Python Exceptions Handling

Python provides two very important features to handle any unexpected error in your Python programs and to add debugging capabilities in them:

• Exception Handling: This would be covered in this tutorial. Here is a list standard Exceptions available in Python: Standard Exceptions.
  
• Assertions: This would be covered in Assertions in Python tutorial.

What is Exception?

An exception is an event, which occurs during the execution of a program, that disrupts the normal flow of the program's instructions. In general, when a Python script encounters a situation that it can't cope with, it raises an exception. An exception is a Python object that represents an error.
When a Python script raises an exception, it must either handle the exception immediately otherwise it would terminate and come out.

Handling an exception:

If you have some suspicious code that may raise an exception, you can defend your program by placing the suspicious code in a try: block. After the try: block, include an except: statement, followed by a block of code which handles the problem as elegantly as possible.

Syntax:

Here is simple syntax of try....except...else blocks:

try:
   You do your operations here;
   ......................
except ExceptionI:
   If there is ExceptionI, then execute this block.
except ExceptionII:
   If there is ExceptionII, then execute this block.
   ......................
else:
   If there is no exception then execute this block. 

Here are few important points about the above-mentioned syntax:

• A single try statement can have multiple except statements. This is useful when the try block contains statements that may throw different types of exceptions.
• You can also provide a generic except clause, which handles any exception.
• After the except clause(s), you can include an else-clause. The code in the else-block executes if the code in the try: block does not raise an exception.
• The else-block is a good place for code that does not need the try: block's protection.

Example:

Here is simple example, which opens a file and writes the content in the file and comes out gracefully because there is no problem at all:

#!/usr/bin/python

try:
   fh = open("testfile", "w")
   fh.write("This is my test file for exception handling!!")
except IOError:
   print "Error: can\'t find file or read data"
else:
   print "Written content in the file successfully"
   fh.close()

This will produce the following result:

Written content in the file successfully

Example:

Here is one more simple example, which tries to open a file where you do not have permission to write in the file, so it raises an exception:

#!/usr/bin/python

try:
   fh = open("testfile", "r")
   fh.write("This is my test file for exception handling!!")
except IOError:
   print "Error: can\'t find file or read data"
else:
   print "Written content in the file successfully"

This will produce the following result:

Error: can't find file or read data

The except clause with no exceptions:

You can also use the except statement with no exceptions defined as follows:

try:
   You do your operations here;
   ......................
except:
   If there is any exception, then execute this block.
   ......................
else:
   If there is no exception then execute this block. 

This kind of a try-except statement catches all the exceptions that occur. Using this kind of try-except statement is not considered a good programming practice though, because it catches all exceptions but does not make the programmer identify the root cause of the problem that may occur.

The except clause with multiple exceptions:

You can also use the same except statement to handle multiple exceptions as follows:

try:
   You do your operations here;
   ......................
except(Exception1[, Exception2[,...ExceptionN]]]):
   If there is any exception from the given exception list, 
   then execute this block.
   ......................
else:
   If there is no exception then execute this block. 

The try-finally clause:

You can use a finally: block along with a try: block. The finally block is a place to put any code that must execute, whether the try-block raised an exception or not. The syntax of the try-finally statement is this:

try:
   You do your operations here;
   ......................
   Due to any exception, this may be skipped.
finally:
   This would always be executed.
   ......................

Note that you can provide except clause(s), or a finally clause, but not both. You can not use else clause as well along with a finally clause.

Example:

#!/usr/bin/python

try:
   fh = open("testfile", "w")
   fh.write("This is my test file for exception handling!!")
finally:
   print "Error: can\'t find file or read data"

If you do not have permission to open the file in writing mode, then this will produce the following result:

Error: can't find file or read data

Same example can be written more cleanly as follows:

#!/usr/bin/python

try:
   fh = open("testfile", "w")
   try:
      fh.write("This is my test file for exception handling!!")
   finally:
      print "Going to close the file"
      fh.close()
except IOError:
   print "Error: can\'t find file or read data"

When an exception is thrown in the try block, the execution immediately passes to the finally block. After all the statements in the finally block are executed, the exception is raised again and is handled in the except statements if present in the next higher layer of the try-except statement.

Argument of an Exception:

An exception can have an argument, which is a value that gives additional information about the problem. The contents of the argument vary by exception. You capture an exception's argument by supplying a variable in the except clause as follows:

try:
   You do your operations here;
   ......................
except ExceptionType, Argument:
   You can print value of Argument here...

If you are writing the code to handle a single exception, you can have a variable follow the name of the exception in the except statement. If you are trapping multiple exceptions, you can have a variable follow the tuple of the exception.
This variable will receive the value of the exception mostly containing the cause of the exception. The variable can receive a single value or multiple values in the form of a tuple. This tuple usually contains the error string, the error number, and an error location.

Example:

Following is an example for a single exception:

#!/usr/bin/python

# Define a function here.
def temp_convert(var):
   try:
      return int(var)
   except ValueError, Argument:
      print "The argument does not contain numbers\n", Argument

# Call above function here.
temp_convert("xyz");

This would produce the following result:

The argument does not contain numbers
invalid literal for int() with base 10: 'xyz'

Raising an exceptions:

You can raise exceptions in several ways by using the raise statement. The general syntax for the raise statement.

Syntax:

raise [Exception [, args [, traceback]]]

Here, Exception is the type of exception (for example, NameError) and argument is a value for the exception argument. The argument is optional; if not supplied, the exception argument is None.
The final argument, traceback, is also optional (and rarely used in practice), and if present, is the traceback object used for the exception.

