Book Image

Python Data Analysis Cookbook

By : Ivan Idris
Book Image

Python Data Analysis Cookbook

By: Ivan Idris

Overview of this book

Data analysis is a rapidly evolving field and Python is a multi-paradigm programming language suitable for object-oriented application development and functional design patterns. As Python offers a range of tools and libraries for all purposes, it has slowly evolved as the primary language for data science, including topics on: data analysis, visualization, and machine learning. Python Data Analysis Cookbook focuses on reproducibility and creating production-ready systems. You will start with recipes that set the foundation for data analysis with libraries such as matplotlib, NumPy, and pandas. You will learn to create visualizations by choosing color maps and palettes then dive into statistical data analysis using distribution algorithms and correlations. You’ll then help you find your way around different data and numerical problems, get to grips with Spark and HDFS, and then set up migration scripts for web mining. In this book, you will dive deeper into recipes on spectral analysis, smoothing, and bootstrapping methods. Moving on, you will learn to rank stocks and check market efficiency, then work with metrics and clusters. You will achieve parallelism to improve system performance by using multiple threads and speeding up your code. By the end of the book, you will be capable of handling various data analysis techniques in Python and devising solutions for problem scenarios.
Table of Contents (23 chapters)
Python Data Analysis Cookbook
Credits
About the Author
About the Reviewers
www.PacktPub.com
Preface
Glossary
Index

Analyzing signals with the discrete cosine transform


The discrete cosine transform (DCT) is a transform similar to the Fourier transform, but it tries to represent a signal by a sum of cosine terms only (refer to equation 6.11). The DCT is used for signal compression and in the calculation of the mel frequency spectrum, which I mentioned in the Analyzing the frequency spectrum of audio recipe. We can convert normal frequencies to the mel frequency (a frequency more appropriate for the analysis of speech and music) with the following equation:

The steps to create the mel frequency spectrum are not complicated, but there are quite a few of them. The relevant Wikipedia page is available at https://en.wikipedia.org/wiki/Mel-frequency_cepstrum (retrieved September 2015). If you do a quick web search, you can find a couple of Python libraries that implement the algorithm. I implemented a very simple version of the computation in this recipe.

How to do it...

  1. The imports are as follows:

    import dautil...