Features of SweetViz Library

• Target analysis: This shows how a target value relates to other features.
• Mixed-type associations: Sweetviz integrates associations for categorical (uncertainty coefficient), numerical (Pearson’s correlation) & categorical-numerical (correlation ratio) datatypes smoothly, to deliver maximum information for all the data types.
• Visualize and compare: Distinct datasets (e.g. training vs test data).
• Type inference: It automatically detects numerical, categorical & text features, with optional manual overrides.
• Summary information: 
• Type, missing values, unique values, duplicate rows, & most frequent values.
• Numerical analysis like sum, min/max/range, quartiles, mean, mode, standard deviation, median absolute deviation, coefficient of variation, kurtosis, and skewness.

SweetViz is an open-source Python library, this is used for automated exploratory data analysis (EDA), it helps data analysts/scientists quickly generate beautiful & highly detailed visualizations. The output, we get is a fully self-contained HTML application. The system built reports around quickly visualizing the target values & comparing datasets.

Exploratory data analysis (EDA) is the process of analyzing and summarizing the main characteristics of a dataset, often with the goal of understanding the underlying patterns, relationships, and trends in the data.