In its most complex form, the while statement consists of a header line with a test expression, a body of one or more indented statements, and an optional else part that is executed if control exits the loop without a break statement being encountered. Python keeps evaluating the test at the top and executing the statements nested in the loop body until the test returns a false value:

while <test>:                   # Loop test
    <statements1>               # Loop body
else:                           # Optional else
    <statements2>               # Run if didn't exit loop with break

To illustrate, let’s look at a few simple while loops in action. The first, which consists of a print statement nested in a while loop, just prints a message forever. Recall that True is just a custom version of the integer 1 and always stands for a Boolean true value; because the test is always true, Python keeps executing the body forever, or until you stop its execution. This sort of behavior is usually called an infinite loop:

>>> while True:
...    print('Type Ctrl-C to stop me!')

The next example keeps slicing off the first character of a string until the string is empty and hence false. It’s typical to test an object directly like this instead of using the more verbose equivalent (while x != '':). Later in this chapter, we’ll see other ways to step more directly through the items in a string with a for loop.

>>> x = 'spam'
>>> while x:                  # While x is not empty
...     print(x, end=' ')
...     x = x[1:]             # Strip first character off x
...
spam pam am m

Note the end=' ' keyword argument used here to place all outputs on the same line separated by a space; see Chapter 11 if you’ve forgotten why this works as it does. The following code counts from the value of a up to, but not including, b. We’ll see an easier way to do this with a Python for loop and the built-in range function later:

>>> a=0; b=10
>>> while a < b:              # One way to code counter loops
...     print(a, end=' ')
...     a += 1                # Or, a = a + 1
...
0 1 2 3 4 5 6 7 8 9

Finally, notice that Python doesn’t have what some languages call a “do until” loop statement. However, we can simulate one with a test and break at the bottom of the loop body:

while True:
    ...loop body...
    if exitTest(): break

To fully understand how this structure works, we need to move on to the next section and learn more about the break statement.

Factoring in break and continue statements, the general format of the while loop looks like this:

while <test1>:
    <statements1>
    if <test2>: break              # Exit loop now, skip else
    if <test3>: continue           # Go to top of loop now, to test1
else:
    <statements2>                  # Run if we didn't hit a 'break'

break and continue statements can appear anywhere inside the while (or for) loop’s body, but they are usually coded further nested in an if test to take action in response to some condition.

Let’s turn to a few simple examples to see how these statements come together in practice.

Simple things first: the pass statement is a no-operation placeholder that is used when the syntax requires a statement, but you have nothing useful to say. It is often used to code an empty body for a compound statement. For instance, if you want to code an infinite loop that does nothing each time through, do it with a pass:

while True: pass                   # Type Ctrl-C to stop me!

Because the body is just an empty statement, Python gets stuck in this loop. pass is roughly to statements as None is to objects—an explicit nothing. Notice that here the while loop’s body is on the same line as the header, after the colon; as with if statements, this only works if the body isn’t a compound statement.

This example does nothing forever. It probably isn’t the most useful Python program ever written (unless you want to warm up your laptop computer on a cold winter’s day!); frankly, though, I couldn’t think of a better pass example at this point in the book.

We’ll see other places where pass makes more sense later—for instance, to ignore exceptions caught by try statements, and to define empty class objects with attributes that behave like “structs” and “records” in other languages. A pass is also sometimes coded to mean “to be filled in later,” to stub out the bodies of functions temporarily:

def func1():
    pass                           # Add real code here later

def func2():
    pass

We can’t leave the body empty without getting a syntax error, so we say pass instead.

def func1():
    ...                   # Alternative to pass

def func2():
    ...

func1()                   # Does nothing if called

def func1(): ...          # Works on same line too
def func2(): ...

