted as Python object types.

Cython provides an accelerated and typed equivalent of a Python tuple, the ctuple. A ctuple is assembled from any valid C types. For example

def main():
    bar: (cython.double, cython.int)

They compile down to C-structures and can be used as efficient alternatives to Python tuples.

While these C types can be vastly faster, they have C semantics. Specifically, the integer types overflow and the C float type only has 32 bits of precision (as opposed to the 64-bit C double which Python floats wrap and is typically what one wants). If you want to use these numeric Python types simply omit the type declaration and let them be objects.

It is also possible to declare Extension Types (declared with cdef class or the @cclass decorator). This does allow subclasses. This typing is mostly used to access cdef/@cfunc methods and attributes of the extension type. The C code uses a variable which is a pointer to a structure of the specific type, something like struct MyExtensionTypeObject*.

Grouping multiple C declarations

If you have a series of declarations that all begin with cdef, you can group them into a cdef block like this:

Note

This is supported only in Cython’s cdef syntax.

from __future__ import print_function

cdef:
    struct Spam:
        int tons

    int i
    float a
    Spam *p

    void f(Spam *s) except *:
        print(s.tons, "Tons of spam")

Python functions vs. C functions

There are two kinds of function definition in Cython:

Python functions are defined using the def statement, as in Python. They take Python objects as parameters and return Python objects.

C functions are defined using the cdef statement in Cython syntax or with the @cfunc decorator. They take either Python objects or C values as parameters, and can return either Python objects or C values.

Within a Cython module, Python functions and C functions can call each other freely, but only Python functions can be called from outside the module by interpreted Python code. So, any functions that you want to “export” from your Cython module must be declared as Python functions using def. There is also a hybrid function, declared with cpdef in .pyx files or with the @ccall decorator. These functions can be called from anywhere, but use the faster C calling convention when being called from other Cython code. They can also be overridden by a Python method on a subclass or an instance attribute, even when called from Cython. If this happens, most performance gains are of course lost and even if it does not, there is a tiny overhead in calling such a method from Cython compared to calling a C method.

Parameters of either type of function can be declared to have C data types, using normal C declaration syntax. For example,

def spam(i: cython.int, s: cython.p_char):
    ...

@cython.cfunc
def eggs(l: cython.ulong, f: cython.float) -> cython.int:
    ...

ctuples may also be used

@cython.cfunc
def chips(t: (cython.long, cython.long, cython.double)) -> (cython.int, cython.float):
    ...

When a parameter of a Python function is declared to have a C data type, it is passed in as a Python object and automatically converted to a C value, if possible. In other words, the definition of spam above is equivalent to writing

def spam(python_i, python_s):
    i: cython.int = python_i
    s: cython.p_char = python_s
    ...

Automatic conversion is currently only possible for numeric types, string types and structs (composed recursively of any of these types); attempting to use any other type for the parameter of a Python function will result in a compile-time error. Care must be taken with strings to ensure a reference if the pointer is to be used after the call. Structs can be obtained from Python mappings, and again care must be taken with string attributes if they are to be used after the function returns.

C functions, on the other hand, can have parameters of any type, since they’re passed in directly using a normal C function call.

C Functions declared using cdef or the @cfunc decorator with a Python object return type, like Python functions, will return a None value when execution leaves the function body without an explicit return value. This is in contrast to C/C++, which leaves the return value undefined. In the case of non-Python object return types, the equivalent of zero is returned, for example, 0 for int, False for bint and NULL for pointer types.

A more complete comparison of the pros and cons of these different method types can be found at Early Binding for Speed.

Python objects as parameters and return values

If no type is specified for a parameter or return value, it is assumed to be a Python object. (Note that this is different from the C convention, where it would default to int.) For example, the following defines a C function that takes two Python objects as parameters and returns a Python object

@cython.cfunc
def spamobjs(x, y):
    ...

Reference counting for these objects is performed automatically according to the standard Python/C API rules (i.e. borrowed references are taken as parameters and a new reference is returned).

Warning

This only applies to Cython code. Other Python packages which are implemented in C like NumPy may not follow these conventions.

