NumPy and SciPy are the ones that I know of (and I don't even write Python, so there is surely more).

numpy, scipy, scikit-learn, matplotlib, pandas, biopython, sage, ipython and ipython notebook — now jupyter — (with both matplotlib and clusters management integrations), non-C accelerators (numba, cython), anaconda (a science and engineering semi-proprietary bundling & package manager).

Many of these have their roots in the late 90s or early 00s. I guess Python got its fangs (haha) into scientific computing at the turn of the century, from the late 90s you find articles mentioning the introduction of scripting languages as glue in scientific pipelines variously mentioning Perl, Python and Tcl, and by and large it seems to have slowly accreted around Python.

Some of the Numpy and Scipy routines go back earlier than the 90's. AFAIK, they trace their roots to numeric routines on Fortran punch cards.

> AFAIK, they trace their roots to numeric routines on Fortran punch cards.

Oh yes they can bind to much older libraries (e.g. BLAS), I was talking about the packages themselves. For instance Numpy is the unification of Numeric and Numarray, the former having gotten started circa 1995[0] on matrix-sig[1] and the latter originally indented to replace the former. So numpy itself has its roots in 1995.

[0] https://mail.python.org/pipermail/matrix-sig/1995-August/000...

[1] https://mail.python.org/pipermail/matrix-sig/

There are gems in these early messages too, for instance one of the early threads suggested allowing parens-less tuples and slice literals (a:b) in tuples within indexing: https://mail.python.org/pipermail/matrix-sig/1995-September/...

Slice literals, let alone slice literals in tuples, remain indexing-only features to this day.

So the language has a long history of adapting itself to numerical computing concerns.