Exception Handling

1. What is Exception Handling

Exception handling is the mechanism used to detect, manage, and respond to runtime errors without crashing the program.

x = 10
y = 0

print(x / y)  # Raises ZeroDivisionError

Without handling, the program terminates abruptly.


2. Basic try-except Block

try:
    result = 10 / 0
except ZeroDivisionError:
    print("Cannot divide by zero")

Safely captures and handles runtime exceptions.


3. Handling Multiple Exceptions

try:
    value = int("abc")
except (ValueError, TypeError):
    print("Conversion failed")

Handles multiple exception types in a single block.


4. Generic Exception Handling

Catches any unexpected runtime error.


5. Using else with try-except

else executes only when no exception occurs.


6. Using finally Block

Ensures cleanup execution regardless of outcome.


7. Raising Exceptions Manually

Used for enforcing business rules.


8. Custom Exception Handling Pattern

Enables domain-specific error design.


9. Chained Exceptions

Preserves original error trace.


10. Enterprise-Grade Exception Handling Example

Common in microservices, validation systems, and API pipelines.


Exception Handling Flow

Block
Purpose

try

Code that may fail

except

Handle error

else

Executes if no error

finally

Always executes


Common Exception Types

Exception
Scenario

ZeroDivisionError

Division by zero

ValueError

Wrong data type or value

TypeError

Wrong argument type

FileNotFoundError

Missing file

KeyError

Missing dictionary key


Best Practices

  • Avoid bare except clauses

  • Catch specific exceptions

  • Log exceptions for diagnostics

  • Use custom exceptions for domain logic

  • Avoid suppressing critical errors


Common Mistakes

  • Overusing generic except Exception

  • Silent exception swallowing

  • Mixing logic and error handling

  • Not re-raising critical exceptions


Enterprise Importance

Proper exception handling ensures:

  • Application stability

  • Fault tolerance

  • Graceful degradation

  • Traceable error diagnostics

  • System resiliency

Crucial in:

  • Distributed systems

  • Financial applications

  • AI model inference engines

  • DevOps pipelines

  • API error management


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