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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
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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
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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
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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
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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
Source: http://www.swaroopch.com/mediawiki/index.php?oldid=1582
Contributors: Swaroop, 5 anonymous edits
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.
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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:
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$ 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?
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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.
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Python en:Object Oriented
Programming
Introduction
In all the programs we wrote till now, we have designed our program around functions i.e.
blocks of statements which manipulate data. This is called the procedure-oriented way of programming. There is another way of organizing your program which is to combine data
and functionality and wrap it inside something called an object. This is called the object oriented programming paradigm. Most of the time you can use procedural programming, but when writing large programs or have a problem that is better suited to this method, you can use object oriented programming techniques.
Classes and objects are the two main aspects of object oriented programming. A class creates a new type where objects are instances of the class. An analogy is that you can have variables of type int which translates to saying that variables that store integers are variables which are instances (objects) of the int class.
Note for Static Language Programmers
Note that even integers are treated as objects (of the int class). This is unlike C++
and Java (before version 1.5) where integers are primitive native types. See
help(int) for more details on the class.
C# and Java 1.5 programmers will find this similar to the boxing and unboxing
concept.
Objects can store data using ordinary variables that belong to the object. Variables that belong to an object or class are referred to as fields. Objects can also have functionality by using functions that belong to a class. Such functions are called methods of the class. This terminology is important because it helps us to differentiate between functions and
variables which are independent and those which belong to a class or object. Collectively, the fields and methods can be referred to as the attributes of that class.
Fields are of two types - they can belong to each instance/object of the class or they can belong to the class itself. They are called instance variables and class variables respectively.
A class is created using the class keyword. The fields and methods of the class are listed in an indented block.
The self
Class methods have only one specific difference from ordinary functions - they must have an extra first name that has to be added to the beginning of the parameter list, but you do not give a value for this parameter when you call the method, Python will provide it. This particular variable refers to the object itself, and by convention, it is given the name self.
Although, you can give any name for this parameter, it is strongly recommended that you use the name self - any other name is definitely frowned upon. There are many
advantages to using a standard name - any reader of your program will immediately
recognize it and even specialized IDEs (Integrated Development Environments) can help
you if you use self.
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Note for C++/Java/C# Programmers
The self in Python is equivalent to the this pointer in C++ and the this reference
in Java and C#.
You must be wondering how Python gives the value for self and why you don't need to
give a value for it. An example will make this clear. Say you have a class called MyClass and an instance of this class called myobject. When you call a method of this object as myobject.method(arg1, arg2), this is automatically converted by Python into
MyClass.method(myobject, arg1, arg2) - this is all the special self is about.
This also means that if you have a method which takes no arguments, then you still have to have one argument - the self.
Classes
The simplest class possible is shown in the following example.
#!/usr/bin/python
# Filename: simplestclass.py
class Person:
pass # An empty block
p = Person()
print(p)
Output:
$ python simplestclass.py
<__main__.Person object at 0x019F85F0>
How It Works:
We create a new class using the class statement and the name of the class. This is
followed by an indented block of statements which form the body of the class. In this case, we have an empty block which is indicated using the pass statement.
Next, we create an object/instance of this class using the name of the class followed by a pair of parentheses. (We will learn more about instantiation in the next section). For our verification, we confirm the type of the variable by simply printing it. It tells us that we have an instance of the Person class in the __main__ module.
Notice that the address of the computer memory where your object is stored is also printed.
The address will have a different value on your computer since Python can store the object wherever it finds space.
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Object Methods
We have already discussed that classes/objects can have methods just like functions except that we have an extra self variable. We will now see an example.
#!/usr/bin/python
# Filename: method.py
class Person:
def sayHi(self):
print('Hello, how are you?')
p = Person()
p.sayHi()
# This short example can also be written as Person().sayHi()
Output:
$ python method.py
Hello, how are you?
How It Works:
Here we see the self in action. Notice that the sayHi method takes no parameters but
still has the self in the function definition.
The __init__method
There are many method names which have special significance in Python classes. We will see the significance of the __init__ method now.
The __init__ method is run as soon as an object of a class is instantiated. The method is useful to do any initialization you want to do with your object. Notice the double underscores both at the beginning and at the end of the name.