The name object can also be used to explicitly declare something as a Python object. This can be useful if the name being declared would otherwise be taken as the name of a type, for example,

@cython.cfunc
def ftang(int: object):
    ...

declares a parameter called int which is a Python object. You can also use object as the explicit return type of a function, e.g.:

cdef object ftang(object int):
    ...

Note

Currently, Cython contains a bug not allowing object as return annotation in pure Python from a C function. (GitHub issue #2529)

In the interests of clarity, it is probably a good idea to always be explicit about object parameters in C functions.

To create a borrowed reference, specify the parameter type as PyObject*. Cython won’t perform automatic Py_INCREF, or Py_DECREF, e.g.:

from __future__ import print_function

from cython.cimports.cpython.ref import PyObject

import sys

python_dict = {"abc": 123}
python_dict_refcount = sys.getrefcount(python_dict)

@cython.cfunc
def owned_reference(obj: object):
    refcount = sys.getrefcount(python_dict)
    print('Inside owned_reference: {refcount}'.format(refcount=refcount))

@cython.cfunc
def borrowed_reference(obj: cython.pointer(PyObject)):
    refcount = obj.ob_refcnt
    print('Inside borrowed_reference: {refcount}'.format(refcount=refcount))

def main():
    print('Initial refcount: {refcount}'.format(refcount=python_dict_refcount))
    owned_reference(python_dict)
    borrowed_reference(cython.cast(cython.pointer(PyObject), python_dict))

will display:

Initial refcount: 2
Inside owned_reference: 3
Inside borrowed_reference: 2

Optional Arguments

Unlike C, it is possible to use optional arguments in C and cpdef/@ccall functions. There are differences though whether you declare them in a .pyx/.py file or the corresponding .pxd file.

To avoid repetition (and potential future inconsistencies), default argument values are not visible in the declaration (in .pxd files) but only in the implementation (in .pyx files).

When in a .pyx/.py file, the signature is the same as it is in Python itself:

optional_subclassing.py
from __future__ import print_function

@cython.cclass
class A:
    @cython.cfunc
    def foo(self):
        print("A")

@cython.cclass
class B(A):
    @cython.cfunc
    def foo(self, x=None):
        print("B", x)

@cython.cclass
class C(B):
    @cython.ccall
    def foo(self, x=True, k:cython.int = 3):
        print("C", x, k)

When in a .pxd file, the signature is different like this example: cdef foo(x=*). This is because the program calling the function just needs to know what signatures are possible in C, but doesn’t need to know the value of the default arguments.:

optional_subclassing.pxd
cdef class A:
    cdef foo(self)

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

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

Note

The number of arguments may increase when subclassing, but the arg types and order must be the same, as shown in the example above.

There may be a slight performance penalty when the optional arg is overridden with one that does not have default values.

Keyword-only Arguments

As in Python 3, def functions can have keyword-only arguments listed after a "*" parameter and before a "**" parameter if any:

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


# We cannot call f with less verbosity than this.
foo = f(4, "bar", c=68, e=1.0)

As shown above, the c, d and e arguments can not be passed as positional arguments and must be passed as keyword arguments. Furthermore, c and e are required keyword arguments since they do not have a default value.

A single "*" without argument name can be used to terminate the list of positional arguments:

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

# We cannot call g with less verbosity than this.
foo = g(4.0, "something", c=68, d="other")

Shown above, the signature takes exactly two positional parameters and has two required keyword parameters.

Function Pointers

Functions declared in a struct are automatically converted to function pointers.

For using error return values with function pointers, see the note at the bottom of Error return values.

Error return values

In Python (more specifically, in the CPython runtime), exceptions that occur inside of a function are signaled to the caller and propagated up the call stack through defined error return values. For functions that return a Python object (and thus, a pointer to such an object), the error return value is simply the NULL pointer, so any function returning a Python object has a well-defined error return value.

While this is always the case for C functions, functions defined as C functions or cpdef/@ccall functions can return arbitrary C types, which do not have such a well-defined error return value. Thus, if an exception is detected in such a function, a warning message is printed, the exception is ignored, and the function returns immediately without propagating the exception to its caller.

