Avoid headaches when debugging in one line of code

2 divided by 1 is equal to 2.0.
Traceback (most recent call last):
  File "loguru_example.py", line 17, in <module>
    divide_numbers(num_list)
  File "loguru_example.py", line 11, in divide_numbers
    res = division(num1, num2)
  File "loguru_example.py", line 5, in division
    return num1/num2
ZeroDivisionError: division by zero

You might also want to visualize which lines of code are being executed and how many times they are executed in real-time:

pip install loguru

from itertools import combinations

def division(num1: int, num2: int):
    return num1/num2

def divide_numbers(num_list: list):
  """Division of 2 numbers in the number list """
  
    for comb in combinations(num_list, 2):
        num1, num2 = comb 
        res = division(num1, num2)
        print(f"{num1} divided by {num2} is equal to {res}.")


if __name__ =='__main__':
    num_list = [2, 1, 0]
    divide_numbers(num_list)

Note that combinations([2,1,0], 2) returns [(2, 1), (2, 0), (1, 0)] . After running the code above, we get this error:

2 divided by 1 is equal to 2.0.
Traceback (most recent call last):
  File "loguru_example.py", line 17, in <module>
    divide_numbers(num_list)
  File "loguru_example.py", line 11, in divide_numbers
    res = division(num1, num2)
  File "loguru_example.py", line 5, in division
    return num1/num2
ZeroDivisionError: division by zero

From the output, we know that the line return num1/num2 is where the error occurs, but we don’t know which values of num1 and num2 cause the error. Luckily, this can be easily tracked by adding Loguru’s logger.catch decorator:

from loguru import logger 
from itertools import combinations

def division(num1: int, num2: int):
    return num1/num2

@logger.catch # Add this to track errors
def divide_numbers(num_list: list):
    for comb in combinations(num_list, 2):
        num1, num2 = comb 
        res = division(num1, num2)
        print(f"{num1} divided by {num2} is equal to {res}.")


if __name__ =='__main__':
    num_list = [2, 1, 0]
    divide_numbers(num_list)

Output:

By adding logger.catch, the exceptions are much easier to understand! It turns out that the error occurs when dividing 2 by 0.

snoop — Print the Lines of Code being Executed in a Function

What if there is no error in the code, but we want to figure out what is going on in the code? That is when snoop comes in handy.

snoop is a Python package that prints the lines of code being executed along with the values of each variable by adding only one decorator.

To install snoop, type:

pip install snoop

Let’s imagine we have a function calledfactorial that finds the factorial of an integer.

import snoop 

def factorial(x: int):
    if x == 1:
        return 1
    else:
        return (x * factorial(x-1))
        
if __name__ == "__main__":
    num = 5
    print(f"The factorial of {num} is {factorial(num)}")

Output:

The factorial of 5 is 120

To understand why the output of factorial(5) is 20 , we can add snoop decorator to the function factorial .

import snoop 

@snoop
def factorial(x):
    if x == 1:
        return 1
    else:
        return (x * factorial(x-1))


if __name__ == "__main__":
    num = 5
    print(f"The factorial of {num} is {factorial(num)}")

Output:

In the output above, we can view the values of the variables and which lines of code are executed. Now we can understand how recursion works much better!

heartrate — Visualize the Execution of a Python Program in Real-Time

If you want to visualize which lines are executed and how many times they are executed, try heartrate.

heartrate is also created by the creator of snoop. To install heartrate, type:

pip install heartrate

Now let’s add heartrate.trace(browser=True) to our previous code. This will open a browser window displaying the visualization of the file where trace() was called.

import heartrate 
heartrate.trace(browser=True)

def factorial(x):
    if x == 1:
        return 1
    else:
        return (x * factorial(x-1))


if __name__ == "__main__":
    num = 5
    print(f"The factorial of {num} is {factorial(num)}")

A new browser should pop up when you run the code above. If not, go to http://localhost:9999. You should see the output like below:

Cool! The bars show the lines that have been hit. The longer bars mean more hits, lighter colors mean more recent.

From the output above, we can see that the program executes:

• if x==1 5 times
• return 1 once
• return (x * factorial(x-1)) 4 times

The output makes sense since the initial value of x is 5 and the function is called repetitively until x equals to 1 .

Now let’s see what it is like to visualize the execution of a Python program in real-time using heartrate. Let’s add sleep(0.5) so that the program runs a little bit slower and increase num to 20 .

import heartrate 
from time import sleep

heartrate.trace(browser=True)


def factorial(x):
    if x == 1:
        return 1
    else:
        sleep(0.5)
        return (x * factorial(x-1))


if __name__ == "__main__":
    num = 20
    print(f"The factorial of {num} is {factorial(num)}")

Awesome! We can see which lines of code are being executed and how many times each of them has been executed in real-time.

Conclusion

Congratulations! You have just learned 3 tools to track and visualize the execution of your Python code. I hope debugging will be less painful for you when using these 3 tools. Since these tools only require one line of code, why not give them a try to see how helpful they are?

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