Part 1: Python Tutorial (DRAFT)#

Author: Guido van Rossum
Dept. CST, CWI, Kruislaan 413
1098 SJ Amsterdam, The Netherlands
E-mail: guido@cwi.nl

Python is a simple, yet powerful programming language that bridges the gap between C and shell programming, and is thus ideally suited for 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, Amoeba, and the Apple Macintosh O.S.

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 as the examples are self-contained, the tutorial can be read off-line as well.

For a description of standard objects and modules, see Part 2 (Library Reference). The Language Reference document (XXX not yet existing) gives a more formal reference to the language.

1. 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 all such cases, Python is 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 most simple 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 for 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, and even a generic interface to window systems (STDWIN).

Python is an interpreted language, which saves 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: no declarations are necessary (all type checking is dynamic); statement grouping is done by indentation instead of begin/end brackets; and the high-level data types allow you to express complex operations in a single statement.

Python is extensible: if you know how to program in C it is easy to add a new built-in module to the interpreter, either to perform critical operations at maximum speed, or to link Python programs to libraries that may be only 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.

Where From Here#

Since the best introduction to a language is using it, the next section explains the mechanics of using the interpreter. 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 classes.

2. Using the Python Interpreter#

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

python

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.

If available, the script name and additional arguments thereafter are passed to the script in the variable sys.argv, which is a list of strings.

When standard input is a tty, the interpreter is said to be in interactive mode. In this mode it prompts for the next command with the primary prompt, usually three greater-than signs (>>>); for continuation lines it prompts with the secondary prompt, by default three dots (...). Typing an EOF (Control-D) at the primary prompt causes the interpreter to exit with a zero exit status.

When an error occurs in interactive mode, the interpreter prints a message and a stack trace and returns to the primary prompt; with input from a file, it exits with a nonzero exit status. All error messages are written to the standard error stream; normal output from the executed commands is written to standard output.

Typing an interrupt (normally Control-C or DEL) to the primary or secondary prompt cancels the input and returns to the primary prompt. Typing an interrupt while a command is being executed raises the KeyboardInterrupt exception, which may be handled by a try statement.

When a module named foo is imported, the interpreter searches for a file named foo.py in a 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, an installation-dependent default path is used, usually .:/usr/local/lib/python.

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

#! /usr/local/python

at the beginning of the script and giving the file an executable mode.

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.

The most important editing keys are:

• C-A – move to beginning of line
• C-E – move to end of line
• C-B / C-F – move left / right one character
• Backspace – erase character to the left
• C-D – erase character to the right
• C-K – kill rest of line to the right
• C-Y – yank back last killed string
• C-_ – undo last change

History substitution:

• C-P / C-N – move up / down in history buffer
• C-R / C-S – incremental reverse / forward search
• Return – pass current line to interpreter

The Readline library can be customized via $HOME/.inputrc. Example:

# 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
Control-u: universal-argument

Note that the default binding for TAB in Python is to insert a TAB instead of Readline’s default filename completion function.

3. An Informal Introduction to Python#

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

Using Python as a Calculator#

The interpreter acts as a simple calculator. Expression syntax is straightforward: the operators +, -, * and / work as in most other languages; parentheses can be used for grouping:

>>> # This is a comment
>>> 2+2
4
>>> (50-5+5*6+25)/4
25
>>> # Division truncates towards zero:
>>> 7/3
2

The equal sign (=) is used to assign a value to a variable:

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

There is some support for floating point, but you can’t mix floating point and integral numbers in an expression (yet):

>>> 10.0 / 3.3
3.0303030303

Python can also manipulate strings, enclosed in single quotes:

>>> 'foo bar'
'foo bar'
>>> 'doesn\'t'
'doesn\'t'

Strings can be concatenated with + and repeated with *:

>>> word = 'Help' + 'A'
>>> word
'HelpA'
>>> '<' + word*5 + '>'
'<HelpAHelpAHelpAHelpAHelpA>'