Example:

An exception can be a string, a class or an object. Most of the exceptions that the Python core raises are classes, with an argument that is an instance of the class. Defining new exceptions is quite easy and can be done as follows:

def functionName( level ):
   if level < 1:
      raise "Invalid level!", level
      # The code below to this would not be executed
      # if we raise the exception

Note: In order to catch an exception, an "except" clause must refer to the same exception thrown either class object or simple string. For example, to capture above exception, we must write our except clause as follows:

try:
   Business Logic here...
except "Invalid level!":
   Exception handling here...
else:
   Rest of the code here...

User-Defined Exceptions:

Python also allows you to create your own exceptions by deriving classes from the standard built-in exceptions.
Here is an example related to RuntimeError. Here, a class is created that is subclassed from RuntimeError. This is useful when you need to display more specific information when an exception is caught.
In the try block, the user-defined exception is raised and caught in the except block. The variable e is used to create an instance of the class Networkerror.

class Networkerror(RuntimeError):
   def __init__(self, arg):
      self.args = arg

So once you defined above class, you can raise your exception as follows:

try

Python Files I/O

This chapter will cover all the basic I/O functions available in Python. For more functions, please refer to standard Python documentation.

Printing to the Screen:

The simplest way to produce output is using the print statement where you can pass zero or more expressions separated by commas. This function converts the expressions you pass into a string and writes the result to standard output as follows:

#!/usr/bin/python

print "Python is really a great language,", "isn't it?";

This would produce the following result on your standard screen:

Python is really a great language, isn't it?

Reading Keyboard Input:

Python provides two built-in functions to read a line of text from standard input, which by default comes from the keyboard. These functions are:

• raw_input
• input

The raw_input Function:

The raw_input([prompt]) function reads one line from standard input and returns it as a string (removing the trailing newline).

#!/usr/bin/python

str = raw_input("Enter your input: ");
print "Received input is : ", str

This would prompt you to enter any string and it would display same string on the screen. When I typed "Hello Python!", its output is like this:

Enter your input: Hello Python
Received input is :  Hello Python

The input Function:

The input([prompt]) function is equivalent to raw_input, except that it assumes the input is a valid Python expression and returns the evaluated result to you.

#!/usr/bin/python

str = input("Enter your input: ");
print "Received input is : ", str

This would produce the following result against the entered input:

Enter your input: [x*5 for x in range(2,10,2)]
Recieved input is :  [10, 20, 30, 40]

Opening and Closing Files:

Until now, you have been reading and writing to the standard input and output. Now, we will see how to play with actual data files.
Python provides basic functions and methods necessary to manipulate files by default. You can do your most of the file manipulation using a file object.

The open Function:

Before you can read or write a file, you have to open it using Python's built-in open() function. This function creates a file object, which would be utilized to call other support methods associated with it.

Syntax:

file object = open(file_name [, access_mode][, buffering])

Here is paramters' detail:

• file_name: The file_name argument is a string value that contains the name of the file that you want to access.
• access_mode: The access_mode determines the mode in which the file has to be opened, i.e., read, write, append, etc. A complete list of possible values is given below in the table. This is optional parameter and the default file access mode is read (r).
• buffering: If the buffering value is set to 0, no buffering will take place. If the buffering value is 1, line buffering will be performed while accessing a file. If you specify the buffering value as an integer greater than 1, then buffering action will be performed with the indicated buffer size. If negative, the buffer size is the system default(default behavior).

Here is a list of the different modes of opening a file:

Modes	Description
r	Opens a file for reading only. The file pointer is placed at the beginning of the file. This is the default mode.
rb	Opens a file for reading only in binary format. The file pointer is placed at the beginning of the file. This is the default mode.
r+	Opens a file for both reading and writing. The file pointer will be at the beginning of the file.
rb+	Opens a file for both reading and writing in binary format. The file pointer will be at the beginning of the file.
w	Opens a file for writing only. Overwrites the file if the file exists. If the file does not exist, creates a new file for writing.
wb	Opens a file for writing only in binary format. Overwrites the file if the file exists. If the file does not exist, creates a new file for writing.
w+	Opens a file for both writing and reading. Overwrites the existing file if the file exists. If the file does not exist, creates a new file for reading and writing.
wb+	Opens a file for both writing and reading in binary format. Overwrites the existing file if the file exists. If the file does not exist, creates a new file for reading and writing.
a	Opens a file for appending. The file pointer is at the end of the file if the file exists. That is, the file is in the append mode. If the file does not exist, it creates a new file for writing.
ab	Opens a file for appending in binary format. The file pointer is at the end of the file if the file exists. That is, the file is in the append mode. If the file does not exist, it creates a new file for writing.
a+	Opens a file for both appending and reading. The file pointer is at the end of the file if the file exists. The file opens in the append mode. If the file does not exist, it creates a new file for reading and writing.
ab+	Opens a file for both appending and reading in binary format. The file pointer is at the end of the file if the file exists. The file opens in the append mode. If the file does not exist, it creates a new file for reading and writing.

The file object attributes:

Once a file is opened and you have one file object, you can get various information related to that file.
Here is a list of all attributes related to file object:

Attribute	Description
file.closed	Returns true if file is closed, false otherwise.
file.mode	Returns access mode with which file was opened.
file.name	Returns name of the file.
file.softspace	Returns false if space explicitly required with print, true otherwise.

Example:

#!/usr/bin/python

# Open a file
fo = open("foo.txt", "wb")
print "Name of the file: ", fo.name
print "Closed or not : ", fo.closed
print "Opening mode : ", fo.mode
print "Softspace flag : ", fo.softspace

This would produce the following result:

Name of the file:  foo.txt
Closed or not :  False
Opening mode :  wb
Softspace flag :  0

The close() Method:

The close() method of a file object flushes any unwritten information and closes the file object, after which no more writing can be done.
Python automatically closes a file when the reference object of a file is reassigned to another file. It is a good practice to use the close() method to close a file.

