Exploratory Data Analysis (EDA) is a process of investigating and summarizing the key insights of a data set using graphs or python functions. EDA can help to understand the distribution, null values, outliers, correlations and other characteristics of the data, and to identify potential problems or opportunities for further analysis. For water quality data, EDA can help to explore the variables that affect the quality of water, such as pH, dissolved oxygen, conductivity, temperature, etc., and to visualize their relationships and patterns. There are many python libraries and tools that can help to perform EDA on water quality data, such as pandas, numpy, matplotlib, seaborn, plotly, etc. One can also use Kaggle notebooks or GitHub repositories to find examples of EDA on water quality data using python functions.