A structured, notebook-based Python reference: from syntax basics to object oriented programming, iteration tools, the standard library, regular expressions and type hints. Every topic is a notebook with a quick summary table, explanations and code examples.
Read it online: Python Complete Reference Notebook on Kaggle
- Offline notebooks in
notebooks/: 97 notebooks in 14 folders, one topic per notebook, meant for studying and quick lookup. - One unified notebook in
kaggle/: all 14 parts in a single file (341 cells, 152 of them code) with a clickable roadmap, a table of contents and an A-Z index of every name it explains. It is designed to run top to bottom without manual input and is the source of the Kaggle notebook above.
A beginner-to-intermediate refresher: short explanations, quick tables and runnable examples, meant for revision and quick lookup rather than deep dives.
Not covered: metaclasses, C extensions, CPython internals, packaging and publishing, threads, processes and asyncio, networking (urllib, socket), subprocess, web frameworks and data-science libraries.
The parts follow a learning path: each part builds on the ones before it. A few one-line examples in Parts 1 and 5 use a construct that is explained later (for example try in Data Types). The Suggested background column lists the earlier parts whose ideas a part uses. Quick References (keywords and built-in functions) is meant for lookup at any time.
| You want to... | Use |
|---|---|
| Look up a name (function, method, keyword) | The A-Z index at the end of the unified notebook |
| Find the tool for a job ("how do I remove duplicates?") | 14 - Quick References/04 - Task Index |
See every method of str, list, tuple, dict or set |
14 - Quick References/03 - Methods of Built-in Types |
| Recall a syntax or the options of a topic | The quick table at the top of every notebook |
| See all keywords or built-in functions | The first two notebooks of Quick References |
Part N of the unified notebook is the folder N - ... in notebooks/, and section N.M is file M - ... inside that folder. For example, section 4.9 (Decorators) is 04 - Functions/09 - Decorators.
| Part | Topic | Notebooks | What it covers | Suggested background |
|---|---|---|---|---|
| 1 | Python Basics | 8 | Comments, variables, data types, operators, type conversion, user input, string formatting, modules and import |
Start here |
| 2 | Control Flow | 4 | if / elif / else, while and for loops, match / case |
Part 1 |
| 3 | Data Structures | 6 | Lists, tuples, sets & frozen sets, dictionaries, references and copying, booleans, container comparison guide | Parts 1-2 |
| 4 | Functions | 13 | Return values, parameters, defaults, *args / **kwargs, scope, recursion, lambda, decorators, docstrings, type hints, PEP 8 |
Parts 1-2 |
| 5 | Errors, Debugging & Profiling | 4 | Exception types, raise, try / except / else / finally, tracebacks, assert, pdb, timeit, cProfile |
Parts 1-2, 4 |
| 6 | Comprehensions, Iterators & Generators | 7 | List, set, dict and generator expressions, nested comprehensions, iterables vs iterators, generators, scope and the walrus operator | Parts 1-2, 4-5 |
| 7 | Standard Library Essentials | 6 | math, statistics, decimal, random, datetime, collections, functools |
Parts 1-2, 4-6 |
| 8 | Itertools | 8 | Infinite iterators, slicing and filtering, chaining and zipping, accumulate / pairwise / batched / tee, groupby, combinatorics, recipes |
Parts 1-2, 4-7 |
| 9 | Files & Data Formats | 6 | Reading and writing files, file modes, pathlib, os / sys / shutil, JSON, CSV |
Parts 1-2, 4-7 |
| 10 | Regular Expressions | 5 | Regex syntax, character classes, quantifiers, groups, assertions, the re functions, flags and performance |
Parts 1-2, 4 |
| 11 | Object Oriented Programming | 16 | Classes, encapsulation, inheritance and MRO, custom exceptions, polymorphism, ABCs, dunder methods, context managers, descriptors, dataclasses, class patterns, design patterns | Parts 1-2, 4-7, 9 |
| 12 | Advanced Type Hinting | 4 | TypeVar and generics, protocols, TypedDict, Self, TypeGuard, overload, ParamSpec, runtime introspection, 3.14 notes |
Parts 1-2, 4-5, 7, 11 |
| 13 | Building Real Programs | 6 | Packages and the __main__ guard, command-line arguments, external packages with pip, logging, unittest, doctest |
Parts 1-2, 4-6, 9, 11 |
| 14 | Quick References | 4 | All 35 keywords with examples, the built-in functions by category, every method of str, list, tuple, dict and set, and a task index (how do I...?) |
Parts 1-2, 4-9, 11 |
Open 00 - Python Notebook Content for a quick offline index, or use the list below.
