ISBN 978-1-449-31979-3
Publisher: O'Reilly, 2012
Paperback, 452 pages.

Editorial Reviews.

Looking for complete instructions on manipulating, processing, cleaning, and crunching structured data in Python? This hands-on book is packed with practical case studies that show you how to effectively solve a broad set of data analysis problems, using several Python libraries-including NumPy, pandas, matplotlib, and IPython.
Written by Wes McKinney, the main author of the pandas library, Python for Data Analysis also serves as a practical, modern introduction to scientific computing in Python for data-intensive applications. It’s ideal for analysts new to Python and for Python programmers new to scientific computing.

• Use the IPython interactive shell as your primary development environment
• Learn basic and advanced NumPy (Numerical Python) features
• Get started with data analysis tools in the pandas library
• Use high-performance tools to load, clean, transform, merge, and reshape data
• Create scatter plots and static or interactive visualizations with matplotlib
• Apply the pandas groupby facility to slice, dice, and summarize datasets
• Work with time series data in many different forms
• Learn how to solve problems in web analytics, social sciences, finance, and economics, through detailed examples
Wes McKinney is the main author of pandas, the popular open source Python library for data analysis. Wes is an active speaker and participant 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.