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!

pandas-profiling v2.2.0

New release introducing variable size binning (via astropy), PyCharm integration and various fixes and optimizations.

• Added Variable bin sizing via Bayesian Boxing (feature request [#216])
• PyCharm integration, console attempts to detect file type.
• Fixed bug [#215].
• Updated the missingno package to 0.4.2, fixing the font size in the bar diagram.
• Various optimizations

Thanks to:
@Utsav37 @mansenfranzen @jakevdp

pandas-profiling v2.1.2

Fix [#211] and README

pandas-profiling v2.1.1

• Fix of [#206]
• Improve code maintainability of the view (HTML templates, notebook)
• Fix bug in dendrogram sizing

pandas-profiling v2.1.0

The pandas-profiling release version 2.1.0 includes:

• Correlations: correlation calculations are now more fault tolerant ([#51] and [#197]), correlation names in the report are clarified.
• Jupyter Notebook: rendering a profiling report is done inside the srcdoc attribute (which fixes [#199]), a full-width option is added and the column layout is improved.
• User experience: The table styling and sample section formatting is improved.
• Warnings: detection added for categorical variable that is suspected to be of the datetime type.
• Documentation and community:
  • The Contribution page helps users that want to contribute.
  • Typo's fixed [#195], Thank you @abhilashshakti
  • Added more examples.
• Other bugfixes and improvements:
  • Add version information to console interface.
  • Fix: Remove one-time used logger [#202]
  • Fix: Dealing with string indices [#200]

Contributors:
@abhilashshakti @adamrossnelson @manycoding @InsciteAnalytics

pandas-profiling v2.0.3

Bugfix on version structure for 2.0.2.

pandas-profiling v2.0.2

Revised version structure, fixed recursion preventing installation of dependencies ([#184]).

The setup.py file used to include utils from the package prior to installation.
This causes errors when the dependencies are not yet present.

pandas-profiling v2.0.1

• Add offline support [#177], [#179] and [#180]

pandas-profiling v2.0.0

With 23 commits, 123 files changes and 20+ issues resolved, Pandas Profiling v2.0.0 is a big leap forward.

Thanks to the great contributions from everyone involved! Special thanks to @JosPolfliet @conradoqg @eyaltra.