master-algorithms-py

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👾🐍 master algorithms with python (and my book)

📖 algorithms and data structures: learn with my examples! (2023)

• 𝟘𝟘𝟘𝟙. arrays and strings
• 𝟘𝟘𝟙𝟘. bit operations
• 𝟘𝟘𝟙𝟙. dynamic programming
• 𝟘𝟙𝟘𝟘. graphs
• 𝟘𝟙𝟘𝟙. hash objects
• 𝟘𝟙𝟙𝟘. heaps
• 𝟘𝟙𝟙𝟙. linked lists
• 𝟙𝟘𝟘𝟘. math
• 𝟙𝟘𝟘𝟙. queues
• 𝟙𝟘𝟙𝟘. searching
• 𝟙𝟘𝟙𝟙. sets
• 𝟙𝟙𝟘𝟘. sorting
• 𝟙𝟙𝟘𝟙. stacks
• 𝟙𝟙𝟙𝟘. trees
• 𝟙𝟙𝟙𝟙. tries

📖 my book on algorithms and data structure: open-source for you! (2014)

• ➡️ one of the first-ever publications solving classic algorithm and data structure problems in python, published by hanbit media.
• ➡️ last time i checked, it had 4.6/5 stars and 33 reviews (not bad for a book written in school by a self-taught!).
• ➡️ this repo used to have 600+ stars and 300 forks before 💩 happened 😞 (here is a proof).
• ➡️ just for fun: this book as a reference for a CMU computer science class.

the zen of phython

Beautiful is better than ugly.
Explicit is better than implicit.
Simple is better than complex.
Complex is better than complicated.
Flat is better than nested.
Sparse is better than dense.
Readability counts.
Special cases aren't special enough to break the rules.
Although practicality beats purity.
Errors should never pass silently.
Unless explicitly silenced.
In the face of ambiguity, refuse the temptation to guess.
There should be one-- and preferably only one --obvious way to do it.
Although that way may not be obvious at first unless you're Dutch.
Now is better than never.
Although never is often better than *right* now.
If the implementation is hard to explain, it's a bad idea.
If the implementation is easy to explain, it may be a good idea.
Namespaces are one honking great idea -- let's do more of those!

external resources

• big-o complexities chart and explanation