In data science, handling exceptions is crucial for dealing with potential errors that may arise during data processing or analysis.
Example Code:
# Function to divide two numbers, handling division by zero exception
def safe_divide(a, b):
try:
result = a / b
except ZeroDivisionError:
result = "Error: Cannot divide by zero!"
return result
print(safe_divide(10, 2)) # Output: 5.0
print(safe_divide(10, 0)) # Output: Error: Cannot divide by zero!
Handling exceptions ensures that your code does not crash and allows you to gracefully handle unexpected scenarios during data processing.
