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Python for Data Analysis

Data Wrangling with Pandas, NumPy, and IPython

Python for Data Analysis by Wes McKinney
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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
O'Reilly Media; September 2017
544 pages; ISBN 9781491957615
Read online, or download in DRM-free EPUB or DRM-free PDF format
Title: Python for Data Analysis
Author: Wes McKinney