Strings can be subscripted; the first character has subscript 0. Substrings can be specified with slice notation (two indices separated by a colon):

>>> word[4]
'A'
>>> word[0:2]
'He'
>>> word[2:4]
'lp'
>>> word[:2]    # Take first two characters
'He'
>>> word[2:]    # Drop first two characters
'lpA'
>>> word[:3] + word[3:]
'HelpA'

Degenerate cases are handled gracefully:

>>> word[1:100]
'elpA'
>>> word[10:]
''
>>> word[2:1]
''

Slice indices may be negative, to start counting from the right:

>>> word[-2:]    # Take last two characters
'pA'
>>> word[:-2]    # Drop last two characters
'Hel'
>>> word[-0:]    # (since -0 equals 0)
'HelpA'

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:

 +---+---+---+---+---+
 | H | e | l | p | A |
 +---+---+---+---+---+
 0   1   2   3   4   5
-5  -4  -3  -2  -1

len() computes the length of a string:

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

The most versatile compound data type is the list:

>>> a = ['foo', 'bar', 100, 1234]
>>> a
['foo', 'bar', 100, 1234]
>>> a[0]
'foo'
>>> a[3]
1234
>>> a[1:3]
['bar', 100]
>>> a[:2] + ['bletch', 2*2]
['foo', 'bar', 'bletch', 4]

Unlike strings, which are immutable, individual elements of a list can be changed:

>>> a[2] = a[2] + 23
>>> a
['foo', 'bar', 123, 1234]

Assignment to slices may change the size of the list:

>>> a[0:2] = [1, 12]
>>> a
[1, 12, 123, 1234]
>>> a[0:2] = []
>>> a
[123, 1234]
>>> a[1:1] = ['bletch', 'xyzzy']
>>> a
[123, 'bletch', 'xyzzy', 1234]

len() also applies to lists:

>>> len(a)
4

Tuples and Sequences#

XXX To Be Done.

First Steps Towards Programming#

We can write an initial subsequence of the Fibonacci series as follows:

>>> a, b = 0, 1
>>> while b < 100:
...       print b
...       a, b = b, a+b
...
1
1
2
3
5
8
13
21
34
55
89

This example introduces several new features:

• The first line contains a multiple assignment: a and b simultaneously get the new values 0 and 1. On the last line this is used again, demonstrating that 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 < 100) remains true. In Python, as in C, any non-zero integer value is true; zero is false. The standard comparison operators are <, >, =, <=, >= and <>.

• The body of the loop is indented: indentation is Python’s way of grouping statements.

• The print statement writes the value of the expression(s) it is given. Strings are written without quotes and a space is inserted between items:

  >>> 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

More Control Flow Tools#

If Statements#

>>> 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.

For Statements#

The for statement in Python iterates over the items of any sequence (e.g., a list or a string):

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

The range() Function#

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

>>> range(10)
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
>>> 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():

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

Break Statements and Else Clauses on Loops#

The break statement breaks out of the smallest enclosing for or while loop. Loop statements may have an else clause; it is executed when the loop terminates normally but not when terminated by break:

>>> 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

Pass Statements#

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

>>> while 1:
...       pass # Busy-wait for keyboard interrupt

Conditions Revisited#

XXX To Be Done.

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
...     a, b = 0, 1
...     while b <= n:
...           print b,
...           a, b = b, a+b
...
>>> 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 execution of a function introduces a new symbol table used for the local variables of the function. Variable assignments in a function store the value in the local symbol table; 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, the global symbol table is read-only within a function. Arguments are passed using call by value.

A function definition introduces the function name in the global symbol table. The value can be assigned to another name which can then also be used as a function:

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

In Python, procedures are just functions that don’t return a value. They return None (a built-in name):

>>> print fib(0)
None

A function that returns a list of Fibonacci numbers instead of printing them:

>>> 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
...
>>> f100 = fib2(100)
>>> f100
[1, 1, 2, 3, 5, 8, 13, 21, 34, 55, 89]

The return statement returns with a value from a function. The method append adds a new element at the end of the list; it is equivalent to ret = ret + [b], but more efficient.

