Python Reference Manual

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# Python Reference Manual
Guido van Rossum
Dept. AA, CWI, P.O. Box 94079
1090 GB Amsterdam, The Netherlands
E-mail: guido@cwi.nl
10 April 1995
Release 1.2
Copyright © 1991-1995 by Stichting Mathematisch Centrum,
Amsterdam, The Netherlands.
All Rights Reserved
Permission to use, copy, modify, and distribute this software and its
documentation for any purpose and without fee is hereby granted,
provided that the above copyright notice appear in all copies and that
both that copyright notice and this permission notice appear in
supporting documentation, and that the names of Stichting Mathematisch
Centrum or CWI not be used in advertising or publicity pertaining to
distribution of the software without specific, written prior permission.
STICHTING MATHEMATISCH CENTRUM DISCLAIMS ALL WARRANTIES WITH REGARD TO
THIS SOFTWARE, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY AND
FITNESS, IN NO EVENT SHALL STICHTING MATHEMATISCH CENTRUM BE LIABLE
FOR ANY SPECIAL, INDIRECT OR CONSEQUENTIAL DAMAGES OR ANY DAMAGES
WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS, WHETHER IN AN
ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS ACTION, ARISING OUT
OF OR IN CONNECTION WITH THE USE OR PERFORMANCE OF THIS SOFTWARE.
### Abstract:
Python is a simple, yet powerful, interpreted programming language
that bridges the gap between C and shell programming, and is thus
ideally suited for ``throw-away programming'' and rapid prototyping.
Its syntax is put together from constructs borrowed from a variety of
other languages; most prominent are influences from ABC, C, Modula-3
and Icon.
The Python interpreter is easily extended with new functions and data
types implemented in C. Python is also suitable as an extension
language for highly customizable C applications such as editors or
window managers.
Python is available for various operating systems, amongst which
several flavors of Unix (including Linux), the Apple Macintosh O.S.,
MS-DOS, MS-Windows 3.1, Windows NT, and OS/2.
This reference manual describes the syntax and ``core semantics'' of
the language. It is terse, but attempts to be exact and complete.
The semantics of non-essential built-in object types and of the
built-in functions and modules are described in the Python
Library Reference. For an informal introduction to the language, see
the Python Tutorial.
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- Contents (node1.html#SECTION00100000000000000000)
Introduction (node2.html#SECTION00200000000000000000)
- Notation (node3.html#SECTION00210000000000000000)
Lexical analysis (node4.html#SECTION00300000000000000000)
- Line structure (node5.html#SECTION00310000000000000000)
- Comments (node6.html#SECTION00311000000000000000)
Explicit line joining (node7.html#SECTION00312000000000000000)
Implicit line joining (node8.html#SECTION00313000000000000000)
Blank lines (node9.html#SECTION00314000000000000000)
Indentation (node10.html#SECTION00315000000000000000)
Other tokens (node11.html#SECTION00320000000000000000)
Identifiers (node12.html#SECTION00330000000000000000)
- Keywords (node13.html#SECTION00331000000000000000)
Literals (node14.html#SECTION00340000000000000000)
- String literals (node15.html#SECTION00341000000000000000)
Numeric literals (node16.html#SECTION00342000000000000000)
Operators (node17.html#SECTION00350000000000000000)
Delimiters (node18.html#SECTION00360000000000000000)
Data model (node19.html#SECTION00400000000000000000)
- Objects, values and types (node20.html#SECTION00410000000000000000)
The standard type hierarchy (node21.html#SECTION00420000000000000000)
Special method names (node22.html#SECTION00430000000000000000)
- Special methods for any type (node23.html#SECTION00431000000000000000)
Special methods for attribute access (node24.html#SECTION00432000000000000000)
Special methods for sequence and mapping types (node25.html#SECTION00433000000000000000)
Special methods for sequence types (node26.html#SECTION00434000000000000000)
Special methods for numeric types (node27.html#SECTION00435000000000000000)
Execution model (node28.html#SECTION00500000000000000000)
- Code blocks, execution frames, and name spaces (node29.html#SECTION00510000000000000000)
Exceptions (node30.html#SECTION00520000000000000000)
Expressions and conditions (node31.html#SECTION00600000000000000000)
- Arithmetic conversions (node32.html#SECTION00610000000000000000)
Atoms (node33.html#SECTION00620000000000000000)
- Identifiers (Names) (node34.html#SECTION00621000000000000000)
Literals (node35.html#SECTION00622000000000000000)
Parenthesized forms (node36.html#SECTION00623000000000000000)
List displays (node37.html#SECTION00624000000000000000)
Dictionary displays (node38.html#SECTION00625000000000000000)
String conversions (node39.html#SECTION00626000000000000000)
Primaries (node40.html#SECTION00630000000000000000)
- Attribute references (node41.html#SECTION00631000000000000000)
Subscriptions (node42.html#SECTION00632000000000000000)
Slicings (node43.html#SECTION00633000000000000000)
Calls (node44.html#SECTION00634000000000000000)
Unary arithmetic operations (node45.html#SECTION00640000000000000000)
Binary arithmetic operations (node46.html#SECTION00650000000000000000)
Shifting operations (node47.html#SECTION00660000000000000000)
Binary bit-wise operations (node48.html#SECTION00670000000000000000)
Comparisons (node49.html#SECTION00680000000000000000)
Boolean operations (node50.html#SECTION00690000000000000000)
Expression lists and condition lists (node51.html#SECTION006100000000000000000)
Summary (node52.html#SECTION006110000000000000000)
Simple statements (node53.html#SECTION00700000000000000000)
- Expression statements (node54.html#SECTION00710000000000000000)
Assignment statements (node55.html#SECTION00720000000000000000)
The pass statement (node56.html#SECTION00730000000000000000)
The del statement (node57.html#SECTION00740000000000000000)
The print statement (node58.html#SECTION00750000000000000000)
The return statement (node59.html#SECTION00760000000000000000)
The raise statement (node60.html#SECTION00770000000000000000)
The break statement (node61.html#SECTION00780000000000000000)
The continue statement (node62.html#SECTION00790000000000000000)
The import statement (node63.html#SECTION007100000000000000000)
The global statement (node64.html#SECTION007110000000000000000)
The access statement (node65.html#SECTION007120000000000000000)
The exec statement (node66.html#SECTION007130000000000000000)
Compound statements (node67.html#SECTION00800000000000000000)
- The if statement (node68.html#SECTION00810000000000000000)
The while statement (node69.html#SECTION00820000000000000000)
The for statement (node70.html#SECTION00830000000000000000)
The try statement (node71.html#SECTION00840000000000000000)
Function definitions (node72.html#SECTION00850000000000000000)
Class definitions (node73.html#SECTION00860000000000000000)
Top-level components (node74.html#SECTION00900000000000000000)
- Complete Python programs (node75.html#SECTION00910000000000000000)
File input (node76.html#SECTION00920000000000000000)
Interactive input (node77.html#SECTION00930000000000000000)
Expression input (node78.html#SECTION00940000000000000000)
Index (node79.html#SECTION001000000000000000000)
About this document ... (node80.html#SECTION001100000000000000000)
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guido@cwi.nl


