Awesome GitHub RepositoriesProfiling Reports

This library provides a diagnostic toolkit for automated data profiling and exploratory analysis. It generates comprehensive statistical summaries and visual reports for tabular datasets, enabling users to identify distribution patterns, missing values, and quality anomalies through a unified interface. The project distinguishes itself by offering differential analysis, which allows for the comparison of two dataset versions to track structural and statistical changes over time. It supports large-scale data processing through lazy evaluation and provides interactive widgets that embed directl

This project is an exploratory data analysis framework and profiling tool designed to generate comprehensive statistical reports from Pandas and Spark DataFrames. It functions as a data quality profiler that identifies missing values, duplicates, and high correlations within tabular datasets. The tool distinguishes itself through specialized capabilities for time-series analysis, extracting temporal statistics, seasonality, and auto-correlation plots. It also includes a dataset comparison utility to identify structural or content changes between different versions of a dataset. The analysis

This project is an exploratory data analysis library and profiling tool for Pandas and Spark DataFrames. It automates the initial investigation of datasets by generating comprehensive descriptive analysis reports, statistical summaries, and data quality warnings. The system functions as a data quality profiler to detect missing values, duplicate rows, and type inconsistencies. It includes a dataset comparison tool for identifying structural and content shifts between different versions of the same data, as well as specialized tools for time-series analysis to calculate auto-correlation and se

Lux ist ein automatisiertes Tool zur explorativen Datenanalyse, das entwickelt wurde, um intelligente visuelle Darstellungen von pandas Dataframes zu generieren. Es identifiziert Muster und Trends, indem es optimale Diagrammtypen und Achsen-Mappings basierend auf den statistischen Attributen eines Datensatzes empfiehlt. Das Tool fungiert als interaktive Datenprofilierungsschicht, die es Benutzern ermöglicht, Sammlungen von Diagrammen mithilfe von Filtern und Platzhaltern zu durchsuchen und abzufragen. Es dient zudem als Visualisierungs-Code-Generator, der automatisch erstellte Diagramme in programmatischen Code oder HTML zur manuellen Verfeinerung in externen Bibliotheken übersetzt. Das System deckt ein breites Spektrum an explorativen Analysefunktionen ab, einschließlich automatisierter Diagramm-Kodierung, geführter Entdeckung durch Schritt-Empfehlungen und der Möglichkeit, visuelle Konfigurationen als deklarative Spezifikationen zu exportieren. Dieses Projekt integriert sich direkt in pandas, um das Standard-Dataframe-Drucken durch interaktive Visualisierungskomponenten zu überschreiben.

dtale is a web-based interactive grid and visualizer for pandas dataframes, designed as an exploratory data analysis tool. It provides a browser-based interface for analyzing tabular data structures, allowing users to calculate statistics, detect outliers, and compute correlations without writing manual code. The project functions as an embedded data viewer that can be integrated into web applications via iframes or custom routes, with specific support for Django, Flask, and Streamlit. It enables the exploration of datasets through a combination of an interactive data grid and a data visualiz