| Author: | Dave Kuhlman |
|---|---|
| Contact: |<EMAIL_ADDRESS>|
| Address: | http://www.davekuhlman.org |
| Revision: | 1.4c |
| Date: | January 10, 2014 |
| Copyright: | Copyright (c) 2013 Dave Kuhlman. All Rights Reserved. This software is subject to the provisions of the MIT License http://www.opensource.org/licenses/mit-license.php. |
|---|---|
| Abstract: | This document provides notes and an outline of an introductory course on programming in Python. |
Contents
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++.
 (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 docstrings 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. Docstrings can be viewed with several tools, e.g. help(), obj.__doc__, and, in IPython, a question mark (?) after a name will produce help.
A docstring 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 docstrings that span multiple lines.
There are also tools that extract and format docstrings, 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.
There is also a math module. See: http://docs.python.org/2/library/math.html#module-math Example:
In [8]: import math In [9]: math.fabs(-12.345) Out[9]: 12.345
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([('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()
Modes for opening files:
| Modes | Description |
|---|---|
| r | read |
| w | write; truncates file |
| a | write; append; position at end |
| r+ | update; position at beginning for write |
| w+ | update; truncates file |
| a+ | update; position at end for write |
| b | binary (examples: 'rb', 'wb', 'rb+', etc) |
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. For help, type:
$ python --help
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:
Examples:
from xml.dom.minidom import parseString print "1.", parseString from xml.dom import minidom print "2.", minidom.parseString import xml.dom.minidom print "3.", xml.dom.minidom.parseString from xml.dom.minidom import parseString as prsStr print "4.", prsStr
When we run the above, we see:
$ python import_example.py 1. <function parseString at 0x7f869d8c60c8> 2. <function parseString at 0x7f869d8c60c8> 3. <function parseString at 0x7f869d8c60c8> 4. <function parseString at 0x7f869d8c60c8>
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: <IP_ADDRESS> 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:
In [1]: a = (11, 22) In [2]: b = (11, 22) In [3]: a is b Out[3]: False In [4]: d = {a: 'abc'} In [5]: a in d Out[5]: True In [6]: b in d Out[6]: True
Comparison operators, for example ==, !=, <, <=, ...
Short circuit evaluation:
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 -- 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
Comprehensions for sets -- They look like list comprehensions, but are surrounded by curley brackets instead of square brackets. Example:
In [1]: a = [11, 22, 33] In [2]: b = {x * 4 for x in a} In [3]: print b set([88, 132, 44])
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 as 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" (easier to ask for forgiveness than permission) and "LBYL" (look before you leap) 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 as exp: print exp
Throw or raise an exception.
Forms:
The raise statement takes:
See http://docs.python.org/2/reference/simple_stmts.html#the-raise-statement
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:
Here is a more extensive example:
import sys import time class SimpleContext(object): """A simple context manager""" def __init__(self, label): self.label = label def __enter__(self): print "entering block" return self def __exit__(self, exc_type, exc_value, traceback): if exc_type is None: print "leaving block" else: sys.stderr.write("caught exception -- type: %s\n" % (exc_type, )) return False def print_msg(self, msg, outfile=sys.stdout): outfile.write("%s (%s) %s\n" % (self.label, time.ctime(), msg, )) def test_with(): print 'stage 1' with SimpleContext('logging 1') as context: context.print_msg('message 1') context.print_msg('message 2') context.print_msg('message 3') context.label = 'logging 2' context.print_msg('message 4') context.print_msg('message 5') context.print_msg('message 6') print 'stage 2' with SimpleContext('logging 3') as context: context.print_msg('message 7') # cause a NameError exception print unknown_variable context.print_msg('message 8') print 'stage 3' def test(): try: test_with() except NameError as exp: print 'caught exception up above -- exp: %s' % exp test()
When we run the above example we see the following output:
$ python with_as2.py stage 1 entering block logging 1 (Mon Dec 23 10:52:59 2013) message 1 logging 1 (Mon Dec 23 10:52:59 2013) message 2 logging 1 (Mon Dec 23 10:52:59 2013) message 3 logging 2 (Mon Dec 23 10:52:59 2013) message 4 logging 2 (Mon Dec 23 10:52:59 2013) message 5 logging 2 (Mon Dec 23 10:52:59 2013) message 6 leaving block stage 2 entering block logging 3 (Mon Dec 23 10:52:59 2013) message 7 caught exception -- type: <type 'exceptions.NameError'> caught exception up above -- exp: global name 'unknown_variable' is not defined
Notes:
The del statement can be used to:
The del statement does not delete the object referred 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'
The assert statement enables to check for error and abnormal conditions.
Here is an example:
def test(value): assert value % 2 == 0 print 'value:', value def main(): print '__debug__:', __debug__ test(4) test(3) if __name__ == '__main__': main()
Notes:
The above code will produce an AssertionError exception when test(3) is called.
Assertions are checked only when the __debug__ builtin variable is True. Turn it off and optimize compiled code by running Python with the -O command line option. Example:
$ python -O myscript.py
See: http://docs.python.org/2/reference/simple_stmts.html#the-assert-statement
There is no case statement in Python. Use the if: statement with a sequence of elif: clauses.
Or, use a dictionary of functions. Example:
def f(): print 'hi' def g(): print 'bye' Funcs = {'say-hi': f, 'say-bye': g} def run_task(task): Funcs[task]() def test(): run_task('say-hi') run_task('say-bye') test()
And, if we run the above, we see:
$ python say_hi_bye.py hi bye
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:
Extra 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 and dictionaries 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:
Some_global_variable = 'nil' Another_global_variable = 'nil' def fn(): global Some_global_variable, Another_global_variable Some_global_variable = 'hello' ...
Another example:
def set_globals1(x, y): print x, y global Gx Gx = x global Gy Gy = y def set_globals2(x, y): global Gx, Gy print x, y Gx = x Gy = y
The global statement should be lexically before first use of variables mentioned in it.
But in the following example, the global statement is not needed. We need global when we assign or bind a new value to a global variable.
Gz = [] def set_globals3(x): # global statement not needed # global Gz Gz.append(x)
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 section Docstrings for classes for more information on docstrings.
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 iterator protocol 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. For more on the iterator protocol, see: http://docs.python.org/2/library/stdtypes.html#iterator-types
Note that the iterator protocol has changed in Python 3. See: http://docs.python.org/3/library/stdtypes.html#iterator-types
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 section Docstrings for classes for more information on docstrings.
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 instance (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. To avoid these problems, you may want to consider using mixins -- see http://en.wikipedia.org/wiki/Mixin. Or, perhaps, implement a "has-a" relationship, where an instance of one class has or holds an instance of another class in an instance variable, but does not inherit from that 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.
In a class method, you can use the first parameter (usually "cls") to reference the class variable instead of using the hard coded class name. Example:
class A(object): size = -1 @classmethod def incr_size(cls, increment=1): cls.size += increment
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 decorator (and 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 docstring but no implementation. Example:
class Sample(object): """Demonstrate and interface definition.""" def display(self): """Display the instance.""" pass def validate(self, severity=1): """Validate the instance.""" pass
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.
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.
There are several good code, style, and syntax checkers for Python. I can recommend flake8, and you may also want to look at pylint:
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 docstring 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:


