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5. Data Structures

5. Data Structures¶

This chapter describes some things you’ve learned about already in more detail,
and adds some new things as well.

5.1. More on Lists¶

The list data type has some more methods. Here are all of the methods of list

list. append ( x )

Add an item to the end of the list. Equivalent to a[len(a):] = [x] .

list. extend ( iterable )

Extend the list by appending all the items from the iterable. Equivalent to
a[len(a):] = iterable .

list. insert ( i , x )

Insert an item at a given position. The first argument is the index of the
element before which to insert, so a.insert(0, x) inserts at the front of
the list, and a.insert(len(a), x) is equivalent to a.append(x) .

list. remove ( x )

Remove the first item from the list whose value is equal to x . It raises a
ValueError if there is no such item.

list. pop ( [ i ] )

Remove the item at the given position in the list, and return it. If no index
is specified, a.pop() removes and returns the last item in the list. (The
square brackets around the i in the method signature denote that the parameter
is optional, not that you should type square brackets at that position. You
will see this notation frequently in the Python Library Reference.)

list. clear ( )

Remove all items from the list. Equivalent to del a[:] .

list. index ( x [ , start [ , end ] ] )

Return zero-based index in the list of the first item whose value is equal to x .
Raises a ValueError if there is no such item.

The optional arguments start and end are interpreted as in the slice
notation and are used to limit the search to a particular subsequence of
the list. The returned index is computed relative to the beginning of the full
sequence rather than the start argument.

list. count ( x )

Return the number of times x appears in the list.

list. sort ( * , key = None , reverse = False )

Sort the items of the list in place (the arguments can be used for sort
customization, see sorted() for their explanation).

list. reverse ( )

Reverse the elements of the list in place.

list. copy ( )

Return a shallow copy of the list. Equivalent to a[:] .

An example that uses most of the list methods:

>>> fruits = ['orange', 'apple', 'pear', 'banana', 'kiwi', 'apple', 'banana']
>>> fruits.count('apple')
>>> fruits.count('tangerine')
>>> fruits.index('banana')
>>> fruits.index('banana', 4) # Find next banana starting at position 4
>>> fruits.reverse()
>>> fruits
['banana', 'apple', 'kiwi', 'banana', 'pear', 'apple', 'orange']
>>> fruits.append('grape')
>>> fruits
['banana', 'apple', 'kiwi', 'banana', 'pear', 'apple', 'orange', 'grape']
>>> fruits.sort()
>>> fruits
['apple', 'apple', 'banana', 'banana', 'grape', 'kiwi', 'orange', 'pear']
>>> fruits.pop()

You might have noticed that methods like insert , remove or sort that
only modify the list have no return value printed – they return the default
None . 1 This is a design principle for all mutable data structures in

Another thing you might notice is that not all data can be sorted or
compared. For instance, [None, 'hello', 10] doesn’t sort because
integers can’t be compared to strings and None can’t be compared to
other types. Also, there are some types that don’t have a defined
ordering relation. For example, 3+4j < 5+7j isn’t a valid

5.1.1. Using Lists as Stacks¶

The list methods make it very easy to use a list as a stack, where the last
element added is the first element retrieved (“last-in, first-out”). To add an
item to the top of the stack, use append() . To retrieve an item from the
top of the stack, use pop() without an explicit index. For example:

>>> stack = [3, 4, 5]
>>> stack.append(6)
>>> stack.append(7)
>>> stack
[3, 4, 5, 6, 7]
>>> stack.pop()
>>> stack
[3, 4, 5, 6]
>>> stack.pop()
>>> stack.pop()
>>> stack
[3, 4]

5.1.2. Using Lists as Queues¶

It is also possible to use a list as a queue, where the first element added is
the first element retrieved (“first-in, first-out”); however, lists are not
efficient for this purpose. While appends and pops from the end of list are
fast, doing inserts or pops from the beginning of a list is slow (because all
of the other elements have to be shifted by one).

To implement a queue, use collections.deque which was designed to
have fast appends and pops from both ends. For example:

>>> from collections import deque
>>> queue = deque(["Eric", "John", "Michael"])
>>> queue.append("Terry") # Terry arrives
>>> queue.append("Graham") # Graham arrives
>>> queue.popleft() # The first to arrive now leaves
>>> queue.popleft() # The second to arrive now leaves
>>> queue # Remaining queue in order of arrival
deque(['Michael', 'Terry', 'Graham'])

5.1.3. List Comprehensions¶

List comprehensions provide a concise way to create lists.
Common applications are to make new lists where each element is the result of
some operations applied to each member of another sequence or iterable, or to
create a subsequence of those elements that satisfy a certain condition.

For example, assume we want to create a list of squares, like:

>>> squares = []
>>> for x in range(10):
... squares.append(x**2)
>>> squares
[0, 1, 4, 9, 16, 25, 36, 49, 64, 81]

Note that this creates (or overwrites) a variable named x that still exists
after the loop completes. We can calculate the list of squares without any
side effects using:

squares = list(map(lambda x: x**2, range(10)))

or, equivalently:

squares = [x**2 for x in range(10)]

which is more concise and readable.

A list comprehension consists of brackets containing an expression followed
by a for clause, then zero or more for or if
clauses. The result will be a new list resulting from evaluating the expression
in the context of the for and if clauses which follow it.
For example, this listcomp combines the elements of two lists if they are not

>>> [(x, y) for x in [1,2,3] for y in [3,1,4] if x != y]
[(1, 3), (1, 4), (2, 3), (2, 1), (2, 4), (3, 1), (3, 4)]

and it’s equivalent to:

>>> combs = []
>>> for x in [1,2,3]:
... for y in [3,1,4]:
... if x != y:
... combs.append((x, y))
>>> combs
[(1, 3), (1, 4), (2, 3), (2, 1), (2, 4), (3, 1), (3, 4)]

Note how the order of the for and if statements is the
same in both these snippets.

If the expression is a tuple (e.g. the (x, y) in the previous example),
it must be parenthesized.

>>> vec = [-4, -2, 0, 2, 4]
>>> # create a new list with the values doubled
>>> [x*2 for x in vec]
[-8, -4, 0, 4, 8]
>>> # filter the list to exclude negative numbers
>>> [x for x in vec if x >= 0]
[0, 2, 4]
>>> # apply a function to all the elements
>>> [abs(x) for x in vec]
[4, 2, 0, 2, 4]
>>> # call a method on each element
>>> freshfruit = [' banana', ' loganberry ', 'passion fruit ']
>>> [weapon.strip() for weapon in freshfruit]
['banana', 'loganberry', 'passion fruit']
>>> # create a list of 2-tuples like (number, square)
>>> [(x, x**2) for x in range(6)]
[(0, 0), (1, 1), (2, 4), (3, 9), (4, 16), (5, 25)]
>>> # the tuple must be parenthesized, otherwise an error is raised
>>> [x, x**2 for x in range(6)]
File "<stdin>", line 1
[x, x**2 for x in range(6)]
SyntaxError: did you forget parentheses around the comprehension target?
>>> # flatten a list using a listcomp with two 'for'
>>> vec = [[1,2,3], [4,5,6], [7,8,9]]
>>> [num for elem in vec for num in elem]
[1, 2, 3, 4, 5, 6, 7, 8, 9]

List comprehensions can contain complex expressions and nested functions:

>>> from math import pi
>>> [str(round(pi, i)) for i in range(1, 6)]
['3.1', '3.14', '3.142', '3.1416', '3.14159']

5.1.4. Nested List Comprehensions¶

The initial expression in a list comprehension can be any arbitrary expression,
including another list comprehension.

Consider the following example of a 3x4 matrix implemented as a list of
3 lists of length 4:

>>> matrix = [
... [1, 2, 3, 4],
... [5, 6, 7, 8],
... [9, 10, 11, 12],
... ]

The following list comprehension will transpose rows and columns:

>>> [[row[i] for row in matrix] for i in range(4)]
[[1, 5, 9], [2, 6, 10], [3, 7, 11], [4, 8, 12]]

As we saw in the previous section, the inner list comprehension is evaluated in
the context of the for that follows it, so this example is
equivalent to:

>>> transposed = []
>>> for i in range(4):
... transposed.append([row[i] for row in matrix])
>>> transposed
[[1, 5, 9], [2, 6, 10], [3, 7, 11], [4, 8, 12]]

which, in turn, is the same as:

>>> transposed = []
>>> for i in range(4):
... # the following 3 lines implement the nested listcomp
... transposed_row = []
... for row in matrix:
... transposed_row.append(row[i])
... transposed.append(transposed_row)
>>> transposed
[[1, 5, 9], [2, 6, 10], [3, 7, 11], [4, 8, 12]]

In the real world, you should prefer built-in functions to complex flow statements.
The zip() function would do a great job for this use case:

>>> list(zip(*matrix))
[(1, 5, 9), (2, 6, 10), (3, 7, 11), (4, 8, 12)]

See Unpacking Argument Lists for details on the asterisk in this line.

5.2. The del statement¶

There is a way to remove an item from a list given its index instead of its
value: the del statement. This differs from the pop() method
which returns a value. The del statement can also be used to remove
slices from a list or clear the entire list (which we did earlier by assignment
of an empty list to the slice). For example:

>>> a = [-1, 1, 66.25, 333, 333, 1234.5]
>>> del a[0]
>>> a
[1, 66.25, 333, 333, 1234.5]
>>> del a[2:4]
>>> a
[1, 66.25, 1234.5]
>>> del a[:]
>>> a

del can also be used to delete entire variables:

>>> del a

Referencing the name a hereafter is an error (at least until another value
is assigned to it). We’ll find other uses for del later.

5.3. Tuples and Sequences¶

We saw that lists and strings have many common properties, such as indexing and
slicing operations. They are two examples of sequence data types (see
Sequence Types — list, tuple, range ). Since Python is an evolving language, other sequence data
types may be added. There is also another standard sequence data type: the
tuple .

A tuple consists of a number of values separated by commas, for instance:

>>> t = 12345, 54321, 'hello!'
>>> t[0]
>>> t
(12345, 54321, 'hello!')
>>> # Tuples may be nested:
... u = t, (1, 2, 3, 4, 5)
>>> u
((12345, 54321, 'hello!'), (1, 2, 3, 4, 5))
>>> # Tuples are immutable:
... t[0] = 88888
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
TypeError: 'tuple' object does not support item assignment
>>> # but they can contain mutable objects:
... v = ([1, 2, 3], [3, 2, 1])
>>> v
([1, 2, 3], [3, 2, 1])

As you see, on output tuples are always enclosed in parentheses, so that nested
tuples are interpreted correctly; they may be input with or without surrounding
parentheses, although often parentheses are necessary anyway (if the tuple is
part of a larger expression). It is not possible to assign to the individual
items of a tuple, however it is possible to create tuples which contain mutable
objects, such as lists.

Though tuples may seem similar to lists, they are often used in different
situations and for different purposes.
Tuples are immutable , and usually contain a heterogeneous sequence of
elements that are accessed via unpacking (see later in this section) or indexing
(or even by attribute in the case of namedtuples ).
Lists are mutable , and their elements are usually homogeneous and are
accessed by iterating over the list.

A special problem is the construction of tuples containing 0 or 1 items: the
syntax has some extra quirks to accommodate these. Empty tuples are constructed
by an empty pair of parentheses; a tuple with one item is constructed by
following a value with a comma (it is not sufficient to enclose a single value
in parentheses). Ugly, but effective. For example:

>>> empty = ()
>>> singleton = 'hello', # <-- note trailing comma
>>> len(empty)
>>> len(singleton)
>>> singleton

The statement t = 12345, 54321, 'hello!' is an example of tuple packing :
the values 12345 , 54321 and 'hello!' are packed together in a tuple.
The reverse operation is also possible:

>>> x, y, z = t

This is called, appropriately enough, sequence unpacking and works for any
sequence on the right-hand side. Sequence unpacking requires that there are as
many variables on the left side of the equals sign as there are elements in the
sequence. Note that multiple assignment is really just a combination of tuple
packing and sequence unpacking.

5.4. Sets¶

Python also includes a data type for sets . A set is an unordered collection
with no duplicate elements. Basic uses include membership testing and
eliminating duplicate entries. Set objects also support mathematical operations
like union, intersection, difference, and symmetric difference.

Curly braces or the set() function can be used to create sets. Note: to
create an empty set you have to use set() , not {} ; the latter creates an
empty dictionary, a data structure that we discuss in the next section.

