.. _pysupported:

=========================
Supported Python features
=========================

Apart from the :ref:`pysupported-language` part below, which applies to both
:term:`object mode` and :term:`nopython mode`, this page only lists the
features supported in :term:`nopython mode`.

.. _pysupported-language:

Language
========

Constructs
----------

Numba strives to support as much of the Python language as possible, but
some language features are not available inside Numba-compiled functions. The following Python language features are not currently supported:

* Class definition
* Exception handling (``try .. except``, ``try .. finally``)
* Context management (the ``with`` statement)
* Some comprehensions (list comprehension is supported, but not dict, set or generator comprehensions)
* Generator delegation (``yield from``)

The ``raise`` statement is supported in several forms:

* ``raise`` (to re-raise the current exception)
* ``raise SomeException``
* ``raise SomeException(<arguments>)``: in :term:`nopython mode`, constructor
  arguments must be :term:`compile-time constants <compile-time constant>`

Similarly, the ``assert`` statement is supported with or without an error
message.

Inner function and closure
--------------------------

Numba now supports inner functions as long as they are non-recursive
and only called locally, but not passed as argument or returned as
result. The use of closure variables (variables defined in outer scopes)
within an inner function is also supported.

Function calls
--------------

Numba supports function calls using positional and named arguments, as well
as arguments with default values and ``*args`` (note the argument for
``*args`` can only be a tuple, not a list).  Explicit ``**kwargs`` are
not supported.

Function calls to locally defined inner functions are supported as long as
they can be fully inlined.

Recursive calls
'''''''''''''''

Most recursive call patterns are supported.  The only restriction is that the
recursive callee must have a control-flow path that returns without recursing.
Numba is able to type-infer recursive functions without specifying the function
type signature (which is required in numba 0.28 and earlier).
Recursive calls can even call into a different overload of the function.

.. XXX add reference to NBEP

Generators
----------

Numba supports generator functions and is able to compile them in
:term:`object mode` and :term:`nopython mode`.  The returned generator
can be used both from Numba-compiled code and from regular Python code.

Coroutine features of generators are not supported (i.e. the
:meth:`generator.send`, :meth:`generator.throw`, :meth:`generator.close`
methods).

.. _pysupported-builtin-types:

Built-in types
==============

int, bool
---------

Arithmetic operations as well as truth values are supported.

The following attributes and methods are supported:

* ``.conjugate()``
* ``.real``
* ``.imag``

float, complex
--------------

Arithmetic operations as well as truth values are supported.

The following attributes and methods are supported:

* ``.conjugate()``
* ``.real``
* ``.imag``

tuple
-----

The following operations are supported:

* tuple construction
* tuple unpacking
* comparison between tuples
* iteration and indexing over homogenous tuples
* addition (concatenation) between tuples
* slicing tuples with a constant slice

list
----

Creating and returning lists from JIT-compiled functions is supported,
as well as all methods and operations.  Lists must be strictly homogenous:
Numba will reject any list containing objects of different types, even if
the types are compatible (for example, ``[1, 2.5]`` is rejected as it
contains a :class:`int` and a :class:`float`).

.. note::
   When passing a list into a JIT-compiled function, any modifications
   made to the list will not be visible to the Python interpreter until
   the function returns.

.. warning::
   List sorting currently uses a quicksort algorithm, which has different
   performance characterics than the algorithm used by Python.

List comprehension
''''''''''''''''''

Numba supports list comprehension, but not the creation of nested list.

Numba also supports "array comprehension" that is a list comprehension
followed immediately by a call to :func:`numpy.array`. The following
is an example that produces a 2D Numpy array::

    from numba import jit
    import numpy as np
    
    @jit(nopython=True)
    def f(n):
      return np.array([ [ x * y for x in range(n) ] for y in range(n) ])

In this case, Numba is able to optimize the program to allocate and
initialize the result array directly without allocating intermediate
list objects.  Therefore, the nesting of list comprehension here is
not a problem since a multi-dimensional array is being created here
instead of a nested list.

Additionally, Numba supports parallel array comphension when combined
with the :ref:`parallel_jit_option` option on CPUs.

set
---

All methods and operations on sets are supported in JIT-compiled functions.

Sets must be strictly homogenous: Numba will reject any set containing
objects of different types, even if the types are compatible (for example,
``{1, 2.5}`` is rejected as it contains a :class:`int` and a :class:`float`).

.. note::
   When passing a set into a JIT-compiled function, any modifications
   made to the set will not be visible to the Python interpreter until
   the function returns.

None
----

The None value is supported for identity testing (when using an
:class:`~numba.optional` type).

bytes, bytearray, memoryview
----------------------------

The :class:`bytearray` type and, on Python 3, the :class:`bytes` type
support indexing, iteration and retrieving the len().

