10 must-see books from Python Reptile Little White Advanced Data Analysis God

I: Python Advanced Data Analysis

By Sayan Muhopadia

Recommendation: This book contains examples of data analysis, covers a wide range of areas from basic statistics to ETL, deep learning, and the Internet of Things, giving the concept of various technical aspects of industrial analysis projects.

About the author: Sayan Mukhopadhyay has over 13 years of industry experience and a deep understanding of data analytics applications in investment banking, online payments, online advertising, IT architecture, and retail. His area of expertise is high-performance computing in distributed and data-driven environments such as real-time analytics, high-frequency trading, and more.

Python Advanced Data Analysis: Machine Learning, Deep Learning, and NLP Instances

Author: Sayan Mukhopadhyay

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Ii: Python Data Analysis and Data Operations

Author: Song Tianlong

Recommendation: first-line data analysis experts write, more than 10 Internet cafes and a number of data science related organizations jointly recommended, including 50 data workflow knowledge points, 14 data analysis and mining topics, 8 comprehensive operational cases, covering members, goods, traffic, content 4 big data operation topics, 360 degrees of pulse operation problems and fit the data scene landing.

About the author: Tony Song, senior big data technology expert, data director of Softcom Power Group Big Data Research Institute, head of technology and consulting for Webtrekk China, and manager of Gome Online's big data center. Specializing in data mining, modeling, analytics, and operations, mastering end-to-end data value scenario design, business requirements transformation, data structure grooming, data modeling and learning, and data engineering delivery.

Python Data Analytics and Data Operations

Author: Song Tianlong

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Iii: Python Data Analysis and Mining Practice

Author: Zhang Liangdu Wang Lu Tan Liyun Su Jianlin, etc

Recommendation: More than 10 senior experts and researchers in the field of data mining, more than 10 years of big data mining consulting and implementation experience crystallization. Starting from the application of data mining, with real cases in power, aviation, medical, Internet, manufacturing and public service industries as the main line, the paper introduces the Python data mining modeling process in depth, which is very practical.

About the author: Zhang Liangdu, senior big data mining expert and pattern recognition expert, senior information project manager, has more than 10 years of big data mining applications, consulting and training experience. F or telecommunications, power, government, Internet, manufacturing, retail, banking, biology, chemicals, medicine and other industries hundreds of large enterprises have provided data mining applications and consulting services, practical experience is very rich. In addition, he is well versed in Java EE enterprise application development and is the author of bestsellers such as "Practical Tutorials on Neural Networks", "Data Mining: Practical Case Studies", "MATLAB Data Analysis and Mining Practice", "R Language Data Analysis and Mining Practice".

Python data analysis and mining

Author: Zhang Liangdu Wang Lu Tan Liyun Su Jianlin, etc

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Iv: Data Analysis with Python

Author: Wes McKinney

Recommendation: Still struggling to find a complete course in controlling, processing, organizing, and analyzing structured data with Python? This book contains a number of practical cases, and you'll learn how to efficiently solve a wide variety of data analysis problems with a variety of Python libraries, including Numpy, Pandas, Matplotlib, and IPython.

About the author: Wes McKinney senior data analysis expert, has extensive research on various Python libraries (including NumPy, pandas, matplotlib, IPython, etc.) and has accumulated extensive experience in a wide range of practices. H e has written a large number of classic articles related to Python data analysis, which have been reprinted by the major technology communities and are recognized as one of the leading figures in Python and the open source technology community. Pandas, a well-known open source Python library for data analysis, has been developed and has been well received by users.

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Use Python for data analysis

Author: (AMERICAN) MCKINNEY, TANG XUEXUAN, ET AL

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V: Python Data Mining: Concepts, Methods and Practices

By Megan Squire

Recommendations: This book uses the Python programming language and project-based approach to introduce a variety of often overlooked data mining concepts, such as association rules, entity matching, network analysis, text mining, and anomaly detection. Each chapter provides a comprehensive description of the basics of a particular data mining technique, providing alternatives to assess its effectiveness, and implementing the technology with real data to help you "know it, know what it is" and move on to the path of a data mining expert.

