Skip to main content

pandas-profiling


⚠️ pandas-profiling package naming was changed. To continue profiling data use ydata-profiling instead!

This repo implements the brownout strategy for deprecating the pandas-profiling package on PyPI.⚠️


Pandas Profiling Logo

🎊 New year, new face, more functionalities!

Thank you for using and following pandas-profiling developments. Yet, we have a new exciting feature - we are now thrilled to announce that Spark is now part of the Data Profiling family from version 4.0.0 onwards

With its introduction, there was also the need for a new naming, one that will allow to decouple the concept of profiling from the Pandas Dataframes - ydata-profiling!

But fear not, pip install pandas-profiling will still be a valid for a while, and we will keep investing in growing the best open-source for data profiling, so you can use it for even more use cases.

How to fix the error for the main use cases

  • use pip install ydata-profiling rather than pip install pandas-profiling
  • replace pandas-profiling by ydata-profiling in your pip requirements files (requirements.txt, setup.py, setup.cfg, Pipfile, etc ...)
  • if the pandas-profiling package is used by one of your dependencies it would be great if you take some time to track which package uses pandas_profiling instead of ydata_profiling for the imports

Schedule for deprecation

  • ydata-profiling was launched in February 1st.
  • pip install pandas-profiling will still be supported until April 1st, but a warning will be thrown. from pandas_profiling import ProfileReport will be supported until April 1st.
  • After April 1st, an error will be thrown if pip install pandas-profiling is used. Use pip install ydata-profiling instead.
  • After April 1st, an error will be thrown if from pandas_profiling import ProfileReport is used. Use from ydata_profiling import ProfileReport instead.

About pandas-profiling

pandas-profiling primary goal is to provide a one-line Exploratory Data Analysis (EDA) experience in a consistent and fast solution. Like pandas df.describe() function, that is so handy, pandas-profiling delivers an extended analysis of a DataFrame while alllowing the data analysis to be exported in different formats such as html and json.

The package outputs a simple and digested analysis of a dataset, including time-series and text.

Documentation | Discord | Stack Overflow | Latest changelog

Do you like this project? Show us your love and give feedback!

Release files for pandas-profiling 3.6.6

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for pandas-profiling 3.6.6
File Size Uploaded
pandas-profiling-3.6.6.tar.gz 253.5 kB Details

Built distribution (wheel)

Table of built distributions (wheels) for pandas-profiling 3.6.6
File Interpreter ABI Platform
pandas_profiling-3.6.6-py2.py3-none-any.whl Python 2, Python 3 none any Details

Total release size: 577.9 kB

Release files / pandas-profiling-3.6.6.tar.gz

Download URL pandas-profiling-3.6.6.tar.gz
Size 253.5 kB
Tags Source
SHA-256 checksum
How to use checksums
1ef14a9cfa647ff95e13fd0b589f74897c0b078ef8ebcd44760b4031dcbf52a2
BLAKE2b-256 checksum
How to use checksums
34c0cd405caab2739ced0161ef4f7bed55d73aaf6e0554b95f4dd21088d94710
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
No
Uploaded via twine/4.0.1 CPython/3.11.1

Release files / pandas_profiling-3.6.6-py2.py3-none-any.whl

Download URL pandas_profiling-3.6.6-py2.py3-none-any.whl
Size 324.4 kB
Tags Python 2 Python 3
SHA-256 checksum
How to use checksums
69297626426358672ccbb357abbfea7fa35a7feda4388dd6377bd521d15cfaf2
BLAKE2b-256 checksum
How to use checksums
accbf038b9b57c6edaaf6f4e400b0bb1d954bc08dc6bc61a4b0861acecd3789c
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
No
Uploaded via twine/4.0.1 CPython/3.11.1

Release history Release notifications | RSS feed

This release

3.6.6 This release

2 release files

3.6.5

2 release files

3.6.4

2 release files

3.6.3

2 release files

3.6.2

2 release files

3.6.1

2 release files

3.6.0

2 release files

3.5.0

2 release files

3.4.0

2 release files

3.3.0

2 release files

3.2.0

2 release files

3.1.0

2 release files

3.0.0

2 release files

Anthropic, PBC Visionary sponsor Bloomberg Visionary sponsor Hudson River Trading Visionary sponsor Meta Visionary sponsor NVIDIA Visionary sponsor Microsoft Sustainability sponsor Depot Continuous Integration AWS Cloud computing and Security Sponsor Datadog Monitoring Fastly CDN Google Download Analytics Sentry Error logging StatusPage Status page