# PyDataRoad
**Repository Path**: zuokuijun/PyDataRoad
## Basic Information
- **Project Name**: PyDataRoad
- **Description**: open source for wechat-official-account (ID: PyDataLab)
- **Primary Language**: Unknown
- **License**: Not specified
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No
## Statistics
- **Stars**: 0
- **Forks**: 0
- **Created**: 2021-08-10
- **Last Updated**: 2021-08-10
## Categories & Tags
**Categories**: Uncategorized
**Tags**: None
## README
## 个人官网
欢迎访问个人官网： [http://liyangbit.com](http://liyangbit.com)
## 微信公众号
欢迎关注个人微信公众号“**Python数据之道**”（公号ID：**PyDataLab** ）。
微信公众号上目前已发布的部分文章链接，以及对应的代码或数据文件如下：
|文章发布日期|文章名称及链接|代码 / 数据文件|
|-------|---------|---------|
|20210131|[财经数据神器 Tushare，股票数据全搞定](http://liyangbit.com/)|[请点击链接](https://github.com/liyangbit/PyDataRoad/tree/master/comprehensive/Tushare)|
|20190619|[用Python快速分析、可视化和预测股票价格](https://mp.weixin.qq.com/s/fVN4ImUd4xDszJOKecwIhg)|[请点击链接](https://github.com/liyangbit/PyDataRoad/tree/master/projects/Stock-prediction-with-Python)|
|20190323|[Python 可视化神器：Plotly Express 入门之路](http://liyangbit.com/pythonvisualization/Plotly-Express-introduction-cn/)|[请点击链接](https://github.com/liyangbit/PyDataRoad/tree/master/comprehensive/Plotly-Express-Introducing)|
|20190304|[干货推荐：掌握这几点，轻松玩转 Bokeh 可视化 （项目实战经验分享）](http://liyangbit.com/pythonvisualization/Bokeh-Data-Visualization/)|[请点击链接](https://github.com/liyangbit/PyDataRoad/tree/master/comprehensive/Bokeh-data-visualization)|
|20190225|[推荐：免费获取《Python知识手册》](http://liyangbit.com/python-knowledge-handbook/)|[请点击链接](https://github.com/liyangbit/Python-Knowledge-Handbook)|
|20190131|[轻松玩转Python发送新春祝福给指定好友](http://liyangbit.com/comprehensive/wechat-msg-sent/)|[请点击链接](https://github.com/liyangbit/PyDataRoad/tree/master/comprehensive/wechat-msg-send)|
|20190121|[轻松用 Seaborn 进行数据可视化](http://liyangbit.com/pythonvisualization/Data-Visualization-with-Seaborn/)|[请点击链接](https://github.com/liyangbit/PyDataRoad/tree/master/comprehensive/Seaborn-data-visualization)|
|20181224|[Matplotlib 必须掌握的 50 个可视化图表（附完整 Python 源代码）](http://liyangbit.com/pythonvisualization/matplotlib-top-50-visualizations/)|[请点击链接](https://github.com/liyangbit/PyDataRoad/tree/master/projects/matplotlib-top-50-visualizations)|
|20180625|[第二波分析：德国是2018世界杯夺冠最大热门？ Python数据分析来揭开神秘面纱…](http://liyangbit.com/projects/projects-world-cup-predict-2nd-post/)|[请点击链接](https://github.com/liyangbit/PyDataRoad/tree/master/projects/football-world-cup)|
|20180619|[Matplotlib小册子：饼图概览](http://mp.weixin.qq.com/s?__biz=MzI2NjY5NzI0NA==&mid=2247484694&idx=1&sn=c48c2013e1035153e9bba98b8db9ee51&chksm=ea8b6a65ddfce37399d293d2730899da3167d0ab0aba33e078bef4ecbbcf75735728f1de1fb8#rd)|[请点击链接](https://github.com/liyangbit/PyDataRoad/tree/master/comprehensive/matplotlib-pie)|
