5 (1)

5 (1)

#### Overview of this book

Data analysis enables you to generate value from small and big data by discovering new patterns and trends, and Python is one of the most popular tools for analyzing a wide variety of data. With this book, you’ll get up and running using Python for data analysis by exploring the different phases and methodologies used in data analysis and learning how to use modern libraries from the Python ecosystem to create efficient data pipelines. Starting with the essential statistical and data analysis fundamentals using Python, you’ll perform complex data analysis and modeling, data manipulation, data cleaning, and data visualization using easy-to-follow examples. You’ll then understand how to conduct time series analysis and signal processing using ARMA models. As you advance, you’ll get to grips with smart processing and data analytics using machine learning algorithms such as regression, classification, Principal Component Analysis (PCA), and clustering. In the concluding chapters, you’ll work on real-world examples to analyze textual and image data using natural language processing (NLP) and image analytics techniques, respectively. Finally, the book will demonstrate parallel computing using Dask. By the end of this data analysis book, you’ll be equipped with the skills you need to prepare data for analysis and create meaningful data visualizations for forecasting values from data.
Preface
Section 1: Foundation for Data Analysis
Free Chapter
Getting Started with Python Libraries
Section 2: Exploratory Data Analysis and Data Cleaning
Data Visualization
Cleaning Messy Data
Signal Processing and Time Series
Section 3: Deep Dive into Machine Learning
Supervised Learning - Regression Analysis
Supervised Learning - Classification Techniques
Unsupervised Learning - PCA and Clustering
Section 4: NLP, Image Analytics, and Parallel Computing
Analyzing Textual Data
Analyzing Image Data
Other Books You May Enjoy

# Spectral analysis filtering

In the previous section, we discussed the amplitude spectrum of the dataset. Now is the time to explore the power spectrum. The power spectrum of any physical signal can display the energy distribution of the signal. We can easily change the code and display the power spectrum by squaring the transformed signal using the following syntax:

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

We can also plot the phase spectrum using the following Python syntax:

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

Let's see the complete code for the power and phase spectrum for the Sunspot dataset:

1. Import the libraries and read the dataset:
`# Import required libraryimport numpy as npimport statsmodels.api as smfrom scipy.fftpack import rfftfrom scipy.fftpack import fftshiftimport matplotlib.pyplot as plt# Read the datasetdata = sm.datasets.sunspots.load_pandas().data`
1. Compute FFT, Spectrum, and Phase:
`# Compute FFTtransformed = fftshift...`