Leo — Learning Under Constraint

Leo began his coding session in an unusual way:

“For better network speed, I have to stand in an uncomfortable position beside the dinner table.”

Despite the environment, he worked through complex number multiplication and successfully implemented a product_of function.

He then noticed something important: edge cases in his Complex class were not displaying cleanly.

After class, he continued refining his work:

• Replaced hardcoded tests with user input
• Debugged the __str__ method
• Tested all sign combinations of real and imaginary parts

Final __str__ implementation:

def __str__(self):
    if self.im < 0 and self.re != 0:
        return f"{self.re} - {abs(self.im)}i"
    elif self.im < 0 and self.re == 0:
        return str(self.im) + "i"
    elif self.im == 0 and self.re == 0:
        return "0"
    elif self.im == 0 and self.re != 0:
        return str(self.re)
    elif self.im != 0 and self.re == 0:
        return str(self.im) + "i"
    else:
        return f"{self.re} + {self.im}i"
      

Result examples:

1.0 + 1.0i
1.0 - 1.0i
-1.0 + 1.0i
-1.0 - 1.0i
      

What stood out most was not correctness, but mindset: noticing edge cases, refining behavior, and continuing after class.

Learning is not a single execution. It is iterative refinement.

Small constraints often produce strong focus. Mathematics, logic, and code quietly reward persistence.