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Comment by npalli | Hacker News Reader

Python is nothing without it’s batteries.

The design and success of e.g. Golang is pretty strong support for the idea that you can't and shouldn't separate a language from its broader ecosystem of tooling and packages.

The success of python is due to not needing a broader ecosystem for A LOT of things.

They are of course now abandoning this idea.

> The success of python is due to not needing a broader ecosystem for A LOT of things.

I honestly think that was a coincidence. Perl and Ruby had other disadvantages, Python won despite having bad package management and a bloated standard library, not because of it.

It's because Ruby captured the web market and Python everything else, and I get everything is more timeless than a single segment.

Ruby was competing on the web market and lost to many others, including Python. In part, because python had a much broader ecosystem, and php had wide adoption through wordpress and others, and javascript was expanding from browsers.

The bloated standard library is the only reason I kept using python in spite of the packaging nightmare. I can do most things with no dependencies, or with one dependency I need over and over like matplotlib

If python had been lean and needed packages to do anything useful, while still having a packaging nightmare, it would have been unusable

Well, sure, but equally I think there would have been a lot more effort to fix the packaging nightmare if it had been more urgent.

There was a massive effort though, the proliferation of several different package managers is evidence of that.

Maybe. A lot of them felt like one-person projects that not many people cared about. I think that on the contrary, part of the reason so many different package managers could coexist with no clear winner emerging was that the problem wasn't very serious for a lot of the community.

The bloated standard library is the reason why you can send around a single .py file to others and they can execute it instantly.

Most of the python users are not able nor aware of venv, uv, pip and all of that.

Python is its batteries.

But why whenever I try to use it, it tries to hurt me like it's kicking me right in my batteries?

What language is used to write the batteries

C/C++, in large part

And below that, FORTRAN :)

These days it's a whole lot of Rust.

These days it’s still a whole lot of Fortran, with some Rust sprinkled on top. (:

Which since Fortran 2003, or even Fortran 95, has gotten rather nice to use.

IDK it's become too verbose IMHO, looks almost like COBOL now. (I think it was Fortran 66 that was the last Fortran true to its nature as a "Formula Translator"...)

We are way beyond comparing languages to COBOL, now that plenty folks type whole book sized descriptions into tiny chat windows for their AI overloads.

I hear this so much from Python people -- almost like they are paid by the word to say it. Is it different from Perl, Ruby, Java, or C# (DotNet)? Not in my experience, except people from those communities don't repeat that phrase so much.

The irony here: We are talking about data science. 98% of "data science" Python projects start by creating a virtual env and adding Pandas and NumPy which have numerous (really: squillions of) dependencies outside the foundation library.

Someone correct me if I'm completely wrong, but by default (i.e. precompiled wheels) numpy has 0 dependencies and pandas has 5, one of which is numpy. So not really "squillions" of dependencies.

pandas==2.3.3

├── numpy [required: >=1.22.4, installed: 2.2.6]

├── python-dateutil [required: >=2.8.2, installed: 2.9.0.post0]

│ └── six [required: >=1.5, installed: 1.17.0]

├── pytz [required: >=2020.1, installed: 2025.2]

└── tzdata [required: >=2022.7, installed: 2025.2]

I don't know about _squillions_, but numpy definitely has _requirements_, even if they're not represented as such in the python graph.

e.g.

  https://github.com/numpy/numpy/blob/main/.gitmodules (some source code requirements)
  https://github.com/numpy/numpy/tree/main/requirements (mostly build/ci/... requirements)
  ...

They're not represented, because those are build-time dependencies. Most users when they do pip install numpy or equivalent, just get the precompiled binaries and none of those get installed. And even if you compile it yourself, you still don't need those for running numpy.

Read https://numpy.org/devdocs/building/blas_lapack.html.

NumPy will fall back to internal and very slow BLAS and LAPACK implementations if your system does not have a better one, but assuming you're using NumPy for its performance and not just the convenience of adding array programming features to Python, you're really gonna want better ones, and what that is heavily depends on the computer you're using.

This isn't really a Python thing, though. It's a hard problem to solve with any kind of scientific computing. If you insist on using a dynamic interpreted language, which you probably have to do for exploratory interactive analysis, and you still need speed over large datasets, you're gonna need to have a native FFI and link against native libraries. Thanks to standardization, you'll have many choices and which is fastest depends heavily on your hardware setup.

The wheels will most likely come with openblas, so while you can get the original blas (which is really only slow by comparison, for small tasks it's likely users won't notice), this is generally not an issue.

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