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, Mac OS X, 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.
The Python interpreter is usually installed as /usr/local/bin/python3.8
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:
python3.8
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 python3.8 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 with single quotes.
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.
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.
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:
$ python3.8 Python 3.8 (default, Sep 16 2015, 09:25:04) [GCC 4.8.2] 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.
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 -*-
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.
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."
Let's try some simple Python commands.
Start the interpreter and wait for the
primary prompt, >>>.
(It shouldn't take long.)
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 (discarding any fractional 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 # result * 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).
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
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'
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'
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'
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
Strings are examples of sequence types, and support the common operations supported by such types.
Strings support a large number of methods for basic transformations and searching.
String literals that have embedded expressions.
Information about string formatting with str.format().
The old formatting operations invoked when strings are
the left operand of the % operator are described in more detail here.
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'
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
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.
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.
Besides the while statement just introduced, Python uses the usual
flow control statements known from other languages, with some twists.
if StatementsPerhaps 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.
for StatementsThe 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:
# 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
range() FunctionIf 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'):
range(5, 10) 5, 6, 7, 8, 9 range(0, 10, 3) 0, 3, 6, 9 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:
>>> print(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 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.
Lastly, maybe you are curious about how to get a list from a range.
Here is the solution:
>>> list(range(4)) [0, 1, 2, 3]
In chapter Data Structures, we will discuss in more detail about
list().
break and continue Statements, and else Clauses on LoopsThe 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 a number", num) Found an even number 2 Found a number 3 Found an even number 4 Found a number 5 Found an even number 6 Found a number 7 Found an even number 8 Found a number 9
pass StatementsThe 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! ...
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, a new
local symbol table is created for that call.
A function definition introduces the function name in the current symbol table.
The value of the function name has a type that is recognized by the interpreter
as a user-defined function.
This value can be assigned to another name which
can then also be used as a function.
This serves as a general renaming
mechanism:
>>> 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.
It is also possible to define functions with a variable number of arguments. There are three forms, which can be combined.
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
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 keyword 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.
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.
If / and * are not present in the function definition, arguments may
be passed to a function by position or by keyword.
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.
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.
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 an unexpected keyword argument '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 an unexpected keyword argument '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 to 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.
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.
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'
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 !
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')]
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.
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 positional argument, a keyword 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'
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
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).
This chapter describes some things you've learned about already in more detail, and adds some new things as well.
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 a 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.
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]
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'])
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, in <module> [x, x**2 for x in range(6)] ^ SyntaxError: invalid syntax >>> # 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']
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 nested listcomp 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.
del statementThere 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.
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.
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'}
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}
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 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]
The conditions used in while and if statements can contain any
operators, not just comparisons.
The comparison operators in and not in check whether a value occurs
(does not occur) in a sequence.
The operators is and is not compare
whether two objects are really the same object; this only matters for mutable
objects like lists.
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.
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
Other languages may return the mutated object, which allows method
chaining, such as d->insert("a")->remove("b")->sort();.
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 enter the names of the functions defined in fibo directly in
the current symbol table; it only enters 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
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 symbol table, which is used as the global symbol
table 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 are placed in the importing module's global
symbol table.
There is a variant of the import statement that imports names from a
module directly into the importing module's symbol table.
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 symbol table (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).
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).
When a module named spam is imported, the interpreter first searches for
a built-in module with that name.
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.
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.
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.
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')
dir() FunctionThe 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) ['__displayhook__', '__doc__', '__excepthook__', '__loader__', '__name__', '__package__', '__stderr__', '__stdin__', '__stdout__', '_clear_type_cache', '_current_frames', '_debugmallocstats', '_getframe', '_home', '_mercurial', '_xoptions', 'abiflags', 'api_version', 'argv', 'base_exec_prefix', 'base_prefix', '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', 'getcheckinterval', 'getdefaultencoding', 'getdlopenflags', 'getfilesystemencoding', 'getobjects', 'getprofile', 'getrecursionlimit', 'getrefcount', 'getsizeof', 'getswitchinterval', 'gettotalrefcount', 'gettrace', 'hash_info', 'hexversion', 'implementation', 'int_info', 'intern', 'maxsize', 'maxunicode', 'meta_path', 'modules', 'path', 'path_hooks', 'path_importer_cache', 'platform', 'prefix', 'ps1', 'setcheckinterval', 'setdlopenflags', 'setprofile', 'setrecursionlimit', 'setswitchinterval', 'settrace', 'stderr', 'stdin', 'stdout', 'thread_info', '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']
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.
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 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.
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.
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
In fact function definitions are also ‘statements' that are ‘executed'; the execution of a module-level function definition enters the function name in the module's global symbol table.
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.
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.
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'.
For a reference on these format specifications, see the reference guide for the Format Specification Mini-Language.
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 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.
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'
The % operator can also be used for string formatting.
It interprets the
left argument much like a sprintf()-style format string to be applied
to the right argument, and returns the string resulting from this formatting
operation.
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.
open() returns a file object, and is most commonly used with
two arguments: open(filename, mode).
>>> f = open('workfile', 'w')
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()). 'b' appended to the mode opens the file in
binary mode: now the data is read and written in the form of bytes
objects.
This mode should be used for all files that don't contain text.
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') 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.
