Python en:Input Output
Introduction
There will be situations where your program has to interact with the user. For example, you
would want to take input from the user and then print some results back. We can achieve
this using the input() and print() functions 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.
Input from user
#!/usr/bin/python
# user_input.py
def reverse(text):
return text[::-1]
def is_palindrome(text):
return text == reverse(text)
something = input('Enter text: ')
if (is_palindrome(something)):
print("Yes, it is a palindrome")
else:
print("No, it is not a palindrome")
Output:
$ python user_input.py
Enter text: sir
No, it is not a palindrome
$ python user_input.py
Enter text: madam
Yes, it is a palindrome
$ python user_input.py
Enter text: racecar
Yes, it is a palindrome
How It Works:
We use the slicing feature to reverse the text. We've already seen how we can make slices
from sequences using the seq[a:b] code starting from position a to position b. We can
also provide a third argument that determines the step by which the slicing is done. The
Python en:Input Output 91
default step is 1 because of which it returns a continuous part of the text. Giving a negative
step, i.e., -1 will return the text in reverse.
The input() function takes a string as argument and displays it to the user. Then it waits
for the user to type something and press the return key. Once the user has entered, the
input() function will then return that text.
We take that text and reverse it. If the original text and reversed text are equal, then the
text is a palindrome (http:/ / en. wiktionary. org/ wiki/ palindrome).
Homework exercise:
Checking whether a text is a palindrome should also ignore punctuation, spaces and case.
For example, "Rise to vote, sir." is also a palindrome but our current program doesn't say it
is. Can you improve the above program to recognize this palindrome?
Files
You can open and use files for reading or writing by creating an object of the file class
and using its read, readline or write methods appropriately to read from or write to the
file. The ability to read or write to the file depends on the mode you have specified for the
file opening. Then finally, when you are finished with the file, you call the close method to
tell Python that we are done using the file.
Example:
#!/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 = open('poem.txt', 'w') # open for 'w'riting
f.write(poem) # write text to file
f.close() # close the file
f = open('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, end='')
f.close() # close the file
Output:
$ python using_file.py
Programming is fun
Python en:Input Output 92
When the work is done
if you wanna make your work also fun:
use Python!
How It Works:
First, open a file by using the built-in open function and 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'). We can also by dealing with a text file ('t') or a binary
file ('b'). There are actually many more modes available and help(open) will give you
more details about them. By default, open() considers the file to be a 't'ext file and opens it
in 'r'ead mode.
In our example, we first open the file in write text mode and use the write method of the
file object to write to the file and then we finally close the file.
Next, we open the same file again for reading. We don't need to specify a mode because
'read text file' is the default mode. 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. When an empty string is returned, it means that we have reached the
end of the file and we 'break' out of the loop.
By deafult, the print() function prints the text as well as an automatic newline to the
screen. We are suppressing the newline by specifying end='' because the line that is read
from the file already ends with a newline character. Then, we finally close the file.
Now, check the contents of the poem.txt file to confirm that the program has indeed
written and read from that file.
Pickle
Python provides a standard module called pickle using which you can store any Python
object in a file and then get it back later. This is called storing the object persistently.
Example:
#!/usr/bin/python
# Filename: pickling.py
import pickle
# the name of the file where we will store the object
shoplistfile = 'shoplist.data'
# the list of things to buy
shoplist = ['apple', 'mango', 'carrot']
# Write to the file
f = open(shoplistfile, 'wb')
pickle.dump(shoplist, f) # dump the object to a file
f.close()
del shoplist # destroy the shoplist variable
# Read back from the storage
Python en:Input Output 93
f = open(shoplistfile, 'rb')
storedlist = pickle.load(f) # load the object from the file
print(storedlist)
Output:
$ python pickling.py
['apple', 'mango', 'carrot']
How It Works:
To store an object in a file, we have to first open the file in 'w'rite 'b'inary mode and then
call the dump function of the pickle module. This process is called pickling.
Next, we retrieve the object using the load function of the pickle module which returns
the object. This process is called unpickling.
Summary
We have discussed various types of input/output and also file handling and using the pickle
module.
Next, we will explore the concept of exceptions.

Python en:Problem Solving
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 idea is to learn how to write a Python script on your own.
The Problem
The problem is "I want a program which creates a backup of all my important files".
Although, this is a simple problem, there is not enough information for us to get started
with the solution. A little more analysis is required. For example, how do we specify which
files are to be backed up? How are they stored? Where are they stored?
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 analysis
since every person has their own way of doing things, so that is perfectly okay.
1. The files and directories to be backed up are specified in a list.
2. The backup must be stored in a main backup directory.
3. The files are backed up into a zip file.
4. The name of the zip archive is the current date and time.
5. We use the standard zip command available by default in any standard Linux/Unix
distribution. Windows users can install (http:/ / gnuwin32. sourceforge. net/ downlinks/
zip. php) from the GnuWin32 project page (http:/ / gnuwin32. sourceforge. net/ packages/
zip. htm) and add C:\Program Files\GnuWin32\bin to your system PATH environment
variable, similar to what we did for recognizing the python command itself. 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.
Python en:Problem Solving 73
The Solution
As the design of our program is now reasonably stable, we can write the code which is an
implementation of our solution.
#!/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 = ['"C:\\My Documents"', 'C:\\Code']
# Notice we had to use double quotes inside the string for names with
spaces in it.
# 2. The backup must be stored in a main backup directory
target_dir = '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 + os.sep + time.strftime('%Y%m%d%H%M%S') + '.zip'
# 5. We use the zip command to put the files in a zip archive
zip_command = "zip -qr {0} {1}".format(target, ' '.join(source))
# Run the backup
if os.system(zip_command) == 0:
print('Successful backup to', target)
else:
print('Backup FAILED')
Output:
$ python backup_ver1.py
Successful backup to E:\Backup\20080702185040.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.
If the above program does not work for you, put a print(zip_command) just before the
os.system call and run the program. Now copy/paste the printed zip_command to the shell
prompt and see if it runs properly on its own. If this command fails, check the zip command
manual on what could be wrong. If this command succeeds, then check the Python program
if it exactly matches the program written above.
How It Works:
You will notice how we have converted our design into code in a step-by-step manner.
Python en:Problem Solving 74
We make use of the os and time modules by first importing 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 find out using the
time.strftime() function. It will also have the .zip extension and will be stored in the
target_dir directory.
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 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 century. 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
(http:/ / docs. python. org/ dev/ 3. 0/ library/ time. html#time. strftime).
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
all the subdirectories and files. The two options are combined and specified in a shortcut 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.
That's it, we have created a script to take a backup of our important files!
Note to Windows Users
Instead of double backslash escape sequences, you can also use raw strings. For
example, use 'C:\\Documents' or r'C:\Documents'. However, do not use
'C:\Documents' since you end up 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.
Python en:Problem Solving 75
Second Version
The first version of our script works. However, we can make some refinements to it so that
it can work better on a daily basis. This is called the maintenance phase of the software.
