This is Info file /home/pdm/tmp/Python-1.5.2p1/Doc/tut/python-tut.info, produced by Makeinfo version 1.68 from the input file tut.texi. July 6, 1999 1.5.2  File: python-tut.info, Node: Top, Next: Front Matter, Prev: (dir), Up: (dir) Python Tutorial *************** * Menu: * Front Matter:: * Whetting Your Appetite:: * Using the Python Interpreter:: * An Informal Introduction to Python:: * More Control Flow Tools:: * Data Structures:: * Modules:: * Input and Output:: * Errors and Exceptions:: * Classes:: * What Now?:: * Interactive Input Editing and History Substitution:: * Module Index:: * Class-Exception-Object Index:: * Function-Method-Variable Index:: * Miscellaneous Index::  File: python-tut.info, Node: Front Matter, Next: Whetting Your Appetite, Prev: Top, Up: Top Front Matter ************ Copyright (C) 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 or Corporation for National Research Initiatives or CNRI not be used in advertising or publicity pertaining to distribution of the software without specific, written prior permission. While CWI is the initial source for this software, a modified version is made available by the Corporation for National Research Initiatives (CNRI) at the Internet address `ftp://ftp.python.org'. STICHTING MATHEMATISCH CENTRUM AND CNRI DISCLAIM ALL WARRANTIES WITH REGARD TO THIS SOFTWARE, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS, IN NO EVENT SHALL STICHTING MATHEMATISCH CENTRUM OR CNRI 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. Python is an easy to learn, powerful programming language. It has efficient high-level data structures and a simple but effective approach to object-oriented programming. Python's elegant syntax and dynamic typing, together with its interpreted nature, make it an ideal language for scripting and rapid application development in many areas on most platforms. The Python interpreter and the extensive standard library are freely available in source or binary form for all major platforms from the Python web site, `http://www.python.org', and can be freely distributed. The same site also contains distributions of and pointers to many free third party Python modules, programs and tools, and additional documentation. The Python interpreter is easily extended with new functions and data types implemented in C or C++ (or other languages callable from C). Python is also suitable as an extension language for customizable applications. This tutorial introduces the reader informally to the basic concepts and features of the Python language and system. It helps to have a Python interpreter handy for hands-on experience, but all examples are self-contained, so the tutorial can be read off-line as well. For a description of standard objects and modules, see the *Python Library Reference* document. The *Python Reference Manual* gives a more formal definition of the language. To write extensions in C or C++, read the *Extending and Embedding* and *Python/C API* manuals. There are also several books covering Python in depth. This tutorial does not attempt to be comprehensive and cover every single feature, or even every commonly used feature. Instead, it introduces many of Python's most noteworthy features, and will give you a good idea of the language's flavor and style. After reading it, you will be able to read and write Python modules and programs, and you will be ready to learn more about the various Python library modules described in the *Python Library Reference*.  File: python-tut.info, Node: Whetting Your Appetite, Next: Using the Python Interpreter, Prev: Front Matter, Up: Top 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 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 you're not sufficiently familiar with C. Another situation: perhaps you have to work with several C libraries, and the usual C write/compile/test/re-compile cycle is too slow. You need to develop software more quickly. Possibly perhaps you've written a program that could use an extension language, and you don't want to design a language, write and debug an interpreter for it, then tie it into your application. 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 interfaces to GUI toolkits like Tk. Python is an interpreted language, which can save you considerable time during program development because no compilation and linking is necessary. The interpreter can be used interactively, which makes it easy to experiment with features of the language, to write throw-away programs, or to test functions during bottom-up program development. It is also a handy desk calculator. Python 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. Making references to Monty Python skits in documentation is not only allowed, it is encouraged! * Menu: * Where From Here::  File: python-tut.info, Node: Where From Here, Prev: Whetting Your Appetite, Up: Whetting Your Appetite 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 through examples, beginning with simple expressions, statements and data types, through functions and modules, and finally touching upon advanced concepts like exceptions and user-defined classes.  