Python Tutorial

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

The break statement breaks out of the innermost enclosing for or while loop:

for n in range(2, 10):
    for x in range(2, n):
        if n % x == 0:
            print(f"{n} equals {x} * {n//x}")
            break

4 equals 2 * 2
6 equals 2 * 3
8 equals 2 * 4
9 equals 3 * 3

The continue statement continues with the next iteration of the loop:

for num in range(2, 10):
    if num % 2 == 0:
        print(f"Found an even number {num}")
        continue
    print(f"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. else Clauses on Loops

In a for or while loop the break statement may be paired with an else clause. If the loop finishes without executing the break, the else clause executes.

In a for loop, the else clause is executed after the loop finishes its final iteration, that is, if no break occurred.

In a while loop, it’s executed after the loop’s condition becomes false.

In either kind of loop, the else clause is not executed if the loop was terminated by a break. Of course, other ways of ending the loop early, such as a return or a raised exception, will also skip execution of the else clause.

This is exemplified in the following for 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.)

One way to think of the else clause is to imagine it paired with the if inside the loop. As the loop executes, it will run a sequence like if/if/if/else. The if is inside the loop, encountered a number of times. If the condition is ever true, a break will happen. If the condition is never true, the else clause outside the loop will execute.

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.

4.6. 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!

4.7. 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:
    def __init__(self, x, y):
        self.x = x
        self.y = y

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, with __match_args__ added, we could match it like this:

class Point:
    __match_args__ = ('x', 'y')
    def __init__(self, x, y):
        self.x = x
        self.y = y

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.

4.8. Defining Functions

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

def fib(n):    # write Fibonacci series less than n
    """Print a Fibonacci series less than 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.

4.9. 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:
        reply = input(prompt)
        if reply in {'y', 'ye', 'yes'}:
            return True
        if reply 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'

4.10. 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.