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Python Data Analysis

You're reading from   Python Data Analysis Learn how to apply powerful data analysis techniques with popular open source Python modules

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Product type Paperback
Published in Oct 2014
Publisher
ISBN-13 9781783553358
Length 348 pages
Edition 1st Edition
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Author (1):
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Ivan Idris Ivan Idris
Author Profile Icon Ivan Idris
Ivan Idris
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Toc

Table of Contents (17) Chapters Close

Preface 1. Getting Started with Python Libraries 2. NumPy Arrays FREE CHAPTER 3. Statistics and Linear Algebra 4. pandas Primer 5. Retrieving, Processing, and Storing Data 6. Data Visualization 7. Signal Processing and Time Series 8. Working with Databases 9. Analyzing Textual Data and Social Media 10. Predictive Analytics and Machine Learning 11. Environments Outside the Python Ecosystem and Cloud Computing 12. Performance Tuning, Profiling, and Concurrency A. Key Concepts
B. Useful Functions C. Online Resources
Index

Chapter 7. Signal Processing and Time Series

Signal processing is a field of engineering and applied mathematics that analyzes analog and digital signals, corresponding to variables that vary with time. One of the categories of signal processing techniques is time series analysis. A time series is an ordered list of data points starting with the oldest measurements first. The data points are usually equidistant, for instance, consistent with daily or annual sampling. In time series analysis, the order of the values is important. It's common to try to derive a relation between a value and another data point or combination of data points a fixed number of periods in the past, in the same time series.

The time series examples in this chapter use annual sunspot cycles data. This data is provided by the statsmodels package (an open source Python project). The examples use NumPy/SciPy, pandas, and also statsmodels.

We will cover the following topics in this chapter:

  • Moving averages...
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