Syntax:

fileObject.close();

Example:

#!/usr/bin/python

# Open a file
fo = open("foo.txt", "wb")
print "Name of the file: ", fo.name

# Close opend file
fo.close()

This would produce the following result:

Name of the file:  foo.txt

Reading and Writing Files:

The file object provides a set of access methods to make our lives easier. We would see how to use read() and write() methods to read and write files.

The write() Method:

The write() method writes any string to an open file. It is important to note that Python strings can have binary data and not just text.
The write() method does not add a newline character ('\n') to the end of the string:

Syntax:

fileObject.write(string);

Here, passed parameter is the content to be written into the opened file.

Example:

#!/usr/bin/python

# Open a file
fo = open("foo.txt", "wb")
fo.write( "Python is a great language.\nYeah its great!!\n");

# Close opend file
fo.close()

The above method would create foo.txt file and would write given content in that file and finally it would close that file. If you would open this file, it would have following content.

Python is a great language.
Yeah its great!!

The read() Method:

The read() method reads a string from an open file. It is important to note that Python strings can have binary data and not just text.

Syntax:

fileObject.read([count]);

Here, passed parameter is the number of bytes to be read from the opened file. This method starts reading from the beginning of the file and if count is missing, then it tries to read as much as possible, maybe until the end of file.

Example:

Let's take a file foo.txt, which we have created above.

#!/usr/bin/python

# Open a file
fo = open("foo.txt", "r+")
str = fo.read(10);
print "Read String is : ", str
# Close opend file
fo.close()

This would produce the following result:

Read String is :  Python is

File Positions:

The tell() method tells you the current position within the file; in other words, the next read or write will occur at that many bytes from the beginning of the file.
The seek(offset[, from]) method changes the current file position. The offset argument indicates the number of bytes to be moved. The from argument specifies the reference position from where the bytes are to be moved.
If from is set to 0, it means use the beginning of the file as the reference position and 1 means use the current position as the reference position and if it is set to 2 then the end of the file would be taken as the reference position.

Example:

Let's take a file foo.txt, which we have created above.

#!/usr/bin/python

# Open a file
fo = open("foo.txt", "r+")
str = fo.read(10);
print "Read String is : ", str

# Check current position
position = fo.tell();
print "Current file position : ", position

# Reposition pointer at the beginning once again
position = fo.seek(0, 0);
str = fo.read(10);
print "Again read String is : ", str
# Close opend file
fo.close()

This would produce the following result:

Read String is :  Python is
Current file position :  10
Again read String is :  Python is

Renaming and Deleting Files:

Python os module provides methods that help you perform file-processing operations, such as renaming and deleting files.
To use this module you need to import it first and then you can call any related functions.

The rename() Method:

The rename() method takes two arguments, the current filename and the new filename.

Syntax:

os.rename(current_file_name, new_file_name)

Example:

Following is the example to rename an existing file test1.txt:

#!/usr/bin/python
import os

# Rename a file from test1.txt to test2.txt
os.rename( "test1.txt", "test2.txt" )

The remove() Method:

You can use the remove() method to delete files by supplying the name of the file to be deleted as the argument.

Syntax:

os.remove(file_name)

Example:

Following is the example to delete an existing file test2.txt:

#!/usr/bin/python
import os

# Delete file test2.txt
os.remove("text2.txt")

Directories in Python:

All files are contained within various directories, and Python has no problem handling these too. The os module has several methods that help you create, remove and change directories.

The mkdir() Method:

You can use the mkdir() method of the os module to create directories in the current directory. You need to supply an argument to this method which contains the name of the directory to be created.

Syntax:

os.mkdir("newdir")

Example:

Following is the example to create a directory test in the current directory:

#!/usr/bin/python
import os

# Create a directory "test"
os.mkdir("test")

The chdir() Method:

You can use the chdir() method to change the current directory. The chdir() method takes an argument, which is the name of the directory that you want to make the current directory.

Syntax:

os.chdir("newdir")

Example:

Following is the example to go into "/home/newdir" directory:

#!/usr/bin/python
import os

# Changing a directory to "/home/newdir"
os.chdir("/home/newdir")

The getcwd() Method:

The getcwd() method displays the current working directory.

Syntax:

os.getcwd()

Example:

Following is the example to give current directory:

#!/usr/bin/python
import os

# This would give location of the current directory
os.getcwd()

The rmdir() Method:

The rmdir() method deletes the directory, which is passed as an argument in the method.
Before removing a directory, all the contents in it should be removed.

Syntax:

os.rmdir('dirname')

Example:

Following is the example to remove "/tmp/test" directory. It is required to give fully qualified name of the directory, otherwise it would search for that directory in the current directory.

#!/usr/bin/python
import os

# This would  remove "/tmp/test"  directory.
os.rmdir( "/tmp/test"  )

File & Directory Related Methods:

There are three important sources, which provide a wide range of utility methods to handle and manipulate files & directories on Windows and Unix operating systems. They are as follows:

• File Object Methods: The file object provides functions to manipulate files.
• OS Object Methods: This provides methods to process files as well as directories.

Python Modules

A module allows you to logically organize your Python code. Grouping related code into a module makes the code easier to understand and use. A module is a Python object with arbitrarily named attributes that you can bind and reference.
Simply, a module is a file consisting of Python code. A module can define functions, classes and variables. A module can also include runnable code.

