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.

pandas-profiling v1.4.3

• Fix the correlation images (#160).

Contributors:
@kazetof

pandas-profiling v1.4.2

• Multiple Bugfixes
• Enable Travis CI builds

Contributors:
@Aylr @LeonardAukea @kevanshea @endremborza @romainx @drkarthi

pandas-profiling v1.4.1

Enhancements

• Performance enhancement. It is now possible to disable some heavy resource operations and achieve better performances (see also #76):
  • Correlation checking by turning check_correlation to False (#43)
  • Recoded checking by turning check_recoded to False.
• Possibility to install using conda
• Implementation of a new Boolean variable type (#25)
• Add new badges for zeros and highly skewed (#63)
• Code refactoring (internal improvement) to split on main modules in 4 modules (#65)
• Improve types handling
  • types like list, tuple and dict are now officially unsupported until we improve them
  • mixed columns are also correctly handled
  • New Binary variable type supporting native boolean type and also binary numeric values (#77)
• Warnings column names have link to corresponding detail in variables section in order to ease the navigation (#66)
• Spearman and Pearson Correlation matrix diagrams added in the report (#83)

Bug fixes

• #56 Incorrect calculation for % unique for variables with missing values bug
• #11 Avoid to throw an error when calling get_rejected_variables while correlation has not been computed
• #68 Avoid to set the matplotlib backend if not necessary