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Python Data Analysis

You're reading from   Python Data Analysis Perform data collection, data processing, wrangling, visualization, and model building using Python

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Product type Paperback
Published in Feb 2021
Publisher Packt
ISBN-13 9781789955248
Length 478 pages
Edition 3rd Edition
Languages
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Authors (2):
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Ivan Idris Ivan Idris
Author Profile Icon Ivan Idris
Ivan Idris
Avinash Navlani Avinash Navlani
Author Profile Icon Avinash Navlani
Avinash Navlani
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Toc

Table of Contents (20) Chapters Close

Preface 1. Section 1: Foundation for Data Analysis
2. Getting Started with Python Libraries FREE CHAPTER 3. NumPy and pandas 4. Statistics 5. Linear Algebra 6. Section 2: Exploratory Data Analysis and Data Cleaning
7. Data Visualization 8. Retrieving, Processing, and Storing Data 9. Cleaning Messy Data 10. Signal Processing and Time Series 11. Section 3: Deep Dive into Machine Learning
12. Supervised Learning - Regression Analysis 13. Supervised Learning - Classification Techniques 14. Unsupervised Learning - PCA and Clustering 15. Section 4: NLP, Image Analytics, and Parallel Computing
16. Analyzing Textual Data 17. Analyzing Image Data 18. Parallel Computing Using Dask 19. Other Books You May Enjoy

Generating periodic signals

Many natural phenomena are regular and trustworthy, such as an accurate clock. Some phenomena exhibit patterns that seem regular. A group of scientists found three cycles in the sunspot activity with the Hilbert-Huang transform (see https://en.wikipedia.org/wiki/Hilbert%E2%80%93Huang_transform). The cycles have a duration of 11, 22, and 100 years, approximately. Normally, we would simulate a periodic signal using trigonometric functions such as a sine function. You probably remember a bit of trigonometry from high school. That's all we need for this example. Since we have three cycles, it seems reasonable to create a model that is a linear combination of three sine functions. This just requires a tiny adjustment of the code for the autoregressive model:

  1. Create model, error, and fit functions:
# Import required libraries
import numpy as np
import statsmodels.api as sm
from scipy.optimize import leastsq
import matplotlib.pyplot as plt

# Create model function...
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