If you want such a C function to be able to propagate exceptions, you need to declare an exception return value for it as a contract with the caller. Here is an example

@cython.cfunc
@cython.exceptval(-1)
def spam() -> cython.int:
    ...

With this declaration, whenever an exception occurs inside spam, it will immediately return with the value -1. From the caller’s side, whenever a call to spam returns -1, the caller will assume that an exception has occurred and can now process or propagate it.

When you declare an exception value for a function, you should never explicitly or implicitly return that value. This includes empty return statements, without a return value, for which Cython inserts the default return value (e.g. 0 for C number types). In general, exception return values are best chosen from invalid or very unlikely return values of the function, such as a negative value for functions that return only non-negative results, or a very large value like INT_MAX for a function that “usually” only returns small results.

If all possible return values are legal and you can’t reserve one entirely for signalling errors, you can use an alternative form of exception value declaration

@cython.cfunc
@cython.exceptval(-1, check=True)
def spam() -> cython.int:
    ...

The keyword argument check=True indicates that the value -1 _may_ signal an error.

In this case, Cython generates a call to PyErr_Occurred() if the exception value is returned, to make sure it really received an exception and not just a normal result.

There is also a third form of exception value declaration

@cython.cfunc
@cython.exceptval(check=True)
def spam() -> cython.int:
    ...

This form causes Cython to generate a call to PyErr_Occurred() after every call to spam, regardless of what value it returns. If you have a function returning void that needs to propagate errors, you will have to use this form, since there isn’t any error return value to test. Otherwise, an explicit error return value allows the C compiler to generate more efficient code and is thus generally preferable.

An external C++ function that may raise an exception can be declared with:

cdef int spam() except +

Note

These declarations are not used in Python code, only in .pxd and .pyx files.

See Using C++ in Cython for more details.

Some things to note:

  • Exception values can only be declared for functions returning a C integer, enum, float or pointer type, and the value must be a constant expression. Functions that return void, or a struct/union by value, can only use the except * or exceptval(check=True) form.

  • The exception value specification is part of the signature of the function. If you’re passing a pointer to a function as a parameter or assigning it to a variable, the declared type of the parameter or variable must have the same exception value specification (or lack thereof). Here is an example of a pointer-to-function declaration with an exception value:

    int (*grail)(int, char*) except -1
    

    Note

    Pointers to functions are currently not supported by pure Python mode. (GitHub issue #4279)

  • You don’t need to (and shouldn’t) declare exception values for functions which return Python objects. Remember that a function with no declared return type implicitly returns a Python object. (Exceptions on such functions are implicitly propagated by returning NULL.)

Checking return values of non-Cython functions

It’s important to understand that the except clause does not cause an error to be raised when the specified value is returned. For example, you can’t write something like:

cdef extern FILE *fopen(char *filename, char *mode) except NULL # WRONG!

and expect an exception to be automatically raised if a call to fopen() returns NULL. The except clause doesn’t work that way; its only purpose is for propagating Python exceptions that have already been raised, either by a Cython function or a C function that calls Python/C API routines. To get an exception from a non-Python-aware function such as fopen(), you will have to check the return value and raise it yourself, for example:

from cython.cimports.libc.stdio import FILE, fopen
from cython.cimports.libc.stdlib import malloc, free
from cython.cimports.cpython.exc import PyErr_SetFromErrnoWithFilenameObject

def open_file():
    p = fopen("spam.txt", "r")   # The type of "p" is "FILE*", as returned by fopen().

    if p is cython.NULL:
        PyErr_SetFromErrnoWithFilenameObject(OSError, "spam.txt")
    ...


def allocating_memory(number=10):
    # Note that the type of the variable "my_array" is automatically inferred from the assignment.
    my_array = cython.cast(p_double, malloc(number * cython.sizeof(double)))
    if not my_array:  # same as 'is NULL' above
        raise MemoryError()
    ...
    free(my_array)

