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.
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
andfinally
(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
andasync def
- class definition:
class
(except for :ref:`@jitclass <jitclass>`) - set, dict and generator comprehensions
- generator delegation:
yield from
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 can be passed as argument into another function. But, they cannot be returned. For example:
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.
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.
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.
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).
The raise
statement is only supported in the following forms:
raise SomeException
raise SomeException(<arguments>)
It is currently unsupported to re-raise an exception created in compiled code.
The try .. except
construct is partially supported. The following forms
of are supported:
the bare except that captures all exceptions:
try: ... except: ...
using exactly the
Exception
class in theexcept
clause:try: ... except Exception: ...
This will match any exception that is a subclass of
Exception
as expected. Currently, instances ofException
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:
try:
do_work()
except Exception:
handle_error_case()
return error_code
The else
block and the finally
block of a try .. except
are
supported:
>>> @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.
Arithmetic operations as well as truth values are supported.
The following attributes and methods are supported:
.conjugate()
.real
.imag
Arithmetic operations as well as truth values are supported.
The following attributes and methods are supported:
.conjugate()
.real
.imag
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 constructors, functions, attributes and methods are currently supported:
str(int)
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()
Regular string literals (e.g. "ABC"
) as well as f-strings without format specs
(e.g. "ABC_{a+1}"
)
that only use string and integer variables (types with str()
overload)
are supported in :term:`nopython mode`.
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 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.
An example of a homogeneous tuple:
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.
An example of a heterogeneous tuple:
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]
, where7
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:
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`:
- :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:
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())
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])]
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.
Warning
This is an experimental feature!
Lists that:
- Are constructed using the square braces syntax
- Have values of a literal type
will have their initial value stored in the .initial_value
property on the
type so as to permit inspection of these values at compile time. If required,
to force value based dispatch the :ref:`literally <developer-literally>`
function will accept such a list.
Example:
.. literalinclude:: ../../../numba/tests/doc_examples/test_literal_container_usage.py :language: python :caption: from ``test_ex_initial_value_list_compile_time_consts`` of ``numba/tests/doc_examples/test_literal_container_usage.py`` :start-after: magictoken.test_ex_initial_value_list_compile_time_consts.begin :end-before: magictoken.test_ex_initial_value_list_compile_time_consts.end :dedent: 12 :linenos:
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:
Warning
This is an experimental feature!
Numba supports the use of literal lists containing any values, for example:
l = ['a', 1, 2j, np.zeros(5,)]
the predominant use of these lists is for use as a configuration object.
The lists appear as a LiteralList
type which inherits from Literal
, as a
result the literal values of the list items are available at compile time.
For example:
.. literalinclude:: ../../../numba/tests/doc_examples/test_literal_container_usage.py :language: python :caption: from ``test_ex_literal_list`` of ``numba/tests/doc_examples/test_literal_container_usage.py`` :start-after: magictoken.test_ex_literal_list.begin :end-before: magictoken.test_ex_literal_list.end :dedent: 12 :linenos:
Important things to note about these kinds of lists:
- They are immutable, use of mutating methods e.g.
.pop()
will result in compilation failure. Read-only static access and read only methods are supported e.g.len()
. - Dynamic access of items is not possible, e.g.
some_list[x]
, for a valuex
which is not a compile time constant. This is because it's impossible to statically determine the type of the item being accessed. - Inside the compiler, these lists are actually just tuples with some extra things added to make them look like they are lists.
- They cannot be returned to the interpreter from a compiled function.
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.
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`).
The use of reference counted types, e.g. strings, in sets is unsupported.
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.
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 supports the use of dict()
. 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 also supports, explicitly, the dict(iterable)
constructor.
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 ``test_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 ``test_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 ``test_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.
Numba supports dictionary comprehension under the assumption that a
numba.typed.Dict
instance can be created from the comprehension. For
example:
In [1]: from numba import njit In [2]: @njit ...: def foo(n): ...: return {i: i**2 for i in range(n)} ...: In [3]: foo(3) Out[3]: DictType[int64,int64]<iv=None>({0: 0, 1: 1, 2: 4})
Warning
This is an experimental feature!
Typed dictionaries that:
- Are constructed using the curly braces syntax
- Have literal string keys
- Have values of a literal type
will have their initial value stored in the .initial_value
property on the
type so as to permit inspection of these values at compile time. If required,
to force value based dispatch the :ref:`literally <developer-literally>`
function will accept a typed dictionary.
