CodingInterviews2-ByPython

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CodingInterviews2-ByPython

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此项目是《剑指offer》第二版里算法面试题的Python3实现版本，作为一本经典书籍，可以时常拿出来看一看、翻一翻、记一记。同时也是为了Python程序员能够更好的通过公司的技术面试，拿到心仪的offer。

CodingInterviews2-ByPython 최신버젼 다운로드

최종 버전 다운로드 (.zip)

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## 项目介绍

- 此项目是《剑指offer》第二版里算法面试题的Python3实现版本。
- 包括数组、字符串、链表、树、栈、队列和哈希表等数据结构的面试题。
- 查找、排序、动态规划、贪心等算法题。
- 收录的都是各大公司常考的题目。

扎实的基本功是成为优秀程序员的前提条件，而这些体现在编程语言、数据结构和算法上，这也是此项目的初衷。帮助自己的同时也帮助各位同学在各自的道路上能够走得更远。

## 目录

**项目持续更新中......**

| 类型     | 题型                                                         |
| -------- | ------------------------------------------------------------ |
| 数据结构 | [Interview-3：数组中重复的数字](https://github.com/NLP-LOVE/CodingInterviews2-ByPython/blob/master/Algorithm/Interview-3.md) |
| 数据结构 | [Interview-4：二维数组中的查找](https://github.com/NLP-LOVE/CodingInterviews2-ByPython/blob/master/Algorithm/Interview-4.md) |
| 数据结构 | [Interview-5：字符串替换空格](https://github.com/NLP-LOVE/CodingInterviews2-ByPython/blob/master/Algorithm/Interview-5.md) |
| 数据结构 | [Interview-6：从尾到头打印链表](https://github.com/NLP-LOVE/CodingInterviews2-ByPython/blob/master/Algorithm/Interview-6.md) |
| 数据结构 | [Interview-7：重建二叉树](https://github.com/NLP-LOVE/CodingInterviews2-ByPython/blob/master/Algorithm/Interview-7.md) |
| 数据结构 | [Interview-8：二叉树中序遍历的下一个节点](https://github.com/NLP-LOVE/CodingInterviews2-ByPython/blob/master/Algorithm/Interview-8.md) |
| 数据结构 | [Interview-9：用两个栈实现队列](https://github.com/NLP-LOVE/CodingInterviews2-ByPython/blob/master/Algorithm/Interview-9.md) |
| 数据结构 | [Interview-10：斐波那契数列](https://github.com/NLP-LOVE/CodingInterviews2-ByPython/blob/master/Algorithm/Interview-10.md) |
| 数据结构 | [Inverview-59：队列的最大值](https://github.com/NLP-LOVE/CodingInterviews2-ByPython/blob/master/Algorithm/Inverview-59.md) |
| 数据结构 | [Inverview-62：圆圈中最后剩下的数字](https://github.com/NLP-LOVE/CodingInterviews2-ByPython/blob/master/Algorithm/Interview-62.md) |
| 查找     | [LeetCode-704：二分查找](https://github.com/NLP-LOVE/CodingInterviews2-ByPython/blob/master/Algorithm/LeetCode-704_binary_search.md) |

**其他项目推荐：** [personal-llm-api 轻量级的 LLM 接口服务](https://github.com/NLP-LOVE/personal-llm-api)

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