The :class:`memoryview` type supports indexing, slicing, iteration,
retrieving the len(), and also the following attributes:

* :attr:`~memoryview.contiguous`
* :attr:`~memoryview.c_contiguous`
* :attr:`~memoryview.f_contiguous`
* :attr:`~memoryview.itemsize`
* :attr:`~memoryview.nbytes`
* :attr:`~memoryview.ndim`
* :attr:`~memoryview.readonly`
* :attr:`~memoryview.shape`
* :attr:`~memoryview.strides`


Built-in functions
==================

The following built-in functions are supported:

* :func:`abs`
* :class:`bool`
* :class:`complex`
* :func:`divmod`
* :func:`enumerate`
* :class:`float`
* :class:`int`: only the one-argument form
* :func:`iter`: only the one-argument form
* :func:`len`
* :func:`min`
* :func:`max`
* :func:`next`: only the one-argument form
* :func:`print`: only numbers and strings; no ``file`` or ``sep`` argument
* :class:`range`: semantics are similar to those of Python 3 even in Python 2:
  a range object is returned instead of an array of values.
* :func:`round`
* :func:`sorted`: the ``key`` argument is not supported
* :func:`type`: only the one-argument form, and only on some types
  (e.g. numbers and named tuples)
* :func:`zip`


Standard library modules
========================

``array``
---------

Limited support for the :class:`array.array` type is provided through
the buffer protocol.  Indexing, iteration and taking the len() is supported.
All type codes are supported except for ``"u"``.

``cmath``
---------

The following functions from the :mod:`cmath` module are supported:

* :func:`cmath.acos`
* :func:`cmath.acosh`
* :func:`cmath.asin`
* :func:`cmath.asinh`
* :func:`cmath.atan`
* :func:`cmath.atanh`
* :func:`cmath.cos`
* :func:`cmath.cosh`
* :func:`cmath.exp`
* :func:`cmath.isfinite`
* :func:`cmath.isinf`
* :func:`cmath.isnan`
* :func:`cmath.log`
* :func:`cmath.log10`
* :func:`cmath.phase`
* :func:`cmath.polar`
* :func:`cmath.rect`
* :func:`cmath.sin`
* :func:`cmath.sinh`
* :func:`cmath.sqrt`
* :func:`cmath.tan`
* :func:`cmath.tanh`

``collections``
---------------

Named tuple classes, as returned by :func:`collections.namedtuple`, are
supported in the same way regular tuples are supported.  Attribute access
and named parameters in the constructor are also supported.

Creating a named tuple class inside Numba code is *not* supported; the class
must be created at the global level.

``ctypes``
----------

Numba is able to call ctypes-declared functions with the following argument
and return types:

* :class:`ctypes.c_int8`
* :class:`ctypes.c_int16`
* :class:`ctypes.c_int32`
* :class:`ctypes.c_int64`
* :class:`ctypes.c_uint8`
* :class:`ctypes.c_uint16`
* :class:`ctypes.c_uint32`
* :class:`ctypes.c_uint64`
* :class:`ctypes.c_float`
* :class:`ctypes.c_double`
* :class:`ctypes.c_void_p`

``enum``
--------

Both :class:`enum.Enum` and :class:`enum.IntEnum` subclasses are supported.

``math``
--------

The following functions from the :mod:`math` module are supported:

* :func:`math.acos`
* :func:`math.acosh`
* :func:`math.asin`
* :func:`math.asinh`
* :func:`math.atan`
* :func:`math.atan2`
* :func:`math.atanh`
* :func:`math.ceil`
* :func:`math.copysign`
* :func:`math.cos`
* :func:`math.cosh`
* :func:`math.degrees`
* :func:`math.erf`
* :func:`math.erfc`
* :func:`math.exp`
* :func:`math.expm1`
* :func:`math.fabs`
* :func:`math.floor`
* :func:`math.frexp`
* :func:`math.gamma`
* :func:`math.hypot`
* :func:`math.isfinite`
* :func:`math.isinf`
* :func:`math.isnan`
* :func:`math.ldexp`
* :func:`math.lgamma`
* :func:`math.log`
* :func:`math.log10`
* :func:`math.log1p`
* :func:`math.pow`
* :func:`math.radians`
* :func:`math.sin`
* :func:`math.sinh`
* :func:`math.sqrt`
* :func:`math.tan`
* :func:`math.tanh`
* :func:`math.trunc`

``operator``
------------

The following functions from the :mod:`operator` module are supported:

