Python: A Simple Tutorial CIS 530 – Fall 2010 Highly adapted from slides for CIS 530 originally...

Python: A Simple Tutorial

CIS 530 – Fall 2010 Highly adapted from slides for CIS 530 originally by

Prof. Mitch Marcus I am your course TA (Varun Aggarwala) :) Email address avarun@seas

Python Python is an open source scripting language. Developed by Guido van Rossum in the early 1990s Named after Monty Python Available on eniac Available for download from http://www.python.org

CIS 530 - Intro to NLP 2

Why Python? Very Object Oriented

Python much less verbose than Java

NLP Processing: Symbolic Python has built-in datatypes for strings, lists, and more.

NLP Processing: Statistical Python has strong numeric processing capabilities: matrix

operations, etc. Suitable for probability and machine learning code.

NLTK: Natural Language Tool Kit Widely used for teaching NLP First developed for this course Implemented as a set of Python modules Provides adequate libraries for many NLP building blocks

Google “NLTK” for more info, code, data sets, book..

Why Python?

Powerful but unobtrusive object system Every value is an object Classes guide but do not dominate object

construction

Powerful collection and iteration abstractions Dynamic typing makes generics easy

Python

Interpreted language: works with an evaluator for language expressions

Dynamically typed: variables do not have a predefined type

Rich, built-in collection types: Lists Tuples Dictionaries (maps) Sets

Concise

Language features

Indentation instead of braces Newline separates statements Several sequence types

Strings ’…’: made of characters, immutable Lists […]: made of anything, mutable Tuples (…) : made of anything, immutable

Powerful subscripting (slicing) Functions are independent entities (not all

functions are methods) Exceptions as in JavaCIS 530 - Intro to NLP 6

Dynamic typing

Java: statically typed Variables are declared to refer to objects of a

given type Methods use type signatures to enforce

contracts Python

Variables come into existence when first assigned to

A variable can refer to an object of any type All types are (almost) treated the same way Main drawback: type errors are only caught

at runtimeCIS 530 - Intro to NLP 7

Playing with Python (1)

>>> 2+35>>> 2/30>>> 2.0/30.66666666666666663>>> x=4.5>>> int(x)4

Playing with Python (2)>>> x='abc'>>> x[0]'a'>>> x[1:3]'bc'>>> x[:2]'ab’>>> x[1]='d'Traceback (most recent call last): File "<pyshell#14>", line 1, in <module> x[1]='d'TypeError: 'str' object does not support item

assignment

>>> x=['a','b','c']>>> x[1]'b'>>> x[1:]['b', 'c']>>> x[1]='d'>>> x['a', 'd', 'c']

>>> def p(x): if len(x) < 2: return True else: return x[0] == x[-1] and p(x[1:-1])>>> p('abc')False>>> p('aba')True>>> p([1,2,3])False>>> p([1,’a’,’a’,1])True>>> p((False,2,2,False))True>>> p((’a’,1,1))False

Python dictionaries (Maps)

>>> d={'alice':1234, 'bob':5678, 'clare':9012}>>> d['alice']1234>>> d['bob']5678>>> d['bob'] = 7777>>> d{'clare': 9012, 'bob': 7777, 'alice': 1234}>>> d.keys()['clare', 'bob', 'alice']>>> d.items()[('clare', 9012), ('bob', 7777), ('alice', 1234)]>>> del d['bob']>>> d{'clare': 9012, 'alice': 1234}

Technical Issues

Installing & Running Python

The Python Interpreter Interactive interface to Python

% pythonPython 2.6 (r26:66714, Feb 3 2009, 20:49:49) [GCC 4.3.2 [gcc-4_3-branch revision 141291]] on linux2

Type "help", "copyright", "credits" or "license" for more information.

Python interpreter evaluates inputs:

>>> 3*(7+2)27

The IDLE GUI Environment (Windows)

IDLE Development Environment Shell for interactive evaluation. Text editor with color-coding and smart indenting

for creating Python files. Menu commands for changing system settings

and running files.

