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1. Whetting Your Appetite





1. Whetting Your Appetite¶


If you do much work on computers, eventually you find that there’s some task
you’d like to automate. For example, you may wish to perform a
search-and-replace over a large number of text files, or rename and rearrange a
bunch of photo files in a complicated way. Perhaps you’d like to write a small
custom database, or a specialized GUI application, or a simple game.


If you’re a professional software developer, you may have to work with several
C/C++/Java libraries but find the usual write/compile/test/re-compile cycle is
too slow. Perhaps you’re writing a test suite for such a library and find
writing the testing code a tedious task. Or maybe you’ve written a program that
could use an extension language, and you don’t want to design and implement a
whole new language for your application.


Python is just the language for you.


You could write a Unix shell script or Windows batch files for some of these
tasks, but shell scripts are best at moving around files and changing text data,
not well-suited for GUI applications or games. You could write a C/C++/Java
program, but it can take a lot of development time to get even a first-draft
program. Python is simpler to use, available on Windows, macOS, and Unix
operating systems, and will help you get the job done more quickly.


Python is simple to use, but it is a real programming language, offering much
more structure and support for large programs than shell scripts or batch files
can offer. On the other hand, Python also offers much more error checking than
C, and, being a very-high-level language, it has high-level data types built
in, such as flexible arrays and dictionaries. Because of its more general data
types Python is applicable to a much larger problem domain than Awk or even
Perl, yet many things are at least as easy in Python as in those languages.


Python allows you to split your program into modules that can be reused in other
Python programs. It comes with a large collection of standard modules that you
can use as the basis of your programs — or as examples to start learning to
program in Python. Some of these modules provide things like file I/O, system
calls, sockets, and even interfaces to graphical user interface toolkits like
Tk.


Python is an interpreted language, which can save you considerable time during
program development because no compilation and linking is necessary. The
interpreter can be used interactively, which makes it easy to experiment with
features of the language, to write throw-away programs, or to test functions
during bottom-up program development. It is also a handy desk calculator.


Python enables programs to be written compactly and readably. Programs written
in Python are typically much shorter than equivalent C, C++, or Java programs,
for several reasons:



  • the high-level data types allow you to express complex operations in a single
    statement;


  • statement grouping is done by indentation instead of beginning and ending
    brackets;


  • no variable or argument declarations are necessary.



Python is extensible: if you know how to program in C it is easy to add a new
built-in function or module to the interpreter, either to perform critical
operations at maximum speed, or to link Python programs to libraries that may
only be available in binary form (such as a vendor-specific graphics library).
Once you are really hooked, you can link the Python interpreter into an
application written in C and use it as an extension or command language for that
application.


By the way, the language is named after the BBC show “Monty Python’s Flying
Circus” and has nothing to do with reptiles. Making references to Monty
Python skits in documentation is not only allowed, it is encouraged!


Now that you are all excited about Python, you’ll want to examine it in some
more detail. Since the best way to learn a language is to use it, the tutorial
invites you to play with the Python interpreter as you read.


In the next chapter, the mechanics of using the interpreter are explained. This
is rather mundane information, but essential for trying out the examples shown
later.


The rest of the tutorial introduces various features of the Python language and
system through examples, beginning with simple expressions, statements and data
types, through functions and modules, and finally touching upon advanced
concepts like exceptions and user-defined classes.








Read article
2. Using the Python Interpreter





2. Using the Python Interpreter¶



2.1. Invoking the Interpreter¶


The Python interpreter is usually installed as /usr/local/bin/python3.11
on those machines where it is available; putting /usr/local/bin in your
Unix shell’s search path makes it possible to start it by typing the command:


python3.11


to the shell. 1 Since the choice of the directory where the interpreter lives
is an installation option, other places are possible; check with your local
Python guru or system administrator. (E.g., /usr/local/python is a
popular alternative location.)


On Windows machines where you have installed Python from the Microsoft Store, the python3.11 command will be available. If you have
the py.exe launcher installed, you can use the py
command. See Excursus: Setting environment variables for other ways to launch Python.


Typing an end-of-file character (Control-D on Unix, Control-Z on
Windows) at the primary prompt causes the interpreter to exit with a zero exit
status. If that doesn’t work, you can exit the interpreter by typing the
following command: quit().


The interpreter’s line-editing features include interactive editing, history
substitution and code completion on systems that support the GNU Readline library.
Perhaps the quickest check to see whether command line editing is supported is
typing Control-P to the first Python prompt you get. If it beeps, you
have command line editing; see Appendix Interactive Input Editing and History Substitution for an
introduction to the keys. If nothing appears to happen, or if ^P is
echoed, command line editing isn’t available; you’ll only be able to use
backspace to remove characters from the current line.


The interpreter operates somewhat like the Unix shell: when called with standard
input connected to a tty device, it reads and executes commands interactively;
when called with a file name argument or with a file as standard input, it reads
and executes a script from that file.


