Detailed course introduction to python classes and objects with examples

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Python Classes

Command line and environment

The CPython interpreter scans the command line and the environment for various settings.

CPython implementation detail: Other implementations' command line schemes may differ. See Alternate Implementations for further resources.

1.1. Command line

When invoking Python, you may specify any of these options:

python [-BdEiOQsStuUvVWxX3?] [-c command | -m module-name | script | - ] [args]

The most common use case is, of course, a simple invocation of a script:

python myscript.py

1.1.1. Interface options

The interpreter interface resembles that of the UNIX shell, but provides some additional methods of invocation:

When called with standard input connected to a tty device, it prompts for commands and executes them until an EOF (an end-of-file character, you can produce that with Ctrl-D on UNIX or Ctrl-Z, Enter on Windows) is read.

When called with a file name argument or with a file as standard input, it reads and executes a script from that file.

When called with a directory name argument, it reads and executes an appropriately named script from that directory.

When called with -c command, it executes the Python statement(s) given as command. Here command may contain multiple statements separated by newlines. Leading whitespace is significant in Python statements! When called with -m module-name, the given module is located on the Python module path and executed as a script.

In non-interactive mode, the entire input is parsed before it is executed.

An interface option terminates the list of options consumed by the interpreter, all consecutive arguments will end up in sys.argv â€“ note that the first element, subscript zero (sys.argv[0]), is a string reflecting the program's source.

-c

Execute the Python code in command. command can be one or more statements separated by newlines, with significant leading whitespace as in normal module code.

If this option is given, the first element of sys.argv will be "-c" and the current directory will be added to the start of sys.path (allowing modules in that directory to be imported as top level modules).

-m

Search sys.path for the named module and execute its contents as the __main__ module.

Since the argument is a module name, you must not give a file extension (.py). The module-name should be a valid Python module name, but the implementation may not always enforce this (e.g. it may allow you to use a name that includes a hyphen).

Note: This option cannot be used with built-in modules and extension modules written in C, since they do not have Python module files. However, it can still be used for precompiled modules, even if the original source file is not available.

If this option is given, the first element of sys.argv will be the full path to the module file. As with the -c option, the current directory will be added to the start of sys.path.

Many standard library modules contain code that is invoked on their execution as a script. An example is the timeit module:

python -mtimeit -s 'setup here' 'benchmarked code here' python -mtimeit -h # for details

See also: runpy.run_module() The actual implementation of this feature. PEP 338 â€“ Executing modules as scripts

New in version 2.4.

Changed in version 2.5: The named module can now be located inside a package.

Read commands from standard input (sys.stdin). If standard input is a terminal, -i is implied.

If this option is given, the first element of sys.argv will be "-" and the current directory will be added to the start of sys.path.

Execute the Python code contained in script, which must be a filesystem path (absolute or relative) referring to either a Python file, a directory containing a __main__.py file, or a zipfile containing a __main__.py file.

If this option is given, the first element of sys.argv will be the script name as given on the command line.

If the script name refers directly to a Python file, the directory containing that file is added to the start of sys.path, and the file is executed as the __main__ module.

If the script name refers to a directory or zipfile, the script name is added to the start of sys.path and the __main__.py file in that location is executed as the __main__ module.

Changed in version 2.5: Directories and zipfiles containing a __main__.py file at the top level are now considered valid Python scripts.

If no interface option is given, -i is implied, sys.argv[0] is an empty string ("") and the current directory will be added to the start of sys.path.

See also: Invoking the Interpreter

1.1.2. Generic options

-h

--help

Print a short description of all command line options.

Changed in version 2.5: The --help variant.

-V

--version

Print the Python version number and exit. Example output could be:

Python 2.5.1

Changed in version 2.5: The --version variant.

1.1.3. Miscellaneous options

-B

If given, Python won't try to write .pyc or .pyo files on the import of source modules. See also PYTHONDONTWRITEBYTECODE.

New in version 2.6.

-d

Turn on parser debugging output (for wizards only, depending on compilation options). See also PYTHONDEBUG.

-E

Ignore all PYTHON* environment variables, e.g. PYTHONPATH and PYTHONHOME, that might be set.

New in version 2.2.

-i

When a script is passed as first argument or the -c option is used, enter interactive mode after executing the script or the command, even when sys.stdin does not appear to be a terminal. The PYTHONSTARTUP file is

not read.

This can be useful to inspect global variables or a stack trace when a script raises an exception. See also PYTHONINSPECT.

-O

Turn on basic optimizations. This changes the filename extension for compiled (bytecode) files from .pyc to .pyo. See also PYTHONOPTIMIZE.

-OO

Discard docstrings in addition to the -O optimizations.

-Q

Division control. The argument must be one of the following:

old division of int/int and long/long return an int or long (default)

new new division semantics, i.e. division of int/int and long/long returns a float

warn old division semantics with a warning for int/int and long/long

warnall

old division semantics with a warning for all uses of the division operator

See also: Tools/scripts/fixdiv.py for a use of warnall

PEP 238 â€“ Changing the division operator

-s

Don't add user site directory to sys.path

New in version 2.6.

See also: PEP 370 â€“ Per user site-packages directory

-S

Disable the import of the module site and the site-dependent manipulations of sys.path that it entails.

-t

Issue a warning when a source file mixes tabs and spaces for indentation in a way that makes it depend on the worth of a tab expressed in spaces. Issue an error when the option is given twice (-tt).

-u

Force stdin, stdout and stderr to be totally unbuffered. On systems where it matters, also put stdin, stdout and stderr in binary mode.

Note that there is internal buffering in file.readlines() and File Objects (for

line in sys.stdin) which is not influenced by this option. To work around this, you will want to use file.readline() inside a while 1: loop.

-3

Warn about Python 3.x incompatibilities which cannot be fixed trivially by 2to3. Among these are:

dict.has_key() apply() callable() coerce() execfile() reduce() reload()

Using these will emit a DeprecationWarning.

New in version 2.6.

1.1.4. Options you shouldn't use

-J

Reserved for use by Jython.

-U

Turns all string literals into unicodes globally. Do not be tempted to use this option as it will probably break your world. It also produces .pyc files with a different magic number than normal. Instead, you can enable unicode literals on a per-module basis by using:

from __future__ import unicode_literals

at the top of the file. See __future__ for details.

-X

Reserved for alternative implementations of Python to use for their own purposes.

1.2. Environment variables

These environment variables influence Python's behavior.

PYTHONHOME

Change the location of the standard Python libraries. By default, the

libraries are searched in prefix/lib/pythonversion and exec_prefix/lib/pythonversion, where prefix and exec_prefix are installation-dependent directories, both defaulting to /usr/local.

When PYTHONHOME is set to a single directory, its value replaces both prefix and exec_prefix. To specify different values for these, set PYTHONHOME to prefix:exec_prefix.

PYTHONPATH

Augment the default search path for module files. The format is the same as the shell's PATH: one or more directory pathnames separated by os.pathsep (e.g. colons on Unix or semicolons on Windows). Non-existent

directories are silently ignored.

In addition to normal directories, individual PYTHONPATH entries may refer to zipfiles containing pure Python modules (in either source or compiled form). Extension modules cannot be imported from zipfiles.

The default search path is installation dependent, but generally begins with prefix/lib/pythonversion (see PYTHONHOME above). It is always appended to PYTHONPATH.

An additional directory will be inserted in the search path in front of PYTHONPATH as described above under Interface options. The search path can be manipulated from within a Python program as the variablesys.path.

PYTHONSTARTUP

If this is the name of a readable file, the Python commands in that file are executed before the first prompt is displayed in interactive mode. The file is executed in the same namespace where interactive commands are executed so that objects defined or imported in it can be used without qualification in the interactive session. You can also change the prompts sys.ps1 and sys.ps2 in this file.

PYTHONY2K

Set this to a non-empty string to cause the time module to require dates specified as strings to include 4-digit years, otherwise 2-digit years are converted based on rules described in the time module documentation.

PYTHONOPTIMIZE

If this is set to a non-empty string it is equivalent to specifying the -O option. If set to an integer, it is equivalent to specifying -O multiple times.

PYTHONDEBUG

If this is set to a non-empty string it is equivalent to specifying the -d option. If set to an integer, it is equivalent to specifying -d multiple times.

PYTHONINSPECT

If this is set to a non-empty string it is equivalent to specifying the -i option.

This variable can also be modified by Python code using os.environ to force inspect mode on program termination.

PYTHONUNBUFFERED

If this is set to a non-empty string it is equivalent to specifying the -u option.

PYTHONVERBOSE

If this is set to a non-empty string it is equivalent to specifying the -v option. If set to an integer, it is equivalent to specifying -v multiple times.

PYTHONCASEOK

If this is set, Python ignores case in import statements. This only works on Windows.

PYTHONDONTWRITEBYTECODE

If this is set, Python won't try to write .pyc or .pyo files on the import of source modules.

New in version 2.6.

