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

Partitioning data using k-means clustering

k-means is one of the simplest, most popular, and most well-known clustering algorithms. It is a kind of partitioning clustering method. It partitions input data by defining a random initial cluster center based on a given number of clusters. In the next iteration, it associates the data items to the nearest cluster center using Euclidean distance. In this algorithm, the initial cluster center can be chosen manually or randomly. k-means takes data and the number of clusters as input and performs the following steps:

  1. Select k random data items as the initial centers of clusters.
  2. Allocate the data items to the nearest cluster center.
  1. Select the new cluster center by averaging the values of other cluster items.
  2. Repeat steps 2 and 3 until there is no change in the clusters.

This algorithm aims to minimize the sum of squared errors:

k-means is one of the fastest and most robust algorithms of its kind. It works best with a dataset with distinct...