A second way of starting the interpreter is python -c command [arg] ...,
which executes the statement(s) in command, analogous to the shell’s
-c option. Since Python statements often contain spaces or other
characters that are special to the shell, it is usually advised to quote
command in its entirety.


Some Python modules are also useful as scripts. These can be invoked using
python -m module [arg] ..., which executes the source file for module as
if you had spelled out its full name on the command line.


When a script file is used, it is sometimes useful to be able to run the script
and enter interactive mode afterwards. This can be done by passing -i
before the script.


All command line options are described in Command line and environment.



2.1.1. Argument Passing¶


When known to the interpreter, the script name and additional arguments
thereafter are turned into a list of strings and assigned to the argv
variable in the sys module. You can access this list by executing import
sys
. The length of the list is at least one; when no script and no arguments
are given, sys.argv[0] is an empty string. When the script name is given as
'-' (meaning standard input), sys.argv[0] is set to '-'. When
-c command is used, sys.argv[0] is set to '-c'. When
-m module is used, sys.argv[0] is set to the full name of the
located module. Options found after -c command or -m
module are not consumed by the Python interpreter’s option processing but
left in sys.argv for the command or module to handle.




2.1.2. Interactive Mode¶


When commands are read from a tty, the interpreter is said to be in interactive
mode
. In this mode it prompts for the next command with the primary prompt,
usually three greater-than signs (>>>); for continuation lines it prompts
with the secondary prompt, by default three dots (...). The interpreter
prints a welcome message stating its version number and a copyright notice
before printing the first prompt:


$ python3.11
Python 3.11 (default, April 4 2021, 09:25:04)
[GCC 10.2.0] on linux
Type "help", "copyright", "credits" or "license" for more information.
>>>


Continuation lines are needed when entering a multi-line construct. As an
example, take a look at this if statement:


>>> the_world_is_flat = True
>>> if the_world_is_flat:
... print("Be careful not to fall off!")
...
Be careful not to fall off!


For more on interactive mode, see Interactive Mode.





2.2. The Interpreter and Its Environment¶



2.2.1. Source Code Encoding¶


By default, Python source files are treated as encoded in UTF-8. In that
encoding, characters of most languages in the world can be used simultaneously
in string literals, identifiers and comments — although the standard library
only uses ASCII characters for identifiers, a convention that any portable code
should follow. To display all these characters properly, your editor must
recognize that the file is UTF-8, and it must use a font that supports all the
characters in the file.


To declare an encoding other than the default one, a special comment line
should be added as the first line of the file. The syntax is as follows:


# -*- coding: encoding -*-


where encoding is one of the valid codecs supported by Python.


For example, to declare that Windows-1252 encoding is to be used, the first
line of your source code file should be:


# -*- coding: cp1252 -*-


One exception to the first line rule is when the source code starts with a
UNIX “shebang” line. In this case, the encoding
declaration should be added as the second line of the file. For example:


#!/usr/bin/env python3
# -*- coding: cp1252 -*-


Footnotes



1

On Unix, the Python 3.x interpreter is by default not installed with the
executable named python, so that it does not conflict with a
simultaneously installed Python 2.x executable.












Read article
3. An Informal Introduction to Python





3. An Informal Introduction to Python¶


In the following examples, input and output are distinguished by the presence or
absence of prompts (>>> and …): to repeat the example, you must type
everything after the prompt, when the prompt appears; lines that do not begin
with a prompt are output from the interpreter. Note that a secondary prompt on a
line by itself in an example means you must type a blank line; this is used to
end a multi-line command.


You can toggle the display of prompts and output by clicking on >>>
in the upper-right corner of an example box. If you hide the prompts
and output for an example, then you can easily copy and paste the input
lines into your interpreter.


Many of the examples in this manual, even those entered at the interactive
prompt, include comments. Comments in Python start with the hash character,
#, and extend to the end of the physical line. A comment may appear at the
start of a line or following whitespace or code, but not within a string
literal. A hash character within a string literal is just a hash character.
Since comments are to clarify code and are not interpreted by Python, they may
be omitted when typing in examples.


Some examples:


# this is the first comment
spam = 1 # and this is the second comment
# ... and now a third!
text = "# This is not a comment because it's inside quotes."



3.1. Using Python as a Calculator¶


Let’s try some simple Python commands. Start the interpreter and wait for the
primary prompt, >>>. (It shouldn’t take long.)



3.1.1. Numbers¶


The interpreter acts as a simple calculator: you can type an expression at it
and it will write the value. Expression syntax is straightforward: the
operators +, -, * and / work just like in most other languages
(for example, Pascal or C); parentheses (()) can be used for grouping.
For example:


>>> 2 + 2
4
>>> 50 - 5*6
20
>>> (50 - 5*6) / 4
5.0
>>> 8 / 5 # division always returns a floating point number
1.6


The integer numbers (e.g. 2, 4, 20) have type int,
the ones with a fractional part (e.g. 5.0, 1.6) have type
float. We will see more about numeric types later in the tutorial.


