A Byte of Python - A very good Python tutorial for beginners
http://www.byteofpython.info/
A Byte of Python
Swaroop C H
www.byteofpython.info
Version 1.20
Copyright © 2003-2005 Swaroop C H
This book is released under the Creative Commons
Attribution-NonCommercial-ShareAlike License 2.0 .
Abstract
This book will help you to learn the Python programming language,
whether you are new to computers or are an experienced programmer.
_________________________________________________________
Table of Contents
Preface
Who This Book Is For
History Lesson
Status of the book
Official Website
License Terms
Feedback
Something To Think About
1. Introduction
Introduction
Features of Python
Summary
Why not Perl?
What Programmers Say
2. Installing Python
For Linux/BSD users
For Windows Users
Summary
3. First Steps
Introduction
Using the interpreter prompt
Choosing an Editor
Using a Source File
Output
How It Works
Executable Python programs
Getting Help
Summary
4. The Basics
Literal Constants
Numbers
Strings
Variables
Identifier Naming
Data Types
Objects
Output
How It Works
Logical and Physical Lines
Indentation
Summary
5. Operators and Expressions
Introduction
Operators
Operator Precedence
Order of Evaluation
Associativity
Expressions
Using Expressions
Summary
6. Control Flow
Introduction
The if statement
Using the if statement
How It Works
The while statement
Using the while statement
The for loop
Using the for statement
The break statement
Using the break statement
The continue statement
Using the continue statement
Summary
7. Functions
Introduction
Defining a Function
Function Parameters
Using Function Parameters
Local Variables
Using Local Variables
Using the global statement
Default Argument Values
Using Default Argument Values
Keyword Arguments
Using Keyword Arguments
The return statement
Using the literal statement
DocStrings
Using DocStrings
Summary
8. Modules
Introduction
Using the sys module
Byte-compiled .pyc files
The from..import statement
A module's __name__
Using a module's __name__
Making your own Modules
Creating your own Modules
from..import
The dir() function
Using the dir function
Summary
9. Data Structures
Introduction
List
Quick introduction to Objects and Classes
Using Lists
Tuple
Using Tuples
Tuples and the print statement
Dictionary
Using Dictionaries
Sequences
Using Sequences
References
Objects and References
More about Strings
String Methods
Summary
10. Problem Solving - Writing a Python Script
The Problem
The Solution
First Version
Second Version
Third Version
Fourth Version
More Refinements
The Software Development Process
Summary
11. Object-Oriented Programming
Introduction
The self
Classes
Creating a Class
object Methods
Using Object Methds
The __init__ method
Using the __init__ method
Class and Object Variables
Using Class and Object Variables
Inheritance
Using Inheritance
Summary
12. Input/Output
Files
Using file
Pickle
Pickling and Unpickling
Summary
13. Exceptions
Errors
Try..Except
Handling Exceptions
Raising Exceptions
How To Raise Exceptions
Try..Finally
Using Finally
Summary
14. The Python Standard Library
Introduction
The sys module
Command Line Arguments
More sys
The os module
Summary
15. More Python
Special Methods
Single Statement Blocks
List Comprehension
Using List Comprehensions
Receiving Tuples and Lists in Functions
Lambda Forms
Using Lambda Forms
The exec and eval statements
The assert statement
The repr function
Summary
16. What Next?
Graphical Software
Summary of GUI Tools
Explore More
Summary
A. Free/Libré and Open Source Software (FLOSS)
B. About
Colophon
About the Author
C. Revision History
Timestamp
List of Tables
5.1. Operators and their usage
5.2. Operator Precedence
15.1. Some Special Methods
List of Examples
3.1. Using the python interpreter prompt
3.2. Using a Source File
4.1. Using Variables and Literal constants
5.1. Using Expressions
6.1. Using the if statement
6.2. Using the while statement
6.3. Using the for statement
6.4. Using the break statement
6.5. Using the continue statement
7.1. Defining a function
7.2. Using Function Parameters
7.3. Using Local Variables
7.4. Using the global statement
7.5. Using Default Argument Values
7.6. Using Keyword Arguments
7.7. Using the literal statement
7.8. Using DocStrings
8.1. Using the sys module
8.2. Using a module's __name__
8.3. How to create your own module
8.4. Using the dir function
9.1. Using lists
9.2. Using Tuples
9.3. Output using tuples
9.4. Using dictionaries
9.5. Using Sequences
9.6. Objects and References
9.7. String Methods
10.1. Backup Script - The First Version
10.2. Backup Script - The Second Version
10.3. Backup Script - The Third Version (does not work!)
10.4. Backup Script - The Fourth Version
11.1. Creating a Class
11.2. Using Object Methods
11.3. Using the __init__ method
11.4. Using Class and Object Variables
11.5. Using Inheritance
12.1. Using files
12.2. Pickling and Unpickling
13.1. Handling Exceptions
13.2. How to Raise Exceptions
13.3. Using Finally
14.1. Using sys.argv
15.1. Using List Comprehensions
15.2. Using Lambda Forms
Preface
Table of Contents
Who This Book Is For
History Lesson
Status of the book
Official Website
License Terms
Feedback
Something To Think About
Python is probably one of the few programming languages which is
both simple and powerful. This is good for both and beginners as
well as experts, and more importantly, is fun to program with. This
book aims to help you learn this wonderful language and show how to
get things done quickly and painlessly - in effect 'The Perfect
Anti-venom to your programming problems'.
Who This Book Is For
This book serves as a guide or tutorial to the Python programming
language. It is mainly targeted at newbies. It is useful for
experienced programmers as well.
The aim is that if all you know about computers is how to save text
files, then you can learn Python from this book. If you have
previous programming experience, then you can also learn Python from
this book.
If you do have previous programming experience, you will be
interested in the differences between Python and your favorite
programming language - I have highlighted many such differences. A
little warning though, Python is soon going to become your favorite
programming language!
History Lesson
I first started with Python when I needed to write an installer for
my software Diamond so that I could make the installation easy. I
had to choose between Python and Perl bindings for the Qt library. I
did some research on the web and I came across an article where Eric
S. Raymond, the famous and respected hacker, talked about how Python
has become his favorite programming language. I also found out that
the PyQt bindings were very good compared to Perl-Qt. So, I decided
that Python was the language for me.
Then, I started searching for a good book on Python. I couldn't find
any! I did find some O'Reilly books but they were either too
expensive or were more like a reference manual than a guide. So, I
settled for the documentation that came with Python. However, it was
too brief and small. It did give a good idea about Python but was
not complete. I managed with it since I had previous programming
experience, but it was unsuitable for newbies.
About six months after my first brush with Python, I installed the
(then) latest Red Hat 9.0 Linux and I was playing around with KWord.
I got excited about it and suddenly got the idea of writing some
stuff on Python. I started writing a few pages but it quickly became
30 pages long. Then, I became serious about making it more useful in
a book form. After a lot of rewrites, it has reached a stage where
it has become a useful guide to learning the Python language. I
consider this book to be my contribution and tribute to the open
source community.
This book started out as my personal notes on Python and I still
consider it in the same way, although I've taken a lot of effort to
make it more palatable to others :)
In the true spirit of open source, I have received lots of
constructive suggestions, criticisms and feedback from enthusiastic
readers which has helped me improve this book a lot.
Status of the book
This book is a work-in-progress. Many chapters are constantly being
changed and improved. However, the book has matured a lot. You
should be able to learn Python easily from this book. Please do tell
me if you find any part of the book to be incorrect or
incomprehensible.
More chapters are planned for the future, such as on wxPython,
Twisted and maybe even Boa Constructor.
Official Website
The official website of the book is www.byteofpython.info . From the
website, you can read the whole book online or you can download the
latest versions of the book, and also send me feedback.
License Terms
This book is licensed under the Creative Commons
Attribution-NonCommercial-ShareAlike License 2.0 .
Basically, you are free to copy, distribute, and display the book,
as long as you give credit to me. The restrictions are that you
cannot use the book for commercial purposes without my permission.
You are free to modify and build upon this work, provided that you
clearly mark all changes and release the modified work under the
same license as this book.
Please visit the Creative Commons website for the full and exact
text of the license, or for an easy-to-understand version. There is
even a comic strip explaining the terms of the license.
Feedback
I have put in a lot of effort to make this book as interesting and
as accurate as possible. However, if you find some material to be
inconsistent or incorrect, or simply needs improvement, then please
do inform me, so that I can make suitable improvements. You can
reach me at .
Something To Think About
There are two ways of constructing a software design: one way is
to make it so simple that there are obviously no deficiencies; the
other is to make it so complicated that there are no obvious
deficiencies.
--C. A. R. Hoare
Success in life is a matter not so much of talent and opportunity
as of concentration and perseverance.
--C. W. Wendte
Chapter 1. Introduction
Table of Contents
Introduction
Features of Python
Summary
Why not Perl?
What Programmers Say
Introduction
Python is one of those rare languages which can claim to be both
simple and powerful. You will find that you will be pleasantly
surprised on how easy it is to concentrate on the solution to the
problem rather than the syntax and structure of the language you are
programming in.
The official introduction to Python is
Python is an easy to learn, powerful programming language. It has
efficient high-level data structures and a simple but effective
approach to object-oriented programming. Python's elegant syntax
and dynamic typing, together with its interpreted nature, make it
an ideal language for scripting and rapid application development
in many areas on most platforms.
I will discuss most of these features in more detail in the next
section.
Note
Guido van Rossum, the creator of the Python language, named the
language after the BBC show "Monty Python's Flying Circus ". He
doesn't particularly like snakes that kill animals for food by
winding their long bodies around them and crushing them.
Features of Python
Simple
Python is a simple and minimalistic language. Reading a good
Python program feels almost like reading English, although
very strict English! This pseudo-code nature of Python is one
of its greatest strengths. It allows you to concentrate on
the solution to the problem rather than the language itself.
Easy to Learn
As you will see, Python is extremely easy to get started
with. Python has an extraordinarily simple syntax, as already
mentioned.
Free and Open Source
Python is an example of a FLOSS (Free/Libré and Open Source
Software). In simple terms, you can freely distribute copies
of this software, read it's source code, make changes to it,
use pieces of it in new free programs, and that you know you
can do these things. FLOSS is based on the concept of a
community which shares knowledge. This is one of the reasons
why Python is so good - it has been created and is constantly
improved by a community who just want to see a better Python.
High-level Language
When you write programs in Python, you never need to bother
about the low-level details such as managing the memory used
by your program, etc.
Portable
Due to its open-source nature, Python has been ported (i.e.
changed to make it work on) to many platforms. All your
Python programs can work on any of these platforms without
requiring any changes at all if you are careful enough to
avoid any system-dependent features.
You can use Python on Linux, Windows, FreeBSD, Macintosh,
Solaris, OS/2, Amiga, AROS, AS/400, BeOS, OS/390, z/OS, Palm
OS, QNX, VMS, Psion, Acorn RISC OS, VxWorks, PlayStation,
Sharp Zaurus, Windows CE and even PocketPC !
Interpreted
This requires a bit of explanation.
A program written in a compiled language like C or C++ is
converted from the source language i.e. C or C++ into a
language that is spoken by your computer (binary code i.e. 0s
and 1s) using a compiler with various flags and options. When
you run the program, the linker/loader software copies the
program from hard disk to memory and starts running it.
Python, on the other hand, does not need compilation to
binary. You just run the program directly from the source
code. Internally, Python converts the source code into an
intermediate form called bytecodes and then translates this
into the native language of your computer and then runs it.
All this, actually, makes using Python much easier since you
don't have to worry about compiling the program, making sure
that the proper libraries are linked and loaded, etc, etc.
This also makes your Python programs much more portable,
since you can just copy your Python program onto another
computer and it just works!
Object Oriented
Python supports procedure-oriented programming as well as
object-oriented programming. In procedure-oriented languages,
the program is built around procedures or functions which are
nothing but reusable pieces of programs. In object-oriented
languages, the program is built around objects which combine
data and functionality. Python has a very powerful but
simplistic way of doing OOP, especially when compared to big
languages like C++ or Java.
Extensible
If you need a critical piece of code to run very fast or want
to have some piece of algorithm not to be open, you can code
that part of your program in C or C++ and then use them from
your Python program.
Embeddable
You can embed Python within your C/C++ programs to give
'scripting' capabilities for your program's users.
Extensive Libraries
The Python Standard Library is huge indeed. It can help you
do various things involving regular expressions,
documentation generation, unit testing, threading, databases,
web browsers, CGI, ftp, email, XML, XML-RPC, HTML, WAV files,
cryptography, GUI (graphical user interfaces), Tk, and other
system-dependent stuff. Remember, all this is always
available wherever Python is installed. This is called the
'Batteries Included' philosophy of Python.
Besides, the standard library, there are various other
high-quality libraries such as wxPython, Twisted, Python
Imaging Library and many more.
Summary
Python is indeed an exciting and powerful language. It has the right
combination of performance and features that make writing programs
in Python both fun and easy.
Why not Perl?
If you didn't know already, Perl is another extremely popular open
source interpreted programming language.
If you have ever tried writing a large program in Perl, you would
have answered this question yourself! In other words, Perl programs
are easy when they are small and it excels at small hacks and
scripts to 'get work done'. However, they quickly become unwieldy
once you start writing bigger programs and I am speaking this out of
experience of writing large Perl programs at Yahoo!
When compared to Perl, Python programs are definitely simpler,
clearer, easier to write and hence more understandable and
maintainable. I do admire Perl and I do use it on a daily basis for
various things but whenever I write a program, I always start
thinking in terms of Python because it has become so natural for me.
Perl has undergone so many hacks and changes, that it feels like it
is one big (but one hell of a) hack. Sadly, the upcoming Perl 6 does
not seem to be making any improvements regarding this.
The only and very significant advantage that I feel Perl has, is its
huge CPAN library - the Comprehensive Perl Archive Network. As the
name suggests, this is a humongous collection of Perl modules and it
is simply mind-boggling because of its sheer size and depth - you
can do virtually anything you can do with a computer using these
modules. One of the reasons that Perl has more libraries than Python
is that it has been around for a much longer time than Python. Maybe
I should suggest a port-Perl-modules-to-Python hackathon on
comp.lang.python :)
Also, the new Parrot virtual machine is designed to run both the
completely redesigned Perl 6 as well as Python and other interpreted
languages like Ruby, PHP and Tcl. What this means to you is that
maybe you will be able to use all Perl modules from Python in the
future, so that will give you the best of both worlds - the powerful
CPAN library combined with the powerful Python language. However, we
will have to just wait and see what happens.
What Programmers Say
You may find it interesting to read what great hackers like ESR have
to say about Python:
* Eric S. Raymond is the author of 'The Cathedral and the Bazaar'
and is also the person who coined the term 'Open Source'. He
says that Python has become his favorite programming language.
This article was the real inspiration for my first brush with
Python.
* Bruce Eckel is the author of the famous 'Thinking in Java' and
'Thinking in C++' books. He says that no language has made him
more productive than Python. He says that Python is perhaps the
only language that focuses on making things easier for the
programmer. Read the complete interview for more details.
* Peter Norvig is a well-known Lisp author and Director of Search
Quality at Google (thanks to Guido van Rossum for pointing that
out). He says that Python has always been an integral part of
Google. You can actually verify this statement by looking at the
Google Jobs page which lists Python knowledge as a requirement
for software engineers.
* Bruce Perens is a co-founder of OpenSource.org and the UserLinux
project. UserLinux aims to create a standardized Linux
distribution supported by multiple vendors. Python has beaten
contenders like Perl and Ruby to become the main programming
language that will be supported by UserLinux.
Chapter 2. Installing Python
Table of Contents
For Linux/BSD users
For Windows Users
Summary
For Linux/BSD users
If you are using a Linux distribution such as Fedora or Mandrake or
{put your choice here}, or a BSD system such as FreeBSD, then you
probably already have Python installed on your system.
To test if you have Python already installed on your Linux box, open
a shell program (like konsole or gnome-terminal) and enter the
command python -V as shown below.
$ python -V
Python 2.3.4
Note
$ is the prompt of the shell. It will be different for you depending
on the settings of your OS, hence I will indicate the prompt by just
the $ symbol.
If you see some version information like the one shown above, then
you have Python installed already.
However, if you get a message like this one:
$ python -V
bash: python: command not found
then, you don't have Python installed. This is highly unlikely but
possible.
In this case, you have two ways of installing Python on your system.
* Install the binary packages using the package management
software that comes with your OS, such as yum in Fedora Linux,
urpmi in Mandrake Linux, apt-get in Debian Linux, pkg_add in
FreeBSD, etc. Note that you will need an internet connection to
use this method.
Alternatively, you can download the binaries from somewhere else
and then copy to your PC and install it.
* You can compile Python from the source code and install it. The
compilation instructions are provided at the website.
For Windows Users
Visit Python.org/download and download the latest version from this
website (which was 2.3.4 as of this writing. This is just 9.4 MB
which is very compact compared to most other languages. The
installation is just like any other Windows-based software.
Caution
When you are given the option of unchecking any optional components,
don't uncheck any! Some of these components can be useful for you,
especially IDLE.
An interesting fact is that about 70% of Python downloads are by
Windows users. Of course, this doesn't give the complete picture
since almost all Linux users will have Python installed already on
their systems by default.
Using Python in the Windows command line
If you want to be able to use Python from the Windows command line,
then you need to set the PATH variable appropriately.
For Windows 2000, XP, 2003 , click on Control Panel -> System ->
Advanced -> Environment Variables. Click on the variable named PATH
in the 'System Variables' section, then select Edit and add
;C:/Python23 (without the quotes) to the end of what is already
there. Of course, use the appropriate directory name.
For older versions of Windows, add the following line to the file
C:/AUTOEXEC.BAT : 'PATH=%PATH%;C:/Python23' (without the quotes) and
restart the system. For Windows NT, use the AUTOEXEC.NT file.
Summary
For a Linux system, you most probably already have Python installed
on your system. Otherwise, you can install it using the package
management software that comes with your distribution. For a Windows
system, installing Python is as easy as downloading the installer
and double-clicking on it. From now on, we will assume that you have
Python installed on your system.
Next, we will write our first Python program.
Chapter 3. First Steps
Table of Contents
Introduction
Using the interpreter prompt
Choosing an Editor
Using a Source File
Output
How It Works
Executable Python programs
Getting Help
Summary
Introduction
We will now see how to run a traditional 'Hello World' program in
Python. This will teach you how to write, save and run Python
programs.
There are two ways of using Python to run your program - using the
interactive interpreter prompt or using a source file. We will now
see how to use both the methods.
Using the interpreter prompt
Start the intepreter on the command line by entering python at the
shell prompt. Now enter print 'Hello World' followed by the Enter
key. You should see the words Hello World as output.
For Windows users, you can run the interpreter in the command line
if you have set the PATH variable appropriately. Alternatively, you
can use the IDLE program. IDLE is short for Integrated DeveLopment
Environment. Click on Start -> Programs -> Python 2.3 -> IDLE
(Python GUI). Linux users can use IDLE too.
Note that the <<< signs are the prompt for entering Python
statements.
Example 3.1. Using the python interpreter prompt
$ python
Python 2.3.4 (#1, Oct 26 2004, 16:42:40)
[GCC 3.4.2 20041017 (Red Hat 3.4.2-6.fc3)] on linux2
Type "help", "copyright", "credits" or "license" for more information.
>>> print 'hello world'
hello world
>>>
Notice that Python gives you the output of
the line immediately! What you just entered is a single Python
statement. We use print to (unsurprisingly) print any value that you
supply to it. Here, we are supplying the text Hello World and this
is promptly printed to the screen.
How to quit the Python prompt
To exit the prompt, press Ctrl-d if you are using IDLE or are using
a Linux/BSD shell. In case of the Windows command prompt, press
Ctrl-z followed by Enter.
Choosing an Editor
Before we move on to writing Python programs in source files, we
need an editor to write the source files. The choice of an editor is
crucial indeed. You have to choose an editor as you would choose a
car you would buy. A good editor will help you write Python programs
easily, making your journey more comfortable and helps you reach
your destination (achieve your goal) in a much faster and safer way.
One of the very basic requirements is syntax highlighting where all
the different parts of your Python program are colorized so that you
can see your program and visualize its running.
If you are using Windows, then I suggest that you use IDLE. IDLE
does syntax highlighting and a lot more such as allowing you to run
your programs within IDLE among other things. A special note: don't
use Notepad - it is a bad choice because it does not do syntax
highlighting and also importantly it does not support indentation of
the text which is very important in our case as we will see later.
Good editors such as IDLE (and also VIM) will automatically help you
do this.
If you are using Linux/FreeBSD, then you have a lot of choices for
an editor. If you are an experienced programmer, then you must be
already using VIM or Emacs. Needless to say, these are two of the
most powerful editors and you will be benefitted by using them to
write your Python programs. I personally use VIM for most of my
programs. If you are a beginner programmer, then you can use Kate
which is one of my favorites. In case you are willing to take the
time to learn VIM or Emacs, then I highly recommend that you do
learn to use either of them as it will be very useful for you in the
long run.
If you still want to explore other choices of an editor, see the
comprehensive list of Python editors and make your choice. You can
also choose an IDE (Integrated Development Environment) for Python.
See the comprehensive list of IDEs that support Python for more
details. Once you start writing large Python programs, IDEs can be
very useful indeed.
I repeat once again, please choose a proper editor - it can make
writing Python programs more fun and easy.
Using a Source File
Now let's get back to programming. There is a tradition that
whenever you learn a new programming language, the first program
that you write and run is the 'Hello World' program - all it does is
just say 'Hello World' when you run it. As Simon Cozens ^[1] puts
it, it is the 'traditional incantation to the programming gods to
help you learn the language better' :) .
Start your choice of editor, enter the following program and save it
as helloworld.py
Example 3.2. Using a Source File
#!/usr/bin/python
# Filename : helloworld.py
print 'Hello World'
(Source file: code/helloworld.py)
Run this program by opening a shell (Linux terminal or DOS prompt)
and entering the command python helloworld.py. If you are using
IDLE, use the menu Edit -> Run Script or the keyboard shortcut
Ctrl-F5. The output is as shown below.
Output
$ python helloworld.py
Hello World
If you got the output as shown above,
congratulations! - you have successfully run your first Python
program.
In case you got an error, please type the above program exactly as
shown and above and run the program again. Note that Python is
case-sensitive i.e. print is not the same as Print - note the
lowercase p in the former and the uppercase P in the latter. Also,
ensure there are no spaces or tabs before the first character in
each line - we will see why this is important later.
How It Works
Let us consider the first two lines of the program. These are called
comments - anything to the right of the # symbol is a comment and is
mainly useful as notes for the reader of the program.
Python does not use comments except for the special case of the
first line here. It is called the shebang line - whenever the first
two characters of the source file are #! followed by the location of
a program, this tells your Linux/Unix system that this program
should be run with this interpreter when you execute the program.
This is explained in detail in the next section. Note that you can
always run the program on any platform by specifying the interpreter
directly on the command line such as the command python
helloworld.py .
Important
Use comments sensibly in your program to explain some important
details of your program - this is useful for readers of your program
so that they can easily understand what the program is doing.
Remember, that person can be yourself after six months!
The comments are followed by a Python statement - this just prints
the text 'Hello World'. The print is actually an operator and 'Hello
World' is referred to as a string - don't worry, we will explore
these terminologies in detail later.
Executable Python programs
This applies only to Linux/Unix users but Windows users might be
curious as well about the first line of the program. First, we have
to give the program executable permission using the chmod command
then run the source program.
$ chmod a+x helloworld.py
$ ./helloworld.py
Hello World
The chmod command is used here to change the mode of
the file by giving execute permission to all users of the system.
Then, we execute the program directly by specifying the location of
the source file. We use the ./ to indicate that the program is
located in the current directory.
To make things more fun, you can rename the file to just helloworld
and run it as ./helloworld and it will still work since the system
knows that it has to run the program using the interpreter whose
location is specified in the first line in the source file.
You are now able to run the program as long as you know the exact
path of the program - but what if you wanted to be able to run the
program from anywhere? You can do this by storing the program in one
of the directories listed in the PATH environment variable. Whenever
you run any program, the system looks for that program in each of
the directories listed in the PATH environment variable and then
runs that program. We can make this program available everywhere by
simply copying this source file to one of the directories listed in
PATH.
$ echo $PATH
/opt/mono/bin:/usr/local/bin:/usr/bin:/bin:/usr/X11R6/bin:/home/swaroop
/bin
$ cp helloworld.py /home/swaroop/bin/helloworld
$ helloworld
Hello World
We can display the PATH variable using the echo
command and prefixing the variable name by $ to indicate to the
shell that we need the value of this variable. We see that
/home/swaroop/bin is one of the directories in the PATH variable
where swaroop is the username I am using in my system. There will
usually be a similar directory for your username on your system.
Alternatively, you can add a directory of your choice to the PATH
variable - this can be done by running
PATH=$PATH:/home/swaroop/mydir where '/home/swaroop/mydir' is the
directory I want to add to the PATH variable.
This method is very useful if you want to write useful scripts that
you want to run the program anytime, anywhere. It is like creating
your own commands just like cd or any other commands that you use in
the Linux terminal or DOS prompt.
Caution
W.r.t. Python, a program or a script or software all mean the same
thing.
Getting Help
If you need quick information about any function or statement in
Python, then you can use the built-in help functionality. This is
very useful especially when using the interpreter prompt. For
example, run help(str) - this displays the help for the str class
which is used to store all text (strings) that you use in your
program. Classes will be explained in detail in the chapter on
object-oriented programming.
Note
Press q to exit the help.
Similarly, you can obtain information about almost anything in
Python. Use help() to learn more about using help itself!
In case you need to get help for operators like print, then you need
to set the PYTHONDOCS environment variable appropriately. This can
be done easily on Linux/Unix using the env command.
$ env PYTHONDOCS=/usr/share/doc/python-docs-2.3.4/html/ python
Python 2.3.4 (#1, Oct 26 2004, 16:42:40)
[GCC 3.4.2 20041017 (Red Hat 3.4.2-6.fc3)] on linux2
Type "help", "copyright", "credits" or "license" for more information.
>>> help('print')
You will notice that I have used quotes to specify
'print' so that Python can understand that I want to fetch help
about 'print' and I am not asking it to print something.
Note that the location I have used is the location in Fedora Core 3
Linux - it may be different for different distributions and
versions.
Summary
You should now be able to write, save and run Python programs at
ease. Now that you are a Python user, let's learn some more Python
concepts.
_________________________________________________________
^[1] one of the leading Perl6/Parrot hackers and the author of the
amazing 'Beginning Perl' book
Chapter 4. The Basics
Table of Contents
Literal Constants
Numbers
Strings
Variables
Identifier Naming
Data Types
Objects
Output
How It Works
Logical and Physical Lines
Indentation
Summary
Just printing 'Hello World' is not enough, is it? You want to do
more than that - you want to take some input, manipulate it and get
something out of it. We can achieve this in Python using constants
and variables.
Literal Constants
An example of a literal constant is a number like 5, 1.23, 9.25e-3
or a string like 'This is a string' or "It's a string!". It is
called a literal because it is literal - you use its value
literally. The number 2 always represents itself and nothing else -
it is a constant because its value cannot be changed. Hence, all
these are referred to as literal constants.
Numbers
Numbers in Python are of four types - integers, long integers,
floating point and complex numbers.
* Examples of integers are 2 which are just whole numbers.
* Long integers are just bigger whole numbers.
* Examples of floating point numbers (or floats for short) are
3.23 and 52.3E-4. The E notation indicates powers of 10. In this
case, 52.3E-4 means 52.3 * 10^-4.
* Examples of complex numbers are (-5+4j) and (2.3 - 4.6j)
Strings
A string is a sequence of characters. Strings are basically just a
bunch of words.
I can almost guarantee that you will be using strings in almost
every Python program that you write, so pay attention to the
following part. Here's how you use strings in Python:
* Using Single Quotes (')
You can specify strings using single quotes such as 'Quote me on
this' . All white space i.e. spaces and tabs are preserved
as-is.
* Using Double Quotes (")
Strings in double quotes work exactly the same way as strings in
single quotes. An example is "What's your name?"
* Using Triple Quotes (''' or """)
You can specify multi-line strings using triple quotes. You can
use single quotes and double quotes freely within the triple
quotes. An example is
'''This is a multi-line string. This is the first line.
This is the second line.
"What's your name?," I asked.
He said "Bond, James Bond."
'''
* Escape Sequences
Suppose, you want to have a string which contains a single quote
('), how will you specify this string? For example, the string
is What's your name?. You cannot specify 'What's your name?'
because Python will be confused as to where the string starts
and ends. So, you will have to specify that this single quote
does not indicate the end of the string. This can be done with
the help of what is called an escape sequence. You specify the
single quote as /' - notice the backslash. Now, you can specify
the string as 'What/'s your name?'.
Another way of specifying this specific string would be "What's
your name?" i.e. using double quotes. Similarly, you have to use
an escape sequence forusing a double quote itself in a double
quoted string. Also, you have to indicate the backslash itself
using the escape sequence //.
What if you wanted to specify a two-line string? One way is to
use a triple-quoted string as shown above or you can use an
escape sequence for the newline character - /n to indicate the
start of a new line. An example is This is the first line/nThis
is the second line . Another useful escape sequence to know is
the tab - /t. There are many more escape sequences but I have
mentioned only the most useful ones here.
One thing to note is that in a string, a single backslash at the
end of the line indicates that the string is continued in the
next line, but no newline is added. For example,
"This is the first sentence./
This is the second sentence."
is equivalent to "This is the
first sentence. This is the second sentence."
* Raw Strings
If you need to specify some strings where no special processing
such as escape sequences are handled, then what you need is to
specify a raw string by prefixing r or R to the string. An
example is r"Newlines are indicated by /n".
* Unicode Strings
Unicode is a standard way of writing international text. If you
want to write text in your native language such as Hindi or
Arabic, then you need to have a Unicode-enabled text editor.
Similarly, Python allows you to handle Unicode text - all you
need to do is prefix u or U. For example, u"This is a Unicode
string.".
Remember to use Unicode strings when you are dealing with text
files, especially when you know that the file will contain text
written in languages other than English.
* Strings are immutable
This means that once you have created a string, you cannot
change it. Although this might seem like a bad thing, it really
isn't. We will see why this is not a limitation in the various
programs that we see later on.
* String literal concatenation
If you place two string literals side by side, they are
automatically concatenated by Python. For example, 'What/'s'
'your name?' is automatically converted in to "What's your
name?".
Note for C/C++ Programmers
There is no separate char data type in Python. There is no real need
for it and I am sure you won't miss it.
Note for Perl/PHP Programmers
Remember that single-quoted strings and double-quoted strings are
the same - they do not differ in any way.
Note for Regular Expression Users
Always use raw strings when dealing with regular expressions.
Otherwise, a lot of backwhacking may be required. For example,
backreferences can be referred to as '//1' or r'/1'.
Variables
Using just literal constants can soon become boring - we need some
way of storing any information and manipulate them as well. This is
where variables come into the picture. Variables are exactly what
they mean - their value can vary i.e. you can store anything using a
variable. Variables are just parts of your computer's memory where
you store some information. Unlike literal constants, you need some
method of accessing these variables and hence you give them names.
Identifier Naming
Variables are examples of identifiers. Identifiers are names given
to identify something. There are some rules you have to follow for
naming identifiers:
* The first character of the identifier must be a letter of the
alphabet (upper or lowercase) or an underscore ('_').
* The rest of the identifier name can consist of letters (upper or
lowercase), underscores ('_') or digits (0-9).
* Identifier names are case-sensitive. For example, myname and
myName are not the same. Note the lowercase n in the former and
the uppercase N in te latter.
* Examples of valid identifier names are i, __my_name, name_23 and
a1b2_c3.
* Examples of invalid identifier names are 2things, this is spaced
out and my-name.
Data Types
Variables can hold values of different types called data types. The
basic types are numbers and strings, which we have already
discussed. In later chapters, we will see how to create our own
types using classes.
Objects
Remember, Python refers to anything used in a program as an object.
This is meant in the generic sense. Instead of saying 'the
something', we say 'the object'.
Note for Object Oriented Programming users
Python is strongly object-oriented in the sense that everything is
an object including numbers, strings and even functions.
We will now see how to use variables along with literal constants.
Save the following example and run the program.
How to write Python programs
Henceforth, the standard procedure to save and run a Python program
is as follows:
1. Open your favorite editor.
2. Enter the program code given in the example.
3. Save it as a file with the filename mentioned in the comment. I
follow the convention of having all Python programs saved with
the extension .py.
4. Run the interpreter with the command python program.py or use
IDLE to run the programs. You can also use the executable method
as explained earlier.
Example 4.1. Using Variables and Literal constants
# Filename : var.py
i = 5
print i
i = i + 1
print i
s = '''This is a multi-line string.
This is the second line.'''
print s
Output
$ python var.py
5
6
This is a multi-line string.
This is the second line.
How It Works
Here's how this program works. First, we assign the literal constant
value 5 to the variable i using the assignment operator (=). This
line is called a statement because it states that something should
be done and in this case, we connect the variable name i to the
value 5. Next, we print the value of i using the print statement
which, unsurprisingly, just prints the value of the variable to the
screen.
The we add 1 to the value stored in i and store it back. We then
print it and expectedly, we get the value 6.
Similarly, we assign the literal string to the variable s and then
print it.
Note for C/C++ Programmers
Variables are used by just assigning them a value. No declaration or
data type definition is needed/used.
Logical and Physical Lines
A physical line is what you see when you write the program. A
logical line is what Python sees as a single statement. Python
implicitly assumes that each physical line corresponds to a logical
line.
An example of a logical line is a statement like print 'Hello World'
- if this was on a line by itself (as you see it in an editor), then
this also corresponds to a physical line.
Implicitly, Python encourages the use of a single statement per line
which makes code more readable.
If you want to specify more than one logical line on a single
physical line, then you have to explicitly specify this using a
semicolon (;) which indicates the end of a logical line/statement.
For example,
i = 5
print i
is effectively same as
i = 5;
print i;
and the same can be written as
i = 5; print i;
or even
i = 5; print i
However, I strongly recommend that you stick to
writing a single logical line in a single physical line only. Use
more than one physical line for a single logical line only if the
logical line is really long. The idea is to avoid the semicolon as
far as possible since it leads to more readable code. In fact, I
have never used or even seen a semicolon in a Python program.
An example of writing a logical line spanning many physical lines
follows. This is referred to as explicit line joining.
s = 'This is a string. /
This continues the string.'
print s
This gives the output:
This is a string. This continues the string.
Similarly,
print /
i
is the same as
print i
Sometimes, there is an implicit assumption where you
don't need to use a backslash. This is the case where the logical
line uses parentheses, square brackets or curly braces. This is is
called implicit line joining. You can see this in action when we
write programs using lists in later chapters.
Indentation
Whitespace is important in Python. Actually, whitespace at the
beginning of the line is important. This is called indentation.
Leading whitespace (spaces and tabs) at the beginning of the logical
line is used to determine the indentation level of the logical line,
which in turn is used to determine the grouping of statements.
This means that statements which go together must have the same
indentation. Each such set of statements is called a block. We will
see examples of how blocks are important in later chapters.
One thing you should remember is how wrong indentation can give rise
to errors. For example:
i = 5
print 'Value is', i # Error! Notice a single space at the start of the
line
print 'I repeat, the value is', i
When you run this, you get the following error:
File "whitespace.py", line 4
print 'Value is', i # Error! Notice a single space at the start of
the line
^
SyntaxError: invalid syntax
Notice that there is a single space at the beginning
of the second line. The error indicated by Python tells us that the
syntax of the program is invalid i.e. the program was not properly
written. What this means to you is that you cannot arbitrarily start
new blocks of statements (except for the main block which you have
been using all along, of course). Cases where you can use new blocks
will be detailed in later chapters such as the control flow chapter.
How to indent
Do not use a mixture of tabs and spaces for the indentation as it
does not work across different platforms properly. I strongly
recommend that you use a single tab or two or four spaces for each
indentation level.
Choose any of these three indentation styles. More importantly,
choose one and use it consistently i.e. use that indentation style
only.
Summary
Now that we have gone through many nitty-gritty details, we can move
on to more interesting stuff such as control flow statements. Be
sure to become comfortable with what you have read in this chapter.
Chapter 5. Operators and Expressions
Table of Contents
Introduction
Operators
Operator Precedence
Order of Evaluation
Associativity
Expressions
Using Expressions
Summary
Introduction
Most statements (logical lines) that you write will contain
expressions. A simple example of an expression is 2 + 3. An
expression can be broken down into operators and operands.
Operators are functionality that do something and can be represented
by symbols such as + or by special keywords. Operators require some
data to operate on and such data are called operands. In this case,
2 and 3 are the operands.
Operators
We will briefly take a look at the operators and their usage:
Tip
You can evaluate the expressions given in the examples using the
interpreter interactively. For example, to test the expression 2 +
3, use the interactive Python interpreter prompt:
>>> 2 + 3
5
>>> 3 * 5
15
>>>
Table 5.1. Operators and their usage
Operator Name Explanation Examples
+ Plus Adds the two objects 3 + 5 gives 8. 'a' + 'b' gives 'ab'.
- Minus Either gives a negative number or gives the subtraction of
one number from the other -5.2 gives a negative number. 50 - 24
gives 26.
* Multiply Gives the multiplication of the two numbers or returns
the string repeated that many times. 2 * 3 gives 6. 'la' * 3 gives
'lalala'.
