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

This item is fairly worn, but continues to work perfectly. Signs of wear can include aesthetic issues such as scratches, dents, worn corners, bends, tears, small stains, and partial water damage. All pages and the cover are intact but the dust cover may be missing, if applicable. Pages may include excessive notes and highlighting, but the text is not obscured or unreadale. Satisfaction Guaranteed. Seller Inventory # 4BQGBJ013O6Q_ns

Bibliographic Details

Title: Python Data Science Handbook: Essential ...

Publisher: O'Reilly Media

Publication Date: 2017

Binding: paperback

Condition: Acceptable

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 allâ??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, youâ??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.