Division (/) always returns a float. To do floor division and
get an integer result you can use the // operator; to calculate
the remainder you can use %:


>>> 17 / 3  # classic division returns a float
5.666666666666667
>>>
>>> 17 // 3 # floor division discards the fractional part
5
>>> 17 % 3 # the % operator returns the remainder of the division
2
>>> 5 * 3 + 2 # floored quotient * divisor + remainder
17


With Python, it is possible to use the ** operator to calculate powers 1:


>>> 5 ** 2  # 5 squared
25
>>> 2 ** 7 # 2 to the power of 7
128


The equal sign (=) is used to assign a value to a variable. Afterwards, no
result is displayed before the next interactive prompt:


>>> width = 20
>>> height = 5 * 9
>>> width * height
900


If a variable is not “defined” (assigned a value), trying to use it will
give you an error:


>>> n  # try to access an undefined variable
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
NameError: name 'n' is not defined


There is full support for floating point; operators with mixed type operands
convert the integer operand to floating point:


>>> 4 * 3.75 - 1
14.0


In interactive mode, the last printed expression is assigned to the variable
_. This means that when you are using Python as a desk calculator, it is
somewhat easier to continue calculations, for example:


>>> tax = 12.5 / 100
>>> price = 100.50
>>> price * tax
12.5625
>>> price + _
113.0625
>>> round(_, 2)
113.06


This variable should be treated as read-only by the user. Don’t explicitly
assign a value to it — you would create an independent local variable with the
same name masking the built-in variable with its magic behavior.


In addition to int and float, Python supports other types of
numbers, such as Decimal and Fraction.
Python also has built-in support for complex numbers,
and uses the j or J suffix to indicate the imaginary part
(e.g. 3+5j).




3.1.2. Strings¶


Besides numbers, Python can also manipulate strings, which can be expressed
in several ways. They can be enclosed in single quotes ('...') or
double quotes ("...") with the same result 2. \ can be used
to escape quotes:


>>> 'spam eggs'  # single quotes
'spam eggs'
>>> 'doesn\'t' # use \' to escape the single quote...
"doesn't"
>>> "doesn't" # ...or use double quotes instead
"doesn't"
>>> '"Yes," they said.'
'"Yes," they said.'
>>> "\"Yes,\" they said."
'"Yes," they said.'
>>> '"Isn\'t," they said.'
'"Isn\'t," they said.'


In the interactive interpreter, the output string is enclosed in quotes and
special characters are escaped with backslashes. While this might sometimes
look different from the input (the enclosing quotes could change), the two
strings are equivalent. The string is enclosed in double quotes if
the string contains a single quote and no double quotes, otherwise it is
enclosed in single quotes. The print() function produces a more
readable output, by omitting the enclosing quotes and by printing escaped
and special characters:


>>> '"Isn\'t," they said.'
'"Isn\'t," they said.'
>>> print('"Isn\'t," they said.')
"Isn't," they said.
>>> s = 'First line.\nSecond line.' # \n means newline
>>> s # without print(), \n is included in the output
'First line.\nSecond line.'
>>> print(s) # with print(), \n produces a new line
First line.
Second line.


If you don’t want characters prefaced by \ to be interpreted as
special characters, you can use raw strings by adding an r before
the first quote:


>>> print('C:\some\name')  # here \n means newline!
C:\some
ame
>>> print(r'C:\some\name') # note the r before the quote
C:\some\name


There is one subtle aspect to raw strings: a raw string may not end in
an odd number of \ characters; see
the FAQ entry for more information
and workarounds.


String literals can span multiple lines. One way is using triple-quotes:
"""...""" or '''...'''. End of lines are automatically
included in the string, but it’s possible to prevent this by adding a \ at
the end of the line. The following example:


print("""\
Usage: thingy [OPTIONS]
-h Display this usage message
-H hostname Hostname to connect to
""")


produces the following output (note that the initial newline is not included):


Usage: thingy [OPTIONS]
-h Display this usage message
-H hostname Hostname to connect to


Strings can be concatenated (glued together) with the + operator, and
repeated with *:


>>> # 3 times 'un', followed by 'ium'
>>> 3 * 'un' + 'ium'
'unununium'


Two or more string literals (i.e. the ones enclosed between quotes) next
to each other are automatically concatenated.


>>> 'Py' 'thon'
'Python'


This feature is particularly useful when you want to break long strings:


>>> text = ('Put several strings within parentheses '
... 'to have them joined together.')
>>> text
'Put several strings within parentheses to have them joined together.'


