Numba-compiled numerical algorithms in Python can approach the speeds of C or FORTRAN. You don't need to replace the Python interpreter, run a separate compilation step, or even …

Numba-compiled numerical algorithms in Python can approach the speeds of C or FORTRAN. You don't need to replace the Python interpreter, run a separate compilation step, or even …

Numba documentation ¶ This is the Numba documentation. Unless you are already acquainted with Numba, we suggest you start with the User manual.

Numba is a just-in-time compiler for Python that works best on code that uses NumPy arrays and functions, and loops. The most common way to use Numba is through its collection of …

One way to compile a function is by using the numba.jit decorator with an explicit signature. Later, we will see that we can get by without providing such a signature by letting numba figure out …

Numba generated code will evaluate the full expression in one go, for each element. The numba approach approach avoids having temporal intermmediate arrays built, as well as avoiding …

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 …

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 …

We are now uploading packages to the numba channel on Anaconda Cloud for 32-bit little-endian, ARMv7-based boards, which currently includes the Raspberry Pi 2 and 3, but not the Pi 1 or …

All numba array operations that are supported by Case study: Array Expressions, which include common arithmetic functions between Numpy arrays, and between arrays and scalars, as well …