Which is an admirable sentiment… but the title of this thread is not “Python: still doing useful work” but “Python: now 20% faster”, and being ridiculously self-congratulatory about this when the correct response is to laugh at the silly pointless frivolity of it. It's an interpreter implementation technique.

There has been many faster python threads lately, I'm looking forward to improvements, especially, those that reach CPython! From the blog post: I think it's private. Maybe they can invite uwi to help with Java solutions. Ofc. The link below describes an alternate way if you don't have virtualenv. I am playing with the AtCoder Library Practice Contest problems and it turns out the networkx algos have terrible constant factors. Likely because pytorch uses C++ under the hood. Optimize for performance--Go is literally hundreds or thousands of times better here. You also want to kill it. Could you open document_en/index.html file? It may take years for almost everything to drift to newer versions, but there's a noticeable performance benefit we all realize over time.

From now, we can measure the amount of implementation excluding the library part, but that's the only change. It's a drop-in replacement. There are also a few red coders on codeforces.com who mostly use pypy (cpython is completely unviable there because numpy and numba is not installed), https://codeforces.com/submissions/pajenegod, https://codeforces.com/submissions/conqueror_of_tourist. Typically "typesafe generics" is what people talk about when they discuss "generics", but since you chose to pick the "dynamically typed languages are generic" nit, I assumed you were talking about (1). In some cases a rewrite may save more money than moving to a 20% faster python. That the CPython maintainers have, time and time again, decided for a "simple implementation" has pushed away many professional VM engineers and researchers who would be more than willing to help maintain a JIT. Making statements based on opinion; back them up with references or personal experience. FFT is shorter than segtree. These contests may contain some dummy tasks that are irrelevant to the library, so don't try to think problems like "ok, maybe this task requires that library, so the solution should be...". I'm curious what the outcome would be if we ran a poll asking what people find easier. Before contest Codeforces Round #673 … > From my perspective Julia is sacrifising some amount of "duct tape UX" to gain speed, and that's the wrong direction. I don't think so (and tip, we can remove all assert with #define NDEBUG). For example: https://atcoder.jp/contests/practice2/submissions?f.Task=&f.... (the user "maspy" is rated at 2750 using only cpython!!! There are ways to mitigate Pandas memory usage (10x is a sign that something has gone very horribly wrong), and sometimes Pandas is simply the wrong tool for the job.

But that doesn't mean it's not useful to have a faster Python, only that there are likely to be limits and trade-offs involved in doing that. What with problems about modifying these algorithms? But if you stopped naming servers a long time ago, and if the pricing structure is favorable, it could mean a huge cost saving without an expensive rewrite. Of course I think it's better to be replaced with more idiomatic expressions afterwards. I think the right directions are starting with a super simple library, containing only highly useful DS and algos, and only after it is highly used, start by introducing new features. This is just a comment on my personal use of Python for competitive programming: I've never used numpy for competitive programming or thought that it would be a good tool for that.

Your error message makes it sound like an outdated or minimal version of git, I'd try updating that, and if that doesn't work add some additional debug. You want at least a magnitude faster to justify the cost and risk of switching. If the code that is executed in PyPy is pure Python, then the speed offered by PyPy is usually noticeable. There are "fair use" terms, which allow some things without permissions -- but things like "copying the binary to company-internal repo so CI runners can pick it" really need explicit permissions if you want to be above the board. In case of software, here is how the law works [0]. You're getting some pushback, but I tend to agree with you on matplotlib and pandas. For example, we never used segment trees with lazy propagation in our contests. https://github.com/python/cpython/blob/0564aafb71a153dd0aca4... https://en.wikipedia.org/wiki/Threaded_code. Possibly - I'm keeping my eye on it. As someone who learned on python (many years ago), I think the balance of being allowed to shoot myself in the foot, while only having to learn complexity when complex concepts came up, was a good combination rather than a bad one. Not to minimize speeding up the glue code, but I don't know if they will see that large of an improvement. Especially when the reference implementation isn’t formally specified. So the thing you really doesn't like is about having some specified knowledge, because the argument about having something badly implemented isn't very good imo. Pypy is not always faster and somehow manages to be slower for networkx.bipartite.maximum_matching: https://atcoder.jp/contests/practice2/submissions/16587795, https://atcoder.jp/contests/practice2/submissions/16587790. Optimize for deployment story (single small artifact vs hundreds of megabytes of dependencies). The early Go compiler reused the code generator from Limbo. For all of numpy's faults I find it odd that you choose its indexing and broadcasting API as the thing that's confusing and worse than equivalents in Mathematica and R. I can't speak to Julia or Matlab, but I find working with arrays in the other two is like pulling teeth. If you're seeing 10x on a web app, either something is wrong or you probably shouldn't be using Pandas. Plotting seems to tend towards magic because plots are basically art, with all the desire for aesthetic customization that applies, and it's a very common task so users also want brevity (magic). Also, does it mean that we'll finally see some suffix automatons? But if this is still a concern, I'm sure there will be quite a few Java users from our community who might be willing to help with a Java version of the ACL library.

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