Python Error Types: Common Errors and How to Handle Them

Errors are not something that should make you panic; there’s no coding without errors. They signal that something has gone wrong in your code. Every programming language has its own types of errors.

Python generates specific errors that appear when developers make mistakes in their code. Python provides complete error messages, which explain the error type along with the specific problem and its location in your code.

Want to log Python errors properly? Start with our Python Logging Basics guide.

Understanding these errors is important. The knowledge of these errors enables you to stop bugs before they occur and debug your code more efficiently while creating cleaner code and improving program reliability.

Common Python Error Types and Their Causes

Errors are common in programming, but they don’t just show up. Something must trigger them to occur. They might occur due to missing values, not adhering to specific syntax, or incorrectly writing a unique name.

Syntax Errors

Python syntax errors emerge when your code violates the language rules that define its expected structure. The Python interpreter stops execution right away to raise a SyntaxError because it fails to understand the code when this error occurs.

Common causes of syntax errors include:

1. Missing colons (:) after control statements like if, for, or function definitions.

if True
    print("This will cause a syntax error")

Output:

File "syntax-error.py", line 1
    if True
          ^
SyntaxError: expected ':'

To fix this, add a colon after if True

2. Incorrect Indentation

def greet():
print("Hello, World!")  # Should be indented inside the function

Output:

 File "syntax-error.py", line 2
    print("Hello, World!")
    ^
IndentationError: expected an indented block after function definition on line 2

This error message helps you identify the exact spot where the error took place.

Name Errors

A NameError occurs when you try to use a variable or function name that hasn’t been defined yet. This can also happen when you forget to import a module or function, or you’re referencing a name that is out of scope.

Examples:

1. Using an undefined variable

print(x)

x has never been assigned a value before being printed, so it’ll throw a NameError.

2. Using a variable before assignment in a function

def my_func():
    print(num)
    num = 10
my_func()

This code will throw an error because you’re trying to reference “num” before assignment. 

Type Errors

Python works as a dynamically typed language because it does not require explicit data type declarations, yet it performs type-based operations during runtime. The TypeError appears when you attempt to use an operation or function on an object that Python does not support.

Examples:

1. Adding a String to an Integer

age = 25
message = "I am " + age + " years old."

Output:

TypeError: can only concatenate str (not "int") to str

The + operator does not allow direct concatenation between strings and integers. Python requires both operands to be strings.

You can do this instead:

message = "I am " + str(age) + " years old."
print(message)

Output: I am 25 years old.

2. Calling a function with the wrong argument type

def square(number):
    return number * number
print(square("4"))

You can’t multiply two strings.

Value Errors

A ValueError occurs when a function receives a value that is different from the argument type. That is, the data type is correct, but the data itself is not.

Example:

1. Converting a non-numeric string to an integer or a float

num = int("hello")

You can’t convert the string “hello” to an integer even with the int() function.

2. Passing a negative number to math.sqrt()

import math
result = math.sqrt(-16)

A negative value passed to the function results in a ValueError because the square root of negative numbers does not exist.

Index Errors

The attempt to access an invalid index in a sequence (list, tuple, string) results in an IndexError.  The sequence length is exceeded when you try to access indices that are greater than or equal to the sequence length, or less than zero (apart from negative indexing).

Examples:

1. Accessing index out of range

fruits = ["apple", "banana", "cherry"]
print(fruits[3])  # Index 3 does not exist since valid indices are 0, 1, 2

Output:

IndexError: list index out of range

Index 3 does not exist. The valid indices are 0, 1, and 2.

2. Negative index beyond sequence start

numbers = [10, 20, 30]
print(numbers[-4])  # Valid negative indices are -1, -2, -3 only

Output:

IndexError: list index out of range

Key Errors

A KeyError occurs when you try to access a value from a dictionary using a key that does not exist.

Examples:

1. Accessing a non-existent key

student_grades = {
    "Alice": 95,
    "Bob": 88,
    "Charlie": 92
}
print(student_grades["David"])

student_grades does not contain the key ‘David’. So it’ll return KeyError.

2. Misspelled key

config = {
    "host": "localhost",
    "port": 8080
}

print(config["pot"])

We’re trying to access “pot” instead of “port” which returned a KeyError.

Attribute Errors

The AttributeError appears when you attempt to access or invoke an attribute or method that does not exist on an object. The attributes of objects and classes consist of variables and methods. An AttributeError comes up when you try to access these attributes that either have a spelling mistake or do not exist for the object type.

Examples:

1. Calling a non-existent method on a built-in type

text = "hello world"
print(text.push())  # There is no 'push' method for strings

Output:

AttributeError: 'str' object has no attribute 'push'

Python doesn’t have a method call push for strings. So, the above code will return AttributeError.

2. Misspelling an attribute name

class Car:
    def __init__(self):
        self.color = "red"
my_car = Car()
print(my_car.colour)

The attribute name is color, but color was accessed, so it returned an error.

Import Errors

An ImportError occurs when Python has trouble importing a module, package, or object (name) from a module.

The error contains two typical subcases:

• Module not found: The Python interpreter fails to locate the specified module. Modern Python generates ModuleNotFoundError, which extends from ImportError.
• Import failed: Python detects the module but fails to complete the import operation due to internal module issues.

