4.1. if Statements¶

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

>>> x = int(input("Please enter an integer: "))
Please enter an integer: 42
>>> if x < 0:
...     x = 0
...     print('Negative changed to zero')
... elif x == 0:
...     print('Zero')
... elif x == 1:
...     print('Single')
... else:
...     print('More')
...
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.

If you’re comparing the same value to several constants, or checking for specific types or
attributes, you may also find the match statement useful. For more
details see match Statements .

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 (a list or a string), in the order that
they appear in the sequence. For example (no pun intended):

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

Code that modifies a collection while iterating over that same collection can
be tricky to get right. Instead, it is usually more straight-forward to loop
over a copy of the collection or to create a new collection:

# Create a sample collection
users = {'Hans': 'active', 'Éléonore': 'inactive', '景太郎': 'active'}

# Strategy:  Iterate over a copy
for user, status in users.copy().items():
    if status == 'inactive':
        del users[user]

# Strategy:  Create a new collection
active_users = {}
for user, status in users.items():
    if status == 'active':
        active_users[user] = status

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 arithmetic progressions:

>>> for i in range(5):
...     print(i)
...
0
1
2
3
4

The given end point is never part of the generated sequence; range(10) generates
10 values, 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; sometimes this is called the ‘step’):

>>> list(range(5, 10))
[5, 6, 7, 8, 9]

>>> list(range(0, 10, 3))
[0, 3, 6, 9]

>>> list(range(-10, -100, -30))
[-10, -40, -70]

To iterate over the indices of a sequence, you can 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

In most such cases, however, it is convenient to use the enumerate()
function, see Looping Techniques .

A strange thing happens if you just print a range:

>>> range(10)
range(0, 10)

In many ways the object returned by range() behaves as if it is a list,
but in fact it isn’t. It is an object which returns the successive items of
the desired sequence when you iterate over it, but it doesn’t really make
the list, thus saving space.

We say such an object is iterable , that is, suitable as a target for
functions and constructs that expect something from which they can
obtain successive items until the supply is exhausted. We have seen that
the for statement is such a construct, while an example of a function
that takes an iterable is sum() :

>>> sum(range(4))  # 0 + 1 + 2 + 3
6

Later we will see more functions that return iterables and take iterables as
arguments. In chapter Data Structures , we will discuss in more detail about
list() .

4.4. break and continue Statements, and else Clauses on Loops¶

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

Loop statements may have an else clause; it is executed when the loop
terminates through exhaustion of the iterable (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:
...         # loop fell through without finding a factor
...         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

(Yes, this is the correct code. Look closely: the else clause belongs to
the for loop, not the if statement.)

When used with a loop, the else clause has more in common with the
else clause of a try statement than it does with that of
if statements: a try statement’s else clause runs
when no exception occurs, and a loop’s else clause runs when no break
occurs. For more on the try statement and exceptions, see
Handling Exceptions .

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

>>> for num in range(2, 10):
...     if num % 2 == 0:
...         print("Found an even number", num)
...         continue
...     print("Found an odd number", num)
...
Found an even number 2
Found an odd number 3
Found an even number 4
Found an odd number 5
Found an even number 6
Found an odd number 7
Found an even number 8
Found an odd number 9

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 True:
...     pass  # Busy-wait for keyboard interrupt (Ctrl+C)
...

This is commonly used for creating minimal classes:

>>> class MyEmptyClass:
...     pass
...

Another place pass can be used is as a place-holder for a function or
conditional body when you are working on new code, allowing you to keep thinking
at a more abstract level. The pass is silently ignored:

>>> def initlog(*args):
...     pass   # Remember to implement this!
...

4.6. match Statements¶

A match statement takes an expression and compares its value to successive
patterns given as one or more case blocks. This is superficially
similar to a switch statement in C, Java or JavaScript (and many
other languages), but it’s more similar to pattern matching in
languages like Rust or Haskell. Only the first pattern that matches
gets executed and it can also extract components (sequence elements
or object attributes) from the value into variables.

The simplest form compares a subject value against one or more literals:

def http_error(status):
    match status:
        case 400:
            return "Bad request"
        case 404:
            return "Not found"
        case 418:
            return "I'm a teapot"
        case _:
            return "Something's wrong with the internet"

Note the last block: the “variable name” _ acts as a wildcard and
never fails to match. If no case matches, none of the branches is executed.

