Python 3 — 7 Modules · 50+ Examples

Python from Zero to Interview.

Complete Python 3 reference — variables, data structures, control flow, OOP with all four pillars, decorators, generators, essential libraries, and 10+ hands-on programs. The one guide you need to go from beginner to interview-ready.

Start with Basics Python Interview →

⬡Python 3.10+ ⬡50+ runnable examples ⬡Interview-focused ⬡OOP fully covered

7 Modules Basics → Practice

50+ Examples Runnable code snippets

4 OOP Pillars All fully covered

35+ Interview Qs In the Python track

Recommended Learning Path

01BasicsStarter → 02CollectionsStarter → 03Control FlowStarter → 04FunctionsMid → 05OOPMid → 06LibrariesMid → 07PracticeAdvanced

Curriculum

All 7 Python modules

Follow the path top-to-bottom or jump to the module you need.

Module 01

Python Basics

VariablesData Types Type ConversionNone

Where every Python journey starts. Variables, primitive types (int, float, str, bool), type conversion, and how Python stores data in memory.

ListsTuples SetsDictsComprehensions

Python's built-in data structures — essential for DSA and data manipulation. Includes all comprehension forms and when to use each type.

if/elsefor whilebreak/continue

Decision-making and iteration — conditionals, loops, and all Python operators for building program logic.

DecoratorsGenerators RecursionExceptionsFile I/O

Write reusable, elegant code. Covers decorators, generators, lambda, *args/**kwargs, exception handling, and file I/O — all heavily tested in interviews.

Mid level

Learn →

Module 05

Object-Oriented Programming

ClassesInheritance PolymorphismAbstraction Magic MethodsSOLID

Complete OOP — all four pillars, dunder/magic methods, class vs instance methods, multiple inheritance, MRO, and Python-specific design patterns for professional code.

NumPyPandas MatplotlibRequests

Real-world Python libraries for data science, visualization, and web APIs. The toolkit every Python developer needs.

AlgorithmsOOP Projects Interview Qs

10+ hands-on programs from basic to advanced — sorting, recursion, OOP projects, and coding interview exercises in Python.

Advanced

Practice →

Why Python?

The language worth mastering first

For interviews, AI/ML, backend, and scripting — Python is the pragmatic choice.

Interview Standard

Most technical interviews accept Python. Its concise syntax lets you focus on the algorithm, not the boilerplate — a real advantage under time pressure.

AI/ML Dominance

PyTorch, TensorFlow, scikit-learn, Pandas — the entire AI/ML stack runs on Python. If you want to work in AI, Python is non-negotiable.

Backend Ready

Django, Flask, and FastAPI make Python a strong choice for backend web development — from REST APIs to full production services used by Instagram, Spotify, and Dropbox.

Readable by Default

Python's syntax enforces clarity. Code looks like English, which makes it easier to write correct logic fast — and easier for reviewers to spot issues.

Fast to Ship

From idea to working prototype in minutes. Python's REPL, rich stdlib, and massive ecosystem (PyPI has 500k+ packages) make development uniquely fast.

Everywhere

Scripting, automation, data analysis, web, ML, game dev, security research — Python reaches into almost every domain of software development.

Watch Out

Python gotchas that trip up interviews

Common surprises — know these cold before your next interview.

MUTABLE DEFAULT ARGUMENT

def append_to(item, lst=[]):  #  BAD
    lst.append(item)
    return lst

# Fix: use None as default
def append_to(item, lst=None):  # ✓
    if lst is None: lst = []
    lst.append(item)
    return lst

Default arguments are evaluated once at function definition, not each call. Mutable defaults persist across calls.

IS VS ==

a = [1, 2, 3]
b = [1, 2, 3]

a == b  # True  — same value
a is b  # False — different objects

# Small ints (-5 to 256) are cached
x = 256; y = 256
x is y  # True  — same object!

is checks identity (same object in memory). == checks equality (same value). Always use == for value comparison.

LATE BINDING CLOSURES

fns = [lambda: i for i in range(3)]
[f() for f in fns]  # [2, 2, 2] 

# Fix: capture i at definition time
fns = [lambda i=i: i for i in range(3)]
[f() for f in fns]  # [0, 1, 2] ✓

Closures look up variables at call time, not definition time. By the time the lambdas run, i is already 2.

LIST COPY VS REFERENCE

a = [1, 2, 3]
b = a          #  same object
b.append(4)
print(a)       # [1, 2, 3, 4]

# Shallow copy options:
b = a.copy()   # ✓
b = a[:]       # ✓
b = list(a)    # ✓

Assignment copies the reference, not the object. For nested lists, use copy.deepcopy() to fully isolate.

Code Preview

Taste the content

Three patterns from across the curriculum — click to switch.

python_examples.py

Decorator

Generator

OOP Magic

# A timing decorator — measures function runtime
import time
import functools

def timer(func):
    @functools.wraps(func)
    def wrapper(*args, **kwargs):
        start = time.perf_counter()
        result = func(*args, **kwargs)
        elapsed = time.perf_counter() - start
        print(f"{func.__name__} ran in {elapsed:.4f}s")
        return result
    return wrapper

@timer
def bubble_sort(arr):
    n = len(arr)
    for i in range(n):
        for j in range(0, n - i - 1):
            if arr[j] > arr[j + 1]:
                arr[j], arr[j + 1] = arr[j + 1], arr[j]
    return arr

bubble_sort([64, 34, 25, 12, 22])
# bubble_sort ran in 0.0001s

# Generator — infinite Fibonacci sequence
def fibonacci():
    a, b = 0, 1
    while True:
        yield a
        a, b = b, a + b

# Only generates values on demand — O(1) memory
gen = fibonacci()
first_ten = [next(gen) for _ in range(10)]
print(first_ten)
# [0, 1, 1, 2, 3, 5, 8, 13, 21, 34]

# Generator expression — even more concise
squares = (x**2 for x in range(10) if x % 2 == 0)
print(list(squares))
# [0, 4, 16, 36, 64]

# Magic methods — make your class feel native
class Vector:
    def __init__(self, x, y):
        self.x, self.y = x, y

    def __repr__(self):
        return f"Vector({self.x}, {self.y})"

    def __add__(self, other):        # v1 + v2
        return Vector(self.x + other.x, self.y + other.y)

    def __mul__(self, scalar):      # v * 3
        return Vector(self.x * scalar, self.y * scalar)

    def __len__(self):               # len(v)
        return int((self.x**2 + self.y**2) ** 0.5)

v1 = Vector(2, 3)
v2 = Vector(1, 4)
print(v1 + v2)   # Vector(3, 7)
print(v1 * 3)    # Vector(6, 9)
print(len(v1))   # 3

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