.. _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`.

.. warning::
    Numba behavior differs from Python semantics in some situations.  We
    strongly advise reviewing :ref:`pysemantics` to become familiar with these
    differences.


.. _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.
Below is a quick reference for the support level of Python constructs.


**Supported** constructs:

- conditional branch: ``if .. elif .. else``
- loops: ``while``, ``for .. in``, ``break``, ``continue``
- basic generator: ``yield``
- assertion: ``assert``

**Partially supported** constructs:

- exceptions: ``try .. except``, ``raise``, ``else`` and ``finally``
  (See details in this :ref:`section <pysupported-exception-handling>`)

- context manager:
  ``with`` (only support :ref:`numba.objmode() <with_objmode>`)

- list comprehension (see details in this
  :ref:`section <pysupported-comprehension>`)

**Unsupported** constructs:

- async features: ``async with``, ``async for`` and ``async def``
- class definition: ``class`` (except for :ref:`@jitclass <jitclass>`)
- set, dict and generator comprehensions
- generator delegation: ``yield from``

Functions
---------

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.

Functions as arguments
''''''''''''''''''''''

Functions can be passed as argument into another function.  But, they cannot
be returned. For example:

.. code-block:: python

  from numba import jit

  @jit
  def add1(x):
      return x + 1

  @jit
  def bar(fn, x):
      return fn(x)

  @jit
  def foo(x):
      return bar(add1, x)

  # Passing add1 within numba compiled code.
  print(foo(1))
  # Passing add1 into bar from interpreted code
  print(bar(add1, 1))

.. note:: Numba does not handle function objects as real objects.  Once a
          function is assigned to a variable, the variable cannot be
          re-assigned to a different function.


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.

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-exception-handling:

Exception handling
------------------

``raise`` statement
'''''''''''''''''''

The ``raise`` statement is only supported in the following forms:

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

It is currently unsupported to re-raise an exception created in compiled code.

``try .. except``
'''''''''''''''''

The ``try .. except`` construct is partially supported. The following forms
of are supported:

* the *bare* except that captures all exceptions:

  .. code-block:: python

    try:
        ...
    except:
        ...

* using exactly the ``Exception`` class in the ``except`` clause:

  .. code-block:: python

    try:
      ...
    except Exception:
      ...

  This will match any exception that is a subclass of ``Exception`` as
  expected. Currently, instances of ``Exception`` and it's subclasses are the
  only kind of exception that can be raised in compiled code.

.. warning:: Numba currently masks signals like ``KeyboardInterrupt`` and
  ``SystemExit``. These signaling exceptions are ignored during the execution of
  Numba compiled code. The Python interpreter will handle them as soon as
  the control is returned to it.

Currently, exception objects are not materialized inside compiled functions.
As a result, it is not possible to store an exception object into a user
variable or to re-raise an exception. With this limitation, the only realistic
use-case would look like:

.. code-block:: python

  try:
     do_work()
  except Exception:
     handle_error_case()
     return error_code

``try .. except .. else .. finally``
''''''''''''''''''''''''''''''''''''

The ``else`` block and the ``finally`` block of a ``try .. except`` are
supported:

  .. code-block:: python

    >>> @jit(nopython=True)
    ... def foo():
    ...     try:
    ...         print('main block')
    ...     except Exception:
    ...         print('handler block')
    ...     else:
    ...         print('else block')
    ...     finally:
    ...         print('final block')
    ...
    >>> foo()
    main block
    else block
    final block

The ``try .. finally`` construct without the ``except`` clause is also
supported.

.. _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``

str
---

Numba supports (Unicode) strings in Python 3.  Strings can be passed into
:term:`nopython mode` as arguments, as well as constructed and returned from
:term:`nopython mode`. As in Python, slices (even of length 1) return a new,
reference counted string.  Optimized code paths for efficiently accessing
single characters may be introduced in the future.

The in-memory representation is the same as was introduced in Python 3.4, with
each string having a tag to indicate whether the string is using a 1, 2, or 4
byte character width in memory.  When strings of different encodings are
combined (as in concatenation), the resulting string automatically uses the
larger character width of the two input strings.  String slices also use the
same character width as the original string, even if the slice could be
represented with a narrower character width.  (These details are invisible to
the user, of course.)

