Ecosyste.ms: Awesome
An open API service indexing awesome lists of open source software.
https://github.com/go-outside-labs/master-algorithms-py
👾 𝗺𝘆 𝗱𝗲𝘁𝗮𝗶𝗹𝗲𝗱 𝘄𝗮𝗹𝗸-𝘁𝗵𝗿𝗼𝘂𝗴𝗵 𝗳𝗼𝗿 𝗺𝗮𝘀𝘁𝗲𝗿𝗶𝗻𝗴 𝗰𝗹𝗮𝘀𝘀𝗶𝗰𝗮𝗹 𝗮𝗹𝗴𝗼𝗿𝗶𝘁𝗵𝗺 𝗮𝗻𝗱 𝗱𝗮𝘁𝗮 𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲 𝗽𝗿𝗼𝗯𝗹𝗲𝗺𝘀 (𝗮𝗻𝗱 𝘁𝗵𝗲 𝗯𝗼𝗼𝗸 𝗶 𝗽𝘂𝗯𝗹𝗶𝘀𝗵𝗲𝗱 𝗮 𝗱𝗲𝗰𝗮𝗱𝗲 𝗮𝗴𝗼)
https://github.com/go-outside-labs/master-algorithms-py
algorithm algorithms breath-first-search code-interview data-structure data-structures depth-first-search exercise graphs-algorithms interview learn-algorithm python python-solution queues tries
Last synced: 3 months ago
JSON representation
👾 𝗺𝘆 𝗱𝗲𝘁𝗮𝗶𝗹𝗲𝗱 𝘄𝗮𝗹𝗸-𝘁𝗵𝗿𝗼𝘂𝗴𝗵 𝗳𝗼𝗿 𝗺𝗮𝘀𝘁𝗲𝗿𝗶𝗻𝗴 𝗰𝗹𝗮𝘀𝘀𝗶𝗰𝗮𝗹 𝗮𝗹𝗴𝗼𝗿𝗶𝘁𝗵𝗺 𝗮𝗻𝗱 𝗱𝗮𝘁𝗮 𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲 𝗽𝗿𝗼𝗯𝗹𝗲𝗺𝘀 (𝗮𝗻𝗱 𝘁𝗵𝗲 𝗯𝗼𝗼𝗸 𝗶 𝗽𝘂𝗯𝗹𝗶𝘀𝗵𝗲𝗱 𝗮 𝗱𝗲𝗰𝗮𝗱𝗲 𝗮𝗴𝗼)
- Host: GitHub
- URL: https://github.com/go-outside-labs/master-algorithms-py
- Owner: go-outside-labs
- Archived: true
- Created: 2013-09-10T08:04:08.000Z (about 11 years ago)
- Default Branch: master
- Last Pushed: 2024-03-14T20:10:36.000Z (8 months ago)
- Last Synced: 2024-07-27T19:16:50.937Z (3 months ago)
- Topics: algorithm, algorithms, breath-first-search, code-interview, data-structure, data-structures, depth-first-search, exercise, graphs-algorithms, interview, learn-algorithm, python, python-solution, queues, tries
- Language: Python
- Homepage:
- Size: 16.6 MB
- Stars: 19
- Watchers: 3
- Forks: 9
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
## 👾🐍 master algorithms with python (and my book)
---
### 📖 algorithms and data structures: learn with my examples! (2023)
* 𝟘𝟘𝟘𝟙. **[arrays and strings](arrays_and_strings)**
* 𝟘𝟘𝟙𝟘. **[bit operations](bit_operations)**
* 𝟘𝟘𝟙𝟙. **[dynamic programming](dynamic_programming)**
* 𝟘𝟙𝟘𝟘. **[graphs](graphs)**
* 𝟘𝟙𝟘𝟙. **[hash objects](hash_objects)**
* 𝟘𝟙𝟙𝟘. **[heaps](heaps)**
* 𝟘𝟙𝟙𝟙. **[linked lists](linked_lists)**
* 𝟙𝟘𝟘𝟘. **[math](math)**
* 𝟙𝟘𝟘𝟙. **[queues](queues)**
* 𝟙𝟘𝟙𝟘. **[searching](searching)**
* 𝟙𝟘𝟙𝟙. **[sets](sets)**
* 𝟙𝟙𝟘𝟘. **[sorting](sorting)**
* 𝟙𝟙𝟘𝟙. **[stacks](stacks)**
* 𝟙𝟙𝟙𝟘. **[trees](trees)**
* 𝟙𝟙𝟙𝟙. **[tries](tries)**
---
### [📖 my book on algorithms and data structure: open-source for you! (2014)](MY_BOOK)
- **➡️ [one of the first-ever publications solving classic algorithm and data structure problems in python, published by hanbit media](https://www.hanbit.co.kr/store/books/look.php?p_code=B8465804191)**.
- **➡️ [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!)](https://www.hanbit.co.kr/store/books/look.php?p_code=B8465804191)**.
- **➡️ [this repo used to have 600+ stars and 300 forks before 💩 happened 😞 (here is a proof)](MY_BOOK/600_stars.png)**.
- **➡️ [just for fun: this book as a reference for a CMU computer science class](https://www.andrew.cmu.edu/user/ramesh/teaching/course/48784.pdf)**.
----
### 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](https://www.bigocheatsheet.com/)**