Python Course

Master Python from scratch with our comprehensive, interactive curriculum — 125 lessons from Beginner to Advanced.

A complete free Python course from zero to advanced — variables, functions, loops, OOP, file handling and async — with interactive lessons and projects.

👋 New to Python? No experience needed.

Start with Lesson 1 and build real skills step by step — at your own pace.

Continue where you left off

Lessons in this course

• Introduction to Python — Install Python, run your first script, and understand how Python works
• Variables and Data Types — Learn how to store text, numbers, and booleans in your programs
• Operators and Expressions — Arithmetic, comparison, and logical operators explained with examples
• Control Flow - If/Else — Make decisions in code with if, elif, and else statements
• Loops - For and While — Repeat actions automatically with for loops and while loops
• Functions — Write reusable blocks of code and pass data in and out with parameters
• Lists and Collections — Store and manipulate ordered sequences of data with Python lists
• Dictionaries — Work with key-value pairs to structure and look up real-world data
• *args & **kwargs — Accept any number of positional and keyword arguments in your functions
• Lambda Functions — Write small anonymous functions for sorting, mapping and filtering
• f-strings & String Formatting — Format strings cleanly with f-strings — the modern Python standard
• String Methods — split, join, strip, replace and the string methods you'll use daily
• Sets — Store unique values and do fast membership tests, unions and intersections
• Tuples — Immutable sequences, tuple unpacking, and when to use them over lists
• Comprehensions — Build lists, dicts and sets in one readable line
• enumerate & zip — Loop with an index, and loop over multiple iterables at once
• Checkpoint: Python Essentials — Review & combine the core concepts in a build challenge — then a quiz
• Regular Expressions (re) — Search, match and replace text patterns with the re module
• Dates & Times (datetime) — Work with dates, times, durations and formatting
• Working with JSON — Parse and create JSON with json.loads and json.dumps
• HTTP Requests & APIs — Call REST APIs and handle responses with the requests library
• Reading CSV & Excel Files — Read and write spreadsheet data with csv and openpyxl/pandas
• Web Scraping with BeautifulSoup — Extract data from web pages with requests and BeautifulSoup
• itertools — Iteration power tools — chain, groupby, combinations and more
• Modern File Paths with pathlib — Handle file paths cleanly with the modern pathlib module
• Checkpoint: A Real-World Script — Combine APIs, dates, regex and CSV into one real script — then a quiz
• File Handling — Read from and write to files — txt, csv, and more
• Exception Handling — Handle errors gracefully so your programs don't crash unexpectedly
• Modules and Packages — Organise code into reusable files and import the Python standard library
• Object-Oriented Programming — Model real-world concepts using classes, objects, and methods
• Inheritance and Polymorphism — Build class hierarchies and reuse code through inheritance
• Decorators & Advanced Features — Wrap functions to add behaviour without modifying their code
• Advanced Functions & Parameters Masterclass — args, kwargs, keyword-only args, and function signatures in depth
• Higher-Order Functions & Function Factories — Pass and return functions, build factories and pipelines
• Closures & Lexical Scope in Real Projects — Understand variable capture and build stateful function patterns
• Context Managers & the with Statement in Depth — Write custom context managers for resource and lifecycle control
• Generators & Iterators Mastery — Produce values lazily for memory-efficient data pipelines
• Advanced Async & Await Patterns — Write clean asynchronous code with async/await and coroutines
• AsyncIO: Event Loop, Tasks & Futures — Deep dive into the AsyncIO event loop, tasks, and concurrent I/O
• Concurrency in Python: Threads vs Processes — Understand the GIL and choose between threading and multiprocessing
• Parallelism with concurrent.futures — Run CPU-bound tasks in parallel with ProcessPoolExecutor
• Profiling & Optimising Python Performance — Find bottlenecks with cProfile, timeit, and memory profilers
• Memory Management & Garbage Collection Internals — How Python allocates, tracks, and frees memory under the hood
• Type Hints & Static Typing with mypy — Add type annotations and catch bugs before runtime with mypy
• Data Classes & Advanced Class Patterns — Reduce boilerplate with @dataclass and slots
• Magic Methods & the Python Data Model — Implement __repr__, __len__, __eq__, and more to integrate with Python
• Operator Overloading & Custom Behaviours — Make your objects work with +, -, *, and comparison operators
• Mixins, Multiple Inheritance & OOP Patterns — Compose behaviour with mixins and navigate multiple inheritance safely
• Design Patterns in Python (Singleton, Factory, Strategy) — Apply classic GoF patterns idiomatically in Python
• Module & Package Architecture for Large Codebases — Structure growing projects with namespaces, imports, and __init__.py
• Logging, Debugging & Error Handling at Scale — Add structured logging, use pdb, and handle errors in production
• Testing with pytest: Fixtures, Parametrisation & Mocks — Write reliable tests with pytest fixtures, parametrize, and unittest.mock
• Building Command-Line Tools with argparse & Typer — Build polished CLI tools with argument parsing and subcommands
• Virtual Environments & Dependency Management Best Practices — Isolate projects with venv and manage packages with pip and Poetry
• Packaging & Publishing Python Libraries to PyPI — Package your code and publish it so others can pip install it
• Working with Files, Streams & Large Datasets — Handle large files with streaming, chunking, and pathlib
• Advanced Collections, itertools & functools — Use Counter, defaultdict, chain, groupby, partial and more
• Functional Programming Techniques in Python — Write pure, composable code with map, filter, reduce, and comprehensions
• Metaprogramming & Introspection with inspect — Dynamically inspect, modify, and create classes and functions at runtime
• Building and Reusing Custom Decorator Libraries — Create your own decorator toolkit with functools.wraps and class decorators
• Using SQLite & ORMs (SQLAlchemy) in Python — Query databases with raw SQLite and the SQLAlchemy ORM
