| Author: | Dave Kuhlman |
|---|---|
| Contact: | dkuhlman (at) davekuhlman (dot) org |
| Address: | http://www.reifywork.com |
| Revision: | 1.3a |
|---|---|
| Date: | October 05, 2014 |
| Copyright: | Copyright (c) 2009 Dave Kuhlman. All Rights Reserved. This document is subject to the provisions of the Open Source MIT License http://www.opensource.org/licenses/mit-license.php. |
|---|---|
| Abstract: | This document is a self-learning document for a course in Python programming. This course contains (1) a part for beginners, (2) a discussion of several advanced topics that are of interest to Python programmers, and (3) a Python workbook with lots of exercises. |
Contents
[table of contents]
Preface
This book is a collection of materials that I've used when conducting Python training and also materials from my Web site that are intended for self-instruction.
You may prefer a machine readable copy of this book. You can find it in various formats here:
And, let me thank the students in my Python classes. Their questions and suggestions were a great help in the preparation of these materials.
Introductions
Practical matters: restrooms, breakroom, lunch and break times, etc.
Starting the Python interactive interpreter. Also, IPython and Idle.
Running scripts
Editors -- Choose an editor which you can configure so that it indents with 4 spaces, not tab characters. For a list of editors for Python, see: http://wiki.python.org/moin/PythonEditors. A few possible editors:
Interactive interpreters:
IDEs -- Also see http://en.wikipedia.org/wiki/List_of_integrated_development_environments_for_Python:
Where else to get help:
Python home page -- http://www.python.org
Python standard documentation -- http://www.python.org/doc/.
You will also find links to tutorials there.
FAQs -- http://www.python.org/doc/faq/.
The Python Wiki -- http://wiki.python.org/
The Python Package Index -- Lots of Python packages -- https://pypi.python.org/pypi
Special interest groups (SIGs) -- http://www.python.org/sigs/
Other python related mailing lists and lists for specific applications (for example, Zope, Twisted, etc). Try: http://dir.gmane.org/search.php?match=python.
http://sourceforge.net -- Lots of projects. Search for "python".
USENET -- comp.lang.python. Can also be accessed through Gmane: http://dir.gmane.org/gmane.comp.python.general.
The Python tutor email list -- http://mail.python.org/mailman/listinfo/tutor
Local documentation:
On MS Windows, the Python documentation is installed with the standard installation.
Install the standard Python documentation on your machine from http://www.python.org/doc/.
pydoc. Example, on the command line, type: pydoc re.
Import a module, then view its .__doc__ attribute.
At the interactive prompt, use help(obj). You might need to import it first. Example:
>>> import urllib >>> help(urllib)
In IPython, the question mark operator gives help. Example:
In [13]: open?
Type: builtin_function_or_method
Base Class: <type 'builtin_function_or_method'>
String Form: <built-in function open>
Namespace: Python builtin
Docstring:
open(name[, mode[, buffering]]) -> file object
Open a file using the file() type, returns a file object.
Constructor Docstring:
x.__init__(...) initializes x; see x.__class__.__doc__ for signature
Callable: Yes
Call def: Calling definition not available.Call docstring:
x.__call__(...) <==> x(...)
Python is a high-level general purpose programming language:
Important features of Python:
Some things you will need to know:
Python uses indentation to show block structure. Indent one level to show the beginning of a block. Out-dent one level to show the end of a block. As an example, the following C-style code:
if (x)
{
if (y)
{
f1()
}
f2()
}
in Python would be:
if x:
if y:
f1()
f2()
And, the convention is to use four spaces (and no hard tabs) for each level of indentation. Actually, it's more than a convention; it's practically a requirement. Following that "convention" will make it so much easier to merge your Python code with code from other sources.
An overview of Python:
A scripting language -- Python is suitable (1) for embedding, (2) for writing small unstructured scripts, (3) for "quick and dirty" programs.
Not a scripting language -- (1) Python scales. (2) Python encourages us to write code that is clear and well-structured.
Interpreted, but also compiled to byte-code. Modules are automatically compiled (to .pyc) when imported, but may also be explicitly compiled.
Provides an interactive command line and interpreter shell. In fact, there are several.
Dynamic -- For example:
Strongly typed at run-time, not compile-time. Objects (values) have a type, but variables do not.
Reasonably high level -- High level built-in data types; high level control structures (for walking lists and iterators, for example).
Object-oriented -- Almost everything is an object. Simple object definition. Data hiding by agreement. Multiple inheritance. Interfaces by convention. Polymorphism.
Highly structured -- Statements, functions, classes, modules, and packages enable us to write large, well-structured applications. Why structure? Readability, locate-ability, modifiability.
Explicitness
First-class objects:
Indented block structure -- "Python is pseudo-code that runs."
Embedding and extending Python -- Python provides a well-documented and supported way (1) to embed the Python interpreter in C/C++ applications and (2) to extend Python with modules and objects implemented in C/C++.
Automatic garbage collection. (But, there is a gc module to allow explicit control of garbage collection.)
Comparison with other languages: compiled languages (e.g. C/C++); Java; Perl, Tcl, and Ruby. Python excells at: development speed, execution speed, clarity and maintainability.
Varieties of Python:
CPython -- Standard Python 2.x implemented in C.
Jython -- Python for the Java environment -- http://www.jython.org/
PyPy -- Python with a JIT compiler and stackless mode -- http://pypy.org/
Stackless -- Python with enhanced thread support and microthreads etc. -- http://www.stackless.com/
IronPython -- Python for .NET and the CLR -- http://ironpython.net/
Python 3 -- The new, new Python. This is intended as a replacement for Python 2.x. -- http://www.python.org/doc/. A few differences (from Python 2.x):
For a more information about differences between Python 2.x and Python 3.x, see the description of the various fixes that can be applied with the 2to3 tool: http://docs.python.org/3/library/2to3.html#fixers
The migration tool, 2to3, eases the conversion of 2.x code to 3.x.
Also see The Zen of Python -- http://www.python.org/peps/pep-0020.html. Or, at the Python interactive prompt, type:
>>> import this
If you execute Python from the command line with no script (no arguments), Python gives you an interactive prompt. This is an excellent facility for learning Python and for trying small snippets of code. Many of the examples that follow were developed using the Python interactive prompt.
Start the Python interactive interpreter by typing python with no arguments at the command line. For example:
$ python Python 2.6.1 (r261:67515, Jan 11 2009, 15:19:23) [GCC 4.3.2] on linux2 Type "help", "copyright", "credits" or "license" for more information. >>> print 'hello' hello >>>
You may also want to consider using IDLE. IDLE is a graphical integrated development environment for Python; it contains a Python shell. It is likely that Idle was installed for you when you installed Python. You will find a script to start up IDLE in the Tools/scripts directory of your Python distribution. IDLE requires Tkinter.
In addition, there are tools that will give you a more powerful and fancy Python interactive interpreter. One example is IPython, which is available at http://ipython.scipy.org/.
Everything after "#" on a line is ignored. No block comments, but doc strings are a comment in quotes at the beginning of a module, class, method or function. Also, editors with support for Python often provide the ability to comment out selected blocks of code, usually with "##".
Python represents block structure and nested block structure with indentation, not with begin and end brackets.
The empty block -- Use the pass no-op statement.
Benefits of the use of indentation to indicate structure:
Editor considerations -- The standard is 4 spaces (no hard tabs) for each indentation level. You will need a text editor that helps you respect that.
Doc strings are like comments, but they are carried with executing code. Doc strings can be viewed with several tools, e.g. help(), obj.__doc__, and, in IPython, a question mark (?) after a name will produce help.
A doc string is written as a quoted string that is at the top of a module or the first lines after the header line of a function or class.
We can use triple-quoting to create doc strings that span multiple lines.
There are also tools that extract and format doc strings, for example:
See the following for suggestions and more information on doc strings: Docstring conventions -- http://www.python.org/dev/peps/pep-0257/.
See: http://docs.python.org/ref/operators.html. Python defines the following operators:
+ - * ** / // % << >> & | ^ ~ < > <= >= == != <>
The comparison operators <> and != are alternate spellings of the same operator. != is the preferred spelling; <> is obsolescent.
Logical operators:
and or is not in
There are also (1) the dot operator, (2) the subscript operator [], and the function/method call operator ().
For information on the precedences of operators, see the table at http://docs.python.org/2/reference/expressions.html#operator-precedence, which is reproduced below.
For information on what the different operators do, the section in the "Python Language Reference" titled "Special method names" may be of help: http://docs.python.org/2/reference/datamodel.html#special-method-names
The following table summarizes the operator precedences in Python, from lowest precedence (least binding) to highest precedence (most binding). Operators on the same line have the same precedence. Unless the syntax is explicitly given, operators are binary. Operators on the same line group left to right (except for comparisons, including tests, which all have the same precedence and chain from left to right -- see section 5.9 -- and exponentiation, which groups from right to left):
Operator Description
======================== ==================
lambda Lambda expression
or Boolean OR
and Boolean AND
not x Boolean NOT
in, not in Membership tests
is, is not Identity tests
<, <=, >, >=, <>, !=, == Comparisons
| Bitwise OR
^ Bitwise XOR
& Bitwise AND
<<, >> Shifts
+, - Addition and subtraction
*, /, % Multiplication, division, remainder
+x, -x Positive, negative
~x Bitwise not
** Exponentiation
x.attribute Attribute reference
x[index] Subscription
x[index:index] Slicing
f(arguments...) Function call
(expressions...) Binding or tuple display
[expressions...] List display
{key:datum...} Dictionary display
`expressions...` String conversion
Note that most operators result in calls to methods with special names, for example __add__, __sub__, __mul__, etc. See Special method names http://docs.python.org/2/reference/datamodel.html#special-method-names
Later, we will see how these operators can be emulated in classes that you define yourself, through the use of these special names.
For more on lexical matters and Python styles, see:
Understanding the Python execution model -- How Python evaluates and executes your code.
Evaluating expressions.
Creating names/variables -- Binding -- The following all create names (variables) and bind values (objects) to them: (1) assignment, (2) function definition, (3) class definition, (4) function and method call, (5) importing a module, ...
First class objects -- Almost all objects in Python are first class. Definition: An object is first class if: (1) we can put it in a structured object; (2) we can pass it to a function; (3) we can return it from a function.
References -- Objects (or references to them) can be shared. What does this mean?
print -- Example:
print obj print "one", "two", 'three'
for: -- Example:
stuff = ['aa', 'bb', 'cc']
for item in stuff:
print item
Learn what the type of an object is -- Example:
type(obj)
Learn what attributes an object has and what it's capabilities are -- Example:
dir(obj) value = "a message" dir(value)
Get help on a class or an object -- Example:
help(str)
help("")
value = "abc"
help(value)
help(value.upper)
In IPython (but not standard Python), you can also get help at the interactive prompt by typing "?" and "??" after an object. Example:
In [48]: a = '' In [49]: a.upper? Type: builtin_function_or_method String Form:<built-in method upper of str object at 0x7f1c426e0508> Docstring: S.upper() -> string Return a copy of the string S converted to uppercase.
For information on built-in data types, see section Built-in Types -- http://docs.python.org/lib/types.html in the Python standard documentation.
The numeric types are:
See 2.3.4 Numeric Types -- int, float, long, complex -- http://docs.python.org/lib/typesnumeric.html.
Python does mixed arithmetic.
Integer division truncates. This is changed in Python 3. Use float(n) to force coercion to a float. Example:
In [8]: a = 4 In [9]: b = 5 In [10]: a / b Out[10]: 0 # possibly wrong? In [11]: float(a) / b Out[11]: 0.8
Applying the function call operator (parentheses) to a type or class creates an instance of that type or class.
Scientific and heavily numeric programming -- High level Python is not very efficient for numerical programming. But, there are libraries that help -- Numpy and SciPy -- See: SciPy: Scientific Tools for Python -- http://scipy.org/
List -- A list is a dynamic array/sequence. It is ordered and indexable. A list is mutable.
List constructors: [], list().
range() and xrange():
Tuples -- A tuple is a sequence. A tuple is immutable.
Tuple constructors: (), but really a comma; also tuple().
Tuples are like lists, but are not mutable.
Python lists are (1) heterogeneous (2) indexable, and (3) dynamic. For example, we can add to a list and make it longer.
Notes on sequence constructors:
The length of a tuple or list (or other container): len(mylist).
Operators for lists:
Try: list1 + list2, list1 * n, list1 += list2, etc.
Comparison operators: <, ==, >=, etc.
Test for membership with the in operator. Example:
In [77]: a = [11, 22, 33] In [78]: a Out[78]: [11, 22, 33] In [79]: 22 in a Out[79]: True In [80]: 44 in a Out[80]: False
Subscription:
Operations on tuples -- No operations that change the tuple, since tuples are immutable. We can do iteration and subscription. We can do "contains" (the in operator) and get the length (the len() operator). We can use certain boolean operators.
Operations on lists -- Operations similar to tuples plus:
List operators -- +, *, etc.
For more operations and operators on sequences, see: http://docs.python.org/2/library/stdtypes.html#sequence-types-str-unicode-list-tuple-bytearray-buffer-xrange.
Exercises:
Create an empty list. Append 4 strings to the list. Then pop one item off the end of the list. Solution:
In [25]: a = []
In [26]: a.append('aaa')
In [27]: a.append('bbb')
In [28]: a.append('ccc')
In [29]: a.append('ddd')
In [30]: print a
['aaa', 'bbb', 'ccc', 'ddd']
In [31]: a.pop()
Out[31]: 'ddd'
Use the for statement to print the items in the list. Solution:
In [32]: for item in a: ....: print item ....: aaa bbb ccc
Use the string join operation to concatenate the items in the list. Solution:
In [33]: '||'.join(a) Out[33]: 'aaa||bbb||ccc'
Use lists containing three (3) elements to create and show a tree:
In [37]: b = ['bb', None, None] In [38]: c = ['cc', None, None] In [39]: root = ['aa', b, c] In [40]: In [40]: In [40]: def show_tree(t): ....: if not t: ....: return ....: print t[0] ....: show_tree(t[1]) ....: show_tree(t[2]) ....: ....: In [41]: show_tree(root) aa bb cc
Note that we will learn a better way to represent tree structures when we cover implementing classes in Python.
Strings are sequences. They are immutable. They are indexable. They are iterable.
For operations on strings, see http://docs.python.org/lib/string-methods.html or use:
>>> help(str)
Or:
>>> dir("abc")
String operations (methods).
String operators, e.g. +, <, <=, ==, etc..
Constructors/literals:
Escape characters in strings -- \t, \n, \\, etc.
String formatting -- See: http://docs.python.org/2/library/stdtypes.html#string-formatting-operations
Examples:
In [18]: name = 'dave' In [19]: size = 25 In [20]: factor = 3.45 In [21]: print 'Name: %s Size: %d Factor: %3.4f' % (name, size, factor, ) Name: dave Size: 25 Factor: 3.4500 In [25]: print 'Name: %s Size: %d Factor: %08.4f' % (name, size, factor, ) Name: dave Size: 25 Factor: 003.4500
If the right-hand argument to the formatting operator is a dictionary, then you can (actually, must) use the names of keys in the dictionary in your format strings. Examples:
In [115]: values = {'vegetable': 'chard', 'fruit': 'nectarine'}
In [116]: 'I love %(vegetable)s and I love %(fruit)s.' % values
Out[116]: 'I love chard and I love nectarine.'
Also consider using the right justify and left justify operations. Examples: mystring.rjust(20), mystring.ljust(20, ':').
In Python 3, the str.format method is preferred to the string formatting operator. This method is also available in Python 2.7. It has benefits and advantages over the string formatting operator. You can start learning about it here: http://docs.python.org/2/library/stdtypes.html#string-methods
Exercises:
Use a literal to create a string containing (1) a single quote, (2) a double quote, (3) both a single and double quote. Solutions:
"Some 'quoted' text." 'Some "quoted" text.' 'Some "quoted" \'extra\' text.'
Write a string literal that spans multiple lines. Solution:
"""This string spans several lines because it is a little long. """
Use the string join operation to create a string that contains a colon as a separator. Solution:
>>> content = []
>>> content.append('finch')
>>> content.append('sparrow')
>>> content.append('thrush')
>>> content.append('jay')
>>> contentstr = ':'.join(content)
>>> print contentstr
finch:sparrow:thrush:jay
Use string formatting to produce a string containing your last and first names, separated by a comma. Solution:
>>> first = 'Dave' >>> last = 'Kuhlman' >>> full = '%s, %s' % (last, first, ) >>> print full Kuhlman, Dave
Incrementally building up large strings from lots of small strings -- the old way -- Since strings in Python are immutable, appending to a string requires a re-allocation. So, it is faster to append to a list, then use join. Example:
In [25]: strlist = []
In [26]: strlist.append('Line #1')
In [27]: strlist.append('Line #2')
In [28]: strlist.append('Line #3')
In [29]: str = '\n'.join(strlist)
In [30]: print str
Line #1
Line #2
Line #3
Incrementally building up large strings from lots of small strings -- the new way -- The += operation on strings has been optimized. So, when you do this str1 += str2, even many times, it is efficient.
The translate method enables us to map the characters in a string, replacing those in one table by those in another. And, the maketrans function in the string module, makes it easy to create the mapping table:
import string
def test():
a = 'axbycz'
t = string.maketrans('abc', '123')
print a
print a.translate(t)
test()
The new way to do string formatting (which is standard in Python 3 and perhaps preferred for new code in Python 2) is to use the string.format method. See here:
Some examples:
In [1]: 'aaa {1} bbb {0} ccc {1} ddd'.format('xx', 'yy', )
Out[1]: 'aaa yy bbb xx ccc yy ddd'
In [2]: 'number: {0:05d} ok'.format(25)
Out[2]: 'number: 00025 ok'
In [4]: 'n1: {num1} n2: {num2}'.format(num2=25, num1=100)
Out[4]: 'n1: 100 n2: 25'
In [5]: 'n1: {num1} n2: {num2} again: {num1}'.format(num2=25, num1=100)
Out[5]: 'n1: 100 n2: 25 again: 100'
In [6]: 'number: {:05d} ok'.format(25)
Out[6]: 'number: 00025 ok'
In [7]: values = {'name': 'dave', 'hobby': 'birding'}
In [8]: 'user: {name} activity: {hobby}'.format(**values)
Out[8]: 'user: dave activity: birding'
Representing unicode:
In [96]: a = u'abcd'
In [97]: a
Out[97]: u'abcd'
In [98]: b = unicode('efgh')
In [99]: b
Out[99]: u'efgh'
Convert to unicode: a_string.decode(encoding). Examples:
In [102]: 'abcd'.decode('utf-8')
Out[102]: u'abcd'
In [103]:
In [104]: 'abcd'.decode(sys.getdefaultencoding())
Out[104]: u'abcd'
Convert out of unicode: a_unicode_string.encode(encoding). Examples:
In [107]: a = u'abcd'
In [108]: a.encode('utf-8')
Out[108]: 'abcd'
In [109]: a.encode(sys.getdefaultencoding())
Out[109]: 'abcd'
In [110]: b = u'Sel\xe7uk'
In [111]: print b.encode('utf-8')
Selçuk
Test for unicode type -- Example:
In [122]: import types In [123]: a = u'abcd' In [124]: type(a) is types.UnicodeType Out[124]: True In [125]: In [126]: type(a) is type(u'') Out[126]: True
Or better:
In [127]: isinstance(a, unicode) Out[127]: True
An example with a character "c" with a hachek:
In [135]: name = 'Ivan Krsti\xc4\x87'
In [136]: name.decode('utf-8')
Out[136]: u'Ivan Krsti\u0107'
In [137]:
In [138]: len(name)
Out[138]: 12
In [139]: len(name.decode('utf-8'))
Out[139]: 11
You can also create a unicode character by using the unichr() built-in function:
In [2]: a = 'aa' + unichr(170) + 'bb'
In [3]: a
Out[3]: u'aa\xaabb'
In [6]: b = a.encode('utf-8')
In [7]: b
Out[7]: 'aa\xc2\xaabb'
In [8]: print b
aaªbb
Guidance for use of encodings and unicode -- If you are working with a multibyte character set:
For more information, see:
If you are reading and writing multibyte character data from or to a file, then look at the codecs.open() in the codecs module -- http://docs.python.org/2/library/codecs.html#codecs.open.
Handling multi-byte character sets in Python 3 is easier, I think, but different. One hint is to use the encoding keyword parameter to the open built-in function. Here is an example:
def test():
infile = open('infile1.txt', 'r', encoding='utf-8')
outfile = open('outfile1.txt', 'w', encoding='utf-8')
for line in infile:
line = line.upper()
outfile.write(line)
infile.close()
outfile.close()
test()
A dictionary is a collection, whose values are accessible by key. It is a collection of name-value pairs.
The order of elements in a dictionary is undefined. But, we can iterate over (1) the keys, (2) the values, and (3) the items (key-value pairs) in a dictionary. We can set the value of a key and we can get the value associated with a key.
Keys must be immutable objects: ints, strings, tuples, ...
Literals for constructing dictionaries:
d1 = {}
d2 = {key1: value1, key2: value2, }
Constructor for dictionaries -- dict() can be used to create instances of the class dict. Some examples:
dict({'one': 2, 'two': 3})
dict({'one': 2, 'two': 3}.items())
dict({'one': 2, 'two': 3}.iteritems())
dict(zip(('one', 'two'), (2, 3)))
dict([['two', 3], ['one', 2]])
dict(one=2, two=3)
dict([(['one', 'two'][i-2], i) for i in (2, 3)])
For operations on dictionaries, see http://docs.python.org/lib/typesmapping.html or use:
>>> help({})
Or:
>>> dir({})
Indexing -- Access or add items to a dictionary with the indexing operator [ ]. Example:
In [102]: dict1 = {}
In [103]: dict1['name'] = 'dave'
In [104]: dict1['category'] = 38
In [105]: dict1
Out[105]: {'category': 38, 'name': 'dave'}
Some of the operations produce the keys, the values, and the items (pairs) in a dictionary. Examples:
In [43]: d = {'aa': 111, 'bb': 222}
In [44]: d.keys()
Out[44]: ['aa', 'bb']
In [45]: d.values()
Out[45]: [111, 222]
In [46]: d.items()
Out[46]: [('aa', 111), ('bb', 222)]
When iterating over large dictionaries, use methods iterkeys(), itervalues(), and iteritems(). Example:
In [47]:
In [47]: d = {'aa': 111, 'bb': 222}
In [48]: for key in d.iterkeys():
....: print key
....:
....:
aa
bb
To test for the existence of a key in a dictionary, use the in operator or the mydict.has_key(k) method. The in operator is preferred. Example:
>>> d = {'tomato': 101, 'cucumber': 102}
>>> k = 'tomato'
>>> k in d
True
>>> d.has_key(k)
True
You can often avoid the need for a test by using method get. Example:
>>> d = {'tomato': 101, 'cucumber': 102}
>>> d.get('tomato', -1)
101
>>> d.get('chard', -1)
-1
>>> if d.get('eggplant') is None:
... print 'missing'
...
missing
Dictionary "view" objects provide dynamic (automatically updated) views of the keys or the values or the items in a dictionary. View objects also support set operations. Create views with mydict.viewkeys(), mydict.viewvalues(), and mydict.viewitems(). See: http://docs.python.org/2/library/stdtypes.html#dictionary-view-objects.
The dictionary setdefault method provides a way to get the value associated with a key from a dictionary and to set that value if the key is missing. Example:
In [106]: a
Out[106]: {}
In [108]: a.setdefault('cc', 33)
Out[108]: 33
In [109]: a
Out[109]: {'cc': 33}
In [110]: a.setdefault('cc', 44)
Out[110]: 33
In [111]: a
Out[111]: {'cc': 33}
Exercises:
Write a literal that defines a dictionary using both string literals and variables containing strings. Solution:
>>> first = 'Dave'
>>> last = 'Kuhlman'
>>> name_dict = {first: last, 'Elvis': 'Presley'}
>>> print name_dict
{'Dave': 'Kuhlman', 'Elvis': 'Presley'}
Write statements that iterate over (1) the keys, (2) the values, and (3) the items in a dictionary. (Note: Requires introduction of the for statement.) Solutions:
>>> d = {'aa': 111, 'bb': 222, 'cc': 333}
>>> for key in d.keys():
... print key
...
aa
cc
bb
>>> for value in d.values():
... print value
...
111
333
222
>>> for item in d.items():
... print item
...
('aa', 111)
('cc', 333)
('bb', 222)
>>> for key, value in d.items():
... print key, '::', value
...
aa :: 111
cc :: 333
bb :: 222
Additional notes on dictionaries:
You can use iterkeys(), itervalues(), iteritems() to obtain iterators over keys, values, and items.
A dictionary itself is iterable: it iterates over its keys. So, the following two lines are equivalent:
for k in myDict: print k for k in myDict.iterkeys(): print k
The in operator tests for a key in a dictionary. Example:
In [52]: mydict = {'peach': 'sweet', 'lemon': 'tangy'}
In [53]: key = 'peach'
In [54]: if key in mydict:
....: print mydict[key]
....:
sweet
Open a file with the open factory method. Example:
In [28]: f = open('mylog.txt', 'w')
In [29]: f.write('message #1\n')
In [30]: f.write('message #2\n')
In [31]: f.write('message #3\n')
In [32]: f.close()
In [33]: f = file('mylog.txt', 'r')
In [34]: for line in f:
....: print line,
....:
message #1
message #2
message #3
In [35]: f.close()
Notes:
Use the (built-in) open(path, mode) function to open a file and create a file object. You could also use file(), but open() is recommended.
A file object that is open for reading a text file supports the iterator protocol and, therefore, can be used in a for statement. It iterates over the lines in the file. This is most likely only useful for text files.
open is a factory method that creates file objects. Use it to open files for reading, writing, and appending. Examples:
infile = open('myfile.txt', 'r') # open for reading
outfile = open('myfile.txt', 'w') # open for (over-) writing
log = open('myfile.txt', 'a') # open for appending to existing content
When you have finished with a file, close it. Examples:
infile.close() outfile.close()
You can also use the with: statement to automatically close the file. Example:
with open('tmp01.txt', 'r') as infile:
for x in infile:
print x,
The above works because a file is a context manager: it obeys the context manager protocol. A file has methods __enter__ and __exit__, and the __exit__ method automatically closes the file for us. See the section on the with: statement.
In order to open multiple files, you can nest with: statements, or use a single with: statement with multiple "expression as target" clauses. Example:
def test():
#
# use multiple nested with: statements.
with open('small_file.txt', 'r') as infile:
with open('tmp_outfile.txt', 'w') as outfile:
for line in infile:
outfile.write('line: %s' % line.upper())
print infile
print outfile
#
# use a single with: statement.
with open('small_file.txt', 'r') as infile, \
open('tmp_outfile.txt', 'w') as outfile:
for line in infile:
outfile.write('line: %s' % line.upper())
print infile
print outfile
test()
file is the file type and can be used as a constructor to create file objects. But, open is preferred.
Lines read from a text file have a newline. Strip it off with something like: line.rstrip('\n').
For binary files you should add the binary mode, for example: rb, wb. For more about modes, see the description of the open() function at Built-in Functions -- http://docs.python.org/lib/built-in-funcs.html.
Learn more about file objects and the methods they provide at: 2.3.9 File Objects -- http://docs.python.org/2/library/stdtypes.html#file-objects.
You can also append to an existing file. Note the "a" mode in the following example:
In [39]: f = open('mylog.txt', 'a')
In [40]: f.write('message #4\n')
In [41]: f.close()
In [42]: f = file('mylog.txt', 'r')
In [43]: for line in f:
....: print line,
....:
message #1
message #2
message #3
message #4
In [44]: f.close()
For binary files, add "b" to the mode. Not strictly necessary on UNIX, but needed on MS Windows. And, you will want to make your code portable across platforms. Example:
In [62]: import zipfile
In [63]: outfile = open('tmp1.zip', 'wb')
In [64]: zfile = zipfile.ZipFile(outfile, 'w', zipfile.ZIP_DEFLATED)
In [65]: zfile.writestr('entry1', 'my heroes have always been cowboys')
In [66]: zfile.writestr('entry2', 'and they still are it seems')
In [67]: zfile.writestr('entry3', 'sadly in search of and')
In [68]: zfile.writestr('entry4', 'on step in back of')
In [69]:
In [70]: zfile.writestr('entry4', 'one step in back of')
In [71]: zfile.writestr('entry5', 'themselves and their slow moving ways')
In [72]: zfile.close()
In [73]: outfile.close()
In [75]:
$
$ unzip -lv tmp1.zip
Archive: tmp1.zip
Length Method Size Ratio Date Time CRC-32 Name
-------- ------ ------- ----- ---- ---- ------ ----
34 Defl:N 36 -6% 05-29-08 17:04 f6b7d921 entry1
27 Defl:N 29 -7% 05-29-08 17:07 10da8f3d entry2
22 Defl:N 24 -9% 05-29-08 17:07 3fd17fda entry3
18 Defl:N 20 -11% 05-29-08 17:08 d55182e6 entry4
19 Defl:N 21 -11% 05-29-08 17:08 1a892acd entry4
37 Defl:N 39 -5% 05-29-08 17:09 e213708c entry5
-------- ------- --- -------
157 169 -8% 6 files
Exercises:
Read all of the lines of a file into a list. Print the 3rd and 5th lines in the file/list. Solution:
In [55]: f = open('tmp1.txt', 'r')
In [56]: lines = f.readlines()
In [57]: f.close()
In [58]: lines
Out[58]: ['the\n', 'big\n', 'brown\n', 'dog\n', 'had\n', 'long\n', 'hair\n']
In [59]: print lines[2]
brown
In [61]: print lines[4]
had
More notes:
Other built-in data types are described in section Built-in Types -- http://docs.python.org/lib/types.html in the Python standard documentation.
The unique value None is used to indicate "no value", "nothing", "non-existence", etc. There is only one None value; in other words, it's a singleton.
Use is to test for None. Example:
>>> flag = None >>> >>> if flag is None: ... print 'clear' ... clear >>> if flag is not None: ... print 'hello' ... >>>
True and False are the boolean values.
The following values also count as false, for example, in an if: statement: False, numeric zero, None, the empty string, an empty list, an empty dictionary, any empty container, etc. All other values, including True, act as true values.
A set is an unordered collection of immutable objects. A set does not contain duplicates.
Sets support several set operations, for example: union, intersection, difference, ...
A frozenset is like a set, except that a frozenset is immutable. Therefore, a frozenset is hash-able and can be used as a key in a dictionary, and it can be added to a set.
Create a set with the set constructor. Examples:
>>> a = set()
>>> a
set([])
>>> a.add('aa')
>>> a.add('bb')
>>> a
set(['aa', 'bb'])
>>> b = set([11, 22])
>>> b
set([11, 22])
>>> c = set([22, 33])
>>> b.union(c)
set([33, 11, 22])
>>> b.intersection(c)
set([22])
For more information on sets, see: Set Types -- set, frozenset -- http://docs.python.org/lib/types-set.html
Structured code -- Python programs are made up of expressions, statements, functions, classes, modules, and packages.
Python objects are first-class objects.
Expressions are evaluated.
Statements are executed.
Functions (1) are objects and (2) are callable.
Object-oriented programming in Python. Modeling "real world" objects. (1) Encapsulation; (2) data hiding; (3) inheritance. Polymorphism.
Classes -- (1) encapsulation; (2) data hiding; (3) inheritance.
An overview of the structure of a typical class: (1) methods; (2) the constructor; (3) class (static) variables; (4) super/subclasses.
Form -- target = expression.
Possible targets:
Identifier
Tuple or list -- Can be nested. Left and right sides must have equivalent structure. Example:
>>> x, y, z = 11, 22, 33 >>> [x, y, z] = 111, 222, 333 >>> a, (b, c) = 11, (22, 33) >>> a, B = 11, (22, 33)
This feature can be used to simulate an enum:
In [22]: LITTLE, MEDIUM, LARGE = range(1, 4) In [23]: LITTLE Out[23]: 1 In [24]: MEDIUM Out[24]: 2
Subscription of a sequence, dictionary, etc. Example:
In [10]: a = range(10)
In [11]: a
Out[11]: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
In [12]: a[3] = 'abc'
In [13]: a
Out[13]: [0, 1, 2, 'abc', 4, 5, 6, 7, 8, 9]
In [14]:
In [14]: b = {'aa': 11, 'bb': 22}
In [15]: b
Out[15]: {'aa': 11, 'bb': 22}
In [16]: b['bb'] = 1000
In [17]: b['cc'] = 2000
In [18]: b
Out[18]: {'aa': 11, 'bb': 1000, 'cc': 2000}
A slice of a sequence -- Note that the sequence must be mutable. Example:
In [1]: a = range(10) In [2]: a Out[2]: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9] In [3]: a[2:5] = [11, 22, 33, 44, 55, 66] In [4]: a Out[4]: [0, 1, 11, 22, 33, 44, 55, 66, 5, 6, 7, 8, 9]
Attribute reference -- Example:
>>> class MyClass: ... pass ... >>> anObj = MyClass() >>> anObj.desc = 'pretty' >>> print anObj.desc pretty
There is also augmented assignment. Examples:
>>> index = 0 >>> index += 1 >>> index += 5 >>> index += f(x) >>> index -= 1 >>> index *= 3
Things to note:
Assignment to a name creates a new variable (if it does not exist in the namespace) and a binding. Specifically, it binds a value to the new name. Calling a function also does this to the (formal) parameters within the local namespace.
In Python, a language with dynamic typing, the data type is associated with the value, not the variable, as is the case in statically typed languages.
Assignment can also cause sharing of an object. Example:
obj1 = A() obj2 = obj1
Check to determine that the same object is shared with id(obj) or the is operator. Example:
In [23]: a = range(10) In [24]: a Out[24]: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9] In [25]: b = a In [26]: b Out[26]: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9] In [27]: b[3] = 333 In [28]: b Out[28]: [0, 1, 2, 333, 4, 5, 6, 7, 8, 9] In [29]: a Out[29]: [0, 1, 2, 333, 4, 5, 6, 7, 8, 9] In [30]: a is b Out[30]: True In [31]: print id(a), id(b) 31037920 31037920
You can also do multiple assignment in a single statement. Example:
In [32]: a = b = 123 In [33]: a Out[33]: 123 In [34]: b Out[34]: 123 In [35]: In [35]: In [35]: a = b = [11, 22] In [36]: a is b Out[36]: True
You can interchange (swap) the value of two variables using assignment and packing/unpacking:
>>> a = 111 >>> b = 222 >>> a, b = b, a >>> a 222 >>> b 111
Make module (or objects in the module) available.
