A Guide To Assert In Python With Examples

May 16, 2023
A Guide To Assert in Python with Examples

Asserts in Python are nothing but sanity checks to help you test the truthiness of your code and catch bugs before production. They help increase reliability and make your syntax error-free. Python asserts help in every development stage and enables developers to document, debug, and test codes in Python like a pro.

Every popular programming language, including Java, C, & Python, has a feature called assertions. In simple, they are statements used to test the code’s correctness and tie a conditional loop around it. The code compilation ensues if the condition remains true and throws an exception when false.

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Let’s explore more about Python asserts to understand better what they are, what they do, and when to use them.

What Can Python Asserts Do?

Some people think Python asserts only help debug codes. However, they can also streamline documenting and testing. Regardless, their primary duty is to sound an alarm at first sight of a bug.

In practice, you can use assertions to evaluate pre-conditions and post-conditions during development. Place them where the function begins to validate the input or after to validate the output.

Adding an assert in Python clarifies the need to check truthiness. Moreover, they enable developers to add a message to elaborate on the condition. These small messages help document codes better and save time lost in trying to remember the issue.

In addition, Python asserts also help write test cases. Using assertions, you can check the truthiness of a condition on the spot and check whether the code passes the test.

Asserts in Python: The Syntax

Python has its own built-in assertions made exclusively for it. These asserts vary with statement or expression yet always remain true, as the program halts and throws an Assertion exception at first false. In Python, assert is a keyword, so don’t be surprised to see the word appear in a different color.

assert <condition>

assert <condition>, <error message>

The above syntax can take in two parameters; condition and a message. The first is compulsory, as it dictates what to check or test, while the second is optional. 

Regardless, it is best to add a message with every assertion as it defines the reason for the stop and helps with documentation.  

How Python Asserts Work?  

The best way to understand how asserts in Python work is with an example. The below code entails a function to calculate the sum of numbers. The condition is list cannot be empty. Let’s run it to see asserts in action. 

Example # 1 

def Calculate_sum(numbers): 

    assert len(numbers) != 0 # Condition: List cannot be empty 

    return sum(numbers)/len(numbers) 

num_1 = [1,2,3,4,5,6,7,8,9] 

print( " Calculated sum of given numbers: ", Calculate_sum(num_1)) 

num_2 = [] 

print( " Calculated sum of given numbers: ", Calculate_sum(num_2))


 Calculated sum of given numbers: 5.0 

Traceback (most recent call last): 

 File “main.py”, line 9, in <module> 

   Print( “ Calculated sum of given numbers: “, Calculate_sum(num_2)) 

 File “main.py”, line 2, in Calculate_sum 

   Assert lens(numbers) != 0 # Condition: List cannot be empty 


In the example above, the assertion error appears at the bottom as the num_2 list on line # 8 is empty. This makes the assertion stop further execution and the compilation then and there.  

Below is another code with the same assertion but tweaked input to counter the error. Let’s run: 

Example # 2 

def Calculate_sum(numbers): 

    assert len(numbers) != 0 # Condition: List cannot be empty 

    return sum(numbers)/len(numbers) 

num_1 = [1,2,3,4,5,6,7,8,9] 

print( " Calculated sum of given numbers: ", Calculate_sum(num_1)) 

num_2 = [8,5,6,7,4,3] 

print( " Calculated sum of given numbers: ", Calculate_sum(num_2)) 


Calculated sum of given numbers: 5.0 

Calculated sum of given numbers: 5.5 

When Not To Use Python Asserts? 

The assertions in Python have many functionalities and provide developers with an efficient way to debug, document, and test their codes before production. However, limiting their usage to the domains described above is best.  

Asserts in Python are best not to use for data validation or data processing, as the features let a coder disable assertions during production. Then, all validation codes and assertion-based processing data get lost forever.  

Despite this, many developers still use Python asserts for these prohibited purposes and end up neck-deep in data trouble.  

So, now you know what are asserts in Python, what they can do, how they work, and what not to use them. Go code efficiently! 

Also Read: How To Implement Caching In Python?

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