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

1 Line of code data quality profiling & exploratory data analysis for Pandas and Spark DataFrames.

Repository: https://github.com/Data-Centric-AI-Community/fg-data-profiling
Canonical: https://ross.abutalabs.com/products/fg-data-profiling
Homepage: https://docs.sdk.ydata.ai
Language: Python
License: MIT
License Family: permissive
Topics: pandas-profiling, pandas-dataframe, statistics, jupyter-notebook, exploration, data-science, python, pandas, machine-learning, deep-learning, exploratory-data-analysis, eda, data-quality, html-report, data-exploration, data-analysis, jupyter, big-data-analytics, data-profiling, hacktoberfest
Last push: 2026-04-22T23:26:39+00:00

## Health v2 (maintenance only)
Score: 83/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 78, release rhythm 80, longevity 100
- inputs: {"age_days": 3889, "days_push": 133, "days_rel": 133, "gap_med": 25.5, "n_releases_24m": 17}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 13682, forks 1795 (observed 2026-08-28T04:11:04.521141+00:00)

## What it is
A Python library that generates one-line data quality profiling and exploratory data analysis reports for Pandas and Spark DataFrames. It produces extended statistics summaries exportable to HTML and JSON, including support for time-series and text data.

## Use cases
- profile a pandas dataframe with one line of code
- generate an eda report for a dataset
- check data quality before training a model
- explore missing values and correlations in a dataframe
- create an html summary of a csv dataset
- profile large spark dataframes
- summarize time-series data statistics

## When to choose
- you need quick automated EDA for pandas or Spark DataFrames
- you want shareable HTML/JSON profiling reports
- you are doing data quality checks in a Jupyter notebook
- you need dataset statistics like distributions, correlations, and missing values without writing boilerplate

## When to avoid
- you need interactive dashboards with custom visualizations rather than generated reports
- you need a full data catalog integrated with databases and storage systems
- your project depends on the old pandas-profiling/ydata-profiling package name which no longer receives updates

## Facets
- artifact type: library
- maturity: active
- function: data-science, analytics, data-visualization, developer-tools
- domain: data-science, analytics, data-visualization, big-data
- platform: python, cross-platform
- tags: pandas, spark, eda, data-profiling, data-quality, html-report, jupyter, exploratory-data-analysis

## Member repositories
- Data-Centric-AI-Community/fg-data-profiling (main) score 83

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:11:04.521141+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-29T17:13:07.488015+00:00, confidence not recorded.
  - readme: https://github.com/Data-Centric-AI-Community/fg-data-profiling (fetched 2026-08-28T04:11:04.521141+00:00, sha 519d489d2664)
  - homepage: https://docs.sdk.ydata.ai (fetched 2026-08-29T08:07:33.381912+00:00, sha 36c6c3c2e4f9)
  - registry_pypi: https://pypi.org/pypi/fg-data-profiling/json (fetched 2026-08-29T08:07:33.384487+00:00, sha 3d6225a08835)
- Data as of 2026-08-30T08:39:29.467469+00:00.