Python for data analysis : data wrangling with pandas, NumPy, and Jupyter / by Wes McKinney

By:

McKinney, Wes

Material type:

TextLanguage: English Publication details: Sebastopol: O'REILLY, 2022.Edition: 3rd edDescription: 561 p. Paper BackISBN:

9789355421906

Subject(s):

DDC classification:

005.133 WES-P

Contents:

Preliminaries -- Python language basics IPython, and Jupyter notebooks -- Built-in data structures, functions, and files -- NumPy basics: arrays and vectorized computation -- Getting started with pandas -- Data loading, storage, and file formats -- Data cleaing and preparation -- Data wrangling: join, combine, reshape -- Plotting and visualization -- Data aggregation and group operations -- Time series -- Introduction to modeling libraries in Python -- Data analysis examples -- Advanced NumPy -- More on the IPython system.

Summary: "Get the definitive handbook for manipulating, processing, cleaning, and crunching datasets in Python. Updated for Python 3.9 and pandas 1.2, the third edition of this hands-on guide is packed with practical case studies that show you how to solve a broad set of data analysis problems effectively. You'll learn the latest versions of pandas, NumPy, and Jupyter in the process. It's ideal for analysts new to Python and for Python programmers new to data science and scientific computing." --

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	Books	ST. THOMAS COLLEGE LIBRARY, PALAI		Physics	Physics	005.133 WES-P (Browse shelf(Opens below))					Available			93166
	Books	ST. THOMAS COLLEGE LIBRARY, PALAI		Economics	Economics	005.133 WES-P (Browse shelf(Opens below))					Available			92920

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"Get the definitive handbook for manipulating, processing, cleaning, and crunching datasets in Python. Updated for Python 3.9 and pandas 1.2, the third edition of this hands-on guide is packed with practical case studies that show you how to solve a broad set of data analysis problems effectively. You'll learn the latest versions of pandas, NumPy, and Jupyter in the process. It's ideal for analysts new to Python and for Python programmers new to data science and scientific computing." --

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