Running Interactively on UNIX (ENIAC)On Unix…

% python

>>> 3+3

Python prompts with ‘>>>’. To exit Python (not Idle):

In Unix, type CONTROL-D In Windows, type CONTROL-Z + <Enter>

Running Programs on UNIX% python filename.py

You can make a python file executable by adding following text as the first line of the file to make it runable: #!/usr/bin/python

The Basics

A Code Sample (in IDLE) x = 34 - 23 # A comment. y = “Hello” # Another one. z = 3.45 if z == 3.45 or y == “Hello”: x = x + 1 y = y + “ World” # String concat. print x print y

Enough to Understand the Code Indentation matters to the meaning of the code:

Block structure indicated by indentation The first assignment to a variable creates it.

Variable types don’t need to be declared. Python figures out the variable types on its own.

Assignment uses = and comparison uses ==. For numbers + - * / % are as expected.

Special use of + for string concatenation. Special use of % for string formatting (as with printf in C)

Logical operators are words (and, or, not) not symbols

Simple printing can be done with print.

Basic Datatypes Integers (default for numbers)z = 5 / 2 # Answer is 2, integer division. Floatsx = 3.456 Strings

Can use “” or ‘’ to specify. “abc” ‘abc’ (Same thing.)

Unmatched can occur within the string. “matt’s”

Use triple double-quotes for multi-line strings or strings than contain both ‘ and “ inside of them: “““a‘b“c”””

WhitespaceWhitespace is meaningful in Python: especially

indentation and placement of newlines. Use a newline to end a line of code.

Use \ when must go to next line prematurely.

No braces { } to mark blocks of code in Python… Use consistent indentation instead. The first line with less indentation is outside of the block. The first line with more indentation starts a nested block

Often a colon appears at the start of a new block. (E.g. for function and class definitions.)

Comments Start comments with # – the rest of line is ignored. Can include a “documentation string” as the first line of

any new function or class that you define. The development environment, debugger, and other tools

use it: it’s good style to include one.def my_function(x, y):

“““This is the docstring. This function does blah blah blah.”””# The code would go here...

Assignment Binding a variable in Python means setting a

name to hold a reference to some object. Assignment creates references, not copies

Names in Python do not have an intrinsic type. Objects have types. Python determines the type of the reference automatically

based on what data is assigned to it.

You create a name the first time it appears on the left side of an assignment expression:

A reference is deleted via garbage collection after any names bound to it have passed out of scope.

Python uses reference semantics (more later)

Naming Rules Names are case sensitive and cannot start with a number.

They can contain letters, numbers, and underscores. bob Bob _bob _2_bob_ bob_2 BoB There are some reserved words:

and, assert, break, class, continue, def, del, elif, else, except, exec, finally, for, from, global, if, import, in, is, lambda, not, or, pass, print, raise, return, try, while

Accessing Non-Existent Name

If you try to access a name before it’s been properly created (by placing it on the left side of an assignment), you’ll get an error.

Traceback (most recent call last): File "<pyshell#16>", line 1, in -toplevel- yNameError: name ‘y' is not defined>>> y = 3>>> y3

Sequence types:Tuples, Lists, and Strings

Sequence Types1. Tuple

A simple immutable ordered sequence of items Items can be of mixed types, including collection types

2. Strings Immutable Conceptually very much like a tuple (8-bit characters. Unicode strings use 2-byte

characters.)

3. List Mutable ordered sequence of items of mixed types

Similar Syntax All three sequence types (tuples, strings, and

lists) share much of the same syntax and functionality.

Key difference: Tuples and strings are immutable Lists are mutable

The operations shown in this section can be applied to all sequence types most examples will just show the operation

performed on one

Sequence Types 1

Tuples are defined using parentheses (and commas).>>> tu = (23, ‘abc’, 4.56, (2,3), ‘def’)

Lists are defined using square brackets (and commas).>>> li = [“abc”, 34, 4.34, 23]

Strings are defined using quotes (“, ‘, or “““).>>> st = “Hello World”

>>> st = ‘Hello World’

>>> st = “““This is a multi-line

string that uses triple quotes.”””

