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When working with large datasets, it’s often necessary to understand data types, distributions, and potential issues (e.g., missing values, outliers) before analysis. While libraries like pandas-profiling exist, there is still room for an extensible, easy-to-use, and highly customizable profiler that integrates data validation.

Key Features:
Automatic Data Summaries: Provide insights like distribution, unique values, missing values, and more for each column.
Anomaly Detection: Automatically flag columns or rows with unusual distributions, outliers, or inconsistent data.
Data Validation: Set validation rules (e.g., no missing values in specific columns, data type constraints) and get alerts if the data violates these rules.
Custom Reports: Generate visual reports (e.g., HTML, PDF) with configurable thresholds for what counts as an anomaly.
Data Drift Detection: Track changes in data distributions over time to identify shifts in data quality or content.
Benefits:
DataProfiler would be invaluable to data scientists and engineers dealing with exploratory data analysis, data quality checks, and ETL pipelines, reducing manual data investigation.


To Use :

'from megaprofiler import MegaProfiler'


