Python data science handbook : essential tools for working with data

By:

Vanderplas, Jake

Material type: Text

TextPublication details: Mumbai O'Reilly Shroff Publishers & Distributors Pvt Ltd 2023Edition: 2nd edDescription: xxiv, 563pISBN:

9789355422552 (pbk.)

Subject(s):

DDC classification:

005.133 VAN

Summary: "All Indian Reprints of O'Reilly are printed in Grayscale 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 allIPython, 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"

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Holdings
Item type	Current library	Collection	Call number	Status	Date due	Barcode
Book	Plaksha University Library	Computer science	005.133 VAN (Browse shelf(Opens below))	Checked out	23/01/2025	004549

https://www.shroffpublishers.com/books/9789355422552/

"All Indian Reprints of O'Reilly are printed in Grayscale

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 allIPython, 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"

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