5.1 More on Lists

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---
# 5.1 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@cnri.reston.va.us


5.2 The del statement

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---
# 5.2 The del statement
There is a way to remove an item from a list given its index instead
of its value: the del statement. This 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@cnri.reston.va.us


5.3 Tuples and Sequences

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---
# 5.3 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@cnri.reston.va.us


5.4 Dictionaries

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---
# 5.4 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@cnri.reston.va.us


5.5 More on Conditions

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---
# 5.5 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@cnri.reston.va.us


1.2 Introduction

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---
# 1.2 Introduction
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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guido@cnri.reston.va.us


5.6 Comparing Sequences and Other Types

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---
# 5.6 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#283
---
guido@cnri.reston.va.us


6 Modules

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---
# 6 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
>>>
```
---
- 6.1 More on Modules (node42.html#SECTION00710000000000000000)
6.2 Standard Modules (node43.html#SECTION00720000000000000000)
6.3 The dir() function (node44.html#SECTION00730000000000000000)
---
node42.html tut.html node40.html node1.html
guido@cnri.reston.va.us


6.1 More on Modules

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---
# 6.1 More on Modules
A module can contain executable statements as well as function
definitions.
These statements are intended to initialize the module.
They are executed only the
first
time the module is imported somewhere.footnode.html#296
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@cnri.reston.va.us


6.2 Standard Modules

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---
# 6.2 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@cnri.reston.va.us


6.3 The dir() function

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---
# 6.3 The dir() function
The built-in function dir is used to find out which names a module
defines. It returns a sorted list of strings:
```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@cnri.reston.va.us


7 Output Formatting

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---
# 7 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'
>>>
```
---
node46.html tut.html node44.html node1.html
guido@cnri.reston.va.us


8 Errors and Exceptions

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---
# 8 Errors and Exceptions
Until now error messages haven't been more than mentioned, but if you
have tried out the examples you have probably seen some. There are
(at least) two distinguishable kinds of errors: syntax errors
and exceptions.
---
- 8.1 Syntax Errors (node47.html#SECTION00910000000000000000)
8.2 Exceptions (node48.html#SECTION00920000000000000000)
8.3 Handling Exceptions (node49.html#SECTION00930000000000000000)
8.4 Raising Exceptions (node50.html#SECTION00940000000000000000)
8.5 User-defined Exceptions (node51.html#SECTION00950000000000000000)
8.6 Defining Clean-up Actions (node52.html#SECTION00960000000000000000)
---
guido@cnri.reston.va.us


8.1 Syntax Errors

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---
# 8.1 Syntax Errors
Syntax errors, also known as parsing errors, are perhaps the most common
kind of complaint you get while you are still learning Python:
```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@cnri.reston.va.us


8.2 Exceptions

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


8.3 Handling Exceptions

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---
# 8.3 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@cnri.reston.va.us


1.3 Where From Here

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---
# 1.3 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@cnri.reston.va.us


8.4 Raising Exceptions

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---
# 8.4 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@cnri.reston.va.us


8.5 User-defined Exceptions

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---
# 8.5 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@cnri.reston.va.us


