Subsections

- 4.1 `if` Statements
- 4.2 `for` Statements
- 4.3 The `range()` Function
- 4.4 `break` and `continue` Statements, and `else` Clauses on Loops
- 4.5 `pass` Statements
- 4.6 Defining Functions
- 4.7 More on Defining Functions
  - 4.7.1 Default Argument Values
  - 4.7.2 Keyword Arguments
  - 4.7.3 Arbitrary Argument Lists
  - 4.7.4 Lambda Forms
  - 4.7.5 Documentation Strings

#
4. More Control Flow Tools

Besides the `while` statement just introduced, Python knows the usual control flow statements known from other languages, with some twists.

#
4.1 `if` Statements

Perhaps the most well-known statement type is the `if` statement. For example:

      >>> #  [Code which sets 'x' to a value...]
      >>> if x < 0:
      ...      x = 0
      ...      print 'Negative changed to zero'
      ... elif x == 0:
      ...      print 'Zero'
      ... elif x == 1:
      ...      print 'Single'
      ... else:
      ...      print 'More'
      ...

There can be zero or more `elif` parts, and the `else` part is optional. The keyword ``elif`' is short for `else if', and is useful to avoid excessive indentation. An `if` ... `elif` ... `elif` ... sequence is a substitute for the switch or case statements found in other languages.

#
4.2 `for` Statements

The `for` statement in Python differs a bit from what you may be used to in C or Pascal. Rather than always iterating over an arithmetic progression of numbers (like in Pascal), or giving the user the ability to define both the iteration step and halting condition (as C), Python's `for` statement iterates over the items of any sequence (e.g., a list or a string), in the order that they appear in the sequence. For example (no pun intended):

      >>> # Measure some strings:
      ... a = ['cat', 'window', 'defenestrate']
      >>> for x in a:
      ...     print x, len(x)
      ...
      cat 3
      window 6
      defenestrate 12

It is not safe to modify the sequence being iterated over in the loop (this can only happen for mutable sequence types, i.e., lists). If you need to modify the list you are iterating over, e.g., duplicate selected items, you must iterate over a copy. The slice notation makes this particularly convenient:

      >>> for x in a[:]: # make a slice copy of the entire list
      ...    if len(x) > 6: a.insert(0, x)
      ...
      >>> a
      ['defenestrate', 'cat', 'window', 'defenestrate']

#
4.3 The `range()` Function

If you do need to iterate over a sequence of numbers, the built-in function `range()` comes in handy. It generates lists containing arithmetic progressions, e.g.:

      >>> range(10)
      [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]

The given end point is never part of the generated list; `range(10)` generates a list of 10 values, exactly the legal indices for items of a sequence of length 10. It is possible to let the range start at another number, or to specify a different increment (even negative):

      >>> range(5, 10)
      [5, 6, 7, 8, 9]
      >>> range(0, 10, 3)
      [0, 3, 6, 9]
      >>> range(-10, -100, -30)
      [-10, -40, -70]

To iterate over the indices of a sequence, combine `range()` and `len()` as follows:

      >>> a = ['Mary', 'had', 'a', 'little', 'lamb']
      >>> for i in range(len(a)):
      ...     print i, a[i]
      ...
      0 Mary
      1 had
      2 a
      3 little
      4 lamb

#
4.4 `break` and `continue` Statements, and `else` Clauses on Loops

The `break` statement, like in C, breaks out of the smallest enclosing `for` or `while` loop.

The `continue` statement, also borrowed from C, continues with the next iteration of the loop.

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. This is exemplified by the following loop, which searches for prime numbers:

      >>> for n in range(2, 10):
      ...     for x in range(2, n):
      ...         if n % x == 0:
      ...            print n, 'equals', x, '*', n/x
      ...            break
      ...     else:
      ...          print n, 'is a prime number'
      ...
      2 is a prime number
      3 is a prime number
      4 equals 2 * 2
      5 is a prime number
      6 equals 2 * 3
      7 is a prime number
      8 equals 2 * 4
      9 equals 3 * 3

#
4.5 `pass` Statements

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

      >>> while 1:
      ...       pass # Busy-wait for keyboard interrupt
      ...

#
4.6 Defining Functions

We can create a function that writes the Fibonacci series to an arbitrary boundary:

      >>> def fib(n):    # write Fibonacci series up to n
      ...     "Print a Fibonacci series up to n"
      ...     a, b = 0, 1
      ...     while b < n:
      ...         print b,
      ...         a, b = b, a+b
      ...
      >>> # Now call the function we just defined:
      ... fib(2000)
      1 1 2 3 5 8 13 21 34 55 89 144 233 377 610 987 1597

The keyword `def` introduces a function definition. It must be followed by the function name and the parenthesized list of formal parameters. The statements that form the body of the function start at the next line, indented by a tab stop. The first statement of the function body can optionally be a string literal; this string literal is the function's documentation string, or docstring. There are tools which use docstrings to automatically produce printed documentation, or to let the user interactively browse through code; it's good practice to include docstrings in code that you write, so try to make a habit of it.

The execution of a function introduces a new symbol table used for the local variables of the function. More precisely, all variable assignments in a function store the value in the local symbol table; whereas variable references first look in the local symbol table, then in the global symbol table, and then in the table of built-in names. Thus, global variables cannot be directly assigned a value within a function (unless named in a `global` statement), although they may be referenced.

The actual parameters (arguments) to a function call are introduced in the local symbol table of the called function when it is called; thus, arguments are passed using call by value.4.1When a function calls another function, a new local symbol table is created for that call.

A function definition introduces the function name in the current symbol table. The value of the function name has a type that is recognized by the interpreter as a user-defined function. This value can be assigned to another name which can then also be used as a function. This serves as a general renaming mechanism:

      >>> fib
      <function object at 10042ed0>
      >>> f = fib
      >>> f(100)
      1 1 2 3 5 8 13 21 34 55 89

You might object that `fib` is not a function but a procedure. In Python, like in C, procedures are just functions that don't return a value. In fact, technically speaking, procedures do return a value, albeit a rather boring one. This value is called `None` (it's a built-in name). Writing the value `None` is normally suppressed by the interpreter if it would be the only value written. You can see it if you really want to:

      >>> print fib(0)
      None

It is simple to write a function that returns a list of the numbers of the Fibonacci series, instead of printing it:

      >>> def fib2(n): # return Fibonacci series up to n
      ...     "Return a list containing the Fibonacci series up to n"
      ...     result = []
      ...     a, b = 0, 1
      ...     while b < n:
      ...         result.append(b)    # see below
      ...         a, b = b, a+b
      ...     return result
      ...
      >>> f100 = fib2(100)    # call it
      >>> f100                # write the result
      [1, 1, 2, 3, 5, 8, 13, 21, 34, 55, 89]

This example, as usual, demonstrates some new Python features:

- The `return` statement returns with a value from a function. `return` without an expression argument is used to return from the middle of a procedure (falling off the end also returns from a procedure), in which case the `None` value is returned.

- The statement `result.append(b)` calls a method of the list object `result`. A method is a function that `belongs' to an object and is named `obj.methodname`, where `obj` is some object (this may be an expression), and `methodname` is the name of a method that is defined by the object's type. Different types define different methods. Methods of different types may have the same name without causing ambiguity. (It is possible to define your own object types and methods, using classes, as discussed later in this tutorial.) The method `append()` shown in the example, is defined for list objects; it adds a new element at the end of the list. In this example it is equivalent to "`result = result + [b]`", but more efficient.

#
4.7 More on Defining Functions

It is also possible to define functions with a variable number of arguments. There are three forms, which can be combined.

##
4.7.1 Default Argument Values

The most useful form is to specify a default value for one or more arguments. This creates a function that can be called with fewer arguments than it is defined, e.g.

      def ask_ok(prompt, retries=4, complaint='Yes or no, please!'):
          while 1:
              ok = raw_input(prompt)
              if ok in ('y', 'ye', 'yes'): return 1
              if ok in ('n', 'no', 'nop', 'nope'): return 0
              retries = retries - 1
              if retries < 0: raise IOError, 'refusenik user'
              print complaint

This function can be called either like this: `ask_ok('Do you really want to quit?')` or like this: `ask_ok('OK to overwrite the file?', 2)`.

The default values are evaluated at the point of function definition in the defining scope, so that e.g.

      i = 5
      def f(arg = i): print arg
      i = 6
      f()

will print `5`.