You can combine several literals in a single pattern using | (“or”):

case 401 | 403 | 404:
    return "Not allowed"

Patterns can look like unpacking assignments, and can be used to bind
variables:

# point is an (x, y) tuple
match point:
    case (0, 0):
        print("Origin")
    case (0, y):
        print(f"Y={y}")
    case (x, 0):
        print(f"X={x}")
    case (x, y):
        print(f"X={x}, Y={y}")
    case _:
        raise ValueError("Not a point")

Study that one carefully! The first pattern has two literals, and can
be thought of as an extension of the literal pattern shown above. But
the next two patterns combine a literal and a variable, and the
variable binds a value from the subject ( point ). The fourth
pattern captures two values, which makes it conceptually similar to
the unpacking assignment (x, y) = point .

If you are using classes to structure your data
you can use the class name followed by an argument list resembling a
constructor, but with the ability to capture attributes into variables:

class Point:
    x: int
    y: int

def where_is(point):
    match point:
        case Point(x=0, y=0):
            print("Origin")
        case Point(x=0, y=y):
            print(f"Y={y}")
        case Point(x=x, y=0):
            print(f"X={x}")
        case Point():
            print("Somewhere else")
        case _:
            print("Not a point")

You can use positional parameters with some builtin classes that provide an
ordering for their attributes (e.g. dataclasses). You can also define a specific
position for attributes in patterns by setting the __match_args__ special
attribute in your classes. If it’s set to (“x”, “y”), the following patterns are all
equivalent (and all bind the y attribute to the var variable):

Point(1, var)
Point(1, y=var)
Point(x=1, y=var)
Point(y=var, x=1)

A recommended way to read patterns is to look at them as an extended form of what you
would put on the left of an assignment, to understand which variables would be set to
what.
Only the standalone names (like var above) are assigned to by a match statement.
Dotted names (like foo.bar ), attribute names (the x= and y= above) or class names
(recognized by the “(…)” next to them like Point above) are never assigned to.

Patterns can be arbitrarily nested. For example, if we have a short
list of points, we could match it like this:

match points:
    case []:
        print("No points")
    case [Point(0, 0)]:
        print("The origin")
    case [Point(x, y)]:
        print(f"Single point {x}, {y}")
    case [Point(0, y1), Point(0, y2)]:
        print(f"Two on the Y axis at {y1}, {y2}")
    case _:
        print("Something else")

We can add an if clause to a pattern, known as a “guard”. If the
guard is false, match goes on to try the next case block. Note
that value capture happens before the guard is evaluated:

match point:
    case Point(x, y) if x == y:
        print(f"Y=X at {x}")
    case Point(x, y):
        print(f"Not on the diagonal")

Several other key features of this statement:

• Like unpacking assignments, tuple and list patterns have exactly the
  same meaning and actually match arbitrary sequences. An important
  exception is that they don’t match iterators or strings.




• Sequence patterns support extended unpacking: [x, y, *rest] and (x, y,
*rest) work similar to unpacking assignments. The
  name after * may also be _ , so (x, y, *_) matches a sequence
  of at least two items without binding the remaining items.




• Mapping patterns: {"bandwidth": b, "latency": l} captures the
  "bandwidth" and "latency" values from a dictionary. Unlike sequence
  patterns, extra keys are ignored. An unpacking like **rest is also
  supported. (But **_ would be redundant, so it is not allowed.)




• Subpatterns may be captured using the as keyword:

  
  

  case (Point(x1, y1), Point(x2, y2) as p2): ...
  

  
  
  
  

  will capture the second element of the input as p2 (as long as the input is
  a sequence of two points)

  
  



• Most literals are compared by equality, however the singletons True ,
  False and None are compared by identity.




• Patterns may use named constants. These must be dotted names
  to prevent them from being interpreted as capture variable:

  
  

  from enum import Enum
  class Color(Enum):
      RED = 'red'
      GREEN = 'green'
      BLUE = 'blue'
  
  color = Color(input("Enter your choice of 'red', 'blue' or 'green': "))
  
  match color:
      case Color.RED:
          print("I see red!")
      case Color.GREEN:
          print("Grass is green")
      case Color.BLUE:
          print("I'm feeling the blues :(")
  

  
  
  
  

For a more detailed explanation and additional examples, you can look into
PEP 636 which is written in a tutorial format.

