Book Image

Python Data Analysis - Third Edition

By : Avinash Navlani, Ivan Idris
5 (1)
Book Image

Python Data Analysis - Third Edition

5 (1)
By: Avinash Navlani, Ivan Idris

Overview of this book

Data analysis enables you to generate value from small and big data by discovering new patterns and trends, and Python is one of the most popular tools for analyzing a wide variety of data. With this book, you’ll get up and running using Python for data analysis by exploring the different phases and methodologies used in data analysis and learning how to use modern libraries from the Python ecosystem to create efficient data pipelines. Starting with the essential statistical and data analysis fundamentals using Python, you’ll perform complex data analysis and modeling, data manipulation, data cleaning, and data visualization using easy-to-follow examples. You’ll then understand how to conduct time series analysis and signal processing using ARMA models. As you advance, you’ll get to grips with smart processing and data analytics using machine learning algorithms such as regression, classification, Principal Component Analysis (PCA), and clustering. In the concluding chapters, you’ll work on real-world examples to analyze textual and image data using natural language processing (NLP) and image analytics techniques, respectively. Finally, the book will demonstrate parallel computing using Dask. By the end of this data analysis book, you’ll be equipped with the skills you need to prepare data for analysis and create meaningful data visualizations for forecasting values from data.
Table of Contents (20 chapters)
Section 1: Foundation for Data Analysis
Section 2: Exploratory Data Analysis and Data Cleaning
Section 3: Deep Dive into Machine Learning
Section 4: NLP, Image Analytics, and Parallel Computing

Hierarchical clustering

Hierarchical clustering groups data items based on different levels of a hierarchy. It combines the items in groups based on different levels of a hierarchy using top-down or bottom-up strategies. Based on the strategy used, hierarchical clustering can be of two types – agglomerative or divisive:

  • The agglomerative type is the most widely used hierarchical clustering technique. It groups similar data items in the form of a hierarchy based on similarity. This method is also called Agglomerative Nesting (AGNES). This algorithm starts by considering every data item as an individual cluster and combines clusters based on similarity. It iteratively collects small clusters and combines them into a single large cluster. This algorithm gives its result in the form of a tree structure. It works in a bottom-up manner; that is, every item is initially considered as a single element cluster and in each iteration of the algorithm, the two most similar clusters are combined...