Data Science Python Functions

How it works

A function gives a calculation a name and a defined input/output boundary. Prefer a function whose result depends on its arguments rather than hidden mutable state. Validate domain conditions that would make the result misleading. A per-hour measure needs positive opening hours; dividing by zero or quietly substituting one hour would conceal a data problem.

Run the code

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Local review: Python 3.12.14, NumPy 2.5.4, Pandas 3.0.6 and SciPy 1.18.0. Browser review: Pyodide 0.27.7, Python 3.12.7, NumPy 2.0.2, Pandas 2.2.3 and SciPy 1.14.1 in their respective editors. Save the download as python-functions.py and run python python-functions.py in your separate environment. Apache and MySQL are not needed. Environment setup.

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Example

import math
def litres_per_hour(litres, hours):
    if not math.isfinite(litres) or not math.isfinite(hours) or litres < 0 or hours <= 0:
        raise ValueError("nonnegative litres and positive finite hours required")
    return litres / hours
assert litres_per_hour(21., 3.) == 7.
try:
    litres_per_hour(21., 0.)
except ValueError:
    print("Rate 7.0 L/hour; zero hours rejected")
else:
    raise AssertionError("zero hours accepted")

Expected result

Rate 7.0 L/hour; zero hours rejected

The validation rejects invalid values before division. The example checks both a known rate and a boundary where no meaningful rate exists. A list of daily rates and a total-period rate answer different questions; the denominator policy must be part of the function’s documented purpose rather than guessed by its caller.