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Python for Data Analysis : Data Wrangling with Pandas, NumPy, and IPython by Wes McKinney (2017, Trade Paperback)
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- ¡Cómpralo ya!Python for Data Analysis 2nd Edition Paperback
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Product Identifiers
PublisherO'reilly Media, Incorporated
ISBN-101491957662
ISBN-139781491957660
eBay Product ID (ePID)229642588
Product Key Features
Number of Pages547 Pages
LanguageEnglish
Publication NamePython for Data Analysis : Data Wrangling with Pandas, Numpy, and Ipython
SubjectData Modeling & Design, Data Processing, Databases / Data Mining, Programming Languages / Python
Publication Year2017
TypeTextbook
AuthorWes Mckinney
Subject AreaComputers
FormatTrade Paperback
Dimensions
Item Height1.3 in
Item Weight33.2 Oz
Item Length9.2 in
Item Width7 in
Additional Product Features
Edition Number2
Intended AudienceTrade
LCCN2018-302150
Dewey Edition23
IllustratedYes
Dewey Decimal005.133
SynopsisGet complete instructions for manipulating, processing, cleaning, and crunching datasets in Python. Updated for Python 3.6, the second 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, IPython, 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 IPython shell and Jupyter notebook for exploratory computing Learn basic and advanced features in NumPy (Numerical Python) 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 2017
