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

Project overview

The project reduces time-consuming manual EDA coding to a one-line invocation while spanning both Pandas and Spark workflows, offering a consistent and fast solution for data quality profiling.

Project type

Data Processing

Use cases

Data Analysis

Deployment

Refer to project documentation

License

MIT

Best for

• Users who perform exploratory data analysis or data quality profiling on Pandas or Spark DataFrames and want to reduce manual coding effort with a fast, consistent one-line solution.

Key capabilities

• Generates an extended analysis of a DataFrame in a single line of code.
• Provides support for profiling Spark DataFrames for big dataset analysis.

Limitations and risks

• The CLI is limited to standard formatted CSV files that can be read directly by pandas without additional settings.

Getting started

• Install via pip: `pip install fg-data-profiling`. Then instantiate a report: `ProfileReport(df, title="Profiling Report")`. Coding is required to use the library. Interaction is available as a library or via CLI.

Evidence and sources

• GitHub project description: 1 Line of code data quality profiling & exploratory data analysis for Pandas and Spark DataFrames.
• README: fg-data-profiling primary goal is to provide a one-line Exploratory Data Analysis (EDA) experience in a consistent and fast solution.
• README: profile = ProfileReport(df, title="Profiling Report")
• README: pip install fg-data-profiling
• README: conda install -c conda-forge fg-data-profiling

Reviewed at: 2026-07-17T23:42:35Z