The list object type has two more methods:

• insert(i, x) – inserts an item at a given position. a.insert(0, x) inserts at the front, a.insert(len(a), x) is equivalent to a.append(x).
• sort() – sorts the elements of the list.

>>> a = [10, 100, 1, 1000]
>>> a.insert(2, -1)
>>> a
[10, 100, -1, 1, 1000]
>>> a.sort()
>>> a
[-1, 1, 10, 100, 1000]
>>> b = ['Mary', 'had', 'a', 'little', 'boy']
>>> b.sort()
>>> b
['Mary', 'a', 'boy', 'had', 'little']

Modules#

If you quit from the Python interpreter and enter it again, the definitions you have made are lost. To write a longer program, use a text editor to prepare the input for the interpreter and run it with that file as input. Such a file is called a script. You may also want to split a program into several files, or to use a handy function in several programs. 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.

A module is a file containing Python definitions and statements. The file name is the module name with the suffix .py appended. For instance, create a file called fibo.py with the following contents:

# Fibonacci numbers module

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

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

Then import this module:

>>> import fibo
>>> 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]

If you intend to use a function often you can assign it to a local name:

>>> fib = fibo.fib
>>> fib(500)
1 1 2 3 5 8 13 21 34 55 89 144 233 377

More on Modules#

A module can contain executable statements as well as function definitions. These statements are intended to initialize the module and are executed only the first time the module is imported.

Each module has its own private symbol table, which is used as the global symbol table by all functions defined in the module. You can touch a module’s global variables with the same notation used to refer to its functions: modname.itemname.

There is a variant of the import statement that imports names from a module directly into the importing module’s symbol table:

>>> from fibo import fib, fib2
>>> fib(500)
1 1 2 3 5 8 13 21 34 55 89 144 233 377

There is even a variant to import all names that a module defines:

>>> from fibo import *
>>> fib(500)
1 1 2 3 5 8 13 21 34 55 89 144 233 377

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

Standard Modules#

Python comes with a library of standard modules. One particular module deserves some attention: sys, which is built into every Python interpreter. The variables sys.ps1 and sys.ps2 define the strings used as primary and secondary prompts:

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

The variable sys.path is a list of strings that determine the interpreter’s search path for modules. It is initialized from PYTHONPATH or from a built-in default. You can modify it using standard list operations:

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

Errors and Exceptions#

There are (at least) two distinguishable kinds of errors: syntax errors and exceptions.

Syntax Errors#

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

>>> while 1 print 'Hello world'
Parsing error: file <stdin>, line 1:
while 1 print 'Hello world'
             ^
Unhandled exception: run-time error: syntax error

The parser repeats the offending line and displays an arrow pointing at the earliest point where the error was detected.

Exceptions#

Even if a statement is syntactically correct, it may cause an error when executed:

>>> 10 * (1/0)
Unhandled exception: run-time error: integer division by zero
Stack backtrace (innermost last):
  File "<stdin>", line 1
>>> 4 + foo*3
Unhandled exception: undefined name: foo
Stack backtrace (innermost last):
  File "<stdin>", line 1
>>> '2' + 2
Unhandled exception: type error: illegal argument type for built-in operation
Stack backtrace (innermost last):
  File "<stdin>", line 1

Common exception types:

• Run-time errors – caused by wrong data used by the program.
• Undefined name errors – usually caused by misspelled identifiers.
• Type errors – using data the wrong way; the error can be glanced from the object type(s) alone.

Handling Exceptions#

The try statement handles selected exceptions:

>>> numbers = [0.3333, 2.5, 0.0, 10.0]
>>> for x in numbers:
...     print x,
...     try:
...         print 1.0 / x
...     except RuntimeError:
...         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:

1. The try clause (statements between try and except) is executed.
2. If no exception occurs, the except clause is skipped.
3. If an exception occurs and its type matches the exception named after except, the except clause is executed.
4. If an exception occurs which does not match, it is passed on to outer try statements; if no handler is found, it is an unhandled exception and execution stops.

