Data is a collection of discrete objects, events, and facts in the form of numbers, text, pictures, videos, objects, audio, and other entities. Processing data provides a great deal of information. But the million-dollar question is—how do we get meaningful information from data? The answer to this question is Exploratory Data Analysis (EDA), which is the process of investigating datasets, elucidating subjects, and visualizing outcomes. EDA is an approach to data analysis that applies a variety of techniques to maximize specific insights into a dataset, reveal an underlying structure, extract significant variables, detect outliers and anomalies, test assumptions, develop models, and determine best parameters for future estimations. This book, Hands-On Exploratory Data Analysis with Python, aims to provide practical knowledge about the main pillars of EDA, including data cleansing, data preparation, data exploration, and data visualization. Why visualization? Well, several research studies have shown that portraying data in graphical form makes complex statistical data analyses and business intelligence more marketable. 

You will get the opportunity to explore open source datasets including healthcare datasets, demographics datasets, a Titanic dataset, a wine quality dataset, automobile datasets, a Boston housing pricing dataset, and many others. Using these real-life datasets, you will get hands-on practice in understanding data, summarize data's characteristics, and visualizing data for business intelligence purposes. This book expects you to use pandas, a powerful library for working with data, and other core Python libraries including NumPy, scikit-learn, SciPy, StatsModels for regression, and Matplotlib for visualization.