pandas-profiling
Deprecated 'pandas-profiling' package, use 'ydata-profiling' instead
Decision gist · record as of 2026-08-14
No. Do not install this package. It is deprecated and will error after April 1st. Install ydata-profiling instead, which provides the same functionality under the new name. If you encounter pandas-profiling as a transitive dependency, file an issue with the upstream package to migrate to ydata-profiling.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Package is deprecated and will throw errors after April 1st; install ydata-profiling instead for continued support.
- Low install friction, but the package is in active deprecation.
- Last release was over a year ago; the repo remains active but this version is a brownout implementation directing users to ydata-profiling instead.
License · maintenance · safety
MIT (permissive) — MIT license (permissive) allows commercial and private use, but the package itself is deprecated and unsupported—license permissiveness is moot when the package is being phased out.
last release 2023-01-31 (1291 days) · last repo commit 2026-04-22 · 13,670 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 417,617 downloads/mo, #6,813 on PyPI
Alternatives
Verify before relying
pip install pandas-profiling
from pandas_profiling import ProfileReport
# Note: This import path will error after April 1st; use ydata-profiling instead- Whether the April 1st deprecation deadline has passed relative to current date.
- Whether dependencies of this package have migrated to ydata-profiling, potentially making this a transitive blocker.
- Exact functionality differences between this version and ydata-profiling's current capabilities.
What it is and what it does
pandas-profiling was a one-line exploratory data analysis tool that extended pandas' df.describe() to produce detailed HTML and JSON reports on DataFrames, including time-series and text analysis. The package has been renamed to ydata-profiling to decouple profiling from pandas-specific DataFrames.
This specific version (3.6.6) is a brownout implementation: it remains installable via pip but is deprecated and will cease functioning after April 1st. The package now serves only to redirect users to ydata-profiling. New installations should use ydata-profiling directly; existing code using pandas-profiling imports will break after the deprecation deadline.
Use it for
- Migrating legacy code: if you have existing pandas-profiling imports, this package documents the migration path to ydata-profiling.
- Temporary compatibility: if a dependency still requires pandas-profiling, this package provides a bridge until that dependency updates.
- Understanding the deprecation: reviewing this package's brownout strategy and timeline for sunsetting old naming conventions.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
No.
Do not install this package. It is deprecated and will error after April 1st. Install ydata-profiling instead, which provides the same functionality under the new name. If you encounter pandas-profiling as a transitive dependency, file an issue with the upstream package to migrate to ydata-profiling.
Install
pandas-profiling on PyPI
Before you install
Low install friction, but the package is in active deprecation. Last release was over a year ago; the repo remains active but this version is a brownout implementation directing users to ydata-profiling instead.
Package is deprecated and will throw errors after April 1st; install ydata-profiling instead for continued support.
License in practice
MIT license (permissive) allows commercial and private use, but the package itself is deprecated and unsupported—license permissiveness is moot when the package is being phased out.
Quickstart
pip install pandas-profiling
from pandas_profiling import ProfileReport
# Note: This import path will error after April 1st; use ydata-profiling instead
Verify before relying
- Whether the April 1st deprecation deadline has passed relative to current date.
- Whether dependencies of this package have migrated to ydata-profiling, potentially making this a transitive blocker.
- Exact functionality differences between this version and ydata-profiling's current capabilities.
Package facts
| License | MIT permissive |
| Python support | Capped below the current Python release >=3.7, <3.11 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 1 packageydata-profiling |
| Maintenance | Actively maintained 1,291 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 417,617 / month, #6,813 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 5 - Production/StableEnvironment :: ConsoleFramework :: IPythonIntended Audience :: DevelopersIntended Audience :: Financial and Insurance IndustryIntended Audience :: Healthcare IndustryIntended Audience :: Science/ResearchLicense :: OSI Approved :: MIT LicenseOperating System :: OS IndependentProgramming Language :: Python :: 3Topic :: Scientific/EngineeringTopic :: Software Development :: Build Tools |
Evidence: pandas_profiling-3.6.6-py2.py3-none-any.whl
Tags
Let your AI agent find packages like this
Example. Real query, live index.
You found this page by searching. An agent finds it by wishing: SkillFed indexes 14,416 PyPI packages by what they can do, searchable in plain language.
wish › “automated eda report generation”
- pandas-profilingThis package is deprecated; it redirects users to ydata-profiling for…
- sweetvizSweetviz generates interactive HTML visualizations for exploratory…
- ydata-profilingGenerates comprehensive exploratory data analysis reports for pandas…
Give your agent the search over MCP, or paste the wish link into any chat.
More Scientific/Engineering packages
NumPy provides an N-dimensional array object and a comprehensive suite of mathematical, linear algebra, Fourier transform, and random number functions for scientific computing in Python.
pandas provides fast, flexible data structures (Series and DataFrame) for loading, cleaning, transforming, and analyzing labeled or relational data in Python.
scipy provides numerical algorithms for mathematics, science, and engineering—including optimization, integration, linear algebra, Fourier transforms, signal and image processing, and ODE solvers—built on numpy arrays.
scikit-learn provides a comprehensive Python library for supervised and unsupervised machine learning, including classification, regression, clustering, dimensionality reduction, and model evaluation tools built on NumPy and SciPy.
Install it if you need to train, evaluate, or deploy supervised or unsupervised learning models.
dill extends Python's pickle module to serialize and deserialize a much wider range of Python objects, including functions, lambdas, classes, and interpreter sessions, to byte streams for storage or network transmission.
Multiprocess is an enhanced fork of Python's standard multiprocessing library that uses dill for better serialization, allowing you to spawn processes with a threading-like API and share complex objects between them.
Install it if you use multiprocessing and encounter pickle serialization limits with lambdas or complex objects.
See also percentify · ydata-profiling · pandas-summary · sweetviz · dtale · sklearn · itables · pandas-td · facets-overview · pandasql