8.6 Defining Clean-up Actions

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---
# 8.6 Defining Clean-up Actions
The try statement has another optional clause which is intended to
define clean-up actions that must be executed under all circumstances.
For example:
```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@cnri.reston.va.us


9 Classes

node54.html tut.html node52.html node1.html
---
# 9 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.
---
- 9.1 A word about terminology (node54.html#SECTION001010000000000000000)
9.2 Python scopes and name spaces (node55.html#SECTION001020000000000000000)
9.3 A first look at classes (node56.html#SECTION001030000000000000000)
- 9.3.1 Class definition syntax (node57.html#SECTION001031000000000000000)
9.3.2 Class objects (node58.html#SECTION001032000000000000000)
9.3.3 Instance objects (node59.html#SECTION001033000000000000000)
9.3.4 Method objects (node60.html#SECTION001034000000000000000)
9.4 Random remarks (node61.html#SECTION001040000000000000000)
9.5 Inheritance (node62.html#SECTION001050000000000000000)
- 9.5.1 Multiple inheritance (node63.html#SECTION001051000000000000000)
9.6 Odds and ends (node64.html#SECTION001060000000000000000)
---
guido@cnri.reston.va.us


9.1 A word about terminology

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---
# 9.1 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.
---
guido@cnri.reston.va.us


9.2 Python scopes and name spaces

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---
# 9.2 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#844
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@cnri.reston.va.us


9.3 A first look at classes

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---
# 9.3 A first look at classes
Classes introduce a little bit of new syntax, three new object types,
and some new semantics.
---
- 9.3.1 Class definition syntax (node57.html#SECTION001031000000000000000)
9.3.2 Class objects (node58.html#SECTION001032000000000000000)
9.3.3 Instance objects (node59.html#SECTION001033000000000000000)
9.3.4 Method objects (node60.html#SECTION001034000000000000000)
---
guido@cnri.reston.va.us


9.3.1 Class definition syntax

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---
## 9.3.1 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).
---
guido@cnri.reston.va.us


9.3.2 Class objects

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---
## 9.3.2 Class objects
Class objects support two kinds of operations: attribute references
and instantiation.
Attribute references use the standard syntax used for all
attribute references in Python: `obj.name`. Valid attribute
names are all the names that were in the class's 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`.
---
guido@cnri.reston.va.us


9.3.3 Instance objects

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---
## 9.3.3 Instance objects
Now what can we do with instance objects? The only operations
understood by instance objects are attribute references. There are
two kinds of valid attribute names.
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.
---
guido@cnri.reston.va.us


2 Using the Python Interpreter

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---
# 2 Using the Python Interpreter
---
- 2.1 Invoking the Interpreter (node7.html#SECTION00310000000000000000)
- 2.1.1 Argument Passing (node8.html#SECTION00311000000000000000)
2.1.2 Interactive Mode (node9.html#SECTION00312000000000000000)
2.2 The Interpreter and its Environment (node10.html#SECTION00320000000000000000)
- 2.2.1 Error Handling (node11.html#SECTION00321000000000000000)
2.2.2 The Module Search Path (node12.html#SECTION00322000000000000000)
2.2.3 ``Compiled'' Python files (node13.html#SECTION00323000000000000000)
2.2.4 Executable Python scripts (node14.html#SECTION00324000000000000000)
2.2.5 The Interactive Startup File (node15.html#SECTION00325000000000000000)
2.3 Interactive Input Editing and History Substitution (node16.html#SECTION00330000000000000000)
- 2.3.1 Line Editing (node17.html#SECTION00331000000000000000)
2.3.2 History Substitution (node18.html#SECTION00332000000000000000)
2.3.3 Key Bindings (node19.html#SECTION00333000000000000000)
2.3.4 Commentary (node20.html#SECTION00334000000000000000)
---
guido@cnri.reston.va.us


9.3.4 Method objects

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---
## 9.3.4 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@cnri.reston.va.us


9.4 Random remarks

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---
# 9.4 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@cnri.reston.va.us


9.5 Inheritance

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---
# 9.5 Inheritance
Of course, a language feature would not be worthy of the name ``class''
without supporting inheritance. The syntax for a derived class
definition looks as follows:
```text
class DerivedClassName(BaseClassName):
<statement-1>
.
.
.
<statement-N>
```
The name `BaseClassName` must be defined in a scope containing
the derived class definition. Instead of a base class name, an
expression is also allowed. This is useful when the base class is
defined in another module, e.g.,
```text
class DerivedClassName(modname.BaseClassName):
```
Execution of a derived class definition proceeds the same as for a
base class. When the class object is constructed, the base class is
remembered. This is used for resolving attribute references: if a
requested attribute is not found in the class, it is searched in the
base class. This rule is applied recursively if the base class itself
is derived from some other class.
There's nothing special about instantiation of derived classes:
`DerivedClassName()` creates a new instance of the class. Method
references are resolved as follows: the corresponding class attribute
is searched, descending down the chain of base classes if necessary,
and the method reference is valid if this yields a function object.
Derived classes may override methods of their base classes. Because
methods have no special privileges when calling other methods of the
same object, a method of a base class that calls another method
defined in the same base class, may in fact end up calling a method of
a derived class that overrides it. (For C++ programmers: all methods
in Python are ``virtual functions''.)
An overriding method in a derived class may in fact want to extend
rather than simply replace the base class method of the same name.
There is a simple way to call the base class method directly: just
call `BaseClassName.methodname(self, arguments)`. This is
occasionally useful to clients as well. (Note that this only works if
the base class is defined or imported directly in the global scope.)
---
- 9.5.1 Multiple inheritance (node63.html#SECTION001051000000000000000)
---
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guido@cnri.reston.va.us


9.5.1 Multiple inheritance

node64.html node62.html node62.html node1.html
---
## 9.5.1 Multiple inheritance
Python supports a limited form of multiple inheritance as well. A
class definition with multiple base classes looks as follows:
```text
class DerivedClassName(Base1, Base2, Base3):
<statement-1>
.
.
.
<statement-N>
```
The only rule necessary to explain the semantics is the resolution
rule used for class attribute references. This is depth-first,
left-to-right. Thus, if an attribute is not found in
`DerivedClassName`, it is searched in `Base1`, then
(recursively) in the base classes of `Base1`, and only if it is
not found there, it is searched in `Base2`, and so on.
(To some people breadth first--searching `Base2` and
`Base3` before the base classes of `Base1`--looks more
natural. However, this would require you to know whether a particular
attribute of `Base1` is actually defined in `Base1` or in
one of its base classes before you can figure out the consequences of
a name conflict with an attribute of `Base2`. The depth-first
rule makes no differences between direct and inherited attributes of
`Base1`.)
It is clear that indiscriminate use of multiple inheritance is a
maintenance nightmare, given the reliance in Python on conventions to
avoid accidental name conflicts. A well-known problem with multiple
inheritance is a class derived from two classes that happen to have a
common base class. While it is easy enough to figure out what happens
in this case (the instance will have a single copy of ``instance
variables'' or data attributes used by the common base class), it is
not clear that these semantics are in any way useful.
---
guido@cnri.reston.va.us


9.6 Odds and ends

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---
# 9.6 Odds and ends
Sometimes it is useful to have a data type similar to the Pascal
``record'' or C ``struct'', bundling together a couple of named data
items. An empty class definition will do nicely, e.g.:
```text
class Employee:
pass