>>> X = ...               # Alternative to None
>>> X
Ellipsis

The continue statement causes an immediate jump to the top of a loop. It also sometimes lets you avoid statement nesting. The next example uses continue to skip odd numbers. This code prints all even numbers less than 10 and greater than or equal to 0. Remember, 0 means false and % is the remainder of division operator, so this loop counts down to 0, skipping numbers that aren’t multiples of 2 (it prints 8 6 4 2 0):

x = 10
while x:
    x = x−1                        # Or, x -= 1
    if x % 2 != 0: continue        # Odd? -- skip print
    print(x, end=' ')

Because continue jumps to the top of the loop, you don’t need to nest the print statement inside an if test; the print is only reached if the continue is not run. If this sounds similar to a “goto” in other languages, it should. Python has no “goto” statement, but because continue lets you jump about in a program, many of the warnings about readability and maintainability you may have heard about goto apply. continue should probably be used sparingly, especially when you’re first getting started with Python. For instance, the last example might be clearer if the print were nested under the if:

x = 10
while x:
    x = x−1
    if x % 2 == 0:                 # Even? -- print
        print(x, end=' ')

The break statement causes an immediate exit from a loop. Because the code that follows it in the loop is not executed if the break is reached, you can also sometimes avoid nesting by including a break. For example, here is a simple interactive loop (a variant of a larger example we studied in Chapter 10) that inputs data with input (known as raw_input in Python 2.6) and exits when the user enters “stop” for the name request:

>>> while True:
...     name = input('Enter name:')
...     if name == 'stop': break
...     age  = input('Enter age: ')
...     print('Hello', name, '=>', int(age) ** 2)
...
Enter name:mel
Enter age: 40
Hello mel => 1600
Enter name:bob
Enter age: 30
Hello bob => 900
Enter name:stop

Notice how this code converts the age input to an integer with int before raising it to the second power; as you’ll recall, this is necessary because input returns user input as a string. In Chapter 35, you’ll see that input also raises an exception at end-of-file (e.g., if the user types Ctrl-Z or Ctrl-D); if this matters, wrap input in try statements.

When combined with the loop else clause, the break statement can often eliminate the need for the search status flags used in other languages. For instance, the following piece of code determines whether a positive integer y is prime by searching for factors greater than 1:

x = y // 2                                # For some y > 1
while x > 1:
    if y % x == 0:                        # Remainder
        print(y, 'has factor', x)
        break                             # Skip else
    x -= 1
else:                                     # Normal exit
    print(y, 'is prime')

Rather than setting a flag to be tested when the loop is exited, it inserts a break where a factor is found. This way, the loop else clause can assume that it will be executed only if no factor is found; if you don’t hit the break, the number is prime.

The loop else clause is also run if the body of the loop is never executed, as you don’t run a break in that event either; in a while loop, this happens if the test in the header is false to begin with. Thus, in the preceding example you still get the “is prime” message if x is initially less than or equal to 1 (for instance, if y is 2).

found = False
while x and not found:
    if match(x[0]):                  # Value at front?
        print('Ni')
        found = True
    else:
        x = x[1:]                    # Slice off front and repeat
if not found:
    print('not found')

while x:                             # Exit when x empty
    if match(x[0]):
        print('Ni')
        break                        # Exit, go around else
    x = x[1:]
else:
    print('Not found')               # Only here if exhausted x

while ((x = next()) != NULL) {...process x...}

while True:
    x = next()
    if not x: break
    ...process x...

x = True
while x:
    x = next()
    if x:
        ...process x...

x = next()
while x:
    ...process x...
    x = next()

The Python for loop begins with a header line that specifies an assignment target (or targets), along with the object you want to step through. The header is followed by a block of (normally indented) statements that you want to repeat:

for <target> in <object>:             # Assign object items to target
    <statements>                      # Repeated loop body: use target
else:
    <statements>                      # If we didn't hit a 'break'

When Python runs a for loop, it assigns the items in the sequence object to the target one by one and executes the loop body for each. The loop body typically uses the assignment target to refer to the current item in the sequence as though it were a cursor stepping through the sequence.

The name used as the assignment target in a for header line is usually a (possibly new) variable in the scope where the for statement is coded. There’s not much special about it; it can even be changed inside the loop’s body, but it will automatically be set to the next item in the sequence when control returns to the top of the loop again. After the loop this variable normally still refers to the last item visited, which is the last item in the sequence unless the loop exits with a break statement.

