Imports and Standard-Library Modules

Unit ID: M05-U06 Estimated active time: 22-30 minutes

A module groups reusable Python code

Python's standard library provides modules installed with Python.

import math

planned_minutes = 225
planned_hours = math.ceil(planned_minutes / 60)
print(planned_hours)

math.ceil() rounds upward, producing 4.

The module name keeps provenance visible. A reader can see that ceil comes from math.

Import one name deliberately

from statistics import mean

average = mean([8, 12, 4])

This is concise, but the origin is less visible at the call. Use the style that matches the codebase and avoids name conflicts.

Aliases

Libraries often use established aliases:

import statistics as stats
print(stats.mean([8, 12, 4]))

Do not invent cryptic aliases merely to save typing.

Avoid wildcard imports

from math import *

This brings many names into the current namespace and makes origins unclear. Prefer explicit imports.

Your own module

If course_tools.py contains:

def clean_status(raw_status: str) -> str:
    return raw_status.strip().lower()

Another file in the same project may use:

from course_tools import clean_status

Notebook import behaviour depends on the current working directory and Python path. The course package will provide a fixed folder structure.

Import should not run a report unexpectedly

Keep demonstration code under:

if __name__ == "__main__":
    print("Run a direct demonstration here")

When imported, __name__ is the module name and the demonstration is skipped. When run directly, __name__ is "__main__".

Practice

Use statistics.mean() to calculate the average of [8, 12, 4]. Inspect help(statistics.mean). Then import the same function directly and compare readability.

Takeaway

Modules group reusable code. Use explicit imports, clear provenance, and a main guard so import does not trigger unrelated output. Next, we will distinguish modules from installed packages and isolated environments.