PYTHONIOENCODING

Overrides the encoding used for stdin/stdout/stderr, in the syntax encodingname:errorhandler. The :errorhandler part is optional and has the same

meaning as in str.encode().

New in version 2.6.

PYTHONNOUSERSITE

If this is set, Python won't add the user site directory to sys.path

New in version 2.6.

See also: PEP 370 â€“ Per user site-packages directory

PYTHONUSERBASE

Sets the base directory for the user site directory

New in version 2.6.

See also: PEP 370 â€“ Per user site-packages directory

PYTHONEXECUTABLE

If this environment variable is set, sys.argv[0] will be set to its value instead of the value got through the C runtime. Only works on Mac OS X.

1.2.1. Debug-mode variables

Setting these variables only has an effect in a debug build of Python, that is, if Python was configured with the --with-pydebug build option.

PYTHONTHREADDEBUG

If set, Python will print threading debug info.

Changed in version 2.6: Previously, this variable was called THREADDEBUG.

PYTHONDUMPREFS

If set, Python will dump objects and reference counts still alive after shutting down the interpreter.

PYTHONMALLOCSTATS

If set, Python will print memory allocation statistics every time a new object arena is created, and on shutdown.

Using the Python Interpreter

2.1. Invoking the Interpreter

The Python interpreter is usually installed as /usr/local/bin/python 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

python

to the shell. 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, the Python installation is usually placed in C:\Python26, though you can change this when you're running the installer. To add this directory to your path, you can type the following command into the command prompt in a DOS box:

set path=%path%;C:\python26

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 usually aren't very sophisticated. On Unix, whoever installed the interpreter may have enabled support for the GNU readline library, which adds more elaborate interactive editing and history features. 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 AppendixInteractive 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.

Note that there is a difference between python file and python input() and raw_input(), are satisfied from file. Since this file has already been read until the end by the parser before the program starts executing, the program will encounter end-of-file immediately. In the former case (which is usually what you want) they are satisfied from whatever file or device is connected to standard input of the Python interpreter.

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. (This does not work if the script is read from standard input, for the same reason as explained in the previous paragraph.)

2.1.1. Argument Passing

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

2.1.2. Interactive Mode

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

python

Python 2.6 (#1, Feb 28 2007, 00:02:06)

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 = 1 >>> if the_world_is_flat:

... print "Be careful not to fall off!" ...

Be careful not to fall off!

2.2. The Interpreter and Its Environment

2.2.1. Error Handling

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 DEL) 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 theKeyboardInterrupt exception, which may be handled by a try statement.

2.2.2. Executable Python Scripts

On BSD'ish Unix systems, Python scripts can be made directly executable, like shell scripts, by putting the line

#! /usr/bin/env python

(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 â€œexecutable modeâ€. 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.

2.2.3. Source Code Encoding

It is possible to use encodings different than ASCII in Python source files. The best way to do it is to put one more special comment line right after the #! line to define the source file encoding:

# -*- coding: encoding -*-

With that declaration, all characters in the source file will be treated as having the encoding encoding, and it will be possible to directly write Unicode string literals in the selected encoding. The list of possible encodings can be found in the Python Library Reference, in the section on codecs.

For example, to write Unicode literals including the Euro currency symbol, the ISO-8859-15 encoding can be used, with the Euro symbol having the ordinal value 164. This script will print the value 8364 (the Unicode codepoint corresponding to the Euro symbol) and then exit:

# -*- coding: iso-8859-15 -*-

currency = u"â‚¬" print ord(currency)

If your editor supports saving files as UTF-8 with a UTF-8 byte order mark (aka BOM), you can use that instead of an encoding declaration. IDLE supports this capability if Options/General/Default Source Encoding/UTF-8 is set. Notice that this signature is not understood in older Python releases (2.2 and earlier), and also not understood by the operating system for script files with #! lines (only used on Unix systems).

By using UTF-8 (either through the signature or an encoding declaration), characters of most languages in the world can be used simultaneously in string literals and comments. Using non-ASCII characters in identifiers is not supported. 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.

2.2.4. The Interactive Startup File

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'): execfile('.pythonrc.py'). 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): execfile(filename)

Footnotes

[1] A problem with the GNU Readline package may prevent this.

An Informal Introduction to Python

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

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! STRING = "# This is not a comment."

3.1. Using Python as a Calculator

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

3.1.1. Numbers

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

2+2

4

>>> # This is a comment

... 2+2

>>> 2+2 # and a comment on the same line as code

>>> (50-5*6)/4

5

>>> # Integer division returns the floor:

... 7/3

>>> 7/-3

-3

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

A value can be assigned to several variables simultaneously:

>>> x = y = z = 0 # Zero x, y and z

>>> x

>>> y

>>> z

Variables must be â€œdefinedâ€ (assigned a value) before they can be used, or an error will occur:

>>> # try to access an undefined variable

... n

Traceback (most recent call last):

File "", line 1, in

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:

>>> 3 * 3.75 / 1.5

>>> 7.0 / 2

Complex numbers are also supported; imaginary numbers are written with a suffix of j or J. Complex numbers with a nonzero real component are written as (real+imagj), or can be created with the complex(real, imag) function.

>>> 1j * 1J

(-1+0j)

>>> 1j * complex(0,1)

(-1+0j)

>>> 3+1j*3

(3+3j)

>>> (3+1j)*3

(9+3j)

>>> (1+2j)/(1+1j)

(1.5+0.5j)

Complex numbers are always represented as two floating point numbers, the real and imaginary part. To extract these parts from a complex number z, use z.real and z.imag.

>>> a=1.5+0.5j

>>> a.real

1.5

>>> a.imag

0.5

The conversion functions to floating point and integer (float(), int() and long()) don't work for complex numbers there is no one correct way to convert a complex number to a real number. Use abs(z) to get its magnitude (as a float) or z.real to get its real part.

>>> a=3.0+4.0j

>>> float(a)

Traceback (most recent call last):

File "", line 1, in ?

TypeError: can't convert complex to float; use abs(z)

>>> a.real

3.0

>>> a.imag

4.0

>>> abs(a) # sqrt(a.real**2 + a.imag**2)

5.0

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.

3.1.2. Strings

Besides numbers, Python can also manipulate strings, which can be expressed in several ways. They can be enclosed in single quotes or double quotes:

>>> 'spam eggs'

'spam eggs'

>>> 'doesn\'t'

"doesn't"

>>> "doesn't"

"doesn't"

>>> '"Yes," he said.'

'"Yes," he said.'

>>> "\"Yes,\" he said."

'"Yes," he said.'

>>> '"Isn\'t," she said.'

'"Isn\'t," she said.'

String literals can span multiple lines in several ways. Continuation lines can be used, with a backslash as the last character on the line indicating that the next line is a logical continuation of the line:

hello = "This is a rather long string containing\n\ several lines of text just as you would do in C.\n\ Note that whitespace at the beginning of the line is\ significant." print hello

Note that newlines still need to be embedded in the string using \n; the newline following the trailing backslash is discarded. This example would print the following:

This is a rather long string containing several lines of text just as you would do in C.

Note that whitespace at the beginning of the line is significant.

Or, strings can be surrounded in a pair of matching triple-quotes: """ or '''.

End of lines do not need to be escaped when using triple-quotes, but they will be included in the string.

print """

Usage: thingy [OPTIONS]

-h Display this usage message -H hostname Hostname to connect to """

produces the following output:

Usage: thingy [OPTIONS]

-h Display this usage message

-H hostname Hostname to connect to

If we make the string literal a â€œrawâ€ string, \n sequences are not converted to newlines, but the backslash at the end of the line, and the newline character in the source, are both included in the string as data. Thus, the example:

hello = r"This is a rather long string containing\n\ several lines of text much as you would do in C." print hello

would print:

This is a rather long string containing\n\ several lines of text much as you would do in C.

The interpreter prints the result of string operations in the same way as they are typed for input: inside quotes, and with quotes and other funny characters escaped by backslashes, to show the precise value. The string is enclosed in double quotes if the string contains a single quote and no double quotes, else it's enclosed in single quotes. (The print statement, described later, can be used to write strings without quotes or escapes.)

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

word = 'Help' + 'A'

>>> word

'HelpA'

>>> '<' + word*5 + '>'

Two string literals next to each other are automatically concatenated; the first line above could also have been written word = 'Help' 'A'; this only works with two literals, not with arbitrary string expressions:

>>> 'str' 'ing' #

'string'

>>> 'str'.strip() + 'ing' #

'string'

>>> 'str'.strip() 'ing' #

File "", line 1, in ?

'str'.strip() 'ing'

SyntaxError: invalid syntax

Strings can be subscripted (indexed); like in C, the first character of a string has subscript (index) 0. There is no separate character type; a character is simply a string of size one. Like in Icon, substrings can be specified with the slice notation: two indices separated by a colon.

>>> word[4]

'A'

>>> word[0:2]

'He'

>>> word[2:4]

'lp'

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] # The first two characters

'He'

>>> word[2:] # Everything except the first two characters 'lpA'

Unlike a C string, Python strings cannot be changed. Assigning to an indexed position in the string results in an error:

word[0] = 'x'

Traceback (most recent call last):

File "", line 1, in ?

TypeError: object does not support item assignment

>>> word[:1] = 'Splat'

Traceback (most recent call last):

File "", line 1, in ?

TypeError: object does not support slice assignment

However, creating a new string with the combined content is easy and efficient:

>>> 'x' + word[1:]

'xelpA'

>>> 'Splat' + word[4]

'SplatA'

Here's a useful invariant of slice operations: s[:i] + s[i:] equals s.

>>> word[:2] + word[2:]

'HelpA'

>>> word[:3] + word[3:]

'HelpA'

Degenerate slice indices are handled gracefully: an index that is too large is replaced by the string size, an upper bound smaller than the lower bound returns an empty string.

>>> word[1:100]

'elpA'

>>> word[10:] ''

>>> word[2:1]

Indices may be negative numbers, to start counting from the right. For example:

>>> word[-1] # The last character

'A'

>>> word[-2] # The last-but-one character

'p'

>>> word[-2:] # The last two characters

'pA'

>>> word[:-2] # Everything except the last two characters 'Hel'

But note that -0 is really the same as 0, so it does not count from the right!

word[-0] # (since -0 equals 0) 'H'

Out-of-range negative slice indices are truncated, but don't try this for singleelement (non-slice) indices:

>>> word[-100:]

'HelpA'

>>> word[-10] # error

Traceback (most recent call last):

File "", line 1, in ?

IndexError: string index out of range

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:

+---+---+---+---+---+

| H | e | l | p | A |

+---+---+---+---+---+

0 1 2 3 4 5

-5 -4 -3 -2 -1

The first row of numbers gives the position of the indices 0...5 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.

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

>>> s = 'supercalifragilisticexpialidocious'

>>> len(s)

See also:

Sequence Types str, unicode, list, tuple, buffer, xrange

Strings, and the Unicode strings described in the next section, are examples of sequence types, and support the common operations supported by such types.

String Methods

Both strings and Unicode strings support a large number of methods for basic transformations and searching.

String Formatting

Information about string formatting with str.format() is described here.

String Formatting Operations

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

3.1.3. Unicode Strings

Starting with Python 2.0 a new data type for storing text data is available to the programmer: the Unicode object. It can be used to store and manipulate Unicode data and integrates well with the existing string objects, providing auto-conversions where necessary.

Unicode has the advantage of providing one ordinal for every character in every script used in modern and ancient texts. Previously, there were only 256 possible ordinals for script characters. Texts were typically bound to a code page which mapped the ordinals to script characters. This lead to very much confusion especially with respect to internationalization (usually written as i18n 'i' + 18 characters + 'n') of software. Unicode solves these problems by defining one code page for all scripts.

Creating Unicode strings in Python is just as simple as creating normal strings:

>>> u'Hello World !' u'Hello World !'

The small 'u' in front of the quote indicates that a Unicode string is supposed to be created. If you want to include special characters in the string, you can do so by using the Python Unicode-Escape encoding. The following example shows how:

>>> u'Hello\u0020World !' u'Hello World !'

The escape sequence \u0020 indicates to insert the Unicode character with the ordinal value 0x0020 (the space character) at the given position.

Other characters are interpreted by using their respective ordinal values directly as Unicode ordinals. If you have literal strings in the standard Latin-1 encoding that is used in many Western countries, you will find it convenient that the lower 256 characters of Unicode are the same as the 256 characters of Latin-1.

For experts, there is also a raw mode just like the one for normal strings. You have to prefix the opening quote with â€˜ur' to have Python use the Raw-Unicode-Escape encoding. It will only apply the above \uXXXX conversion if there is an uneven number of backslashes in front of the small â€˜u'.

>>> ur'Hello\u0020World !' u'Hello World !'

>>> ur'Hello\\u0020World !' u'Hello\\\\u0020World !'

The raw mode is most useful when you have to enter lots of backslashes, as can be necessary in regular expressions.

Apart from these standard encodings, Python provides a whole set of other ways of creating Unicode strings on the basis of a known encoding.

The built-in function unicode() provides access to all registered Unicode codecs

(COders and DECoders). Some of the more well known encodings which these codecs can convert are Latin-1, ASCII, UTF-8, and UTF-16. The latter two are variable-length encodings that store each Unicode character in one or more bytes. The default encoding is normally set to ASCII, which passes through characters in the range 0 to 127 and rejects any other characters with an error. When a Unicode string is printed, written to a file, or converted with

str(), conversion takes place using this default encoding.

>>> u"abc" u'abc' >>> str(u"abc")

'abc' >>> u"Ã¤Ã¶Ã¼" u'\xe4\xf6\xfc' >>> str(u"Ã¤Ã¶Ã¼")

Traceback (most recent call last):

File "", line 1, in ?

UnicodeEncodeError: 'ascii' codec can't encode characters in position 0-2: ordinal not in range

To convert a Unicode string into an 8-bit string using a specific encoding, Unicode objects provide an encode() method that takes one argument, the name of the encoding. Lowercase names for encodings are preferred.

>>> u"Ã¤Ã¶Ã¼".encode('utf-8') '\xc3\xa4\xc3\xb6\xc3\xbc'

If you have data in a specific encoding and want to produce a corresponding Unicode string from it, you can use the unicode() function with the encoding name as the second argument.

>>> unicode('\xc3\xa4\xc3\xb6\xc3\xbc', 'utf-8') u'\xe4\xf6\xfc'

3.1.4. Lists

Python knows a number of compound data types, used to group together other values. The most versatile is the list, which can be written as a list of comma-separated values (items) between square brackets. List items need not all have the same type.

>>> a = ['spam', 'eggs', 100, 1234]

>>> a

['spam', 'eggs', 100, 1234]

Like string indices, list indices start at 0, and lists can be sliced, concatenated and so on:

>>> a[0]

'spam'

>>> a[3]

1234

>>> a[-2]

100

>>> a[1:-1]

['eggs', 100]

>>> a[:2] + ['bacon', 2*2]

['spam', 'eggs', 'bacon', 4]

>>> 3*a[:3] + ['Boo!']

['spam', 'eggs', 100, 'spam', 'eggs', 100, 'spam', 'eggs', 100, 'Boo!']

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

>>> a[:]

['spam', 'eggs', 100, 1234]

Unlike strings, which are immutable, it is possible to change individual elements of a list:

>>> a

['spam', 'eggs', 100, 1234]

>>> a[2] = a[2] + 23

>>> a

['spam', 'eggs', 123, 1234]

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

>>> # Replace some items:

... a[0:2] = [1, 12]

>>> a

[1, 12, 123, 1234] >>> # Remove some:

... a[0:2] = []

>>> a

[123, 1234]

>>> # Insert some:

... a[1:1] = ['bletch', 'xyzzy']

>>> a

[123, 'bletch', 'xyzzy', 1234]

>>> # Insert (a copy of) itself at the beginning

>>> a[:0] = a

>>> a

[123, 'bletch', 'xyzzy', 1234, 123, 'bletch', 'xyzzy', 1234]

>>> # Clear the list: replace all items with an empty list

>>> a[:] = []

>>> a

[]

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

>>> a = ['a', 'b', 'c', 'd']

>>> len(a)

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

>>> q = [2, 3]

>>> p = [1, q, 4]

>>> len(p)

>>> p[1]

[2, 3]

>>> p[1][0]

>>> p[1].append('xtra') # See section 5.1

>>> p

[1, [2, 3, 'xtra'], 4]

>>> q

[2, 3, 'xtra']

Note that in the last example, p[1] and q really refer to the same object! We'll come back to object semantics later.

3.2. First Steps Towards Programming

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

>>> # Fibonacci series:

... # the sum of two elements defines the next

... a, b = 0, 1 >>> while b < 10:

... print b ... a, b = b, a+b ...

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: b < 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. Python does not (yet!) provide an intelligent input line editing facility, so 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; most 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 statement writes the value of the expression(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 expressions 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

A trailing comma avoids the newline after the output:

>>> a, b = 0, 1 >>> while b < 1000:

... print b, ... a, b = b, a+b ...

1 1 2 3 5 8 13 21 34 55 89 144 233 377 610 987

Note that the interpreter inserts a newline before it prints the next prompt if the last line was not completed.

More Control Flow Tools

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

4.1. if Statements

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

>>> x = int(raw_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' ...

There can be zero or more elif parts, and the else part is optional. The keyword â€˜elifâ€˜ is short for â€˜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.

4.2. for Statements

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

1 of 13

>>> # Measure some strings:

... a = ['cat', 'window', 'defenestrate'] >>> for x in a: ... print x, len(x) ... cat 3 window 6 defenestrate 12

It is not safe to modify the sequence being iterated over in the loop (this can only happen for mutable sequence types, such as lists). If you need to modify the list you are iterating over (for example, to duplicate selected items) you must iterate over a copy. The slice notation makes this particularly convenient:

>>> for x in a[:]: # make a slice copy of the entire list ... if len(x) > 6: a.insert(0, x) ...

>>> a

['defenestrate', 'cat', 'window', 'defenestrate']

4.3. The range() Function

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

>>> range(10)

[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]

The given end point is never part of the generated list; range(10) generates a list of 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 â€˜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.

4.4. break and continue Statements, and else Clauses on

Loops

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

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

Loop statements may have an else clause; it is executed when the loop terminates through exhaustion of the list (with for) or when the condition becomes false (with while), but not when the loop is terminated by a breakstatement. 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 * 1

7 is a prime number

8 equals 2 * 4

9 equals 3 * 3

4.5. pass Statements

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

>>> while True:

... pass # Busy-wait for keyboard interrupt (Ctrl+C) ...

This is commonly used for creating minimal classes:

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

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

>>> def initlog(*args):

... pass # Remember to implement this!

...

4.6. Defining Functions

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

>>> def fib(n): # write Fibonacci series up to n

... """Print a Fibonacci series up to n."""

... a, b = 0, 1 ... while a < n: ... print a, ... a, b = b, a+b ...

>>> # 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 cannot be directly assigned a value within a function (unless named in a global 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 objectreference, 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

>>> 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 â€˜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 methodappend() shown in the example is defined for list objects; it adds a new element at the end of the list. In this example it is equivalent to

result = result + [a], but more efficient.

4.7. More on Defining Functions

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

4.7.1. Default Argument Values

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

def ask_ok(prompt, retries=4, complaint='Yes or no, please!'): while True: ok = raw_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 IOError('refusenik user') print complaint

This function can be called in several ways:

giving only the mandatory argument: ask_ok('Do you really want to quit?') giving one of the optional arguments: ask_ok('OK to overwrite the file?', 2) or even giving all arguments: ask_ok('OK to overwrite the file?', 2, 'Come on, only yes or no!')

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

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

i = 5

def f(arg=i): print arg

i = 6 f()

will print 5.

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

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

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

This will print

[1]

[1, 2]

[1, 2, 3]

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

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

4.7.2. Keyword Arguments

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

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

could be called in any of the following ways:

parrot(1000)

parrot(action = 'VOOOOOM', voltage = 1000000) parrot('a thousand', state = 'pushing up the daisies') parrot('a million', 'bereft of life', 'jump')

but the following calls would all be invalid:

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

In general, an argument list must have any positional arguments followed by any keyword arguments, where the keywords must be chosen from the formal parameter names. It's not important whether a formal parameter has a default value or not. No argument may receive a value more than once formal parameter names corresponding to positional arguments cannot be used as keywords in the same calls. Here's an example that fails due to this restriction:

>>> def function(a): ... pass ...

>>> function(0, a=0)

Traceback (most recent call last):

File "", line 1, in ?

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 keys = keywords.keys() keys.sort() for kw in keys: 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. ---------------------------------------client : John Cleese shopkeeper : Michael Palin sketch : Cheese Shop Sketch

Note that the sort() method of the list of keyword argument names is called before printing the contents of the keywords dictionary; if this is not done, the order in which the arguments are printed is undefined.

4.7.3. Arbitrary Argument Lists

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

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

4.7.4. Unpacking Argument Lists

The reverse situation occurs when the arguments are already in a list or tuple but need to be unpacked for a function call requiring separate positional arguments. For instance, the built-in range() function expects separatestart 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:

>>> range(3, 6) # normal call with separate arguments

[3, 4, 5]

>>> args = [3, 6]

>>> 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,

... print "if you put", voltage, "volts through it.", ... print "E's", state, "!" ...

>>> d = {"voltage": "four million", "state": "bleedin' demised", "action": "VOOM"}

>>> parrot(**d)

-- This parrot wouldn't VOOM if you put four million volts through it. E's bleedin' demised !

4.7.5. Lambda Forms

By popular demand, a few features commonly found in functional programming languages like Lisp have been added to Python. With the lambda keyword, small anonymous functions can be created. Here's a function that returns the sum of its two arguments: lambda a, b: a+b. Lambda forms 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 forms can reference variables from the containing scope:

>>> def make_incrementor(n): ... return lambda x: x + n ...

>>> f = make_incrementor(42)

>>> f(0)

4.7.6. Documentation Strings

There are emerging 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 lineafter 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 â€œequivalentâ€ to this indentation is then stripped from the start of all lines of the string. Lines that are indented less should not occur, but if they occur all their leading whitespace should be stripped. Equivalence of whitespace should be tested after expansion of tabs (to 8 spaces, normally).

Here is an example of a multi-line docstring:

>>> def my_function():

... """Do nothing, but document it.

...

... No, really, it doesn't do anything.

... """ ... pass ...

>>> print my_function.__doc__ Do nothing, but document it.

No, really, it doesn't do anything.

4.8. Intermezzo: Coding Style

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

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

Use 4-space indentation, and no tabs.

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

Wrap lines so that they don't exceed 79 characters.

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

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

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

Use docstrings.

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

Name your classes and functions consistently; the convention is to use CamelCase for classes and lower_case_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. Plain ASCII works best in any case.

Footnotes

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

Data Structures

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

5.1. More on Lists

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

list.append(x)

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

list.extend(L)

Extend the list by appending all the items in the given list; equivalent to a[len(a):] = L.

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 x. It is an error 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.index(x)

Return the index in the list of the first item whose value is x. It is an error if there is no such item.

list.count(x)

Return the number of times x appears in the list.

list.sort()

Sort the items of the list, in place.

list.reverse()

Reverse the elements of the list, in place.

An example that uses most of the list methods:

>>> a = [66.25, 333, 333, 1, 1234.5]

>>> print a.count(333), a.count(66.25), a.count('x') 2 1 0

>>> a.insert(, -1)

>>> a.append(333)

>>> a

[66.25, 333, -1, 333, 1, 1234.5, 333]

>>> a.index(333)

>>> a.remove(333)

>>> a

[66.25, -1, 333, 1, 1234.5, 333]

>>> a.reverse()

>>> a

[333, 1234.5, 1, 333, -1, 66.25]

>>> a.sort()

>>> a

[-1, 1, 66.25, 333, 333, 1234.5]

5.1.1. Using Lists as Stacks

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

>>> stack = [3, 4, 5]

>>> stack.append(6)

>>> stack.append(7)

>>> stack

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

>>> stack

[3, 4, 5, 6]

>>> stack.pop()

>>> stack [3, 4]

5.1.2. Using Lists as Queues

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

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

>>> from collections import deque

>>> queue = deque(["Eric", "John", "Michael"])

>>> queue.append("Terry") # Terry arrives

>>> queue.append("Graham") # Graham arrives

>>> queue.popleft() # The first to arrive now leaves 'Eric'

>>> queue.popleft() # The second to arrive now leaves 'John'

>>> queue # Remaining queue in order of arrival deque(['Michael', 'Terry', 'Graham'])

5.1.3. Functional Programming Tools

There are three built-in functions that are very useful when used with lists: filter(), map(), and reduce().

filter(function, sequence) returns a sequence consisting of those items from the sequence for which function(item) is true. If sequence is a string or tuple, the result will be of the same type; otherwise, it is always a list. For example, to compute some primes:

>>> def f(x): return x % 2 != 0 and x % 3 != 0 ...

>>> filter(f, range(2, 25)) [5, 7, 11, 13, 17, 19, 23]

map(function, sequence) calls function(item) for each of the sequence's items and

returns a list of the return values. For example, to compute some cubes:

>>> def cube(x): return x*x*x ...

>>> map(cube, range(1, 11))

[1, 8, 27, 64, 125, 216, 343, 512, 729, 1000]

More than one sequence may be passed; the function must then have as

many arguments as there are sequences and is called with the corresponding item from each sequence (or None if some sequence is shorter than another). For example:

>>> seq = range(8)

>>> def add(x, y): return x+y ...

>>> map(add, seq, seq)

[0, 2, 4, 6, 8, 10, 12, 14]

reduce(function, sequence) returns a single value constructed by calling the binary function function on the first two items of the sequence, then on the result and the next item, and so on. For example, to compute the sum of the numbers 1 through 10:

>>> def add(x,y): return x+y ...

>>> reduce(add, range(1, 11))

If there's only one item in the sequence, its value is returned; if the sequence is empty, an exception is raised.

A third argument can be passed to indicate the starting value. In this case the starting value is returned for an empty sequence, and the function is first applied to the starting value and the first sequence item, then to the result and the next item, and so on. For example,

>>> def sum(seq):

... def add(x,y): return x+y ... return reduce(add, seq, 0) ...

>>> sum(range(1, 11))

>>> sum([])

Don't use this example's definition of sum(): since summing numbers is such a common need, a built-in function sum(sequence) is already provided, and works exactly like this.

New in version 2.3.

5.1.4. List Comprehensions

List comprehensions provide a concise way to create lists without resorting to use of map(), filter() and/or lambda. The resulting list definition tends often to be clearer than lists built using those constructs. Each list comprehension consists of an expression followed by a for clause, then zero or more for or if clauses. The result will be a list resulting from evaluating the expression in the context of the for and if clauses which follow it. If the expression would evaluate to a tuple, it must be parenthesized.

>>> freshfruit = [' banana', ' loganberry ', 'passion fruit ']

>>> [weapon.strip() for weapon in freshfruit] ['banana', 'loganberry', 'passion fruit']

>>> vec = [2, 4, 6]

>>> [3*x for x in vec]

[6, 12, 18]

>>> [3*x for x in vec if x > 3]

[12, 18]

>>> [3*x for x in vec if x < 2]

[]

>>> [[x,x**2] for x in vec]

[[2, 4], [4, 16], [6, 36]]

>>> [x, x**2 for x in vec] # error - parens required for tuples

File "", line 1, in ?

[x, x**2 for x in vec]

SyntaxError: invalid syntax

>>> [(x, x**2) for x in vec]

[(2, 4), (4, 16), (6, 36)]

>>> vec1 = [2, 4, 6]

>>> vec2 = [4, 3, -9]

>>> [x*y for x in vec1 for y in vec2] [8, 6, -18, 16, 12, -36, 24, 18, -54] >>> [x+y for x in vec1 for y in vec2]

[6, 5, -7, 8, 7, -5, 10, 9, -3]

>>> [vec1[i]*vec2[i] for i in range(len(vec1))]

[8, 12, -54]

List comprehensions are much more flexible than map() and can be applied to complex expressions and nested functions:

>>> [str(round(355/113.0, i)) for i in range(1,6)] ['3.1', '3.14', '3.142', '3.1416', '3.14159']

5.1.5. Nested List Comprehensions

If you've got the stomach for it, list comprehensions can be nested. They are a powerful tool but â€“ like all powerful tools â€“ they need to be used carefully, if at all.

Consider the following example of a 3x3 matrix held as a list containing three lists, one list per row:

>>> mat = [

... [1, 2, 3],

... [4, 5, 6],

... [7, 8, 9],

... ]

Now, if you wanted to swap rows and columns, you could use a list comprehension:

>>> print [[row[i] for row in mat] for i in [0, 1, 2]]

[[1, 4, 7], [2, 5, 8], [3, 6, 9]]

Special care has to be taken for the nested list comprehension:

To avoid apprehension when nesting list comprehensions, read from right to left.

A more verbose version of this snippet shows the flow explicitly:

for i in [0, 1, 2]: for row in mat: print row[i], print

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

>>> zip(*mat)

[(1, 4, 7), (2, 5, 8), (3, 6, 9)]

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

5.2. The del statement

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

a = [-1, 1, 66.25, 333, 333, 1234.5] del a[0]

>>> a

[1, 66.25, 333, 333, 1234.5]

>>> del a[2:4]

>>> a

[1, 66.25, 1234.5]

>>> del a[:]

>>> a []

del can also be used to delete entire variables:

>>> del a

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

5.3. Tuples and Sequences

We saw that lists and strings have many common properties, such as indexing and slicing operations. They are two examples of sequence data types (see Sequence Types str, unicode, list, tuple, buffer, xrange). 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))

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).

Tuples have many uses. For example: (x, y) coordinate pairs, employee records from a database, etc. Tuples, like strings, are immutable: it is not possible to assign to the individual items of a tuple (you can simulate much of the same effect with slicing and concatenation, though). It is also possible to create tuples which contain mutable objects, such as lists.

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', #

>>> len(empty)

>>> len(singleton)

>>> 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 the list of variables on the left to have the same number of elements as the length of the sequence. Note that multiple assignment is really just a combination of tuple packing and sequence unpacking.

5.4. Sets

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

Here is a brief demonstration:

basket = ['apple', 'orange', 'apple', 'pear', 'orange', 'banana'] fruit = set(basket) # create a set without duplicates

>>> fruit

set(['orange', 'pear', 'apple', 'banana'])

>>> 'orange' in fruit # fast membership testing

True

>>> 'crabgrass' in fruit

False

>>> # Demonstrate set operations on unique letters from two words ...

>>> a = set('abracadabra')

>>> b = set('alacazam')

>>> a # unique letters in a set(['a', 'r', 'b', 'c', 'd'])

>>> a - b # letters in a but not in b set(['r', 'd', 'b'])

>>> a | b # letters in either a or b set(['a', 'c', 'r', 'd', 'b', 'm', 'z', 'l'])

>>> a & b # letters in both a and b set(['a', 'c'])

>>> a ^ b # letters in a or b but not both set(['r', 'd', 'b', 'm', 'z', 'l'])

5.5. Dictionaries

Another useful data type built into Python is the dictionary (see Mapping

Types dict). Dictionaries are sometimes found in other languages as â€œassociative memoriesâ€ or â€œassociative arraysâ€. Unlike sequences, which are indexed by a range of numbers, dictionaries are indexed bykeys, 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 an unordered 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.

The keys() method of a dictionary object returns a list of all the keys used in the dictionary, in arbitrary order (if you want it sorted, just apply the sort() method to the list of keys). 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

{'sape': 4139, 'guido': 4127, 'jack': 4098}

>>> tel['jack']

4098

>>> del tel['sape']

>>> tel['irv'] = 4127

>>> tel

{'guido': 4127, 'irv': 4127, 'jack': 4098}

>>> tel.keys()

['guido', 'irv', 'jack']

>>> 'guido' in tel

True

The dict() constructor builds dictionaries directly from lists of key-value pairs stored as tuples. When the pairs form a pattern, list comprehensions can compactly specify the key-value list.

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

{'sape': 4139, 'jack': 4098, 'guido': 4127}

>>> dict([(x, x**2) for x in (2, 4, 6)]) # use a list comprehension {2: 4, 4: 16, 6: 36}

Later in the tutorial, we will learn about Generator Expressions which are even better suited for the task of supplying key-values pairs to the dict() constructor.

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, 'jack': 4098, 'guido': 4127}

5.6. Looping Techniques

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

knights = {'gallahad': 'the pure', 'robin': 'the brave'} for k, v in knights.iteritems(): ... 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(xrange(1,10,2)): ... print i ... 9

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

5.7. More on Conditions

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

The comparison operators in and not in 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 cannot occur inside expressions. C programmers may grumble about this, but it avoids a common class of problems encountered in C programs: typing = in an expression when == was intended.

5.8. Comparing Sequences and Other Types

Sequence objects 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 ASCII ordering for 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 is legal. The outcome is deterministic but arbitrary: the types are ordered by their name. Thus, a list is always smaller than a string, a string is always smaller than a tuple, etc. [1] Mixed numeric types are compared according to their numeric value, so 0 equals 0.0, etc.

Footnotes

[1] The rules for comparing objects of different types should not be relied upon; they may change in a future version of the language.

Modules

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

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

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

# Fibonacci numbers module

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

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

1 1 2 3 5 8 13 21 34 55 89 144 233 377 610 987

>>> fibo.fib2(100)

[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)

1 1 2 3 5 8 13 21 34 55 89 144 233 377

6.1. More on Modules

A module can contain executable statements as well as function definitions. These statements are intended to initialize the module. They are executed only the first time the module is imported somewhere. [1]

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)

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)

1 1 2 3 5 8 13 21 34 55 89 144 233 377

This imports all names except those beginning with an underscore (_).

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.

Note: For efficiency reasons, each module is only imported once per interpreter session. Therefore, if you change your modules, you must restart the interpreter â€“ or, if it's just one module you want to test interactively, use reload(), e.g. reload(modulename).

6.1.1. Executing modules as scripts

When you run a Python module with

python fibo.py

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 â€œmainâ€ file:

$ python fibo.py 50

1 1 2 3 5 8 13 21 34

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

>>> import fibo

>>>

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

6.1.2. The Module Search Path

When a module named spam is imported, the interpreter searches for a file named spam.py in the current directory, and then in the list of directories specified by the environment variable PYTHONPATH. This has the same syntax as the shell variable PATH, that is, a list of directory names. When PYTHONPATH is not set, or when the file is not found there, the search continues in an installation-dependent default path; on Unix, this is usually .:/usr/local/lib/python.

Actually, modules are searched in the list of directories given by the variable sys.path which is initialized from the directory containing the input script (or the current directory), PYTHONPATH and the installation- dependent default. This allows Python programs that know what they're doing to modify or replace the module search path. Note that because the directory containing the script being run is on the search path, it is important that the script not have the same name as a standard module, or Python will attempt to load the script as a module when that module is imported. This will generally be an error. See section Standard Modules for more information.

6.1.3. â€œCompiledâ€ Python files

As an important speed-up of the start-up time for short programs that use a lot of standard modules, if a file called spam.pyc exists in the directory where spam.py is found, this is assumed to contain an already-â€œbyte-compiledâ€ version

of the module spam. The modification time of the version of spam.py used to create spam.pyc is recorded in spam.pyc, and the .pyc file is ignored if these don't match.

Normally, you don't need to do anything to create the spam.pyc file. Whenever spam.py is successfully compiled, an attempt is made to write the compiled

version to spam.pyc. It is not an error if this attempt fails; if for any reason the file is not written completely, the resulting spam.pyc file will be recognized as invalid and thus ignored later. The contents of the spam.pyc file are platform independent, so a Python module directory can be shared by machines of different architectures.

Some tips for experts:

When the Python interpreter is invoked with the -O flag, optimized code

is generated and stored in .pyo files. The optimizer currently doesn't help much; it only removes assert statements. When -O is used, all bytecode is optimized; .pyc files are ignored and .py files are compiled to optimized bytecode.

Passing two -O flags to the Python interpreter (-OO) will cause the bytecode compiler to perform optimizations that could in some rare cases result in malfunctioning programs. Currently only __doc__ strings are removed from the bytecode, resulting in more compact .pyo files. Since some programs may rely on having these available, you should only use this option if you know what you're doing.

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

When a script is run by giving its name on the command line, the bytecode for the script is never written to a .pyc or .pyo file. Thus, the startup time of a script may be reduced by moving most of its code to a module and having a small bootstrap script that imports that module. It is also possible to name a .pyc or .pyo file directly on the command line.

It is possible to have a file called spam.pyc (or spam.pyo when -O is used) without a file spam.py for the same module. This can be used to distribute a library of Python code in a form that is moderately hard to reverse engineer.

The module compileall can create .pyc files (or .pyo files when -O is used) for all modules in a directory.

6.2. Standard Modules

Python comes with a library of standard modules, described in a separate document, the Python Library Reference (â€œLibrary Referenceâ€ 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!

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 ifPYTHONPATH is not set. You can modify it using standard list operations:

>>> import sys

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

6.3. The dir() Function

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

>>> import fibo, sys

>>> dir(fibo)

['__name__', 'fib', 'fib2']

>>> dir(sys)

['__displayhook__', '__doc__', '__excepthook__', '__name__', '__stderr__',

'__stdin__', '__stdout__', '_getframe', 'api_version', 'argv',

'builtin_module_names', 'byteorder', 'callstats', 'copyright',

'displayhook', 'exc_clear', 'exc_info', 'exc_type', 'excepthook',

'exec_prefix', 'executable', 'exit', 'getdefaultencoding', 'getdlopenflags',

'getrecursionlimit', 'getrefcount', 'hexversion', 'maxint', 'maxunicode',

'meta_path', 'modules', 'path', 'path_hooks', 'path_importer_cache',

'platform', 'prefix', 'ps1', 'ps2', 'setcheckinterval', 'setdlopenflags',

'setprofile', 'setrecursionlimit', 'settrace', 'stderr', 'stdin', 'stdout',

'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__', '__doc__', '__file__', '__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 __builtin__:

>>> import __builtin__

>>> dir(__builtin__)

['ArithmeticError', 'AssertionError', 'AttributeError', 'DeprecationWarning',

'EOFError', 'Ellipsis', 'EnvironmentError', 'Exception', 'False',

'FloatingPointError', 'FutureWarning', 'IOError', 'ImportError',

'IndentationError', 'IndexError', 'KeyError', 'KeyboardInterrupt',

'LookupError', 'MemoryError', 'NameError', 'None', 'NotImplemented',

'NotImplementedError', 'OSError', 'OverflowError',

'PendingDeprecationWarning', 'ReferenceError', 'RuntimeError',

'RuntimeWarning', 'StandardError', 'StopIteration', 'SyntaxError',

'SyntaxWarning', 'SystemError', 'SystemExit', 'TabError', 'True',

'TypeError', 'UnboundLocalError', 'UnicodeDecodeError',

'UnicodeEncodeError', 'UnicodeError', 'UnicodeTranslateError',

'UserWarning', 'ValueError', 'Warning', 'WindowsError',

'ZeroDivisionError', '_', '__debug__', '__doc__', '__import__',

'__name__', 'abs', 'apply', 'basestring', 'bool', 'buffer',

'callable', 'chr', 'classmethod', 'cmp', 'coerce', 'compile',

'complex', 'copyright', 'credits', 'delattr', 'dict', 'dir', 'divmod',

'enumerate', 'eval', 'execfile', 'exit', 'file', 'filter', 'float',

'frozenset', 'getattr', 'globals', 'hasattr', 'hash', 'help', 'hex',

'id', 'input', 'int', 'intern', 'isinstance', 'issubclass', 'iter',

'len', 'license', 'list', 'locals', 'long', 'map', 'max', 'min',

'object', 'oct', 'open', 'ord', 'pow', 'property', 'quit', 'range',

'raw_input', 'reduce', 'reload', 'repr', 'reversed', 'round', 'set',

'setattr', 'slice', 'sorted', 'staticmethod', 'str', 'sum', 'super', 'tuple', 'type', 'unichr', 'unicode', 'vars', 'xrange', 'zip']

6.4. Packages

Packages are a way of structuring Python's module namespace by using â€œdotted module namesâ€. 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 the Python Imaging Library from having to worry about each other's module names.

Suppose you want to design a collection of modules (a â€œpackageâ€) 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 the directories as containing packages; this is done to prevent directories with a common name, such as string, from unintentionally hiding valid modules that occur later on the module search path. In the simplest case, __init__.py can just be an empty file, but it can also execute initialization code for the package or set the __all__ variable, described later.

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

import sound.effects.echo

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

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

An alternative way of importing the submodule is:

from sound.effects import echo

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

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

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

from sound.effects.echo import echofilter

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

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

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

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

6.4.1. Importing * From a Package

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

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

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

6.4.2. Intra-package References

The submodules often need to refer to each other. For example, the surround module might use the echo module. In fact, such references are so common that the import statement first looks in the containing package before looking in the standard module search path. Thus, the surround module can simply use import echo or from echo import echofilter. If the imported module is not found in the current package (the package of which the current module is a submodule), the import statement looks for a top-level module with the given name.

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.vocoderneeds to use the echo module in the sound.effects package, it can use from sound.effects import echo.

Starting with Python 2.5, in addition to the implicit relative imports described above, you can write explicit relative imports with the from module import name form of import statement. These explicit relative 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 both explicit and implicit 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 should always use absolute imports.

6.4.3. Packages in Multiple Directories

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

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

Footnotes

[1] In fact function definitions are also â€˜statements' that are â€˜executed'; the execution of a module-level function enters the function name in the module's global symbol table.

Input and Output

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

7.1. Fancier Output Formatting

So far we've encountered two ways of writing values: expression statements and the print statement. (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 two ways to format your output; the first way is to do all the string handling yourself; using string slicing and concatenation operations you can create any layout you can imagine. The standard module string contains some useful operations for padding strings to a given column width; these will be discussed shortly. The second way is to use the str.format() method.

One question remains, of course: how do you convert values to strings? Luckily, Python has ways to convert any value to a string: pass it to 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 not 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 and floating point numbers, in particular, have two distinct representations.

Some examples:

>>> s = 'Hello, world.'

>>> str(s)

'Hello, world.'

>>> repr(s)

"'Hello, world.'"

>>> str(0.1)

'0.1'

>>> repr(0.1)

'0.10000000000000001'

>>> 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'))"

Here are two ways to write a table of squares and cubes:

>>> for x in range(1, 11):

... print repr(x).rjust(2), repr(x*x).rjust(3),

... # Note trailing comma 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

>>> 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

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

This example demonstrates the rjust() method of string objects, which rightjustifies a string in a field of a given width by padding it with spaces on the left. There are similar methods ljust() and 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, 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'

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

>>> print 'We are the {0} who say "{1}!"'.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 format() method. A number in the brackets refers to the position of the object passed into the 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 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.

'!s' (apply str()) and '!r' (apply repr()) can be used to convert the value before it is formatted.

>>> import math

>>> print 'The value of PI is approximately {0}.'.format(math.pi)

The value of PI is approximately 3.14159265359.

>>> print 'The value of PI is approximately {0!r}.'.format(math.pi) The value of PI is approximately 3.141592653589793.

An optional ':' and format specifier can follow the field name. This allows greater control over how the value is formatted. The following example truncates Pi to three places after the decimal.

>>> import math

>>> print 'The value of PI is approximately {0:.3f}.'.format(math.pi) 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 tables pretty.

>>> table = {'Sjoerd': 4127, 'Jack': 4098, 'Dcab': 7678}

>>> for name, phone in table.items():

... print '{0:10} ==> {1:10d}'.format(name, phone) ...

Jack ==> 4098

Dcab ==> 7678

Sjoerd ==> 4127

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 â€˜**' 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 new built-in vars() function, which returns a dictionary containing all local variables.

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

7.1.1. Old string formatting

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.

Since str.format() is quite new, a lot of Python code still uses the % operator. However, because this old style of formatting will eventually be removed from the language, str.format() should generally be used.

More information can be found in the String Formatting Operations section.

7.2. Reading and Writing Files

open() returns a file object, and is most commonly used with two arguments: open(filename, mode).

>>> f = open('/tmp/workfile', 'w')

>>> print f

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 modeargument is optional; 'r' will be assumed if it's omitted.

On Windows, 'b' appended to the mode opens the file in binary mode, so there are also modes like 'rb', 'wb', and 'r+b'. Python on Windows makes a distinction between text and binary files; the end-of-line characters in text files are automatically altered slightly when data is read or written. This behind-the-scenes modification to file data is fine for ASCII text files, but it'll corrupt binary data like that in JPEG or EXE files. Be very careful to use binary mode when reading and writing such files. On Unix, it doesn't hurt to append a 'b' to the mode, so you can use it platform-independently for all binary files.

7.2.1. Methods of File Objects

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

To read a file's contents, call f.read(size), which reads some quantity of data and returns it as a string. 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 bytes 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() ''

f.readlines() returns a list containing all the lines of data in the file. If given an optional parameter sizehint, it reads that many bytes from the file and enough more to complete a line, and returns the lines from that. This is often used to allow efficient reading of a large file by lines, but without having to load the entire file in memory. Only complete lines will be returned.

>>> f.readlines()

['This is the first line of the file.\n', 'Second line of the file\n']

An alternative approach to reading lines is to loop over the file object. This is memory efficient, fast, and leads to simpler code:

>>> for line in f: print line,

This is the first line of the file.

Second line of the file

The alternative approach is simpler but does not provide as fine-grained control. Since the two approaches manage line buffering differently, they should not be mixed.

f.write(string) writes the contents of string to the file, returning None.

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

To write something other than a string, it needs to be converted to a string first:

>>> value = ('the answer', 42)

>>> s = str(value) >>> f.write(s)

f.tell() returns an integer giving the file object's current position in the file, measured in bytes from the beginning of the file. To change the file object's position, use f.seek(offset, from_what). The position is computed from adding offset to a reference point; the reference point is selected by the from_what argument. A from_what 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. from_what can be omitted and defaults to 0, using the beginning of the file as the reference point.

>>> f = open('/tmp/workfile', 'r+')

>>> f.write('0123456789abcdef')

>>> f.seek(5) # Go to the 6th byte in the file

>>> f.read(1)

'5'

>>> f.seek(-3, 2) # Go to the 3rd byte before the end

>>> f.read(1)

'd'

When you're done with a file, call f.close() to close it and free up any system resources taken up by the open file. After calling f.close(), attempts to use the file object will automatically fail.

>>> f.close()

>>> f.read()

Traceback (most recent call last):

File "", line 1, in ?

ValueError: I/O operation on closed file

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

>>> with open('/tmp/workfile', 'r') as f:

... read_data = f.read()

>>> f.closed

True

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

7.2.2. The pickle Module

Strings can easily be written to and read from a file. Numbers take a bit more effort, since the read() method only returns strings, which will have to be passed to a function like int(), which takes a string like '123' and returns its numeric value 123. However, when you want to save more complex data types like lists, dictionaries, or class instances, things get a lot more complicated.

Rather than have users be constantly writing and debugging code to save complicated data types, Python provides a standard module called pickle. This is an amazing module that can take almost any Python object (even some forms of Python code!), and convert it to a string representation; this process is called pickling. Reconstructing the object from the string representation is called unpickling. Between pickling and unpickling, the string representing the object may have been stored in a file or data, or sent over a network connection to some distant machine.

If you have an object x, and a file object f that's been opened for writing, the simplest way to pickle the object takes only one line of code:

pickle.dump(x, f)

To unpickle the object again, if f is a file object which has been opened for reading:

x = pickle.load(f)

(There are other variants of this, used when pickling many objects or when you don't want to write the pickled data to a file; consult the complete documentation for pickle in the Python Library Reference.)

pickle is the standard way to make Python objects which can be stored and

reused by other programs or by a future invocation of the same program; the technical term for this is a persistent object. Because pickle is so widely used, many authors who write Python extensions take care to ensure that new data types such as matrices can be properly pickled and unpickled.

Errors and Exceptions

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

8.1. Syntax Errors

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

>>> while True print 'Hello world'

File "", line 1, in ?

while True print 'Hello world'

SyntaxError: invalid syntax

The parser repeats the offending line and displays a little â€˜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 keyword print, since a colon (':') is missing before it. File name and line number are printed so you know where to look in case the input came from a script.

8.2. Exceptions

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

>>> 10 * (/0)

Traceback (most recent call last):

File "", line 1, in ?

ZeroDivisionError: integer division or modulo by zero

>>> 4 + spam*3

Traceback (most recent call last):

File "", line 1, in ?

NameError: name 'spam' is not defined

>>> '2' + 2

Traceback (most recent call last):

File "", line 1, in ?

TypeError: cannot concatenate 'str' and 'int' objects

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.

8.3. Handling Exceptions

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

>>> while True:

... try:

... x = int(raw_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

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 IOError as (errno, strerror): print "I/O error({0}): {1}".format(errno, strerror) 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 IOError: 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 (or tuple).

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 printed directly

... x, y = inst # __getitem__ allows args to be unpacked directly

... print 'x =', x

... print 'y =', y ...

('spam', 'eggs') ('spam', 'eggs') x = spam y = eggs

If an exception has an argument, it is printed as the last part (â€˜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 detail: ... print 'Handling run-time error:', detail ...

Handling run-time error: integer division or modulo by zero

8.4. Raising Exceptions

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

>>> raise NameError('HiThere')

Traceback (most recent call last):

File "", line 1, in ?

NameError: HiThere

The argument to raise is an exception class or instance to be raised. There is a deprecated alternate syntax that separates class and constructor arguments; the above could be written as raise NameError, 'HiThere'. Since it once was the only one available, the latter form is prevalent in older code.

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 "", line 2, in ?

NameError: HiThere

8.5. User-defined Exceptions

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

>>> class MyError(Exception):

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

... def __str__(self): ... return repr(self.value) ...

>>> try:

... raise MyError(2*2) ... except MyError as e:

... print 'My exception occurred, value:', e.value ...

My exception occurred, value: 4

>>> raise MyError('oops!') Traceback (most recent call last):

File "", line 1, in ?

__main__.MyError: 'oops!'

In this example, the default __init__() of Exception has been overridden. The new behavior simply creates the value attribute. This replaces the default behavior of creating the args attribute.

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: expr -- input expression in which the error occurred msg -- explanation of the error

"""

def __init__(self, expr, msg): self.expr = expr self.msg = msg

class TransitionError(Error):

"""Raised when an operation attempts a state transition that's not allowed.

Attributes:

prev -- state at beginning of transition next -- attempted new state

msg -- explanation of why the specific transition is not allowed

"""

def __init__(self, prev, next, msg): self.prev = prev self.next = next self.msg = msg

Most exceptions are defined with names that end in â€œError,â€ 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.

8.6. Defining Clean-up Actions

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

>>> try:

... raise KeyboardInterrupt ... finally:

... print 'Goodbye, world!' ...

Goodbye, world!

KeyboardInterrupt

A finally clause is always executed before leaving the try statement, whether an exception has occurred or not. When an exception has occurred in the try clause and has not been handled by an except clause (or it has occurred in a except or else clause), it is re-raised after the finally clause has been executed. The finally clause is also executed â€œon the way outâ€ when any other clause of the try statement is left via a break,continue or return statement. A more complicated example (having except and finally clauses in the same try statement works as of Python 2.5):

>>> 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

executing finally clause >>> divide(2, 0) division by zero! executing finally clause >>> divide("2", "1") executing finally clause

Traceback (most recent call last):

File "", line 1, in ?

File "", line 3, in divide

TypeError: unsupported operand type(s) for /: 'str' and 'str'

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

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

8.7. Predefined Clean-up Actions

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

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

The problem with this code is that it leaves the file open for an indeterminate amount of time after the code has finished executing. This is not an issue in simple scripts, but can be a problem for larger applications. The withstatement 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

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

Classes

Python's class mechanism adds classes to the language with a minimum of new syntax and semantics. It is a mixture of the class mechanisms found in C++ and Modula-3. As is true for modules, classes in Python do not put an absolute barrier between definition and user, but rather rely on the politeness of the user not to â€œbreak into the definition.â€ The most important features of classes are retained with full power, however: 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 an arbitrary amount of data.

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

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

9.1. A Word About Names and Objects

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

9.2. Python Scopes and Namespaces

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

Let's begin with some definitions.

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

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

Attributes may be read-only or writable. In the latter case, assignment to attributes is possible. Module attributes are writable: you can write modname.the_answer = 42. Writable attributes may also be deleted with the delstatement. 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 __builtin__.)

