Let's be real.

For most developers — yes, even experienced ones — Python built-in math module gets ignored unless writing a calculator, some school project or is in dire need of getting the square root.

But the truth?

The math module is seriously underutilized and can help to make your code faster, cleaner, and more intelligent — if you know what to use.

Here, I will breakdown:

A summary of why devs underutilize/pull the math module more than they should

⦿ What secret resources it holds

⦿ How to be an advanced user right after it

⦿Time to put this to rest once and for all.

Why the math Module Is Ignored by Developers?

Here are the usual suspects:

  1. They believe Python does math «good enough» out of the box.

So they stick to +, -, *, /, and maybe pow().

2. They use NumPy for any advanced working.

NumPy = powerful, true — but when you only need a few, a fast and precise functions it's overkill.

3. Then they do not know about what is in the math module.

And that's the real tragedy. The Python standard library has incredible tools come built-in — and most devs don't even get close to using most of them.

What Are You Probably Not Aware Of That Is In math

Now, onto the good stuff that 90% of the devs miss out on:

1. math.isclose(a, b)

What do you get when you compare two floating-point numbers? Weirdness?

0.1 + 0.2 == 0.3 # False 😡

Use math.isclose() instead:

import math
math.isclose(0.1 + 0.2, 0.3)

✅ It considers the errors due to floating-point precision. If you're comparing floats, just always use it.

2. math.prod(iterable)

Tired of doing:

result = 1
  for x in nums:
    result *= x

You can make use of this one-liner:

math.prod([2, 3, 4]) # ➝ 24

👏 Cleaner. Faster. More readable.

3. math.comb(n, k) and math.perm(n, k)

Need to compute combinations or permutations? The formula is now gone :

math.comb(5, 2) # ➝ 10
math.perm(5, 2) # ➝ 20

Suitably for algorithms, interviews, and data science.

4. math.frexp(x) and math.ldexp(m, e)

Low-level but powerful.

math.frexp(8) # ➝ (0.5, 4)
math.ldexp(0.5, 4) # ➝ 8.0

Floating point construction and destruction with a base-2 exponentiation. Extremely helpful for example bit-level hacks or special-purpose optimizations.

5. math.dist(p, q) and math.hypot(x, y)

Have to find the distance between two points?

math.dist([0, 0], [3, 4]) # ➝ 5.0
math.hypot(3, 4) # ➝ 5.0

✅ No more writing distance formula by yourself Python is already there to help you.

6. math.fsum(iterable)

Want accurate float addition?

None

Great for scientific, financial, and data-sensitive use cases.

7. math.factorial(n)

You can stop writing recursive functions or importing SymPy:

math.factorial(5) # ➝ 120

Is very fast, it comes built-in with python and provides very good numerical support including big numbers.

Best of all, you are not restricted to just math-in Python thinking.

Imagine this:

You're creating a small utility, script, or server-side application. You need some very fast, very accurate math — but no reason to pull in SciPy, NumPy, TensorFlow.

Use math.

It's:

⦿ Lightweight

⦿ Standard

⦿ Under guided C-optimizations

Python gives you power. You just need to use it.

🛠️ TL;DR: Fix Your Math Game in Python

🚫 Stop writing manual loops.

🚫 Stop using == on floats

🚫Stop overlooking the power of built-in

✅ Start using:

⦿ math.isclose()

⦿ math.prod()

⦿ math. comb(), math. perm()

⦿ math. dist(), math. hypot()

⦿ math. fsum() and math. factorial()

Final Thought

Math is not only for scientists or nerds, there is a math modules.

For just about any Python developer with a desire for clean, sexy code

So the next time that you are going to Google how to compute combinations in Python…

Note that math was already aware of it.

Thank you for being a part of the community

Before you go: