Book Image

Mastering Python Data Analysis

By : Magnus Vilhelm Persson
Book Image

Mastering Python Data Analysis

By: Magnus Vilhelm Persson

Overview of this book

Python, a multi-paradigm programming language, has become the language of choice for data scientists for data analysis, visualization, and machine learning. Ever imagined how to become an expert at effectively approaching data analysis problems, solving them, and extracting all of the available information from your data? Well, look no further, this is the book you want! Through this comprehensive guide, you will explore data and present results and conclusions from statistical analysis in a meaningful way. You’ll be able to quickly and accurately perform the hands-on sorting, reduction, and subsequent analysis, and fully appreciate how data analysis methods can support business decision-making. You’ll start off by learning about the tools available for data analysis in Python and will then explore the statistical models that are used to identify patterns in data. Gradually, you’ll move on to review statistical inference using Python, Pandas, and SciPy. After that, we’ll focus on performing regression using computational tools and you’ll get to understand the problem of identifying clusters in data in an algorithmic way. Finally, we delve into advanced techniques to quantify cause and effect using Bayesian methods and you’ll discover how to use Python’s tools for supervised machine learning.
Table of Contents (15 chapters)
Mastering Python Data Analysis
Credits
About the Authors
About the Reviewer
www.PacktPub.com
Preface

Hierarchical clustering analysis


Hierarchical clustering is connectivity-based clustering. It assumes that the clusters are connected, or in another word, linked. For example, we can classify animals and plants based on this assumption. We have all developed from something common. This makes it possible for us to assume that every observation is its own cluster on one hand and, on the other, all observations are in one and the same group. This also forms the basis for two approaches to hierarchical clustering algorithms, agglomerative and divisive:

  • Agglomerative clustering starts out with each point in its own cluster and then merges the two clusters with the lowest dissimilarity, that is, the bottom-up approach

  • Divisive clustering is, as the name suggests, a top-down approach where we start out with one single cluster that is divided into smaller and smaller clusters

In contrast to k-means, it gives us a way to identify the clusters without initial guesses of the number of clusters or cluster...