4.7. 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 a < n:
...         print(a, end=' ')
...         a, b = b, a+b
...     print()
...
>>> # Now call the function we just defined:
... fib(2000)
0 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, and
must be indented.

The first statement of the function body can optionally be a string literal;
this string literal is the function’s documentation string, or docstring .
(More about docstrings can be found in the section Documentation Strings .)
There are tools which use docstrings to automatically produce online or printed
documentation, or to let the user interactively browse through code; it’s good
practice to include docstrings in code that you write, so 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 local symbol tables of
enclosing functions, then in the global symbol table, and finally in the table
of built-in names. Thus, global variables and variables of enclosing functions
cannot be directly assigned a value within a function (unless, for global
variables, named in a global statement, or, for variables of enclosing
functions, named in a nonlocal 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 (where the value is always an object reference ,
not the value of the object). 1 When a function calls another function,
or calls itself recursively, a new
local symbol table is created for that call.

A function definition associates the function name with the function object in
the current symbol table. The interpreter recognizes the object pointed to by
that name as a user-defined function. Other names can also point to that same
function object and can also be used to access the function:

>>> fib
<function fib at 10042ed0>
>>> f = fib
>>> f(100)
0 1 1 2 3 5 8 13 21 34 55 89

Coming from other languages, you might object that fib is not a function but
a procedure since it doesn’t return a value. In fact, even functions without a
return statement 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 using print() :

>>> fib(0)
>>> 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 a < n:
...         result.append(a)    # see below
...         a, b = b, a+b
...     return result
...
>>> f100 = fib2(100)    # call it
>>> f100                # write the result
[0, 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 returns None . Falling off
  the end of a function also returns None .




• The statement result.append(a) 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 , see Classes )
  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 + [a] , but more efficient.

4.8. 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.

def ask_ok(prompt, retries=4, reminder='Please try again!'):
    while True:
        ok = input(prompt)
        if ok in ('y', 'ye', 'yes'):
            return True
        if ok in ('n', 'no', 'nop', 'nope'):
            return False
        retries = retries - 1
        if retries < 0:
            raise ValueError('invalid user response')
        print(reminder)

i = 5

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

i = 6
f()

def f(a, L=[]):
    L.append(a)
    return L

print(f(1))
print(f(2))
print(f(3))

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

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

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

parrot(1000)                                          # 1 positional argument
parrot(voltage=1000)                                  # 1 keyword argument
parrot(voltage=1000000, action='VOOOOOM')             # 2 keyword arguments
parrot(action='VOOOOOM', voltage=1000000)             # 2 keyword arguments
parrot('a million', 'bereft of life', 'jump')         # 3 positional arguments
parrot('a thousand', state='pushing up the daisies')  # 1 positional, 1 keyword

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

>>> def function(a):
...     pass
...
>>> function(0, a=0)
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
TypeError: function() got multiple values for argument 'a'

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:
        print(kw, ":", keywords[kw])

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

-- 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.
----------------------------------------
shopkeeper : Michael Palin
client : John Cleese
sketch : Cheese Shop Sketch

def f(pos1, pos2, /, pos_or_kwd, *, kwd1, kwd2):
      -----------    ----------     ----------
        |             |                  |
        |        Positional or keyword   |
        |                                - Keyword only
         -- Positional only

>>> def standard_arg(arg):
...     print(arg)
...
>>> def pos_only_arg(arg, /):
...     print(arg)
...
>>> def kwd_only_arg(*, arg):
...     print(arg)
...
>>> def combined_example(pos_only, /, standard, *, kwd_only):
...     print(pos_only, standard, kwd_only)

>>> standard_arg(2)
2

>>> standard_arg(arg=2)
2

>>> pos_only_arg(1)
1

>>> pos_only_arg(arg=1)
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
TypeError: pos_only_arg() got some positional-only arguments passed as keyword arguments: 'arg'

>>> kwd_only_arg(3)
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
TypeError: kwd_only_arg() takes 0 positional arguments but 1 was given

>>> kwd_only_arg(arg=3)
3

>>> combined_example(1, 2, 3)
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
TypeError: combined_example() takes 2 positional arguments but 3 were given

