You are viewing archived documentation from the old Numba documentation site. The current documentation is located at https://numba.readthedocs.io.


Numba is a compiler for Python array and numerical functions that gives you the power to speed up your applications with high performance functions written directly in Python.

Numba generates optimized machine code from pure Python code using the LLVM compiler infrastructure. With a few simple annotations, array-oriented and math-heavy Python code can be just-in-time optimized to performance similar as C, C++ and Fortran, without having to switch languages or Python interpreters.

Numba’s main features are:

  • on-the-fly code generation (at import time or runtime, at the user’s preference)
  • native code generation for the CPU (default) and GPU hardware
  • integration with the Python scientific software stack (thanks to Numpy)

Here is how a Numba-optimized function, taking a Numpy array as argument, might look like:

def sum2d(arr):
    M, N = arr.shape
    result = 0.0
    for i in range(M):
        for j in range(N):
            result += arr[i,j]
    return result