A Guide to Exploratory Data Analysis in Python

Exploratory Data Analysis (EDA) is an approach to data analysis that focuses on understanding the structure of your data. It involves visualizing and summarizing the data to gain insights into its characteristics and relationships between variables. EDA helps in selecting appropriate analytical techniques, models, and interpreting their outputs accurately. The process includes checking the size of the dataset, column names, data types, missing values, and descriptive statistics. Data visualization libraries such as Seaborn can be used to create plots like boxplots, scatter plots, histograms, and heatmaps for better understanding of the data. EDA is crucial before moving into more sophisticated modeling techniques, as it helps in identifying potential issues or biases within the dataset.