Pythonic ways to use 'else' in a for loop [duplicate]

Question

I have hardly ever noticed a python program that uses else in a for loop.

I recently used it to perform an action based on the loop variable condition while exiting; as it is in the scope.

What is the pythonic way to use an else in a for loop? Are there any notable use cases?

And, yea. I dislike using break statement. I'd rather set the looping condition complex. Would I be able to get any benefit out of it, if I don't like to use break statement anyway.

Worth noting that for loop has an else since the language inception, the first ever version.

Answer 1

What could be more pythonic than PyPy?

Look at what I discovered starting at line 284 in ctypes_configure/configure.py:

    for i in range(0, info['size'] - csize + 1, info['align']):
        if layout[i:i+csize] == [None] * csize:
            layout_addfield(layout, i, ctype, '_alignment')
            break
    else:
        raise AssertionError("unenforceable alignment %d" % (
            info['align'],))

And here, from line 425 in pypy/annotation/annrpython.py (clicky)

if cell.is_constant():
    return Constant(cell.const)
else:
    for v in known_variables:
        if self.bindings[v] is cell:
            return v
    else:
        raise CannotSimplify

In pypy/annotation/binaryop.py, starting at line 751:

def is_((pbc1, pbc2)):
    thistype = pairtype(SomePBC, SomePBC)
    s = super(thistype, pair(pbc1, pbc2)).is_()
    if not s.is_constant():
        if not pbc1.can_be_None or not pbc2.can_be_None:
            for desc in pbc1.descriptions:
                if desc in pbc2.descriptions:
                    break
            else:
                s.const = False    # no common desc in the two sets
    return s

A non-one-liner in pypy/annotation/classdef.py, starting at line 176:

def add_source_for_attribute(self, attr, source):
    """Adds information about a constant source for an attribute.
    """
    for cdef in self.getmro():
        if attr in cdef.attrs:
            # the Attribute() exists already for this class (or a parent)
            attrdef = cdef.attrs[attr]
            s_prev_value = attrdef.s_value
            attrdef.add_constant_source(self, source)
            # we should reflow from all the reader's position,
            # but as an optimization we try to see if the attribute
            # has really been generalized
            if attrdef.s_value != s_prev_value:
                attrdef.mutated(cdef) # reflow from all read positions
            return
    else:
        # remember the source in self.attr_sources
        sources = self.attr_sources.setdefault(attr, [])
        sources.append(source)
        # register the source in any Attribute found in subclasses,
        # to restore invariant (III)
        # NB. add_constant_source() may discover new subdefs but the
        #     right thing will happen to them because self.attr_sources
        #     was already updated
        if not source.instance_level:
            for subdef in self.getallsubdefs():
                if attr in subdef.attrs:
                    attrdef = subdef.attrs[attr]
                    s_prev_value = attrdef.s_value
                    attrdef.add_constant_source(self, source)
                    if attrdef.s_value != s_prev_value:
                        attrdef.mutated(subdef) # reflow from all read positions

Later in the same file, starting at line 307, an example with an illuminating comment:

def generalize_attr(self, attr, s_value=None):
    # if the attribute exists in a superclass, generalize there,
    # as imposed by invariant (I)
    for clsdef in self.getmro():
        if attr in clsdef.attrs:
            clsdef._generalize_attr(attr, s_value)
            break
    else:
        self._generalize_attr(attr, s_value)

Answer 2

Basically, it simplifies any loop that uses a boolean flag like this:

found = False                # <-- initialize boolean
for divisor in range(2, n):
    if n % divisor == 0:
        found = True         # <-- update boolean
        break  # optional, but continuing would be a waste of time

if found:                    # <-- check boolean
    print(n, "is divisible by", divisor)
else:
    print(n, "is prime")

and allows you to skip the management of the flag:

for divisor in range(2, n):
    if n % divisor == 0:
        print(n, "is divisible by", divisor)
        break
else:
    print(n, "is prime")

Note that there is already a natural place for code to execute when you do find a divisor - right before the break. The only new feature here is a place for code to execute when you tried all divisors and did not find any.

