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

Quantizing colors


In ancient times, computer games were practically monochromatic. Many years later, the Internet allowed us to download images, but the Web was slow, so compact images with few colors were preferred. We can conclude that restricting the number of colors is traditional. Color is a dimension of images, so we can speak of dimensionality reduction if we remove colors from an image. The actual process is called color quantization.

Usually, we represent RGB (red, green, and blue) values in three-dimensional space for each pixel and then cluster the points. For each cluster, we are left with a corresponding average color. In this recipe, we will use k-means clustering (refer to the Clustering streaming data with Spark recipe), although this is not necessarily the best algorithm.

Getting ready

Follow the instructions in the Setting up OpenCV recipe.

How to do it...

The code is in the quantizing_colors.ipynb file in this book's code bundle:

  1. The imports are as follows:

    import numpy as np...