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-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 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 …

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 …

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 …

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 …

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 …

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 …