This helps only in conjuction with break. You still need booleans if you can't break (e.g. because you looking for the last match, or have to track several conditions in parallel).

Oh, and BTW, this works for while loops just as well.

any/all

If the only purpose of the loop is a yes-or-no answer, any()/all() functions with a generator or generator expression can be utilized:

if any(n % divisor == 0 
       for divisor in range(2, n)):
    print(n, "is composite")
else:
    print(n, "is prime")

Note the elegancy! The code is 1:1 what you want to say — "if any divisor divides n evenly, from 2 to n, then ...".

[This has similar efficiency to a loop with a break, because the any() function is short-circuiting, only running the generator expression until it yeilds True.]

But that won't give you the actual divisor, as any() always returns exactly True or False. A loop with else: is hard to beat when you need both (A) access to current value that was "found" (B) separate code paths for "found" vs. "not found" cases.

Answer 3

If you have a for loop you don't really have any condition statement. So break is your choice if you like to abort and then else can serve perfectly to handle the case where you were not happy.

for fruit in basket:
   if fruit.kind in ['Orange', 'Apple']:
       fruit.eat()
       break
else:
   print 'The basket contains no desirable fruit'

Answer 4

Without using break, else blocks have no benefit for for and while statements. The following two examples are equivalent:

for x in range(10):
  pass
else:
  print "else"

for x in range(10):
  pass
print "else"

The only reason for using else with for or while is to do something after the loop if it terminated normally, meaning without an explicit break.

After a lot of thinking, I can finally come up with a case where this might be useful:

def commit_changes(directory):
    for file in directory:
        if file_is_modified(file):
            break
    else:
        # No changes
        return False

    # Something has been changed
    send_directory_to_server()
    return True

Answer 5

Perhaps the best answer comes from the official Python tutorial:

break and continue Statements, and else Clauses on Loops:

Loop statements may have an else clause; it is executed when the loop terminates through exhaustion of the list (with for) or when the condition becomes false (with while), but not when the loop is terminated by a break statement

Answer 6

I was introduced to a wonderful idiom in which you can use a for/break/else scheme with an iterator to save both time and LOC. The example at hand was searching for the candidate for an incompletely qualified path. If you care to see the original context, please see the original question.

def match(path, actual):
    path = path.strip('/').split('/')
    actual = iter(actual.strip('/').split('/'))
    for pathitem in path:
        for item in actual:
            if pathitem == item:
                break
        else:
            return False
    return True

What makes the use of for/else so great here is the elegance of avoiding juggling a confusing boolean around. Without else, but hoping to achieve the same amount of short-circuiting, it might be written like so:

def match(path, actual):
    path = path.strip('/').split('/')
    actual = iter(actual.strip('/').split('/'))
    failed = True
    for pathitem in path:
        failed = True
        for item in actual:
            if pathitem == item:
                failed = False
                break
        if failed:
            break
    return not failed

I think the use of else makes it more elegant and more obvious.

Answer 7

A use case of the else clause of loops is breaking out of nested loops:

while True:
    for item in iterable:
        if condition:
            break
        suite
    else:
        continue
    break

It avoids repeating conditions:

while not condition:
    for item in iterable:
        if condition:
            break
        suite

Answer 8

Here you go:

a = ('y','a','y')
for x in a:
  print x,
else:
  print '!'

It's for the caboose.

edit:

# What happens if we add the ! to a list?

def side_effect(your_list):
  your_list.extend('!')
  for x in your_list:
    print x,

claimant = ['A',' ','g','u','r','u']
side_effect(claimant)
print claimant[-1]

# oh no, claimant now ends with a '!'

edit:

a = (("this","is"),("a","contrived","example"),("of","the","caboose","idiom"))
for b in a:
  for c in b:
    print c,
    if "is" == c:
      break
  else:
    print