One of the refinements I felt was useful is a better file-naming mechanism - using the time
as the name of the file within a directory with the current date as a directory within the
main backup directory. First advantage is that your backups are stored in a hierarchical
manner and therefore it is much easier to manage. Second advantage is that the length of
the filenames are much shorter. Third 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.
#!/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 = ['"C:\\My Documents"', 'C:\\Code']
# Notice we had to use double quotes inside the string for names with
spaces in it.
# 2. The backup must be stored in a main backup directory
target_dir = '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 + os.sep + 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 to put the files in a zip archive
zip_command = "zip -qr {0} {1}".format(target, ' '.join(source))
# Run the backup
if os.system(zip_command) == 0:
print('Successful backup to', target)
Python en:Problem Solving 76
else:
print('Backup FAILED')
Output:
$ python backup_ver2.py
Successfully created directory E:\Backup\20080702
Successful backup to E:\Backup\20080702\202311.zip
$ python backup_ver2.py
Successful backup to E:\Backup\20080702\202325.zip
How It Works:
Most of the program remains the same. The changes is that we check if there is a directory
with the current day as name inside the main backup directory using the os.path.exists
function. If it doesn't exist, we create it using the os.mkdir function.
Third Version
The second version works fine when I do many backups, but when there are lots of
backups, I am finding it hard to differentiate what the backups were for! For example, I
might have made some major changes to a program or presentation, then I want to
associate what those changes are with the name of the zip archive. This can be easily
achieved by attaching a user-supplied comment to the name of the zip archive.
Note
The following program does not work, so do not be alarmed, please follow along
because there's a lesson in here.
#!/usr/bin/python
# Filename: backup_ver3.py
import os
import time
# 1. The files and directories to be backed up are specified in a list.
source = ['"C:\\My Documents"', 'C:\\Code']
# Notice we had to use double quotes inside the string for names with
spaces in it.
# 2. The backup must be stored in a main backup directory
target_dir = '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 + os.sep + time.strftime('%Y%m%d')
# The current time is the name of the zip archive
now = time.strftime('%H%M%S')
Python en:Problem Solving 77
# Take a comment from the user to create the name of the zip file
comment = 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 to put the files in a zip archive
zip_command = "zip -qr {0} {1}".format(target, ' '.join(source))
# Run the backup
if os.system(zip_command) == 0:
print('Successful backup to', target)
else:
print('Backup FAILED')
Output:
$ python backup_ver3.py
File "backup_ver3.py", line 25
target = today + os.sep + now + '_' +
^
SyntaxError: invalid syntax
How This (does not) Work:
This program does not work! Python says there is a syntax error which means that the
script does not satisfy the structure that Python expects to see. When we observe the error
given by Python, it also tells us the place where it detected the error as well. So we start
debugging our program from that line.
On careful observation, we see that the single logical line has been split into two physical
lines but we have not specified that these two physical lines belong together. Basically,
Python has found the addition operator (+) without any operand in that logical line and
hence it doesn't know how to continue. Remember that we can specify that the logical line
continues in the next physical line by the use of a backslash at the end of the physical line.
So, we make this correction to our program. This correction of the program when we find
errors is called bug fixing.
Python en:Problem Solving 78
Fourth Version
#!/usr/bin/python
# Filename: backup_ver4.py
import os
import time
# 1. The files and directories to be backed up are specified in a list.
source = ['"C:\\My Documents"', 'C:\\Code']
# Notice we had to use double quotes inside the string for names with
spaces in it.
# 2. The backup must be stored in a main backup directory
target_dir = '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 + os.sep + 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 = 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 to put the files in a zip archive
zip_command = "zip -qr {0} {1}".format(target, ' '.join(source))
# Run the backup
if os.system(zip_command) == 0:
print('Successful backup to', target)
else:
print('Backup FAILED')
Output:
Python en:Problem Solving 79
$ python backup_ver4.py
Enter a comment --> added new examples
Successful backup to
E:\Backup\20080702\202836_added_new_examples.zip
$ python backup_ver4.py
Enter a comment -->
Successful backup to E:\Backup\20080702\202839.zip
How It Works:
This program now works! Let us go through the actual enhancements that we had made in
version 3. We take in the user's comments using the 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 without entering anything (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 filenames without spaces are much easier.
More Refinements
The fourth version is a satisfactorily working script for most users, but there is always room
for improvement. For example, you can include a verbosity level for the program where you
can specify a -v option to make your program become more talkative.
Another possible enhancement would be to allow extra files and directories to be passed to
the script at the command line. We can get these names from the sys.argv list and we can
add them to our source list using the extend method provided by the list class.
The most important refinement would be to not use the os.system way of creating archives
and instead using the zipfile or tarfile built-in module to create these archives. They
are part of the standard library and available already for you to use without external
dependencies on the zip program to be available on your computer.
However, I have been using the os.system way of creating a backup in the above examples
purely for pedagogical purposes, so that the example is simple enough to be understood by
everybody but real enough to be useful.
Can you try writing the fifth version that uses the zipfile (http:/ / docs. python. org/ dev/ 3.
0/ library/ zipfile. html) module instead of the os.system call?
Python en:Problem Solving 80
The Software Development Process
We have now gone through the various phases in the process of writing a software. These
phases can be summarised as follows:
1. What (Analysis)
2. How (Design)
3. Do It (Implementation)
4. Test (Testing and Debugging)
5. Use (Operation or Deployment)
6. Maintain (Refinement)
A recommended way of writing programs is the procedure we have followed in creating the
backup script: Do the analysis and design. Start implementing with a simple version. Test
and debug it. Use it to ensure that it works as expected. Now, add any features that you
want and continue to repeat the Do It-Test-Use cycle as many times as required.
Remember, Software is grown, not built.
Summary
We have seen how to create our own Python programs/scripts and the various stages
involved in writing such programs. You may find it useful to create your own program just
like we did in this chapter so that you become comfortable with Python as well as
problem-solving.
Next, we will discuss object-oriented programming.

Python en:Data Structures
Introduction
Data structures are basically just that - they are structures which can hold some data
together. In other words, they are used to store a collection of related data.
There are four built-in data structures in Python - list, tuple, dictionary and set. We will see
how to use each of them and how they make life easier for us.
List
A list is a data structure that holds an ordered collection of items i.e. you can store a
sequence of items in a list. This is easy to imagine if you can think of a shopping list where
you have a list of items to buy, except that you probably have each item on a separate line
in your shopping list whereas in Python you put commas in between them.
The list of items should be enclosed in square brackets so that Python understands that you
are specifying a list. Once you have created a list, you can add, remove or search for items
in the list. Since we can add and remove items, we say that a list is a mutable data type i.e.
this type can be altered.
Quick Introduction To Objects And Classes
Although I've been generally delaying the discussion of objects and classes till now, a little
explanation is needed right now so that you can understand lists better. We will explore this
topic in detail later in its own chapter.
A list is an example of usage of objects and classes. When we use a variable i and assign a
value to it, say integer 5 to it, you can think of it as creating an object (i.e. instance) i of
class (i.e. type) int. In fact, you can read 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:
#!/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:', end=' ')
Python en:Data Structures 63
for item in shoplist:
print(item, end=' ')
print('\nI also have to buy rice.')
shoplist.append('rice')
print('My shopping list is now', shoplist)
print('I will sort my list now')
shoplist.sort()
print('Sorted shopping list is', shoplist)
print('The first item I will buy is', shoplist[0])
olditem = shoplist[0]
del shoplist[0]
print('I bought the', olditem)
print('My shopping list is now', shoplist)
Output:
$ python using_list.py
I have 4 items to purchase.
These items are: apple mango carrot banana
I also have to buy rice.
My shopping list is now ['apple', 'mango', 'carrot', 'banana',
'rice']
I will sort my list now
Sorted shopping list is ['apple', 'banana', 'carrot', 'mango',
'rice']
The first item I will buy is apple
I bought the apple
My shopping list is now ['banana', 'carrot', 'mango', 'rice']
How It Works:
The variable shoplist is a shopping list for someone who is going to the market. In
shoplist, we only store strings of the names of the items to buy but 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 the use of the end keyword argument to the print function to indicate that we
want to end the output with a space instead of the usual line break.
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 neatly.
Then, we sort the list by using the sort method of the list. It is important to 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
Python en:Data Structures 64
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 from the 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 details.
Tuple
Tuples are used to hold together multiple objects. Think of them as similar to lists, but
without the extensive functionality that the list class gives you. One major feature of tuples
is that they are immutable like strings i.e. you cannot modify tuples.
Tuples are defined by specifying items separated by commas within an optional 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:
#!/usr/bin/python
# Filename: using_tuple.py
zoo = ('python', 'elephant', 'penguin') # remember the parentheses are
optional
print('Number of animals in the zoo is', len(zoo))
new_zoo = ('monkey', 'camel', zoo)
print('Number of cages 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])
print('Number of animals in the new zoo is',
len(new_zoo)-1+len(new_zoo[2]))
Output:
$ python using_tuple.py
Number of animals in the zoo is 3
Number of cages in the new zoo is 3
All animals in new zoo are ('monkey', 'camel', ('python',
'elephant', 'penguin'))
Animals brought from old zoo are ('python', 'elephant', 'penguin')
Last animal brought from old zoo is penguin
Number of animals in the new zoo is 5
How It Works:
The variable zoo refers to a tuple of items. We see that the len function can be used to get
the length of the tuple. This also indicates that a tuple is a sequence as well.
Python en:Data Structures 65
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 within the
third item in the new_zoo tuple by specifying new_zoo[2][2]. This is pretty simple once
you've understood the idiom.
Parentheses
Although the parentheses is optional, I prefer always having them to make it obvious
that it is a tuple, especially because it avoids ambiguity. For example, print(1,2,3)
and print( (1,2,3) ) mean two different things - the former prints three numbers
whereas the latter prints a tuple (which contains three numbers).
Tuple with 0 or 1 items
An empty tuple is constructed by an empty pair of parentheses such as myempty = ().
However, a tuple with a single item is not so simple. You have to specify it using a
comma following the first (and only) item so that Python can differentiate between a
tuple and a pair of parentheses surrounding the object in an expression i.e. you have to
specify singleton = (2 , ) if you mean you want a tuple containing the item 2.
Note for Perl programmers
A list within a list does not lose its identity i.e. lists are not flattened as in Perl. The
same applies to a tuple within a tuple, or a tuple within a list, or a list within a tuple,
etc. As far as Python is concerned, they are just objects stored using another object,
that's all.
Dictionary
A dictionary is like an address-book where you can find the address or contact details of a
person by knowing only his/her name i.e. we associate keys (name) with values (details).
Note that the key must be unique just like you cannot find out the correct information if you
have two persons with the exact same name.
Note that you can use only immutable objects (like strings) for the keys of a dictionary but
you can use either immutable or mutable objects for the values of the dictionary. This
basically translates to say that you should use only simple objects for keys.
Pairs of keys and values are specified in a dictionary by using the notation d = {key1 :
value1, key2 : value2 }. Notice that the 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 braces.
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:
#!/usr/bin/python
# Filename: using_dict.py
Python en:Data Structures 66
# 'ab' is short for 'a'ddress'b'ook
ab = { 'Swaroop' : 'swaroop@swaroopch.com',
'Larry' : 'larry@wall.org',
'Matsumoto' : 'matz@ruby-lang.org',
'Spammer' : 'spammer@hotmail.com'
}
print("Swaroop's address is", ab['Swaroop'])
# Deleting a key-value pair
del ab['Spammer']
print('\nThere are {0} contacts in the address-book\n'.format(len(ab)))
for name, address in ab.items():
print('Contact {0} at {1}'.format(name, address))
# Adding a key-value pair
ab['Guido'] = 'guido@python.org'
if 'Guido' in ab: # OR ab.has_key('Guido')
print("\nGuido's address is", ab['Guido'])
Output:
$ python using_dict.py
Swaroop's address is swaroop@swaroopch.com
There are 3 contacts in the address-book
Contact Swaroop at swaroop@swaroopch.com
Contact Matsumoto at matz@ruby-lang.org
Contact Larry at larry@wall.org
Guido's address is guido@python.org
How It Works:
We create the dictionary ab using the notation already discussed. We then access key-value
pairs by specifying the key using the indexing operator as discussed in the context of lists
and tuples. Observe the simple syntax.
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
Python en:Data Structures 67
for-block.
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 check if a key-value pair exists using the in operator or even the has_key method
of the dict class. You can see the documentation for the complete list of methods of the
dict class using help(dict).
Keyword Arguments and Dictionaries
On a different note, if you have used keyword arguments in your functions, you have
already used dictionaries! Just think about it - the key-value pair is specified by you in
the parameter list of the function definition and when you access variables within your
function, it is just a key access of a dictionary (which is called the symbol table in
compiler design terminology).
Sequences
Lists, tuples and strings are examples of sequences, but what are sequences and what is so
special about them?
The major features is that they have membership tests (i.e. the in and not in expressions)
and indexing operations. The indexing operation which allows us to fetch a particular item
in the sequence directly.
The three types of sequences mentioned above - lists, tuples and strings, also have a
slicing operation which allows us to retrieve a slice of the sequence i.e. a part of the
sequence.
Example:
#!/usr/bin/python
# Filename: seq.py
shoplist = ['apple', 'mango', 'carrot', 'banana']
name = 'swaroop'
# 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])
print('Character 0 is', name[0])
# 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
Python en:Data Structures 68
print('characters 1 to 3 is', name[1:3])
print('characters 2 to end is', name[2:])
print('characters 1 to -1 is', name[1:-1])
print('characters start to end is', name[:])
Output:
$ python seq.py
Item 0 is apple
Item 1 is mango
Item 2 is carrot
Item 3 is banana
Item -1 is banana
Item -2 is carrot
Character 0 is s
Item 1 to 3 is ['mango', 'carrot']
Item 2 to end is ['carrot', 'banana']
Item 1 to -1 is ['mango', 'carrot']
Item start to end is ['apple', 'mango', 'carrot', 'banana']
characters 1 to 3 is wa
characters 2 to end is aroop
characters 1 to -1 is waroo
characters start to end is swaroop
How It Works:
First, we see how to use indexes to get individual items of a sequence. This is also referred
to as the subscription operation. Whenever you specify a number to a sequence within
square brackets as shown above, Python will fetch you the item corresponding to that
position in the sequence. Remember that Python starts counting numbers from 0. Hence,
shoplist[0] fetches the first item and shoplist[3] fetches the fourth item in the
shoplist sequence.