* :func:`operator.add`
* :func:`operator.and_`
* :func:`operator.div` (Python 2 only)
* :func:`operator.eq`
* :func:`operator.floordiv`
* :func:`operator.ge`
* :func:`operator.gt`
* :func:`operator.iadd`
* :func:`operator.iand`
* :func:`operator.idiv` (Python 2 only)
* :func:`operator.ifloordiv`
* :func:`operator.ilshift`
* :func:`operator.imatmul` (Python 3.5 and above)
* :func:`operator.imod`
* :func:`operator.imul`
* :func:`operator.invert`
* :func:`operator.ior`
* :func:`operator.ipow`
* :func:`operator.irshift`
* :func:`operator.isub`
* :func:`operator.itruediv`
* :func:`operator.ixor`
* :func:`operator.le`
* :func:`operator.lshift`
* :func:`operator.lt`
* :func:`operator.matmul` (Python 3.5 and above)
* :func:`operator.mod`
* :func:`operator.mul`
* :func:`operator.ne`
* :func:`operator.neg`
* :func:`operator.not_`
* :func:`operator.or_`
* :func:`operator.pos`
* :func:`operator.pow`
* :func:`operator.rshift`
* :func:`operator.sub`
* :func:`operator.truediv`
* :func:`operator.xor`

.. _pysupported-random:

``random``
----------

Numba supports top-level functions from the :mod:`random` module, but does
not allow you to create individual Random instances.  A Mersenne-Twister
generator is used, with a dedicated internal state.  It is initialized at
startup with entropy drawn from the operating system.

* :func:`random.betavariate`
* :func:`random.expovariate`
* :func:`random.gammavariate`
* :func:`random.gauss`
* :func:`random.getrandbits`: number of bits must not be greater than 64
* :func:`random.lognormvariate`
* :func:`random.normalvariate`
* :func:`random.paretovariate`
* :func:`random.randint`
* :func:`random.random`
* :func:`random.randrange`
* :func:`random.seed`: with an integer argument only
* :func:`random.shuffle`: the sequence argument must be a one-dimension
  Numpy array or buffer-providing object (such as a :class:`bytearray`
  or :class:`array.array`); the second (optional) argument is not supported
* :func:`random.uniform`
* :func:`random.triangular`
* :func:`random.vonmisesvariate`
* :func:`random.weibullvariate`

.. note::
   Calling :func:`random.seed` from non-Numba code (or from :term:`object mode`
   code) will seed the Python random generator, not the Numba random generator.

.. note::
   The generator is not thread-safe when :ref:`releasing the GIL <jit-nogil>`.

   Also, under Unix, if creating a child process using :func:`os.fork` or the
   :mod:`multiprocessing` module, the child's random generator will inherit
   the parent's state and will therefore produce the same sequence of
   numbers (except when using the "forkserver" start method under Python 3.4
   and later).

.. seealso::
   Numba also supports most additional distributions from the :ref:`Numpy
   random module <numpy-random>`.


Third-party modules
===================

.. I put this here as there's only one module (apart from Numpy), otherwise
   it should be a separate page.

``cffi``
--------

Similarly to ctypes, Numba is able to call into `cffi`_-declared external
functions, using the following C types and any derived pointer types:

* :c:type:`char`
* :c:type:`short`
* :c:type:`int`
* :c:type:`long`
* :c:type:`long long`
* :c:type:`unsigned char`
* :c:type:`unsigned short`
* :c:type:`unsigned int`
* :c:type:`unsigned long`
* :c:type:`unsigned long long`
* :c:type:`int8_t`
* :c:type:`uint8_t`
* :c:type:`int16_t`
* :c:type:`uint16_t`
* :c:type:`int32_t`
* :c:type:`uint32_t`
* :c:type:`int64_t`
* :c:type:`uint64_t`
* :c:type:`float`
* :c:type:`double`
* :c:type:`ssize_t`
* :c:type:`size_t`
* :c:type:`void`

The ``from_buffer()`` method of ``cffi.FFI`` and ``CompiledFFI`` objects is
supported for passing Numpy arrays and other buffer-like objects.  Only
*contiguous* arguments are accepted.  The argument to ``from_buffer()``
is converted to a raw pointer of the appropriate C type (for example a
``double *`` for a ``float64`` array).

Additional type mappings for the conversion from a buffer to the appropriate C
type may be registered with Numba. This may include struct types, though it is
only permitted to call functions that accept pointers to structs - passing a
struct by value is unsupported. For registering a mapping, use:

.. function:: numba.cffi_support.register_type(cffi_type, numba_type)

Out-of-line cffi modules must be registered with Numba prior to the use of any
of their functions from within Numba-compiled functions:

.. function:: numba.cffi_support.register_module(mod)

   Register the cffi out-of-line module ``mod`` with Numba.

Inline cffi modules require no registration.

.. _cffi: https://cffi.readthedocs.org/
