IPython Interactive Computing and Visualization Cookbook, 2nd-Packt2018.pdf下载

weixin_39820780 2020-06-18 06:30:32
We are becoming awash in the flood of digital data from scientific
research, engineering, economics, politics, journalism, business, and
many other domains. As a result, analyzing, visualizing, and harnessing
data is the occupation of an increasingly large and diverse set of people.
Quantitative ski
相关下载链接://download.csdn.net/download/windstand/10309197?utm_source=bbsseo
...全文
10 回复 打赏 收藏 转发到动态 举报
写回复
用AI写文章
回复
切换为时间正序
请发表友善的回复…
发表回复
We are becoming awash in the flood of digital data from scientific research, engineering, economics, politics, journalism, business, and many other domains. As a result, analyzing, visualizing, and harnessing data is the occupation of an increasingly large and diverse set of people. Quantitative skills such as programming, numerical computing, mathematics, statistics, and data mining, which form the core of data science, are more and more appreciated in a seemingly endless plethora of fields. Python, a widely-known programming language, is also one of the leading open platforms for data science. IPython is a mature Python project that provides scientist-friendly interactive access to Python. It is part of the broader Project Jupyter, which aims to provide high-quality environments for interactive computing, data analysis, visualization, and the authoring of interactive scientific documents. Jupyter is estimated to have several million users today. The prequel of this book, Learning IPython for Interactive Computing and Data Visualization Second Edition, Packt Publishing was published in 2015, two years after the first edition. It is a beginner-level introduction to data science and numerical computing with Python, IPython, and Jupyter. This book, the first edition of which was published in 2014, continues that journey by presenting more than 100 recipes for interactive scientific computing and data science. These recipes not only cover programming topics such as numerical computing, high-performance computing, parallel computing, and interactive visualization, but also data analysis topics such as statistics, data mining, machine learning, signal processing, graph theory, numerical optimization, and many others. This second edition is fully compatible with the latest versions of the platform and its libraries. It includes new recipes to better leverage the latest features of Python 3, and it introduces promising new projects such as JupyterLab, Altair, and Dask.
We are becoming awash in the flood of digital data from scientific research, engineering, economics, politics, journalism, business, and many other domains. As a result, analyzing, visualizing, and harnessing data is the occupation of an increasingly large and diverse set of people. Quantitative skills such as programming, numerical computing, mathematics, statistics, and data mining, which form the core of data science, are more and more appreciated in a seemingly endless plethora of fields. Python, a widely-known programming language, is also one of the leading open platforms for data science. IPython is a mature Python project that provides scientist-friendly interactive access to Python. It is part of the broader Project Jupyter, which aims to provide high-quality environments for interactive computing, data analysis, visualization, and the authoring of interactive scientific documents. Jupyter is estimated to have several million users today. The prequel of this book, Learning IPython for Interactive Computing and Data Visualization Second Edition, Packt Publishing was published in 2015, two years after the first edition. It is a beginner-level introduction to data science and numerical computing with Python, IPython, and Jupyter. This book, the first edition of which was published in 2014, continues that journey by presenting more than 100 recipes for interactive scientific computing and data science. These recipes not only cover programming topics such as numerical computing, high-performance computing, parallel computing, and interactive visualization, but also data analysis topics such as statistics, data mining, machine learning, signal processing, graph theory, numerical optimization, and many others. This second edition is fully compatible with the latest versions of the platform and its libraries. It includes new recipes to better leverage the latest features of Python 3, and it introduces promising new projects such as JupyterLab, Altair, and Dask.

12,694

社区成员

发帖
与我相关
我的任务
社区描述
CSDN 下载资源悬赏专区
其他 技术论坛(原bbs)
社区管理员
  • 下载资源悬赏专区社区
加入社区
  • 近7日
  • 近30日
  • 至今
社区公告
暂无公告

试试用AI创作助手写篇文章吧