The index can also be a negative number, in which case, the position is calculated from the
end of the sequence. Therefore, shoplist[-1] refers to the last item in the sequence and
shoplist[-2] fetches the second last item in the sequence.
The slicing operation is used by specifying the name of the sequence followed by an
optional pair of numbers separated by a colon within square brackets. Note that this is very
similar to the indexing operation you have been using till now. 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.
Python en:Data Structures 69
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.
You can also provide a third argument for the slice, which is the step for the slicing (by
default, the step size is 1):
>>> shoplist = ['apple', 'mango', 'carrot', 'banana']
>>> shoplist[::1]
['apple', 'mango', 'carrot', 'banana']
>>> shoplist[::2]
['apple', 'carrot']
>>> shoplist[::3]
['apple', 'banana']
>>> shoplist[::-1]
['banana', 'carrot', 'mango', 'apple']
Notice that when the step is 2, we get the items with position 0, 2, ... When the step size is
3, we get the items with position 0, 3, etc.
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!
Set
Sets are unordered collections of simple objects. These are used when the existence of an
object in a collection is more important than the order or how many times it occurs.
Using sets, you can test for membership, whether it is a subset of another set, find the
intersection between two sets, and so on.
>>> bri = set(['brazil', 'russia', 'india'])
>>> 'india' in bri
True
>>> 'usa' in bri
False
>>> bric = bri.copy()
>>> bric.add('china')
>>> bric.issuperset(bri)
True
>>> bri.remove('russia')
>>> bri & bric # OR bri.intersection(bric)
{'brazil', 'india'}
How It Works:
The example is pretty much self-explanatory because it involves basic set theory
mathematics taught in school.
Python en:Data Structures 70
References
When you create an object and assign it to a variable, the variable only refers to the object
and does not represent the object itself! That is, the variable name points to that part of
your computer's memory where the object is stored. This is called as binding of the name
to the object.
Generally, you don't need to be worried about this, but there is a subtle effect due to
references which you need to be aware of:
Example:
#!/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
Output:
$ python reference.py
Simple Assignment
shoplist is ['mango', 'carrot', 'banana']
mylist is ['mango', 'carrot', 'banana']
Copy by making a full slice
shoplist is ['mango', 'carrot', 'banana']
mylist is ['carrot', 'banana']
How It Works:
Most of the explanation is available in the comments.
Remember 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
Python en:Data Structures 71
to the same object and this could be trouble if you are not careful.
Note for Perl programmers
Remember that an assignment statement for lists does not create a copy. You have to
use slicing operation to make a copy of the sequence.
More About Strings
We have already discussed strings in detail earlier. What more can there be to know? Well,
did you know that strings are also objects and have methods which do everything from
checking part of a string to stripping spaces!
The strings that you use in program are all objects of the class str. Some useful methods of
this class are demonstrated in the next example. For a complete list of such methods, see
help(str).
Example:
#!/usr/bin/python
# Filename: str_methods.py
name = 'Swaroop' # This is a string object
if name.startswith('Swa'):
print('Yes, the string starts with "Swa"')
if 'a' in name:
print('Yes, it contains the string "a"')
if name.find('war') != -1:
print('Yes, it contains the string "war"')
delimiter = '_*_'
mylist = ['Brazil', 'Russia', 'India', 'China']
print(delimiter.join(mylist))
Output:
$ python str_methods.py
Yes, the string starts with "Swa"
Yes, it contains the string "a"
Yes, it contains the string "war"
Brazil_*_Russia_*_India_*_China
How It Works:
Here, we see a lot of the string methods in action. The startswith method is used to find
out whether the string starts with the given string. The in operator is used to check if a
given string is a part of the string.
The find method is used to do find the position of the given string in the string or returns
-1 if it is not successful to find the substring. The str class also has a neat method to join
the items of a sequence with the string acting as a delimiter between each item of the
sequence and returns a bigger string generated from this.
Python en:Data Structures 72
Summary
We have explored the various built-in data structures of Python in detail. These data
structures will be essential for writing programs of reasonable size.
Now that we have a lot of the basics of Python in place, we will next see how to design and
write a real-world Python program.
Previous Next

Python en:Modules
Introduction
You have seen how you can reuse code in your program by defining functions once. What if
you wanted to reuse a number of functions in other programs that you write? As you might
have guessed, the answer is modules.
There are various methods of writing modules, but the simplest way is to create a file with a
.py extension that contains functions and variables.
Another method is to write the modules in the native language in which the Python
interpreter itself was written. For example, you can write modules in the C programming
language (http:/ / docs. python. org/ extending/ ) and when compiled, they can be used from
your Python code when using the standard Python interpreter.
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:
#!/usr/bin/python
# Filename: using_sys.py
import sys
print('The command line arguments are:')
for i in sys.argv:
print(i)
print('\n\nThe PYTHONPATH is', sys.path, '\n')
Output:
$ python using_sys.py we are arguments
The command line arguments are:
using_sys.py
we
are
arguments
The PYTHONPATH is ['', 'C:\\Windows\\system32\\python30.zip',
'C:\\Python30\\DLLs', 'C:\\Python30\\lib',
'C:\\Python30\\lib\\plat-win', 'C:\\Python30',
'C:\\Python30\\lib\\site-packages']
How It Works:
First, we import the sys module using the import statement. Basically, this translates to
us telling Python that we want to use this module. The sys module contains functionality
related to the Python interpreter and its environment i.e. the system.
Python en:Modules 56
When Python executes the import sys statement, it looks for the sys module. In this case,
it is one of the built-in modules, and hence Python knows where to find it.
If it was not a compiled module i.e. a module written in Python, then the Python interpreter
will search for it in the directories listed in its sys.path variable. If the module is found,
then the statements in the body 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.
The argv variable in the sys module is accessed using the dotted notation i.e. sys.argv. It
clearly indicates that this name is part of the sys module. Another advantage of this
approach is that the name does not clash with any argv variable used in your program.
The sys.argv variable is a list of strings (lists are explained in detail in a later chapter.
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 the command line arguments in the sys.argv
variable for us to use.
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.
Note that the current directory is the directory from which the program is launched. Run
import os; print(os.getcwd()) to find out the current directory of your program.
Byte- compiled . pyc files
Importing a module is a relatively costly affair, so Python does some tricks to make it faster.
One way is to create byte-compiled files with the extension .pyc which is an intermediate
form that Python transforms the program into (remember the introduction 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 a portion of the processing required in
importing a module is already done. Also, these byte-compiled files are
platform-independent.