john = Employee() # Create an empty employee record

# Fill the fields of the record
john.name = 'John Doe'
john.dept = 'computer lab'
john.salary = 1000
```
A piece of Python code that expects a particular abstract data type
can often be passed a class that emulates the methods of that data
type instead. For instance, if you have a function that formats some
data from a file object, you can define a class with methods
`read()` and `readline()` that gets the data from a string
buffer instead, and pass it as an argument. (Unfortunately, this
technique has its limitations: a class can't define operations that
are accessed by special syntax such as sequence subscripting or
arithmetic operators, and assigning such a ``pseudo-file'' to
`sys.stdin` will not cause the interpreter to read further input
from it.)
Instance method objects have attributes, too: `m.im_self` is the
object of which the method is an instance, and `m.im_func` is the
function object corresponding to the method.
---
guido@cnri.reston.va.us


10 Recent Additions as of Release 1.1

node66.html tut.html node64.html node1.html
---
# 10 Recent Additions as of Release 1.1
Python is an evolving language. Since this tutorial was last
thoroughly revised, several new features have been added to the
language. While ideally I should revise the tutorial to incorporate
them in the mainline of the text, lack of time currently requires me
to take a more modest approach. In this chapter I will briefly list the
most important improvements to the language and how you can use them
to your benefit.
---
- 10.1 The Last Printed Expression (node66.html#SECTION001110000000000000000)
10.2 String Literals (node67.html#SECTION001120000000000000000)
- 10.2.1 Double Quotes (node68.html#SECTION001121000000000000000)
10.2.2 Continuation Of String Literals (node69.html#SECTION001122000000000000000)
10.2.3 Triple-quoted strings (node70.html#SECTION001123000000000000000)
10.2.4 String Literal Juxtaposition (node71.html#SECTION001124000000000000000)
10.3 The Formatting Operator (node72.html#SECTION001130000000000000000)
- 10.3.1 Basic Usage (node73.html#SECTION001131000000000000000)
10.3.2 Referencing Variables By Name (node74.html#SECTION001132000000000000000)
10.4 Optional Function Arguments (node75.html#SECTION001140000000000000000)
- 10.4.1 Default Argument Values (node76.html#SECTION001141000000000000000)
10.4.2 Arbitrary Argument Lists (node77.html#SECTION001142000000000000000)
10.5 Lambda And Functional Programming Tools (node78.html#SECTION001150000000000000000)
- 10.5.1 Lambda Forms (node79.html#SECTION001151000000000000000)
10.5.2 Map, Reduce and Filter (node80.html#SECTION001152000000000000000)
- Map. (node81.html#SECTION001152100000000000000)
Filter. (node82.html#SECTION001152200000000000000)
Reduce. (node83.html#SECTION001152300000000000000)
10.6 Continuation Lines Without Backslashes (node84.html#SECTION001160000000000000000)
10.7 Regular Expressions (node85.html#SECTION001170000000000000000)
10.8 Generalized Dictionaries (node86.html#SECTION001180000000000000000)
10.9 Miscellaneous New Built-in Functions (node87.html#SECTION001190000000000000000)
10.10 Else Clause For Try Statement (node88.html#SECTION0011100000000000000000)
10.11 New Class Features in Release 1.1 (node89.html#SECTION0011110000000000000000)
- 10.11.1 New Operator Overloading (node90.html#SECTION0011111000000000000000)
10.11.2 Trapping Attribute Access (node91.html#SECTION0011112000000000000000)
10.11.3 Calling a Class Instance (node92.html#SECTION0011113000000000000000)
---
guido@cnri.reston.va.us


10.1 The Last Printed Expression

node67.html node65.html node65.html node1.html
---
# 10.1 The Last Printed Expression
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:
```text
>>> tax = 17.5 / 100
>>> price = 3.50
>>> price * tax
0.6125
>>> price + _
4.1125
>>> round(_, 2)
4.11
>>>
```
For reasons too embarrassing to explain, this variable is implemented
as a built-in (living in the module `__builtin__`), so it should
be treated as read-only by the user. I.e. 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.
---
guido@cnri.reston.va.us


10.2 String Literals

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---
# 10.2 String Literals
---
- 10.2.1 Double Quotes (node68.html#SECTION001121000000000000000)
10.2.2 Continuation Of String Literals (node69.html#SECTION001122000000000000000)
10.2.3 Triple-quoted strings (node70.html#SECTION001123000000000000000)
10.2.4 String Literal Juxtaposition (node71.html#SECTION001124000000000000000)
---
guido@cnri.reston.va.us


10.2.1 Double Quotes

node69.html node67.html node67.html node1.html
---
## 10.2.1 Double Quotes
Python can now also use double quotes to surround string literals,
e.g. `"this doesn't hurt a bit"`. There is no semantic
difference between strings surrounded by single or double quotes.
---
guido@cnri.reston.va.us


10.2.2 Continuation Of String Literals

node70.html node67.html node68.html node1.html
---
## 10.2.2 Continuation Of String Literals
String literals can span multiple lines by escaping newlines with
backslashes, e.g.
```text
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:
```text
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.
```
---
guido@cnri.reston.va.us