Here is a brief demonstration:

>>> basket = {'apple', 'orange', 'apple', 'pear', 'orange', 'banana'}
>>> print(basket) # show that duplicates have been removed
{'orange', 'banana', 'pear', 'apple'}
>>> 'orange' in basket # fast membership testing
>>> 'crabgrass' in basket

>>> # Demonstrate set operations on unique letters from two words
>>> a = set('abracadabra')
>>> b = set('alacazam')
>>> a # unique letters in a
{'a', 'r', 'b', 'c', 'd'}
>>> a - b # letters in a but not in b
{'r', 'd', 'b'}
>>> a | b # letters in a or b or both
{'a', 'c', 'r', 'd', 'b', 'm', 'z', 'l'}
>>> a & b # letters in both a and b
{'a', 'c'}
>>> a ^ b # letters in a or b but not both
{'r', 'd', 'b', 'm', 'z', 'l'}

Similarly to list comprehensions , set comprehensions
are also supported:

>>> a = {x for x in 'abracadabra' if x not in 'abc'}
>>> a
{'r', 'd'}

5.5. Dictionaries¶

Another useful data type built into Python is the dictionary (see
Mapping Types — dict ). Dictionaries are sometimes found in other languages as
“associative memories” or “associative arrays”. Unlike sequences, which are
indexed by a range of numbers, dictionaries are indexed by keys , which can be
any immutable type; strings and numbers can always be keys. Tuples can be used
as keys if they contain only strings, numbers, or tuples; if a tuple contains
any mutable object either directly or indirectly, it cannot be used as a key.
You can’t use lists as keys, since lists can be modified in place using index
assignments, slice assignments, or methods like append() and
extend() .

It is best to think of a dictionary as a set of key: value pairs,
with the requirement that the keys are unique (within one dictionary). A pair of
braces creates an empty dictionary: {} . Placing a comma-separated list of
key:value pairs within the braces adds initial key:value pairs to the
dictionary; this is also the way dictionaries are written on output.

The main operations on a dictionary are storing a value with some key and
extracting the value given the key. It is also possible to delete a key:value
pair with del . If you store using a key that is already in use, the old
value associated with that key is forgotten. It is an error to extract a value
using a non-existent key.

Performing list(d) on a dictionary returns a list of all the keys
used in the dictionary, in insertion order (if you want it sorted, just use
sorted(d) instead). To check whether a single key is in the
dictionary, use the in keyword.

Here is a small example using a dictionary:

>>> tel = {'jack': 4098, 'sape': 4139}
>>> tel['guido'] = 4127
>>> tel
{'jack': 4098, 'sape': 4139, 'guido': 4127}
>>> tel['jack']
>>> del tel['sape']
>>> tel['irv'] = 4127
>>> tel
{'jack': 4098, 'guido': 4127, 'irv': 4127}
>>> list(tel)
['jack', 'guido', 'irv']
>>> sorted(tel)
['guido', 'irv', 'jack']
>>> 'guido' in tel
>>> 'jack' not in tel

The dict() constructor builds dictionaries directly from sequences of
key-value pairs:

>>> dict([('sape', 4139), ('guido', 4127), ('jack', 4098)])
{'sape': 4139, 'guido': 4127, 'jack': 4098}

In addition, dict comprehensions can be used to create dictionaries from
arbitrary key and value expressions:

>>> {x: x**2 for x in (2, 4, 6)}
{2: 4, 4: 16, 6: 36}

When the keys are simple strings, it is sometimes easier to specify pairs using
keyword arguments:

>>> dict(sape=4139, guido=4127, jack=4098)
{'sape': 4139, 'guido': 4127, 'jack': 4098}

5.6. Looping Techniques¶

When looping through dictionaries, the key and corresponding value can be
retrieved at the same time using the items() method.

>>> knights = {'gallahad': 'the pure', 'robin': 'the brave'}
>>> for k, v in knights.items():
... print(k, v)
gallahad the pure
robin the brave

When looping through a sequence, the position index and corresponding value can
be retrieved at the same time using the enumerate() function.

>>> for i, v in enumerate(['tic', 'tac', 'toe']):
... print(i, v)
0 tic
1 tac
2 toe

To loop over two or more sequences at the same time, the entries can be paired
with the zip() function.

>>> questions = ['name', 'quest', 'favorite color']
>>> answers = ['lancelot', 'the holy grail', 'blue']
>>> for q, a in zip(questions, answers):
... print('What is your {0}? It is {1}.'.format(q, a))
What is your name? It is lancelot.
What is your quest? It is the holy grail.
What is your favorite color? It is blue.

To loop over a sequence in reverse, first specify the sequence in a forward
direction and then call the reversed() function.

>>> for i in reversed(range(1, 10, 2)):
... print(i)

To loop over a sequence in sorted order, use the sorted() function which
returns a new sorted list while leaving the source unaltered.

>>> basket = ['apple', 'orange', 'apple', 'pear', 'orange', 'banana']
>>> for i in sorted(basket):
... print(i)

Using set() on a sequence eliminates duplicate elements. The use of
sorted() in combination with set() over a sequence is an idiomatic
way to loop over unique elements of the sequence in sorted order.

>>> basket = ['apple', 'orange', 'apple', 'pear', 'orange', 'banana']
>>> for f in sorted(set(basket)):
... print(f)

It is sometimes tempting to change a list while you are looping over it;
however, it is often simpler and safer to create a new list instead.

>>> import math
>>> raw_data = [56.2, float('NaN'), 51.7, 55.3, 52.5, float('NaN'), 47.8]
>>> filtered_data = []
>>> for value in raw_data:
... if not math.isnan(value):
... filtered_data.append(value)
>>> filtered_data
[56.2, 51.7, 55.3, 52.5, 47.8]

5.7. More on Conditions¶

The conditions used in while and if statements can contain any
operators, not just comparisons.

The comparison operators in and not in are membership tests that
determine whether a value is in (or not in) a container. The operators is
and is not compare whether two objects are really the same object. All
comparison operators have the same priority, which is lower than that of all
numerical operators.

Comparisons can be chained. For example, a < b == c tests whether a is
less than b and moreover b equals c .

Comparisons may be combined using the Boolean operators and and or , and
the outcome of a comparison (or of any other Boolean expression) may be negated
with not . These have lower priorities than comparison operators; between
them, not has the highest priority and or the lowest, so that A and
not B or C
is equivalent to (A and (not B)) or C . As always, parentheses
can be used to express the desired composition.

The Boolean operators and and or are so-called short-circuit
operators: their arguments are evaluated from left to right, and evaluation
stops as soon as the outcome is determined. For example, if A and C are
true but B is false, A and B and C does not evaluate the expression
C . When used as a general value and not as a Boolean, the return value of a
short-circuit operator is the last evaluated argument.

It is possible to assign the result of a comparison or other Boolean expression
to a variable. For example,

>>> string1, string2, string3 = '', 'Trondheim', 'Hammer Dance'
>>> non_null = string1 or string2 or string3
>>> non_null

Note that in Python, unlike C, assignment inside expressions must be done
explicitly with the
walrus operator := .
This avoids a common class of problems encountered in C programs: typing =
in an expression when == was intended.

5.8. Comparing Sequences and Other Types¶

Sequence objects typically may be compared to other objects with the same sequence
type. The comparison uses lexicographical ordering: first the first two
items are compared, and if they differ this determines the outcome of the
comparison; if they are equal, the next two items are compared, and so on, until
either sequence is exhausted. If two items to be compared are themselves
sequences of the same type, the lexicographical comparison is carried out
recursively. If all items of two sequences compare equal, the sequences are
considered equal. If one sequence is an initial sub-sequence of the other, the
shorter sequence is the smaller (lesser) one. Lexicographical ordering for
strings uses the Unicode code point number to order individual characters.
Some examples of comparisons between sequences of the same type:

(1, 2, 3)              < (1, 2, 4)
[1, 2, 3] < [1, 2, 4]
'ABC' < 'C' < 'Pascal' < 'Python'
(1, 2, 3, 4) < (1, 2, 4)
(1, 2) < (1, 2, -1)
(1, 2, 3) == (1.0, 2.0, 3.0)
(1, 2, ('aa', 'ab')) < (1, 2, ('abc', 'a'), 4)

Note that comparing objects of different types with < or > is legal
provided that the objects have appropriate comparison methods. For example,
mixed numeric types are compared according to their numeric value, so 0 equals
0.0, etc. Otherwise, rather than providing an arbitrary ordering, the
interpreter will raise a TypeError exception.



Other languages may return the mutated object, which allows method
chaining, such as d->insert("a")->remove("b")->sort(); .

6. Modules

6. Modules¶

If you quit from the Python interpreter and enter it again, the definitions you
have made (functions and variables) are lost. Therefore, if you want to write a
somewhat longer program, you are better off using a text editor to prepare the
input for the interpreter and running it with that file as input instead. This
is known as creating a script . As your program gets longer, you may want to
split it into several files for easier maintenance. You may also want to use a
handy function that you’ve written in several programs without copying its
definition into each program.

To support this, Python has a way to put definitions in a file and use them in a
script or in an interactive instance of the interpreter. Such a file is called a
module ; definitions from a module can be imported into other modules or into
the main module (the collection of variables that you have access to in a
script executed at the top level and in calculator mode).

A module is a file containing Python definitions and statements. The file name
is the module name with the suffix .py appended. Within a module, the
module’s name (as a string) is available as the value of the global variable
__name__ . For instance, use your favorite text editor to create a file
called fibo.py in the current directory with the following contents:

# Fibonacci numbers module

def fib(n): # write Fibonacci series up to n
a, b = 0, 1
while a < n:
print(a, end=' ')
a, b = b, a+b

def fib2(n): # return Fibonacci series up to n
result = []
a, b = 0, 1
while a < n:
a, b = b, a+b
return result

Now enter the Python interpreter and import this module with the following

>>> import fibo

This does not add the names of the functions defined in fibo directly to
the current namespace (see Python Scopes and Namespaces for more details);
it only adds the module name fibo there. Using
the module name you can access the functions:

>>> fibo.fib(1000)
0 1 1 2 3 5 8 13 21 34 55 89 144 233 377 610 987
>>> fibo.fib2(100)
[0, 1, 1, 2, 3, 5, 8, 13, 21, 34, 55, 89]
>>> fibo.__name__

If you intend to use a function often you can assign it to a local name:

>>> fib = fibo.fib
>>> fib(500)
0 1 1 2 3 5 8 13 21 34 55 89 144 233 377

6.1. More on Modules¶

A module can contain executable statements as well as function definitions.
These statements are intended to initialize the module. They are executed only
the first time the module name is encountered in an import statement. 1
(They are also run if the file is executed as a script.)

Each module has its own private namespace, which is used as the global namespace
by all functions defined in the module. Thus, the author of a module can
use global variables in the module without worrying about accidental clashes
with a user’s global variables. On the other hand, if you know what you are
doing you can touch a module’s global variables with the same notation used to
refer to its functions, modname.itemname .

Modules can import other modules. It is customary but not required to place all
import statements at the beginning of a module (or script, for that
matter). The imported module names, if placed at the top level of a module
(outside any functions or classes), are added to the module’s global namespace.

There is a variant of the import statement that imports names from a
module directly into the importing module’s namespace. For example:

>>> from fibo import fib, fib2
>>> fib(500)
0 1 1 2 3 5 8 13 21 34 55 89 144 233 377

This does not introduce the module name from which the imports are taken in the
local namespace (so in the example, fibo is not defined).

There is even a variant to import all names that a module defines:

>>> from fibo import *
>>> fib(500)
0 1 1 2 3 5 8 13 21 34 55 89 144 233 377

This imports all names except those beginning with an underscore ( _ ).
In most cases Python programmers do not use this facility since it introduces
an unknown set of names into the interpreter, possibly hiding some things
you have already defined.

Note that in general the practice of importing * from a module or package is
frowned upon, since it often causes poorly readable code. However, it is okay to
use it to save typing in interactive sessions.

If the module name is followed by as , then the name
following as is bound directly to the imported module.

>>> import fibo as fib
>>> fib.fib(500)
0 1 1 2 3 5 8 13 21 34 55 89 144 233 377

This is effectively importing the module in the same way that import fibo
will do, with the only difference of it being available as fib .

It can also be used when utilising from with similar effects:

>>> from fibo import fib as fibonacci
>>> fibonacci(500)
0 1 1 2 3 5 8 13 21 34 55 89 144 233 377


For efficiency reasons, each module is only imported once per interpreter
session. Therefore, if you change your modules, you must restart the
interpreter – or, if it’s just one module you want to test interactively,
use importlib.reload() , e.g. import importlib;

6.1.1. Executing modules as scripts¶

When you run a Python module with

python fibo.py <arguments>

the code in the module will be executed, just as if you imported it, but with
the __name__ set to "__main__" . That means that by adding this code at
the end of your module:

if __name__ == "__main__":
import sys

you can make the file usable as a script as well as an importable module,
because the code that parses the command line only runs if the module is
executed as the “main” file:

$ python fibo.py 50
0 1 1 2 3 5 8 13 21 34

If the module is imported, the code is not run:

>>> import fibo

This is often used either to provide a convenient user interface to a module, or
for testing purposes (running the module as a script executes a test suite).

6.1.2. The Module Search Path¶

When a module named spam is imported, the interpreter first searches for
a built-in module with that name. These module names are listed in
sys.builtin_module_names . If not found, it then searches for a file
named spam.py in a list of directories given by the variable
sys.path . sys.path is initialized from these locations:

  • The directory containing the input script (or the current directory when no
    file is specified).

  • PYTHONPATH (a list of directory names, with the same syntax as the
    shell variable PATH ).