Footnotes

guido@cwi.nl


Contents

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## Contents
- Whetting Your Appetite (node2.html#SECTION00200000000000000000)
- Where From Here (node3.html#SECTION00210000000000000000)
Using the Python Interpreter (node4.html#SECTION00300000000000000000)
- Invoking the Interpreter (node5.html#SECTION00310000000000000000)
- Argument Passing (node6.html#SECTION00311000000000000000)
Interactive Mode (node7.html#SECTION00312000000000000000)
The Interpreter and its Environment (node8.html#SECTION00320000000000000000)
- Error Handling (node9.html#SECTION00321000000000000000)
The Module Search Path (node10.html#SECTION00322000000000000000)
``Compiled'' Python files (node11.html#SECTION00323000000000000000)
Executable Python scripts (node12.html#SECTION00324000000000000000)
The Interactive Startup File (node13.html#SECTION00325000000000000000)
Interactive Input Editing and History Substitution (node14.html#SECTION00330000000000000000)
- Line Editing (node15.html#SECTION00331000000000000000)
History Substitution (node16.html#SECTION00332000000000000000)
Key Bindings (node17.html#SECTION00333000000000000000)
Commentary (node18.html#SECTION00334000000000000000)
An Informal Introduction to Python (node19.html#SECTION00400000000000000000)
- Using Python as a Calculator (node20.html#SECTION00410000000000000000)
- Numbers (node21.html#SECTION00411000000000000000)
Strings (node22.html#SECTION00412000000000000000)
Lists (node23.html#SECTION00413000000000000000)
First Steps Towards Programming (node24.html#SECTION00420000000000000000)
More Control Flow Tools (node25.html#SECTION00500000000000000000)
- If Statements (node26.html#SECTION00510000000000000000)
For Statements (node27.html#SECTION00520000000000000000)
The range() Function (node28.html#SECTION00530000000000000000)
Break and Continue Statements, and Else Clauses on Loops (node29.html#SECTION00540000000000000000)
Pass Statements (node30.html#SECTION00550000000000000000)
Defining Functions (node31.html#SECTION00560000000000000000)
Odds and Ends (node32.html#SECTION00600000000000000000)
- More on Lists (node33.html#SECTION00610000000000000000)
The del statement (node34.html#SECTION00620000000000000000)
Tuples and Sequences (node35.html#SECTION00630000000000000000)
Dictionaries (node36.html#SECTION00640000000000000000)
More on Conditions (node37.html#SECTION00650000000000000000)
Comparing Sequences and Other Types (node38.html#SECTION00660000000000000000)
Modules (node39.html#SECTION00700000000000000000)
- More on Modules (node40.html#SECTION00710000000000000000)
Standard Modules (node41.html#SECTION00720000000000000000)
The dir() function (node42.html#SECTION00730000000000000000)
Output Formatting (node43.html#SECTION00800000000000000000)
Errors and Exceptions (node44.html#SECTION00900000000000000000)
- Syntax Errors (node45.html#SECTION00910000000000000000)
Exceptions (node46.html#SECTION00920000000000000000)
Handling Exceptions (node47.html#SECTION00930000000000000000)
Raising Exceptions (node48.html#SECTION00940000000000000000)
User-defined Exceptions (node49.html#SECTION00950000000000000000)
Defining Clean-up Actions (node50.html#SECTION00960000000000000000)
Classes (node51.html#SECTION001000000000000000000)
- A word about terminology (node52.html#SECTION001010000000000000000)
Python scopes and name spaces (node53.html#SECTION001020000000000000000)
A first look at classes (node54.html#SECTION001030000000000000000)
- Class definition syntax (node55.html#SECTION001031000000000000000)
Class objects (node56.html#SECTION001032000000000000000)
Instance objects (node57.html#SECTION001033000000000000000)
Method objects (node58.html#SECTION001034000000000000000)
Random remarks (node59.html#SECTION001040000000000000000)
Inheritance (node60.html#SECTION001050000000000000000)
- Multiple inheritance (node61.html#SECTION001051000000000000000)
Odds and ends (node62.html#SECTION001060000000000000000)
Recent Additions (node63.html#SECTION001100000000000000000)
- The Last Printed Expression (node64.html#SECTION001110000000000000000)
String Literals (node65.html#SECTION001120000000000000000)
- Double Quotes (node66.html#SECTION001121000000000000000)
Continuation Of String Literals (node67.html#SECTION001122000000000000000)
Triple-quoted strings (node68.html#SECTION001123000000000000000)
String Literal Juxtaposition (node69.html#SECTION001124000000000000000)
The Formatting Operator (node70.html#SECTION001130000000000000000)
- Basic Usage (node71.html#SECTION001131000000000000000)
Referencing Variables By Name (node72.html#SECTION001132000000000000000)
Optional Function Arguments (node73.html#SECTION001140000000000000000)
- Default Argument Values (node74.html#SECTION001141000000000000000)
Arbitrary Argument Lists (node75.html#SECTION001142000000000000000)
Lambda And Functional Programming Tools (node76.html#SECTION001150000000000000000)
- Lambda Forms (node77.html#SECTION001151000000000000000)
Map, Reduce and Filter (node78.html#SECTION001152000000000000000)
- Map. (node79.html#SECTION001152100000000000000)
Filter. (node80.html#SECTION001152200000000000000)
Reduce. (node81.html#SECTION001152300000000000000)
Continuation Lines Without Backslashes (node82.html#SECTION001160000000000000000)
Regular Expressions (node83.html#SECTION001170000000000000000)
Generalized Dictionaries (node84.html#SECTION001180000000000000000)
Miscellaneous New Built-in Functions (node85.html#SECTION001190000000000000000)
Else Clause For Try Statement (node86.html#SECTION0011100000000000000000)
New Class Features in Release 1.1 (node87.html#SECTION0011110000000000000000)
- New Operator Overloading (node88.html#SECTION0011111000000000000000)
Trapping Attribute Access (node89.html#SECTION0011112000000000000000)
Calling a Class Instance (node90.html#SECTION0011113000000000000000)
New in Release 1.2 (node91.html#SECTION001200000000000000000)
- New Class Features (node92.html#SECTION001210000000000000000)
Unix Signal Handling (node93.html#SECTION001220000000000000000)
Exceptions Can Be Classes (node94.html#SECTION001230000000000000000)
Object Persistency and Object Copying (node95.html#SECTION001240000000000000000)
- Persistent Objects (node96.html#SECTION001241000000000000000)
Copying Objects (node97.html#SECTION001242000000000000000)
Documentation Strings (node98.html#SECTION001250000000000000000)
Customizing Import and Built-Ins (node99.html#SECTION001260000000000000000)
Python and the World-Wide Web (node100.html#SECTION001270000000000000000)
Miscellaneous (node101.html#SECTION001280000000000000000)
About this document ... (node102.html#SECTION001300000000000000000)
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guido@cwi.nl


