Investors who have purchased long stock positions would obviously like to sell stocks at or near their all-time highs. This, of course, is very difficult to do in practice, especially if a stock price has only spent a small portion of its history above a certain threshold. We can use boolean indexing to find all points in time that a stock has spent above or below a certain value. This exercise may help us gain perspective as to what a common range for some stock to be trading within.

Pandas Cookbook
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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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