Examples:

1. Importing a non-existent module

import non_existent_module

Output:

ModuleNotFoundError: No module named 'non_existent_module'

Python will raise an error if you attempt to import the non_existent_module module since it does not exist.

2. Importing a non-existent name from a module

from math import squareroot

The math module has no function named squareroot. The correct name is sqrt.

Arithmetic Errors

In Python, all errors that arise during numerical computations are represented by an ArithmeticError. Though there are subclasses, such as ZeroDivisonEror and OverflowError. ArithmeticError itself is not raised directly in code.

Examples:

1. ZeroDivisionError

The most common arithmetic error occurs when you try to divide by zero.

result = 10 / 0

Dividing any number by zero is mathematically undefined and results in a runtime error.

2. OverflowError

OverflowError occurs when a numerical operation results in a number too large to be represented within Python’s numeric limits (mostly for floating-point numbers).

Example with the math module’s exponential function:

import math
result = math.exp(1000)

The value returned by math.exp(10000) is too large for Python to represent as a floating-point number.

Runtime Errors

Python raises a RuntimeError as a general-purpose error when an error occurs but does not match any built-in error category. The error indicates that a program execution failure occurred for which there is no specific error type, such as ValueError or SyntaxError.

def some_process(flag):
    if not flag:
        # Signal error due to unexpected condition
        raise RuntimeError("Process cannot proceed: invalid flag value.")
try:
    some_process(False)
except RuntimeError as e:
    print("Caught RuntimeError:", e)

Output:

Caught RuntimeError: Process cannot proceed: invalid flag value.

Handling Errors in Python

Python provides built-in tools to manage errors effectively. This section will teach you how to handle common errors with simple methods.

1. Make use of try-except Blocks

The most popular method for dealing with errors in Python is with try-except. It allows you to “try” a block of code that might result in an error and then “catch” that error to keep your program from crashing.

Basic structure:

try:
    # error code
except SomeError:
    # what to do if that error happens

If an error happens inside the try block, Python stops executing it and jumps to the except block.

Example: Handling ZeroDivisionError

try:
    result = 10 / 0
except ZeroDivisionError:
    print("You can't divide by zero!")

Output:

You can't divide by zero!

Without try-except, this would crash your program.

2. Raising Exceptions

Your code requires intentional error raising for specific situations when you need to halt execution or notify users and developers about unexpected system behavior.

Syntax:

raise ErrorType("Custom error message")

The raise statement requires an exception type (built-in or custom) to be used together. The raise statement accepts an optional error message, which enhances clarity

Example: Raising a ValueError

age = -5

if age < 0:
    raise ValueError("Age cannot be negative")

Output:

ValueError: Age cannot be negative

This is helpful when you’re validating data and want to enforce rules.

3. Clear Error Messages

The way you describe errors during Python error handling makes a significant difference. A poorly defined error message will prolong debugging time, but an exact error description helps developers avoid spending many hours on troubleshooting. For example:

Vague message:

raise ValueError("Invalid input")

Clear message:

raise ValueError("Expected a number between 1 and 10, got -3")

Whether you’re using raise or except, always aim to provide helpful context.

You can also format Python logs for better readability and consistency across environments.

Example: Custom Error Messages in try-except

try:
    age = int(input("Enter your age: "))
    if age < 0:
        raise ValueError("Age cannot be negative")
except ValueError as e:
    print(f"Input error: {e}")

Output:

Input error: Age cannot be negative

You can also include variable info in the message

username = ""
if not username:
    raise Exception(f"Username is required, got '{username}'")

Output:

Exception: Username is required, got ''

Looking to optimize how you handle and store logs? Follow these Python logging best practices to make your debugging process seamless.

How Middleware Helps You Detect and Fix Python Errors

When talking about Python error types and handling, you need an understanding of how modern observability platforms like Middleware can help you detect and diagnose errors before they affect users or business operations.

Middleware can monitor your Python applications by integrating with language-specific agents or SDKs. Once set up, it tracks all the error types explained in this article, such as SyntaxErrors, ValueErrors, and RuntimeErrors.

For complete visibility, combine error tracking with Python performance monitoring using Middleware.

Key Features

Key features that support Python error handling include:

Error Aggregation and Tracing

The middleware dashboard collects error data through type-based aggregation, frequency and endpoint-based aggregation, and custom dimensions to help you identify which Python exception types occur most frequently or have the most impact in your application.

Real-Time Root Cause Analysis

The system displays error origins together with their occurrence times to help you understand when user impact starts. The combination of traces and logs enables you to pinpoint the exact line of code, API call, or deployment change that initiated the problem.

Automatic Alerts

The system sends immediate alerts about error spikes, new issues, and anomalies through email or Slack channels, or other channels based on your defined thresholds. The system prevents you from missing essential bugs or outages.

Conclusion

We have talked about Python errors and learned practical methods to handle them. The ability to understand and control these errors is vital for developing Python code that operates dependably in actual deployment environments.

Handling errors stops the application from crashing. It also enhances user experience and reduces debugging time. Every Python developer needs to learn error handling as a fundamental skill.

Want to track Python errors in real time? Try Middleware to monitor, analyze, and resolve Python exceptions across your projects effortlessly.

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