2.1 Invoking the Interpreter

node8.html node6.html node6.html node1.html
---
# 2.1 Invoking the Interpreter
The Python interpreter is usually installed as /usr/local/bin/python
on those machines where it is available; putting /usr/local/bin in
your Unix shell's search path makes it possible to start it by
typing the command
```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.)
---
- 2.1.1 Argument Passing (node8.html#SECTION00311000000000000000)
2.1.2 Interactive Mode (node9.html#SECTION00312000000000000000)
---
node8.html node6.html node6.html node1.html
guido@cnri.reston.va.us


10.2.3 Triple-quoted strings

node71.html node67.html node69.html node1.html
---
## 10.2.3 Triple-quoted strings
In some cases, when you need to include really long strings (e.g.
containing several paragraphs of informational text), it is annoying
that you have to terminate each line with `\n\`, especially if
you would like to reformat the text occasionally with a powerful text
editor like Emacs. For such situations, ``triple-quoted'' strings can
be used, e.g.
```text
hello = """

This string is bounded by triple double quotes (3 times ").
Unescaped newlines in the string are retained, though \
it is still possible\nto use all normal escape sequences.

Whitespace at the beginning of a line is
significant. If you need to include three opening quotes
you have to escape at least one of them, e.g. \""".

This string ends in a newline.
"""
```
Triple-quoted strings can be surrounded by three single quotes as
well, again without semantic difference.
---
guido@cnri.reston.va.us


10.2.4 String Literal Juxtaposition

node72.html node67.html node70.html node1.html
---
## 10.2.4 String Literal Juxtaposition
One final twist: you can juxtapose multiple string literals. Two or
more adjacent string literals (but not arbitrary expressions!)
separated only by whitespace will be concatenated (without intervening
whitespace) into a single string object at compile time. This makes
it possible to continue a long string on the next line without
sacrificing indentation or performance, unlike the use of the string
concatenation operator `+` or the continuation of the literal
itself on the next line (since leading whitespace is significant
inside all types of string literals). Note that this feature, like
all string features except triple-quoted strings, is borrowed from
Standard C.
---
guido@cnri.reston.va.us


10.3 The Formatting Operator

node73.html node65.html node71.html node1.html
---
# 10.3 The Formatting Operator
---
- 10.3.1 Basic Usage (node73.html#SECTION001131000000000000000)
10.3.2 Referencing Variables By Name (node74.html#SECTION001132000000000000000)
---
guido@cnri.reston.va.us


10.3.1 Basic Usage

node74.html node72.html node72.html node1.html
---
## 10.3.1 Basic Usage
The chapter on output formatting is really out of date: there is now
an almost complete interface to C-style printf formats. This is done
by overloading the modulo operator (`%`) for a left operand
which is a string, e.g.
```text
>>> import math
>>> print 'The value of PI is approximately %5.3f.' % math.pi
The value of PI is approximately 3.142.
>>>
```
If there is more than one format in the string you pass a tuple as
right operand, e.g.
```text
>>> table = {'Sjoerd': 4127, 'Jack': 4098, 'Dcab': 8637678}
>>> for name, phone in table.items():
... print '%-10s ==> %10d' % (name, phone)
...
Jack ==> 4098
Dcab ==> 8637678
Sjoerd ==> 4127
>>>
```
Most formats work exactly as in C and require that you pass the proper
type (however, if you don't you get an exception, not a core dump).
The `%s` format is more relaxed: if the corresponding argument is
not a string object, it is converted to string using the `str()`
built-in function. Using `*` to pass the width or precision in
as a separate (integer) argument is supported. The C formats
`%n` and `%p` are not supported.
---
guido@cnri.reston.va.us


10.3.2 Referencing Variables By Name

node75.html node72.html node73.html node1.html
---
## 10.3.2 Referencing Variables By Name
If you have a really long format string that you don't want to split
up, it would be nice if you could reference the variables to be
formatted by name instead of by position. This can be done by using
an extension of C formats using the form `%(name)format`, e.g.
```text
>>> table = {'Sjoerd': 4127, 'Jack': 4098, 'Dcab': 8637678}
>>> print 'Jack: %(Jack)d; Sjoerd: %(Sjoerd)d; Dcab: %(Dcab)d' % table
Jack: 4098; Sjoerd: 4127; Dcab: 8637678
>>>
```
This is particularly useful in combination with the new built-in
`vars()` function, which returns a dictionary containing all
local variables.
---
guido@cnri.reston.va.us


10.4 Optional Function Arguments

node76.html node65.html node74.html node1.html
---
# 10.4 Optional Function Arguments
It is now possible to define functions with a variable number of
arguments. There are two forms, which can be combined.
---
- 10.4.1 Default Argument Values (node76.html#SECTION001141000000000000000)
10.4.2 Arbitrary Argument Lists (node77.html#SECTION001142000000000000000)
---
guido@cnri.reston.va.us


10.4.1 Default Argument Values

node77.html node75.html node75.html node1.html
---
## 10.4.1 Default Argument Values
The most useful form is to specify a default value for one or more
arguments. This creates a function that can be called with fewer
arguments than it is defined, e.g.
```text
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.
```text
i = 5
def f(arg = i): print arg
i = 6
f()
```
will print `5`.
---
guido@cnri.reston.va.us