A try statement may have more than one except clause. An except clause may name multiple exceptions as a parenthesized list:

... except (RuntimeError, TypeError, NameError):
...     pass

When an exception has an argument, the except clause may specify a variable to receive the argument’s value:

>>> try:
...     foo()
... except NameError, x:
...     print 'name', x, 'undefined'
...
name foo undefined

Standard exception names and values:

EOFError              'end-of-file read'
KeyboardInterrupt     'keyboard interrupt'
MemoryError           'out of memory'           *
NameError             'undefined name'          *
RuntimeError          'run-time error'          *
SystemError           'system error'            *
TypeError             'type error'              *

Those with a * have an argument.

Exception handlers also handle exceptions that occur inside functions called (even indirectly) in the try clause:

>>> def this_fails():
...     x = 1/0
...
>>> try:
...     this_fails()
... except RuntimeError, detail:
...     print 'Handling run-time error:', detail
...
Handling run-time error: domain error or zero division

Raising Exceptions#

The raise statement forces a specified exception to occur:

>>> raise NameError, 'Hi There!'
Unhandled exception: undefined name: Hi There!
Stack backtrace (innermost last):
  File "<stdin>", line 1

User-defined Exceptions#

Programs may name their own exceptions by assigning a string to a variable:

>>> my_exc = 'nobody likes me!'
>>> try:
...     raise my_exc, 2*2
... except my_exc, val:
...     print 'My exception occured, value:', val
...
My exception occured, value: 4
>>> raise my_exc, 1
Unhandled exception: nobody likes me!: 1

Defining Clean-up Actions#

The try statement has an optional finally clause for clean-up actions that must execute under all circumstances:

>>> try:
...     raise KeyboardInterrupt
... finally:
...     print 'Goodbye, world!'
...
Goodbye, world!
Unhandled exception: keyboard interrupt

The finally clause is executed whether or not an exception occurred. It is also executed when the try statement is left via a break or return statement.

Classes#

Classes in Python make it possible to play the game of encapsulation in a somewhat different way than with modules. Classes are an advanced topic and are probably best skipped on the first encounter with Python.

Prologue#

Python’s class mechanism is a mixture of the class mechanisms found in C++ and Modula-3. The most important features are retained with full power: the class inheritance mechanism allows multiple base classes, a derived class can override any method 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 data members) are public, and all member functions (methods) 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.

A Simple Example#

Consider the following example, which defines a class Set representing a (finite) mathematical set:

class Set():
    def new(self):
        self.elements = []
        return self
    def add(self, e):
        if e not in self.elements:
            self.elements.append(e)
    def remove(self, e):
        if e in self.elements:
            for i in range(len(self.elements)):
                if self.elements[i] = e:
                    del self.elements[i]
                    break
    def is_element(self, e):
        return e in self.elements
    def size(self):
        return len(self.elements)

Assuming this class definition is the only contents of the module file SetClass.py, we can use it as follows:

>>> from SetClass import Set
>>> a = Set().new() # create a Set object
>>> a.add(2)
>>> a.add(3)
>>> a.add(1)
>>> a.add(1)
>>> if a.is_element(3): print '3 is in the set'
...
3 is in the set
>>> if not a.is_element(4): print '4 is not in the set'
...
4 is not in the set
>>> print 'a has', a.size(), 'elements'
a has 3 elements
>>> a.remove(1)
>>> print 'now a has', a.size(), 'elements'
now a has 2 elements

The functions defined in the class (e.g., add) can be called using the member notation for the object a. The member function is called with one less argument than it is defined: the object is implicitly passed as the first argument. Thus, a.add(2) is equivalent to Set.add(a, 2).

XXX This section is not complete yet!

XXX P.M.#

Still to be written:

• The del statement
• The dir() function
• Tuples
• Dictionaries
• Objects and types in general
• Backquotes
• And/Or/Not