Sequence Types 2 We can access individual members of a tuple, list, or string

using square bracket “array” notation. Note that all are 0 based…

>>> tu = (23, ‘abc’, 4.56, (2,3), ‘def’)>>> tu[1] # Second item in the tuple. ‘abc’

>>> li = [“abc”, 34, 4.34, 23] >>> li[1] # Second item in the list. 34

>>> st = “Hello World”>>> st[1] # Second character in string. ‘e’

Positive and negative indices

>>> t = (23, ‘abc’, 4.56, (2,3), ‘def’)

Positive index: count from the left, starting with 0.>>> t[1]

‘abc’

Negative lookup: count from right, starting with –1.>>> t[-3]

Slicing: Return Copy of a Subset 1

>>> t = (23, ‘abc’, 4.56, (2,3), ‘def’)

Return a copy of the container with a subset of the original members. Start copying at the first index, and stop copying before the second index.

>>> t[1:4](‘abc’, 4.56, (2,3))

You can also use negative indices when slicing. >>> t[1:-1](‘abc’, 4.56, (2,3))

Slicing: Return Copy of a Subset 2

>>> t = (23, ‘abc’, 4.56, (2,3), ‘def’)

Omit the first index to make a copy starting from the beginning of the container.

>>> t[:2] (23, ‘abc’)

Omit the second index to make a copy starting at the first index and going to the end of the container.

>>> t[2:](4.56, (2,3), ‘def’)

The ‘in’ Operator

Boolean test whether a value is inside a collection (often called a container in Python:

>>> t = [1, 2, 4, 5]>>> 3 in tFalse>>> 4 in tTrue>>> 4 not in tFalse

For strings, tests for substrings>>> a = 'abcde'>>> 'c' in aTrue>>> 'cd' in aTrue>>> 'ac' in aFalse

Be careful: the in keyword is also used in the syntax of for loops and list comprehensions.

The + Operator

The + operator produces a new tuple, list, or string whose value is the concatenation of its arguments.

>>> (1, 2, 3) + (4, 5, 6) (1, 2, 3, 4, 5, 6)

>>> [1, 2, 3] + [4, 5, 6] [1, 2, 3, 4, 5, 6]

>>> “Hello” + “ ” + “World” ‘Hello World’

Mutability:Tuples vs. Lists

Lists: Mutable

>>> li = [‘abc’, 23, 4.34, 23]

>>> li[1] = 45

>>> li[‘abc’, 45, 4.34, 23]

We can change lists in place. Name li still points to the same memory reference when

we’re done.

Tuples: Immutable

>>> t = (23, ‘abc’, 4.56, (2,3), ‘def’)>>> t[2] = 3.14

Traceback (most recent call last): File "<pyshell#75>", line 1, in -toplevel- tu[2] = 3.14TypeError: object doesn't support item assignment

You can’t change a tuple. You can make a fresh tuple and assign its reference to a previously

used name.>>> t = (23, ‘abc’, 3.14, (2,3), ‘def’)

The immutability of tuples means they’re faster than lists.

Operations on Lists Only 1

>>> li = [1, 11, 3, 4, 5]

>>> li.append(‘a’) # Note the method syntax

>>> li

[1, 11, 3, 4, 5, ‘a’]

>>> li.insert(2, ‘i’)

[1, 11, ‘i’, 3, 4, 5, ‘a’]

The extend method vs the + operator. + creates a fresh list (with a new memory reference) extend operates on list li in place.