Example:
#!/usr/bin/python
# Filename: class_init.py
class Person:
def __init__(self, name):
self.name = name
def sayHi(self):
print('Hello, my name is', self.name)
p = Person('Swaroop')
p.sayHi()
# This short example can also be written as Person('Swaroop').sayHi()
Output:
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$ python class_init.py
Hello, my name is Swaroop
How It Works:
Here, we define the __init__ method as taking a parameter name (along with the usual
self). Here, we just create a new field also called name. Notice these are two different variables even though they are both called 'name'. The dotted notation allows us to
differentiate between them.
Most importantly, notice that we do not explicitly call the __init__ method but pass the arguments in the parentheses following the class name when creating a new instance of the class. This is the special significance of this method.
Now, we are able to use the self.name field in our methods which is demonstrated in the sayHi method.
Class And Object Variables
We have already discussed the functionality part of classes and objects (i.e. methods), now let us learn about the data part. The data part, i.e. fields, are nothing but ordinary variables that are bound to the namespaces of the classes and objects. This means that these names are valid within the context of these classes and objects only. That's why they are called name spaces.
There are two types of fields - class variables and object variables which are classified depending on whether the class or the object owns the variables respectively.
Class variables are shared - they can be accessed by all instances of that class. There is only one copy of the class variable and when any one object makes a change to a class
variable, that change will be seen by all the other instances.
Object variables are owned by each individual object/instance of the class. In this case, each object has its own copy of the field i.e. they are not shared and are not related in any way to the field by the same name in a different instance. An example will make this easy to understand:
#!/usr/bin/python
# Filename: objvar.py
class Robot:
'''Represents a robot, with a name.'''
# A class variable, counting the number of robots
population = 0
def __init__(self, name):
'''Initializes the data.'''
self.name = name
print('(Initializing {0})'.format(self.name))
# When this person is created, the robot
# adds to the population
Robot.population += 1
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85
def __del__(self):
'''I am dying.'''
print('{0} is being destroyed!'.format(self.name))
Robot.population -= 1
if Robot.population == 0:
print('{0} was the last one.'.format(self.name))
else:
print('There are still {0:d} robots
working.'.format(Robot.population))
def sayHi(self):
'''Greeting by the robot.
Yeah, they can do that.'''
print('Greetings, my masters call me {0}.'.format(self.name))
def howMany():
'''Prints the current population.'''
print('We have {0:d} robots.'.format(Robot.population))
howMany = staticmethod(howMany)
droid1 = Robot('R2-D2')
droid1.sayHi()
Robot.howMany()
droid2 = Robot('C-3PO')
droid2.sayHi()
Robot.howMany()
print(" \nRobots can do some work here. \n")
print("Robots have finished their work. So let's destroy them.")
del droid1
del droid2
Robot.howMany()
Output:
(Initializing R2-D2)
Greetings, my masters call me R2-D2.
We have 1 robots.
(Initializing C-3PO)
Greetings, my masters call me C-3PO.
We have 2 robots.
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Robots can do some work here.
Robots have finished their work. So let's destroy them.
R2-D2 is being destroyed!
There are still 1 robots working.
C-3PO is being destroyed!
C-3PO was the last one.
We have 0 robots.
How It Works:
This is a long example but helps demonstrate the nature of class and object variables. Here, population belongs to the Robot class and hence is a class variable. The name variable belongs to the object (it is assigned using self) and hence is an object variable.
Thus, we refer to the population class variable as Robot.population and not as
self.population. We refer to the object variable name using self.name notation in the
methods of that object. Remember this simple difference between class and object
variables. Also note that an object variable with the same name as a class variable will hide the class variable!
The howMany is actually a method that belongs to the class and not to the object. This means we can define it as either a classmethod or a staticmethod depending on whether
we need to know which class we are part of. Since we don't need such information, we will go for staticmethod.
We could have also achieved the same using decorators (http:/ / www. ibm. com/
developerworks/linux/library/l-cpdecor.html):
@staticmethod
def howMany():
'''Prints the current population.'''
print('We have {0:d} robots.'.format(Robot.population))
Decorators can be imagined to be a shortcut to calling an explicit statement, as we have seen in this example.
Observe that the __init__ method is used to initialize the Robot instance with a name. In this method, we increase the population count by 1 since we have one more robot being
added. Also observe that the values of self.name is specific to each object which indicates the nature of object variables.
Remember, that you must refer to the variables and methods of the same object using the self only. This is called an attribute reference.
In this program, we also see the use of docstrings for classes as well as methods. We can access the class docstring at runtime using Robot.__doc__ and the method docstring as
Robot.sayHi.__doc__
Just like the __init__ method, there is another special method __del__ which is called when an object is going to die i.e. it is no longer being used and is being returned to the computer system for reusing that piece of memory. In this method, we simply decrease the Robot.population count by 1.