The for statement also supports an optional else block, which works exactly as it does in a while loop—it’s executed if the loop exits without running into a break statement (i.e., if all items in the sequence have been visited). The break and continue statements introduced earlier also work the same in a for loop as they do in a while. The for loop’s complete format can be described this way:

for <target> in <object>:             # Assign object items to target
    <statements>
    if <test>: break                  # Exit loop now, skip else
    if <test>: continue               # Go to top of loop now
else:
    <statements>                      # If we didn't hit a 'break'

Let’s type a few for loops interactively now, so you can see how they are used in practice.

>>> for x in ["spam", "eggs", "ham"]:
...     print(x, end=' ')
...
spam eggs ham

>>> sum = 0
>>> for x in [1, 2, 3, 4]:
...     sum = sum + x
...
>>> sum
10
>>> prod = 1
>>> for item in [1, 2, 3, 4]: prod *= item
...
>>> prod
24

>>> S = "lumberjack"
>>> T = ("and", "I'm", "okay")

>>> for x in S: print(x, end=' ')     # Iterate over a string
...
l u m b e r j a c k

>>> for x in T: print(x, end=' ')     # Iterate over a tuple
...
and I'm okay

>>> T = [(1, 2), (3, 4), (5, 6)]
>>> for (a, b) in T:                   # Tuple assignment at work
...     print(a, b)
...
1 2
3 4
5 6

>>> D = {'a': 1, 'b': 2, 'c': 3}
>>> for key in D:
...    print(key, '=>', D[key])             # Use dict keys iterator and index
...
a => 1
c => 3
b => 2

>>> list(D.items())
[('a', 1), ('c', 3), ('b', 2)]

>>> for (key, value) in D.items():
...    print(key, '=>', value)              # Iterate over both keys and values
...
a => 1
c => 3
b => 2

>>> T
[(1, 2), (3, 4), (5, 6)]

>>> for both in T:
...     a, b = both                         # Manual assignment equivalent
...     print(a, b)
...
1 2
3 4
5 6

>>> ((a, b), c) = ((1, 2), 3)               # Nested sequences work too
>>> a, b, c
(1, 2, 3)

>>> for ((a, b), c) in [((1, 2), 3), ((4, 5), 6)]: print(a, b, c)
...
1 2 3
4 5 6

>>> for ((a, b), c) in [([1, 2], 3), ['XY', 6]]: print(a, b, c)
...
1 2 3
X Y 6

>>> a, b, c = (1, 2, 3)                               # Tuple assignment
>>> a, b, c
(1, 2, 3)

>>> for (a, b, c) in [(1, 2, 3), (4, 5, 6)]:          # Used in for loop
...     print(a, b, c)
...
1 2 3
4 5 6

>>> a, *b, c = (1, 2, 3, 4)                           # Extended seq assignment
>>> a, b, c
(1, [2, 3], 4)

>>> for (a, *b, c) in [(1, 2, 3, 4), (5, 6, 7, 8)]:
...     print(a, b, c)
...
1 [2, 3] 4
5 [6, 7] 8

>>> for all in [(1, 2, 3, 4), (5, 6, 7, 8)]:          # Manual slicing in 2.6
...     a, b, c = all[0], all[1:3], all[3]
...     print(a, b, c)
...
1 (2, 3) 4
5 (6, 7) 8

>>> items = ["aaa", 111, (4, 5), 2.01]   # A set of objects
>>> tests = [(4, 5), 3.14]               # Keys to search for
>>>
>>> for key in tests:                    # For all keys
...     for item in items:               # For all items
...         if item == key:              # Check for match
...             print(key, "was found")
...             break
...     else:
...         print(key, "not found!")
...
(4, 5) was found
3.14 not found!

>>> for key in tests:                    # For all keys
...     if key in items:                 # Let Python check for a match
...         print(key, "was found")
...     else:
...         print(key, "not found!")
...
(4, 5) was found
3.14 not found!