What import does:
Evaluate the content of a module.
Likely to create variables in the local (module) namespace.
Evaluation of a specific module only happens once during a given run of the program. Therefore, a module is shared across an application.
A module is evaluated from top to bottom. Later statements can replace values created earlier. This is true of functions and classes, as well as (other) variables.
Which statements are evaluated? Assignment, class, def, ...
Use the following idiom to make a module both run-able and import-able:
if __name__ == '__main__':
# import pdb; pdb.set_trace()
main() # or "test()" or some other function defined in module
Notes:
Where import looks for modules:
sys.path shows where it looks.
There are some standard places.
Add additional directories by setting the environment variable PYTHONPATH.
You can also add paths by modifying sys.path, for example:
import sys sys.path.insert(0, '/path/to/my/module')
Packages need a file named __init__.py.
Extensions -- To determine what extensions import looks for, do:
>>> import imp
>>> imp.get_suffixes()
[('.so', 'rb', 3), ('module.so', 'rb', 3), ('.py', 'U', 1), ('.pyc', 'rb', 2)]
Forms of the import statement:
More notes on the import statement:
The import statement and packages -- A file named __init__.py is required in a package. This file is evaluated the first time either the package is imported or a file in the package is imported. Question: What is made available when you do import aPackage? Answer: All variables (names) that are global inside the __init__.py module in that package. But, see notes on the use of __all__: The import statement -- http://docs.python.org/ref/import.html
The use of if __name__ == "__main__": -- Makes a module both import-able and executable.
Using dots in the import statement -- From the Python language reference manual:
"Hierarchical module names:when the module names contains one or more dots, the module search path is carried out differently. The sequence of identifiers up to the last dot is used to find a package; the final identifier is then searched inside the package. A package is generally a subdirectory of a directory on sys.path that has a file __init__.py."
See: The import statement -- http://docs.python.org/ref/import.html
Exercises:
Import a module from the standard library, for example re.
Import an element from a module from the standard library, for example import compile from the re module.
Create a simple Python package with a single module in it. Solution:
Create a directory named simplepackage in the current directory.
Create an (empty) __init__.py in the new directory.
Create an simple.py in the new directory.
Add a simple function name test1 in simple.py.
Import using any of the following:
>>> import simplepackage.simple >>> from simplepackage import simple >>> from simplepackage.simple import test1 >>> from simplepackage.simple import test1 as mytest
print sends output to sys.stdout. It adds a newline, unless an extra comma is added.
Arguments to print:
String formatting -- Arguments are a tuple. Reference: 2.3.6.2 String Formatting Operations -- http://docs.python.org/lib/typesseq-strings.html.
Can also use sys.stdout. Note that a carriage return is not automatically added. Example:
>>> import sys
>>> sys.stdout.write('hello\n')
Controlling the destination and format of print -- Replace sys.stdout with an instance of any class that implements the method write taking one parameter. Example:
import sys
class Writer:
def __init__(self, file_name):
self.out_file = file(file_name, 'a')
def write(self, msg):
self.out_file.write('[[%s]]' % msg)
def close(self):
self.out_file.close()
def test():
writer = Writer('outputfile.txt')
save_stdout = sys.stdout
sys.stdout = writer
print 'hello'
print 'goodbye'
writer.close()
# Show the output.
tmp_file = file('outputfile.txt')
sys.stdout = save_stdout
content = tmp_file.read()
tmp_file.close()
print content
test()
There is an alternative form of the print statement that takes a file-like object, in particular an object that has a write method. For example:
In [1]: outfile = open('tmp.log', 'w')
In [2]: print >> outfile, 'Message #1'
In [3]: print >> outfile, 'Message #2'
In [4]: print >> outfile, 'Message #3'
In [5]: outfile.close()
In [6]:
In [6]: infile = open('tmp.log', 'r')
In [7]: for line in infile:
...: print 'Line:', line.rstrip('\n')
...:
Line: Message #1
Line: Message #2
Line: Message #3
In [8]: infile.close()
Future deprecation warning -- There is no print statement in Python 3. There is a print built-in function.
A template for the if: statement:
if condition1:
statements
elif condition2:
statements
elif condition3:
statements
else:
statements
The elif and else clauses are optional.
Conditions -- Expressions -- Anything that returns a value. Compare with eval() and exec.
Truth values:
Operators:
and and or -- Note that both and and or do short circuit evaluation.
not
is and is not -- The identical object. Cf. a is b and id(a) == id(b). Useful to test for None, for example:
if x is None:
...
if x is not None:
...
in and not in -- Can be used to test for existence of a key in a dictionary or for the presence of a value in a collection.
The in operator tests for equality, not identity.
Example:
>>> d = {'aa': 111, 'bb': 222}
>>> 'aa' in d
True
>>> 'aa' not in d
False
>>> 'xx' in d
False
Comparison operators, for example ==, !=, <, <=, ...
There is an if expression. Example:
>>> a = 'aa' >>> b = 'bb' >>> x = 'yes' if a == b else 'no' >>> x 'no'
Notes:
Exercises:
Iterate over a sequence or an "iterable" object.
Form:
for x in y:
block
Iterator -- Some notes on what it means to be iterable:
Testing for "iterability":
Some ways to produce iterators:
iter() and enumerate() -- See: http://docs.python.org/lib/built-in-funcs.html.
some_dict.iterkeys(), some_dict.itervalues(), some_dict.iteritems().
Use a sequence in an iterator context, for example in a for statement. Lists, tuples, dictionaries, and strings can be used in an iterator context to produce an iterator.
Generator expressions -- Latest Python only. Syntactically like list comprehensions, but (1) surrounded by parentheses instead of square brackets and (2) use lazy evaluation.
A class that implements the iterator protocol -- Example:
class A(object):
def __init__(self):
self.data = [11,22,33]
self.idx = 0
def __iter__(self):
return self
def next(self):
if self.idx < len(self.data):
x = self.data[self.idx]
self.idx +=1
return x
else:
raise StopIteration
def test():
a = A()
for x in a:
print x
test()
Note that the iterator protocol changes in Python 3.
A function containing a yield statement. See:
Also see itertools module in the Python standard library for much more help with iterators: itertools — Functions creating iterators for efficient looping -- http://docs.python.org/2/library/itertools.html#module-itertools
The for: statement can also do unpacking. Example:
In [25]: items = ['apple', 'banana', 'cherry', 'date'] In [26]: for idx, item in enumerate(items): ....: print '%d. %s' % (idx, item, ) ....: 0. apple 1. banana 2. cherry 3. date
The for statement can also have an optional else: clause. The else: clause is executed if the for statement completes normally, that is if a break statement is not executed.
Helpful functions with for:
enumerate(iterable) -- Returns an iterable that produces pairs (tuples) containing count (index) and value. Example:
>>> for idx, value in enumerate([11,22,33]): ... print idx, value ... 0 11 1 22 2 33
range([start,] stop[, step]) and xrange([start,] stop[, step]).
List comprehensions -- Since list comprehensions create lists, they are useful in for statements, although, when the number of elements is large, you should consider using a generator expression instead. A list comprehension looks a bit like a for: statement, but is inside square brackets, and it is an expression, not a statement. Two forms (among others):
Generator expressions -- A generator expression looks similar to a list comprehension, except that it is surrounded by parentheses rather than square brackets. Example:
In [28]: items = ['apple', 'banana', 'cherry', 'date'] In [29]: gen1 = (item.upper() for item in items) In [30]: for x in gen1: ....: print 'x:', x ....: x: APPLE x: BANANA x: CHERRY x: DATE
Exercises:
Write a list comprehension that returns all the keys in a dictionary whose associated values are greater than zero.
Write a list comprehension that produces even integers from 0 to 10. Use a for statement to iterate over those values. Solution:
for x in [y for y in range(10) if y % 2 == 0]:
print 'x: %s' % x
Write a list comprehension that iterates over two lists and produces all the combinations of items from the lists. Solution:
In [19]: a = range(4) In [20]: b = [11,22,33] In [21]: a Out[21]: [0, 1, 2, 3] In [22]: b Out[22]: [11, 22, 33] In [23]: c = [(x, y) for x in a for y in b] In [24]: print c [(0, 11), (0, 22), (0, 33), (1, 11), (1, 22), (1, 33), (2, 11), (2, 22), (2, 33), (3, 11), (3, 22), (3, 33)]
But, note that in the previous exercise, a generator expression would often be better. A generator expression is like a list comprehension, except that, instead of creating the entire list, it produces a generator that can be used to produce each of the elements.
The break and continue statements are often useful in a for statement. See continue and break statements
The for statement can also have an optional else: clause. The else: clause is executed if the for statement completes normally, that is if a break statement is not executed. Example:
for item in data1:
if item > 100:
value1 = item
break
else:
value1 = 'not found'
print 'value1:', value1
When run, it prints:
value1: not found
Form:
while condition:
block
The while: statement is not often used in Python because the for: statement is usually more convenient, more idiomatic, and more Pythonic.
Exercises:
Write a while statement that prints integers from zero to 5. Solution:
count = 0
while count < 5:
count += 1
print count
The break and continue statements are often useful in a while statement. See continue and break statements
The while statement can also have an optional else: clause. The else: clause is executed if the while statement completes normally, that is if a break statement is not executed.
The break statement exits from a loop.
The continue statement causes execution to immediately continue at the start of the loop.
Can be used in for: and while:.
When the for: statement or the while: statement has an else: clause, the block in the else: clause is executed only if a break statement is not executed.
Exercises:
Using break, write a while statement that prints integers from zero to 5. Solution:
count = 0
while True:
count += 1
if count > 5:
break
print count
Notes:
Using continue, write a while statement that processes only even integers from 0 to 10. Note: % is the modulo operator. Solution:
count = 0
while count < 10:
count += 1
if count % 2 == 0:
continue
print count
Exceptions are a systematic and consistent way of processing errors and "unusual" events in Python.
Caught and un-caught exceptions -- Uncaught exceptions terminate a program.
The try: statement catches an exception.
Almost all errors in Python are exceptions.
Evaluation (execution model) of the try statement -- When an exception occurs in the try block, even if inside a nested function call, execution of the try block ends and the except clauses are searched for a matching exception.
Tracebacks -- Also see the traceback module: http://docs.python.org/lib/module-traceback.html
Exceptions are classes.
Exception classes -- subclassing, args.
An exception class in an except: clause catches instances of that exception class and all subclasses, but not superclasses.
Built-in exception classes -- See:
User defined exception classes -- subclasses of Exception.
Example:
try:
raise RuntimeError('this silly error')
except RuntimeError, exp:
print "[[[%s]]]" % exp
Reference: http://docs.python.org/lib/module-exceptions.html
You can also get the arguments passed to the constructor of an exception object. In the above example, these would be:
exp.args
Why would you define your own exception class? One answer: You want a user of your code to catch your exception and no others.
Catching an exception by exception class catches exceptions of that class and all its subclasses. So:
except SomeExceptionClass, exp:
matches and catches an exception if SomeExceptionClass is the exception class or a base class (superclass) of the exception class. The exception object (usually an instance of some exception class) is bound to exp.
A more "modern" syntax is:
except SomeExceptionClass as exp:
So:
class MyE(ValueError):
pass
try:
raise MyE()
except ValueError:
print 'caught exception'
will print "caught exception", because ValueError is a base class of MyE.
Also see the entries for "EAFP" and "LBYL" in the Python glossary: http://docs.python.org/3/glossary.html.
Exercises:
Write a very simple, empty exception subclass. Solution:
class MyE(Exception):
pass
Write a try:except: statement that raises your exception and also catches it. Solution:
try:
raise MyE('hello there dave')
except MyE, e:
print e
Throw or raise an exception.
Forms:
The raise statement takes:
See http://docs.python.org/ref/raise.html.
For a list of built-in exceptions, see http://docs.python.org/lib/module-exceptions.html.
The following example defines an exception subclass and throws an instance of that subclass. It also shows how to pass and catch multiple arguments to the exception:
class NotsobadError(Exception):
pass
def test(x):
try:
if x == 0:
raise NotsobadError('a moderately bad error', 'not too bad')
except NotsobadError, e:
print 'Error args: %s' % (e.args, )
test(0)
Which prints out the following:
Error args: ('a moderately bad error', 'not too bad')
Notes:
The following example does a small amount of processing of the arguments:
class NotsobadError(Exception):
"""An exception class.
"""
def get_args(self):
return '::::'.join(self.args)
def test(x):
try:
if x == 0:
raise NotsobadError('a moderately bad error', 'not too bad')
except NotsobadError, e:
print 'Error args: {{{%s}}}' % (e.get_args(), )
test(0)
The with statement enables us to use a context manager (any object that satisfies the context manager protocol) to add code before (on entry to) and after (on exit from) a block of code.
A context manager is an instance of a class that satisfies this interface:
class Context01(object):
def __enter__(self):
pass
def __exit__(self, exc_type, exc_value, traceback):
pass
Here is an example that uses the above context manager:
class Context01(object):
def __enter__(self):
print 'in __enter__'
return 'some value or other' # usually we want to return self
def __exit__(self, exc_type, exc_value, traceback):
print 'in __exit__'
Notes:
The __enter__ method is called before our block of code is entered.
Usually, but not always, we will want the __enter__ method to return self, that is, the instance of our context manager class. We do this so that we can write:
with MyContextManager() as obj:
pass
and then use the instance (obj in this case) in the nested block.
The __exit__ method is called when our block of code is exited either normally or because of an exception.
If an exception is supplied, and the method wishes to suppress the exception (i.e., prevent it from being propagated), it should return a true value. Otherwise, the exception will be processed normally upon exit from this method.
If the block exits normally, the value of exc_type, exc_value, and traceback will be None.
For more information on the with: statement, see Context Manager Types -- http://docs.python.org/2/library/stdtypes.html#context-manager-types.
See module contextlib for strange ways of writing context managers: http://docs.python.org/2/library/contextlib.html#module-contextlib
Here are examples:
# example 1
with Context01():
print 'in body'
# example 2
with Context02() as a_value:
print 'in body'
print 'a_value: "%s"' % (a_value, )
a_value.some_method_in_Context02()
# example 3
with open(infilename, 'r') as infile, open(outfilename, 'w') as outfile:
for line in infile:
line = line.rstrip()
outfile.write('%s\n' % line.upper())
Notes:
The del statement can be used to:
If name is listed in a global statement, then del removes name from the global namespace.
Names can be a (nested) list. Examples:
>>> del a >>> del a, b, c
We can also delete items from a list or dictionary (and perhaps from other objects that we can subscript). Examples:
In [9]:d = {'aa': 111, 'bb': 222, 'cc': 333}
In [10]:print d
{'aa': 111, 'cc': 333, 'bb': 222}
In [11]:del d['bb']
In [12]:print d
{'aa': 111, 'cc': 333}
In [13]:
In [13]:a = [111, 222, 333, 444]
In [14]:print a
[111, 222, 333, 444]
In [15]:del a[1]
In [16]:print a
[111, 333, 444]
And, we can delete an attribute from an instance. Example:
In [17]:class A: ....: pass ....: In [18]:a = A() In [19]:a.x = 123 In [20]:dir(a) Out[20]:['__doc__', '__module__', 'x'] In [21]:print a.x 123 In [22]:del a.x In [23]:dir(a) Out[23]:['__doc__', '__module__'] In [24]:print a.x ---------------------------------------------- exceptions.AttributeError Traceback (most recent call last) /home/dkuhlman/a1/Python/Test/<console> AttributeError: A instance has no attribute 'x'
There is no case statement in Python. Use the if: statement with a sequence of elif: clauses. Or, use a dictionary of functions.
The def statement is used to define functions and methods.
The def statement is evaluated. It produces a function/method (object) and binds it to a variable in the current name-space.
Although the def statement is evaluated, the code in its nested block is not executed. Therefore, many errors may not be detected until each and every path through that code is tested. Recommendations: (1) Use a Python code checker, for example flake8 or pylint; (2) Do thorough testing and use the Python unittest framework. Pythonic wisdom: If it's not tested, it's broken.
The return statement is used to return values from a function.
The return statement takes zero or more values, separated by commas. Using commas actually returns a single tuple.
The default value is None.
To return multiple values, use a tuple or list. Don't forget that (assignment) unpacking can be used to capture multiple values. Returning multiple items separated by commas is equivalent to returning a tuple. Example:
In [8]: def test(x, y): ...: return x * 3, y * 4 ...: In [9]: a, b = test(3, 4) In [10]: print a 9 In [11]: print b 16
Default values -- Example:
In [53]: def t(max=5): ....: for val in range(max): ....: print val ....: ....: In [54]: t(3) 0 1 2 In [55]: t() 0 1 2 3 4
Giving a parameter a default value makes that parameter optional.
Note: If a function has a parameter with a default value, then all "normal" arguments must proceed the parameters with default values. More completely, parameters must be given from left to right in the following order:
List parameters -- *args. It's a tuple.
Keyword parameters -- **kwargs. It's a dictionary.
When calling a function, values may be passed to a function with positional arguments or keyword arguments.
Positional arguments must placed before (to the left of) keyword arguments.
Passing lists to a function as multiple arguments -- some_func(*aList). Effectively, this syntax causes Python to unroll the arguments. Example:
def fn1(*args, **kwargs):
fn2(*args, **kwargs)
Creating local variables -- Any binding operation creates a local variable. Examples are (1) parameters of a function; (2) assignment to a variable in a function; (3) the import statement; (4) etc. Contrast with accessing a variable.
Variable look-up -- The LGB/LEGB rule -- The local, enclosing, global, built-in scopes are searched in that order. See: http://www.python.org/dev/peps/pep-0227/
The global statement -- Inside a function, we must use global when we want to set the value of a global variable. Example:
def fn():
global Some_global_variable, Another_global_variable
Some_global_variable = 'hello'
...
Functions are first-class -- You can store them in a structure, pass them to a function, and return them from a function.
Function calls can take keyword arguments. Example:
>>> test(size=25)
Formal parameters to a function can have default values. Example:
>>> def test(size=0):
...
Do not use mutable objects as default values.
You can "capture" remaining arguments with *args, and **kwargs. (Spelling is not significant.) Example:
In [13]: def test(size, *args, **kwargs):
....: print size
....: print args
....: print kwargs
....:
....:
In [14]: test(32, 'aa', 'bb', otherparam='xyz')
32
('aa', 'bb')
{'otherparam': 'xyz'}
Normal arguments must come before default arguments which must come before keyword arguments.
A function that does not explicitly return a value, returns None.
In order to set the value of a global variable, declare the variable with global.
Exercises:
Write a function that takes a single argument, prints the value of the argument, and returns the argument as a string. Solution:
>>> def t(x): ... print 'x: %s' % x ... return '[[%s]]' % x ... >>> t(3) x: 3 '[[3]]'
Write a function that takes a variable number of arguments and prints them all. Solution:
>>> def t(*args):
... for arg in args:
... print 'arg: %s' % arg
...
>>> t('aa', 'bb', 'cc')
arg: aa
arg: bb
arg: cc
Write a function that prints the names and values of keyword arguments passed to it. Solution:
>>> def t(**kwargs): ... for key in kwargs.keys(): ... print 'key: %s value: %s' % (key, kwargs[key], ) ... >>> t(arg1=11, arg2=22) key: arg1 value: 11 key: arg2 value: 22
By default, assignment in a function or method creates local variables.
Reference (not assignment) to a variable, accesses a local variable if it has already been created, else accesses a global variable.
In order to assign a value to a global variable, declare the variable as global at the beginning of the function or method.
If in a function or method, you both reference and assign to a variable, then you must either:
The global statement declares one or more variables, separated by commas, to be global.
Some examples:
In [1]: In [1]: X = 3 In [2]: def t(): ...: print X ...: In [3]: In [3]: t() 3 In [4]: def s(): ...: X = 4 ...: In [5]: In [5]: In [5]: s() In [6]: t() 3 In [7]: X = -1 In [8]: def u(): ...: global X ...: X = 5 ...: In [9]: In [9]: u() In [10]: t() 5 In [16]: def v(): ....: x = X ....: X = 6 ....: return x ....: In [17]: In [17]: v() ------------------------------------------------------------ Traceback (most recent call last): File "<ipython console>", line 1, in <module> File "<ipython console>", line 2, in v UnboundLocalError: local variable 'X' referenced before assignment In [18]: def w(): ....: global X ....: x = X ....: X = 7 ....: return x ....: In [19]: In [19]: w() Out[19]: 5 In [20]: X Out[20]: 7
Add docstrings as a triple-quoted string beginning with the first line of a function or method. See epydoc for a suggested format.
A decorator performs a transformation on a function. Examples of decorators that are built-in functions are: @classmethod, @staticmethod, and @property. See: http://docs.python.org/2/library/functions.html#built-in-functions
A decorator is applied using the "@" character on a line immediately preceeding the function definition header. Examples:
class SomeClass(object):
@classmethod
def HelloClass(cls, arg):
pass
## HelloClass = classmethod(HelloClass)
@staticmethod
def HelloStatic(arg):
pass
## HelloStatic = staticmethod(HelloStatic)
#
# Define/implement a decorator.
def wrapper(fn):
def inner_fn(*args, **kwargs):
print '>>'
result = fn(*args, **kwargs)
print '<<'
return result
return inner_fn
#
# Apply a decorator.
@wrapper
def fn1(msg):
pass
## fn1 = wrapper(fn1)
Notes:
Use a lambda, as a convenience, when you need a function that both:
Example:
In [1]: fn = lambda x, y, z: (x ** 2) + (y * 2) + z
In [2]: fn(4, 5, 6)
Out[2]: 32
In [3]:
In [3]: format = lambda obj, category: 'The "%s" is a "%s".' % (obj, category, )
In [4]: format('pine tree', 'conifer')
Out[4]: 'The "pine tree" is a "conifer".'
A lambda can take multiple arguments and can return (like a function) multiple values. Example:
In [79]: a = lambda x, y: (x * 3, y * 4, (x, y)) In [80]: In [81]: a(3, 4) Out[81]: (9, 16, (3, 4))
Suggestion: In some cases, a lambda may be useful as an event handler.
Example:
class Test:
def __init__(self, first='', last=''):
self.first = first
self.last = last
def test(self, formatter):
"""
Test for lambdas.
formatter is a function taking 2 arguments, first and last
names. It should return the formatted name.
"""
msg = 'My name is %s' % (formatter(self.first, self.last),)
print msg
def test():
t = Test('Dave', 'Kuhlman')
t.test(lambda first, last: '%s %s' % (first, last, ))
t.test(lambda first, last: '%s, %s' % (last, first, ))
test()
A lambda enables us to define "functions" where we do not need names for those functions. Example:
In [45]: a = [ ....: lambda x: x, ....: lambda x: x * 2, ....: ] In [46]: In [46]: a Out[46]: [<function __main__.<lambda>>, <function __main__.<lambda>>] In [47]: a[0](3) Out[47]: 3 In [48]: a[1](3) Out[48]: 6
Reference: http://docs.python.org/2/reference/expressions.html#lambda
Concepts:
An object satisfies the iterator protocol if it does the following:
For more information on iterators, see the section on iterator types in the Python Library Reference -- http://docs.python.org/2/library/stdtypes.html#iterator-types.
For more on the yield statement, see: http://docs.python.org/2/reference/simple_stmts.html#the-yield-statement
Actually, yield is an expression. For more on yield expressions and on the next() and send() generator methods, as well as others, see: Yield expression -- http://docs.python.org/2/reference/expressions.html#yield-expressions in the Python language reference manual.
A function or method containing a yield statement implements a generator. Adding the yield statement to a function or method turns that function or method into one which, when called, returns a generator, i.e. an object that implements the iterator protocol.
A generator (a function containing yield) provides a convenient way to implement a filter. But, also consider:
Here are a few examples:
def simplegenerator():
yield 'aaa' # Note 1
yield 'bbb'
yield 'ccc'
def list_tripler(somelist):
for item in somelist:
item *= 3
yield item
def limit_iterator(somelist, max):
for item in somelist:
if item > max:
return # Note 2
yield item
def test():
print '1.', '-' * 30
it = simplegenerator()
for item in it:
print item
print '2.', '-' * 30
alist = range(5)
it = list_tripler(alist)
for item in it:
print item
print '3.', '-' * 30
alist = range(8)
it = limit_iterator(alist, 4)
for item in it:
print item
print '4.', '-' * 30
it = simplegenerator()
try:
print it.next() # Note 3
print it.next()
print it.next()
print it.next()
except StopIteration, exp: # Note 4
print 'reached end of sequence'
if __name__ == '__main__':
test()
Notes:
And here is the output from running the above example:
$ python test_iterator.py 1. ------------------------------ aaa bbb ccc 2. ------------------------------ 0 3 6 9 12 3. ------------------------------ 0 1 2 3 4 4. ------------------------------ aaa bbb ccc reached end of sequence
An instance of a class which implements the __iter__ method, returning an iterator, is iterable. For example, it can be used in a for statement or in a list comprehension, or in a generator expression, or as an argument to the iter() built-in method. But, notice that the class most likely implements a generator method which can be called directly.
Examples -- The following code implements an iterator that produces all the objects in a tree of objects:
class Node:
def __init__(self, data, children=None):
self.initlevel = 0
self.data = data
if children is None:
self.children = []
else:
self.children = children
def set_initlevel(self, initlevel): self.initlevel = initlevel
def get_initlevel(self): return self.initlevel
def addchild(self, child):
self.children.append(child)
def get_data(self):
return self.data
def get_children(self):
return self.children
def show_tree(self, level):
self.show_level(level)
print 'data: %s' % (self.data, )
for child in self.children:
child.show_tree(level + 1)
def show_level(self, level):
print ' ' * level,
#
# Generator method #1
# This generator turns instances of this class into iterable objects.
#
def walk_tree(self, level):
yield (level, self, )
for child in self.get_children():
for level1, tree1 in child.walk_tree(level+1):
yield level1, tree1
def __iter__(self):
return self.walk_tree(self.initlevel)
#
# Generator method #2
# This generator uses a support function (walk_list) which calls
# this function to recursively walk the tree.
# If effect, this iterates over the support function, which
# iterates over this function.
#
def walk_tree(tree, level):
yield (level, tree)
for child in walk_list(tree.get_children(), level+1):
yield child
def walk_list(trees, level):
for tree in trees:
for tree in walk_tree(tree, level):
yield tree
#
# Generator method #3
# This generator is like method #2, but calls itself (as an iterator),
# rather than calling a support function.
#
def walk_tree_recur(tree, level):
yield (level, tree,)
for child in tree.get_children():
for level1, tree1 in walk_tree_recur(child, level+1):
yield (level1, tree1, )
def show_level(level):
print ' ' * level,
def test():
a7 = Node('777')
a6 = Node('666')
a5 = Node('555')
a4 = Node('444')
a3 = Node('333', [a4, a5])
a2 = Node('222', [a6, a7])
a1 = Node('111', [a2, a3])
initLevel = 2
a1.show_tree(initLevel)
print '=' * 40
for level, item in walk_tree(a1, initLevel):
show_level(level)
print 'item:', item.get_data()
print '=' * 40
for level, item in walk_tree_recur(a1, initLevel):
show_level(level)
print 'item:', item.get_data()
print '=' * 40
a1.set_initlevel(initLevel)
for level, item in a1:
show_level(level)
print 'item:', item.get_data()
iter1 = iter(a1)
print iter1
print iter1.next()
print iter1.next()
print iter1.next()
print iter1.next()
print iter1.next()
print iter1.next()
print iter1.next()
## print iter1.next()
return a1
if __name__ == '__main__':
test()
Notes:
A module is a Python source code file.
A module can be imported. When imported, the module is evaluated, and a module object is created. The module object has attributes. The following attributes are of special interest:
A module can be run.
To make a module both import-able and run-able, use the following idiom (at the end of the module):
def main():
o
o
o
if __name__ == '__main__':
main()
Where Python looks for modules:
Notes about modules and objects:
Add docstrings as a triple-quoted string at or near the top of the file. See epydoc for a suggested format.
A package is a directory on the file system which contains a file named __init__.py.
The __init__.py file:
Why is it there? -- It makes modules in the directory "import-able".
Can __init__.py be empty? -- Yes. Or, just include a comment.
When is it evaluated? -- It is evaluated the first time that an application imports anything from that directory/package.
What can you do with it? -- Any code that should be executed exactly once and during import. For example:
Define a variable named __all__ to specify the list of names that will be imported by from my_package import *. For example, if the following is present in my_package/__init__.py:
__all__ = ['func1', 'func2',]
Then, from my_package import * will import func1 and func2, but not other names defined in my_package.
Note that __all__ can be used at the module level, as well as at the package level.
For more information, see the section on packages in the Python tutorial: http://docs.python.org/2/tutorial/modules.html#packages.
Guidance and suggestions:
"Flat is better" -- Use the __init__.py file to present a "flat" view of the API for your code. Enable your users to do import mypackage and then reference:
Where item1, item2, etc compose the API you want your users to use, even though the implementation of these items may be buried deep in your code.
Use the __init__.py module to present a "clean" API. Present only the items that you intend your users to use, and by doing so, "hide" items you do not intend them to use.
Classes model the behavior of objects in the "real" world. Methods implement the behaviors of these types of objects. Member variables hold (current) state. Classes enable us to implement new data types in Python.
The class: statement is used to define a class. The class: statement creates a class object and binds it to a name.
In [104]: class A: .....: pass .....: In [105]: a = A()
To define a new style class (recommended), inherit from object or from another class that does. Example:
In [21]: class A(object): ....: pass ....: In [22]: In [22]: a = A() In [23]: a Out[23]: <__main__.A object at 0x82fbfcc>
A method is a function defined in class scope and with first parameter self:
In [106]: class B(object): .....: def show(self): .....: print 'hello from B' .....: In [107]: b = B() In [108]: b.show() hello from B
A method as we describe it here is more properly called an instance method, in order to distinguish it from class methods and static methods.
The constructor is a method named __init__.
Exercise: Define a class with a member variable name and a show method. Use print to show the name. Solution:
In [109]: class A(object):
.....: def __init__(self, name):
.....: self.name = name
.....: def show(self):
.....: print 'name: "%s"' % self.name
.....:
In [111]: a = A('dave')
In [112]: a.show()
name: "dave"
Notes:
Defining member variables -- Member variables are created with assignment. Example:
class A(object):
def __init__(self, name):
self.name = name
A small gotcha -- Do this:
In [28]: class A(object): ....: def __init__(self, items=None): ....: if items is None: ....: self.items = [] ....: else: ....: self.items = items
Do not do this:
In [29]: class B: ....: def __init__(self, items=[]): # wrong. list ctor evaluated only once. ....: self.items = items
In the second example, the def statement and the list constructor are evaluated only once. Therefore, the item member variable of all instances of class B, will share the same value, which is most likely not what you want.
Use the instance and the dot operator.
Calling a method defined in the same class or a superclass:
From outside the class -- Use the instance:
some_object.some_method() an_array_of_of_objects[1].another_method()
From within the same class -- Use self:
self.a_method()
From with a subclass when the method is in the superclass and there is a method with the same name in the current class -- Use the class (name) or use super:
SomeSuperClass.__init__(self, arg1, arg2) super(CurrentClass, self).__init__(arg1, arg2)
Calling a method defined in a specific superclass -- Use the class (name).
Referencing superclasses -- Use the built-in super or the explicit name of the superclass. Use of super is preferred. For example:
In [39]: class B(A): ....: def __init__(self, name, size): ....: super(B, self).__init__(name) ....: # A.__init__(self, name) # an older alternative form ....: self.size = size
The use of super() may solve problems searching for the base class when using multiple inheritance. A better solution is to not use multiple inheritance.
You can also use multiple inheritance. But, it can cause confusion. For example, in the following, class C inherits from both A and B:
class C(A, B):
...
Python searches superclasses MRO (method resolution order). If only single inheritance is involved, there is little confusion. If multiple inheritance is being used, the search order of super classes can get complex -- see here: http://www.python.org/download/releases/2.3/mro
For more information on inheritance, see the tutorial in the standard Python documentation set: 9.5 Inheritance and 9.5.1 Multiple Inheritance.
Watch out for problems with inheriting from multiple classes that have a common base class.
Also called static data.
A class variable is shared by instances of the class.
Define at class level with assignment. Example:
class A(object):
size = 5
def get_size(self):
return A.size
Reference with classname.variable.
Caution: self.variable = x creates a new member variable.
Instance (plain) methods:
Class methods:
Static methods:
Notes on decorators:
A decorator of the form @afunc is the same as m = afunc(m). So, this:
@afunc def m(self): pass
is the same as:
def m(self): pass m = afunc(m)
You can use decorators @classmethod and @staticmethod (instead of the classmethod() and staticmethod() built-in functions) to declare class methods and static methods.
Example:
class B(object):
Count = 0
def dup_string(x):
s1 = '%s%s' % (x, x,)
return s1
dup_string = staticmethod(dup_string)
@classmethod
def show_count(cls, msg):
print '%s %d' % (msg, cls.Count, )
def test():
print B.dup_string('abcd')
B.show_count('here is the count: ')
An alternative way to implement "static methods" -- Use a "plain", module-level function. For example:
In [1]: def inc_count(): ...: A.count += 1 ...: In [2]: In [2]: def dec_count(): ...: A.count -= 1 ...: In [3]: In [3]: class A: ...: count = 0 ...: def get_count(self): ...: return A.count ...: In [4]: In [4]: a = A() In [5]: a.get_count() Out[5]: 0 In [6]: In [6]: In [6]: inc_count() In [7]: inc_count() In [8]: a.get_count() Out[8]: 2 In [9]: In [9]: b = A() In [10]: b.get_count() Out[10]: 2
The property built-in function enables us to write classes in a way that does not require a user of the class to use getters and setters. Example:
class TestProperty(object):
def __init__(self, description):
self._description = description
def _set_description(self, description):
print 'setting description'
self._description = description
def _get_description(self):
print 'getting description'
return self._description
description = property(_get_description, _set_description)
The property built-in function is also a decorator. So, the following is equivalent to the above example:
class TestProperty(object):
def __init__(self, description):
self._description = description
@property
def description(self):
print 'getting description'
return self._description
@description.setter
def description(self, description):
print 'setting description'
self._description = description
Notes:
For more information on properties, see Built-in Functions -- properties -- http://docs.python.org/2/library/functions.html#property
In Python, to implement an interface is to implement a method with a specific name and a specific arguments.