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87
The __del__ method is run when the object is no longer in use and there is no guarantee when that method will be run. If you want to explicitly see it in action, we have to use the del statement which is what we have done here.
Note for C++/Java/C# Programmers
All class members (including the data members) are public and all the methods are virtual in Python.
One exception: If you use data members with names using the double underscore
prefix such as __privatevar, Python uses name-mangling to effectively make it a private variable.
Thus, the convention followed is that any variable that is to be used only within the
class or object should begin with an underscore and all other names are public and can be used by other classes/objects. Remember that this is only a convention and is not
enforced by Python (except for the double underscore prefix).
Inheritance
One of the major benefits of object oriented programming is reuse of code and one of the ways this is achieved is through the inheritance mechanism. Inheritance can be best imagined as implementing a type and subtype relationship between classes.
Suppose you want to write a program which has to keep track of the teachers and students in a college. They have some common characteristics such as name, age and address. They also have specific characteristics such as salary, courses and leaves for teachers and, marks and fees for students.
You can create two independent classes for each type and process them but adding a new common characteristic would mean adding to both of these independent classes. This
quickly becomes unwieldy.
A better way would be to create a common class called SchoolMember and then have the
teacher and student classes inherit from this class i.e. they will become sub-types of this type (class) and then we can add specific characteristics to these sub-types.
There are many advantages to this approach. If we add/change any functionality in
SchoolMember, this is automatically reflected in the subtypes as well. For example, you can add a new ID card field for both teachers and students by simply adding it to the
SchoolMember class. However, changes in the subtypes do not affect other subtypes.
Another advantage is that if you can refer to a teacher or student object as a SchoolMember object which could be useful in some situations such as counting of the number of school members. This is called polymorphism where a sub-type can be substituted in any situation where a parent type is expected i.e. the object can be treated as an instance of the parent class.
Also observe that we reuse the code of the parent class and we do not need to repeat it in the different classes as we would have had to in case we had used independent classes.
The SchoolMember class in this situation is known as the base class or the superclass. The Teacher and Student classes are called the derived classes or subclasses.
We will now see this example as a program.
#!/usr/bin/python
# Filename: inherit.py
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88
class SchoolMember:
'''Represents any school member.'''
def __init__(self, name, age):
self.name = name
self.age = age
print('(Initialized SchoolMember: {0})'.format(self.name))
def tell(self):
'''Tell my details.'''
print('Name:"{0}" Age:"{1}"'.format(self.name, self.age), end="
")
class Teacher(SchoolMember):
'''Represents a teacher.'''
def __init__(self, name, age, salary):
SchoolMember.__init__(self, name, age)
self.salary = salary
print('(Initialized Teacher: {0})'.format(self.name))
def tell(self):
SchoolMember.tell(self)
print('Salary: "{0:d}"'.format(self.salary))
class Student(SchoolMember):
'''Represents a student.'''
def __init__(self, name, age, marks):
SchoolMember.__init__(self, name, age)
self.marks = marks
print('(Initialized Student: {0})'.format(self.name))
def tell(self):
SchoolMember.tell(self)
print('Marks: "{0:d}"'.format(self.marks))
t = Teacher('Mrs. Shrividya', 40, 30000)
s = Student('Swaroop', 25, 75)
print() # prints a blank line
members = [t, s]
for member in members:
member.tell() # works for both Teachers and Students
Output:
$ python inherit.py
(Initialized SchoolMember: Mrs. Shrividya)
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89
(Initialized Teacher: Mrs. Shrividya)
(Initialized SchoolMember: Swaroop)
(Initialized Student: Swaroop)
Name:"Mrs. Shrividya" Age:"40" Salary: "30000"
Name:"Swaroop" Age:"25" Marks: "75"
How It Works:
To use inheritance, we specify the base class names in a tuple following the class name in the class definition. Next, we observe that the __init__ method of the base class is
explicitly called using the self variable so that we can initialize the base class part of the object. This is very important to remember - Python does not automatically call the
constructor of the base class, you have to explicitly call it yourself.
We also observe that we can call methods of the base class by prefixing the class name to the method call and then pass in the self variable along with any arguments.
Notice that we can treat instances of Teacher or Student as just instances of the
SchoolMember when we use the tell method of the SchoolMember class.
Also, observe that the tell method of the subtype is called and not the tell method of the SchoolMember class. One way to understand this is that Python always starts looking for methods in the actual type, which in this case it does. If it could not find the method, it starts looking at the methods belonging to its base classes one by one in the order they are specified in the tuple in the class definition.
