Python Data Science Handbook

Jake Vanderplas

ISBN 10: 1098121228 / ISBN 13: 9781098121228

Editore: O'reilly Media Jan 2023, 2023

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Neuware -'Python is a first-class tool for many researchers, primarily 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 new edition of 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 the second edition of 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.'--Publisher marketing. 563 pp. Englisch. Codice articolo 9781098121228

Dati bibliografici

Titolo: Python Data Science Handbook

Casa editrice: O'reilly Media Jan 2023

Data di pubblicazione: 2023

Legatura: Taschenbuch

Condizione: Neu

Edizione: seconda edizione

Descrizione articolo

Riassunto:

Python is a first-class tool for many researchers, primarily 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 new edition of 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 the second edition of 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:

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