Overriding in extension types

cpdef/@ccall methods can override C methods:

from __future__ import print_function

@cython.cclass
class A:
    @cython.cfunc
    def foo(self):
        print("A")

@cython.cclass
class B(A):
    @cython.cfunc
    def foo(self, x=None):
        print("B", x)

@cython.cclass
class C(B):
    @cython.ccall
    def foo(self, x=True, k:cython.int = 3):
        print("C", x, k)

When subclassing an extension type with a Python class, Python methods can override cpdef/@ccall methods but not plain C methods:

from __future__ import print_function

@cython.cclass
class A:
    @cython.cfunc
    def foo(self):
        print("A")

@cython.cclass
class B(A):
    @cython.ccall
    def foo(self):
        print("B")

class C(B):  # NOTE: no cclass decorator
    def foo(self):
        print("C")

If C above would be an extension type (cdef class), this would not work correctly. The Cython compiler will give a warning in that case.

Automatic type conversions

In most situations, automatic conversions will be performed for the basic numeric and string types when a Python object is used in a context requiring a C value, or vice versa. The following table summarises the conversion possibilities.

C types

From Python types

To Python types

[unsigned] char, [unsigned] short, int, long

int, long

int

unsigned int, unsigned long, [unsigned] long long

int, long

long

float, double, long double

int, long, float

float

char*

str/bytes

str/bytes 3

C array

iterable

list 5

struct, union

dict 4

3

The conversion is to/from str for Python 2.x, and bytes for Python 3.x.

4

The conversion from a C union type to a Python dict will add a value for each of the union fields. Cython 0.23 and later, however, will refuse to automatically convert a union with unsafe type combinations. An example is a union of an int and a char*, in which case the pointer value may or may not be a valid pointer.

5

Other than signed/unsigned char[]. The conversion will fail if the length of C array is not known at compile time, and when using a slice of a C array.

Caveats when using a Python string in a C context

You need to be careful when using a Python string in a context expecting a char*. In this situation, a pointer to the contents of the Python string is used, which is only valid as long as the Python string exists. So you need to make sure that a reference to the original Python string is held for as long as the C string is needed. If you can’t guarantee that the Python string will live long enough, you will need to copy the C string.

Cython detects and prevents some mistakes of this kind. For instance, if you attempt something like

def main():
    s: cython.p_char
    s = pystring1 + pystring2

then Cython will produce the error message Storing unsafe C derivative of temporary Python reference. The reason is that concatenating the two Python strings produces a new Python string object that is referenced only by a temporary internal variable that Cython generates. As soon as the statement has finished, the temporary variable will be decrefed and the Python string deallocated, leaving s dangling. Since this code could not possibly work, Cython refuses to compile it.

The solution is to assign the result of the concatenation to a Python variable, and then obtain the char* from that, i.e.

def main():
    s: cython.p_char
    p = pystring1 + pystring2
    s = p

It is then your responsibility to hold the reference p for as long as necessary.

Keep in mind that the rules used to detect such errors are only heuristics. Sometimes Cython will complain unnecessarily, and sometimes it will fail to detect a problem that exists. Ultimately, you need to understand the issue and be careful what you do.

Type Casting

The Cython language supports type casting in a similar way as C. Where C uses "(" and ")", Cython uses "<" and ">". In pure python mode, the cython.cast() function is used. For example:

def main():
    p: cython.p_char
    q: cython.p_float
    p = cython.cast(cython.p_char, q)

When casting a C value to a Python object type or vice versa, Cython will attempt a coercion. Simple examples are casts like cast(int, pyobj_value), which convert a Python number to a plain C int value, or the statement cast(bytes, charptr_value), which copies a C char* string into a new Python bytes object.

Note

Cython will not prevent a redundant cast, but emits a warning for it.