This only works with two literals though, not with variables or expressions:


>>> prefix = 'Py'
>>> prefix 'thon' # can't concatenate a variable and a string literal
File "<stdin>", line 1
prefix 'thon'
^^^^^^
SyntaxError: invalid syntax
>>> ('un' * 3) 'ium'
File "<stdin>", line 1
('un' * 3) 'ium'
^^^^^
SyntaxError: invalid syntax


If you want to concatenate variables or a variable and a literal, use +:


>>> prefix + 'thon'
'Python'


Strings can be indexed (subscripted), with the first character having index 0.
There is no separate character type; a character is simply a string of size
one:


>>> word = 'Python'
>>> word[0] # character in position 0
'P'
>>> word[5] # character in position 5
'n'


Indices may also be negative numbers, to start counting from the right:


>>> word[-1]  # last character
'n'
>>> word[-2] # second-last character
'o'
>>> word[-6]
'P'


Note that since -0 is the same as 0, negative indices start from -1.


In addition to indexing, slicing is also supported. While indexing is used
to obtain individual characters, slicing allows you to obtain substring:


>>> word[0:2]  # characters from position 0 (included) to 2 (excluded)
'Py'
>>> word[2:5] # characters from position 2 (included) to 5 (excluded)
'tho'


Slice indices have useful defaults; an omitted first index defaults to zero, an
omitted second index defaults to the size of the string being sliced.


>>> word[:2]   # character from the beginning to position 2 (excluded)
'Py'
>>> word[4:] # characters from position 4 (included) to the end
'on'
>>> word[-2:] # characters from the second-last (included) to the end
'on'


Note how the start is always included, and the end always excluded. This
makes sure that s[:i] + s[i:] is always equal to s:


>>> word[:2] + word[2:]
'Python'
>>> word[:4] + word[4:]
'Python'


One way to remember how slices work is to think of the indices as pointing
between characters, with the left edge of the first character numbered 0.
Then the right edge of the last character of a string of n characters has
index n, for example:


 +---+---+---+---+---+---+
| P | y | t | h | o | n |
+---+---+---+---+---+---+
0 1 2 3 4 5 6
-6 -5 -4 -3 -2 -1


The first row of numbers gives the position of the indices 0…6 in the string;
the second row gives the corresponding negative indices. The slice from i to
j consists of all characters between the edges labeled i and j,
respectively.


For non-negative indices, the length of a slice is the difference of the
indices, if both are within bounds. For example, the length of word[1:3] is
2.


Attempting to use an index that is too large will result in an error:


>>> word[42]  # the word only has 6 characters
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
IndexError: string index out of range


However, out of range slice indexes are handled gracefully when used for
slicing:


>>> word[4:42]
'on'
>>> word[42:]
''


Python strings cannot be changed — they are immutable.
Therefore, assigning to an indexed position in the string results in an error:


>>> word[0] = 'J'
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
TypeError: 'str' object does not support item assignment
>>> word[2:] = 'py'
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
TypeError: 'str' object does not support item assignment


If you need a different string, you should create a new one:


>>> 'J' + word[1:]
'Jython'
>>> word[:2] + 'py'
'Pypy'


The built-in function len() returns the length of a string:


>>> s = 'supercalifragilisticexpialidocious'
>>> len(s)
34



See also



Text Sequence Type — str

Strings are examples of sequence types, and support the common
operations supported by such types.



String Methods

Strings support a large number of methods for
basic transformations and searching.



Formatted string literals

String literals that have embedded expressions.



Format String Syntax

Information about string formatting with str.format().



printf-style String Formatting

The old formatting operations invoked when strings are
the left operand of the % operator are described in more detail here.







3.1.3. Lists¶


Python knows a number of compound data types, used to group together other
values. The most versatile is the list, which can be written as a list of
comma-separated values (items) between square brackets. Lists might contain
items of different types, but usually the items all have the same type.


>>> squares = [1, 4, 9, 16, 25]
>>> squares
[1, 4, 9, 16, 25]


Like strings (and all other built-in sequence types), lists can be
indexed and sliced:


>>> squares[0]  # indexing returns the item
1
>>> squares[-1]
25
>>> squares[-3:] # slicing returns a new list
[9, 16, 25]


All slice operations return a new list containing the requested elements. This
means that the following slice returns a
shallow copy of the list:


>>> squares[:]
[1, 4, 9, 16, 25]


Lists also support operations like concatenation:


>>> squares + [36, 49, 64, 81, 100]
[1, 4, 9, 16, 25, 36, 49, 64, 81, 100]


Unlike strings, which are immutable, lists are a mutable
type, i.e. it is possible to change their content:


>>> cubes = [1, 8, 27, 65, 125]  # something's wrong here
>>> 4 ** 3 # the cube of 4 is 64, not 65!
64
>>> cubes[3] = 64 # replace the wrong value
>>> cubes
[1, 8, 27, 64, 125]


You can also add new items at the end of the list, by using
the append() method (we will see more about methods later):


>>> cubes.append(216)  # add the cube of 6
>>> cubes.append(7 ** 3) # and the cube of 7
>>> cubes
[1, 8, 27, 64, 125, 216, 343]


Assignment to slices is also possible, and this can even change the size of the
list or clear it entirely:


>>> letters = ['a', 'b', 'c', 'd', 'e', 'f', 'g']
>>> letters
['a', 'b', 'c', 'd', 'e', 'f', 'g']
>>> # replace some values
>>> letters[2:5] = ['C', 'D', 'E']
>>> letters
['a', 'b', 'C', 'D', 'E', 'f', 'g']
>>> # now remove them
>>> letters[2:5] = []
>>> letters
['a', 'b', 'f', 'g']
>>> # clear the list by replacing all the elements with an empty list
>>> letters[:] = []
>>> letters
[]


The built-in function len() also applies to lists:


>>> letters = ['a', 'b', 'c', 'd']
>>> len(letters)
4


It is possible to nest lists (create lists containing other lists), for
example:


>>> a = ['a', 'b', 'c']
>>> n = [1, 2, 3]
>>> x = [a, n]
>>> x
[['a', 'b', 'c'], [1, 2, 3]]
>>> x[0]
['a', 'b', 'c']
>>> x[0][1]
'b'





3.2. First Steps Towards Programming¶


Of course, we can use Python for more complicated tasks than adding two and two
together. For instance, we can write an initial sub-sequence of the
Fibonacci series
as follows:


>>> # Fibonacci series:
... # the sum of two elements defines the next
... a, b = 0, 1
>>> while a < 10:
... print(a)
... a, b = b, a+b
...
0
1
1
2
3
5
8


This example introduces several new features.



  • The first line contains a multiple assignment: the variables a and b
    simultaneously get the new values 0 and 1. On the last line this is used again,
    demonstrating that the expressions on the right-hand side are all evaluated
    first before any of the assignments take place. The right-hand side expressions
    are evaluated from the left to the right.


  • The while loop executes as long as the condition (here: a < 10)
    remains true. In Python, like in C, any non-zero integer value is true; zero is
    false. The condition may also be a string or list value, in fact any sequence;
    anything with a non-zero length is true, empty sequences are false. The test
    used in the example is a simple comparison. The standard comparison operators
    are written the same as in C: < (less than), > (greater than), ==
    (equal to), <= (less than or equal to), >= (greater than or equal to)
    and != (not equal to).


  • The body of the loop is indented: indentation is Python’s way of grouping
    statements. At the interactive prompt, you have to type a tab or space(s) for
    each indented line. In practice you will prepare more complicated input
    for Python with a text editor; all decent text editors have an auto-indent
    facility. When a compound statement is entered interactively, it must be
    followed by a blank line to indicate completion (since the parser cannot
    guess when you have typed the last line). Note that each line within a basic
    block must be indented by the same amount.


  • The print() function writes the value of the argument(s) it is given.
    It differs from just writing the expression you want to write (as we did
    earlier in the calculator examples) in the way it handles multiple arguments,
    floating point quantities, and strings. Strings are printed without quotes,
    and a space is inserted between items, so you can format things nicely, like
    this:


    >>> i = 256*256
    >>> print('The value of i is', i)
    The value of i is 65536


    The keyword argument end can be used to avoid the newline after the output,
    or end the output with a different string:


    >>> a, b = 0, 1
    >>> while a < 1000:
    ... print(a, end=',')
    ... a, b = b, a+b
    ...
    0,1,1,2,3,5,8,13,21,34,55,89,144,233,377,610,987,




Footnotes



1

Since ** has higher precedence than -, -3**2 will be
interpreted as -(3**2) and thus result in -9. To avoid this
and get 9, you can use (-3)**2.



2

Unlike other languages, special characters such as \n have the
same meaning with both single ('...') and double ("...") quotes.
The only difference between the two is that within single quotes you don’t
need to escape " (but you have to escape \') and vice versa.











Read article
4. More Control Flow Tools





4. More Control Flow Tools¶


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



4.1. if Statements¶


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


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


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


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




4.2. for Statements¶


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


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


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


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

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

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




4.3. The range() Function¶


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


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


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


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

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

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


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


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


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


A strange thing happens if you just print a range:


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


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


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


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


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




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


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


Loop statements may have an else clause; it is executed when the loop
terminates through exhaustion of the iterable (with for) or when the
condition becomes false (with while), but not when the loop is
terminated by a break statement. This is exemplified by the
following loop, which searches for prime numbers:


>>> for n in range(2, 10):
... for x in range(2, n):
... if n % x == 0:
... print(n, 'equals', x, '*', n//x)
... break
... else:
... # loop fell through without finding a factor
... print(n, 'is a prime number')
...
2 is a prime number
3 is a prime number
4 equals 2 * 2
5 is a prime number
6 equals 2 * 3
7 is a prime number
8 equals 2 * 4
9 equals 3 * 3


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


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


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


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




4.5. pass Statements¶


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


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


This is commonly used for creating minimal classes:


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


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


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




4.6. match Statements¶


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


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


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


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


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


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


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


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


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


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


class Point:
x: int
y: int

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


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


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


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


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


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


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


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


Several other key features of this statement:



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


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


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


  • Subpatterns may be captured using the as keyword:


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


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



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


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


    from enum import Enum
    class Color(Enum):
    RED = 'red'
    GREEN = 'green'
    BLUE = 'blue'

    color = Color(input("Enter your choice of 'red', 'blue' or 'green': "))

    match color:
    case Color.RED:
    print("I see red!")
    case Color.GREEN:
    print("Grass is green")
    case Color.BLUE:
    print("I'm feeling the blues :(")




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




4.7. Defining Functions¶


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


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


The keyword def introduces a function definition. It must be
followed by the function name and the parenthesized list of formal parameters.
The statements that form the body of the function start at the next line, and
must be indented.