10.4.2 Arbitrary Argument Lists

node78.html node75.html node76.html node1.html
---
## 10.4.2 Arbitrary Argument Lists
It is also possible 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, e.g.
```text
def fprintf(file, format, *args):
file.write(format % args)
```
This feature may be combined with the previous, e.g.
```text
def but_is_it_useful(required, optional = None, *remains):
print "I don't know"
```
---
guido@cnri.reston.va.us


node79.html node65.html node77.html node1.html
---
# 10.5 Lambda And Functional Programming Tools
---
- 10.5.1 Lambda Forms (node79.html#SECTION001151000000000000000)
10.5.2 Map, Reduce and Filter (node80.html#SECTION001152000000000000000)
- Map. (node81.html#SECTION001152100000000000000)
Filter. (node82.html#SECTION001152200000000000000)
Reduce. (node83.html#SECTION001152300000000000000)
---
guido@cnri.reston.va.us


10.5.1 Lambda Forms

node80.html node78.html node78.html node1.html
---
## 10.5.1 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.
```text
def make_incrementor(n):
return lambda x, incr=n: x+incr
```
---
guido@cnri.reston.va.us


2.1.1 Argument Passing

node9.html node7.html node7.html node1.html
---
## 2.1.1 Argument Passing
When known to the interpreter, the script name and additional
arguments thereafter are passed to the script in the variable
sys.argv, which is a list of strings. Its length is at least one;
when no script and no arguments are given, sys.argv[0] is an
empty string. When the script name is given as '-' (meaning
standard input), sys.argv[0] is set to '-'. When -c
command is used, sys.argv[0] is set to '-c'. 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.
---
guido@cnri.reston.va.us


10.5.2 Map, Reduce and Filter

node81.html node78.html node79.html node1.html
---
## 10.5.2 Map, Reduce and Filter
Three new built-in functions on sequences are good candidate to pass
lambda forms.
---
- Map. (node81.html#SECTION001152100000000000000)
Filter. (node82.html#SECTION001152200000000000000)
Reduce. (node83.html#SECTION001152300000000000000)
---
guido@cnri.reston.va.us


Map.

node82.html node80.html node80.html node1.html
---
### Map.
`map(function, sequence)` calls `function(item)` for each of
the sequence's items and returns a list of the return values. For
example, to compute some cubes:
```text
>>> map(lambda x: x*x*x, range(1, 11))
[1, 8, 27, 64, 125, 216, 343, 512, 729, 1000]
>>>
```
More than one sequence may be passed; the function must then have as
many arguments as there are sequences and is called with the
corresponding item from each sequence (or `None` if some sequence
is shorter than another). If `None` is passed for the function,
a function returning its argument(s) is substituted.
Combining these two special cases, we see that
`map(None, list1, list2)` is a convenient way of turning a pair
of lists into a list of pairs. For example:
```text
>>> seq = range(8)
>>> map(None, seq, map(lambda x: x*x, seq))
[(0, 0), (1, 1), (2, 4), (3, 9), (4, 16), (5, 25), (6, 36), (7, 49)]
>>>
```
---
guido@cnri.reston.va.us


Filter.

node83.html node80.html node81.html node1.html
---
### Filter.
`filter(function, sequence)` returns a sequence (of the same
type, if possible) consisting of those items from the sequence for
which `function(item)` is true. For example, to compute some
primes:
```text
>>> filter(lambda x: x%2 != 0 and x%3 != 0, range(2, 25))
[5, 7, 11, 13, 17, 19, 23]
>>>
```
---
guido@cnri.reston.va.us


Reduce.

node84.html node80.html node82.html node1.html
---
### Reduce.
`reduce(function, sequence)` returns a single value constructed
by calling the (binary) function on the first two items of the
sequence, then on the result and the next item, and so on. For
example, to compute the sum of the numbers 1 through 10:
```text
>>> reduce(lambda x, y: x+y, range(1, 11))
55
>>>
```
If there's only one item in the sequence, its value is returned; if
the sequence is empty, an exception is raised.
A third argument can be passed to indicate the starting value. In this
case the starting value is returned for an empty sequence, and the
function is first applied to the starting value and the first sequence
item, then to the result and the next item, and so on. For example,
```text
>>> def sum(seq):
... return reduce(lambda x, y: x+y, seq, 0)
...
>>> sum(range(1, 11))
55
>>> sum([])
0
>>>
```
---
guido@cnri.reston.va.us


node85.html node65.html node83.html node1.html
---
# 10.6 Continuation Lines Without Backslashes
While the general mechanism for continuation of a source line on the
next physical line remains to place a backslash on the end of the
line, expressions inside matched parentheses (or square brackets, or
curly braces) can now also be continued without using a backslash.
This is particularly useful for calls to functions with many
arguments, and for initializations of large tables.
For example:
```text
month_names = ['Januari', 'Februari', 'Maart',
'April', 'Mei', 'Juni',
'Juli', 'Augustus', 'September',
'Oktober', 'November', 'December']
```
and
```text
CopyInternalHyperLinks(self.context.hyperlinks,
copy.context.hyperlinks,
uidremap)
```
---
guido@cnri.reston.va.us