  • The installation-dependent default (by convention including a
    site-packages directory, handled by the site module).

More details are at The initialization of the sys.path module search path .


On file systems which support symlinks, the directory containing the input
script is calculated after the symlink is followed. In other words the
directory containing the symlink is not added to the module search path.

After initialization, Python programs can modify sys.path . The
directory containing the script being run is placed at the beginning of the
search path, ahead of the standard library path. This means that scripts in that
directory will be loaded instead of modules of the same name in the library
directory. This is an error unless the replacement is intended. See section
Standard Modules for more information.

6.1.3. “Compiled” Python files¶

To speed up loading modules, Python caches the compiled version of each module
in the __pycache__ directory under the name module. version .pyc ,
where the version encodes the format of the compiled file; it generally contains
the Python version number. For example, in CPython release 3.3 the compiled
version of spam.py would be cached as __pycache__/spam.cpython-33.pyc . This
naming convention allows compiled modules from different releases and different
versions of Python to coexist.

Python checks the modification date of the source against the compiled version
to see if it’s out of date and needs to be recompiled. This is a completely
automatic process. Also, the compiled modules are platform-independent, so the
same library can be shared among systems with different architectures.

Python does not check the cache in two circumstances. First, it always
recompiles and does not store the result for the module that’s loaded directly
from the command line. Second, it does not check the cache if there is no
source module. To support a non-source (compiled only) distribution, the
compiled module must be in the source directory, and there must not be a source

Some tips for experts:

  • You can use the -O or -OO switches on the Python command
    to reduce the size of a compiled module. The -O switch removes assert
    statements, the -OO switch removes both assert statements and __doc__
    strings. Since some programs may rely on having these available, you should
    only use this option if you know what you’re doing. “Optimized” modules have
    an opt- tag and are usually smaller. Future releases may
    change the effects of optimization.

  • A program doesn’t run any faster when it is read from a .pyc
    file than when it is read from a .py file; the only thing that’s faster
    about .pyc files is the speed with which they are loaded.

  • The module compileall can create .pyc files for all modules in a

  • There is more detail on this process, including a flow chart of the
    decisions, in PEP 3147 .

6.2. Standard Modules¶

Python comes with a library of standard modules, described in a separate
document, the Python Library Reference (“Library Reference” hereafter). Some
modules are built into the interpreter; these provide access to operations that
are not part of the core of the language but are nevertheless built in, either
for efficiency or to provide access to operating system primitives such as
system calls. The set of such modules is a configuration option which also
depends on the underlying platform. For example, the winreg module is only
provided on Windows systems. One particular module deserves some attention:
sys , which is built into every Python interpreter. The variables
sys.ps1 and sys.ps2 define the strings used as primary and secondary

>>> import sys
>>> sys.ps1
'>>> '
>>> sys.ps2
'... '
>>> sys.ps1 = 'C> '
C> print('Yuck!')

These two variables are only defined if the interpreter is in interactive mode.

The variable sys.path is a list of strings that determines the interpreter’s
search path for modules. It is initialized to a default path taken from the
environment variable PYTHONPATH , or from a built-in default if
PYTHONPATH is not set. You can modify it using standard list

>>> import sys
>>> sys.path.append('/ufs/guido/lib/python')

6.3. The dir() Function¶

The built-in function dir() is used to find out which names a module
defines. It returns a sorted list of strings:

>>> import fibo, sys
>>> dir(fibo)
['__name__', 'fib', 'fib2']
>>> dir(sys)
['__breakpointhook__', '__displayhook__', '__doc__', '__excepthook__',
'__interactivehook__', '__loader__', '__name__', '__package__', '__spec__',
'__stderr__', '__stdin__', '__stdout__', '__unraisablehook__',
'_clear_type_cache', '_current_frames', '_debugmallocstats', '_framework',
'_getframe', '_git', '_home', '_xoptions', 'abiflags', 'addaudithook',
'api_version', 'argv', 'audit', 'base_exec_prefix', 'base_prefix',
'breakpointhook', 'builtin_module_names', 'byteorder', 'call_tracing',
'callstats', 'copyright', 'displayhook', 'dont_write_bytecode', 'exc_info',
'excepthook', 'exec_prefix', 'executable', 'exit', 'flags', 'float_info',
'float_repr_style', 'get_asyncgen_hooks', 'get_coroutine_origin_tracking_depth',
'getallocatedblocks', 'getdefaultencoding', 'getdlopenflags',
'getfilesystemencodeerrors', 'getfilesystemencoding', 'getprofile',
'getrecursionlimit', 'getrefcount', 'getsizeof', 'getswitchinterval',
'gettrace', 'hash_info', 'hexversion', 'implementation', 'int_info',
'intern', 'is_finalizing', 'last_traceback', 'last_type', 'last_value',
'maxsize', 'maxunicode', 'meta_path', 'modules', 'path', 'path_hooks',
'path_importer_cache', 'platform', 'prefix', 'ps1', 'ps2', 'pycache_prefix',
'set_asyncgen_hooks', 'set_coroutine_origin_tracking_depth', 'setdlopenflags',
'setprofile', 'setrecursionlimit', 'setswitchinterval', 'settrace', 'stderr',
'stdin', 'stdout', 'thread_info', 'unraisablehook', 'version', 'version_info',

Without arguments, dir() lists the names you have defined currently:

>>> a = [1, 2, 3, 4, 5]
>>> import fibo
>>> fib = fibo.fib
>>> dir()
['__builtins__', '__name__', 'a', 'fib', 'fibo', 'sys']

Note that it lists all types of names: variables, modules, functions, etc.

dir() does not list the names of built-in functions and variables. If you
want a list of those, they are defined in the standard module
builtins :

>>> import builtins
>>> dir(builtins)
['ArithmeticError', 'AssertionError', 'AttributeError', 'BaseException',
'BlockingIOError', 'BrokenPipeError', 'BufferError', 'BytesWarning',
'ChildProcessError', 'ConnectionAbortedError', 'ConnectionError',
'ConnectionRefusedError', 'ConnectionResetError', 'DeprecationWarning',
'EOFError', 'Ellipsis', 'EnvironmentError', 'Exception', 'False',
'FileExistsError', 'FileNotFoundError', 'FloatingPointError',
'FutureWarning', 'GeneratorExit', 'IOError', 'ImportError',
'ImportWarning', 'IndentationError', 'IndexError', 'InterruptedError',
'IsADirectoryError', 'KeyError', 'KeyboardInterrupt', 'LookupError',
'MemoryError', 'NameError', 'None', 'NotADirectoryError', 'NotImplemented',
'NotImplementedError', 'OSError', 'OverflowError',
'PendingDeprecationWarning', 'PermissionError', 'ProcessLookupError',
'ReferenceError', 'ResourceWarning', 'RuntimeError', 'RuntimeWarning',
'StopIteration', 'SyntaxError', 'SyntaxWarning', 'SystemError',
'SystemExit', 'TabError', 'TimeoutError', 'True', 'TypeError',
'UnboundLocalError', 'UnicodeDecodeError', 'UnicodeEncodeError',
'UnicodeError', 'UnicodeTranslateError', 'UnicodeWarning', 'UserWarning',
'ValueError', 'Warning', 'ZeroDivisionError', '_', '__build_class__',
'__debug__', '__doc__', '__import__', '__name__', '__package__', 'abs',
'all', 'any', 'ascii', 'bin', 'bool', 'bytearray', 'bytes', 'callable',
'chr', 'classmethod', 'compile', 'complex', 'copyright', 'credits',
'delattr', 'dict', 'dir', 'divmod', 'enumerate', 'eval', 'exec', 'exit',
'filter', 'float', 'format', 'frozenset', 'getattr', 'globals', 'hasattr',
'hash', 'help', 'hex', 'id', 'input', 'int', 'isinstance', 'issubclass',
'iter', 'len', 'license', 'list', 'locals', 'map', 'max', 'memoryview',
'min', 'next', 'object', 'oct', 'open', 'ord', 'pow', 'print', 'property',
'quit', 'range', 'repr', 'reversed', 'round', 'set', 'setattr', 'slice',
'sorted', 'staticmethod', 'str', 'sum', 'super', 'tuple', 'type', 'vars',

6.4. Packages¶

Packages are a way of structuring Python’s module namespace by using “dotted
module names”. For example, the module name A.B designates a submodule
named B in a package named A . Just like the use of modules saves the
authors of different modules from having to worry about each other’s global
variable names, the use of dotted module names saves the authors of multi-module
packages like NumPy or Pillow from having to worry about
each other’s module names.

Suppose you want to design a collection of modules (a “package”) for the uniform
handling of sound files and sound data. There are many different sound file
formats (usually recognized by their extension, for example: .wav ,
.aiff , .au ), so you may need to create and maintain a growing
collection of modules for the conversion between the various file formats.
There are also many different operations you might want to perform on sound data
(such as mixing, adding echo, applying an equalizer function, creating an
artificial stereo effect), so in addition you will be writing a never-ending
stream of modules to perform these operations. Here’s a possible structure for
your package (expressed in terms of a hierarchical filesystem):

sound/                          Top-level package
__init__.py Initialize the sound package
formats/ Subpackage for file format conversions
effects/ Subpackage for sound effects
filters/ Subpackage for filters

When importing the package, Python searches through the directories on
sys.path looking for the package subdirectory.

The __init__.py files are required to make Python treat directories
containing the file as packages. This prevents directories with a common name,
such as string , unintentionally hiding valid modules that occur later
on the module search path. In the simplest case, __init__.py can just be
an empty file, but it can also execute initialization code for the package or
set the __all__ variable, described later.

Users of the package can import individual modules from the package, for

import sound.effects.echo

This loads the submodule sound.effects.echo . It must be referenced with
its full name.

sound.effects.echo.echofilter(input, output, delay=0.7, atten=4)

An alternative way of importing the submodule is:

from sound.effects import echo

This also loads the submodule echo , and makes it available without its
package prefix, so it can be used as follows:

echo.echofilter(input, output, delay=0.7, atten=4)

Yet another variation is to import the desired function or variable directly:

from sound.effects.echo import echofilter

Again, this loads the submodule echo , but this makes its function
echofilter() directly available:

echofilter(input, output, delay=0.7, atten=4)

Note that when using from package import item , the item can be either a
submodule (or subpackage) of the package, or some other name defined in the
package, like a function, class or variable. The import statement first
tests whether the item is defined in the package; if not, it assumes it is a
module and attempts to load it. If it fails to find it, an ImportError
exception is raised.

Contrarily, when using syntax like import item.subitem.subsubitem , each item
except for the last must be a package; the last item can be a module or a
package but can’t be a class or function or variable defined in the previous

6.4.1. Importing * From a Package¶

Now what happens when the user writes from sound.effects import * ? Ideally,
one would hope that this somehow goes out to the filesystem, finds which
submodules are present in the package, and imports them all. This could take a
long time and importing sub-modules might have unwanted side-effects that should
only happen when the sub-module is explicitly imported.

The only solution is for the package author to provide an explicit index of the
package. The import statement uses the following convention: if a package’s
__init__.py code defines a list named __all__ , it is taken to be the
list of module names that should be imported when from package import * is
encountered. It is up to the package author to keep this list up-to-date when a
new version of the package is released. Package authors may also decide not to
support it, if they don’t see a use for importing * from their package. For
example, the file sound/effects/__init__.py could contain the following

__all__ = ["echo", "surround", "reverse"]

This would mean that from sound.effects import * would import the three
named submodules of the sound.effects package.

If __all__ is not defined, the statement from sound.effects import *
does not import all submodules from the package sound.effects into the
current namespace; it only ensures that the package sound.effects has
been imported (possibly running any initialization code in __init__.py )
and then imports whatever names are defined in the package. This includes any
names defined (and submodules explicitly loaded) by __init__.py . It
also includes any submodules of the package that were explicitly loaded by
previous import statements. Consider this code:

import sound.effects.echo
import sound.effects.surround
from sound.effects import *

In this example, the echo and surround modules are imported in the
current namespace because they are defined in the sound.effects package
when the from...import statement is executed. (This also works when
__all__ is defined.)

Although certain modules are designed to export only names that follow certain
patterns when you use import * , it is still considered bad practice in
production code.

Remember, there is nothing wrong with using from package import
! In fact, this is the recommended notation unless the
importing module needs to use submodules with the same name from different

6.4.2. Intra-package References¶

When packages are structured into subpackages (as with the sound package
in the example), you can use absolute imports to refer to submodules of siblings
packages. For example, if the module sound.filters.vocoder needs to use
the echo module in the sound.effects package, it can use from
sound.effects import echo

You can also write relative imports, with the from module import name form
of import statement. These imports use leading dots to indicate the current and
parent packages involved in the relative import. From the surround
module for example, you might use:

from . import echo
from .. import formats
from ..filters import equalizer

Note that relative imports are based on the name of the current module. Since
the name of the main module is always "__main__" , modules intended for use
as the main module of a Python application must always use absolute imports.