** Power Returns x to the power of y 3 ** 4 gives 81 (i.e. 3 * 3 * 3
* 3)
/ Divide Divide x by y 4/3 gives 1 (division of integers gives an
integer). 4.0/3 or 4/3.0 gives 1.3333333333333333
// Floor Division Returns the floor of the quotient 4 // 3.0 gives
1.0
% Modulo Returns the remainder of the division 8%3 gives 2.
-25.5%2.25 gives 1.5 .
<< Left Shift Shifts the bits of the number to the left by the
number of bits specified. (Each number is represented in memory by
bits or binary digits i.e. 0 and 1) 2 << 2 gives 8. - 2 is
represented by 10 in bits. Left shifting by 2 bits gives 1000 which
represents the decimal 8.
>> Right Shift Shifts the bits of the number to the right by the
number of bits specified. 11 >> 1 gives 5 - 11 is represented in
bits by 1011 which when right shifted by 1 bit gives 101 which is
nothing but decimal 5.
& Bitwise AND Bitwise AND of the numbers 5 & 3 gives 1.
| Bit-wise OR Bitwise OR of the numbers 5 | 3 gives 7
^ Bit-wise XOR 5 ^ 3 gives 6
~ Bit-wise invert The bit-wise inversion of x is -(x+1) ~5 gives -6.
< Less Than Returns whether x is less than y. All comparison
operators return 1 for true and 0 for false. This is equivalent to
the special variables True and False respectively. Note the
capitalization of these variables' names. 5 < 3 gives 0 (i.e. False)
and 3 < 5 gives 1 (i.e. True). Comparisons can be chained
arbitrarily: 3 < 5 < 7 gives True.
> Greater Than Returns whether x is greater than y 5 < 3 returns
True. If both operands are numbers, they are first converted to a
common type. Otherwise, it always returns False.
<= Less Than or Equal To Returns whether x is less than or equal to
y x = 3; y = 6; x <= y returns True.
>= Greater Than or Equal To Returns whether x is greater than or
equal to y x = 4; y = 3; x >= 3 returns True.
== Equal To Compares if the objects are equal x = 2; y = 2; x == y
returns True. x = 'str'; y = 'stR'; x == y returns False. x = 'str';
y = 'str'; x == y returns True.
!= Not Equal To Compares if the objects are not equal x = 2; y = 3;
x != y returns True.
not Boolean NOT If x is True, it returns False. If x is False, it
returns True. x = True; not y returns False.
and Boolean AND x and y returns False if x is False, else it returns
evaluation of y x = False; y = True; x and y returns False since x
is False. In this case, Python will not evaluate y since it knows
that the value of the expression will has to be false (since x is
False). This is called short-circuit evaluation.
or Boolean OR If x is True, it returns True, else it returns
evaluation of y x = True; y = False; x or y returns True.
Short-circuit evaluation applies here as well.
Operator Precedence
If you had an expression such as 2 + 3 * 4, is the addition done
first or the multiplication? Our high school maths tells us that the
multiplication should be done first - this means that the
multiplication operator has higher precedence than the addition
operator.
The following table gives the operator precedence table for Python,
from the lowest precedence (least binding) to the highest precedence
(most binding). This means that in a given expression, Python will
first evaluate the operators lower in the table before the operators
listed higher in the table.
The following table (same as the one in the Python reference manual)
is provided for the sake of completeness. However, I advise you to
use parentheses for grouping of operators and operands in order to
explicitly specify the precedence and to make the program as
readable as possible. For example, 2 + (3 * 4) is definitely more
clearer than 2 + 3 * 4. As with everything else, the parentheses
shold be used sensibly and should not be redundant (as in 2 + (3 +
4)).
Table 5.2. Operator Precedence
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 and 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
The operators which we have not already come across will be
explained in later chapters.
Operators with the same same precedence are listed in the same row
in the above table. For example, + and - have the same precedence.
Order of Evaluation
By default, the operator precedence table decides which operators
are evaluated before others. However, if you want to change the orer
in which they are evaluated, you can use parentheses. For example,
if you want addition to be evaluated before multiplication in an
expression, then you can write something like (2 + 3) * 4.
Associativity
Operators are usually associated from left to right i.e. operators
with same precedence are evaluated in a left to right manner. For
example, 2 + 3 + 4 is evaluated as (2 + 3) + 4. Some operators like
assignment operators have right to left associativity i.e. a = b = c
is treated as a = (b = c).
Expressions
Using Expressions
Example 5.1. Using Expressions
#!/usr/bin/python
# Filename: expression.py
length = 5
breadth = 2
area = length * breadth
print 'Area is', area
print 'Perimeter is', 2 * (length + breadth)
Output
$ python expression.py
Area is 10
Perimeter is 14
How It Works
The length and breadth of the rectangle are stored in variables by
the same name. We use these to calculate the area and perimieter of
the rectangle with the help of expressions. We store the result of
the expression length * breadth in the variable area and then print
it using the print statement. In the second case, we directly use
the value of the expression 2 * (length + breadth) in the print
statement.
Also, notice how Python 'pretty-prints' the output. Even though we
have not specified a space between 'Area is' and the variable area,
Python puts it for us so that we get a clean nice output and the
program is much more readable this way (since we don't need to worry
about spacing in the output). This is an example of how Python makes
life easy for the programmer.
Summary
We have seen how to use operators, operands and expressions - these
are the basic building blocks of any program. Next, we will see how
to make use of these in our programs using statements.
Chapter 6. Control Flow
Table of Contents
Introduction
The if statement
Using the if statement
How It Works
The while statement
Using the while statement
The for loop
Using the for statement
The break statement
Using the break statement
The continue statement
Using the continue statement
Summary
Introduction
In the programs we have seen till now, there has always been a
series of statements and Python faithfully executes them in the same
order. What if you wanted to change the flow of how it works? For
example, you want the program to take some decisions and do
different things depending on different situations such as printing
'Good Morning' or 'Good Evening' depending on the time of the day?
As you might have guessed, this is achieved using control flow
statements. There are three control flow statements in Python - if,
for and while.
The if statement
The if statement is used to check a condition and if the condition
is true, we run a block of statements (called the if-block), else we
process another block of statements (called the else-block). The
else clause is optional.
Using the if statement
Example 6.1. Using the if statement
#!/usr/bin/python
# Filename: if.py
number = 23
guess = int(raw_input('Enter an integer : '))
if guess == number:
print 'Congratulations, you guessed it.' # New block starts her
e
print "(but you do not win any prizes!)" # New block ends here
elif guess < number:
print 'No, it is a little higher than that' # Another block
# You can do whatever you want in a block ...
else:
print 'No, it is a little lower than that'
# you must have guess > number to reach here
print 'Done'
# This last statement is always executed, after the if statement is exe
cuted
Output
$ python if.py
Enter an integer : 50
No, it is a little lower than that
Done
$ python if.py
Enter an integer : 22
No, it is a little higher than that
Done
$ python if.py
Enter an integer : 23
Congratulations, you guessed it.
(but you do not win any prizes!)
Done
How It Works
In this program, we take guesses from the user and check if it is
the number that we have. We set the variable number to any integer
we want, say 23. Then, we take the user's guess using the
raw_input() function. Functions are just reusable pieces of
programs. We'll read more about them in the next chapter.
We supply a string to the built-in raw_input function which prints
it to the screen and waits for input from the user. Once we enter
something and press enter, the function returns the input which in
the case of raw_input is a string. We then convert this string to an
integer using int and then store it in the variable guess. Actually,
the int is a class but all you need to know right now is that you
can use it to convert a string to an integer (assuming the string
contains a valid integer in the text).
Next, we compare the guess of the user with the number we have
chosen. If they are equal, we print a success message. Notice that
we use indentation levels to tell Python which statements belong to
which block. This is why indentation is so important in Python. I
hope you are sticking to 'one tab per indentation level' rule. Are
you?
Notice how the if statement contains a colon at the end - we are
indicating to Python that a block of statements follows.
Then, we check if the guess is less than the number, and if so, we
inform the user to guess a little higher than that. What we have
used here is the elif clause which actually combines two related if
else-if else statements into one combined if-elif-else statement.
This makes the program easier and reduces the amount of indentation
required.
The elif and else statements must also have a colon at the end of
the logical line followed by their corresponding block of statements
(with proper indentation, of course)
You can have another if statement inside the if-block of an if
statement and so on - this is called a nested if statement.
Remember that the elif and else parts are optional. A minival valid
if statement is
if True:
print 'Yes, it is true'
After Python has finished executing the
complete if statement along with the assocated elif and else
clauses, it moves on to the next statement in the block containing
the if statement. In this case, it is the main block where execution
of the program starts and the next statement is the print 'Done'
statement. After this, Python sees the ends of the program and
simply finishes up.
Although this is a very simple program, I have been pointing out a
lot of things that you should notice even in this simple program.
All these are pretty straightforward (and surprisingly simple for
those of you from C/C++ backgrounds) and requires you to become
aware of all these initially, but after that, you will become
comfortable with it and it'll feel 'natural' to you.
Note for C/C++ Programmers
There is no switch statement in Python. You can use an
if..elif..else statement to do the same thing (and in some cases,
use a dictionary to do it quickly)
The while statement
The while statement allows you to repeatedly execute a block of
statements as long as a condition is true. A while statement is an
example of what is called a looping statement. A while statement can
have an optional else clause.
Using the while statement
Example 6.2. Using the while statement
#!/usr/bin/python
# Filename: while.py
number = 23
running = True
while running:
guess = int(raw_input('Enter an integer : '))
if guess == number:
print 'Congratulations, you guessed it.'
running = False # this causes the while loop to stop
elif guess < number:
print 'No, it is a little higher than that.'
else:
print 'No, it is a little lower than that.'
else:
print 'The while loop is over.'
# Do anything else you want to do here
print 'Done'
Output
$ python while.py
Enter an integer : 50
No, it is a little lower than that.
Enter an integer : 22
No, it is a little higher than that.
Enter an integer : 23
Congratulations, you guessed it.
The while loop is over.
Done
How It Works
In this program, we are still playing the guessing game, but the
advantage is that the user is allowed to keep guessing until he
guesses correctly - there is no need to repeatedly execute the
program for each guess as we have done previously. This aptly
demonstrates the use of the while statement.
We move the raw_input and if statements to inside the while loop and
set the variable running to True before the while loop. First, we
check if the variable running is True and then proceed to execute
the corresponding while-block. After this block is executed, the
condition is again checked which in this case is the running
variable. If it is true, we execute the while-block again, else we
continue to execute the optional else-block and then continue to the
next statement.
The else block is executed when the while loop condition becomes
False - this may even be the first time that the condition is
checked. If there is an else clause for a while loop, it is always
executed unless you have a while loop which loops forever without
ever breaking out!
The True and False are called Boolean types and you can consider
them to be equivalent to the value 1 and 0 respecitvely. It's
important to use these where the condition or checking is important
and not the actual value such as 1.
The else-block is actually redundant since you can put those
statements in the same block (as the while statement) after the
while statement to get the same effect.
Note for C/C++ Programmers
Remember that you can have an else clause for the while loop.
The for loop
The for..in statement is another looping statement which iterates
over a sequence of objects i.e. go through each item in a sequence.
We will see more about sequences in detail in later chapters. What
you need to know right now is that a sequence is just an ordered
collection of items.
Using the for statement
Example 6.3. Using the for statement
#!/usr/bin/python
# Filename: for.py
for i in range(1, 5):
print i
else:
print 'The for loop is over'
Output
$ python for.py
1
2
3
4
The for loop is over
How It Works
In this program, we are printing a sequence of numbers. We generate
this sequence of numbers using hte built-in range function.
What we do here is supply it two numbers and range returns a
sequence of numbers starting from the first number and up to the
second number. For example, range(1,5) gives the sequence [1, 2, 3,
4]. By default, range takes a step count of 1. If we supply a third
number to range, then that becomes the step count. For example,
range(1,5,2) gives [1,3]. Remember that the range extends up to the
second number i.e. it does not include the second number.
The for loop then iterates over this range - for i in range(1,5) is
equivalent to for i in [1, 2, 3, 4] which is like assigning each
number (or object) in the sequence to i, one at a time, and then
executing the block of statements for each value of i. In this case,
we just print the value in the block of statements.
Remember that the else part is optional. When included, it is always
executed once after the for loop is over unless a break statement is
encountered.
Remember that the for..in loop works for any sequence. Here, we have
a list of numbers generated by the built-in range function, but in
general we can use any kind of sequence of any kind of objects! We
will explore this idea in detail in later chapters.
Note for C/C++/Java/C# Programmers
The Python for loop is radically different from the C/C++ for loop.
C# programmers will note that the for loop in Python is similar to
the foreach loop in C#. Java programmers will note that the same is
similar to for (int i : IntArray) in Java 1.5 .
In C/C++, if you want to write for (int i = 0; i < 5; i++), then in
Python you write just for i in range(0,5). As you can see, the for
loop is simpler, more expressive and less error prone in Python.
The break statement
The break statement is used to break out of a loop statement i.e.
stop the execution of a looping statement, even if the loop
condition has not become False or the sequence of items has been
completely iterated over.
An important note is that if you break out of a for or while loop,
any corresponding loop else block is not executed.
Using the break statement
Example 6.4. Using the break statement
#!/usr/bin/python
# Filename: break.py
while True:
s = raw_input('Enter something : ')
if s == 'quit':
break
print 'Length of the string is', len(s)
print 'Done'
Output
$ python break.py
Enter something : Programming is fun
Length of the string is 18
Enter something : When the work is done
Length of the string is 21
Enter something : if you wanna make your work also fun:
Length of the string is 37
Enter something : use Python!
Length of the string is 12
Enter something : quit
Done
How It Works
In this program, we repeatedly take the user's input and print the
length of each input each time. We are providing a special condition
to stop the program by checking if the user input is 'quit'. We stop
the program by breaking out of the loop and reach the end of the
program.
The length of the input string can be found out using the built-in
len function.
Remember that the break statement can be used with the for loop as
well.
G2's Poetic Python
The input I have used here is a mini poem I have written called G2's
Poetic Python:
Programming is fun
When the work is done
if you wanna make your work also fun:
use Python!
The continue statement
The continue statement is used to tell Python to skip the rest of
the statements in the current loop block and to continue to the next
iteration of the loop.
Using the continue statement
Example 6.5. Using the continue statement
#!/usr/bin/python
# Filename: continue.py
while True:
s = raw_input('Enter something : ')
if s == 'quit':
break
if len(s) < 3:
continue
print 'Input is of sufficient length'
# Do other kinds of processing here...
Output
$ python continue.py
Enter something : a
Enter something : 12
Enter something : abc
Input is of sufficient length
Enter something : quit
How It Works
In this program, we accept input from the user, but we process them
only if they are at least 3 characters long. So, we use the built-in
len function to get the length and if the length is less than 3, we
skip the rest of the statements in the block by using the continue
statement. Otherwise, the rest of the statements in the loop are
executed and we can do any kind of processing we want to do here.
Note that the continue statement works with the for loop as well.
Summary
We have seen how to use the three control flow statements - if,
while and for along with their associated break and continue
statements. These are some of the most often used parts of Python
and hence, becoming comfortable with them is essential.
Next, we will see how to create and use functions.
Chapter 7. Functions
Table of Contents
Introduction
Defining a Function
Function Parameters
Using Function Parameters
Local Variables
Using Local Variables
Using the global statement
Default Argument Values
Using Default Argument Values
Keyword Arguments
Using Keyword Arguments
The return statement
Using the literal statement
DocStrings
Using DocStrings
Summary
Introduction
Functions are reusable pieces of programs. They allow you to give a
name to a block of statements and you can run that block using that
name anywhere in your program and any number of times. This is known
as calling the function. We have already used many built-in
functions such as the len and range.
Functions are defined using the def keyword. This is followed by an
identifier name for the function followed by a pair of parentheses
which may enclose some names of variables and the line ends with a
colon. Next follows the block of statements that are part of this
function. An example will show that this is actually very simple:
Defining a Function
Example 7.1. Defining a function
#!/usr/bin/python
# Filename: function1.py
def sayHello():
print 'Hello World!' # block belonging to the function
# End of function
sayHello() # call the function
Output
$ python function1.py
Hello World!
How It Works
We define a function called sayHello using the syntax as explained
above. This function takes no parameters and hence there are no
variables declared in the parentheses. Parameters to functions are
just input to the function so that we can pass in different values
to it and get back corresponding results.
Function Parameters
A function can take parameters which are just values you supply to
the function so that the function can do something utilising those
values. These parameters are just like variables except that the
values of these variables are defined when we call the function and
are not assigned values within the function itself.
Parameters are specified within the pair of parentheses in the
function definition, separated by commas. When we call the function,
we supply the values in the same way. Note the terminology used -
the names given in the function definition are called parameters
whereas the values you supply in the function call are called
arguments.
Using Function Parameters
Example 7.2. Using Function Parameters
#!/usr/bin/python
# Filename: func_param.py
def printMax(a, b):
if a > b:
print a, 'is maximum'
else:
print b, 'is maximum'
printMax(3, 4) # directly give literal values
x = 5
y = 7
printMax(x, y) # give variables as arguments
Output
$ python func_param.py
4 is maximum
7 is maximum
How It Works
Here, we define a function called printMax where we take two
parameters called a and b. We find out the greater number using a
simple if..else statement and then print the bigger number.
In the first usage of printMax, we directly supply the numbers i.e.
arguments. In the second usage, we call the function using
variables. printMax(x, y) causes value of argument x to be assigned
to parameter a and the value of argument y assigned to parameter b.
The printMax function works the same in both the cases.
Local Variables
When you declare variables inside a function definition, they are
not related in any way to other variables with the same names used
outside the function i.e. variable names are local to the function.
This is called the scope of the variable. All variables have the
scope of the block they are declared in starting from the point of
definition of the name.
Using Local Variables
Example 7.3. Using Local Variables
#!/usr/bin/python
# Filename: func_local.py
def func(x):
print 'x is', x
x = 2
print 'Changed local x to', x
x = 50
func(x)
print 'x is still', x
Output
$ python func_local.py
x is 50
Changed local x to 2
x is still 50
How It Works
In the function, the first time that we use the value of the name x,
Python uses the value of the parameter declared in the function.
Next, we assign the value 2 to x. The name x is local to our
function. So, when we change the value of x in the function, the x
defined in the main block remains unaffected.
In the last print statement, we confirm that the value of x in the
main block is actually unaffected.
Using the global statement
If you want to assign a value to a name defined outside the
function, then you have to tell Python that the name is not local,
but it is global. We do this using the global statement. It is
impossible to assign a value to a variable defined outside a
function without the global statement.
You can use the values of such variables defined outside the
function (assuming there is no variable with the same name within
the function). However, this is not encouraged and should be avoided
since it becomes unclear to the reader of the program as to where
that variable's definition is. Using the global statement makes it
amply clear that the variable is defined in an outer block.
Example 7.4. Using the global statement
#!/usr/bin/python
# Filename: func_global.py
def func():
global x
print 'x is', x
x = 2
print 'Changed global x to', x
x = 50
func()
print 'Value of x is', x
Output
$ python func_global.py
x is 50
Changed global x to 2
Value of x is 2
How It Works
The global statement is used to decare that x is a global variable -
hence, when we assign a value to x inside the function, that change
is reflected when we use the value of x in the main block.
You can specify more than one global variable using the same global
statement. For example, global x, y, z.
Default Argument Values
For some functions, you may want to make some of its parameters as
optional and use default values if the user does not want to provide
values for such parameters. This is done with the help of default
argument values. You can specify default argument values for
parameters by following the parameter name in the function
definition with the assignment operator (=) followed by the default
value.
Note that the default argument value should be a constant. More
precisely, the default argument value should be immutable - this is
explained in detail in later chapters. For now, just remember this.
Using Default Argument Values
Example 7.5. Using Default Argument Values
#!/usr/bin/python
# Filename: func_default.py
def say(message, times = 1):
print message * times
say('Hello')
say('World', 5)
Output
$ python func_default.py
Hello
WorldWorldWorldWorldWorld
How It Works
The function named say is used to print a string as many times as
want. If we don't supply a value, then by default, the string is
printed just once. We achieve this by specifying a default argument
value of 1 to the parameter times.
In the first usage of say, we supply only the string and it prints
the string once. In the second usage of say, we supply both the
string and an argument 5 stating that we want to say the string
message 5 times.
Important
Only those parameters which are at the end of the parameter list can
be given default argument values i.e. you cannot have a parameter
with a default argument value before a parameter without a default
argument value in the order of parameters declared in the function
parameter list.
This is because the values are assigned to the parameters by
position. For example, def func(a, b=5) is valid, but def func(a=5,
b) is not valid.
Keyword Arguments
If you have some functions with many parameters and you want to
specify only some of them, then you can give values for such
parameters by naming them - this is called keyword arguments - we
use the name (keyword) instead of the position (which we have been
using all along) to specify the arguments to the function.
There are two advantages - one, using the function is easier since
we do not need to worry about the order of the arguments. Two, we
can give values to only those parameters which we want, provided
that the other parameters have default argument values.
Using Keyword Arguments
Example 7.6. Using Keyword Arguments
#!/usr/bin/python
# Filename: func_key.py
def func(a, b=5, c=10):
print 'a is', a, 'and b is', b, 'and c is', c
func(3, 7)
func(25, c=24)
func(c=50, a=100)
Output
$ python func_key.py
a is 3 and b is 7 and c is 10
a is 25 and b is 5 and c is 24
a is 100 and b is 5 and c is 50
How It Works
The function named func has one parameter without default argument
values, followed by two parameters with default argument values.
In the first usage, func(3, 7), the parameter a gets the value 3,
the parameter b gets the value 5 and c gets the default value of 10.
In the second usage func(25, c=24), the variable a gets the value of
25 due to the position of the argument. Then, the parameter c gets
the value of 24 due to naming i.e. keyword arguments. The variable b
gets the default value of 5.
In the third usage func(c=50, a=100), we use keyword arguments
completely to specify the values. Notice, that we are specifying
value for parameter c before that for a even though a is defined
before c in the function definition.
The return statement
The return statement is used to return from a function i.e. break
out of the function. We can optionally return a value from the
function as well.
Using the literal statement
Example 7.7. Using the literal statement
#!/usr/bin/python
# Filename: func_return.py
def maximum(x, y):
if x > y:
return x
else:
return y
print maximum(2, 3)
Output
$ python func_return.py
3
How It Works
The maximum function returns the maximum of the parameters, in this
case the numbers supplied to the function. It uses a simple if..else
statement to find the greater value and then returns that value.
Note that a return statement without a value is equivalent to return
None. None is a special type in Python that represents nothingness.
For example, it is used to indicate that a variable has no value if
it has a value of None.
Every function implicitly contains a return None statement at the
end unless you have written your own return statement. You can see
this by running print someFunction() where the function someFunction
does not use the return statement such as:
def someFunction():
pass
The pass statement is used in Python
to indicate an empty block of statements.
DocStrings
Python has a nifty feature called documentation strings which is
usually referred to by its shorter name docstrings. DocStrings are
an important tool that you should make use of since it helps to
document the program better and makes it more easy to understand.
Amazingly, we can even get back the docstring from, say a function,
when the program is actually running!
Using DocStrings
Example 7.8. Using DocStrings
#!/usr/bin/python
# Filename: func_doc.py
def printMax(x, y):
'''Prints the maximum of two numbers.
The two values must be integers.'''
x = int(x) # convert to integers, if possible
y = int(y)
if x > y:
print x, 'is maximum'
else:
print y, 'is maximum'
printMax(3, 5)
print printMax.__doc__
Output
$ python func_doc.py
5 is maximum
Prints the maximum of two numbers.
The two values must be integers.
How It Works
A string on the first logical line of a function is the docstring
for that function. Note that DocStrings also apply to modules and
classes which we will learn about in the respective chapters.
The convention followed for a docstring is a multi-line string where
the first line starts with a capital letter and ends with a dot.
Then the second line is blank followed by any detailed explanation
starting from the third line. You are strongly advised to follow
this convention for all your docstrings for all your non-trivial
functions.
We can access the docstring of the printMax function using the
__doc__ (notice the double underscores) attribute (name belonging
to) of the function. Just remember that Python treats everything as
an object and this includes functions. We'll learn more about
objects in the chapter on classes.
If you have used the help() in Python, then you have already seen
the usage of docstrings! What it does is just fetch the __doc__
attribute of that function and displays it in a neat manner for you.
You can try it out on the function above - just include
help(printMax) in your program. Remember to press q to exit the
help.
Automated tools can retrieve the documentation from your program in
this manner. Therefore, I strongly recommend that you use docstrings
for any non-trivial function that you write. The pydoc command that
comes with your Python distribution works similarly to help() using
docstrings.
Summary
We have seen so many aspects of functions but note that we still
haven't covered all aspects of it. However, we have already covered
most of what you'll use regarding Python functions on an everyday
basis.
Next, we will see how to use as well as create Python modules.
Chapter 8. Modules
Table of Contents
Introduction
Using the sys module
Byte-compiled .pyc files
The from..import statement
A module's __name__
Using a module's __name__
Making your own Modules
Creating your own Modules
from..import
The dir() function
Using the dir function
Summary
Introduction
You have seen how you can reuse code in your program by defining
functions once. What if you wanted to reuse a number of functions in
other programs that you write? As you might have guessed, the answer
is modules. A module is basically a file containing all your
functions and variables that you have defined. To reuse the module
in other programs, the filename of the module must have a .py
extension.
A module can be imported by another program to make use of its
functionality. This is how we can use the Python standard library as
well. First, we will see how to use the standard library modules.
Using the sys module
Example 8.1. Using the sys module
#!/usr/bin/python
# Filename: using_sys.py
import sys
print 'The command line arguments are:'
for i in sys.argv:
print i
print '/n/nThe PYTHONPATH is', sys.path, '/n'
Output
$ python using_sys.py we are arguments
The command line arguments are:
using_sys.py
we
are
arguments
The PYTHONPATH is ['/home/swaroop/byte/code', '/usr/lib/python23.zip',
'/usr/lib/python2.3', '/usr/lib/python2.3/plat-linux2',
'/usr/lib/python2.3/lib-tk', '/usr/lib/python2.3/lib-dynload',
'/usr/lib/python2.3/site-packages', '/usr/lib/python2.3/site-packages/g
tk-2.0']
How It Works
First, we import the sys module using the import statement.
Basically, this translates to us telling Python that we want to use
this module. The sys module contains functionality related to the
Python interpreter and its environment.
When Python executes the import sys statement, it looks for the
sys.py module in one of the directores listed in its sys.path
variable. If the file is found, then the statements in the main
block of that module is run and then the module is made available
for you to use. Note that the initialization is done only the first
time that we import a module. Also, 'sys' is short for 'system'.
The argv variable in the sys module is referred to using the dotted
notation - sys.argv - one of the advantages of this approach is that
the name does not clash with any argv variable used in your program.
Also, it indicates clearly that this name is part of the sys module.
The sys.argv variable is a list of strings (lists are explained in
detail in later sections). Specifically, the sys.argv contains the
list of command line arguments i.e. the arguments passed to your
program using the command line.
If you are using an IDE to write and run these programs, look for a
way to specify command line arguments to the program in the menus.
Here, when we execute python using_sys.py we are arguments, we run
the module using_sys.py with the python command and the other things
that follow are arguments passed to the program. Python stores it in
the sys.argv variable for us.
Remember, the name of the script running is always the first
argument in the sys.argv list. So, in this case we will have
'using_sys.py' as sys.argv[0], 'we' as sys.argv[1], 'are' as
sys.argv[2] and 'arguments' as sys.argv[3] . Notice that Python
starts counting from 0 and not 1.
The sys.path contains the list of directory names where modules are
imported from. Observe that the first string in sys.path is empty -
this empty string indicates that the current directory is also part
of the sys.path which is same as the PYTHONPATH environment
variable. This means that you can directly import modules located in
the current directory. Otherwise, you will have to place your module
in one of the directories listed in sys.path .
Byte-compiled .pyc files
Importing a module is a relatively costly affair, so Python does
some tricks to make it faster. One way is to create byte-compiled
files with the extension .pyc which is related to the intermediate
form that Python transforms the program into (remember the intro
section on how Python works ?). This .pyc file is useful when you
import the module the next time from a different program - it will
be much faster since part of the processing required in importing a
module is already done. Also, these byte-compiled files are
platform-independent. So, now you know what those .pyc files really
are.
The from..import statement
If you want to directly import the argv variable into your program
(to avoid typing the sys. everytime for it), then you can use the
from sys import argv statement. If you want to import all the names
used in the sys module, then you can use the from sys import *
statement. This works for any module. In general, avoid using the
from..import statement and use the import statement instead since
your program will be much more readable and will avoid any name
clashes that way.
A module's __name__
Every module has a name and statements in a module can find out the
name of its module. This is especially handy in one particular
situation - As mentioned previously, when a module is imported for
the first time, the main block in that module is run. What if we
want to run the block only if the program was used by itself and not
when it was imported from another module? This can be achieved using
the __name__ attribute of the module.
Using a module's __name__
Example 8.2. Using a module's __name__
#!/usr/bin/python
# Filename: using_name.py
if __name__ == '__main__':
print 'This program is being run by itself'
else:
print 'I am being imported from another module'
Output
$ python using_name.py
This program is being run by itself
$ python
>>> import using_name
I am being imported from another module
>>>
How It Works
Every Python module has it's __name__ defined and if this is
'__main__', it implies that the module is being run standalone by
the user and we can do corresponding appropriate actions.
Making your own Modules
Creating your own modules is easy, you've been doing it all along!
Every Python program is also a module. You just have to make sure it
has a .py extension. The following example should make it clear.
Creating your own Modules
Example 8.3. How to create your own module
#!/usr/bin/python
# Filename: mymodule.py
def sayhi():
print 'Hi, this is mymodule speaking.'
version = '0.1'
# End of mymodule.py
The above was a sample module. As
you can see, there is nothing particularly special about compared to
our usual Python program. We will next see how to use this module in
our other Python programs.
Remember that the module should be placed in the same directory as
the program that we import it in, or the module should be in one of
the directories listed in sys.path .
#!/usr/bin/python
# Filename: mymodule_demo.py
import mymodule
mymodule.sayhi()
print 'Version', mymodule.version
Output
$ python mymodule_demo.py
Hi, this is mymodule speaking.
Version 0.1
How It Works
Notice that we use the same dotted notation to access members of the
module. Python makes good reuse of the same notation to give the
distinctive 'Pythonic' feel to it so that we don't have to keep
learning new ways to do things.
from..import
Here is a version utilising the from..import syntax.
#!/usr/bin/python
# Filename: mymodule_demo2.py
from mymodule import sayhi, version
# Alternative:
# from mymodule import *
sayhi()
print 'Version', version
The output of mymodule_demo2.py is same as
the output of mymodule_demo.py.
The dir() function
You can use the built-in dir function to list the identifiers that a
module defines. The identifiers are the functions, classes and
variables defined in that module.
When you supply a module name to the dir() function, it returns the
list of the names defined in that module. When no argument is
applied to it, it returns the list of names defined in the current
module.
Using the dir function
Example 8.4. Using the dir function
$ python
>>> import sys
>>> dir(sys) # get list of attributes for sys module
['__displayhook__', '__doc__', '__excepthook__', '__name__', '__stderr_
_',
'__stdin__', '__stdout__', '_getframe', 'api_version', 'argv',
'builtin_module_names', 'byteorder', 'call_tracing', 'callstats',
'copyright', 'displayhook', 'exc_clear', 'exc_info', 'exc_type',
'excepthook', 'exec_prefix', 'executable', 'exit', 'getcheckinterval',
'getdefaultencoding', 'getdlopenflags', 'getfilesystemencoding',
'getrecursionlimit', 'getrefcount', 'hexversion', 'maxint', 'maxunicode
',
'meta_path','modules', 'path', 'path_hooks', 'path_importer_cache',
'platform', 'prefix', 'ps1', 'ps2', 'setcheckinterval', 'setdlopenflags
',
'setprofile', 'setrecursionlimit', 'settrace', 'stderr', 'stdin', 'stdo
ut',
'version', 'version_info', 'warnoptions']
>>> dir() # get list of attributes for current module
['__builtins__', '__doc__', '__name__', 'sys']
>>>
>>> a = 5 # create a new variable 'a'
>>> dir()
['__builtins__', '__doc__', '__name__', 'a', 'sys']
>>>
>>> del a # delete/remove a name
>>>
>>> dir()
['__builtins__', '__doc__', '__name__', 'sys']
>>>
How It Works
First, we see the usage of dir on the imported sys module. We can
see the huge list of attributes that it contains.
Next, we use the dir function without passing parameters to it - by
default, it returns the list of attributes for the current module.
Notice that the list of imported modules is also part of this list.
In order to observe the dir in action, we define a new variable a
and assign it a value and then check dir and we observe that there
is an additional value in the list of the same name. We remove the
variable/attribute of the current module using the del statement and
the change is reflected again in the output of the dir function.
A note on del - this statement is used to delete a variable/name and
after the statement has run, in this case del a, you can no longer
access the variable a - it is as if it never existed before at all.
Summary
Modules are useful because they provide services and functionality
that you can reuse in other programs. The standard library that
comes with Python is an example of such a set of modules. We have
seen how to use these modules and create our own modules as well.
Next, we will learn about some interesting concepts called data
structures.
Chapter 9. Data Structures
Table of Contents
Introduction
List
Quick introduction to Objects and Classes
Using Lists
Tuple
Using Tuples
Tuples and the print statement
Dictionary
Using Dictionaries
Sequences
Using Sequences
References
Objects and References
More about Strings
String Methods
Summary
Introduction
Data structures are basically just that - they are structures which
can hold some data together. In other words, they are used to store
a collection of related data.
There are three built-in data structures in Python - list, tuple and
dictionary. We will see how to use each of them and how they make
life easier.
List
A list is a data structure that holds an ordered collection of items
i.e. you can store a sequence of items in a list. This is easy to
imagine if you can think of a shopping list where you have a list of
items to buy, except that you probbly have each item on a separate
line in your shopping list whereas in Python you put commas in
between them.
The list of items should be enclosed in square brackets so that
Python understands that you are specifying a list. Once you have
created a list, you can add, remove or search for items in the list.
Since, we can add and remove items, we say that a list is a mutable
data type i.e. this type can be altered.
Quick introduction to Objects and Classes
Although, I've been generally delaying the discussion of objects and
classes till now, a little explanation is needed right now so that
you can understand lists better. We will still explore this topic in
detail in its own chapter.
A list is an example of usage of objects and classes. When you use a
variable i and assign a value to it, say integer 5 to it, you can
think of it as creating an object (instance) i of class (type) int.
In fact, you can see help(int) to understand this better.
A class can also have methods i.e. functions defined for use with
respect to that class only. You can use these pieces of
functionality only when you have an object of that class. For
example, Python provides an append method for the list class which
allows you to add an item to the end of the list. For example,
mylist.append('an item') will add that string to the list mylist.
Note the use of dotted notation for accessing methods of the
objects.
A class can also have fields which are nothing but variables defined
for use with respect to that class only. You can use these
variables/names only when you have an object of that class. Fields
are also accessed by the dotted notation, for example, mylist.field
.
Using Lists
Example 9.1. Using lists
#!/usr/bin/python
# Filename: using_list.py
# This is my shopping list
shoplist = ['apple', 'mango', 'carrot', 'banana']
print 'I have', len(shoplist), 'items to purchase.'
print 'These items are:', # Notice the comma at end of the line
for item in shoplist:
print item,
print '/nI also have to buy rice.'
shoplist.append('rice')
print 'My shopping list is now', shoplist
print 'I will sort my list now'
shoplist.sort()
print 'Sorted shopping list is', shoplist
print 'The first item I will buy is', shoplist[0]
olditem = shoplist[0]
del shoplist[0]
print 'I bought the', olditem
print 'My shopping list is now', shoplist
Output
$ python using_list.py
I have 4 items to purchase.
These items are: apple mango carrot banana
I also have to buy rice.
My shopping list is now ['apple', 'mango', 'carrot', 'banana', 'rice']
I will sort my list now
Sorted shopping list is ['apple', 'banana', 'carrot', 'mango', 'rice']
The first item I will buy is apple
I bought the apple
My shopping list is now ['banana', 'carrot', 'mango', 'rice']
How It Works
The variable shoplist is a shopping list for someone who is going to
the market. In shoplist, we only store strings of the names of the
items to buy but remember you can add any kind of object to a list
including numbers and even other lists.
We have also used the for..in loop to iterate through the items of
the list. By now, you must have realised that a list is also a
sequence. The speciality of sequences will be discussed in a later
section
Notice that we use a comma at the end of the print statement to
suppress the automatic printing of a line break after every print
statement. This is a bit of an ugly way of doing it, but it is
simple and gets the job done.
Next, we add an item to the list using the append method of the list
object, as already discussed before. Then, we check that the item
has been indeed added to the list by printing the contents of the
list by simply passing the list to the print statement which prints
it in a neat manner for us.
Then, we sort the list by using the sort method of the list.
Understand that this method affects the list itself and does not
return a modified list - this is different from the way strings
work. This is what we mean by saying that lists are mutable and that
strings are immutable.
Next, when we finish buying an item in the market, we want to remove
it from the list. We achieve this by using the del statement. Here,
we mention which item of the list we want to remove and the del
statement removes it fromt he list for us. We specify that we want
to remove the first item from the list and hence we use del
shoplist[0] (remember that Python starts counting from 0).
If you want to know all the methods defined by the list object, see
help(list) for complete details.
