World's Best AI Learning Platform with profoundly Demanding Certification Programs
Designed by IITian's, only for AI Learners.
Download our e-book of Introduction To Python
Related Blog
Top Discussion
about installing the softwere Exception Type: JSONDecodeError at /update/ Exception Value: Expecting value: line 1 column 1 (char 0) How to leave/exit/deactivate a Python virtualenvironment What is the difference between covariance and correlation? TypeError: 'int' object is not subscriptable Which are different modes to open a file ? How to handle imbalanced data and achieve good performance? What does it mean to cross-validate a machine learning model? Join Discussion
Top Courses
Machine Learning with Python & Statistics
4 (4,001 Ratings)
218 Learners
Webinars
Python Design Patterns - Exception Handling
Neha Kumawat
5 months ago
Table of Content
- Why use exceptions?
- Example – Division by zero
- Output
- Raising Exceptions
- Syntax
Handling exceptions is also a primary criterion of design patterns. An exception is an error that happens during the execution of a program. When a particular error occurs, it is important to generate an exception. This helps in curbing program crashes.
Why use exceptions?
Exceptions are convenient ways of handling errors and special conditions in a program. When a user thinks that the specified code can produce an error then it is important to use exception handling.
Example – Division by zero
import sys
randomList = ['a', 0, 2]
for entry in randomList:
try:
print("The entry is", entry)
r = 1/int(entry)
break
except:
print("Oops!",sys.exc_info()[0],"occured.")
print("Next entry.")
print()
print("The reciprocal of",entry,"is",r)
Output
The above program generates the following output −
Raising Exceptions
In Python programming specifically, exceptions are raised when corresponding error of code occurs at run time. This can be forcefully raised using the “raise” keyword.
Syntax
raise KeyboardInterrupt
Traceback (most recent call last):
...
KeyboardInterrupt
Like the Blog, then Share it with your friends and colleagues to make this AI community stronger.
To learn more about nuances of Artificial Intelligence, Python Programming, Deep Learning, Data Science and Machine Learning, visit our insideAIML blog page.
Keep Learning. Keep Growing.