The second edition of this how-to guide, updated for Python 3.6, is packed with practical examples that show you how to effectively tackle a wide range of data analysis tasks. Along the way, you will learn the latest versions of pandas, NumPy, IPython, and Jupiter.
Python for Data Analysis PDF
Written by Wes McKinney, creator of the Python Pandas project, this book provides a practical and modern introduction to data analysis tools in Python. It is ideal for analysts new to Python and for Python programmers new to data science and scientific computing. Data files and related materials are available on GitHub.
Python for Data Analysis: from the author
The first edition of this book was published in 2012, during a time when open source data analysis libraries for Python (such as pandas) were very new and were developing rapidly. In this updated and expanded second edition, I have revised the chapters to take into account the incompatible changes and depreciations, as well as new features that have occurred over the past five years.
I've also added new content to introduce tools that didn't exist in 2012 or weren't mature enough to make the first cut. Finally, I tried to avoid writing about new or cutting-edge open source projects that might not have had a chance to mature. I would like readers of this issue to discover that content is still almost as relevant in 2020 or 2021 as it is in 2017.