"Python Scientific Computing" (Recommended learning: Python video tutorials)
Starting from the installation of the distribution, this book takes the common libraries for scientific computation and visualization, such as numpy, scipy, sympy, matplotlib, traits, tvtk, mayavi, opencv, and so on. are introduced in more detail. Because of the wide range of functions, it may not be deep enough for individual libraries, but this book allows people to quickly get started, a comprehensive understanding of the common libraries used in scientific computing. It is then relatively easy to select the libraries you need for in-depth study on this basis.
"NumPyBeginner's Guide 2nd"/"Python Data Analysis Tutorial: NumPy Study Guide (2nd Edition)
NumPy is a beginner's guide to Numpy. The whole book can be described as short, concise, and well-organized, and the basics of Numpy are clearly explained. The author of this book has also written a "NumPyCookbook" / "NumPy Strategy: Python Scientific Computing and Data Analysis", but this book compared to the former, it seems that the structure is a bit messy, the content is also a bit of on and off, if you want to look at it, it is recommended that you read the first book and then come to see this one. Here I also want to spit out the Chinese translation of the title of these two books. In order to be able to sell a few more books, the publisher is also quite desperate, trying to find ways to be linked to a few words with the data analysis, as if some books now always have to pull on the cloud and big data. In addition, there is a book "LearningSciPy for Numerical and Scientific Computing", which can be used as an introductory tutorial for SciPy (it seems that the Chinese version is not yet available).
"Pythonfor Data Analysis"/"Data Analysis with Python"
This book also starts with numpy, and focuses on the various processes of data analysis, including data access, regularization, visualization, and so on. In addition, this book also covers the library pandas, which you can check out if you are interested.
"MachineLearning in Action"/"Machine Learning in Action"
White-box introductory tutorial on machine learning in Python, focusing on various types of commonly used algorithms for machine learning and how to implement them in Python. It's a book that teaches you how to build wheels, but the wheels you build don't seem to work very well that's all. Still, for people who aspire to build cars, it's essential to understand the structure and principles of the wheel. In addition, before reading this book, if you have forgotten much of your high math line algebra probability theory, it is better to catch up.
BuildingMachine Learning Systems with Python / Designing Machine Learning Systems
Black-box introduction to machine learning in Python. If the last book taught you how to assemble a wheel, this book is straight up telling you how to turn the wheel and how you can do it better. As for why the wheel turns, see the previous book. In addition, you can read the book Learning scikit-learn: Machine Learning in Python together with this book (no Chinese version available). This book is for Python's machine learning library scikit-learn to explain a special book, 100 pages or so, can be used as the official documentation to expand the reading.
"Pythonfor Finance"
The book that teaches you how to use Python to process financial data is supposed to be written by a Chinese person and published by Packt, but it seems that there is no Chinese version yet. Compared to the previous books, this book is a bit more specialized, focusing on financial data analysis. I haven't read much of this book, and I can't write a more detailed introduction. The reason why I listed it is because when I was checking the information, I found that O'Reilly seems to be ready to publish a book "Python for Finance" at the end of the year. It seems that Python is really getting hot.
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