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Modules & Built-in Functions

Python comes with a huge standard library of pre-written modules — and dozens of built-in functions that you can use without importing anything.

Built-in functions you'll use every day

nums = [3, 1, 4, 1, 5, 9, 2, 6]

print(len(nums))          # 8
print(sum(nums))          # 31
print(min(nums))          # 1
print(max(nums))          # 9
print(sorted(nums))       # [1, 1, 2, 3, 4, 5, 6, 9]
print(list(reversed(nums)))  # reversed
print(abs(-10))           # 10
print(round(3.7))         # 4
print(pow(2, 10))         # 1024 — same as 2 ** 10

type(), isinstance() — check types:

print(type(5))                     # <class 'int'>
print(type(5.0))                   # <class 'float'>
print(isinstance(5, int))          # True
print(isinstance(5, (int, float))) # True — accepts a tuple of types

enumerate(), zip() — already used in Loops:

fruits = ["apple", "banana", "cherry"]

for i, fruit in enumerate(fruits):
    print(i, fruit)

prices = [50, 30, 80]
for fruit, price in zip(fruits, prices):
    print(f"{fruit}: ₹{price}")

any(), all() — combine boolean checks:

nums = [2, 4, 6, 8, 9]

print(all(x > 0 for x in nums))      # True — every one positive
print(all(x % 2 == 0 for x in nums)) # False — 9 is odd
print(any(x > 5 for x in nums))      # True — 6, 8, 9 are > 5

map(), filter(), reduce() — see Functions.

input(), print(), int(), str(), float(), bool() — already covered.

Importing modules

A module is a .py file (or a folder of them) with reusable code.

import math

print(math.pi)                  # 3.141592653589793
print(math.sqrt(16))            # 4.0
print(math.factorial(5))        # 120
print(math.floor(3.7))          # 3
print(math.ceil(3.2))           # 4
print(math.log(100, 10))        # 2.0

Other import styles:

# Import specific names
from math import pi, sqrt
print(pi, sqrt(25))

# Rename on import
import math as m
print(m.pi)

# Import everything (avoid in real code — pollutes the namespace)
# from math import *

The most useful standard-library modules

random — random numbers

import random

print(random.random())                  # float in [0.0, 1.0)
print(random.randint(1, 10))            # int in [1, 10] inclusive
print(random.uniform(0, 100))           # float in range
print(random.choice(["red", "green", "blue"]))   # pick one
print(random.sample(range(100), 5))     # 5 unique picks
deck = list(range(1, 11))
random.shuffle(deck)                     # in-place shuffle
print(deck)

datetime — dates and times

from datetime import datetime, date, timedelta

now = datetime.now()
print("Now:", now)
print("Today:", date.today())

# Formatting
print(now.strftime("%Y-%m-%d %H:%M:%S"))
print(now.strftime("%A, %d %B %Y"))     # full names

# Parsing
parsed = datetime.strptime("2025-01-31 14:30", "%Y-%m-%d %H:%M")
print(parsed)

# Arithmetic with timedelta
future = now + timedelta(days=7, hours=3)
print("Next week:", future)
diff = future - now
print("Difference:", diff)

os and pathlib — work with files and paths

import os
from pathlib import Path

# Current working directory
print(os.getcwd())

# List a folder
print(os.listdir("."))

# pathlib is the modern way
p = Path("./README.md")
print(p.name)         # 'README.md'
print(p.suffix)       # '.md'
print(p.stem)         # 'README'
print(p.exists())

json — JSON encode/decode

import json

# Python → JSON string
data = {"name": "Alice", "age": 25, "skills": ["Python", "ML"]}
json_str = json.dumps(data, indent=2)
print(json_str)

# JSON string → Python
text = '{"city": "Mumbai", "pin": 400001}'
obj = json.loads(text)
print(obj["city"], obj["pin"])

re — regular expressions

import re

text = "Call me at 555-1234 or 555-5678"

