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

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ISBN-13: 9781491957660
ISBN-10: 1491957662
Edition: 2
Author: Wes McKinney
Publication date: 2017
Publisher: O'Reilly Media
Format: Paperback 550 pages
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Book details

ISBN-13: 9781491957660
ISBN-10: 1491957662
Edition: 2
Author: Wes McKinney
Publication date: 2017
Publisher: O'Reilly Media
Format: Paperback 550 pages
Category: Statistics , Computers

Summary

Acknowledged authors Wes McKinney wrote Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython comprising 550 pages back in 2017. Textbook and eTextbook are published under ISBN 1491957662 and 9781491957660. Since then Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython textbook received total rating of 4 stars and was available to sell back to BooksRun online for the top buyback price of $ 11.85 or rent at the marketplace.

Description

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

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