<DIV><p><i>Python for Data Analysis</i> is concerned with the nuts and bolts of manipulating, processing, cleaning, and crunching data in Python. It is also a practical, modern introduction to scientific computing in Python, tailored for data-intensive applications. This is a book about the parts of the Python language and libraries you’ll need to effectively solve a broad set of data analysis problems. This book is not an exposition on analytical methods using Python as the implementation language.</p><p>Written by Wes McKinney, the main author of the pandas library, this hands-on book is packed with practical cases studies. It’s ideal for analysts new to Python and for Python programmers new to scientific computing.</p><ul><li>Use the IPython interactive shell as your primary development environment </li><li>Learn basic and advanced NumPy (Numerical Python) features </li><li>Get started with data analysis tools in the pandas library </li><li>Use high-performance tools to load, clean, transform, merge, and reshape data </li><li>Create scatter plots and static or interactive visualizations with matplotlib </li><li>Apply the pandas groupby facility to slice, dice, and summarize datasets </li><li>Measure data by points in time, whether it’s specific instances, fixed periods, or intervals </li><li>Learn how to solve problems in web analytics, social sciences, finance, and economics, through detailed examples </li></ul></div>