10.7 Regular Expressions

node86.html node65.html node84.html node1.html
---
# 10.7 Regular Expressions
While C's printf-style output formats, transformed into Python, are
adequate for most output formatting jobs, C's scanf-style input
formats are not very powerful. Instead of scanf-style input, Python
offers Emacs-style regular expressions as a powerful input and
scanning mechanism. Read the corresponding section in the Library
Reference for a full description.
---
guido@cnri.reston.va.us


10.8 Generalized Dictionaries

node87.html node65.html node85.html node1.html
---
# 10.8 Generalized Dictionaries
The keys of dictionaries are no longer restricted to strings -- they
can be any immutable basic type including strings, numbers, tuples, or
(certain) class instances. (Lists and dictionaries are not acceptable
as dictionary keys, in order to avoid problems when the object used as
a key is modified.)
Dictionaries have two new methods: `d.values()` returns a list of
the dictionary's values, and `d.items()` returns a list of the
dictionary's (key, value) pairs. Like `d.keys()`, these
operations are slow for large dictionaries. Examples:
```text
>>> d = {100: 'honderd', 1000: 'duizend', 10: 'tien'}
>>> d.keys()
[100, 10, 1000]
>>> d.values()
['honderd', 'tien', 'duizend']
>>> d.items()
[(100, 'honderd'), (10, 'tien'), (1000, 'duizend')]
>>>
```
---
guido@cnri.reston.va.us


node88.html node65.html node86.html node1.html
---
# 10.9 Miscellaneous New Built-in Functions
The function `vars()` returns a dictionary containing the current
local variables. With a module argument, it returns that module's
global variables. The old function `dir(x)` returns
`vars(x).keys()`.
The function `round(x)` returns a floating point number rounded
to the nearest integer (but still expressed as a floating point
number). E.g. `round(3.4) == 3.0` and `round(3.5) == 4.0`.
With a second argument it rounds to the specified number of digits,
e.g. `round(math.pi, 4) == 3.1416` or even
`round(123.4, -2) == 100.0`.
The function `hash(x)` returns a hash value for an object.
All object types acceptable as dictionary keys have a hash value (and
it is this hash value that the dictionary implementation uses).
The function `id(x)` return a unique identifier for an object.
For two objects x and y, `id(x) == id(y)` if and only if
`x is y`. (In fact the object's address is used.)
The function `hasattr(x, name)` returns whether an object has an
attribute with the given name (a string value). The function
`getattr(x, name)` returns the object's attribute with the given
name. The function `setattr(x, name, value)` assigns a value to
an object's attribute with the given name. These three functions are
useful if the attribute names are not known beforehand. Note that
`getattr(x, 'spam')` is equivalent to `x.spam`, and
`setattr(x, 'spam', y)` is equivalent to `x.spam = y`. By
definition, `hasattr(x, name)` returns true if and only if
`getattr(x, name)` returns without raising an exception.
---
guido@cnri.reston.va.us


10.10 Else Clause For Try Statement

node89.html node65.html node87.html node1.html
---
# 10.10 Else Clause For Try Statement
The `try...except` statement now has an optional `else`
clause, which must follow all `except` clauses. It is useful to
place code that must be executed if the `try` clause does not
raise an exception. For example:
```text
for arg in sys.argv:
try:
f = open(arg, 'r')
except IOError:
print 'cannot open', arg
else:
print arg, 'has', len(f.readlines()), 'lines'
f.close()
```
---
guido@cnri.reston.va.us


10.11 New Class Features in Release 1.1

node90.html node65.html node88.html node1.html
---
# 10.11 New Class Features in Release 1.1
Some changes have been made to classes: the operator overloading
mechanism is more flexible, providing more support for non-numeric use
of operators (including calling an object as if it were a function),
and it is possible to trap attribute accesses.
---
- 10.11.1 New Operator Overloading (node90.html#SECTION0011111000000000000000)
10.11.2 Trapping Attribute Access (node91.html#SECTION0011112000000000000000)
10.11.3 Calling a Class Instance (node92.html#SECTION0011113000000000000000)
---
guido@cnri.reston.va.us


2.1.2 Interactive Mode

node10.html node7.html node8.html node1.html
---
## 2.1.2 Interactive Mode
When commands are read from a tty, the interpreter is said to be in
interactive mode. In this mode it prompts for the next command
with the primary prompt, usually three greater-than signs (
>>>); for continuation lines it prompts with the
secondary prompt,
by default three dots (...). 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.
The interpreter prints a welcome message stating its version number
and a copyright notice before printing the first prompt, e.g.:
```text
python
Python 1.4 (Oct 25 1996) [GCC 2.7.2]
Copyright 1991-1996 Stichting Mathematisch Centrum, Amsterdam
>>>
```
---
guido@cnri.reston.va.us