6.4.3. Packages in Multiple Directories¶

Packages support one more special attribute, __path__ . This is
initialized to be a list containing the name of the directory holding the
package’s __init__.py before the code in that file is executed. This
variable can be modified; doing so affects future searches for modules and
subpackages contained in the package.

While this feature is not often needed, it can be used to extend the set of
modules found in a package.



In fact function definitions are also ‘statements’ that are ‘executed’; the
execution of a module-level function definition adds the function name to
the module’s global namespace.

Read article
7. Input and Output

7. Input and Output¶

There are several ways to present the output of a program; data can be printed
in a human-readable form, or written to a file for future use. This chapter will
discuss some of the possibilities.

7.1. Fancier Output Formatting¶

So far we’ve encountered two ways of writing values: expression statements and
the print() function. (A third way is using the write() method
of file objects; the standard output file can be referenced as sys.stdout .
See the Library Reference for more information on this.)

Often you’ll want more control over the formatting of your output than simply
printing space-separated values. There are several ways to format output.

  • To use formatted string literals , begin a string
    with f or F before the opening quotation mark or triple quotation mark.
    Inside this string, you can write a Python expression between { and }
    characters that can refer to variables or literal values.

    >>> year = 2016
    >>> event = 'Referendum'
    >>> f'Results of the {year} {event}'
    'Results of the 2016 Referendum'

  • The str.format() method of strings requires more manual
    effort. You’ll still use { and } to mark where a variable
    will be substituted and can provide detailed formatting directives,
    but you’ll also need to provide the information to be formatted.

    >>> yes_votes = 42_572_654
    >>> no_votes = 43_132_495
    >>> percentage = yes_votes / (yes_votes + no_votes)
    >>> '{:-9} YES votes {:2.2%}'.format(yes_votes, percentage)
    ' 42572654 YES votes 49.67%'

  • Finally, you can do all the string handling yourself by using string slicing and
    concatenation operations to create any layout you can imagine. The
    string type has some methods that perform useful operations for padding
    strings to a given column width.

When you don’t need fancy output but just want a quick display of some
variables for debugging purposes, you can convert any value to a string with
the repr() or str() functions.

The str() function is meant to return representations of values which are
fairly human-readable, while repr() is meant to generate representations
which can be read by the interpreter (or will force a SyntaxError if
there is no equivalent syntax). For objects which don’t have a particular
representation for human consumption, str() will return the same value as
repr() . Many values, such as numbers or structures like lists and
dictionaries, have the same representation using either function. Strings, in
particular, have two distinct representations.

Some examples:

>>> s = 'Hello, world.'
>>> str(s)
'Hello, world.'
>>> repr(s)
"'Hello, world.'"
>>> str(1/7)
>>> x = 10 * 3.25
>>> y = 200 * 200
>>> s = 'The value of x is ' + repr(x) + ', and y is ' + repr(y) + '...'
>>> print(s)
The value of x is 32.5, and y is 40000...
>>> # The repr() of a string adds string quotes and backslashes:
... hello = 'hello, world\n'
>>> hellos = repr(hello)
>>> print(hellos)
'hello, world\n'
>>> # The argument to repr() may be any Python object:
... repr((x, y, ('spam', 'eggs')))
"(32.5, 40000, ('spam', 'eggs'))"

The string module contains a Template class that offers
yet another way to substitute values into strings, using placeholders like
$x and replacing them with values from a dictionary, but offers much less
control of the formatting.

7.1.1. Formatted String Literals¶

Formatted string literals (also called f-strings for
short) let you include the value of Python expressions inside a string by
prefixing the string with f or F and writing expressions as
{expression} .

An optional format specifier can follow the expression. This allows greater
control over how the value is formatted. The following example rounds pi to
three places after the decimal:

>>> import math
>>> print(f'The value of pi is approximately {math.pi:.3f}.')
The value of pi is approximately 3.142.

Passing an integer after the ':' will cause that field to be a minimum
number of characters wide. This is useful for making columns line up.

>>> table = {'Sjoerd': 4127, 'Jack': 4098, 'Dcab': 7678}
>>> for name, phone in table.items():
... print(f'{name:10} ==> {phone:10d}')
Sjoerd ==> 4127
Jack ==> 4098
Dcab ==> 7678

Other modifiers can be used to convert the value before it is formatted.
'!a' applies ascii() , '!s' applies str() , and '!r'
applies repr() :

>>> animals = 'eels'
>>> print(f'My hovercraft is full of {animals}.')
My hovercraft is full of eels.
>>> print(f'My hovercraft is full of {animals!r}.')
My hovercraft is full of 'eels'.

The = specifier can be used to expand an expression to the text of the
expression, an equal sign, then the representation of the evaluated expression:

>>> bugs = 'roaches'
>>> count = 13
>>> area = 'living room'
>>> print(f'Debugging {bugs=} {count=} {area=}')
Debugging bugs='roaches' count=13 area='living room'

See self-documenting expressions for more information
on the = specifier. For a reference on these format specifications, see
the reference guide for the Format Specification Mini-Language .

7.1.2. The String format() Method¶

Basic usage of the str.format() method looks like this:

>>> print('We are the {} who say "{}!"'.format('knights', 'Ni'))
We are the knights who say "Ni!"

The brackets and characters within them (called format fields) are replaced with
the objects passed into the str.format() method. A number in the
brackets can be used to refer to the position of the object passed into the
str.format() method.

>>> print('{0} and {1}'.format('spam', 'eggs'))
spam and eggs
>>> print('{1} and {0}'.format('spam', 'eggs'))
eggs and spam

If keyword arguments are used in the str.format() method, their values
are referred to by using the name of the argument.

>>> print('This {food} is {adjective}.'.format(
... food='spam', adjective='absolutely horrible'))
This spam is absolutely horrible.

Positional and keyword arguments can be arbitrarily combined:

>>> print('The story of {0}, {1}, and {other}.'.format('Bill', 'Manfred',
... other='Georg'))
The story of Bill, Manfred, and Georg.

If you have a really long format string that you don’t want to split up, it
would be nice if you could reference the variables to be formatted by name
instead of by position. This can be done by simply passing the dict and using
square brackets '[]' to access the keys.

>>> table = {'Sjoerd': 4127, 'Jack': 4098, 'Dcab': 8637678}
>>> print('Jack: {0[Jack]:d}; Sjoerd: {0[Sjoerd]:d}; '
... 'Dcab: {0[Dcab]:d}'.format(table))
Jack: 4098; Sjoerd: 4127; Dcab: 8637678

This could also be done by passing the table dictionary as keyword arguments with the **

>>> table = {'Sjoerd': 4127, 'Jack': 4098, 'Dcab': 8637678}
>>> print('Jack: {Jack:d}; Sjoerd: {Sjoerd:d}; Dcab: {Dcab:d}'.format(**table))
Jack: 4098; Sjoerd: 4127; Dcab: 8637678

This is particularly useful in combination with the built-in function
vars() , which returns a dictionary containing all local variables.

As an example, the following lines produce a tidily aligned
set of columns giving integers and their squares and cubes:

>>> for x in range(1, 11):
... print('{0:2d} {1:3d} {2:4d}'.format(x, x*x, x*x*x))
1 1 1
2 4 8
3 9 27
4 16 64
5 25 125
6 36 216
7 49 343
8 64 512
9 81 729
10 100 1000

For a complete overview of string formatting with str.format() , see
Format String Syntax .

7.1.3. Manual String Formatting¶

Here’s the same table of squares and cubes, formatted manually:

>>> for x in range(1, 11):
... print(repr(x).rjust(2), repr(x*x).rjust(3), end=' ')
... # Note use of 'end' on previous line
... print(repr(x*x*x).rjust(4))
1 1 1
2 4 8
3 9 27
4 16 64
5 25 125
6 36 216
7 49 343
8 64 512
9 81 729
10 100 1000

(Note that the one space between each column was added by the
way print() works: it always adds spaces between its arguments.)

The str.rjust() method of string objects right-justifies a string in a
field of a given width by padding it with spaces on the left. There are
similar methods str.ljust() and str.center() . These methods do
not write anything, they just return a new string. If the input string is too
long, they don’t truncate it, but return it unchanged; this will mess up your
column lay-out but that’s usually better than the alternative, which would be
lying about a value. (If you really want truncation you can always add a
slice operation, as in x.ljust(n)[:n] .)

There is another method, str.zfill() , which pads a numeric string on the
left with zeros. It understands about plus and minus signs:

>>> '12'.zfill(5)
>>> '-3.14'.zfill(7)
>>> '3.14159265359'.zfill(5)

7.1.4. Old string formatting¶

The % operator (modulo) can also be used for string formatting. Given 'string'
% values
, instances of % in string are replaced with zero or more
elements of values . This operation is commonly known as string
interpolation. For example:

>>> import math
>>> print('The value of pi is approximately %5.3f.' % math.pi)
The value of pi is approximately 3.142.

More information can be found in the printf-style String Formatting section.

7.2. Reading and Writing Files¶

open() returns a file object , and is most commonly used with
two positional arguments and one keyword argument:
open(filename, mode, encoding=None)

>>> f = open('workfile', 'w', encoding="utf-8")

The first argument is a string containing the filename. The second argument is
another string containing a few characters describing the way in which the file
will be used. mode can be 'r' when the file will only be read, 'w'
for only writing (an existing file with the same name will be erased), and
'a' opens the file for appending; any data written to the file is
automatically added to the end. 'r+' opens the file for both reading and
writing. The mode argument is optional; 'r' will be assumed if it’s

Normally, files are opened in text mode , that means, you read and write
strings from and to the file, which are encoded in a specific encoding .
If encoding is not specified, the default is platform dependent
(see open() ).
Because UTF-8 is the modern de-facto standard, encoding="utf-8" is
recommended unless you know that you need to use a different encoding.
Appending a 'b' to the mode opens the file in binary mode .
Binary mode data is read and written as bytes objects.
You can not specify encoding when opening file in binary mode.

In text mode, the default when reading is to convert platform-specific line
endings ( \n on Unix, \r\n on Windows) to just \n . When writing in
text mode, the default is to convert occurrences of \n back to
platform-specific line endings. This behind-the-scenes modification
to file data is fine for text files, but will corrupt binary data like that in
JPEG or EXE files. Be very careful to use binary mode when
reading and writing such files.

It is good practice to use the with keyword when dealing
with file objects. The advantage is that the file is properly closed
after its suite finishes, even if an exception is raised at some
point. Using with is also much shorter than writing
equivalent try - finally blocks:

>>> with open('workfile', encoding="utf-8") as f:
... read_data = f.read()

>>> # We can check that the file has been automatically closed.
>>> f.closed

If you’re not using the with keyword, then you should call
f.close() to close the file and immediately free up any system
resources used by it.


Calling f.write() without using the with keyword or calling
f.close() might result in the arguments
of f.write() not being completely written to the disk, even if the
program exits successfully.

After a file object is closed, either by a with statement
or by calling f.close() , attempts to use the file object will
automatically fail.

>>> f.close()
>>> f.read()
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
ValueError: I/O operation on closed file.

7.2.1. Methods of File Objects¶

The rest of the examples in this section will assume that a file object called
f has already been created.

To read a file’s contents, call f.read(size) , which reads some quantity of
data and returns it as a string (in text mode) or bytes object (in binary mode).
size is an optional numeric argument. When size is omitted or negative, the
entire contents of the file will be read and returned; it’s your problem if the
file is twice as large as your machine’s memory. Otherwise, at most size
characters (in text mode) or size bytes (in binary mode) are read and returned.
If the end of the file has been reached, f.read() will return an empty
string ( '' ).

>>> f.read()
'This is the entire file.\n'
>>> f.read()

f.readline() reads a single line from the file; a newline character ( \n )
is left at the end of the string, and is only omitted on the last line of the
file if the file doesn’t end in a newline. This makes the return value
unambiguous; if f.readline() returns an empty string, the end of the file
has been reached, while a blank line is represented by '\n' , a string
containing only a single newline.

>>> f.readline()
'This is the first line of the file.\n'
>>> f.readline()
'Second line of the file\n'
>>> f.readline()

For reading lines from a file, you can loop over the file object. This is memory
efficient, fast, and leads to simple code:

>>> for line in f:
... print(line, end='')
This is the first line of the file.
Second line of the file

If you want to read all the lines of a file in a list you can also use
list(f) or f.readlines() .

f.write(string) writes the contents of string to the file, returning
the number of characters written.

>>> f.write('This is a test\n')

Other types of objects need to be converted – either to a string (in text mode)
or a bytes object (in binary mode) – before writing them:

>>> value = ('the answer', 42)
>>> s = str(value) # convert the tuple to string
>>> f.write(s)

f.tell() returns an integer giving the file object’s current position in the file
represented as number of bytes from the beginning of the file when in binary mode and
an opaque number when in text mode.

To change the file object’s position, use f.seek(offset, whence) . The position is computed
from adding offset to a reference point; the reference point is selected by
the whence argument. A whence value of 0 measures from the beginning
of the file, 1 uses the current file position, and 2 uses the end of the file as
the reference point. whence can be omitted and defaults to 0, using the
beginning of the file as the reference point.

