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 have a C/C++ …

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 have a C/C++ …

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 decorators …

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 the …

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 revisiting …

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 Zero.

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 as …

Numba implements the ability to run loops in parallel, similar to OpenMP parallel for loops and Cython’s prange. The loops body is scheduled in seperate threads, and they execute in a nopython numba …

This is a short guide to features present in Numba that can help with obtaining the best performance from code. Two examples are used, both are entirely contrived and exist purely for pedagogical …