Example:
.. literalinclude:: ../../../numba/tests/doc_examples/test_literal_container_usage.py :language: python :caption: from ``test_ex_initial_value_dict_compile_time_consts`` of ``numba/tests/doc_examples/test_literal_container_usage.py`` :start-after: magictoken.test_ex_initial_value_dict_compile_time_consts.begin :end-before: magictoken.test_ex_initial_value_dict_compile_time_consts.end :dedent: 12 :linenos:
Warning
This is an experimental feature!
Numba supports the use of statically declared string key to any value dictionaries, for example:
d = {'a': 1, 'b': 'data', 'c': 2j}
the predominant use of these dictionaries is to orchestrate advanced compilation
dispatch or as a container for use as a configuration object. The dictionaries
appear as a LiteralStrKeyDict
type which inherits from Literal
, as a
result the literal values of the keys and the types of the items are available
at compile time. For example:
.. literalinclude:: ../../../numba/tests/doc_examples/test_literal_container_usage.py :language: python :caption: from ``test_ex_literal_dict_compile_time_consts`` of ``numba/tests/doc_examples/test_literal_container_usage.py`` :start-after: magictoken.test_ex_literal_dict_compile_time_consts.begin :end-before: magictoken.test_ex_literal_dict_compile_time_consts.end :dedent: 12 :linenos:
Important things to note about these kinds of dictionaries:
- They are immutable, use of mutating methods e.g.
.pop()
will result in compilation failure. Read-only static access and read only methods are supported e.g.len()
. - Dynamic access of items is not possible, e.g.
some_dictionary[x]
, for a valuex
which is not a compile time constant. This is because it's impossible statically determine the type of the item being accessed. - Inside the compiler, these dictionaries are actually just named tuples with some extra things added to make them look like they are dictionaries.
- They cannot be returned to the interpreter from a compiled function.
- The
.keys()
,.values()
and.items()
methods all functionally operate but return tuples opposed to iterables.
The None value is supported for identity testing (when using an :class:`~numba.optional` type).
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`
The following built-in functions are supported:
Warning
Support for isinstance
is an experimental feature. This feature is
automatically enabled by simply using isinstance
in JIT compiled code.
- :func:`abs`
- :class:`bool`
- :func:`chr`
- :class:`complex`
- :func:`divmod`
- :func:`enumerate`
- :func:`filter`
- :class:`float`
- :func:`getattr`: the attribute must be a string literal and the return type
cannot be a function type (e.g.
getattr(numpy, 'cos')
is not supported as it returns a function type). - :func:`hasattr`
- :func:`hash` (see :ref:`pysupported-hashing` below)
- :class:`int`: only the one-argument form
- :func:`iter`: only the one-argument form
- :func:`isinstance` (experimental support only)
- :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
orsep
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:`repr`
- :func:`round`
- :func:`sorted`: the
key
argument is not supported - :func:`sum`
- :func:`str`
- :func:`type`: only the one-argument form, and only on some types (e.g. numbers and named tuples)
- :func:`zip`
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.
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"
.
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`
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.
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`
Both :class:`enum.Enum` and :class:`enum.IntEnum` subclasses are supported.
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`
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`
The :func:`functools.reduce` function is supported but the initializer argument is required.
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`
Warning
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. To seed the Numba random generator, see the example below.
from numba import njit
import random
@njit
def seed(a):
random.seed(a)
@njit
def rand():
return random.random()
# Incorrect seeding
random.seed(1234)
print(rand())
random.seed(1234)
print(rand())
# Correct seeding
seed(1234)
print(rand())
seed(1234)
print(rand())
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>`.
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.
Similarly to ctypes, Numba is able to call into cffi-declared external functions, using the following C types and any derived pointer types:
- :c:expr:`char`
- :c:expr:`short`
- :c:expr:`int`
- :c:expr:`long`
- :c:expr:`long long`
- :c:expr:`unsigned char`
- :c:expr:`unsigned short`
- :c:expr:`unsigned int`
- :c:expr:`unsigned long`
- :c:expr:`unsigned long long`
- :c:expr:`int8_t`
- :c:expr:`uint8_t`
- :c:expr:`int16_t`
- :c:expr:`uint16_t`
- :c:expr:`int32_t`
- :c:expr:`uint32_t`
- :c:expr:`int64_t`
- :c:expr:`uint64_t`
- :c:expr:`float`
- :c:expr:`double`
- :c:expr:`ssize_t`
- :c:expr:`size_t`
- :c:expr:`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.