The other scoping issue in python that always struck me as strange is that loop variables aren't scoped to the loop, they continue to exist after the loop completes. I can see the logic for this feature even if I don't agree with it, but what I really don't get is that the loop variables are not defined if you iterate over something that is empty:

   >>> for item in [1]:
   ...   print item
   1
   >>> item
   1

   >>> for i in []:
   ...   print i
   
   >>> i
   Traceback (most recent call last):
     File "<stdin>", line 1, in <module>
   NameError: name 'i' is not defined

I would expect i == None. That oddity makes it dangerous to use the feature unless you're really careful (e.g. using a for - else construct).

Then there's for-else:

    In [1]: for i in []:
       ...:     pass
       ...: else:
       ...:     print 'Else!'
       ...:
    Else!

    In [2]: for i in []:
       ...:     break
       ...: else:
       ...:    print 'Else!'
       ...:
    Else!

    In [3]: for i in range(2):
       ...:     break
       ...: else:
       ...:     print 'Else!'
       ...:

    In [4]: for i in range(2):
       ...:     pass
       ...: else:
       ...:     print 'Else!'
       ...:
    Else!

The syntax could be interpreted as:

  if len(l) == 0:
    print "Else!"
  else:
    for i in l:
      pass

The "catch cases where a `break` is triggered" case isn't common enough for this syntax feature to be encountered very often, leading to confusion when people come across it (though at least it's not a bug where a common use-case has weird behavior to new-comers).

> but what I really don't get is that the loop variables are not defined if you iterate over something that is empty

if you conceptualize how a for-loop has to work as a while-loop using Python's iterator protocol (which is the only way the iterator protocol itself makes sense), it seems pretty intuitive.

That is, this:

  for item in items:
      ...1
  else:
      ...2

becomes, approximately:

  try:
      while True:
          __hidden_iter = items.iter()
          try: 
              item = __hidden_iter.next()
          except StopIteration:
              raise __NormalLoopExit
          ...1
  except __NormalLoopExit:
      ...2

If you have an empty loop, the first assignment doesn't complete (instead raising StopIteration in evaluating the right side, which raises the notional exception __NormalLoopExit, which invokes the else: clause, if any) so the variable never gets around to being created.

> if an object is immutable, the behavior of Python matches what the naive developer expects

If the object was immutable then append wouldn't work. That's hardly matching expectations.

Most immutable objects don't have methods that would mutate the value, but fail because the object is immutable...

I guess the clarification to what I was saying is that, in the simple case (integers, strings, None) the objects are immutable. It's only getting into cases where the value of the object itself is mutable, that you run into issues. If all objects (or all objects 'allowed' as default values) were immutable, then this behavior would not trigger.

So saying that mutability has nothing to do with it isn't entirely true. It's the immutability of the types of values used in most simple cases that hides this issue from developers until they run into a more complex case.

Read my post that has the "correct answers" which show you how to do it. The key is setting the default to None and then doing something like:

if val is None: val = []

or the more idiomatic python way:

    val = val or []

I believe the former is more idiomatic, but I don't have a reference.

You want to explicitly check against `None` so that you're not overwriting all falsey values of `val` - even though you should generally try to enforce argument types, your second example would cause unexpected behavior in some cases, particularly those that have non-falsey 'default' assignments

I'm well aware of that way to do it, but it doesn't excuse a different way being unintuitive.

Why? You would just get a new list back.

I can see why you might think so, but remember the Zen of Python is to have only one obvious way to do something.

    >>> [1,2,3] + [4,5]
    [1, 2, 3, 4, 5]

Thus appending should do something different than addition.

    >>> x = [1,2,3]
    >>> x.append([4,5])
    >>> x
    [1, 2, 3, [4, 5]]

I'm sorry, but I don't understand why you would think this as unexpected behaviour? For the class A, the list l is a class-level attribute, hence it can be referred via either a or b objects, but for class B, after initialisation, l is an object attribute, so it is different for both c and d.

It's not the concept that can be confusing, it's the syntax python chose.

In most of the languages I'm familiar with, there are very clear syntax differences when working with class attributes. For example, in many languages class attributes have to be accessed via the class name instead of from an instance of the class making it clear to the programmer they are working with a class attribute, e.g. MyClass.myClassVariable not myInstance.myClassVariable. Additionally, the way you define class attributes in python is the way you define instance attributes in many languages, which just adds to the confusion. e.g. in Java or C# you can define class variables directly in the class body, but an explicit 'static' keyword is needed, undecorated definitions are assumed to be instance variables.

Finally, I think the definition of class B above is a little more nuanced, class B has both a class attribute named l AND an instance attribute named l.

B.l == None and B().l == []

Ah, gotcha!

It's been a while since I've done major OOP coding in any language other than Python, so I'm a little rusty. The issues you raise are perfectly legitimate and would be understandably confusing to newcomers to the language. :)