One of the most basic and common operations to perform during a data analysis is to select rows containing the largest value of some column within a group. For instance, this would be like finding the highest rated film of each year or the highest grossing film by content rating. To accomplish this task, we need to sort the groups as well as the column used to rank each member of the group, and then extract the highest member of each group.

Pandas Cookbook
By :

Pandas Cookbook
By:
Overview of this book
This 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.
Table of Contents (12 chapters)
Preface
Pandas Foundations
Essential DataFrame Operations
Beginning Data Analysis
Selecting Subsets of Data
Boolean Indexing
Index Alignment
Grouping for Aggregation, Filtration, and Transformation
Restructuring Data into a Tidy Form
Combining Pandas Objects
Time Series Analysis
Visualization with Matplotlib, Pandas, and Seaborn
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