Example:

The Python code for a module named aname normally resides in a file named aname.py. Here's an example of a simple module, support.py

def print_func( par ):
   print "Hello : ", par
   return

The import Statement:

You can use any Python source file as a module by executing an import statement in some other Python source file. The import has the following syntax:

import module1[, module2[,... moduleN]

When the interpreter encounters an import statement, it imports the module if the module is present in the search path. A search path is a list of directories that the interpreter searches before importing a module. For example, to import the module hello.py, you need to put the following command at the top of the script:

#!/usr/bin/python

# Import module support
import support

# Now you can call defined function that module as follows
support.print_func("Zara")

When the above code is executed, it produces the following result:

Hello : Zara

A module is loaded only once, regardless of the number of times it is imported. This prevents the module execution from happening over and over again if multiple imports occur.

The from...import Statement

Python's from statement lets you import specific attributes from a module into the current namespace. The from...import has the following syntax:

from modname import name1[, name2[, ... nameN]]

For example, to import the function fibonacci from the module fib, use the following statement:

from fib import fibonacci

This statement does not import the entire module fib into the current namespace; it just introduces the item fibonacci from the module fib into the global symbol table of the importing module.

The from...import * Statement:

It is also possible to import all names from a module into the current namespace by using the following import statement:

from modname import *

This provides an easy way to import all the items from a module into the current namespace; however, this statement should be used sparingly.

Locating Modules:

When you import a module, the Python interpreter searches for the module in the following sequences:

• The current directory.
• If the module isn't found, Python then searches each directory in the shell variable PYTHONPATH.
• If all else fails, Python checks the default path. On UNIX, this default path is normally /usr/local/lib/python/.

The module search path is stored in the system module sys as the sys.path variable. The sys.path variable contains the current directory, PYTHONPATH, and the installation-dependent default.

The PYTHONPATH Variable:

The PYTHONPATH is an environment variable, consisting of a list of directories. The syntax of PYTHONPATH is the same as that of the shell variable PATH.
Here is a typical PYTHONPATH from a Windows system:

set PYTHONPATH=c:\python20\lib;

And here is a typical PYTHONPATH from a UNIX system:

set PYTHONPATH=/usr/local/lib/python

Namespaces and Scoping:

Variables are names (identifiers) that map to objects. A namespace is a dictionary of variable names (keys) and their corresponding objects (values).
A Python statement can access variables in a local namespace and in the global namespace. If a local and a global variable have the same name, the local variable shadows the global variable.
Each function has its own local namespace. Class methods follow the same scoping rule as ordinary functions.
Python makes educated guesses on whether variables are local or global. It assumes that any variable assigned a value in a function is local.
Therefore, in order to assign a value to a global variable within a function, you must first use the global statement.
The statement global VarName tells Python that VarName is a global variable. Python stops searching the local namespace for the variable.
For example, we define a variable Money in the global namespace. Within the function Money, we assign Money a value, therefore Python assumes Money as a local variable. However, we accessed the value of the local variable Money before setting it, so an UnboundLocalError is the result. Uncommenting the global statement fixes the problem.

#!/usr/bin/python

Money = 2000
def AddMoney():
   # Uncomment the following line to fix the code:
   # global Money
   Money = Money + 1

print Money
AddMoney()
print Money

The dir( ) Function:

The dir() built-in function returns a sorted list of strings containing the names defined by a module.
The list contains the names of all the modules, variables and functions that are defined in a module. Following is a simple example:

#!/usr/bin/python

# Import built-in module math
import math

content = dir(math)

print content;

When the above code is executed, it produces the following result:

['__doc__', '__file__', '__name__', 'acos', 'asin', 'atan', 
'atan2', 'ceil', 'cos', 'cosh', 'degrees', 'e', 'exp', 
'fabs', 'floor', 'fmod', 'frexp', 'hypot', 'ldexp', 'log',
'log10', 'modf', 'pi', 'pow', 'radians', 'sin', 'sinh', 
'sqrt', 'tan', 'tanh']

Here, the special string variable __name__ is the module's name, and __file__ is the filename from which the module was loaded.

The globals() and locals() Functions:

The globals() and locals() functions can be used to return the names in the global and local namespaces depending on the location from where they are called.
If locals() is called from within a function, it will return all the names that can be accessed locally from that function.
If globals() is called from within a function, it will return all the names that can be accessed globally from that function.
The return type of both these functions is dictionary. Therefore, names can be extracted using the keys() function.

The reload() Function:

When the module is imported into a script, the code in the top-level portion of a module is executed only once.
Therefore, if you want to reexecute the top-level code in a module, you can use the reload() function. The reload() function imports a previously imported module again. The syntax of the reload() function is this:

reload(module_name)

Here, module_name is the name of the module you want to reload and not the string containing the module name. For example, to reload hello module, do the following:

reload(hello)

Packages in Python:

A package is a hierarchical file directory structure that defines a single Python application environment that consists of modules and subpackages and sub-subpackages, and so on.
Consider a file Pots.py available in Phone directory. This file has following line of source code:

#!/usr/bin/python

def Pots():
   print "I'm Pots Phone"

Similar way, we have another two files having different functions with the same name as above:

• Phone/Isdn.py file having function Isdn()
• Phone/G3.py file having function G3()

Now, create one more file __init__.py in Phone directory:

• Phone/__init__.py

To make all of your functions available when you've imported Phone, you need to put explicit import statements in __init__.py as follows:

from Pots import Pots
from Isdn import Isdn
from G3 import G3

After you've added these lines to __init__.py, you have all of these classes available when you've imported the Phone package.