Note
These .pyc files are usually created in the same directory as the corresponding .py
files. If Python does not have permission to write to files in that directory, then the
.pyc files will not be created.
Python en:Modules 57
The from . . . import . . . statement
If you want to directly import the argv variable into your program (to avoid typing the
sys. everytime for it), then you can use the from sys import argv statement. If you want
to import all the names used in the sys module, then you can use the from sys import *
statement. This works for any module.
In general, you should avoid using this statement and use the import statement instead
since your program will avoid name clashes and will be more readable.
A module's _ _ name_ _
Every module has a name and statements in a module can find out the name of its module.
This is handy in the particular situation of figuring out if the module is being run standalone
or being imported. As mentioned previously, when a module is imported for the first time,
the code in that module is executed. We can use this concept to alter the behavior of the
module 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:
#!/usr/bin/python
# Filename: using_name.py
if __name__ == '__main__':
print('This program is being run by itself')
else:
print('I am being imported from another module')
Output:
$ python using_name.py
This program is being run by itself
$ python
>>> import using_name
I am being imported from another module
>>>
How It Works:
Every Python module has it's __name__ defined and if this is '__main__', it implies that the
module is being run standalone by the user and we can take appropriate actions.
Python en:Modules 58
Making Your Own Modules
Creating your own modules is easy, you've been doing it all along! This is because 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:
#!/usr/bin/python
# Filename: mymodule.py
def sayhi():
print('Hi, this is mymodule speaking.')
__version__ = '0.1'
# End of mymodule.py
The above was a sample module. As you can see, there is nothing particularly special about
compared to our usual Python program. We will next see how to use this module in our
other Python programs.
Remember that the module should be placed in the same directory as the program that we
import it in, or the module should be in one of the directories listed in sys.path.
#!/usr/bin/python
# Filename: mymodule_demo.py
import mymodule
mymodule.sayhi()
print ('Version', mymodule.__version__)
Output:
$ python mymodule_demo.py
Hi, this is mymodule speaking.
Version 0.1
How It Works:
Notice that we use the same dotted notation to access members of the module. Python
makes good reuse of the same notation to give the distinctive 'Pythonic' feel to it so that we
don't have to keep learning new ways to do things.
Here is a version utilising the from..import syntax:
#!/usr/bin/python
# Filename: mymodule_demo2.py
from mymodule import sayhi, __version__
sayhi()
print('Version', __version__)
Python en:Modules 59
The output of mymodule_demo2.py is same as the output of mymodule_demo.py.
Notice that if there was already a __version__ name declared in the module that imports
mymodule, there would be a clash. This is also likely because it is common practice for each
module to declare it's version number using this name. Hence, it is always recommended to
prefer the import statement even though it might make your program a little longer.
You could also use:
from mymodule import *
This will import all public names such as sayhi but would not import __version__
because it starts with double underscores.
Zen of Python
One of Python's guiding principles is that "Explicit is better than Implicit". Run import
this to learn more and see this discussion (http:/ / stackoverflow. com/ questions/
228181/ zen-of-python) which lists examples for each of the principles.
The dir function
You can use the built-in dir function to list the identifiers that an object defines. For
example, for a module, the identifiers include 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:
$ python
>>> import sys # get list of attributes, in this case, for the sys module
>>> dir(sys)
['__displayhook__', '__doc__', '__excepthook__', '__name__',
'__package__', '__s
tderr__', '__stdin__', '__stdout__', '_clear_type_cache',
'_compact_freelists',
'_current_frames', '_getframe', 'api_version', 'argv',
'builtin_module_names', '
byteorder', 'call_tracing', 'callstats', 'copyright', 'displayhook',
'dllhandle'
, 'dont_write_bytecode', 'exc_info', 'excepthook', 'exec_prefix',
'executable',
'exit', 'flags', 'float_info', 'getcheckinterval',
'getdefaultencoding', 'getfil
esystemencoding', 'getprofile', 'getrecursionlimit', 'getrefcount',
'getsizeof',
'gettrace', 'getwindowsversion', 'hexversion', 'intern', 'maxsize',
'maxunicode
', 'meta_path', 'modules', 'path', 'path_hooks', 'path_importer_cache',
Python en:Modules 60
'platfor
m', 'prefix', 'ps1', 'ps2', 'setcheckinterval', 'setprofile',
'setrecursionlimit
', 'settrace', 'stderr', 'stdin', 'stdout', 'subversion', 'version',
'version_in
fo', 'warnoptions', 'winver']
>>> dir() # get list of attributes for current module
['__builtins__', '__doc__', '__name__', '__package__', 'sys']
>>> a = 5 # create a new variable 'a'
>>> dir()
['__builtins__', '__doc__', '__name__', '__package__', 'a', 'sys']
>>> del a # delete/remove a name
>>> dir()
['__builtins__', '__doc__', '__name__', '__package__', 'sys']
>>>
How It Works:
First, we see the usage of dir on the imported sys module. We can see the huge list of
attributes that it contains.
Next, we use the dir function without passing parameters to it. By default, it returns the
list of attributes for the current module. Notice that the list of imported modules is also part
of this list.
In order to observe the dir in action, we define a new variable a and assign it a value and
then check dir and we observe that there is an additional value in the list of the same
name. We remove the variable/attribute of the current module using the del statement and
the change is reflected again in the output of the dir function.
A note on del - this statement is used to delete a variable/name and after the statement
has run, in this case del a, you can no longer access the variable a - it is as if it never
existed before at all.
Note that the dir() function works on any object. For example, run dir(print) to learn
about the attributes of the print function, or dir(str) for the attributes of the str class.
Python en:Modules 61
Packages
By now, you must have started observing the hierarchy of organizing your programs.
Variables usually go inside functions. Functions and global variables usually go inside
modules. What if you wanted to organize modules? That's where packages come into the
picture.
Packages are just folders of modules with a special __init__.py file that indicates to
Python that this folder is special because it contains Python modules.
Let's say you want to create a package called 'world' with subpackages 'asia', 'africa', etc.
and these subpackages in turn contain modules like 'india', 'madagascar', etc.
This is how you would structure the folders:
- /
- world/
- __init__.py
- asia/
- __init__.py
- india/
- __init__.py
- foo.py
- africa/
- __init__.py
- madagascar/
- __init__.py
- bar.py
Packages are just a convenience to hierarchically organize modules. You will see many
instances of this in the standard library.
Summary
Just like functions are reusable parts of programs, modules are reusable programs.
Packages are another hierarchy to organize modules. The standard library that comes with
Python is an example of such a set of packages and modules.
We have seen how to use these modules and create our own modules.
Next, we will learn about some interesting concepts called data structures.

Python en:Functions
Introduction
Functions are reusable pieces of programs. They allow you to give a name to a block of
statements and you can run that block using that name anywhere in your program and any
number of times. This is known as calling the function. We have already used many built-in
functions such as the len and range.
The function concept is probably the most important building block of any non-trivial
software (in any programming language), so we will explore various aspects of functions in
this chapter.
