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Supported NumPy features

One objective of Numba is having a seamless integration with NumPy. NumPy arrays provide an efficient storage method for homogeneous sets of data. NumPy dtypes provide type information useful when compiling, and the regular, structured storage of potentially large amounts of data in memory provides an ideal memory layout for code generation. Numba excels at generating code that executes on top of NumPy arrays.

NumPy support in Numba comes in many forms:

  • Numba understands calls to NumPy ufuncs and is able to generate equivalent native code for many of them.
  • NumPy arrays are directly supported in Numba. Access to NumPy arrays is very efficient, as indexing is lowered to direct memory accesses when possible.
  • Numba is able to generate ufuncs and gufuncs. This means that it is possible to implement ufuncs and gufuncs within Python, getting speeds comparable to that of ufuncs/gufuncs implemented in C extension modules using the NumPy C API.

The following sections focus on the NumPy features supported in :term:`nopython mode`, unless otherwise stated.

Scalar types

Numba supports the following NumPy scalar types:

  • Integers: all integers of either signedness, and any width up to 64 bits
  • Booleans
  • Real numbers: single-precision (32-bit) and double-precision (64-bit) reals
  • Complex numbers: single-precision (2x32-bit) and double-precision (2x64-bit) complex numbers
  • Datetimes and timestamps: of any unit
  • Character sequences (but no operations are available on them)
  • Structured scalars: structured scalars made of any of the types above and arrays of the types above

The following scalar types and features are not supported:

  • Arbitrary Python objects
  • Half-precision and extended-precision real and complex numbers
  • Nested structured scalars the fields of structured scalars may not contain other structured scalars

The operations supported on NumPy scalars are almost the same as on the equivalent built-in types such as int or float. You can use a type's constructor to convert from a different type or width. In addition you can use the view(np.<dtype>) method to bitcast all int and float types within the same width. However, you must define the scalar using a NumPy constructor within a jitted function. For example, the following will work:

>>> import numpy as np
>>> from numba import njit
>>> @njit
... def bitcast():
...     i = np.int64(-1)
...     print(i.view(np.uint64))
...
>>> bitcast()
18446744073709551615

Whereas the following will not work:

>>> import numpy as np
>>> from numba import njit
>>> @njit
... def bitcast(i):
...     print(i.view(np.uint64))
...
>>> bitcast(np.int64(-1))
---------------------------------------------------------------------------
TypingError                               Traceback (most recent call last)
    ...
TypingError: Failed in nopython mode pipeline (step: ensure IR is legal prior to lowering)
'view' can only be called on NumPy dtypes, try wrapping the variable with 'np.<dtype>()'

File "<ipython-input-3-fc40aaab84c4>", line 3:
def bitcast(i):
    print(i.view(np.uint64))

Structured scalars support attribute getting and setting, as well as member lookup using constant strings. Strings stored in a local or global tuple are considered constant strings and can be used for member lookup.

.. literalinclude:: ../../../numba/tests/doc_examples/test_rec_array.py
   :language: python
   :start-after: magictoken.ex_rec_arr_const_index.begin
   :end-before: magictoken.ex_rec_arr_const_index.end
   :dedent: 8

It is also possible to use local or global tuples together with literal_unroll:

.. literalinclude:: ../../../numba/tests/doc_examples/test_rec_array.py
   :language: python
   :start-after: magictoken.ex_rec_arr_lit_unroll_index.begin
   :end-before: magictoken.ex_rec_arr_lit_unroll_index.end
   :dedent: 8

Record subtyping

Warning

This is an experimental feature.

Numba allows width subtyping of structured scalars. For example, dtype([('a', 'f8'), ('b', 'i8')]) will be considered a subtype of dtype([('a', 'f8')], because the second is a strict subset of the first, i.e. field a is of the same type and is in the same position in both types. The subtyping relationship will matter in cases where compilation for a certain input is not allowed, but the input is a subtype of another, allowed type.

import numpy as np
from numba import njit, typeof
from numba.core import types
record1 = np.array([1], dtype=[('a', 'f8')])[0]
record2 = np.array([(2,3)], dtype=[('a', 'f8'), ('b', 'f8')])[0]

@njit(types.float64(typeof(record1)))
def foo(rec):
    return rec['a']

foo(record1)
foo(record2)

Without subtyping the last line would fail. With subtyping, no new compilation will be triggered, but the compiled function for record1 will be used for record2.

.. seealso::
   `NumPy scalars <http://docs.scipy.org/doc/numpy/reference/arrays.scalars.html>`_
   reference.

Array types

NumPy arrays of any of the scalar types above are supported, regardless of the shape or layout.