"Duck typing" -- If it walks like a duck and quacks like a duck ...
One way to define an "interface" is to define a class containing methods that have a header and a doc string but no implementation.
Additional notes on interfaces:
A new-style class is one that subclasses object or a class that subclasses object (that is, another new-style class).
You can subclass Python's built-in data-types.
A simple example -- the following class extends the list data-type:
class C(list):
def get_len(self):
return len(self)
c = C((11,22,33))
c.get_len()
c = C((11,22,33,44,55,66,77,88))
print c.get_len()
# Prints "8".
A slightly more complex example -- the following class extends the dictionary data-type:
class D(dict):
def __init__(self, data=None, name='no_name'):
if data is None:
data = {}
dict.__init__(self, data)
self.name = name
def get_len(self):
return len(self)
def get_keys(self):
content = []
for key in self:
content.append(key)
contentstr = ', '.join(content)
return contentstr
def get_name(self):
return self.name
def test():
d = D({'aa': 111, 'bb':222, 'cc':333})
# Prints "3"
print d.get_len()
# Prints "'aa, cc, bb'"
print d.get_keys()
# Prints "no_name"
print d.get_name()
Some things to remember about new-style classes:
For more on new-style classes, see: http://www.python.org/doc/newstyle/
Exercises:
Write a class and a subclass of this class.
Solution:
class A(object):
def __init__(self, name):
self.name = name
def show(self):
print 'name: %s' % (self.name, )
class B(A):
def __init__(self, name, desc):
A.__init__(self, name)
self.desc = desc
def show(self):
A.show(self)
print 'desc: %s' % (self.desc, )
Add docstrings as a (triple-quoted) string beginning with the first line of a class. See epydoc for a suggested format.
Add an leading underscore to a member name (method or data variable) to "suggest" that the member is private.
pdb -- The Python debugger:
Start the debugger by running an expression:
pdb.run('expression')
Example:
if __name__ == '__main__':
import pdb
pdb.run('main()')
Start up the debugger at a specific location with the following:
import pdb; pdb.set_trace()
Example:
if __name__ == '__main__':
import pdb
pdb.set_trace()
main()
Get help from within the debugger. For example:
(Pdb) help (Pdb) help next
Can also embed IPython into your code. See http://ipython.scipy.org/doc/manual/manual.html.
ipdb -- Also consider using ipdb (and IPython). The ipdb debugger interactive prompt has some additional features, for example, it does tab name completion.
Inspecting:
Miscellaneous tools:
pdb is implemented with the cmd module in the Python standard library. You can implement similar command line interfaces by using cmd. See: cmd -- Support for line-oriented command interpreters -- http://docs.python.org/lib/module-cmd.html.
Create a file object. Use open().
This example reads and prints each line of a file:
def test():
f = file('tmp.py', 'r')
for line in f:
print 'line:', line.rstrip()
f.close()
test()
Notes:
A text file is an iterable. It iterates over the lines in a file. The following is a common idiom:
infile = file(filename, 'r')
for line in infile:
process_a_line(line)
infile.close()
string.rstrip() strips new-line and other whitespace from the right side of each line. To strip new-lines only, but not other whitespace, try rstrip('\n').
Other ways of reading from a file/stream object: my_file.read(), my_file.readline(), my_file.readlines(),
This example writes lines of text to a file:
def test():
f = file('tmp.txt', 'w')
for ch in 'abcdefg':
f.write(ch * 10)
f.write('\n')
f.close()
test()
Notes:
And, don't forget the with: statement. It makes closing files automatic. The following example converts all the vowels in an input file to upper case and writes the converted lines to an output file:
import string
def show_file(infilename, outfilename):
tran_table = string.maketrans('aeiou', 'AEIOU')
with open(infilename, 'r') as infile, open(outfilename, 'w') as outfile:
for line in infile:
line = line.rstrip()
outfile.write('%s\n' % line.translate(tran_table))
For more documentation on the unit test framework, see unittest -- Unit testing framework -- http://docs.python.org/2/library/unittest.html#module-unittest
For help and more information do the following at the Python interactive prompt:
>>> import unittest >>> help(unittest)
And, you can read the source: Lib/unittest.py in the Python standard library.
Here is a very simple example. You can find more information about this primitive way of structuring unit tests in the library documentation for the unittest module Basic example -- http://docs.python.org/lib/minimal-example.html
import unittest
class UnitTests02(unittest.TestCase):
def testFoo(self):
self.failUnless(False)
class UnitTests01(unittest.TestCase):
def testBar01(self):
self.failUnless(False)
def testBar02(self):
self.failUnless(False)
def main():
unittest.main()
if __name__ == '__main__':
main()
Notes:
The call to unittest.main() runs all tests in all test fixtures in the module. It actually creates an instance of class TestProgram in module Lib/unittest.py, which automatically runs tests.
Test fixtures are classes that inherit from unittest.TestCase.
Within a test fixture (a class), the tests are any methods whose names begin with the prefix "test".
In any test, we check for success or failure with inherited methods such as failIf(), failUnless(), assertNotEqual(), etc. For more on these methods, see the library documentation for the unittest module TestCase Objects -- http://docs.python.org/lib/testcase-objects.html.
If you want to change (1) the test method prefix or (2) the function used to sort (the order of) execution of tests within a test fixture, then you can create your own instance of class unittest.TestLoader and customize it. For example:
def main():
my_test_loader = unittest.TestLoader()
my_test_loader.testMethodPrefix = 'check'
my_test_loader.sortTestMethodsUsing = my_cmp_func
unittest.main(testLoader=my_test_loader)
if __name__ == '__main__':
main()
But, see the notes in section Additional unittest features for instructions on a (possibly) better way to do this.
Here is another, not quite so simple, example:
#!/usr/bin/env python
import sys, popen2
import getopt
import unittest
class GenTest(unittest.TestCase):
def test_1_generate(self):
cmd = 'python ../generateDS.py -f -o out2sup.py -s out2sub.py people.xsd'
outfile, infile = popen2.popen2(cmd)
result = outfile.read()
outfile.close()
infile.close()
self.failUnless(len(result) == 0)
def test_2_compare_superclasses(self):
cmd = 'diff out1sup.py out2sup.py'
outfile, infile = popen2.popen2(cmd)
outfile, infile = popen2.popen2(cmd)
result = outfile.read()
outfile.close()
infile.close()
#print 'len(result):', len(result)
# Ignore the differing lines containing the date/time.
#self.failUnless(len(result) < 130 and result.find('Generated') > -1)
self.failUnless(check_result(result))
def test_3_compare_subclasses(self):
cmd = 'diff out1sub.py out2sub.py'
outfile, infile = popen2.popen2(cmd)
outfile, infile = popen2.popen2(cmd)
result = outfile.read()
outfile.close()
infile.close()
# Ignore the differing lines containing the date/time.
#self.failUnless(len(result) < 130 and result.find('Generated') > -1)
self.failUnless(check_result(result))
def check_result(result):
flag1 = 0
flag2 = 0
lines = result.split('\n')
len1 = len(lines)
if len1 <= 5:
flag1 = 1
s1 = '\n'.join(lines[:4])
if s1.find('Generated') > -1:
flag2 = 1
return flag1 and flag2
# Make the test suite.
def suite():
# The following is obsolete. See Lib/unittest.py.
#return unittest.makeSuite(GenTest)
loader = unittest.TestLoader()
# or alternatively
# loader = unittest.defaultTestLoader
testsuite = loader.loadTestsFromTestCase(GenTest)
return testsuite
# Make the test suite and run the tests.
def test():
testsuite = suite()
runner = unittest.TextTestRunner(sys.stdout, verbosity=2)
runner.run(testsuite)
USAGE_TEXT = """
Usage:
python test.py [options]
Options:
-h, --help Display this help message.
Example:
python test.py
"""
def usage():
print USAGE_TEXT
sys.exit(-1)
def main():
args = sys.argv[1:]
try:
opts, args = getopt.getopt(args, 'h', ['help'])
except:
usage()
relink = 1
for opt, val in opts:
if opt in ('-h', '--help'):
usage()
if len(args) != 0:
usage()
test()
if __name__ == '__main__':
main()
#import pdb
#pdb.run('main()')
Notes:
And, the following example shows several additional features. See the notes that follow the code:
import unittest
class UnitTests02(unittest.TestCase):
def testFoo(self):
self.failUnless(False)
def checkBar01(self):
self.failUnless(False)
class UnitTests01(unittest.TestCase):
# Note 1
def setUp(self):
print 'setting up UnitTests01'
def tearDown(self):
print 'tearing down UnitTests01'
def testBar01(self):
print 'testing testBar01'
self.failUnless(False)
def testBar02(self):
print 'testing testBar02'
self.failUnless(False)
def function_test_1():
name = 'mona'
assert not name.startswith('mo')
def compare_names(name1, name2):
if name1 < name2:
return 1
elif name1 > name2:
return -1
else:
return 0
def make_suite():
suite = unittest.TestSuite()
# Note 2
suite.addTest(unittest.makeSuite(UnitTests01, sortUsing=compare_names))
# Note 3
suite.addTest(unittest.makeSuite(UnitTests02, prefix='check'))
# Note 4
suite.addTest(unittest.FunctionTestCase(function_test_1))
return suite
def main():
suite = make_suite()
runner = unittest.TextTestRunner()
runner.run(suite)
if __name__ == '__main__':
main()
Notes:
Why should we use unit tests? Many reasons, including:
Additional notes:
In a test class, instance methods setUp and tearDown are run automatically before each and after each individual test.
In a test class, class methods setUpClass and tearDownClass are run automatically once before and after all the tests in a class.
Module level functions setUpModule and tearDownModule are run before and after any tests in a module.
In some cases you can also run tests directly from the command line. Do the following for help:
$ python -m unittest --help
For simple test harnesses, consider using doctest. With doctest you can (1) run a test at the Python interactive prompt, then (2) copy and paste that test into a doc string in your module, and then (3) run the tests automatically from within your module under doctest.
There are examples and explanation in the standard Python documentation: 5.2 doctest -- Test interactive Python examples -- http://docs.python.org/lib/module-doctest.html.
A simple way to use doctest in your module:
Run several tests in the Python interactive interpreter. Note that because doctest looks for the interpreter's ">>>" prompt, you must use the standard interpreter, and not, for example, IPython. Also, make sure that you include a line with the ">>>" prompt after each set of results; this enables doctest to determine the extent of the test results.
Use copy and paste, to insert the tests and their results from your interactive session into the docstrings.
Add the following code at the bottom of your module:
def _test():
import doctest
doctest.testmod()
if __name__ == "__main__":
_test()
Here is an example:
def f(n):
"""
Print something funny.
>>> f(1)
10
>>> f(2)
-10
>>> f(3)
0
"""
if n == 1:
return 10
elif n == 2:
return -10
else:
return 0
def test():
import doctest, test_doctest
doctest.testmod(test_doctest)
if __name__ == '__main__':
test()
And, here is the output from running the above test with the -v flag:
$ python test_doctest.py -v
Running test_doctest.__doc__
0 of 0 examples failed in test_doctest.__doc__
Running test_doctest.f.__doc__
Trying: f(1)
Expecting: 10
ok
Trying: f(2)
Expecting: -10
ok
Trying: f(3)
Expecting: 0
ok
0 of 3 examples failed in test_doctest.f.__doc__
Running test_doctest.test.__doc__
0 of 0 examples failed in test_doctest.test.__doc__
2 items had no tests:
test_doctest
test_doctest.test
1 items passed all tests:
3 tests in test_doctest.f
3 tests in 3 items.
3 passed and 0 failed.
Test passed.
Python database API defines a standard interface for access to a relational database.
In order to use this API you must install the database adapter (interface module) for your particular database, e.g. PostgreSQL, MySQL, Oracle, etc.
You can learn more about the Python DB-API here: http://www.python.org/dev/peps/pep-0249/
The following simple example uses sqlite3 -- http://docs.python.org/2/library/sqlite3.html
#!/usr/bin/env python
"""
Create a relational database and a table in it.
Add some records.
Read and display the records.
"""
import sys
import sqlite3
def create_table(db_name):
con = sqlite3.connect(db_name)
cursor = con.cursor()
cursor.execute('''CREATE TABLE plants
(name text, desc text, cat int)''')
cursor.execute(
'''INSERT INTO plants VALUES ('tomato', 'red and juicy', 1)''')
cursor.execute(
'''INSERT INTO plants VALUES ('pepper', 'green and crunchy', 2)''')
cursor.execute('''INSERT INTO plants VALUES ('pepper', 'purple', 2)''')
con.commit()
con.close()
def retrieve(db_name):
con = sqlite3.connect(db_name)
cursor = con.cursor()
cursor.execute('''select * from plants''')
rows = cursor.fetchall()
print rows
print '-' * 40
cursor.execute('''select * from plants''')
for row in cursor:
print row
con.close()
def test():
args = sys.argv[1:]
if len(args) != 1:
sys.stderr.write('\nusage: test_db.py <db_name>\n\n')
sys.exit(1)
db_name = args[0]
create_table(db_name)
retrieve(db_name)
test()
Simple:
$ python setup.py build $ python setup.py install # as root
More complex:
Look for a README or INSTALL file at the root of the package.
Type the following for help:
$ python setup.py cmd --help $ python setup.py --help-commands $ python setup.py --help [cmd1 cmd2 ...]
And, for even more details, see Installing Python Modules -- http://docs.python.org/inst/inst.html
pip is becoming popular for installing and managing Python packages. See: https://pypi.python.org/pypi/pip
Also, consider using virtualenv, especially if you suspect or worry that installing some new package will alter the behavior of a package currently installed on your machine. See: https://pypi.python.org/pypi/virtualenv. virtualenv creates a directory and sets up a Python environment into which you can install and use Python packages without changing your usual Python installation.
[As time permits, explain more features and do more exercises as requested by class members.]
Thanks to David Goodger for the following list or references. His "Code Like a Pythonista: Idiomatic Python" (http://python.net/~goodger/projects/pycon/2007/idiomatic/) is worth a careful reading:
This document is intended as notes for a course on (slightly) advanced Python topics.
For more help on regular expressions, see:
A regular expression pattern is a sequence of characters that will match sequences of characters in a target.
The patterns or regular expressions can be defined as follows:
Because of the use of backslashes in patterns, you are usually better off defining regular expressions with raw strings, e.g. r"abc".
When a regular expression is to be used more than once, you should consider compiling it. For example:
import sys, re
pat = re.compile('aa[bc]*dd')
while 1:
line = raw_input('Enter a line ("q" to quit):')
if line == 'q':
break
if pat.search(line):
print 'matched:', line
else:
print 'no match:', line
Comments:
Use match() to match at the beginning of a string (or not at all).
Use search() to search a string and match the first string from the left.
Here are some examples:
>>> import re
>>> pat = re.compile('aa[0-9]*bb')
>>> x = pat.match('aa1234bbccddee')
>>> x
<_sre.SRE_Match object at 0x401e9608>
>>> x = pat.match('xxxxaa1234bbccddee')
>>> x
>>> type(x)
<type 'NoneType'>
>>> x = pat.search('xxxxaa1234bbccddee')
>>> x
<_sre.SRE_Match object at 0x401e9608>
Notes:
When a match or search is successful, it returns a match object. When it fails, it returns None.
You can also call the corresponding functions match and search in the re module, e.g.:
>>> x = re.search(pat, 'xxxxaa1234bbccddee') >>> x <_sre.SRE_Match object at 0x401e9560>
For a list of functions in the re module, see Module Contents -- http://docs.python.org/library/re.html#module-contents.
Match objects enable you to extract matched sub-strings after performing a match. A match object is returned by successful match. The part of the target available in the match object is the portion matched by groups in the pattern, that is the portion of the pattern inside parentheses. For example:
In [69]: mo = re.search(r'height: (\d*) width: (\d*)', 'height: 123 width: 456')
In [70]: mo.groups()
Out[70]: ('123', '456')
Here is another example:
import sys, re
Targets = [
'There are <<25>> sparrows.',
'I see <<15>> finches.',
'There is nothing here.',
]
def test():
pat = re.compile('<<([0-9]*)>>')
for line in Targets:
mo = pat.search(line)
if mo:
value = mo.group(1)
print 'value: %s' % value
else:
print 'no match'
test()
When we run the above, it prints out the following:
value: 25 value: 15 no match
Explanation:
In addition, you can:
Use "values = mo.groups()" to get a tuple containing the strings matched by all groups.
Use "mo.expand()" to interpolate the group values into a string. For example, "mo.expand(r'value1: \1 value2: \2')"inserts the values of the first and second group into a string. If the first group matched "aaa" and the second matched "bbb", then this example would produce "value1: aaa value2: bbb". For example:
In [76]: mo = re.search(r'h: (\d*) w: (\d*)', 'h: 123 w: 456') In [77]: mo.expand(r'Height: \1 Width: \2') Out[77]: 'Height: 123 Width: 456'
You can extract multiple items with a single search. Here is an example:
import sys, re
pat = re.compile('aa([0-9]*)bb([0-9]*)cc')
while 1:
line = raw_input('Enter a line ("q" to quit):')
if line == 'q':
break
mo = pat.search(line)
if mo:
value1, value2 = mo.group(1, 2)
print 'value1: %s value2: %s' % (value1, value2)
else:
print 'no match'
Comments:
A simple way to perform multiple replacements using a regular expression is to use the re.subn() function. Here is an example:
In [81]: re.subn(r'\d+', '***', 'there are 203 birds sitting in 2 trees')
Out[81]: ('there are *** birds sitting in *** trees', 2)
For more complex replacements, use a function instead of a constant replacement string:
import re
def repl_func(mo):
s1 = mo.group(1)
s2 = '*' * len(s1)
return s2
def test():
pat = r'(\d+)'
in_str = 'there are 2034 birds in 21 trees'
out_str, count = re.subn(pat, repl_func, in_str)
print 'in: "%s"' % in_str
print 'out: "%s"' % out_str
print 'count: %d' % count
test()
And when we run the above, it produces:
in: "there are 2034 birds in 21 trees" out: "there are **** birds in ** trees" count: 2
Notes:
Here is an even more complex example -- You can locate sub-strings (slices) of a match and replace them:
import sys, re
pat = re.compile('aa([0-9]*)bb([0-9]*)cc')
while 1:
line = raw_input('Enter a line ("q" to quit): ')
if line == 'q':
break
mo = pat.search(line)
if mo:
value1, value2 = mo.group(1, 2)
start1 = mo.start(1)
end1 = mo.end(1)
start2 = mo.start(2)
end2 = mo.end(2)
print 'value1: %s start1: %d end1: %d' % (value1, start1, end1)
print 'value2: %s start2: %d end2: %d' % (value2, start2, end2)
repl1 = raw_input('Enter replacement #1: ')
repl2 = raw_input('Enter replacement #2: ')
newline = (line[:start1] + repl1 + line[end1:start2] +
repl2 + line[end2:])
print 'newline: %s' % newline
else:
print 'no match'
Explanation:
Alternatively, use "mo.span(1)" instead of "mo.start(1)" and "mo.end(1)" in order to get the start and end of a sub-match in a single operation. "mo.span(1)"returns a tuple: (start, end).
Put together a new string with string concatenation from pieces of the original string and replacement values. You can use string slices to get the sub-strings of the original string. In our case, the following gets the start of the string, adds the first replacement, adds the middle of the original string, adds the second replacement, and finally, adds the last part of the original string:
newline = line[:start1] + repl1 + line[end1:start2] + repl2 + line[end2:]
You can also use the sub function or method to do substitutions. Here is an example:
import sys, re
pat = re.compile('[0-9]+')
print 'Replacing decimal digits.'
while 1:
target = raw_input('Enter a target line ("q" to quit): ')
if target == 'q':
break
repl = raw_input('Enter a replacement: ')
result = pat.sub(repl, target)
print 'result: %s' % result
Here is another example of the use of a function to insert calculated replacements.
import sys, re, string
pat = re.compile('[a-m]+')
def replacer(mo):
return string.upper(mo.group(0))
print 'Upper-casing a-m.'
while 1:
target = raw_input('Enter a target line ("q" to quit): ')
if target == 'q':
break
result = pat.sub(replacer, target)
print 'result: %s' % result
Notes:
This is also a convenient use for a lambda instead of a named function, for example:
import sys, re, string
pat = re.compile('[a-m]+')
print 'Upper-casing a-m.'
while 1:
target = raw_input('Enter a target line ("q" to quit): ')
if target == 'q':
break
result = pat.sub(
lambda mo: string.upper(mo.group(0)),
target)
print 'result: %s' % result
Note 1: You will need a sufficiently recent version of Python in order to use iterators and generators. I believe that they were introduced in Python 2.2.
Note 2: The iterator protocol has changed slightly in Python version 3.0.
Goals for this section:
Definitions:
A few additional basic points:
This section attempts to provide examples that illustrate the generator/iterator pattern.
Why is this important?
Examples - The remainder of this section provides a set of examples which implement and use iterators.
This function contains a yield statement. Therefore, when we call it, it produces an iterator:
def generateItems(seq):
for item in seq:
yield 'item: %s' % item
anIter = generateItems([])
print 'dir(anIter):', dir(anIter)
anIter = generateItems([111,222,333])
for x in anIter:
print x
anIter = generateItems(['aaa', 'bbb', 'ccc'])
print anIter.next()
print anIter.next()
print anIter.next()
print anIter.next()
Running this example produces the following output:
dir(anIter): ['__class__', '__delattr__', '__doc__', '__getattribute__',
'__hash__', '__init__', '__iter__', '__new__', '__reduce__',
'__reduce_ex__', '__repr__', '__setattr__', '__str__', 'gi_frame',
'gi_running', 'next']
item: 111
item: 222
item: 333
item: aaa
item: bbb
item: ccc
Traceback (most recent call last):
File "iterator_generator.py", line 14, in ?
print anIter.next()
StopIteration
Notes and explanation:
An alternative and perhaps simpler way to create an interator is to use a generator expression. This can be useful when you already have a collection or iterator to work with.
Then following example implements a function that returns a generator object. The effect is to generate the objects in a collection which excluding items in a separte collection:
DATA = [
'lemon',
'lime',
'grape',
'apple',
'pear',
'watermelon',
'canteloupe',
'honeydew',
'orange',
'grapefruit',
]
def make_producer(collection, excludes):
gen = (item for item in collection if item not in excludes)
return gen
def test():
iter1 = make_producer(DATA, ('apple', 'orange', 'honeydew', ))
print '%s' % iter1
for fruit in iter1:
print fruit
test()
When run, this example produces the following:
$ python workbook063.py <generator object <genexpr> at 0x7fb3d0f1bc80> lemon lime grape pear watermelon canteloupe grapefruit
Notes:
Each time this method is called, it produces a (new) iterator object. This method is analogous to the iterkeys and itervalues methods in the dictionary built-in object:
#
# A class that provides an iterator generator method.
#
class Node:
def __init__(self, name='<noname>', value='<novalue>', children=None):
self.name = name
self.value = value
self.children = children
if children is None:
self.children = []
else:
self.children = children
def set_name(self, name): self.name = name
def get_name(self): return self.name
def set_value(self, value): self.value = value
def get_value(self): return self.value
def iterchildren(self):
for child in self.children:
yield child
#
# Print information on this node and walk over all children and
# grandchildren ...
def walk(self, level=0):
print '%sname: %s value: %s' % (
get_filler(level), self.get_name(), self.get_value(), )
for child in self.iterchildren():
child.walk(level + 1)
#
# An function that is the equivalent of the walk() method in
# class Node.
#
def walk(node, level=0):
print '%sname: %s value: %s' % (
get_filler(level), node.get_name(), node.get_value(), )
for child in node.iterchildren():
walk(child, level + 1)
def get_filler(level):
return ' ' * level
def test():
a7 = Node('gilbert', '777')
a6 = Node('fred', '666')
a5 = Node('ellie', '555')
a4 = Node('daniel', '444')
a3 = Node('carl', '333', [a4, a5])
a2 = Node('bill', '222', [a6, a7])
a1 = Node('alice', '111', [a2, a3])
# Use the walk method to walk the entire tree.
print 'Using the method:'
a1.walk()
print '=' * 30
# Use the walk function to walk the entire tree.
print 'Using the function:'
walk(a1)
test()
Running this example produces the following output:
Using the method:
name: alice value: 111
name: bill value: 222
name: fred value: 666
name: gilbert value: 777
name: carl value: 333
name: daniel value: 444
name: ellie value: 555
==============================
Using the function:
name: alice value: 111
name: bill value: 222
name: fred value: 666
name: gilbert value: 777
name: carl value: 333
name: daniel value: 444
name: ellie value: 555
Notes and explanation:
This class implements the iterator protocol. Therefore, instances of this class are iterators. The presence of the next() and __iter__() methods means that this class implements the iterator protocol and makes instances of this class iterators.
Note that when an iterator is "exhausted" it, normally, cannot be reused to iterate over the sequence. However, in this example, we provide a refresh method which enables us to "rewind" and reuse the iterator instance:
#
# An iterator class that does *not* use ``yield``.
# This iterator produces every other item in a sequence.
#
class IteratorExample:
def __init__(self, seq):
self.seq = seq
self.idx = 0
def next(self):
self.idx += 1
if self.idx >= len(self.seq):
raise StopIteration
value = self.seq[self.idx]
self.idx += 1
return value
def __iter__(self):
return self
def refresh(self):
self.idx = 0
def test_iteratorexample():
a = IteratorExample('edcba')
for x in a:
print x
print '----------'
a.refresh()
for x in a:
print x
print '=' * 30
a = IteratorExample('abcde')
try:
print a.next()
print a.next()
print a.next()
print a.next()
print a.next()
print a.next()
except StopIteration, e:
print 'stopping', e
test_iteratorexample()
Running this example produces the following output:
d b ---------- d b ============================== b d stopping
Notes and explanation:
There may be times when the next method is easier and more straight-forward to implement using yield. If so, then this class might serve as an model. If you do not feel the need to do this, then you should ignore this example:
#
# An iterator class that uses ``yield``.
# This iterator produces every other item in a sequence.
#
class YieldIteratorExample:
def __init__(self, seq):
self.seq = seq
self.iterator = self._next()
self.next = self.iterator.next
def _next(self):
flag = 0
for x in self.seq:
if flag:
flag = 0
yield x
else:
flag = 1
def __iter__(self):
return self.iterator
def refresh(self):
self.iterator = self._next()
self.next = self.iterator.next
def test_yielditeratorexample():
a = YieldIteratorExample('edcba')
for x in a:
print x
print '----------'
a.refresh()
for x in a:
print x
print '=' * 30
a = YieldIteratorExample('abcde')
try:
print a.next()
print a.next()
print a.next()
print a.next()
print a.next()
print a.next()
except StopIteration, e:
print 'stopping', e
test_yielditeratorexample()
Running this example produces the following output:
d b ---------- d b ============================== b d stopping
Notes and explanation:
Because the _next method uses yield, calling it (actually, calling the iterator object it produces) in an iterator context causes it to be "resumed" immediately after the yield statement. This reduces bookkeeping a bit.
However, with this style, we must explicitly produce an iterator. We do this by calling the _next method, which contains a yield statement, and is therefore a generator. The following code in our constructor (__init__) completes the set-up of our class as an iterator class:
self.iterator = self._next() self.next = self.iterator.next
Remember that we need both __iter__() and next() methods in YieldIteratorExample to satisfy the iterator protocol. The __iter__() method is already there and the above code in the constructor creates the next() method.
A list comprehension looks a bit like an iterator, but it produces a list. See: The Python Language Reference: List displays -- http://docs.python.org/reference/expressions.html#list-displays for more on list comprehensions.
Here is an example:
In [4]: def f(x): ...: return x * 3 ...: In [5]: list1 = [11, 22, 33] In [6]: list2 = [f(x) for x in list1] In [7]: print list2 [33, 66, 99]
A generator expression looks quite similar to a list comprehension, but is enclosed in parentheses rather than square brackets. Unlike a list comprehension, a generator expression does not produce a list; it produces an generator object. A generator object is an iterator.
For more on generator expressions, see The Python Language Reference: Generator expressions -- http://docs.python.org/reference/expressions.html#generator-expressions.
The following example uses a generator expression to produce an iterator:
mylist = range(10)
def f(x):
return x*3
genexpr = (f(x) for x in mylist)
for x in genexpr:
print x
Notes and explanation:
Unit test and the Python unit test framework provide a convenient way to define and run tests that ensure that a Python application produces specified results.
This section, while it will not attempt to explain everything about the unit test framework, will provide examples of several straight-forward ways to construct and run tests.
Some assumptions:
In the test class, implement a number of methods to perform your tests. Name your test methods with the prefix "test". Here is an example:
import unittest
class MyTest(unittest.TestCase):
def test_one(self):
# some test code
pass
def test_two(self):
# some test code
pass
Create a test harness. Here is an example:
import unittest
# make the test suite.
def suite():
loader = unittest.TestLoader()
testsuite = loader.loadTestsFromTestCase(MyTest)
return testsuite
# Make the test suite; run the tests.
def test():
testsuite = suite()
runner = unittest.TextTestRunner(sys.stdout, verbosity=2)
result = runner.run(testsuite)
Here is a more complete example:
import sys, StringIO, string
import unittest
import webserv_example_heavy_sub
# A comparison function for case-insenstive sorting.
def mycmpfunc(arg1, arg2):
return cmp(string.lower(arg1), string.lower(arg2))
class XmlTest(unittest.TestCase):
def test_import_export1(self):
inFile = file('test1_in.xml', 'r')
inContent = inFile.read()
inFile.close()
doc = webserv_example_heavy_sub.parseString(inContent)
outFile = StringIO.StringIO()
outFile.write('<?xml version="1.0" ?>\n')
doc.export(outFile, 0)
outContent = outFile.getvalue()
outFile.close()
self.failUnless(inContent == outContent)
# make the test suite.
def suite():
loader = unittest.TestLoader()
# Change the test method prefix: test --> trial.
#loader.testMethodPrefix = 'trial'
# Change the comparison function that determines the order of tests.
#loader.sortTestMethodsUsing = mycmpfunc
testsuite = loader.loadTestsFromTestCase(XmlTest)
return testsuite
# Make the test suite; run the tests.
def test_main():
testsuite = suite()
runner = unittest.TextTestRunner(sys.stdout, verbosity=2)
result = runner.run(testsuite)
if __name__ == "__main__":
test_main()
Running the above script produces the following output:
test_import_export (__main__.XmlTest) ... ok ---------------------------------------------------------------------- Ran 1 test in 0.035s OK
A few notes on this example:
This example tests the ability to parse an xml document test1_in.xml and export that document back to XML. The test succeeds if the input XML document and the exported XML document are the same.
The code which is being tested parses an XML document returned by a request to Amazon Web services. You can learn more about Amazon Web services at: http://www.amazon.com/webservices. This code was generated from an XML Schema document by generateDS.py. So we are in effect, testing generateDS.py. You can find generateDS.py at: http://www.reifywork.com/#generateds-py.
Testing for success/failure and reporting failures -- Use the methods listed at http://www.python.org/doc/current/lib/testcase-objects.html to test for and report success and failure. In our example, we used "self.failUnless(inContent == outContent)" to ensure that the content we parsed and the content that we exported were the same.
Add additional tests by adding methods whose names have the prefix "test". If you prefer a different prefix for tests names, add something like the following to the above script:
loader.testMethodPrefix = 'trial'
By default, the tests are run in the order of their names sorted by the cmp function. So, if needed, you can control the order of execution of tests by selecting their names, for example, using names like test_1_checkderef, test_2_checkcalc, etc. Or, you can change the comparison function by adding something like the following to the above script:
loader.sortTestMethodsUsing = mycmpfunc
As a bit of motivation for creating and using unit tests, while developing this example, I discovered several errors (or maybe "special features") in generateDS.py.
Extending vs. embedding -- They are different but related:
Documentation -- The two important sources for information about extending and embedding are the following:
Types of extensions:
Tools -- There are several tools that support the development of Python extensions:
Writing an extension module by hand -- What to do:
Implementing a wrapper function -- What to do:
Capture the arguments with PyArg_ParseTuple. The format string specifies how arguments are to be converted and captured. See 1.7 Extracting Parameters in Extension Functions. Here are some of the most commonly used types:
Use "i", "s", "f", etc to convert and capture simple types such as integers, strings, floats, etc.
Use "O" to get a pointer to Python "complex" types such as lists, tuples, dictionaries, etc.
Use items in parentheses to capture and unpack sequences (e.g. lists and tuples) of fixed length. Example:
if (!PyArg_ParseTuple(args, "(ii)(ii)", &x, &y, &width, &height))
{
return NULL;
} /* if */
A sample call might be:
lowerLeft = (x1, y1) extent = (width1, height1) scan(lowerLeft, extent)
Use ":aName" (colon) at the end of the format string to provide a function name for error messages. Example:
if (!PyArg_ParseTuple(args, "O:setContentHandler", &pythonInstance))
{
return NULL;
} /* if */
Use ";an error message" (semicolon) at the end of the format string to provide a string that replaces the default error message.
Docs are available at: http://www.python.org/doc/current/ext/parseTuple.html.
Write the logic.
Handle errors and exceptions -- You will need to understand how to (1) clearing errors and exceptions and (2) Raise errors (exceptions).
Many functions in the Python C API raise exceptions. You will need to check for and clear these exceptions. Here is an example:
char * message;
int messageNo;
message = NULL;
messageNo = -1;
/* Is the argument a string?
*/
if (! PyArg_ParseTuple(args, "s", &message))
{
/* It's not a string. Clear the error.
* Then try to get a message number (an integer).
*/
PyErr_Clear();
if (! PyArg_ParseTuple(args, "i", &messageNo))
{
o
o
o
You can also raise exceptions in your C code that can be caught (in a "try:except:" block) back in the calling Python code. Here is an example:
if (n == 0)
{
PyErr_SetString(PyExc_ValueError, "Value must not be zero");
return NULL;
}
See Include/pyerrors.h in the Python source distribution for more exception/error types.