Important warning: The default value is evaluated only once. This makes a difference when the default is a mutable object such as a list or dictionary. For example, the following function accumulates the arguments passed to it on subsequent calls:

      def f(a, l = []):
          l.append(a)
          return l
      print f(1)
      print f(2)
      print f(3)

This will print

      [1]
      [1, 2]
      [1, 2, 3]

If you don't want the default to be shared between subsequent calls, you can write the function like this instead:

      def f(a, l = None):
          if l is None:
              l = []
          l.append(a)
          return l

##
4.7.2 Keyword Arguments

Functions can also be called using keyword arguments of the form "`keyword = value`". For instance, the following function:

      def parrot(voltage, state='a stiff', action='voom', type='Norwegian Blue'):
          print "-- This parrot wouldn't", action,
          print "if you put", voltage, "Volts through it."
          print "-- Lovely plumage, the", type
          print "-- It's", state, "!"

could be called in any of the following ways:

      parrot(1000)
      parrot(action = 'VOOOOOM', voltage = 1000000)
      parrot('a thousand', state = 'pushing up the daisies')
      parrot('a million', 'bereft of life', 'jump')

but the following calls would all be invalid:

      parrot()                     # required argument missing
      parrot(voltage=5.0, 'dead')  # non-keyword argument following keyword
      parrot(110, voltage=220)     # duplicate value for argument
      parrot(actor='John Cleese')  # unknown keyword

In general, an argument list must have any positional arguments followed by any keyword arguments, where the keywords must be chosen from the formal parameter names. It's not important whether a formal parameter has a default value or not. No argument must receive a value more than once -- formal parameter names corresponding to positional arguments cannot be used as keywords in the same calls.

When a final formal parameter of the form `**name` is present, it receives a dictionary containing all keyword arguments whose keyword doesn't correspond to a formal parameter. This may be combined with a formal parameter of the form `*name`(described in the next subsection) which receives a tuple containing the positional arguments beyond the formal parameter list. (`*name` must occur before `**name`.) For example, if we define a function like this:

      def cheeseshop(kind, *arguments, **keywords):
          print "-- Do you have any", kind, '?'
          print "-- I'm sorry, we're all out of", kind
          for arg in arguments: print arg
          print '-'*40
          for kw in keywords.keys(): print kw, ':', keywords[kw]

It could be called like this:

      cheeseshop('Limburger', "It's very runny, sir.",
                 "It's really very, VERY runny, sir.",
                 client='John Cleese',
                 shopkeeper='Michael Palin',
                 sketch='Cheese Shop Sketch')

and of course it would print:

      -- Do you have any Limburger ?
      -- I'm sorry, we're all out of Limburger
      It's very runny, sir.
      It's really very, VERY runny, sir.
      ----------------------------------------
      client : John Cleese
      shopkeeper : Michael Palin
      sketch : Cheese Shop Sketch

##
4.7.3 Arbitrary Argument Lists

Finally, the least frequently used option is to specify that a function can be called with an arbitrary number of arguments. These arguments will be wrapped up in a tuple. Before the variable number of arguments, zero or more normal arguments may occur.

      def fprintf(file, format, *args):
          file.write(format % args)

##
4.7.4 Lambda Forms

By popular demand, a few features commonly found in functional programming languages and Lisp have been added to Python. With the `lambda` keyword, small anonymous functions can be created. Here's a function that returns the sum of its two arguments: "`lambda a, b: a+b`". Lambda forms can be used wherever function objects are required. They are syntactically restricted to a single expression. Semantically, they are just syntactic sugar for a normal function definition. Like nested function definitions, lambda forms cannot reference variables from the containing scope, but this can be overcome through the judicious use of default argument values, e.g.

      def make_incrementor(n):
          return lambda x, incr=n: x+incr

##
4.7.5 Documentation Strings

There are emerging conventions about the content and formatting of documentation strings.

The first line should always be a short, concise summary of the object's purpose. For brevity, it should not explicitly state the object's name or type, since these are available by other means (except if the name happens to be a verb describing a function's operation). This line should begin with a capital letter and end with a period.

If there are more lines in the documentation string, the second line should be blank, visually separating the summary from the rest of the description. The following lines should be one or more paragraphs describing the object's calling conventions, its side effects, etc.

The Python parser does not strip indentation from multi-line string literals in Python, so tools that process documentation have to strip indentation. This is done using the following convention. The first non-blank line after the first line of the string determines the amount of indentation for the entire documentation string. (We can't use the first line since it is generally adjacent to the string's opening quotes so its indentation is not apparent in the string literal.) Whitespace ``equivalent'' to this indentation is then stripped from the start of all lines of the string. Lines that are indented less should not occur, but if they occur all their leading whitespace should be stripped. Equivalence of whitespace should be tested after expansion of tabs (to 8 spaces, normally).

#### Footnotes

... value.4.1
  Actually, call by object reference would be a better description, since if a mutable object is passed, the caller will see any changes the callee makes to it (e.g., items inserted into a list).