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interface ReactivateeScope
See Also
value |
adds the current Reactivatee as an observer for this ReactiveField, and returns the current value of this ReactiveField. abstract val <T> ReactiveField<T>.value: T |
invoke |
A shorthand for value. open operator fun <T> ReactiveField<T>.invoke(): T |


SPICE (Simulation Program with Integrated Circuit Emphasis) is a widely used tool for simulating electrical circuits. The original version was developed at the Electronics Research Laboratory of the University of California, Berkeley. Dozens of variations of SPICE exist, with both free and commercial versions available.
This site allows you to run ngspice simulations from a web browser environment. Whether you are using a Windows, Linux, or Mac computer, a smart phone, or a tablet, you always should be able to run a SPICE simulation.
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Please write your ngspice netlist in the below editor.


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UKPRN:12345678
16 to 19 allocation 2017 to 2018
Programme Funding Formula
| Lagged Students - 118 | |||
| Exceptional Variations to Lagged Students - 0 | |||
| National Funding Rate | £455,909 | ||
| Disadvantage funding | £135,462 |
| £1,600 |
| Total | £514,329 |
{% endblock %}
UKPRN:12345678
| Lagged Students - 118 | |||
| Exceptional Variations to Lagged Students - 0 | |||
| National Funding Rate | £455,909 | ||
| Disadvantage funding | £135,462 |
| £1,600 |
| Total | £514,329 |