{"id":20580701,"url":"https://github.com/data-camp/pandas","last_synced_at":"2025-04-14T19:45:23.374Z","repository":{"id":111003455,"uuid":"123240882","full_name":"Data-Camp/pandas","owner":"Data-Camp","description":"pandas应知必回教程","archived":false,"fork":false,"pushed_at":"2018-03-09T02:45:54.000Z","size":9798,"stargazers_count":7,"open_issues_count":0,"forks_count":1,"subscribers_count":1,"default_branch":"master","last_synced_at":"2025-03-28T08:11:16.769Z","etag":null,"topics":["pandas","python","tutorial"],"latest_commit_sha":null,"homepage":"","language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/Data-Camp.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2018-02-28T06:34:04.000Z","updated_at":"2024-08-04T11:52:07.000Z","dependencies_parsed_at":null,"dependency_job_id":"7fe4b44c-a2f2-42e4-a843-a0f4c1020478","html_url":"https://github.com/Data-Camp/pandas","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Data-Camp%2Fpandas","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Data-Camp%2Fpandas/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Data-Camp%2Fpandas/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Data-Camp%2Fpandas/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/Data-Camp","download_url":"https://codeload.github.com/Data-Camp/pandas/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":248950364,"owners_count":21188255,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["pandas","python","tutorial"],"created_at":"2024-11-16T06:24:58.370Z","updated_at":"2025-04-14T19:45:23.368Z","avatar_url":"https://github.com/Data-Camp.png","language":"Jupyter Notebook","readme":"# Pandas中文手册\n[![Binder](https://mybinder.org/badge.svg)](https://mybinder.org/v2/gh/Data-Camp/pandas/master)\n\n[pandas](http://pandas.pydata.org/) 是一个用于数据分析的Python库，它能让你快速处理一些探索性的工作。\n\n本教程改进自[IA-CAS](https://github.com/ia-cas/pandas-cookbook)对[pandas-cookbook](https://github.com/jvns/pandas-cookbook)的翻译。本手册的目的通过一些实际的例子来让你开始使用pandas。\nPandas 的[帮助文档](http://pandas.pydata.org/pandas-docs/stable/)已经相当全面了。不过，经常会有人询问应该怎样上手。接下来我讲讲如何用pandas来处理一些真实世界中的数据，如你所料，这些数据包含各种bug和异常值。\n\n接下来我会使用以下3个数据集：这些数据已经包含在本目录下\n* 311 calls in New York\n* How many people were on Montréal's bike paths in 2012\n* Montreal's weather for 2012, hourly\n\n## 目录\n* **Jupyter Notebook快速入门**\n  \u003cbr\u003e 展示了如何使用Jupyter的tab自动补齐和魔法函数\n* **Chapter 1 - 读取CSV**\n  \u003cbr\u003e 将数据导入到pandas是相当容易的一件事，即使有编码错误也不是问题！\n* **Chapter 2 - 选取数据和数据描述**\n  \u003cbr\u003e从pandas的DataFrame中选择数据有时候显得不那么直观,在这一部分我将解释一些基本的东西（比如怎么做切片操作，选取指定列）\n* **Chapter 3 - 探索性数据分析_基础**\n  \u003cbr\u003e这部分将继续介绍如何对数据切片、切块以及过滤处理。\n* **Chapter 4 - 探索性数据分析_Groupby和Aggregate**\n  \u003cbr\u003e groupby/aggregate操作 是我最喜欢pandas的地方，我几乎无时不刻都在用它。这部分必读！\n* **Chapter 5 - 合并DataFrames和简单爬取数据**\n  \u003cbr\u003e这部分将会探索Montreal的冬天冷不冷（答案：冷！），用pandas来做网页抓取相当有意思。\n* **Chapter 6 - 字符串操作**\n  \u003cbr\u003e pandas对string的操作非常好，它包含所有向量化的string操作。这部分内容将一系列包含Snow的字符串转换成向量化的数值来表示。\n* **Chapter 7 - 清理杂乱数据**\n  \u003cbr\u003e 处理脏数据可不轻松，不过对于pandas来说，那就是另外一回事了\n* **Chapter 8 - 如何处理时间戳**\n  \u003cbr\u003e 这个小技巧花了我两天才弄明白。。。\n* **Chapter 9 - 从SQL数据库读取数据**\n  \u003cbr\u003e 本部分将介绍如何从 SQLite3, PostgreSQL及MySQL中导入数据到pandas\n\n## 视频课程\n本教程的完整视频和语音讲解演示版由`数据帮 (Data Camp)`在[网易云课堂](https://study.163.com)的`pandas应知必回课程`中付费提供，可根据个人情况酌情选择。\n\n## 怎么使用Panda手册\n### 在线环境\n推荐使用Binder的出色在线Jupyter Notebook运行环境，[点击这里即可开始](https://mybinder.org/v2/gh/Data-Camp/pandas/master)。对网速要求不高，基本均可流畅运行。\n\n### 本地环境\n首先，你需要更新下Jupyter Notebook(\u0026gt;= 3.0) 以及 pandas(\u0026gt;=0.13)\n\n用pip可以完成更新操作：\n\n```\npip install pandas\n```\n\n编译和配置这些有时候挺繁琐的，我自己是用的[Anaconda](https://www.anaconda.com/what-is-anaconda/),这个软件把几乎所有你能想到的库都包含了，并且是免费和开源的。\n\n用conda也可以完成更新操作：\n```\nconda upgrade pandas\n```\n\n安装好Jupyter和pandas后，就可以使用Git命令或GitHub客户端下载\n\n```\ngit clone https://github.com/qzcool/DataCamp_pandas_cookbook.git\ncd DataCamp_pandas_cookbook/cookbook\njupyter notebook\n```\n\n执行完以上命令后，你的浏览器会自动打开一个地址为 `http://localhost:8888`的页面。\n\n## 待办\n- [ ] join 操作\n- [ ] 使用 stack/unstack\n- [ ] append\n","funding_links":[],"categories":[],"sub_categories":[],"project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fdata-camp%2Fpandas","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fdata-camp%2Fpandas","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fdata-camp%2Fpandas/lists"}