Python Data Science Handbook: Essential Tools for Working with Data, Second Edition (Grayscale Indian Edition)

Python Data Science Handbook: Essential Tools for Working with Data, Second Edition (Grayscale Indian Edition) (Paperback, Jake VanderPlas)

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Python Data Science Handbook: Essential Tools for Working with Data, Second Edition (Grayscale Indian Edition)  (Paperback, Jake VanderPlas)

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    Highlights
    • Binding: Paperback
    • Publisher: Shroff/O'Reilly
    • Genre: Higher Education and Professional Books
    • ISBN: 9789355422552, 9355422555
    • Edition: Second, 2022
    • Pages: 592
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  • Description
    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.
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    Book Details
    Imprint
    • SPD
    Publication Year
    • 2022 2022-12-24
    University Books Details
    Specialization
    • Data Science
    Dimensions
    Width
    • 7
    Height
    • 9
    Depth
    • 1.1
    Weight
    • 1000
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    Questions and Answers
    Q:Is good for beginners?
    A:YES .. This book explains about Python libraries with practical examples, easy to follow set up and use essential data science and machine learning tools. Must have for your reference .
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    Q:Is this book used for artificial intelligence and data science students 2024
    A:This book is for learning Data Science with coding examples in Python.
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