>>> li.extend([9, 8, 7]) >>>li[1, 2, ‘i’, 3, 4, 5, ‘a’, 9, 8, 7]

Confusing: extend takes a list as an argument. append takes a singleton as an argument.>>> li.append([10, 11, 12])>>> li[1, 2, ‘i’, 3, 4, 5, ‘a’, 9, 8, 7, [10, 11, 12]]

Operations on Lists Only 3>>> li = [‘a’, ‘b’, ‘c’, ‘b’]

>>> li.index(‘b’) # index of first occurrence*

*more complex forms exist

>>> li.count(‘b’) # number of occurrences

>>> li.remove(‘b’) # remove first occurrence

>>> li

[‘a’, ‘c’, ‘b’]

Operations on Lists Only 4>>> li = [5, 2, 6, 8]

>>> li.reverse() # reverse the list *in place*>>> li [8, 6, 2, 5]

>>> li.sort() # sort the list *in place*>>> li [2, 5, 6, 8]

>>> li.sort(some_function) # sort in place using user-defined comparison

Summary: Tuples vs. Lists Lists slower but more powerful than tuples.

Lists can be modified, and they have lots of handy operations we can perform on them.

Tuples are immutable and have fewer features.

To convert between tuples and lists use the list() and tuple() functions:

li = list(tu)

tu = tuple(li)

Dictionaries: a mapping collection type

Dictionaries: A Mapping type Dictionaries store a mapping between a set of keys

and a set of values. Keys can be any immutable type. Values can be any type Values and keys can be of different types in a single dictionary

You can define modify view lookup delete

the key-value pairs in the dictionary.

Creating and accessing dictionaries

>>> d = {‘user’:‘bozo’, ‘pswd’:1234}

>>> d[‘user’] ‘bozo’

>>> d[‘pswd’]1234

>>> d[‘bozo’]

Traceback (innermost last): File ‘<interactive input>’ line 1, in ?KeyError: bozo

Updating Dictionaries

>>> d = {‘user’:‘bozo’, ‘pswd’:1234}

>>> d[‘user’] = ‘clown’>>> d{‘user’:‘clown’, ‘pswd’:1234}

Keys must be unique. Assigning to an existing key replaces its value.

>>> d[‘id’] = 45>>> d{‘user’:‘clown’, ‘id’:45, ‘pswd’:1234}

Dictionaries are unordered New entry might appear anywhere in the output.

(Dictionaries work by hashing)

Removing dictionary entries>>> d = {‘user’:‘bozo’, ‘p’:1234, ‘i’:34}

>>> del d[‘user’] # Remove one.

{‘p’:1234, ‘i’:34}

>>> d.clear() # Remove all.

>>> a=[1,2]>>> del a[1] # (del also works on lists)

Useful Accessor Methods>>> d = {‘user’:‘bozo’, ‘p’:1234, ‘i’:34}

>>> d.keys() # List of current keys[‘user’, ‘p’, ‘i’]

>>> d.values() # List of current values.[‘bozo’, 1234, 34]

>>> d.items() # List of item tuples.[(‘user’,‘bozo’), (‘p’,1234), (‘i’,34)]

Functions in Python

(Methods later)

Defining Functions

No header file or declaration of types of function or arguments.

The indentation matters…First line with less indentation is considered to beoutside of the function definition.

def get_final_answer(filename):

“Documentation String” line1

return total_counter

Function definition begins with def Function name and its arguments.

The keyword ‘return’ indicates the value to be sent back to the caller.

Colon.

Python and Types

Python determines the data types of variable

bindings in a program automatically. “Dynamic Typing”

But Python’s not casual about types, it enforces the types of objects. “Strong Typing”

So, for example, you can’t just append an integer to a string. You must first convert the integer to a string itself.

x = “the answer is ” # Decides x is bound to a string.

y = 23 # Decides y is bound to an integer.

print x + y # Python will complain about this.

Calling a Function

The syntax for a function call is: >>> def myfun(x, y):

return x * y

>>> myfun(3, 4)

Parameters in Python are “Call by Assignment.” Old values for the variables that are parameter names are hidden,

and these variables are simply made to refer to the new values All assignment in Python, including binding function parameters,

uses reference semantics. (Many web discussions of this are simply confused.)

Functions without returns

All functions in Python have a return value even if no return line inside the code.

