🐍 Visualizing Function Execution in Python with a Simple Progress Bar

Have you ever wanted to track the progress of a series of function calls in Python — maybe during some tasks that take time to complete?

In this short post, I’ll show you a minimal example of how to execute a list of functions sequentially while displaying a simple progress bar to track completion in the terminal.

🔧 Code Walkthrough

import time def test_1(): time.sleep(5) print('this is function 1') def test_2(): time.sleep(2) print('this is function 2') def test_3(): time.sleep(3) print('this is function 3') def test_4(): time.sleep(1) print('this is function 4') func_list = [ {'index': 1, 'func': test_1}, {'index': 2, 'func': test_2}, {'index': 3, 'func': test_3}, {'index': 4, 'func': test_4}, ] for func_dict in func_list: index = func_dict['index'] function = func_dict['func'] # call function  function() # progress bar  progress_total = len(func_list) progress_bar = int((index / progress_total) * progress_total) # percent  percent = int((index / progress_total) * 100) print('🟩' * progress_bar, f'{percent} %') 

🧠 What’s Happening Here?

1. - time.sleep() is used to simulate time-consuming operations.
2. - We store functions in a list of dictionaries with their respective index.
3. - After each function call, we calculate:
4. - Progress bar length
5. - Percent complete
6. - A simple emoji-based progress bar (🟩) is printed after each function.

✅ Sample Output

this is function 1 🟩 25 % this is function 2 🟩🟩 50 % this is function 3 🟩🟩🟩 75 % this is function 4 🟩🟩🟩🟩 100 % 

💡 Ideas for Improvement

• Add concurrent execution using asyncio or concurrent.futures.
• Turn the progress bar into a dynamic inline update (e.g., using \r or libraries like tqdm).
• Add color using the colorama library or similar.

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Let me know in the comments: How would you improve this basic progress tracker?