>>> seq1 = "spam"
>>> seq2 = "scam"
>>>
>>> res = []                             # Start empty
>>> for x in seq1:                       # Scan first sequence
...     if x in seq2:                    # Common item?
...         res.append(x)                # Add to result end
...
>>> res
['s', 'a', 'm']

file = open('test.txt', 'r')   # Read contents into a string
print(file.read())

file = open('test.txt')
while True:
    char = file.read(1)         # Read by character
    if not char: break
    print(char)

for char in open('test.txt').read():
    print(char)

file = open('test.txt')
while True:
    line = file.readline()      # Read line by line
    if not line: break
    print(line, end='')         # Line already has a 


file = open('test.txt', 'rb')
while True:
    chunk = file.read(10)       # Read byte chunks: up to 10 bytes
    if not chunk: break
    print(chunk)

for line in open('test.txt').readlines():
    print(line, end='')

for line in open('test.txt'):   # Use iterators: best text input mode
    print(line, end='')

The range function is really a general tool that can be used in a variety of contexts. Although it’s used most often to generate indexes in a for, you can use it anywhere you need a list of integers. In Python 3.0, range is an iterator that generates items on demand, so we need to wrap it in a list call to display its results all at once (more on iterators in Chapter 14):

>>> list(range(5)), list(range(2, 5)), list(range(0, 10, 2))
([0, 1, 2, 3, 4], [2, 3, 4], [0, 2, 4, 6, 8])

With one argument, range generates a list of integers from zero up to but not including the argument’s value. If you pass in two arguments, the first is taken as the lower bound. An optional third argument can give a step; if it is used, Python adds the step to each successive integer in the result (the step defaults to 1). Ranges can also be nonpositive and nonascending, if you want them to be:

>>> list(range(−5, 5))
[−5, −4, −3, −2, −1, 0, 1, 2, 3, 4]

>>> list(range(5, −5, −1))
[5, 4, 3, 2, 1, 0, −1, −2, −3, −4]

Although such range results may be useful all by themselves, they tend to come in most handy within for loops. For one thing, they provide a simple way to repeat an action a specific number of times. To print three lines, for example, use a range to generate the appropriate number of integers; for loops force results from range automatically in 3.0, so we don’t need list here:

>>> for i in range(3):
...     print(i, 'Pythons')
...
0 Pythons
1 Pythons
2 Pythons

range is also commonly used to iterate over a sequence indirectly. The easiest and fastest way to step through a sequence exhaustively is always with a simple for, as Python handles most of the details for you:

>>> X = 'spam'
>>> for item in X: print(item, end=' ')           # Simple iteration
...
s p a m

Internally, the for loop handles the details of the iteration automatically when used this way. If you really need to take over the indexing logic explicitly, you can do it with a while loop:

>>> i = 0
>>> while i < len(X):                             # while loop iteration
...     print(X[i], end=' ')
...     i += 1
...
s p a m

You can also do manual indexing with a for, though, if you use range to generate a list of indexes to iterate through. It’s a multistep process, but it’s sufficient to generate offsets, rather than the items at those offsets:

>>> X
'spam'
>>> len(X)                                        # Length of string
4
>>> list(range(len(X)))                           # All legal offsets into X
[0, 1, 2, 3]
>>>
>>> for i in range(len(X)): print(X[i], end=' ')  # Manual for indexing
...
s p a m

Note that because this example is stepping over a list of offsets into X, not the actual items of X, we need to index back into X within the loop to fetch each item.

The last example in the prior section works, but it’s not the fastest option. It’s also more work than we need to do. Unless you have a special indexing requirement, you’re always better off using the simple for loop form in Python—as a general rule, use for instead of while whenever possible, and don’t use range calls in for loops except as a last resort. This simpler solution is better:

>>> for item in X: print(item)                    # Simple iteration
...