Array access

Arrays support normal iteration. Full basic indexing and slicing is supported. A subset of advanced indexing is also supported: only one advanced index is allowed, and it has to be a one-dimensional array (it can be combined with an arbitrary number of basic indices as well).

.. seealso::
   `NumPy indexing <http://docs.scipy.org/doc/numpy/reference/arrays.indexing.html>`_
   reference.

Structured array access

Numba presently supports accessing fields of individual elements in structured arrays by attribute as well as by getting and setting. This goes slightly beyond the NumPy API, which only allows accessing fields by getting and setting. For example:

from numba import njit
import numpy as np

record_type = np.dtype([("ival", np.int32), ("fval", np.float64)], align=True)

def f(rec):
    value = 2.5
    rec[0].ival = int(value)
    rec[0].fval = value
    return rec

arr = np.ones(1, dtype=record_type)

cfunc = njit(f)

# Works
print(cfunc(arr))

# Does not work
print(f(arr))

The above code results in the output:

[(2, 2.5)]
Traceback (most recent call last):
  File "repro.py", line 22, in <module>
    print(f(arr))
  File "repro.py", line 9, in f
    rec[0].ival = int(value)
AttributeError: 'numpy.void' object has no attribute 'ival'

The Numba-compiled version of the function executes, but the pure Python version raises an error because of the unsupported use of attribute access.

Note

This behavior will eventually be deprecated and removed.

Attributes

The following attributes of NumPy arrays are supported:

The flags object

The object returned by the :attr:`~numpy.ndarray.flags` attribute supports the contiguous, c_contiguous and f_contiguous attributes.

The flat object

The object returned by the :attr:`~numpy.ndarray.flat` attribute supports iteration and indexing, but be careful: indexing is very slow on non-C-contiguous arrays.

The real and imag attributes

NumPy supports these attributes regardless of the dtype but Numba chooses to limit their support to avoid potential user error. For numeric dtypes, Numba follows NumPy's behavior. The :attr:`~numpy.ndarray.real` attribute returns a view of the real part of the complex array and it behaves as an identity function for other numeric dtypes. The :attr:`~numpy.ndarray.imag` attribute returns a view of the imaginary part of the complex array and it returns a zero array with the same shape and dtype for other numeric dtypes. For non-numeric dtypes, including all structured/record dtypes, using these attributes will result in a compile-time (TypingError) error. This behavior differs from NumPy's but it is chosen to avoid the potential confusion with field names that overlap these attributes.

Calculation

The following methods of NumPy arrays are supported in their basic form (without any optional arguments):

The corresponding top-level NumPy functions (such as :func:`numpy.prod`) are similarly supported.

Other methods

The following methods of NumPy arrays are supported:

Where applicable, the corresponding top-level NumPy functions (such as :func:`numpy.argmax`) are similarly supported.

Warning

Sorting may be slightly slower than NumPy's implementation.

Functions

Linear algebra

Basic linear algebra is supported on 1-D and 2-D contiguous arrays of floating-point and complex numbers:

Note

The implementation of these functions needs SciPy to be installed.

Reductions

The following reduction functions are supported:

Other functions

The following top-level functions are supported:

The following constructors are supported, both with a numeric input (to construct a scalar) or a sequence (to construct an array):

The following machine parameter classes are supported, with all purely numerical attributes:

Literal arrays

Neither Python nor Numba has actual array literals, but you can construct arbitrary arrays by calling :func:`numpy.array` on a nested tuple:

a = numpy.array(((a, b, c), (d, e, f)))

(nested lists are not yet supported by Numba)

Modules

random

Generator Objects

Numba supports :py:class:`numpy.random.Generator()` objects. As of version 0.56, users can pass individual NumPy :py:class:`Generator` objects into Numba functions and use their methods inside the functions. The same algorithms are used as NumPy for random number generation hence maintaining parity between the random number generated using NumPy and Numba under identical arguments (also the same documentation notes as NumPy :py:class:`Generator` methods apply). The current Numba support for :py:class:`Generator` is not thread-safe, hence we do not recommend using :py:class:`Generator` methods in methods with parallel execution logic.

Note

NumPy's :py:class:`Generator` objects rely on :py:class:`BitGenerator` to manage state and generate the random bits, which are then transformed into random values from useful distributions. Numba will unbox the :py:class:`Generator` objects and will maintain a reference to the underlying :py:class:`BitGenerator` objects using NumPy's ctypes interface bindings. Hence :py:class:`Generator` objects can cross the JIT boundary and their functions be used within Numba-Jit code. Note that since only references to :py:class:`BitGenerator` objects are maintained, any change to the state of a particular :py:class:`Generator` object outside Numba code would affect the state of :py:class:`Generator` inside the Numba code.