10.11.1 New Operator Overloading

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---
## 10.11.1 New Operator Overloading
It is no longer necessary to coerce both sides of an operator to the
same class or type. A class may still provide a `__coerce__`
method, but this method may return objects of different types or
classes if it feels like it. If no `__coerce__` is defined, any
argument type or class is acceptable.
In order to make it possible to implement binary operators where the
right-hand side is a class instance but the left-hand side is not,
without using coercions, right-hand versions of all binary operators
may be defined. These have an `r' prepended to their name,
e.g. `__radd__`.
For example, here's a very simple class for representing times. Times
are initialized from a number of seconds (like time.time()). Times
are printed like this: `Wed Mar 15 12:28:48 1995`. Subtracting
two Times gives their difference in seconds. Adding or subtracting a
Time and a number gives a new Time. You can't add two times, nor can
you subtract a Time from a number.
```text
import time

class Time:
def __init__(self, seconds):
self.seconds = seconds
def __repr__(self):
return time.ctime(self.seconds)
def __add__(self, x):
return Time(self.seconds + x)
__radd__ = __add__ # support for x+t
def __sub__(self, x):
if hasattr(x, 'seconds'): # test if x could be a Time
return self.seconds - x.seconds
else:
return self.seconds - x

now = Time(time.time())
tomorrow = 24*3600 + now
yesterday = now - today
print tomorrow - yesterday # prints 172800
```
---
guido@cnri.reston.va.us


10.11.2 Trapping Attribute Access

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---
## 10.11.2 Trapping Attribute Access
You can define three new ``magic'' methods in a class now:
`__getattr__(self, name)`, `__setattr__(self, name, value)`
and `__delattr__(self, name)`.
The `__getattr__` method is called when an attribute access fails,
i.e. when an attribute access would otherwise raise AttributeError --
this is after the instance's dictionary and its class hierarchy
have been searched for the named attribute. Note that if this method
attempts to access any undefined instance attribute it will be called
recursively!
The `__setattr__` and `__delattr__` methods are called when
assignment to, respectively deletion of an attribute are attempted.
They are called instead of the normal action (which is to insert
or delete the attribute in the instance dictionary). If either of
these methods most set or delete any attribute, they can only do so by
using the instance dictionary directly -- `self.__dict__` -- else
they would be called recursively.
For example, here's a near-universal ``Wrapper'' class that passes all
its attribute accesses to another object. Note how the
`__init__` method inserts the wrapped object in
`self.__dict__` in order to avoid endless recursion
(`__setattr__` would call `__getattr__` which would call
itself recursively).
```text
class Wrapper:
def __init__(self, wrapped):
self.__dict__['wrapped'] = wrapped
def __getattr__(self, name):
return getattr(self.wrapped, name)
def __setattr__(self, name, value):
setattr(self.wrapped, name, value)
def __delattr__(self, name):
delattr(self.wrapped, name)

import sys
f = Wrapper(sys.stdout)
f.write('hello world\n') # prints 'hello world'
```
A simpler example of `__getattr__` is an attribute that is
computed each time (or the first time) it it accessed. For instance:
```text
from math import pi

class Circle:
def __init__(self, radius):
self.radius = radius
def __getattr__(self, name):
if name == 'circumference':
return 2 * pi * self.radius
if name == 'diameter':
return 2 * self.radius
if name == 'area':
return pi * pow(self.radius, 2)
raise AttributeError, name
```
---
guido@cnri.reston.va.us


10.11.3 Calling a Class Instance

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---
## 10.11.3 Calling a Class Instance
If a class defines a method `__call__` it is possible to call its
instances as if they were functions. For example:
```text
class PresetSomeArguments:
def __init__(self, func, *args):
self.func, self.args = func, args
def __call__(self, *args):
return apply(self.func, self.args + args)

f = PresetSomeArguments(pow, 2) # f(i) computes powers of 2
for i in range(10): print f(i), # prints 1 2 4 8 16 32 64 128 256 512
print # append newline
```
---
guido@cnri.reston.va.us


11 New in Release 1.2

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---
# 11 New in Release 1.2
This chapter describes even more recent additions to the Python
language and library.
---
- 11.1 New Class Features (node94.html#SECTION001210000000000000000)
11.2 Unix Signal Handling (node95.html#SECTION001220000000000000000)
11.3 Exceptions Can Be Classes (node96.html#SECTION001230000000000000000)
11.4 Object Persistency and Object Copying (node97.html#SECTION001240000000000000000)
- 11.4.1 Persistent Objects (node98.html#SECTION001241000000000000000)
11.4.2 Copying Objects (node99.html#SECTION001242000000000000000)
11.5 Documentation Strings (node100.html#SECTION001250000000000000000)
11.6 Customizing Import and Built-Ins (node101.html#SECTION001260000000000000000)
11.7 Python and the World-Wide Web (node102.html#SECTION001270000000000000000)
11.8 Miscellaneous (node103.html#SECTION001280000000000000000)
---
guido@cnri.reston.va.us