File: python-tut.info, Node: Using the Python Interpreter, Next: An Informal Introduction to Python, Prev: Whetting Your Appetite, Up: Top Using the Python Interpreter **************************** * Menu: * Invoking the Interpreter:: * Interpreter and Its Environment::  File: python-tut.info, Node: Invoking the Interpreter, Next: Interpreter and Its Environment, Prev: Using the Python Interpreter, Up: Using the Python Interpreter Invoking the Interpreter ======================== The Python interpreter is usually installed as `/usr/local/bin/python' on those machines where it is available; putting `/usr/local/bin' in your UNIX shell's search path makes it possible to start it by typing the command python to the shell. Since the choice of the directory where the interpreter lives is an installation option, other places are possible; check with your local Python guru or system administrator. (E.g., `/usr/local/python' is a popular alternative location.) Typing an EOF character (Control-D on UNIX, Control-Z on DOS or Windows) at the primary prompt causes the interpreter to exit with a zero exit status. If that doesn't work, you can exit the interpreter by typing the following commands: `import sys; sys.exit()'. The interpreter's line-editing features usually aren't very sophisticated. On UNIX, whoever installed the interpreter may have enabled support for the GNU readline library, which adds more elaborate interactive editing and history features. Perhaps the quickest check to see whether command line editing is supported is typing Control-P to the first Python prompt you get. If it beeps, you have command line editing; see Appendix A for an introduction to the keys. If nothing appears to happen, or if `^P' is echoed, command line editing isn't available; you'll only be able to use backspace to remove characters from the current line. The interpreter operates somewhat like the UNIX shell: when called with standard input connected to a tty device, it reads and executes commands interactively; when called with a file name argument or with a file as standard input, it reads and executes a *script* from that file. A 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 >> '); for continuation lines it prompts with the *secondary prompt*, by default three dots (`... '). The interpreter prints a welcome message stating its version number and a copyright notice before printing the first prompt, e.g.: python Python 1.5.2b2 (#1, Feb 28 1999, 00:02:06) [GCC 2.8.1] on sunos5 Copyright 1991-1995 Stichting Mathematisch Centrum, Amsterdam >>>  File: python-tut.info, Node: Interpreter and Its Environment, Prev: Invoking the Interpreter, Up: Using the Python Interpreter The Interpreter and Its Environment =================================== * Menu: * Error Handling:: * Executable Python Scripts:: * Interactive Startup File::  File: python-tut.info, Node: Error Handling, Next: Executable Python Scripts, Prev: Interpreter and Its Environment, Up: Interpreter and Its Environment Error Handling -------------- When an error occurs, the interpreter prints an error message and a stack trace. In interactive mode, it then returns to the primary prompt; when input came from a file, it exits with a nonzero exit status after printing the stack trace. (Exceptions handled by an `except' clause in a `try' statement are not errors in this context.) Some errors are unconditionally fatal and cause an exit with a nonzero exit; this applies to internal inconsistencies and some cases of running out of memory. All error messages are written to the standard error stream; normal output from the executed commands is written to standard output. Typing the interrupt character (usually Control-C or DEL) to the primary or secondary prompt cancels the input and returns to the primary prompt.(1) Typing an interrupt while a command is executing raises the `KeyboardInterrupt' exception, which may be handled by a `try' statement. ---------- Footnotes ---------- (1) A problem with the GNU Readline package may prevent this.  File: python-tut.info, Node: Executable Python Scripts, Next: Interactive Startup File, Prev: Error Handling, Up: Interpreter and Its Environment Executable Python Scripts ------------------------- On BSD'ish UNIX systems, Python scripts can be made directly executable, like shell scripts, by putting the line #! /usr/bin/env python (assuming that the interpreter is on the user's `PATH') at the beginning of the script and giving the file an executable mode. The `#!' must be the first two characters of the file. Note that the hash, or pound, character, `#', is used to start a comment in Python.  File: python-tut.info, Node: Interactive Startup File, Prev: Executable Python Scripts, Up: Interpreter and Its Environment 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.py')'. If you want to use the startup file in a script, you must do this explicitly in the script: import os if os.environ.get('PYTHONSTARTUP') \ and os.path.isfile(os.environ['PYTHONSTARTUP']): execfile(os.environ['PYTHONSTARTUP'])  File: python-tut.info, Node: An Informal Introduction to Python, Next: More Control Flow Tools, Prev: Using the Python Interpreter, Up: Top An Informal Introduction to Python ********************************** In the following examples, input and output are distinguished by the presence or absence of prompts (`>>> ' and `... '): to repeat the example, you must type everything after the prompt, when the prompt appears; lines that do not begin with a prompt are output from the interpreter. Note that a secondary prompt on a line by itself in an example means you must type a blank line; this is used to end a multi-line command. Many of the examples in this manual, even those entered at the interactive prompt, include comments. Comments in Python start with the hash character, `#', and extend to the end of the physical line. A comment may appear at the start of a line or following whitespace or code, but not within a string literal. A hash character within a string literal is just a hash character. Some examples: # this is the first comment SPAM = 1 # and this is the second comment # ... and now a third! STRING = "# This is not a comment." * Menu: * Using Python as a Calculator:: * First Steps Towards Programming::  File: python-tut.info, Node: Using Python as a Calculator, Next: First Steps Towards Programming, Prev: An Informal Introduction to Python, Up: An Informal Introduction to Python 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.) * Menu: * Numbers:: * Strings:: * Lists::  File: python-tut.info, Node: Numbers, Next: Strings, Prev: Using Python as a Calculator, Up: Using Python as a Calculator 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: >>> 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: >>> width = 20 >>> height = 5*9 >>> width * height 900 A value can be assigned to several variables simultaneously: >>> 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: >>> 4 * 2.5 / 3.3 3.0303030303 >>> 7.0 / 2 3.5 Complex numbers are also supported; imaginary numbers are written with a suffix of `j' or `J'. Complex numbers with a nonzero real component are written as `(REAL+IMAGj)', or can be created with the `complex(REAL, IMAG)' function. >>> 1j * 1J (-1+0j) >>> 1j * complex(0,1) (-1+0j) >>> 3+1j*3 (3+3j) >>> (3+1j)*3 (9+3j) >>> (1+2j)/(1+1j) (1.5+0.5j) Complex numbers are always represented as two floating point numbers, the real and imaginary part. To extract these parts from a complex number Z, use `Z.real' and `Z.imag'. >>> a=1.5+0.5j >>> a.real 1.5 >>> a.imag 0.5 The conversion functions to floating point and integer (`float()', `int()' and `long()') don't work for complex numbers -- there is no one correct way to convert a complex number to a real number. Use `abs(Z)' to get its magnitude (as a float) or `z.real' to get its real part. >>> a=1.5+0.5j >>> float(a) Traceback (innermost last): File "", line 1, in ? TypeError: can't convert complex to float; use e.g. abs(z) >>> a.real 1.5 >>> abs(a) 1.58113883008 In interactive mode, the last printed expression is assigned to the variable `_'. This means that when you are using Python as a desk calculator, it is somewhat easier to continue calculations, for example: >>> tax = 17.5 / 100 >>> price = 3.50 >>> price * tax 0.6125 >>> price + _ 4.1125 >>> round(_, 2) 4.11 This variable should be treated as read-only by the user. Don't explicitly assign a value to it -- you would create an independent local variable with the same name masking the built-in variable with its magic behavior.  File: python-tut.info, Node: Strings, Next: Lists, Prev: Numbers, Up: Using Python as a Calculator Strings ------- Besides numbers, Python can also manipulate strings, which can be expressed in several ways. They can be enclosed in single quotes or double quotes: >>> 'spam eggs' 'spam eggs' >>> 'doesn\'t' "doesn't" >>> "doesn't" "doesn't" >>> '"Yes," he said.' '"Yes," he said.' >>> "\"Yes,\" he said." '"Yes," he said.' >>> '"Isn\'t," she said.' '"Isn\'t," she said.' String literals can span multiple lines in several ways. Newlines can be escaped with backslashes, e.g.: hello = "This is a rather long string containing\n\ several lines of text just as you would do in C.\n\ Note that whitespace at the beginning of the line is\ significant.