If you don't explicitly close a file, Python's
garbage collector will eventually destroy the object and close the
open file for you, but the file may stay open for a while.
Another
risk is that different Python implementations will do this clean-up at
different times.
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.
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.
jsonStrings 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 >>> json.dumps([1, 'simple', 'list']) '[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 text file object which has
been opened for reading:
x = json.load(f)
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.
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.
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.
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't convert 'int' object to str implicitly
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
happened, 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.
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 statement.
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.
The last except clause may omit the exception name(s), to serve as a wildcard. Use this with extreme caution, since it is easy to mask a real programming error in this way! It can also be used to print an error message and then re-raise the exception (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: {0}".format(err)) except ValueError: print("Could not convert data to an integer.") except: print("Unexpected error:", sys.exc_info()[0]) 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.
When an exception occurs, it may have an associated value, also known as the
exception's argument.
The presence and type of the argument depend on the
exception type.
The except clause may specify a variable after the exception name.
The
variable is bound to an exception instance with the arguments stored in
instance.args.
For convenience, the exception instance defines
__str__() so the arguments can be printed directly without having to
reference .args.
One may also instantiate an exception first before
raising it and add any attributes to it as desired.
>>> 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
If an exception has arguments, they are printed as the last part (‘detail') of the message for unhandled exceptions.
Exception handlers don't just handle exceptions if they occur immediately in the
try clause, but also if they 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
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 Exception).
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
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.
When
creating a module that can raise several distinct errors, a common practice is
to create a base class for exceptions defined by that module, and subclass that
to create specific exception classes for different error conditions:
class Error(Exception): """Base class for exceptions in this module.""" pass class InputError(Error): """Exception raised for errors in the input. Attributes: expression -- input expression in which the error occurred message -- explanation of the error """ def __init__(self, expression, message): self.expression = expression self.message = message class TransitionError(Error): """Raised when an operation attempts a state transition that's not allowed. Attributes: previous -- state at beginning of transition next -- attempted new state message -- explanation of why the specific transition is not allowed """ def __init__(self, previous, next, message): self.previous = previous self.next = next self.message = message
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.
More information on classes is presented in
chapter Classes.
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 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 finally clause's return statement will execute before, and instead of, the return statement in a try clause.
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.
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.
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.)
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.
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 at least three 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, contains 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 middle 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 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.
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.
Classes introduce a little bit of new syntax, three new object types, and some new semantics.
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).
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)
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.
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.
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']
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__.
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.
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/.
“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.
Sometimes it is useful to have a data type similar to the Pascal “record” or C
“struct”, bundling together a few named data items.
An empty class definition
will do nicely:
class Employee: pass john = Employee() # Create an empty employee record # Fill the fields of the record john.name = 'John Doe' 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.
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 <iterator object at 0x00A1DB50> >>> 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
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.
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
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.
The os module provides dozens of functions for interacting with the
operating system:
>>> import os >>> os.getcwd() # Return the current working directory 'C:\\Python38' >>> 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'
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']
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'].
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().
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'
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.
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://tycho.usno.navy.mil/cgi-bin/timer.pl') as response: ... for line in response: ... line = line.decode('utf-8') # Decoding the binary data to text. ... if 'EST' in line or 'EDT' in line: # look for Eastern Time ... print(line) <BR>Nov.
25, 09:43:32 PM EST >>> 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.)
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
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
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.
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
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.
This second tour covers more advanced modules that support professional
programming needs.
These modules rarely occur in small scripts.
The reprlib module provides a version of repr() customized for
abbreviated displays of large or deeply nested containers:
>>> import reprlib >>> reprlib.repr(set('supercalifragilisticexpialidocious')) "{'a', 'c', 'd', 'e', 'f', 'g', ...}"
The pprint module offers more sophisticated control over printing both
built-in and user defined objects in a way that is readable by the interpreter.
When the result is longer than one line, the “pretty printer” adds line breaks
and indentation to more clearly reveal data structure:
>>> import pprint >>> t = [[[['black', 'cyan'], 'white', ['green', 'red']], [['magenta', ... 'yellow'], 'blue']]] ... >>> pprint.pprint(t, width=30) [[[['black', 'cyan'], 'white', ['green', 'red']], [['magenta', 'yellow'], 'blue']]]
The textwrap module formats paragraphs of text to fit a given screen
width:
>>> import textwrap >>> doc = """The wrap() method is just like fill() except that it returns ...a list of strings instead of one big string with newlines to separate ...the wrapped lines.""" ... >>> print(textwrap.fill(doc, width=40)) The wrap() method is just like fill() except that it returns a list of strings instead of one big string with newlines to separate the wrapped lines.
The locale module accesses a database of culture specific data formats.
The grouping attribute of locale's format function provides a direct way of
formatting numbers with group separators:
>>> import locale >>> locale.setlocale(locale.LC_ALL, 'English_United States.1252') 'English_United States.1252' >>> conv = locale.localeconv() # get a mapping of conventions >>> x = 1234567.8 >>> locale.format("%d", x, grouping=True) '1,234,567' >>> locale.format_string("%s%.*f", (conv['currency_symbol'], ... conv['frac_digits'], x), grouping=True) '$1,234,567.80'
The string module includes a versatile Template class
with a simplified syntax suitable for editing by end-users.
This allows users
to customize their applications without having to alter the application.
The format uses placeholder names formed by $ with valid Python identifiers
(alphanumeric characters and underscores).
Surrounding the placeholder with
braces allows it to be followed by more alphanumeric letters with no intervening
spaces.
Writing $$ creates a single escaped $:
>>> from string import Template >>> t = Template('${village}folk send $$10 to $cause.') >>> t.substitute(village='Nottingham', cause='the ditch fund') 'Nottinghamfolk send $10 to the ditch fund.'
The substitute() method raises a KeyError when a
placeholder is not supplied in a dictionary or a keyword argument.
For
mail-merge style applications, user supplied data may be incomplete and the
safe_substitute() method may be more appropriate —
it will leave placeholders unchanged if data is missing:
>>> t = Template('Return the $item to $owner.') >>> d = dict(item='unladen swallow') >>> t.substitute(d) Traceback (most recent call last): ... KeyError: 'owner' >>> t.safe_substitute(d) 'Return the unladen swallow to $owner.'
Template subclasses can specify a custom delimiter.
For example, a batch
renaming utility for a photo browser may elect to use percent signs for
placeholders such as the current date, image sequence number, or file format:
>>> import time, os.path >>> photofiles = ['img_1074.jpg', 'img_1076.jpg', 'img_1077.jpg'] >>> class BatchRename(Template): ... delimiter = '%' >>> fmt = input('Enter rename style (%d-date %n-seqnum %f-format): ') Enter rename style (%d-date %n-seqnum %f-format): Ashley_%n%f >>> t = BatchRename(fmt) >>> date = time.strftime('%d%b%y') >>> for i, filename in enumerate(photofiles): ... base, ext = os.path.splitext(filename) ... newname = t.substitute(d=date, n=i, f=ext) ... print('{0} --> {1}'.format(filename, newname)) img_1074.jpg --> Ashley_0.jpg img_1076.jpg --> Ashley_1.jpg img_1077.jpg --> Ashley_2.jpg
Another application for templating is separating program logic from the details
of multiple output formats.
This makes it possible to substitute custom
templates for XML files, plain text reports, and HTML web reports.
The struct module provides pack() and
unpack() functions for working with variable length binary
record formats.
The following example shows
how to loop through header information in a ZIP file without using the
zipfile module.
Pack codes "H" and "I" represent two and four
byte unsigned numbers respectively.
The "<" indicates that they are
standard size and in little-endian byte order:
import struct with open('myfile.zip', 'rb') as f: data = f.read() start = 0 for i in range(3): # show the first 3 file headers start += 14 fields = struct.unpack('<IIIHH', data[start:start+16]) crc32, comp_size, uncomp_size, filenamesize, extra_size = fields start += 16 filename = data[start:start+filenamesize] start += filenamesize extra = data[start:start+extra_size] print(filename, hex(crc32), comp_size, uncomp_size) start += extra_size + comp_size # skip to the next header
Threading is a technique for decoupling tasks which are not sequentially
dependent.
Threads can be used to improve the responsiveness of applications
that accept user input while other tasks run in the background.
A related use
case is running I/O in parallel with computations in another thread.
The following code shows how the high level threading module can run
tasks in background while the main program continues to run:
import threading, zipfile class AsyncZip(threading.Thread): def __init__(self, infile, outfile): threading.Thread.__init__(self) self.infile = infile self.outfile = outfile def run(self): f = zipfile.ZipFile(self.outfile, 'w', zipfile.ZIP_DEFLATED) f.write(self.infile) f.close() print('Finished background zip of:', self.infile) background = AsyncZip('mydata.txt', 'myarchive.zip') background.start() print('The main program continues to run in foreground.') background.join() # Wait for the background task to finish print('Main program waited until background was done.')
The principal challenge of multi-threaded applications is coordinating threads
that share data or other resources.
To that end, the threading module provides
a number of synchronization primitives including locks, events, condition
variables, and semaphores.
While those tools are powerful, minor design errors can result in problems that
are difficult to reproduce.
So, the preferred approach to task coordination is
to concentrate all access to a resource in a single thread and then use the
queue module to feed that thread with requests from other threads.
Applications using Queue objects for inter-thread communication and
coordination are easier to design, more readable, and more reliable.
The logging module offers a full featured and flexible logging system.
At its simplest, log messages are sent to a file or to sys.stderr:
import logging logging.debug('Debugging information') logging.info('Informational message') logging.warning('Warning:config file %s not found', 'server.conf') logging.error('Error occurred') logging.critical('Critical error -- shutting down')
This produces the following output:
WARNING:root:Warning:config file server.conf not found ERROR:root:Error occurred CRITICAL:root:Critical error -- shutting down
By default, informational and debugging messages are suppressed and the output
is sent to standard error.
Other output options include routing messages
through email, datagrams, sockets, or to an HTTP Server.
New filters can select
different routing based on message priority: DEBUG,
INFO, WARNING, ERROR,
and CRITICAL.
The logging system can be configured directly from Python or can be loaded from a user editable configuration file for customized logging without altering the application.
Python does automatic memory management (reference counting for most objects and
garbage collection to eliminate cycles).
The memory is freed shortly
after the last reference to it has been eliminated.
This approach works fine for most applications but occasionally there is a need
to track objects only as long as they are being used by something else.
Unfortunately, just tracking them creates a reference that makes them permanent.
The weakref module provides tools for tracking objects without creating a
reference.
When the object is no longer needed, it is automatically removed
from a weakref table and a callback is triggered for weakref objects.
Typical
applications include caching objects that are expensive to create:
>>> import weakref, gc >>> class A: ... def __init__(self, value): ... self.value = value ... def __repr__(self): ... return str(self.value) ... >>> a = A(10) # create a reference >>> d = weakref.WeakValueDictionary() >>> d['primary'] = a # does not create a reference >>> d['primary'] # fetch the object if it is still alive 10 >>> del a # remove the one reference >>> gc.collect() # run garbage collection right away 0 >>> d['primary'] # entry was automatically removed Traceback (most recent call last): File "<stdin>", line 1, in <module> d['primary'] # entry was automatically removed File "C:/python38/lib/weakref.py", line 46, in __getitem__ o = self.data[key]() KeyError: 'primary'
Many data structure needs can be met with the built-in list type.
However,
sometimes there is a need for alternative implementations with different
performance trade-offs.
The array module provides an array() object that is like
a list that stores only homogeneous data and stores it more compactly.
The
following example shows an array of numbers stored as two byte unsigned binary
numbers (typecode "H") rather than the usual 16 bytes per entry for regular
lists of Python int objects:
>>> from array import array >>> a = array('H', [4000, 10, 700, 22222]) >>> sum(a) 26932 >>> a[1:3] array('H', [10, 700])
The collections module provides a deque() object
that is like a list with faster appends and pops from the left side but slower
lookups in the middle.
These objects are well suited for implementing queues
and breadth first tree searches:
>>> from collections import deque >>> d = deque(["task1", "task2", "task3"]) >>> d.append("task4") >>> print("Handling", d.popleft()) Handling task1
unsearched = deque([starting_node]) def breadth_first_search(unsearched): node = unsearched.popleft() for m in gen_moves(node): if is_goal(m): return m unsearched.append(m)
In addition to alternative list implementations, the library also offers other
tools such as the bisect module with functions for manipulating sorted
lists:
>>> import bisect >>> scores = [(100, 'perl'), (200, 'tcl'), (400, 'lua'), (500, 'python')] >>> bisect.insort(scores, (300, 'ruby')) >>> scores [(100, 'perl'), (200, 'tcl'), (300, 'ruby'), (400, 'lua'), (500, 'python')]
The heapq module provides functions for implementing heaps based on
regular lists.
The lowest valued entry is always kept at position zero.
This
is useful for applications which repeatedly access the smallest element but do
not want to run a full list sort:
>>> from heapq import heapify, heappop, heappush >>> data = [1, 3, 5, 7, 9, 2, 4, 6, 8, 0] >>> heapify(data) # rearrange the list into heap order >>> heappush(data, -5) # add a new entry >>> [heappop(data) for i in range(3)] # fetch the three smallest entries [-5, 0, 1]
The decimal module offers a Decimal datatype for
decimal floating point arithmetic.
Compared to the built-in float
implementation of binary floating point, the class is especially helpful for
financial applications and other uses which require exact decimal representation,
control over precision,
control over rounding to meet legal or regulatory requirements,
tracking of significant decimal places, or
applications where the user expects the results to match calculations done by hand.
For example, calculating a 5% tax on a 70 cent phone charge gives different
results in decimal floating point and binary floating point.
The difference
becomes significant if the results are rounded to the nearest cent:
>>> from decimal import * >>> round(Decimal('0.70') * Decimal('1.05'), 2) Decimal('0.74') >>> round(.70 * 1.05, 2) 0.73
The Decimal result keeps a trailing zero, automatically
inferring four place significance from multiplicands with two place
significance.
Decimal reproduces mathematics as done by hand and avoids
issues that can arise when binary floating point cannot exactly represent
decimal quantities.
Exact representation enables the Decimal class to perform
modulo calculations and equality tests that are unsuitable for binary floating
point:
>>> Decimal('1.00') % Decimal('.10') Decimal('0.00') >>> 1.00 % 0.10 0.09999999999999995 >>> sum([Decimal('0.1')]*10) == Decimal('1.0') True >>> sum([0.1]*10) == 1.0 False
The decimal module provides arithmetic with as much precision as needed:
>>> getcontext().prec = 36 >>> Decimal(1) / Decimal(7) Decimal('0.142857142857142857142857142857142857')
Python applications will often use packages and modules that don't
come as part of the standard library.
Applications will sometimes
need a specific version of a library, because the application may
require that a particular bug has been fixed or the application may be
written using an obsolete version of the library's interface.
This means it may not be possible for one Python installation to meet
the requirements of every application.
If application A needs version
1.0 of a particular module but application B needs version 2.0, then
the requirements are in conflict and installing either version 1.0 or 2.0
will leave one application unable to run.
The solution for this problem is to create a virtual environment, a self-contained directory tree that contains a Python installation for a particular version of Python, plus a number of additional packages.
Different applications can then use different virtual environments. To resolve the earlier example of conflicting requirements, application A can have its own virtual environment with version 1.0 installed while application B has another virtual environment with version 2.0. If application B requires a library be upgraded to version 3.0, this will not affect application A's environment.
The module used to create and manage virtual environments is called
venv. venv will usually install the most recent version of
Python that you have available.
If you have multiple versions of Python on your
system, you can select a specific Python version by running python3 or
whichever version you want.
To create a virtual environment, decide upon a directory where you want to
place it, and run the venv module as a script with the directory path:
python3 -m venv tutorial-env
This will create the tutorial-env directory if it doesn't exist,
and also create directories inside it containing a copy of the Python
interpreter, the standard library, and various supporting files.
A common directory location for a virtual environment is .venv.
This name keeps the directory typically hidden in your shell and thus
out of the way while giving it a name that explains why the directory
exists.
It also prevents clashing with .env environment variable
definition files that some tooling supports.
Once you've created a virtual environment, you may activate it.
On Windows, run:
tutorial-env\Scripts\activate.bat
On Unix or MacOS, run:
source tutorial-env/bin/activate
(This script is written for the bash shell.
If you use the
csh or fish shells, there are alternate
activate.csh and activate.fish scripts you should use
instead.)
Activating the virtual environment will change your shell's prompt to show what
virtual environment you're using, and modify the environment so that running
python will get you that particular version and installation of Python.
For example:
$ source ~/envs/tutorial-env/bin/activate (tutorial-env) $ python Python 3.5.1 (default, May 6 2016, 10:59:36) ... >>> import sys >>> sys.path ['', '/usr/local/lib/python35.zip', ..., '~/envs/tutorial-env/lib/python3.5/site-packages'] >>>
You can install, upgrade, and remove packages using a program called
pip.
By default pip will install packages from the Python
Package Index, <https://pypi.org>.
You can browse the Python
Package Index by going to it in your web browser, or you can use pip's
limited search feature:
(tutorial-env) $ pip search astronomy skyfield - Elegant astronomy for Python gary - Galactic astronomy and gravitational dynamics. novas - The United States Naval Observatory NOVAS astronomy library astroobs - Provides astronomy ephemeris to plan telescope observations PyAstronomy - A collection of astronomy related tools for Python. ...
pip has a number of subcommands: “search”, “install”, “uninstall”,
“freeze”, etc.
(Consult the Installing Python Modules guide for
complete documentation for pip.)
You can install the latest version of a package by specifying a package's name:
(tutorial-env) $ pip install novas Collecting novas Downloading novas-3.1.1.3.tar.gz (136kB) Installing collected packages: novas Running setup.py install for novas Successfully installed novas-3.1.1.3
You can also install a specific version of a package by giving the
package name followed by == and the version number:
(tutorial-env) $ pip install requests==2.6.0 Collecting requests==2.6.0 Using cached requests-2.6.0-py2.py3-none-any.whl Installing collected packages: requests Successfully installed requests-2.6.0
If you re-run this command, pip will notice that the requested
version is already installed and do nothing.
You can supply a
different version number to get that version, or you can run pip
install --upgrade to upgrade the package to the latest version:
(tutorial-env) $ pip install --upgrade requests Collecting requests Installing collected packages: requests Found existing installation: requests 2.6.0 Uninstalling requests-2.6.0: Successfully uninstalled requests-2.6.0 Successfully installed requests-2.7.0
pip uninstall followed by one or more package names will remove the
packages from the virtual environment.
pip show will display information about a particular package:
(tutorial-env) $ pip show requests --- Metadata-Version: 2.0 Name: requests Version: 2.7.0 Summary: Python HTTP for Humans. Home-page: http://python-requests.org Author: Kenneth Reitz Author-email: me@kennethreitz.com License: Apache 2.0 Location: /Users/akuchling/envs/tutorial-env/lib/python3.4/site-packages Requires:
pip list will display all of the packages installed in the virtual
environment:
(tutorial-env) $ pip list novas (3.1.1.3) numpy (1.9.2) pip (7.0.3) requests (2.7.0) setuptools (16.0)
pip freeze will produce a similar list of the installed packages,
but the output uses the format that pip install expects.
A common convention is to put this list in a requirements.txt file:
(tutorial-env) $ pip freeze > requirements.txt (tutorial-env) $ cat requirements.txt novas==3.1.1.3 numpy==1.9.2 requests==2.7.0
The requirements.txt can then be committed to version control and
shipped as part of an application.
Users can then install all the
necessary packages with install -r:
(tutorial-env) $ pip install -r requirements.txt Collecting novas==3.1.1.3 (from -r requirements.txt (line 1)) ... Collecting numpy==1.9.2 (from -r requirements.txt (line 2)) ... Collecting requests==2.7.0 (from -r requirements.txt (line 3)) ... Installing collected packages: novas, numpy, requests Running setup.py install for novas Successfully installed novas-3.1.1.3 numpy-1.9.2 requests-2.7.0
pip has many more options.
Consult the Installing Python Modules
guide for complete documentation for pip.
When you've written
a package and want to make it available on the Python Package Index,
consult the Distributing Python Modules guide.
Reading this tutorial has probably reinforced your interest in using Python —
you should be eager to apply Python to solving your real-world problems.
Where
should you go to learn more?
This tutorial is part of Python's documentation set.
Some other documents in
the set are:
You should browse through this manual, which gives complete (though terse)
reference material about types, functions, and the modules in the standard
library.
The standard Python distribution includes a lot of additional code.
There are modules to read Unix mailboxes, retrieve documents via HTTP, generate
random numbers, parse command-line options, write CGI programs, compress data,
and many other tasks.
Skimming through the Library Reference will give you an
idea of what's available.
Installing Python Modules explains how to install additional modules written by other Python users.
The Python Language Reference: A detailed explanation of Python's syntax and
semantics.
It's heavy reading, but is useful as a complete guide to the
language itself.
More Python resources:
https://www.python.org: The major Python Web site.
It contains code,
documentation, and pointers to Python-related pages around the Web.
This Web
site is mirrored in various places around the world, such as Europe, Japan, and
Australia; a mirror may be faster than the main site, depending on your
geographical location.
https://docs.python.org: Fast access to Python's documentation.
https://pypi.org: The Python Package Index, previously also nicknamed
the Cheese Shop 1, is an index of user-created Python modules that are available
for download.
Once you begin releasing code, you can register it here so that
others can find it.
https://code.activestate.com/recipes/langs/python/: The Python Cookbook is a sizable collection of code examples, larger modules, and useful scripts. Particularly notable contributions are collected in a book also titled Python Cookbook (O'Reilly & Associates, ISBN 0-596-00797-3.)
http://www.pyvideo.org collects links to Python-related videos from conferences and user-group meetings.
https://scipy.org: The Scientific Python project includes modules for fast array computations and manipulations plus a host of packages for such things as linear algebra, Fourier transforms, non-linear solvers, random number distributions, statistical analysis and the like.
For Python-related questions and problem reports, you can post to the newsgroup
comp.lang.python, or send them to the mailing list at
python-list@python.org.
The newsgroup and mailing list are gatewayed, so
messages posted to one will automatically be forwarded to the other.
There are
hundreds of postings a day, asking (and
answering) questions, suggesting new features, and announcing new modules.
Mailing list archives are available at https://mail.python.org/pipermail/.
Before posting, be sure to check the list of
Frequently Asked Questions (also called the FAQ).
The
FAQ answers many of the questions that come up again and again, and may
already contain the solution for your problem.
Footnotes
“Cheese Shop” is a Monty Python's sketch: a customer enters a cheese shop, but whatever cheese he asks for, the clerk says it's missing.
Some versions of the Python interpreter support editing of the current input
line and history substitution, similar to facilities found in the Korn shell and
the GNU Bash shell.
This is implemented using the GNU Readline library,
which supports various styles of editing.
This library has its own
documentation which we won't duplicate here.
Completion of variable and module names is
automatically enabled at interpreter startup so
that the Tab key invokes the completion function; it looks at
Python statement names, the current local variables, and the available
module names.
For dotted expressions such as string.a, it will evaluate
the expression up to the final '.' and then suggest completions from
the attributes of the resulting object.
Note that this may execute
application-defined code if an object with a __getattr__() method
is part of the expression.
The default configuration also saves your
history into a file named .python_history in your user directory.
The history will be available again during the next interactive interpreter
session.
This facility is an enormous step forward compared to earlier versions of the
interpreter; however, some wishes are left: It would be nice if the proper
indentation were suggested on continuation lines (the parser knows if an indent
token is required next).
The completion mechanism might use the interpreter's
symbol table.
A command to check (or even suggest) matching parentheses,
quotes, etc., would also be useful.
One alternative enhanced interactive interpreter that has been around for quite
some time is IPython, which features tab completion, object exploration and
advanced history management.
It can also be thoroughly customized and embedded
into other applications.
Another similar enhanced interactive environment is
bpython.
Floating-point numbers are represented in computer hardware as base 2 (binary)
fractions.
For example, the decimal fraction
0.125
has value 1/10 + 2/100 + 5/1000, and in the same way the binary fraction
0.001
has value 0/2 + 0/4 + 1/8.
These two fractions have identical values, the only
real difference being that the first is written in base 10 fractional notation,
and the second in base 2.
Unfortunately, most decimal fractions cannot be represented exactly as binary
fractions.
A consequence is that, in general, the decimal floating-point
numbers you enter are only approximated by the binary floating-point numbers
actually stored in the machine.
The problem is easier to understand at first in base 10.
Consider the fraction
1/3.
You can approximate that as a base 10 fraction:
0.3
or, better,
0.33
or, better,
0.333
and so on.
No matter how many digits you're willing to write down, the result
will never be exactly 1/3, but will be an increasingly better approximation of
1/3.
In the same way, no matter how many base 2 digits you're willing to use, the
decimal value 0.1 cannot be represented exactly as a base 2 fraction.
In base
2, 1/10 is the infinitely repeating fraction
0.0001100110011001100110011001100110011001100110011...
Stop at any finite number of bits, and you get an approximation.
On most
machines today, floats are approximated using a binary fraction with
the numerator using the first 53 bits starting with the most significant bit and
with the denominator as a power of two.
In the case of 1/10, the binary fraction
is 3602879701896397 / 2 ** 55 which is close to but not exactly
equal to the true value of 1/10.
Many users are not aware of the approximation because of the way values are
displayed.
Python only prints a decimal approximation to the true decimal
value of the binary approximation stored by the machine.
On most machines, if
Python were to print the true decimal value of the binary approximation stored
for 0.1, it would have to display
>>> 0.1 0.1000000000000000055511151231257827021181583404541015625
That is more digits than most people find useful, so Python keeps the number of digits manageable by displaying a rounded value instead
>>> 1 / 10 0.1
Just remember, even though the printed result looks like the exact value of 1/10, the actual stored value is the nearest representable binary fraction.
Interestingly, there are many different decimal numbers that share the same
nearest approximate binary fraction.
For example, the numbers 0.1 and
0.10000000000000001 and
0.1000000000000000055511151231257827021181583404541015625 are all
approximated by 3602879701896397 / 2 ** 55.
Since all of these decimal
values share the same approximation, any one of them could be displayed
while still preserving the invariant eval(repr(x)) == x.
Historically, the Python prompt and built-in repr() function would choose
the one with 17 significant digits, 0.10000000000000001.
Starting with
Python 3.1, Python (on most systems) is now able to choose the shortest of
these and simply display 0.1.
Note that this is in the very nature of binary floating-point: this is not a bug
in Python, and it is not a bug in your code either.
You'll see the same kind of
thing in all languages that support your hardware's floating-point arithmetic
(although some languages may not display the difference by default, or in all
output modes).
For more pleasant output, you may wish to use string formatting to produce a limited number of significant digits:
>>> format(math.pi, '.12g') # give 12 significant digits '3.14159265359' >>> format(math.pi, '.2f') # give 2 digits after the point '3.14' >>> repr(math.pi) '3.141592653589793'
It's important to realize that this is, in a real sense, an illusion: you're simply rounding the display of the true machine value.
One illusion may beget another.
For example, since 0.1 is not exactly 1/10,
summing three values of 0.1 may not yield exactly 0.3, either:
>>> .1 + .1 + .1 == .3 False
Also, since the 0.1 cannot get any closer to the exact value of 1/10 and
0.3 cannot get any closer to the exact value of 3/10, then pre-rounding with
round() function cannot help:
>>> round(.1, 1) + round(.1, 1) + round(.1, 1) == round(.3, 1) False
Though the numbers cannot be made closer to their intended exact values,
the round() function can be useful for post-rounding so that results
with inexact values become comparable to one another:
>>> round(.1 + .1 + .1, 10) == round(.3, 10) True
Binary floating-point arithmetic holds many surprises like this.
The problem
with “0.1” is explained in precise detail below, in the “Representation Error”
section.
See The Perils of Floating Point
for a more complete account of other common surprises.
As that says near the end, “there are no easy answers.” Still, don't be unduly
wary of floating-point! The errors in Python float operations are inherited
from the floating-point hardware, and on most machines are on the order of no
more than 1 part in 2**53 per operation.
That's more than adequate for most
tasks, but you do need to keep in mind that it's not decimal arithmetic and
that every float operation can suffer a new rounding error.
While pathological cases do exist, for most casual use of floating-point
arithmetic you'll see the result you expect in the end if you simply round the
display of your final results to the number of decimal digits you expect.
str() usually suffices, and for finer control see the str.format()
method's format specifiers in Format String Syntax.
For use cases which require exact decimal representation, try using the
decimal module which implements decimal arithmetic suitable for
accounting applications and high-precision applications.
Another form of exact arithmetic is supported by the fractions module
which implements arithmetic based on rational numbers (so the numbers like
1/3 can be represented exactly).
If you are a heavy user of floating point operations you should take a look
at the Numerical Python package and many other packages for mathematical and
statistical operations supplied by the SciPy project.
See <https://scipy.org>.
Python provides tools that may help on those rare occasions when you really
do want to know the exact value of a float.
The
float.as_integer_ratio() method expresses the value of a float as a
fraction:
>>> x = 3.14159 >>> x.as_integer_ratio() (3537115888337719, 1125899906842624)
Since the ratio is exact, it can be used to losslessly recreate the original value:
>>> x == 3537115888337719 / 1125899906842624 True
The float.hex() method expresses a float in hexadecimal (base
16), again giving the exact value stored by your computer:
>>> x.hex() '0x1.921f9f01b866ep+1'
This precise hexadecimal representation can be used to reconstruct the float value exactly:
>>> x == float.fromhex('0x1.921f9f01b866ep+1') True
Since the representation is exact, it is useful for reliably porting values across different versions of Python (platform independence) and exchanging data with other languages that support the same format (such as Java and C99).
Another helpful tool is the math.fsum() function which helps mitigate
loss-of-precision during summation.
It tracks “lost digits” as values are
added onto a running total.
That can make a difference in overall accuracy
so that the errors do not accumulate to the point where they affect the
final total:
>>> sum([0.1] * 10) == 1.0 False >>> math.fsum([0.1] * 10) == 1.0 True
This section explains the “0.1” example in detail, and shows how you can perform
an exact analysis of cases like this yourself.
Basic familiarity with binary
floating-point representation is assumed.
Representation error refers to the fact that some (most, actually) decimal fractions cannot be represented exactly as binary (base 2) fractions. This is the chief reason why Python (or Perl, C, C++, Java, Fortran, and many others) often won't display the exact decimal number you expect.
Why is that? 1/10 is not exactly representable as a binary fraction.
Almost all
machines today (November 2000) use IEEE-754 floating point arithmetic, and
almost all platforms map Python floats to IEEE-754 “double precision”.
754
doubles contain 53 bits of precision, so on input the computer strives to
convert 0.1 to the closest fraction it can of the form J/2**N where J is
an integer containing exactly 53 bits.
Rewriting
1 / 10 ~= J / (2**N)
as
J ~= 2**N / 10
and recalling that J has exactly 53 bits (is >= 2**52 but < 2**53),
the best value for N is 56:
>>> 2**52 <= 2**56 // 10 < 2**53 True
That is, 56 is the only value for N that leaves J with exactly 53 bits.
The
best possible value for J is then that quotient rounded:
>>> q, r = divmod(2**56, 10) >>> r 6
Since the remainder is more than half of 10, the best approximation is obtained by rounding up:
>>> q+1 7205759403792794
Therefore the best possible approximation to 1/10 in 754 double precision is:
7205759403792794 / 2 ** 56
Dividing both the numerator and denominator by two reduces the fraction to:
3602879701896397 / 2 ** 55
Note that since we rounded up, this is actually a little bit larger than 1/10;
if we had not rounded up, the quotient would have been a little bit smaller than
1/10.
But in no case can it be exactly 1/10!
So the computer never “sees” 1/10: what it sees is the exact fraction given above, the best 754 double approximation it can get:
>>> 0.1 * 2 ** 55 3602879701896397.0
If we multiply that fraction by 10**55, we can see the value out to 55 decimal digits:
>>> 3602879701896397 * 10 ** 55 // 2 ** 55 1000000000000000055511151231257827021181583404541015625
meaning that the exact number stored in the computer is equal to the decimal value 0.1000000000000000055511151231257827021181583404541015625. Instead of displaying the full decimal value, many languages (including older versions of Python), round the result to 17 significant digits:
>>> format(0.1, '.17f') '0.10000000000000001'
The fractions and decimal modules make these calculations
easy:
>>> from decimal import Decimal >>> from fractions import Fraction >>> Fraction.from_float(0.1) Fraction(3602879701896397, 36028797018963968) >>> (0.1).as_integer_ratio() (3602879701896397, 36028797018963968) >>> Decimal.from_float(0.1) Decimal('0.1000000000000000055511151231257827021181583404541015625') >>> format(Decimal.from_float(0.1), '.17') '0.10000000000000001'
When an error occurs, the interpreter prints an error message and a stack trace.
In interactive mode, it then returns to the primary prompt; when input came from
a file, it exits with a nonzero exit status after printing the stack trace.
(Exceptions handled by an except clause in a try statement
are not errors in this context.) Some errors are unconditionally fatal and
cause an exit with a nonzero exit; this applies to internal inconsistencies and
some cases of running out of memory.
All error messages are written to the
standard error stream; normal output from executed commands is written to
standard output.
Typing the interrupt character (usually Control-C or Delete) to the primary or
secondary prompt cancels the input and returns to the primary prompt.
1
Typing an interrupt while a command is executing raises the
KeyboardInterrupt exception, which may be handled by a try
statement.
On BSD'ish Unix systems, Python scripts can be made directly executable, like shell scripts, by putting the line
#!/usr/bin/env python3.5
(assuming that the interpreter is on the user's PATH) at the beginning
of the script and giving the file an executable mode.
The #! must be the
first two characters of the file.
On some platforms, this first line must end
with a Unix-style line ending ('\n'), not a Windows ('\r\n') line
ending.
Note that the hash, or pound, character, '#', is used to start a
comment in Python.
The script can be given an executable mode, or permission, using the chmod command.
$ chmod +x myscript.py
On Windows systems, there is no notion of an “executable mode”.
The Python
installer automatically associates .py files with python.exe so that
a double-click on a Python file will run it as a script.
The extension can
also be .pyw, in that case, the console window that normally appears is
suppressed.
When you use Python interactively, it is frequently handy to have some standard
commands executed every time the interpreter is started.
You can do this by
setting an environment variable named PYTHONSTARTUP to the name of a
file containing your start-up commands.
This is similar to the .profile
feature of the Unix shells.
This file is only read in interactive sessions, not when Python reads commands
from a script, and not when /dev/tty is given as the explicit source of
commands (which otherwise behaves like an interactive session).
It is executed
in the same namespace where interactive commands are executed, so that objects
that it defines or imports can be used without qualification in the interactive
session.
You can also change the prompts sys.ps1 and sys.ps2 in this
file.
If you want to read an additional start-up file from the current directory, you
can program this in the global start-up file using code like if
os.path.isfile('.pythonrc.py'): exec(open('.pythonrc.py').read()).
If you want to use the startup file in a script, you must do this explicitly
in the script:
import os filename = os.environ.get('PYTHONSTARTUP') if filename and os.path.isfile(filename): with open(filename) as fobj: startup_file = fobj.read() exec(startup_file)
Python provides two hooks to let you customize it: sitecustomize and
usercustomize.
To see how it works, you need first to find the location
of your user site-packages directory.
Start Python and run this code:
>>> import site >>> site.getusersitepackages() '/home/user/.local/lib/python3.5/site-packages'
Now you can create a file named usercustomize.py in that directory and
put anything you want in it.
It will affect every invocation of Python, unless
it is started with the -s option to disable the automatic import.
sitecustomize works in the same way, but is typically created by an
administrator of the computer in the global site-packages directory, and is
imported before usercustomize.
See the documentation of the site
module for more details.
Footnotes
A problem with the GNU Readline package may prevent this.