• REST API Clients & External Service Integrations — Call REST APIs with requests, handle auth, and parse JSON responses
• Automation & Scripting for DevOps and System Tasks — Automate file operations, shell commands, and scheduled tasks
• Integrating Python with Other Languages (C, Rust & more) — Call C extensions with ctypes and Cython, and interface with Rust
• Architecture Patterns for Python Apps (MVC, Clean Architecture) — Structure large Python applications with proven architectural patterns
• Slicing Lists & Strings — Extract sub-sequences with [start:stop:step], negative indices, and [::-1]
• Booleans & Truthiness — Truthy/falsy values, short-circuit and/or, and any()/all()
• None & NoneType — Use None correctly — is None vs ==, and the mutable-default-arg trap
• Conditional Expressions (Ternary) — Pick a value inline with a if cond else b, including in comprehensions
• The Walrus Operator (:=) — Assign inside expressions to simplify loops and comprehensions
• Structural Pattern Matching (match/case) — Match literals, sequences, mappings and classes with guards (3.10+)
• Unpacking & Star Expressions — Destructure with a, *rest = ... and splat args with * and **
• Sorting with sorted() & key — Order data with sorted/sort, reverse, and key functions (multi-key too)
• Checkpoint: Core Syntax — Combine slicing, unpacking, sorting and match/case — then a quiz
• functools: lru_cache, partial, reduce — Memoize with lru_cache, pre-fill args with partial, and fold with reduce
• Enums (the enum module) — Model fixed sets of named constants with Enum, auto(), and IntEnum
• namedtuple & NamedTuple — Lightweight immutable records with named fields and defaults
• Randomness with the random Module — randint, choice, shuffle and seeded reproducibility (and when to use secrets)
• Running Commands with subprocess — Run external programs with subprocess.run, capture output, and stay safe
• Hashing with hashlib — Compute SHA-256/HMAC digests, hash files, and understand salts vs encryption
• Testing with unittest — Write TestCase classes with asserts, setUp/tearDown, and assertRaises
• Advanced Type Hints (Generics, Protocol, TypedDict) — Generics with TypeVar, structural typing with Protocol, and TypedDict
• Checkpoint: The Standard Library — Combine enums, namedtuples, functools and hashlib in a build — then a quiz
• Dictionary Methods Deep Dive — get, setdefault, update, pop, items and dict comprehensions in depth
• Counter & defaultdict — Count and group data effortlessly with collections.Counter and defaultdict
• deque & Queues — O(1) appends and pops at both ends with collections.deque
• Heaps with heapq (Priority Queues) — Build priority queues and find top-k with the heapq module
• Binary Search with bisect — Search and insert into sorted lists in O(log n) with bisect
• Numbers: int, float, Decimal & Fraction — Exact money math with Decimal, exact ratios with Fraction, and float pitfalls
• Strings, Bytes & Unicode Encodings — str vs bytes, encode/decode, UTF-8, and fixing UnicodeDecodeError
• Iterators: __iter__ & __next__ — How the iterator protocol powers every for loop — write your own
• Checkpoint: Data Structures — Combine Counter, heapq, deque and bisect in a build — then a quiz
• Abstract Base Classes (abc) — Define and enforce interfaces with abc.ABC and @abstractmethod
• Properties & Descriptors — Computed, validated attributes with @property and the descriptor protocol
• __slots__ & Memory Optimization — Cut memory and speed up attribute access by dropping __dict__
• Scope: global, nonlocal & LEGB — Master name resolution and avoid the UnboundLocalError trap
• Custom Exceptions & Exception Chaining — Build exception hierarchies and chain causes with raise ... from
• Time Zones with zoneinfo — Aware datetimes, converting between zones, and storing UTC correctly
• Serialization with pickle — Save and load Python objects — and the critical security warning
• Config Files with configparser — Read and write INI configuration with sections, types, and defaults
• Checkpoint: OOP & Standard Library — Combine abc, properties, custom exceptions and config in a build — then a quiz
• Threading & the GIL — Run concurrent I/O with threads and understand why the GIL limits CPU work
• multiprocessing: True Parallelism — Use multiple processes to bypass the GIL for CPU-bound work
• Thread-Safe Queues (queue) — Coordinate producers and consumers safely with queue.Queue
• contextlib: contextmanager, suppress, ExitStack — Write your own with-statements and manage dynamic resources
• Temporary Files & Directories — Create auto-cleaning temp files and folders with the tempfile module
• Finding Files with glob & fnmatch — Match file paths with wildcards using glob and fnmatch
• High-Level File Operations (shutil) — Copy, move, archive and delete trees with the shutil module
• Network Programming with sockets — Build TCP clients and servers with Python's socket module
• Checkpoint: Concurrency & I/O — Combine threads, queues, temp files and sockets in a build — then a quiz
• The statistics Module — Compute mean, median, mode, stdev and quantiles from the standard library
• The operator Module — Use itemgetter, attrgetter and operator functions for fast, clean code
• Binary Data with struct — Pack and unpack C-style binary data and read file headers
• Text Wrapping & Formatting (textwrap) — Wrap, indent, dedent and shorten text cleanly with textwrap
• Benchmarking with timeit — Measure tiny code snippets accurately and compare approaches
• The warnings Module — Emit, filter and capture warnings — and turn them into errors
• Introspection with inspect — Examine signatures, source and members of live objects at runtime
• Weak References (weakref) — Reference objects without keeping them alive — caches and cycle-breaking
• Checkpoint: Stdlib Power Tools — Combine struct, statistics and textwrap in a build — then a quiz
• Final Project Ideas — Guided project ideas to put everything you've learned into practice
• Data Validation with Pydantic — Validate and parse data with typed BaseModel classes, field constraints and validators
• NumPy for Numerical Computing — The ndarray, vectorized math, broadcasting and fast aggregations over arrays
• Data Visualization with Matplotlib — Line, scatter, bar and histogram plots with the pyplot and object-oriented APIs
• Building APIs with FastAPI — Typed path operations, Pydantic models, dependency injection and auto Swagger docs
• Static Type Checking with mypy — Catch bugs before runtime with gradual typing, strict mode, generics and Protocols