And, you can test whether a function in the Python C API that you have called has raised an exception. For example:
if (PyErr_Occurred())
{
/* An exception was raised.
* Do something about it.
*/
o
o
o
For more documentation on errors and exceptions, see: http://www.python.org/doc/current/api/exceptionHandling.html.
Create and return a value:
Note: Our discussion and examples are for SWIG version 1.3
SWIG will often enable you to generate wrappers for functions in an existing C function library. SWIG does not understand everything in C header files. But it does a fairly impressive job. You should try it first before resorting to the hard work of writing wrappers by hand.
More information on SWIG is at http://www.swig.org.
Here are some steps that you can follow:
Create an interface file -- Even when you are wrapping functions defined in an existing header file, creating an interface file is a good idea. Include your existing header file into it, then add whatever else you need. Here is an extremely simple example of a SWIG interface file:
%module MyLibrary
%{
#include "MyLibrary.h"
%}
%include "MyLibrary.h"
Comments:
The "%{" and "%}" brackets are directives to SWIG. They say: "Add the code between these brackets to the generated wrapper file without processing it.
The "%include" statement says: "Copy the file into the interface file here. In effect, you are asking SWIG to generate wrappers for all the functions in this header file. If you want wrappers for only some of the functions in a header file, then copy or reproduce function declarations for the desired functions here. An example:
%module MyLibrary
%{
#include "MyLibrary.h"
%}
int calcArea(int width, int height);
int calcVolume(int radius);
This example will generate wrappers for only two functions.
You can find more information about the directives that are used in SWIG interface files in the SWIG User Manual, in particular at:
Generate the wrappers:
swig -python MyLibrary.i
Compile and link the library. On Linux, you can use something like the following:
gcc -c MyLibrary.c gcc -c -I/usr/local/include/python2.3 MyLibrary_wrap.c gcc -shared MyLibrary.o MyLibrary_wrap.o -o _MyLibrary.so
Note that we produce a shared library whose name is the module name prefixed with an underscore. SWIG also generates a .py file, without the leading underscore, which we will import from our Python code and which, in turn, imports the shared library.
Use the extension module in your python code:
Python 2.3b1 (#1, Apr 25 2003, 20:36:09) [GCC 2.95.4 20011002 (Debian prerelease)] on linux2 Type "help", "copyright", "credits" or "license" for more information. >>> import MyLibrary >>> MyLibrary.calcArea(4.0, 5.0) 20.0
Here is a makefile that will execute swig to generate wrappers, then compile and link the extension.
CFLAGS = -I/usr/local/include/python2.3
all: _MyLibrary.so
- _MyLibrary.so: MyLibrary.o MyLibrary_wrap.o
- gcc -shared MyLibrary.o MyLibrary_wrap.o -o _MyLibrary.so
- MyLibrary.o: MyLibrary.c
- gcc -c MyLibrary.c -o MyLibrary.o
- MyLibrary_wrap.o: MyLibrary_wrap.c
- gcc -c ${CFLAGS} MyLibrary_wrap.c -o MyLibrary_wrap.o
- MyLibrary_wrap.c: MyLibrary.i
- swig -python MyLibrary.i
- clean:
- rm -f MyLibrary.py MyLibrary.o MyLibrary_wrap.c
- MyLibrary_wrap.o _MyLibrary.so
Here is an example of running this makefile:
$ make -f MyLibrary_makefile clean
rm -f MyLibrary.py MyLibrary.o MyLibrary_wrap.c \
MyLibrary_wrap.o _MyLibrary.so
$ make -f MyLibrary_makefile
gcc -c MyLibrary.c -o MyLibrary.o
swig -python MyLibrary.i
gcc -c -I/usr/local/include/python2.3 MyLibrary_wrap.c -o MyLibrary_wrap.o
gcc -shared MyLibrary.o MyLibrary_wrap.o -o _MyLibrary.so
And, here are C source files that can be used in our example.
MyLibrary.h:
/* MyLibrary.h */ float calcArea(float width, float height); float calcVolume(float radius); int getVersion(); int getMode();
MyLibrary.c:
/* MyLibrary.c
*/
float calcArea(float width, float height)
{
return (width * height);
}
float calcVolume(float radius)
{
return (3.14 * radius * radius);
}
int getVersion()
{
return 123;
}
int getMode()
{
return 1;
}
Pyrex is a useful tool for writing Python extensions. Because the Pyrex language is similar to Python, writing extensions in Pyrex is easier than doing so in C. Cython appears to be the a newer version of Pyrex.
More information is on Pyrex and Cython is at:
Here is a simple function definition in Pyrex:
# python_201_pyrex_string.pyx
import string
def formatString(object s1, object s2):
s1 = string.strip(s1)
s2 = string.strip(s2)
s3 = '<<%s||%s>>' % (s1, s2)
s4 = s3 * 4
return s4
And, here is a make file:
CFLAGS = -DNDEBUG -O3 -Wall -Wstrict-prototypes -fPIC \
-I/usr/local/include/python2.3
all: python_201_pyrex_string.so
python_201_pyrex_string.so: python_201_pyrex_string.o
gcc -shared python_201_pyrex_string.o -o python_201_pyrex_string.so
python_201_pyrex_string.o: python_201_pyrex_string.c
gcc -c ${CFLAGS} python_201_pyrex_string.c -o python_201_pyrex_string.o
python_201_pyrex_string.c: python_201_pyrex_string.pyx
pyrexc python_201_pyrex_string.pyx
clean:
rm -f python_201_pyrex_string.so python_201_pyrex_string.o \
python_201_pyrex_string.c
Here is another example. In this one, one function in the .pyx file calls another. Here is the implementation file:
# python_201_pyrex_primes.pyx
def showPrimes(int kmax):
plist = primes(kmax)
for p in plist:
print 'prime: %d' % p
cdef primes(int kmax):
cdef int n, k, i
cdef int p[1000]
result = []
if kmax > 1000:
kmax = 1000
k = 0
n = 2
while k < kmax:
i = 0
while i < k and n % p[i] <> 0:
i = i + 1
if i == k:
p[k] = n
k = k + 1
result.append(n)
n = n + 1
return result
And, here is a make file:
#CFLAGS = -DNDEBUG -g -O3 -Wall -Wstrict-prototypes -fPIC # -I/usr/local/include/python2.3 CFLAGS = -DNDEBUG -I/usr/local/include/python2.3
all: python_201_pyrex_primes.so
- python_201_pyrex_primes.so: python_201_pyrex_primes.o
- gcc -shared python_201_pyrex_primes.o -o python_201_pyrex_primes.so
- python_201_pyrex_primes.o: python_201_pyrex_primes.c
- gcc -c ${CFLAGS} python_201_pyrex_primes.c -o python_201_pyrex_primes.o
- python_201_pyrex_primes.c: python_201_pyrex_primes.pyx
- pyrexc python_201_pyrex_primes.pyx
- clean:
- rm -f python_201_pyrex_primes.so python_201_pyrex_primes.o
- python_201_pyrex_primes.c
Here is the output from running the makefile:
$ make -f python_201_pyrex_makeprimes clean
rm -f python_201_pyrex_primes.so python_201_pyrex_primes.o \
python_201_pyrex_primes.c
$ make -f python_201_pyrex_makeprimes
pyrexc python_201_pyrex_primes.pyx
gcc -c -DNDEBUG -I/usr/local/include/python2.3 python_201_pyrex_primes.c -o python_201_pyrex_primes.o
gcc -shared python_201_pyrex_primes.o -o python_201_pyrex_primes.so
Here is an interactive example of its use:
$ python Python 2.3b1 (#1, Apr 25 2003, 20:36:09) [GCC 2.95.4 20011002 (Debian prerelease)] on linux2 Type "help", "copyright", "credits" or "license" for more information. >>> import python_201_pyrex_primes >>> dir(python_201_pyrex_primes) ['__builtins__', '__doc__', '__file__', '__name__', 'showPrimes'] >>> python_201_pyrex_primes.showPrimes(5) prime: 2 prime: 3 prime: 5 prime: 7 prime: 11
This next example shows how to use Pyrex to implement a new extension type, that is a new Python built-in type. Notice that the class is declared with the cdef keyword, which tells Pyrex to generate the C implementation of a type instead of a class.
Here is the implementation file:
# python_201_pyrex_clsprimes.pyx
"""An implementation of primes handling class
for a demonstration of Pyrex.
"""
cdef class Primes:
"""A class containing functions for
handling primes.
"""
def showPrimes(self, int kmax):
"""Show a range of primes.
Use the method primes() to generate the primes.
"""
plist = self.primes(kmax)
for p in plist:
print 'prime: %d' % p
def primes(self, int kmax):
"""Generate the primes in the range 0 - kmax.
"""
cdef int n, k, i
cdef int p[1000]
result = []
if kmax > 1000:
kmax = 1000
k = 0
n = 2
while k < kmax:
i = 0
while i < k and n % p[i] <> 0:
i = i + 1
if i == k:
p[k] = n
k = k + 1
result.append(n)
n = n + 1
return result
And, here is a make file:
CFLAGS = -DNDEBUG -I/usr/local/include/python2.3
all: python_201_pyrex_clsprimes.so
python_201_pyrex_clsprimes.so: python_201_pyrex_clsprimes.o
gcc -shared python_201_pyrex_clsprimes.o -o python_201_pyrex_clsprimes.so
python_201_pyrex_clsprimes.o: python_201_pyrex_clsprimes.c
gcc -c ${CFLAGS} python_201_pyrex_clsprimes.c -o python_201_pyrex_clsprimes.o
python_201_pyrex_clsprimes.c: python_201_pyrex_clsprimes.pyx
pyrexc python_201_pyrex_clsprimes.pyx
clean:
rm -f python_201_pyrex_clsprimes.so python_201_pyrex_clsprimes.o \
python_201_pyrex_clsprimes.c
Here is output from running the makefile:
$ make -f python_201_pyrex_makeclsprimes clean
rm -f python_201_pyrex_clsprimes.so python_201_pyrex_clsprimes.o \
python_201_pyrex_clsprimes.c
$ make -f python_201_pyrex_makeclsprimes
pyrexc python_201_pyrex_clsprimes.pyx
gcc -c -DNDEBUG -I/usr/local/include/python2.3 python_201_pyrex_clsprimes.c -o python_201_pyrex_clsprimes.o
gcc -shared python_201_pyrex_clsprimes.o -o python_201_pyrex_clsprimes.so
And here is an interactive example of its use:
$ python Python 2.3b1 (#1, Apr 25 2003, 20:36:09) [GCC 2.95.4 20011002 (Debian prerelease)] on linux2 Type "help", "copyright", "credits" or "license" for more information. >>> import python_201_pyrex_clsprimes >>> dir(python_201_pyrex_clsprimes) ['Primes', '__builtins__', '__doc__', '__file__', '__name__'] >>> primes = python_201_pyrex_clsprimes.Primes() >>> dir(primes) ['__class__', '__delattr__', '__doc__', '__getattribute__', '__hash__', '__init__', '__new__', '__reduce__', '__reduce_ex__', '__repr__', '__setattr__', '__str__', 'primes', 'showPrimes'] >>> primes.showPrimes(4) prime: 2 prime: 3 prime: 5 prime: 7
Documentation -- Also notice that Pyrex preserves the documentation for the module, the class, and the methods in the class. You can show this documentation with pydoc, as follows:
$ pydoc python_201_pyrex_clsprimes
Or, in Python interactive mode, use:
$ python Python 2.3b1 (#1, Apr 25 2003, 20:36:09) [GCC 2.95.4 20011002 (Debian prerelease)] on linux2 Type "help", "copyright", "credits" or "license" for more information. >>> import python_201_pyrex_clsprimes >>> help(python_201_pyrex_clsprimes)
Choose SWIG when:
Choose Pyrex when:
Here is a simple example that uses Cython to wrap a function implemented in C.
First the C header file:
/* test_c_lib.h */ int calculate(int width, int height);
And, the C implementation file:
/* test_c_lib.c */
#include "test_c_lib.h"
int calculate(int width, int height)
{
int result;
result = width * height * 3;
return result;
}
Here is a Cython file that calls our C function:
# test_c.pyx
# Declare the external C function.
cdef extern from "test_c_lib.h":
int calculate(int width, int height)
def test(w, h):
# Call the external C function.
result = calculate(w, h)
print 'result from calculate: %d' % result
We can compile our code using this script (on Linux):
#!/bin/bash -x cython test_c.pyx gcc -c -fPIC -I/usr/local/include/python2.6 -o test_c.o test_c.c gcc -c -fPIC -I/usr/local/include/python2.6 -o test_c_lib.o test_c_lib.c gcc -shared -fPIC -I/usr/local/include/python2.6 -o test_c.so test_c.o test_c_lib.o
Here is a small Python file that uses the wrapper that we wrote in Cython:
# run_test_c.py
import test_c
def test():
test_c.test(4, 5)
test_c.test(12, 15)
if __name__ == '__main__':
test()
And, when we run it, we see the following:
$ python run_test_c.py result from calculate: 60 result from calculate: 540
The goal -- A new built-in data type for Python.
Existing examples -- Objects/listobject.c, Objects/stringobject.c, Objects/dictobject.c, etc in the Python source code distribution.
In older versions of the Python source code distribution, a template for the C code was provided in Objects/xxobject.c. Objects/xxobject.c is no longer included in the Python source code distribution. However:
And, you can use Pyrex to generate a new built-in type. To do so, implement a Python/Pyrex class and declare the class with the Pyrex keyword cdef. In fact, you may want to use Pyrex to generate a minimal extension type, and then edit that generated code to insert and add functionality by hand. See the Pyrex section for an example.
Pyrex also goes some way toward giving you access to (existing) C structs and functions from Python.
Extension classes the easy way -- SWIG shadow classes.
Start with an implementation of a C++ class and its header file.
Use the following SWIG flags:
swig -c++ -python mymodule.i
More information is available with the SWIG documentation at: http://www.swig.org/Doc1.3/Python.html.
Extension classes the Pyrex way -- An alternatie is to use Pyrex to compile a class definition that does not have the cdef keyword. Using cdef on the class tells Pyrex to generate an extension type instead of a class. You will have to determine whether you want an extension class or an extension type.
Python is an excellent language for text analysis.
In some cases, simply splitting lines of text into words will be enough. In these cases use string.split().
In other cases, regular expressions may be able to do the parsing you need. If so, see the section on regular expressions in this document.
However, in some cases, more complex analysis of input text is required. This section describes some of the ways that Python can help you with this complex parsing and analysis.
There are a number of special purpose parsers which you will find in the Python standard library:
XML parsers and XML tools -- There is lots of support for parsing and processing XML in Python. Here are a few places to look for support:
For simple grammars, this is not so hard.
You will need to implement:
As an example, we'll implement a recursive descent parser written in Python for the following grammer:
Prog ::= Command | Command Prog
Command ::= Func_call
Func_call ::= Term '(' Func_call_list ')'
Func_call_list ::= Func_call | Func_call ',' Func_call_list
Term = <word>
Here is an implementation of a recursive descent parser for the above grammar:
#!/usr/bin/env python
"""
A recursive descent parser example.
Usage:
python rparser.py [options] <inputfile>
Options:
-h, --help Display this help message.
Example:
python rparser.py myfile.txt
The grammar:
Prog ::= Command | Command Prog
Command ::= Func_call
Func_call ::= Term '(' Func_call_list ')'
Func_call_list ::= Func_call | Func_call ',' Func_call_list
Term = <word>
"""
import sys
import string
import types
import getopt
#
# To use the IPython interactive shell to inspect your running
# application, uncomment the following lines:
#
## from IPython.Shell import IPShellEmbed
## ipshell = IPShellEmbed((),
## banner = '>>>>>>>> Into IPython >>>>>>>>',
## exit_msg = '<<<<<<<< Out of IPython <<<<<<<<')
#
# Then add the following line at the point in your code where
# you want to inspect run-time values:
#
# ipshell('some message to identify where we are')
#
# For more information see: http://ipython.scipy.org/moin/
#
#
# Constants
#
# AST node types
NoneNodeType = 0
ProgNodeType = 1
CommandNodeType = 2
FuncCallNodeType = 3
FuncCallListNodeType = 4
TermNodeType = 5
# Token types
NoneTokType = 0
LParTokType = 1
RParTokType = 2
WordTokType = 3
CommaTokType = 4
EOFTokType = 5
# Dictionary to map node type values to node type names
NodeTypeDict = {
NoneNodeType: 'NoneNodeType',
ProgNodeType: 'ProgNodeType',
CommandNodeType: 'CommandNodeType',
FuncCallNodeType: 'FuncCallNodeType',
FuncCallListNodeType: 'FuncCallListNodeType',
TermNodeType: 'TermNodeType',
}
#
# Representation of a node in the AST (abstract syntax tree).
#
class ASTNode:
def __init__(self, nodeType, *args):
self.nodeType = nodeType
self.children = []
for item in args:
self.children.append(item)
def show(self, level):
self.showLevel(level)
print 'Node -- Type %s' % NodeTypeDict[self.nodeType]
level += 1
for child in self.children:
if isinstance(child, ASTNode):
child.show(level)
elif type(child) == types.ListType:
for item in child:
item.show(level)
else:
self.showLevel(level)
print 'Child:', child
def showLevel(self, level):
for idx in range(level):
print ' ',
#
# The recursive descent parser class.
# Contains the "recognizer" methods, which implement the grammar
# rules (above), one recognizer method for each production rule.
#
class ProgParser:
def __init__(self):
pass
def parseFile(self, infileName):
self.infileName = infileName
self.tokens = None
self.tokenType = NoneTokType
self.token = ''
self.lineNo = -1
self.infile = file(self.infileName, 'r')
self.tokens = genTokens(self.infile)
try:
self.tokenType, self.token, self.lineNo = self.tokens.next()
except StopIteration:
raise RuntimeError, 'Empty file'
result = self.prog_reco()
self.infile.close()
self.infile = None
return result
def parseStream(self, instream):
self.tokens = genTokens(instream, '<instream>')
try:
self.tokenType, self.token, self.lineNo = self.tokens.next()
except StopIteration:
raise RuntimeError, 'Empty file'
result = self.prog_reco()
return result
def prog_reco(self):
commandList = []
while 1:
result = self.command_reco()
if not result:
break
commandList.append(result)
return ASTNode(ProgNodeType, commandList)
def command_reco(self):
if self.tokenType == EOFTokType:
return None
result = self.func_call_reco()
return ASTNode(CommandNodeType, result)
def func_call_reco(self):
if self.tokenType == WordTokType:
term = ASTNode(TermNodeType, self.token)
self.tokenType, self.token, self.lineNo = self.tokens.next()
if self.tokenType == LParTokType:
self.tokenType, self.token, self.lineNo = self.tokens.next()
result = self.func_call_list_reco()
if result:
if self.tokenType == RParTokType:
self.tokenType, self.token, self.lineNo = \
self.tokens.next()
return ASTNode(FuncCallNodeType, term, result)
else:
raise ParseError(self.lineNo, 'missing right paren')
else:
raise ParseError(self.lineNo, 'bad func call list')
else:
raise ParseError(self.lineNo, 'missing left paren')
else:
return None
def func_call_list_reco(self):
terms = []
while 1:
result = self.func_call_reco()
if not result:
break
terms.append(result)
if self.tokenType != CommaTokType:
break
self.tokenType, self.token, self.lineNo = self.tokens.next()
return ASTNode(FuncCallListNodeType, terms)
#
# The parse error exception class.
#
class ParseError(Exception):
def __init__(self, lineNo, msg):
RuntimeError.__init__(self, msg)
self.lineNo = lineNo
self.msg = msg
def getLineNo(self):
return self.lineNo
def getMsg(self):
return self.msg
def is_word(token):
for letter in token:
if letter not in string.ascii_letters:
return None
return 1
#
# Generate the tokens.
# Usage:
# gen = genTokens(infile)
# tokType, tok, lineNo = gen.next()
# ...
def genTokens(infile):
lineNo = 0
while 1:
lineNo += 1
try:
line = infile.next()
except:
yield (EOFTokType, None, lineNo)
toks = line.split()
for tok in toks:
if is_word(tok):
tokType = WordTokType
elif tok == '(':
tokType = LParTokType
elif tok == ')':
tokType = RParTokType
elif tok == ',':
tokType = CommaTokType
yield (tokType, tok, lineNo)
def test(infileName):
parser = ProgParser()
#ipshell('(test) #1\nCtrl-D to exit')
result = None
try:
result = parser.parseFile(infileName)
except ParseError, exp:
sys.stderr.write('ParseError: (%d) %s\n' % \
(exp.getLineNo(), exp.getMsg()))
if result:
result.show(0)
def usage():
print __doc__
sys.exit(1)
def main():
args = sys.argv[1:]
try:
opts, args = getopt.getopt(args, 'h', ['help'])
except:
usage()
relink = 1
for opt, val in opts:
if opt in ('-h', '--help'):
usage()
if len(args) != 1:
usage()
inputfile = args[0]
test(inputfile)
if __name__ == '__main__':
#import pdb; pdb.set_trace()
main()
Comments and explanation:
And, here is a sample of the data we can apply this parser to:
aaa ( ) bbb ( ccc ( ) ) ddd ( eee ( ) , fff ( ggg ( ) , hhh ( ) , iii ( ) ) )
And, if we run the parser on the this input data, we see:
$ python workbook045.py workbook045.data
Node -- Type ProgNodeType
Node -- Type CommandNodeType
Node -- Type FuncCallNodeType
Node -- Type TermNodeType
Child: aaa
Node -- Type FuncCallListNodeType
Node -- Type CommandNodeType
Node -- Type FuncCallNodeType
Node -- Type TermNodeType
Child: bbb
Node -- Type FuncCallListNodeType
Node -- Type FuncCallNodeType
Node -- Type TermNodeType
Child: ccc
Node -- Type FuncCallListNodeType
Node -- Type CommandNodeType
Node -- Type FuncCallNodeType
Node -- Type TermNodeType
Child: ddd
Node -- Type FuncCallListNodeType
Node -- Type FuncCallNodeType
Node -- Type TermNodeType
Child: eee
Node -- Type FuncCallListNodeType
Node -- Type FuncCallNodeType
Node -- Type TermNodeType
Child: fff
Node -- Type FuncCallListNodeType
Node -- Type FuncCallNodeType
Node -- Type TermNodeType
Child: ggg
Node -- Type FuncCallListNodeType
Node -- Type FuncCallNodeType
Node -- Type TermNodeType
Child: hhh
Node -- Type FuncCallListNodeType
Node -- Type FuncCallNodeType
Node -- Type TermNodeType
Child: iii
Node -- Type FuncCallListNodeType
Lexical analysis -- The tokenizer in our recursive descent parser example was (for demonstration purposes) overly simple. You can always write more complex tokenizers by hand. However, for more complex (and real) tokenizers, you may want to use a tool to build your tokenizer.
In this section we'll describe Plex and use it to produce a tokenizer for our recursive descent parser.
You can obtain Plex at http://www.cosc.canterbury.ac.nz/~greg/python/Plex/.
In order to use it, you may want to add Plex-1.1.4/Plex to your PYTHONPATH.
Here is a simple example from the Plex tutorial:
#!/usr/bin/env python
"""
Sample Plex lexer
Usage:
python plex_example.py inputfile
"""
import sys
import Plex
def count_lines(scanner, text):
scanner.line_count += 1
print '-' * 60
def test(infileName):
letter = Plex.Range("AZaz")
digit = Plex.Range("09")
name = letter + Plex.Rep(letter | digit)
number = Plex.Rep1(digit)
space = Plex.Any(" \t")
endline = Plex.Str('\n')
#comment = Plex.Str('"') + Plex.Rep( Plex.AnyBut('"')) + Plex.Str('"')
resword = Plex.Str("if", "then", "else", "end")
lexicon = Plex.Lexicon([
(endline, count_lines),
(resword, 'keyword'),
(name, 'ident'),
(number, 'int'),
( Plex.Any("+-*/=<>"), 'operator'),
(space, Plex.IGNORE),
#(comment, 'comment'),
(Plex.Str('('), 'lpar'),
(Plex.Str(')'), 'rpar'),
# comments surrounded by (* and *)
(Plex.Str("(*"), Plex.Begin('comment')),
Plex.State('comment', [
(Plex.Str("*)"), Plex.Begin('')),
(Plex.AnyChar, Plex.IGNORE),
]),
])
infile = open(infileName, "r")
scanner = Plex.Scanner(lexicon, infile, infileName)
scanner.line_count = 0
while True:
token = scanner.read()
if token[0] is None:
break
position = scanner.position()
posstr = ('(%d, %d)' % (position[1], position[2], )).ljust(10)
tokstr = '"%s"' % token[1]
tokstr = tokstr.ljust(20)
print '%s tok: %s tokType: %s' % (posstr, tokstr, token[0],)
print 'line_count: %d' % scanner.line_count
def usage():
print __doc__
sys.exit(1)
def main():
args = sys.argv[1:]
if len(args) != 1:
usage()
infileName = args[0]
test(infileName)
if __name__ == '__main__':
#import pdb; pdb.set_trace()
main()
Here is a bit of data on which we can use the above lexer:
mass = (height * (* some comment *) width * depth) / density totalmass = totalmass + mass
And, when we apply the above test program to this data, here is what we see:
$ python plex_example.py plex_example.data
(1, 0) tok: "mass" tokType: ident
(1, 5) tok: "=" tokType: operator
(1, 7) tok: "(" tokType: lpar
(1, 8) tok: "height" tokType: ident
(1, 15) tok: "*" tokType: operator
(1, 36) tok: "width" tokType: ident
(1, 42) tok: "*" tokType: operator
(1, 44) tok: "depth" tokType: ident
(1, 49) tok: ")" tokType: rpar
(1, 51) tok: "/" tokType: operator
(1, 53) tok: "density" tokType: ident
------------------------------------------------------------
(2, 0) tok: "totalmass" tokType: ident
(2, 10) tok: "=" tokType: operator
(2, 12) tok: "totalmass" tokType: ident
(2, 22) tok: "+" tokType: operator
(2, 24) tok: "mass" tokType: ident
------------------------------------------------------------
line_count: 2
Comments and explanation:
And, here are some comments on constructing the patterns used in a lexicon:
Now let's revisit our recursive descent parser, this time with a tokenizer built with Plex. The tokenizer is trivial, but will serve as an example of how to hook it into a parser:
#!/usr/bin/env python
"""
A recursive descent parser example using Plex.
This example uses Plex to implement a tokenizer.
Usage:
python python_201_rparser_plex.py [options] <inputfile>
Options:
-h, --help Display this help message.
Example:
python python_201_rparser_plex.py myfile.txt
The grammar:
Prog ::= Command | Command Prog
Command ::= Func_call
Func_call ::= Term '(' Func_call_list ')'
Func_call_list ::= Func_call | Func_call ',' Func_call_list
Term = <word>
"""
import sys, string, types
import getopt
import Plex
## from IPython.Shell import IPShellEmbed
## ipshell = IPShellEmbed((),
## banner = '>>>>>>>> Into IPython >>>>>>>>',
## exit_msg = '<<<<<<<< Out of IPython <<<<<<<<')
#
# Constants
#
# AST node types
NoneNodeType = 0
ProgNodeType = 1
CommandNodeType = 2
FuncCallNodeType = 3
FuncCallListNodeType = 4
TermNodeType = 5
# Token types
NoneTokType = 0
LParTokType = 1
RParTokType = 2
WordTokType = 3
CommaTokType = 4
EOFTokType = 5
# Dictionary to map node type values to node type names
NodeTypeDict = {
NoneNodeType: 'NoneNodeType',
ProgNodeType: 'ProgNodeType',
CommandNodeType: 'CommandNodeType',
FuncCallNodeType: 'FuncCallNodeType',
FuncCallListNodeType: 'FuncCallListNodeType',
TermNodeType: 'TermNodeType',
}
#
# Representation of a node in the AST (abstract syntax tree).
#
class ASTNode:
def __init__(self, nodeType, *args):
self.nodeType = nodeType
self.children = []
for item in args:
self.children.append(item)
def show(self, level):
self.showLevel(level)
print 'Node -- Type %s' % NodeTypeDict[self.nodeType]
level += 1
for child in self.children:
if isinstance(child, ASTNode):
child.show(level)
elif type(child) == types.ListType:
for item in child:
item.show(level)
else:
self.showLevel(level)
print 'Child:', child
def showLevel(self, level):
for idx in range(level):
print ' ',
#
# The recursive descent parser class.
# Contains the "recognizer" methods, which implement the grammar
# rules (above), one recognizer method for each production rule.
#
class ProgParser:
def __init__(self):
self.tokens = None
self.tokenType = NoneTokType
self.token = ''
self.lineNo = -1
self.infile = None
self.tokens = None
def parseFile(self, infileName):
self.tokens = None
self.tokenType = NoneTokType
self.token = ''
self.lineNo = -1
self.infile = file(infileName, 'r')
self.tokens = genTokens(self.infile, infileName)
try:
self.tokenType, self.token, self.lineNo = self.tokens.next()
except StopIteration:
raise RuntimeError, 'Empty file'
result = self.prog_reco()
self.infile.close()
self.infile = None
return result
def parseStream(self, instream):
self.tokens = None
self.tokenType = NoneTokType
self.token = ''
self.lineNo = -1
self.tokens = genTokens(self.instream, '<stream>')
try:
self.tokenType, self.token, self.lineNo = self.tokens.next()
except StopIteration:
raise RuntimeError, 'Empty stream'
result = self.prog_reco()
self.infile.close()
self.infile = None
return result
def prog_reco(self):
commandList = []
while 1:
result = self.command_reco()
if not result:
break
commandList.append(result)
return ASTNode(ProgNodeType, commandList)
def command_reco(self):
if self.tokenType == EOFTokType:
return None
result = self.func_call_reco()
return ASTNode(CommandNodeType, result)
def func_call_reco(self):
if self.tokenType == WordTokType:
term = ASTNode(TermNodeType, self.token)
self.tokenType, self.token, self.lineNo = self.tokens.next()
if self.tokenType == LParTokType:
self.tokenType, self.token, self.lineNo = self.tokens.next()
result = self.func_call_list_reco()
if result:
if self.tokenType == RParTokType:
self.tokenType, self.token, self.lineNo = \
self.tokens.next()
return ASTNode(FuncCallNodeType, term, result)
else:
raise ParseError(self.lineNo, 'missing right paren')
else:
raise ParseError(self.lineNo, 'bad func call list')
else:
raise ParseError(self.lineNo, 'missing left paren')
else:
return None
def func_call_list_reco(self):
terms = []
while 1:
result = self.func_call_reco()
if not result:
break
terms.append(result)
if self.tokenType != CommaTokType:
break
self.tokenType, self.token, self.lineNo = self.tokens.next()
return ASTNode(FuncCallListNodeType, terms)
#
# The parse error exception class.
#
class ParseError(Exception):
def __init__(self, lineNo, msg):
RuntimeError.__init__(self, msg)
self.lineNo = lineNo
self.msg = msg
def getLineNo(self):
return self.lineNo
def getMsg(self):
return self.msg
#
# Generate the tokens.
# Usage - example
# gen = genTokens(infile)
# tokType, tok, lineNo = gen.next()
# ...
def genTokens(infile, infileName):
letter = Plex.Range("AZaz")
digit = Plex.Range("09")
name = letter + Plex.Rep(letter | digit)
lpar = Plex.Str('(')
rpar = Plex.Str(')')
comma = Plex.Str(',')
comment = Plex.Str("#") + Plex.Rep(Plex.AnyBut("\n"))
space = Plex.Any(" \t\n")
lexicon = Plex.Lexicon([
(name, 'word'),
(lpar, 'lpar'),
(rpar, 'rpar'),
(comma, 'comma'),
(comment, Plex.IGNORE),
(space, Plex.IGNORE),
])
scanner = Plex.Scanner(lexicon, infile, infileName)
while 1:
tokenType, token = scanner.read()
name, lineNo, columnNo = scanner.position()
if tokenType == None:
tokType = EOFTokType
token = None
elif tokenType == 'word':
tokType = WordTokType
elif tokenType == 'lpar':
tokType = LParTokType
elif tokenType == 'rpar':
tokType = RParTokType
elif tokenType == 'comma':
tokType = CommaTokType
else:
tokType = NoneTokType
tok = token
yield (tokType, tok, lineNo)
def test(infileName):
parser = ProgParser()
#ipshell('(test) #1\nCtrl-D to exit')
result = None
try:
result = parser.parseFile(infileName)
except ParseError, exp:
sys.stderr.write('ParseError: (%d) %s\n' % \
(exp.getLineNo(), exp.getMsg()))
if result:
result.show(0)
def usage():
print __doc__
sys.exit(-1)
def main():
args = sys.argv[1:]
try:
opts, args = getopt.getopt(args, 'h', ['help'])
except:
usage()
for opt, val in opts:
if opt in ('-h', '--help'):
usage()
if len(args) != 1:
usage()
infileName = args[0]
test(infileName)
if __name__ == '__main__':
#import pdb; pdb.set_trace()
main()
And, here is a sample of the data we can apply this parser to:
# Test for recursive descent parser and Plex. # Command #1 aaa() # Command #2 bbb (ccc()) # An end of line comment. # Command #3 ddd(eee(), fff(ggg(), hhh(), iii())) # End of test
And, when we run our parser, it produces the following:
$ python plex_recusive.py plex_recusive.data
Node -- Type ProgNodeType
Node -- Type CommandNodeType
Node -- Type FuncCallNodeType
Node -- Type TermNodeType
Child: aaa
Node -- Type FuncCallListNodeType
Node -- Type CommandNodeType
Node -- Type FuncCallNodeType
Node -- Type TermNodeType
Child: bbb
Node -- Type FuncCallListNodeType
Node -- Type FuncCallNodeType
Node -- Type TermNodeType
Child: ccc
Node -- Type FuncCallListNodeType
Node -- Type CommandNodeType
Node -- Type FuncCallNodeType
Node -- Type TermNodeType
Child: ddd
Node -- Type FuncCallListNodeType
Node -- Type FuncCallNodeType
Node -- Type TermNodeType
Child: eee
Node -- Type FuncCallListNodeType
Node -- Type FuncCallNodeType
Node -- Type TermNodeType
Child: fff
Node -- Type FuncCallListNodeType
Node -- Type FuncCallNodeType
Node -- Type TermNodeType
Child: ggg
Node -- Type FuncCallListNodeType
Node -- Type FuncCallNodeType
Node -- Type TermNodeType
Child: hhh
Node -- Type FuncCallListNodeType
Node -- Type FuncCallNodeType
Node -- Type TermNodeType
Child: iii
Node -- Type FuncCallListNodeType
Comments:
For complex parsing tasks, you may want to consider the following tools:
And, for lexical analysis, you may also want to look here:
In the sections below, we give examples and notes about the use of PLY and pyparsing.
