Python Data Science Handbook

Jake Vanderplas

ISBN 10: 1491912057 / ISBN 13: 9781491912058

Editore: O'Reilly Media, United States, Sebastopol, 2016

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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. The book has been read, but is in excellent condition. Pages are intact and not marred by notes or highlighting. The spine remains undamaged.

Codice articolo GOR008657258

Dati bibliografici

• Titolo: Python Data Science Handbook
• Editore: O'Reilly Media, United States, Sebastopol
• Data di pubblicazione: 2016
• Lingua: Inglese
• ISBN 10: 1491912057
• ISBN 13: 9781491912058
• Rilegatura: Paperback
• Condizione: Very Good
• Peso: 921 grammi

Descrizione articolo

Riassunto

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

Informazioni sull?autore

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.