[SatvikBeri]

Python is one of my least favorite languages, but it is a language I absolutely have to use for data work – it would be impractical to rewrite everything in Julia.

Type annotations help a lot. They're not perfect, but for long-running jobs it's a huge help to catch something when PyCharm highlights it instead of 30 minutes into the job.

[dreamer7]

I'm curious to know what makes it one of your least favourite languages. Could you elaborate?

The reason I'm interested is because I love python and would like to hear differing opinions

[SatvikBeri]

Sure! Broadly speaking, I think Python emphasizes a combination of features that lead to code that isn't very composable or decoupled, but without the error-checking benefits you might get in a language like Scala.

For example, consider implementing complex rational numbers. If you have a complex number type, and a rational number type, it should be relatively easy to combine them. Julia does this with literally 11 lines of code in Rational.jl, and 0 lines of code in Complex.jl . In Python, this is extremely difficult, to the point that nobody does it – you would need to restructure the class hierarchy, and you can't do that without access to both.

Another example is the lack of extension methods. For example, if you want to add a bit of functionality to a string or a sqlalchemy engine, you can either add functions (which have a different syntax) or inherit and follow the awful "MyString" pattern.

Python chose to emphasize list comprehensions over map + filter. The problem with that is that you now have syntax that doesn't generalize to other collections, especially custom collections.

Some patterns like async/await, decorators, and with syntax encourage hardcoding decisions when writing a function, as opposed to when using it. This makes your functions less flexible and means you have to write more of them. E.g. consider Julia's do-block syntax[1], which is very similar to Python's with but based on function composition and far more general as a result. Or compare Julia's @spawn to Python's async/await.

[1]: https://docs.julialang.org/en/v1/manual/functions/#Do-Block-...

[dragonwriter]

> For example, consider implementing complex rational numbers. If you have a complex number type, and a rational number type, it should be relatively easy to combine them. Julia does this with literally 11 lines of code in Rational.jl, and 0 lines of code in Complex.jl . In Python, this is extremely difficult, to the point that nobody does it

It's really not difficult; yes, it's more verbose, sure.

> Python chose to emphasize list comprehensions over map + filter. The problem with that is that you now have syntax that doesn't generalize to other collections, especially custom collections.

Python has map/filter/reduce and they work fine. Comprehensions (and genexps) are more concise and cleae for 95% of use cases, I find, so I think the right choice was made there, though I do think people do tend to reach for them sometimes when map, filter, and friends are more appropriate.

> consider Julia’s do-block syntax[1], which is very similar to Python's with

It's more similar to Ruby’s block notation than Python with; it constructs an anonymous function and passes it to another function being called. It is true that with multiline lambdas at all, much less a convenience notation for passing them to other functions, with notation would be superfluous.

> Or compare Julia’s @spawn to Python’s async/await.

Why? They solve different problems. @spawn is equivalent to Python’s concurrent.futures.submit, and it’s buddy fetch() is concurrent.futures.Future.result(). Python has had those longer than it has async/await.

[SatvikBeri]

> It's really not difficult; yes, it's more verbose, sure.

Can you point to a case where it's actually been done? I don't even see how you would do it in Python without modifying the standard library or reimplementing significant chunks of cmath.py or fractions.py . In languages like Julia or Haskell this is possible (and relatively easy) as a third party who's just importing the two types.

> Python has map/filter/reduce and they work fine.

Python's map has a few problems, e.g. crippled lambdas make it less useful and it returns a map object instead of the same type as the original collection. It's better than nothing, but it's noticeably weaker than map in other languages.

> Comprehensions (and genexps) are more concise and cleae for 95% of use cases, I find, so I think the right choice was made there, though I do think people do tend to reach for them sometimes when map, filter, and friends are more appropriate.

One big use case where it doesn't apply is numpy arrays or pytorch tensors. It's very common, at least in my domain, for people to initially write code with lists and then switch to arrays for performance. But despite the fact that they should have mostly the same interface, this requires lots of little syntax changes and it's easy to introduce a bug.

Haskell has this issue too to some extent, linked lists are the default data structure and `map` doesn't work with others, you need `fmap`.

> It's more similar to Ruby’s block notation than Python with; it constructs an anonymous function and passes it to another function being called. It is true that with multiline lambdas at all, much less a convenience notation for passing them to other functions, with notation would be superfluous.

Good point.

> Why? They solve different problems. @spawn is equivalent to Pythons submit from concurrent.futures, and it's buddy fetch() is Future.result(). Python has had that longer than async/await.

I do like concurrent.futures, but most blog posts, documentation etc. recommend using asyncio when either one would work, and the majority of libraries at this point use asyncio. I don't think it's really possible to avoid asyncio at this point.

[dragonwriter]

> I don’t even see how you would do it in Python without modifying the standard library or reimplementing significant chunks of cmath.py or fractions.py

If I understand the problem correctly, and you are trying to implement Gaussian Rationals, you’d inherit from numbers.Complex, storing the real and imaginary parts as instances of numbers.Rational (you could use the concrete fractions.Fraction, but I don’t think you actually would need to reference the concrete type to do the implementation.)

There’s a bit of boilerplate isinstance-stuff implementing the operations, which is somewhat tedious, but not difficult (I think its less involved than the custom integral class used as an example in the numbers module documentation, because you should be able to get by only handling same-class, Rational, and Complex cases for most ops.

> One big use case where it doesn’t apply is numpy arrays or pytorch tensors.

Good point. It is not one I’ve hit a lot because of the stuff most of python code use is on doesn’t hit those, but I’ve seen that.

> I do like concurrent.futures, but most blog posts, documentation etc. recommend using asyncio when either one would work, and the majority of libraries at this point use asyncio. I don’t think it’s really possible to avoid asyncio at this point.