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Mastering Pandas for Finance

You're reading from   Mastering Pandas for Finance Master pandas, an open source Python Data Analysis Library, for financial data analysis

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Product type Paperback
Published in May 2015
Publisher Packt
ISBN-13 9781783985104
Length 298 pages
Edition 1st Edition
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Author (1):
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Michael Heydt Michael Heydt
Author Profile Icon Michael Heydt
Michael Heydt
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Table of Contents (11) Chapters Close

Preface 1. Getting Started with pandas Using Wakari.io FREE CHAPTER 2. Introducing the Series and DataFrame 3. Reshaping, Reorganizing, and Aggregating 4. Time-series 5. Time-series Stock Data 6. Trading Using Google Trends 7. Algorithmic Trading 8. Working with Options 9. Portfolios and Risk Index

Chapter 2. Introducing the Series and DataFrame

pandas provides a comprehensive set of data structures for working with and manipulating data and performing various statistical and financial analyses. The two primary data structures in pandas are Series and DataFrame. In this chapter, we will examine the Series object and how it extends a NumPy ndarray to provide operations such as indexed data retrieval, axis labeling, and automatic alignment. Then, we will move on to examine how DataFrame extends the capabilities of Series to use columnar/tabular data, which can be of more than one data type.

The intention of this chapter is to be not only a refresher for those with basic familiarity with pandas, but also a means by which someone who is not initiated with pandas can gain enough familiarity with the two data structures and have a good foundation as we move into more finance-related subjects in later chapters. We will not cover all the details of using Series and DataFrame but...

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