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https://github.com/albanie/algorithms-and-data-structures

Video descriptions and minimalist Python implementations of algorithms and data structures.
https://github.com/albanie/algorithms-and-data-structures

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Video descriptions and minimalist Python implementations of algorithms and data structures.

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# Introduction to data structures and algorithms



This repo aims to provide minimalist implementations of data structures and algorithms in Python to accompany a [lecture course on this topic](https://www.youtube.com/playlist?list=PL9t0xVFP90GBPScODccB2_PpouUUZ4j8C).

The bare necessities. Each data structure is accompanied by a video lecture (and pdf slides).



## Data structures, algorithms and concepts



### Binary Search Trees



- :movie_camera: [video lecture](https://youtu.be/0woI8l0ZWmA)

- :bar_chart: [slides (pdf)](https://samuelalbanie.com/files/digest-slides/2022-10-brief-guide-to-binary-search-trees.pdf)

- :hammer: [binary_search_tree.py](binary_search_tree.py)

- :fountain_pen: [detailed references](https://samuelalbanie.com/digests/2022-10-brief-guide-to-binary-search-trees/)



### Red-Black Trees



- :movie_camera: [video lecture](https://youtu.be/t-oiZnplv7g)

- :bar_chart: [slides (pdf)](https://samuelalbanie.com/files/digest-slides/2022-12-brief-guide-to-red-black-trees.pdf)

- :hammer: [red_black_tree.py](red_black_tree.py)

- :hammer: [script.js (D3 visualisation)](visualisations/script.js)

- :fountain_pen: [detailed references](https://samuelalbanie.com/digests/2022-12-brief-guide-to-red-black-trees/)



### B-trees



- :movie_camera: [video lecture](https://youtu.be/7MqaHGWRS3E)

- :bar_chart: [slides (pdf)](https://samuelalbanie.com/files/digest-slides/2022-12-brief-guide-to-b-trees.pdf)

- :hammer: [btree.py](btree.py) ([visualisation of outputs](graphviz-walkthroughs/btree.md))

- :fountain_pen: [detailed references](http://samuelalbanie.com/digests/2022-12-brief-guide-to-b-trees)



### Hash Tables



- :movie_camera: [video lecture](https://www.youtube.com/watch?v=r1XZGP5ppqQ)

- :bar_chart: [slides (pdf)](https://samuelalbanie.com/files/digest-slides/2022-09-brief-guide-to-hash-tables.pdf)

- :hammer: [hash_table.py](hash_table.py)

- :fountain_pen: [detailed references](https://samuelalbanie.com/digests/2022-09-brief-guide-to-hash-tables/)



### Heapsort and Binary Heaps



- :movie_camera: [video lecture](https://youtu.be/ryRfapIQHW0)

- :bar_chart: [slides (pdf)](https://samuelalbanie.com/files/digest-slides/2022-12-brief-guide-to-heapsort-and-binary-heaps.pdf)

- :hammer: [heapsort.py](heapsort.py) (related topic: [priority_queue_with_heap.py](priority_queue_with_heap.py))

- :fountain_pen: [detailed references](http://samuelalbanie.com/digests/2022-12-brief-guide-to-heapsort-and-binary-heaps)



### Quicksort



- :movie_camera: [video lecture](https://youtu.be/kbiKn1K08RM)

- :bar_chart: [slides (pdf)](https://samuelalbanie.com/files/digest-slides/2023-01-brief-guide-to-quicksort.pdf)

- :hammer: [quicksort.py](quicksort.py)

- :fountain_pen: [detailed references](http://samuelalbanie.com/digests/2023-01-brief-guide-to-quicksort)



### Lower Bounds for Comparison Sorts



- :movie_camera: [video lecture](https://youtu.be/JWSiXs9aB5U)

- :bar_chart: [slides (pdf)](https://samuelalbanie.com/files/digest-slides/2023-01-2023-01-brief-guide-to-comparison-sorting-lower-bounds.pdf)

- :fountain_pen: [detailed references](http://samuelalbanie.com/digests/2023-01-brief-guide-to-comparison-sorting-lower-bounds)



### Counting Sort



- :movie_camera: [video lecture](https://youtu.be/0aMcZpAySjw)

- :bar_chart: [slides (pdf)](https://samuelalbanie.com/files/digest-slides/2023-01-brief-guide-to-counting-sort.pdf)

- :hammer: [counting_sort.py](counting_sort.py)

- :fountain_pen: [detailed references](http://samuelalbanie.com/digests/2023-01-brief-guide-to-counting-sort)



### Radix Sort



- :movie_camera: [video lecture](https://youtu.be/HzPbzQi9404)

- :bar_chart: [slides (pdf)](https://samuelalbanie.com/files/digest-slides/2023-01-brief-guide-to-radix-sort.pdf)

- :hammer: [radix_sort.py](radix_sort.py)

- :fountain_pen: [detailed references](http://samuelalbanie.com/digests/2023-01-brief-guide-to-radix-sort)



### Bucket Sort



- :movie_camera: [video lecture](https://youtu.be/mz2fBJyoEVc)

- :bar_chart: [slides (pdf)](https://samuelalbanie.com/files/digest-slides/2023-01-brief-guide-to-bucket-sort.pdf)

