More Control Flow Tools

4. More Control Flow Tools¶
As well as the while
statement just introduced, Python uses a few more
that we will encounter in this chapter.
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 list()
in more
detail.
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!
...
For this last case, many people use the ellipsis literal ...
instead of
pass
. This use has no special meaning to Python, and is not part of
the language definition (you could use any constant expression here), but
...
is used conventionally as a placeholder body as well.
See The Ellipsis Object.
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. If no case matches,
none of the branches is executed.
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.
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
andNone
are compared by identity.Patterns may use named constants. These must be dotted names to prevent them from being interpreted as capture variables:
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 returnsNone
. Falling off the end of a function also returnsNone
.The statement
result.append(a)
calls a method of the list objectresult
. A method is a function that ‘belongs’ to an object and is namedobj.methodname
, whereobj
is some object (this may be an expression), andmethodname
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 methodappend()
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 toresult = 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.
4.9.1. 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 to allow. For example:
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)
This function can be called in several ways:
giving only the mandatory argument:
ask_ok('Do you really want to quit?')
giving one of the optional arguments:
ask_ok('OK to overwrite the file?', 2)
or even giving all arguments:
ask_ok('OK to overwrite the file?', 2, 'Come on, only yes or no!')
This example also introduces the in
keyword. This tests whether or
not a sequence contains a certain value.
The default values are evaluated at the point of function definition in the defining scope, so that
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, dictionary, or instances of most classes. 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
4.9.2. Keyword Arguments¶
Functions can also be called using keyword arguments
of the form kwarg=value
. For instance, the following function:
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, "!")
accepts one required argument (voltage
) and three optional arguments
(state
, action
, and type
). This function can be called in any
of the following ways:
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
but all the following calls would be invalid:
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
In a function call, keyword arguments must follow positional arguments.
All the keyword arguments passed must match one of the arguments
accepted by the function (e.g. actor
is not a valid argument for the
parrot
function), and their order is not important. This also includes
non-optional arguments (e.g. parrot(voltage=1000)
is valid too).
No argument may receive a value more than once.
Here’s an example that fails due to this restriction:
>>> 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'
When a final formal parameter of the form **name
is present, it receives a
dictionary (see Mapping Types — dict) containing all keyword arguments except for
those corresponding 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:
print(kw, ":", keywords[kw])
It could be called like this:
cheeseshop("Limburger", "It's very runny, sir.",
"It's really very, VERY runny, sir.",
shopkeeper="Michael Palin",
client="John Cleese",
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.
----------------------------------------
shopkeeper : Michael Palin
client : John Cleese
sketch : Cheese Shop Sketch
Note that the order in which the keyword arguments are printed is guaranteed to match the order in which they were provided in the function call.
4.9.3. Special parameters¶
By default, arguments may be passed to a Python function either by position or explicitly by keyword. For readability and performance, it makes sense to restrict the way arguments can be passed so that a developer need only look at the function definition to determine if items are passed by position, by position or keyword, or by keyword.
A function definition may look like:
def f(pos1, pos2, /, pos_or_kwd, *, kwd1, kwd2):
----------- ---------- ----------
| | |
| Positional or keyword |
| - Keyword only
-- Positional only
where /
and *
are optional. If used, these symbols indicate the kind of
parameter by how the arguments may be passed to the function:
positional-only, positional-or-keyword, and keyword-only. Keyword parameters
are also referred to as named parameters.
4.9.3.1. Positional-or-Keyword Arguments¶
If /
and *
are not present in the function definition, arguments may
be passed to a function by position or by keyword.
4.9.3.2. Positional-Only Parameters¶
Looking at this in a bit more detail, it is possible to mark certain parameters
as positional-only. If positional-only, the parameters’ order matters, and
the parameters cannot be passed by keyword. Positional-only parameters are
placed before a /
(forward-slash). The /
is used to logically
separate the positional-only parameters from the rest of the parameters.
If there is no /
in the function definition, there are no positional-only
parameters.
Parameters following the /
may be positional-or-keyword or keyword-only.
4.9.3.3. Keyword-Only Arguments¶
To mark parameters as keyword-only, indicating the parameters must be passed
by keyword argument, place an *
in the arguments list just before the first
keyword-only parameter.