#!/usr/bin/python

# Now import your Phone Package.
import Phone

Phone.Pots()
Phone.Isdn()
Phone.G3()

When the above code is executed, it produces the following result:

I'm Pots Phone
I'm 3G Phone
I'm ISDN Phone

In the above example, we have taken example of a single functions in each file, but you can keep multiple functions in your files. You can also define different Python classes in those files and then you can create your packages out of those classes.

Python Functions

A function is a block of organized, reusable code that is used to perform a single, related action. Functions provide better modularity for your application and a high degree of code reusing.
As you already know, Python gives you many built-in functions like print(), etc. but you can also create your own functions. These functions are called user-defined functions.

Defining a Function

You can define functions to provide the required functionality. Here are simple rules to define a function in Python.

• Function blocks begin with the keyword def followed by the function name and parentheses ( ( ) ).
• Any input parameters or arguments should be placed within these parentheses. You can also define parameters inside these parentheses.
• The first statement of a function can be an optional statement - the documentation string of the function or docstring.
• The code block within every function starts with a colon (:) and is indented.
• The statement return [expression] exits a function, optionally passing back an expression to the caller. A return statement with no arguments is the same as return None.

Syntax:

def functionname( parameters ):
   "function_docstring"
   function_suite
   return [expression]

By default, parameters have a positional behavior and you need to inform them in the same order that they were defined.

Example:

Here is the simplest form of a Python function. This function takes a string as input parameter and prints it on standard screen.

def printme( str ):
   "This prints a passed string into this function"
   print str
   return

Calling a Function

Defining a function only gives it a name, specifies the parameters that are to be included in the function and structures the blocks of code.
Once the basic structure of a function is finalized, you can execute it by calling it from another function or directly from the Python prompt. Following is the example to call printme() function:

#!/usr/bin/python

# Function definition is here
def printme( str ):
   "This prints a passed string into this function"
   print str;
   return;

# Now you can call printme function
printme("I'm first call to user defined function!");
printme("Again second call to the same function");

When the above code is executed, it produces the following result:

I'm first call to user defined function!
Again second call to the same function

Pass by reference vs value

All parameters (arguments) in the Python language are passed by reference. It means if you change what a parameter refers to within a function, the change also reflects back in the calling function. For example:

#!/usr/bin/python

# Function definition is here
def changeme( mylist ):
   "This changes a passed list into this function"
   mylist.append([1,2,3,4]);
   print "Values inside the function: ", mylist
   return

# Now you can call changeme function
mylist = [10,20,30];
changeme( mylist );
print "Values outside the function: ", mylist

Here, we are maintaining reference of the passed object and appending values in the same object. So, this would produce the following result:

Values inside the function:  [10, 20, 30, [1, 2, 3, 4]]
Values outside the function:  [10, 20, 30, [1, 2, 3, 4]]

There is one more example where argument is being passed by reference and the reference is being overwritten inside the called function.

#!/usr/bin/python

# Function definition is here
def changeme( mylist ):
   "This changes a passed list into this function"
   mylist = [1,2,3,4]; # This would assig new reference in mylist
   print "Values inside the function: ", mylist
   return

# Now you can call changeme function
mylist = [10,20,30];
changeme( mylist );
print "Values outside the function: ", mylist

The parameter mylist is local to the function changeme. Changing mylist within the function does not affect mylist. The function accomplishes nothing and finally this would produce the following result:

Values inside the function:  [1, 2, 3, 4]
Values outside the function:  [10, 20, 30]

Function Arguments:

You can call a function by using the following types of formal arguments:

• Required arguments
• Keyword arguments
• Default arguments
• Variable-length arguments

Required arguments:

Required arguments are the arguments passed to a function in correct positional order. Here, the number of arguments in the function call should match exactly with the function definition.
To call the function printme(), you definitely need to pass one argument, otherwise it would give a syntax error as follows:

#!/usr/bin/python

# Function definition is here
def printme( str ):
   "This prints a passed string into this function"
   print str;
   return;

# Now you can call printme function
printme();

When the above code is executed, it produces the following result:

Traceback (most recent call last):
  File "test.py", line 11, in <module>
    printme();
TypeError: printme() takes exactly 1 argument (0 given)

Keyword arguments:

Keyword arguments are related to the function calls. When you use keyword arguments in a function call, the caller identifies the arguments by the parameter name.
This allows you to skip arguments or place them out of order because the Python interpreter is able to use the keywords provided to match the values with parameters. You can also make keyword calls to the printme() function in the following ways:

#!/usr/bin/python

# Function definition is here
def printme( str ):
   "This prints a passed string into this function"
   print str;
   return;

# Now you can call printme function
printme( str = "My string");

When the above code is executed, it produces the following result:

My string

Following example gives more clear picture. Note, here order of the parameter does not matter.

#!/usr/bin/python

# Function definition is here
def printinfo( name, age ):
   "This prints a passed info into this function"
   print "Name: ", name;
   print "Age ", age;
   return;

# Now you can call printinfo function
printinfo( age=50, name="miki" );

When the above code is executed, it produces the following result:

Name:  miki
Age  50

Default arguments:

A default argument is an argument that assumes a default value if a value is not provided in the function call for that argument. Following example gives an idea on default arguments, it would print default age if it is not passed:

#!/usr/bin/python

# Function definition is here
def printinfo( name, age = 35 ):
   "This prints a passed info into this function"
   print "Name: ", name;
   print "Age ", age;
   return;

# Now you can call printinfo function
printinfo( age=50, name="miki" );
printinfo( name="miki" );

When the above code is executed, it produces the following result:

Name:  miki
Age  50
Name:  miki
Age  35

Variable-length arguments:

You may need to process a function for more arguments than you specified while defining the function. These arguments are called variable-length arguments and are not named in the function definition, unlike required and default arguments.
The general syntax for a function with non-keyword variable arguments is this:

def functionname([formal_args,] *var_args_tuple ):
   "function_docstring"
   function_suite
   return [expression]

An asterisk (*) is placed before the variable name that will hold the values of all nonkeyword variable arguments. This tuple remains empty if no additional arguments are specified during the function call. Following is a simple example:

#!/usr/bin/python

# Function definition is here
def printinfo( arg1, *vartuple ):
   "This prints a variable passed arguments"
   print "Output is: "
   print arg1
   for var in vartuple:
      print var
   return;

# Now you can call printinfo function
printinfo( 10 );
printinfo( 70, 60, 50 );

When the above code is executed, it produces the following result:

Output is:
10
Output is:
70
60
50

The Anonymous Functions:

You can use the lambda keyword to create small anonymous functions. These functions are called anonymous because they are not declared in the standard manner by using the def keyword.

• Lambda forms can take any number of arguments but return just one value in the form of an expression. They cannot contain commands or multiple expressions.
• An anonymous function cannot be a direct call to print because lambda requires an expression.
• Lambda functions have their own local namespace and cannot access variables other than those in their parameter list and those in the global namespace.
• Although it appears that lambda's are a one-line version of a function, they are not equivalent to inline statements in C or C++, whose purpose is by passing function stack allocation during invocation for performance reasons.

Syntax:

The syntax of lambda functions contains only a single statement, which is as follows:

lambda [arg1 [,arg2,.....argn]]:expression

Following is the example to show how lambda form of function works:

#!/usr/bin/python

# Function definition is here
sum = lambda arg1, arg2: arg1 + arg2;

 

# Now you can call sum as a function
print "Value of total : ", sum( 10, 20 )
print "Value of total : ", sum( 20, 20 )

When the above code is executed, it produces the following result:

Value of total :  30
Value of total :  40

The return Statement:

The statement return [expression] exits a function, optionally passing back an expression to the caller. A return statement with no arguments is the same as return None.
All the above examples are not returning any value, but if you like you can return a value from a function as follows:

#!/usr/bin/python

# Function definition is here
def sum( arg1, arg2 ):
   # Add both the parameters and return them."
   total = arg1 + arg2
   print "Inside the function : ", total
   return total;

# Now you can call sum function
total = sum( 10, 20 );
print "Outside the function : ", total 

When the above code is executed, it produces the following result:

Inside the function :  30
Outside the function :  30

Scope of Variables:

All variables in a program may not be accessible at all locations in that program. This depends on where you have declared a variable.
The scope of a variable determines the portion of the program where you can access a particular identifier. There are two basic scopes of variables in Python:

• Global variables
• Local variables

Global vs. Local variables:

Variables that are defined inside a function body have a local scope, and those defined outside have a global scope.
This means that local variables can be accessed only inside the function in which they are declared, whereas global variables can be accessed throughout the program body by all functions. When you call a function, the variables declared inside it are brought into scope. Following is a simple example:

#!/usr/bin/python

total = 0; # This is global variable.
# Function definition is here
def sum( arg1, arg2 ):
   # Add both the parameters and return them."
   total = arg1 + arg2; # Here total is local variable.
   print "Inside the function local total : ", total
   return total;

# Now you can call sum function
sum( 10, 20 );
print "Outside the function global total : ", total 

When the above code is executed, it produces the following result:

Inside the function local total :  30
Outside the function global total :  0

Python Date & Time

A Python program can handle date & time in several ways. Converting between date formats is a common chore for computers. Python's time and calendar modules help track dates and times.

What is Tick?

Time intervals are floating-point numbers in units of seconds. Particular instants in time are expressed in seconds since 12:00am, January 1, 1970(epoch).
There is a popular time module available in Python which provides functions for working with times, and for converting between representations. The function time.time() returns the current system time in ticks since 12:00am, January 1, 1970(epoch).

Example:

#!/usr/bin/python
import time;  # This is required to include time module.

ticks = time.time()
print "Number of ticks since 12:00am, January 1, 1970:", ticks

This would produce a result something as follows:

Number of ticks since 12:00am, January 1, 1970: 7186862.73399

Date arithmetic is easy to do with ticks. However, dates before the epoch cannot be represented in this form. Dates in the far future also cannot be represented this way - the cutoff point is sometime in 2038 for UNIX and Windows.

What is TimeTuple?

Many of Python's time functions handle time as a tuple of 9 numbers, as shown below:

Index	Field	Values
0	4-digit year	2008
1	Month	1 to 12
2	Day	1 to 31
3	Hour	0 to 23
4	Minute	0 to 59
5	Second	0 to 61 (60 or 61 are leap-seconds)
6	Day of Week	0 to 6 (0 is Monday)
7	Day of year	1 to 366 (Julian day)
8	Daylight savings	-1, 0, 1, -1 means library determines DST

The above tuple is equivalent to struct_time structure. This structure has following attributes:

Index	Attributes	Values
0	tm_year	2008
1	tm_mon	1 to 12
2	tm_mday	1 to 31
3	tm_hour	0 to 23
4	tm_min	0 to 59
5	tm_sec	0 to 61 (60 or 61 are leap-seconds)
6	tm_wday	0 to 6 (0 is Monday)
7	tm_yday	1 to 366 (Julian day)
8	tm_isdst	-1, 0, 1, -1 means library determines DST

Getting current time -:

To translate a time instant from a seconds since the epoch floating-point value into a time-tuple, pass the floating-point value to a function (e.g., localtime) that returns a time-tuple with all nine items valid.

