Numba¶

Version:: 2025-10

Everyone knows: Python is slow — especially when it comes to loops.

Instead of vectorizing everything or rewriting your logic in C/C++, there’s a hidden gem that deserves more attention: Numba.

What is Numba?¶

Introduction¶

Numba is a Just-In-Time (JIT) compiler for Python, powered by LLVM. It takes your regular Python functions and compiles them into native machine code.

Result? Performance close to C, without leaving Python.

Extension in pandas¶

The apply method in Pandas is typically something we try to avoid, as it often slows down code execution significantly.

But sometimes, we simply have no other choice.

So, how can we speed up apply without installing any extra libraries?

Since version 2.2, it’s possible to run the function using Numba instead of plain Python, thanks to the engine parameter.

Numba enables faster computation by compiling the code using the Just-In-Time (JIT) method to optimize performance.

Moreover, operations can now be parallelized, further boosting execution speed.

Example: Compute the Sum of Squares¶

Without Numba (slow):

def sum_of_squares(arr):
    total = 0
    for x in arr:
        total += x * x
    return total

With Numba (fast):

from numba import njit

@njit
def sum_of_squares(arr):
    total = 0
    for x in arr:
        total += x * x
    return total

Performance Comparison¶

With an array of 10 million elements:

Pure Python: ~1.3 seconds
With Numba: ~0.02 seconds

Why It’s Great¶

You keep your code clean and native
You compile only the critical parts
Perfect for custom algorithms and complex loops

Takeaway¶

If you’re looking to optimize your code without breaking it or switching languages, add Numba to your data science toolbox.