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

Determining bias


When teaching probability, it is customary to give examples of coin tosses. Whether it is going to rain or not is more or less like a coin toss. If we have two possible outcomes, the binomial distribution is appropriate. This distribution requires two parameters: the probability and the sample size.

In statistics, there are two generally accepted approaches. In the frequentist approach, we measure the number of coin tosses and use that frequency for further analysis. Bayesian analysis is named after its founder the Reverend Thomas Bayes. The Bayesian approach is more incremental and requires a prior distribution, which is the distribution we assume before performing experiments. The posterior distribution is the distribution we are interested in and which we obtain after getting new data from experiments. Let's first have a look at the following equations:

(3.7) and (3.8) describe the probability mass function for the binomial distribution. (3.9) comes from an essay published...