lauriat

vor 4 Jahrendiscuss

There is https://github.com/lauriat/funct which has some of those higher level functions.

vor 4 Jahrendiscuss

Neat. There's also sc but I wanted to make something light and simple.

vor 6 Jahrendiscuss

Thanks for the feedback! Redesigned swiss-army-knife is well put.

I’m sure Array’s not for everyone, but for some, including me it’s a nifty tool. I don’t expect people to memorise all the features of the library - the aim was to name and document each feature clearly such that finding the right method would be easy with the help of an IDE.

lauriat

vor 6 Jahrendiscuss

Fair enough. Readability is subjective but I understand the sentiment. Constructing list comprehensions of such long chained expressions can be rather tedious and error prone, though (as your example shows).

lauriat

vor 6 Jahrendiscuss

That's why the A is capitalised ;)

lauriat

vor 6 Jahrendiscuss

cheers!

lauriat

vor 6 Jahrendiscuss

I agree, and yes, the line may be a bit excessive. The idea of Arrays is not just to cram a heap of functions to a single line. The readability (at least to me) is improved even with e.g. a single map

  arr.map(func)

vs.

  list(map(func, arr))

lauriat

vor 6 Jahrendiscuss

If you're doing matrix multiplication or other math operations on fixed size sequences, you shouldn't.

If, however, you need the dynamic nature of the built-in list or functional methods with a touch of numpyness, you should give Array a spin.

lauriat

vor 6 Jahrendiscuss

Fair enough, the example is a bit exaggerated. You could implement it with comprehensions

  all(func3(y) for y in (func1(x) for x in zip(a, b)) if func2(y))

It most likely is a bit faster, but I wouldn't say it's more readable.

lauriat

vor 6 Jahrendiscuss

You can already do that with

  d.get("non_existant_key", default)

lauriat

vor 6 Jahrendiscuss

My explanation was pretty poor, let me rephrase

For example, when calling

  Array((x, y), (z, w)).index((z, w))

the following piece of code is executed

  bool(Array((x, y)).__eq__((z, w)))
  = bool(Array(False, False))

If __bool__ returned whether the Array is nonempty, bool(Array(False, False)) would evaluate to True and the method would wrongly return 0.

You're right that it would be more clear if __bool__ would behave similarly, but since Array computes operations element-wise, it isn't possible.

lauriat

vor 6 Jahrendiscuss

I understand the unpythonic nature of Arrays may startle some hardcore pythonistas, but ability to chain functions was one of the main reasons why I wrote the package as I find nested function calls ugly and sometimes rather hard to decipher.

Regarding the perfomance, Arrays aren't meant to be super high performing but rather a simple way to manipulate sequences. For the best performance you should go with generic python, toolz or other.

lauriat

vor 6 Jahrendiscuss

Thanks!

Good point. However setting

  def __bool__(self): return self.nonEmpty

would mess up certain methods e.g. .index for nested Arrays as __eq__ is computed elementwise and bool(Array(False, False)) would evaluate to True.

Maybe a warning would be appropriate? (as is the case with ndarrays)

lauriat

vor 6 Jahrendiscuss

Thank you for taking the time to check it out!

Naturally if you're dealing with big arrays/tensors, numpy is the best choice for operating on sequences.

However, ndarrays have downsides for certain use cases - as ndarrays are fixed size, adding elements is very slow, also they don't support functional methods (or rather you have to create a new array every time you apply e.g. a map), and ndarrays of any other type than numbers doesn't really make sense.

Many of the methods are wrappers for built-ins, but I find the syntax of Arrays cleaner than the weirdness of the builtins.

For example, while applying an async "starmap" to an Array is just a method call, with built-in lists you would have go through the whole hassle of importing both ThreadPoolExecutor and starmap, creating an executor, scheduling the function, and finally converting the result back to a list.

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