[lauriat]

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)

[pansa2]

> bool(Array(False, False)) would evaluate to True

Isn't that consistent with the built-in `list`, though, because `bool([False, False])` is True?

[lauriat]

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.

[wodenokoto]

I think the complaint is that you are using `bool` as a `all()` call on your Arrays.

If you used `all()` in your implementation instead, you could be compatible with the idiomatic use of `bool(my_list)` and the _very_ common `if my_list:` structure could be used with Arrays too (like most people probably would expect from a "better list type")

Regardless, Pandas struggles with the same problem, so you are at least in good company :)