Academic Tutorials

English | French | Portugese | German | Italian
Home Advertise Payments Recommended Websites Interview Questions FAQs
News Source Codes E-Books Downloads Jobs Web Hosting

Introduction to Python
Using the Python Interpreter
An Informal Introduction to Python
More Control Flow Tools
Data Structures
Input and Output
Errors and Exceptions
Brief Tour of the Standard Library
Brief Tour of the Standard Library - Part II

HTML Tutorials
HTML Tutorial
XHTML Tutorial
CSS Tutorial
TCP/IP Tutorial
CSS 1.0
CSS 2.0
XML Tutorials
XML Tutorial
XSL Tutorial
XSLT Tutorial
DTD Tutorial
Schema Tutorial
XForms Tutorial
XSL-FO Tutorial
XML DOM Tutorial
XLink Tutorial
XQuery Tutorial
XPath Tutorial
XPointer Tutorial
RDF Tutorial
SOAP Tutorial
WSDL Tutorial
RSS Tutorial
WAP Tutorial
Web Services Tutorial
Browser Scripting
JavaScript Tutorial
VBScript Tutorial
DHTML Tutorial
HTML DOM Tutorial
WMLScript Tutorial
E4X Tutorial
Server Scripting
ASP Tutorial
PERL Tutorial
SQL Tutorial
ADO Tutorial
Apple Script
PL/SQL Tutorial
SQL Server
.NET (dotnet)
.Net Mobile
C# : C Sharp
SVG Tutorial
Flash Tutorial
Media Tutorial
SMIL Tutorial
Photoshop Tutorial
Gimp Tutorial
Gnuplot Programming
GIF Animation Tutorial
Scientific Visualization Tutorial
Web Building
Web Browsers
Web Hosting
W3C Tutorial
Web Building
Web Quality
Web Semantic
Web Careers
Weblogic Tutorial
Web Site Hosting
Domain Name
Java Tutorials
Java Tutorial
JSP Tutorial
Servlets Tutorial
Struts Tutorial
EJB Tutorial
JMS Tutorial
JMX Tutorial
Programming Langauges
C Tutorial
C++ Tutorial
Visual Basic Tutorial
Data Structures Using C
Assembly Language
Forth Programming
Lisp Programming
Data Warehousing
CGI Programming
Emacs Tutorial
Soft Skills
Communication Skills
Time Management
Project Management
Team Work
Leadership Skills
Corporate Communication
Negotiation Skills
Database Tutorials
Operating System
Software Testing
SAP Module
Business Warehousing
SAP Basis
Material Management
Sales & Distribution
Human Resource
Customer Relationship Management
Production and Planning
Networking Programming
Corba Tutorial
Networking Tutorial
Microsoft Office
Microsoft Word
Microsoft Outlook
Microsoft PowerPoint
Microsoft Publisher
Microsoft Excel
Microsoft Front Page
Microsoft InfoPath
Microsoft Access
Financial Accounting
Managerial Accounting
Network Sites

Data Structures

Previoushome Next

Data Structures

This chapter describes some things you've learned about already in more detail, and adds some new things as well.


More on Lists

The list data type has some more methods. Here are all of the methods of list objects:

append( x)
Add an item to the end of the list; equivalent to a[len(a):] = [x].
extend( L)
Extend the list by appending all the items in the given list; equivalent to a[len(a):] = L.
insert( i, x)
Insert an item at a given position. The first argument is the index of the element before which to insert, so a.insert(0, x) inserts at the front of the list, and a.insert(len(a), x) is equivalent to a.append(x).
remove( x)
Remove the first item from the list whose value is x. It is an error if there is no such item.
pop( [i])
Remove the item at the given position in the list, and return it. If no index is specified, a.pop() removes and returns the last item in the list. (The square brackets around the i in the method signature denote that the parameter is optional, not that you should type square brackets at that position.)
index( x)
Return the index in the list of the first item whose value is x. It is an error if there is no such item.
count( x)
Return the number of times x appears in the list.
sort( )
Sort the items of the list, in place.
reverse( )
Reverse the elements of the list, in place.

An example that uses most of the list methods:

>>> a = [66.25, 333, 333, 1, 1234.5]
>>> print a.count(333), a.count(66.25), a.count('x')
2 1 0
>>> a.insert(2, -1)
>>> a.append(333)
>>> a
[66.25, 333, -1, 333, 1, 1234.5, 333]
>>> a.index(333)
>>> a.remove(333)
>>> a
[66.25, -1, 333, 1, 1234.5, 333]
>>> a.reverse()
>>> a
[333, 1234.5, 1, 333, -1, 66.25]
>>> a.sort()
>>> a
[-1, 1, 66.25, 333, 333, 1234.5]

Using Lists as Stacks

The list methods make it very easy to use a list as a stack, where the last element added is the first element retrieved (``last-in, first-out''). To add an item to the top of the stack, use append(). To retrieve an item from the top of the stack, use pop() without an explicit index. For example:

>>> stack = [3, 4, 5]
>>> stack.append(6)
>>> stack.append(7)
>>> stack
[3, 4, 5, 6, 7]
>>> stack.pop()
>>> stack
[3, 4, 5, 6]
>>> stack.pop()
>>> stack.pop()
>>> stack
[3, 4]

Using Lists as Queues

You can also use a list conveniently as a queue, where the first element added is the first element retrieved (``first-in, first-out''). To add an item to the back of the queue, use append(). To retrieve an item from the front of the queue, use pop() with 0 as the index. For example:

>>> queue = ["Eric", "John", "Michael"]
>>> queue.append("Terry")           # Terry arrives
>>> queue.append("Graham")          # Graham arrives
>>> queue.pop(0)
>>> queue.pop(0)
>>> queue
['Michael', 'Terry', 'Graham']

Functional Programming Tools

There are three built-in functions that are very useful when used with lists: filter(), map(), and reduce().

"filter(function, sequence)" returns a sequence consisting of those items from the sequence for which function(item) is true. If sequence is a string or tuple, the result will be of the same type; otherwise, it is always a list. For example, to compute some primes:

>>> def f(x): return x % 2 != 0 and x % 3 != 0
>>> filter(f, range(2, 25))
[5, 7, 11, 13, 17, 19, 23]

"map(function, sequence)" calls function(item) for each of the sequence's items and returns a list of the return values. For example, to compute some cubes:

>>> def cube(x): return x*x*x
>>> map(cube, range(1, 11))
[1, 8, 27, 64, 125, 216, 343, 512, 729, 1000]

More than one sequence may be passed; the function must then have as many arguments as there are sequences and is called with the corresponding item from each sequence (or None if some sequence is shorter than another). For example:

>>> seq = range(8)
>>> def add(x, y): return x+y
>>> map(add, seq, seq)
[0, 2, 4, 6, 8, 10, 12, 14]

"reduce(function, sequence)" returns a single value constructed by calling the binary function function on the first two items of the sequence, then on the result and the next item, and so on. For example, to compute the sum of the numbers 1 through 10:

>>> def add(x,y): return x+y
>>> reduce(add, range(1, 11))

If there's only one item in the sequence, its value is returned; if the sequence is empty, an exception is raised.

A third argument can be passed to indicate the starting value. In this case the starting value is returned for an empty sequence, and the function is first applied to the starting value and the first sequence item, then to the result and the next item, and so on. For example,

>>> def sum(seq):
...     def add(x,y): return x+y
...     return reduce(add, seq, 0)
>>> sum(range(1, 11))
>>> sum([])

Don't use this example's definition of sum(): since summing numbers is such a common need, a built-in function sum(sequence) is already provided, and works exactly like this. New in version 2.3.

List Comprehensions

List comprehensions provide a concise way to create lists without resorting to use of map(), filter() and/or lambda. The resulting list definition tends often to be clearer than lists built using those constructs. Each list comprehension consists of an expression followed by a for clause, then zero or more for or if clauses. The result will be a list resulting from evaluating the expression in the context of the for and if clauses which follow it. If the expression would evaluate to a tuple, it must be parenthesized.