- :hammer: [bucket_sort.py](bucket_sort.py)

- :fountain_pen: [detailed references](http://samuelalbanie.com/digests/2023-01-brief-guide-to-bucket-sort)



### Task-Parallel Computing



- :movie_camera: [video lecture](https://youtu.be/tUQV_9AzhkE)

- :bar_chart: [slides (pdf)](https://samuelalbanie.com/files/digest-slides/2023-02-brief-guide-to-task-parallel-computing.pdf)

- :hammer: [parallel_fibonacci.py](parallel_fibonacci.py)

- :fountain_pen: [detailed references](http://samuelalbanie.com/digests/2023-02-brief-guide-to-task-parallel-computing)



### NP-complete Problems



- :movie_camera: [video lecture](https://youtu.be/TnGTjht3TF0)

- :bar_chart: [slides (pdf)](https://samuelalbanie.com/files/digest-slides/2023-03-brief-guide-to-np-complete-problems.pdf)

- :fountain_pen: [detailed references](http://samuelalbanie.com/digests/2023-03-brief-guide-to-np-complete-problems)



### Foundation models for code



Until recently, code for implementing data structures was largely written by hand. As of 2021, there have been exploratory efforts to employ neural networks for the task of generating code from natural language descriptions (this approach underpins the GitHub Copilot tool, for example). If you'd like to learn more, the video below describes Codex, a popular foundation model for code generation.



- :movie_camera: [YouTube video](https://www.youtube.com/watch?v=Wc7dcwF7QaA)

- :bar_chart: [slides (pdf)](https://samuelalbanie.com/files/digest-slides/2022-07-codex.pdf)

- :page_facing_up: [arxiv paper](https://arxiv.org/abs/2107.03374)

- :fountain_pen: [detailed references](https://samuelalbanie.com/digests/2022-07-codex/)



## Further background



### Big O notation (and $\Theta$, $\Omega$, $o$, $\omega$ too)



- :movie_camera: [video lecture](https://www.youtube.com/watch?v=nsIQyK4Gf48)

- :bar_chart: [slides (pdf)](https://samuelalbanie.com/files/digest-slides/2022-10-big-o-notation-and-its-companions.pdf)

- :fountain_pen: [detailed references](https://samuelalbanie.com/digests/2022-10-big-o-notation-and-its-companions/)



### Row-major order vs column-major order



- :movie_camera: [video lecture](https://youtu.be/b5lYGvcBjy4)

- :bar_chart: [slides (pdf)](https://samuelalbanie.com/files/digest-slides/2022-09-row-major-vs-column-major.pdf)

- :fountain_pen: [detailed references](https://samuelalbanie.com/digests/2022-09-row-major-vs-column-major/)



### Why numbering should start at zero (according to Dijkstra)



- :movie_camera: [video lecture](https://youtu.be/saZnPDPyQHA)

- :bar_chart: [slides (pdf)](https://samuelalbanie.com/files/digest-slides/2022-09-why-numbering-should-start-at-zero-dijkstra.pdf)

- :fountain_pen: [detailed references](https://samuelalbanie.com/digests/2022-09-why-numbering-should-start-at-zero-dijkstra/)



## Further resources



### Books



The books below represent (in my opinion) high-quality learning/reference materials. 



- :orange_book: [Introduction to Algorithms (a.k.a "CLRS")](https://mitpress.mit.edu/9780262046305/introduction-to-algorithms/) by Thomas H. Cormen et al.  - this was the primary reference used when developing the materials above. If you have a local library, it's worth checking in case they have a copy.

- :green_book: [algorithms.wtf](http://algorithms.wtf/) by Jeff Erickson (open-source)

- :green_book: + :hammer: [Elementary Algorithms](https://github.com/liuxinyu95/AlgoXY) by Liu Xinyu (open-source)

- :orange_book: [The Art of Computer Programming](https://www-cs-faculty.stanford.edu/~knuth/taocp.html) by Donald E. Knuth. This is a series of books rather than a single book. The content is engaging, humorous and extraordinarily comprehensive. It is perhaps most useful as a reference for deep dives on topics, rather than an introductory learning resource (simply because the level of detail is so high).



### Visualisations



#### Red-black trees visualisation



![red-black tree growth gif](visualisations/red-black-tree-growth.gif)



#### B-trees visualisation



![btree visualisation](figs/btree.png)



## Requirements for install



To visualise the b-trees as done above, you'll need to have a copy of pygraphviz:



```bash

conda install -c anaconda graphviz

pip install pygraphviz

```



If you'd like to run the parallel code without the GIL, you need Sam Gross' [nogil](https://nogil.dev) version of Python. This is not strictly necessary to run the code, but without it the parallel code with be slower than the serial code.



```bash

# first, install pyenv. Then:

pyenv install nogil-3.9.10

# activate the nogil

pyenv local nogil-3.9.10



# run your code here



# (for afterwards) deactivate the nogil (if you want to go back to the system python)

pyenv local system

```



## Additional comments



The repo is a work in progress. It aims to provide a reference for learning, not code for production (it has not been extensively tested to handle all edge cases).



Pull requests are welcome.