01 - Python Basics (8 notebooks)
02 - Control Flow (4 notebooks)
03 - Data Structures (6 notebooks)
04 - Functions (13 notebooks)
01 - Functions Basics02 - Return Statement03 - Parameters & Arguments04 - Default Parameters05 - Args Kwargs & Unpacking06 - Function Scope07 - Recursion08 - Lambda Functions09 - Decorators10 - Docstrings11 - Type Hints Foundations12 - Collections, Union Types & Type Aliases13 - Coding Style (PEP 8)
05 - Errors, Debugging & Profiling (4 notebooks)
06 - Comprehensions, Iterators & Generators (7 notebooks)
07 - Standard Library Essentials (6 notebooks)
08 - Itertools (8 notebooks)
09 - Files & Data Formats (6 notebooks)
10 - Regular Expressions (5 notebooks)
11 - Object Oriented Programming (16 notebooks)
01 - OOP Foundations02 - Classes, Instances & __init__03 - Attributes & Methods04 - Encapsulation & Properties05 - Class & Static Methods06 - Inheritance & super07 - Custom Exceptions & Chaining08 - Multiple Inheritance, MRO & Mixins09 - Polymorphism, Duck Typing & Composition10 - Abstract Base Classes11 - Dunder Methods & Operator Overloading12 - Context Managers13 - Object Model, Descriptors & __slots__14 - Dataclasses & Enums15 - Class Patterns16 - OOP Design, Advanced Patterns & Best Practices
12 - Advanced Type Hinting (4 notebooks)
13 - Building Real Programs (6 notebooks)
14 - Quick References (4 notebooks)
Export the unified notebook to a single HTML file. It opens in any browser without Python or an internet connection, and the roadmap links and Ctrl+F search work:
jupyter nbconvert --to html --execute kaggle/Python-Complete-Reference.ipynb
--execute runs every cell first and stops at the first error, so the command also checks that the notebook still runs top to bottom.
Only Python and JupyterLab are needed. The notebooks use the standard library only, so there is nothing else to install.
cd python-complete-reference
pip install jupyterlab
jupyter labNotes:
- Python version: use Python 3.13 or newer for the offline notebooks. Two notebooks demonstrate newer features (
typealiases need 3.12+,typing.TypeIsneeds 3.13+). The unified notebook inkaggle/includes version guards and is written to run on Python 3.11 or newer. It was executed from top to bottom on Python 3.12. - Run cell by cell in the offline notebooks. Some cells show errors or wait for keyboard input on purpose, so "Run All" can stop early there. The unified notebook is the version designed for "Run All".
- Language: everything in this repository is in English.
python-complete-reference/
├── README.md
├── LICENSE
├── kaggle/
│ ├── Python-Complete-Reference.ipynb # unified notebook (source of the Kaggle notebook)
│ └── kernel-metadata.json # Kaggle API metadata
└── notebooks/
├── 00 - Python Notebook Content.ipynb
├── 01 - Python Basics/
├── 02 - Control Flow/
├── 03 - Data Structures/
├── 04 - Functions/
├── 05 - Errors, Debugging & Profiling/
├── 06 - Comprehensions, Iterators & Generators/
├── 07 - Standard Library Essentials/
├── 08 - Itertools/
├── 09 - Files & Data Formats/
├── 10 - Regular Expressions/
├── 11 - Object Oriented Programming/
├── 12 - Advanced Type Hinting/
├── 13 - Building Real Programs/
└── 14 - Quick References/
After editing kaggle/Python-Complete-Reference.ipynb, push it with the Kaggle CLI:
kaggle kernels push -p kaggle
Keep your Kaggle API credentials (kaggle.json) out of the repository. It is already listed in .gitignore.
Maintained by mahmoud15 on Kaggle. Issues and suggestions are welcome.