>>> f = open('workfile', 'rb+')
>>> f.write(b'0123456789abcdef')
>>> f.seek(5) # Go to the 6th byte in the file
>>> f.read(1)
>>> f.seek(-3, 2) # Go to the 3rd byte before the end
>>> f.read(1)

In text files (those opened without a b in the mode string), only seeks
relative to the beginning of the file are allowed (the exception being seeking
to the very file end with seek(0, 2) ) and the only valid offset values are
those returned from the f.tell() , or zero. Any other offset value produces
undefined behaviour.

File objects have some additional methods, such as isatty() and
truncate() which are less frequently used; consult the Library
Reference for a complete guide to file objects.

7.2.2. Saving structured data with json ¶

Strings can easily be written to and read from a file. Numbers take a bit more
effort, since the read() method only returns strings, which will have to
be passed to a function like int() , which takes a string like '123'
and returns its numeric value 123. When you want to save more complex data
types like nested lists and dictionaries, parsing and serializing by hand
becomes complicated.

Rather than having users constantly writing and debugging code to save
complicated data types to files, Python allows you to use the popular data
interchange format called JSON (JavaScript Object Notation). The standard module called json can take Python
data hierarchies, and convert them to string representations; this process is
called serializing . Reconstructing the data from the string representation
is called deserializing . Between serializing and deserializing, the
string representing the object may have been stored in a file or data, or
sent over a network connection to some distant machine.


The JSON format is commonly used by modern applications to allow for data
exchange. Many programmers are already familiar with it, which makes
it a good choice for interoperability.

If you have an object x , you can view its JSON string representation with a
simple line of code:

>>> import json
>>> x = [1, 'simple', 'list']
>>> json.dumps(x)
'[1, "simple", "list"]'

Another variant of the dumps() function, called dump() ,
simply serializes the object to a text file . So if f is a
text file object opened for writing, we can do this:

json.dump(x, f)

To decode the object again, if f is a binary file or
text file object which has been opened for reading:

x = json.load(f)


JSON files must be encoded in UTF-8. Use encoding="utf-8" when opening
JSON file as a text file for both of reading and writing.

This simple serialization technique can handle lists and dictionaries, but
serializing arbitrary class instances in JSON requires a bit of extra effort.
The reference for the json module contains an explanation of this.

See also

pickle - the pickle module

Contrary to JSON , pickle is a protocol which allows
the serialization of arbitrarily complex Python objects. As such, it is
specific to Python and cannot be used to communicate with applications
written in other languages. It is also insecure by default:
deserializing pickle data coming from an untrusted source can execute
arbitrary code, if the data was crafted by a skilled attacker.

Read article
8. Errors and Exceptions

8. Errors and Exceptions¶

Until now error messages haven’t been more than mentioned, but if you have tried
out the examples you have probably seen some. There are (at least) two
distinguishable kinds of errors: syntax errors and exceptions .

8.1. Syntax Errors¶

Syntax errors, also known as parsing errors, are perhaps the most common kind of
complaint you get while you are still learning Python:

>>> while True print('Hello world')
File "<stdin>", line 1
while True print('Hello world')
SyntaxError: invalid syntax

The parser repeats the offending line and displays a little ‘arrow’ pointing at
the earliest point in the line where the error was detected. The error is
caused by (or at least detected at) the token preceding the arrow: in the
example, the error is detected at the function print() , since a colon
( ':' ) is missing before it. File name and line number are printed so you
know where to look in case the input came from a script.

8.2. Exceptions¶

Even if a statement or expression is syntactically correct, it may cause an
error when an attempt is made to execute it. Errors detected during execution
are called exceptions and are not unconditionally fatal: you will soon learn
how to handle them in Python programs. Most exceptions are not handled by
programs, however, and result in error messages as shown here:

>>> 10 * (1/0)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
ZeroDivisionError: division by zero
>>> 4 + spam*3
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
NameError: name 'spam' is not defined
>>> '2' + 2
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
TypeError: can only concatenate str (not "int") to str

The last line of the error message indicates what happened. Exceptions come in
different types, and the type is printed as part of the message: the types in
the example are ZeroDivisionError , NameError and TypeError .
The string printed as the exception type is the name of the built-in exception
that occurred. This is true for all built-in exceptions, but need not be true
for user-defined exceptions (although it is a useful convention). Standard
exception names are built-in identifiers (not reserved keywords).

The rest of the line provides detail based on the type of exception and what
caused it.

The preceding part of the error message shows the context where the exception
occurred, in the form of a stack traceback. In general it contains a stack
traceback listing source lines; however, it will not display lines read from
standard input.

Built-in Exceptions lists the built-in exceptions and their meanings.

8.3. Handling Exceptions¶

It is possible to write programs that handle selected exceptions. Look at the
following example, which asks the user for input until a valid integer has been
entered, but allows the user to interrupt the program (using Control - C or
whatever the operating system supports); note that a user-generated interruption
is signalled by raising the KeyboardInterrupt exception.

>>> while True:
... try:
... x = int(input("Please enter a number: "))
... break
... except ValueError:
... print("Oops! That was no valid number. Try again...")

The try statement works as follows.

  • First, the try clause (the statement(s) between the try and
    except keywords) is executed.

  • If no exception occurs, the except clause is skipped and execution of the
    try statement is finished.

  • If an exception occurs during execution of the try clause, the rest of the
    clause is skipped. Then, if its type matches the exception named after the
    except keyword, the except clause is executed, and then execution
    continues after the try/except block.

  • If an exception occurs which does not match the exception named in the except
    , it is passed on to outer try statements; if no handler is
    found, it is an unhandled exception and execution stops with a message as
    shown above.

A try statement may have more than one except clause , to specify
handlers for different exceptions. At most one handler will be executed.
Handlers only handle exceptions that occur in the corresponding try clause ,
not in other handlers of the same try statement. An except clause
may name multiple exceptions as a parenthesized tuple, for example:

... except (RuntimeError, TypeError, NameError):
... pass

A class in an except clause is compatible with an exception if it is
the same class or a base class thereof (but not the other way around — an
except clause listing a derived class is not compatible with a base class).
For example, the following code will print B, C, D in that order:

class B(Exception):

class C(B):

class D(C):

for cls in [B, C, D]:
raise cls()
except D:
except C:
except B:

Note that if the except clauses were reversed (with except B first), it
would have printed B, B, B — the first matching except clause is triggered.

When an exception occurs, it may have associated values, also known as the
exception’s arguments . The presence and types of the arguments depend on the
exception type.

The except clause may specify a variable after the exception name. The
variable is bound to the exception instance which typically has an args
attribute that stores the arguments. For convenience, builtin exception
types define __str__() to print all the arguments without explicitly
accessing .args .

>>> try:
... raise Exception('spam', 'eggs')
... except Exception as inst:
... print(type(inst)) # the exception instance
... print(inst.args) # arguments stored in .args
... print(inst) # __str__ allows args to be printed directly,
... # but may be overridden in exception subclasses
... x, y = inst.args # unpack args
... print('x =', x)
... print('y =', y)
<class 'Exception'>
('spam', 'eggs')
('spam', 'eggs')
x = spam
y = eggs

The exception’s __str__() output is printed as the last part (‘detail’)
of the message for unhandled exceptions.

BaseException is the common base class of all exceptions. One of its
subclasses, Exception , is the base class of all the non-fatal exceptions.
Exceptions which are not subclasses of Exception are not typically
handled, because they are used to indicate that the program should terminate.
They include SystemExit which is raised by sys.exit() and
KeyboardInterrupt which is raised when a user wishes to interrupt
the program.

Exception can be used as a wildcard that catches (almost) everything.
However, it is good practice to be as specific as possible with the types
of exceptions that we intend to handle, and to allow any unexpected
exceptions to propagate on.

The most common pattern for handling Exception is to print or log
the exception and then re-raise it (allowing a caller to handle the
exception as well):

import sys

f = open('myfile.txt')
s = f.readline()
i = int(s.strip())
except OSError as err:
print("OS error:", err)
except ValueError:
print("Could not convert data to an integer.")
except Exception as err:
print(f"Unexpected {err=}, {type(err)=}")

The try … except statement has an optional else
, which, when present, must follow all except clauses . It is useful
for code that must be executed if the try clause does not raise an exception.
For example:

for arg in sys.argv[1:]:
f = open(arg, 'r')
except OSError:
print('cannot open', arg)
print(arg, 'has', len(f.readlines()), 'lines')

The use of the else clause is better than adding additional code to
the try clause because it avoids accidentally catching an exception
that wasn’t raised by the code being protected by the try …
except statement.

Exception handlers do not handle only exceptions that occur immediately in the
try clause , but also those that occur inside functions that are called (even
indirectly) in the try clause . For example:

>>> def this_fails():
... x = 1/0
>>> try:
... this_fails()
... except ZeroDivisionError as err:
... print('Handling run-time error:', err)
Handling run-time error: division by zero

8.4. Raising Exceptions¶

The raise statement allows the programmer to force a specified
exception to occur. For example:

>>> raise NameError('HiThere')
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
NameError: HiThere

The sole argument to raise indicates the exception to be raised.
This must be either an exception instance or an exception class (a class that
derives from BaseException , such as Exception or one of its
subclasses). If an exception class is passed, it will be implicitly
instantiated by calling its constructor with no arguments:

raise ValueError  # shorthand for 'raise ValueError()'

If you need to determine whether an exception was raised but don’t intend to
handle it, a simpler form of the raise statement allows you to
re-raise the exception:

>>> try:
... raise NameError('HiThere')
... except NameError:
... print('An exception flew by!')
... raise
An exception flew by!
Traceback (most recent call last):
File "<stdin>", line 2, in <module>
NameError: HiThere

8.5. Exception Chaining¶

If an unhandled exception occurs inside an except section, it will
have the exception being handled attached to it and included in the error

>>> try:
... open("database.sqlite")
... except OSError:
... raise RuntimeError("unable to handle error")
Traceback (most recent call last):
File "<stdin>", line 2, in <module>
FileNotFoundError: [Errno 2] No such file or directory: 'database.sqlite'

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
File "<stdin>", line 4, in <module>
RuntimeError: unable to handle error

To indicate that an exception is a direct consequence of another, the
raise statement allows an optional from clause:

# exc must be exception instance or None.
raise RuntimeError from exc

This can be useful when you are transforming exceptions. For example:

>>> def func():
... raise ConnectionError
>>> try:
... func()
... except ConnectionError as exc:
... raise RuntimeError('Failed to open database') from exc
Traceback (most recent call last):
File "<stdin>", line 2, in <module>
File "<stdin>", line 2, in func

The above exception was the direct cause of the following exception:

Traceback (most recent call last):
File "<stdin>", line 4, in <module>
RuntimeError: Failed to open database

It also allows disabling automatic exception chaining using the from None

>>> try:
... open('database.sqlite')
... except OSError:
... raise RuntimeError from None
Traceback (most recent call last):
File "<stdin>", line 4, in <module>

For more information about chaining mechanics, see Built-in Exceptions .

8.6. User-defined Exceptions¶

Programs may name their own exceptions by creating a new exception class (see
Classes for more about Python classes). Exceptions should typically
be derived from the Exception class, either directly or indirectly.

Exception classes can be defined which do anything any other class can do, but
are usually kept simple, often only offering a number of attributes that allow
information about the error to be extracted by handlers for the exception.

Most exceptions are defined with names that end in “Error”, similar to the
naming of the standard exceptions.

Many standard modules define their own exceptions to report errors that may
occur in functions they define.

8.7. Defining Clean-up Actions¶

The try statement has another optional clause which is intended to
define clean-up actions that must be executed under all circumstances. For

>>> try:
... raise KeyboardInterrupt
... finally:
... print('Goodbye, world!')
Goodbye, world!
Traceback (most recent call last):
File "<stdin>", line 2, in <module>

If a finally clause is present, the finally
clause will execute as the last task before the try
statement completes. The finally clause runs whether or
not the try statement produces an exception. The following
points discuss more complex cases when an exception occurs:

  • If an exception occurs during execution of the try
    clause, the exception may be handled by an except
    clause. If the exception is not handled by an except
    clause, the exception is re-raised after the finally
    clause has been executed.

  • An exception could occur during execution of an except
    or else clause. Again, the exception is re-raised after
    the finally clause has been executed.

  • If the finally clause executes a break ,
    continue or return statement, exceptions are not

  • If the try statement reaches a break ,
    continue or return statement, the
    finally clause will execute just prior to the
    break , continue or return
    statement’s execution.

  • If a finally clause includes a return
    statement, the returned value will be the one from the
    finally clause’s return statement, not the
    value from the try clause’s return

For example:

>>> def bool_return():
... try:
... return True
... finally:
... return False
>>> bool_return()

A more complicated example:

>>> def divide(x, y):
... try:
... result = x / y
... except ZeroDivisionError:
... print("division by zero!")
... else:
... print("result is", result)
... finally:
... print("executing finally clause")
>>> divide(2, 1)
result is 2.0
executing finally clause
>>> divide(2, 0)
division by zero!
executing finally clause
>>> divide("2", "1")
executing finally clause
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "<stdin>", line 3, in divide
TypeError: unsupported operand type(s) for /: 'str' and 'str'

As you can see, the finally clause is executed in any event. The
TypeError raised by dividing two strings is not handled by the
except clause and therefore re-raised after the finally
clause has been executed.