# Find all phone-number-like patterns
phones = re.findall(r"\d{3}-\d{4}", text)
print(phones)

# Replace
clean = re.sub(r"\d{3}-\d{4}", "XXX-XXXX", text)
print(clean)

# Match start of string
m = re.match(r"(\w+) (\w+)", "Hello World")
if m:
    print(m.group(1), m.group(2))

Common regex elements:

Pattern Matches
\d digit (0-9)
\w word character (letters, digits, _)
\s whitespace
. any character (except newline)
* 0 or more
+ 1 or more
? 0 or 1
{n} exactly n
[abc] a, b, or c
^ start of string
$ end of string

collections — specialized containers

from collections import Counter, defaultdict, deque, namedtuple

# Counter — counting things
print(Counter("mississippi"))    # {'i': 4, 's': 4, 'p': 2, 'm': 1}

# defaultdict — auto-create missing keys
dd = defaultdict(list)
dd["fruits"].append("apple")     # no need to initialize the list
dd["fruits"].append("banana")
print(dict(dd))

# deque — fast append/pop on both ends
dq = deque([1, 2, 3])
dq.appendleft(0)
dq.append(4)
print(dq)

# namedtuple — readable tuple
Point = namedtuple("Point", ["x", "y"])
p = Point(3, 4)
print(p.x, p.y)

itertools — efficient looping

from itertools import product, combinations, permutations, chain

# All pairs from two lists
print(list(product([1, 2], ['a', 'b'])))     # [(1,'a'), (1,'b'), (2,'a'), (2,'b')]

# Pick 2 from 4 (order doesn't matter)
print(list(combinations(['A', 'B', 'C', 'D'], 2)))

# Order matters
print(list(permutations(['A', 'B', 'C'], 2)))

# Flatten lists
print(list(chain([1, 2], [3, 4], [5, 6])))

Writing your own module

Create a file mymath.py: 

# mymath.py
def square(x):
    return x * x

def cube(x):
    return x ** 3

PI = 3.14159

Use it from another file in the same folder: 

import mymath

print(mymath.square(5))
print(mymath.PI)

Installing third-party packages

pip install requests numpy pandas

Then in your code: 

import requests
r = requests.get("https://api.github.com")
print(r.status_code)

Practice

What does this print?

Expected: 5.0

import math
print(math.sqrt(25))

Print pi with 4-decimal precision

Expected: 3.1416

import math
print(f"{math.pi}")           # bug: no format spec — prints full precision

Quiz — Quick check

What you remember

Q1. Which import style is least recommended in production code?

  • import math
  • from math import sqrt
  • import math as m
  • from math import *

Why: from module import * pulls every name into your namespace and can silently shadow your own variables. Be explicit about what you import.

Q2. Which module would you use to read/write JSON?

  • csv
  • pickle
  • json
  • xml

Why: json.dumps(obj) converts Python → JSON string; json.loads(s) converts JSON → Python. pickle is for Python-only binary serialization (don't use it for external data).

Q3. What's the difference between random.randint(1, 10) and random.randrange(1, 10)?

  • No difference
  • randint is inclusive of 10; randrange is exclusive of 10 (gives 1–9)
  • randint returns a float; randrange returns an int
  • randrange is deprecated

Why: randint(a, b) mirrors common math notation (both ends included). randrange(a, b) follows Python's range() convention (stop is exclusive).

Common doubts

What's the difference between a module and a package?

A module is a single .py file. A package is a folder of modules with an __init__.py file. You import both the same way — import mypackage.utils reaches into a folder.

Why use pathlib over os.path?

pathlib (Python 3.4+) is the modern, object-oriented way to work with paths. p = Path("data") / "file.csv" is clearer than os.path.join("data", "file.csv"). Methods like .exists(), .read_text(), .glob() live on the Path object directly. Use os only when you need OS-level functions that pathlib doesn't expose.

Where do pip install packages go?

Globally by default — installed into your Python's site-packages folder, shared across every project. That's a problem when projects need different versions of a library. The fix is a virtual environment: python -m venv .venv && source .venv/bin/activate. Now pip install only affects this project. Use one per project.

What's next

→ Object-Oriented Programming (OOP)