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Pandas Cookbook: Recipes for Scientific Computing, Time Series Analysis and Data Visualization using Python,New
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Publisher's Note: A new second edition, updated completely for pandas 1.x with additional chapters, has now been published. This edition from 2017 is outdated and is based on pandas 0.20. Key Features Use the power of pandas 0.20 to solve most complex scientific computing problems with ease Leverage fast, robust data structures in pandas 0.20 to gain useful insights from your data Practical, easy to implement recipes for quick solutions to common problems in data using pandas 0.20 Book DescriptionThis book will provide you with unique, idiomatic, and fun recipes for both fundamental and advanced data manipulation tasks with pandas 0.20. Some recipes focus on achieving a deeper understanding of basic principles, or comparing and contrasting two similar operations. Other recipes will dive deep into a particular dataset, uncovering new and unexpected insights along the way.The pandas library is massive, and it's common for frequent users to be unaware of many of its more impressive features. The official pandas documentation, while thorough, does not contain many useful examples of how to piece together multiple commands like one would do during an actual analysis. This book guides you, as if you were looking over the shoulder of an expert, through practical situations that you are highly likely to encounter.Many advanced recipes combine several different features across the pandas 0.20 library to generate results. What you will learn Master the fundamentals of pandas 0.20 to quickly begin exploring any dataset Isolate any subset of data by properly selecting and querying the data Split data into independent groups before applying aggregations and transformations to each group Restructure data into tidy form to make data analysis and visualization easier Prepare realworld messy datasets for machine learning Combine and merge data from different sources through pandas SQLlike operations Utilize pandas unparalleled time series functionality Create beautiful and insightful visualizations through pandas 0.20 direct hooks to Matplotlib and Seaborn
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- Q: What is the main focus of the Pandas Cookbook? A: The Pandas Cookbook focuses on providing hands-on recipes for scientific computing, time series analysis, and data visualization using Python's pandas library. It covers both fundamental and advanced data manipulation tasks.
- Q: Who is the author of the Pandas Cookbook? A: The author of the Pandas Cookbook is Theodore Petrou.
- Q: What version of pandas does this book cover? A: This book covers pandas version 0.20, providing recipes and techniques relevant to that specific version.
- Q: Is this book suitable for beginners? A: Yes, the Pandas Cookbook is suitable for both novice and advanced users, with practical recipes and tips to assist those new to pandas.
- Q: How many pages does the Pandas Cookbook have? A: The Pandas Cookbook contains a total of 532 pages.
- Q: What type of binding does this book have? A: The Pandas Cookbook is available in paperback binding.
- Q: When was the Pandas Cookbook published? A: The Pandas Cookbook was published on October 24, 2017.
- Q: What can I learn from the Pandas Cookbook? A: You can learn to master the fundamentals of pandas, isolate data subsets, restructure data, prepare datasets for machine learning, and create visualizations using pandas and libraries like Matplotlib and Seaborn.
- Q: Are there any prerequisites for reading this book? A: Some understanding of pandas is helpful but not mandatory to benefit from the recipes and concepts presented in the book.
- Q: What topics are covered in the advanced recipes? A: The advanced recipes cover combining multiple features of pandas, generating insights from complex datasets, and leveraging pandas' time series functionality.