The Module Search Path

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## The Module Search Path
When a module named spam is imported, the interpreter searches
for a file named spam.py in the list of directories specified by
the environment variable PYTHONPATH. It has the same syntax as
the Unix shell variable PATH, i.e., a list of colon-separated
directory names. When PYTHONPATH is not set, or when the file
is not found there, the search continues in an installation-dependent
default path, usually .:/usr/local/lib/python.
Actually, modules are searched in the list of directories given by the
variable sys.path which is initialized from PYTHONPATH and
the installation-dependent default. This allows Python programs that
know what they're doing to modify or replace the module search path.
See the section on Standard Modules later.
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Python and the World-Wide Web

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# Python and the World-Wide Web
There is a growing number of modules available for writing WWW tools.
The previous release already sported modules `gopherlib`,
`ftplib`, `httplib` and `urllib` (which unifies the
other three) for accessing data through the commonest WWW protocols.
This release also provides `cgi`, to ease the writing of
server-side scripts that use the Common Gateway Interface protocol,
supported by most WWW servers. The module `urlparse` provides
precise parsing of a URL string into its components (address scheme,
network location, path, parameters, query, and fragment identifier).
A rudimentary, parser for HTML files is available in the module
`htmllib`. It currently supports a subset of HTML 1.0 (if you
bring it up to date, I'd love to receive your fixes!). Unfortunately
Python seems to be too slow for real-time parsing and formatting of
HTML such as required by interactive WWW browsers --- but it's good
enough to write a ``robot'' (an automated WWW browser that searches
the web for information).
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Miscellaneous

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# Miscellaneous
- The `socket` module now exports all the needed constants used for
socket operations, such as `SO_BROADCAST`.
The functions `popen()` and `fdopen()` in the `os`
module now follow the pattern of the built-in function `open()`:
the default mode argument is `'r'` and the optional third
argument specifies the buffer size, where `0` means unbuffered,
`1` means line-buffered, and any larger number means the size of
the buffer in bytes.
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guido@cwi.nl


About this document ...

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# About this document ...
Python Tutorial
This document was generated using the LaTeX2HTML (http://cbl.leeds.ac.uk/nikos/tex2html/doc/latex2html/latex2html.html) translator Version 95.1 (Fri Jan 20 1995) Copyright © 1993, 1994, Nikos Drakos (http://cbl.leeds.ac.uk/nikos/personal.html), Computer Based Learning Unit, University of Leeds.
The command line arguments were:
latex2html -address guido@cwi.nl -dont_include myformat tut.tex.
The translation was initiated by Guido van Rossum on Tue Apr 11 00:17:44 MDT 1995
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``Compiled'' Python files

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## ``Compiled'' 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-``compiled'' 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 file is ignored if
these don't match.
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.
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Executable Python scripts

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## Executable Python scripts
On BSD'ish Unix systems, Python scripts can be made directly
executable, like shell scripts, by putting the line
```text
#! /usr/local/bin/python
```
(assuming that's the name of the interpreter) at the beginning of the
script and giving the file an executable mode. The #! must be
the first two characters of the file.
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The Interactive Startup File

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## 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 name space 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, e.g.
`execfile('.pythonrc')`. If you want to use the startup file
in a script, you must write this explicitly in the script, e.g.
`import os;` `execfile(os.environ['PYTHONSTARTUP'])`.
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# Interactive Input Editing and History Substitution
Some versions of the Python interpreter support editing of the current
input line and history substitution, similar to facilities found in
the Korn shell and the GNU Bash shell. This is implemented using the
GNU Readline library, which supports Emacs-style and vi-style
editing. This library has its own documentation which I won't
duplicate here; however, the basics are easily explained.
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. If nothing appears to
happen, or if `^P` is echoed, you can skip the rest of this
section.
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History Substitution (node16.html#SECTION00332000000000000000)
Key Bindings (node17.html#SECTION00333000000000000000)
Commentary (node18.html#SECTION00334000000000000000)
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guido@cwi.nl


Line Editing

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## Line Editing
If supported, input line editing is active whenever the interpreter
prints a primary or secondary prompt. The current line can be edited
using the conventional Emacs control characters. The most important
of these are: C-A (Control-A) moves the cursor to the beginning of the
line, C-E to the end, C-B moves it one position to the left, C-F to
the right. Backspace erases the character to the left of the cursor,
C-D the character to its right. C-K kills (erases) the rest of the
line to the right of the cursor, C-Y yanks back the last killed
string. C-underscore undoes the last change you made; it can be
repeated for cumulative effect.
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guido@cwi.nl


History Substitution

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## History Substitution
History substitution works as follows. All non-empty input lines
issued are saved in a history buffer, and when a new prompt is given
you are positioned on a new line at the bottom of this buffer. C-P
moves one line up (back) in the history buffer, C-N moves one down.
Any line in the history buffer can be edited; an asterisk appears in
front of the prompt to mark a line as modified. Pressing the Return
key passes the current line to the interpreter. C-R starts an
incremental reverse search; C-S starts a forward search.
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guido@cwi.nl


Key Bindings

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## Key Bindings
The key bindings and some other parameters of the Readline library can
be customized by placing commands in an initialization file called
$HOME/.inputrc. Key bindings have the form
```text
key-name: function-name
```
or
```text
"string": function-name
```
and options can be set with
```text
set option-name value
```
For example:
```text
# I prefer vi-style editing:
set editing-mode vi
# Edit using a single line:
set horizontal-scroll-mode On
# Rebind some keys:
Meta-h: backward-kill-word
"\C-u": universal-argument
"\C-x\C-r": re-read-init-file
```
Note that the default binding for TAB in Python is to insert a TAB
instead of Readline's default filename completion function. If you
insist, you can override this by putting
```text
TAB: complete
```
in your $HOME/.inputrc. (Of course, this makes it hard to type
indented continuation lines...)
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Commentary

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## Commentary
This facility is an enormous step forward compared to previous
versions of the interpreter; however, some wishes are left: It would
be nice if the proper indentation were suggested on continuation lines
(the parser knows if an indent token is required next). The
completion mechanism might use the interpreter's symbol table. A
command to check (or even suggest) matching parentheses, quotes etc.
would also be useful.
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An Informal Introduction to Python

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# 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.
footnode.html#108
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.
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- Using Python as a Calculator (node20.html#SECTION00410000000000000000)
- Numbers (node21.html#SECTION00411000000000000000)
Strings (node22.html#SECTION00412000000000000000)
Lists (node23.html#SECTION00413000000000000000)
First Steps Towards Programming (node24.html#SECTION00420000000000000000)
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guido@cwi.nl