The following functions, attributes and methods are currently supported:

* ``len()``
* ``+`` (concatenation of strings)
* ``*`` (repetition of strings)
* ``in``, ``.contains()``
* ``==``, ``<``, ``<=``, ``>``, ``>=`` (comparison)
* ``.capitalize()``
* ``.casefold()``
* ``.center()``
* ``.count()``
* ``.endswith()``
* ``.endswith()``
* ``.expandtabs()``
* ``.find()``
* ``.index()``
* ``.isalnum()``
* ``.isalpha()``
* ``.isdecimal()``
* ``.isdigit()``
* ``.isidentifier()``
* ``.islower()``
* ``.isnumeric()``
* ``.isprintable()``
* ``.isspace()``
* ``.istitle()``
* ``.isupper()``
* ``.join()``
* ``.ljust()``
* ``.lower()``
* ``.lstrip()``
* ``.partition()``
* ``.replace()``
* ``.rfind()``
* ``.rindex()``
* ``.rjust()``
* ``.rpartition()``
* ``.rsplit()``
* ``.rstrip()``
* ``.split()``
* ``.splitlines()``
* ``.startswith()``
* ``.strip()``
* ``.swapcase()``
* ``.title()``
* ``.upper()``
* ``.zfill()``

Additional operations as well as support for Python 2 strings / Python 3 bytes
will be added in a future version of Numba.  Python 2 Unicode objects will
likely never be supported.

.. warning::
    The performance of some operations is known to be slower than the CPython
    implementation. These include substring search (``in``, ``.contains()``
    and ``find()``) and string creation (like ``.split()``).  Improving the
    string performance is an ongoing task, but the speed of CPython is
    unlikely to be surpassed for basic string operation in isolation.
    Numba is most successfully used for larger algorithms that happen to
    involve strings, where basic string operations are not the bottleneck.


tuple
-----

Tuple support is categorised into two categories based on the contents of a
tuple. The first category is homogeneous tuples, these are tuples where the type
of all the values in the tuple are the same, the second is heterogeneous tuples,
these are tuples where the types of the values are different.

.. note::

    The ``tuple()`` constructor itself is NOT supported.

homogeneous tuples
------------------

An example of a homogeneous tuple:

.. code-block:: python

    homogeneous_tuple = (1, 2, 3, 4)

The following operations are supported on homogeneous tuples:

* Tuple construction.
* Tuple unpacking.
* Comparison between tuples.
* Iteration and indexing.
* Addition (concatenation) between tuples.
* Slicing tuples with a constant slice.
* The index method on tuples.

heterogeneous tuples
--------------------

An example of a heterogeneous tuple:

.. code-block:: python

    heterogeneous_tuple = (1, 2j, 3.0, "a")

The following operations are supported on heterogeneous tuples:

* Comparison between tuples.
* Indexing using an index value that is a compile time constant
  e.g. ``mytuple[7]``, where ``7`` is evidently a constant.
* Iteration over a tuple (requires experimental :func:`literal_unroll` feature,
  see below).

.. warning::
   The following feature (:func:`literal_unroll`) is experimental and was added
   in version 0.47.

To permit iteration over a heterogeneous tuple the special function
:func:`numba.literal_unroll` must be used. This function has no effect other
than to act as a token to permit the use of this feature. Example use:

.. code-block:: python

    from numba import njit, literal_unroll

    @njit
    def foo()
        heterogeneous_tuple = (1, 2j, 3.0, "a")
        for i in literal_unroll(heterogeneous_tuple):
            print(i)

.. warning::
    The following restrictions apply to the use of :func:`literal_unroll`:

    * This feature is only available for Python versions >= 3.6.
    * :func:`literal_unroll` can only be used on tuples and constant lists of
      compile time constants, e.g. ``[1, 2j, 3, "a"]`` and the list not being
      mutated.
    * The only supported use pattern for :func:`literal_unroll` is loop
      iteration.
    * Only one :func:`literal_unroll` call is permitted per loop nest (i.e.
      nested heterogeneous tuple iteration loops are forbidden).
    * The usual type inference/stability rules still apply.

