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

Running multiple threads with the threading module


A computer process is an instance of a running program. Processes are actually heavyweight, so we may prefer threads, which are lighter. In fact, threads are often just subunits of a process. Processes are separated from each other, while threads can share instructions and data.

Operating systems typically assign one thread to each core (if there are more than one), or switch between threads periodically; this is called time slicing. Threads as processes can have different priorities and the operating system has daemon threads running in the background with very low priority.

It's easier to switch between threads than between processes; however, because threads share information, they are more dangerous to use. For instance, if multiple threads are able to increment a counter at the same time, this will make the code nondeterministic and potentially incorrect. One way to minimize risks is to make sure that only one thread can access a shared...