In this section we will show how to implement our parser example with PLY.
First down-load PLY. It is available here: PLY (Python Lex-Yacc) -- http://www.dabeaz.com/ply/
Then add the PLY directory to your PYTHONPATH.
Learn how to construct lexers and parsers with PLY by reading doc/ply.html in the distribution of PLY and by looking at the examples in the distribution.
For those of you who want a more complex example, see A Python Parser for the RELAX NG Compact Syntax, which is implemented with PLY.
Now, here is our example parser. Comments and explanations are below:
#!/usr/bin/env python
"""
A parser example.
This example uses PLY to implement a lexer and parser.
The grammar:
Prog ::= Command*
Command ::= Func_call
Func_call ::= Term '(' Func_call_list ')'
Func_call_list ::= Func_call*
Term = <word>
Here is a sample "program" to use as input:
# Test for recursive descent parser and Plex.
# Command #1
aaa()
# Command #2
bbb (ccc()) # An end of line comment.
# Command #3
ddd(eee(), fff(ggg(), hhh(), iii()))
# End of test
"""
import sys
import types
import getopt
import ply.lex as lex
import ply.yacc as yacc
#
# Globals
#
startlinepos = 0
#
# Constants
#
# AST node types
NoneNodeType = 0
ProgNodeType = 1
CommandNodeType = 2
CommandListNodeType = 3
FuncCallNodeType = 4
FuncCallListNodeType = 5
TermNodeType = 6
# Dictionary to map node type values to node type names
NodeTypeDict = {
NoneNodeType: 'NoneNodeType',
ProgNodeType: 'ProgNodeType',
CommandNodeType: 'CommandNodeType',
CommandListNodeType: 'CommandListNodeType',
FuncCallNodeType: 'FuncCallNodeType',
FuncCallListNodeType: 'FuncCallListNodeType',
TermNodeType: 'TermNodeType',
}
#
# Representation of a node in the AST (abstract syntax tree).
#
class ASTNode:
def __init__(self, nodeType, *args):
self.nodeType = nodeType
self.children = []
for item in args:
self.children.append(item)
def append(self, item):
self.children.append(item)
def show(self, level):
self.showLevel(level)
print 'Node -- Type: %s' % NodeTypeDict[self.nodeType]
level += 1
for child in self.children:
if isinstance(child, ASTNode):
child.show(level)
elif type(child) == types.ListType:
for item in child:
item.show(level)
else:
self.showLevel(level)
print 'Value:', child
def showLevel(self, level):
for idx in range(level):
print ' ',
#
# Exception classes
#
class LexerError(Exception):
def __init__(self, msg, lineno, columnno):
self.msg = msg
self.lineno = lineno
self.columnno = columnno
def show(self):
sys.stderr.write('Lexer error (%d, %d) %s\n' % \
(self.lineno, self.columnno, self.msg))
class ParserError(Exception):
def __init__(self, msg, lineno, columnno):
self.msg = msg
self.lineno = lineno
self.columnno = columnno
def show(self):
sys.stderr.write('Parser error (%d, %d) %s\n' % \
(self.lineno, self.columnno, self.msg))
#
# Lexer specification
#
tokens = (
'NAME',
'LPAR','RPAR',
'COMMA',
)
# Tokens
t_LPAR = r'\('
t_RPAR = r'\)'
t_COMMA = r'\,'
t_NAME = r'[a-zA-Z_][a-zA-Z0-9_]*'
# Ignore whitespace
t_ignore = ' \t'
# Ignore comments ('#' to end of line)
def t_COMMENT(t):
r'\#[^\n]*'
pass
def t_newline(t):
r'\n+'
global startlinepos
startlinepos = t.lexer.lexpos - 1
t.lineno += t.value.count("\n")
def t_error(t):
global startlinepos
msg = "Illegal character '%s'" % (t.value[0])
columnno = t.lexer.lexpos - startlinepos
raise LexerError(msg, t.lineno, columnno)
#
# Parser specification
#
def p_prog(t):
'prog : command_list'
t[0] = ASTNode(ProgNodeType, t[1])
def p_command_list_1(t):
'command_list : command'
t[0] = ASTNode(CommandListNodeType, t[1])
def p_command_list_2(t):
'command_list : command_list command'
t[1].append(t[2])
t[0] = t[1]
def p_command(t):
'command : func_call'
t[0] = ASTNode(CommandNodeType, t[1])
def p_func_call_1(t):
'func_call : term LPAR RPAR'
t[0] = ASTNode(FuncCallNodeType, t[1])
def p_func_call_2(t):
'func_call : term LPAR func_call_list RPAR'
t[0] = ASTNode(FuncCallNodeType, t[1], t[3])
def p_func_call_list_1(t):
'func_call_list : func_call'
t[0] = ASTNode(FuncCallListNodeType, t[1])
def p_func_call_list_2(t):
'func_call_list : func_call_list COMMA func_call'
t[1].append(t[3])
t[0] = t[1]
def p_term(t):
'term : NAME'
t[0] = ASTNode(TermNodeType, t[1])
def p_error(t):
global startlinepos
msg = "Syntax error at '%s'" % t.value
columnno = t.lexer.lexpos - startlinepos
raise ParserError(msg, t.lineno, columnno)
#
# Parse the input and display the AST (abstract syntax tree)
#
def parse(infileName):
startlinepos = 0
# Build the lexer
lex.lex(debug=1)
# Build the parser
yacc.yacc()
# Read the input
infile = file(infileName, 'r')
content = infile.read()
infile.close()
try:
# Do the parse
result = yacc.parse(content)
# Display the AST
result.show(0)
except LexerError, exp:
exp.show()
except ParserError, exp:
exp.show()
USAGE_TEXT = __doc__
def usage():
print USAGE_TEXT
sys.exit(-1)
def main():
args = sys.argv[1:]
try:
opts, args = getopt.getopt(args, 'h', ['help'])
except:
usage()
relink = 1
for opt, val in opts:
if opt in ('-h', '--help'):
usage()
if len(args) != 1:
usage()
infileName = args[0]
parse(infileName)
if __name__ == '__main__':
#import pdb; pdb.set_trace()
main()
Applying this parser to the following input:
# Test for recursive descent parser and Plex. # Command #1 aaa() # Command #2 bbb (ccc()) # An end of line comment. # Command #3 ddd(eee(), fff(ggg(), hhh(), iii())) # End of test
produces the following output:
Node -- Type: ProgNodeType
Node -- Type: CommandListNodeType
Node -- Type: CommandNodeType
Node -- Type: FuncCallNodeType
Node -- Type: TermNodeType
Value: aaa
Node -- Type: CommandNodeType
Node -- Type: FuncCallNodeType
Node -- Type: TermNodeType
Value: bbb
Node -- Type: FuncCallListNodeType
Node -- Type: FuncCallNodeType
Node -- Type: TermNodeType
Value: ccc
Node -- Type: CommandNodeType
Node -- Type: FuncCallNodeType
Node -- Type: TermNodeType
Value: ddd
Node -- Type: FuncCallListNodeType
Node -- Type: FuncCallNodeType
Node -- Type: TermNodeType
Value: eee
Node -- Type: FuncCallNodeType
Node -- Type: TermNodeType
Value: fff
Node -- Type: FuncCallListNodeType
Node -- Type: FuncCallNodeType
Node -- Type: TermNodeType
Value: ggg
Node -- Type: FuncCallNodeType
Node -- Type: TermNodeType
Value: hhh
Node -- Type: FuncCallNodeType
Node -- Type: TermNodeType
Value: iii
Comments and explanation:
pyparsing is a relatively new parsing package for Python. It was implemented and is supported by Paul McGuire and it shows promise. It appears especially easy to use and seems especially appropriate in particular for quick parsing tasks, although it has features that make some complex parsing tasks easy. It follows a very natural Python style for constructing parsers.
Good documentation comes with the pyparsing distribution. See file HowToUseParsing.html. So, I won't try to repeat that here. What follows is an attempt to provide several quick examples to help you solve simple parsing tasks as quickly as possible.
You will also want to look at the samples in the examples directory, which are very helpful. My examples below are fairly simple. You can see more of the ability of pyparsing to handle complex tasks in the examples.
Where to get it - You can find pyparsing at: Pyparsing Wiki Home -- http://pyparsing.wikispaces.com/
How to install it - Put the pyparsing module somewhere on your PYTHONPATH.
And now, here are a few examples.
Note: This example is for demonstration purposes only. If you really to need to parse comma delimited fields, you can probably do so much more easily with the CSV (comma separated values) module in the Python standard library.
Here is a simple grammar for lines containing fields separated by commas:
import sys
from pyparsing import alphanums, ZeroOrMore, Word
fieldDef = Word(alphanums)
lineDef = fieldDef + ZeroOrMore("," + fieldDef)
def test():
args = sys.argv[1:]
if len(args) != 1:
print 'usage: python pyparsing_test1.py <datafile.txt>'
sys.exit(-1)
infilename = sys.argv[1]
infile = file(infilename, 'r')
for line in infile:
fields = lineDef.parseString(line)
print fields
test()
Here is some sample data:
abcd,defg 11111,22222,33333
And, when we run our parser on this data file, here is what we see:
$ python comma_parser.py sample1.data ['abcd', ',', 'defg'] ['11111', ',', '22222', ',', '33333']
Notes and explanation:
Note how the grammar is constructed from normal Python calls to function and object/class constructors. I've constructed the parser in-line because my example is simple, but constructing the parser in a function or even a module might make sense for more complex grammars. pyparsing makes it easy to use these these different styles.
Use "+" to specify a sequence. In our example, a lineDef is a fieldDef followed by ....
Use ZeroOrMore to specify repetition. In our example, a lineDef is a fieldDef followed by zero or more occurances of comma and fieldDef. There is also OneOrMore when you want to require at least one occurance.
Parsing comma delimited text happens so frequently that pyparsing provides a shortcut. Replace:
lineDef = fieldDef + ZeroOrMore("," + fieldDef)
with:
lineDef = delimitedList(fieldDef)
And note that delimitedList takes an optional argument delim used to specify the delimiter. The default is a comma.
This example parses expressions of the form func(arg1, arg2, arg3):
from pyparsing import Word, alphas, alphanums, nums, ZeroOrMore, Literal
lparen = Literal("(")
rparen = Literal(")")
identifier = Word(alphas, alphanums + "_")
integer = Word( nums )
functor = identifier
arg = identifier | integer
args = arg + ZeroOrMore("," + arg)
expression = functor + lparen + args + rparen
def test():
content = raw_input("Enter an expression: ")
parsedContent = expression.parseString(content)
print parsedContent
test()
Explanation:
This example parses expressions having the following form:
Input format: [name] [phone] [city, state zip] Last, first 111-222-3333 city, ca 99999
Here is the parser:
import sys
from pyparsing import alphas, nums, ZeroOrMore, Word, Group, Suppress, Combine
lastname = Word(alphas)
firstname = Word(alphas)
city = Group(Word(alphas) + ZeroOrMore(Word(alphas)))
state = Word(alphas, exact=2)
zip = Word(nums, exact=5)
name = Group(lastname + Suppress(",") + firstname)
phone = Combine(Word(nums, exact=3) + "-" + Word(nums, exact=3) + "-" + Word(nums, exact=4))
location = Group(city + Suppress(",") + state + zip)
record = name + phone + location
def test():
args = sys.argv[1:]
if len(args) != 1:
print 'usage: python pyparsing_test3.py <datafile.txt>'
sys.exit(-1)
infilename = sys.argv[1]
infile = file(infilename, 'r')
for line in infile:
line = line.strip()
if line and line[0] != "#":
fields = record.parseString(line)
print fields
test()
And, here is some sample input:
Jabberer, Jerry 111-222-3333 Bakersfield, CA 95111 Kackler, Kerry 111-222-3334 Fresno, CA 95112 Louderdale, Larry 111-222-3335 Los Angeles, CA 94001
Here is output from parsing the above input:
[['Jabberer', 'Jerry'], '111-222-3333', [['Bakersfield'], 'CA', '95111']] [['Kackler', 'Kerry'], '111-222-3334', [['Fresno'], 'CA', '95112']] [['Louderdale', 'Larry'], '111-222-3335', [['Los', 'Angeles'], 'CA', '94001']]
Comments:
This example (thanks to Paul McGuire) parses a more complex structure and produces a dictionary.
Here is the code:
from pyparsing import Literal, Word, Group, Dict, ZeroOrMore, alphas, nums,\
delimitedList
import pprint
testData = """
+-------+------+------+------+------+------+------+------+------+
| | A1 | B1 | C1 | D1 | A2 | B2 | C2 | D2 |
+=======+======+======+======+======+======+======+======+======+
| min | 7 | 43 | 7 | 15 | 82 | 98 | 1 | 37 |
| max | 11 | 52 | 10 | 17 | 85 | 112 | 4 | 39 |
| ave | 9 | 47 | 8 | 16 | 84 | 106 | 3 | 38 |
| sdev | 1 | 3 | 1 | 1 | 1 | 3 | 1 | 1 |
+-------+------+------+------+------+------+------+------+------+
"""
# Define grammar for datatable
heading = (Literal(
"+-------+------+------+------+------+------+------+------+------+") +
"| | A1 | B1 | C1 | D1 | A2 | B2 | C2 | D2 |" +
"+=======+======+======+======+======+======+======+======+======+").suppress()
vert = Literal("|").suppress()
number = Word(nums)
rowData = Group( vert + Word(alphas) + vert + delimitedList(number,"|") +
vert )
trailing = Literal(
"+-------+------+------+------+------+------+------+------+------+").suppress()
datatable = heading + Dict( ZeroOrMore(rowData) ) + trailing
def main():
# Now parse data and print results
data = datatable.parseString(testData)
print "data:", data
print "data.asList():",
pprint.pprint(data.asList())
print "data keys:", data.keys()
print "data['min']:", data['min']
print "data.max:", data.max
if __name__ == '__main__':
main()
When we run this, it produces the following:
data: [['min', '7', '43', '7', '15', '82', '98', '1', '37'], ['max', '11', '52', '10', '17', '85', '112', '4', '39'], ['ave', '9', '47', '8', '16', '84', '106', '3', '38'], ['sdev', '1', '3', '1', '1', '1', '3', '1', '1']] data.asList():[['min', '7', '43', '7', '15', '82', '98', '1', '37'], ['max', '11', '52', '10', '17', '85', '112', '4', '39'], ['ave', '9', '47', '8', '16', '84', '106', '3', '38'], ['sdev', '1', '3', '1', '1', '1', '3', '1', '1']] data keys: ['ave', 'min', 'sdev', 'max'] data['min']: ['7', '43', '7', '15', '82', '98', '1', '37'] data.max: ['11', '52', '10', '17', '85', '112', '4', '39']
Notes:
This section will help you to put a GUI (graphical user interface) in your Python program.
We will use a particular GUI library: PyGTK. We've chosen this because it is reasonably light-weight and our goal is to embed light-weight GUI interfaces in an (possibly) existing application.
For simpler GUI needs, consider EasyGUI, which is also described below.
For more heavy-weight GUI needs (for example, complete GUI applications), you may want to explore WxPython. See the WxPython home page at: http://www.wxpython.org/
Information about PyGTK is here: The PyGTK home page -- http://www.pygtk.org//.
In this section we explain how to pop up a simple dialog box from your Python application.
To do this, do the following:
Here is a sample that displays a message box:
#!/usr/bin/env python
import sys
import getopt
import gtk
class MessageBox(gtk.Dialog):
def __init__(self, message="", buttons=(), pixmap=None,
modal= True):
gtk.Dialog.__init__(self)
self.connect("destroy", self.quit)
self.connect("delete_event", self.quit)
if modal:
self.set_modal(True)
hbox = gtk.HBox(spacing=5)
hbox.set_border_width(5)
self.vbox.pack_start(hbox)
hbox.show()
if pixmap:
self.realize()
pixmap = Pixmap(self, pixmap)
hbox.pack_start(pixmap, expand=False)
pixmap.show()
label = gtk.Label(message)
hbox.pack_start(label)
label.show()
for text in buttons:
b = gtk.Button(text)
b.set_flags(gtk.CAN_DEFAULT)
b.set_data("user_data", text)
b.connect("clicked", self.click)
self.action_area.pack_start(b)
b.show()
self.ret = None
def quit(self, *args):
self.hide()
self.destroy()
gtk.main_quit()
def click(self, button):
self.ret = button.get_data("user_data")
self.quit()
# create a message box, and return which button was pressed
def message_box(title="Message Box", message="", buttons=(), pixmap=None,
modal= True):
win = MessageBox(message, buttons, pixmap=pixmap, modal=modal)
win.set_title(title)
win.show()
gtk.main()
return win.ret
def test():
result = message_box(title='Test #1',
message='Here is your message',
buttons=('Ok', 'Cancel'))
print 'result:', result
USAGE_TEXT = """
Usage:
python simple_dialog.py [options]
Options:
-h, --help Display this help message.
Example:
python simple_dialog.py
"""
def usage():
print USAGE_TEXT
sys.exit(-1)
def main():
args = sys.argv[1:]
try:
opts, args = getopt.getopt(args, 'h', ['help'])
except:
usage()
relink = 1
for opt, val in opts:
if opt in ('-h', '--help'):
usage()
if len(args) != 0:
usage()
test()
if __name__ == '__main__':
#import pdb; pdb.set_trace()
main()
Some explanation:
And, here is an example that displays an text input dialog:
#!/usr/bin/env python
import sys
import getopt
import gtk
class EntryDialog( gtk.Dialog):
def __init__(self, message="", default_text='', modal=True):
gtk.Dialog.__init__(self)
self.connect("destroy", self.quit)
self.connect("delete_event", self.quit)
if modal:
self.set_modal(True)
box = gtk.VBox(spacing=10)
box.set_border_width(10)
self.vbox.pack_start(box)
box.show()
if message:
label = gtk.Label(message)
box.pack_start(label)
label.show()
self.entry = gtk.Entry()
self.entry.set_text(default_text)
box.pack_start(self.entry)
self.entry.show()
self.entry.grab_focus()
button = gtk.Button("OK")
button.connect("clicked", self.click)
button.set_flags(gtk.CAN_DEFAULT)
self.action_area.pack_start(button)
button.show()
button.grab_default()
button = gtk.Button("Cancel")
button.connect("clicked", self.quit)
button.set_flags(gtk.CAN_DEFAULT)
self.action_area.pack_start(button)
button.show()
self.ret = None
def quit(self, w=None, event=None):
self.hide()
self.destroy()
gtk.main_quit()
def click(self, button):
self.ret = self.entry.get_text()
self.quit()
def input_box(title="Input Box", message="", default_text='',
modal=True):
win = EntryDialog(message, default_text, modal=modal)
win.set_title(title)
win.show()
gtk.main()
return win.ret
def test():
result = input_box(title='Test #2',
message='Enter a valuexxx:',
default_text='a default value')
if result is None:
print 'Canceled'
else:
print 'result: "%s"' % result
USAGE_TEXT = """
Usage:
python simple_dialog.py [options]
Options:
-h, --help Display this help message.
Example:
python simple_dialog.py
"""
def usage():
print USAGE_TEXT
sys.exit(-1)
def main():
args = sys.argv[1:]
try:
opts, args = getopt.getopt(args, 'h', ['help'])
except:
usage()
relink = 1
for opt, val in opts:
if opt in ('-h', '--help'):
usage()
if len(args) != 0:
usage()
test()
if __name__ == '__main__':
#import pdb; pdb.set_trace()
main()
Most of the explanation for the message box example is relevant to this example, too. Here are some differences:
This example shows a file selection dialog box:
#!/usr/bin/env python
import sys
import getopt
import gtk
class FileChooser(gtk.FileSelection):
def __init__(self, modal=True, multiple=True):
gtk.FileSelection.__init__(self)
self.multiple = multiple
self.connect("destroy", self.quit)
self.connect("delete_event", self.quit)
if modal:
self.set_modal(True)
self.cancel_button.connect('clicked', self.quit)
self.ok_button.connect('clicked', self.ok_cb)
if multiple:
self.set_select_multiple(True)
self.ret = None
def quit(self, *args):
self.hide()
self.destroy()
gtk.main_quit()
def ok_cb(self, b):
if self.multiple:
self.ret = self.get_selections()
else:
self.ret = self.get_filename()
self.quit()
def file_sel_box(title="Browse", modal=False, multiple=True):
win = FileChooser(modal=modal, multiple=multiple)
win.set_title(title)
win.show()
gtk.main()
return win.ret
def file_open_box(modal=True):
return file_sel_box("Open", modal=modal, multiple=True)
def file_save_box(modal=True):
return file_sel_box("Save As", modal=modal, multiple=False)
def test():
result = file_open_box()
print 'open result:', result
result = file_save_box()
print 'save result:', result
USAGE_TEXT = """
Usage:
python simple_dialog.py [options]
Options:
-h, --help Display this help message.
Example:
python simple_dialog.py
"""
def usage():
print USAGE_TEXT
sys.exit(-1)
def main():
args = sys.argv[1:]
try:
opts, args = getopt.getopt(args, 'h', ['help'])
except:
usage()
relink = 1
for opt, val in opts:
if opt in ('-h', '--help'):
usage()
if len(args) != 0:
usage()
test()
if __name__ == '__main__':
main()
#import pdb
#pdb.run('main()')
A little guidance:
Note that there are also predefined dialogs for font selection (FontSelectionDialog) and color selection (ColorSelectionDialog)
If your GUI needs are minimalist (maybe a pop-up dialog or two) and your application is imperative rather than event driven, then you may want to consider EasyGUI. As the name suggests, it is extremely easy to use.
How to know when you might be able to use EasyGUI:
EasyGUI plus documentation and examples are available at EasyGUI home page at SourceForge -- http://easygui.sourceforge.net/
EasyGUI provides functions for a variety of commonly needed dialog boxes, including:
See the documentation at the EasyGUI Web site for more features.
For a demonstration of EasyGUI's capabilities, run the easygui.py as a Python script:
$ python easygui.py
Here is a simple example that prompts the user for an entry, then shows the response in a message box:
import easygui
def testeasygui():
response = easygui.enterbox(msg='Enter your name:', title='Name Entry')
easygui.msgbox(msg=response, title='Your Response')
testeasygui()
This example presents a dialog to allow the user to select a file:
import easygui
def test():
response = easygui.fileopenbox(msg='Select a file')
print 'file name: %s' % response
test()
Python has an excellent range of implementation organization structures. These range from statements and control structures (at a low level) through functions, methods, and classes (at an intermediate level) and modules and packages at an upper level.
This section provides some guidance with the use of packages. In particular:
A Python package is a collection of Python modules in a disk directory.
In order to be able to import individual modules from a directory, the directory must contain a file named __init__.py. (Note that requirement does not apply to directories that are listed in PYTHONPATH.) The __init__.py serves several purposes:
One simple way to enable the user to import and use a package is to instruct the use to import individual modules from the package.
A second, slightly more advanced way to enable the user to import the package is to expose those features of the package in the __init__ module. Suppose that module mod1 contains functions fun1a and fun1b and suppose that module mod2 contains functions fun2a and fun2b. Then file __init__.py might contain the following:
from mod1 import fun1a, fun1b from mod2 import fun2a, fun2b
Then, if the following is evaluated in the user's code:
import testpackages
Then testpackages will contain fun1a, fun1b, fun2a, and fun2b.
For example, here is an interactive session that demostrates importing the package:
>>> import testpackages >>> print dir(testpackages) [`__builtins__', `__doc__', `__file__', `__name__', `__path__', `fun1a', `fun1b', `fun2a', `fun2b', `mod1', `mod2']
Distutils (Python Distribution Utilities) has special support for distrubuting and installing packages. Learn more here: Distributing Python Modules -- http://docs.python.org/distutils/index.html.
As our example, imagine that we have a directory containing the following:
Testpackages Testpackages/README Testpackages/MANIFEST.in Testpackages/setup.py Testpackages/testpackages/__init__.py Testpackages/testpackages/mod1.py Testpackages/testpackages/mod2.py
Notice the sub-directory Testpackages/testpackages containing the file __init__.py. This is the Python package that we will install.
We'll describe how to configure the above files so that they can be packaged as a single distribution file and so that the Python package they contain can be installed as a package by Distutils.
The MANIFEST.in file lists the files that we want included in our distribution. Here is the contents of our MANIFEST.in file:
include README MANIFEST MANIFEST.in include setup.py include testpackages/*.py
The setup.py file describes to Distutils (1) how to package the distribution file and (2) how to install the distribution. Here is the contents of our sample setup.py:
#!/usr/bin/env python
from distutils.core import setup # [1]
long_description = 'Tests for installing and distributing Python packages'
setup(name = 'testpackages', # [2]
version = '1.0a',
description = 'Tests for Python packages',
maintainer = 'Dave Kuhlman',
maintainer_email = 'dkuhlman (at) davekuhlman (dot) org
url = 'http://www.reifywork.com
long_description = long_description,
packages = ['testpackages'] # [3]
)
Explanation:
Now, to create a distribution file, we run the following:
python setup.py sdist --formats=gztar
which will create a file testpackages-1.0a.tar.gz under the directory dist.
Then, you can give this distribution file to a potential user, who can install it by doing the following:
$ tar xvzf testpackages-1.0a.tar.gz $ cd testpackages-1.0a $ python setup.py build $ python setup.py install # as root
This document takes a workbook and exercise-with-solutions approach to Python training. It is hoped that those who feel a need for less explanation and more practical exercises will find this useful.
A few notes about the exercises:
The latest version of this document is at my Web site (URL above).
If you have comments or suggestions, please send them my way.
A name is any combination of letters, digits, and the underscore, but the first character must be a letter or an underscore. Names may be of any length.
Case is significant.
Exercises:
Solutions:
The following are keywords in Python and should not be used as variable names:
and del from not while as elif global or with assert else if pass yield break except import print class exec in raise continue finally is return def for lambda try
Exercises:
Solutions:
The following are operators in Python and will separate names:
+ - * ** / // % << >> & | ^ ~ < > <= >= == != <> and or is not in Also: () [] . (dot)
But, note that the Python style guide suggests that you place blanks around binary operators. One exception to this rule is function arguments and parameters for functions: it is suggested that you not put blanks around the equal sign (=) used to specify keyword arguments and default parameters.
Exercises:
Solutions:
In Python, normally we write one statement per line. In fact, Python assumes this. Therefore:
Extending a Python statement to a subsequent line -- Follow these two rules:
Exercises:
Extend the following statement to a second line using parentheses:
total_count = tree_count + vegetable_count + fruit_count
Extend the following statement to a second line using the backslash line continuation character:
total_count = tree_count + vegetable_count + fruit_count
Solutions:
Parentheses create an open context that tells Python that a statement extends to the next line:
total_count = (tree_count +
vegetable_count + fruit_count)
A backslash as the last character on line tells Python that the current statement extends to the next line:
total_count = tree_count + \
vegetable_count + fruit_count
For extending a line on a subsequent line, which is better, parentheses or a backslash? Here is a quote:
"The preferred way of wrapping long lines is by using Python's implied line continuation inside parentheses, brackets and braces. If necessary, you can add an extra pair of parentheses around an expression, but sometimes using a backslash looks better."
-- PEP 8: Style Guide for Python Code -- http://www.python.org/dev/peps/pep-0008/
Python uses indentation to indicate program structure. That is to say, in order to nest a block of code inside a compound statement, you indent that nested code. This is different from many programming languages which use some sort of begin and end markers, for example curly brackets.
The standard coding practice for Python is to use four spaces per indentation level and to not use hard tabs. (See the Style Guide for Python Code.) Because of this, you will want to use a text editor that you can configure so that it will use four spaces for indentation. See here for a list of Python-friendly text editors: PythonEditors.
Exercises:
Given the following, nest the print statement inside the if statement:
if x > 0: print x
Nest these two lines:
z = x + y print z
inside the following function definition statement:
def show_sum(x, y):
Solutions:
Indentation indicates that one statement is nested inside another statement:
if x > 0:
print x
Indentation indicates that a block of statements is nested inside another statement:
def show_sum(x, y):
z = x + y
print z
Here are a few rules:
Exercises:
Will the following code produce an error?
show_version()
def show_version():
print 'Version 1.0a'
Will the following code produce an error?
def test():
show_version()
def show_version():
print 'Version 1.0a'
test()
Will the following code produce an error? Assume that show_config is not defined:
x = 3
if x > 5:
show_config()
Solutions:
Answer: Yes, it generates an error. The name show_version would not be created and bound to a value until the def function definition statement binds a function object to it. That is done after the attempt to use (call) that object.
Answer: No. The function test() does call the function show_version(), but since test() is not called until after show_version() is defined, that is OK.
Answer: No. It's bad code, but in this case will not generate an error. Since x is less than 5, the body of the if statement is not evaluated.
N.B. This example shows why it is important during testing that every line of code in your Python program be evaluated. Here is good Pythonic advice: "If it's not tested, it's broken."
Each of the subsections in this section on built-in data types will have a similar structure:
The numbers you will use most commonly are likely to be integers and floats. Python also has long integers and complex numbers.
A few facts about numbers (in Python):
Python will convert to using a long integer automatically when needed. You do not need to worry about exceeding the size of a (standard) integer.
The size of the largest integer in your version of Python is in sys.maxint. To learn what it is, do:
>>> import sys >>> print sys.maxint 9223372036854775807
The above show the maximum size of an integer on a 64-bit version of Python.
You can convert from integer to float by using the float constructor. Example:
>>> x = 25 >>> y = float(x) >>> print y 25.0
Python does "mixed arithmetic". You can add, multiply, and divide integers and floats. When you do, Python "promotes" the result to a float.
An integer is constructed with a series of digits or the integer constructor (int(x)). Be aware that a sequence of digits beginning with zero represents an octal value. Examples:
>>> x1 = 1234
>>> x2 = int('1234')
>>> x3 = -25
>>> x1
1234
>>> x2
1234
>>> x3
-25
A float is constructed either with digits and a dot (example, 12.345) or with engineering/scientific notation or with the float constructor (float(x)). Examples:
>>> x1 = 2.0e3
>>> x1 = 1.234
>>> x2 = -1.234
>>> x3 = float('1.234')
>>> x4 = 2.0e3
>>> x5 = 2.0e-3
>>> print x1, x2, x3, x4, x5
1.234 -1.234 1.234 2000.0 0.002
Exercises:
Construct these numeric values:
Solutions:
0
0.0, 0., or .0
101
1000.0
1e3 or 1.0e3
Asigning integer values to variables:
In [7]: value1 = 23 In [8]: value2 = -14 In [9]: value3 = 0 In [10]: value1 Out[10]: 23 In [11]: value2 Out[11]: -14 In [12]: value3 Out[12]: 0
Assigning expression values to variables:
value1 = 4 * (3 + 5) value2 = (value1 / 3.0) - 2
Assigning floats to variables:
value1 = 0.01 value2 = -3.0 value3 = 3e-4
Assigning expressions containing varialbes:
value4 = value1 * (value2 - value3) value4 = value1 + value2 + value3 - value4
Mixed arithmetic:
x = 5 y = 8 z = float(x) / y
You can also construct integers and floats using the class. Calling a class (using parentheses after a class name, for example) produces an instance of the class.
Exercises:
Solutions:
Use the int data type to construct an integer instance from a string:
int("123")
Use the float data type to construct a float instance from an integer:
float(123)
Use the int data type to construct an integer instance from a float:
int(12.345) # --> 12
Notice that the result is truncated to the integer part.
You can use most of the familiar operators with numbers, for example:
+ - * ** / // % << >> & | ^ ~ < > <= >= == != <>
Look here for an explanation of these operators when applied to numbers: Numeric Types -- int, float, long, complex -- http://docs.python.org/lib/typesnumeric.html.
Some operators take precedence over others. The table in the Web page just referenced above also shows that order of priority.
Here is a bit of that table:
All numeric types (except complex) support the following operations,
sorted by ascending priority (operations in the same box have the same
priority; all numeric operations have a higher priority than comparison
operations):
Operation Result
--------- ------
x + y sum of x and y
x - y difference of x and y
x * y product of x and y
x / y quotient of x and y
x // y (floored) quotient of x and y
x % y remainder of x / y
-x x negated
+x x unchanged
abs(x) absolute value or magnitude of x
int(x) x converted to integer
long(x) x converted to long integer
float(x) x converted to floating point
complex(re,im) a complex number with real part re, imaginary part
im. im defaults to zero.
c.conjugate() conjugate of the complex number c
divmod(x, y) the pair (x // y, x % y)
pow(x, y) x to the power y
x ** y x to the power y
Notice also that the same operator may perform a different function depending on the data type of the value to which it is applied.
Exercises:
Solutions:
Arithmetic expressions are follow standard infix algebraic syntax:
3 + 4 + 5
Use another infix expression:
2 + 3 * 4
Or:
2 + (3 * 4)
But, in this case the parentheses are not necessary because the * operator binds more tightly than the + operator.
Use parentheses to control order of evaluation:
(2 + 3) * 4
Note that the * operator has precedence over (binds tighter than) the + operator, so the parentheses are needed.
Python does mixed arithemetic. When you apply an operation to an integer and a float, it promotes the result to the "higher" data type, a float.
If you need to perform an operation on several integers, but want use a floating point operation, first convert one of the integers to a float using float(x), which effectively creates an instance of class float.
Try the following at your Python interactive prompt:
Exercises:
Given the following assignments:
x = 20 y = 50
Divide x by y giving a float result.
Solutions:
Promote one of the integers to float before performing the division:
z = float(x) / y
Most of the methods implemented by the data types (classes) int and float are special methods that are called through the use of operators. Special methods often have names that begin and end with a double underscore. To see a list of the special names and a bit of an indication of when each is called, do any of the following at the Python interactive prompt:
>>> help(int) >>> help(32) >>> help(float) >>> help(1.23) >>> dir(1) >>> dir(1.2)
Lists are a container data type that acts as a dynamic array. That is to say, a list is a sequence that can be indexed into and that can grow and shrink.