4.9.3.4. Function Examples¶
Consider the following example function definitions paying close attention to the
markers /
and *
:
>>> 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)
The first function definition, standard_arg
, the most familiar form,
places no restrictions on the calling convention and arguments may be
passed by position or keyword:
>>> standard_arg(2)
2
>>> standard_arg(arg=2)
2
The second function pos_only_arg
is restricted to only use positional
parameters as there is a /
in the function definition:
>>> 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'
The third function kwd_only_arg
only allows keyword arguments as indicated
by a *
in the function definition:
>>> 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
And the last uses all three calling conventions in the same function definition:
>>> 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'
Finally, consider this function definition which has a potential collision between the positional argument name
and **kwds
which has name
as a key:
def foo(name, **kwds):
return 'name' in kwds
There is no possible call that will make it return True
as the keyword 'name'
will always bind to the first parameter. For example:
>>> foo(1, **{'name': 2})
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
TypeError: foo() got multiple values for argument 'name'
>>>
But using /
(positional only arguments), it is possible since it allows name
as a positional argument and 'name'
as a key in the keyword arguments:
>>> def foo(name, /, **kwds):
... return 'name' in kwds
...
>>> foo(1, **{'name': 2})
True
In other words, the names of positional-only parameters can be used in
**kwds
without ambiguity.
4.9.3.5. Recap¶
The use case will determine which parameters to use in the function definition:
def f(pos1, pos2, /, pos_or_kwd, *, kwd1, kwd2):
As guidance:
Use positional-only if you want the name of the parameters to not be available to the user. This is useful when parameter names have no real meaning, if you want to enforce the order of the arguments when the function is called or if you need to take some positional parameters and arbitrary keywords.
Use keyword-only when names have meaning and the function definition is more understandable by being explicit with names or you want to prevent users relying on the position of the argument being passed.
For an API, use positional-only to prevent breaking API changes if the parameter’s name is modified in the future.
4.9.4. 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 (see Tuples and Sequences). Before the variable number of arguments, zero or more normal arguments may occur.
def write_multiple_items(file, separator, *args):
file.write(separator.join(args))
Normally, these variadic arguments will be last in the list of formal
parameters, because they scoop up all remaining input arguments that are
passed to the function. Any formal parameters which occur after the *args
parameter are ‘keyword-only’ arguments, meaning that they can only be used as
keywords rather than positional arguments.
>>> def concat(*args, sep="/"):
... return sep.join(args)
...
>>> concat("earth", "mars", "venus")
'earth/mars/venus'
>>> concat("earth", "mars", "venus", sep=".")
'earth.mars.venus'
4.9.5. Unpacking Argument Lists¶
The reverse situation occurs when the arguments are already in a list or tuple
but need to be unpacked for a function call requiring separate positional
arguments. For instance, the built-in range()
function expects separate
start and stop arguments. If they are not available separately, write the
function call with the *
-operator to unpack the arguments out of a list
or tuple:
>>> 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]
In the same fashion, dictionaries can deliver keyword arguments with the
**
-operator:
>>> 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 !
4.9.6. Lambda Expressions¶
Small anonymous functions can be created with the lambda
keyword.
This function returns the sum of its two arguments: lambda a, b: a+b
.
Lambda functions 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 functions can reference variables from the containing
scope:
>>> def make_incrementor(n):
... return lambda x: x + n
...
>>> f = make_incrementor(42)
>>> f(0)
42
>>> f(1)
43
The above example uses a lambda expression to return a function. Another use
is to pass a small function as an argument. For instance, list.sort()
takes a sorting key function key which can be a lambda function:
>>> 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')]
4.9.7. Documentation Strings¶
Here are some 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 strips indentation from multi-line string literals when they serve as module, class, or function docstrings.
Here is an example of a multi-line docstring:
>>> def my_function():
... """Do nothing, but document it.
...
... No, really, it doesn't do anything:
...
... >>> my_function()
... >>>
... """
... pass
...
>>> print(my_function.__doc__)
Do nothing, but document it.
No, really, it doesn't do anything:
>>> my_function()
>>>
4.9.8. Function Annotations¶
Function annotations are completely optional metadata information about the types used by user-defined functions (see PEP 3107 and PEP 484 for more information).
