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]
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.
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)
...