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

Caching with a least recently used cache


Caching involves storing results, usually from a function call, in memory or on disk. If done correctly, caching helps by reducing the number of function calls. In general, we want to keep the cache small for space reasons. If we are able to find items in cache, we talk about hits; otherwise, we have misses. Obviously, we want to have as many hits as possible and as few misses as possible. This means that we want to maximize the hits-misses ratio.

Many caching algorithms exist, of which we will cover the least recently used (LRU) algorithm. This algorithm keeps track of when a cache item was used. If the cache is about to exceed its maximum specified size, LRU gets rid of the least recently used item. The reasoning is that these items are possibly older and, therefore, not as relevant any more. There are several variations of LRU. Other algorithms do the opposite—removing the most recent item, the least frequently used item, or a random item.

The standard...