>>> freshfruit = ['  banana', '  loganberry ', 'passion fruit  ']
>>> [weapon.strip() for weapon in freshfruit]
['banana', 'loganberry', 'passion fruit']
>>> vec = [2, 4, 6]
>>> [3*x for x in vec]
[6, 12, 18]
>>> [3*x for x in vec if x > 3]
[12, 18]
>>> [3*x for x in vec if x < 2]
>>> [[x,x**2] for x in vec]
[[2, 4], [4, 16], [6, 36]]
>>> [x, x**2 for x in vec]	# error - parens required for tuples
  File "<stdin>", line 1, in ?
    [x, x**2 for x in vec]
SyntaxError: invalid syntax
>>> [(x, x**2) for x in vec]
[(2, 4), (4, 16), (6, 36)]
>>> vec1 = [2, 4, 6]
>>> vec2 = [4, 3, -9]
>>> [x*y for x in vec1 for y in vec2]
[8, 6, -18, 16, 12, -36, 24, 18, -54]
>>> [x+y for x in vec1 for y in vec2]
[6, 5, -7, 8, 7, -5, 10, 9, -3]
>>> [vec1[i]*vec2[i] for i in range(len(vec1))]
[8, 12, -54]

List comprehensions are much more flexible than map() and can be applied to complex expressions and nested functions:

>>> [str(round(355/113.0, i)) for i in range(1,6)]
['3.1', '3.14', '3.142', '3.1416', '3.14159']

The del statement

There is a way to remove an item from a list given its index instead of its value: the del statement. This differs from the pop() method which returns a value. The del statement can also be used to remove slices from a list or clear the entire list (which we did earlier by assignment of an empty list to the slice). For example:

>>> a = [-1, 1, 66.25, 333, 333, 1234.5]
>>> del a[0]
>>> a
[1, 66.25, 333, 333, 1234.5]
>>> del a[2:4]
>>> a
[1, 66.25, 1234.5]
>>> del a[:]
>>> a

del can also be used to delete entire variables:

>>> del a

Referencing the name a hereafter is an error (at least until another value is assigned to it). We'll find other uses for del later.

Tuples and Sequences

We saw that lists and strings have many common properties, such as indexing and slicing operations. They are two examples of sequence data types. Since Python is an evolving language, other sequence data types may be added. There is also another standard sequence data type: the tuple.

A tuple consists of a number of values separated by commas, for instance:

>>> t = 12345, 54321, 'hello!'
>>> t[0]
>>> t
(12345, 54321, 'hello!')
>>> # Tuples may be nested:
... u = t, (1, 2, 3, 4, 5)
>>> u
((12345, 54321, 'hello!'), (1, 2, 3, 4, 5))

As you see, on output tuples are always enclosed in parentheses, so that nested tuples are interpreted correctly; they may be input with or without surrounding parentheses, although often parentheses are necessary anyway (if the tuple is part of a larger expression).

Tuples have many uses. For example: (x, y) coordinate pairs, employee records from a database, etc. Tuples, like strings, are immutable: it is not possible to assign to the individual items of a tuple (you can simulate much of the same effect with slicing and concatenation, though). It is also possible to create tuples which contain mutable objects, such as lists.

A special problem is the construction of tuples containing 0 or 1 items: the syntax has some extra quirks to accommodate these. Empty tuples are constructed by an empty pair of parentheses; a tuple with one item is constructed by following a value with a comma (it is not sufficient to enclose a single value in parentheses). Ugly, but effective. For example:

>>> empty = ()
>>> singleton = 'hello',    # <-- note trailing comma
>>> len(empty)
>>> len(singleton)
>>> singleton

The statement t = 12345, 54321, 'hello!' is an example of tuple packing: the values 12345, 54321 and 'hello!' are packed together in a tuple. The reverse operation is also possible:

>>> x, y, z = t

This is called, appropriately enough, sequence unpacking. Sequence unpacking requires the list of variables on the left to have the same number of elements as the length of the sequence. Note that multiple assignment is really just a combination of tuple packing and sequence unpacking!

There is a small bit of asymmetry here: packing multiple values always creates a tuple, and unpacking works for any sequence.


Python also includes a data type for sets. A set is an unordered collection with no duplicate elements. Basic uses include membership testing and eliminating duplicate entries. Set objects also support mathematical operations like union, intersection, difference, and symmetric difference.