Annotations are stored in the __annotations__
attribute of the function as a dictionary and have no effect on any other part of the
function. Parameter annotations are defined by a colon after the parameter name, followed
by an expression evaluating to the value of the annotation. Return annotations are
defined by a literal ->
, followed by an expression, between the parameter
list and the colon denoting the end of the def
statement. The
following example has a required argument, an optional argument, and the return
value annotated:
>>> 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 concisely, 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 andlowercase_with_underscores
for functions and methods. Always useself
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


An Informal Introduction to Python

3. An Informal Introduction to Python¶
In the following examples, input and output are distinguished by the presence or absence of prompts (>>> and …): to repeat the example, you must type everything after the prompt, when the prompt appears; lines that do not begin with a prompt are output from the interpreter. Note that a secondary prompt on a line by itself in an example means you must type a blank line; this is used to end a multi-line command.
You can use the “Copy” button (it appears in the upper-right corner when hovering over or tapping a code example), which strips prompts and omits output, to copy and paste the input lines into your interpreter.
Many of the examples in this manual, even those entered at the interactive
prompt, include comments. Comments in Python start with the hash character,
#
, and extend to the end of the physical line. A comment may appear at the
start of a line or following whitespace or code, but not within a string
literal. A hash character within a string literal is just a hash character.
Since comments are to clarify code and are not interpreted by Python, they may
be omitted when typing in examples.
Some examples:
# this is the first comment
spam = 1 # and this is the second comment
# ... and now a third!
text = "# This is not a comment because it's inside quotes."
3.1. Using Python as a Calculator¶
Let’s try some simple Python commands. Start the interpreter and wait for the
primary prompt, >>>
. (It shouldn’t take long.)
3.1.1. Numbers¶
The interpreter acts as a simple calculator: you can type an expression into it
and it will write the value. Expression syntax is straightforward: the
operators +
, -
, *
and /
can be used to perform
arithmetic; parentheses (()
) can be used for grouping.
For example:
>>> 2 + 2
4
>>> 50 - 5*6
20
>>> (50 - 5*6) / 4
5.0
>>> 8 / 5 # division always returns a floating-point number
1.6
The integer numbers (e.g. 2
, 4
, 20
) have type int
,
the ones with a fractional part (e.g. 5.0
, 1.6
) have type
float
. We will see more about numeric types later in the tutorial.
Division (/
) always returns a float. To do floor division and
get an integer result you can use the //
operator; to calculate
the remainder you can use %
:
>>> 17 / 3 # classic division returns a float
5.666666666666667
>>>
>>> 17 // 3 # floor division discards the fractional part
5
>>> 17 % 3 # the % operator returns the remainder of the division
2
>>> 5 * 3 + 2 # floored quotient * divisor + remainder
17
With Python, it is possible to use the **
operator to calculate powers [1]:
>>> 5 ** 2 # 5 squared
25
>>> 2 ** 7 # 2 to the power of 7
128
The equal sign (=
) is used to assign a value to a variable. Afterwards, no
result is displayed before the next interactive prompt:
>>> width = 20
>>> height = 5 * 9
>>> width * height
900
If a variable is not “defined” (assigned a value), trying to use it will give you an error:
>>> n # try to access an undefined variable
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
NameError: name 'n' is not defined
There is full support for floating point; operators with mixed type operands convert the integer operand to floating point:
>>> 4 * 3.75 - 1
14.0
In interactive mode, the last printed expression is assigned to the variable
_
. This means that when you are using Python as a desk calculator, it is
somewhat easier to continue calculations, for example:
>>> tax = 12.5 / 100
>>> price = 100.50
>>> price * tax
12.5625
>>> price + _
113.0625
>>> round(_, 2)
113.06
This variable should be treated as read-only by the user. Don’t explicitly assign a value to it — you would create an independent local variable with the same name masking the built-in variable with its magic behavior.
In addition to int
and float
, Python supports other types of
numbers, such as Decimal
and Fraction
.
Python also has built-in support for complex numbers,
and uses the j
or J
suffix to indicate the imaginary part
(e.g. 3+5j
).
3.1.2. Text¶
Python can manipulate text (represented by type str
, so-called
“strings”) as well as numbers. This includes characters “!
”, words
“rabbit
”, names “Paris
”, sentences “Got your back.
”, etc.
“Yay! :)
”. They can be enclosed in single quotes ('...'
) or double
quotes ("..."