.. literalinclude:: ../../../numba/tests/doc_examples/test_numpy_generators.py
   :language: python
   :start-after: magictoken.npgen_usage.begin
   :end-before: magictoken.npgen_usage.end
   :dedent: 8

The following :py:class:`Generator` methods are supported:

RandomState and legacy Random number generation

Numba supports top-level functions from the numpy.random module, but does not allow you to create individual RandomState instances. The same algorithms are used as for :ref:`the standard random module <pysupported-random>` (and therefore the same notes apply), but with an independent internal state: seeding or drawing numbers from one generator won't affect the other.

The following functions are supported.

Initialization

Warning

Calling :func:`numpy.random.seed` from interpreted code (including from :term:`object mode` code) will seed the NumPy random generator, not the Numba random generator. To seed the Numba random generator, see the example below.

from numba import njit
import numpy as np

@njit
def seed(a):
    np.random.seed(a)

@njit
def rand():
    return np.random.rand()


# Incorrect seeding
np.random.seed(1234)
print(rand())

np.random.seed(1234)
print(rand())

# Correct seeding
seed(1234)
print(rand())

seed(1234)
print(rand())

Simple random data

Permutations

Distributions

The following functions support all arguments.

Note

Calling :func:`numpy.random.seed` from non-Numba code (or from :term:`object mode` code) will seed the NumPy 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.

stride_tricks

The following function from the :mod:`numpy.lib.stride_tricks` module is supported:

Standard ufuncs

One objective of Numba is having all the standard ufuncs in NumPy understood by Numba. When a supported ufunc is found when compiling a function, Numba maps the ufunc to equivalent native code. This allows the use of those ufuncs in Numba code that gets compiled in :term:`nopython mode`.

Limitations

Right now, only a selection of the standard ufuncs work in :term:`nopython mode`. Following is a list of the different standard ufuncs that Numba is aware of, sorted in the same way as in the NumPy documentation.

Math operations

UFUNC MODE
name object mode nopython mode
add Yes Yes
subtract Yes Yes
multiply Yes Yes
divide Yes Yes
logaddexp Yes Yes
logaddexp2 Yes Yes
true_divide Yes Yes
floor_divide Yes Yes
negative Yes Yes
power Yes Yes
float_power Yes Yes
remainder Yes Yes
mod Yes Yes
fmod Yes Yes
divmod (*) Yes Yes
abs Yes Yes
absolute Yes Yes
fabs Yes Yes
rint Yes Yes
sign Yes Yes
conj Yes Yes
exp Yes Yes
exp2 Yes Yes
log Yes Yes
log2 Yes Yes
log10 Yes Yes
expm1 Yes Yes
log1p Yes Yes
sqrt Yes Yes
square Yes Yes
cbrt Yes Yes
reciprocal Yes Yes
conjugate Yes Yes
gcd Yes Yes
lcm Yes Yes

(*) not supported on timedelta types

Trigonometric functions

UFUNC MODE
name object mode nopython mode
sin Yes Yes
cos Yes Yes
tan Yes Yes
arcsin Yes Yes
arccos Yes Yes
arctan Yes Yes
arctan2 Yes Yes
hypot Yes Yes
sinh Yes Yes
cosh Yes Yes
tanh Yes Yes
arcsinh Yes Yes
arccosh Yes Yes
arctanh Yes Yes
deg2rad Yes Yes
rad2deg Yes Yes
degrees Yes Yes
radians Yes Yes

Bit-twiddling functions

UFUNC MODE
name object mode nopython mode
bitwise_and Yes Yes
bitwise_or Yes Yes
bitwise_xor Yes Yes
bitwise_not Yes Yes
invert Yes Yes
left_shift Yes Yes
right_shift Yes Yes

Comparison functions

UFUNC MODE
name object mode nopython mode
greater Yes Yes
greater_equal Yes Yes
less Yes Yes
less_equal Yes Yes
not_equal Yes Yes
equal Yes Yes
logical_and Yes Yes
logical_or Yes Yes
logical_xor Yes Yes
logical_not Yes Yes
maximum Yes Yes
minimum Yes Yes
fmax Yes Yes
fmin Yes Yes

Floating functions

UFUNC MODE
name object mode nopython mode
isfinite Yes Yes
isinf Yes Yes
isnan Yes Yes
signbit Yes Yes
copysign Yes Yes
nextafter Yes Yes
modf Yes No
ldexp Yes (*) Yes
frexp Yes No
floor Yes Yes
ceil Yes Yes
trunc Yes Yes
spacing Yes Yes

(*) not supported on windows 32 bit

Datetime functions

UFUNC MODE
name object mode nopython mode
isnat Yes Yes