Here is a brief demonstration:

>>> basket = ['apple', 'orange', 'apple', 'pear', 'orange', 'banana']
>>> fruit = set(basket)               # create a set without duplicates
>>> fruit
set(['orange', 'pear', 'apple', 'banana'])
>>> 'orange' in fruit                 # fast membership testing
>>> 'crabgrass' in fruit

>>> # Demonstrate set operations on unique letters from two words
>>> a = set('abracadabra')
>>> b = set('alacazam')
>>> a                                  # unique letters in a
set(['a', 'r', 'b', 'c', 'd'])
>>> a - b                              # letters in a but not in b
set(['r', 'd', 'b'])
>>> a | b                              # letters in either a or b
set(['a', 'c', 'r', 'd', 'b', 'm', 'z', 'l'])
>>> a & b                              # letters in both a and b
set(['a', 'c'])
>>> a ^ b                              # letters in a or b but not both
set(['r', 'd', 'b', 'm', 'z', 'l'])


Another useful data type built into Python is the dictionary. Dictionaries are sometimes found in other languages as ``associative memories'' or ``associative arrays''. Unlike sequences, which are indexed by a range of numbers, dictionaries are indexed by keys, which can be any immutable type; strings and numbers can always be keys. Tuples can be used as keys if they contain only strings, numbers, or tuples; if a tuple contains any mutable object either directly or indirectly, it cannot be used as a key. You can't use lists as keys, since lists can be modified in place using index assignments, slice assignments, or methods like append() and extend().

It is best to think of a dictionary as an unordered set of key: value pairs, with the requirement that the keys are unique (within one dictionary). A pair of braces creates an empty dictionary: {}. Placing a comma-separated list of key:value pairs within the braces adds initial key:value pairs to the dictionary; this is also the way dictionaries are written on output.

The main operations on a dictionary are storing a value with some key and extracting the value given the key. It is also possible to delete a key:value pair with del. If you store using a key that is already in use, the old value associated with that key is forgotten. It is an error to extract a value using a non-existent key.

The keys() method of a dictionary object returns a list of all the keys used in the dictionary, in arbitrary order (if you want it sorted, just apply the sort() method to the list of keys). To check whether a single key is in the dictionary, either use the dictionary's has_key() method or the in keyword.

Here is a small example using a dictionary:

>>> tel = {'jack': 4098, 'sape': 4139}
>>> tel['guido'] = 4127
>>> tel
{'sape': 4139, 'guido': 4127, 'jack': 4098}
>>> tel['jack']
>>> del tel['sape']
>>> tel['irv'] = 4127
>>> tel
{'guido': 4127, 'irv': 4127, 'jack': 4098}
>>> tel.keys()
['guido', 'irv', 'jack']
>>> tel.has_key('guido')
>>> 'guido' in tel

The dict() constructor builds dictionaries directly from lists of key-value pairs stored as tuples. When the pairs form a pattern, list comprehensions can compactly specify the key-value list.

>>> dict([('sape', 4139), ('guido', 4127), ('jack', 4098)])
{'sape': 4139, 'jack': 4098, 'guido': 4127}
>>> dict([(x, x**2) for x in (2, 4, 6)])     # use a list comprehension
{2: 4, 4: 16, 6: 36}

Later in the tutorial, we will learn about Generator Expressions which are even better suited for the task of supplying key-values pairs to the dict() constructor.

When the keys are simple strings, it is sometimes easier to specify pairs using keyword arguments:

>>> dict(sape=4139, guido=4127, jack=4098)
{'sape': 4139, 'jack': 4098, 'guido': 4127}

Looping Techniques

When looping through dictionaries, the key and corresponding value can be retrieved at the same time using the iteritems() method.

>>> knights = {'gallahad': 'the pure', 'robin': 'the brave'}
>>> for k, v in knights.iteritems():
...     print k, v
gallahad the pure
robin the brave

When looping through a sequence, the position index and corresponding value can be retrieved at the same time using the enumerate() function.

>>> for i, v in enumerate(['tic', 'tac', 'toe']):
...     print i, v
0 tic
1 tac
2 toe

To loop over two or more sequences at the same time, the entries can be paired with the zip() function.

>>> questions = ['name', 'quest', 'favorite color']
>>> answers = ['lancelot', 'the holy grail', 'blue']
>>> for q, a in zip(questions, answers):
...     print 'What is your %s?  It is %s.' % (q, a)
What is your name?  It is lancelot.
What is your quest?  It is the holy grail.
What is your favorite color?  It is blue.

