2.5. Supported Python features

Apart from the Language part below, which applies to both object mode and nopython mode, this page only lists the features supported in nopython mode.

2.5.1. 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:

  • Function definition
  • Class definition
  • Exception handling (try .. except, try .. finally)
  • Context management (the with statement)
  • Comprehensions (either list, dict, set or generator comprehensions)
  • Generator delegation (yield from)

The raise statement is supported in several forms:

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

Numba supports generator functions and is able to compile them in object mode and 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 generator.send(), generator.throw(), generator.close() methods).

2.5.2. 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 int and a float).


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


List sorting currently uses a quicksort algorithm, which has different performance characterics than the algorithm used by Python. None

The None value is supported for identity testing (when using an optional type). bytes, bytearray, memoryview

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

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

2.5.3. Built-in functions

The following built-in functions are supported:

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

2.5.4. Standard library modules array

Limited support for the 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". collections

Named tuple classes, as returned by 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: random

Numba supports top-level functions from the 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.


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


The generator is not thread-safe when releasing the GIL.

Also, under Unix, if creating a child process using os.fork() or the 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).

See also

Numba also supports most additional distributions from the Numpy random module.

2.5.5. Third-party modules cffi

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

  • char
  • short
  • int
  • long
  • long long
  • unsigned char
  • unsigned short
  • unsigned int
  • unsigned long
  • unsigned long long
  • int8_t
  • uint8_t
  • int16_t
  • uint16_t
  • int32_t
  • uint32_t
  • int64_t
  • uint64_t
  • float
  • double
  • ssize_t
  • size_t
  • 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:

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:


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

Inline cffi modules require no registration.