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:
#!/usr/bin/python
# Filename: function1.py
def sayHello():
print('Hello World!') # block belonging to the function
# End of function
Python en:Functions 45
sayHello() # call the function
sayHello() # call the function again
Output:
$ python function1.py
Hello World!
Hello World!
How It Works:
We define a function called sayHello using the syntax as explained above. This function
takes no parameters and hence there are no variables declared in the parentheses.
Parameters to functions are just input to the function so that we can pass in different values
to it and get back corresponding results.
Notice that we can call the same function twice which means we do not have to write the
same code again.
Function Parameters
A function can take parameters, which are 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
already assigned values when the function runs.
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:
#!/usr/bin/python
# Filename: func_param.py
def printMax(a, b):
if a > b:
print(a, 'is maximum')
elif a == b:
print(a, 'is equal to', b)
else:
print(b, 'is maximum')
printMax(3, 4) # directly give literal values
x = 5
y = 7
printMax(x, y) # give variables as arguments
Output:
Python en:Functions 46
$ python func_param.py
4 is maximum
7 is maximum
How It Works:
Here, we define a function called printMax where we take two parameters called a and b.
We find out the greater number using a simple if..else statement and then print the
bigger number.
In the first usage of printMax, we directly supply the numbers i.e. arguments. In the second
usage, we call the function using variables. printMax(x, y) causes value of argument x to
be assigned to parameter a and the value of argument y assigned to parameter b. The
printMax function works the same in both the cases.
Local Variables
When you declare variables inside a function definition, they are not related in any way to
other variables with the same names used outside the function i.e. variable names are local
to the function. This is called the scope of the variable. All variables have the scope of the
block they are declared in starting from the point of definition of the name.
Example:
#!/usr/bin/python
# Filename: func_local.py
x = 50
def func(x):
print('x is', x)
x = 2
print('Changed local x to', x)
func(x)
print('x is still', x)
Output:
$ python func_local.py
x is 50
Changed local x to 2
x is still 50
How It Works:
In the function, the first time that we use the value of the name x, Python uses the value of
the parameter declared in the function.
Next, we assign the value 2 to x. The name x is local to our function. So, when we change
the value of x in the function, the x defined in the main block remains unaffected.
In the last print function call, we display the value of x in the main block and confirm that
it is actually unaffected.
Python en:Functions 47
Using The global Statement
If you want to assign a value to a name defined at the top level of the program (i.e. not
inside any kind of scope such as functions or classes), 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 outermost block.
Example:
#!/usr/bin/python
# Filename: func_global.py
x = 50
def func():
global x
print('x is', x)
x = 2
print('Changed global x to', x)
func()
print('Value of x is', x)
Output:
$ python func_global.py
x is 50
Changed global x to 2
Value of x is 2
How It Works:
The global statement is used to declare 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.
Python en:Functions 48
Using nonlocal statement
We have seen how to access variables in the local and global scope above. There is another
kind of scope called "nonlocal" scope which is in-between these two types of scopes.
Nonlocal scopes are observed when you define functions inside functions.
Since everything in Python is just executable code, you can define functions anywhere.
Let's take an example:
#!/usr/bin/python
# Filename: func_nonlocal.py
def func_outer():
x = 2
print('x is', x)
def func_inner():
nonlocal x
x = 5
func_inner()
print('Changed local x to', x)
func_outer()
Output:
$ python func_nonlocal.py
x is 2
Changed local x to 5
How It Works:
When we are inside func_inner, the 'x' defined in the first line of func_outer is relatively
neither in local scope nor in global scope. We declare that we are using this x by nonlocal
x and hence we get access to that variable.
Try changing the nonlocal x to global x and also by removing the statement itself and
observe the difference in behavior in these two cases.
Default Argument Values
For some functions, you may want to make some of its parameters as optional and use
default values if the user does not want to provide values for such parameters. This is done
with the help of default argument values. You can specify default argument values for
parameters by following the parameter name in the function definition with the assignment
operator (=) followed by the default value.
Note that the default argument value should be a constant. More precisely, the default
argument value should be immutable - this is explained in detail in later chapters. For now,
just remember this.
Example:
Python en:Functions 49
#!/usr/bin/python
# Filename: func_default.py
def say(message, times = 1):
print(message * times)
say('Hello')
say('World', 5)
Output:
$ python func_default.py
Hello
WorldWorldWorldWorldWorld
How It Works:
The function named say is used to print a string as many times as specified. If we don't
supply a value, then by default, the string is printed just once. We achieve this by specifying
a default argument value of 1 to the parameter times.
In the first usage of say, we supply only the string and it prints the string once. In the
second usage of say, we supply both the string and an argument 5 stating that we want to
say the string message 5 times.
Important
Only those parameters which are at the end of the parameter list can be given default
argument values i.e. you cannot have a parameter with a default argument value
before a parameter without a default argument value in the order of parameters
declared in the function parameter list.
This is because the values are assigned to the parameters by position. For example,
def func(a, b=5) is valid, but def func(a=5, b) is not valid.
Keyword Arguments
If you have some functions with many parameters and you want to specify only some of
them, then you can give values for such parameters by naming them - this is called keyword
arguments - we use the name (keyword) instead of the position (which we have been using
all along) to specify the arguments to the function.
There are two advantages - one, using the function is easier since we do not need to worry
about the order of the arguments. Two, we can give values to only those parameters which
we want, provided that the other parameters have default argument values.
Example:
#!/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)
Python en:Functions 50
func(25, c=24)
func(c=50, a=100)
Output:
$ python func_key.py
a is 3 and b is 7 and c is 10
a is 25 and b is 5 and c is 24
a is 100 and b is 5 and c is 50
How It Works:
The function named func has one parameter without default argument values, followed by
two parameters with default argument values.
In the first usage, func(3, 7), the parameter a gets the value 3, the parameter b gets the
value 7 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.
VarArgs parameters
TODO
Should I write about this in a later chapter since we haven't talked about lists and
dictionaries yet?
Sometimes you might want to define a function that can take any number of parameters,
this can be achieved by using the stars:
#!/usr/bin/python
# Filename: total.py
def total(initial=5, *numbers, **keywords):
count = initial
for number in numbers:
count += number
for key in keywords:
count += keywords[key]
return count
print(total(10, 1, 2, 3, vegetables=50, fruits=100))
Output:
$ python total.py
166
How It Works:
Python en:Functions 51
When we declare a starred parameter such as *param, then all the positional arguments
from that point till the end are collected as a list called 'param'.
Similarly, when we declare a double-starred parameter such as **param, then all the
keyword arguments from that point till the end are collected as a dictionary called 'param'.
We will explore lists and dictionaries in a later chapter.
Keyword- only Parameters
If we want to specify certain keyword parameters to be available as keyword-only and not
as positional arguments, they can be declared after a starred parameter:
#!/usr/bin/python
# Filename: keyword_only.py
def total(initial=5, *numbers, vegetables):
count = initial
for number in numbers:
count += number
count += vegetables
return count
print(total(10, 1, 2, 3, vegetables=50))
print(total(10, 1, 2, 3))
# Raises error because we have not supplied a default argument value
for 'vegetables'
Output:
$ python keyword_only.py
66
Traceback (most recent call last):
File "test.py", line 12, in
print(total(10, 1, 2, 3))
TypeError: total() needs keyword-only argument vegetables
How It Works:
Declaring parameters after a starred parameter results in keyword-only arguments. If these
arguments are not supplied a default value, then calls to the function will raise an error if
the keyword argument is not supplied, as seen above.
