This means the exception has no message attached. Print the exception type:

print repr(e)

You may also want to print the traceback:

import traceback

# ...
except BaseException as e:
    traceback.print_exc()

You want to avoid catching BaseException however, this is no better than a blanket except: statement. Catch more specific exceptions instead.

If you literally want to raise an exception only on the empty string, you'll need to do that manually:

try:
    user_input = input() # raw_input in Python 2.x
    if not user_input:
        raise ValueError('empty string')
except ValueError as e:
    print(e)

But that "integer input" part of the comment makes me think what you really want is to raise an exception on anything other than an integer, including but not limited to the empty string.

If so, open up your interactive interpreter and see what happens when you type things like int('2'), int('abc'), int(''), etc., and the answer should be pretty obvious.

But then how do you distinguish an empty string from something different? Simple: Just do the user_input = input() before the try, and check whether user_input is empty within the except. (You put if statements inside except handlers all the time in real code, e.g., to distinguish an OSError with an EINTR errno from one with a different errno.)

Method 2 is wrong because you are doing an operation in two steps when it could be done in one. In method 2, you check if the queue is empty, and then later (very soon, but still later), try to get the item. What if you have two threads pulling items from the queue? The get() could still fail with an empty queue. What if an item is added to the queue after you checked that it was empty? These are the sort of tiny windows of opportunity where bugs creep in to concurrent code.

Do it in one step, it's by far the better choice.

import Queue

q = Queue.Queue()

try:
    task = q.get(False)
except Queue.Empty:
    # Handle empty queue here
    pass
else:
    # Handle task here and call q.task_done()

Don't get hung up on "exceptions should be errors". Exceptions are simply another channel of communication, use them. Use the "else" clause here to narrow the scope of the exception clause.

try:
    do_something()
except:
    pass

You will use the pass statement.

The pass statement does nothing. It can be used when a statement is required syntactically but the program requires no action.

The thing that I think you're missing here is that the StopIteration is what actually makes the for loop stop. This is why you can write custom iterators that work with Python's for loop transparently. It doesn't do any checking to see if the iterator is empty, and the for loop does not keep track of of the iterator's state. This is an effect of the iterator protocol, and was an intentional choice in the language design. You can read more about the iterator protocol on Python's docs site if you want to.

It also makes more sense if you think about the number of items in e.g. your list corresponding directly to the number of times your loop is executed.

-----------------------------------|
| No. of items | No. times executed|
------------------------------------
|      5       |         5         |
|      4       |         4         |
|      3       |         3         |
|      2       |         2         |
|      1       |         1         |
|      0       |         0         |
------------------------------------

If the for loop special-cased an empty iterable, this invariant would be lost.

It would also complicate the protocol on writing custom iterators, because you would have to have an extra method to signal to the for loop that your iterable is empty before the iteration began.

Here's a (stupid) example of a custom iterator that always acts like it's empty:

class EmptyIterator():
    def __iter__(self): return self
    def __next__(self): raise StopIteration

for blah in EmptyIterator():
    print('this is never reached')
try:
    next(EmptyIterator())
except StopIteration:
    print('Oh hi, I was empty so I raised this Exception for you.')

The recommendation in Python is to use exceptions to indicate failure. This is true even if you expect failure on a regular basis.

Look at it from the perspective of the caller of your code:

my_status = get_abe_status(my_url)

What if we return None? If the caller doesn't specifically handle the case that get_abe_status failed, it will simply try to continue on with my_stats being None. That may produce a difficult to diagnose bug later on. Even if you do check for None, this code has no clue why get_abe_status() failed.

But what if we raise an exception? If the caller doesn't specifically handle the case, the exception will propagate upward eventually hitting the default exception handler. That may not be the what you want, but its better then introducing a subtle bug elsewhere in the program. Additionally, the exception gives information about what went wrong which is lost in the first version.

From the caller's perspective, its simply more convenient to get an exception than a return value. And that's the python style, to use exceptions to indicate failure conditions not return values.

Some will take a different perspective and argue that you should only use exceptions for cases you never really expect to happen. They argue that normally running running could should not raise any exceptions. One reason that is given for this is that exceptions are grossly inefficient, but that's not actually true for Python.

A couple of points on your code:

try:
    hits[0]
except IndexError:
    raise NotFoundError("No mentions found.")

That's a really confusing way to check for an empty list. Don't induce an exception just to check something. Use an if.

# say we expect four hits...
if len(hits) != 4:
    raise Warning("An unexpected number of hits.")
    logger.warning("An unexpected number of hits.")

You do realize that the logger.warning line will never run right?

The Empty exception you're looking for isn't available directly in the multiprocessing module, because multiprocessing borrows it from the queue module (which used to be named Queue in Python 2). To make your code work, just do an import queue at the top:

Try this:

import multiprocessing
import queue # or Queue in Python 2

f = multiprocessing.Queue()
try:
    f.get(True,0.1)
except queue.Empty: # queue here refers to the module, not a class
    print('foo')