Tuple
Tuples are just like lists except that they are immutable like
strings i.e. you cannot modify tuples. Tuples are defined by
specifying items separated by commas within a pair of parentheses.
Tuples are usually used in cases where a statement or a user-defined
function can safely assume that the collection of values i.e. the
tuple of values used will not change.
Using Tuples
Example 9.2. Using Tuples
#!/usr/bin/python
# Filename: using_tuple.py
zoo = ('wolf', 'elephant', 'penguin')
print 'Number of animals in the zoo is', len(zoo)
new_zoo = ('monkey', 'dolphin', zoo)
print 'Number of animals in the new zoo is', len(new_zoo)
print 'All animals in new zoo are', new_zoo
print 'Animals brought from old zoo are', new_zoo[2]
print 'Last animal brought from old zoo is', new_zoo[2][2]
Output
$ python using_tuple.py
Number of animals in the zoo is 3
Number of animals in the new zoo is 3
All animals in new zoo are ('monkey', 'dolphin', ('wolf', 'elephant', '
penguin'))
Animals brought from old zoo are ('wolf', 'elephant', 'penguin')
Last animal brought from old zoo is penguin
How It Works
The variable zoo refers to a tuple of items. We see that the len
function can be used to get the length of the tuple. This also
indicates that a tuple is a sequence as well.
We are now shifting these animals to a new zoo since the old zoo is
being closed. Therefore, the new_zoo tuple contains some animals
which are already there along with the animals brought over from the
old zoo. Back to reality, note that a tuple within a tuple does not
lose its identity.
We can access the items in the tuple by specifying the item's
position within a pair of square brackets just like we did for
lists. This is called the indexing operator. We access the third
item in new_zoo by specifying new_zoo[2] and we access the third
item in the third item in the new_zoo tuple by specifying
new_zoo[2][2]. This is pretty simple once you've understood the
idiom.
Tuple with 0 or 1 items. An empty tuple is constructed by an empty
pair of parentheses such as myempty = (). However, a tuple with a
single item is not so simple. You have to specify it using a comma
following the first (and only) item so that Python can differentiate
between a tuple and a pair of parentheses surrounding the object in
an expression i.e. you have to specify singleton = (2 , ) if you
mean you want a tuple containing the item 2.
Note for Perl programmers
A list within a list does not lose its identity i.e. lists are not
flattened as in Perl. The same applies to a tuple within a tuple, or
a tuple within a list, or a list within a tuple, etc. As far as
Python is concerned, they are just objects stored using another
object, that's all.
Tuples and the print statement
One of the most common usage of tuples is with the print statement.
Here is an example:
Example 9.3. Output using tuples
#!/usr/bin/python
# Filename: print_tuple.py
age = 22
name = 'Swaroop'
print '%s is %d years old' % (name, age)
print 'Why is %s playing with that python?' % name
Output
$ python print_tuple.py
Swaroop is 22 years old
Why is Swaroop playing with that python?
How It Works
The print statement can take a string using certain specifications
followed by the % symbol followed by a tuple of items matching the
specification. The specifications are used to format the output in a
certain way. The specification can be like %s for strings and %d for
integers. The tuple must have items corresponding to these
specifications in the same order.
Observe the first usage where we use %s first and this corresponds
to the variable name which is the first item in the tuple and the
second specification is %d corresponding to age which is the second
item in the tuple.
What Python does here is that it converts each item in the tuple
into a string and substitutes that string value into the place of
the specification. Therefore the %s is replaced by the value of the
variable name and so on.
This usage of the print statement makes writing output extremely
easy and avoids lot of string manipulation to achieve the same. It
also avoids using commas everywhere as we have done till now.
Most of the time, you can just use the %s specification and let
Python take care of the rest for you. This works even for numbers.
However, you may want to give the correct specifications since this
adds one level of checking that your program is correct.
In the second print statement, we are using a single specification
followed by the % symbol followed by a single item - there are no
pair of parentheses. This works only in the case where there is a
single specification in the string.
Dictionary
A dictionary is like an address-book where you can find the address
or contact details of a person by knowing only his/her name i.e. we
associate keys (name) with values (details). Note that the key must
be unique just like you cannot find out the correct information if
you have two persons with the exact same name.
Note that you can use only immutable objects (like strings) for the
keys of a dictionary but you can use either immutable or mutable
objects for the values of the dictionary. This basically translates
to say that you should use only simple objects for keys.
Pairs of keys and valus are specified in a dictionary by using the
notation d = {key1 : value1, key2 : value2 }. Notice that they
key/value pairs are separated by a colon and the pairs are separated
themselves by commas and all this is enclosed in a pair of curly
brackets.
Remember that key/value pairs in a dictionary are not ordered in any
manner. If you want a particular order, then you will have to sort
them yourself before using it.
The dictionaries that you will be using are instances/objects of the
dict class.
Using Dictionaries
Example 9.4. Using dictionaries
#!/usr/bin/python
# Filename: using_dict.py
# 'ab' is short for 'a'ddress'b'ook
ab = { 'Swaroop' : '[email protected]',
'Larry' : '[email protected]',
'Matsumoto' : '[email protected]',
'Spammer' : '[email protected]'
}
print "Swaroop's address is %s" % ab['Swaroop']
# Adding a key/value pair
ab['Guido'] = '[email protected]'
# Deleting a key/value pair
del ab['Spammer']
print '/nThere are %d contacts in the address-book/n' % len(ab)
for name, address in ab.items():
print 'Contact %s at %s' % (name, address)
if 'Guido' in ab: # OR ab.has_key('Guido')
print "/nGuido's address is %s" % ab['Guido']
Output
$ python using_dict.py
Swaroop's address is [email protected]
There are 4 contacts in the address-book
Contact Swaroop at [email protected]
Contact Matsumoto at [email protected]
Contact Larry at [email protected]
Contact Guido at [email protected]
Guido's address is [email protected]
How It Works
We create the dictionary ab using the notation already discussed. We
then access key/value pairs by specifying the key using the indexing
operator as discussed in the context of lists and tuples. Observe
that the syntax is very simple for dictionaries as well.
We can add new key/value pairs by simply using the indexing operator
to access a key and assign that value, as we have done for Guido in
the above case.
We can delete key/value pairs using our old friend - the del
statement. We simply specify the dictionary and the indexing
operator for the key to be removed and pass it to the del statement.
There is no need to know the value corresponding to the key for this
operation.
Next, we access each key/value pair of the dictionary using the
items method of the dictionary which returns a list of tuples where
each tuple contains a pair of items - the key followed by the value.
We retrieve this pair and assign it to the variables name and
address correspondingly for each pair using the for..in loop and
then print these values in the for-block.
We can check if a key/value pair exists using the in operator or
even the has_key method of the dict class. You can see the
documentation for the complete list of methods of the dict class
using help(dict).
Keyword Arguments and Dictionaries. On a different note, if you
have used keyword arguments in your functions, you have already used
dictionaries! Just think about it - the key/value pair is specified
by you in the parameter list of the function definition and when you
access variables within your function, it is just a key access of a
dictionary (which is called the symbol table in compiler design
terminology).
Sequences
Lists, tuples and strings are examples of sequences, but what are
sequences and what is so special about them? Two of the main
features of a sequence is the indexing operation which allows us to
fetch a particular item in the sequence directly and the slicing
operation which allows us to retrieve a slice of the sequence i.e. a
part of the sequence.
Using Sequences
Example 9.5. Using Sequences
#!/usr/bin/python
# Filename: seq.py
shoplist = ['apple', 'mango', 'carrot', 'banana']
# Indexing or 'Subscription' operation
print 'Item 0 is', shoplist[0]
print 'Item 1 is', shoplist[1]
print 'Item 2 is', shoplist[2]
print 'Item 3 is', shoplist[3]
print 'Item -1 is', shoplist[-1]
print 'Item -2 is', shoplist[-2]
# Slicing on a list
print 'Item 1 to 3 is', shoplist[1:3]
print 'Item 2 to end is', shoplist[2:]
print 'Item 1 to -1 is', shoplist[1:-1]
print 'Item start to end is', shoplist[:]
# Slicing on a string
name = 'swaroop'
print 'characters 1 to 3 is', name[1:3]
print 'characters 2 to end is', name[2:]
print 'characters 1 to -1 is', name[1:-1]
print 'characters start to end is', name[:]
Output
$ python seq.py
Item 0 is apple
Item 1 is mango
Item 2 is carrot
Item 3 is banana
Item -1 is banana
Item -2 is carrot
Item 1 to 3 is ['mango', 'carrot']
Item 2 to end is ['carrot', 'banana']
Item 1 to -1 is ['mango', 'carrot']
Item start to end is ['apple', 'mango', 'carrot', 'banana']
characters 1 to 3 is wa
characters 2 to end is aroop
characters 1 to -1 is waroo
characters start to end is swaroop
How It Works
First, we see how to use indexes to get individual items of a
sequence. This is also referred to as the subscription operation.
Whenever you specify a number to a sequence within square brackets
as shown above, Python will fetch you the item corresponding to that
position in the sequence. Remember that Python starts counting
numbers from 0. Hence, shoplist[0] fetches the first item and
shoplist[3] fetches the fourth item in the shoplist sequence.
The index can also be a negative number, in which case, the position
is calculated from the end of the sequence. Therefore, shoplist[-1]
refers to the last item in the sequence and shoplist[-2] fetches the
second last item in the sequence.
The slicing operation is used by specifying the name of the sequence
followed by an optional pair of numbers separated by a colon within
square brackets. Note that this is very very similar to the indexing
operation you have been using til lnow. Remember the numbers are
optional but the colon isn't.
The first number (before the colon) in the slicing operation refers
to the position from where the slice starts and the second number
(after the colon) indicates where the slice will stop at. If the
first number is not specified, Python will start at the beginning of
the sequence. If the second number is left out, Python will stop at
the end of the sequence. Note that the slice returned starts at the
start position and will end just before the end position i.e. the
start position is included but the end position is excluded from the
sequence slice.
Thus, shoplist[1:3] returns a slice of the sequence starting at
position 1, includes position 2 but stops at position 3 and
therefore a slice of two items is returned. Similarly, shoplist[:]
returns a copy of the whole sequence.
You can also do slicing with negative positions. Negative numbers
are used for positions from the end of the sequence. For example,
shoplist[:-1] will return a slice of the sequence which excludes the
last item of the sequence but contains everything else.
Try various combinations of such slice specifications using the
Python interpreter interactively i.e. the prompt so that you can see
the results immediately. The great thing about sequences is that you
can access tuples, lists and strings all in the same way!
References
When you create an object and assign it to a variable, the variable
only refers to the object and does not represent the object itself!
That is, the variable name points to that part of your computer's
memory where the object is stored. This is called as binding of the
name to the object.
Generally, you don't need to be worried about this, but there is a
subtle effect due to references which you need to be aware of. This
is demonstrated by the following example.
Objects and References
Example 9.6. Objects and References
#!/usr/bin/python
# Filename: reference.py
print 'Simple Assignment'
shoplist = ['apple', 'mango', 'carrot', 'banana']
mylist = shoplist # mylist is just another name pointing to the same ob
ject!
del shoplist[0] # I purchased the first item, so I remove it from the l
ist
print 'shoplist is', shoplist
print 'mylist is', mylist
# notice that both shoplist and mylist both print the same list without
# the 'apple' confirming that they point to the same object
print 'Copy by making a full slice'
mylist = shoplist[:] # make a copy by doing a full slice
del mylist[0] # remove first item
print 'shoplist is', shoplist
print 'mylist is', mylist
# notice that now the two lists are different
Output
$ python reference.py
Simple Assignment
shoplist is ['mango', 'carrot', 'banana']
mylist is ['mango', 'carrot', 'banana']
Copy by making a full slice
shoplist is ['mango', 'carrot', 'banana']
mylist is ['carrot', 'banana']
How It Works
Most of the explanation is available in the comments itself. What
you need to remember is that if you want to make a copy of a list or
such kinds of sequences or complex objects (not simple objects such
as integers), then you have to use the slicing operation to make a
copy. If you just assign the variable name to another name, both of
them will refer to the same object and this could lead to all sorts
of trouble if you are not careful.
Note for Perl programmers
Remember that an assignment statement for lists does not create a
copy. You have to use slicing operation to make a copy of the
sequence.
More about Strings
We have already discussed strings in detail earlier. What more can
there be to know? Well, did you know that strings are also objects
and have methods which do everything from checking part of a string
to stripping spaces!
The strings that you use in program are all objects of the class
str. Some useful methods of this class are demonstrated in the next
example. For a complete list of such methods, see help(str).
String Methods
Example 9.7. String Methods
#!/usr/bin/python
# Filename: str_methods.py
name = 'Swaroop' # This is a string object
if name.startswith('Swa'):
print 'Yes, the string starts with "Swa"'
if 'a' in name:
print 'Yes, it contains the string "a"'
if name.find('war') != -1:
print 'Yes, it contains the string "war"'
delimiter = '_*_'
mylist = ['Brazil', 'Russia', 'India', 'China']
print delimiter.join(mylist)
Output
$ python str_methods.py
Yes, the string starts with "Swa"
Yes, it contains the string "a"
Yes, it contains the string "war"
Brazil_*_Russia_*_India_*_China
How It Works
Here, we see a lot of the string methods in action. The startswith
method is used to find out whether the string starts with the given
string. The in operator is used to check if a given string is a part
of the string.
The find method is used to do find the position of the given string
in the string or returns -1 if it is not successful to find the
substring. The str class also has a neat method to join the items of
a sequence with the string acting as a delimiter between each item
of the sequence and returns a bigger string generated from this.
Summary
We have explored the various built-in data structures of Python in
detail. These data structures will be essential for writing programs
of reasonable size.
Now that we have a lot of the basics of Python in place, we will
next see how to design and write a real-world Python program.
Chapter 10. Problem Solving - Writing a Python Script
Table of Contents
The Problem
The Solution
First Version
Second Version
Third Version
Fourth Version
More Refinements
The Software Development Process
Summary
We have explored various parts of the Python language and now we
will take a look at how all these parts fit together, by designing
and writing a program which does something useful.
The Problem
The problem is 'I want a program which creates a backup of all my
important files'.
Although, this is a simple problem, there is not enough information
for us to get started with the solution. A little more analysis is
required. For example, how do we specify which files are to be
backed up? Where is the backup stored? How are they stored in the
backup?
After analyzing the problem properly, we design our program. We make
a list of things about how our program should work. In this case, I
have created the following list on how I want it to work. If you do
the design, you may not come up with the same kind of problem -
every person has their own way of doing things, this is ok.
1. The files and directories to be backed up are specified in a
list.
2. The backup must be stored in a main backup directory.
3. The files are backed up into a zip file.
4. The name of the zip archive is the current date and time.
5. We use the standard zip command available by default in any
standard Linux/Unix distribution. Windows users can use the
Info-Zip program. Note that you can use any archiving command
you want as long as it has a command line interface so that we
can pass arguments to it from our script.
The Solution
As the design of our program is now stable, we can write the code
which is an implementation of our solution.
First Version
Example 10.1. Backup Script - The First Version
#!/usr/bin/python
# Filename: backup_ver1.py
import os
import time
# 1. The files and directories to be backed up are specified in a list.
source = ['/home/swaroop/byte', '/home/swaroop/bin']
# If you are using Windows, use source = [r'C:/Documents', r'D:/Work']
or something like that
# 2. The backup must be stored in a main backup directory
target_dir = '/mnt/e/backup/' # Remember to change this to what you wil
l be using
# 3. The files are backed up into a zip file.
# 4. The name of the zip archive is the current date and time
target = target_dir + time.strftime('%Y%m%d%H%M%S') + '.zip'
# 5. We use the zip command (in Unix/Linux) to put the files in a zip a
rchive
zip_command = "zip -qr '%s' %s" % (target, ' '.join(source))
# Run the backup
if os.system(zip_command) == 0:
print 'Successful backup to', target
else:
print 'Backup FAILED'
Output
$ python backup_ver1.py
Successful backup to /mnt/e/backup/20041208073244.zip
Now, we are in the testing phase
where we test that our program works properly. If it doesn't behave
as expected, then we have to debug our program i.e. remove the bugs
(errors) from the program.
How It Works
You will notice how we have converted our design into code in a
step-by-step manner.
We make use of the os and time modules and so we import them. Then,
we specify the files and directories to be backed up in the source
list. The target directory is where store all the backup files and
this is specified in the target_dir variable. The name of the zip
archive that we are going to create is the current date and time
which we fetch using the time.strftime() function. It will also have
the .zip extension and will be stored in the target_dir directory.
The time.strftime() function takes a specification such as the one
we have used in the above program. The %Y specification will be
replaced by the year without the cetury. The %m specification will
be replaced by the month as a decimal number between 01 and 12 and
so on. The complete list of such specifications can be found in the
[Python Reference Manual] that comes with your Python distribution.
Notice that this is similar to (but not same as) the specification
used in print statement (using the % followed by tuple).
We create the name of the target zip file using the addition
operator which concatenates the strings i.e. it joins the two
strings together and returns a new one. Then, we create a string
zip_command which contains the command that we are going to execute.
You can check if this command works by running it on the shell
(Linux terminal or DOS prompt).
The zip command that we are using has some options and parameters
passed. The -q option is used to indicate that the zip command
should work quietly. The -r option specifies that the zip command
should work recursively for directories i.e. it should include
subdirectories and files within the subdirectories as well. The two
options are combined and specified in a shorter way as -qr. The
options are followed by the name of the zip archive to create
followed by the list of files and directories to backup. We convert
the source list into a string using the join method of strings which
we have already seen how to use.
Then, we finally run the command using the os.system function which
runs the command as if it was run from the system i.e. in the shell
- it returns 0 if the command was successfully, else it returns an
error number.
Depending on the outcome of the command, we print the appropriate
message that the backup has failed or succeeded and that's it, we
have created a script to take a backup of our important files!
Note to Windows Users
You can set the source list and target directory to any file and
directory names but you have to be a little careful in Windows. The
problem is that Windows uses the backslash (/) as the directory
separator character but Python uses backslashes to represent escape
sequences!
So, you have to represent a backslash itself using an escape
sequence or you have to use raw strings. For example, use
'C://Documents' or r'C:/Documents' but do not use 'C:/Documents' -
you are using an unknown escape sequence /D !
Now that we have a working backup script, we can use it whenever we
want to take a backup of the files. Linux/Unix users are advised to
use the executable method as discussed earlier so that they can run
the backup script anytime anywhere. This is called the operation
phase or the deployment phase of the software.
The above program works properly, but (usually) first programs do
not work exactly as you expect. For example, there might be problems
if you have not designed the program properly or if you have made a
mistake in typing the code, etc. Appropriately, you will have to go
back to the design phase or you will have to debug your program.
Second Version
The first version of our script works. However, we can make some
refinements to it so that it can work better on a daily basis. This
is called the maintenance phase of the software.