A tuple is an index-able container, like a list, except that a tuple is immutable.
A few characteristics of lists and tuples:
The literal representation of a list is square brackets containing zero or more items separated by commas.
Examples:
Try these at the Python interactive prompt:
>>> [11, 22, 33] >>> ['aa', 'bb', 'cc', ] >>> [100, 'apple', 200, 'banana', ] # The last comma is >>> optional.
A list can contain lists. In fact a list can contain any kind of object:
>>> [1, [2, 3], 4, [5, 6, 7, ], 8]
Lists are heterogenous, that is, different kinds of objects can be in the same list. Here is a list that contains a number, a string, and another list:
>>> [123, 'abc', [456, 789]]
Exercises:
Solutions:
We can define list literals at the Python or IPython interactive prompt:
Create a tuple using commas, optionally with parentheses:
In [1]: a1 = (11, 22, 33, ) In [2]: a1 Out[2]: (11, 22, 33)
Quoted characters separated by commas create a tuple of strings:
In [3]: a2 = ('aaa', 'bbb', 'ccc')
In [4]: a2
Out[4]: ('aaa', 'bbb', 'ccc')
Items separated by commas inside square brackets create a list:
In [26]: a3 = [100, 200, 300, ] In [27]: a3 Out[27]: [100, 200, 300]
Strings separated by commas inside square brackets create a list of strings:
In [5]: a3 = ['basil', 'parsley', 'coriander'] In [6]: a3 Out[6]: ['basil', 'parsley', 'coriander'] In [7]:
A tuple or a list can contain tuples and lists:
In [8]: a5 = [(11, 22), (33, 44), (55,)] In [9]: a5 Out[9]: [(11, 22), (33, 44), (55,)]
A list or tuple can contain items of different types:
In [10]: a6 = [101, 102, 'abc', "def", (201, 202), ('ghi', 'jkl')]
In [11]: a6
Out[11]: [101, 102, 'abc', 'def', (201, 202), ('ghi', 'jkl')]
In order to create a tuple containing exactly one item, we must use a comma:
In [13]: a7 = (6,) In [14]: a7 Out[14]: (6,)
In order to create an empty tuple, use the tuple class/type to create an instance of a empty tuple:
In [21]: a = tuple() In [22]: a Out[22]: () In [23]: type(a) Out[23]: <type 'tuple'>
There are several operators that are applicable to lists. Here is how to find out about them:
Exercises:
Solutions:
The plus operator, applied to two lists produces a new list that is a concatenation of two lists:
>>> [11, 22] + ['aa', 'bb']
Multiplying a list by an integer n creates a new list that repeats the original list n times:
>>> [11, 'abc', 4.5] * 3
The comparison operators can be used to compare lists:
>>> [11, 22] == [11, 22] >>> [11, 22] < [11, 33]
Again, use dir() and help() to learn about the methods supported by lists.
Examples:
Solutions:
The extend method adds elements from another list, or other iterable:
>>> a = [11, 22, 33, 44, ] >>> b = [55, 66] >>> a.extend(b) >>> a [11, 22, 33, 44, 55, 66]
Use the append method on a list to add/append an item to the end of a list:
>>> a = ['aa', 11]
>>> a.append('bb')
>>> a.append(22)
>>> a
['aa', 11, 'bb', 22]
The insert method on a list enables us to insert items at a given position in a list:
>>> a = [11, 22, 33, 44, ] >>> a.insert(0, 'aa') >>> a ['aa', 11, 22, 33, 44] >>> a.insert(2, 'bb') >>> a ['aa', 11, 'bb', 22, 33, 44]
But, note that we use append to add items at the end of a list.
The pop method on a list returns the "right-most" item from a list and removes that item from the list:
>>> a = [11, 22, 33, 44, ] >>> >>> b = a.pop() >>> a [11, 22, 33] >>> b 44 >>> b = a.pop() >>> a [11, 22] >>> b 33
Note that the append and pop methods taken together can be used to implement a stack, that is a LIFO (last in first out) data structure.
A list comprehension is a convenient way to produce a list from an iterable (a sequence or other object that can be iterated over).
In its simplest form, a list comprehension resembles the header line of a for statement inside square brackets. However, in a list comprehension, the for statement header is prefixed with an expression and surrounded by square brackets. Here is a template:
[expr(x) for x in iterable]
where:
Here is an example:
>>> a = [11, 22, 33, 44] >>> b = [x * 2 for x in a] >>> b [22, 44, 66, 88]
Exercises:
Given the following list of strings:
names = ['alice', 'bertrand', 'charlene']
produce the following lists: (1) a list of all upper case names; (2) a list of capitalized (first letter upper case);
Given the following function which calculates the factorial of a number:
def t(n):
if n <= 1:
return n
else:
return n * t(n - 1)
and the following list of numbers:
numbers = [2, 3, 4, 5]
create a list of the factorials of each of the numbers in the list.
Solutions:
For our expression in a list comprehension, use the upper and capitalize methods:
>>> names = ['alice', 'bertrand', 'charlene'] >>> [name.upper() for name in names] ['ALICE', 'BERTRAND', 'CHARLENE'] >>> [name.capitalize() for name in names] ['Alice', 'Bertrand', 'Charlene']
The expression in our list comprehension calls the factorial function:
def t(n):
if n <= 1:
return n
else:
return n * t(n - 1)
def test():
numbers = [2, 3, 4, 5]
factorials = [t(n) for n in numbers]
print 'factorials:', factorials
if __name__ == '__main__':
test()
A list comprehension can also contain an if clause. Here is a template:
[expr(x) for x in iterable if pred(x)]
where:
Only values for which the if clause evaluates to true are included in creating the resulting list.
Examples:
>>> a = [11, 22, 33, 44] >>> b = [x * 3 for x in a if x % 2 == 0] >>> b [66, 132]
Exercises:
Given two lists, generate a list of all the strings in the first list that are not in the second list. Here are two sample lists:
names1 = ['alice', 'bertrand', 'charlene', 'daniel'] names2 = ['bertrand', 'charlene']
Solutions:
The if clause of our list comprehension checks for containment in the list names2:
def test():
names1 = ['alice', 'bertrand', 'charlene', 'daniel']
names2 = ['bertrand', 'charlene']
names3 = [name for name in names1 if name not in names2]
print 'names3:', names3
if __name__ == '__main__':
test()
When run, this script prints out the following:
names3: ['alice', 'daniel']
A string is an ordered sequence of characters. Here are a few characteristics of strings:
Create strings with single quotes or double quotes. You can put single quotes inside double quotes and you can put double quotes inside single quotes. You can also escape characters with a backslash.
Exercises:
Solutions:
Create a string with double quotes to include single quotes inside the string:
>>> str1 = "that is jerry's ball"
Create a string enclosed with single quotes in order to include double quotes inside the string:
>>> str1 = 'say "goodbye", bullwinkle'
Take your choice. Escape either the single quotes or the double quotes with a backslash:
>>> str1 = 'say "hello" to jerry\'s mom' >>> str2 = "say \"hello\" to jerry's mom" >>> str1 'say "hello" to jerry\'s mom' >>> str2 'say "hello" to jerry\'s mom'
Triple quotes enable you to create a string that spans multiple lines. Use three single quotes or three double quotes to create a single quoted string.
Examples:
Solutions:
Use triple single quotes or triple double quotes to create multi-line strings:
String1 = '''This string extends
across several lines. And, so it has
end-of-line characters in it.
'''
String2 = """
This string begins and ends with an end-of-line
character. It can have both 'single'
quotes and "double" quotes in it.
"""
def test():
print String1
print String2
if __name__ == '__main__':
test()
Python does not have a distinct character type. In Python, a character is a string of length 1. You can use the ord() and chr() built-in functions to convert from character to integer and back.
Exercises:
Solutions:
The character "a" is a plain string of length 1:
>>> x = 'a'
The integer equivalent of the letter "A":
>>> x = "A" >>> ord(x) 65
You can concatenate strings with the "+" operator.
You can create multiple concatenated copies of a string with the "*" operator.
And, augmented assignment (+= and *=) also work.
Examples:
>>> 'cat' + ' and ' + 'dog' 'cat and dog' >>> '#' * 40 '########################################' >>> >>> s1 = 'flower' >>> s1 += 's' >>> s1 'flowers'
Exercises:
Given these strings:
>>> s1 = 'abcd' >>> s2 = 'efgh'
create a new string composed of the first string followed by (concatenated with) the second.
Create a single string containing 5 copies of the string 'abc'.
Use the multiplication operator to create a "line" of 50 dashes.
Here are the components of a path to a file on the file system: "home", "myusername", "Workdir", "notes.txt". Concatenate these together separating them with the path separator to form a complete path to that file. (Note that if you use the backslash to separate components of the path, you will need to use a double backslash, because the backslash is the escape character in strings.
Solutions:
The plus (+) operator applied to a string can be used to concatenate strings:
>>> s3 = s1 + s2 >>> s3 'abcdefgh'
The multiplication operator (*) applied to a string creates a new string that concatenates a string with itself some number of times:
>>> s1 = 'abc' * 5 >>> s1 'abcabcabcabcabc'
The multiplication operator (*) applied to a string can be used to create a "horizontal divider line":
>>> s1 = '-' * 50 >>> print s1 --------------------------------------------------
The sep member of the os module gives us a platform independent way to construct paths:
>>> import os >>> >>> a = ["home", "myusername", "Workdir", "notes.txt"] >>> path = a[0] + os.sep + a[1] + os.sep + a[2] + os.sep + a[3] >>> path 'home/myusername/Workdir/notes.txt'
And, a more concise solution:
>>> import os >>> a = ["home", "myusername", "Workdir", "notes.txt"] >>> os.sep.join(a) 'home/myusername/Workdir/notes.txt'
Notes:
String support a variety of operations. You can obtain a list of these methods by using the dir() built-in function on any string:
>>> dir("")
['__add__', '__class__', '__contains__', '__delattr__', '__doc__',
'__eq__', '__ge__', '__getattribute__', '__getitem__',
'__getnewargs__', '__getslice__', '__gt__', '__hash__', '__init__',
'__le__', '__len__', '__lt__', '__mod__', '__mul__', '__ne__',
'__new__', '__reduce__', '__reduce_ex__', '__repr__', '__rmod__',
'__rmul__', '__setattr__', '__str__', 'capitalize', 'center',
'count', 'decode', 'encode', 'endswith', 'expandtabs', 'find',
'index', 'isalnum', 'isalpha', 'isdigit', 'islower', 'isspace',
'istitle', 'isupper', 'join', 'ljust', 'lower', 'lstrip',
'partition', 'replace', 'rfind', 'rindex', 'rjust', 'rpartition',
'rsplit', 'rstrip', 'split', 'splitlines', 'startswith', 'strip',
'swapcase', 'title', 'translate', 'upper', 'zfill']
And, you can get help on any specific method by using the help() built-in function. Here is an example:
>>> help("".strip)
Help on built-in function strip:
strip(...)
S.strip([chars]) -> string or unicode
Return a copy of the string S with leading and trailing
whitespace removed.
If chars is given and not None, remove characters in chars instead.
If chars is unicode, S will be converted to unicode before stripping
Exercises:
Solutions:
The rstrip() method strips whitespace off the right side of a string:
>>> s1 = 'some text \n' >>> s1 'some text \n' >>> s2 = s1.rstrip() >>> s2 'some text'
The center(n) method centers a string within a padded string of width n:
>>> s1 = 'Dave' >>> s2 = s1.center(20) >>> s2 ' Dave '
The upper() method produces a new string that converts all alpha characters in the original to upper case:
>>> s1 = 'Banana' >>> s1 'Banana' >>> s2 = s1.upper() >>> s2 'BANANA'
The split(sep) method produces a list of strings that are separated by sep in the original string. If sep is omitted, whitespace is treated as the separator:
>>> s1 = """how does it feel ... to be on your own ... no directions known ... like a rolling stone ... """ >>> words = s1.split() >>> words ['how', 'does', 'it', 'feel', 'to', 'be', 'on', 'your', 'own', 'no', 'directions', 'known', 'like', 'a', 'rolling', 'stone']
Note that the split() function in the re (regular expression) module is useful when the separator is more complex than whitespace or a single character.
The join() method concatenates strings from a list of strings to form a single string:
>>> lines = []
>>> lines.append('how does it feel')
>>> lines.append('to be on your own')
>>> lines.append('no directions known')
>>> lines.append('like a rolling stone')
>>> lines
['how does it feel', 'to be on your own', 'no directions known',
'like a rolling stone']
>>> s1 = ''.join(lines)
>>> s2 = ' '.join(lines)
>>> s3 = '\n'.join(lines)
>>> s1
'how does it feelto be on your ownno directions knownlike a rolling stone'
>>> s2
'how does it feel to be on your own no directions known like a rolling stone'
>>> s3
'how does it feel\nto be on your own\nno directions known\nlike a rolling stone'
>>> print s3
how does it feel
to be on your own
no directions known
like a rolling stone
Raw strings give us a convenient way to include the backslash character in a string without escaping (with an additional backslash). Raw strings look like plain literal strings, but are prefixed with an "r" or "R". See String literals http://docs.python.org/reference/lexical_analysis.html#string-literals
Excercises:
Solutions:
We use an "r" prefix to define a raw string:
>>> print 'abc \\ def' abc \ def >>> print r'abc \ def' abc \ def
Unicode strings give us a consistent way to process character data from a variety of character encodings.
Excercises:
Solutions:
We can represent unicode string with either the "u" prefix or with a call to the unicode type:
def exercise1():
a = u'abcd'
print a
b = unicode('efgh')
print b
We convert a string from another character encoding into unicode with the decode() string method:
import sys
def exercise2():
a = 'abcd'.decode('utf-8')
print a
b = 'abcd'.decode(sys.getdefaultencoding())
print b
We can convert a unicode string to another character encoding with the encode() string method:
import sys
def exercise3():
a = u'abcd'
print a.encode('utf-8')
print a.encode(sys.getdefaultencoding())
Here are two ways to check the type of a string:
import types
def exercise4():
a = u'abcd'
print type(a) is types.UnicodeType
print type(a) is type(u'')
We can encode unicode characters in a string in several ways, for example, (1) by defining a utf-8 string and converting it to unicode or (2) defining a string with an embedded unicode character or (3) concatenating a unicode characher into a string:
def exercise5():
utf8_string = 'Ivan Krsti\xc4\x87'
unicode_string = utf8_string.decode('utf-8')
print unicode_string.encode('utf-8')
print len(utf8_string)
print len(unicode_string)
unicode_string = u'aa\u0107bb'
print unicode_string.encode('utf-8')
unicode_string = 'aa' + unichr(263) + 'bb'
print unicode_string.encode('utf-8')
Guidance for use of encodings and unicode:
Convert/decode from an external encoding to unicode early:
my_source_string.decode(encoding)
Do your work (Python processing) in unicode.
Convert/encode to an external encoding late (for example, just before saving to an external file):
my_unicode_string.encode(encoding)
For more information, see:
A dictionary is an un-ordered collection of key-value pairs.
A dictionary has a length, specifically the number of key-value pairs.
A dictionary provides fast look up by key.
The keys must be immutable object types.
Curley brackets are used to represent a dictionary. Each pair in the dictionary is represented by a key and value separated by a colon. Multiple pairs are separated by comas. For example, here is an empty dictionary and several dictionaries containing key/value pairs:
In [4]: d1 = {}
In [5]: d2 = {'width': 8.5, 'height': 11}
In [6]: d3 = {1: 'RED', 2: 'GREEN', 3: 'BLUE', }
In [7]: d1
Out[7]: {}
In [8]: d2
Out[8]: {'height': 11, 'width': 8.5}
In [9]: d3
Out[9]: {1: 'RED', 2: 'GREEN', 3: 'BLUE'}
Notes:
Exercises:
Define a dictionary that has the following key-value pairs:
Key Value Eggplant Purple Tomato Red Parsley Green Lemon Yellow Pepper Green, Red, Yellow
Define a dictionary to represent the "enum" days of the week: Sunday, Monday, Tuesday, ...
Solutions:
A dictionary whose keys and values are strings can be used to represent this table:
vegetables = {
'Eggplant': 'Purple',
'Tomato': 'Red',
'Parsley': 'Green',
'Lemon': 'Yellow',
'Pepper': 'Green',
}
Note that the open curly bracket enables us to continue this statement across multiple lines without using a backslash.
We might use strings for the names of the days of the week as keys:
DAYS = {
'Sunday': 1,
'Monday': 2,
'Tuesday': 3,
'Wednesday': 4,
'Thrusday': 5,
'Friday': 6,
'Saturday': 7,
}
Dictionaries support the following "operators":
Length -- len(d) returns the number of pairs in a dictionary.
Indexing -- You can both set and get the value associated with a key by using the indexing operator [ ]. Examples:
In [12]: d3[2] Out[12]: 'GREEN' In [13]: d3[0] = 'WHITE' In [14]: d3[0] Out[14]: 'WHITE'
Test for key -- The in operator tests for the existence of a key in a dictionary. Example:
In [6]: trees = {'poplar': 'deciduous', 'cedar': 'evergreen'}
In [7]: if 'cedar' in trees:
...: print 'The cedar is %s' % (trees['cedar'], )
...:
The cedar is evergreen
Exercises:
Create an empty dictionary, then use the indexing operator [ ] to in sert the following name-value pairs:
"red" -- "255:0:0" "green" -- "0:255:0" "blue" -- "0:0:255"
Print out the number of items in your dictionary.
Solutions:
We can use "[ ]" to set the value of a key in a dictionary:
def test():
colors = {}
colors["red"] = "255:0:0"
colors["green"] = "0:255:0"
colors["blue"] = "0:0:255"
print 'The value of red is "%s"' % (colors['red'], )
print 'The colors dictionary contains %d items.' % (len(colors), )
test()
When we run this, we see:
The value of red is "255:0:0" The colors dictionary contains 3 items.
The len() built-in function gives us the number of items in a dictionary. See the previous solution for an example of this.
Here is a table that describes the methods applicable to dictionarys:
| Operation | Result |
|---|---|
| len(a) | the number of items in a |
| a[k] | the item of a with key k |
| a[k] = v | set a[k] to v |
| del a[k] | remove a[k] from a |
| a.clear() | remove all items from a |
| a.copy() | a (shallow) copy of a |
| k in a | True if a has a key k, else False |
| k not in a | equivalent to not k in a |
| a.has_key(k) | equivalent to k in a, use that form in new code |
| a.items() | a copy of a's list of (key, value) pair |
| a.keys() | a copy of a's list of keys |
| a.update([b]) | updates a with key/value pairs from b, overwriting existing keys, returns None |
| a.fromkeys(seq[, value]) | creates a new dictionary with keys from seq and values set to value |
| a.values() | a copy of a's list of values |
| a.get(k[, x]) | a[k] if k in a, else x) |
| a.setdefault(k[, x]) | a[k] if k in a, else x (also setting it) |
| a.pop(k[, x]) | a[k] if k in a, else x (and remove k) (8) |
| a.popitem() | remove and return an arbitrary (key, value) pair |
| a.iteritems() | return an iterator over (key, value) pairs |
| a.iterkeys() | return an iterator over the mapping's keys |
| a.itervalues() | return an iterator over the mapping's values |
You can also find this table at the standard documentation Web site in the "Python Library Reference": Mapping Types -- dict http://docs.python.org/lib/typesmapping.html
Exercises:
Solutions:
We can use the d.items() method to retrieve a list of tuples containing key-value pairs, then use unpacking to capture the key and value:
Vegetables = {
'Eggplant': 'Purple',
'Tomato': 'Red',
'Parsley': 'Green',
'Lemon': 'Yellow',
'Pepper': 'Green',
}
def test():
for key, value in Vegetables.items():
print 'key:', key, ' value:', value
test()
We retrieve a list of keys with the keys() method, the sort it with the list sort() method:
Vegetables = {
'Eggplant': 'Purple',
'Tomato': 'Red',
'Parsley': 'Green',
'Lemon': 'Yellow',
'Pepper': 'Green',
}
def test():
keys = Vegetables.keys()
keys.sort()
for key in keys:
print 'key:', key, ' value:', Vegetables[key]
test()
To test for the existence of a key in a dictionary, we can use either the in operator (preferred) or the d.has_key() method (old style):
Vegetables = {
'Eggplant': 'Purple',
'Tomato': 'Red',
'Parsley': 'Green',
'Lemon': 'Yellow',
'Pepper': 'Green',
}
def test():
if 'Eggplant' in Vegetables:
print 'we have %s egplants' % Vegetables['Eggplant']
if 'Banana' not in Vegetables:
print 'yes we have no bananas'
if Vegetables.has_key('Parsley'):
print 'we have leafy, %s parsley' % Vegetables['Parsley']
test()
Which will print out:
we have Purple egplants yes we have no bananas we have leafy, Green parsley
A Python file object represents a file on a file system.
A file object open for reading a text file is iterable. When we iterate over it, it produces the lines in the file.
A file may be opened in these modes:
The open() built-in function is used to create a file object. For example, the following code (1) opens a file for writing, then (2) for reading, then (3) for appending, and finally (4) for reading again:
def test(infilename):
# 1. Open the file in write mode, which creates the file.
outfile = open(infilename, 'w')
outfile.write('line 1\n')
outfile.write('line 2\n')
outfile.write('line 3\n')
outfile.close()
# 2. Open the file for reading.
infile = open(infilename, 'r')
for line in infile:
print 'Line:', line.rstrip()
infile.close()
# 3. Open the file in append mode, and add a line to the end of
# the file.
outfile = open(infilename, 'a')
outfile.write('line 4\n')
outfile.close()
print '-' * 40
# 4. Open the file in read mode once more.
infile = open(infilename, 'r')
for line in infile:
print 'Line:', line.rstrip()
infile.close()
test('tmp.txt')
Exercises:
Solutions:
Use the open() built-in function to open the file and create a file object. Use the read() method on the file object to read the entire file. Use the split() or splitlines() methods to split the file into lines:
>>> infile = open('tmp.txt', 'r')
>>> content = infile.read()
>>> infile.close()
>>> lines = content.splitlines()
>>> print lines
['line 1', 'line 2', 'line 3', '']
The f.readlines() method returns a list of lines in a file:
>>> infile = open('tmp.txt', 'r')
>>> lines = infile.readlines()
>>> infile.close()
>>> print lines
['line 1\n', 'line 2\n', 'line 3\n']
Since a file object (open for reading) is itself an iterator, we can iterate over it in a for statement:
"""
Test iteration over a text file.
Usage:
python test.py in_file_name
"""
import sys
def test(infilename):
infile = open(infilename, 'r')
for line in infile:
# Strip off the new-line character and any whitespace on
# the right.
line = line.rstrip()
# Print only non-blank lines.
if line:
print line
infile.close()
def main():
args = sys.argv[1:]
if len(args) != 1:
print __doc__
sys.exit(1)
infilename = args[0]
test(infilename)
if __name__ == '__main__':
main()
Notes:
None is a singleton. There is only one instance of None. Use this value to indicate the absence of any other "real" value.
Test for None with the identity operator is.
Exercises:
Solutions:
The identity operators is and is not can be used to test for None:
>>> a = [11, None, 'abc', None, {}]
>>> a
[11, None, 'abc', None, {}]
>>> count = 0
>>> for item in a:
... if item is None:
... count += 1
...
>>>
>>> print count
2
Python has the two boolean values True and False. Many comparison operators return True and False.
Examples:
What value is returned by 3 > 2?
Answer: The boolean value True.
Given these variable definitions:
x = 3 y = 4 z = 5
What does the following print out:
print y > x and z > y
Answer -- Prints out "True"
The assignment statement uses the assignment operator =.
The assignment statement is a binding statement: it binds a value to a name within a namespace.
Exercises:
Solutions:
1. The = operator is an assignment statement that binds a value to a variable:
>>> vegetable = "eggplant"
There is also augmented assignment using the operators +=, -=, *=, /=, etc.
Exercises:
Solutions:
The += operator increments the value of an integer:
>>> count = 0 >>> count += 1 >>> count 1 >>> count += 1 >>> count 2
The += operator appends characters to the end of a string:
>>> buffer = 'abcde' >>> buffer += 'fgh' >>> buffer 'abcdefgh'
The += operator appends items in one list to another:
In [20]: a = [11, 22, 33] In [21]: b = [44, 55] In [22]: a += b In [23]: a Out[23]: [11, 22, 33, 44, 55]
The -= operator decrements the value of an integer:
>>> count = 5 >>> count 5 >>> count -= 1 >>> count 4
You can also assign a value to (1) an element of a list, (2) an item in a dictionary, (3) an attribute of an object, etc.
Exercises:
Create a list of three items, then assign a new value to the 2nd element in the list.
Create a dictionary, then assign values to the keys "vegetable" and "fruit" in that dictionary.
Use the following code to create an instance of a class:
class A(object):
pass
a = A()
Then assign values to an attribue named category in that instance.
Solutions:
Assignment with the indexing operator [] assigns a value to an element in a list:
>>> trees = ['pine', 'oak', 'elm'] >>> trees ['pine', 'oak', 'elm'] >>> trees[1] = 'cedar' >>> trees ['pine', 'cedar', 'elm']
Assignment with the indexing operator [] assigns a value to an item (a key-value pair) in a dictionary:
>>> foods = {}
>>> foods
{}
>>> foods['vegetable'] = 'green beans'
>>> foods['fruit'] = 'nectarine'
>>> foods
{'vegetable': 'green beans', 'fruit': 'nectarine'}
Assignment along with the dereferencing operator . (dot) enables us to assign a value to an attribute of an object:
>>> class A(object):
... pass
...
>>> a = A()
>>> a.category = 25
>>> a.__dict__
{'category': 25}
>>> a.category
25
Warning: Be aware that the print statement will go away in Python version 3.0. It will be replaced by the built-in print() function.
The print statement sends output to standard output. It provides a somewhat more convenient way of producing output than using sys.stdout.write().
The print statement takes a series of zero or more objects separated by commas. Zero objects produces a blank line.
The print statement normally adds a newline at the end of its output. To eliminate that, add a comma at the end.
Exercises:
Solutions:
We can print a literal string:
>>> print 'Hello, there' Hello, there
We can print literals and the value of variables:
>>> description = 'cute' >>> print 'I am a', description, 'kid.' I am a cute kid.
The string formatting operator gives more control over formatting output:
>>> name = 'Alice' >>> print 'My name is "%s".' % (name, ) My name is "Alice".
The if statement is a compound statement that enables us to conditionally execute blocks of code.
The if statement also has optional elif: and else: clauses.
The condition in an if: or elif: clause can be any Python expression, in other words, something that returns a value (even if that value is None).
In the condition in an if: or elif: clause, the following values count as "false":
All other values count as true.
Exercises:
Given the following list:
>>> bananas = ['banana1', 'banana2', 'banana3',]
Print one message if it is an empty list and another messge if it is not.
Here is one way of defining a Python equivalent of an "enum":
NO_COLOR, RED, GREEN, BLUE = range(4)
Write an if: statement which implements the effect of a "switch" statement in Python. Print out a unique message for each color.
Solutions:
We can test for an empty or non-empty list:
>>> bananas = ['banana1', 'banana2', 'banana3',] >>> if not bananas: ... print 'yes, we have no bananas' ... else: ... print 'yes, we have bananas' ... yes, we have bananas
We can simulate a "switch" statement using if:elif: ...:
NO_COLOR, RED, GREEN, BLUE = range(4)
def test(color):
if color == RED:
print "It's red."
elif color == GREEN:
print "It's green."
elif color == BLUE:
print "It's blue."
def main():
color = BLUE
test(color)
if __name__ == '__main__':
main()
Which, when run prints out the following:
It's blue.
The for: statement is the Python way to iterate over and process the elements of a collection or other iterable.
The basic form of the for: statement is the following:
for X in Y:
statement
o
o
o
where:
Exercises:
Solutions:
The for: statement can iterate over the items in a list:
In [13]: a = [11, 22, 33, ] In [14]: for value in a: ....: print 'value: %d' % value ....: ....: value: 11 value: 22 value: 33
The for: statement can iterate over the characters in a string:
In [16]: b = 'chocolate' In [17]: for chr1 in b: ....: print 'character: %s' % chr1 ....: ....: character: c character: h character: o character: c character: o character: l character: a character: t character: e
Notes:
When we need a sequential index, we can use the range() built-in function to create a list of integers. And, the xrange() built-in function produces an interator that produces a sequence of integers without creating the entire list. To iterate over a large sequence of integers, use xrange() instead of range().
Exercises:
Print out the integers from 0 to 5 in sequence.
Compute the sum of all the integers from 0 to 99999.
Given the following generator function:
import urllib
Urls = [
'http://yahoo.com',
'http://python.org',
'http://gimp.org', # The GNU image manipulation program
]
def walk(url_list):
for url in url_list:
f = urllib.urlopen(url)
stuff = f.read()
f.close()
yield stuff
Write a for: statement that uses this iterator generator to print the lengths of the content at each of the Web pages in that list.
Solutions:
The range() built-in function gives us a sequence to iterate over:
In [5]: for idx in range(6): ...: print 'idx: %d' % idx ...: ...: idx: 0 idx: 1 idx: 2 idx: 3 idx: 4 idx: 5
Since that sequence is a bit large, we'll use xrange() instead of range():
In [8]: count = 0 In [9]: for n in xrange(100000): ...: count += n ...: ...: In [10]: count Out[10]: 4999950000
The for: statement enables us to iterate over iterables as well as collections:
import urllib
Urls = [
'http://yahoo.com',
'http://python.org',
'http://gimp.org', # The GNU image manipulation program
]
def walk(url_list):
for url in url_list:
f = urllib.urlopen(url)
stuff = f.read()
f.close()
yield stuff
def test():
for url in walk(Urls):
print 'length: %d' % (len(url), )
if __name__ == '__main__':
test()
When I ran this script, it prints the following:
length: 9562 length: 16341 length: 12343
If you need an index while iterating over a sequence, consider using the enumerate() built-in function.
Exercises:
Given the following two lists of integers of the same length:
a = [1, 2, 3, 4, 5] b = [100, 200, 300, 400, 500]
Add the values in the first list to the corresponding values in the second list.
Solutions:
The enumerate() built-in function gives us an index and values from a sequence. Since enumerate() gives us an interator that produces a sequence of two-tuples, we can unpack those tuples into index and value variables in the header line of the for statement:
In [13]: a = [1, 2, 3, 4, 5] In [14]: b = [100, 200, 300, 400, 500] In [15]: In [16]: for idx, value in enumerate(a): ....: b[idx] += value ....: ....: In [17]: b Out[17]: [101, 202, 303, 404, 505]
A while: statement executes a block of code repeatedly as long as a condition is true.
Here is a template for the while: statement:
while condition:
statement
o
o
o
Where:
Exercises:
Solutions:
A while: loop with an index variable can be used to modify each element of a list:
def test_while():
numbers = [11, 22, 33, 44, ]
print 'before: %s' % (numbers, )
idx = 0
while idx < len(numbers):
numbers[idx] *= 2
idx += 1
print 'after: %s' % (numbers, )
But, notice that this task is easier using the for: statement and the built-in enumerate() function:
def test_for():
numbers = [11, 22, 33, 44, ]
print 'before: %s' % (numbers, )
for idx, item in enumerate(numbers):
numbers[idx] *= 2
print 'after: %s' % (numbers, )
The continue statement skips the remainder of the statements in the body of a loop and starts immediately at the top of the loop again.
A break statement in the body of a loop terminates the loop. It exits from the immediately containing loop.
break and continue can be used in both for: and while: statements.
Exercises:
Solutions:
The continue statement enables us to "skip" items that satisfy a condition or test:
def test():
numbers = [11, 22, 33, 44, 55, 66, ]
print 'before: %s' % (numbers, )
for idx, item in enumerate(numbers):
if item % 2 != 0:
continue
numbers[idx] *= 3
print 'after: %s' % (numbers, )
test()
The break statement enables us to exit from a loop when we find a zero:
def test():
numbers = [11, 22, 33, 0, 44, 55, 66, ]
print 'numbers: %s' % (numbers, )
sum = 0
for item in numbers:
if item == 0:
break
sum += item
print 'sum: %d' % (sum, )
test()
The try:except: statement enables us to catch an exception that is thrown from within a block of code, or from code called from any depth withing that block.
The raise statement enables us to throw an exception.
An exception is a class or an instance of an exception class. If an exception is not caught, it results in a traceback and termination of the program.
There is a set of standard exceptions. You can learn about them here: Built-in Exceptions -- http://docs.python.org/lib/module-exceptions.html.
You can define your own exception classes. To do so, create an empty subclass of the class Exception. Defining your own exception will enable you (or others) to throw and then catch that specific exception type while ignore others exceptions.
Exercises:
Write a try:except: statement that attempts to open a file for reading and catches the exception thrown when the file does not exist.
Question: How do you find out the name of the exception that is thrown for an input/output error such as the failure to open a file?
Define an exception class. Then write a try:except: statement in which you throw and catch that specific exception.
Define an exception class and use it to implement a multi-level break from an inner loop, by-passing an outer loop.
Solutions:
Use the Python interactive interpreter to learn the exception type thrown when a I/O error occurs. Example:
>>> infile = open('xx_nothing__yy.txt', 'r')
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
IOError: [Errno 2] No such file or directory: 'xx_nothing__yy.txt'
>>>
In this case, the exception type is IOError.
