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Data-Centric-AI-Community/fg-data-profiling

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Fg Data Profiling

This project is a data profiling and exploratory data analysis tool designed to generate automated quality reports for Pandas and Spark dataframes. It serves as a system for computing descriptive statistics, identifying correlations, and analyzing univariate and multivariate data patterns.

The tool provides specialized capabilities for comparing different versions of datasets to identify changes in data quality and distributions. It includes a dedicated profiler for time-dependent data to extract statistical information such as seasonality and auto-correlation.

The software covers a broad analysis surface including text data composition, multivariate interaction mapping, and the extraction of technical file metadata. Analysis results can be exported as HTML files, JSON data, or interactive widgets.

A command line interface is available for processing CSV files and generating reports using local configuration files.

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Features

  • Data Profiling - Provides automated statistical profiling to analyze data patterns and validate data quality.
  • Data Observability Profilings - Provides automated profiling of data distributions to detect shifts and quality issues in Pandas DataFrames.
  • Univariate Statistical Profiling - Computes descriptive statistics and generates distribution histograms for individual variables.
  • Automated - Provides a unified system for automated data quality reporting and profiling for both Pandas and Spark.
  • Data Quality Reports - Produces human-readable exploratory data analysis reports featuring descriptive statistics and quality alerts.
  • Statistical Aggregators - Computes descriptive statistics and distribution histograms for individual columns to determine central tendency.
  • Automated Exploratory Analysis - Automatically generates statistical summaries and visual insights to discover patterns and anomalies in new datasets.
  • Dataset Comparators - Provides utilities for calculating statistical differences and drift between different versions of dataframes.
  • Agnostic Interfaces - Implements a unified API to execute profiling logic across both Pandas and Spark data structures.
  • Distributed Dataframe Analysis - Implements large-scale data quality checks and exploratory analysis specifically for Spark DataFrames.
  • Multivariate Relationship Visualizers - Calculates pairwise interactions and correlations between multiple variables to identify dataset dependencies.
  • Time Series Analysis - Calculates temporal trends, seasonality, and auto-correlation for time-dependent datasets.
  • Time-Series Statistical Profiling - Extracts statistical information from time-dependent data, including seasonality and auto-correlation plots.
  • Time Series Decomposition - Analyzes temporal data using auto-correlation and partial auto-correlation to identify seasonality and trends.
  • Analysis Result Exporters - Serializes profiling reports into HTML, JSON, and interactive widgets for external system integration.
  • Time Series Analysis Tools - Provides specialized tools for calculating statistical properties of temporal data, such as seasonality and auto-correlation.
  • Command Line Interfaces - Provides a command line tool for processing CSV files and generating profiling reports via local configurations.
  • Analysis Report Serialization - Transforms analysis results into HTML, JSON, and interactive widgets for external viewing and integration.
  • Data Analysis - Generates data profiling reports for DataFrames.
  • Data Analysis and Processing - Data quality profiling for Pandas and Spark.
13,609 stars·1,792 forks·Python·MIT·19 views

Star history

Star history chart for data-centric-ai-community/fg-data-profilingStar history chart for data-centric-ai-community/fg-data-profiling

How this analysis was created: This summary and feature list are AI-generated from collected project material and can contain mistakes. Stars, license and language are imported from GitHub. Inclusion does not mean that we have tested or audited this project. Check the source documentation for any feature you depend on. Learn more on our About page.

Frequently asked questions

What does data-centric-ai-community/fg-data-profiling do?

This project is a data profiling and exploratory data analysis tool designed to generate automated quality reports for Pandas and Spark dataframes. It serves as a system for computing descriptive statistics, identifying correlations, and analyzing univariate and multivariate data patterns.

What are the main features of data-centric-ai-community/fg-data-profiling?

The main features of data-centric-ai-community/fg-data-profiling are: Data Profiling, Data Observability Profilings, Univariate Statistical Profiling, Automated, Data Quality Reports, Statistical Aggregators, Automated Exploratory Analysis, Dataset Comparators.

Which projects share features with data-centric-ai-community/fg-data-profiling?

Projects with overlapping indexed features include: ydataai/pandas-profiling — This project is an exploratory data analysis framework and profiling tool designed to generate comprehensive… ydataai/ydata-profiling — Ydata-profiling is an automated exploratory data analysis framework designed to generate comprehensive statistical… pandas-profiling/pandas-profiling — This project is an exploratory data analysis library and profiling tool for Pandas and Spark DataFrames. It automates… hosseinmoein/dataframe — DataFrame is a C++ tabular data library and manipulation engine designed for managing heterogeneous data in contiguous… pandas-dev/pandas — Pandas is a high-performance data analysis library that provides a comprehensive framework for manipulating, cleaning,… saulpw/visidata — VisiData is a terminal-based interactive data analysis tool and browser designed for exploring, filtering, and sorting…

Projects sharing features with Fg Data Profiling

These projects share indexed features with Fg Data Profiling. Shared tags can include platform or build tooling; verify the primary use case before treating a result as a replacement.
  • ydataai/pandas-profilingydataai avatar

    ydataai/pandas-profiling

    13,610View on GitHub↗

    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

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  • ydataai/ydata-profilingydataai avatar

    ydataai/ydata-profiling

    13,388View on GitHub↗

    Ydata-profiling is an automated exploratory data analysis framework designed to generate comprehensive statistical reports and visual summaries from dataframes. It functions as a diagnostic tool for assessing data quality, identifying missing values, duplicates, and outliers, while providing a scalable engine for profiling massive datasets across distributed enterprise environments. The project distinguishes itself through its ability to handle large-scale data through distributed task orchestration and lazy stream processing, which minimizes memory overhead during complex computations. It in

    Pythonbig-data-analyticsdata-analysisdata-exploration
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  • pandas-profiling/pandas-profilingpandas-profiling avatar

    pandas-profiling/pandas-profiling

    13,609View on GitHub↗

    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

    Python
    View on GitHub↗13,609
  • hosseinmoein/dataframehosseinmoein avatar

    hosseinmoein/DataFrame

    2,917View on GitHub↗

    DataFrame is a C++ tabular data library and manipulation engine designed for managing heterogeneous data in contiguous memory. It functions as a statistical analysis framework and time series analysis toolkit, providing the means to store, index, and transform multidimensional datasets. The project distinguishes itself through a high-performance execution model that utilizes column-major storage, SIMD-aligned memory allocation, and a thread-pool for parallel computations. It employs a visitor-based algorithm dispatch system and policy-driven transformations to decouple data processing logic f

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