A more involved use of :func:`literal_unroll` might be type specific dispatch,
recall that string and integer literal values are considered their own type,
for example:

.. code-block:: python

    from numba import njit, types, literal_unroll
    from numba.extending import overload

    def dt(x):
        # dummy function to overload
        pass

    @overload(dt, inline='always')
    def ol_dt(li):
        if isinstance(li, types.StringLiteral):
            value = li.literal_value
            if value == "apple":
                def impl(li):
                    return 1
            elif value == "orange":
                def impl(li):
                    return 2
            elif value == "banana":
                def impl(li):
                    return 3
            return impl
        elif isinstance(li, types.IntegerLiteral):
            value = li.literal_value
            if value == 0xca11ab1e:
                def impl(li):
                    # capture the dispatcher literal value
                    return 0x5ca1ab1e + value
                return impl

    @njit
    def foo():
        acc = 0
        for t in literal_unroll(('apple', 'orange', 'banana', 3390155550)):
            acc += dt(t)
        return acc

    print(foo())


list
----


.. warning::
    As of version 0.45.x the internal implementation for the list datatype in
    Numba is changing. Until recently, only a single implementation of the list
    datatype was available, the so-called *reflected-list* (see below).
    However, it was scheduled for deprecation from version 0.44.0 onwards due
    to its limitations. As of version 0.45.0 a new implementation, the
    so-called *typed-list* (see below), is available as an experimental
    feature. For more information, please see: :ref:`deprecation`.

Creating and returning lists from JIT-compiled functions is supported,
as well as all methods and operations.  Lists must be strictly homogeneous:
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`).

For example, to create a list of arrays::

  In [1]: from numba import njit

  In [2]: import numpy as np

  In [3]: @njit
    ...: def foo(x):
    ...:     lst = []
    ...:     for i in range(x):
    ...:         lst.append(np.arange(i))
    ...:     return lst
    ...:

  In [4]: foo(4)
  Out[4]: [array([], dtype=int64), array([0]), array([0, 1]), array([0, 1, 2])]


.. _feature-reflected-list:

List Reflection
'''''''''''''''

In nopython mode, Numba does not operate on Python objects.  ``list`` are
compiled into an internal representation.  Any ``list`` arguments must be
converted into this representation on the way in to nopython mode and their
contained elements must be restored in the original Python objects via a
process called :term:`reflection`.  Reflection is required to maintain the same
semantics as found in regular Python code.  However, the reflection process
can be expensive for large lists and it is not supported for lists that contain
reflected data types.  Users cannot use list-of-list as an argument because
of this limitation.

.. 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.  (A limitation of the reflection process.)

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


.. _feature-typed-list:

Typed List
''''''''''

.. note::
  ``numba.typed.List`` is an experimental feature, if you encounter any bugs in
  functionality or suffer from unexpectedly bad performance, please report
  this, ideally by opening an issue on the Numba issue tracker.

As of version 0.45.0 a new implementation of the list data type is available,
the so-called *typed-list*. This is compiled library backed, type-homogeneous
list data type that is an improvement over the *reflected-list* mentioned
above.  Additionally, lists can now be arbitrarily nested. Since the
implementation is considered experimental, you will need to import it
explicitly from the `numba.typed` module::

    In [1]: from numba.typed import List

    In [2]: from numba import njit

    In [3]: @njit
    ...: def foo(l):
    ...:     l.append(23)
    ...:     return l
    ...:

    In [4]: mylist = List()

    In [5]: mylist.append(1)

    In [6]: foo(mylist)
    Out[6]: ListType[int64]([1, 23])


.. note::
    As the typed-list stabilizes it will fully replace the reflected-list and the
    constructors `[]` and `list()` will create a typed-list instead of a
    reflected one.