Functions without a return return the special value None. None is a special constant in the language. None is used like NULL, void, or nil in other languages. None is also logically equivalent to False. The interpreter doesn’t print None

Function overloading? No.

There is no function overloading in Python. Unlike C++, a Python function is specified by its name alone

The number, order, names, or types of its arguments cannot be used to distinguish between two functions with the same name.

Two different functions can’t have the same name, even if they have different arguments.

But: see operator overloading in later slides

(Note: van Rossum playing with function overloading for the future)

Functions are first-class objects in Python

Functions can be used as any other data type They can be

Arguments to function Return values of functions Assigned to variables Parts of tuples, lists, etc …

>>> def myfun(x): return x*3

>>> def applier(q, x): return q(x)

>>> applier(myfun, 7)21

Logical Expressions

True and False

True and False are constants in Python.

Other values equivalent to True and False: False: zero, None, empty container or object True: non-zero numbers, non-empty objects

Comparison operators: ==, !=, <, <=, etc. X and Y have same value: X == Y Compare with X is Y :

X and Y are two variables that refer to the identical same object.

Boolean Logic Expressions You can also combine Boolean expressions.

True if a is True and b is True: a and b True if a is True or b is True: a or b True if a is False: not a

Use parentheses as needed to disambiguate complex Boolean expressions.

Actually, evaluation of expressions is lazy…

Special Properties of and and or Actually and and or don’t return True or False. They return the value of one of their sub-expressions

(which may be a non-Boolean value). X and Y and Z

If all are true, returns value of Z. Otherwise, returns value of first false sub-expression.

X or Y or Z If all are false, returns value of Z. Otherwise, returns value of first true sub-expression.

And and or use lazy evaluation, so no further expressions are evaluated

The “and-or” Trick

An old deprecated trick to implement a simple conditional result = test and expr1 or expr2

When test is True, result is assigned expr1. When test is False, result is assigned expr2. Works almost like (test ? expr1 : expr2) expression of C++.

But if the value of expr1 is ever False, the trick doesn’t work. Don’t use it, but you may see it in the code. Made unnecessary by conditional expressions in Python 2.5

(see next slide)

Conditional Expressions: New in Python 2.5

x = true_value if condition else false_value

Uses lazy evaluation: First, condition is evaluated If True, true_value is evaluated and returned If False, false_value is evaluated and returned

Standard use: x = (true_value if condition else false_value)

Control of Flow

if Statements if x == 3:

print “X equals 3.”elif x == 2:

print “X equals 2.”else:

print “X equals something else.”print “This is outside the ‘if’.”

Be careful! The keyword if is also used in the syntax of filtered list comprehensions.Note: Use of indentation for blocks Colon (:) after boolean expression

while Loops>>> x = 3>>> while x < 5:

print x, "still in the loop"x = x + 1

3 still in the loop4 still in the loop>>> x = 6>>> while x < 5:

print x, "still in the loop"

break and continue You can use the keyword break inside a loop to

leave the while loop entirely.

You can use the keyword continue inside a loop to stop processing the current iteration of the loop and to immediately go on to the next one.

assert An assert statement will check to make sure that

something is true during the course of a program. If the condition if false, the program stops

(more accurately: the program throws an exception)

assert(number_of_players < 5)

Also in Java, we just didn’t mention it

For Loops

For Loops / List Comprehensions Python’s list comprehensions provide a natural

idiom that usually requires a for-loop in other programming languages. As a result, Python code uses many fewer for-loops Nevertheless, it’s important to learn about for-loops.

Caveat! The keywords for and in are also used in the syntax of list comprehensions, but this is a totally different construction.

For Loops 1 For-each is Python’s only for construction A for loop steps through each of the items in a collection

type, or any other type of object which is “iterable”

for <item> in <collection>:<statements>

If <collection> is a list or a tuple, then the loop steps through each element of the sequence.