However, the coding pattern used in the prior example does allow us to do more specialized sorts of traversals. For instance, we can skip items as we go:

>>> S = 'abcdefghijk'
>>> list(range(0, len(S), 2))
[0, 2, 4, 6, 8, 10]

>>> for i in range(0, len(S), 2): print(S[i], end=' ')
...
a c e g i k

Here, we visit every second item in the string S by stepping over the generated range list. To visit every third item, change the third range argument to be 3, and so on. In effect, using range this way lets you skip items in loops while still retaining the simplicity of the for loop construct.

Still, this is probably not the ideal best-practice technique in Python today. If you really want to skip items in a sequence, the extended three-limit form of the slice expression, presented in Chapter 7, provides a simpler route to the same goal. To visit every second character in S, for example, slice with a stride of 2:

>>> S = 'abcdefghijk'
>>> for c in S[::2]: print(c, end=' ')
...
a c e g i k

The result is the same, but substantially easier for you to write and for others to read. The only real advantage to using range here instead is that it does not copy the string and does not create a list in 3.0; for very large strings, it may save memory.

Another common place where you may use the range and for combination is in loops that change a list as it is being traversed. Suppose, for example, that you need to add 1 to every item in a list. You can try this with a simple for loop, but the result probably won’t be exactly what you want:

>>> L = [1, 2, 3, 4, 5]

>>> for x in L:
...     x += 1
...
>>> L
[1, 2, 3, 4, 5]
>>> x
6

This doesn’t quite work—it changes the loop variable x, not the list L. The reason is somewhat subtle. Each time through the loop, x refers to the next integer already pulled out of the list. In the first iteration, for example, x is integer 1. In the next iteration, the loop body sets x to a different object, integer 2, but it does not update the list where 1 originally came from.

To really change the list as we march across it, we need to use indexes so we can assign an updated value to each position as we go. The range/len combination can produce the required indexes for us:

>>> L = [1, 2, 3, 4, 5]

>>> for i in range(len(L)):          # Add one to each item in L
...     L[i] += 1                    # Or L[i] = L[i] + 1
...
>>> L
[2, 3, 4, 5, 6]

When coded this way, the list is changed as we proceed through the loop. There is no way to do the same with a simple for x in L:-style loop, because such a loop iterates through actual items, not list positions. But what about the equivalent while loop? Such a loop requires a bit more work on our part, and likely runs more slowly:

>>> i = 0
>>> while i < len(L):
...     L[i] += 1
...     i += 1
...
>>> L
[3, 4, 5, 6, 7]

Here again, though, the range solution may not be ideal either. A list comprehension expression of the form:

[x+1 for x in L]

would do similar work, albeit without changing the original list in-place (we could assign the expression’s new list object result back to L, but this would not update any other references to the original list). Because this is such a central looping concept, we’ll save a complete exploration of list comprehensions for the next chapter.

As we’ve seen, the range built-in allows us to traverse sequences with for in a nonexhaustive fashion. In the same spirit, the built-in zip function allows us to use for loops to visit multiple sequences in parallel. In basic operation, zip takes one or more sequences as arguments and returns a series of tuples that pair up parallel items taken from those sequences. For example, suppose we’re working with two lists:

>>> L1 = [1,2,3,4]
>>> L2 = [5,6,7,8]

To combine the items in these lists, we can use zip to create a list of tuple pairs (like range, zip is an iterable object in 3.0, so we must wrap it in a list call to display all its results at once—more on iterators in the next chapter):

>>> zip(L1, L2)
<zip object at 0x026523C8>
>>> list(zip(L1, L2))                       # list() required in 3.0, not 2.6
[(1, 5), (2, 6), (3, 7), (4, 8)]

Such a result may be useful in other contexts as well, but when wedded with the for loop, it supports parallel iterations:

>>> for (x, y) in zip(L1, L2):
...     print(x, y, '--', x+y)
...
1 5 -- 6
2 6 -- 8
3 7 -- 10
4 8 -- 12

Here, we step over the result of the zip call—that is, the pairs of items pulled from the two lists. Notice that this for loop again uses the tuple assignment form we met earlier to unpack each tuple in the zip result. The first time through, it’s as though we ran the assignment statement (x, y) = (1, 5).

