Why does my Python algorithm return None instead of the expected result

# Why Does My Python Algorithm Return None Instead of the Expected Result?

## Summary
- A Python function returns `None` when called because **no return value is explicitly specified**.
- Functions without a `return` statement or with an empty `return` default to `None`.
- The output appears as `None` when printing the function's result due to this missing return declaration.

## Root Cause
- Python functions are **procedures by default** without explicit return statements.
- **Missing `return`**: When a function computes a value but fails to explicitly return it, Python implicitly returns `None`.
- **Late `return` placement**: The `return` statement must execute *during* the function call—placing it after loops or conditionals without being reached also causes `None`.

## Why This Happens in Real Systems
- **Implicit returns**: Developers accustomed to languages with implicit returns (e.g., Ruby) might expect similar behavior in Python.
- **Side-effect focus**: Functions designed for I/O operations (e.g., printing results) are called without returning values. Later repurposing for computation retains this behavior.
- **Complex control flow**: Branches (`if`/`else`) and loops that skip the `return` statement path lead to unintentional `None`.
- **Debugging leftovers**: Temporary `print()` statements replacing `return` during debugging are accidentally left in place.

## Real-World Impact
- **Silent failures**: `None` propagates through downstream code, causing `TypeError` exceptions later (e.g., `None + 5`).
- **Logic errors**: Unhandled `None` values corrupt data pipelines or cause application crashes.
- **Debugging bottlenecks**: Tracing why "correctly computed" values disappear becomes time-consuming.

## Example or Code
**Problematic Implementation**
```python
def calculate_total(a, b):
result = a + b
# Missing return statement

print(calculate_total(2, 3))  # Output: None

Fixed Implementation

def calculate_total(a, b):
result = a + b
return result  # Explicitly return the value

print(calculate_total(2, 3))  # Output: 5

How Senior Engineers Fix It

Explicit Returns: Ensure every logical path returns a value, especially in conditionals:
```
def safe_divide(a, b):
if b == 0:
return 0.0  # Handle edge case
return a / b
```

Type Hints: Use annotations to enforce return types and catch issues early:

def calculate_total(a: int, b: int) -> int:  # Flags missing returns via linters
return a + b

Unit Tests: Validate return values with tests:
```
assert calculate_total(2, 3) == 5
```
Linter Rules: Enable static analysis tools (e.g., mypy, pylint) to detect missing returns.

Why Juniors Miss It

Misunderstanding flow: Assuming computed values “automatically” become the return value.
Confusing print with return: Believing printing inside the function replaces the need to return.
Overlooking branches: Missing return paths in complex conditional/loop structures.
Testing gaps: Running code without validating outputs for edge cases or intermediate steps.
Copy-paste habits: Reusing functions designed for side effects (e.g., logging) without adapting for returns.