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. â€œDirectly accessibleâ€ 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. Otherwise, all variables found outside of the innermost scope 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 â€œcompileâ€ time, so don't rely on dynamic name resolution! (In fact, local variables are already determined statically.)

A special quirk of Python is that â€“ if no global statement is in effect â€“ 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.)

9.3. A First Look at Classes

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

9.3.1. Class Definition Syntax

The simplest form of class definition looks like this:

class ClassName: .

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

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

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

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

9.3.2. Class Objects

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

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

class MyClass:

"""A simple example class""" i = 12345 def f(self): return 'hello world'

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

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

x = MyClass()

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

The instantiation operation (â€œcallingâ€ a class object) creates an empty object. Many classes like to create objects with instances customized to a specific initial state. Therefore a class may define a special method named__init__(), like this:

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

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

x = MyClass()

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

>>> class Complex:

... def __init__(self, realpart, imagpart):

... self.r = realpart

... self.i = imagpart ...

>>> x = Complex(3.0, -4.5)

>>> x.r, x.i

(3.0, -4.5)

9.3.3. Instance Objects

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

data attributes correspond to â€œinstance variablesâ€ in Smalltalk, and to â€œdata membersâ€ 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 â€œbelongs toâ€ an object. (In Python, the term method is not unique to class instances: other object types can have methods as well. For example, list objects have methods called append, insert, remove, sort, and so on. However, in the following discussion, we'll use the term method exclusively to mean methods of class instance objects, unless explicitly stated otherwise.)

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

9.3.4. Method Objects

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

x.f()

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

xf = x.f while True: print xf()

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

What exactly happens when a method is called? You may have noticed that x.f() was called without an argument above, even though the function

definition for f() specified an argument. What happened to the argument? Surely Python raises an exception when a function that requires an argument is called without any even if the argument isn't actually used...

Actually, you may have guessed the answer: the special thing about methods is that the 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 object before the first argument.

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

9.4. Random Remarks

Data attributes override method attributes with the same name; to avoid accidental name conflicts, which may cause hard-to-find bugs in large programs, it is wise to use some kind of convention that minimizes the chance of conflicts. Possible conventions include capitalizing method names, prefixing data attribute names with a small unique string (perhaps just an underscore), or using verbs for methods and nouns for data attributes.

Data attributes may be referenced by methods as well as by ordinary users (â€œclientsâ€) 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 the class definition. (The class itself is never used as a global scope.) While one rarely encounters a good reason for using global data in a method, there are many legitimate uses of the global scope: for one thing, functions and modules imported into the global scope can be used by methods, as well as functions and classes defined in it. Usually, the class containing the method is itself defined in this global scope, and in the next section we'll find some good reasons why a method would want to reference its own class.

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

9.5. Inheritance

Of course, a language feature would not be worthy of the name â€œclassâ€ without supporting inheritance. The syntax for a derived class definition looks like this:

class DerivedClassName(BaseClassName): .

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

class DerivedClassName(modname.BaseClassName):

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

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

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

An overriding method in a derived class may in fact want to extend rather than simply replace the base class method of the same name. There is a simple way to call the base class method directly: just call

BaseClassName.methodname(self, 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(unicode, str) is False since unicode is not a subclass of str (they only share a common ancestor, basestring).

9.5.1. Multiple Inheritance

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

class DerivedClassName(Base1, Base2, Base3): .

For old-style classes, the only rule is depth-first, left-to-right. Thus, if an attribute is not found in DerivedClassName, it is searched in Base1, then (recursively) in the base classes of Base1, and only if it is not found there, it is searched in Base2, and so on.

(To some people breadth first searching Base2 and Base3 before the base classes of Base1 looks more natural. However, this would require you to know whether a particular attribute of Base1 is actually defined in Base1 or in one of its base classes before you can figure out the consequences of a name conflict with an attribute of Base2. The depth-first rule makes no differences between direct and inherited attributes ofBase1.)

For new-style classes, 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.

With new-style classes, dynamic ordering is necessary because all cases of multiple inheritance exhibit one or more diamond relationships (where one at least one of the parent classes can be accessed through multiple paths from the bottommost class). For example, all new-style 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.

9.6. Private Variables

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

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

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, eval() or execfile() does not consider the classname of the invoking class to be the current class; this is similar to the effect of the global statement, the effect of which is likewise restricted to code that is byte-compiled together. The same restriction applies to getattr(),

setattr() and delattr(), as well as when referencing __dict__ directly.

9.7. Odds and Ends

Sometimes it is useful to have a data type similar to the Pascal â€œrecordâ€ or C â€œstructâ€, 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.im_self is the instance object with the method m(), and m.im_func is the function object corresponding to the method.

9.8. Exceptions Are Classes Too

User-defined exceptions are identified by classes as well. Using this mechanism it is possible to create extensible hierarchies of exceptions.

There are two new valid (semantic) forms for the raise statement:

raise Class, instance raise instance

In the first form, instance must be an instance of Class or of a class derived from it. The second form is a shorthand for:

raise instance.__class__, instance

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: pass class C(B): pass class D(C): pass

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

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

When an error message is printed for an unhandled exception, the exception's class name is printed, then a colon and a space, and finally the instance converted to a string using the built-in function str().

9.9. Iterators

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

for element in [1, 2, 3]:

print element for element in (1, 2, 3): print element for key in {'one':1, 'two':2}:

print key for char in "123": print char for line in open("myfile.txt"): print line

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. This example shows how it all works:

>>> s = 'abc'

>>> it = iter(s)

>>> it

>>> it.next()

'a'

>>> it.next()

'b'

>>> it.next()

'c'

>>> it.next()

Traceback (most recent call last):

File "", line 1, in ? it.next() 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)

>>> for char in rev: ... print char ... m a p s

9.10. Generators

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

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

>>> for char in reverse('golf'): ... print char ... f l o g

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

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

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

9.11. Generator Expressions

Some simple generators can be coded succinctly as expressions using a syntax similar to list comprehensions but with parentheses instead of 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

>>> from math import pi, sin

>>> sine_table = dict((x, sin(x*pi/180)) for x in range(0, 91))

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

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

>>> data = 'golf'

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

Footnotes

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

Brief Tour of the Standard Library

10.1. Operating System Interface

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

>>> import os

>>> os.system('time 0:02')

>>> os.getcwd() # Return the current working directory

'C:\\Python26'

>>> os.chdir('/server/accesslogs')

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)

>>> help(os)

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

>>> import shutil

>>> shutil.copyfile('data.db', 'archive.db')

>>> shutil.move('/build/executables', 'installdir')

10.2. File Wildcards

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

>>> import glob

>>> glob.glob('*.py')

['primes.py', 'random.py', 'quote.py']

10.3. Command Line Arguments

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

>>> import sys

>>> print sys.argv

['demo.py', 'one', 'two', 'three']

The getopt module processes sys.argv using the conventions of the Unix getopt() function. More powerful and flexible command line processing is

provided by the optparse module.

10.4. Error Output Redirection and Program Termination

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

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

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

10.5. String Pattern Matching

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

>>> import re

>>> re.findall(r'\bf[a-z]*', 'which foot or hand fell fastest')

['foot', 'fell', 'fastest']

>>> re.sub(r'(\b[a-z]+) \1', r'\1', 'cat in the the hat') 'cat in the hat'

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

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

'tea for two'

10.6. Mathematics

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

>>> import math

>>> math.cos(math.pi / 4.0)

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(xrange(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

Detailed course introduction to python classes and objects with examples

Python Classes

Command line and environment

1.1. Command line

1.1.1. Interface options

1.1.2. Generic options

-3

1.1.4. Options you shouldn't use

1.2. Environment variables

Using the Python Interpreter

2.1. Invoking the Interpreter

2.1.1. Argument Passing

2.1.2. Interactive Mode

2.2.1. Error Handling

2.2.2. Executable Python Scripts

2.2.3. Source Code Encoding

2.2.4. The Interactive Startup File

An Informal Introduction to Python

3.1.1. Numbers

4

5

3.1.2. Strings

3.1.3. Unicode Strings

3.1.4. Lists

3.2. First Steps Towards Programming

More Control Flow Tools

4.1. if Statements

4.2. for Statements

4.5. pass Statements

4.6. Defining Functions

4.7. More on Defining Functions

4.7.1. Default Argument Values

4.7.3. Arbitrary Argument Lists

4.7.4. Unpacking Argument Lists

4.7.5. Lambda Forms

4.7.6. Documentation Strings

5.7. More on Conditions

5.8. Comparing Sequences and Other Types

6.1. More on Modules

Classes

9.1. A Word About Names and Objects

9.2. Python Scopes and Namespaces

9.3. A First Look at Classes

9.3.1. Class Definition Syntax

9.3.2. Class Objects

9.6. Private Variables

9.7. Odds and Ends

9.8. Exceptions Are Classes Too