\n" print hello which would print the following: This is a rather long string containing several lines of text just as you would do in C. Note that whitespace at the beginning of the line is significant. Or, strings can be surrounded in a pair of matching triple-quotes: `"""' or `''''. End of lines do not need to be escaped when using triple-quotes, but they will be included in the string. print """ Usage: thingy [OPTIONS] -h Display this usage message -H hostname Hostname to connect to """ produces the following output: Usage: thingy [OPTIONS] -h Display this usage message -H hostname Hostname to connect to The interpreter prints the result of string operations in the same way as they are typed for input: inside quotes, and with quotes and other funny characters escaped by backslashes, to show the precise value. The string is enclosed in double quotes if the string contains a single quote and no double quotes, else it's enclosed in single quotes. (The `print' statement, described later, can be used to write strings without quotes or escapes.) Strings can be concatenated (glued together) with the `+' operator, and repeated with `*': >>> word = 'Help' + 'A' >>> word 'HelpA' >>> '<' + word*5 + '>' '' Two string literals next to each other are automatically concatenated; the first line above could also have been written `word = 'Help' 'A''; this only works with two literals, not with arbitrary string expressions: >>> 'str' 'ing' # <- This is ok 'string' >>> string.strip('str') + 'ing' # <- This is ok 'string' >>> string.strip('str') 'ing' # <- This is invalid File "", line 1 string.strip('str') 'ing' ^ SyntaxError: invalid syntax Strings can be subscripted (indexed); like in C, the first character of a string has subscript (index) 0. There is no separate character type; a character is simply a string of size one. Like in Icon, substrings can be specified with the *slice notation*: two indices separated by a colon. >>> word[4] 'A' >>> word[0:2] 'He' >>> word[2:4] 'lp' Slice indices have useful defaults; an omitted first index defaults to zero, an omitted second index defaults to the size of the string being sliced. >>> word[:2] # The first two characters 'He' >>> word[2:] # All but the first two characters 'lpA' Here's a useful invariant of slice operations: `s[:i] + s[i:]' equals `s'. >>> word[:2] + word[2:] 'HelpA' >>> word[:3] + word[3:] 'HelpA' Degenerate slice indices are handled gracefully: an index that is too large is replaced by the string size, an upper bound smaller than the lower bound returns an empty string. >>> word[1:100] 'elpA' >>> word[10:] '' >>> word[2:1] '' Indices may be negative numbers, to start counting from the right. For example: >>> word[-1] # The last character 'A' >>> word[-2] # The last-but-one character 'p' >>> word[-2:] # The last two characters 'pA' >>> word[:-2] # 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! >>> 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: >>> word[-100:] 'HelpA' >>> word[-10] # error Traceback (innermost last): File "", 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: +---+---+---+---+---+ | 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: >>> s = 'supercalifragilisticexpialidocious' >>> len(s) 34  File: python-tut.info, Node: Lists, Prev: Strings, Up: Using Python as a Calculator Lists ----- Python knows a number of *compound* data types, used to group together other values. The most versatile is the *list*, which can be written as a list of comma-separated values (items) between square brackets. List items need not all have the same type. >>> a = ['spam', 'eggs', 100, 1234] >>> a ['spam', 'eggs', 100, 1234] Like string indices, list indices start at 0, and lists can be sliced, concatenated and so on: >>> a[0] 'spam' >>> a[3] 1234 >>> a[-2] 100 >>> a[1:-1] ['eggs', 100] >>> a[:2] + ['bacon', 2*2] ['spam', 'eggs', 'bacon', 4] >>> 3*a[:3] + ['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: >>> 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: >>> # 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: >>> len(a) 8 It is possible to nest lists (create lists containing other lists), for example: >>> 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.  File: python-tut.info, Node: First Steps Towards Programming, Prev: Using Python as a Calculator, Up: An Informal Introduction to Python 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: >>> # 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: >>> i = 256*256 >>> print 'The value of i is', i The value of i is 65536 A trailing comma avoids the newline after the output: >>> a, b = 0, 1 >>> while b < 1000: ... print b, ... a, b = b, a+b ... 1 1 2 3 5 8 13 21 34 55 89 144 233 377 610 987 Note that the interpreter inserts a newline before it prints the next prompt if the last line was not completed.  File: python-tut.info, Node: More Control Flow Tools, Next: Data Structures, Prev: An Informal Introduction to Python, Up: Top More Control Flow Tools *********************** Besides the `while' statement just introduced, Python knows the usual control flow statements known from other languages, with some twists. * Menu: * if Statements:: * for Statements:: * range Function:: * break and continue Statements:: * pass Statements:: * Defining Functions:: * More on Defining Functions::  File: python-tut.info, Node: if Statements, Next: for Statements, Prev: More Control Flow Tools, Up: More Control Flow Tools `if' Statements =============== Perhaps the most well-known statement type is the `if' statement. For example: >>> # [Code which sets 'x' to a value...] >>> 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.  