The first statement of the function body can optionally be a string literal;
this string literal is the function’s documentation string, or docstring.
(More about docstrings can be found in the section Documentation Strings.)
There are tools which use docstrings to automatically produce online or printed
documentation, or to let the user interactively browse through code; it’s good
practice to include docstrings in code that you write, so make a habit of it.


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


The actual parameters (arguments) to a function call are introduced in the local
symbol table of the called function when it is called; thus, arguments are
passed using call by value (where the value is always an object reference,
not the value of the object). 1 When a function calls another function,
or calls itself recursively, a new
local symbol table is created for that call.


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


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


Coming from other languages, you might object that fib is not a function but
a procedure since it doesn’t return a value. In fact, even functions without a
return statement do return a value, albeit a rather boring one. This
value is called None (it’s a built-in name). Writing the value None is
normally suppressed by the interpreter if it would be the only value written.
You can see it if you really want to using print():


>>> fib(0)
>>> print(fib(0))
None


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


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


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



  • The return statement returns with a value from a function.
    return without an expression argument returns None. Falling off
    the end of a function also returns None.


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





4.8. More on Defining Functions¶


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



4.8.1. Default Argument Values¶


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


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


This function can be called in several ways:



  • giving only the mandatory argument:
    ask_ok('Do you really want to quit?')


  • giving one of the optional arguments:
    ask_ok('OK to overwrite the file?', 2)


  • or even giving all arguments:
    ask_ok('OK to overwrite the file?', 2, 'Come on, only yes or no!')



This example also introduces the in keyword. This tests whether or
not a sequence contains a certain value.


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


i = 5

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

i = 6
f()


will print 5.


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


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

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


This will print


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


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


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




4.8.2. Keyword Arguments¶


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


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


accepts one required argument (voltage) and three optional arguments
(state, action, and type). This function can be called in any
of the following ways:


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


but all the following calls would be invalid:


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


In a function call, keyword arguments must follow positional arguments.
All the keyword arguments passed must match one of the arguments
accepted by the function (e.g. actor is not a valid argument for the
parrot function), and their order is not important. This also includes
non-optional arguments (e.g. parrot(voltage=1000) is valid too).
No argument may receive a value more than once.
Here’s an example that fails due to this restriction:


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


When a final formal parameter of the form **name is present, it receives a
dictionary (see Mapping Types — dict) containing all keyword arguments except for
those corresponding to a formal parameter. This may be combined with a formal
parameter of the form *name (described in the next subsection) which
receives a tuple containing the positional
arguments beyond the formal parameter list. (*name must occur
before **name.) For example, if we define a function like this:


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


It could be called like this:


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


and of course it would print:


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


Note that the order in which the keyword arguments are printed is guaranteed
to match the order in which they were provided in the function call.




4.8.3. Special parameters¶


By default, arguments may be passed to a Python function either by position
or explicitly by keyword. For readability and performance, it makes sense to
restrict the way arguments can be passed so that a developer need only look
at the function definition to determine if items are passed by position, by
position or keyword, or by keyword.


A function definition may look like:


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


where / and * are optional. If used, these symbols indicate the kind of
parameter by how the arguments may be passed to the function:
positional-only, positional-or-keyword, and keyword-only. Keyword parameters
are also referred to as named parameters.



4.8.3.1. Positional-or-Keyword Arguments¶


If / and * are not present in the function definition, arguments may
be passed to a function by position or by keyword.




4.8.3.2. Positional-Only Parameters¶


Looking at this in a bit more detail, it is possible to mark certain parameters
as positional-only. If positional-only, the parameters’ order matters, and
the parameters cannot be passed by keyword. Positional-only parameters are
placed before a / (forward-slash). The / is used to logically
separate the positional-only parameters from the rest of the parameters.
If there is no / in the function definition, there are no positional-only
parameters.


Parameters following the / may be positional-or-keyword or keyword-only.




4.8.3.3. Keyword-Only Arguments¶


To mark parameters as keyword-only, indicating the parameters must be passed
by keyword argument, place an * in the arguments list just before the first
keyword-only parameter.




