Fast Python for Data Science

Fast Python for Data Science is a hands-on guide to writing Python code that processes more data faster with less resources. It takes a holistic approach to Python performance, covering code, libraries, and computing architecture optimization. Written for experienced practitioners, it provides practical solutions for improving computation and storage efficiency, with examples such as rewriting games in Cython and implementing a MapReduce framework. It also covers Python GPU computing and how modern hardware has rehabilitated some former antipatterns.

Format: Paperback / softback
Length: 375 pages
Publication date: 30 June 2023
Publisher: Manning Publications

A comprehensive guide to optimizing Python code for data science, "Fast Python for Data Science" offers practical solutions for improving computation and storage efficiency. Written for experienced practitioners, the book dives right into practical solutions for improving computation and storage efficiency, including rewriting games in lower-level Cython and implementing a MapReduce framework from scratch. It also explores Python GPU computing and how modern hardware has rehabilitated some former antipatterns and made counterintuitive ideas the most efficient way of working.

In today's data-driven world, fast and accurate systems are essential for handling the vast datasets and complex analytical algorithms that are prevalent in modern data science. Python programmers must enhance performance by writing faster pure-Python programs, optimizing the use of libraries, and leveraging modern multi-processor hardware. "Fast Python for Data Science" provides a comprehensive guide to achieving these goals.

The book takes a holistic approach to Python performance, examining how code, libraries, and computing architecture interact and can be optimized together. It begins by introducing the basics of Python programming and covers essential concepts such as data types, loops, functions, and object-oriented programming. From there, it delves into advanced topics such as parallel computing, memory management, and performance optimization techniques.

One of the key strengths of "Fast Python for Data Science" is its practical nature. The book offers numerous hands-on examples and exercises that allow readers to apply the techniques learned to real-world data science problems. These examples range from rewriting games in lower-level Cython to implementing a MapReduce framework from scratch. By experimenting with these examples, readers can gain a deeper understanding of the concepts and apply them to their own projects.

In addition to its practical focus, "Fast Python for Data Science" also covers the latest developments in Python technology for data science. It discusses the use of libraries such as NumPy, SciPy, and Pandas, which are widely used in data analysis and manipulation. The book also explores the use of modern multi-processor hardware and parallel computing techniques, which can significantly boost the performance of Python code.

Another notable feature of "Fast Python for Data Science" is its emphasis on writing efficient and readable code. The book encourages programmers to follow best practices such as using proper indentation, naming variables consistently, and using descriptive function names. By writing clean and concise code, readers can make their code more maintainable and easier to understand, which is essential for collaboration and long-term success in data science.

Whether you are a Python programmer looking to improve your performance or a data scientist seeking to optimize your code for better results, "Fast Python for Data Science" is an essential resource. Its comprehensive coverage of Python performance, practical examples, and up-to-date information make it a valuable tool for anyone working with data in Python.

In conclusion, "Fast Python for Data Science" is a comprehensive guide to optimizing Python code for data science. Written for experienced practitioners, the book offers practical solutions for improving computation and storage efficiency, including rewriting games in lower-level Cython and implementing a MapReduce framework from scratch. It also explores Python GPU computing and how modern hardware has rehabilitated some former antipatterns and made counterintuitive ideas the most efficient way of working. With its practical focus, emphasis on writing efficient and readable code, and up-to-date coverage of Python technology, "Fast Python for Data Science" is an essential resource for anyone working with data in Python.

Weight: 572g
Dimension: 188 x 237 x 22 (mm)
ISBN-13: 9781617297939