throwaway2037 9 hours ago

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.

▲ m55au 9 hours ago | parent [-]

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]

▲ noitpmeder 4 hours ago | parent [-]

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)
  ...

	▲	m55au 3 hours ago \| parent [-]
		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.