4.8.3.4. Function Examples¶


Consider the following example function definitions paying close attention to the
markers / and *:


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


The first function definition, standard_arg, the most familiar form,
places no restrictions on the calling convention and arguments may be
passed by position or keyword:


>>> standard_arg(2)
2

>>> standard_arg(arg=2)
2


The second function pos_only_arg is restricted to only use positional
parameters as there is a / in the function definition:


>>> pos_only_arg(1)
1

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


The third function kwd_only_args only allows keyword arguments as indicated
by a * in the function definition:


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

>>> kwd_only_arg(arg=3)
3


And the last uses all three calling conventions in the same function
definition:


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

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

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

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


Finally, consider this function definition which has a potential collision between the positional argument name and **kwds which has name as a key:


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


There is no possible call that will make it return True as the keyword 'name'
will always bind to the first parameter. For example:


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


But using / (positional only arguments), it is possible since it allows name as a positional argument and 'name' as a key in the keyword arguments:


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


In other words, the names of positional-only parameters can be used in
**kwds without ambiguity.




4.8.3.5. Recap¶


The use case will determine which parameters to use in the function definition:


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


As guidance:



  • Use positional-only if you want the name of the parameters to not be
    available to the user. This is useful when parameter names have no real
    meaning, if you want to enforce the order of the arguments when the function
    is called or if you need to take some positional parameters and arbitrary
    keywords.


  • Use keyword-only when names have meaning and the function definition is
    more understandable by being explicit with names or you want to prevent
    users relying on the position of the argument being passed.


  • For an API, use positional-only to prevent breaking API changes
    if the parameter’s name is modified in the future.






4.8.4. Arbitrary Argument Lists¶


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


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


Normally, these variadic arguments will be last in the list of formal
parameters, because they scoop up all remaining input arguments that are
passed to the function. Any formal parameters which occur after the *args
parameter are ‘keyword-only’ arguments, meaning that they can only be used as
keywords rather than positional arguments.


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




4.8.5. Unpacking Argument Lists¶


The reverse situation occurs when the arguments are already in a list or tuple
but need to be unpacked for a function call requiring separate positional
arguments. For instance, the built-in range() function expects separate
start and stop arguments. If they are not available separately, write the
function call with the *-operator to unpack the arguments out of a list
or tuple:


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


In the same fashion, dictionaries can deliver keyword arguments with the
**-operator:


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




4.8.6. Lambda Expressions¶


Small anonymous functions can be created with the lambda keyword.
This function returns the sum of its two arguments: lambda a, b: a+b.
Lambda functions can be used wherever function objects are required. They are
syntactically restricted to a single expression. Semantically, they are just
syntactic sugar for a normal function definition. Like nested function
definitions, lambda functions can reference variables from the containing
scope:


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


The above example uses a lambda expression to return a function. Another use
is to pass a small function as an argument:


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




4.8.7. Documentation Strings¶


Here are some conventions about the content and formatting of documentation
strings.


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


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


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


Here is an example of a multi-line docstring:


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

No, really, it doesn't do anything.




4.8.8. Function Annotations¶


Function annotations are completely optional metadata
information about the types used by user-defined functions (see PEP 3107 and
PEP 484 for more information).


Annotations are stored in the __annotations__
attribute of the function as a dictionary and have no effect on any other part of the
function. Parameter annotations are defined by a colon after the parameter name, followed
by an expression evaluating to the value of the annotation. Return annotations are
defined by a literal ->, followed by an expression, between the parameter
list and the colon denoting the end of the def statement. The
following example has a required argument, an optional argument, and the return
value annotated:


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





4.9. Intermezzo: Coding Style¶


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


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



  • Use 4-space indentation, and no tabs.


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



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


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



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


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


  • Use docstrings.


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


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


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


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



Footnotes



1

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











Read article
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
objects:




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')
2
>>> fruits.count('tangerine')
0
>>> fruits.index('banana')
3
>>> fruits.index('banana', 4) # Find next banana starting at position 4
6
>>> 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()
'pear'


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
Python.


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
comparison.



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()
7
>>> stack
[3, 4, 5, 6]
>>> stack.pop()
6
>>> stack.pop()
5
>>> 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
'Eric'
>>> queue.popleft() # The second to arrive now leaves
'John'
>>> 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
equal:


>>> [(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]
12345
>>> 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)
0
>>> len(singleton)
1
>>> singleton
('hello',)


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
True
>>> 'crabgrass' in basket
False

>>> # 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']
4098
>>> 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
True
>>> 'jack' not in tel
False


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)
...
9
7
5
3
1


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)
...
apple
apple
banana
orange
orange
pear


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)
...
apple
banana
orange
pear


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
'Trondheim'


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.


Footnotes



1

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











Read article
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
print()

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


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


>>> 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__
'fibo'


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



Note


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;
importlib.reload(modulename)
.




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
fib(int(sys.argv[1]))


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.



Note


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
module.


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
    directory.


  • 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
prompts:


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


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
operations:


>>> 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',
'warnoptions']


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',
'zip']




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
__init__.py
wavread.py
wavwrite.py
aiffread.py
aiffwrite.py
auread.py
auwrite.py
...
effects/ Subpackage for sound effects
__init__.py
echo.py
surround.py
reverse.py
...
filters/ Subpackage for filters
__init__.py
equalizer.py
vocoder.py
karaoke.py
...