File: python-tut.info, Node: for Statements, Next: range Function, Prev: if Statements, Up: More Control Flow Tools `for' Statements ================ The `for' statement in Python differs a bit from what you may be used to in C or Pascal. Rather than always iterating over an arithmetic progression of numbers (like in Pascal), or giving the user the ability to define both the iteration step and halting condition (as C), Python's `for'statement iterates over the items of any sequence (e.g., a list or a string), in the order that they appear in the sequence. For example (no pun intended): >>> # 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: >>> 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']  File: python-tut.info, Node: range Function, Next: break and continue Statements, Prev: for Statements, Up: More Control Flow Tools 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.: >>> 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): >>> 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: >>> 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  File: python-tut.info, Node: break and continue Statements, Next: pass Statements, Prev: range Function, Up: More Control Flow Tools `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: >>> 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  File: python-tut.info, Node: pass Statements, Next: Defining Functions, Prev: break and continue Statements, Up: More Control Flow Tools `pass' Statements ================= The `pass' statement does nothing. It can be used when a statement is required syntactically but the program requires no action. For example: >>> while 1: ... pass # Busy-wait for keyboard interrupt ...  File: python-tut.info, Node: Defining Functions, Next: More on Defining Functions, Prev: pass Statements, Up: More Control Flow Tools Defining Functions ================== We can create a function that writes the Fibonacci series to an arbitrary boundary: >>> def fib(n): # write Fibonacci series up to n ... "Print a Fibonacci series up to n" ... a, b = 0, 1 ... while 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 start at the next line, indented by a tab stop. The first statement of the function body can optionally be a string literal; this string literal is the function's documentation string, or "docstring". There are tools which use docstrings to automatically produce printed documentation, or to let the user interactively browse through code; it's good practice to include docstrings in code that you write, so try to make a habit of it. The *execution* of a function introduces a new symbol table used for the local variables of the function. More precisely, all variable assignments in a function store the value in the local symbol table; whereas variable references first look in the local symbol table, then in the 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*.(1) When a function calls another function, a new local symbol table is created for that call. A function definition introduces the function name in the current symbol table. The value of the function name has a type that is recognized by the interpreter as a user-defined function. This value can be assigned to another name which can then also be used as a function. This serves as a general renaming mechanism: >>> fib >>> f = fib >>> f(100) 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: >>> print fib(0) None It is simple to write a function that returns a list of the numbers of the Fibonacci series, instead of printing it: >>> def fib2(n): # return Fibonacci series up to n ... "Return a list containing the Fibonacci series up to n" ... result = [] ... a, b = 0, 1 ... while 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. ---------- Footnotes ---------- (1) Actually, *call by object reference* would be a better description, since if a mutable object is passed, the caller will see any changes the callee makes to it (e.g., items inserted into a list).  File: python-tut.info, Node: More on Defining Functions, Prev: Defining Functions, Up: More Control Flow Tools More on Defining Functions ========================== It is also possible to define functions with a variable number of arguments. There are three forms, which can be combined. * Menu: * Default Argument Values:: * Keyword Arguments:: * Arbitrary Argument Lists:: * Lambda Forms:: * Documentation Strings::  File: python-tut.info, Node: Default Argument Values, Next: Keyword Arguments, Prev: More on Defining Functions, Up: More on Defining Functions Default Argument Values ----------------------- The most useful form is to specify a default value for one or more arguments. This creates a function that can be called with fewer arguments than it is defined, e.g. def ask_ok(prompt, retries=4, complaint='Yes or no, please!'): while 1: ok = raw_input(prompt) if ok in ('y', 'ye', 'yes'): return 1 if ok in ('n', 'no', 'nop', 'nope'): return 0 retries = retries - 1 if