21–30 of 158 posts
If Numpy, Pandas, etc. were wrappable from JavaScript this could have easily been titled "Why I use Node.js for High Performance Scientific Computing". The "Python" here isn't particularly material to the result, it's mostly a wrapper around C. Toss in Cython, and now you've really gone outside the bounds of "I'm just using 'Python' for HPC!". I agree some of the tooling and niceties are beyond a doubt best in breed…
Except, when you're on Windows, and it just doesn't. Just installing things and doing the 'hello world' for aforementioned libraries is laughably impossible.
So, use Python, but use it only on Linux.
(Okay, if you absolutely must do it in Windows: Use Anaconda).
If Numpy, Pandas, etc. were wrappable from JavaScript this could have easily been titled "Why I use Node.js for High Performance Scientific Computing". The "Python" here isn't particularly material to the result, it's mostly a wrapper around C. Toss in Cython, and now you've really gone outside the bounds of "I'm just using 'Python' for HPC!". I agree some of the tooling and niceties are beyond a doubt best in breed…
But they aren't, of course.
For science, the fact that so many great libraries are available and they all use Numpy arrays as base datatype (from image processing to GIS to machine learning etc etc) is a real strength of Python and I doubt another lanuage is going to achieve that any time soon.
If Numpy, Pandas, etc. were wrappable from JavaScript this could have easily been titled "Why I use Node.js for High Performance Scientific Computing". The "Python" here isn't particularly material to the result, it's mostly a wrapper around C. Toss in Cython, and now you've really gone outside the bounds of "I'm just using 'Python' for HPC!". I agree some of the tooling and niceties are beyond a doubt best in breed…
Completely bizarre attitude (creator of pandas here).
Summary in the conclusion: "The end result is an implementation several orders of magnitude faster than the current reference implementation in Java. ... [Python] makes the first version easy to implement and provides plenty of powerful tools for optimization later when you understand where and how you need it." [edited to be a statement instead of rhetorical question]
All the points the author makes through the post are interesting, and Python is definitely great for protoyping, but I think the initial premise is false: "people don't tend to think of [Python] as a high performance language; for that you would want a compiled language -- ideally C or C++ but Java would do." Java is compiled to bytecode, but it isn't a "compiled language" since that bytecode has to be interpreted by…
Earlier quoted context omitted.
Completely bizarre attitude (creator of pandas here).
Not disagreeing with you, but would you mind elaborating on that?
This may be a liiiitle bit off-topic, but I really need to get it off my chest: Python for high-performance scientific computer works beautifully ... it's a dream. Scipy/numpy, matplotlib, pandas, ipython. They're all unbelievably awesome. It all just works. Except , when you're on Windows, and it just doesn't. Just installing things and doing the 'hello world' for aforementioned libraries is laughably impossible. So…
Good article. Couldn't find who wrote it since it doesn't have a byline. I'm guessing it was Leland McInnes?
https://github.com/lmcinnes/hdbscan/commits/master/notebooks...
Summary in the conclusion: "The end result is an implementation several orders of magnitude faster than the current reference implementation in Java. ... [Python] makes the first version easy to implement and provides plenty of powerful tools for optimization later when you understand where and how you need it." [edited to be a statement instead of rhetorical question]
Actually, even the subsection headings in bold give a very succinct summary: - Python has easy development ( https://xkcd.com/353/ ) - Great libraries (ie, free matlab) - Cython for efficiency via C - The algorithms themselves determine speediness (ie numerical methods)
This is so important I wish people would focus more on it. I recently rewrote some Javascript code in (pure) python and got a good 2 orders of magnitude speed up on large inputs just by picking the right data structures and replacing an O(n^3) nested loop with an O(n log n) approach.
Earlier quoted context omitted.
Not disagreeing with you, but would you mind elaborating on that?
Claiming that using wrapped libraries written in another programming language (LAPACK, anyone?) is an inauthentic usage of the language (here, Python) is pedantic and unhelpful. C is just a wrapper for assembly, then, right?