A hard exercise to help build the right mental model for Python data.

The “Solution” link visualizes execution and reveals what’s actually happening using 𝗺𝗲𝗺𝗼𝗿𝘆_𝗴𝗿𝗮𝗽𝗵: https://github.com/bterwijn/memory_graph

  • bterwijn@programming.devOP
    3·
    1 day ago

    If you don’t want mutability you have to go to a pure functional language like Haskell, but then you have to copy a big list every time you make a change. There are ways to optimize copying by secretly sharing data behind the scene but you pay a performance price in some way. Then again, Python is slow and has mutability but popular for other reasons.

    • lad@programming.devEnglish
      3·
      17 hours ago

      I don’t think mutability is wrong as a concept, albeit I enjoyed learning and toying with Haskell, but I really think these functions in Python should have been two sets of operations, one set to do what a += b does and one set to do what a = a + b does.

      I’ve been doing C++ for quite some time and amount of implicit things that happen magically and not everyone get them right and this leads to bugs and confusion had really grown on me with time, and I feel like this is the same pattern here, where we get implicit magic instead of being clear with intentions and results

      • bterwijn@programming.devOP
        3·
        17 hours ago

        I think the confusion comes from a += b being equivalent to a = a + b for immutable types, so some people generalize that incorrectly to mutable types too. Otherwise I think it’s pretty clear a += b mutates a, and a = a + b first computes a + b and then reassigns that to a so that its identity changes, just like in c = a + b.

        If you implement these operations in a class you have to implement each dunder, __iadd__(self, other) and __add__(self, other) separately, same thing in C++.

    • a_non_monotonic_function@lemmy.world
      4·
      1 day ago

      Like anything else, these sorts if issues are rather murky and are directly impacted by the user’s competence in the tools.

      Does python have obvious overhead issues? Yes.

      Does Python have to be super inefficient? No.

      Take basic set operations, for example. Do it manually in the language and it will be dog slow. Do it using the set class? You are leveraging the speed of the underlying C implementation.

      E.g., In competitive programming eventually need C or Java, but a strong Python user can move the bar significantly in terms of how many problems are possible with Python.

      If you don’t want mutability you have to go to a pure functional language like Haskell, but then you have to copy a big list every time you make a change. There are ways to optimize copying by secretly sharing data behind the scene but you pay a performance price in some way.

      I don’t believe this to be the case. The immutability is precisely why efficient structural sharing is possible without screwing up other data structures. And for standard stuff, it is actually happening behind the scenes already.

      You see similar claims about recursion in general, but those claims are often so broad that they don’t hold water. I mean, yea, if your language sucks at optimizing recursion it isn’t going to be a pleasant experience, but tail call elimination, lazy evaluation, etc. mean you can write some really awesome and efficient code.

      I think the bigger problem is that it takes a lot of time to internalize what is going on under the hood when you make a call or initialize a data structure.