>>> combined_example(1, 2, kwd_only=3)
1 2 3

>>> combined_example(1, standard=2, kwd_only=3)
1 2 3

>>> combined_example(pos_only=1, standard=2, kwd_only=3)
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
TypeError: combined_example() got some positional-only arguments passed as keyword arguments: 'pos_only'

def foo(name, **kwds):
    return 'name' in kwds

>>> foo(1, **{'name': 2})
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
TypeError: foo() got multiple values for argument 'name'
>>>

>>> def foo(name, /, **kwds):
...     return 'name' in kwds
...
>>> foo(1, **{'name': 2})
True

def f(pos1, pos2, /, pos_or_kwd, *, kwd1, kwd2):

def write_multiple_items(file, separator, *args):
    file.write(separator.join(args))

>>> def concat(*args, sep="/"):
...     return sep.join(args)
...
>>> concat("earth", "mars", "venus")
'earth/mars/venus'
>>> concat("earth", "mars", "venus", sep=".")
'earth.mars.venus'

>>> list(range(3, 6))            # normal call with separate arguments
[3, 4, 5]
>>> args = [3, 6]
>>> list(range(*args))            # call with arguments unpacked from a list
[3, 4, 5]

>>> def parrot(voltage, state='a stiff', action='voom'):
...     print("-- This parrot wouldn't", action, end=' ')
...     print("if you put", voltage, "volts through it.", end=' ')
...     print("E's", state, "!")
...
>>> d = {"voltage": "four million", "state": "bleedin' demised", "action": "VOOM"}
>>> parrot(**d)
-- This parrot wouldn't VOOM if you put four million volts through it. E's bleedin' demised !

>>> def make_incrementor(n):
...     return lambda x: x + n
...
>>> f = make_incrementor(42)
>>> f(0)
42
>>> f(1)
43

>>> pairs = [(1, 'one'), (2, 'two'), (3, 'three'), (4, 'four')]
>>> pairs.sort(key=lambda pair: pair[1])
>>> pairs
[(4, 'four'), (1, 'one'), (3, 'three'), (2, 'two')]

>>> def my_function():
...     """Do nothing, but document it.
...
...     No, really, it doesn't do anything.
...     """
...     pass
...
>>> print(my_function.__doc__)
Do nothing, but document it.

    No, really, it doesn't do anything.

>>> def f(ham: str, eggs: str = 'eggs') -> str:
...     print("Annotations:", f.__annotations__)
...     print("Arguments:", ham, eggs)
...     return ham + ' and ' + eggs
...
>>> f('spam')
Annotations: {'ham': <class 'str'>, 'return': <class 'str'>, 'eggs': <class 'str'>}
Arguments: spam eggs
'spam and eggs'

4.9. Intermezzo: Coding Style¶

Now that you are about to write longer, more complex pieces of Python, it is a
good time to talk about coding style . Most languages can be written (or more
concise, formatted ) in different styles; some are more readable than others.
Making it easy for others to read your code is always a good idea, and adopting
a nice coding style helps tremendously for that.

For Python, PEP 8 has emerged as the style guide that most projects adhere to;
it promotes a very readable and eye-pleasing coding style. Every Python
developer should read it at some point; here are the most important points
extracted for you:

• Use 4-space indentation, and no tabs.

  
  

  4 spaces are a good compromise between small indentation (allows greater
  nesting depth) and large indentation (easier to read). Tabs introduce
  confusion, and are best left out.

  
  



• Wrap lines so that they don’t exceed 79 characters.

  
  

  This helps users with small displays and makes it possible to have several
  code files side-by-side on larger displays.

  
  



• Use blank lines to separate functions and classes, and larger blocks of
  code inside functions.




• When possible, put comments on a line of their own.




• Use docstrings.




• Use spaces around operators and after commas, but not directly inside
  bracketing constructs: a = f(1, 2) + g(3, 4) .




• Name your classes and functions consistently; the convention is to use
  UpperCamelCase for classes and lowercase_with_underscores for functions
  and methods. Always use self as the name for the first method argument
  (see A First Look at Classes for more on classes and methods).




• Don’t use fancy encodings if your code is meant to be used in international
  environments. Python’s default, UTF-8, or even plain ASCII work best in any
  case.




• Likewise, don’t use non-ASCII characters in identifiers if there is only the
  slightest chance people speaking a different language will read or maintain
  the code.

Footnotes

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 (items inserted into a list).