Here's an example using ``List()`` to create ``numba.typed.List`` inside a
jit-compiled function and letting the compiler infer the item type:

.. literalinclude:: ../../../numba/tests/doc_examples/test_typed_list_usage.py
   :language: python
   :caption: from ``ex_inferred_list_jit`` of ``numba/tests/doc_examples/test_typed_list_usage.py``
   :start-after: magictoken.ex_inferred_list_jit.begin
   :end-before: magictoken.ex_inferred_list_jit.end
   :dedent: 12
   :linenos:

Here's an example of using ``List()`` to create a ``numba.typed.List`` outside of
a jit-compiled function and then using it as an argument to a jit-compiled
function:

.. literalinclude:: ../../../numba/tests/doc_examples/test_typed_list_usage.py
   :language: python
   :caption: from ``ex_inferred_list`` of ``numba/tests/doc_examples/test_typed_list_usage.py``
   :start-after: magictoken.ex_inferred_list.begin
   :end-before: magictoken.ex_inferred_list.end
   :dedent: 12
   :linenos:

Finally, here's an example of using a nested `List()`:

.. literalinclude:: ../../../numba/tests/doc_examples/test_typed_list_usage.py
   :language: python
   :caption: from ``ex_nested_list`` of ``numba/tests/doc_examples/test_typed_list_usage.py``
   :start-after: magictoken.ex_nested_list.begin
   :end-before: magictoken.ex_nested_list.end
   :dedent: 12
   :linenos:

.. _pysupported-comprehension:

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

Numba supports list comprehension.  For example::


  In [1]: from numba import njit

  In [2]: @njit
    ...: def foo(x):
    ...:     return [[i for i in range(n)] for n in range(x)]
    ...:

  In [3]: foo(3)
  Out[3]: [[], [0], [0, 1]]


.. note::
  Prior to version 0.39.0, Numba did not support the creation of nested lists.


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 comprehension 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 homogeneous: 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.

.. _feature-typed-dict:

Typed Dict
''''''''''

.. warning::
  ``numba.typed.Dict`` is an experimental feature.  The API may change
  in the future releases.

.. note::
  ``dict()`` was not supported in versions prior to 0.44.  Currently, calling
  ``dict()`` translates to calling ``numba.typed.Dict()``.

Numba only supports the use of ``dict()`` without any arguments.  Such use is
semantically equivalent to ``{}`` and ``numba.typed.Dict()``.  It will create
an instance of ``numba.typed.Dict`` where the key-value types will be later
inferred by usage.

Numba does not fully support the Python ``dict`` because it is an untyped
container that can have any Python types as members. To generate efficient
machine code, Numba needs the keys and the values of the dictionary to have
fixed types, declared in advance. To achieve this, Numba has a typed dictionary,
``numba.typed.Dict``, for which the type-inference mechanism must be able to
infer the key-value types by use, or the user must explicitly declare the
key-value type using the ``Dict.empty()`` constructor method.
This typed dictionary has the same API as the Python ``dict``,  it implements
the ``collections.MutableMapping`` interface and is usable in both interpreted
Python code and JIT-compiled Numba functions.
Because the typed dictionary stores keys and values in Numba's native,
unboxed data layout, passing a Numba dictionary into nopython mode has very low
overhead. However, this means that using a typed dictionary from the Python
interpreter is slower than a regular dictionary because Numba has to box and
unbox key and value objects when getting or setting items.

An important difference of the typed dictionary in comparison to Python's
``dict`` is that **implicit casting** occurs when a key or value is stored.
As a result the *setitem* operation may fail should the type-casting fail.

It should be noted that the Numba typed dictionary is implemented using the same
algorithm as the CPython 3.7 dictionary. As a consequence, the typed dictionary
is ordered and has the same collision resolution as the CPython implementation.

Further to the above in relation to type specification, there are limitations
placed on the types that can be used as keys and/or values in the typed
dictionary, most notably the Numba ``Set`` and ``List`` types are currently
unsupported. Acceptable key/value types include but are not limited to: unicode
strings, arrays (value only), scalars, tuples. It is expected that these
limitations will be relaxed as Numba continues to improve.

