Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython

McKinney, Wes

ISBN 10: 1491957662 / ISBN 13: 9781491957660

Edité par O'Reilly Media, 2017

Item in very good condition! Textbooks may not include supplemental items i.e. CDs, access codes etc. N° de réf. du vendeur 00096425352

Synopsis :

Get 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

À propos de l?auteur: 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 a quantitative analyst at AQR Capital Management and Python consultant before founding DataPad, a data analytics company, in 2013. He graduated from MIT with an S.B. in Mathematics.

Détails bibliographiques

Titre : Python for Data Analysis: Data Wrangling ...
Éditeur : O'Reilly Media
Date d'édition : 2017
Reliure : Couverture souple
Etat : Very Good
Edition : 2ème Édition