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“Writing a textbook” holds immeasurable merit as it allows us to save others’ time with our own. In today’s impetuous and realistic society, I have dedicated myself to writing textbooks, knowing that they may not be counted among my personal achievements. However, I find immense joy in the process of writing this textbook. As the old saying goes, “If you’re afraid, don’t do it; if you’re doing it, don’t be afraid!” It has taken me 18 months of dedicated effort simply because I want to utilize my time to save the valuable time of the readers. “Writing a textbook” requires an exceptional top-down design and stepwise refinement. Through years of teaching experience, I have come to realize the urgent need for an excellent Python textbook for the education of data science and big data professionals. Existing textbooks face several issues: firstly, they teach (or learn) Python as if it were Java or C, failing to capture the unique characteristics of Python. Secondly, the style of “knowledge first, code later” and the dominance of knowledge over practical implementation seem to invert their proper order of importance. Thirdly, there is no clear distinction between Python textbooks used for data science and computer science, leading to confusion. Lastly, some authors treat the readers (or themselves) as programming novices, neglecting the fact that most readers possess prior knowledge of Java or C and are learning Python as a second programming language. They do not require repetitive explanations of low-level concepts or redundant explanations of the same knowledge in different languages. Overcoming these limitations and exploring new teaching and textbook-writing patterns was my original intention in writing this book. Whether or not I have achieved this goal remains to be seen and depends on your careful reading and fair judgment. “Writing a textbook” necessitates the knowledge and practice of countless resources. Throughout the writing process, I extensively referred to monographs, textbooks, papers, open-source projects, and original data. The reference list contains detailed citations for the sources I have used. However, I may have inadvertently missed a few. If so, I sincerely apologize to the relevant scholars. This book also incorporates data science research and engineering projects completed by my team since 2012, as well as the questions and discussions raised by my students. The course slides, raw data, source codes, and errata list for this textbook can be found at github.com. For further information, please contact me at chaolemen@ruc.edu.cn. “Writing a textbook” is impossible without the help of others. The leaders and editors at Springer Press and Publishing House of Electronics Industry, especially editor Zhang Haitao, have made significant contributions to the publication of this book. I would like to express my gratitude to the Ministry of Education-IBM Industry- University Cooperation Collaborative Education Project for their funding and support. Special thanks go to Zhang Chen, Xiao Jiwen, Liu Xuan, Tianyi Zhang, Meng Gang, Sun Zhizhong, Wang Rui, Liu Yan, Yang Canjun, Li Haojing, Wang Yuqing, Qu Hanqing, Zhao Qun, Li Xueming, Ji Jiayu, and other students at Renmin University of China for their invaluable proofreading assistance. “Writing a textbook” is a lengthy process of iterative refinement. This edition may still have some shortcomings, and I genuinely welcome your feedback and suggestions. This textbook is my third book after Data Science and Data Science Theory and Practice. Someone once said to me, “Prof. Chaolemen, you have already achieved so much, why do you still work so tirelessly? You will become the number one in the field of data science.” I replied, “No, that is not my purpose. I have undertaken all these endeavors with the belief that I strive to be the one who works the hardest and is most willing to invest time and energy in this field. As for other matters, I am not concerned. My hope is that my dedication and effort will inspire you to do the same! In this textbook, I have aimed to provide a comprehensive and cohesive guide to Python programming. I have taken great care in addressing the shortcomings I observed in existing textbooks. Rather than treating Python as a mere translation of Java or C, I have emphasized its unique features and characteristics. The content is structured in a logical and progressive manner, ensuring that knowledge and practical implementation are presented in the right order of importance.