) with the same result [2].
>>> 'spam eggs' # single quotes
'spam eggs'
>>> "Paris rabbit got your back :)! Yay!" # double quotes
'Paris rabbit got your back :)! Yay!'
>>> '1975' # digits and numerals enclosed in quotes are also strings
'1975'
To quote a quote, we need to “escape” it, by preceding it with \
.
Alternatively, we can use the other type of quotation marks:
>>> 'doesn\'t' # use \' to escape the single quote...
"doesn't"
>>> "doesn't" # ...or use double quotes instead
"doesn't"
>>> '"Yes," they said.'
'"Yes," they said.'
>>> "\"Yes,\" they said."
'"Yes," they said.'
>>> '"Isn\'t," they said.'
'"Isn\'t," they said.'
In the Python shell, the string definition and output string can look
different. The print()
function produces a more readable output, by
omitting the enclosing quotes and by printing escaped and special characters:
>>> s = 'First line.\nSecond line.' # \n means newline
>>> s # without print(), special characters are included in the string
'First line.\nSecond line.'
>>> print(s) # with print(), special characters are interpreted, so \n produces new line
First line.
Second line.
If you don’t want characters prefaced by \
to be interpreted as
special characters, you can use raw strings by adding an r
before
the first quote:
>>> print('C:\some\name') # here \n means newline!
C:\some
ame
>>> print(r'C:\some\name') # note the r before the quote
C:\some\name
There is one subtle aspect to raw strings: a raw string may not end in
an odd number of \
characters; see
the FAQ entry for more information
and workarounds.
String literals can span multiple lines. One way is using triple-quotes:
"""..."""
or '''...'''
. End-of-line characters are automatically
included in the string, but it’s possible to prevent this by adding a \
at
the end of the line. In the following example, the initial newline is not
included:
>>> print("""\
... Usage: thingy [OPTIONS]
... -h Display this usage message
... -H hostname Hostname to connect to
... """)
Usage: thingy [OPTIONS]
-h Display this usage message
-H hostname Hostname to connect to
>>>
Strings can be concatenated (glued together) with the +
operator, and
repeated with *
:
>>> # 3 times 'un', followed by 'ium'
>>> 3 * 'un' + 'ium'
'unununium'
Two or more string literals (i.e. the ones enclosed between quotes) next to each other are automatically concatenated.
>>> 'Py' 'thon'
'Python'
This feature is particularly useful when you want to break long strings:
>>> text = ('Put several strings within parentheses '
... 'to have them joined together.')
>>> text
'Put several strings within parentheses to have them joined together.'
This only works with two literals though, not with variables or expressions:
>>> prefix = 'Py'
>>> prefix 'thon' # can't concatenate a variable and a string literal
File "<stdin>", line 1
prefix 'thon'
^^^^^^
SyntaxError: invalid syntax
>>> ('un' * 3) 'ium'
File "<stdin>", line 1
('un' * 3) 'ium'
^^^^^
SyntaxError: invalid syntax
If you want to concatenate variables or a variable and a literal, use +
:
>>> prefix + 'thon'
'Python'
Strings can be indexed (subscripted), with the first character having index 0. There is no separate character type; a character is simply a string of size one:
>>> word = 'Python'
>>> word[0] # character in position 0
'P'
>>> word[5] # character in position 5
'n'
Indices may also be negative numbers, to start counting from the right:
>>> word[-1] # last character
'n'
>>> word[-2] # second-last character
'o'
>>> word[-6]
'P'
Note that since -0 is the same as 0, negative indices start from -1.
In addition to indexing, slicing is also supported. While indexing is used to obtain individual characters, slicing allows you to obtain a substring:
>>> word[0:2] # characters from position 0 (included) to 2 (excluded)
'Py'
>>> word[2:5] # characters from position 2 (included) to 5 (excluded)
'tho'
Slice indices have useful defaults; an omitted first index defaults to zero, an omitted second index defaults to the size of the string being sliced.
