Standard Library Tour

Python is often described as "batteries included". Every installation comes with the standard library, a large collection of modules for everyday jobs: counting things, random numbers, statistics, file operations, logging, compression and much more. You do not install anything; you just import them.

You have already met several of these modules in earlier lessons. This tour shows some new ones that save you real work, plus a map so you know where to look before writing code yourself or reaching for pip.

collections: smarter containers

The collections module adds container types that solve common problems in one line.

Counter counts how often each item appears. defaultdict is a dictionary that creates a default value for a missing key, so you never need to check whether a key exists first. deque (pronounced "deck") is a list-like queue that adds and removes items quickly at both ends, and can keep only the last few items with maxlen.

from collections import Counter, defaultdict, deque

votes = ["Meera", "Kabir", "Meera", "Isha", "Meera", "Kabir"]
tally = Counter(votes)
print(tally)
print("Class monitor:", tally.most_common(1)[0][0])

students = [("Aarav", "Rohtak"), ("Priya", "Jaipur"), ("Rohan", "Rohtak")]
by_city = defaultdict(list)
for name, city in students:
    by_city[city].append(name)
print(dict(by_city))

recent = deque(maxlen=3)
for search in ["laptop bag", "earphones", "power bank", "phone cover"]:
    recent.append(search)
print(list(recent))

Counter({'Meera': 3, 'Kabir': 2, 'Isha': 1})
Class monitor: Meera
{'Rohtak': ['Aarav', 'Rohan'], 'Jaipur': ['Priya']}
['earphones', 'power bank', 'phone cover']

With a normal dictionary, by_city[city].append(name) would raise KeyError the first time a city appears. defaultdict(list) creates an empty list automatically. The deque kept only the three most recent searches and dropped the oldest.

random, secrets and statistics

random makes pseudo-random choices for games, simulations and shuffling. Calling random.seed() with a fixed number makes the sequence repeat exactly, which is useful for testing.

import random
import statistics

random.seed(2026)
team = ["Aarav", "Priya", "Kabir", "Meera", "Rohan", "Isha"]
print(random.choice(team))
print(random.sample(team, 3))
print(random.randint(1, 6))

runs = [45, 12, 88, 67, 30, 101, 54]
print(round(statistics.mean(runs), 1), statistics.median(runs))
print(round(statistics.stdev(runs), 2))

Aarav
['Kabir', 'Rohan', 'Aarav']
2
56.7 54
31.37

statistics gives you the mean (average), median (middle value), standard deviation and more without any maths of your own.

import secrets

otp = f"{secrets.randbelow(10**6):06d}"
token = secrets.token_urlsafe(16)
print(len(otp), otp.isdigit())
print(len(token) > 16)

6 True
True

The actual OTP and token change on every run, so this example prints only checks about them. The :06d format pads the number with zeros so an OTP like 4,213 becomes 004213.

textwrap and hashlib

textwrap formats long text to a fixed width, which is handy for terminal output and receipts. hashlib computes a hash: a short fingerprint of some data. The same data always gives the same hash, and any change gives a completely different one, so hashes are used to check that a downloaded file was not damaged.

import hashlib
import textwrap

notice = ("The college library will remain closed on Saturday for stock "
          "checking. Students must return all borrowed books by Friday evening.")
print(textwrap.fill(notice, width=40))
print(textwrap.shorten(notice, width=45, placeholder=" ..."))

data = "Fee receipt: Ananya, ₹12,500".encode("utf-8")
print(hashlib.sha256(data).hexdigest()[:16])
print(hashlib.sha256(data + b" ").hexdigest()[:16])

The college library will remain closed
on Saturday for stock checking. Students
must return all borrowed books by Friday
evening.
The college library will remain closed on ...
89386cde500f5b83
e78a181167a4f3f5

Adding just one space changed the fingerprint completely. Note that a plain hash is not the right way to store passwords; real login systems use special slow password-hashing functions.

logging: better than print for programs

print() is fine while learning, but real programs use logging. Each message gets a level (DEBUG, INFO, WARNING, ERROR, CRITICAL), and you choose which levels to show, without deleting lines from your code. Messages can include the time and go to a file instead of the screen.

import logging

logging.basicConfig(level=logging.INFO, format="%(levelname)s: %(message)s")
log = logging.getLogger("shop")

log.debug("Cart contents: %s", ["pen", "notebook"])    # hidden: below INFO
log.info("Order %d placed by %s", 1042, "Farhan")
log.warning("Only %d notebooks left", 3)
log.error("Payment gateway did not respond")

INFO: Order 1042 placed by Farhan
WARNING: Only 3 notebooks left
ERROR: Payment gateway did not respond

The debug message is hidden because the level is set to INFO. Change it to logging.DEBUG while hunting a bug, and back again when you are done. Log messages go to the error stream (stderr) by default, which terminals show just like normal output.

A map of the standard library

Before you write something complicated or install a package, check whether a standard module already does it:

The full list is in the official library reference. Skim it once; you will be surprised by how much is already there.

Common mistakes

• Writing your own version of a standard tool. Counting with a manual dictionary loop is fine for learning, but Counter is shorter and clearer.
• Using random for security. Use secrets for OTPs, tokens and passwords.
• Leaving debug print() calls everywhere. Use logging with levels so you can turn messages on and off.
• Naming your files after standard modules. A file called logging.py or statistics.py hides the real module; see Modules & Imports.

Key points

• The standard library ships with Python; import its modules without installing anything.
• collections offers Counter, defaultdict and deque for counting, grouping and queues.
• random is for games and simulations; secrets is for anything security-related.
• statistics, textwrap and hashlib handle averages, text layout and data fingerprints.
• logging replaces debug prints with levelled messages you can switch on and off.
• Python 3.14 adds compression.zstd; check the library map before installing a package.

from collections import Counter, defaultdict

answers = "python java python c python javascript java python c".split()

for language, count in Counter(answers).most_common(2):
    print(language, count)

by_letter = defaultdict(list)
for word in sorted(set(answers)):
    by_letter[word[0]].append(word)
print(dict(by_letter))

python 4
java 2
{'c': ['c'], 'j': ['java', 'javascript'], 'p': ['python']}