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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

Fourier analysis


Fourier analysis is based on the Fourier series named after the mathematician Joseph Fourier. The Fourier series is a mathematical method used to represent functions as an infinite series of sine and cosine terms. The functions in question can be real or complex valued:

The most efficient algorithm for Fourier analysis is the Fast Fourier Transform (FFT). This algorithm is implemented in SciPy and NumPy. When applied to the time series data, the Fourier analysis transforms maps onto the frequency domain, producing a frequency spectrum. The frequency spectrum displays harmonics as distinct spikes at certain frequencies. Music, for example, is composed from different frequencies with the note A at 440 Hz. The note A can be produced by a pitch fork. We can produce this and other notes with musical instruments such as a piano. White noise is a signal consisting of many frequencies, which are represented equally. White light is a mix of all the visible frequencies of light, also...

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