Frequently asked questions

Is Python hard to learn for beginners?

Python is widely considered one of the easiest programming languages to learn. Its syntax reads almost like plain English, there are no semicolons or curly braces to manage, and you can write a working program in just a few lines. Most beginners can write useful scripts within their first week.

How long does it take to learn Python?

You can grasp the fundamentals (variables, loops, functions, lists) in 2–4 weeks of consistent practice. Reaching a job-ready level with projects, OOP, and a chosen specialism (web, data, automation) typically takes 4–8 months. The key is building real projects, not just watching tutorials.

What can you build with Python?

Python powers web backends (Django, Flask, FastAPI), data analysis and machine learning (pandas, scikit-learn, PyTorch), automation scripts, web scrapers, desktop apps, and even game prototypes. It is the most popular language for data science and AI.

Do I need any prior experience to start this Python course?

No. This course starts from absolute zero — installing Python, printing your first message, and understanding variables — before moving through loops, functions, OOP, file handling and async. Every concept is explained before it is used.

Is Python still worth learning in 2026?

Yes. Python remains one of the most in-demand languages thanks to its dominance in AI, machine learning, data science and automation, plus a huge job market for backend and scripting roles.

More for this course

• Python cheat sheet — Quick syntax reference you can scan while you code
• Python Decorators: A Practical Guide — Master Python decorators with practical examples for logging, authentication, caching, and more. Learn to write cleaner, more reusable, professional code.
• 10 Python Tips Every Beginner Should Know — Discover essential Python tips that will help you write cleaner, more efficient code from day one.
• The Two Things That Actually Confused Me Learning Python (That No Course Explains) — A genuine beginner's notes: the jargon courses never define, and the 'for lists in my_lists' naming illusion that makes Python code look like magic. Both are fixable in five minutes — once someone actually explains them.
• Error Handling Best Practices in Python — Master professional error handling in Python. Learn try/except patterns, logging strategies, custom exceptions, and production-ready techniques for reliable applications.