>>> word[:2] # character from the beginning to position 2 (excluded)
'Py'
>>> word[4:] # characters from position 4 (included) to the end
'on'
>>> word[-2:] # characters from the second-last (included) to the end
'on'
Note how the start is always included, and the end always excluded. This
makes sure that s[:i] + s[i:]
is always equal to s
:
>>> word[:2] + word[2:]
'Python'
>>> word[:4] + word[4:]
'Python'
One way to remember how slices work is to think of the indices as pointing between characters, with the left edge of the first character numbered 0. Then the right edge of the last character of a string of n characters has index n, for example:
+---+---+---+---+---+---+
| P | y | t | h | o | n |
+---+---+---+---+---+---+
0 1 2 3 4 5 6
-6 -5 -4 -3 -2 -1
The first row of numbers gives the position of the indices 0…6 in the string; the second row gives the corresponding negative indices. The slice from i to j consists of all characters between the edges labeled i and j, respectively.
For non-negative indices, the length of a slice is the difference of the
indices, if both are within bounds. For example, the length of word[1:3]
is
2.
Attempting to use an index that is too large will result in an error:
>>> word[42] # the word only has 6 characters
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
IndexError: string index out of range
However, out of range slice indexes are handled gracefully when used for slicing:
>>> word[4:42]
'on'
>>> word[42:]
''
Python strings cannot be changed — they are immutable. Therefore, assigning to an indexed position in the string results in an error:
>>> word[0] = 'J'
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
TypeError: 'str' object does not support item assignment
>>> word[2:] = 'py'
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
TypeError: 'str' object does not support item assignment
If you need a different string, you should create a new one:
>>> 'J' + word[1:]
'Jython'
>>> word[:2] + 'py'
'Pypy'
The built-in function len()
returns the length of a string:
>>> s = 'supercalifragilisticexpialidocious'
>>> len(s)
34
See also
- Text Sequence Type — str
Strings are examples of sequence types, and support the common operations supported by such types.
- String Methods
Strings support a large number of methods for basic transformations and searching.
- f-strings
String literals that have embedded expressions.
- Format String Syntax
Information about string formatting with
str.format()
.- printf-style String Formatting
The old formatting operations invoked when strings are the left operand of the
%
operator are described in more detail here.
3.1.3. Lists¶
Python knows a number of compound data types, used to group together other values. The most versatile is the list, which can be written as a list of comma-separated values (items) between square brackets. Lists might contain items of different types, but usually the items all have the same type.
>>> squares = [1, 4, 9, 16, 25]
>>> squares
[1, 4, 9, 16, 25]
Like strings (and all other built-in sequence types), lists can be indexed and sliced:
>>> squares[0] # indexing returns the item
1
>>> squares[-1]
25
>>> squares[-3:] # slicing returns a new list
[9, 16, 25]
Lists also support operations like concatenation:
>>> squares + [36, 49, 64, 81, 100]
[1, 4, 9, 16, 25, 36, 49, 64, 81, 100]
Unlike strings, which are immutable, lists are a mutable type, i.e. it is possible to change their content:
>>> cubes = [1, 8, 27, 65, 125] # something's wrong here
>>> 4 ** 3 # the cube of 4 is 64, not 65!
64
>>> cubes[3] = 64 # replace the wrong value
>>> cubes
[1, 8, 27, 64, 125]
You can also add new items at the end of the list, by using
the list.append()
method (we will see more about methods later):
>>> cubes.append(216) # add the cube of 6
>>> cubes.append(7 ** 3) # and the cube of 7
>>> cubes
[1, 8, 27, 64, 125, 216, 343]
Simple assignment in Python never copies data. When you assign a list to a variable, the variable refers to the existing list. Any changes you make to the list through one variable will be seen through all other variables that refer to it.:
>>> rgb = ["Red", "Green", "Blue"]
>>> rgba = rgb
>>> id(rgb) == id(rgba) # they reference the same object
True
>>> rgba.append("Alph")
>>> rgb
["Red", "Green", "Blue", "Alph"]
All slice operations return a new list containing the requested elements. This means that the following slice returns a shallow copy of the list:
>>> correct_rgba = rgba[:]
>>> correct_rgba[-1] = "Alpha"
>>> correct_rgba
["Red", "Green", "Blue", "Alpha"]
>>> rgba
["Red", "Green", "Blue", "Alph"]
Assignment to slices is also possible, and this can even change the size of the list or clear it entirely:
>>> letters = ['a', 'b', 'c', 'd', 'e', 'f', 'g']
>>> letters
['a', 'b', 'c', 'd', 'e', 'f', 'g']
>>> # replace some values
>>> letters[2:5] = ['C', 'D', 'E']
>>> letters
['a', 'b', 'C', 'D', 'E', 'f', 'g']
>>> # now remove them
>>> letters[2:5] = []
>>> letters
['a', 'b', 'f', 'g']
>>> # clear the list by replacing all the elements with an empty list
>>> letters[:] = []
>>> letters
[]
The built-in function len()
also applies to lists:
>>> letters = ['a', 'b', 'c', 'd']
>>> len(letters)
4
It is possible to nest lists (create lists containing other lists), for example:
>>> a = ['a', 'b', 'c']
>>> n = [1, 2, 3]
>>> x = [a, n]
>>> x
[['a', 'b', 'c'], [1, 2, 3]]
>>> x[0]
['a', 'b', 'c']
>>> x[0][1]
'b'
3.2. First Steps Towards Programming¶
Of course, we can use Python for more complicated tasks than adding two and two together. For instance, we can write an initial sub-sequence of the Fibonacci series as follows:
>>> # Fibonacci series:
>>> # the sum of two elements defines the next
>>> a, b = 0, 1
>>> while a < 10:
... print(a)
... a, b = b, a+b
...