To get the address of some Python object, use a cast to a pointer type like cast(p_void, ...) or cast(pointer(PyObject), ...). You can also cast a C pointer back to a Python object reference with cast(object, ...), or to a more specific builtin or extension type (e.g. cast(MyExtType, ptr)). This will increase the reference count of the object by one, i.e. the cast returns an owned reference. Here is an example:

casting_python.pxd
cdef extern from *:
    ctypedef Py_ssize_t Py_intptr_t
casting_python.py
from cython.cimports.cpython.ref import PyObject

def main():

    python_string = "foo"

    # Note that the variables below are automatically inferred
    # as the correct pointer type that is assigned to them.
    # They do not need to be typed explicitly.

    ptr = cython.cast(cython.p_void, python_string)
    adress_in_c = cython.cast(Py_intptr_t, ptr)
    address_from_void = adress_in_c        # address_from_void is a python int

    ptr2 = cython.cast(cython.pointer(PyObject), python_string)
    address_in_c2 = cython.cast(Py_intptr_t, ptr2)
    address_from_PyObject = address_in_c2  # address_from_PyObject is a python int

    assert address_from_void == address_from_PyObject == id(python_string)

    print(cython.cast(object, ptr))                     # Prints "foo"
    print(cython.cast(object, ptr2))                    # prints "foo"

Casting with cast(object, ...) creates an owned reference. Cython will automatically perform a Py_INCREF and Py_DECREF operation. Casting to cast(pointer(PyObject), ...) creates a borrowed reference, leaving the refcount unchanged.

Checked Type Casts

A cast like <MyExtensionType>x or cast(MyExtensionType, x) will cast x to the class MyExtensionType without any checking at all.

To have a cast checked, use <MyExtensionType?>x in Cython syntax or cast(MyExtensionType, x, typecheck=True). In this case, Cython will apply a runtime check that raises a TypeError if x is not an instance of MyExtensionType. This tests for the exact class for builtin types, but allows subclasses for Extension Types.

Statements and expressions

Control structures and expressions follow Python syntax for the most part. When applied to Python objects, they have the same semantics as in Python (unless otherwise noted). Most of the Python operators can also be applied to C values, with the obvious semantics.

If Python objects and C values are mixed in an expression, conversions are performed automatically between Python objects and C numeric or string types.

Reference counts are maintained automatically for all Python objects, and all Python operations are automatically checked for errors, with appropriate action taken.

Differences between C and Cython expressions

There are some differences in syntax and semantics between C expressions and Cython expressions, particularly in the area of C constructs which have no direct equivalent in Python.

  • An integer literal is treated as a C constant, and will be truncated to whatever size your C compiler thinks appropriate. To get a Python integer (of arbitrary precision), cast immediately to an object (e.g. <object>100000000000000000000 or cast(object, 100000000000000000000)). The L, LL, and U suffixes have the same meaning in Cython syntax as in C.

  • There is no -> operator in Cython. Instead of p->x, use p.x

  • There is no unary * operator in Cython. Instead of *p, use p[0]

  • There is an & operator in Cython, with the same semantics as in C. In pure python mode, use the cython.address() function instead.

  • The null C pointer is called NULL, not 0. NULL is a reserved word in Cython and special object in pure python mode.

  • Type casts are written <type>value or cast(type, value), for example,

    def main():
        p: cython.p_char
        q: cython.p_float
    
        p = cython.cast(cython.p_char, q)
    

Scope rules

Cython determines whether a variable belongs to a local scope, the module scope, or the built-in scope completely statically. As with Python, assigning to a variable which is not otherwise declared implicitly declares it to be a variable residing in the scope where it is assigned. The type of the variable depends on type inference, except for the global module scope, where it is always a Python object.

Built-in Functions

Cython compiles calls to most built-in functions into direct calls to the corresponding Python/C API routines, making them particularly fast.

Only direct function calls using these names are optimised. If you do something else with one of these names that assumes it’s a Python object, such as assign it to a Python variable, and later call it, the call will be made as a Python function call.