|20180611|[世界杯：用Python分析热门夺冠球队](http://liyangbit.com/projects/projects-world-cup-top3-predict/)|[请点击链接](https://github.com/liyangbit/PyDataRoad/tree/master/projects/football-world-cup)|
|20180603|[Pandas：日期数据处理](http://mp.weixin.qq.com/s?__biz=MzI2NjY5NzI0NA==&mid=2247484626&idx=1&sn=35ab4f1a120d66170f564b7547c481da&chksm=ea8b6ba1ddfce2b7e401aeff2b66d2e79f4b89c47d08d14d6d501594178adf0b25cb0d6d9317&scene=21#wechat_redirect)|[数据文件（"date.csv"）请点击链接](https://github.com/liyangbit/PyDataRoad/tree/master/01data)|
|20170921|[Cheat sheet for Jupyter notebook](http://mp.weixin.qq.com/s?__biz=MzI2NjY5NzI0NA==&mid=2247484189&idx=1&sn=0d7f064d6c48dad3d78b571735849fe7&chksm=ea8b6c6eddfce578a0429dda924f28ea57a38de3ab3b16565144bd6981fc7a39b292de1f7723&scene=21#wechat_redirect)|[请点击链接](https://github.com/liyangbit/PyDataRoad/tree/master/comprehensive/cheat-sheet)|
|20170717|[数据集资源分享！！！](http://mp.weixin.qq.com/s?__biz=MzI2NjY5NzI0NA==&mid=2247483972&idx=1&sn=6f59757fba7fd64df759f1cc4d63691d&chksm=ea8b6d37ddfce4215417691c6cd299b1bf4dcc8f2e3c22a6b608ff3f462655307d3a378007dd&scene=21#wechat_redirect)|[请点击链接](https://github.com/liyangbit/PyDataRoad/tree/master/comprehensive/dataset)|
|20170710| **Python数据分析项目实战之福布斯系列**
(1) 20170719 [福布斯系列之数据分析思路篇](http://mp.weixin.qq.com/s?__biz=MzI2NjY5NzI0NA==&mid=2247483983&idx=1&sn=949d01b3917605084e4aed47737b3260&chksm=ea8b6d3cddfce42aa3a5b9b4bfd7c5b71b04ee9f4cc43dab0ebb1dead18897920901d17180c5&scene=21#wechat_redirect)
(2) 20170721 [福布斯系列之数据采集](http://mp.weixin.qq.com/s?__biz=MzI2NjY5NzI0NA==&mid=2247483984&idx=1&sn=7ab4e0cfa75bb6fea553b9c4837d7283&chksm=ea8b6d23ddfce435f65b9a3a808d43bdeee3a1343a00ae6f70f98335a3604db02a60e40af72f&scene=21#wechat_redirect)
(3) 20170802 [福布斯系列之数据完整性检查 - Python数据分析项目实战](http://mp.weixin.qq.com/s?__biz=MzI2NjY5NzI0NA==&mid=2247484019&idx=1&sn=b75fa8c5378c90f20ee68f6439ef88b7&chksm=ea8b6d00ddfce41668c74233eb14e273b4310371a3755141773f397ec26ded4ffebb65026b1d&scene=21#wechat_redirect)
(4) 20170809 [福布斯系列之补充数据收集 - Python数据分析项目实战](http://mp.weixin.qq.com/s?__biz=MzI2NjY5NzI0NA==&mid=2247484031&idx=1&sn=20374dc68a4d038cf878c6184d3fd1dd&chksm=ea8b6d0cddfce41a8a19fd16abfd1d1c8f102089b3021d337b6d0eb49abe938a4a26943fe102&scene=21#wechat_redirect)
(5) 20170814 [福布斯系列之数据清洗（1） - Python数据分析项目实战](http://mp.weixin.qq.com/s?__biz=MzI2NjY5NzI0NA==&mid=2247484067&idx=1&sn=955df45c829b6f9d8092fda5234b5566&chksm=ea8b6dd0ddfce4c66bf1ec45122493f2a719aef4eafdede2ed34ca29d9565534bda7bd4a53ad&scene=21#wechat_redirect)