If <collection> is a string, then the loop steps through each character of the string.

for someChar in “Hello World”: print someChar

For Loops 2for <item> in <collection>:<statements>

<item> can be more complex than a single variable name. When the elements of <collection> are themselves

sequences, then <item> can match the structure of the elements.

This multiple assignment can make it easier to access the individual parts of each element.

for (x, y) in [(a,1), (b,2), (c,3), (d,4)]:

print x

For loops and the range() function Since a variable often ranges over some sequence of

numbers, the range() function returns a list of numbers from 0 up to but not including the number we pass to it.

range(5) returns [0,1,2,3,4] So we could say:

for x in range(5): print x

(There are more complex forms of range() that provide richer functionality…)

xrange() returns an iterator that provides the same functionality here more efficiently

For Loops and Dictionaries

>>> ages = { "Sam " :4, "Mary " :3, "Bill " :2 }

>>> ages

{'Bill': 2, 'Mary': 3, 'Sam': 4}

>>> for name in ages.keys():

print name, ages[name]

Bill 2

Mary 3

String Operations A number of methods for the string class perform

useful formatting operations:

>>> “hello”.upper()‘HELLO’

Check the Python documentation for many other handy string operations.

Helpful hint: use <string>.strip() to strip off final newlines from lines read from files

Printing with Python

You can print a string to the screen using “print.” Using the % string operator in combination with the print

command, we can format our output text. >>> print “%s xyz %d” % (“abc”, 34)abc xyz 34

“Print” automatically adds a newline to the end of the string. If you include a list of strings, it will concatenate them with a space between them.

>>> print “abc” >>> print “abc”, “def”abc abc def

Useful trick: >>> print “abc”, doesn’t add newline just a single space

Convert Anything to a String The built-in str() function can convert an instance

of any data type into a string. You can define how this function behaves for user-created

data types. You can also redefine the behavior of this function for many types.

>>> “Hello ” + str(2)

“Hello 2”

Importing and Modules

Importing and Modules Use classes & functions defined in another file.

A Python module is a single file with the same name (plus the .py extension)

Like Java import

Where does Python look for module files? The list of directories where Python looks: sys.path

To add a directory of your own to this list, append it to this list.sys.path.append(‘/my/new/path’)

Import Iimport somefile

Everything in somefile.py can be referred to by:

somefile.className.method(“abc”)somefile.myFunction(34)

from somefile import * Everything in somefile.py can be referred to by:

className.method(“abc”)myFunction(34)

Caveat! This can easily overwrite the definition of an existing function or variable!

Import IIfrom somefile import className

Only the item className in somefile.py gets imported. Refer to it without a module prefix. Caveat! This can overwrite an existing definition.

className.method(“abc”) This was imported

myFunction(34) Not this one

Commonly Used Modules

Some useful modules to import, included with Python:

Module: sys - Lots of handy stuff. Maxint

Module: os - OS specific code. Module: os.path - Directory processing.

More Commonly Used Modules Module: math - Mathematical code.

Exponents sqrt

Module: Random - Random number code. Randrange Uniform Choice Shuffle

To see what’s in the standard library of modules, check out the Python Library Reference: http://docs.python.org/lib/lib.html

Or O’Reilly’s Python in a Nutshell: http://proquest.safaribooksonline.com/0596100469 (URL works inside of UPenn, afaik, otherwise see the course web

Object Oriented Programmingin Python: Defining Classes

It’s all objects… Everything in Python is really an object.

We’ve seen hints of this already…“hello”.upper()list3.append(‘a’)dict2.keys()

These look like Java or C++ method calls.

Programming in Python is typically done in an object oriented fashion.

Defining a Class A class is a special data type which defines how

to build a certain kind of object. The class also stores some data items that are shared by all

the instances of this class. But no static variables!

Python doesn’t use separate class interface definitions much. You just define the class and then use it.

Methods in Classes Define a method in a class by including function

definitions within the scope of the class block.

There must be a special first argument self in all of method definitions which gets bound to the calling instance. Self is like this in Java

Self always refers to the current class instance

A constructor for a class is a method called __init__ defined within the class.