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
example:


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
item.



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
code:


__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
specific_submodule
! In fact, this is the recommended notation unless the
importing module needs to use submodules with the same name from different
packages.




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.


Footnotes



1

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)
'0.14285714285714285'
>>> 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 **
notation.


>>> 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)
'00012'
>>> '-3.14'.zfill(7)
'-003.14'
>>> '3.14159265359'.zfill(5)
'3.14159265359'




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
omitted.


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
True


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.



Warning


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')
15


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)
18


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')
16
>>> f.seek(5) # Go to the 6th byte in the file
5
>>> f.read(1)
b'5'
>>> f.seek(-3, 2) # Go to the 3rd byte before the end
13
>>> f.read(1)
b'd'


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.



Note


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)



Note


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
    clause
    , 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):
pass

class C(B):
pass

class D(C):
pass

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

try:
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)=}")
raise


The try … except statement has an optional else
clause
, 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:]:
try:
f = open(arg, 'r')
except OSError:
print('cannot open', arg)
else:
print(arg, 'has', len(f.readlines()), 'lines')
f.close()


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
message:


>>> 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
ConnectionError

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
idiom:


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


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
example:


>>> try:
... raise KeyboardInterrupt
... finally:
... print('Goodbye, world!')
...
Goodbye, world!
KeyboardInterrupt
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
    re-raised.


  • 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
    statement.



For example:


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


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
+------------------------------------
>>>









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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
programmer.


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
binding:


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"
do_local()
print("After local assignment:", spam)
do_nonlocal()
print("After nonlocal assignment:", spam)
do_global()
print("After global assignment:", spam)

scope_test()
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
binding.


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:
<statement-1>
.
.
.
<statement-N>


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
example).




9.3.2. Class Objects¶


Class objects support two kinds of operations: attribute references and
instantiation.


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
print(x.counter)
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:


x.f()


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:
print(xf())


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
'canine'
>>> e.kind # shared by all dogs
'canine'
>>> d.name # unique to d
'Fido'
>>> e.name # unique to e
'Buddy'


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
instances:


class Dog:

tricks = [] # mistaken use of a class variable

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

def add_trick(self, trick):
self.tricks.append(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):
self.tricks.append(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
argument:


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

def add(self, x):
self.data.append(x)

def addtwice(self, x):
self.add(x)
self.add(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
this:


class DerivedClassName(BaseClassName):
<statement-1>
.
.
.
<statement-N>


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,
arguments)
. This is occasionally useful to clients as well. (Note that this
only works if the base class is accessible as BaseClassName in the global
scope.)


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):
<statement-1>
.
.
.
<statement-N>


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
https://www.python.org/download/releases/2.3/mro/.





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 = []
self.__update(iterable)

def update(self, iterable):
for item in iterable:
self.items_list.append(item)

__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):
self.items_list.append(item)


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

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


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


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]:
print(element)
for element in (1, 2, 3):
print(element)
for key in {'one':1, 'two':2}:
print(key)
for char in "123":
print(char)
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)
'a'
>>> next(it)
'b'
>>> next(it)
'c'
>>> next(it)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
next(it)
StopIteration


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)
...
m
a
p
s




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)
...
f
l
o
g


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.


Examples:


>>> sum(i*i for i in range(10))                 # sum of squares
285

>>> xvec = [10, 20, 30]
>>> yvec = [7, 5, 3]
>>> sum(x*y for x,y in zip(xvec, yvec)) # dot product
260

>>> 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']


Footnotes



1

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.











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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
'C:\\Python311'
>>> os.chdir('/server/accesslogs') # Change current working directory
>>> os.system('mkdir today') # Run the command mkdir in the system shell
0


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')
'archive.db'
>>> shutil.move('/build/executables', 'installdir')
'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
three
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(
prog='top',
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()
print(args)


When run at the command line with python top.py --lines=5 alpha.txt
beta.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)
0.70710678118654757
>>> math.log(1024, 2)
10.0


The random module provides tools for making random selections:


>>> import random
>>> random.choice(['apple', 'pear', 'banana'])
'apple'
>>> random.sample(range(100), 10) # sampling without replacement
[30, 83, 16, 4, 8, 81, 41, 50, 18, 33]
>>> random.random() # random float
0.17970987693706186
>>> random.randrange(6) # random integer chosen from range(6)
4


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)
1.6071428571428572
>>> statistics.median(data)
1.25
>>> statistics.variance(data)
1.3720238095238095


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




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
aware.


>>> # 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
14368




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)
41
>>> t = zlib.compress(s)
>>> len(t)
37
>>> zlib.decompress(t)
b'witch which has which witches wrist watch'
>>> zlib.crc32(s)
226805979




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()
0.57535828626024577
>>> Timer('a,b = b,a', 'a=1; b=2').timeit()
0.54962537085770791


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
documentation:


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

>>> print(average([20, 30, 70]))
40.0
"""
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
file:


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):
average([])
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
    protocols.


  • 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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