One of the refinements I felt was useful is a better file-naming
mechanism - using the time as the name of the file within a
directory with the current date as a directory within the main
backup directory. One advantage is that your backups are stored in a
hierarchical manner and therefore it is much easier to manage.
Another advantage is that the length of the filenames are much
shorter this way. Yet another advantage is that separate directories
will help you to easily check if you have taken a backup for each
day since the directory would be created only if you have taken a
backup for that day.
Example 10.2. Backup Script - The Second Version
#!/usr/bin/python
# Filename: backup_ver2.py
import os
import time
# 1. The files and directories to be backed up are specified in a list.
source = ['/home/swaroop/byte', '/home/swaroop/bin']
# If you are using Windows, use source = [r'C:/Documents', r'D:/Work']
or something like that
# 2. The backup must be stored in a main backup directory
target_dir = '/mnt/e/backup/' # Remember to change this to what you wil
l be using
# 3. The files are backed up into a zip file.
# 4. The current day is the name of the subdirectory in the main direct
ory
today = target_dir + time.strftime('%Y%m%d')
# The current time is the name of the zip archive
now = time.strftime('%H%M%S')
# Create the subdirectory if it isn't already there
if not os.path.exists(today):
os.mkdir(today) # make directory
print 'Successfully created directory', today
# The name of the zip file
target = today + os.sep + now + '.zip'
# 5. We use the zip command (in Unix/Linux) to put the files in a zip a
rchive
zip_command = "zip -qr '%s' %s" % (target, ' '.join(source))
# Run the backup
if os.system(zip_command) == 0:
print 'Successful backup to', target
else:
print 'Backup FAILED'
Output
$ python backup_ver2.py
Successfully created directory /mnt/e/backup/20041208
Successful backup to /mnt/e/backup/20041208/080020.zip
$ python backup_ver2.py
Successful backup to /mnt/e/backup/20041208/080428.zip
How It Works
Most of the program remains the same. The changes is that we check
if there is a directory with the current day as name inside the main
backup directory using the os.exists function. If it doesn't exist,
we create it using the os.mkdir function.
Notice the use of os.sep variable - this gives the directory
separator according to your operating system i.e. it will be '/' in
Linux, Unix, it will be '//' in Windows and ':' in Mac OS. Using
os.sep instead of these characters directly will make our program
portable and work across these systems.
Third Version
The second version works fine when I do many backups, but when there
are lots of backups, I am finding it hard to differentiate what the
backups were for! For example, I might have made some major changes
to a program or presentation, then I want to associate what those
changes are with the name of the zip archive. This can be easily
achieved by attaching a user-supplied comment to the name of the zip
archive.
Example 10.3. Backup Script - The Third Version (does not work!)
#!/usr/bin/python
# Filename: backup_ver2.py
import os
import time
# 1. The files and directories to be backed up are specified in a list.
source = ['/home/swaroop/byte', '/home/swaroop/bin']
# If you are using Windows, use source = [r'C:/Documents', r'D:/Work']
or something like that
# 2. The backup must be stored in a main backup directory
target_dir = '/mnt/e/backup/' # Remember to change this to what you wil
l be using
# 3. The files are backed up into a zip file.
# 4. The current day is the name of the subdirectory in the main direct
ory
today = target_dir + time.strftime('%Y%m%d')
# The current time is the name of the zip archive
now = time.strftime('%H%M%S')
# Take a comment from the user to create the name of the zip file
comment = raw_input('Enter a comment --> ')
if len(comment) == 0: # check if a comment was entered
target = today + os.sep + now + '.zip'
else:
target = today + os.sep + now + '_' +
comment.replace(' ', '_') + '.zip'
# Create the subdirectory if it isn't already there
if not os.path.exists(today):
os.mkdir(today) # make directory
print 'Successfully created directory', today
# 5. We use the zip command (in Unix/Linux) to put the files in a zip a
rchive
zip_command = "zip -qr '%s' %s" % (target, ' '.join(source))
# Run the backup
if os.system(zip_command) == 0:
print 'Successful backup to', target
else:
print 'Backup FAILED'
Output
$ python backup_ver3.py
File "backup_ver3.py", line 25
target = today + os.sep + now + '_' +
^
SyntaxError: invalid syntax
How This (does not) Work
This program does not work!. Python says there is a syntax error
which means that the script does not satisfy the structure that
Python expects to see. When we observe the error given by Python, it
also tells us the place where it detected the error as well. So we
start debugging our program from that line.
On careful observation, we see that the single logical line has been
split into two physical lines but we have not specified that these
two physical lines belong together. Basically, Python has found the
addition operator (+) without any operand in that logical line and
hence it doesn't know how to continue. Remember that we can specify
that the logical line continues in the next physical line by the use
of a backslash at the end of the physical line. So, we make this
correction to our program. This is called bug fixing.
Fourth Version
Example 10.4. Backup Script - The Fourth Version
#!/usr/bin/python
# Filename: backup_ver2.py
import os, time
# 1. The files and directories to be backed up are specified in a list.
source = ['/home/swaroop/byte', '/home/swaroop/bin']
# If you are using Windows, use source = [r'C:/Documents', r'D:/Work']
or something like that
# 2. The backup must be stored in a main backup directory
target_dir = '/mnt/e/backup/' # Remember to change this to what you wil
l be using
# 3. The files are backed up into a zip file.
# 4. The current day is the name of the subdirectory in the main direct
ory
today = target_dir + time.strftime('%Y%m%d')
# The current time is the name of the zip archive
now = time.strftime('%H%M%S')
# Take a comment from the user to create the name of the zip file
comment = raw_input('Enter a comment --> ')
if len(comment) == 0: # check if a comment was entered
target = today + os.sep + now + '.zip'
else:
target = today + os.sep + now + '_' + /
comment.replace(' ', '_') + '.zip'
# Notice the backslash!
# Create the subdirectory if it isn't already there
if not os.path.exists(today):
os.mkdir(today) # make directory
print 'Successfully created directory', today
# 5. We use the zip command (in Unix/Linux) to put the files in a zip a
rchive
zip_command = "zip -qr '%s' %s" % (target, ' '.join(source))
# Run the backup
if os.system(zip_command) == 0:
print 'Successful backup to', target
else:
print 'Backup FAILED'
Output
$ python backup_ver4.py
Enter a comment --> added new examples
Successful backup to /mnt/e/backup/20041208/082156_added_new_examples.z
ip
$ python backup_ver4.py
Enter a comment -->
Successful backup to /mnt/e/backup/20041208/082316.zip
How It Works
This program now works! Let us go through the actual enhancements
that we had made in version 3. We take in the user's comments using
the raw_input function and then check if the user actually entered
something by finding out the length of the input using the len
function. If the user has just pressed enter for some reason (maybe
it was just a routine backup or no special changes were made), then
we proceed as we have done before.
However, if a comment was supplied, then this is attached to the
name of the zip archive just before the .zip extension. Notice that
we are replacing spaces in the comment with underscores - this is
because managing such filenames are much easier.
More Refinements
The fourth version is a satisfactorily working script for most
users, but there is always room for improvement. For example, you
can include a verbosity level for the program where you can specify
a -v option to make your program become more talkative.
Another possible enhancement would be to allow extra files and
directories to be passed to the script at the command line. We will
get these from the sys.argv list and we can add them to our source
list using the extend method provided by the list class.
One refinement I prefer is the use of the tar command instead of the
zip command. One advantage is that when you use the tar command
along with gzip, the backup is much faster and the backup created is
also much smaller. If I need to use this archive in Windows, then
WinZip handles such .tar.gz files easily as well. The tar command is
available by default on most Linux/Unix systems. Windows users can
download and install it as well.
The command string will now be:
tar = 'tar -cvzf %s %s -X /home/swaroop/excludes.txt' % (target, ' '.jo
in(srcdir))
The options are explained below.
* -c indicates creation of an archive.
* -v indicates verbose i.e. the command should be more talkative.
* -z indicates the gzip filter should be used.
* -f indicates force in creation of archive i.e. it should replace
if there is a file by the same name already.
* -X indicates a file which contains a list of filenames which
must be excluded from the backup. For example, you can specify
*~ in this file to not include any filenames ending with ~ in
the backup.
Important
The most preferred way of creating such kind of archives would be
using the zipfile or tarfile module respectively. They are part of
the Python Standard Library and available for you to use already.
Using these libraries also avoids the use of the os.system which is
generally not advisable to use because it is very easy to make
costly mistakes using it.
However, I have been using the os.system way of creating a backup
purely for pedagogical purposes, so that the example is simple
enough to be understood by everybody but real enough to be useful.
The Software Development Process
We have now gone through the various phases in the process of
writing a software. These phases can be summarised as follows:
1. What (Analysis)
2. How (Design)
3. Do It (Implementation)
4. Test (Testing and Debugging)
5. Use (Operation or Deployment)
6. Maintain (Refinement)
Important
A recommended way of writing programs is the procedure we have
followed in creating the backup script - Do the analysis and design.
Start implementing with a simple version. Test and debug it. Use it
to ensure that it works as expected. Now, add any features that you
want and continue to repeat the Do It-Test-Use cycle as many times
as required. Remember, 'Software is grown, not built'.
Summary
We have seen how to create our own Python programs/scripts and the
various stages involved in writing such programs. You may find it
useful to create your own program just like we did in this chapter
so that you become comfortable with Python as well as
problem-solving.
Next, we will discuss object-oriented programming.
Chapter 11. Object-Oriented Programming
Table of Contents
Introduction
The self
Classes
Creating a Class
object Methods
Using Object Methds
The __init__ method
Using the __init__ method
Class and Object Variables
Using Class and Object Variables
Inheritance
Using Inheritance
Summary
Introduction
In all our programs till now, we have designed our program around
functions or blocks of statements which manipulate data. This is
called the procedure-oriented way of programming. There is another
way of organizing your program which is to combine data and
functionality and wrap it inside what is called an object. This is
called the object oriented programming paradigm. Most of the time
you can use procedural programming but sometimes when you want to
write large programs or have a solution that is better suited to it,
you can use object oriented programming techniques.
Classes and objects are the two main aspecs of object oriented
programming. A class creates a new type where objects are instances
of the class. An analogy is that you can have variables of type int
which translates to saying that variables that store integers are
variables which are instances (objects) of the int class.
Note for C/C++/Java/C# Programmers
Note that even integers are treated as objects (of the int class).
This is unlike C++ and Java (before version 1.5) where integers are
primitive native types. See help(int) for more details on the class.
C# and Java 1.5 programmers will be familiar with this concept since
it is similar to the boxing and unboxing concept.
Objects can store data using ordinary variables that belong to the
object. Variables that belong to an object or class are called as
fields. Objects can also have functionality by using functions that
belong to a class. Such functions are called methods of the class.
This terminology is important because it helps us to differentiate
between functions and variables which are separate by itself and
those which belong to a class or object. Collectively, the fields
and methods can be referred to as the attributes of that class.
Fields are of two types - they can belong to each instance/object of
the class or they can belong to the class itself. They are called
instance variables and class variables respectively.
A class is created using the class keyword. The fields and methods
of the class are listed in an indented block.
The self
Class methods have only one specific difference from ordinary
functions - they must have an extra first name that has to be added
to the beginning of the parameter list, but you do do not give a
value for this parameter when you call the method, Python will
provide it. This particular variable refers to the object itself,
and by convention, it is given the name self.
Although, you can give any name for this parameter, it is strongly
recommended that you use the name self - any other name is
definitely frowned upon. There are many advantages to using a
standard name - any reader of your program will immediately
recognize it and even specialized IDEs (Integrated Development
Environments) can help you if you use self.
Note for C++/Java/C# Programmers
The self in Python is equivalent to the self pointer in C++ and the
this reference in Java and C#.
You must be wondering how Python gives the value for self and why
you don't need to give a value for it. An example will make this
clear. Say you have a class called MyClass and an instance of this
class called MyObject. When you call a method of this object as
MyObject.method(arg1, arg2), this is automatically converted by
Python into MyClass.method(MyObject, arg1, arg2 - this is what the
special self is all about.
This also means that if you have a method which takes no arguments,
then you still have to define the method to have a self argument.
Classes
The simplest class possible is shown in the following example.
Creating a Class
Example 11.1. Creating a Class
#!/usr/bin/python
# Filename: simplestclass.py
class Person:
pass # An empty block
p = Person()
print p
Output
$ python simplestclass.py
<__main__.Person instance at 0xf6fcb18c>
How It Works
We create a new class using the class statement followed by the name
of the class. This follows an indented block of statements which
form the body of the class. In this case, we have an empty block
which is indicated using the pass statement.
Next, we create an object/instance of this class using the name of
the class followed by a pair of parentheses. (We will learn more
about instantiation in the next section). For our verification, we
confirm the type of the variable by simply printing it. It tells us
that we have an instance of the Person class in the __main__ module.
Notice that the address of the computer memory where your object is
stored is also printed. The address will have a different value on
your computer since Python can store the object wherever it finds
space.
object Methods
We have already discussed that classes/objects can have methods just
like functions except that we have an extra self variable. We will
now see an example.
Using Object Methds
Example 11.2. Using Object Methods
#!/usr/bin/python
# Filename: method.py
class Person:
def sayHi(self):
print 'Hello, how are you?'
p = Person()
p.sayHi()
# This short example can also be written as Person().sayHi()
Output
$ python method.py
Hello, how are you?
How It Works
Here we see the self in action. Notice that the sayHi method takes
no parameters but still has the self in the function definition.
The __init__ method
There are many method names which have special significance in
Python classes. We will see the significance of the __init__ method
now.
The __init__ method is run as soon as an object of a class is
instantiated. The method is useful to do any initialization you want
to do with your object. Notice the double underscore both in the
beginning and at the end in the name.
Using the __init__ method
Example 11.3. Using the __init__ method
#!/usr/bin/python
# Filename: class_init.py
class Person:
def __init__(self, name):
self.name = name
def sayHi(self):
print 'Hello, my name is', self.name
p = Person('Swaroop')
p.sayHi()
# This short example can also be written as Person('Swaroop').sayHi()
Output
$ python class_init.py
Hello, my name is Swaroop
How It Works
Here, we define the __init__ method as taking a parameter name
(along with the usual self). Here, we just create a new field also
called name. Notice these are two different variables even though
they have the same name. The dotted notation allows us to
differentiate between them.
Most importantly, notice that we do not explicitly call the __init__
method but pass the arguments in the parentheses following the class
name when creating a new instance of the class. This is the special
significance of this method.
Now, we are able to use the self.name field in our methods which is
demonstrated in the sayHi method.
Note for C++/Java/C# Programmers
The __init__ method is analogous to a constructor in C++, C# or
Java.
Class and Object Variables
We have already discussed the functionality part of classes and
objects, now we'll see the data part of it. Actually, they are
nothing but ordinary variables which are bound to the classes and
objects namespaces i.e. the names are valid within the context of
these classes and objects only.
There are two types of fields - class variables and object variables
which are classified depending on whether the class or the object
owns the variables respectively.
Class variables are shared in the sense that they are accessed by
all objects (instances) of that class. There is only copy of the
class variable and when any one object makes a change to a class
variable, the change is reflected in all the other instances as
well.
Object variables are owned by each individual object/instance of the
class. In this case, each object has its own copy of the field i.e.
they are not shared and are not related in any way to the field by
the samen name in a different instance of the same class. An example
will make this easy to understand.
Using Class and Object Variables
Example 11.4. Using Class and Object Variables
#!/usr/bin/python
# Filename: objvar.py
class Person:
'''Represents a person.'''
population = 0
def __init__(self, name):
'''Initializes the person's data.'''
self.name = name
print '(Initializing %s)' % self.name
# When this person is created, he/she
# adds to the population
Person.population += 1
def __del__(self):
'''I am dying.'''
print '%s says bye.' % self.name
Person.population -= 1
if Person.population == 0:
print 'I am the last one.'
else:
print 'There are still %d people left.' % Perso
n.population
def sayHi(self):
'''Greeting by the person.
Really, that's all it does.'''
print 'Hi, my name is %s.' % self.name
def howMany(self):
'''Prints the current population.'''
if Person.population == 1:
print 'I am the only person here.'
else:
print 'We have %d persons here.' % Person.popul
ation
swaroop = Person('Swaroop')
swaroop.sayHi()
swaroop.howMany()
kalam = Person('Abdul Kalam')
kalam.sayHi()
kalam.howMany()
swaroop.sayHi()
swaroop.howMany()
Output
$ python objvar.py
(Initializing Swaroop)
Hi, my name is Swaroop.
I am the only person here.
(Initializing Abdul Kalam)
Hi, my name is Abdul Kalam.
We have 2 persons here.
Hi, my name is Swaroop.
We have 2 persons here.
Abdul Kalam says bye.
There are still 1 people left.
Swaroop says bye.
I am the last one.
How It Works
This is a long example but helps demonstrate the nature of class and
object variables. Here, population belongs to the Person class and
hence is a class variable. The name variable belongs to the object
(it is assigned using self) and hence is an object variable.
Thus, we refer to the population class variable as Person.population
and not as self.population. Note that an object variable with the
same name as a class variable will hide the class variable! We refer
to the object variable name using self.name notation in the methods
of that object. Remember this simple difference between class and
object variables.
Observe that the __init__ method is used to initialize the Person
instance with a name. In this method, we increase the population
count by 1 since we have one more person being added. Also observe
that the values of self.name is specific to each object which
indicates the nature of object variables.
Remember, that you must refer to the variables and methods of the
same object using the self variable only. This is called an
attribute reference.
In this program, we also see the use of docstrings for classes as
well as methods. We can access the class docstring at runtime using
Person.__doc__ and the method docstring as Person.sayHi.__doc__
Just like the __init__ method, there is another special method
__del__ which is called when an object is going to die i.e. it is no
longer being used and is being returned to the system for reusing
that piece of memory. In this method, we simply decrease the
Person.population count by 1.
The __del__ method is run when the object is no longer in use and
there is no guarantee when that method will be run. If you want to
explicitly do this, you just have to use the del statement which we
have used in previous examples.
Note for C++/Java/C# Programmers
All class members (including the data members) are public and all
the methods are virtual in Python.
One exception: If you use data members with names using the double
underscore prefix such as __privatevar, Python uses name-mangling
to effectively make it a private variable.
Thus, the convention followed is that any variable that is to be
used only within the class or object should begin with an underscore
and all other names are public and can be used by other
classes/objects. Remember that this is only a convention and is not
enforced by Python (except for the double underscore prefix).
Also, note that the __del__ method is analogous to the concept of a
destructor.
Inheritance
One of the major benefits of object oriented programming is reuse of
code and one of the ways this is achieved is through the inheritance
mechanism. Inheritance can be best imagined as implementing a type
and subtype relationship between classes.
Suppose you want to write a program which has to keep track of the
teachers and students in a college. They have some common
characteristics such as name, age and address. They also have
specific characteristics such as salary, courses and leaves for
teachers and, marks and fees for students.