To loop over a sequence in reverse, first specify the sequence in a forward direction and then call the reversed() function.

>>> for i in reversed(xrange(1,10,2)):
...     print i

To loop over a sequence in sorted order, use the sorted() function which returns a new sorted list while leaving the source unaltered.

>>> basket = ['apple', 'orange', 'apple', 'pear', 'orange', 'banana']
>>> for f in sorted(set(basket)):
...     print f

More on Conditions

The conditions used in while and if statements can contain any operators, not just comparisons.

The comparison operators in and not in check whether a value occurs (does not occur) in a sequence. The operators is and is not compare whether two objects are really the same object; this only matters for mutable objects like lists. All comparison operators have the same priority, which is lower than that of all numerical operators.

Comparisons can be chained. For example, a < b == c tests whether a is less than b and moreover b equals c.

Comparisons may be combined using the Boolean operators and and or, and the outcome of a comparison (or of any other Boolean expression) may be negated with not. These have lower priorities than comparison operators; between them, not has the highest priority and or the lowest, so that A and not B or C is equivalent to (A and (not B)) or C. As always, parentheses can be used to express the desired composition.

The Boolean operators and and or are so-called short-circuit operators: their arguments are evaluated from left to right, and evaluation stops as soon as the outcome is determined. For example, if A and C are true but B is false, A and B and C does not evaluate the expression C. When used as a general value and not as a Boolean, the return value of a short-circuit operator is the last evaluated argument.

It is possible to assign the result of a comparison or other Boolean expression to a variable. For example,

>>> string1, string2, string3 = '', 'Trondheim', 'Hammer Dance'
>>> non_null = string1 or string2 or string3
>>> non_null

Note that in Python, unlike C, assignment cannot occur inside expressions. C programmers may grumble about this, but it avoids a common class of problems encountered in C programs: typing = in an expression when == was intended.

Comparing Sequences and Other Types

Sequence objects may be compared to other objects with the same sequence type. The comparison uses lexicographical ordering: first the first two items are compared, and if they differ this determines the outcome of the comparison; if they are equal, the next two items are compared, and so on, until either sequence is exhausted. If two items to be compared are themselves sequences of the same type, the lexicographical comparison is carried out recursively. If all items of two sequences compare equal, the sequences are considered equal. If one sequence is an initial sub-sequence of the other, the shorter sequence is the smaller (lesser) one. Lexicographical ordering for strings uses the ASCII ordering for individual characters. Some examples of comparisons between sequences of the same type:

(1, 2, 3)              < (1, 2, 4)
[1, 2, 3]              < [1, 2, 4]
'ABC' < 'C' < 'Pascal' < 'Python'
(1, 2, 3, 4)           < (1, 2, 4)
(1, 2)                 < (1, 2, -1)
(1, 2, 3)             == (1.0, 2.0, 3.0)
(1, 2, ('aa', 'ab'))   < (1, 2, ('abc', 'a'), 4)

Note that comparing objects of different types is legal. The outcome is deterministic but arbitrary: the types are ordered by their name. Thus, a list is always smaller than a string, a string is always smaller than a tuple, etc. Mixed numeric types are compared according to their numeric value, so 0 equals 0.0, etc.

Be the first one to comment on this page.

  Python eBooks
More Links » »
 Python FAQs
More Links » »
 Python Interview Questions
More Links » »
 Python Articles

No Python Articles could be found as of now.

 Python News

No News on Python could be found as of now.

 Python Jobs

No Python Articles could be found as of now.

Share And Enjoy:These icons link to social bookmarking sites where readers can share and discover new web pages.
  • blinkbits
  • BlinkList
  • blogmarks
  • co.mments
  • connotea
  • digg
  • Fark
  • feedmelinks
  • Furl
  • LinkaGoGo
  • Ma.gnolia
  • NewsVine
  • Netvouz
  • RawSugar
  • Reddit
  • scuttle
  • Shadows
  • Simpy
  • Smarking
  • Spurl
  • TailRank
  • Wists
  • YahooMyWeb

Previoushome Next

Keywords: Data Structures, Python Tutorial, Python tutorial pdf, history of Python, basic Python, syntax use in Python, Python training courses, Python tool kit, Python switch.