0
1
1
2
3
5
8
This example introduces several new features.
The first line contains a multiple assignment: the variables
a
andb
simultaneously get the new values 0 and 1. On the last line this is used again, demonstrating that the expressions on the right-hand side are all evaluated first before any of the assignments take place. The right-hand side expressions are evaluated from the left to the right.The
while
loop executes as long as the condition (here:a < 10
) remains true. In Python, like in C, any non-zero integer value is true; zero is false. The condition may also be a string or list value, in fact any sequence; anything with a non-zero length is true, empty sequences are false. The test used in the example is a simple comparison. The standard comparison operators are written the same as in C:<
(less than),>
(greater than),==
(equal to),<=
(less than or equal to),>=
(greater than or equal to) and!=
(not equal to).The body of the loop is indented: indentation is Python’s way of grouping statements. At the interactive prompt, you have to type a tab or space(s) for each indented line. In practice you will prepare more complicated input for Python with a text editor; all decent text editors have an auto-indent facility. When a compound statement is entered interactively, it must be followed by a blank line to indicate completion (since the parser cannot guess when you have typed the last line). Note that each line within a basic block must be indented by the same amount.
The
print()
function writes the value of the argument(s) it is given. It differs from just writing the expression you want to write (as we did earlier in the calculator examples) in the way it handles multiple arguments, floating-point quantities, and strings. Strings are printed without quotes, and a space is inserted between items, so you can format things nicely, like this:>>> i = 256*256 >>> print('The value of i is', i) The value of i is 65536
The keyword argument end can be used to avoid the newline after the output, or end the output with a different string:
>>> a, b = 0, 1 >>> while a < 1000: ... print(a, end=',') ... a, b = b, a+b ... 0,1,1,2,3,5,8,13,21,34,55,89,144,233,377,610,987,
Footnotes

File
, line
, in
:
File
, line
:
File
, line
:
File
, line
, in
:
File
, line
, in
:
File
, line
, in
:


Using the Python Interpreter

2. Using the Python Interpreter¶
2.1. Invoking the Interpreter¶
The Python interpreter is usually installed as /usr/local/bin/python3.14
on those machines where it is available; putting /usr/local/bin
in your
Unix shell’s search path makes it possible to start it by typing the command:
python3.14
to the shell. [1] Since the choice of the directory where the interpreter lives
is an installation option, other places are possible; check with your local
Python guru or system administrator. (E.g., /usr/local/python
is a
popular alternative location.)
On Windows machines where you have installed Python from the Microsoft Store, the python3.14
command will be available. If you have
the py.exe launcher installed, you can use the py
command. See Python install manager for other ways to launch Python.
Typing an end-of-file character (Control-D on Unix, Control-Z on
Windows) at the primary prompt causes the interpreter to exit with a zero exit
status. If that doesn’t work, you can exit the interpreter by typing the
following command: quit()
.
The interpreter’s line-editing features include interactive editing, history
substitution and code completion on most systems.
Perhaps the quickest check to see whether command line editing is supported is
typing a word in on the Python prompt, then pressing Left arrow (or Control-b).