A note on terminology - if more than one class is listed in the inheritance tuple, then it is called multiple inheritance.
Summary
We have now explored the various aspects of classes and objects as well as the various terminologies associated with it. We have also seen the benefits and pitfalls of
object-oriented programming. Python is highly object-oriented and understanding these
concepts carefully will help you a lot in the long run.
Next, we will learn how to deal with input/output and how to access files in Python.
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Python en:Input Output
90
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
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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
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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.
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Python en:Exceptions
Introduction
Exceptions occur when certain exceptional situations occur in your program. For example, what if you are going to read a file and the file does not exist? Or what if you accidentally deleted it when the program was running? Such situations are handled using exceptions.
Similarly, what if your program had some invalid statements? This is handled by Python which raises its hands and tells you there is an error.
Errors
Consider a simple print function call. What if we misspelt print as Print? Note the
capitalization. In this case, Python raises a syntax error.
>>> Print('Hello World')
Traceback (most recent call last):
File "<pyshell#0>", line 1, in <module>
Print('Hello World')
NameError: name 'Print' is not defined
>>> print('Hello World')
Hello World
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94
Observe that a NameError is raised and also the location where the error was detected is printed. This is what an error handler for this error does.
Exceptions
We will try to read input from the user. Press ctrl-d and see what happens.
>>> s = input('Enter something --> ')
Enter something -->
Traceback (most recent call last):
File "<pyshell#2>", line 1, in <module>
s = input('Enter something --> ')
EOFError: EOF when reading a line
Python raises an error called EOFError which basically means it found an end of file symbol (which is represented by ctrl-d) when it did not expect to see it.
Handling Exceptions
We can handle exceptions using the try..except statement. We basically put our usual
statements within the try-block and put all our error handlers in the except-block.
#!/usr/bin/python
# Filename: try_except.py
try:
text = input('Enter something --> ')
except EOFError:
print('Why did you do an EOF on me?')
except KeyboardInterrupt:
print('You cancelled the operation.')
else:
print('You entered {0}'.format(text))
Output:
$ python try_except.py
Enter something --> # Press ctrl-d
Why did you do an EOF on me?
$ python try_except.py
Enter something --> # Press ctrl-c
You cancelled the operation.
$ python try_except.py
Enter something --> no exceptions
You entered no exceptions
How It Works:
We put all the statements that might raise exceptions/errors inside the try block and then put handlers for the appropriate errors/exceptions in the except clause/block. The except Python en:Exceptions
95
clause can handle a single specified error or exception, or a parenthesized list of
errors/exceptions. If no names of errors or exceptions are supplied, it will handle all errors and exceptions.
Note that there has to be at least one except clause associated with every try clause.
Otherwise, what's the point of having a try block?
If any error or exception is not handled, then the default Python handler is called which just stops the execution of the program and prints an error message. We have already seen this in action above.
You can also have an else clause associated with a try..except block. The else clause is executed if no exception occurs.
In the next example, we will also see how to get the exception object so that we can retrieve additional information.
Raising Exceptions
You can raise exceptions using the raise statement by providing the name of the error/exception and the exception object that is to be thrown.
The error or exception that you can arise should be class which directly or indirectly must be a derived class of the Exception class.
#!/usr/bin/python
# Filename: raising.py
class ShortInputException(Exception):
'''A user-defined exception class.'''
def __init__(self, length, atleast):
Exception.__init__(self)
self.length = length
self.atleast = atleast
try:
text = input('Enter something --> ')
if len(text) < 3:
raise ShortInputException(len(text), 3)
# Other work can continue as usual here
except EOFError:
print('Why did you do an EOF on me?')
except ShortInputException as ex:
print('ShortInputException: The input was {0} long, expected at
least {1}' \
.format(ex.length, ex.atleast))
else:
print('No exception was raised.')
Output:
$ python raising.py
Enter something --> a
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96
ShortInputException: The input was 1 long, expected at least 3
$ python raising.py
Enter something --> abc
No exception was raised.
How It Works:
Here, we are creating our own exception type. This new exception type is called
ShortInputException. It has two fields - length which is the length of the given input, and atleast which is the minimum length that the program was expecting.
In the except clause, we mention the class of error which will be stored as the variable name to hold the corresponding error/exception object. This is analogous to parameters and arguments in a function call. Within this particular except clause, we use the length and atleast fields of the exception object to print an appropriate message to the user.