You can create two independent classes for each type and process
them but adding a new common characteristic would mean adding to
both of these independent classes. This quickly becomes unwieldy.
A better way would be to create a common class called SchoolMember
and then have the teacher and student classes inherit from this
class i.e. they will become sub-types of this type (class) and then
we can add specific characteristics to these sub-types.
There are many advantages to this approach. If we add/change any
functionality in SchoolMember, this is automatically reflected in
the subtypes as well. For example, you can add a new ID card field
for both teachers and students by simply adding it to the
SchoolMember class. However, changes in the subtypes do not affect
other subtypes. Another advantage is that if you can refer to a
teacher or student object as a SchoolMember object which could be
useful in some situations such as counting of the number of school
members. This is called polymorphism where a sub-type can be
substituted in any situation where a parent type is expected i.e.
the object can be treated as an instance of the parent class.
Also observe that we reuse the code of the parent class and we do
not need to repeat it in the different classes as we would have had
to in case we had used independent classes.
The SchoolMember class in this situation is known as the base class
or the superclass. The Teacher and Student classes are called the
derived classes or subclasses.
We will now see this example as a program.
Using Inheritance
Example 11.5. Using Inheritance
#!/usr/bin/python
# Filename: inherit.py
class SchoolMember:
'''Represents any school member.'''
def __init__(self, name, age):
self.name = name
self.age = age
print '(Initialized SchoolMember: %s)' % self.name
def tell(self):
'''Tell my details.'''
print 'Name:"%s" Age:"%s"' % (self.name, self.age),
class Teacher(SchoolMember):
'''Represents a teacher.'''
def __init__(self, name, age, salary):
SchoolMember.__init__(self, name, age)
self.salary = salary
print '(Initialized Teacher: %s)' % self.name
def tell(self):
SchoolMember.tell(self)
print 'Salary: "%d"' % self.salary
class Student(SchoolMember):
'''Represents a student.'''
def __init__(self, name, age, marks):
SchoolMember.__init__(self, name, age)
self.marks = marks
print '(Initialized Student: %s)' % self.name
def tell(self):
SchoolMember.tell(self)
print 'Marks: "%d"' % self.marks
t = Teacher('Mrs. Shrividya', 40, 30000)
s = Student('Swaroop', 22, 75)
print # prints a blank line
members = [t, s]
for member in members:
member.tell() # works for both Teachers and Students
Output
$ python inherit.py
(Initialized SchoolMember: Mrs. Shrividya)
(Initialized Teacher: Mrs. Shrividya)
(Initialized SchoolMember: Swaroop)
(Initialized Student: Swaroop)
Name:"Mrs. Shrividya" Age:"40" Salary: "30000"
Name:"Swaroop" Age:"22" Marks: "75"
How It Works
To use inheritance, we specify the base class names in a tuple
following the class name in the class definition. Next, we observe
that the __init__ method of the base class is explicitly called
using the self variable so that we can initialize the base class
part of the object. This is very important to remember - Python does
not automatically call the constructor of the base class, you have
to explicitly call it yourself.
We also observe that we can call methods of the base class by
prefixing the class name to the method call and then pass in the
self variable along with any arguments.
Notice that we can treat instances of Teacher or Student as just
instances of the SchoolMember when we use the tell method of the
SchoolMember class.
Also, observe that the tell method of the subtype is called and not
the tell method of the SchoolMember class. One way to understand
this is that Python always starts looking for methods in the type,
which in this case it does. If it could not find the method, it
starts looking at the methods belonging to its base classes one by
one in the order they are specified in the tuple in the class
definition.
A note on terminology - if more than one class is listed in the
inheritance tuple, then it is called multiple inheritance.
Summary
We have now explored the various aspects of classes and objects as
well as the various terminologies associated with it. We have also
seen the benefits and pitfalls of object-oriented programming.
Python is highly object-oriented and understanding these concepts
carefully will help you a lot in the long run.
Next, we will learn how to deal with input/output and how to access
files in Python.
Chapter 12. Input/Output
Table of Contents
Files
Using file
Pickle
Pickling and Unpickling
Summary
There will be lots of times when you want your program to interact
with the user (which could be yourself). You would want to take
input from the user and then print some results back. We can achieve
this using the raw_input and print statements respectively. For
output, we can also use the various methods of the str (string)
class. For example, you can use the rjust method to get a string
which is right justified to a specified width. See help(str) for
more details.
Another common type of input/output is dealing with files. The
ability to create, read and write files is essential to many
programs and we will explore this aspect in this chapter.
Files
You can open and use files for reading or writing by creating an
object of the file class and using its read, readline or write
methods appropriately to read from or write to the file. The ability
to read or write to the file depends on the mode you have specified
for the file opening. Then finally, when you are finished with the
file, you call the close method to tell Python that we are done
using the file.
Using file
Example 12.1. Using files
#!/usr/bin/python
# Filename: using_file.py
poem = '''/
Programming is fun
When the work is done
if you wanna make your work also fun:
use Python!
'''
f = file('poem.txt', 'w') # open for 'w'riting
f.write(poem) # write text to file
f.close() # close the file
f = file('poem.txt') # if no mode is specified, 'r'ead mode is assumed
by default
while True:
line = f.readline()
if len(line) == 0: # Zero length indicates EOF
break
print line, # Notice comma to avoid automatic newline added by
Python
f.close() # close the file
Output
$ python using_file.py
Programming is fun
When the work is done
if you wanna make your work also fun:
use Python!
How It Works
First, we create an instance of the file class by specifying the
name of the file and the mode in which we want to open the file. The
mode can be a read mode ('r'), write mode ('w') or append mode
('a'). There are actually many more modes available and help(file)
will give you more details about them.
We first open the file in write mode and use the write method of the
file class to write to the file and then we finally close the file.
Next, we open the same file again for reading. If we don't specify a
mode, then the read mode is the default one. We read in each line of
the file using the readline method, in a loop. This method returns a
complete line including the newline character at the end of the
line. So, when an empty string is returned, it indicates that the
end of the file has been reached and we stop the loop.
Notice that we use a comma with the print statement to suppress the
automatic newline that the print statement adds because the line
that is read from the file already ends with a newline character.
Then, we finally close the file.
Now, see the contents of the poem.txt file to confirm that the
program has indeed worked properly.
Pickle
Python provides a standard module called pickle using which you can
store any Python object in a file and then get it back later intact.
This is called storing the object persistently.
There is another module called cPickle which functions exactly same
as the pickle module except that it is written in the C language and
is (upto 1000 times) faster. You can use either of these modules,
although we will be using the cPickle module here. Remember though,
that we refer to both these modules as simply the pickle module.
Pickling and Unpickling
Example 12.2. Pickling and Unpickling
#!/usr/bin/python
# Filename: pickling.py
import cPickle as p
#import pickle as p
shoplistfile = 'shoplist.data' # the name of the file where we will sto
re the object
shoplist = ['apple', 'mango', 'carrot']
# Write to the file
f = file(shoplistfile, 'w')
p.dump(shoplist, f) # dump the object to a file
f.close()
del shoplist # remove the shoplist
# Read back from the storage
f = file(shoplistfile)
storedlist = p.load(f)
print storedlist
Output
$ python pickling.py
['apple', 'mango', 'carrot']
How It Works
First, notice that we use the import..as syntax. This is handy since
we can use a shorter name for a module. In this case, it even allows
us to switch to a different module (cPickle or pickle) by simply
changing one line! In the rest of the program, we simply refer to
this module as p.
To store an object in a file, first we open a file object in write
mode and store the object into the open file by calling the dump
function of the pickle module. This process is called pickling.
Next, we retrieve the object using the load function of the pickle
module which returns the object. This process is called unpickling.
Summary
We have discussed various types of input/output and also file
handling and using the pickle module.
Next, we will explore the concept of exceptions.
Chapter 13. Exceptions
Table of Contents
Errors
Try..Except
Handling Exceptions
Raising Exceptions
How To Raise Exceptions
Try..Finally
Using Finally
Summary
Exceptions occur when certain exceptional situations occur in your
program. For example, what if you are going to read a file and the
file does not exist? Or what if you accidentally deleted it when the
program was running? Such situations are handled using exceptions.
What if your program had some invalid statements? This is handled by
Python which raises its hands and tells you there is an error.
Errors
Consider a simple print statement. What if we misspelt print as
Print? Note the capitalization. In this case, Python raises a syntax
error.
>>> Print 'Hello World'
File "", line 1
Print 'Hello World'
^
SyntaxError: invalid syntax
>>> print 'Hello World'
Hello World
Observe that a SyntaxError is raised and also the
location where the error was detected is printed. This is what an
error handler for this error does.
Try..Except
We will try to read input from the user. Press Ctrl-d and see what
happens.
>>> s = raw_input('Enter something --> ')
Enter something --> Traceback (most recent call last):
File "", line 1, in ?
EOFError
Python raises an error called EOFError which
basically means it found an end of file when it did not expect to
(which is represented by Ctrl-d)
Next, we will see how to handle such errors.
Handling Exceptions
We can handle exceptions using the try..except statement. We
basically put our usual statements within the try-block and put all
our error handlers in the except-block.
Example 13.1. Handling Exceptions
#!/usr/bin/python
# Filename: try_except.py
import sys
try:
s = raw_input('Enter something --> ')
except EOFError:
print '/nWhy did you do an EOF on me?'
sys.exit() # exit the program
except:
print '/nSome error/exception occurred.'
# here, we are not exiting the program
print 'Done'
Output
$ python try_except.py
Enter something -->
Why did you do an EOF on me?
$ python try_except.py
Enter something --> Python is exceptional!
Done
How It Works
We put all the statements that might raise an error in the try block
and then handle all the errors and exceptions in the except
clause/block. The except clause can handle a single specified error
or exception, or a parenthesized list of errors/exceptions. If no
names of errors or exceptions are supplied, it will handle all
errors and exceptions. There has to be at least one except clause
associated with every try clause.
If any error or exception is not handled, then the default Python
handler is called which just stops the execution of the program and
prints a message. We have already seen this in action.
You can also have an else clause associated with a try..catch block.
The else clause is executed if no exception occurs.
We can also get the exception object so that we can retrieve
additional information about the exception which has occurred. This
is demonstrated in the next example.
Raising Exceptions
You can raise exceptions using the raise statement. You also have to
specify the name of the error/exception and the exception object
that is to be thrown along with the exception. The error or
exception that you can arise should be class which directly or
indirectly is a derived class of the Error or Exception class
respectively.
How To Raise Exceptions
Example 13.2. How to Raise Exceptions
#!/usr/bin/python
# Filename: raising.py
class ShortInputException(Exception):
'''A user-defined exception class.'''
def __init__(self, length, atleast):
Exception.__init__(self)
self.length = length
self.atleast = atleast
try:
s = raw_input('Enter something --> ')
if len(s) < 3:
raise ShortInputException(len(s), 3)
# Other work can continue as usual here
except EOFError:
print '/nWhy did you do an EOF on me?'
except ShortInputException, x:
print 'ShortInputException: The input was of length %d, /
was expecting at least %d' % (x.length, x.atleast)
else:
print 'No exception was raised.'
Output
$ python raising.py
Enter something -->
Why did you do an EOF on me?
$ python raising.py
Enter something --> ab
ShortInputException: The input was of length 2, was expecting at least
3
$ python raising.py
Enter something --> abc
No exception was raised.
How It Works
Here, we are creating our own exception type although we could've
used any predefined exception/error for demonstration purposes. This
new exception type is the ShortInputException class. It has two
fields - length which is the length of the given input, and atleast
which is the minimum length that the program was expecting.
In the except clause, we mention the class of error as well as the
variable to hold the corresponding error/exception object. This is
analogous to parameters and arguments in a function call. Within
this particular except clause, we use the length and atleast fields
of the exception object to print an appropriate message to the user.
Try..Finally
What if you were reading a file and you wanted to close the file
whether or not an exception was raised? This can be done using the
finally block. Note that you can use an except clause along with a
finally block for the same corresponding try block. You will have to
embed one within another if you want to use both.
Using Finally
Example 13.3. Using Finally
#!/usr/bin/python
# Filename: finally.py
import time
try:
f = file('poem.txt')
while True: # our usual file-reading idiom
line = f.readline()
if len(line) == 0:
break
time.sleep(2)
print line,
finally:
f.close()
print 'Cleaning up...closed the file'
Output
$ python finally.py
Programming is fun
When the work is done
Cleaning up...closed the file
Traceback (most recent call last):
File "finally.py", line 12, in ?
time.sleep(2)
KeyboardInterrupt
How It Works
We do the usual file-reading stuff, but I've arbitrarily introduced
a way of sleeping for 2 seconds before printing each line using the
time.sleep method. The only reason is so that the program runs
slowly (Python is very fast by nature). When the program is still
running, press Ctrl-c to interrupt/cancel the program.
Observe that a KeyboardInterrupt exception is thrown and the program
exits, but before the program exits, the finally clause is executed
and the file is closed.
Summary
We have discussed the usage of the try..except and try..finally
statements. We have seen how to create our own exception types and
how to raise exceptions as well.
Next, we will explore the Python Standard Library.
Chapter 14. The Python Standard Library
Table of Contents
Introduction
The sys module
Command Line Arguments
More sys
The os module
Summary
Introduction
The Python Standard Library is available with every Python
installation. It contains a huge number of very useful modules. It
is important that you become familiar with the Python Standard
Library since most of your problems can be solved more easily and
quickly if you are familiar with this library of modules.
We will explore some of the commonly used modules in this library.
You can find complete details for all of the modules in the Python
Standard Library in the 'Library Reference' section in the
documentation that comes with your Python installation.
The sys module
The sys module contains system-specific functionality. we have
already seen that the sys.argv list contains the command-line
arguments.
Command Line Arguments
Example 14.1. Using sys.argv
#!/usr/bin/python
# Filename: cat.py
import sys
def readfile(filename):
'''Print a file to the standard output.'''
f = file(filename)
while True:
line = f.readline()
if len(line) == 0:
break
print line, # notice comma
f.close()
# Script starts from here
if len(sys.argv) < 2:
print 'No action specified.'
sys.exit()
if sys.argv[1].startswith('--'):
option = sys.argv[1][2:]
# fetch sys.argv[1] but without the first two characters
if option == 'version':
print 'Version 1.2'
elif option == 'help':
print '''/
This program prints files to the standard output.
Any number of files can be specified.
Options include:
--version : Prints the version number
--help : Display this help'''
else:
print 'Unknown option.'
sys.exit()
else:
for filename in sys.argv[1:]:
readfile(filename)
Output
$ python cat.py
No action specified.
$ python cat.py --help
This program prints files to the standard output.
Any number of files can be specified.
Options include:
--version : Prints the version number
--help : Display this help
$ python cat.py --version
Version 1.2
$ python cat.py --nonsense
Unknown option.
$ python cat.py poem.txt
Programming is fun
When the work is done
if you wanna make your work also fun:
use Python!
How It Works
This program tries to mimic the cat command familiar to Linux/Unix
users. You just speicfy the names of some text files and it will
print them to the output.
When a Python program is run i.e. not an interactive mode, there is
always at least one item in the sys.argv list which is the name of
the current program being run and is available as sys.argv[0] since
Python starts counting from 0. Other command line arguments follow
this item.
To make the program user-friendly we have supplied certain options
that the user can specify to learn more about the program. We use
the first argument to check if any options have been specified to
our program. If the --version option is used, the version number of
the program is printed. Similarly, when the --help option is
specified, we give a bit of explanation about the program. We make
use of the sys.exit function to exit the running program. As always,
see help(sys.exit) for more details.
When no options are specified and filenames are passed to the
program, it simply prints out each line of each file, one after the
other in the order specified on the command line.
As an aside, the name cat is short for concatenate which is
basically what this program does - it can print out a file or
attach/concatenate two or more files together in the output.
More sys
The sys.version string gives you information about the version of
Python that you have installed. The sys.version_info tuple gives an
easier way of enabling Python-version specific parts of your
program.
[swaroop@localhost code]$ python
>>> import sys
>>> sys.version
'2.3.4 (#1, Oct 26 2004, 16:42:40) /n[GCC 3.4.2 20041017 (Red Hat 3.4.2
-6.fc3)]'
>>> sys.version_info
(2, 3, 4, 'final', 0)
For experienced programmers, other items of
interest in the sys module include sys.stdin, sys.stdout and
sys.stderr which correspond to the standard input, standard output
and standard error streams of your program respectively.
The os module
This module represents generic operating system functionality. This
module is especially important if you want to make your programs
platform-independent i.e. it allows the program to be written such
that it will run on Linux as well as Windows without any problems
and without requiring changes. An example of this is using the
os.sep variable instead of the operation system-specific path
separator.
Some of the more useful parts of the os module are listed below Most
of them are self-explanatory.
* The os.name string specifies which platform you are using, such
as 'nt' for Windows and 'posix' for Linux/Unix users.
* The os.getcwd() function gets the current working directory i.e.
the path of the directory from which the curent Python script is
working.
* The os.getenv() and os.putenv() functions are used to get and
set environment variables respectively.
* The os.listdir() function returns the name of all files and
directories in the specified directory.
* The os.remove() function is used to delete a file.
* The os.system() function is used to run a shell command.
* The os.linesep string gives the line terminator used in the
current platform. For example, Windows uses '/r/n', Linux uses
'/n' and Mac uses '/r'.
* The os.path.split() function returns the directory name and file
name of the path.
>>> os.path.split('/home/swaroop/byte/code/poem.txt')
('/home/swaroop/byte/code', 'poem.txt')
* The os.path.isfile() and the
os.path.isdir() functions check if the given path refers to a
file or directory respectively. Similarly, the os.path.exists()
function is used to check if a given path actually exists.
You can explore the Python Standard Documentation for more details
on these functions and variables. You can use help(sys), etc. as
well.
Summary
We have seen some of the functionality of the sys module and sys
modules in the Python Standard Library. You should explore the
Python Standard Documentation to find out more about these and other
modules as well.
Next, we will cover various aspects of Python that will make our
tour of Python more complete.
Chapter 15. More Python
Table of Contents
Special Methods
Single Statement Blocks
List Comprehension
Using List Comprehensions
Receiving Tuples and Lists in Functions
Lambda Forms
Using Lambda Forms
The exec and eval statements
The assert statement
The repr function
Summary
Till now, we have covered majority of the various aspects of Python
that you will use. In this chapter, we will cover some more aspects
that will make our knowledge of Python more complete.
Special Methods
There are certain special methods which have special significance in
classes such as the __init__ and __del__ methods whose significance
we have already seen.
Generally, special methods are used to mimic certain behavior. For
example, if you want to use the x[key] indexing operation for your
class (just like you use for lists and tuples) then just implement
the __getitem__() method and your job is done. If you think about
it, this is what Python does for the list class itself!
