try-except Exception Handling

Handling Errors Gracefully

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Basic Syntax

try:
    # Code that might error
    risky_operation()
except ExceptionType:
    # Handle the error
    handle_error()

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Basic Usage

Example 1: Catching Specific Exceptions

try:
    age = int(input("Please enter age: "))
    print(f"Your age is {age}")
except ValueError:
    print(" Please enter a valid number")

Example 2: Catching Multiple Exceptions

try:
    income = int(input("Income: "))
    tax = income / 0.25
except ValueError:
    print(" Income must be a number")
except ZeroDivisionError:
    print(" Divisor cannot be zero")

Example 3: Catching All Exceptions

try:
    # Risky operation
    result = risky_function()
except Exception as e:
    print(f" Error occurred: {e}")

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Complete Syntax

try-except-else-finally

try:
    # Code to attempt
    result = 10 / 2
except ZeroDivisionError:
    # Execute when error occurs
    print(" Division by zero")
else:
    # Execute when no error
    print(f" Result: {result}")
finally:
    # Always executes (cleanup resources)
    print("Calculation complete")

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Real-World Cases

Case 1: Safe Data Loading

import pandas as pd
from pathlib import Path

def load_survey_data(filename):
    """Safely load survey data"""
    try:
        if not Path(filename).exists():
            raise FileNotFoundError(f"File doesn't exist: {filename}")

        df = pd.read_csv(filename)

        # Validate required columns
        required_cols = ['id', 'age', 'income']
        missing_cols = set(required_cols) - set(df.columns)
        if missing_cols:
            raise ValueError(f"Missing columns: {missing_cols}")

        return df

    except FileNotFoundError as e:
        print(f" File error: {e}")
        return None
    except pd.errors.EmptyDataError:
        print(" File is empty")
        return None
    except Exception as e:
        print(f" Unknown error: {e}")
        return None

# Usage
df = load_survey_data('survey.csv')
if df is not None:
    print(f" Successfully loaded {len(df)} rows")

Case 2: Batch Data Processing

# Survey data (may have errors)
responses = [
    {'id': 1, 'age': '25', 'income': '50000'},
    {'id': 2, 'age': 'N/A', 'income': '75000'},
    {'id': 3, 'age': '35', 'income': 'unknown'},
    {'id': 4, 'age': '40', 'income': '85000'}
]

valid_data = []
errors = []

for resp in responses:
    try:
        # Attempt conversion
        clean_resp = {
            'id': resp['id'],
            'age': int(resp['age']),
            'income': float(resp['income'])
        }
        valid_data.append(clean_resp)
    except (ValueError, KeyError) as e:
        errors.append({
            'id': resp.get('id', 'unknown'),
            'error': str(e)
        })

print(f" Valid data: {len(valid_data)} records")
print(f" Errors: {len(errors)} records")

for error in errors:
    print(f"  ID {error['id']}: {error['error']}")

Case 3: API Request Retry

import requests
import time

def fetch_data(url, max_retries=3):
    """API request with retry"""
    for attempt in range(max_retries):
        try:
            response = requests.get(url, timeout=5)
            response.raise_for_status()  # Check HTTP errors
            return response.json()

        except requests.exceptions.Timeout:
            print(f"⏱️  Timeout, retry {attempt + 1}/{max_retries}")
            time.sleep(2 ** attempt)  # Exponential backoff

        except requests.exceptions.HTTPError as e:
            print(f" HTTP error: {e}")
            return None

        except requests.exceptions.RequestException as e:
            print(f" Request error: {e}")
            return None

    print(f" Maximum retries reached")
    return None

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Best Practices

1. Catch Specific Exceptions

#  Too broad
try:
    result = risky_operation()
except:
    print("Error occurred")

#  Specific and clear
try:
    result = risky_operation()
except ValueError:
    print("Value error")
except FileNotFoundError:
    print("File doesn't exist")

2. Use else Clause

try:
    df = pd.read_csv('data.csv')
except FileNotFoundError:
    print(" File doesn't exist")
else:
    # Only executes on success
    print(f" Loaded {len(df)} rows")
    df.to_stata('output.dta')

3. finally for Cleanup

file = None
try:
    file = open('data.txt', 'r')
    content = file.read()
except FileNotFoundError:
    print(" File doesn't exist")
finally:
    # Ensure file is closed
    if file:
        file.close()

# Better way: Use with
with open('data.txt', 'r') as file:
    content = file.read()  # Auto-closes

4. Log Errors

import logging

logging.basicConfig(filename='app.log', level=logging.ERROR)

try:
    result = risky_operation()
except Exception as e:
    logging.error(f"Error: {e}", exc_info=True)
    print(" Error occurred, see log")

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Custom Exceptions

class InvalidAgeError(Exception):
    """Invalid age exception"""
    pass

def validate_respondent(age, income):
    """Validate respondent data"""
    if age < 18 or age > 100:
        raise InvalidAgeError(f"Invalid age: {age}")

    if income < 0:
        raise ValueError(f"Income cannot be negative: {income}")

# Usage
try:
    validate_respondent(age=150, income=50000)
except InvalidAgeError as e:
    print(f" {e}")
except ValueError as e:
    print(f" {e}")

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When to Use try-except?

Suitable Scenarios

• File operations (file may not exist)
• Network requests (may timeout)
• User input (may be invalid)
• Data conversions (may fail)

Unsuitable Scenarios

#  Don't use exceptions to replace normal logic
try:
    value = dict['key']
except KeyError:
    value = None

#  Use conditional logic
value = dict.get('key', None)

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Practice Questions

# Exercise 1: Safe mean calculation
def safe_mean(data):
    """Calculate mean, handle empty list and non-numbers"""
    try:
        # Your code
        pass
    except:
        return None

# Exercise 2: Batch file processing
# Read all CSV files in a folder
# For each file:
# - try to read
# - except record error
# - If successful, add to list

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Next Steps

Next section learns Debugging Tips.

Continue!