# Python, Algorithms, and Data Structures (Book)

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#### This repository contains my book on Algorithms and Data Structure in Python, published by [Hanbit Media](http://www.hanbit.co.kr/) in 2014.

#### 👉 [here is a pic when this repo used to have 600+ stars and 300 forks (before 💩 happened 😞)](600_stars.png)

#### 👉 [this book as a reference for a CMU computer science class](https://www.andrew.cmu.edu/user/ramesh/teaching/course/48784.pdf)

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### Summary


* This book as one of the first publications to solve the classic computer science algorithm and data structure problems in Python.

* [Here is the PDF for the free e-book](https://github.com/bt3gl/Book_on_Python_Algorithms_and_Data_Structure/blob/master/book/ebook_pdf/book_second_edition.pdf).

* [Here is the source code, including abstract structures, bitwise operations, builtin Python data structures, searching and sorting, trees, and real interview problems](https://github.com/bt3gl/Book_on_Python_Algorithms_and_Data_Structure/tree/master/book/ebook_src).

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### [The Zen of Python](https://www.python.org/dev/peps/pep-0020/)

```
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!
```