Whetting Your Appetite

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# Whetting Your Appetite
If you ever wrote a large shell script, you probably know this
feeling: you'd love to add yet another feature, but it's already so
slow, and so big, and so complicated; or the feature involves a system
call or other function that is only accessible from C ...Usually
the problem at hand isn't serious enough to warrant rewriting the
script in C; perhaps because the problem requires variable-length
strings or other data types (like sorted lists of file names) that are
easy in the shell but lots of work to implement in C; or perhaps just
because you're not sufficiently familiar with C.
In such cases, Python may be just the language for you. Python is
simple to use, but it is a real programming language, offering much
more structure and support for large programs than the shell has. On
the other hand, it also offers much more error checking than C, and,
being a very-high-level language, it has high-level data types
built in, such as flexible arrays and dictionaries that would cost you
days to implement efficiently in C. Because of its more general data
types Python is applicable to a much larger problem domain than
Awk or even Perl, yet many things are at least as easy in
Python as in those languages.
Python allows you to split up your program in modules that can be
reused in other Python programs. It comes with a large collection of
standard modules that you can use as the basis of your programs --- or
as examples to start learning to program in Python. There are also
built-in modules that provide things like file I/O, system calls,
sockets, and even a generic interface to window systems (STDWIN).
Python is an interpreted language, which can save you considerable time
during program development because no compilation and linking is
necessary. The interpreter can be used interactively, which makes it
easy to experiment with features of the language, to write throw-away
programs, or to test functions during bottom-up program development.
It is also a handy desk calculator.
Python allows writing very compact and readable programs. Programs
written in Python are typically much shorter than equivalent C
programs, for several reasons:
- the high-level data types allow you to express complex operations in a
single statement;
statement grouping is done by indentation instead of begin/end
brackets;
no variable or argument declarations are necessary.
Python is extensible: if you know how to program in C it is easy
to add a new built-in
function or
module to the interpreter, either to
perform critical operations at maximum speed, or to link Python
programs to libraries that may only be available in binary form (such
as a vendor-specific graphics library). Once you are really hooked,
you can link the Python interpreter into an application written in C
and use it as an extension or command language for that application.
By the way, the language is named after the BBC show ``Monty
Python's Flying Circus'' and has nothing to do with nasty reptiles...
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Using Python as a Calculator

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# 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.)
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- Numbers (node21.html#SECTION00411000000000000000)
Strings (node22.html#SECTION00412000000000000000)
Lists (node23.html#SECTION00413000000000000000)
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Numbers

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## 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 (e.g., Pascal or C); parentheses
can be used for grouping. For example:
```text
>>> 2+2
4
>>> # This is a comment
... 2+2
4
>>> 2+2 # and a comment on the same line as code
4
>>> (50-5*6)/4
5
>>> # Integer division returns the floor:
... 7/3
2
>>> 7/-3
-3
>>>
```
Like in C, the equal sign (=) is used to assign a value to a
variable. The value of an assignment is not written:
```text
>>> width = 20
>>> height = 5*9
>>> width * height
900
>>>
```
A value can be assigned to several variables simultaneously:
```text
>>> x = y = z = 0 # Zero x, y and z
>>> x
0
>>> y
0
>>> z
0
>>>
```
There is full support for floating point; operators with mixed type
operands convert the integer operand to floating point:
```text
>>> 4 * 2.5 / 3.3
3.0303030303
>>> 7.0 / 2
3.5
>>>
```
---
guido@cwi.nl


Strings

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## Strings
Besides numbers, Python can also manipulate strings, enclosed in
single quotes or double quotes:
```text
>>> '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.'
>>>
```
Strings are written 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 *:
```text
>>> word = 'Help' + 'A'
>>> word
'HelpA'
>>> '<' + word*5 + '>'
'<HelpAHelpAHelpAHelpAHelpA>'
>>>
```
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.
```text
>>> 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.
```text
>>> word[:2] # The first two characters
'He'
>>> word[2:] # All but the first two characters
'lpA'
>>>
```
Here's a useful invariant of slice operations: `s[:i] + s[i:]`
equals `s`.
```text
>>> 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.
```text
>>> word[1:100]
'elpA'
>>> word[10:]
''
>>> word[2:1]
''
>>>
```
Indices may be negative numbers, to start counting from the right.
For example:
```text
>>> word[-1] # The last character
'A'
>>> word[-2] # The last-but-one character
'p'
>>> word[-2:] # The last two characters
'pA'
>>> word[:-2] # All but the last two characters
'Hel'
>>>
```
But note that -0 is really the same as 0, so it does not count from
the right!
```text
>>> word[-0] # (since -0 equals 0)
'H'
>>>
```
Out-of-range negative slice indices are truncated, but don't try this
for single-element (non-slice) indices:
```text
>>> word[-100:]
'HelpA'
>>> word[-10] # error
Traceback (innermost last):
File "<stdin>", line 1
IndexError: string index out of range
>>>
```
The best 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:
```text
+---+---+---+---+---+
| 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 nonnegative indices, the length of a slice is the difference of
the indices, if both are within bounds, e.g., the length of
`word[1:3]` is 2.
The built-in function len() returns the length of a string:
```text
>>> s = 'supercalifragilisticexpialidocious'
>>> len(s)
34
>>>
```
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---
guido@cwi.nl


Lists

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## 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.
```text
>>> 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:
```text
>>> 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] + ['Boe!']
['spam', 'eggs', 100, 'spam', 'eggs', 100, 'spam', 'eggs', 100, 'Boe!']
>>>
```
Unlike strings, which are immutable, it is possible to change
individual elements of a list:
```text
>>> 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:
```text
>>> # 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]
>>> a[:0] = a # Insert (a copy of) itself at the beginning
>>> a
[123, 'bletch', 'xyzzy', 1234, 123, 'bletch', 'xyzzy', 1234]
>>>
```
The built-in function len() also applies to lists:
```text
>>> len(a)
8
>>>
```
It is possible to nest lists (create lists containing other lists),
for example:
```text
>>> q = [2, 3]
>>> p = [1, q, 4]
>>> len(p)
3
>>> p[1]
[2, 3]
>>> p[1][0]
2
>>> 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.
---
guido@cwi.nl


First Steps Towards Programming

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# 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
subsequence of the Fibonacci series as follows:
```text
>>> # Fibonacci series:
... # the sum of two elements defines the next
... a, b = 0, 1
>>> while b < 10:
... print b
... a, b = b, a+b
...
1
1
2
3
5
8
>>>
```
This example introduces several new features.
- The first line contains a multiple assignment: the variables
a and b simultaneously get the new values 0 and 1. On the
last line this is used again, demonstrating that the expressions on
the right-hand side are all evaluated first before any of the
assignments take place.
The 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: <, >, ==, <=, >= and !=.
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).
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:
```text
>>> 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:
```text
>>> 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.
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guido@cwi.nl


More Control Flow Tools

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# More Control Flow Tools
Besides the while statement just introduced, Python knows the
usual control flow statements known from other languages, with some
twists.
---
- If Statements (node26.html#SECTION00510000000000000000)
For Statements (node27.html#SECTION00520000000000000000)
The range() Function (node28.html#SECTION00530000000000000000)
Break and Continue Statements, and Else Clauses on Loops (node29.html#SECTION00540000000000000000)
Pass Statements (node30.html#SECTION00550000000000000000)
Defining Functions (node31.html#SECTION00560000000000000000)
---
guido@cwi.nl


If Statements

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# If Statements
Perhaps the most well-known statement type is the if statement.
For example:
```text
>>> 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 `elif' is short for `else if', and is
useful to avoid excessive indentation. An if...elif...elif...
sequence is a substitute for the switch or case statements
found in other languages.
---
guido@cwi.nl


For Statements

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# 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 leaving the user
completely free in the iteration test and step (as C), Python's
for statement iterates over the items of any sequence (e.g., a list
or a string), in the order that they appear in the sequence. For
example (no pun intended):
```text
>>> # 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, i.e., lists). If
you need to modify the list you are iterating over, e.g., duplicate
selected items, you must iterate over a copy. The slice notation
makes this particularly convenient:
```text
>>> 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']
>>>
```
---
guido@cwi.nl


