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Python for Data Analysis : Data Wrangling with Pandas, NumPy, and Jupyter by Wes McKinney (2022, Trade Paperback)
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About this product
Product Identifiers
PublisherO'reilly Media, Incorporated
ISBN-10109810403X
ISBN-139781098104030
eBay Product ID (ePID)14057247928
Product Key Features
Number of Pages579 Pages
LanguageEnglish
Publication NamePython for Data Analysis : Data Wrangling with Pandas, Numpy, and Jupyter
SubjectData Visualization, Data Processing, Databases / Data Mining, Programming Languages / Python
Publication Year2022
TypeTextbook
Subject AreaComputers
AuthorWes Mckinney
FormatTrade Paperback
Dimensions
Item Height1.2 in
Item Weight35.3 Oz
Item Length9.2 in
Item Width7.2 in
Additional Product Features
Edition Number3
LCCN2023-549174
Dewey Edition23
IllustratedYes
Dewey Decimal006.312
SynopsisGet the definitive handbook for manipulating, processing, cleaning, and crunching datasets in Python. Updated for Python 3.10 and pandas 1.4, 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. Written by Wes McKinney, the creator of the Python pandas project, this book is a practical, modern introduction to data science tools in Python. It's ideal for analysts new to Python and for Python programmers new to data science and scientific computing. Data files and related material are available on GitHub. Use the Jupyter notebook and IPython shell for exploratory computing Learn basic and advanced features in NumPy Get started with data analysis tools in the pandas library Use flexible tools to load, clean, transform, merge, and reshape data Create informative visualizations with matplotlib Apply the pandas groupby facility to slice, dice, and summarize datasets Analyze and manipulate regular and irregular time series data Learn how to solve real-world data analysis problems with thorough, detailed examples
LC Classification NumberQA76.73.P98M42 2022
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