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Best Practices for Error Handling and File I/O in Python

Updated: at 04:18 AM

Error handling and file input/output (I/O) are two critical aspects of writing robust Python programs. Properly handling errors and managing file I/O operations following Pythonic best practices can improve code quality, enhance application resilience, and ease maintainability.

This comprehensive guide discusses recommended techniques, conventions, and coding styles for effectively handling exceptions and performing file I/O in Python. Core concepts and Python language constructs relevant to error handling and file I/O are covered first. The guide then provides actionable recommendations and sample code snippets exemplifying ideal practices per the official Python Style Guide PEP 8 and expert advice from the Python community.

Proper error handling and file I/O coding techniques help create optimized Python programs that run smoothly and stably across operating systems. Adopting these best practices is especially beneficial for developers working on large, complex Python projects and applications where resilience, security, scalability, and maintainability are critical. Audiences who will find this guide useful include Python beginners learning proper error handling and file I/O techniques as well as experienced Python programmers seeking to level up their skills.

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Error Handling in Python

Robust error handling is an essential skill for Python developers. Bugs, unanticipated input values, access issues, and runtime exceptions are unavoidable. Python provides structured mechanisms to detect, handle, and recover from errors gracefully using try/except blocks and raising custom exceptions.

Key Error Handling Concepts

Best Practices for Exception Handling

1. Use Specific Exception Classes and Descriptive Messages

Catch specific Python builtin exception classes instead of the generic Exception to handle errors appropriately based on their type. Custom exceptions should inherit from Python’s exception hierarchy at the right level of abstraction. Always pass descriptive messages to exceptions explaining what went wrong.

# Bad practice
   print("An error occurred")

# Good practice
except FileNotFoundError as e:
   print(f"Unable to locate file {file}. Error: {e}")
except ValueError as e:
   print(f"Invalid data format. Error: {e}")

2. Limit Use of Blanket except Statements

Avoid using blanket except statements which blindly suppress all errors. Only use a broad except as a last resort when absolutely needed.

# Bad practice
   #ignores issues

# Good practice
except ValueError:
  #handle specific problem
except Exception as e:
  #log error
  raise #re-raise current exception

3. Print the Stack Trace for Debugging

Accessing the exception stack trace via traceback module helps debugging by revealing where the error occurred. Print the traceback in the exception handler for program crashes.

import traceback

  print('Program error:')

4. Clean Up Resources in finally Block

Use finally blocks to release external resources like files, sockets, database connections etc. This ensures proper cleanup even during unexpected control flow.

db = DatabaseConnection()
  # interact with database
  db.close() # executes after try block ends

5. Use Context Managers for Resource Cleanup

For resource cleanup tasks, prefer Python’s with statement instead of try/finally since it is more readable and reliable. The context manager handles setup/teardown logic.

with DatabaseConnection() as db:
  # perform db operations
# db automatically closed

6. Log Exceptions to Help Troubleshoot Bugs

Log exceptions to record error details like traceback, variables, environment, etc to aid debugging unhandled errors in production. Use built-in logging module or third party logger.

import logging

  raise RuntimeError('Critical error!')
except RuntimeError as e:
  logging.exception(e) #logs full stack trace

7. Re-raise Exceptions after Handling

After handling an exception, re-raise it by calling raise to propagate the error up the call stack if needed. This avoids inadvertently silencing important errors.

except ValueError as e:
  print('ValueError occurred')
  raise # propagate exception

8. Avoid Catching Overly Broad Exceptions

Try to avoid handling Exception or other very broad parent exception classes as it risks masking subtle errors and control flow issues. Overuse of broad exceptions can become problematic code smells.

9. Document Expected Exceptions in Docstrings

Include Raises sections in function/method docstrings to document the exceptions they could raise to support API usability.

def get_user(user_id):
  """Fetch user record by ID.

    ValueError: if user_id is invalid.
    ConnectionError: if could not connect to database.

10. Define Custom Application Exceptions

Create custom exception classes inheriting from Python built-in exception hierarchy to model domain-specific application errors. This improves error handling.

class ValidationError(Exception):
  """Invalid data given by client"""

def validate(data):
  if not valid(data):
    raise ValidationError("Data validation failed")

File Input/Output in Python

Python provides many options for reading and writing files, including text and binary data. Correct usage of file I/O functions and proper structure of I/O code improves code quality and performance.

Key File I/O Concepts

Best Practices for File I/O

1. Use Context Managers for Maintainable Code

Use context managers like the open() function to handle opening and closing files reliably. This avoids leaked resources from inconsistent closes.

# Bad practice
f = open("data.txt")
# ... forget to close file

# Good practice
with open("data.txt") as f:
  # file closed automatically

2. Specify Text vs Binary Modes

Always specify file open mode as text t or binary b based on file contents. Text mode handles encodings when reading/writing from string variables.

# Read utf-8 encoded text
with open('data.txt', 'rt', encoding='utf-8') as file:
  text =

# Write bytes
with open('data.bin', 'wb') as file:

3. Handle File I/O Errors

Robustly handle I/O errors instead of ignoring them as it could silently corrupt data. Catch specific exceptions like OSError. Optionally, only log warnings on expected errors.

  with open('file.txt') as f:
except OSError as e:
  print(f"Error: {e}")

4. Close Files Explicitly as a Backup

Although not necessary when using context managers, also call .close() explicitly as a backup measure for gracefully closing files in all situations.

f = open("data.txt")
  # read file
  f.close() # explicitly close as backup

5. Use with Statement for Reusable File Objects

When passing around a file object to multiple function calls, use a with statement at the start to avoid leaks from forgetting closes.

def process_data(file_obj):
  # read, write file

with open("data.txt") as f:
  # file closed automatically

6. Avoid Unnecessary Closes on Stream Errors

Check for stream errors before closing files to avoid suppressing errors from double closes. Only close once.

f = open("data.txt")
except IOError:
  logging.error("Read error")
  if not f.closed:
    f.close() # only close once

7. Flush File Buffers When Needed

Python automatically flushes standard streams. But call .flush() explicitly on custom file objects if pending writes must be visible externally before closure.

with open("data.txt", "w") as f:
  f.flush() # forces write to disk

8. Avoid file.close() Inside with Block

Do not call .close() inside a with block as the context manager will automatically close the file for you. Doing both will raise an error.

9. Use Absolute Paths for Reliability

Use absolute file paths rather than relative paths to avoid issues with changed working directories. Use os.path functions to normalize paths.

import os
f = open(os.path.abspath("data.bin")) # not reliant on working dir

10. Refactor Repeated Open/Close to Helper

If opening and closing the same file in multiple locations, refactor the open/close logic to a helper function or class to avoid code repetition.


Following Pythonic error handling and file I/O best practices improves code quality and application robustness. Key takeaways include:

Adopting these guidelines helps Python developers write high-quality applications resistant to run-time exceptions, unexpected errors and file operation issues. Robust error handling and correct file I/O techniques are hallmarks of expert Python code.