If you want to have keyword-only arguments but have no need for a starred parameter, then
simply use an empty star without using any name such as def total(initial=5, *,
vegetables).
Python en:Functions 52
The return Statement
The return statement is used to return from a function i.e. break out of the function. We
can optionally return a value from the function as well.
Example:
#!/usr/bin/python
# Filename: func_return.py
def maximum(x, y):
if x > y:
return x
else:
return y
print(maximum(2, 3))
Output:
$ python func_return.py
3
How It Works:
The maximum function returns the maximum of the parameters, in this case the numbers
supplied to the function. It uses a simple if..else statement to find the greater value and
then returns that value.
Note that a return statement without a value is equivalent to return None. None is a
special type in Python that represents nothingness. For example, it is used to indicate that a
variable has no value if it has a value of None.
Every function implicitly contains a return None statement at the end unless you have
written your own return statement. You can see this by running print(someFunction())
where the function someFunction does not use the return statement such as:
def someFunction():
pass
The pass statement is used in Python to indicate an empty block of statements.
Note
There is a built-in function called max that already implements the 'find maximum'
functionality, so use this built-in function whenever possible.
Python en:Functions 53
DocStrings
Python has a nifty feature called documentation strings, 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 easier to understand. Amazingly, we
can even get the docstring back from, say a function, when the program is actually running!
Example:
#!/usr/bin/python
# Filename: func_doc.py
def printMax(x, y):
'''Prints the maximum of two numbers.
The two values must be integers.'''
x = int(x) # convert to integers, if possible
y = int(y)
if x > y:
print(x, 'is maximum')
else:
print(y, 'is maximum')
printMax(3, 5)
print(printMax.__doc__)
Output:
$ python func_doc.py
5 is maximum
Prints the maximum of two numbers.
The two values must be integers.
How It Works:
A string on the first logical line of a function is the docstring for that function. Note that
DocStrings also apply to modules and classes which we will learn about in the respective
chapters.
The convention followed for a docstring is a multi-line string where the first line starts with
a capital letter and ends with a dot. Then the second line is blank followed by any detailed
explanation starting from the third line. You are strongly advised to follow this convention
for all your docstrings for all your non-trivial functions.
We can access the docstring of the printMax function using the __doc__ (notice the
double underscores) attribute (name belonging to) of the function. Just remember that
Python treats everything as an object and this includes functions. We'll learn more about
objects in the chapter on classes.
If you have used 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
Python en:Functions 54
your program. Remember to press the q key to exit 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.
Annotations
Functions have another advanced feature called annotations which are a nifty way of
attaching additional information for each of the parameters as well as the return value.
Since the Python language itself does not interpret these annotations in any way (that
functionality is left to third-party libraries to interpret in any way they want), we will skip
this feature in our discussion. If you are interested to read about annotations, please see
the Python Enhancement Proposal No. 3107 (http:/ / www. python. org/ dev/ peps/
pep-3107/ ).
Summary
We have seen so many aspects of functions but note that we still haven't covered all aspects
of it. However, we have already covered most of what you'll use regarding Python functions
on an everyday basis.
Next, we will see how to use as well as create Python modules.

Python en:Control Flow
Introduction
In the programs we have seen till now, there has always been a series of statements and
Python faithfully executes them in the same order. What if you wanted to change the flow of
how it works? For example, you want the program to take some decisions and do different
things depending on different situations such as printing 'Good Morning' or 'Good Evening'
depending on the time of the day?
As you might have guessed, this is achieved using control flow statements. There are three
control flow statements in Python - if, for and while.
The if statement
The if statement is used to check a condition and if the condition is true, we run a block of
statements (called the if-block), else we process another block of statements (called the
else-block). The else clause is optional.
Example:
#!/usr/bin/python
# Filename: if.py
number = 23
guess = int(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
Output:
$ python if.py
Enter an integer : 50
No, it is a little lower than that
Done
$ python if.py
Enter an integer : 22
Python en:Control Flow 39
No, it is a little higher than that
Done
$ python if.py
Enter an integer : 23
Congratulations, you guessed it.
(but you do not win any prizes!)
Done
How It Works:
In this program, we take guesses from the user and check if it is the number that we have.
We set the variable number to any integer we want, say 23. Then, we take the user's guess
using the 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 input function which prints it to the screen and waits for
input from the user. Once we enter something and press enter key, the input() function
returns what we entered, as 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 the "consistent indentation" 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 minimal valid if statement is:
if True:
print('Yes, it is true')
After Python has finished executing the complete if statement along with the associated
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
Python en:Control Flow 40
aware of all these initially, but after that, you will become comfortable with it and it'll feel
'natural' to you.
Note for C/C++ Programmers
There is no switch statement in Python. You can use an if..elif..else statement to
do the same thing (and in some cases, use a dictionary to do it quickly)
The while Statement
The while statement allows you to repeatedly execute a block of statements as long as a
condition is true. A while statement is an example of what is called a looping statement. A
while statement can have an optional else clause.
Example:
#!/usr/bin/python
# Filename: while.py
number = 23
running = True
while running:
guess = int(input('Enter an integer : '))
if guess == number:
print('Congratulations, you guessed it.')
running = False # this causes the while loop to stop
elif guess < number:
print('No, it is a little higher than that.')
else:
print('No, it is a little lower than that.')
else:
print('The while loop is over.')
# Do anything else you want to do here
print('Done')
Output:
$ python while.py
Enter an integer : 50
No, it is a little lower than that.
Enter an integer : 22
No, it is a little higher than that.
Enter an integer : 23
Congratulations, you guessed it.
The while loop is over.
Done
How It Works:
Python en:Control Flow 41
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 run
the program for each guess, as we have done in the previous section. This aptly
demonstrates the use of the while statement.
We move the 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 break out of the loop with a break statement.
The True and False are called Boolean types and you can consider them to be equivalent
to the value 1 and 0 respectively.
Note for C/C++ Programmers
Remember that you can have an else clause for the while loop.
The for loop
The for..in statement is another looping statement which iterates over a sequence of
objects i.e. go through each item in a sequence. We will see more about sequences in detail
in later chapters. What you need to know right now is that a sequence is just an ordered
collection of items.
Example:
#!/usr/bin/python
# Filename: for.py
for i in range(1, 5):
print(i)
else:
print('The for loop is over')
Output:
$ python for.py
1
2
3
4
The for loop is over
How It Works:
In this program, we are printing a sequence of numbers. We generate this sequence of
numbers using the 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)
Python en:Control Flow 42
gives the sequence [1, 2, 3, 4]. By default, range takes a step count of 1. If we supply a
third number to range, then that becomes the step count. For example, range(1,5,2)
gives [1,3]. Remember that the range extends up to the second number i.e. it does not
include the second number.