HTML Quizzes
CSS Quiz
CSS 1.0 Quiz
CSS 2.0 Quiz
XML Quizzes
XML Quiz
XSL Quiz
DTD Quiz
Schema Quiz
XForms Quiz
XLink Quiz
XQuery Quiz
XPath Quiz
XPointer Quiz
RDF Quiz
RSS Quiz
WAP Quiz
Web Services Quiz
Browser Scripting Quizzes
JavaScript Quiz
VBScript Quiz
WMLScript Quiz
E4X Quiz
Server Scripting Quizzes
ASP Quiz
SQL Quiz
ADO Quiz
CVS Quiz
Python Quiz
Apple Script Quiz
SQL Server Quiz
PHP Quiz
.NET (dotnet) Quizzes
Microsoft.Net Quiz
ASP.Net Quiz
.Net Mobile Quiz
C# : C Sharp Quiz
VC++ Quiz
Multimedia Quizzes
SVG Quiz
Flash Quiz
Media Quiz
Photoshop Quiz
Gimp Quiz
Matlab Quiz
Gnuplot Programming Quiz
GIF Animation Quiz
Scientific Visualization Quiz
Graphics Quiz
Web Building Quizzes
Web Browsers Quiz
Web Hosting Quiz
W3C Quiz
Web Building Quiz
Web Quality Quiz
Web Semantic Quiz
Web Careers Quiz
Weblogic Quiz
SEO Quiz
Web Site Hosting Quiz
Domain Name Quiz
Java Quizzes
Java Quiz
JSP Quiz
Servlets Quiz
Struts Quiz
EJB Quiz
JMS Quiz
JMX Quiz
Eclipse Quiz
J2ME Quiz
Programming Langauges Quizzes
C Quiz
C++ Quiz
Visual Basic Quiz
Data Structures Using C Quiz
Cobol Quiz
Assembly Language Quiz
Mainframe Quiz
Forth Programming Quiz
Lisp Programming Quiz
Pascal Quiz
Delphi Quiz
Fortran Quiz
OOPs Quiz
Data Warehousing Quiz
CGI Programming Quiz
Emacs Quiz
Gnome Quiz
ILU Quiz
Soft Skills Quizzes
Communication Skills Quiz
Time Management Quiz
Project Management Quiz
Team Work Quiz
Leadership Skills Quiz
Corporate Communication Quiz
Negotiation Skills Quiz
Database Quizzes
Oracle Quiz
MySQL Quiz
Operating System Quizzes
BSD Quiz
Symbian Quiz
Unix Quiz
Internet Quiz
IP-Masquerading Quiz
IPC Quiz
Software Testing Quizzes
Testing Quiz
Firewalls Quiz
SAP Module Quizzes
ERP Quiz
Business Warehousing Quiz
SAP Basis Quiz
Material Management Quiz
Sales & Distribution Quiz
Human Resource Quiz
Netweaver Quiz
Customer Relationship Management Quiz
Production and Planning Quiz
Networking Programming Quizzes
Corba Quiz
Networking Quiz
Microsoft Office Quizzes
Microsoft Word Quiz
Microsoft Outlook Quiz
Microsoft PowerPoint Quiz
Microsoft Publisher Quiz
Microsoft Excel Quiz
Microsoft Front Page Quiz
Microsoft InfoPath Quiz
Microsoft Access Quiz
Accounting Quizzes
Financial Accounting Quiz
Managerial Accounting Quiz
Testimonials | Contact Us | Link to Us | Site Map
Copyright 2008. Academic All rights reserved Privacy Policies | About Us
Our Portals : Academic Tutorials | Best eBooksworld | Beyond Stats | City Details | Interview Questions | Discussions World | Excellent Mobiles | Free Bangalore | Give Me The Code | Gog Logo | Indian Free Ads | Jobs Assist | New Interview Questions | One Stop FAQs | One Stop GATE | One Stop GRE | One Stop IAS | One Stop MBA | One Stop SAP | One Stop Testing | Webhosting in India | Dedicated Server in India | Sirf Dosti | Source Codes World | Tasty Food | Tech Archive | Testing Interview Questions | Tests World | The Galz | Top Masala | Vyom | Vyom eBooks | Vyom International | Vyom Links | Vyoms | Vyom World
Copyright 2003-2019 Vyom Technosoft Pvt. Ltd., All Rights Reserved.