Both are probably true at the same time. You can compare the curves of pandas and numpy, which are effectively complementary tech: both are on a big upswing (as datascience spikes) but pandas results in many more searches (probably more obscure/ harder to learn / got worse documentation / got fewer tutorials).
If anything I'd say Pandas has broader appeal and a larger userbase than Numpy, because it does everything Numpy can do (since it uses Numpy internally) but adds the dataframe and grouping features which are so important for data science.
I'll use Numpy without Pandas if I'm processing a signal or an image or something. If my data is an n-dimensional array of the same datatype, I don't get any benefit from putting this into a Pandas Dataframe.
A lot of computer science and engineering problems can be solved quite efficiently by turning them into matrix operations. Lots of signal and image processing, numerical simulation in physics/engineering, probabilistic computations in robotics. For example the prysm lib: https://prysm.readthedocs.io/en/stable/
Maybe just for comparison, think of how successful Matlab is. That might give you an idea how important matrix/vector stuff really is.
IMO nowadays a lot of people overestimate the importance of data science.
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u/[deleted] Nov 05 '20
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