The range() Function

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# 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, e.g.:
```text
>>> 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, exactly 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):
```text
>>> 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, combine range() and
len() as follows:
```text
>>> 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
>>>
```
---
guido@cwi.nl


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---
# 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 break statement. This is exemplified by the
following loop, which searches for prime numbers:
```text
>>> for n in range(2, 10):
... for x in range(2, n):
... if n % x == 0:
... print n, 'equals', x, '*', n/x
... break
... else:
... print n, 'is a prime number'
...
2 is a prime number
3 is a prime number
4 equals 2 * 2
5 is a prime number
6 equals 2 * 3
7 is a prime number
8 equals 2 * 4
9 equals 3 * 3
>>>
```
---
guido@cwi.nl


Where From Here

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# Where From Here
Now that you are all excited about Python, you'll want to examine it
in some more detail. Since the best way to learn a language is
using it, you are invited here to do so.
In the next chapter, the mechanics of using the interpreter are
explained. This is rather mundane information, but essential for
trying out the examples shown later.
The rest of the tutorial introduces various features of the Python
language and system though examples, beginning with simple
expressions, statements and data types, through functions and modules,
and finally touching upon advanced concepts like exceptions
and user-defined classes.
When you're through with the tutorial (or just getting bored), you
should read the Library Reference, which gives complete (though terse)
reference material about built-in and standard types, functions and
modules that can save you a lot of time when writing Python programs.
---
guido@cwi.nl


Pass Statements

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# 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:
```text
>>> while 1:
... pass # Busy-wait for keyboard interrupt
...
```
---
guido@cwi.nl


Defining Functions

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---
# Defining Functions
We can create a function that writes the Fibonacci series to an
arbitrary boundary:
```text
>>> def fib(n): # write Fibonacci series up to n
... a, b = 0, 1
... while b < n:
... print b,
... a, b = b, a+b
...
>>> # Now call the function we just defined:
... fib(2000)
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 starts at
the next line, indented by a tab stop.
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 global symbol table, and then 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.
footnode.html#550
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:
```text
>>> fib
<function object at 10042ed0>
>>> f = fib
>>> f(100)
1 1 2 3 5 8 13 21 34 55 89
>>>
```
You might object that fib is not a function but a procedure. In
Python, like in C, procedures are just functions that don't return a
value. In fact, technically speaking, procedures 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:
```text
>>> 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:
```text
>>> def fib2(n): # return Fibonacci series up to n
... result = []
... a, b = 0, 1
... while b < n:
... result.append(b) # see below
... a, b = b, a+b
... return result
...
>>> f100 = fib2(100) # call it
>>> f100 # write the result
[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 is used to return from the middle
of a procedure (falling off the end also returns from a procedure), in
which case the None value is returned.
The statement result.append(b) calls a method of the list
object result. A method is a function that `belongs' to an
object and is named obj.methodname, where obj is some
object (this may be an expression), and methodname is the name
of a method that is defined by the object's type. Different types
define different methods. Methods of different types may have the
same name without causing ambiguity. (It is possible to define your
own object types and methods, using classes, as discussed later
in this tutorial.)
The method append shown in the example, is defined for
list objects; it adds a new element at the end of the list. In this
example
it is equivalent to result = result + [b], but more efficient.
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guido@cwi.nl


Odds and Ends

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# Odds and Ends
This chapter describes some things you've learned about already in
more detail, and adds some new things as well.
---
- More on Lists (node33.html#SECTION00610000000000000000)
The del statement (node34.html#SECTION00620000000000000000)
Tuples and Sequences (node35.html#SECTION00630000000000000000)
Dictionaries (node36.html#SECTION00640000000000000000)
More on Conditions (node37.html#SECTION00650000000000000000)
Comparing Sequences and Other Types (node38.html#SECTION00660000000000000000)
---
guido@cwi.nl


More on Lists

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---
# More on Lists
The list data type has some more methods. Here are all of the methods
of lists objects:
An example that uses all list methods:
```text
>>> a = [66.6, 333, 333, 1, 1234.5]
>>> print a.count(333), a.count(66.6), a.count('x')
2 1 0
>>> a.insert(2, -1)
>>> a.append(333)
>>> a
[66.6, 333, -1, 333, 1, 1234.5, 333]
>>> a.index(333)
1
>>> a.remove(333)
>>> a
[66.6, -1, 333, 1, 1234.5, 333]
>>> a.reverse()
>>> a
[333, 1234.5, 1, 333, -1, 66.6]
>>> a.sort()
>>> a
[-1, 1, 66.6, 333, 333, 1234.5]
>>>
```
---
guido@cwi.nl


The del statement

---
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---
# 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 can also be used to
remove slices from a list (which we did earlier by assignment of an
empty list to the slice). For example:
```text
>>> a
[-1, 1, 66.6, 333, 333, 1234.5]
>>> del a[0]
>>> a
[1, 66.6, 333, 333, 1234.5]
>>> del a[2:4]
>>> a
[1, 66.6, 1234.5]
>>>
```
del can also be used to delete entire variables:
```text
>>> 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.
---
guido@cwi.nl


Tuples and Sequences

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---
# Tuples and Sequences
We saw that lists and strings have many common properties, e.g.,
indexing and slicing operations. They are two examples of
sequence data types. 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:
```text
>>> 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 alway 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, e.g., (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).
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:
```text
>>> empty = ()
>>> singleton = 'hello', # <-- note trailing comma
>>> len(empty)
0
>>> len(singleton)
1
>>> singleton
('hello',)
>>>
```
The statement t = 12345, 54321, 'hello!' is an example of
tuple packing: the values 12345, 54321 and 'hello!'
are packed together in a tuple. The reverse operation is also
possible, e.g.:
```text
>>> x, y, z = t
>>>
```
This is called, appropriately enough, tuple unpacking. Tuple
unpacking requires that the list of variables on the left has the same
number of elements as the length of the tuple. Note that multiple
assignment is really just a combination of tuple packing and tuple
unpacking!
Occasionally, the corresponding operation on lists is useful: list
unpacking. This is supported by enclosing the list of variables in
square brackets:
```text
>>> a = ['spam', 'eggs', 100, 1234]
>>> [a1, a2, a3, a4] = a
>>>
```
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guido@cwi.nl


Dictionaries

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---
# Dictionaries
Another useful data type built into Python is the dictionary.
Dictionaries are sometimes found in other languages as ``associative
memories'' or ``associative arrays''. Unlike sequences, which are
indexed by a range of numbers, dictionaries are indexed by keys,
which are strings (the use of non-string values as keys
is supported, but beyond the scope of this tutorial).
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 random 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 `has_key()`
method of the dictionary.
Here is a small example using a dictionary:
```text
>>> 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']
>>> tel.has_key('guido')
1
>>>
```
---
guido@cwi.nl


More on Conditions

---
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---
# More on Conditions
The conditions used in while and if statements above can
contain other operators besides 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: e.g., a < b == c tests whether a
is less than b and moreover b equals c.
Comparisons may be combined by the Boolean operators and and
or, and the outcome of a comparison (or of any other Boolean
expression) may be negated with not. These all have lower
priorities than comparison operators again; 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. Of
course, parentheses can be used to express the desired composition.
The Boolean operators and and or are so-called
shortcut operators: their arguments are evaluated from left to right,
and evaluation stops as soon as the outcome is determined. E.g., if
A and C are true but B is false, A and B and C
does not evaluate the expression C. In general, the return value of a
shortcut operator, when used as a general value and not as a Boolean, is
the last evaluated argument.
It is possible to assign the result of a comparison or other Boolean
expression to a variable. For example,
```text
>>> 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.
---
guido@cwi.nl


