Error Handling and Debugging in Python

There is no perfect coder; everyone makes mistakes every now and then from a missing semicolon to errors in logic to even deeper issues in the code. These issues are very common in python coding and thus skills in error handling and debugging are very important for a programmer. Failing to handle errors properly and not knowing how to debug code efficiently can result in inefficient programs and wastage of time in the end.

This article aims to approach these problems from a novice perspective making it more understandable: Understanding how to handle error messages while coding and how to fix those issues when they crop up.

Common Errors in Python

For error handling to be effective, the first step is to know the various types of errors that occur when a python program is run. Someone who is new to coding might wonder what these errors are, well these errors can be categorized into two broad classifications: Syntax errors and Exceptions.

1. Syntax errors: As the name suggests, these errors occur due to the failure in syntax comprehension regarding the code's intended structure by the Python parser, such as missing colons at the end of an 'If' statement or omitting closure for parentheses. Probably, these types of errors are the easiest to correct because, whenever they occur, Python informs the user by display of the line number where the error occurred and gives a brief description of the error.

2. Exceptions: The term refers to error which takes place when the unit program is running something goes wrong and the operation cannot be completed successfully. These errors are the ones happening while executing the program such as division by zero, targeting a file that does not exist, trying to call in a variable that has not been defined. Python's exception system is designed in a manner that enables a programmer to detect and respond to errors before a program crashes.

Error Handling in Python

The features of Python are such that it enables catching and managing exceptions through the `try`, `except`, and `else` blocks. `Try` block is the piece of code that might cause an exception and `except` is where things break, and these exceptions are caught. Perhaps an optional 'Else' block is where the execution flow jumps if all preceding conditions fail or in this case error free code is executed.

Let us start with an exception handling example in Python, which can be implemented in the following way:

Basic Exception Handling Example

  try:
 
     This code could potentially lead to an Exception 
 
    risky_code ( )
 
except ExceptionType:
 
    print("An error occurred.")
 
else:   
 
    print("Did something go wrong?")

Points to note regarding the use of these Keywords:

- `try`: This is where a risky code segment is implemented and the chance of an exception being generated is likely.
- `except`: After executing the try block, if an exception is raised, this block defines actions against the occurred exception. All exceptions can be caught, amongst `ValueError` or `FileNotFoundError`.
- `else`: This block further executes only in a scenario where the try block was successful.

Using a catchAll clause such as an `except` clause is not as ideal as handling exceptions because every type of error can be dealt with appropriately.

Explaining the Catching of Specific Exceptions

Depending on the number of 'except' clauses, you may be able to catch several types of exceptions. A good example would be permissible values when a user is reading a file: one may want to deal with a permissive error differently from what file not found suggests. This is how to capture the meaning of certain exceptions;

Specific Exception Handling Example

  try:
 
    a = 1
 
    5/0
 
except NameError:
 
    print('Exception occurred')

Hence, a user can retrieve and respond to the circumstance in a more effective way instead of issuing a blanket statement.

Explaining the Use of the finally Block

The other useful feature of Python error management is the block 'finally'. This block will run when an exception is raised and also when it is not. However, this is against its normal usage where it is reserved for clearing codes: Releasing resources or closing a file.

Finally Block Example

  try:
 
    a = 0
 
    if a == 0:
 
        raise ZeroDivisionError("Division by zero")
 
    else:
 
        2/a
 
except ZeroDivisionError:
 
    print ('error is raised')
 
finally:
 
    print ('Don't divide a number by zero')

As you may have guessed, the contents inside the "finally" block will run and yes the file will be closed irrespective of an exception being raised or not. This is one of the basic tasks to ensure resources are available.

The Python Debugging Techniques

Debugging is the only way to check for any faults or mistakes in your code. As coders, it goes without saying that we are bound to make errors while writing code. Thankfully Python has a number of tools and techniques which help make matters easier by rectifying errors in the code.

1. Using Print Statements

Inserting print statements when debugging makes this technique quite effective when solving errors. It is easier to identify bugs or faults in the code by inserting print statements in the program at specific points and printing out variable values and intermediate results. However, this method tends to be quite effective especially during large programming tasks. Moreover, when debugging has been finalized, coders tend to forget to delete print statements making their programs much larger than required.

2. Implementing a Debugger

Another debugging method which comes built-in with Python is called `pdb`. With the help of the `pdb` module, you can set breakpoints in your code and then execute your program line by line. This helps in determining the variables used in that specific line and other components of the code. Add the following line at the location where you want the debugger to activate:

PDB Debugger Example

  import pdb; pdb.set_trace()

This command makes the program halt at that specific location therefore allowing you to access the command prompt and enter pertinent details related to the program. Once you end the debugging session, the program continues to execute from the point where it was interrupted.

3. IDE Debuggers

For those who use Integrated Development Environment (IDE) tools such as PyCharm or Visual Studio Code, or any of the most common Python IDEs, a debugger is included as part of the program. These debuggers provide a graphical tool which enables the user to step through the code, change the value of a variable or watch the program execution process. These tools are easier to use than the command-line debugger and are recommended for use in large scale projects.

4. Logging

Debugging using logging is quite effective as well. The Python `logging` module provides a means of determining how the program is executing and how errors occur in a more formalized way than using print statements. While print statements do aid in forming the cause of the error in code, they are limited in that they cannot be retained after the program is run unlike logging which is saved on a separate file thus assisting more in debugging processes.

Logging Example

  import logging
 
  
 
 Setting up the logging configuration
 
logging.basicConfig(level=logging.DEBUG)
 

 
 Logging a debug message
 
logging.debug("This is a debug message.")
 

 
 Logging an info message
 
logging.info("This is an info message.")