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pandas-profiling

Generates a one-line data quality and exploratory analysis report for pandas and Spark DataFrames.Data-Centric-AI-Community/fg-data-profiling

GitHub Download 4.20.0
Stars
13.7k
Forks
1.8k
Licence
MIT
Language
Python
Last update
6 days ago
Type
Library

What it is

fg-data-profiling is a Python package that takes a table of data (a pandas DataFrame) and produces a detailed summary report. The report covers each column's type, basic statistics, charts, missing values, duplicates, and a list of potential data quality problems. It is a renamed continuation of the ydata-profiling project, and the README says the old package will no longer receive updates.

  • Generate a full HTML profiling report for a pandas DataFrame with one call to ProfileReport
  • Compare two versions of the same dataset using the dataset comparison feature
  • Export the analysis as JSON for use in an automated data pipeline
  • Explore a Jupyter notebook dataset through an interactive widget view
  • Review the Alerts section to spot missing values, constant columns, or high correlations

Great for

  • Analysts who want a quick first look at an unfamiliar dataset
  • Data scientists checking data quality before modeling
  • Teams that want a shareable HTML report of a dataset
  • Developers who need a JSON summary of data for automated checks
  • People working with time-series or text columns who want those analyses included

Not ideal for

  • Anyone who needs a full database connector: the README points to a separate paid-tier product, Fabric Data Catalog, for database integration
  • Users who need a large-scale solution without reading the big-data guidance first, since the README links to a separate page on configuring it for large datasets
  • People who only need a few simple summary numbers, since pandas describe() covers that with no extra install
Step-by-step guide: how to use pandas-profilingInstall, first run, examples, tips and FAQs
pandas-profiling alternativespandas-profiling vs Recommenderspandas-profiling vs Weights & Biases

How to get it

  1. 1

    Install with a package manager

    You'll need: Python · pandas

    Install with pip

    pip install fg-data-profiling

    Install with conda

    conda install -c conda-forge fg-data-profiling
  2. 2

    Run it

    • Install the package with pip or conda.
    • Load a pandas DataFrame, for example with pd.read_csv.
    • Import ProfileReport from data_profiling.
    • Run profile = ProfileReport(df, title="Profiling Report").
    • View the report in a Jupyter notebook or export it to HTML or JSON.
  3. 3

    Get the most out of it

    • Use the Alerts section first, since it lists likely data quality issues automatically
    • Use the dataset comparison feature to check changes between dataset versions
    • Follow the big-data guide in the documentation when working with large tables
    • Use the sensitive-data guide if the dataset contains personal information
    • Use the metadata and data dictionary features to add column descriptions to the report

Downloads

Source code (.zip)develop branch · straight from GitHubRelease 4.20.02 files incl. fg_data_profiling-4.20.0-py2.py3-none-any.whlOfficial website & docsdocs.sdk.ydata.ai

Strengths

  • Very short code to start: the README shows a single ProfileReport call producing a full report
  • Outputs in several formats, including HTML, JSON, and Jupyter widgets
  • Wide coverage in one report, including time-series, text, and file or image analysis

Trade-offs

  • The README says the package was renamed and the old name will no longer get fixes, so existing users must migrate imports
  • Spark support is described as released but still has open work, and the README asks for more contributors
  • Some advanced features and the database connections described in the README point to a separate commercial product

How it compares

ProjectStarsTypeLevelUpdatedBest for
fg-data-profiling (this)13.7kLibrarybeginner6 days agoAnalysts who want a quick first look at an unfamiliar dataset
recommenders22kLibraryintermediate4 days agoData scientists prototyping recommendation models
nni14.4kLibraryintermediate2.3 years agoResearchers comparing hyperparameter tuning algorithms such as TPE, Hyperband, or Bayesian methods
wandb11.3kLibrarybeginner4 days agoMachine learning engineers who want to compare training runs over time
awesome-datascience30.3kCurated listbeginner5 days agoBeginners who want a guided set of links to start learning data science
Probabilistic-Programming-and-Bayesian-Methods-for-Hackers28.2kLearning resourcebeginner2.4 years agoProgrammers who know some Python and want to learn Bayesian methods by doing

Stats synced from GitHub. The assessment above was drafted with AI from the project's README on 2026-10-29 and may miss recent changes — check the README before relying on a command. MIT licence: free to use, modify and distribute, including commercially, as long as you keep the copyright notice.