Error Handling in Python using Decorators
Decorators in Python is one of the most useful concepts supported by Python. It takes functions as arguments and also has a nested function. They extend the functionality of the nested function.
Example:
Python3
# defining decorator function def decorator_example(func): print ( "Decorator called" ) # defining inner decorator function def inner_function(): print ( "inner function" ) func() return inner_function # defining outer decorator function @decorator_example def out_function(): print ( "outer function" ) out_function() |
Output:
Decorator called inner function outer function
Error handling using decorators
The following example shows how a general error handling code without use of any decorator looks like:
Python3
def mean(a,b): try : print ((a + b) / 2 ) except TypeError: print ( "wrong data types. enter numeric" ) def square(sq): try : print (sq * sq) except TypeError: print ( "wrong data types. enter numeric" ) def divide(l,b): try : print (b / l) except TypeError: print ( "wrong data types. enter numeric" ) mean( 4 , 5 ) square( 21 ) divide( 8 , 4 ) divide( "two" , "one" ) |
Output :
4.5 441 0.5 wrong data types. enter numeric
Even though there is nothing logically wrong with the above code but it lacks clarity. To make the code more clean and efficient decorators are used for error handling. The following example depicts how the above code can be more presentable and understandable by use of decorators:
Python3
def Error_Handler(func): def Inner_Function( * args, * * kwargs): try : func( * args, * * kwargs) except TypeError: print (f "{func.__name__} wrong data types. enter numeric" ) return Inner_Function @Error_Handler def Mean(a,b): print ((a + b) / 2 ) @Error_Handler def Square(sq): print (sq * sq) @Error_Handler def Divide(l,b): print (b / l) Mean( 4 , 5 ) Square( 14 ) Divide( 8 , 4 ) Square( "three" ) Divide( "two" , "one" ) Mean( "six" , "five" ) |
Output :
4.5
196
0.5
Square wrong data types. enter numeric
Divide wrong data types. enter numeric
Mean wrong data types. enter numeric
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