The net effect is that we scan both L1 and L2 in our loop. We could achieve a similar effect with a while loop that handles indexing manually, but it would require more typing and would likely run more slowly than the for/zip approach.

Strictly speaking, the zip function is more general than this example suggests. For instance, it accepts any type of sequence (really, any iterable object, including files), and it accepts more than two arguments. With three arguments, as in the following example, it builds a list of three-item tuples with items from each sequence, essentially projecting by columns (technically, we get an N-ary tuple for N arguments):

>>> T1, T2, T3 = (1,2,3), (4,5,6), (7,8,9)
>>> T3
(7, 8, 9)
>>> list(zip(T1, T2, T3))
[(1, 4, 7), (2, 5, 8), (3, 6, 9)]

Moreover, zip truncates result tuples at the length of the shortest sequence when the argument lengths differ. In the following, we zip together two strings to pick out characters in parallel, but the result has only as many tuples as the length of the shortest sequence:

>>> S1 = 'abc'
>>> S2 = 'xyz123'
>>>
>>> list(zip(S1, S2))
[('a', 'x'), ('b', 'y'), ('c', 'z')]

>>> S1 = 'abc'
>>> S2 = 'xyz123'

>>> map(None, S1, S2)                        # 2.X only
[('a', 'x'), ('b', 'y'), ('c', 'z'), (None, '1'), (None, '2'), (None,'3')]

>>> list(map(ord, 'spam'))
[115, 112, 97, 109]

>>> res = []
>>> for c in 'spam': res.append(ord(c))
>>> res
[115, 112, 97, 109]

>>> D1 = {'spam':1, 'eggs':3, 'toast':5}
>>> D1
{'toast': 5, 'eggs': 3, 'spam': 1}

>>> D1 = {}
>>> D1['spam']  = 1
>>> D1['eggs']  = 3
>>> D1['toast'] = 5

>>> keys = ['spam', 'eggs', 'toast']
>>> vals = [1, 3, 5]

>>> list(zip(keys, vals))
[('spam', 1), ('eggs', 3), ('toast', 5)]

>>> D2 = {}
>>> for (k, v) in zip(keys, vals): D2[k] = v
...
>>> D2
{'toast': 5, 'eggs': 3, 'spam': 1}

>>> keys = ['spam', 'eggs', 'toast']
>>> vals = [1, 3, 5]

>>> D3 = dict(zip(keys, vals))
>>> D3
{'toast': 5, 'eggs': 3, 'spam': 1}

Earlier, we discussed using range to generate the offsets of items in a string, rather than the items at those offsets. In some programs, though, we need both: the item to use, plus an offset as we go. Traditionally, this was coded with a simple for loop that also kept a counter of the current offset:

>>> S = 'spam'
>>> offset = 0
>>> for item in S:
...     print(item, 'appears at offset', offset)
...     offset += 1
...
s appears at offset 0
p appears at offset 1
a appears at offset 2
m appears at offset 3

This works, but in recent Python releases a new built-in named enumerate does the job for us:

>>> S = 'spam'
>>> for (offset, item) in enumerate(S):
...     print(item, 'appears at offset', offset)
...
s appears at offset 0
p appears at offset 1
a appears at offset 2
m appears at offset 3

The enumerate function returns a generator object—a kind of object that supports the iteration protocol that we will study in the next chapter and will discuss in more detail in the next part of the book. In short, it has a __next__ method called by the next built-in function, which returns an (index, value) tuple each time through the loop. We can unpack these tuples with tuple assignment in the for loop (much like using zip):

>>> E = enumerate(S)
>>> E
<enumerate object at 0x02765AA8>
>>> next(E)
(0, 's')
>>> next(E)
(1, 'p')
>>> next(E)
(2, 'a')

As usual, we don’t normally see this machinery because iteration contexts—including list comprehensions, the subject of Chapter 14—run the iteration protocol automatically:

>>> [c * i for (i, c) in enumerate(S)]
['', 'p', 'aa', 'mmm']

To fully understand iteration concepts like enumerate, zip, and list comprehensions, we need to move on to the next chapter for a more formal dissection.