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

Searching for bright stars


Many stars are visible at night, even without using a telescope or any other optical device. Stars are, in general, larger than planet Earth, but in certain stages of their evolution, they can be smaller. Due to the large distance, they appear as tiny dots. Often, these dots consist of two (a binary system) or more stars. Not all stars emit visible light and not all starlight can reach us.

There are many approaches that we can take to find bright stars in a starry sky image. In this recipe, we will look for local maximums of brightness, which are also above a threshold. To determine brightness, we will convert the image to the HSV color space. In this color space, the three dimensions are hue, saturation, and value (brightness). The OpenCV split() function image values in a color space into the constituent values, for example, hue, saturation, and brightness. This is a relatively slow operation. To find maximums, we can apply the SciPy argrelmax() function.

Getting...