pandas-profiling v2.10.0rc1

The full changelog is available here: https://pandas-profiling.github.io/pandas-profiling/docs/master/rtd/pages/changelog.html.

pandas-profiling v2.9.0

The full changelog is available here: https://pandas-profiling.github.io/pandas-profiling/docs/master/rtd/pages/changelog.html.

pandas-profiling v2.9.0rc1

This release candidate improves handling of sensitive data and futhermore reduces technical debt with various fixes. The full changelog is available here: https://pandas-profiling.github.io/pandas-profiling/docs/master/rtd/pages/changelog.html.

A warm thank you to everyone who has contributed to this release: @gauravkumar37 @Jooong @smaranjitghose @XavierBanos Tam Nguyen @andycraig @mgorsk1 @mbh86 @MHUNCHO @GaelVaroquaux @AmauryLepicard @baluyotraf @pvojnisek @abegong

pandas-profiling v2.8.0

pandas-profiling now has build-in supports for Files and Images, such as extracting file sizes, creation dates and dimensions and scanning for truncated images or those containing EXIF information. Moreover, the text analysis features have also been reworked, providing more informative statistics.

Read the changelog v2.8.0 for more details.

Contributors: @loopyme @Bradley-Butcher @willemhendriks, @IscaAy, @frellnick, @dataverz @ieaves

pandas-profiling v2.7.1

Fix #468 by pinning visions to 0.4.1

pandas-profiling v2.7.0

Announcement and changelog are available in the documentation.

We are grateful for @loopyme and @kyleYang for creating parts of the features on this release.

Thanks for all contributors that made this release possible @1313e @dataprofessor @neomatrix369 @jiangfangfangxm @WesleyTheGeolien @nickyi1990 @ricgu8086.

pandas-profiling v2.6.0

Dependency policy

The current dependency policy is suboptimal. Pinning the dependencies is great for reproducibility (high guarantee to work), but on the downside requires frequent maintenance and introduces compatibility issues with other packages. Therefore, we are moving away from pinning dependencies and instead specify a minimum version.

Pandas v1

Early releases of pandas v1 demonstrated many regressions that broke functionality (as acknowledged by the authors here). At this point, pandas is more stable and we notice high demand for compatibility. We move on to support pandas' latest versions. To ensure compatibility with both versions, we have extended the test matrix to test against both pandas 0.x.y and 1.x.y.

Python 3.6+ features

Python 3.6 introduces ordered dicts and f-strings, which we now rely on. This means that from pandas-profiling 2.6, you should minimally run Python 3.6. For users that for some reason cannot update, you can use pandas-profiling 2.5.0, but you unfortunately won't benefit from updates or maintenance.

Extended continuous integration

Starting from this release, we use Github Actions and Travis CI combined to increase maintainability.
Travis CI handles the testing, Github Actions automates part of the development process by running black and building the docs.

pandas-profiling v2.5.0

• Progress bar added (#224)
• Character analysis for Text/NLP (#278)
• Themes: configuration and demo's (Orange, Dark)
• Tutorial on modifying the report's structure (#362; #281, #259, #253, #234). This jupyter notebook also demonstrates how to use the Kaggle api together with pandas-profiling.
• Toggle descriptions at correlations.

Deprecation:

• This is the last version to support Python 3.5.

Stability:

• The order of columns changed when sort="None" (#377, fixed).
• Pandas v1.0.X is not yet supported (#367, #366, #363, #353, pinned pandas to < 1)
• Improved mixed type detection (#351)
• Refactor of report structures.
• Correlations are more stable (e.g. Phi_k color scale now from 0-1, rows and columns with NaN values are dropped, #329).
• Distinct counts exclude NaNs.
• Fixed alerts in notebooks.

Other improvements:

• Warnings are now sorted.
• Links to Binder and Google Colab are added for notebooks (#349)
• The overview section is tabbed.

• Commit for pandas-profiling v2.5.0

• Progress bar added (#224)
• Character analysis for Text/NLP (#278)
• Themes: configuration and demo's (Orange, Dark)
• Tutorial on modifying the report's structure (#362; #281, #259, #253, #234). This jupyter notebook also demonstrates how to use the Kaggle api together with pandas-profiling.
• Toggle descriptions at correlations.

Deprecation:

• This is the last version to support Python 3.5.

Stability:

• The order of columns changed when sort="None" (#377, fixed).
• Pandas v1.0.X is not yet supported (#367, #366, #363, #353, pinned pandas to < 1)
• Improved mixed type detection (#351)
• Refactor of report structures.
• Correlations are more stable (e.g. Phi_k color scale now from 0-1, rows and columns with NaN values are dropped, #329).
• Distinct counts exclude NaNs.
• Fixed alerts in notebooks.

Other improvements:

• Warnings are now sorted.
• Links to Binder and Google Colab are added for notebooks (#349)
• The overview section is tabbed.

pandas-profiling v2.4.0

The v2.4.0 release decouples the data structure of reports from the actual rendering. It's now much simpler to change the user interface, whether the user is in a jupyter notebook, webpage, native application or just wants a json view of the data.

We are also proud to announce that we are accepted for the GitHub Sponsor programme. You are cordially invited to support me through this programme, because you want to see me continue working on this project and to boost community funding, GitHub will match your contribution!

Other improvements:

• extended configuration with better defaults, including minimal mode for big data (#258, #310)
• more example datasets
• rejection of highly correlated variables is generalized (#284, #299)
• many structural and stability improvements (#254, #274, #239)

Special thanks to @marco-cardoso @ajupton @lvwerra @gliptak @neomatrix369 for their contributions.

pandas-profiling v2.3.0

• (Experimental) Support for "path" type
• Fix numeric precision (#225)
• Force labels in missing values diagram for large number of columns (#222)
• Add pull request template
• Add Census Dataset from the UCI ML Repository

Thanks @bensdm and @huaiweicheng for your valuable contributions to this version!