Try ..Finally
Suppose you are reading a file in your program. How do you ensure that the file object is closed properly whether or not an exception was raised? This can be done using the
finally block. Note that you can use an except clause along with a finally block for the same corresponding try block. You will have to embed one within another if you want to use both.
#!/usr/bin/python
# Filename: finally.py
import time
try:
f = open('poem.txt')
while True: # our usual file-reading idiom
line = f.readline()
if len(line) == 0:
break
print(line, end='')
time.sleep(2) # To make sure it runs for a while
except KeyboardInterrupt:
print('!! You cancelled the reading from the file.')
finally:
f.close()
print('(Cleaning up: Closed the file)')
Output:
$ python finally.py
Programming is fun
When the work is done
if you wanna make your work also fun:
!! You cancelled the reading from the file.
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97
(Cleaning up: Closed the file)
How It Works:
We do the usual file-reading stuff, but we have arbitrarily introduced sleeping for 2 seconds after printing each line using the time.sleep function so that the program runs slowly (Python is very fast by nature). When the program is still running, press ctrl-c to
interrupt/cancel the program.
Observe that the KeyboardInterrupt exception is thrown and the program quits. However, before the program exits, the finally clause is executed and the file object is always closed.
The with statement
Acquiring a resource in the try block and subsequently releasing the resource in the
finally block is a common pattern. Hence, there is also a with statement that enables this to be done in a clean manner:
#!/usr/bin/python
# Filename: using_with.py
with open("poem.txt") as f:
for line in f:
print(line, end='')
How It Works:
The output should be same as the previous example. The difference here is that we are
using the open function with the with statement - we leave the closing of the file to be done automatically by with open.
What happens behind the scenes is that there is a protocol used by the with statement. It fetches the object returned by the open statement, let's call it "thefile" in this case.
It always calls the thefile.__enter__ function before starting the block of code under it and always calls thefile.__exit__ after finishing the block of code.
So the code that we would have written in a finally block is should be taken care of
automatically by the __exit__ method. This is what helps us to avoid having to use explicit try..finally statements repeatedly.
More discussion on this topic is beyond scope of this book, so please refer PEP 343 (http://
www.python.org/dev/peps/pep-0343/) for comprehensive explanation.
Summary
We have discussed the usage of the try..except and try..finally statements. We have
seen how to create our own exception types and how to raise exceptions as well.
Next, we will explore the Python Standard Library.
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Source: http://www.swaroopch.com/mediawiki/index.php?oldid=1470
Contributors: Swaroop, 2 anonymous edits
Python en:Standard Library
98
Python en:Standard Library
Introduction
The Python Standard Library contains a huge number of useful modules and is part of every standard Python installation. It is important to become familiar with the Python Standard Library since many problems can be solved quickly if you are familiar with the range of things that these libraries can do.
We will explore some of the commonly used modules in this library. You can find complete details for all of the modules in the Python Standard Library in the 'Library Reference'
section (http://docs.python.org/dev/3.0/library/) of the documentation that comes with
your Python installation.
Let us explore a few useful modules.
Note
If you find the topics in this chapter too advanced, you may skip this chapter. However, I highly recommend coming back to this chapter when you are more comfortable with
programming using Python.
sys module
The sys module contains system-specific functionality. We have already seen that the
sys.argv list contains the command-line arguments.
Suppose we want to check the version of the Python command being used so that, say, we want to ensure that we are using at least version 3. The sys module gives us such
functionality.
>>> import sys
>>> sys.version_info
(3, 0, 0, 'beta', 2)
>>> sys.version_info[0] >= 3
True
How It Works:
The sys module has a version_info tuple that gives us the version information. The first entry is the major version. We can check this to, for example, ensure the program runs only under Python 3.0:
#!/usr/bin/python
# Filename: versioncheck.py
import sys, warnings
if sys.version_info[0] < 3:
warnings.warn("Need Python 3.0 for this program to run",
RuntimeWarning)
else:
print('Proceed as normal')
Output:
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99
$ python2.5 versioncheck.py
versioncheck.py:6: RuntimeWarning: Need Python 3.0 for this program
to run
RuntimeWarning)
$ python3 versioncheck.py
Proceed as normal
How It Works:
We use another module from the standard library called warnings that is used to display warnings to the end-user. If the Python version number is not at least 3, we display a corresponding warning.
logging module
What if you wanted to have some debugging messages or important messages to be stored
somewhere so that you can check whether your program has been running as you would
expect it? How do you "store somewhere" these messages? This can be achieved using the logging module.