Function and arguments

Return type

Python/C API Equivalent

abs(obj)

object, double, …

PyNumber_Absolute, fabs, fabsf, …

callable(obj)

bint

PyObject_Callable

delattr(obj, name)

None

PyObject_DelAttr

exec(code, [glob, [loc]])

object

dir(obj)

list

PyObject_Dir

divmod(a, b)

tuple

PyNumber_Divmod

getattr(obj, name, [default]) (Note 1)

object

PyObject_GetAttr

hasattr(obj, name)

bint

PyObject_HasAttr

hash(obj)

int / long

PyObject_Hash

intern(obj)

object

Py*_InternFromString

isinstance(obj, type)

bint

PyObject_IsInstance

issubclass(obj, type)

bint

PyObject_IsSubclass

iter(obj, [sentinel])

object

PyObject_GetIter

len(obj)

Py_ssize_t

PyObject_Length

pow(x, y, [z])

object

PyNumber_Power

reload(obj)

object

PyImport_ReloadModule

repr(obj)

object

PyObject_Repr

setattr(obj, name)

void

PyObject_SetAttr

Note 1: Pyrex originally provided a function getattr3(obj, name, default)() corresponding to the three-argument form of the Python builtin getattr(). Cython still supports this function, but the usage is deprecated in favour of the normal builtin, which Cython can optimise in both forms.

Operator Precedence

Keep in mind that there are some differences in operator precedence between Python and C, and that Cython uses the Python precedences, not the C ones.

Integer for-loops

Note

This syntax is supported only in Cython files. Use a normal for-in-range() loop instead.

Cython recognises the usual Python for-in-range integer loop pattern:

for i in range(n):
    ...

If i is declared as a cdef integer type, it will optimise this into a pure C loop. This restriction is required as otherwise the generated code wouldn’t be correct due to potential integer overflows on the target architecture. If you are worried that the loop is not being converted correctly, use the annotate feature of the cython commandline (-a) to easily see the generated C code. See Automatic range conversion

For backwards compatibility to Pyrex, Cython also supports a more verbose form of for-loop which you might find in legacy code:

for i from 0 <= i < n:
    ...

or:

for i from 0 <= i < n by s:
    ...

where s is some integer step size.

Note

This syntax is deprecated and should not be used in new code. Use the normal Python for-loop instead.

Some things to note about the for-from loop:

  • The target expression must be a plain variable name.

  • The name between the lower and upper bounds must be the same as the target name.

  • The direction of iteration is determined by the relations. If they are both from the set {<, <=} then it is upwards; if they are both from the set {>, >=} then it is downwards. (Any other combination is disallowed.)

Like other Python looping statements, break and continue may be used in the body, and the loop may have an else clause.

Cython file types

There are three file types in Cython:

  • The implementation files, carrying a .py or .pyx suffix.

  • The definition files, carrying a .pxd suffix.

  • The include files, carrying a .pxi suffix.

The implementation file

The implementation file, as the name suggest, contains the implementation of your functions, classes, extension types, etc. Nearly all the python syntax is supported in this file. Most of the time, a .py file can be renamed into a .pyx file without changing any code, and Cython will retain the python behavior.

It is possible for Cython to compile both .py and .pyx files. The name of the file isn’t important if one wants to use only the Python syntax, and Cython won’t change the generated code depending on the suffix used. Though, if one want to use the Cython syntax, using a .pyx file is necessary.

In addition to the Python syntax, the user can also leverage Cython syntax (such as cdef) to use C variables, can declare functions as cdef or cpdef and can import C definitions with cimport. Many other Cython features usable in implementation files can be found throughout this page and the rest of the Cython documentation.

There are some restrictions on the implementation part of some Extension Types if the corresponding definition file also defines that type.

Note

When a .pyx file is compiled, Cython first checks to see if a corresponding .pxd file exists and processes it first. It acts like a header file for a Cython .pyx file. You can put inside functions that will be used by other Cython modules. This allows different Cython modules to use functions and classes from each other without the Python overhead. To read more about what how to do that, you can see pxd files.

The definition file

A definition file is used to declare various things.

Any C declaration can be made, and it can be also a declaration of a C variable or function implemented in a C/C++ file. This can be done with cdef extern from. Sometimes, .pxd files are used as a translation of C/C++ header files into a syntax that Cython can understand. This allows then the C/C++ variable and functions to be used directly in implementation files with cimport. You can read more about it in Interfacing with External C Code and Using C++ in Cython.