Now, write a try:except: block which catches that exception:
def test():
infilename = 'nothing_noplace.txt'
try:
infile = open(infilename, 'r')
for line in infile:
print line
except IOError, exp:
print 'cannot open file "%s"' % infilename
test()
We define a exception class as a sub-class of class Exception, then throw it (with the raise statement) and catch it (with a try:except: statement):
class SizeError(Exception):
pass
def test_exception(size):
try:
if size <= 0:
raise SizeError, 'size must be greater than zero'
# Produce a different error to show that it will not be caught.
x = y
except SizeError, exp:
print '%s' % (exp, )
print 'goodbye'
def test():
test_exception(-1)
print '-' * 40
test_exception(1)
test()
When we run this script, it produces the following output:
$ python workbook027.py
size must be greater than zero
goodbye
----------------------------------------
Traceback (most recent call last):
File "workbook027.py", line 20, in <module>
test()
File "workbook027.py", line 18, in test
test_exception(1)
File "workbook027.py", line 10, in test_exception
x = y
NameError: global name 'y' is not defined
Notes:
We define a sub-class of of class Exception, then raise it in an inner loop and catch it outside of an outer loop:
class BreakException1(Exception):
pass
def test():
a = [11, 22, 33, 44, 55, 66, ]
b = [111, 222, 333, 444, 555, 666, ]
try:
for x in a:
print 'outer -- x: %d' % x
for y in b:
if x > 22 and y > 444:
raise BreakException1('leaving inner loop')
print 'inner -- y: %d' % y
print 'outer -- after'
print '-' * 40
except BreakException1, exp:
print 'out of loop -- exp: %s' % exp
test()
Here is what this prints out when run:
outer -- x: 11 inner -- y: 111 inner -- y: 222 inner -- y: 333 inner -- y: 444 inner -- y: 555 inner -- y: 666 outer -- after ---------------------------------------- outer -- x: 22 inner -- y: 111 inner -- y: 222 inner -- y: 333 inner -- y: 444 inner -- y: 555 inner -- y: 666 outer -- after ---------------------------------------- outer -- x: 33 inner -- y: 111 inner -- y: 222 inner -- y: 333 inner -- y: 444 out of loop -- exp: leaving inner loop
A function has these characteristics:
A function is defined with the def: statement. Here is a simple example/template:
def function_name(arg1, arg2):
local_var1 = arg1 + 1
local_var2 = arg2 * 2
return local_var1 + local_var2
And, here is an example of calling this function:
result = function_name(1, 2)
Here are a few notes of explanation:
Exercises:
Solutions:
The return statement enables us to return a value from a function:
def list_sum(values):
sum = 0
for value in values:
sum += value
return sum
def test():
a = [11, 22, 33, 44, ]
print list_sum(a)
if __name__ == '__main__':
test()
You can provide a default value for an argument to a function.
If you do, that argument is optional (when the function is called).
Here are a few things to learn about optional arguments:
Provide a default value with an equal sign and a value. Example:
def sample_func(arg1, arg2, arg3='empty', arg4=0):
All parameters with default values must be after (to the right of) normal parameters.
Do not use a mutable object as a default value. Because the def: statement is evaluated only once and not each time the function is called, the mutable object might be shared across multiple calls to the function. Do not do this:
def sample_func(arg1, arg2=[]):
Instead, do this:
def sample_func(arg1, arg2=None):
if arg2 is None:
arg2 = []
Here is an example that illustrates how this might go wrong:
def adder(a, b=[]):
b.append(a)
return b
def test():
print adder('aaa')
print adder('bbb')
print adder('ccc')
test()
Which, when executed, displays the following:
['aaa'] ['aaa', 'bbb'] ['aaa', 'bbb', 'ccc']
Exercises:
Solutions:
We can pass a file as we would any other object. And, we can use a newline character as a default parameter value:
import sys
def writer(outfile, msg='\n'):
outfile.write(msg)
def test():
writer(sys.stdout, 'aaaaa\n')
writer(sys.stdout)
writer(sys.stdout, 'bbbbb\n')
test()
When run from the command line, this prints out the following:
aaaaa bbbbb
In this solution we are careful not to use a mutable object as a default value:
def add_to_dict(name, value, dic=None):
if dic is None:
dic = {}
dic[name] = value
return dic
def test():
dic1 = {'albert': 'cute', }
print add_to_dict('barry', 'funny', dic1)
print add_to_dict('charlene', 'smart', dic1)
print add_to_dict('darryl', 'outrageous')
print add_to_dict('eddie', 'friendly')
test()
If we run this script, we see:
{'barry': 'funny', 'albert': 'cute'}
{'barry': 'funny', 'albert': 'cute', 'charlene': 'smart'}
{'darryl': 'outrageous'}
{'eddie': 'friendly'}
Notes:
A function, like any other object, can be passed as an argument to a function. This is due the the fact that almost all (maybe all) objects in Python are "first class objects". A first class object is one which we can:
Exercises:
Write a function that takes three arguments: (1) an input file, (2) an output file, and (3) a filter function:
The above function should read each line in the input text file, pass that line through the filter function, then write that (possibly) transformed line to the output file.
Now, write one or more "filter functions" that can be passed to the function described above.
Solutions:
This script adds or removes comment characters to the lines of a file:
import sys
def filter(infile, outfile, filterfunc):
for line in infile:
line = filterfunc(line)
outfile.write(line)
def add_comment(line):
line = '## %s' % (line, )
return line
def remove_comment(line):
if line.startswith('## '):
line = line[3:]
return line
def main():
filter(sys.stdin, sys.stdout, add_comment)
if __name__ == '__main__':
main()
Running this might produce something like the following (note for MS Windows users: use type instead of cat):
$ cat tmp.txt line 1 line 2 line 3 $ cat tmp.txt | python workbook005.py ## line 1 ## line 2 ## line 3
Additional positional arguments passed to a function that are not specified in the function definition (the def: statement``), are collected in an argument preceded by a single asterisk. Keyword arguments passed to a function that are not specified in the function definition can be collected in a dictionary and passed to an argument preceded by a double asterisk.
Examples:
Solutions:
We use *args and **kwargs to collect extra arguments and extra keyword arguments:
def show_args(x, y=-1, *args, **kwargs):
print '-' * 40
print 'x:', x
print 'y:', y
print 'args:', args
print 'kwargs:', kwargs
def test():
show_args(1)
show_args(x=2, y=3)
show_args(y=5, x=4)
show_args(4, 5, 6, 7, 8)
show_args(11, y=44, a=55, b=66)
test()
Running this script produces the following:
$ python workbook006.py
----------------------------------------
x: 1
y: -1
args: ()
kwargs: {}
----------------------------------------
x: 2
y: 3
args: ()
kwargs: {}
----------------------------------------
x: 4
y: 5
args: ()
kwargs: {}
----------------------------------------
x: 4
y: 5
args: (6, 7, 8)
kwargs: {}
----------------------------------------
x: 11
y: 44
args: ()
kwargs: {'a': 55, 'b': 66}
Notes:
We use args and kwargs to catch and pass on all arguments:
def func1(*args, **kwargs):
print 'args: %s' % (args, )
print 'kwargs: %s' % (kwargs, )
def func2(*args, **kwargs):
print 'before'
func1(*args, **kwargs)
print 'after'
def test():
func2('aaa', 'bbb', 'ccc', arg1='ddd', arg2='eee')
test()
When we run this, it prints the following:
before
args: ('aaa', 'bbb', 'ccc')
kwargs: {'arg1': 'ddd', 'arg2': 'eee'}
after
Notes:
In a function definition, arguments must appear in the following order, from left to right:
In a function call, arguments must appear in the following order, from left to right:
If the arguments and return value of a function satisfy some description, then we can say that the function is polymorphic with respect to that description.
If the some of the methods of an object satisfy some description, then we can say that the object is polymorphic with respect to that description.
Basically, what this does is to enable us to use a function or an object anywhere that function satisfies the requirements given by a description.
Exercises:
Solutions:
We can pass a function as an argument to a function:
def fancy(obj):
print 'fancy fancy -- %s -- fancy fancy' % (obj, )
def plain(obj):
print 'plain -- %s -- plain' % (obj, )
def show(func, obj):
func(obj)
def main():
a = {'aa': 11, 'bb': 22, }
show(fancy, a)
show(plain, a)
if __name__ == '__main__':
main()
We can also put functions (function objects) in a data structure (for example, a list), and then pass that data structure to a function:
def fancy(obj):
print 'fancy fancy -- %s -- fancy fancy' % (obj, )
def plain(obj):
print 'plain -- %s -- plain' % (obj, )
Func_list = [fancy, plain, ]
def show(funcs, obj):
for func in funcs:
func(obj)
def main():
a = {'aa': 11, 'bb': 22, }
show(Func_list, a)
if __name__ == '__main__':
main()
Notice that Python supports polymorphism (with or) without inheritance. This type of polymorphism is enabled by what is called duck-typing. For more on this see: Duck typing -- http://en.wikipedia.org/wiki/Duck_typing at Wikipedia.
A recursive function is a function that calls itself.
A recursive function must have a limiting condition, or else it will loop endlessly.
Each recursive call consumes space on the function call stack. Therefore, the number of recursions must have some reasonable upper bound.
Exercises:
Write a recursive function that prints information about each node in the following tree-structure data structure:
Tree = {
'name': 'animals',
'left_branch': {
'name': 'birds',
'left_branch': {
'name': 'seed eaters',
'left_branch': {
'name': 'house finch',
'left_branch': None,
'right_branch': None,
},
'right_branch': {
'name': 'white crowned sparrow',
'left_branch': None,
'right_branch': None,
},
},
'right_branch': {
'name': 'insect eaters',
'left_branch': {
'name': 'hermit thrush',
'left_branch': None,
'right_branch': None,
},
'right_branch': {
'name': 'black headed phoebe',
'left_branch': None,
'right_branch': None,
},
},
},
'right_branch': None,
}
Solutions:
We write a recursive function to walk the whole tree. The recursive function calls itself to process each child of a node in the tree:
Tree = {
'name': 'animals',
'left_branch': {
'name': 'birds',
'left_branch': {
'name': 'seed eaters',
'left_branch': {
'name': 'house finch',
'left_branch': None,
'right_branch': None,
},
'right_branch': {
'name': 'white crowned sparrow',
'left_branch': None,
'right_branch': None,
},
},
'right_branch': {
'name': 'insect eaters',
'left_branch': {
'name': 'hermit thrush',
'left_branch': None,
'right_branch': None,
},
'right_branch': {
'name': 'black headed phoebe',
'left_branch': None,
'right_branch': None,
},
},
},
'right_branch': None,
}
Indents = [' ' * idx for idx in range(10)]
def walk_and_show(node, level=0):
if node is None:
return
print '%sname: %s' % (Indents[level], node['name'], )
level += 1
walk_and_show(node['left_branch'], level)
walk_and_show(node['right_branch'], level)
def test():
walk_and_show(Tree)
if __name__ == '__main__':
test()
Notes:
The "iterator protocol" defines what an iterator object must do in order to be usable in an "iterator context" such as a for statement. The iterator protocol is described in the standard library reference: Iterator Types -- http://docs.python.org/lib/typeiter.html
An easy way to define an object that obeys the iterator protocol is to write a generator function. A generator function is a function that contains one or more yield statements. If a function contains at least one yield statement, then that function when called, returns generator iterator, which is an object that obeys the iterator protocol, i.e. it's an iterator object.
Note that in recent versions of Python, yield is an expression. This enables the consumer to communicate back with the producer (the generator iterator). For more on this, see PEP: 342 Coroutines via Enhanced Generators - http://www.python.org/dev/peps/pep-0342/.
Exercises:
Implement a generator function -- The generator produced should yield all values from a list/iterable that satisfy a predicate. It should apply the transforms before return each value. The function takes these arguments:
values -- A list of values. Actually, it could be any iterable.
predicate -- A function that takes a single argument, performs a test on that value, and returns True or False.
transforms -- (optional) A list of functions. Apply each function in this list and returns the resulting value. So, for example, if the function is called like this:
result = transforms([11, 22], p, [f, g])
then the resulting generator might return:
g(f(11))
Implement a generator function that takes a list of URLs as its argument and generates the contents of each Web page, one by one (that is, it produces a sequence of strings, the HTML page contents).
Solutions:
Here is the implementation of a function which contains yield, and, therefore, produces a generator:
#!/usr/bin/env python
"""
filter_and_transform
filter_and_transform(content, test_func, transforms=None)
Return a generator that returns items from content after applying
the functions in transforms if the item satisfies test_func .
Arguments:
1. ``values`` -- A list of values
2. ``predicate`` -- A function that takes a single argument,
performs a test on that value, and returns True or False.
3. ``transforms`` -- (optional) A list of functions. Apply each
function in this list and returns the resulting value. So,
for example, if the function is called like this::
result = filter_and_transforms([11, 22], p, [f, g])
then the resulting generator might return::
g(f(11))
"""
def filter_and_transform(content, test_func, transforms=None):
for x in content:
if test_func(x):
if transforms is None:
yield x
elif isiterable(transforms):
for func in transforms:
x = func(x)
yield x
else:
yield transforms(x)
def isiterable(x):
flag = True
try:
x = iter(x)
except TypeError, exp:
flag = False
return flag
def iseven(n):
return n % 2 == 0
def f(n):
return n * 2
def g(n):
return n ** 2
def test():
data1 = [11, 22, 33, 44, 55, 66, 77, ]
for val in filter_and_transform(data1, iseven, f):
print 'val: %d' % (val, )
print '-' * 40
for val in filter_and_transform(data1, iseven, [f, g]):
print 'val: %d' % (val, )
print '-' * 40
for val in filter_and_transform(data1, iseven):
print 'val: %d' % (val, )
if __name__ == '__main__':
test()
Notes:
The following function uses the urllib module and the yield function to generate the contents of a sequence of Web pages:
import urllib
Urls = [
'http://yahoo.com',
'http://python.org',
'http://gimp.org', # The GNU image manipulation program
]
def walk(url_list):
for url in url_list:
f = urllib.urlopen(url)
stuff = f.read()
f.close()
yield stuff
def test():
for x in walk(Urls):
print 'length: %d' % (len(x), )
if __name__ == '__main__':
test()
When I run this, I see:
$ python generator_example.py length: 9554 length: 16748 length: 11487
Classes provide Python's way to define new data types and to do OOP (object-oriented programming).
If you have made it this far, you have already used lots of objects. You have been a "consumer" of objects and their services. Now, you will learn how to define and implement new kinds of objects. You will become a "producer" of objects. You will define new classes and you will implement the capabilities (methods) of each new class.
A class is defined with the class statement. The first line of a class statement is a header (it has a colon at the end), and it specifies the name of the class being defined and an (optional) superclass. And that header introduces a compound statement: specifically, the body of the class statement which contains indented, nested statements, importantly, def statements that define the methods that can be called on instances of the objects implemented by this class.
Exercises:
Solutions:
A simple instance method can have the self parameter and no others:
class Demo(object):
def show(self):
print 'hello'
def test():
a = Demo()
a.show()
test()
Notes:
A class can define methods with special names. You have seem some of these before. These names begin and end with a double underscore.
One important special name is __init__. It's the constructor for a class. It is called each time an instance of the class is created. Implementing this method in a class gives us a chance to initialize each instance of our class.
Exercises:
Solutions:
The constructor for a class is a method with the special name __init__:
class Plant(object):
def __init__(self, name, size):
self.name = name
self.size = size
def show(self):
print 'name: "%s" size: %d' % (self.name, self.size, )
def test():
p1 = Plant('Eggplant', 25)
p2 = Plant('Tomato', 36)
plants = [p1, p2, ]
for plant in plants:
plant.show()
test()
Notes:
It is a good idea to initialize all instance variables in the constructor. That enables someone reading our code to learn about all the instance variables of a class by looking in a single location:
# simple_node.py
Indents = [' ' * n for n in range(10)]
class Node(object):
def __init__(self, name=None, children=None):
self.name = name
if children is None:
self.children = []
else:
self.children = children
def show_name(self, indent):
print '%sname: "%s"' % (Indents[indent], self.name, )
def show(self, indent=0):
self.show_name(indent)
indent += 1
for child in self.children:
child.show(indent)
def test():
n1 = Node('N1')
n2 = Node('N2')
n3 = Node('N3')
n4 = Node('N4')
n5 = Node('N5', [n1, n2,])
n6 = Node('N6', [n3, n4,])
n7 = Node('N7', [n5, n6,])
n7.show()
if __name__ == '__main__':
test()
Notes:
A subclass extends or specializes a superclass by adding additional methods to the superclass and by overriding methods (with the same name) that already exist in the superclass.
Exercises:
Solutions:
We can import our previous Node script, then implement classes that have the Node class as a superclass:
from simple_node import Node, Indents
class Plant(Node):
def __init__(self, name, height=-1, children=None):
Node.__init__(self, name, children)
self.height = height
def show(self, indent=0):
self.show_name(indent)
print '%sheight: %s' % (Indents[indent], self.height, )
indent += 1
for child in self.children:
child.show(indent)
class Animal(Node):
def __init__(self, name, color='no color', children=None):
Node.__init__(self, name, children)
self.color = color
def show(self, indent=0):
self.show_name(indent)
print '%scolor: "%s"' % (Indents[indent], self.color, )
indent += 1
for child in self.children:
child.show(indent)
def test():
n1 = Animal('scrubjay', 'gray blue')
n2 = Animal('raven', 'black')
n3 = Animal('american kestrel', 'brown')
n4 = Animal('red-shouldered hawk', 'brown and gray')
n5 = Animal('corvid', 'none', [n1, n2,])
n6 = Animal('raptor', children=[n3, n4,])
n7a = Animal('bird', children=[n5, n6,])
n1 = Plant('valley oak', 50)
n2 = Plant('canyon live oak', 40)
n3 = Plant('jeffery pine', 120)
n4 = Plant('ponderosa pine', 140)
n5 = Plant('oak', children=[n1, n2,])
n6 = Plant('conifer', children=[n3, n4,])
n7b = Plant('tree', children=[n5, n6,])
n8 = Node('birds and trees', [n7a, n7b,])
n8.show()
if __name__ == '__main__':
test()
Notes:
Python also supports class-based polymorphism, which was, by the way, demonstrated in the previous example.
Exercises:
Solution:
We implement three simple classes and then create a list of instances of these classes:
class A(object):
def show(self, msg):
print 'class A -- msg: "%s"' % (msg, )
class B(object):
def show(self, msg):
print 'class B -- msg: "%s"' % (msg, )
class C(object):
def show(self, msg):
print 'class C -- msg: "%s"' % (msg, )
def test():
objs = [A(), B(), C(), A(), ]
for idx, obj in enumerate(objs):
msg = 'message # %d' % (idx + 1, )
obj.show(msg)
if __name__ == '__main__':
test()
Notes:
A method in a class can recusively call itself. This is very similar to the way in which we implemented recursive functions -- see: Recursive functions.
Exercises:
Solutions:
We implement a class with three instance variables: (1) name, (2) left branch, and (3) right branch. Then, we implement a show() method that displays the name and calls itself to show the children in each sub-tree:
Indents = [' ' * idx for idx in range(10)]
class AnimalNode(object):
def __init__(self, name, left_branch=None, right_branch=None):
self.name = name
self.left_branch = left_branch
self.right_branch = right_branch
def show(self, level=0):
print '%sname: %s' % (Indents[level], self.name, )
level += 1
if self.left_branch is not None:
self.left_branch.show(level)
if self.right_branch is not None:
self.right_branch.show(level)
Tree = AnimalNode('animals',
AnimalNode('birds',
AnimalNode('seed eaters',
AnimalNode('house finch'),
AnimalNode('white crowned sparrow'),
),
AnimalNode('insect eaters',
AnimalNode('hermit thrush'),
AnimalNode('black headed phoebe'),
),
),
None,
)
def test():
Tree.show()
if __name__ == '__main__':
test()
Instead of using a left branch and a right branch, in this solution we use a list to represent the children of a node:
class AnimalNode(object):
def __init__(self, data, children=None):
self.data = data
if children is None:
self.children = []
else:
self.children = children
def show(self, level=''):
print '%sdata: %s' % (level, self.data, )
level += ' '
for child in self.children:
child.show(level)
Tree = AnimalNode('animals', [
AnimalNode('birds', [
AnimalNode('seed eaters', [
AnimalNode('house finch'),
AnimalNode('white crowned sparrow'),
AnimalNode('lesser gold finch'),
]),
AnimalNode('insect eaters', [
AnimalNode('hermit thrush'),
AnimalNode('black headed phoebe'),
]),
])
])
def test():
Tree.show()
if __name__ == '__main__':
test()
Notes:
A class variable is one whose single value is shared by all instances of the class and, in fact, is shared by all who have access to the class (object).
"Normal" methods are instance methods. An instance method receives the instance as its first argument. A instance method is defined by using the def statement in the body of a class statement.
A class method receives the class as its first argument. A class method is defined by defining a normal/instance method, then using the classmethod built-in function. For example:
class ASimpleClass(object):
description = 'a simple class'
def show_class(cls, msg):
print '%s: %s' % (cls.description , msg, )
show_class = classmethod(show_class)
A static method does not receive anything special as its first argument. A static method is defined by defining a normal/instance method, then using the staticmethod built-in function. For example:
class ASimpleClass(object):
description = 'a simple class'
def show_class(msg):
print '%s: %s' % (ASimpleClass.description , msg, )
show_class = staticmethod(show_class)
In effect, both class methods and static methods are defined by creating a normal (instance) method, then creating a wrapper object (a class method or static method) using the classmethod or staticmethod built-in function.
Exercises:
Solutions:
We use a class variable named instance_count, rather than an instance variable, to keep a running total of instances. Then, we increment that variable each time an instance is created:
class CountInstances(object):
instance_count = 0
def __init__(self, name='-no name-'):
self.name = name
CountInstances.instance_count += 1
def show(self):
print 'name: "%s"' % (self.name, )
def show_instance_count(cls):
print 'instance count: %d' % (cls.instance_count, )
show_instance_count = classmethod(show_instance_count)
def test():
instances = []
instances.append(CountInstances('apple'))
instances.append(CountInstances('banana'))
instances.append(CountInstances('cherry'))
instances.append(CountInstances())
for instance in instances:
instance.show()
CountInstances.show_instance_count()
if __name__ == '__main__':
test()
Notes:
When we run this script, it prints out the following:
name: "apple" name: "banana" name: "cherry" name: "-no name-" instance count: 4
The call to the classmethod built-in function effectively wraps the show_instance_count method in a class method, that is, in a method that takes a class object as its first argument rather than an instance object. To read more about classmethod, go to Built-in Functions -- http://docs.python.org/lib/built-in-funcs.html and search for "classmethod".
A static method takes neither an instance (self) nor a class as its first paramenter. And, static method is created with the staticmethod() built-in function (rather than with the classmethod() built-in):
class CountInstances(object):
instance_count = 0
def __init__(self, name='-no name-'):
self.name = name
CountInstances.instance_count += 1
def show(self):
print 'name: "%s"' % (self.name, )
def show_instance_count():
print 'instance count: %d' % (
CountInstances.instance_count, )
show_instance_count = staticmethod(show_instance_count)
def test():
instances = []
instances.append(CountInstances('apple'))
instances.append(CountInstances('banana'))
instances.append(CountInstances('cherry'))
instances.append(CountInstances())
for instance in instances:
instance.show()
CountInstances.show_instance_count()
if __name__ == '__main__':
test()
A decorator enables us to do what we did in the previous example with a somewhat simpler syntax.
For simple cases, the decorator syntax enables us to do this:
@functionwrapper
def method1(self):
o
o
o
instead of this:
def method1(self):
o
o
o
method1 = functionwrapper(method1)
So, we can write this:
@classmethod
def method1(self):
o
o
o
instead of this:
def method1(self):
o
o
o
method1 = classmethod(method1)
Exercises:
Solutions:
A decorator is an easier and cleaner way to define a class method (or a static method):
class CountInstances(object):
instance_count = 0
def __init__(self, name='-no name-'):
self.name = name
CountInstances.instance_count += 1
def show(self):
print 'name: "%s"' % (self.name, )
@classmethod
def show_instance_count(cls):
print 'instance count: %d' % (cls.instance_count, )
# Note that the following line has been replaced by
# the classmethod decorator, above.
# show_instance_count = classmethod(show_instance_count)
def test():
instances = []
instances.append(CountInstances('apple'))
instances.append(CountInstances('banana'))
instances.append(CountInstances('cherry'))
instances.append(CountInstances())
for instance in instances:
instance.show()
CountInstances.show_instance_count()
if __name__ == '__main__':
test()
Decorators can be used to "wrap" a function with another function.
When implementing a decorator, it is helpful to remember that the following decorator application:
@dec
def func(arg1, arg2):
pass
is equivalent to:
def func(arg1, arg2):
pass
func = dec(func)
Therefore, to implement a decorator, we write a function that returns a function object, since we replace the value originally bound to the function with this new function object. It may be helpful to take the view that we are creating a function that is a wrapper for the original function.
Exercises:
Solutions:
A function that contains and returns an inner function can be used to wrap a function:
def trace(func):
def inner(*args, **kwargs):
print '>>'
func(*args, **kwargs)
print '<<'
return inner
@trace
def func1(x, y):
print 'x:', x, 'y:', y
func2((x, y))
@trace
def func2(content):
print 'content:', content
def test():
func1('aa', 'bb')
test()
Notes:
Decorators can also take arguments.
The following decorator with arguments:
@dec(argA, argB)
def func(arg1, arg2):
pass
is equivalent to:
def func(arg1, arg2):
pass
func = dec(argA, argB)(func)
Because the decorator's arguments are passed to the result of calling the decorator on the decorated function, you may find it useful to implement a decorator with arguments using a function inside a function inside a function.
Exercises:
Solutions:
Implement this decorator that takes arguments with a function containing a nested function which in turn contains a nested function:
def trace(msg):
def inner1(func):
def inner2(*args, **kwargs):
print '>> [%s]' % (msg, )
retval = func(*args, **kwargs)
print '<< [%s]' % (msg, )
return retval
return inner2
return inner1
@trace('tracing func1')
def func1(x, y):
print 'x:', x, 'y:', y
result = func2((x, y))
return result
@trace('tracing func2')
def func2(content):
print 'content:', content
return content * 3
def test():
result = func1('aa', 'bb')
print 'result:', result
test()
Decorators can be "stacked".
The following stacked decorators:
@dec2
@dec1
def func(arg1, arg2, ...):
pass
are equivalent to:
def func(arg1, arg2, ...):
pass
func = dec2(dec1(func))
Exercises:
Solutions:
Reuse your tracing function from the previous exercise, then write a simple decorator that prints a row of dashes:
def trace(msg):
def inner1(func):
def inner2(*args, **kwargs):
print '>> [%s]' % (msg, )
retval = func(*args, **kwargs)
print '<< [%s]' % (msg, )
return retval
return inner2
return inner1
def horizontal_line(func):
def inner(*args, **kwargs):
print '-' * 50
retval = func(*args, **kwargs)
print '-' * 50
return retval
return inner
@trace('tracing func1')
def func1(x, y):
print 'x:', x, 'y:', y
result = func2((x, y))
return result
@horizontal_line
@trace('tracing func2')
def func2(content):
print 'content:', content
return content * 3
def test():
result = func1('aa', 'bb')
print 'result:', result
test()
Once again, a decorator with arguments can be implemented with a function nested inside a function which is nested inside a function. This remains the same whether the decorator is used as a stacked decorator or not. Here is a solution:
def trace(msg):
def inner1(func):
def inner2(*args, **kwargs):
print '>> [%s]' % (msg, )
retval = func(*args, **kwargs)
print '<< [%s]' % (msg, )
return retval
return inner2
return inner1
def horizontal_line(line_chr):
def inner1(func):
def inner2(*args, **kwargs):
print line_chr * 15
retval = func(*args, **kwargs)
print line_chr * 15
return retval
return inner2
return inner1
@trace('tracing func1')
def func1(x, y):
print 'x:', x, 'y:', y
result = func2((x, y))
return result
@horizontal_line('<**>')
@trace('tracing func2')
def func2(content):
print 'content:', content
return content * 3
def test():
result = func1('aa', 'bb')
print 'result:', result
test()
There is more about decorators here:
Definition: iterable (adjective) -- that which can be iterated over.
A good test of whether something is iterable is whether it can be used in a for: statement. For example, if we can write for item in X:, then X is iterable. Here is another simple test:
def isiterable(x):
try:
y = iter(x)
except TypeError, exp:
return False
return True
Some kinds of iterables:
Exercises:
Solutions:
We implement a class that has __iter__() and next() methods:
import urllib
class WebPages(object):
def __init__(self, urls):
self.urls = urls
self.current_index = 0
def __iter__(self):
self.current_index = 0
return self
def next(self):
if self.current_index >= len(self.urls):
raise StopIteration
url = self.urls[self.current_index]
self.current_index += 1
f = urllib.urlopen(url)
content = f.read()
f.close()
return content
def test():
urls = [
'http://www.python.org',
'http://en.wikipedia.org/',
'http://en.wikipedia.org/wiki/Python_(programming_language)',
]
pages = WebPages(urls)
for page in pages:
print 'length: %d' % (len(page), )
pages = WebPages(urls)
print '-' * 50
page = pages.next()
print 'length: %d' % (len(page), )
page = pages.next()
print 'length: %d' % (len(page), )
page = pages.next()
print 'length: %d' % (len(page), )
page = pages.next()
print 'length: %d' % (len(page), )
test()
The itertools module in the Python standard library has helpers for iterators: http://docs.python.org/library/itertools.html#module-itertools
Exercises:
SAX -- Parse an XML document with SAX, then show some information (tag, attributes, character data) for each element.
Minidom -- Parse an XML document with minidom, then walk the DOM tree and show some information (tag, attributes, character data) for each element.
Here is a sample XML document that you can use for input:
<?xml version="1.0"?>
<people>
<person id="1" value="abcd" ratio="3.2">
<name>Alberta</name>
<interest>gardening</interest>
<interest>reading</interest>
<category>5</category>
</person>
<person id="2">
<name>Bernardo</name>
<interest>programming</interest>
<category></category>
<agent>
<firstname>Darren</firstname>
<lastname>Diddly</lastname>
</agent>
</person>
<person id="3" value="efgh">
<name>Charlie</name>
<interest>people</interest>
<interest>cats</interest>
<interest>dogs</interest>
<category>8</category>
<promoter>
<firstname>David</firstname>
<lastname>Donaldson</lastname>
<client>
<fullname>Arnold Applebee</fullname>
<refid>10001</refid>
</client>
</promoter>
<promoter>
<firstname>Edward</firstname>
<lastname>Eddleberry</lastname>
<client>
<fullname>Arnold Applebee</fullname>
<refid>10001</refid>
</client>
</promoter>
</person>
</people>
ElementTree -- Parse an XML document with ElementTree, then walk the DOM tree and show some information (tag, attributes, character data) for each element.
lxml -- Parse an XML document with lxml, then walk the DOM tree and show some information (tag, attributes, character data) for each element.
Modify document with ElementTree -- Use ElementTree to read a document, then modify the tree. Show the contents of the tree, and then write out the modified document.
XPath -- lxml supports XPath. Use the XPath support in lxml to address each of the following in the above XML instance document:
Solutions:
We can use the SAX support in the Python standard library:
#!/usr/bin/env python
"""
Parse and XML with SAX. Display info about each element.
Usage:
python test_sax.py infilename
Examples:
python test_sax.py people.xml
"""
import sys
from xml.sax import make_parser, handler
class TestHandler(handler.ContentHandler):
def __init__(self):
self.level = 0
def show_with_level(self, value):
print '%s%s' % (' ' * self.level, value, )
def startDocument(self):
self.show_with_level('Document start')
self.level += 1
def endDocument(self):
self.level -= 1
self.show_with_level('Document end')
def startElement(self, name, attrs):
self.show_with_level('start element -- name: "%s"' % (name, ))
self.level += 1
def endElement(self, name):
self.level -= 1
self.show_with_level('end element -- name: "%s"' % (name, ))
def characters(self, content):
content = content.strip()
if content:
self.show_with_level('characters: "%s"' % (content, ))
def test(infilename):
parser = make_parser()
handler = TestHandler()
parser.setContentHandler(handler)
parser.parse(infilename)
def usage():
print __doc__
sys.exit(1)
def main():
args = sys.argv[1:]
if len(args) != 1:
usage()
infilename = args[0]
test(infilename)
if __name__ == '__main__':
main()
The minidom module contains a parse() function that enables us to read an XML document and create a DOM tree:
#!/usr/bin/env python
"""Process an XML document with minidom.
Show the document tree.
Usage:
python minidom_walk.py [options] infilename
"""
import sys
from xml.dom import minidom
def show_tree(doc):
root = doc.documentElement
show_node(root, 0)
def show_node(node, level):
count = 0
if node.nodeType == minidom.Node.ELEMENT_NODE:
show_level(level)
print 'tag: %s' % (node.nodeName, )
for key in node.attributes.keys():
attr = node.attributes.get(key)
show_level(level + 1)
print '- attribute name: %s value: "%s"' % (attr.name,
attr.value, )
if (len(node.childNodes) == 1 and
node.childNodes[0].nodeType == minidom.Node.TEXT_NODE):
show_level(level + 1)
print '- data: "%s"' % (node.childNodes[0].data, )
for child in node.childNodes:
count += 1
show_node(child, level + 1)
return count
def show_level(level):
for x in range(level):
print ' ',
def test():
args = sys.argv[1:]
if len(args) != 1:
print __doc__
sys.exit(1)
docname = args[0]
doc = minidom.parse(docname)
show_tree(doc)
if __name__ == '__main__':
#import pdb; pdb.set_trace()
test()
ElementTree enables us to parse an XML document and create a DOM tree:
#!/usr/bin/env python
"""Process an XML document with elementtree.
Show the document tree.
Usage:
python elementtree_walk.py [options] infilename
"""
import sys
from xml.etree import ElementTree as etree
def show_tree(doc):
root = doc.getroot()
show_node(root, 0)
def show_node(node, level):
show_level(level)
print 'tag: %s' % (node.tag, )
for key, value in node.attrib.iteritems():
show_level(level + 1)
print '- attribute -- name: %s value: "%s"' % (key, value, )
if node.text:
text = node.text.strip()
show_level(level + 1)
print '- text: "%s"' % (node.text, )
if node.tail:
tail = node.tail.strip()
show_level(level + 1)
print '- tail: "%s"' % (tail, )
for child in node.getchildren():
show_node(child, level + 1)
def show_level(level):
for x in range(level):
print ' ',
def test():
args = sys.argv[1:]
if len(args) != 1:
print __doc__
sys.exit(1)
docname = args[0]
doc = etree.parse(docname)
show_tree(doc)
if __name__ == '__main__':
#import pdb; pdb.set_trace()
test()
lxml enables us to parse an XML document and create a DOM tree. In fact, since lxml attempts to mimic the ElementTree API, our code is very similar to that in the solution to the ElementTree exercise:
#!/usr/bin/env python
"""Process an XML document with elementtree.