#!/usr/bin/python
# Filename: use_logging.py
import os, platform, logging
if platform.platform().startswith('Windows'):
logging_file = os.path.join(os.getenv('HOMEDRIVE'),
os.getenv('HOMEPATH'), 'test.log')
else:
logging_file = os.path.join(os.getenv('HOME'), 'test.log')
logging.basicConfig(
level=logging.DEBUG,
format=' %(asctime)s : %(levelname)s : %(message)s',
filename = logging_file,
filemode = 'w',
)
logging.debug("Start of the program")
logging.info("Doing something")
logging.warning("Dying now")
Output:
$python use_logging.py
Logging to C:\Users\swaroop\test.log
If we check the contents of test.log, it will look something like this:
2008-09-03 13:18:16,233 : DEBUG : Start of the program
2008-09-03 13:18:16,233 : INFO : Doing something
2008-09-03 13:18:16,233 : WARNING : Dying now
Python en:Standard Library
100
How It Works:
We use three modules from the standard library - the os module for interacting with the operating system, the platform module for information about the platform i.e. the
operating system and the logging module to log information.
First, we check which operating system we are using by checking the string returned by platform.platform() (for more information, see import platform; help(platform)). If it is Windows, we figure out the home drive, the home folder and the filename where we want to store the information. Putting these three parts together, we get the full location of the file. For other platforms, we need to know just the home folder of the user and we get the full location of the file.
We use the os.path.join() function to put these three parts of the location together. The reason to use a special function rather than just adding the strings together is because this function will ensure the full location matches the format expected by the operating system.
We configure the logging module to write all the messages in a particular format to the file we have specified.
Finally, we can put messages that are either meant for debugging, information, warning or even critical messages. Once the program has run, we can check this file and we will know what happened in the program, even though no information was displayed to the user
running the program.
urllib and json modules
How much fun would it be if we could write our own program that will get search results from the web? Let us explore that now.
This can be achieved using a few modules. First is the urllib module that we can use to fetch any webpage from the internet. We will make use of Yahoo! Search to get the search results and luckily they can give us the results in a format called JSON which is easy for us to parse because of the built-in json module in the standard library.
TODO
This program doesn't work yet which seems to be a bug in Python 3.0 beta 2 (http:/ /
#!/usr/bin/python
# Filename: yahoo_search.py
import sys
if sys.version_info[0] != 3:
sys.exit('This program needs Python 3.0')
import json
import urllib, urllib.parse, urllib.request, urllib.response
# Get your own APP ID at http://developer.yahoo.com/wsregapp/
YAHOO_APP_ID =
'jl22psvV34HELWhdfUJbfDQzlJ2B57KFS_qs4I8D0Wz5U5_yCI1Awv8.lBSfPhwr'
SEARCH_BASE =
'http://search.yahooapis.com/WebSearchService/V1/webSearch'
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101
class YahooSearchError(Exception):
pass
# Taken from http://developer.yahoo.com/python/python-json.html
def search(query, results=20, start=1, **kwargs):
kwargs.update({
'appid': YAHOO_APP_ID,
'query': query,
'results': results,
'start': start,
'output': 'json'
})
url = SEARCH_BASE + '?' + urllib.parse.urlencode(kwargs)
result = json.load(urllib.request.urlopen(url))
if 'Error' in result:
raise YahooSearchError(result['Error'])
return result['ResultSet']
query = input('What do you want to search for? ')
for result in search(query)['Result']:
print("{0} : {1}".format(result['Title'], result['Url']))
Output:
TODO
How It Works:
We can get the search results from a particular website by giving the text we are searching for in a particular format. We have to specify many options which we combine using
key1=value1&key2=value2 format which is handled by the urllib.parse.urlencode()
function.
So for example, open this link in your web browser (http:/ / search. yahooapis. com/
appid=jl22psvV34HELWhdfUJbfDQzlJ2B57KFS_qs4I8D0Wz5U5_yCI1Awv8.
results=20& start=1& output=json) and you will see 20 results, starting from the first
result, for the words "byte of python", and we are asking for the output in JSON format.
We make a connection to this URL using the urllib.request.urlopen() function and pass
that file handle to json.load() which will read the content and simultaneously convert it to a Python object. We then loop through these results and display it to the end-user.
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102
Module of the Week Series
There is much more to be explored in the standard library such as debugging (http://docs.
python. org/ dev/ library/ pdb. html), handling command line options (http:/ / docs. python.