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Python Modules, pip and Virtual Environments (venv) Explained for Beginners

P10.2 Useful Built-in Python Modules: math, datetime, random, statistics

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Why. These come with Python — no install needed — and they cover many daily data tasks.

Module Use it for Example
math rounding up/down, roots, logs math.ceil(4.2) → 5
datetime dates and times date(2026, 3, 1)
random random samples, shuffles random.choice(list)
statistics mean, median, mode statistics.median(x)

How to read this table: each row is one built-in module, what it is for and a tiny example.

How.

Use a built-in module

  1. Import it.
  2. Call the function you need.
  3. Print and check the result.

Worked example A — Round up boxes with math.

Step 1 — What we do. A warehouse needs boxes for 53 items, 12 per box. A half-full box still counts.

Step 2 — Type and run this code.

import math
print(math.ceil(53 / 12))

Step 3 — Output you should see.

5

Step 4 — What it means. ceil rounds up: 5 boxes. (// from the variables chapter gave 4 full boxes.)

Worked example B — Days between dates.

Step 1 — What we do. A loan in Singapore started on 1 Jan 2026; how many days until 15 Mar 2026?

Step 2 — Type and run this code.

from datetime import date
start = date(2026, 1, 1)
end = date(2026, 3, 15)
print((end - start).days)

Step 3 — Output you should see.

73

Step 4 — What it means. Subtracting dates gives a time difference; .days is the number of days.

Worked example C — Median vs mean.

Step 1 — What we do. Salaries in a small IT team, with one very high value.

Step 2 — Type and run this code.

import statistics
salaries = [52000, 55000, 58000, 61000, 250000]
print(statistics.mean(salaries))
print(statistics.median(salaries))

Step 3 — Output you should see.

95200
58000

Step 4 — What it means. One big salary pulls the mean up. The median (58000) shows the typical salary better.

Worked example D — A repeatable random sample.

Step 1 — What we do. We pick 2 stores to audit, and want the same pick every time.

Step 2 — Type and run this code.

import random
random.seed(42)
stores = ["Pune", "Mumbai", "Nagpur", "Nashik", "Aurangabad"]
print(random.sample(stores, 2))

Step 3 — Output you should see.

['Pune', 'Aurangabad']

Step 4 — What it means. seed(42) makes random results repeatable, so a teammate gets the same sample.

Ravindra Bagale's Tip

Use random.seed() whenever you sample data for a report. Then anyone can reproduce your numbers.