Show the document tree.
Usage:
python lxml_walk.py [options] infilename
"""
#
# Imports:
import sys
from lxml import etree
def show_tree(doc):
root = doc.getroot()
show_node(root, 0)
def show_node(node, level):
show_level(level)
print 'tag: %s' % (node.tag, )
for key, value in node.attrib.iteritems():
show_level(level + 1)
print '- attribute -- name: %s value: "%s"' % (key, value, )
if node.text:
text = node.text.strip()
show_level(level + 1)
print '- text: "%s"' % (node.text, )
if node.tail:
tail = node.tail.strip()
show_level(level + 1)
print '- tail: "%s"' % (tail, )
for child in node.getchildren():
show_node(child, level + 1)
def show_level(level):
for x in range(level):
print ' ',
def test():
args = sys.argv[1:]
if len(args) != 1:
print __doc__
sys.exit(1)
docname = args[0]
doc = etree.parse(docname)
show_tree(doc)
if __name__ == '__main__':
#import pdb; pdb.set_trace()
test()
We can modify the DOM tree and write it out to a new file:
#!/usr/bin/env python
"""Process an XML document with elementtree.
Show the document tree.
Modify the document tree and then show it again.
Write the modified XML tree to a new file.
Usage:
python elementtree_walk.py [options] infilename outfilename
Options:
-h, --help Display this help message.
Example:
python elementtree_walk.py myxmldoc.xml myotherxmldoc.xml
"""
import sys
import os
import getopt
import time
# Use ElementTree.
from xml.etree import ElementTree as etree
# Or uncomment to use Lxml.
#from lxml import etree
def show_tree(doc):
root = doc.getroot()
show_node(root, 0)
def show_node(node, level):
show_level(level)
print 'tag: %s' % (node.tag, )
for key, value in node.attrib.iteritems():
show_level(level + 1)
print '- attribute -- name: %s value: "%s"' % (key, value, )
if node.text:
text = node.text.strip()
show_level(level + 1)
print '- text: "%s"' % (node.text, )
if node.tail:
tail = node.tail.strip()
show_level(level + 1)
print '- tail: "%s"' % (tail, )
for child in node.getchildren():
show_node(child, level + 1)
def show_level(level):
for x in range(level):
print ' ',
def modify_tree(doc, tag, attrname, attrvalue):
root = doc.getroot()
modify_node(root, tag, attrname, attrvalue)
def modify_node(node, tag, attrname, attrvalue):
if node.tag == tag:
node.attrib[attrname] = attrvalue
for child in node.getchildren():
modify_node(child, tag, attrname, attrvalue)
def test(indocname, outdocname):
doc = etree.parse(indocname)
show_tree(doc)
print '-' * 50
date = time.ctime()
modify_tree(doc, 'person', 'date', date)
show_tree(doc)
write_output = False
if os.path.exists(outdocname):
response = raw_input('Output file (%s) exists. Over-write? (y/n): ' %
outdocname)
if response == 'y':
write_output = True
else:
write_output = True
if write_output:
doc.write(outdocname)
print 'Wrote modified XML tree to %s' % outdocname
else:
print 'Did not write output file.'
def usage():
print __doc__
sys.exit(1)
def main():
args = sys.argv[1:]
try:
opts, args = getopt.getopt(args, 'h', ['help',
])
except:
usage()
for opt, val in opts:
if opt in ('-h', '--help'):
usage()
if len(args) != 2:
usage()
indocname = args[0]
outdocname = args[1]
test(indocname, outdocname)
if __name__ == '__main__':
#import pdb; pdb.set_trace()
main()
Notes:
When we parse and XML document with lxml, each element (node) has an xpath() method.
# test_xpath.py
from lxml import etree
def test():
doc = etree.parse('people.xml')
root = doc.getroot()
print root.xpath("//name/text()")
print root.xpath("//@id")
test()
And, when we run the above code, here is what we see:
$ python test_xpath.py ['Alberta', 'Bernardo', 'Charlie'] ['1', '2', '3']
For more on XPath see: XML Path Language (XPath) -- http://www.w3.org/TR/xpath
You can find information about database programming in Python here: Database Programming -- http://wiki.python.org/moin/DatabaseProgramming/.
For database access we use the Python Database API. You can find information about it here: Python Database API Specification v2.0 -- http://www.python.org/dev/peps/pep-0249/.
To use the database API we do the following:
Our examples use the gadfly database, which is written in Python. If you want to use gadfly, you can find it here: http://gadfly.sourceforge.net/. gadfly is a reasonable choice if you want an easy to use database on your local machine.
Another reasonable choice for a local database is sqlite3, which is in the Python standard library. Here is a descriptive quote from the SQLite Web site:
"SQLite is a software library that implements a self-contained, serverless, zero-configuration, transactional SQL database engine. SQLite is the most widely deployed SQL database engine in the world. The source code for SQLite is in the public domain."
You can learn about it here:
If you want or need to use another, enterprise class database, for example PostgreSQL, MySQL, Oracle, etc., you will need an interface module for your specific database. You can find information about database interface modules here: Database interfaces -- http://wiki.python.org/moin/DatabaseInterfaces
Excercises:
Solutions:
We can execute a SQL query and then retrieve all the rows with fetchall():
import gadfly
def test():
connection = gadfly.connect("dbtest1", "plantsdbdir")
cur = connection.cursor()
cur.execute('select * from plantsdb order by p_name')
rows = cur.fetchall()
for row in rows:
print '2. row:', row
connection.close()
test()
The cursor itself is an iterator. It iterates over the rows returned by a query. So, we execute a SQL query and then we use the cursor in a for: statement:
import gadfly
def test():
connection = gadfly.connect("dbtest1", "plantsdbdir")
cur = connection.cursor()
cur.execute('select * from plantsdb order by p_name')
for row in cur:
print row
connection.close()
test()
The description attribute in the cursor is a container that has an item describing each field:
import gadfly
def test():
cur.execute('select * from plantsdb order by p_name')
for field in cur.description:
print 'field:', field
rows = cur.fetchall()
for row in rows:
for idx, field in enumerate(row):
content = '%s: "%s"' % (cur.description[idx][0], field, )
print content,
print
connection.close()
test()
Notes:
The solutions using sqlite3 are very similar to those using gadfly. For information on sqlite3, see: sqlite3 — DB-API 2.0 interface for SQLite databases http://docs.python.org/library/sqlite3.html#module-sqlite3.
#!/usr/bin/env python
"""
Perform operations on sqlite3 (plants) database.
Usage:
python py_db_api.py command [arg1, ... ]
Commands:
create -- create new database.
show -- show contents of database.
add -- add row to database. Requires 3 args (name, descrip, rating).
delete - remove row from database. Requires 1 arg (name).
Examples:
python test1.py create
python test1.py show
python test1.py add crenshaw "The most succulent melon" 10
python test1.py delete lemon
"""
import sys
import sqlite3
Values = [
('lemon', 'bright and yellow', '7'),
('peach', 'succulent', '9'),
('banana', 'smooth and creamy', '8'),
('nectarine', 'tangy and tasty', '9'),
('orange', 'sweet and tangy', '8'),
]
Field_defs = [
'p_name varchar',
'p_descrip varchar',
#'p_rating integer',
'p_rating varchar',
]
def createdb():
connection = sqlite3.connect('sqlite3plantsdb')
cursor = connection.cursor()
q1 = "create table plantsdb (%s)" % (', '.join(Field_defs))
print 'create q1: %s' % q1
cursor.execute(q1)
q1 = "create index index1 on plantsdb(p_name)"
cursor.execute(q1)
q1 = "insert into plantsdb (p_name, p_descrip, p_rating) values ('%s', '%s', %s)"
for spec in Values:
q2 = q1 % spec
print 'q2: "%s"' % q2
cursor.execute(q2)
connection.commit()
showdb1(cursor)
connection.close()
def showdb():
connection, cursor = opendb()
showdb1(cursor)
connection.close()
def showdb1(cursor):
cursor.execute("select * from plantsdb order by p_name")
hr()
description = cursor.description
print description
print 'description:'
for rowdescription in description:
print ' %s' % (rowdescription, )
hr()
rows = cursor.fetchall()
print rows
print 'rows:'
for row in rows:
print ' %s' % (row, )
hr()
print 'content:'
for row in rows:
descrip = row[1]
name = row[0]
rating = '%s' % row[2]
print ' %s%s%s' % (
name.ljust(12), descrip.ljust(30), rating.rjust(4), )
def addtodb(name, descrip, rating):
try:
rating = int(rating)
except ValueError, exp:
print 'Error: rating must be integer.'
return
connection, cursor = opendb()
cursor.execute("select * from plantsdb where p_name = '%s'" % name)
rows = cursor.fetchall()
if len(rows) > 0:
ql = "update plantsdb set p_descrip='%s', p_rating='%s' where p_name='%s'" % (
descrip, rating, name, )
print 'ql:', ql
cursor.execute(ql)
connection.commit()
print 'Updated'
else:
cursor.execute("insert into plantsdb values ('%s', '%s', '%s')" % (
name, descrip, rating))
connection.commit()
print 'Added'
showdb1(cursor)
connection.close()
def deletefromdb(name):
connection, cursor = opendb()
cursor.execute("select * from plantsdb where p_name = '%s'" % name)
rows = cursor.fetchall()
if len(rows) > 0:
cursor.execute("delete from plantsdb where p_name='%s'" % name)
connection.commit()
print 'Plant (%s) deleted.' % name
else:
print 'Plant (%s) does not exist.' % name
showdb1(cursor)
connection.close()
def opendb():
connection = sqlite3.connect("sqlite3plantsdb")
cursor = connection.cursor()
return connection, cursor
def hr():
print '-' * 60
def usage():
print __doc__
sys.exit(1)
def main():
args = sys.argv[1:]
if len(args) < 1:
usage()
cmd = args[0]
if cmd == 'create':
if len(args) != 1:
usage()
createdb()
elif cmd == 'show':
if len(args) != 1:
usage()
showdb()
elif cmd == 'add':
if len(args) < 4:
usage()
name = args[1]
descrip = args[2]
rating = args[3]
addtodb(name, descrip, rating)
elif cmd == 'delete':
if len(args) < 2:
usage()
name = args[1]
deletefromdb(name)
else:
usage()
if __name__ == '__main__':
main()
There is support for parsing and generating CSV files in the Python standard library. See: csv — CSV File Reading and Writing http://docs.python.org/library/csv.html#module-csv.
Exercises:
Read a CSV file and print the fields in columns. Here is a sample file to use as input:
# name description rating Lemon,Bright yellow and tart,5 Eggplant,Purple and shiny,6 Tangerine,Succulent,8
Solutions:
Use the CSV module in the Python standard library to read a CSV file:
"""
Read a CSV file and print the contents in columns.
"""
import csv
def test(infilename):
infile = open(infilename)
reader = csv.reader(infile)
print '==== =========== ======'
print 'Name Description Rating'
print '==== =========== ======'
for fields in reader:
if len(fields) == 3:
line = '%s %s %s' % (fields[0].ljust(20),
fields[1].ljust(40), fields[2].ljust(4))
print line
infile.close()
def main():
infilename = 'csv_report.csv'
test(infilename)
if __name__ == '__main__':
main()
And, when run, here is what it displays:
==== =========== ====== Name Description Rating ==== =========== ====== Lemon Bright yellow and tart 5 Eggplant Purple and shiny 6 Tangerine Succulent 8
YAML is a structured text data representation format. It uses indentation to indicate nesting. Here is a description from the YAML Web site:
"YAML: YAML Ain't Markup Language
"What It Is: YAML is a human friendly data serialization standard for all programming languages."
You can learn more about YAML and PyYAML here:
Exercises:
Read the following sample YAML document. Print out the information in it:
american: - Boston Red Sox - Detroit Tigers - New York Yankees national: - New York Mets - Chicago Cubs - Atlanta Braves
Load the YAML data used in the previous exercise, then make a modification (for example, add "San Francisco Giants" to the National League), then dump the modified data to a new file.
Solutions:
Printing out information from YAML is as "simple" as printing out a Python data structure. In this solution, we use the pretty printer from the Python standard library:
import yaml
import pprint
def test():
infile = open('test1.yaml')
data = yaml.load(infile)
infile.close()
pprint.pprint(data)
test()
We could, alternatively, read in and then "load" from a string:
import yaml
import pprint
def test():
infile = open('test1.yaml')
data_str = infile.read()
infile.close()
data = yaml.load(data_str)
pprint.pprint(data)
test()
The YAML dump() function enables us to dump data to a file:
import yaml
import pprint
def test():
infile = open('test1.yaml', 'r')
data = yaml.load(infile)
infile.close()
data['national'].append('San Francisco Giants')
outfile = open('test1_new.yaml', 'w')
yaml.dump(data, outfile)
outfile.close()
test()
Notes:
If we want to produce the standard YAML "block" style rather than the "flow" format, then we could use:
yaml.dump(data, outfile, default_flow_style=False)
Here is a quote from Wikipedia entry for Json:
"JSON (pronounced 'Jason'), short for JavaScript Object Notation, is a lightweight computer data interchange format. It is a text-based, human-readable format for representing simple data structures and associative arrays (called objects)."
The Json text representation looks very similar to Python literal representation of Python builtin data types (for example, lists, dictionaries, numbers, and strings).
Learn more about Json and Python support for Json here:
Excercises:
Write a Python script, using your favorite Python Json implementation (for example python-json or simplejson), that dumps the following data structure to a file:
Data = {
'rock and roll':
['Elis', 'The Beatles', 'The Rolling Stones',],
'country':
['Willie Nelson', 'Hank Williams', ]
}
Write a Python script that reads Json data from a file and loads it into Python data structures.
Solutions:
This solution uses simplejson to store a Python data structure encoded as Json in a file:
import simplejson as json
Data = {
'rock and roll':
['Elis', 'The Beatles', 'The Rolling Stones',],
'country':
['Willie Nelson', 'Hank Williams', ]
}
def test():
fout = open('tmpdata.json', 'w')
content = json.dumps(Data)
fout.write(content)
fout.write('\n')
fout.close()
test()
We can read the file into a string, then decode it from Json:
import simplejson as json
def test():
fin = open('tmpdata.json', 'r')
content = fin.read()
fin.close()
data = json.loads(content)
print data
test()
Note that you may want some control over indentation, character encoding, etc. For simplejson, you can learn about that here: simplejson - JSON encoder and decoder -- http://simplejson.googlecode.com/svn/tags/simplejson-2.0.1/docs/index.html.
This section discusses a specific Python tool, specifically a Python code generator that generates Python bindings for XML files.
Thus, this section will help you in the following ways:
Additional information:
If you plan to work through this tutorial, you may find it helpful to look at the sample code that accompanies this tutorial. You can find it in the distribution under:
tutorial/ tutorial/Code/
You can find additional information about generateDS.py here:
That documentation is also included in the distribution.
generateDS.py generates Python data structures (for example, class definitions) from an XML schema document. These data structures represent the elements in an XML document described by the XML schema. generateDS.py also generates parsers that load an XML document into those data structures. In addition, a separate file containing subclasses (stubs) is optionally generated. The user can add methods to the subclasses in order to process the contents of an XML document.
The generated Python code contains:
Each generated class contains the following:
The generated subclass file contains one (sub-)class definition for each data representation class. If the subclass file is used, then the parser creates instances of the subclasses (instead of creating instances of the superclasses). This enables the user to extend the subclasses with "tree walk" methods, for example, that process the contents of the XML file. The user can also generate and extend multiple subclass files which use a single, common superclass file, thus implementing a number of different processes on the same XML document type.
This document introduces the user to generateDS.py and walks the user through several examples that show how to generate Python code and how to use that generated code.
Note: The sample files used below are under the tutorial/Code/ directory.
Use the following to get help:
$ generateDS.py --help
I'll assume that generateDS.py is in a directory on your path. If not, you should do whatever is necessary to make it accessible and executable.
Here is a simple XML schema document:
And, here is how you might generate classes and subclasses that provide data bindings (a Python API) for the definitions in that schema:
$ generateDS.py -o people_api.py -s people_sub.py people.xsd
And, if you want to automatically over-write the generated Python files, use the -f command line flag to force over-write without asking:
$ generateDS.py -f -o people_api.py -s people_sub.py people.xsd
And, to hard-wire the subclass file so that it imports the API module, use the --super command line file. Example:
$ generateDS.py -o people_api.py people.xsd $ generateDS.py -s people_appl1.py --super=people_api people.xsd
Or, do both at the same time with the following:
$ generateDS.py -o people_api.py -s people_appl1.py --super=people_api people.xsd
And, for your second application:
$ generateDS.py -s people_appl2.py --super=people_api people.xsd
If you take a look inside these two "application" files, you will see and import statement like the following:
import ??? as supermod
If you had not used the --super command line option when generating the "application" files, then you could modify that statement yourself. The --super command line option does this for you.
You can also use the The graphical front-end to configure options and save them in a session file, then use that session file with generateDS.py to specify your command line options. For example:
$ generateDS.py --session=test01.session
You can test the generated code by running it. Try something like the following:
$ python people_api.py people.xml
or:
$ python people_appl1.py people.xml
Why does this work? Why can we run the generated code as a Python script? -- If you look at the generated code, down near the end of the file you'll find a main() function that calls a function named parse(). The parse function does the following:
Except for some indentation (ignorable whitespace), this exported XML should be the same as the original XML document. So, that gives you a reasonably thorough test of your generated code.
And, the code in that parse() function gives you a hint of how you might build your own application-specific code that uses the generated API (those generated Python classes).
Now that you have generated code for your data model, you can test it by running it as an application. Suppose that you have an XML instance document people1.xml that satisfies your schema. Then you can parse that instance document and export it (print it out) with something like the following:
$ python people_api.py people1.xml
And, if you have used the --super command line option, as I have above, to connect your subclass file with the superclass (API) file, then you could use the following to do the same thing:
$ python people_appl1.py people1.xml
You may want to merely skim this section for now, then later refer back to it when some of these options are are used later in this tutorial. Also, remember that you can get information about more command line options used by generateDS.py by typing:
$ python generateDS.py --help
and by reading the document http://www.reifywork.com/generateDS.html
Use this option to tell generateDS.py which of the elements defined in your XM schema is the "root" element. The root element is the outer-most (top-level) element in XML instance documents defined by this schema. In effect, this tells your generated modules which element to use as the root element when parsing and exporting documents.
generateDS.py attempts to guess the root element, usually the first element defined in your XML schema. Use this option when that default is not what you want.
There is also a point-and-click way to run generateDS. It enables you to specify the options needed by generateDS.py through a graphical interface, then to run generateDS.py with those options. It also
You can run it, if you have installed generateDS, by typing the following at a command line:
$ generateds_gui.py
After configuring options, you can save those options in a "session" file, which can be loaded later. Look under the File menu for save and load commands and also consider using the "--session" command line option.
Also note that generateDS.py itself supports a "--session" command line option that enables you to run generateDS.py with the options that you specified and saved with the graphical front-end.
generateDS.py generates Python code which, with no modification, will parse and then export an XML document defined by your schema. However, you are likely to want to go beyond that. In many situations you will want to construct a custom application that processes your XML documents using the generated code.
One strategy is to generate a subclass file and to add your application-specific code to that. Generate the subclass file with the "-s" command line flag:
$ generateDS.py -s myapp.py people.xsd
Now add some application-specific code to myapp.py, for example, if you are using the included "people" sample files:
class peopleTypeSub(supermod.people):
def __init__(self, comments=None, person=None, programmer=None,
python_programmer=None, java_programmer=None):
supermod.people.__init__(self, comments, person, programmer, python_programmer,
java_programmer)
def fancyexport(self, outfile):
outfile.write('Starting fancy export')
for person in self.get_person():
person.fancyexport(outfile)
supermod.people.subclass = peopleTypeSub
# end class peopleTypeSub
class personTypeSub(supermod.person):
def __init__(self, vegetable=None, fruit=None, ratio=None, id=None, value=None,
name=None, interest=None, category=None, agent=None, promoter=None,
description=None):
supermod.person.__init__(self, vegetable, fruit, ratio, id, value,
name, interest, category, agent, promoter, description)
def fancyexport(self, outfile):
outfile.write('Fancy person export -- name: %s' %
self.get_name(), )
supermod.person.subclass = personTypeSub
# end class personTypeSub
In this approach you might do things like the following:
Get to know the generated export API by inspecting the generated code in the superclass file. That's the file generated with the "-o" command line flag.
What to look for:
Now, you can import your generated API module, and use it to construct and manipulate objects. Here is an example using code generated with the "people" schema:
import sys
import people_api as api
def test(names):
people = api.peopleType()
for count, name in enumerate(names):
id = '%d' % (count + 1, )
person = api.personType(name=name, id=id)
people.add_person(person)
people.export(sys.stdout, 0)
test(['albert', 'betsy', 'charlie'])
Run this and you might see something like the following:
$ python tmp.py
<people >
<person id="1">
<name>albert</name>
</person>
<person id="2">
<name>betsy</name>
</person>
<person id="3">
<name>charlie</name>
</person>
</people>
Note: You can find examples of the code in this section in these files:
tutorial/Code/upcase_names.py tutorial/Code/upcase_names_appl.py
Here are the relevant, modified subclasses (upcase_names_appl.py):
import people_api as supermod
class peopleTypeSub(supermod.peopleType):
def __init__(self, comments=None, person=None, specialperson=None, programmer=None, python_programmer=None, java_programmer=None):
super(peopleTypeSub, self).__init__(comments, person, specialperson, programmer, python_programmer, java_programmer, )
def upcase_names(self):
for person in self.get_person():
person.upcase_names()
supermod.peopleType.subclass = peopleTypeSub
# end class peopleTypeSub
class personTypeSub(supermod.personType):
def __init__(self, vegetable=None, fruit=None, ratio=None, id=None, value=None, name=None, interest=None, category=None, agent=None, promoter=None, description=None, range_=None, extensiontype_=None):
super(personTypeSub, self).__init__(vegetable, fruit, ratio, id, value, name, interest, category, agent, promoter, description, range_, extensiontype_, )
def upcase_names(self):
self.set_name(self.get_name().upper())
supermod.personType.subclass = personTypeSub
# end class personTypeSub
Notes:
Here is the application itself (upcase_names.py):
import sys
import upcase_names_appl as appl
def create_people(names):
people = appl.peopleTypeSub()
for count, name in enumerate(names):
id = '%d' % (count + 1, )
person = appl.personTypeSub(name=name, id=id)
people.add_person(person)
return people
def main():
names = ['albert', 'betsy', 'charlie']
people = create_people(names)
print 'Before:'
people.export(sys.stdout, 1)
people.upcase_names()
print '-' * 50
print 'After:'
people.export(sys.stdout, 1)
main()
Notes:
And, when you run this mini-application, here is what you might see:
$ python upcase_names.py
Before:
<people >
<person id="1">
<name>albert</name>
</person>
<person id="2">
<name>betsy</name>
</person>
<person id="3">
<name>charlie</name>
</person>
</people>
--------------------------------------------------
After:
<people >
<person id="1">
<name>ALBERT</name>
</person>
<person id="2">
<name>BETSY</name>
</person>
<person id="3">
<name>CHARLIE</name>
</person>
</people>
There are times when you would like to implement a function or method that can perform operations on a variety of members and that needs type information about each member.
You can get help with this by generating your code with the "--member-specs" command line option. When you use this option, generateDS.py add a list or a dictionary containing an item for each member. If you want a list, then use "--member-specs=list", and if you want a dictionary, with member names as keys, then use "--member-specs=dict".
Here is an example -- In this example, we walk the document/instance tree and convert all string simple types to upper case.
Here is a schema (Code/member_specs.xsd):
<?xml version="1.0"?>
<xs:schema xmlns:xs="http://www.w3.org/2001/XMLSchema">
<xs:element name="contact-list" type="contactlistType" />
<xs:complexType name="contactlistType">
<xs:sequence>
<xs:element name="description" type="xs:string" />
<xs:element name="contact" type="contactType" maxOccurs="unbounded" />
</xs:sequence>
<xs:attribute name="locator" type="xs:string" />
</xs:complexType>
<xs:complexType name="contactType">
<xs:sequence>
<xs:element name="first-name" type="xs:string"/>
<xs:element name="last-name" type="xs:string"/>
<xs:element name="interest" type="xs:string" maxOccurs="unbounded" />
<xs:element name="category" type="xs:integer"/>
</xs:sequence>
<xs:attribute name="id" type="xs:integer" />
<xs:attribute name="priority" type="xs:float" />
<xs:attribute name="color-code" type="xs:string" />
</xs:complexType>
</xs:schema>
We generate code with the following command line:
$ generateDS.py -f \ -o member_specs_api.py \ -s member_specs_upper.py \ --super=member_specs_api \ --member-specs=list \ member_specs.xsd
Notes:
And, here is the subclass file (member_specs_upper.py, generated with the "-s" command line option), to which we have added a bit of code that converts any string-type members to upper case. You can think of this module as a special "application" of the generated classes.
#!/usr/bin/env python
#
# member_specs_upper.py
#
#
# Generated Tue Nov 9 15:54:47 2010 by generateDS.py version 2.2a.
#
import sys
import member_specs_api as supermod
etree_ = None
Verbose_import_ = False
( XMLParser_import_none, XMLParser_import_lxml,
XMLParser_import_elementtree
) = range(3)
XMLParser_import_library = None
try:
# lxml
from lxml import etree as etree_
XMLParser_import_library = XMLParser_import_lxml
if Verbose_import_:
print("running with lxml.etree")
except ImportError:
try:
# cElementTree from Python 2.5+
import xml.etree.cElementTree as etree_
XMLParser_import_library = XMLParser_import_elementtree
if Verbose_import_:
print("running with cElementTree on Python 2.5+")
except ImportError:
try:
# ElementTree from Python 2.5+
import xml.etree.ElementTree as etree_
XMLParser_import_library = XMLParser_import_elementtree
if Verbose_import_:
print("running with ElementTree on Python 2.5+")
except ImportError:
try:
# normal cElementTree install
import cElementTree as etree_
XMLParser_import_library = XMLParser_import_elementtree
if Verbose_import_:
print("running with cElementTree")
except ImportError:
try:
# normal ElementTree install
import elementtree.ElementTree as etree_
XMLParser_import_library = XMLParser_import_elementtree
if Verbose_import_:
print("running with ElementTree")
except ImportError:
raise ImportError("Failed to import ElementTree from any known place")
def parsexml_(*args, **kwargs):
if (XMLParser_import_library == XMLParser_import_lxml and
'parser' not in kwargs):
# Use the lxml ElementTree compatible parser so that, e.g.,
# we ignore comments.
kwargs['parser'] = etree_.ETCompatXMLParser()
doc = etree_.parse(*args, **kwargs)
return doc
#
# Globals
#
ExternalEncoding = 'ascii'
#
# Utility funtions needed in each generated class.
#
def upper_elements(obj):
for item in obj.member_data_items_:
if item.get_data_type() == 'xs:string':
name = remap(item.get_name())
val1 = getattr(obj, name)
if isinstance(val1, list):
for idx, val2 in enumerate(val1):
val1[idx] = val2.upper()
else:
setattr(obj, name, val1.upper())
def remap(name):
newname = name.replace('-', '_')
return newname
#
# Data representation classes
#
class contactlistTypeSub(supermod.contactlistType):
def __init__(self, locator=None, description=None, contact=None):
super(contactlistTypeSub, self).__init__(locator, description, contact, )
def upper(self):
upper_elements(self)
for child in self.get_contact():
child.upper()
supermod.contactlistType.subclass = contactlistTypeSub
# end class contactlistTypeSub
class contactTypeSub(supermod.contactType):
def __init__(self, priority=None, color_code=None, id=None, first_name=None, last_name=None, interest=None, category=None):
super(contactTypeSub, self).__init__(priority, color_code, id, first_name, last_name, interest, category, )
def upper(self):
upper_elements(self)
supermod.contactType.subclass = contactTypeSub
# end class contactTypeSub
def get_root_tag(node):
tag = supermod.Tag_pattern_.match(node.tag).groups()[-1]
rootClass = None
if hasattr(supermod, tag):
rootClass = getattr(supermod, tag)
return tag, rootClass
def parse(inFilename):
doc = parsexml_(inFilename)
rootNode = doc.getroot()
rootTag, rootClass = get_root_tag(rootNode)
if rootClass is None:
rootTag = 'contact-list'
rootClass = supermod.contactlistType
rootObj = rootClass.factory()
rootObj.build(rootNode)
# Enable Python to collect the space used by the DOM.
doc = None
sys.stdout.write('<?xml version="1.0" ?>\n')
rootObj.export(sys.stdout, 0, name_=rootTag,
namespacedef_='')
doc = None
return rootObj
def parseString(inString):
from StringIO import StringIO
doc = parsexml_(StringIO(inString))
rootNode = doc.getroot()
rootTag, rootClass = get_root_tag(rootNode)
if rootClass is None:
rootTag = 'contact-list'
rootClass = supermod.contactlistType
rootObj = rootClass.factory()
rootObj.build(rootNode)
# Enable Python to collect the space used by the DOM.
doc = None
sys.stdout.write('<?xml version="1.0" ?>\n')
rootObj.export(sys.stdout, 0, name_=rootTag,
namespacedef_='')
return rootObj
def parseLiteral(inFilename):
doc = parsexml_(inFilename)
rootNode = doc.getroot()
rootTag, rootClass = get_root_tag(rootNode)
if rootClass is None:
rootTag = 'contact-list'
rootClass = supermod.contactlistType
rootObj = rootClass.factory()
rootObj.build(rootNode)
# Enable Python to collect the space used by the DOM.
doc = None
sys.stdout.write('#from member_specs_api import *\n\n')
sys.stdout.write('import member_specs_api as model_\n\n')
sys.stdout.write('rootObj = model_.contact_list(\n')
rootObj.exportLiteral(sys.stdout, 0, name_="contact_list")
sys.stdout.write(')\n')
return rootObj
USAGE_TEXT = """
Usage: python ???.py <infilename>
"""
def usage():
print USAGE_TEXT
sys.exit(1)
def main():
args = sys.argv[1:]
if len(args) != 1:
usage()
infilename = args[0]
root = parse(infilename)
if __name__ == '__main__':
#import pdb; pdb.set_trace()
main()
Notes:
Here is a test driver (member_specs_test.py) for our (mini-) application:
#!/usr/bin/env python
#
# member_specs_test.py
#
import sys
import member_specs_api as supermod
import member_specs_upper
def process(inFilename):
doc = supermod.parsexml_(inFilename)
rootNode = doc.getroot()
rootClass = member_specs_upper.contactlistTypeSub
rootObj = rootClass.factory()
rootObj.build(rootNode)
# Enable Python to collect the space used by the DOM.
doc = None
sys.stdout.write('<?xml version="1.0" ?>\n')
rootObj.export(sys.stdout, 0, name_="contact-list",
namespacedef_='')
rootObj.upper()
sys.stdout.write('-' * 60)
sys.stdout.write('\n')
rootObj.export(sys.stdout, 0, name_="contact-list",
namespacedef_='')
return rootObj
USAGE_MSG = """\
Synopsis:
Sample application using classes and subclasses generated by generateDS.py
Usage:
python member_specs_test.py infilename
"""
def usage():
print USAGE_MSG
sys.exit(1)
def main():
args = sys.argv[1:]
if len(args) != 1:
usage()
infilename = args[0]
process(infilename)
if __name__ == '__main__':
main()
Notes:
We can use the following command line to run our application:
$ python member_specs_test.py member_specs_data.xml
When we run our application, here is the output:
$ python member_specs_test.py member_specs_data.xml
<?xml version="1.0" ?>
<contact-list locator="http://www.rexx.com/~dkuhlman">
<description>My list of contacts</description>
<contact priority="0.050000" color-code="red" id="1">
<first-name>arlene</first-name>
<last-name>Allen</last-name>
<interest>traveling</interest>
<category>2</category>
</contact>
</contact-list>
------------------------------------------------------------
<contact-list locator="HTTP://WWW.REXX.COM/~DKUHLMAN">
<description>MY LIST OF CONTACTS</description>
<contact priority="0.050000" color-code="RED" id="1">
<first-name>ARLENE</first-name>
<last-name>ALLEN</last-name>
<interest>TRAVELING</interest>
<category>2</category>
</contact>
</contact-list>
Notes:
The following hints are offered for convenience. You can discover them for yourself rather easily by inspecting the generated code.
If a child element is defined in the XML schema with maxOccurs="unbounded" or a value of maxOccurs greater than 1, then access to the child is through a list.
If a child element is defined as a numeric type such as xs:integer, xs:float, or xs:double or as a simple type that is (ultimately) based on a numeric type, then the value is stored (in the Python object) as a Python data type (int, float, etc).
But, when the element itself is defined as mixed="true" or the element a restriction of and has a simple (numeric) as a base, then the valueOf_ instance variable holds the character content and it is always a string, that is it is not converted.
All parameters to the constructors of generated classes have default parameters. Therefore, you can create an "empty" instance of any element by calling the constructor with no parameters.
For example, suppose we have the following XML schema:
<?xml version="1.0"?>
<xs:schema xmlns:xs="http://www.w3.org/2001/XMLSchema">
<xs:element name="plant-list" type="PlantList" />
<xs:complexType name="PlantType">
<xs:sequence>
<xs:element name="description" type="xs:string" />
<xs:element name="catagory" type="xs:integer" />
<xs:element name="fertilizer" type="FertilizerType" maxOccurs="unbounded" />
</xs:sequence>
<xs:attribute name="identifier" type="xs:string" />
</xs:complexType>
<xs:complexType name="FertilizerType">
<xs:sequence>
<xs:element name="name" type="xs:string"/>
<xs:element name="description" type="xs:string"/>
</xs:sequence>
<xs:attribute name="id" type="xs:integer" />
</xs:complexType>
</xs:schema>
And, suppose we generate a module with the following command line:
$ ./generateDS.py -o garden_api.py garden.xsd
Then, for the element named PlantType in the generated module named garden_api.py, you can create an instance as follows:
>>> import garden_api >>> plant = garden_api.PlantType() >>> import sys >>> plant.export(sys.stdout, 0) <PlantType/>