Some useful special methods are listed in the following table. If
you want to know about all the special methods, then a huge list is
available in the Python Reference Manual.
Table 15.1. Some Special Methods
Name Explanation
__init__(self, ...) This method is called just before the newly
created object is returned for usage.
__del__(self) Called just before the object is destroyed
__str__(self) Called when we use the print statement with the object
or when str() is used.
__lt__(self, other) Called when the less than operator ( < ) is
used. Similarly, there are special methods for all the operators (+,
>, etc.)
__getitem__(self, key) Called when x[key] indexing operation is
used.
__len__(self) Called when the built-in len() function is used for
the sequence object.
Single Statement Blocks
By now, you should have firmly understood that each block of
statements is set apart from the rest by its own indentation level.
Well, this is true for the most part but it is not 100% accurate. If
your block of statements contains only one single statement, then
you can specify it on the same line of, say, a conditional statement
or looping statement. The following example should make this clear:
>>> flag = True
>>> if flag: print 'Yes'
...
Yes
As we can see, the single statement is used in-place
and not as a separate block. Although, you can use this for making
your program smaller, I strongly recommend that you do not use this
short-cut method except for error checking, etc. One major reason is
that it will be much easier to add an extra statement if you are
using proper indentation.
Also notice that when the Python interpreter is used in interactive
mode, it helps you enter the statements by changing prompts
appropriately. In the aboe case, after you entered the keyword if,
it changes the prompt to ... to indicate that the statement is not
yet complete. When we do complete the statement in this manner, we
press enter to confirm that the statement is complete. Then, Python
finishes executing the whole statement and returns to the old prompt
waiting for the next input.
List Comprehension
List comprehensions are used to derive a new list from an existing
list. For example, you have a list of numbers and you want to get a
corresponding list with all the numbers multiplied by 2 but only
when the number itself is greater than 2. List comprehensions are
ideal for such situations.
Using List Comprehensions
Example 15.1. Using List Comprehensions
#!/usr/bin/python
# Filename: list_comprehension.py
listone = [2, 3, 4]
listtwo = [2*i for i in listone if i > 2]
print listtwo
Output
$ python list_comprehension.py
[6, 8]
How It Works
Here, we derive a new list by specifying the manipulation to be done
(2*i) when some condition is satisfied (if i > 2). Note that the
original list remains unmodified. Many a time, we use loops to
process each element of a list, the same can be achieved using list
comprehensions in a more precise, compact and explicit manner.
Receiving Tuples and Lists in Functions
There is a special way of receiving parameters to a function as a
tuple or a dictionary using the * or ** prefix respectively. This is
useful when taking variable number of arguments in the function.
>>> def powersum(power, *args):
... '''Return the sum of each argument raised to specified power.''
'
... total = 0
... for i in args:
... total += pow(i, power)
... return total
...
>>> powersum(2, 3, 4)
25
>>> powersum(2, 10)
100
Due to the * prefix on the args variable, all extra
arguments passed to the function are stored in args as a tuple. If a
** prefix had been used instead, the extra parameters would be
considered to be key/value pairs of a dictionary.
Lambda Forms
A lambda statement is used to create new function objects and then
return them at runtime.
Using Lambda Forms
Example 15.2. Using Lambda Forms
#!/usr/bin/python
# Filename: lambda.py
def make_repeater(n):
return lambda s: s * n
twice = make_repeater(2)
print twice('word')
print twice(5)
Output
$ python lambda.py
wordword
10
How It Works
Here, we use a function make_repeater to create new function objects
at runtime and return it. A lambda statement is used to create the
function object. Essentially, the lambda takes a parameter followed
by a single expression only which becomes the body of the function
and the value of this expression is returned by the new function.
Note that even a print statement cannot be used inside a lambda
form, only expressions.
The exec and eval statements
The exec statement is used to execute Python statements which are
stored in a string or file. For example, we can generate a string
containing Python code at runtime and then execute these statements
using the exec statement. A simple example is shown below.
>>> exec 'print "Hello World"'
Hello World
The eval statement is used to evaluate valid Python
expressions which are stored in a string. A simple example is shown
below.
>>> eval('2*3')
6
The assert statement
The assert statement is used to assert that something is true. For
example, if you are very sure that you will have at least one
element in a list you are using and want to check this, and raise an
error if it is not true, then assert statement is ideal in this
situation. When the assert statement fails, an AssertionError is
raised.
>>> mylist = ['item']
>>> assert len(mylist) >= 1
>>> mylist.pop()
'item'
>>> assert len(mylist) >= 1
Traceback (most recent call last):
File "", line 1, in ?
AssertionError
The repr function
The reprt function is used to obtain a canonical string
representation of the object. Backticks (also called conversion or
reverse quotes) do the same thing. Note that you will have
eval(repr(object)) == object most of the time.
>>> i = []
>>> i.append('item')
>>> `i`
"['item']"
>>> repr(i)
"['item']"
Basically, the repr function or the backticks are
used to obtain a printable representation of the object. you can
control what your objects return for the repr function by defining
the __repr__ method in your class.
Summary
We have covered some more features of Python in this chapter and yet
you can be sure we haven't covered all the features of Python.
However, at this stage, we have covered most of what you are ever
going to use in practice. This is sufficient for you to get started
with whatever programs you are going to create.
Next, we will discuss how to explore Python further.
Chapter 16. What Next?
Table of Contents
Graphical Software
Summary of GUI Tools
Explore More
Summary
If you have read this book thoroughly till now and practiced writing
a lot of programs, then you must have become comfortable and
familiar with Python. You have probably created some Python programs
to try out stuff and to exercise your Python skills as well. If you
have not done it already, you should. The question now is 'What
Next?'.
I would suggest that you tackle this problem: create your own
command-line address-book program using which you can add, modify,
delete or search for your contacts such as friends, family and
colleagues and their information such as email address and/or phone
number. Details must be stored for later retrieval.
This is fairly easy if you think about it in terms of all the
various stuff that we have come across till now. If you still want
directions on how to proceed, then here's a hint.
Hint. (You shouldn't be reading this). Create a class to represent
the person's information. Use a dictionary to store person objects
with their name as the key. Use the cPickle module to store the
objects persistently on your hard disk. Use the dictionary built-in
methods to add, delete and modify the persons.
Once you are able to do this, you can claim to be a Python
programmer. Now, immediately send me a mail thanking me for this
great book ;-) . This step is optional but recommended.
Here are some ways to continue your journey with Python:
Graphical Software
GUI Libraries using Python - you need these to create your own
graphical programs using Python. You can create your own IrfanView
or Kuickshow or anything like that using the GUI libraries with
their Python bindings. Bindings are what allow you to write programs
in Python and use the libraries which are themselves written in C or
C++ or other languages.
There are lots of choices for GUI using Python:
* PyQt. This is the Python binding for the Qt toolkit which is
the foundation upon which the KDE is built. Qt is extremely easy
to use and very powerful especially due to the Qt Designer and
the amazing Qt documentation. You can use it for free on Linux
but you will have to pay for it if you want to use it on
Windows. PyQt is free if you want to create free (GPL'ed)
software on Linux/Unix and paid if you want to create
proprietary software. A good resource on PyQt is 'GUI
Programming with Python: Qt Edition'. See the official homepage
for more details.
* PyGTK. This is the Python binding for the GTK+ toolkit which is
the foundation upon which GNOME is built. GTK+ has many quirks
in usage but once you become comfortable, you can create GUI
apps fast. The Glade graphical interface designer is
indispensable. The documentation is yet to improve. GTK+ works
well on Linux but its port to Windows is incomplete. You can
create both free as well as proprietary software using GTK+. See
the official homepage for more details.
* wxPython. This is the Python bindings for the wxWidgets
toolkit. wxPython has a learning curve associated with it.
However, it is very portable and runs on Linux, Windows, Mac and
even embedded platforms. There are many IDEs available for
wxPython which include GUI designers as well such as SPE
(Stani's Python Editor) and the wxGlade GUI builder. You can
create free as well as proprietary software using wxPython. See
the official homepage for more details.
* TkInter. This is one of the oldest GUI toolkits in existence.
If you have used IDLE, you have seen a TkInter program at work.
The documentation for TkInter at PythonWare.org is
comprehensive. TkInter is portable and works on both Linux/Unix
as well as Windows. Importantly, TkInter is part of the standard
Python distribution.
* For more choices, see the GuiProgramming wiki page at Python.org
Summary of GUI Tools
Unfortunately, there is no one standard GUI tool for Python. I
suggest that you choose one of the above tools depending on your
situation. The first factor is whether you are willing to pay to use
any of the GUI tools. The second factor is whether you want the
program to run on Linux or Windows or both. The third factor is
whether you are a KDE or GNOME user on Linux.
Future Chapters
I am contemplating writing 1 or 2 chapters for this book on GUI
Programming. I will be probably be choosing wxPython as the choice
of toolkit. If you would like to present your views on the subject,
please join the byte-of-python mailing list where readers discuss
with me on what improvements can be made to the book.
Explore More
* The Python Standard Library is an extensive library. Most of the
time, this library will have what you are looking for. This is
referred to as the 'batteries included' philosophy of Python. I
highly recommend that you go through the Python Standard
Documentation before you proceed to start writing large Python
programs.
* Python.org - the official homepage of the Python programming
language. You will find the latest versions of the Python
language and interpreter here. There are also various mailing
lists where active discussions on various aspects of Python take
place.
* comp.lang.python is the usenet newsgroup where discussion about
this language takes place. You can post your doubts and queries
to this newsgroup. You can access this online using Google
Groups or join the mailing list which is just a mirror of the
newsgroup.
* Python Cookbook is an extremely valuable collection of recipes
or tips on how to solve certain kinds of problems using Python.
This is a must-read for every Python user.
* Charming Python is an excellent series of Python-related
articles by David Mertz.
* Dive Into Python is a very good book for experienced Python
programmers. If you have thoroughly read the current book you
are reading, then I would highly recommend that you read 'Dive
Into Python' next. It covers a range of topics including XML
Processing, Unit Testing and Functional Programming.
* Jython is an implementation of the Python interpreter in the
Java language. This means that you can write programs in Python
and use the Java libraries as well! Jython is a stable and
mature software. If you are a Java programmer as well, I highly
recommend that you give Jython a try.
* IronPython is an implementation of the Python interpreter in C#
language and can run on the .NET / Mono / DotGNU platform. This
means that you can write programs in Python and use the .NET
Libraries and other libraries provided by these 3 platforms as
well! IronPython is still pre-alpha software and is suitable
only for experimenting as of now. Jim Hugunin, who wrote
IronPython has joined Microsoft and will be working towards a
full version of IronPython in future.
* Lython is a Lisp frontend to the Python language. It is similar
to Common Lisp and compiles directly to Python bytecode which
means that it will interoperate with our usual Python code.
* There are many many more resources on Python. Interesting ones
are Daily Python-URL! which keeps you up to date on the latest
Python happenings, Vaults of Parnassus, ONLamp.com Python
DevCenter, dirtSimple.org, Python Notes and many many more.
Summary
We have now come to the end of this book but, as they say, this is
the the beginning of the end!. You are now an avid Python user and
you are no doubt ready to solve many problems using Python. You can
start automating your computer to do all kinds of previously
unimaginable things or write your own games and much much more. So,
get started!
Appendix A. Free/Libré and Open Source Software (FLOSS)
FLOSS is based on the concept of a community, which itself is based
on the concept of sharing, and particularly the sharing of
knowledge. FLOSS are free for usage, modification and
redistribution.
If you have already read this book, then you are familiar with FLOSS
as well since you have been using Python all along!
If you want to know more about FLOSS, you can explore the following
list. I have listed some big FLOSS as well as those FLOSS which are
cross-platform (i.e. work on Linux, Windows, etc.) so that you can
try using these software without the need to switch to Linux
immediately although you eventually will ;-)
* Linux. This is a FLOSS operating system that the whole world is
slowly embracing! It was started by Linus Torvalds as a student.
Now, it is giving competition to Microsoft Windows. The latest
2.6 kernel is a major breakthrough w.r.t. speed, stability and
scalability. [ Linux Kernel ]
* Knoppix. This is a distribution of Linux which runs off just
the CD! There is no installation required - you can just reboot
your computer, pop the CD in the drive and start using a
full-featured Linux distribution! You can use all the various
FLOSS that comes with a standard Linux distribution such as
running Python programs, compiling C programs, watching movies,
etc. Then, reboot your computer again, remove the CD and use
your existing OS, as if nothing happened at all. [ Knoppix ]
* Fedora. This is a community-driven distribution, sponsored by
Red Hat and is one of the most popular Linux distributions. It
contains the Linux kernel, the KDE, GNOME and XFCE desktops, and
the plethora of FLOSS available and all this in an easy-to-use
and easy-to-install manner.
If you care a complete beginner to Linux, then I would recommend
that you try Mandrake Linux . The newly released Mandrake 10.1
is just awesome. [ Fedora Linux, Mandrake Linux ]
* OpenOffice.org. This is an excellent office suite based on Sun
Microsystems' StarOffice software. OpenOffice has writer,
presentation, spreadsheet and drawing components among other
things. It can even open and edit MS Word and MS PowerPoint
files with ease. It runs on almost all platforms. The upcoming
OpenOffice 2.0 has some radical improvements. [ OpenOffice ]
* Mozilla Firefox. This is the next generation web browser which
is predicted to beat Internet Explorer (in terms of market share
only ;-) in a few years. It is blazingly fast and has gained
critical acclaim for its sensible and impressive features. The
extensions concept allows any kind of functionality to be added
to it.
It's companion product Thunderbird is an excellent email client
that makes reading email a snap. [ Mozilla Firefox, Mozilla
Thunderbird ]
* Mono. This is an open source implementation of the Microsoft
.NET platform. It allows .NET applications to be created and run
on Linux, Windows, FreeBSD, Mac OS and many other platforms as
well. Mono implements the ECMA standards of the CLI and C# which
Microsoft, Intel and HP have submitted for standardization and
they have now become open standards. This is a step in the
direction of ISO standardization for the same.
Currently, there is a complete C# mcs (which itself is written
in C#!), a feature-complete ASP.NET implementation, many ADO.NET
providers for databases and many many more features that are
being improved and added everyday. [ Mono, ECMA, Microsoft .NET
]
* Apache web server. This is the popular open source web server.
In fact, it is the most popular web server on the planet! It
runs nearly 60% of the websites out there. Yes, that's right -
Apache handles more websites than all the competition (including
Microsoft IIS) combined. [ Apache ]
* MySQL. This is an extremely popular open source database
server. It is most famous for it's blazing speed. More features
are being added to it's latest versions. [ MySQL ]
* MPlayer. This is a video player that can play anything from
DivX to MP3 to Ogg to VCDs and DVDs to ... who says open source
ain't fun? ;-) [ MPlayer ]
* Movix. This is a Linux distribution which is based on Knoppix
and runs off the CD but is designed to play movies! You can
create Movix CDs which are just bootable CDs and when you reboot
the computer and pop in the CD, the movie starts playing by
itself! You don't even need a hard disk to watch a movie using
Movix. [ Movix ]
This list is just intended to give you a brief idea - there are many
more excellent FLOSS out there, such as the Perl language, PHP
language, Drupal content management system for websites, PostgreSQL
database server, TORCS racing game, KDevelop IDE, Anjuta IDE, Xine -
the movie player, VIM editor, Quanta+ editor, XMMS audio player,
GIMP image editing program, ... this list could go on forever.
Visit the following websites for more information on FLOSS:
* SourceForge
* FreshMeat
* KDE
* GNOME
To get the latest buzz in the FLOSS world, check out the following
websites:
* OSNews
* LinuxToday
* NewsForge
* SwaroopCH's blog
So, go ahead and explore the vast, free and open world of FLOSS!
Appendix B. About
Table of Contents
Colophon
About the Author
Colophon
Almost all of the software that I have used in the creation of this
book are free and open source software. In the first draft of this
book, I had used Red Hat 9.0 Linux as the foundation of my setup and
now for this sixth draft, I am using Fedora Core 3 Linux as the
basis of my setup.
Initially, I was using KWord to write the book (as explained in the
History Lesson in the preface). Later, I switched to DocBook XML
using Kate but I found it too tedious. So, I switched to OpenOffice
which was just excellent with the level of control it provided for
formatting as well as the PDF generation, but it produced very
sloppy HTML from the document. Finally, I discovered XEmacs and I
rewrote the book from scratch in DocBook XML (again) after I decided
that this format was the long term solution. In this new sixth
draft, I decided to use Quanta+ to do all the editing.
The standard XSL stylesheets that came with Fedora Core 3 Linux are
being used. The standard default fonts are used as well. The
standard fonts are used as well. However, I have written a CSS
document to give color and style to the HTML pages. I have also
written a crude lexical analyzer, in Python of course, which
automatically provides syntax highlighting to all the program
listings.
About the Author
Swaroop C H loves his job which is being a software developer at
Yahoo! in the Bangalore office in India. His interests on the
technological side include FLOSS such as Linux, DotGNU, Qt and
MySQL, great languages like Python and C#, writing stuff like this
book and any software he can create in his spare time, as well as
writing his blog. His other interests include coffee, reading Robert
Ludlum novels, trekking and politics.
If you are still to interested to know more about this guy, check
out his blog at www.swaroopch.info .
Appendix C. Revision History
Table of Contents
Timestamp
Timestamp
This document was generated on January 13, 2005 at 04:03
Revision History
Revision 1.20 13/01/2005
Complete rewrite using Quanta+ on FC3 with lot of corrections and
updates. Many new examples. Re-wrote my DocBook setup from scratch.
Revision 1.15 28/03/2004
Minor revisions
Revision 1.12 16/03/2004
Additions and corrections.
Revision 1.10 09/03/2004
More typo corrections, thanks to many enthusiastic and helpful
readers.
Revision 1.00 08/03/2004
After tremendous feedback and suggestions from readers, I have made
significant revisions to the content along with typo corrections.
Revision 0.99 22/02/2004
Added a new chapter on modules. Added details about variable number
of arguments in functions.
Revision 0.98 16/02/2004
Wrote a Python script and CSS stylesheet to improve XHTML output,
including a crude-yet-functional lexical analyzer for automatic
VIM-like syntax highlighting of the program listings.
Revision 0.97 13/02/2004
Another completely rewritten draft, in DocBook XML (again). Book has
improved a lot - it is more coherent and readable.
Revision 0.93 25/01/2004
Added IDLE talk and more Windows-specific stuff
Revision 0.92 05/01/2004
Changes to few examples.
Revision 0.91 30/12/2003
Corrected typos. Improvised many topics.
Revision 0.90 18/12/2003
Added 2 more chapters. OpenOffice format with revisions.
Revision 0.60 21/11/2003
Fully rewritten and expanded.
Revision 0.20 20/11/2003
Corrected some typos and errors.
Revision 0.15 20/11/2003
Converted to DocBook XML.
Revision 0.10 14/11/2003
Initial draft using KWord.