Explore information representation, compression, error correction, and probabilistic data structures with Python.
Created by python_scripts (Abhijith Warrier).
Understand how information is encoded, compressed, checked, and recovered. Each topic connects a foundational idea to a small Python implementation and an observable result.
The following topics describe the planned scope of the series. This roadmap is not a list of already published notebooks.
| Area | Concepts |
|---|---|
| Representing information | Bits, bytes, Unicode, UTF-8, and encoding. |
| Compression | Entropy, run-length encoding, Huffman coding, and dictionary compression. |
| Detecting and correcting errors | Checksums, hashes, parity bits, and Hamming codes. |
| Approximate representations | Bloom filters, Count-Min Sketch, and HyperLogLog. |
| Sampling signals | Digital sampling and aliasing. |
Once this repository and its notebooks are available, clone it:
git clone https://github.com/abhijithwarrier/Python-Information-Lab.git
cd Python-Information-LabCreate and activate a virtual environment:
python -m venv .venvmacOS / Linux:
source .venv/bin/activateWindows PowerShell:
.venv\Scripts\Activate.ps1Install and launch JupyterLab:
python -m pip install jupyterlab
python -m jupyterlabUse python3 instead of python where required by your system. Open a notebook and run its cells from top to bottom. Any topic-specific packages and Python-version requirements will be stated in that notebook; install packages using its dedicated installation section.
Examples will make distinctions such as detection versus correction, exact versus approximate answers, and storage savings versus information loss explicit. Simplified implementations are for learning, not substitutes for audited security primitives.
Run the example, change an input or assumption, and explain why the result changed. Keep the conceptual lesson connected to the code and its output.
Part of the python_scripts educational series: learning concepts by implementing and exploring them with Python.
Follow @python_scripts on Instagram for accompanying Reels and explanations.
Created by Abhijith Warrier
