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

Accessing resources asynchronously with the asyncio module


It is a basic fact of life that I/O (for example, file or database access) is slow. I/O is not only slow, but also unpredictable. In a common scenario, we wait for data (from a web service or sensors) and write the data to the filesystem or a database. In such a situation, we can find ourselves to be I/O bound—spending more time waiting for the data than actually processing it. We can poll for data periodically or act on event triggers (either check your watch or set an alarm). GUIs usually have special threads that wait for user input in an infinite loop.

The Python asyncio module for asynchronous I/O uses the concept of coroutines with a related function decorator. A brief example of this module was also given in the Scraping the web recipe of Chapter 5, Web Mining, Databases, and Big Data. Subroutines can be thought of as a special case of coroutines. A subroutine has a start and exit point, either through an early exit with a...