LearnPython

A hands-on collection of small, runnable Python programs — from your first Hello, world! to classic algorithms — written for two audiences at once.

Companion to LearnDart, inspired by TheAlgorithms/Python. Every file in this repository is a self-contained lesson: you can open it, read it top-to-bottom, and run it with a single command.

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Table of contents

• Why LearnPython?
• Who this is for
• Quick start
• The learning path (basics/)
• Algorithm categories
• A taste of the code
• How this repo is organized
• Repository conventions
• Roadmap
• Contributing
• Inspiration
• License

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Why LearnPython?

Most language tutorials fall into one of two traps: they either bury you in features before you can write a program, or they hand you a giant final application with no explanation of the pieces. LearnPython takes a third path — one concept per file, one file per commit. You can read the code, the comments, and (if you want) the git history and see exactly how the language was introduced, in order.

• Read it as a book — walk through basics/ in order, no setup beyond installing Python.
• Read it as a reference — jump straight to sorts/, graphs/, dynamic_programming/, etc. when you need a clean Python implementation of a classic algorithm.
• Read it as a git log — every commit is atomic and titled Add: <path> — <what it teaches>.

Who this is for

Audience	Where to start	What you'll get
Non-programmers learning to code for the first time	basics/00_hello_world/	Files are numbered 00, 01, 02... so you always know the next step. Each basics/ file has plain-English comments that explain what the code does and why.
Programmers coming from Dart, JavaScript, Go, Java, C# or similar	basics/02_types/, then the algorithm categories	Skim the basics/ folders for Python-specific idioms (duck typing, comprehensions, generators, decorators, context managers, match), then dive into idiomatic Python implementations of algorithms you already know.
CS students looking for clean reference implementations	Any algorithm category below	Every algorithm is a small, focused file with a main() that exercises it. No frameworks, no packages — just Python and its standard library.

Quick start

1. Install Python — see python.org/downloads. Any CPython 3.10 or newer works.

2. Clone the repo:

   git clone https://github.com/polyglot-learn/LearnPython.git
   cd LearnPython

3. Run any file:

   python3 basics/00_hello_world/hello_world.py

That's the whole workflow. There is no build step, no virtualenv, no pip install — the repo has zero third-party dependencies.

The learning path (basics/)

Follow the numbered folders in order. Inside each folder, read the files in alphabetical order.

#	Folder	You'll learn
00	00_hello_world/	Running a script, print, the __main__ guard
01	01_variables/	Assignment, naming, constants by convention, unpacking
02	02_types/	int, float, str, bool, None, type hints
03	03_operators/	Arithmetic, comparison, logical, identity/membership, walrus
04	04_strings/	f-strings, slicing, common methods, multiline/raw strings
05	05_control_flow/	if/elif/else, for, while, break/continue/else, match
06	06_functions/	Defaults, *args/**kwargs, lambdas, closures, decorators
07	07_collections/	list, tuple, dict, set, comprehensions, slicing
08	08_iterators/	Iterables, generators, yield, itertools
09	09_classes/	Attributes, __init__, dunder methods, inheritance, dataclass, Enum
10	10_async/	async/await, asyncio.gather, async generators
11	11_errors/	try/except/else/finally, raising, custom exceptions
12	12_modules_io/	Imports, pathlib, context managers, JSON

Algorithm categories

Each category holds one algorithm per file, each with a runnable main():

arrays/ · sorts/ · searches/ · strings/ · maths/ · recursion/ · data_structures/ · dynamic_programming/ · graphs/ · greedy/ · backtracking/ · bit_manipulation/ · number_theory/ · geometry/ · ciphers/ · crypto/ · compression/ · concurrency/ · distributed/ · machine_learning/ · probabilistic/ · parsers/ · project_euler/ · puzzles/

See DIRECTORY.md for a flat index of every file.

A taste of the code

Your first Python program — basics/00_hello_world/hello_world.py:

print("Hello, world!")

Python 3.10 structural pattern matching:

def describe(n: int) -> str:
    match n:
        case 0:
            return "zero"
        case 1 | 2 | 3:
            return "small"
        case _ if n < 0:
            return "negative"
        case _:
            return "large or unusual"

A generic stack:

class Stack[T]:
    def __init__(self) -> None:
        self._items: list[T] = []

    def push(self, value: T) -> None:
        self._items.append(value)

    def pop(self) -> T:
        return self._items.pop()

How this repo is organized

LearnPython/
├── basics/                 numbered teaching folders 00_ ... 12_
│   ├── 00_hello_world/
│   ├── 01_variables/
│   └── ...
├── sorts/                  one algorithm per file
├── searches/
├── data_structures/
├── maths/
├── strings/
├── DIRECTORY.md            flat index of every .py file
├── CONTRIBUTING.md         file and commit conventions
├── LICENSE                 MIT
└── README.md               you are here

Repository conventions

• One concept per file. Splittable? Split it.
• One file per commit, one commit per pull request. The git history is the reading order, and every change stays small enough to review at a glance.
• Every file runs. No file exists without a main() you can execute directly.
• Zero dependencies. Every sample runs against the standard library alone.
• basics/ files teach. They have prose comments explaining the what and the why.
• Algorithm files stay clean. They only comment when the why is non-obvious.
• Iterative + recursive = two commits. Same algorithm, two shapes — they get their own files.

Full details in CONTRIBUTING.md.

Roadmap

More samples land in later sessions. Planned expansions mirror LearnDart: the full basics/ path first, then sorts, searches, data structures, graphs, DP, and the advanced categories (crypto, compression, distributed, probabilistic).

Contributing

New samples are always welcome. Please read CONTRIBUTING.md first — the short version:

1. One concept per file. Splittable? Split it.
2. One file per commit, one commit per PR. Message format: Add: <path> — <one-line what it teaches>.
3. Run the file before you submit it.
4. Update DIRECTORY.md when you add files.

Inspiration

• LearnDart — the sibling repository this one mirrors.
• TheAlgorithms/Python — the model for the algorithm categories.
• docs.python.org/3/tutorial — canonical reference for every language feature covered in basics/.

License

Released under the MIT License.