Version 1.20
Copyright © 2003-2005 Swaroop C H
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
List of Tables
List of Examples
Table of Contents
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'.
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!
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.
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.
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.
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.
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 <swaroop (at) byteofpython.info>
.
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 |
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.
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.
As you will see, Python is extremely easy to get started with. Python has an extraordinarily simple syntax, as already mentioned.
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.
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.
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 !
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!
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.
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.
You can embed Python within your C/C++ programs to give 'scripting' capabilities for your program's users.
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.
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.
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.
Table of Contents
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
$
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.
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.
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.
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 PATH in the 'System Variables' section, then select and add ;C:\Python23 (without the quotes) to the end of what is already there. Of course, use the appropriate directory name.
-> -> -> . Click on the variable named
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.
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.
Table of Contents
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.
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 -> ->
-> .
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.
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.
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.
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 ->
or the keyboard shortcut
Ctrl-F5. The output is as shown
below.
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.
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
.
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.
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.
W.r.t. Python, a program or a script or software all mean the same thing.
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.
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.
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.
Table of Contents
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.
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 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)
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:
'
)
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.
"
)
Strings in double quotes work exactly the same way as strings in single
quotes. An example is
"What's your name?"
'''
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."
'''
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."
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 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.
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.
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?"
.
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.
Remember that single-quoted strings and double-quoted strings are the same - they do not differ in any way.
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'
.
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.
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
.
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.
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'.
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.
Henceforth, the standard procedure to save and run a Python program is as follows:
Open your favorite editor.
Enter the program code given in the example.
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
.
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
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.
Variables are used by just assigning them a value. No declaration or data type definition is needed/used.
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.
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.
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.
Table of Contents
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.
We will briefly take a look at the operators and their usage:
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.
|
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.
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
.
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)
.
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)
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.
Table of Contents
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 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.
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 here 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 executed
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.
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 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.
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'
$ 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
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.
Remember that you can have an else
clause for
the while
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.
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'
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.
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 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.
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'
$ 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
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.
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.
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...
$ python continue.py Enter something : a Enter something : 12 Enter something : abc Input is of sufficient length Enter something : quit
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.
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.
Table of Contents
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:
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
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.
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.
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
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.
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.
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
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.
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
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
.
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.
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)
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.
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.
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.
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)
$ 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
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 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.
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)
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.
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!
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__
$ python func_doc.py 5 is maximum Prints the maximum of two numbers. The two values must be integers.
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.
Table of Contents
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.
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'
$ 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/gtk-2.0']
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
.
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.
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.
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.
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'
$ python using_name.py This program is being run by itself $ python >>> import using_name I am being imported from another module >>>
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.
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
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.
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', 'stdout', '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'] >>>
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.
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.
Table of Contents
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.
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.
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
.
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
$ 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']
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.
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.
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]
$ 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
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
.
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.
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
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.
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.
Example 9.4. Using dictionaries
#!/usr/bin/python # Filename: using_dict.py # 'ab' is short for 'a'ddress'b'ook ab = { 'Swaroop' : 'swaroopch@byteofpython.info', 'Larry' : 'larry@wall.org', 'Matsumoto' : 'matz@ruby-lang.org', 'Spammer' : 'spammer@hotmail.com' } print "Swaroop's address is %s" % ab['Swaroop'] # Adding a key/value pair ab['Guido'] = 'guido@python.org' # 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']
$ python using_dict.py Swaroop's address is swaroopch@byteofpython.info There are 4 contacts in the address-book Contact Swaroop at swaroopch@byteofpython.info Contact Matsumoto at matz@ruby-lang.org Contact Larry at larry@wall.org Contact Guido at guido@python.org Guido's address is guido@python.org
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).
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.
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[:]
$ 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
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!
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.
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 object! del shoplist[0] # I purchased the first item, so I remove it from the list 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
$ 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']
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.
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.
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)
.
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)
$ 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
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.
Table of Contents
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 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.
The files and directories to be backed up are specified in a list.
The backup must be stored in a main backup directory.
The files are backed up into a zip file.
The name of the zip archive is the current date and time.
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.
As the design of our program is now stable, we can write the code which is an implementation of our solution.
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 will 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 archive 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'
$ 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.
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!
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.
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 will 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 directory 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 archive 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'
$ 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
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.
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 will 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 directory 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 archive 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'
$ python backup_ver3.py File "backup_ver3.py", line 25 target = today + os.sep + now + '_' + ^ SyntaxError: invalid syntax
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.
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 will 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 directory 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 archive 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'
$ python backup_ver4.py Enter a comment --> added new examples Successful backup to /mnt/e/backup/20041208/082156_added_new_examples.zip $ python backup_ver4.py Enter a comment --> Successful backup to /mnt/e/backup/20041208/082316.zip
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.
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, ' '.join(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.
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.
We have now gone through the various phases in the process of writing a software. These phases can be summarised as follows:
What (Analysis)
How (Design)
Do It (Implementation)
Test (Testing and Debugging)
Use (Operation or Deployment)
Maintain (Refinement)
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'.
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.
Table of Contents
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 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.
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
.
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.
The simplest class possible is shown in the following example.
Example 11.1. Creating a Class
#!/usr/bin/python # Filename: simplestclass.py class Person: pass # An empty block p = Person() print p
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.
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.
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.
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()
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.
The __init__
method is analogous to a
constructor in C++, C# or Java.
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.
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.' % Person.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.population swaroop = Person('Swaroop') swaroop.sayHi() swaroop.howMany() kalam = Person('Abdul Kalam') kalam.sayHi() kalam.howMany() swaroop.sayHi() swaroop.howMany()
$ 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.
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.
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.
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.
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
$ 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"
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.
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.
Table of Contents
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.
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.
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
$ python using_file.py Programming is fun When the work is done if you wanna make your work also fun: use Python!
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.
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.
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 store 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
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.
Table of Contents
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.
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 "<stdin>", 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.
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 "<stdin>", 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.
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'
$ python try_except.py Enter something --> Why did you do an EOF on me? $ python try_except.py Enter something --> Python is exceptional! Done
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.
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.
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.'
$ 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.
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.
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.
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'
$ 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
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.
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 contains system-specific functionality. we have already
seen that the sys.argv
list contains the 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)
$ 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!
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.
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.
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.
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.
Table of Contents
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.
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.
|
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 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.
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
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.
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.
A lambda
statement is used to create new function objects and then
return them at runtime.
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)
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
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 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 "<stdin>", line 1, in ? AssertionError
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.
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.
Table of Contents
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:
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
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.
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.
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.
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!
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:
To get the latest buzz in the FLOSS world, check out the following websites:
So, go ahead and explore the vast, free and open world of FLOSS!
Table of Contents
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.
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 .
Table of Contents
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. |