Comparing Sequences and Other Types

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# 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 subsequence of the other, the
shorted sequence is the smaller one. Lexicographical ordering for
strings uses the ASCII ordering for individual characters. Some
examples of comparisons between sequences with the same types:
```text
(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. Mixed numeric types are compared according
to their numeric value, so 0 equals 0.0, etc.
footnode.html#275
---
guido@cwi.nl


Modules

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---
# 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:
```text
# 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:
```text
>>> 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:
```text
>>> 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:
```text
>>> fib = fibo.fib
>>> fib(500)
1 1 2 3 5 8 13 21 34 55 89 144 233 377
>>>
```
---
- More on Modules (node40.html#SECTION00710000000000000000)
Standard Modules (node41.html#SECTION00720000000000000000)
The dir() function (node42.html#SECTION00730000000000000000)
---
node40.html tut.html node38.html node1.html
---
---
guido@cwi.nl


Using the Python Interpreter

---
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---
# Using the Python Interpreter
---
- Invoking the Interpreter (node5.html#SECTION00310000000000000000)
- Argument Passing (node6.html#SECTION00311000000000000000)
Interactive Mode (node7.html#SECTION00312000000000000000)
The Interpreter and its Environment (node8.html#SECTION00320000000000000000)
- Error Handling (node9.html#SECTION00321000000000000000)
The Module Search Path (node10.html#SECTION00322000000000000000)
``Compiled'' Python files (node11.html#SECTION00323000000000000000)
Executable Python scripts (node12.html#SECTION00324000000000000000)
The Interactive Startup File (node13.html#SECTION00325000000000000000)
Interactive Input Editing and History Substitution (node14.html#SECTION00330000000000000000)
- Line Editing (node15.html#SECTION00331000000000000000)
History Substitution (node16.html#SECTION00332000000000000000)
Key Bindings (node17.html#SECTION00333000000000000000)
Commentary (node18.html#SECTION00334000000000000000)
---
guido@cwi.nl


More on Modules

---
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---
# 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.
footnode.html#288
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:
```text
>>> 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:
```text
>>> 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
(_).
---
guido@cwi.nl


Standard Modules

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# Standard Modules
Python comes with a library of standard modules, described in a separate
document (Python Library Reference). 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; e.g.,
the amoeba module is only provided on systems that somehow support
Amoeba primitives. 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:
```text
>>> import sys
>>> sys.ps1
'>>> '
>>> sys.ps2
'... '
>>> sys.ps1 = 'C> '
C> print 'Yuck!'
Yuck!
C>
```
These two variables are only defined if the interpreter is in
interactive mode.
The variable
sys.path
is a list of strings that determine the interpreter's search path for
modules.
It is initialized to a default path taken from the environment variable
PYTHONPATH,
or from a built-in default if
PYTHONPATH
is not set.
You can modify it using standard list operations, e.g.:
```text
>>> import sys
>>> sys.path.append('/ufs/guido/lib/python')
>>>
```
---
guido@cwi.nl


The dir() function

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---
# 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:
```text
>>> import fibo, sys
>>> dir(fibo)
['__name__', 'fib', 'fib2']
>>> dir(sys)
['__name__', 'argv', 'builtin_module_names', 'copyright', 'exit',
'maxint', 'modules', 'path', 'ps1', 'ps2', 'setprofile', 'settrace',
'stderr', 'stdin', 'stdout', 'version']
>>>
```
Without arguments, dir() lists the names you have defined currently:
```text
>>> a = [1, 2, 3, 4, 5]
>>> import fibo, sys
>>> fib = fibo.fib
>>> dir()
['__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__:
```text
>>> import __builtin__
>>> dir(__builtin__)
['AccessError', 'AttributeError', 'ConflictError', 'EOFError', 'IOError',
'ImportError', 'IndexError', 'KeyError', 'KeyboardInterrupt',
'MemoryError', 'NameError', 'None', 'OverflowError', 'RuntimeError',
'SyntaxError', 'SystemError', 'SystemExit', 'TypeError', 'ValueError',
'ZeroDivisionError', '__name__', 'abs', 'apply', 'chr', 'cmp', 'coerce',
'compile', 'dir', 'divmod', 'eval', 'execfile', 'filter', 'float',
'getattr', 'hasattr', 'hash', 'hex', 'id', 'input', 'int', 'len', 'long',
'map', 'max', 'min', 'oct', 'open', 'ord', 'pow', 'range', 'raw_input',
'reduce', 'reload', 'repr', 'round', 'setattr', 'str', 'type', 'xrange']
>>>
```
---
guido@cwi.nl


Output Formatting

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---
# 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. The key to nice formatting in
Python is to do all the string handling yourself; using string slicing
and concatenation operations you can create any lay-out you can imagine.
The standard module string contains some useful operations for
padding strings to a given column width; these will be discussed shortly.
Finally, the `%` operator (modulo) with a string left argument
interprets this string as a C sprintf format string to be applied to the
right argument, and returns the string resulting from this formatting
operation.
One question remains, of course: how do you convert values to strings?
Luckily, Python has a way to convert any value to a string: just write
the value between reverse quotes (````). Some examples:
```text
>>> x = 10 * 3.14
>>> y = 200*200
>>> s = 'The value of x is ' + `x` + ', and y is ' + `y` + '...'
>>> print s
The value of x is 31.4, and y is 40000...
>>> # Reverse quotes work on other types besides numbers:
... p = [x, y]
>>> ps = `p`
>>> ps
'[31.4, 40000]'
>>> # Converting a string adds string quotes and backslashes:
... hello = 'hello, world\n'
>>> hellos = `hello`
>>> print hellos
'hello, world\012'
>>> # The argument of reverse quotes may be a tuple:
... `x, y, ('spam', 'eggs')`
"(31.4, 40000, ('spam', 'eggs'))"
>>>
```
Here are two ways to write a table of squares and cubes:
```text
>>> import string
>>> for x in range(1, 11):
... print string.rjust(`x`, 2), string.rjust(`x*x`, 3),
... # Note trailing comma on previous line
... print string.rjust(`x*x*x`, 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 '%2d %3d %4d' % (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 one space between each column was added by the way print
works: it always adds spaces between its arguments.)
This example demonstrates the function string.rjust(), which
right-justifies a string in a field of a given width by padding it with
spaces on the left. There are similar functions string.ljust()
and string.center(). These functions 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 string.ljust(x, n)[0:n].)
There is another function, string.zfill, which pads a numeric
string on the left with zeros. It understands about plus and minus
signs:
```text
>>> string.zfill('12', 5)
'00012'
>>> string.zfill('-3.14', 7)
'-003.14'
>>> string.zfill('3.14159265359', 5)
'3.14159265359'
>>>
```
---
node44.html tut.html node42.html node1.html
---
---
guido@cwi.nl