In real world applications, the finally clause is useful for
releasing external resources (such as files or network connections), regardless
of whether the use of the resource was successful.

8.8. Predefined Clean-up Actions¶

Some objects define standard clean-up actions to be undertaken when the object
is no longer needed, regardless of whether or not the operation using the object
succeeded or failed. Look at the following example, which tries to open a file
and print its contents to the screen.

for line in open("myfile.txt"):
print(line, end="")

The problem with this code is that it leaves the file open for an indeterminate
amount of time after this part of the code has finished executing.
This is not an issue in simple scripts, but can be a problem for larger
applications. The with statement allows objects like files to be
used in a way that ensures they are always cleaned up promptly and correctly.

with open("myfile.txt") as f:
for line in f:
print(line, end="")

After the statement is executed, the file f is always closed, even if a
problem was encountered while processing the lines. Objects which, like files,
provide predefined clean-up actions will indicate this in their documentation.

8.9. Raising and Handling Multiple Unrelated Exceptions¶

There are situations where it is necessary to report several exceptions that
have occurred. This is often the case in concurrency frameworks, when several
tasks may have failed in parallel, but there are also other use cases where
it is desirable to continue execution and collect multiple errors rather than
raise the first exception.

The builtin ExceptionGroup wraps a list of exception instances so
that they can be raised together. It is an exception itself, so it can be
caught like any other exception.

>>> def f():
... excs = [OSError('error 1'), SystemError('error 2')]
... raise ExceptionGroup('there were problems', excs)
>>> f()
+ Exception Group Traceback (most recent call last):
| File "<stdin>", line 1, in <module>
| File "<stdin>", line 3, in f
| ExceptionGroup: there were problems
+-+---------------- 1 ----------------
| OSError: error 1
+---------------- 2 ----------------
| SystemError: error 2
>>> try:
... f()
... except Exception as e:
... print(f'caught {type(e)}: e')
caught <class 'ExceptionGroup'>: e

By using except* instead of except , we can selectively
handle only the exceptions in the group that match a certain
type. In the following example, which shows a nested exception
group, each except* clause extracts from the group exceptions
of a certain type while letting all other exceptions propagate to
other clauses and eventually to be reraised.

>>> def f():
... raise ExceptionGroup("group1",
... [OSError(1),
... SystemError(2),
... ExceptionGroup("group2",
... [OSError(3), RecursionError(4)])])
>>> try:
... f()
... except* OSError as e:
... print("There were OSErrors")
... except* SystemError as e:
... print("There were SystemErrors")
There were OSErrors
There were SystemErrors
+ Exception Group Traceback (most recent call last):
| File "<stdin>", line 2, in <module>
| File "<stdin>", line 2, in f
| ExceptionGroup: group1
+-+---------------- 1 ----------------
| ExceptionGroup: group2
+-+---------------- 1 ----------------
| RecursionError: 4

Note that the exceptions nested in an exception group must be instances,
not types. This is because in practice the exceptions would typically
be ones that have already been raised and caught by the program, along
the following pattern:

>>> excs = []
... for test in tests:
... try:
... test.run()
... except Exception as e:
... excs.append(e)
>>> if excs:
... raise ExceptionGroup("Test Failures", excs)

8.10. Enriching Exceptions with Notes¶

When an exception is created in order to be raised, it is usually initialized
with information that describes the error that has occurred. There are cases
where it is useful to add information after the exception was caught. For this
purpose, exceptions have a method add_note(note) that accepts a string and
adds it to the exception’s notes list. The standard traceback rendering
includes all notes, in the order they were added, after the exception.

>>> try:
... raise TypeError('bad type')
... except Exception as e:
... e.add_note('Add some information')
... e.add_note('Add some more information')
... raise
Traceback (most recent call last):
File "<stdin>", line 2, in <module>
TypeError: bad type
Add some information
Add some more information

For example, when collecting exceptions into an exception group, we may want
to add context information for the individual errors. In the following each
exception in the group has a note indicating when this error has occurred.

>>> def f():
... raise OSError('operation failed')
>>> excs = []
>>> for i in range(3):
... try:
... f()
... except Exception as e:
... e.add_note(f'Happened in Iteration {i+1}')
... excs.append(e)
>>> raise ExceptionGroup('We have some problems', excs)
+ Exception Group Traceback (most recent call last):
| File "<stdin>", line 1, in <module>
| ExceptionGroup: We have some problems (3 sub-exceptions)
+-+---------------- 1 ----------------
| Traceback (most recent call last):
| File "<stdin>", line 3, in <module>
| File "<stdin>", line 2, in f
| OSError: operation failed
| Happened in Iteration 1
+---------------- 2 ----------------
| Traceback (most recent call last):
| File "<stdin>", line 3, in <module>
| File "<stdin>", line 2, in f
| OSError: operation failed
| Happened in Iteration 2
+---------------- 3 ----------------
| Traceback (most recent call last):
| File "<stdin>", line 3, in <module>
| File "<stdin>", line 2, in f
| OSError: operation failed
| Happened in Iteration 3

Read article
9. Classes

9. Classes¶

Classes provide a means of bundling data and functionality together. Creating
a new class creates a new type of object, allowing new instances of that
type to be made. Each class instance can have attributes attached to it for
maintaining its state. Class instances can also have methods (defined by its
class) for modifying its state.

Compared with other programming languages, Python’s class mechanism adds classes
with a minimum of new syntax and semantics. It is a mixture of the class
mechanisms found in C++ and Modula-3. Python classes provide all the standard
features of Object Oriented Programming: the class inheritance mechanism allows
multiple base classes, a derived class can override any methods of its base
class or classes, and a method can call the method of a base class with the same
name. Objects can contain arbitrary amounts and kinds of data. As is true for
modules, classes partake of the dynamic nature of Python: they are created at
runtime, and can be modified further after creation.

In C++ terminology, normally class members (including the data members) are
public (except see below Private Variables ), and all member functions are
virtual . As in Modula-3, there are no shorthands for referencing the object’s
members from its methods: the method function is declared with an explicit first
argument representing the object, which is provided implicitly by the call. As
in Smalltalk, classes themselves are objects. This provides semantics for
importing and renaming. Unlike C++ and Modula-3, built-in types can be used as
base classes for extension by the user. Also, like in C++, most built-in
operators with special syntax (arithmetic operators, subscripting etc.) can be
redefined for class instances.

(Lacking universally accepted terminology to talk about classes, I will make
occasional use of Smalltalk and C++ terms. I would use Modula-3 terms, since
its object-oriented semantics are closer to those of Python than C++, but I
expect that few readers have heard of it.)

9.1. A Word About Names and Objects¶

Objects have individuality, and multiple names (in multiple scopes) can be bound
to the same object. This is known as aliasing in other languages. This is
usually not appreciated on a first glance at Python, and can be safely ignored
when dealing with immutable basic types (numbers, strings, tuples). However,
aliasing has a possibly surprising effect on the semantics of Python code
involving mutable objects such as lists, dictionaries, and most other types.
This is usually used to the benefit of the program, since aliases behave like
pointers in some respects. For example, passing an object is cheap since only a
pointer is passed by the implementation; and if a function modifies an object
passed as an argument, the caller will see the change — this eliminates the
need for two different argument passing mechanisms as in Pascal.

9.2. Python Scopes and Namespaces¶

Before introducing classes, I first have to tell you something about Python’s
scope rules. Class definitions play some neat tricks with namespaces, and you
need to know how scopes and namespaces work to fully understand what’s going on.
Incidentally, knowledge about this subject is useful for any advanced Python

Let’s begin with some definitions.

A namespace is a mapping from names to objects. Most namespaces are currently
implemented as Python dictionaries, but that’s normally not noticeable in any
way (except for performance), and it may change in the future. Examples of
namespaces are: the set of built-in names (containing functions such as abs() , and
built-in exception names); the global names in a module; and the local names in
a function invocation. In a sense the set of attributes of an object also form
a namespace. The important thing to know about namespaces is that there is
absolutely no relation between names in different namespaces; for instance, two
different modules may both define a function maximize without confusion —
users of the modules must prefix it with the module name.

By the way, I use the word attribute for any name following a dot — for
example, in the expression z.real , real is an attribute of the object
z . Strictly speaking, references to names in modules are attribute
references: in the expression modname.funcname , modname is a module
object and funcname is an attribute of it. In this case there happens to be
a straightforward mapping between the module’s attributes and the global names
defined in the module: they share the same namespace! 1

Attributes may be read-only or writable. In the latter case, assignment to
attributes is possible. Module attributes are writable: you can write
modname.the_answer = 42 . Writable attributes may also be deleted with the
del statement. For example, del modname.the_answer will remove
the attribute the_answer from the object named by modname .

Namespaces are created at different moments and have different lifetimes. The
namespace containing the built-in names is created when the Python interpreter
starts up, and is never deleted. The global namespace for a module is created
when the module definition is read in; normally, module namespaces also last
until the interpreter quits. The statements executed by the top-level
invocation of the interpreter, either read from a script file or interactively,
are considered part of a module called __main__ , so they have their own
global namespace. (The built-in names actually also live in a module; this is
called builtins .)

The local namespace for a function is created when the function is called, and
deleted when the function returns or raises an exception that is not handled
within the function. (Actually, forgetting would be a better way to describe
what actually happens.) Of course, recursive invocations each have their own
local namespace.

A scope is a textual region of a Python program where a namespace is directly
accessible. “Directly accessible” here means that an unqualified reference to a
name attempts to find the name in the namespace.

Although scopes are determined statically, they are used dynamically. At any
time during execution, there are 3 or 4 nested scopes whose namespaces are
directly accessible:

  • the innermost scope, which is searched first, contains the local names

  • the scopes of any enclosing functions, which are searched starting with the
    nearest enclosing scope, contain non-local, but also non-global names

  • the next-to-last scope contains the current module’s global names

  • the outermost scope (searched last) is the namespace containing built-in names

If a name is declared global, then all references and assignments go directly to
the next-to-last scope containing the module’s global names. To rebind variables
found outside of the innermost scope, the nonlocal statement can be
used; if not declared nonlocal, those variables are read-only (an attempt to
write to such a variable will simply create a new local variable in the
innermost scope, leaving the identically named outer variable unchanged).

Usually, the local scope references the local names of the (textually) current
function. Outside functions, the local scope references the same namespace as
the global scope: the module’s namespace. Class definitions place yet another
namespace in the local scope.

It is important to realize that scopes are determined textually: the global
scope of a function defined in a module is that module’s namespace, no matter
from where or by what alias the function is called. On the other hand, the
actual search for names is done dynamically, at run time — however, the
language definition is evolving towards static name resolution, at “compile”
time, so don’t rely on dynamic name resolution! (In fact, local variables are
already determined statically.)

A special quirk of Python is that – if no global or nonlocal
statement is in effect – assignments to names always go into the innermost scope.
Assignments do not copy data — they just bind names to objects. The same is true
for deletions: the statement del x removes the binding of x from the
namespace referenced by the local scope. In fact, all operations that introduce
new names use the local scope: in particular, import statements and
function definitions bind the module or function name in the local scope.

The global statement can be used to indicate that particular
variables live in the global scope and should be rebound there; the
nonlocal statement indicates that particular variables live in
an enclosing scope and should be rebound there.

9.2.1. Scopes and Namespaces Example¶

This is an example demonstrating how to reference the different scopes and
namespaces, and how global and nonlocal affect variable

def scope_test():
def do_local():
spam = "local spam"

def do_nonlocal():
nonlocal spam
spam = "nonlocal spam"

def do_global():
global spam
spam = "global spam"

spam = "test spam"
print("After local assignment:", spam)
print("After nonlocal assignment:", spam)
print("After global assignment:", spam)

print("In global scope:", spam)

The output of the example code is:

After local assignment: test spam
After nonlocal assignment: nonlocal spam
After global assignment: nonlocal spam
In global scope: global spam

Note how the local assignment (which is default) didn’t change scope_test 's
binding of spam . The nonlocal assignment changed scope_test 's
binding of spam , and the global assignment changed the module-level

You can also see that there was no previous binding for spam before the
global assignment.

9.3. A First Look at Classes¶

Classes introduce a little bit of new syntax, three new object types, and some
new semantics.

9.3.1. Class Definition Syntax¶

The simplest form of class definition looks like this:

class ClassName:

Class definitions, like function definitions ( def statements) must be
executed before they have any effect. (You could conceivably place a class
definition in a branch of an if statement, or inside a function.)

In practice, the statements inside a class definition will usually be function
definitions, but other statements are allowed, and sometimes useful — we’ll
come back to this later. The function definitions inside a class normally have
a peculiar form of argument list, dictated by the calling conventions for
methods — again, this is explained later.

When a class definition is entered, a new namespace is created, and used as the
local scope — thus, all assignments to local variables go into this new
namespace. In particular, function definitions bind the name of the new
function here.

