Python for Data Analysis的创作者· ··· Wes McKinney作者 作者简介· ··· Wes McKinney 资深数据分析专家,对各种Python库(包括NumPy、pandas、matplotlib以及IPython等)等都有深入研究,并在大量的实践中积累了丰富的经验。撰写了大量与Python数据分析相关的经典文章,被各大技术社区争相转载,是Python和开源技术社...
Python for Data Analysis 8.4分 全部书评 (5) 值得已入门者快速翻阅的入门书 每一个数据分析师或是数据科学家都使用各自不同的技术栈。即使同样使用Python做为主力数据分析语言,每个人会用到的工具组合也不尽相同。 但不管怎么说,对于希望使用p... 103 有用 还没到时候,不过是大趋势 这本书是Pandas的模...
Python for Data Analysis, 3rd Edition的创作者· ··· Wes McKinney作者 作者简介· ··· Wes McKinney is a Nashville-based software developer and entrepreneur. After finishing his undergraduate degree in mathematics at MIT in 2007, he went on to do quantitative finance work at AQR Capital Mana...
Book Data and Code Notebooks:https://github.com/wesm/pydata-book What's New in the 2nd Edition? Updated for Python 3.6 Updated for latest pandas (0.20.3) Revamped intro chapters including abridged Python language tutorial, and introductions to Jupyter and IPython Reorganized pandas content to b...
目前国内的中文版是基于Wes McKinney 在2013年出版的《Pythonfor Data Analysis》,里面的代码是基于Python2.7,有些代码已经不能运行了。 国外已经有该书的第2版了,基于最新的Python3,建议英文好的看最新版本,下面提供中文和英文电子版下载链接: 1)电子版(英文)和代码:https://github.com/wesm/pydata-book ...
Python for Data Analysis is concerned with the nuts and bolts of manipulating, processing, cleaning, and crunching data in Python. It is also a practical, modern introduction to scientific computing in Python, tailored for data-intensive applications. This is a book about the parts of the ...
NumPy, short for Numerical Python, is the fundamental package required for high performance scientific computing and data analysis. It is the foundation on which nearly all of the higher-level tools in this book are built. Here are some of the things it provides: ...
Python for Data Analysis is concerned with the nuts and bolts of manipulating, processing, cleaning, and crunching data in Python. It is also a practical, modern introduction to scientific computing … - Selection from Python for Data Analysis [Book]
There is not enough room in the book to give comprehensive treatment of the breadth and depth of functionality in matplotlib. It should be enough to teach you the ropes to get up and running. The matplotlib gallery and documentation are the best resource for learning advanced features. ...
Use the IPython shell and Jupyter notebook for exploratory computing Learn basic and advanced features in NumPy (Numerical Python) Get started with data analysis tools in the pandas library Use flexible tools to load, clean, transform, merge, and reshape data ...