100days
100 days of algorithms
Collection of Jupyter notebooks implementing one algorithm per day for 100 days, classic algorithms in Python with explanations and interactive charts, written as a personal learning challenge with companion Medium articles.
This repository holds Jupyter notebooks written as part of a personal challenge to implement one algorithm per day for 100 consecutive days. The author documented the journey in a series of articles on Medium, and the notebooks contain the actual Python code that accompanied each article.
The algorithms covered span a wide range of classic topics. The code is written in Python 3 and uses Jupyter notebooks, which are interactive documents that mix code with explanations and can display charts and graphs inline. To run the notebooks yourself, you install the Anaconda distribution (a common Python environment for data work), clone the repository, and open it with the Jupyter tool.
The author is upfront that the code was written quickly and should not be treated as polished or production-ready. The goal was learning and exploration, not optimal implementations. Some notebooks include interactive charts using a library called Bokeh, which do not display directly on GitHub and need to be run locally instead.
This is a learning resource and personal log rather than a library or tool you would install in a project. It is aimed at programmers curious about algorithms who want to see varied implementations with accompanying explanations.
Where it fits
- Browse Python implementations of classic algorithms with inline explanations to study algorithmic thinking at your own pace.
- Run interactive Bokeh chart notebooks locally to visualize how sorting or graph algorithms work step by step.
- Use a specific notebook as a concrete starting point before implementing a similar algorithm yourself.