When a class definition is left normally (via the end), a class object is
created. This is basically a wrapper around the contents of the namespace
created by the class definition; we’ll learn more about class objects in the
next section. The original local scope (the one in effect just before the class
definition was entered) is reinstated, and the class object is bound here to the
class name given in the class definition header ( ClassName in the

9.3.2. Class Objects¶

Class objects support two kinds of operations: attribute references and

Attribute references use the standard syntax used for all attribute references
in Python: obj.name . Valid attribute names are all the names that were in
the class’s namespace when the class object was created. So, if the class
definition looked like this:

class MyClass:
"""A simple example class"""
i = 12345

def f(self):
return 'hello world'

then MyClass.i and MyClass.f are valid attribute references, returning
an integer and a function object, respectively. Class attributes can also be
assigned to, so you can change the value of MyClass.i by assignment.
__doc__ is also a valid attribute, returning the docstring belonging to
the class: "A simple example class" .

Class instantiation uses function notation. Just pretend that the class
object is a parameterless function that returns a new instance of the class.
For example (assuming the above class):

x = MyClass()

creates a new instance of the class and assigns this object to the local
variable x .

The instantiation operation (“calling” a class object) creates an empty object.
Many classes like to create objects with instances customized to a specific
initial state. Therefore a class may define a special method named
__init__() , like this:

def __init__(self):
self.data = []

When a class defines an __init__() method, class instantiation
automatically invokes __init__() for the newly created class instance. So
in this example, a new, initialized instance can be obtained by:

x = MyClass()

Of course, the __init__() method may have arguments for greater
flexibility. In that case, arguments given to the class instantiation operator
are passed on to __init__() . For example,

>>> class Complex:
... def __init__(self, realpart, imagpart):
... self.r = realpart
... self.i = imagpart
>>> x = Complex(3.0, -4.5)
>>> x.r, x.i
(3.0, -4.5)

9.3.3. Instance Objects¶

Now what can we do with instance objects? The only operations understood by
instance objects are attribute references. There are two kinds of valid
attribute names: data attributes and methods.

data attributes correspond to “instance variables” in Smalltalk, and to “data
members” in C++. Data attributes need not be declared; like local variables,
they spring into existence when they are first assigned to. For example, if
x is the instance of MyClass created above, the following piece of
code will print the value 16 , without leaving a trace:

x.counter = 1
while x.counter < 10:
x.counter = x.counter * 2
del x.counter

The other kind of instance attribute reference is a method . A method is a
function that “belongs to” an object. (In Python, the term method is not unique
to class instances: other object types can have methods as well. For example,
list objects have methods called append, insert, remove, sort, and so on.
However, in the following discussion, we’ll use the term method exclusively to
mean methods of class instance objects, unless explicitly stated otherwise.)

Valid method names of an instance object depend on its class. By definition,
all attributes of a class that are function objects define corresponding
methods of its instances. So in our example, x.f is a valid method
reference, since MyClass.f is a function, but x.i is not, since
MyClass.i is not. But x.f is not the same thing as MyClass.f — it
is a method object , not a function object.

9.3.4. Method Objects¶

Usually, a method is called right after it is bound:


In the MyClass example, this will return the string 'hello world' .
However, it is not necessary to call a method right away: x.f is a method
object, and can be stored away and called at a later time. For example:

xf = x.f
while True:

will continue to print hello world until the end of time.

What exactly happens when a method is called? You may have noticed that
x.f() was called without an argument above, even though the function
definition for f() specified an argument. What happened to the argument?
Surely Python raises an exception when a function that requires an argument is
called without any — even if the argument isn’t actually used…

Actually, you may have guessed the answer: the special thing about methods is
that the instance object is passed as the first argument of the function. In our
example, the call x.f() is exactly equivalent to MyClass.f(x) . In
general, calling a method with a list of n arguments is equivalent to calling
the corresponding function with an argument list that is created by inserting
the method’s instance object before the first argument.

If you still don’t understand how methods work, a look at the implementation can
perhaps clarify matters. When a non-data attribute of an instance is
referenced, the instance’s class is searched. If the name denotes a valid class
attribute that is a function object, a method object is created by packing
(pointers to) the instance object and the function object just found together in
an abstract object: this is the method object. When the method object is called
with an argument list, a new argument list is constructed from the instance
object and the argument list, and the function object is called with this new
argument list.

9.3.5. Class and Instance Variables¶

Generally speaking, instance variables are for data unique to each instance
and class variables are for attributes and methods shared by all instances
of the class:

class Dog:

kind = 'canine' # class variable shared by all instances

def __init__(self, name):
self.name = name # instance variable unique to each instance

>>> d = Dog('Fido')
>>> e = Dog('Buddy')
>>> d.kind # shared by all dogs
>>> e.kind # shared by all dogs
>>> d.name # unique to d
>>> e.name # unique to e

As discussed in A Word About Names and Objects , shared data can have possibly surprising
effects with involving mutable objects such as lists and dictionaries.
For example, the tricks list in the following code should not be used as a
class variable because just a single list would be shared by all Dog

class Dog:

tricks = [] # mistaken use of a class variable

def __init__(self, name):
self.name = name

def add_trick(self, trick):

>>> d = Dog('Fido')
>>> e = Dog('Buddy')
>>> d.add_trick('roll over')
>>> e.add_trick('play dead')
>>> d.tricks # unexpectedly shared by all dogs
['roll over', 'play dead']

Correct design of the class should use an instance variable instead:

class Dog:

def __init__(self, name):
self.name = name
self.tricks = [] # creates a new empty list for each dog

def add_trick(self, trick):

>>> d = Dog('Fido')
>>> e = Dog('Buddy')
>>> d.add_trick('roll over')
>>> e.add_trick('play dead')
>>> d.tricks
['roll over']
>>> e.tricks
['play dead']

9.4. Random Remarks¶

If the same attribute name occurs in both an instance and in a class,
then attribute lookup prioritizes the instance:

>>> class Warehouse:
... purpose = 'storage'
... region = 'west'
>>> w1 = Warehouse()
>>> print(w1.purpose, w1.region)
storage west
>>> w2 = Warehouse()
>>> w2.region = 'east'
>>> print(w2.purpose, w2.region)
storage east

Data attributes may be referenced by methods as well as by ordinary users
(“clients”) of an object. In other words, classes are not usable to implement
pure abstract data types. In fact, nothing in Python makes it possible to
enforce data hiding — it is all based upon convention. (On the other hand,
the Python implementation, written in C, can completely hide implementation
details and control access to an object if necessary; this can be used by
extensions to Python written in C.)

Clients should use data attributes with care — clients may mess up invariants
maintained by the methods by stamping on their data attributes. Note that
clients may add data attributes of their own to an instance object without
affecting the validity of the methods, as long as name conflicts are avoided —
again, a naming convention can save a lot of headaches here.

There is no shorthand for referencing data attributes (or other methods!) from
within methods. I find that this actually increases the readability of methods:
there is no chance of confusing local variables and instance variables when
glancing through a method.

Often, the first argument of a method is called self . This is nothing more
than a convention: the name self has absolutely no special meaning to
Python. Note, however, that by not following the convention your code may be
less readable to other Python programmers, and it is also conceivable that a
class browser program might be written that relies upon such a convention.

Any function object that is a class attribute defines a method for instances of
that class. It is not necessary that the function definition is textually
enclosed in the class definition: assigning a function object to a local
variable in the class is also ok. For example:

# Function defined outside the class
def f1(self, x, y):
return min(x, x+y)

class C:
f = f1

def g(self):
return 'hello world'

h = g

Now f , g and h are all attributes of class C that refer to
function objects, and consequently they are all methods of instances of
C — h being exactly equivalent to g . Note that this practice
usually only serves to confuse the reader of a program.

Methods may call other methods by using method attributes of the self

class Bag:
def __init__(self):
self.data = []

def add(self, x):

def addtwice(self, x):

Methods may reference global names in the same way as ordinary functions. The
global scope associated with a method is the module containing its
definition. (A class is never used as a global scope.) While one
rarely encounters a good reason for using global data in a method, there are
many legitimate uses of the global scope: for one thing, functions and modules
imported into the global scope can be used by methods, as well as functions and
classes defined in it. Usually, the class containing the method is itself
defined in this global scope, and in the next section we’ll find some good
reasons why a method would want to reference its own class.

Each value is an object, and therefore has a class (also called its type ).
It is stored as object.__class__ .

9.5. Inheritance¶

Of course, a language feature would not be worthy of the name “class” without
supporting inheritance. The syntax for a derived class definition looks like

class DerivedClassName(BaseClassName):

The name BaseClassName must be defined in a scope containing the
derived class definition. In place of a base class name, other arbitrary
expressions are also allowed. This can be useful, for example, when the base
class is defined in another module:

class DerivedClassName(modname.BaseClassName):

Execution of a derived class definition proceeds the same as for a base class.
When the class object is constructed, the base class is remembered. This is
used for resolving attribute references: if a requested attribute is not found
in the class, the search proceeds to look in the base class. This rule is
applied recursively if the base class itself is derived from some other class.

There’s nothing special about instantiation of derived classes:
DerivedClassName() creates a new instance of the class. Method references
are resolved as follows: the corresponding class attribute is searched,
descending down the chain of base classes if necessary, and the method reference
is valid if this yields a function object.

Derived classes may override methods of their base classes. Because methods
have no special privileges when calling other methods of the same object, a
method of a base class that calls another method defined in the same base class
may end up calling a method of a derived class that overrides it. (For C++
programmers: all methods in Python are effectively virtual .)

An overriding method in a derived class may in fact want to extend rather than
simply replace the base class method of the same name. There is a simple way to
call the base class method directly: just call BaseClassName.methodname(self,
. This is occasionally useful to clients as well. (Note that this
only works if the base class is accessible as BaseClassName in the global

Python has two built-in functions that work with inheritance:

  • Use isinstance() to check an instance’s type: isinstance(obj, int)
    will be True only if obj.__class__ is int or some class
    derived from int .

  • Use issubclass() to check class inheritance: issubclass(bool, int)
    is True since bool is a subclass of int . However,
    issubclass(float, int) is False since float is not a
    subclass of int .

9.5.1. Multiple Inheritance¶

Python supports a form of multiple inheritance as well. A class definition with
multiple base classes looks like this:

class DerivedClassName(Base1, Base2, Base3):

For most purposes, in the simplest cases, you can think of the search for
attributes inherited from a parent class as depth-first, left-to-right, not
searching twice in the same class where there is an overlap in the hierarchy.
Thus, if an attribute is not found in DerivedClassName , it is searched
for in Base1 , then (recursively) in the base classes of Base1 ,
and if it was not found there, it was searched for in Base2 , and so on.

In fact, it is slightly more complex than that; the method resolution order
changes dynamically to support cooperative calls to super() . This
approach is known in some other multiple-inheritance languages as
call-next-method and is more powerful than the super call found in
single-inheritance languages.

Dynamic ordering is necessary because all cases of multiple inheritance exhibit
one or more diamond relationships (where at least one of the parent classes
can be accessed through multiple paths from the bottommost class). For example,
all classes inherit from object , so any case of multiple inheritance
provides more than one path to reach object . To keep the base classes
from being accessed more than once, the dynamic algorithm linearizes the search
order in a way that preserves the left-to-right ordering specified in each
class, that calls each parent only once, and that is monotonic (meaning that a
class can be subclassed without affecting the precedence order of its parents).
Taken together, these properties make it possible to design reliable and
extensible classes with multiple inheritance. For more detail, see

9.6. Private Variables¶

“Private” instance variables that cannot be accessed except from inside an
object don’t exist in Python. However, there is a convention that is followed
by most Python code: a name prefixed with an underscore (e.g. _spam ) should
be treated as a non-public part of the API (whether it is a function, a method
or a data member). It should be considered an implementation detail and subject
to change without notice.

Since there is a valid use-case for class-private members (namely to avoid name
clashes of names with names defined by subclasses), there is limited support for
such a mechanism, called name mangling . Any identifier of the form
__spam (at least two leading underscores, at most one trailing underscore)
is textually replaced with _classname__spam , where classname is the
current class name with leading underscore(s) stripped. This mangling is done
without regard to the syntactic position of the identifier, as long as it
occurs within the definition of a class.

Name mangling is helpful for letting subclasses override methods without
breaking intraclass method calls. For example:

class Mapping:
def __init__(self, iterable):
self.items_list = []

def update(self, iterable):
for item in iterable:

__update = update # private copy of original update() method

class MappingSubclass(Mapping):

def update(self, keys, values):
# provides new signature for update()
# but does not break __init__()
for item in zip(keys, values):

The above example would work even if MappingSubclass were to introduce a
__update identifier since it is replaced with _Mapping__update in the
Mapping class and _MappingSubclass__update in the MappingSubclass
class respectively.

Note that the mangling rules are designed mostly to avoid accidents; it still is
possible to access or modify a variable that is considered private. This can
even be useful in special circumstances, such as in the debugger.

