Data Exploration and Analysis with PySpark SQL

Exploratory data analysis (EDA)

Exploratory data analysis (EDA) is a critical step in the data analysis process that involves understanding the dataset, identifying patterns, and gaining insights from the data. In this lesson, we’ll learn how to perform EDA using PySpark SQL with the obesity dataset, which provides information about the classification of individuals based on obesity. The dataset incorporates data from various sources, including medical records, surveys, and self-reported information.

Understanding the dataset

The first step in EDA is to gain a clear understanding of the dataset. This includes loading the dataset, inspecting its structure, examining the schema of the DataFrame, and previewing the data using DataFrame operations. Let’s see how we can achieve these tasks using PySpark SQL.

Previewing the data

After loading the data into a DataFrame, we can use various PySpark DataFrame operations to get a preview of the data. These operations allow us to inspect the data, perform basic transformations, and extract relevant information. Some common DataFrame operations for data preview are shown below: