NumPy Exercises for Data Analysis in Python • Hacker News Board

Re: NumPy Exercises for Data Analysis in Python

#31

Post by shrx » Wed, Feb 28, 2018, 11:21 AM UTC

> 13. How to get the positions where elements of two arrays match?

  > Desired Output:
  > #> (array([1, 3, 5, 7]),)

Why is (array([1, 3, 5, 7]),) the desired output, and not array([1, 3, 5, 7]) ?

Re: NumPy Exercises for Data Analysis in Python

#32

Post by longqzh » Wed, Feb 28, 2018, 11:49 AM UTC

For #15, if the number of elements is large, the speed will be slower than we expected, since maxx function is writren in pure python. But in my experience, it is much faster than for loop in pure python.

Re: NumPy Exercises for Data Analysis in Python

#33

Post by thebooktocome » Wed, Feb 28, 2018, 11:58 AM UTC

mindB wrote: post #26

dman wrote: post #19

Earlier quoted context omitted.

Nice! Ive been meaning to try out Julia for a while now. Is the numpy equivalent in Julia largely written in Julia itself? (as opposed to C/Fortran)

Julia's numpy equivalent is basically the standard Array type from the standard library, which I'm 99% sure is native Julia.

If one is working on small (One downside is that unless you're doing BLAS-style operations, writing non-trivial transformations of StaticArrays always seems to require generated functions.

Anyway, I think this is a feature that numpy doesn't provide.

[1] https://github.com/JuliaArrays/StaticArrays.jl

Re: NumPy Exercises for Data Analysis in Python

#34

Post by Sean1708 » Wed, Feb 28, 2018, 3:29 PM UTC

meken wrote: post #29

stabbles wrote: post #18

Earlier quoted context omitted.

Switch to Julia! Hit @edit unique([1,2,3,2]) in the REPL and you see the implementation.

You can do the same thing in IPython/jupyter with ?? e.g. np.unique??

That only works for functions written in pure Python though, right? Although having said that I'm not sure how many of the functions that you'd actually want to look at the source for are written in C/Cython/Fortran.