Notice that code passed to exec() or eval() does not consider the
classname of the invoking class to be the current class; this is similar to the
effect of the global statement, the effect of which is likewise restricted
to code that is byte-compiled together. The same restriction applies to
getattr() , setattr() and delattr() , as well as when referencing
__dict__ directly.

9.7. Odds and Ends¶

Sometimes it is useful to have a data type similar to the Pascal “record” or C
“struct”, bundling together a few named data items. The idiomatic approach
is to use dataclasses for this purpose:

from dataclasses import dataclass

class Employee:
name: str
dept: str
salary: int

>>> john = Employee('john', 'computer lab', 1000)
>>> john.dept
'computer lab'
>>> john.salary

A piece of Python code that expects a particular abstract data type can often be
passed a class that emulates the methods of that data type instead. For
instance, if you have a function that formats some data from a file object, you
can define a class with methods read() and readline() that get the
data from a string buffer instead, and pass it as an argument.

Instance method objects have attributes, too: m.__self__ is the instance
object with the method m() , and m.__func__ is the function object
corresponding to the method.

9.8. Iterators¶

By now you have probably noticed that most container objects can be looped over
using a for statement:

for element in [1, 2, 3]:
for element in (1, 2, 3):
for key in {'one':1, 'two':2}:
for char in "123":
for line in open("myfile.txt"):
print(line, end='')

This style of access is clear, concise, and convenient. The use of iterators
pervades and unifies Python. Behind the scenes, the for statement
calls iter() on the container object. The function returns an iterator
object that defines the method __next__() which accesses
elements in the container one at a time. When there are no more elements,
__next__() raises a StopIteration exception which tells the
for loop to terminate. You can call the __next__() method
using the next() built-in function; this example shows how it all works:

>>> s = 'abc'
>>> it = iter(s)
>>> it
<str_iterator object at 0x10c90e650>
>>> next(it)
>>> next(it)
>>> next(it)
>>> next(it)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>

Having seen the mechanics behind the iterator protocol, it is easy to add
iterator behavior to your classes. Define an __iter__() method which
returns an object with a __next__() method. If the class
defines __next__() , then __iter__() can just return self :

class Reverse:
"""Iterator for looping over a sequence backwards."""
def __init__(self, data):
self.data = data
self.index = len(data)

def __iter__(self):
return self

def __next__(self):
if self.index == 0:
raise StopIteration
self.index = self.index - 1
return self.data[self.index]

>>> rev = Reverse('spam')
>>> iter(rev)
<__main__.Reverse object at 0x00A1DB50>
>>> for char in rev:
... print(char)

9.9. Generators¶

Generators are a simple and powerful tool for creating iterators. They
are written like regular functions but use the yield statement
whenever they want to return data. Each time next() is called on it, the
generator resumes where it left off (it remembers all the data values and which
statement was last executed). An example shows that generators can be trivially
easy to create:

def reverse(data):
for index in range(len(data)-1, -1, -1):
yield data[index]

>>> for char in reverse('golf'):
... print(char)

Anything that can be done with generators can also be done with class-based
iterators as described in the previous section. What makes generators so
compact is that the __iter__() and __next__() methods
are created automatically.

Another key feature is that the local variables and execution state are
automatically saved between calls. This made the function easier to write and
much more clear than an approach using instance variables like self.index
and self.data .

In addition to automatic method creation and saving program state, when
generators terminate, they automatically raise StopIteration . In
combination, these features make it easy to create iterators with no more effort
than writing a regular function.

9.10. Generator Expressions¶

Some simple generators can be coded succinctly as expressions using a syntax
similar to list comprehensions but with parentheses instead of square brackets.
These expressions are designed for situations where the generator is used right
away by an enclosing function. Generator expressions are more compact but less
versatile than full generator definitions and tend to be more memory friendly
than equivalent list comprehensions.


>>> sum(i*i for i in range(10))                 # sum of squares

>>> xvec = [10, 20, 30]
>>> yvec = [7, 5, 3]
>>> sum(x*y for x,y in zip(xvec, yvec)) # dot product

>>> unique_words = set(word for line in page for word in line.split())

>>> valedictorian = max((student.gpa, student.name) for student in graduates)

>>> data = 'golf'
>>> list(data[i] for i in range(len(data)-1, -1, -1))
['f', 'l', 'o', 'g']



Except for one thing. Module objects have a secret read-only attribute called
__dict__ which returns the dictionary used to implement the module’s
namespace; the name __dict__ is an attribute but not a global name.
Obviously, using this violates the abstraction of namespace implementation, and
should be restricted to things like post-mortem debuggers.

Read article
10. Brief Tour of the Standard Library

10. Brief Tour of the Standard Library¶

10.1. Operating System Interface¶

The os module provides dozens of functions for interacting with the
operating system:

>>> import os
>>> os.getcwd() # Return the current working directory
>>> os.chdir('/server/accesslogs') # Change current working directory
>>> os.system('mkdir today') # Run the command mkdir in the system shell

Be sure to use the import os style instead of from os import * . This
will keep os.open() from shadowing the built-in open() function which
operates much differently.

The built-in dir() and help() functions are useful as interactive
aids for working with large modules like os :

>>> import os
>>> dir(os)
<returns a list of all module functions>
>>> help(os)
<returns an extensive manual page created from the module's docstrings>

For daily file and directory management tasks, the shutil module provides
a higher level interface that is easier to use:

>>> import shutil
>>> shutil.copyfile('data.db', 'archive.db')
>>> shutil.move('/build/executables', 'installdir')

10.2. File Wildcards¶

The glob module provides a function for making file lists from directory
wildcard searches:

>>> import glob
>>> glob.glob('*.py')
['primes.py', 'random.py', 'quote.py']

10.3. Command Line Arguments¶

Common utility scripts often need to process command line arguments. These
arguments are stored in the sys module’s argv attribute as a list. For
instance the following output results from running python demo.py one two
at the command line:

>>> import sys
>>> print(sys.argv)
['demo.py', 'one', 'two', 'three']

The argparse module provides a more sophisticated mechanism to process
command line arguments. The following script extracts one or more filenames
and an optional number of lines to be displayed:

import argparse

parser = argparse.ArgumentParser(
description='Show top lines from each file')
parser.add_argument('filenames', nargs='+')
parser.add_argument('-l', '--lines', type=int, default=10)
args = parser.parse_args()

When run at the command line with python top.py --lines=5 alpha.txt
, the script sets args.lines to 5 and args.filenames
to ['alpha.txt', 'beta.txt'] .

10.4. Error Output Redirection and Program Termination¶

The sys module also has attributes for stdin , stdout , and stderr .
The latter is useful for emitting warnings and error messages to make them
visible even when stdout has been redirected:

>>> sys.stderr.write('Warning, log file not found starting a new one\n')
Warning, log file not found starting a new one

The most direct way to terminate a script is to use sys.exit() .

10.5. String Pattern Matching¶

The re module provides regular expression tools for advanced string
processing. For complex matching and manipulation, regular expressions offer
succinct, optimized solutions:

>>> import re
>>> re.findall(r'\bf[a-z]*', 'which foot or hand fell fastest')
['foot', 'fell', 'fastest']
>>> re.sub(r'(\b[a-z]+) \1', r'\1', 'cat in the the hat')
'cat in the hat'

When only simple capabilities are needed, string methods are preferred because
they are easier to read and debug:

>>> 'tea for too'.replace('too', 'two')
'tea for two'

10.6. Mathematics¶

The math module gives access to the underlying C library functions for
floating point math:

>>> import math
>>> math.cos(math.pi / 4)
>>> math.log(1024, 2)

The random module provides tools for making random selections:

>>> import random
>>> random.choice(['apple', 'pear', 'banana'])
>>> random.sample(range(100), 10) # sampling without replacement
[30, 83, 16, 4, 8, 81, 41, 50, 18, 33]
>>> random.random() # random float
>>> random.randrange(6) # random integer chosen from range(6)

The statistics module calculates basic statistical properties
(the mean, median, variance, etc.) of numeric data:

>>> import statistics
>>> data = [2.75, 1.75, 1.25, 0.25, 0.5, 1.25, 3.5]
>>> statistics.mean(data)
>>> statistics.median(data)
>>> statistics.variance(data)

The SciPy project <https://scipy.org> has many other modules for numerical

10.7. Internet Access¶

There are a number of modules for accessing the internet and processing internet
protocols. Two of the simplest are urllib.request for retrieving data
from URLs and smtplib for sending mail:

>>> from urllib.request import urlopen
>>> with urlopen('http://worldtimeapi.org/api/timezone/etc/UTC.txt') as response:
... for line in response:
... line = line.decode() # Convert bytes to a str
... if line.startswith('datetime'):
... print(line.rstrip()) # Remove trailing newline
datetime: 2022-01-01T01:36:47.689215+00:00

>>> import smtplib
>>> server = smtplib.SMTP('localhost')
>>> server.sendmail('soothsayer@example.org', 'jcaesar@example.org',
... """To: jcaesar@example.org
... From: soothsayer@example.org
... Beware the Ides of March.
... """)
>>> server.quit()

(Note that the second example needs a mailserver running on localhost.)

10.8. Dates and Times¶

The datetime module supplies classes for manipulating dates and times in
both simple and complex ways. While date and time arithmetic is supported, the
focus of the implementation is on efficient member extraction for output
formatting and manipulation. The module also supports objects that are timezone

>>> # dates are easily constructed and formatted
>>> from datetime import date
>>> now = date.today()
>>> now
datetime.date(2003, 12, 2)
>>> now.strftime("%m-%d-%y. %d %b %Y is a %A on the %d day of %B.")
'12-02-03. 02 Dec 2003 is a Tuesday on the 02 day of December.'

>>> # dates support calendar arithmetic
>>> birthday = date(1964, 7, 31)
>>> age = now - birthday
>>> age.days

10.9. Data Compression¶

Common data archiving and compression formats are directly supported by modules
including: zlib , gzip , bz2 , lzma , zipfile and
tarfile .

>>> import zlib
>>> s = b'witch which has which witches wrist watch'
>>> len(s)
>>> t = zlib.compress(s)
>>> len(t)
>>> zlib.decompress(t)
b'witch which has which witches wrist watch'
>>> zlib.crc32(s)

10.10. Performance Measurement¶

Some Python users develop a deep interest in knowing the relative performance of
different approaches to the same problem. Python provides a measurement tool
that answers those questions immediately.

For example, it may be tempting to use the tuple packing and unpacking feature
instead of the traditional approach to swapping arguments. The timeit
module quickly demonstrates a modest performance advantage:

>>> from timeit import Timer
>>> Timer('t=a; a=b; b=t', 'a=1; b=2').timeit()
>>> Timer('a,b = b,a', 'a=1; b=2').timeit()

In contrast to timeit ’s fine level of granularity, the profile and
pstats modules provide tools for identifying time critical sections in
larger blocks of code.

10.11. Quality Control¶

One approach for developing high quality software is to write tests for each
function as it is developed and to run those tests frequently during the
development process.

The doctest module provides a tool for scanning a module and validating
tests embedded in a program’s docstrings. Test construction is as simple as
cutting-and-pasting a typical call along with its results into the docstring.
This improves the documentation by providing the user with an example and it
allows the doctest module to make sure the code remains true to the

def average(values):
"""Computes the arithmetic mean of a list of numbers.

>>> print(average([20, 30, 70]))
return sum(values) / len(values)

import doctest
doctest.testmod() # automatically validate the embedded tests

The unittest module is not as effortless as the doctest module,
but it allows a more comprehensive set of tests to be maintained in a separate

import unittest

class TestStatisticalFunctions(unittest.TestCase):

def test_average(self):
self.assertEqual(average([20, 30, 70]), 40.0)
self.assertEqual(round(average([1, 5, 7]), 1), 4.3)
with self.assertRaises(ZeroDivisionError):
with self.assertRaises(TypeError):
average(20, 30, 70)

unittest.main() # Calling from the command line invokes all tests

10.12. Batteries Included¶

Python has a “batteries included” philosophy. This is best seen through the
sophisticated and robust capabilities of its larger packages. For example:

  • The xmlrpc.client and xmlrpc.server modules make implementing
    remote procedure calls into an almost trivial task. Despite the modules’
    names, no direct knowledge or handling of XML is needed.

  • The email package is a library for managing email messages, including
    MIME and other RFC 2822 -based message documents. Unlike smtplib and
    poplib which actually send and receive messages, the email package has
    a complete toolset for building or decoding complex message structures
    (including attachments) and for implementing internet encoding and header

  • The json package provides robust support for parsing this
    popular data interchange format. The csv module supports
    direct reading and writing of files in Comma-Separated Value format,
    commonly supported by databases and spreadsheets. XML processing is
    supported by the xml.etree.ElementTree , xml.dom and
    xml.sax packages. Together, these modules and packages
    greatly simplify data interchange between Python applications and
    other tools.

  • The sqlite3 module is a wrapper for the SQLite database
    library, providing a persistent database that can be updated and
    accessed using slightly nonstandard SQL syntax.

  • Internationalization is supported by a number of modules including
    gettext , locale , and the codecs package.

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