Errors and Exceptions

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---
# 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.
---
- Syntax Errors (node45.html#SECTION00910000000000000000)
Exceptions (node46.html#SECTION00920000000000000000)
Handling Exceptions (node47.html#SECTION00930000000000000000)
Raising Exceptions (node48.html#SECTION00940000000000000000)
User-defined Exceptions (node49.html#SECTION00950000000000000000)
Defining Clean-up Actions (node50.html#SECTION00960000000000000000)
---
guido@cwi.nl


Syntax Errors

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---
# 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:
```text
>>> while 1 print 'Hello world'
File "<stdin>", line 1
while 1 print 'Hello world'
^
SyntaxError: invalid syntax
>>>
```
The parser repeats the offending line and displays a little `arrow'
pointing at the earliest point in the line where the error was detected.
The error is caused by (or at least detected at) the token
preceding
the arrow: in the example, the error is detected at the 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.
---
guido@cwi.nl


Exceptions

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---
# 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:
```text
>>> 10 * (1/0)
Traceback (innermost last):
File "<stdin>", line 1
ZeroDivisionError: integer division or modulo
>>> 4 + spam*3
Traceback (innermost last):
File "<stdin>", line 1
NameError: spam
>>> '2' + 2
Traceback (innermost last):
File "<stdin>", line 1
TypeError: illegal argument type for built-in operation
>>>
```
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
name for the 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 is a detail whose interpretation depends on the
exception type; its meaning is dependent on the exception type.
The preceding part of the error message shows the context where the
exception happened, in the form of a stack backtrace.
In general it contains a stack backtrace listing source lines; however,
it will not display lines read from standard input.
The Python library reference manual lists the built-in exceptions and
their meanings.
---
guido@cwi.nl


Handling Exceptions

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---
# Handling Exceptions
It is possible to write programs that handle selected exceptions.
Look at the following example, which prints a table of inverses of
some floating point numbers:
```text
>>> numbers = [0.3333, 2.5, 0, 10]
>>> for x in numbers:
... print x,
... try:
... print 1.0 / x
... except ZeroDivisionError:
... print '*** has no inverse ***'
...
0.3333 3.00030003
2.5 0.4
0 *** has no inverse ***
10 0.1
>>>
```
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 rest of the try clause is skipped, 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 list,
e.g.:
```text
... 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!
When an exception occurs, it may have an associated value, also known as
the exceptions's
argument.
The presence and type of the argument depend on the exception type.
For exception types which have an argument, the except clause may
specify a variable after the exception name (or list) to receive the
argument's value, as follows:
```text
>>> try:
... spam()
... except NameError, x:
... print 'name', x, 'undefined'
...
name spam undefined
>>>
```
If an exception has an argument, it is printed as the last part
(`detail') of the message for unhandled exceptions.
Exception handlers don't just handle exceptions if they occur
immediately in the try clause, but also if they occur inside functions
that are called (even indirectly) in the try clause.
For example:
```text
>>> def this_fails():
... x = 1/0
...
>>> try:
... this_fails()
... except ZeroDivisionError, detail:
... print 'Handling run-time error:', detail
...
Handling run-time error: integer division or modulo
>>>
```
---
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---
---
guido@cwi.nl


Raising Exceptions

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---
# Raising Exceptions
The raise statement allows the programmer to force a specified
exception to occur.
For example:
```text
>>> raise NameError, 'HiThere'
Traceback (innermost last):
File "<stdin>", line 1
NameError: HiThere
>>>
```
The first argument to raise names the exception to be raised.
The optional second argument specifies the exception's argument.
---
guido@cwi.nl


User-defined Exceptions

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---
# User-defined Exceptions
Programs may name their own exceptions by assigning a string to a
variable.
For example:
```text
>>> my_exc = 'my_exc'
>>> try:
... raise my_exc, 2*2
... except my_exc, val:
... print 'My exception occurred, value:', val
...
My exception occurred, value: 4
>>> raise my_exc, 1
Traceback (innermost last):
File "<stdin>", line 1
my_exc: 1
>>>
```
Many standard modules use this to report errors that may occur in
functions they define.
---
guido@cwi.nl


Invoking the Interpreter

---
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---
# 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
```text
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.)
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 third 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 best to quote
command in its entirety with double quotes.
Note that there is a difference between ``python file'' and
``python <file''. In the latter case, input requests from the
program, such as calls to 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 EOF 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.)
---
- Argument Passing (node6.html#SECTION00311000000000000000)
Interactive Mode (node7.html#SECTION00312000000000000000)
---
node6.html node4.html node4.html node1.html
---
---
guido@cwi.nl


Defining Clean-up Actions

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---
# 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:
```text
>>> try:
... raise KeyboardInterrupt
... finally:
... print 'Goodbye, world!'
...
Goodbye, world!
Traceback (innermost last):
File "<stdin>", line 2
KeyboardInterrupt
>>>
```
A finally clause is executed whether or not an exception has
occurred in the try clause. When an exception has occurred, it
is re-raised after the finally clause is executed. The
finally clause is also executed ``on the way out'' when the
try statement is left via a break or return
statement.
A try statement must either have one or more except
clauses or one finally clause, but not both.
---
guido@cwi.nl


Classes

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---
# 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 ``break into the
definition.'' 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(es), a method can call the method of a base class with the
same name. Objects can contain an arbitrary amount of private data.
In C++ terminology, all class members (including the data members) are
public, and all member functions are virtual. There are
no special constructors or destructors. 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, albeit in the wider
sense of the word: in Python, all data types are objects. This
provides semantics for importing and renaming. But, just like in C++
or Modula-3, built-in types cannot be used as base classes for
extension by the user. Also, like in C++ but unlike in Modula-3, most
built-in operators with special syntax (arithmetic operators,
subscripting etc.) can be redefined for class members.
---
- A word about terminology (node52.html#SECTION001010000000000000000)
Python scopes and name spaces (node53.html#SECTION001020000000000000000)
A first look at classes (node54.html#SECTION001030000000000000000)
- Class definition syntax (node55.html#SECTION001031000000000000000)
Class objects (node56.html#SECTION001032000000000000000)
Instance objects (node57.html#SECTION001033000000000000000)
Method objects (node58.html#SECTION001034000000000000000)
Random remarks (node59.html#SECTION001040000000000000000)
Inheritance (node60.html#SECTION001050000000000000000)
- Multiple inheritance (node61.html#SECTION001051000000000000000)
Odds and ends (node62.html#SECTION001060000000000000000)
---
guido@cwi.nl


