---
id: ydata-profiling
version: "4.18.4"
license: unclear
license_treatment: permissive
maintenance: active
---

# ydata-profiling — Generate profile report for pandas DataFrame

License: permissive · Maintenance: active · Downloads: 1.9M/mo

## What it is and what it does

ydata-profiling automates exploratory data analysis by generating a single-line report on a pandas DataFrame. It infers column data types, computes descriptive statistics, detects data quality issues (missing values, duplicates, skewness, high correlations), and produces visualizations for univariate and multivariate analysis. The package handles specialized data including time-series (with autocorrelation and seasonality analysis), text (script and block detection), images (dimensions, EXIF metadata), and files (sizes, creation dates).

Reports export to HTML for sharing, JSON for automation, or Jupyter widgets for interactive exploration. The package includes dataset comparison, sensitive data handling, and configuration options for large datasets. It depends on a substantial stack: pandas, numpy, scipy, statsmodels for statistics; matplotlib, seaborn, wordcloud for visualization; pydantic for validation; and specialized libraries like phik for correlation, imagehash for image analysis, and numba for performance.

Use it for:

- Generate a quick statistical summary and quality report on a new dataset before cleaning or modeling
- Compare two versions of the same dataset to identify changes in distributions, missing values, or data quality
- Profile time-series data to detect seasonality, autocorrelation, and temporal patterns in one command
- Embed interactive profiling widgets in Jupyter notebooks for exploratory analysis during data investigation
- Export a shareable HTML report documenting data characteristics and potential issues for stakeholders or documentation

## Worth the install?

AI-flagged interpretation of the facts above — verify before relying.

Generates comprehensive exploratory data analysis reports for pandas DataFrames, automatically detecting data types, computing statistics, and producing visualizations exportable as HTML, JSON, or Jupyter widgets.

Yes, with a migration caveat. The package is actively maintained, has no known vulnerabilities, and provides genuine value for exploratory data analysis workflows. However, install fg-data-profiling instead—ydata-profiling is deprecated and will not receive future updates. If you are already using ydata-profiling, plan migration soon; if starting fresh, use the renamed package directly.

## Install

pip install ydata-profiling
uv add ydata-profiling
poetry add ydata-profiling

## Installing ydata-profiling

Before you install: Low install friction with a pure-Python wheel distribution. Active maintenance with recent commits and no known vulnerabilities. However, the package has been renamed to fg-data-profiling and the old ydata-profiling package will no longer receive updates—migration is recommended for long-term support.

License in practice: MIT license permits unrestricted use, modification, and distribution in both open-source and commercial projects with minimal restrictions.

Quickstart:

pip install ydata-profiling

import pandas as pd
from ydata_profiling import ProfileReport

df = pd.read_csv('data.csv')
profile = ProfileReport(df, title="Analysis")
profile.to_file("report.html")

Requires Python 3.10 or later. The 21 runtime dependencies (scipy, statsmodels, numba, etc.) add significant installation size; ensure adequate disk space and network bandwidth.

Verify before relying:

- Whether the old ydata-profiling package will continue to receive security patches or is fully deprecated in favor of fg-data-profiling
- Performance characteristics when profiling datasets at scale or with very large numbers of columns
- Specific memory requirements for the 21 runtime dependencies, particularly scipy, statsmodels, and numba

## Package facts

- License: not declared (permissive)
- Python support: supports_current
- Install friction: low
- Maintenance: active
- Downloads: 1.9M/month (top 5,000 on PyPI)
- Known vulnerabilities: none known

## Tags

pandas dataframe profiling, exploratory data analysis report, automatic data quality analysis, dataframe summary statistics, data profiling visualization, pandas eda tool, dataset comparison report, data-profiling, eda, pandas-integration

[View on SkillFed](https://skillfed.io/packages/ydata-profiling) · [View on PyPI](https://pypi.org/project/ydata-profiling/)