(6) 20170825 [福布斯系列之数据清洗（2） - Python数据分析项目实战](http://mp.weixin.qq.com/s?__biz=MzI2NjY5NzI0NA==&mid=2247484080&idx=1&sn=6a61e4e57236a2ee96c494473cf87e36&chksm=ea8b6dc3ddfce4d5654421e7a501bf65dc131dcce33c4a7336d9e4928770725e79ef9e764954&scene=21#wechat_redirect)
(7) 20170904 [福布斯系列之数据清洗（3） - Python数据分析项目实战](http://mp.weixin.qq.com/s?__biz=MzI2NjY5NzI0NA==&mid=2247484104&idx=1&sn=0fcc6923f50f95f8d16be50bd933f87c&chksm=ea8b6dbbddfce4ad91b394818e20a8ae9a92ed8b0c8b9d14da84cc0b76f228aa7415adde8209&scene=21#wechat_redirect)
(8) 20170710 [Pandas数据处理实战：福布斯全球上市企业排行榜数据整理](http://mp.weixin.qq.com/s?__biz=MzI2NjY5NzI0NA==&mid=2247483960&idx=1&sn=4f3bc2b8f7dcbe7883c1493440c6daa4&chksm=ea8b6d4bddfce45d2c0d2de3561a7728b3b39b5914c2752ac9d255bbef95c4b995dd45892c97&scene=21#wechat_redirect)
(9) 20170918 [福布斯系列之数据清洗（5） - Python数据分析项目实战](http://mp.weixin.qq.com/s?__biz=MzI2NjY5NzI0NA==&mid=2247484170&idx=1&sn=9865ff152cb7d990cd8fbb1dff6cbc88&chksm=ea8b6c79ddfce56f10e5b60b653c3693f3a806df7112bafe709b62e18db76504792818ae3f09&scene=21#wechat_redirect) | [请点击链接](https://github.com/liyangbit/forbes_global2000)|
|20170618|[Python：一篇文章掌握Numpy的基本用法](http://mp.weixin.qq.com/s?__biz=MzI2NjY5NzI0NA==&mid=2247483867&idx=1&sn=6cafddd7868d4bfd6d2fbc2426cdae9a&chksm=ea8b6ea8ddfce7be7fe108fcc18ad945742f64657007a85805fe8b9ffcb660ae5ab3e3b2f147&scene=21#wechat_redirect)|[请点击链接](https://github.com/liyangbit/PyDataRoad/tree/master/comprehensive/numpy_basic)|
|20170613|[Python：Pandas的DataFrame如何按指定list排序](http://mp.weixin.qq.com/s?__biz=MzI2NjY5NzI0NA==&mid=2247483844&idx=1&sn=f28c73669806a0a21b04bfbbe9eda8a6&chksm=ea8b6eb7ddfce7a155a2528e518891c4b88e80887e681c962d6bd7ce67a3172e8b85aa7585d2&scene=21#wechat_redirect)|[请点击链接](https://github.com/liyangbit/PyDataRoad/tree/master/comprehensive/pandas_df_sortby_custom_list)|
|20170611|[Pycon 2017： Python可视化库大全](http://mp.weixin.qq.com/s?__biz=MzI2NjY5NzI0NA==&mid=2247483838&idx=1&sn=975a7aeb05bde37aca473fd8f6c457b1&chksm=ea8b6ecdddfce7dbc2fb6c66e23f57366f1b0ae7fbf749e5c40be3f7142709b6f15d76cd74e6&scene=21#wechat_redirect)|链接：http://pan.baidu.com/s/1eRDfR7G 密码：uaf3|
|20170605|[50年高考作文题，记录时代变迁](http://mp.weixin.qq.com/s?__biz=MzI2NjY5NzI0NA==&mid=2247483805&idx=1&sn=a368eb3528b2c2bd3f7ebceaec467d42&chksm=ea8b6eeeddfce7f832280c26b6cb08dc2daef0059a024276b7807b8e3d8e24af5cf0f72269b4&scene=21#wechat_redirect)|[请点击链接](https://github.com/liyangbit/PyDataRoad/tree/master/projects/gaokaozw)|
|20170523|[python求职Top10城市，来看看是否有你所在的城市](http://mp.weixin.qq.com/s?__biz=MzI2NjY5NzI0NA==&mid=2247483767&idx=1&sn=26f1e8c43084f9e4859031d54148fe33&chksm=ea8b6e04ddfce7125d2463732557e1f4f4655271f745c83149adcf2feb0fbdecd9eb2566a110&scene=21#wechat_redirect)|[请点击链接](https://github.com/liyangbit/PyDataRoad/tree/master/projects/zhilian_analysis)|
