Learning the Python language is a good choice. On the one hand, Python is widely used in the fields of big data, artificial intelligence, and Web development. On the other hand, the Python language itself is relatively simple, which is very suitable for beginners.
Python is completely self-study. If you have a good foundation in English, you can read the official guide first to establish a systematic understanding of Python, and then learn more about the details of the Python language. There are still a lot of books related to Python. I recommend the following four books:
Book 1: Python Basics (3rd Edition)
This book is more suitable for beginners, and the experimental code of the third edition also starts to use the Python3 version in an all-round way. The structure of this book is very clear and logical, and the explanation of concepts is relatively in place. Another feature is that there are a large number of cases in the book (10 cases) that can be studied. It can be said to be a good introductory book.
The second book: Head First Python (2nd Edition)
This book is quite interesting. It can be said that reading this book will not feel particularly boring (the characteristics of the Head First series of books). There are a lot of visual descriptions in the book, with pictures and texts. This book has a little requirement for the basics, but I still think this book can be used as a zero-based entry.
The third book: Python3 web crawler development practice
This is a book that specifically explains how to use Python to develop crawlers. The book provides targeted explanations for various crawling scenarios. It is worth reading for those who want to use Python for crawler development.
Book Four: Data Analysis Using Python
This book is a best-selling book. It gives a detailed explanation for data analysis in Python. The explanation is relatively in place and it is worth reading. The new version uses Python 3.6 to complete the experiment, and there are many cases. The book has a complete explanation process of data operation, including a series of processes such as data cleaning, regularization, and processing.
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