Python Data Science Handbook: Essential Tools for Working with Data

VanderPlas, Jake

ISBN 10: 1491912057 / ISBN 13: 9781491912058

Published by O'Reilly Media, 2017

About this Item

Cover and pages have light corner creasing. Lamination wrinkling on spine. Spine uncreased. Text unmarked. Good solid binding. Ships Next Business Day.

Seller Inventory # 260221034

Bibliographic Details

• Title: Python Data Science Handbook: Essential Tools for Working with Data
• Publisher: O'Reilly Media
• Publication Date: 2017
• Language: English
• ISBN 10: 1491912057
• ISBN 13: 9781491912058
• Binding: paperback
• Condition: Good
• Weight: 30 ounces

About this title

Synopsis

For many researchers, Python is a first-class tool mainly because of its libraries for storing, manipulating, and gaining insight from data. Several resources exist for individual pieces of this data science stack, but only with the Python Data Science Handbook do you get them all—IPython, NumPy, Pandas, Matplotlib, Scikit-Learn, and other related tools.

Working scientists and data crunchers familiar with reading and writing Python code will find this comprehensive desk reference ideal for tackling day-to-day issues: manipulating, transforming, and cleaning data; visualizing different types of data; and using data to build statistical or machine learning models. Quite simply, this is the must-have reference for scientific computing in Python.

With this handbook, you’ll learn how to use:

• IPython and Jupyter: provide computational environments for data scientists using Python
• NumPy: includes the ndarray for efficient storage and manipulation of dense data arrays in Python
• Pandas: features the DataFrame for efficient storage and manipulation of labeled/columnar data in Python
• Matplotlib: includes capabilities for a flexible range of data visualizations in Python
• Scikit-Learn: for efficient and clean Python implementations of the most important and established machine learning algorithms

About the Author

Jake VanderPlas is a long-time user and developer of the Python scientific stack. He currently works as an interdisciplinary research director at the University of Washington, conducts his own astronomy research, and spends time advising and consulting with local scientists from a wide range of fields.