NumPy

“Most performance sensitive features are already available in python between numpy, numba and Cython”

“NumPy does make the mechanism clearer, but aside from learning purposes, it might be a bit slow.”

“For linear algebra and ML with Jax/Numpy I find it quite readable.”

“Numpy still doesn't have the semantics of a full programming language”

“I’d happily start with the NumPy version and only reach for a vector DB once it actually hurts.”

“numpy style construction is quick to write and performs (and reads) much better than”

“it's much less convenient and less self explanatory than the apl system”

“it took until numpy to provide the right ergonomics for everyday developers to stop thinking in loops”

“NumPy, SciPy, and pandas gave them a decent glue layer over C and Fortran”

“libraries, and specifically NumPy, are the reason Python is still in the picture today”

“NumPy, PyTorch, JAX have non-trivial overhead, and might even be slower than vanilla Python”

“numpy has the same problem as blas basically, right? The limited interface.”

“C extensions written against the CPython C-API such as numpy, lxml and many crypto libraries that either fail or run poorly under PyPy's cpyext”

“not having zero overhead structs that one can in an array handicaps types of performance codes one can write”

“I love how you stuck with pure Python libraries - tkinter, numpy, and sounddevice.”

“Numpy is wrapping around C data structures that the loading process loads into”

“Without NumPy and Pandas, ML in Python would never have been a thing.”

“What's the utility in burning energy to regenerate this all the time when implementations already exist?”

“Just using Numpy also makes sure you get the math right without having to deal with higher level frameworks or c++ libraries.”

“NumPy solves this but is heavyweight and often overkill for 1D data.”

“please do at least use Numba or Numpy so you aren't paying huge overheads for making an object of every little datum”

“numpy is the the de-facto foundation for data science in python”

“Python's combination of readable syntax and powerful libraries (NumPy, SymPy, Matplotlib)”

“Pandas and Numpy made it quite easy to get up and running with a lot of analytics.”

“By the 2010s, I was hearing about all these people that were not using R, but were using NumPy and SciPy.”

“writing matrix maths in numpy is much easier thanks to not having to make this arbitrary distinction”

“numpy tries to be this highly unopinionated tool that can do everything, and ends up annoying to use anywhere”

“Numpy is a bit more verbose but still 1-to-1 with the whiteboard.”

“it's a shame to have to bring in a bazooka to get decent ergonomics and performances”

“Pair it with numpy and matplotlib (two external dependencies that personally I consider part of Python itself)”

“you'd be surprised at the response when I showed some of them how to do it in python numpy”

“If you need speed and efficiency on your local machine, you use NumPy outright”

“it's a solved problem and Python is eating good in terms of quality dataframe libraries”

“extremely easy to use, fast and extensive in the whole ecosystem”

“adding Pandas and NumPy which have numerous (really: squillions of) dependencies outside the foundation library”

“"Hacks" 4-10 could easily be replaced with "use numpy." Performance gains from doing math better in pure Python are minimal compared with numpy.”

“While Numpy has many similar operations, it lacks the terseness, concepts like trains and forks etc.”

“Numpy is a really great library making all of those scientific packages”

“Move your performance critical kernels outside Python. Numpy, Pytorch, external databases, Golang microservice, etc.”

“calling out to external code that isn't written in Python (e.g., NumPy, etc)”