retries < 0: raise IOError, 'refusenik user' print complaint This function can be called either like this: `ask_ok('Do you really want to quit?')' or like this: `ask_ok('OK to overwrite the file?', 2)'. The default values are evaluated at the point of function definition in the *defining* scope, so that e.g. i = 5 def f(arg = i): print arg i = 6 f() will print `5'. *Important warning:* The default value is evaluated only once. This makes a difference when the default is a mutable object such as a list or dictionary. For example, the following function accumulates the arguments passed to it on subsequent calls: def f(a, l = []): l.append(a) return l print f(1) print f(2) print f(3) This will print [1] [1, 2] [1, 2, 3] If you don't want the default to be shared between subsequent calls, you can write the function like this instead: def f(a, l = None): if l is None: l = [] l.append(a) return l  File: python-tut.info, Node: Keyword Arguments, Next: Arbitrary Argument Lists, Prev: Default Argument Values, Up: More on Defining Functions Keyword Arguments ----------------- Functions can also be called using keyword arguments of the form `KEYWORD = VALUE'. For instance, the following function: def parrot(voltage, state='a stiff', action='voom', type='Norwegian Blue'): print "-- This parrot wouldn't", action, print "if you put", voltage, "Volts through it." print "-- Lovely plumage, the", type print "-- It's", state, "!" could be called in any of the following ways: parrot(1000) parrot(action = 'VOOOOOM', voltage = 1000000) parrot('a thousand', state = 'pushing up the daisies') parrot('a million', 'bereft of life', 'jump') but the following calls would all be invalid: parrot() # required argument missing parrot(voltage=5.0, 'dead') # non-keyword argument following keyword parrot(110, voltage=220) # duplicate value for argument parrot(actor='John Cleese') # unknown keyword In general, an argument list must have any positional arguments followed by any keyword arguments, where the keywords must be chosen from the formal parameter names. It's not important whether a formal parameter has a default value or not. No argument may receive a value more than once -- formal parameter names corresponding to positional arguments cannot be used as keywords in the same calls. Here's an example that fails due to this restriction: >>> def function(a): ... pass ... >>> function(0, a=0) Traceback (innermost last): File "", line 1, in ? TypeError: keyword parameter redefined When a final formal parameter of the form `**NAME' is present, it receives a dictionary containing all keyword arguments whose keyword doesn't correspond to a formal parameter. This may be combined with a formal parameter of the form `*NAME' (described in the next subsection) which receives a tuple containing the positional arguments beyond the formal parameter list. (`*NAME' must occur before `**NAME'.) For example, if we define a function like this: def cheeseshop(kind, *arguments, **keywords): print "-- Do you have any", kind, '?' print "-- I'm sorry, we're all out of", kind for arg in arguments: print arg print '-'*40 for kw in keywords.keys(): print kw, ':', keywords[kw] It could be called like this: cheeseshop('Limburger', "It's very runny, sir.", "It's really very, VERY runny, sir.", client='John Cleese', shopkeeper='Michael Palin', sketch='Cheese Shop Sketch') and of course it would print: -- Do you have any Limburger ? -- I'm sorry, we're all out of Limburger It's very runny, sir. It's really very, VERY runny, sir. ---------------------------------------- client : John Cleese shopkeeper : Michael Palin sketch : Cheese Shop Sketch  File: python-tut.info, Node: Arbitrary Argument Lists, Next: Lambda Forms, Prev: Keyword Arguments, Up: More on Defining Functions Arbitrary Argument Lists ------------------------ Finally, the least frequently used option is to specify that a function can be called with an arbitrary number of arguments. These arguments will be wrapped up in a tuple. Before the variable number of arguments, zero or more normal arguments may occur. def fprintf(file, format, *args): file.write(format % args)  File: python-tut.info, Node: Lambda Forms, Next: Documentation Strings, Prev: Arbitrary Argument Lists, Up: More on Defining Functions Lambda Forms ------------ By popular demand, a few features commonly found in functional programming languages and Lisp have been added to Python. With the `lambda' keyword, small anonymous functions can be created. Here's a function that returns the sum of its two arguments: `lambda a, b: a+b'. Lambda forms can be used wherever function objects are required. They are syntactically restricted to a single expression. Semantically, they are just syntactic sugar for a normal function definition. Like nested function definitions, lambda forms cannot reference variables from the containing scope, but this can be overcome through the judicious use of default argument values, e.g. def make_incrementor(n): return lambda x, incr=n: x+incr