A word about terminology

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# A word about terminology
Lacking universally accepted terminology to talk about classes, I'll
make occasional use of Smalltalk and C++ terms. (I'd 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...)
I also have to warn you that there's a terminological pitfall for
object-oriented readers: the word ``object'' in Python does not
necessarily mean a class instance. Like C++ and Modula-3, and unlike
Smalltalk, not all types in Python are classes: the basic built-in
types like integers and lists aren't, and even somewhat more exotic
types like files aren't. However, all Python types share a little
bit of common semantics that is best described by using the word
object.
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 an
(intended!) effect on the semantics of Python code involving mutable
objects such as lists, dictionaries, and most types representing
entities outside the program (files, windows, etc.). 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
obviates the need for two different argument passing mechanisms as in
Pascal.
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Python scopes and name spaces

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# Python scopes and name spaces
Before introducing classes, I first have to tell you something about
Python's scope rules. Class definitions play some neat tricks with
name spaces, and you need to know how scopes and name spaces 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 name space is a mapping from names to objects. Most name
spaces 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 name spaces are: the set of
built-in names (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 name space. The important thing to know about name
spaces is that there is absolutely no relation between names in
different name spaces; 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 name space!
footnode.html#553
Attributes may be read-only or writable. In the latter case,
assignment to attributes is possible. Module attributes are writable:
you can write `modname.the_answer = 42`. Writable attributes may
also be deleted with the del statement, e.g.
`del modname.the_answer`.
Name spaces are created at different moments and have different
lifetimes. The name space containing the built-in names is created
when the Python interpreter starts up, and is never deleted. The
global name space for a module is created when the module definition
is read in; normally, module name spaces 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 name space. (The built-in names
actually also live in a module; this is called `__builtin__`.)
The local name space 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 name space.
A scope is a textual region of a Python program where a name space
is directly accessible. ``Directly accessible'' here means that an
unqualified reference to a name attempts to find the name in the name
space.
Although scopes are determined statically, they are used dynamically.
At any time during execution, exactly three nested scopes are in use
(i.e., exactly three name spaces are directly accessible): the
innermost scope, which is searched first, contains the local names,
the middle scope, searched next, contains the current module's global
names, and the outermost scope (searched last) is the name space
containing built-in names.
Usually, the local scope references the local names of the (textually)
current function. Outside of functions, the local scope references
the same name space as the global scope: the module's name space.
Class definitions place yet another name space 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 name
space, no matter from where or by what alias the function is called.
On the other hand, the actual search for names is done dynamically, at
run time --- however, the language definition is evolving towards
static name resolution, at ``compile'' time, so don't rely on dynamic
name resolution! (In fact, local variables are already determined
statically.)
A special quirk of Python is that assignments 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 name space 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.)
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guido@cwi.nl


A first look at classes

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# A first look at classes
Classes introduce a little bit of new syntax, three new object types,
and some new semantics.
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- Class definition syntax (node55.html#SECTION001031000000000000000)
Class objects (node56.html#SECTION001032000000000000000)
Instance objects (node57.html#SECTION001033000000000000000)
Method objects (node58.html#SECTION001034000000000000000)
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guido@cwi.nl


Class definition syntax

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## Class definition syntax
The simplest form of class definition looks like this:
```text
class ClassName:
<statement-1>
.
.
.
<statement-N>
```
Class definitions, like function definitions (`def` statements)
must be executed before they have any effect. (You could conceivably
place a class definition in a branch of an `if` statement, or
inside a function.)
In practice, the statements inside a class definition will usually be
function definitions, but other statements are allowed, and sometimes
useful --- we'll come back to this later. The function definitions
inside a class normally have a peculiar form of argument list,
dictated by the calling conventions for methods --- again, this is
explained later.
When a class definition is entered, a new name space is created, and
used as the local scope --- thus, all assignments to local variables
go into this new name space. 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 name space 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 definitions was entered) is
reinstated, and the class object is bound here to class name given in
the class definition header (ClassName in the example).
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guido@cwi.nl


Class objects

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## 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 name space when the
class object was created. So, if the class definition looked like
this:
```text
class MyClass:
i = 12345
def f(x):
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.
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):
```text
x = MyClass()
```
creates a new instance of the class and assigns this object to
the local variable `x`.
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guido@cwi.nl


Instance objects

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## 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.
The first I'll call data attributes. These correspond to
``instance variables'' in Smalltalk, and to ``data members'' in C++.
Data attributes need not be declared; like local variables, they
spring into existence when they are first assigned to. For example,
if `x` in the instance of `MyClass` created above, the
following piece of code will print the value 16, without leaving a
trace:
```text
x.counter = 1
while x.counter < 10:
x.counter = x.counter * 2
print x.counter
del x.counter
```
The second kind of attribute references understood by instance objects
are methods. A method is a function that ``belongs to'' an
object. (In Python, the term method is not unique to class instances:
other object types can have methods as well, e.g., list objects have
methods called append, insert, remove, sort, and so on. However,
below, 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 (user-defined) 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.
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guido@cwi.nl


Method objects

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## Method objects
Usually, a method is called immediately, e.g.:
```text
x.f()
```
In our 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
moment, for example:
```text
xf = x.f
while 1:
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, it is unpacked again, a new argument
list is constructed from the instance object and the original argument
list, and the function object is called with this new argument list.
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guido@cwi.nl


Random remarks

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# Random remarks
[These should perhaps be placed more carefully...]
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, e.g., capitalize method names,
prefix data attribute names with a small unique string (perhaps just
an underscore), or use verbs for methods and nouns for data attributes.
Data attributes may be referenced by methods as well as by ordinary
users (``clients'') of an object. In other words, classes are not
usable to implement pure abstract data types. In fact, nothing in
Python makes it possible to enforce data hiding --- it is all based
upon convention. (On the other hand, the Python implementation,
written in C, can completely hide implementation details and control
access to an object if necessary; this can be used by extensions to
Python written in C.)
Clients should use data attributes with care --- clients may mess up
invariants maintained by the methods by stamping on their data
attributes. Note that clients may add data attributes of their own to
an instance object without affecting the validity of the methods, as
long as name conflicts are avoided --- again, a naming convention can
save a lot of headaches here.
There is no shorthand for referencing data attributes (or other
methods!) from within methods. I find that this actually increases
the readability of methods: there is no chance of confusing local
variables and instance variables when glancing through a method.
Conventionally, the first argument of methods is often 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 by other Python programmers, and it is also conceivable that
a class browser program be written which 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:
```text
# 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, e.g.:
```text
class Bag:
def empty(self):
self.data = []
def add(self, x):
self.data.append(x)
def addtwice(self, x):
self.add(x)
self.add(x)
```
The instantiation operation (``calling'' a class object) creates an
empty object. Many classes like to create objects in a known initial
state. Therefore a class may define a special method named
`__init__`, like this:
```text
def __init__(self):
self.empty()
```
When a class defines an `__init__` method, class instantiation
automatically invokes `__init__` for the newly-created class
instance. So in the `Bag` example, a new and initialized instance
can be obtained by:
```text
x = Bag()
```
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,
```text
>>> 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)
>>>
```
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!
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guido@cwi.nl


Argument Passing

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## 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'. Options
found after -c command are not consumed by the Python
interpreter's option processing but left in sys.argv for the
command to handle.
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