Here's an example of using ``dict()`` and ``{}`` to create ``numba.typed.Dict``
instances and letting the compiler infer the key-value types:

.. literalinclude:: ../../../numba/tests/doc_examples/test_typed_dict_usage.py
   :language: python
   :caption: from ``ex_inferred_dict_njit`` of ``numba/tests/doc_examples/test_typed_dict_usage.py``
   :start-after: magictoken.ex_inferred_dict_njit.begin
   :end-before: magictoken.ex_inferred_dict_njit.end
   :dedent: 12
   :linenos:

Here's an example of creating a ``numba.typed.Dict`` instance from interpreted
code and using the dictionary in jit code:

.. literalinclude:: ../../../numba/tests/doc_examples/test_typed_dict_usage.py
   :language: python
   :caption: from ``ex_typed_dict_from_cpython`` of ``numba/tests/doc_examples/test_typed_dict_usage.py``
   :start-after: magictoken.ex_typed_dict_from_cpython.begin
   :end-before: magictoken.ex_typed_dict_from_cpython.end
   :dedent: 12
   :linenos:

Here's an example of creating a ``numba.typed.Dict`` instance from jit code and
using the dictionary in interpreted code:

.. literalinclude:: ../../../numba/tests/doc_examples/test_typed_dict_usage.py
   :language: python
   :caption: from ``ex_typed_dict_njit`` of ``numba/tests/doc_examples/test_typed_dict_usage.py``
   :start-after: magictoken.ex_typed_dict_njit.begin
   :end-before: magictoken.ex_typed_dict_njit.end
   :dedent: 12
   :linenos:

It should be noted that ``numba.typed.Dict`` is not thread-safe.
Specifically, functions which modify a dictionary from multiple
threads will potentially corrupt memory, causing a
range of possible failures. However, the dictionary can be safely read from
multiple threads as long as the contents of the dictionary do not
change during the parallel access.

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`
* :func:`chr`
* :class:`complex`
* :func:`divmod`
* :func:`enumerate`
* :func:`filter`
* :class:`float`
* :func:`hash` (see :ref:`pysupported-hashing` below)
* :class:`int`: only the one-argument form
* :func:`iter`: only the one-argument form
* :func:`len`
* :func:`min`
* :func:`map`
* :func:`max`
* :func:`next`: only the one-argument form
* :func:`ord`
* :func:`print`: only numbers and strings; no ``file`` or ``sep`` argument
* :class:`range`: The only permitted use of range is as a callable function
  (cannot pass range as an argument to a jitted function or return a range from
  a jitted function).
* :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`

.. _pysupported-hashing:

Hashing
-------

The :func:`hash` built-in is supported and produces hash values for all
supported hashable types with the following Python version specific behavior:

Under Python 3, hash values computed by Numba will exactly match those computed
in CPython under the condition that the :attr:`sys.hash_info.algorithm` is
``siphash24`` (default).

The ``PYTHONHASHSEED`` environment variable influences the hashing behavior in
precisely the manner described in the CPython documentation.


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-support:

``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.gcd`
* :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.eq`
* :func:`operator.floordiv`
* :func:`operator.ge`
* :func:`operator.gt`
* :func:`operator.iadd`
* :func:`operator.iand`
* :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`

``functools``
-------------

The :func:`functools.reduce` function is supported but the `initializer`
argument is required.

.. _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::
   Since version 0.28.0, the generator is thread-safe and fork-safe.  Each
   thread and each process will produce independent streams of random numbers.

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

``heapq``
---------

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

* :func:`heapq.heapify`
* :func:`heapq.heappop`
* :func:`heapq.heappush`
* :func:`heapq.heappushpop`
* :func:`heapq.heapreplace`
* :func:`heapq.nlargest` : first two arguments only
* :func:`heapq.nsmallest` : first two arguments only

Note: the heap must be seeded with at least one value to allow its type to be
inferred; heap items are assumed to be homogeneous in type.


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

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

.. _cffi-support:

``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.core.typing.cffi_utils.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.core.typing.cffi_utils.register_module(mod)

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

Inline cffi modules require no registration.

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