It can also contain the definition part of an extension type and the declarations of functions for an external library.

It cannot contain the implementations of any C or Python functions, or any Python class definitions, or any executable statements. It is needed when one wants to access cdef attributes and methods, or to inherit from cdef classes defined in this module.

Note

You don’t need to (and shouldn’t) declare anything in a declaration file public in order to make it available to other Cython modules; its mere presence in a definition file does that. You only need a public declaration if you want to make something available to external C code.

The include statement and include files

Warning

Historically the include statement was used for sharing declarations. Use Sharing Declarations Between Cython Modules instead.

A Cython source file can include material from other files using the include statement, for example,:

include "spamstuff.pxi"

The contents of the named file are textually included at that point. The included file can contain any complete statements or declarations that are valid in the context where the include statement appears, including other include statements. The contents of the included file should begin at an indentation level of zero, and will be treated as though they were indented to the level of the include statement that is including the file. The include statement cannot, however, be used outside of the module scope, such as inside of functions or class bodies.

Note

There are other mechanisms available for splitting Cython code into separate parts that may be more appropriate in many cases. See Sharing Declarations Between Cython Modules.

Conditional Compilation

Some features are available for conditional compilation and compile-time constants within a Cython source file.

Note

This feature has very little use cases. Specifically, it is not a good way to adapt code to platform and environment. Use code generation or (preferably) C compile time adaptation for this. See, for example, Including verbatim C code.

Note

Cython currently does not support conditional compilation and compile-time definitions in Pure Python mode. As it stands, this is unlikely to change.

Compile-Time Definitions

A compile-time constant can be defined using the DEF statement:

DEF FavouriteFood = u"spam"
DEF ArraySize = 42
DEF OtherArraySize = 2 * ArraySize + 17

The right-hand side of the DEF must be a valid compile-time expression. Such expressions are made up of literal values and names defined using DEF statements, combined using any of the Python expression syntax.

The following compile-time names are predefined, corresponding to the values returned by os.uname().

UNAME_SYSNAME, UNAME_NODENAME, UNAME_RELEASE, UNAME_VERSION, UNAME_MACHINE

The following selection of builtin constants and functions are also available:

None, True, False, abs, all, any, ascii, bin, bool, bytearray, bytes, chr, cmp, complex, dict, divmod, enumerate, filter, float, format, frozenset, hash, hex, int, len, list, long, map, max, min, oct, ord, pow, range, reduce, repr, reversed, round, set, slice, sorted, str, sum, tuple, xrange, zip

Note that some of these builtins may not be available when compiling under Python 2.x or 3.x, or may behave differently in both.

A name defined using DEF can be used anywhere an identifier can appear, and it is replaced with its compile-time value as though it were written into the source at that point as a literal. For this to work, the compile-time expression must evaluate to a Python value of type int, long, float, bytes or unicode (str in Py3).

from __future__ import print_function

DEF FavouriteFood = u"spam"
DEF ArraySize = 42
DEF OtherArraySize = 2 * ArraySize + 17

cdef int a1[ArraySize]
cdef int a2[OtherArraySize]
print("I like", FavouriteFood)

Conditional Statements

The IF statement can be used to conditionally include or exclude sections of code at compile time. It works in a similar way to the #if preprocessor directive in C.:

IF UNAME_SYSNAME == "Windows":
    include "icky_definitions.pxi"
ELIF UNAME_SYSNAME == "Darwin":
    include "nice_definitions.pxi"
ELIF UNAME_SYSNAME == "Linux":
    include "penguin_definitions.pxi"
ELSE:
    include "other_definitions.pxi"

The ELIF and ELSE clauses are optional. An IF statement can appear anywhere that a normal statement or declaration can appear, and it can contain any statements or declarations that would be valid in that context, including DEF statements and other IF statements.

The expressions in the IF and ELIF clauses must be valid compile-time expressions as for the DEF statement, although they can evaluate to any Python value, and the truth of the result is determined in the usual Python way.