![]() Some might characterize much of the content of the book as "data manipulation" as opposed to "data analysis." We also use the terms wrangling or munging to refer to data manipulation. My hope is that this book serves as adequate preparation to enable you to move on to a more domain-specific resource. There are now many other books which focus specifically on these more advanced methodologies. The Python open source ecosystem for doing data analysis (or data science) has also expanded significantly since then. Sometime after I originally published this book in 2012, people started using the term data science as an umbrella description for everything from simple descriptive statistics to more advanced statistical analysis and machine learning. This is the Python programming you need for data analysis. ![]() While "data analysis" is in the title of the book, the focus is specifically on Python programming, libraries, and tools as opposed to data analysis methodology. My goal is to offer a guide to the parts of the Python programming language and its data-oriented library ecosystem and tools that will equip you to become an effective data analyst. This book is concerned with the nuts and bolts of manipulating, processing, cleaning, and crunching data in Python. The code examples are MIT licensed and can be found on GitHub or Gitee. The content from this website may not be copied or reproduced. ![]() If you find the online edition of the book useful, please consider ordering a paper copy or a DRM-free eBook to support the author. If you encounter any errata, please report them here. This Open Access web version of Python for Data Analysis 3rd Edition is now available as a companion to the print and digital editions.
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