Python for Data Analysis. Data Wrangling with Pandas, NumPy, and IPython
2nd edition

Par : Wes McKinney

Formats :

Grand Format
61,50 €
Définitivement indisponible
Cet article ne peut plus être commandé sur notre site (ouvrage épuisé ou plus commercialisé). Il se peut néanmoins que l'éditeur imprime une nouvelle édition de cet ouvrage à l'avenir. Nous vous invitons donc à revenir périodiquement sur notre site.
  • Réservation en ligne avec paiement en magasin :
    • Indisponible pour réserver et payer en magasin
  • Nombre de pages544
  • PrésentationBroché
  • FormatGrand Format
  • Poids0.915 kg
  • Dimensions17,8 cm × 23,4 cm × 2,6 cm
  • ISBN978-1-4919-5766-0
  • EAN9781491957660
  • Date de parution01/10/2017
  • ÉditeurO'Reilly

Résumé

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…
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.

A propos de Wes McKinney

Découvrir l'auteur