What books to buy for python data analysis?

Because Python is simple, easy to learn, free and open source, portable, and scalable, its popularity has skyrocketed. In addition, Python has a very rich library, which also makes it more and more widely used in the field of data analysis. If you have decided to learn Python data analysis but have no previous programming experience, then these 6 books will be the right choice for you.

"Python Scientific Computing"(Recommended learning:Python video tutorial)

From Starting from the installation of the distribution version, this book introduces common function libraries for scientific computing and visualization, such as numpy, scipy, sympy, matplotlib, traits, tvtk, mayavi, opencv, etc., in more detail. Because the coverage is too broad, it may not be in-depth enough for a single function library, but this book can help people get started quickly and gain a comprehensive understanding of common function libraries used in scientific computing. On this basis, it is relatively easier to choose the function library you need for in-depth study.

《NumPyBeginner's Guide 2nd》/《PBasic Tutorial on Python Data Analysis: NumPy Study Guide (2nd Edition)》

A Numpy introductory guide for beginners. The whole book can be said to be short, concise and well organized, explaining the basic content of Numpy clearly and clearly. The author of this book has also written a book called "NumPyCookbook"/"NumPy Guide: Python Scientific Computing and Data Analysis". However, compared with the former, the structure of this book is a bit messy and the content is not up to par. If you want If you want to read it, I recommend reading the first one before reading this one. Here I would also like to complain about the translation of the Chinese titles of these two books. In order to sell more copies, publishers are working hard and trying every means to link them to data analysis, just like some books now always mention cloud and big data. In addition, there is also a book "LearningSciPy for Numerical and Scientific Computing", which can be used as an introductory tutorial for SciPy (it seems that there is no Chinese version yet).

"Python for Data Analysis"/"Using Python for Data Analysis"

This book also starts from numpy and focuses on data analysis. Various processes, including data access, organization, visualization, etc. In addition, this book also covers the pandas library, so those who are interested can take a look.

《Machine Learning in Action》/《Machine Learning in Action》

White box introductory tutorial for Python machine learning, focusing on explaining the basics of machine learning Various commonly used algorithms and how to implement them in Python. This is a book that teaches you how to make wheels, but the wheels you make don’t seem to be very easy to use. However, for people who are determined to build cars, it is still necessary to understand the structure and principles of wheels. In addition, before you plan to read this book, if you have almost forgotten all the advanced mathematical linear algebra probability theory, it is better to catch up on it first.

《BuildingMachine Learning Systems with Python》/《Machine Learning System Design》

Black box introductory tutorial for Python machine learning. If the previous book taught you how to assemble a wheel, this book directly tells you how to spin the wheel and how to spin it better. As for why the wheel can turn, please refer to the previous book. In addition, you can read it with the book "Learning scikit-learn: Machine Learning in Python" (no Chinese version yet). This book is a special explanation of Python's machine learning library scikit-learn. It is about 100 pages and can be used as an extended reading of the official documentation.

"Python for Finance"

A book that teaches you how to use Python to process financial data. It should be written by a Chinese and published by Packt, but it seems that it is not available in Chinese yet. Version. Compared with the previous books, this book is more professional and focuses on financial data analysis. I haven’t read this book that much, and I can’t write a more detailed introduction. The reason why I list it is because when I checked the information, I found that O'Reilly seems to be preparing to publish a book "Python for Finance" at the end of the year. It seems that Python is really becoming more and more popular.

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