Reusable Python Analysis Functions and Testing

1. Introduction

An analysis repeated every week should not depend on someone remembering which notebook cells to run. Functions turn a calculation into an explicit contract, and tests catch regressions when data shape or business rules change.

2. Understand the concept

Keep pure calculations separate from file and network operations. Type hints document intended values, while runtime validation still protects against external data. A test should assert observable behavior rather than merely repeat the implementation.

3. Syntax and interpretation

def conversion_rate(conversions: int, visitors: int) -> float:
    if visitors <= 0:
        raise ValueError('visitors must be positive')
    return conversions / visitors

The function rejects a zero or negative denominator rather than returning a meaningless result.

4. Apply it to an analysis

Structure analysis as testable functions and check normal, boundary, and invalid inputs. Start with a small, known example and state what each row or value represents. After the transformation, check row counts, types, keys, and units against the original data. If an assumption fails, stop and investigate instead of presenting the output as a valid result.

5. Common mistakes

• Testing only one ordinary case and missing empty or invalid data.
• Catching exceptions inside a calculation and returning an invented fallback.

6. Practice task

Add a test that verifies conversion_rate raises ValueError when visitors is negative.

7. One possible solution

try:
    conversion_rate(1, -2)
except ValueError:
    print('test passed')

8. Summary and next step

Structure analysis as testable functions and check normal, boundary, and invalid inputs. Use the next lesson in this module to extend the idea with another technique and a more realistic analysis case.