Language Basics

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

The cdef statement is used to declare C variables, either local or module-level:

cdef int i, j, k
cdef float f, g[42], *h

and C struct, union or enum types:

cdef struct Grail:
    int age
    float volume
cdef union Food:
    char *spam
    float *eggs
cdef enum CheeseType:
    cheddar, edam,
    camembert
cdef enum CheeseState:
    hard = 1
    soft = 2
    runny = 3

See also Styles of struct, union and enum declaration

Note

Structs can be declared as cdef packed struct, which has the same effect as the C directive #pragma pack(1).

Declaring an enum as cpdef will create a PEP 435-style Python wrapper:

cpdef enum CheeseState:
    hard = 1
    soft = 2
    runny = 3

There is currently no special syntax for defining a constant, but you can use an anonymous enum declaration for this purpose, for example,:

cdef enum:
    tons_of_spam = 3

Note

the words struct, union and enum are used only when defining a type, not when referring to it. For example, to declare a variable pointing to a Grail you would write:

cdef Grail *gp

and not:

cdef struct Grail *gp # WRONG

There is also a ctypedef statement for giving names to types, e.g.:

ctypedef unsigned long ULong
ctypedef int* IntPtr

It is also possible to declare functions with cdef, making them c functions.

cdef int eggs(unsigned long l, float f):
    ...

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

You can declare classes with cdef, making them Extension Types. Those will have a behavior very close to python classes, but are faster because they use a struct internally to store attributes.

Here is a simple example:

from __future__ import print_function
cdef class Shrubbery:
    cdef int width, height
    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

Cython 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. 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, by appending a * to the base type they point to, e.g. int** for a pointer to a pointer to a C 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:

cdef list foo = []

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. For this kind of typing, Cython uses internally a C variable of type PyObject*. The Python types int, long, and float are not available for static typing and instead interpreted as C int, long, and float respectively, as statically typing variables with these Python types has zero advantages.

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

cdef (double, int) bar

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). This does allow subclasses. This typing is mostly used to access cdef 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:

from __future__ import print_function
cdef:
    struct Spam:
        int tons
    int i
    float a
    Spam *p
    void f(Spam *s):
        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 new cdef statement. 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, called cpdef. A cpdef can be called from anywhere, but uses the faster C calling conventions when being called from other Cython code. A cpdef 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 a cpdef method from Cython compared to calling a cdef method.

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

def spam(int i, char *s):
    ...
cdef int eggs(unsigned long l, float f):
    ...

ctuples may also be used:

cdef (int, float) chips((long, long, double) t):
    ...

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):
    cdef int i = python_i
    cdef char* s = 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.

Functions declared using cdef with 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:

cdef 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,:

cdef ftang(object int):
    ...

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):
    ...

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

Optional Arguments

Unlike C, it is possible to use optional arguments in cdef and cpdef functions. There are differences though whether you declare them in a .pyx 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 file, the signature is the same as it is in Python itself:

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)

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.:

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

If you don’t do anything special, a function declared with cdef that does not return a Python object has no way of reporting Python exceptions to its caller. If an exception is detected in such a function, a warning message is printed and the exception is ignored.

If you want a C function that does not return a Python object to be able to propagate exceptions to its caller, you need to declare an exception value for it. Here is an example:

cdef int spam() except -1:
    ...

With this declaration, whenever an exception occurs inside spam, it will immediately return with the value -1. Furthermore, whenever a call to spam returns -1, an exception will be assumed to have occurred and will be propagated.

When you declare an exception value for a function, you should never explicitly or implicitly return that value. In particular, if the exceptional return value is a False value, then you should ensure the function will never terminate via an implicit or empty return.

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:

cdef int spam() except? -1:
    ...

The “?” indicates that the value -1 only indicates a possible error. In this case, Cython generates a call to PyErr_Occurred() if the exception value is returned, to make sure it really is an error.

There is also a third form of exception value declaration:

cdef int spam() except *:
    ...

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 return value to test. Otherwise there is little use for this form.

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

cdef int spam() except +

See Using C++ in Cython for more details.

Some things to note:

• Exception values can only declared for functions returning an integer, enum, float or pointer type, and the value must be a constant expression. Void functions can only use the except * 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

• 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 libc.stdio cimport FILE, fopen
from libc.stdlib cimport malloc, free
from cpython.exc cimport PyErr_SetFromErrnoWithFilenameObject
def open_file():
    cdef FILE* p
    p = fopen("spam.txt", "r")
    if p is NULL:
        PyErr_SetFromErrnoWithFilenameObject(OSError, "spam.txt")
    ...
def allocating_memory(number=10):
    cdef double *my_array = <double *> malloc(number * sizeof(double))
    if not my_array:  # same as 'is NULL' above
        raise MemoryError()
    ...
    free(my_array)

Overriding in extension types

cpdef methods can override cdef methods:

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)

When subclassing an extension type with a Python class, def methods can override cpdef methods but not cdef methods:

from __future__ import print_function
cdef class A:
    cdef foo(self):
        print("A")
cdef class B(A):
    cpdef foo(self):
        print("B")
class C(B):  # NOTE: not cdef class
    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.

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:

cdef char *s
s = pystring1 + pystring2

then Cython will produce the error message Obtaining char* from temporary Python value. 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.:

cdef char *s
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

Where C uses "(" and ")", Cython uses "<" and ">". For example:

cdef char *p
cdef float *q
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 <int>pyobj, which converts a Python number to a plain C int value, or <bytes>charptr, 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 <void*> or <PyObject*>. You can also cast a C pointer back to a Python object reference with <object>, or a more specific builtin or extension type (e.g. <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:

from cpython.ref cimport PyObject
cdef extern from *:
    ctypedef Py_ssize_t Py_intptr_t
python_string = "foo"
cdef void* ptr = <void*>python_string
cdef Py_intptr_t adress_in_c = <Py_intptr_t>ptr
address_from_void = adress_in_c        # address_from_void is a python int
cdef PyObject* ptr2 = <PyObject*>python_string
cdef Py_intptr_t address_in_c2 = <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(<object>ptr)                     # Prints "foo"
print(<object>ptr2)                    # prints "foo"

The precedence of <...> is such that <type>a.b.c is interpreted as <type>(a.b.c).

Checked Type Casts

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

To have a cast checked, use the syntax like: <MyExtensionType?>x. 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). The L, LL, and U suffixes have the same meaning 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, with the same semantics as in C.

• The null C pointer is called NULL, not 0 (and NULL is a reserved word).

• Type casts are written <type>value , for example,:

  cdef char* p, float* q
  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.

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

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.

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.