The for loop then iterates over this range - for i in range(1,5) is equivalent to for i
in [1, 2, 3, 4] which is like assigning each number (or object) in the sequence to i, one
at a time, and then executing the block of statements for each value of i. In this case, we
just print the value in the block of statements.
Remember that the else part is optional. When included, it is always executed once after
the for loop is over unless a break statement is encountered.
Remember that the for..in loop works for any sequence. Here, we have a list of numbers
generated by the built-in range function, but in general we can use any kind of sequence of
any kind of objects! We will explore this idea in detail in later chapters.
Note for C/C++/Java/C# Programmers
The Python for loop is radically different from the C/C++ for loop. C# programmers
will note that the for loop in Python is similar to the foreach loop in C#. Java
programmers will note that the same is similar to for (int i : IntArray) in Java
1.5 .
In C/C++, if you want to write for (int i = 0; i < 5; i++), then in Python you
write just for i in range(0,5). As you can see, the for loop is simpler, more
expressive and less error prone in Python.
The break Statement
The break statement is used to break out of a loop statement i.e. stop the execution of a
looping statement, even if the loop condition has not become False or the sequence of
items has been completely iterated over.
An important note is that if you break out of a for or while loop, any corresponding loop
else block is not executed.
Example:
#!/usr/bin/python
# Filename: break.py
while True:
s = (input('Enter something : '))
if s == 'quit':
break
print('Length of the string is', len(s))
print('Done')
Output:
$ python break.py
Enter something : Programming is fun
Length of the string is 18
Enter something : When the work is done
Length of the string is 21
Python en:Control Flow 43
Enter something : if you wanna make your work also fun:
Length of the string is 37
Enter something : use Python!
Length of the string is 12
Enter something : quit
Done
How It Works:
In this program, we repeatedly take the user's input and print the length of each input each
time. We are providing a special condition to stop the program by checking if the user input
is 'quit'. We stop the program by breaking out of the loop and reach the end of the
program.
The length of the input string can be found out using the built-in len function.
Remember that the break statement can be used with the for loop as well.
Swaroop's Poetic Python
The input I have used here is a mini poem I have written called Swaroop's Poetic Python:
Programming is fun
When the work is done
if you wanna make your work also fun:
use Python!
The continue Statement
The continue statement is used to tell Python to skip the rest of the statements in the
current loop block and to continue to the next iteration of the loop.
Example:
#!/usr/bin/python
# Filename: continue.py
while True:
s = input('Enter something : ')
if s == 'quit':
break
if len(s) < 3:
print('Too small')
continue
print('Input is of sufficient length')
# Do other kinds of processing here...
Output:
$ python test.py
Enter something : a
Too small
Enter something : 12
Too small
Python en:Control Flow 44
Enter something : abc
Input is of sufficient length
Enter something : quit
How It Works:
In this program, we accept input from the user, but we process them only if they are at
least 3 characters long. So, we use the built-in len function to get the length and if the
length is less than 3, we skip the rest of the statements in the block by using the continue
statement. Otherwise, the rest of the statements in the loop are executed and we can do
any kind of processing we want to do here.
Note that the continue statement works with the for loop as well.
Summary
We have seen how to use the three control flow statements - if, while and for along with
their associated break and continue statements. These are some of the most often used
parts of Python and hence, becoming comfortable with them is essential.
Next, we will see how to create and use functions.

Python en:Operators and Expressions
Introduction
Most statements (logical lines) that you write will contain expressions. A simple example
of an expression is 2 + 3. An expression can be broken down into operators and operands.
Operators are functionality that do something and can be represented by symbols such as +
or by special keywords. Operators require some data to operate on and such data is called
operands. In this case, 2 and 3 are the operands
Operators
We will briefly take a look at the operators and their usage:
Note that 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
>>>
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.
Python en:Operators and Expressions 34
* 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 1.3333333333333333.
// Floor Division Returns the floor of the
quotient
4 // 3 gives 1.
% 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
the 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 Bitwise XOR of the numbers 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 True or False. Note
the capitalization of these
names.
5 < 3 gives False and 3 < 5 gives 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 x 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 left hand side of the 'and'
expression is False which implies that the whole
expression will be False irrespective of the other
values. This is called short-circuit evaluation.
Python en:Operators and Expressions 35
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.
Shortcut for math operation and assignment
It is common to run a math operation on a variable and then assign the result of the
operation back to the variable, hence there is a shortcut for such expressions:
You can write:
a = 2; a = a * 3
as:
a = 2; a *= 3
Notice that var = var operation expression becomes var operation= expression.
Evaluation Order
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 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 and expressions lower in the table
before the ones listed higher in the table.
The following table, taken from the Python reference manual (http:/ / docs. python. org/
dev/ 3. 0/ reference/ expressions. html#evaluation-order), is provided for the sake of
completeness. It is far better to use parentheses to group operators and operands
appropriately in order to explicitly specify the precedence. This makes the program more
readable. See Changing the Order of Evaluation below for details.
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, Floor Division and Remainder
+x, -x Positive, Negative
Python en:Operators and Expressions 36
~x Bitwise NOT
** Exponentiation
x.attribute Attribute reference
x[index] Subscription
x[index1:index2] Slicing
f(arguments ...) Function call
(expressions, ...) Binding or tuple display
[expressions, ...] List display
{key:datum, ...} Dictionary display
The operators which we have not already come across will be explained in later chapters.
Operators with the same precedence are listed in the same row in the above table. For
example, + and - have the same precedence.
Changing the Order Of Evaluation
To make the expressions more readable, we can use parentheses. For example, 2 + (3 *
4) is definitely easier to understand than 2 + 3 * 4 which requires knowledge of the
operator precedences. As with everything else, the parentheses should be used reasonably
(do not overdo it) and should not be redundant (as in 2 + (3 + 4)).
There is an additional advantage to using parentheses - it helps us to change the order of
evaluation. For example, if you want addition to be evaluated before multiplication in an
expression, then you can write something like (2 + 3) * 4.
Associativity
Operators are usually associated from left to right i.e. operators with same precedence are
evaluated in a left to right manner. For example, 2 + 3 + 4 is evaluated as (2 + 3) + 4.
Some operators like assignment operators have right to left associativity i.e. a = b = c is
treated as a = (b = c).
Expressions
Example:
#!/usr/bin/python
# Filename: expression.py
length = 5
breadth = 2
area = length * breadth
print('Area is', area)
print('Perimeter is', 2 * (length + breadth))
Output:
Python en:Operators and Expressions 37
$ python expression.py
Area is 10
Perimeter is 14
How It Works:
The length and breadth of the rectangle are stored in variables by the same name. We use
these to calculate the area and perimeter 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 function. In the second case, we directly use the value of the expression 2
* (length + breadth) in the print function.
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 strings we use for output). This is an example of how Python
makes life easy for the programmer.
Summary
We have seen how to use operators, operands and expressions - these are the basic building
blocks of any program. Next, we will see how to make use of these in our programs using
statements.