11.1 New Class Features

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---
# 11.1 New Class Features
The semantics of `__coerce__` have been changed to be more
reasonable. As an example, the new standard module `Complex`
implements fairly complete complex numbers using this. Additional
examples of classes with and without `__coerce__` methods can be
found in the `Demo/classes` subdirectory, modules `Rat` and
`Dates`.
If a class defines no `__coerce__` method, this is equivalent to
the following definition:
```text
def __coerce__(self, other): return self, other
```
If `__coerce__` coerces itself to an object of a different type,
the operation is carried out using that type -- in release 1.1, this
would cause an error.
Comparisons involving class instances now invoke `__coerce__`
exactly as if `cmp(x, y)` were a binary operator like `+`
(except if `x` and `y` are the same object).
---
guido@cnri.reston.va.us


11.2 Unix Signal Handling

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---
# 11.2 Unix Signal Handling
On Unix, Python now supports signal handling. The module
`signal` exports functions `signal`, `pause` and
`alarm`, which act similar to their Unix counterparts. The
module also exports the conventional names for the various signal
classes (also usable with `os.kill()`) and `SIG_IGN` and
`SIG_DFL`. See the section on `signal` in the Library
Reference Manual for more information.
---
guido@cnri.reston.va.us


11.3 Exceptions Can Be Classes

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---
# 11.3 Exceptions Can Be Classes
User-defined exceptions are no longer limited to being string objects
-- they can be identified by classes as well. Using this mechanism it
is possible to create extensible hierarchies of exceptions.
There are two new valid (semantic) forms for the raise statement:
```text
raise Class, instance

raise instance
```
In the first form, `instance` must be an instance of `Class`
or of a class derived from it. The second form is a shorthand for
```text
raise instance.__class__, instance
```
An except clause may list classes as well as string objects. A class
in an except clause is compatible with an exception if it is the same
class or a base class thereof (but not the other way around -- an
except clause listing a derived class is not compatible with a base
class). For example, the following code will print B, C, D in that
order:
```text
class B:
pass
class C(B):
pass
class D(C):
pass

for c in [B, C, D]:
try:
raise c()
except D:
print "D"
except C:
print "C"
except B:
print "B"
```
Note that if the except clauses were reversed (with ```except B`''
first), it would have printed B, B, B -- the first matching except
clause is triggered.
When an error message is printed for an unhandled exception which is a
class, the class name is printed, then a colon and a space, and
finally the instance converted to a string using the built-in function
`str()`.
In this release, the built-in exceptions are still strings.
---
guido@cnri.reston.va.us


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---
# 11.4 Object Persistency and Object Copying
Two new modules, `pickle` and `shelve`, support storage and
retrieval of (almost) arbitrary Python objects on disk, using the
`dbm` package. A third module, `copy`, provides flexible
object copying operations. More information on these modules is
provided in the Library Reference Manual.
---
- 11.4.1 Persistent Objects (node98.html#SECTION001241000000000000000)
11.4.2 Copying Objects (node99.html#SECTION001242000000000000000)
---
guido@cnri.reston.va.us


11.4.1 Persistent Objects

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---
## 11.4.1 Persistent Objects
The module `pickle` provides a general framework for objects to
disassemble themselves into a stream of bytes and to reassemble such a
stream back into an object. It copes with reference sharing,
recursive objects and instances of user-defined classes, but not
(directly) with objects that have ``magical'' links into the operating
system such as open files, sockets or windows.
The `pickle` module defines a simple protocol whereby
user-defined classes can control how they are disassembled and
assembled. The method `__getinitargs__()`, if defined, returns
the argument list for the constructor to be used at assembly time (by
default the constructor is called without arguments). The methods
`__getstate__()` and `__setstate__()` are used to pass
additional state from disassembly to assembly; by default the
instance's `__dict__` is passed and restored.
Note that `pickle` does not open or close any files -- it can be
used equally well for moving objects around on a network or store them
in a database. For ease of debugging, and the inevitable occasional
manual patch-up, the constructed byte streams consist of printable
ASCII characters only (though it's not designed to be pretty).
The module `shelve` provides a simple model for storing objects
on files. The operation `shelve.open(filename)` returns a
``shelf'', which is a simple persistent database with a
dictionary-like interface. Database keys are strings, objects stored
in the database can be anything that `pickle` will handle.
---
guido@cnri.reston.va.us


11.4.2 Copying Objects

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---
## 11.4.2 Copying Objects
The module `copy` exports two functions: `copy()` and
`deepcopy()`. The `copy()` function returns a ``shallow''
copy of an object; `deepcopy()` returns a ``deep'' copy. The
difference between shallow and deep copying is only relevant for
compound objects (objects that contain other objects, like lists or
class instances):
- A shallow copy constructs a new compound object and then (to the
extent possible) inserts the same objects into in that the
original contains.A deep copy constructs a new compound object and then, recursively,
inserts copies into it of the objects found in the original.
Both functions have the same restrictions and use the same protocols
as `pickle` -- user-defined classes can control how they are
copied by providing methods named `__getinitargs__()`,
`__getstate__()` and `__setstate__()`.
---
guido@cnri.reston.va.us