Python for data analysis / Wes McKinney.

By:

McKinney, Wes

Material type: Text

TextLanguage: English Publication details: Sebastopol, Calif. : O'Reilly, c2013.Description: xiii, 452 p. : ill. ; 24 cmISBN:

9781449319793 (pbk.)
1449319793 (pbk.)

Subject(s):

DDC classification:

006.312 MCK

Contents:

Preliminaries -- Introductory examples -- IPython : an interactive computing and development environment -- NumPy basics : arrays and vectorized computation -- Getting started with pandas -- Data loading, storage, and file formats -- Data wrangling : clean, transform, merge, reshape -- Plotting and visualization -- Data aggregation and group operations -- Time series -- Financial and economic data applications -- Advanced NumPy -- Appendix: Python language essentials.

Summary: Serves as an introduction to Python for data-intensive applications.

Holdings
Item type	Current library	Call number	Copy number	Status	Date due	Barcode
Standard Loan	Thurles Library Main Collection	006.312 MCK (Browse shelf(Opens below))	1	Available		39002100654897

Enhanced descriptions from Syndetics:

Python for Data Analysis is concerned with the nuts and bolts of manipulating, processing, cleaning, and crunching data in Python. It is also a practical, modern introduction to scientific computing in Python, tailored for data-intensive applications. This is a book about the parts of the Python language and libraries you'll need to effectively solve a broad set of data analysis problems. This book is not an exposition on analytical methods using Python as the implementation language.

Written by Wes McKinney, the main author of the pandas library, this hands-on book is packed with practical cases studies. It's ideal for analysts new to Python and for Python programmers new to scientific computing.

Use the IPython interactive shell as your primary development environment Learn basic and advanced NumPy (Numerical Python) features Get started with data analysis tools in the pandas library Use high-performance tools to load, clean, transform, merge, and reshape data Create scatter plots and static or interactive visualizations with matplotlib Apply the pandas groupby facility to slice, dice, and summarize datasets Measure data by points in time, whether it's specific instances, fixed periods, or intervals Learn how to solve problems in web analytics, social sciences, finance, and economics, through detailed examples

Includes index.

Serves as an introduction to Python for data-intensive applications.

Author notes provided by Syndetics

Wes McKinney is the main author of pandas, the popular open sourcePython library for data analysis. Wes is an active speaker andparticipant in the Python and open source communities. He worked as aquantitative analyst at AQR Capital Management and Python consultantbefore founding DataPad, a data analytics company, in 2013. Hegraduated from MIT with an S.B. in Mathematics.