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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 4. Time-series

A time-series is a sequence of data points, typically consisting of successive measurements made at a regular frequency and over a specific time interval. Time-series analysis is composed of various methods for making decisions based upon the data in a time-series by extracting meaningful statistics. Time-series forecasting is the process of developing a model based upon data in a time-series, and using it to predict future values based upon previously observed values. Regression analysis is the process of testing whether one or more independent time-series affect the current value of another time-series.

There is extensive support for working with time-series data in pandas. In this chapter, we will examine representing time-series data with the pandas Series and DataFrame as well as several common techniques for manipulating this data. The techniques learned in this chapter will set the basis for the remaining chapters, where we will examine several financial...

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