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