A simple class definition: studentclass student:“““A class representing a student.”””def __init__(self,n,a): self.full_name = n self.age = adef get_age(self): return self.age

Creating and Deleting Instances

Instantiating Objects There is no “new” keyword as in Java. Merely use the class name with () notation and

assign the result to a variable. __init__ serves as a constructor for the class. Example:

b = student(“Bob”, 21) An __init__ method can take any number of

arguments. Like other functions & methods, arguments can be defined

with default values, making them optional to the caller. But no real overloading

Self Although you must specify self explicitly when

defining the method, you don’t include it when calling the method.

Python passes it for you automatically.

Defining a method: Calling a method:(this code inside a class definition.)

def set_age(self, num): >>> x.set_age(23)self.age = num

Access to Attributes and Methods

Definition of studentclass student:“““A class representing a student.”””def __init__(self,n,a): self.full_name = n self.age = adef get_age(self): return self.age

Traditional Syntax for Access

>>> f = student (“Bob Smith”, 23)

>>> f.full_name # Access an attribute.

“Bob Smith”

>>> f.get_age() # Access a method.

No public, private, protected, etc…

File Processing, Error Handling, Regular Expressions, etc: Coming up…

File Processing with Python

This is a good way to play with the error handing capabilities of Python. Try accessing files without permissions or with non-existent names, etc.

You’ll get plenty of errors to look at and play with!

fileptr = open(‘filename’)

somestring = fileptr.read()

for line in fileptr: print line

fileptr.close()

For more, see section 3.9 of the Python Library reference at http://docs.python.org/lib/bltin-file-objects.html

Exception Handling Exceptions are Python classes

More specific kinds of errors are subclasses of the general Error class.

You use the following commands to interact with them: Try Except Finally Catch

Regular Expressions and Match Objects

Python provides a very rich set of tools for pattern matching against strings in module re (for regular expression)

As central as they are to much of the use of Python, we won’t be using them in this course…

For a gentle introduction to regular expressions in Python see

http://www.diveintopython.org/regular_expressions/index.html

http://www.amk.ca/python/howto/regex/regex.html

Finally…

pass It does absolutely nothing.

Just holds the place of where something should go syntactically. Programmers like to use it to waste time in some code, or to hold the place where they would like put some real code at a later time.

for i in range(1000):

passLike a “no-op” in assembly code, or a set of empty braces {} in C++ or Java.

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Computing & Information Sciences Kansas State University Lecture 6 of 42 CIS 530 / 730 Artificial Intelligence Lecture 6 of 42 William H. Hsu Department

Computing & Information Sciences Kansas State University Lecture 8 of 42 CIS 530 / 730 Artificial Intelligence Lecture 8 of 42 William H. Hsu Department

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Computing & Information Sciences Kansas State University Lecture 13 of 42 CIS 530 / 730 Artificial Intelligence Lecture 13 of 42 William H. Hsu Department

Computing & Information Sciences Kansas State University Lecture 3 of 42 CIS 530 / 730 Artificial Intelligence Lecture 3 of 42 William H. Hsu Department

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Computing & Information Sciences Kansas State University Lecture 14 of 42 CIS 530 / 730 Artificial Intelligence Lecture 14 of 42 William H. Hsu Department

Çankırı Karatekin University...Ders Kodu TDi-101 ATA-101 YDÍ-IOI CIS 109 CIS 103 CIS 101 CIS 107 CIS 105 Ders Kodu CIS 205 CIS 207 CIS CIS 203 CIS 225 CIS 221 CIS 201 CIS 215 CIS

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Computing & Information Sciences Kansas State University Lecture 2 of 42 CIS 530 / 730 Artificial Intelligence Lecture 2 of 42 William H. Hsu Department

Computing & Information Sciences Kansas State University Lecture 7 of 42 CIS 530 / 730 Artificial Intelligence Lecture 7 of 42 William H. Hsu Department