Numba
Hacker ApprovedThe most recommended of 6 JIT compilers
Speeds up numerical Python code by compiling decorated functions, especially numpy and parallel loops. Caveat: only partly compatible with CPython and best on code of a particular shape.
Speeds up numerical Python code by compiling decorated functions, especially numpy and parallel loops. Caveat: only partly compatible with CPython and best on code of a particular shape.
39 people gave an opinion
- recommend it
- 30
- criticize it
- 9
- approval share
- 77%
Recommended in 9 of 13 months
- November 2025: recommended
- December 2025: recommended
- January 2026: recommended
- February 2026: recommended
- March 2026: recommended
- April 2026: not recommended
- May 2026: recommended
- June 2026: recommended
- July 2026: not recommended
- August 2026: recommended
- September 2026: not recommended
- October 2026: recommended
- November 2026: not recommended (month in progress)
Why it carries the badge
- 30 people recommend itNeeds 10 or more, met.
- 3.3 recommenders for every criticNeeds 2 to 1 or better, met.
- Recommended in 9 of 13 monthsNeeds 4 or more, met.
The same three rules apply to every product, and the badge can’t be bought. How the badge works
What people say about it
The reasons people give, with the number of comments that give each one. Open a reason to read what they said.
Why people recommend it
Each mark is one comment
Easy speedups with a decorator
“there's some low hanging fruit with numba. I would recommend this as a 5-minute solution”
Comment from 23 Jun 2026 in “Lossless GIF recompression via exhaustive search” “you add a decorator to your function and it turns it into compiled code”
Comment from 11 Aug 2026 in “Mojo 1.0 Is Here” “tools like numba can beat them all at way less effort.”
Comment from 8 May 2026 in “Mojo 1.0 Beta” - Comment from 14 Mar 2026 in “Python: The Optimization Ladder”
Great for numpy number crunching
“Numba is great at optimizing numpy code but not necessarily other general Python code.”
Comment from 27 Oct 2026 in “Python's experimental JIT to be removed” “It's great for number crunching.”
Comment from 9 Jan 2026 in “Show HN: Various Shape Regularization Algorithms” “please do at least use Numba or Numpy so you aren't paying huge overheads for making an object of every little datum”
Comment from 1 Jan 2026 in “Python Numbers Every Programmer Should Know” Speed close to C or Cython
“Generally as fast as Cython, but much easier to write.”
Comment from 27 Oct 2026 in “Python's experimental JIT to be removed” “it will be roughly as fast as similar C code”
Comment from 1 Jan 2026 in “Python Numbers Every Programmer Should Know”
What people warn about
Each mark is one comment
Heavy install, system requirements
“it was a pain due to extensive system requirements or alternatively requiring use of conda”
Comment from 27 Oct 2026 in “Python's experimental JIT to be removed” “Numba+llvmlite alone is almost 100MB!”
Comment from 16 Aug 2026 in “Numba in the Browser: Unlocking a New Scientific Python Stack in JupyterLite” Only partly compatible with CPython
“use Numba JIT'd Python that is only partially compatible with CPython”
Comment from 23 Aug 2026 in “Thinking in Python” “appears like Python but are nothing like Python as soon as you scratch the surface”
Comment from 8 May 2026 in “Mojo 1.0 Beta” Needs code in a specific shape
“It's JIT only, and it needs to be in a particular shape to be effectively compiled.”
Comment from 12 Aug 2026 in “Mojo 1.0 Is Here” “Numba is great at optimizing numpy code but not necessarily other general Python code.”
Comment from 27 Oct 2026 in “Python's experimental JIT to be removed”
Other JIT compilers people recommend
The four most recommended other JIT compilers, with the same counts for each, so you can compare like for like. The tick on each bar marks the 2 to 1 line the badge needs.
| Product | Recommend | Criticize | Approval | Months | Owned | Badge |
|---|---|---|---|---|---|---|
| NumbaThis page | 30 | 9 | 77% | 9 | — | Approved |
| CPython experimental JIT | 13 | 16 | 45% | 3 | — | — |
| YJIT | 10 | 2 | 83% | 6 | — | Approved |
| HotSpot JVM | 8 | 0 | 100% | 4 | — | — |
| ZJIT | 7 | 2 | 78% | 5 | — | — |
What people said
38 of the 48 comments that mention it, newest first. Each links to the original comment.
“you can reach for numba, and implement your small core of performance code in python”
“Numba is great at its niche.”
“the stuff it does do is obvious and clean and extremely useful”
“PyPI integration is clean”
“it was a pain due to extensive system requirements or alternatively requiring use of conda”
“Generally as fast as Cython, but much easier to write.”
“Most performance sensitive features are already available in python between numpy, numba and Cython”
“Numba is great at optimizing numpy code but not necessarily other general Python code.”
“I don’t know how people who’ve worked with Python and been frustrated with its performance don’t know about Numba.”
“all of them are lacking in different ways”
“use Numba JIT'd Python that is only partially compatible with CPython”
“they achieve the same win, generating GIL-free, bytecode-level perf”
“Numba+llvmlite alone is almost 100MB!”
“Congrats for a serious engineering achievement!”
“numba ( https://numba.pydata.org/ )”
“It's JIT only, and it needs to be in a particular shape to be effectively compiled.”
“you add a decorator to your function and it turns it into compiled code”
“Stuff like Triton, nvidia warp (the language), numba, cupy jax/pallas and so many others really paved the way.”
“there's some low hanging fruit with numba. I would recommend this as a 5-minute solution”
“Also you can use projects like numba”
“I'd still rather use numba or taichi purely because I don't have to evaluate raw strings and can still rely on linters”
“JAX can’t do what Numba can do for example.”
“effective for less complicated / more self contained type problems”
“tools like numba can beat them all at way less effort.”
“They are DSL with a python-looking syntax but made to be optimized, typed and inferred properly.”
“appears like Python but are nothing like Python as soon as you scratch the surface”
“Potentially lots of bang for little buck.”
“so far Numba has shown surprising performance benefits when executing code in parallel, despite being slower when executed sequentially”
“It's great for number crunching.”
“it will be roughly as fast as similar C code”
“tons of tools like Numba, Jax, Triton, etc that you can use to write very fast code”
“please do at least use Numba or Numpy so you aren't paying huge overheads for making an object of every little datum”
“having gone down the path of porting and testing a problem to numba”
“numba is a good trade-off before moving to Julia completely”
“seem to work well enough for non trivial for loop cases”
“numba for gpu kernels... cool!”
“For me I'd rather deal with Jax or Numba”
“the excessive effort I put into trying out Nuitka, Numba, and PyInstaller”
Counts come from 1,795,640 Hacker News comments, November 2025 to November 2026. How we count. Report a mistake.