About the author: Megan Squire, professor of computer science at Elon University, whose main research focus is on the collection, cleaning and analysis of data produced by free and open source software. She is the leader of FLOSSmole.org, FLOSSdata.org and FLOSSpapers.org projects.

Python Data Mining: Concepts, Methods, and Practices

Author: Meghan Squire

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Vi: Mastering Python Web Reptiles

Author: Wei Wei

Recommendation: To the actual combat-oriented, through the Python network crawler core technology and mainstream framework, to help readers quickly and deeply grasp the network crawling technology and anti-climbing skills.

About the author: Wei Wei, a veteran Python programmer who is proficient in the use and development of web reptiles, is currently the co-founder and CEO of Chongqing Xiang Network Technology Co., Ltd. In addition, he is well versed in PHP, Java and project management and is a special lecturer at online educational institutions such as CSDN, 51CTO, Geek Academy, etc.

Proficiency in Python Web Reptiles: Core Technologies, Frameworks, and Project Battles

Author: Wei Wei

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Seven: Python Reptile Development and Project Combat

Author: Fan Chuanhui

Recommendation: Zero-based learning reptile technology, starting from Python and Web front-end foundation, from shallow depth, containing a large number of cases, practical, from static sites to dynamic sites, from stand-alone crawlers to distributed reptiles, covering the use of Scrapy and PySpider frameworks, de-heavy program design and distributed crawler construction.

About the author: Fan Chuanhui, senior networm, Python developer, participated in the development of a number of network applications, in the actual development has accumulated a wealth of practical experience, and good at summing up, contributed a number of technical articles widely praised. Research interests are network security, reptile technology, data analysis, drive development and other technologies.

Python reptile development and project combat

Author: Fan Chuanhui

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Eight: Python Data Analysis Battle

By Ivan Idris

Recommendation: Through more than 140 examples, we explain in detail the practical techniques and best practices for data analysis with Python, and include Docker images of various tools.

About the author: Ivan Idris, Master of Experimental Physics. A fter graduation, he worked for a number of companies in Java, data warehouse development, and QA analysis. C urrently, his interests are focused on business intelligence, big data, and cloud computing. Ivan Idris is interested in writing concise, testable program code and interesting technical articles, and has written books such as "NumPy Beginner's Guide" and "Python Data Analysiss" "NumPy Cookbook" and "Learning NumPy Array."

Python data analysis is a real-world battle

Author: Ivan Idris, Indonesia

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Nine: Python Data Visualization

By Kirthi Raman

Recommendation: A comprehensive explanation of Python's visualization methods in different applications (2) covers Python's various drawing options and contains a large number of practical cases.

About the author: Kirthi Raman is currently the Chief Data Engineer at Neustar. K irthi has been working on data visualization, mastering JavaScript, Python, R, and Java, and is an outstanding engineer. P reviously, he was chief architect, data analyst and information retrieval specialist at Quotient.

Python data visualization

By Kirthi Raman

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Ten: Python Financial Data Analysis

By James Ma Weiming

Recommendation: This book introduces the models and programming modeling methods commonly used in the financial field. I mprove financial applications with Python's powerful scientific computing capabilities. Write basic procedures for modeling, trading, pricing, and analysis.

About the author: James Ma Weiming, graduated from the Stewart School of Business at the Illinois Institute of Technology with a master's degree in finance. H e has written a number of high-frequency, low-latency open source programs and tools. A fter receiving a bachelor's degree in computer engineering from Nanyang Technological University in Singapore and a diploma in information technology from Nanyang Polytechnic University, James began working in Singapore. He has traded in foreign exchange and fixed income products and developed mobile applications for a fund sales platform.

Python Financial Data Analysis

Author: Ma Weiming, Singapore

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