Python Tools for Data Scientists

[Astaroth]

As part of the best-selling Pocket Primer series, this book is designed to provide an introduction to Python tools which are used by data scientists. It includes coverage of fundamental aspects of NumPy and Pandas, how to write regular expressions, and how to perform data cleaning tasks. The first chapter contains a quick tour of basic Python, followed by a chapter introducing NumPy, and followed by a chapter on Pandas. Chapter 4 provides a high-level view of Sklearn and SciPy. Chapter 5 contains an assortment of data cleaning tasks that are solved via Python and the awk programming language. Chapter 6 delves into data visualization with Matplotlib, Seaborn, and Bokeh. Next, one appendix explores issues that can arise with data, followed by an appendix on awk. Numerous code samples are used to illustrate concepts. Companion files with source code are available for downloading from the publisher.
Features
Features coverage of fundamental aspects of NumPy and Pandas, how to write regular expressions, and how to perform data cleaning tasks
Demonstrates concepts using numerous code samples throughout
Includes companion files with source code
About the Author
Oswald Campesato (San Francisco, CA) is an adjunct instructor at UC-Santa Clara and specializes in Deep Learning, Java, Android, and Python. He is the author/co-author of over twenty-five books including Data Wrangling, Python 3 for Machine Learning, and the NLP Using R Pocket Primer (all Mercury Learning).
Part of the best-selling Pocket Primer series, this book provides an introduction to the Python tools used by data scientists. It covers the fundamentals of NumPy and Pandas, covering how to write regular expressions, and how to perform data cleaning tasks. Chapter 1 provides a quick overview of Python basics, followed by a chapter introducing NumPy, and then a chapter on Pandas. Chapter 4 provides an overview of Sklearn and SciPy. Chapter 5 contains a number of data cleaning tasks using Python and the awk programming language. Chapter 6 covers data visualization using Matplotlib, Seaborn, and Bokeh. An appendix then looks at problems that can arise with data, followed by an appendix on awk. Numerous code examples are used to illustrate concepts. The accompanying source code files are available for download from the publisher.
Features
Covers fundamental aspects of NumPy and Pandas, teaches how to write regular expressions, and performs data cleaning tasks
Demonstrates concepts using numerous code examples
Includes accompanying source code files
About the Author
Oswald Campesato (San Francisco, CA) is an associate professor at the University of California, Santa Clara, and specializes in deep learning in Java, Android, and Python. He has authored/co-authored over twenty-five books, including Data Analysis, Python 3 for Machine Learning, and NLP Using R Pocket Primer (all by Mercury Learning).

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