#!/usr/bin/python
import time;

localtime = time.localtime(time.time())
print "Local current time :", localtime

This would produce the following result, which could be formatted in any other presentable form:

Local current time : time.struct_time(tm_year=2013, tm_mon=7, 
tm_mday=17, tm_hour=21, tm_min=26, tm_sec=3, tm_wday=2, tm_yday=198, tm_isdst=0)

Getting formatted time -:

You can format any time as per your requirement, but simple method to get time in readable format is asctime():

#!/usr/bin/python
import time;

localtime = time.asctime( time.localtime(time.time()) )
print "Local current time :", localtime

This would produce the following result:

Local current time : Tue Jan 13 10:17:09 2009

Getting calendar for a month -:

The calendar module gives a wide range of methods to play with yearly and monthly calendars. Here, we print a calendar for a given month ( Jan 2008 ):

#!/usr/bin/python
import calendar

cal = calendar.month(2008, 1)
print "Here is the calendar:"
print cal;

This would produce the following result:

Here is the calendar:
    January 2008
Mo Tu We Th Fr Sa Su
    1  2  3  4  5  6
 7  8  9 10 11 12 13
14 15 16 17 18 19 20
21 22 23 24 25 26 27
28 29 30 31

The time Module:

There is a popular time module available in Python which provides functions for working with times and for converting between representations. Here is the list of all available methods:

SN	Function with Description
1	time.altzone The offset of the local DST timezone, in seconds west of UTC, if one is defined. This is negative if the local DST timezone is east of UTC (as in Western Europe, including the UK). Only use this if daylight is nonzero.
2	time.asctime([tupletime]) Accepts a time-tuple and returns a readable 24-character string such as 'Tue Dec 11 18:07:14 2008'.
3	time.clock( ) Returns the current CPU time as a floating-point number of seconds. To measure computational costs of different approaches, the value of time.clock is more useful than that of time.time().
4	time.ctime([secs]) Like asctime(localtime(secs)) and without arguments is like asctime( )
5	time.gmtime([secs]) Accepts an instant expressed in seconds since the epoch and returns a time-tuple t with the UTC time. Note : t.tm_isdst is always 0
6	time.localtime([secs]) Accepts an instant expressed in seconds since the epoch and returns a time-tuple t with the local time (t.tm_isdst is 0 or 1, depending on whether DST applies to instant secs by local rules).
7	time.mktime(tupletime) Accepts an instant expressed as a time-tuple in local time and returns a floating-point value with the instant expressed in seconds since the epoch.
8	time.sleep(secs) Suspends the calling thread for secs seconds.
9	time.strftime(fmt[,tupletime]) Accepts an instant expressed as a time-tuple in local time and returns a string representing the instant as specified by string fmt.
10	time.strptime(str,fmt='%a %b %d %H:%M:%S %Y') Parses str according to format string fmt and returns the instant in time-tuple format.
11	time.time( ) Returns the current time instant, a floating-point number of seconds since the epoch.
12	time.tzset() Resets the time conversion rules used by the library routines. The environment variable TZ specifies how this is done.

There are following two important attributes available with time module:

SN	Attribute with Description
1	time.timezone Attribute time.timezone is the offset in seconds of the local time zone (without DST) from UTC (>0 in the Americas; <=0 in most of Europe, Asia, Africa).
2	time.tzname Attribute time.tzname is a pair of locale-dependent strings, which are the names of the local time zone without and with DST, respectively.

The calendar Module

The calendar module supplies calendar-related functions, including functions to print a text calendar for a given month or year.
By default, calendar takes Monday as the first day of the week and Sunday as the last one. To change this, call calendar.setfirstweekday() function.
Here is a list of functions available with the calendar module:

SN	Function with Description
1	calendar.calendar(year,w=2,l=1,c=6) Returns a multiline string with a calendar for year year formatted into three columns separated by c spaces. w is the width in characters of each date; each line has length 21*w+18+2*c. l is the number of lines for each week.
2	calendar.firstweekday( ) Returns the current setting for the weekday that starts each week. By default, when calendar is first imported, this is 0, meaning Monday.
3	calendar.isleap(year) Returns True if year is a leap year; otherwise, False.
4	calendar.leapdays(y1,y2) Returns the total number of leap days in the years within range(y1,y2).
5	calendar.month(year,month,w=2,l=1) Returns a multiline string with a calendar for month month of year year, one line per week plus two header lines. w is the width in characters of each date; each line has length 7*w+6. l is the number of lines for each week.
6	calendar.monthcalendar(year,month) Returns a list of lists of ints. Each sublist denotes a week. Days outside month month of year year are set to 0; days within the month are set to their day-of-month, 1 and up.
7	calendar.monthrange(year,month) Returns two integers. The first one is the code of the weekday for the first day of the month month in year year; the second one is the number of days in the month. Weekday codes are 0 (Monday) to 6 (Sunday); month numbers are 1 to 12.
8	calendar.prcal(year,w=2,l=1,c=6) Like print calendar.calendar(year,w,l,c).
9	calendar.prmonth(year,month,w=2,l=1) Like print calendar.month(year,month,w,l).
10	calendar.setfirstweekday(weekday) Sets the first day of each week to weekday code weekday. Weekday codes are 0 (Monday) to 6 (Sunday).
11	calendar.timegm(tupletime) The inverse of time.gmtime: accepts a time instant in time-tuple form and returns the same instant as a floating-point number of seconds since the epoch.
12	calendar.weekday(year,month,day) Returns the weekday code for the given date. Weekday codes are 0 (Monday) to 6 (Sunday); month numbers are 1 (January) to 12 (December).

