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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 FREE CHAPTER 2. NumPy Arrays 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

Spectral analysis


In the previous section, we charted the amplitude spectrum of the dataset. The power spectrum of a physical signal visualizes the energy distribution of the signal. We can modify the code easily to plot the power spectrum, just by squaring the values as follows:

plt.plot(transformed ** 2, label="Power Spectrum")

The phase spectrum visualizes the phase (the initial angle of a sine function) and can be plotted as follows:

plt.plot(np.angle(transformed), label="Phase Spectrum")

Refer to the following graph for the end result:

Please refer to the spectrum.py file in this book's code bundle for the complete code.

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