If the cursor moves, you have command line editing; see Appendix
Interactive Input Editing and History Substitution for an introduction to the keys.
If nothing appears to happen, or if a sequence like ^[[D
or ^B
appears,
command line editing isn’t available; you’ll only be able to use
backspace to remove characters from the current line.
The interpreter operates somewhat like the Unix shell: when called with standard input connected to a tty device, it reads and executes commands interactively; when called with a file name argument or with a file as standard input, it reads and executes a script from that file.
A second way of starting the interpreter is python -c command [arg] ...
,
which executes the statement(s) in command, analogous to the shell’s
-c
option. Since Python statements often contain spaces or other
characters that are special to the shell, it is usually advised to quote
command in its entirety.
Some Python modules are also useful as scripts. These can be invoked using
python -m module [arg] ...
, which executes the source file for module as
if you had spelled out its full name on the command line.
When a script file is used, it is sometimes useful to be able to run the script
and enter interactive mode afterwards. This can be done by passing -i
before the script.
All command line options are described in Command line and environment.
2.1.1. Argument Passing¶
When known to the interpreter, the script name and additional arguments
thereafter are turned into a list of strings and assigned to the argv
variable in the sys
module. You can access this list by executing import
sys
. The length of the list is at least one; when no script and no arguments
are given, sys.argv[0]
is an empty string. When the script name is given as
'-'
(meaning standard input), sys.argv[0]
is set to '-'
. When
-c
command is used, sys.argv[0]
is set to '-c'
. When
-m
module is used, sys.argv[0]
is set to the full name of the
located module. Options found after -c
command or -m
module are not consumed by the Python interpreter’s option processing but
left in sys.argv
for the command or module to handle.
2.1.2. Interactive Mode¶
When commands are read from a tty, the interpreter is said to be in interactive
mode. In this mode it prompts for the next command with the primary prompt,
usually three greater-than signs (>>>
); for continuation lines it prompts
with the secondary prompt, by default three dots (...
). The interpreter
prints a welcome message stating its version number and a copyright notice
before printing the first prompt:
$ python3.14
Python 3.14 (default, April 4 2024, 09:25:04)
[GCC 10.2.0] on linux
Type "help", "copyright", "credits" or "license" for more information.
>>>
Continuation lines are needed when entering a multi-line construct. As an
example, take a look at this if
statement:
>>> the_world_is_flat = True
>>> if the_world_is_flat:
... print("Be careful not to fall off!")
...
Be careful not to fall off!
For more on interactive mode, see Interactive Mode.
2.2. The Interpreter and Its Environment¶
2.2.1. Source Code Encoding¶
By default, Python source files are treated as encoded in UTF-8. In that encoding, characters of most languages in the world can be used simultaneously in string literals, identifiers and comments — although the standard library only uses ASCII characters for identifiers, a convention that any portable code should follow. To display all these characters properly, your editor must recognize that the file is UTF-8, and it must use a font that supports all the characters in the file.
To declare an encoding other than the default one, a special comment line should be added as the first line of the file. The syntax is as follows:
# -*- coding: encoding -*-
where encoding is one of the valid codecs
supported by Python.
For example, to declare that Windows-1252 encoding is to be used, the first line of your source code file should be:
# -*- coding: cp1252 -*-
One exception to the first line rule is when the source code starts with a UNIX “shebang” line. In this case, the encoding declaration should be added as the second line of the file. For example:
#!/usr/bin/env python3
# -*- coding: cp1252 -*-
Footnotes


Security Considerations

Security Considerations¶
The following modules have specific security considerations:
hashlib
: all constructors take a “usedforsecurity” keyword-only argument disabling known insecure and blocked algorithmshttp.server
is not suitable for production use, only implementing basic security checks. See the security considerations.random
shouldn’t be used for security purposes, usesecrets
insteadshelve
: shelve is based on pickle and thus unsuitable for dealing with untrusted sourcestempfile
: mktemp is deprecated due to vulnerability to race conditionszipfile
: maliciously prepared .zip files can cause disk volume exhaustion
The -I
command line option can be used to run Python in isolated
mode. When it cannot be used, the -P
option or the
PYTHONSAFEPATH
environment variable can be used to not prepend a
potentially unsafe path to sys.path
such as the current directory, the
script’s directory or an empty string.