Other Modules & Functions:

If you are interested, then here you would find a list of other important modules and functions to play with date & time in Python:

• The datetime Module
• The pytz Module
• The dateutil Module

Python Dictionary

A dictionary is mutable and is another container type that can store any number of Python objects, including other container types. Dictionaries consist of pairs (called items) of keys and their corresponding values.
Python dictionaries are also known as associative arrays or hash tables. The general syntax of a dictionary is as follows:

dict = {'Alice': '2341', 'Beth': '9102', 'Cecil': '3258'}

You can create dictionary in the following way as well:

dict1 = { 'abc': 456 };
dict2 = { 'abc': 123, 98.6: 37 };

Each key is separated from its value by a colon (:), the items are separated by commas, and the whole thing is enclosed in curly braces. An empty dictionary without any items is written with just two curly braces, like this: {}.
Keys are unique within a dictionary while values may not be. The values of a dictionary can be of any type, but the keys must be of an immutable data type such as strings, numbers, or tuples.

Accessing Values in Dictionary:

To access dictionary elements, you can use the familiar square brackets along with the key to obtain its value. Following is a simple example:

#!/usr/bin/python

dict = {'Name': 'Zara', 'Age': 7, 'Class': 'First'};

print "dict['Name']: ", dict['Name'];
print "dict['Age']: ", dict['Age'];

When the above code is executed, it produces the following result:

dict['Name']:  Zara
dict['Age']:  7

If we attempt to access a data item with a key, which is not part of the dictionary, we get an error as follows:

#!/usr/bin/python

dict = {'Name': 'Zara', 'Age': 7, 'Class': 'First'};

print "dict['Alice']: ", dict['Alice'];

When the above code is executed, it produces the following result:

dict['Zara']:
Traceback (most recent call last):
  File "test.py", line 4, in <module>
    print "dict['Alice']: ", dict['Alice'];
KeyError: 'Alice'

Updating Dictionary:

You can update a dictionary by adding a new entry or item (i.e., a key-value pair), modifying an existing entry, or deleting an existing entry as shown below in the simple example:

#!/usr/bin/python

dict = {'Name': 'Zara', 'Age': 7, 'Class': 'First'};

dict['Age'] = 8; # update existing entry
dict['School'] = "DPS School"; # Add new entry


print "dict['Age']: ", dict['Age'];
print "dict['School']: ", dict['School'];

When the above code is executed, it produces the following result:

dict['Age']:  8
dict['School']:  DPS School

Delete Dictionary Elements:

You can either remove individual dictionary elements or clear the entire contents of a dictionary. You can also delete entire dictionary in a single operation.
To explicitly remove an entire dictionary, just use the del statement. Following is a simple example:

#!/usr/bin/python

dict = {'Name': 'Zara', 'Age': 7, 'Class': 'First'};

del dict['Name']; # remove entry with key 'Name'
dict.clear();     # remove all entries in dict
del dict ;        # delete entire dictionary

print "dict['Age']: ", dict['Age'];
print "dict['School']: ", dict['School'];

This will produce the following result. Note an exception raised, this is because after del dict dictionary does not exist any more:

dict['Age']:
Traceback (most recent call last):
  File "test.py", line 8, in <module>
    print "dict['Age']: ", dict['Age'];
TypeError: 'type' object is unsubscriptable

Note: del() method is discussed in subsequent section.

Properties of Dictionary Keys:

Dictionary values have no restrictions. They can be any arbitrary Python object, either standard objects or user-defined objects. However, same is not true for the keys.
There are two important points to remember about dictionary keys:
(a) More than one entry per key not allowed. Which means no duplicate key is allowed. When duplicate keys encountered during assignment, the last assignment wins. Following is a simple example:

#!/usr/bin/python

dict = {'Name': 'Zara', 'Age': 7, 'Name': 'Manni'};

print "dict['Name']: ", dict['Name'];

When the above code is executed, it produces the following result:

dict['Name']:  Manni

(b) Keys must be immutable. Which means you can use strings, numbers or tuples as dictionary keys but something like ['key'] is not allowed. Following is a simple example:

#!/usr/bin/python

dict = {['Name']: 'Zara', 'Age': 7};

print "dict['Name']: ", dict['Name'];

When the above code is executed, it produces the following result:

Traceback (most recent call last):
  File "test.py", line 3, in <module>
    dict = {['Name']: 'Zara', 'Age': 7};
TypeError: list objects are unhashable

Built-in Dictionary Functions & Methods:

Python includes the following dictionary functions:

SN	Function with Description
1	cmp(dict1, dict2) Compares elements of both dict.
2	len(dict) Gives the total length of the dictionary. This would be equal to the number of items in the dictionary.
3	str(dict) Produces a printable string representation of a dictionary
4	type(variable) Returns the type of the passed variable. If passed variable is dictionary, then it would return a dictionary type.

Python includes following dictionary methods

SN	Methods with Description
1	dict.clear() Removes all elements of dictionary dict
2	dict.copy() Returns a shallow copy of dictionary dict
3	dict.fromkeys() Create a new dictionary with keys from seq and values set to value.
4	dict.get(key, default=None) For key key, returns value or default if key not in dictionary
5	dict.has_key(key) Returns true if key in dictionary dict, false otherwise
6	dict.items() Returns a list of dict's (key, value) tuple pairs
7	dict.keys() Returns list of dictionary dict's keys
8	dict.setdefault(key, default=None) Similar to get(), but will set dict[key]=default if key is not already in dict
9	dict.update(dict2) Adds dictionary dict2's key-values pairs to dict
10	dict.values() Returns list of dictionary dict's values