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


Clustering means grouping items that are similar to each other. Grouping similar products, grouping similar articles or documents, and grouping similar customers for market segmentation are all examples of clustering. The core principle of clustering is minimizing the intra-cluster distance and maximizing the intercluster distance. The intra-cluster distance is the distance between data items within a group, and the inter-cluster distance is the distance between different groups. The data points are not labeled, so clustering is a kind of unsupervised problem. There are various methods for clustering and each method uses a different way to group the data points. The following diagram shows how data observations are grouped together using clustering:

As we are combining similar data points, the question that arises here is how to find the similarity between two data points so we can group similar data objects into the same cluster. In order to measure the similarity or dissimilarity...