dtale

Highlights include:

• #32: unpin jsonschema by moving flasgger to extras_require

Highlights includes:

• Correlations Pearson Matrix filters
• "name" display in title tab
• "Heat Map" toggle
• dropped unused "Flask-Caching" requirement

• Bug fixes for:
  • #28: "Instances" menu option will now be displayed by default
  • #29: add hints to how users can navigate the correlations popup
  • add "unicode" as a string classification for column width calculation

• Bug fixes for:
  • choose between pandas.corr & numpy.corrcoef depending on presence of NaNs
  • hide timeseries correlations when date columns only contain one day

• Bug fixes for:
  • duplicate loading of histogram data
  • string serialization failing when mixing future.str & str in scatter function

Updated correlation calculation to use numpy.corrcoef for performance purposes

Hotfix for failing test under certain versions of future package

Includes bug fixes for:

• display of histogram column information
• reload of hidden "processes" input when loading instances data
• correlations json failures on string conversion

Highlights include:

• fix for incompatible str types when directly altering state of data in running D-Tale instance

Highlights include:

• webbrowser integration (the ability to automatically open a webbrowser upon calling dtale.show())
• flag for hiding the "Shutdown" button for long-running demos
• "Instances" navigator popup for viewing all activate D-Tale instances for the current python process

Highlights include:

• #20: fix for data being overriden with each new instance
• #21: fix for displaying timestamps if they exist
• calling show() now returns an object which can alter the state of a process
  • accessing/altering state through the data property
  • shutting down a process using the kill() function

Highlights include:

• Fix for #13, auto-detection of column widths for strings & floats

Highlights include:

• IE support
• Describe & About popups
• Custom CLI support

This is the initial major release of D-Tale.

Highlights include:

• creation of Flask instances (automatic port detection)
• auto-cleanup of D-Tale instances (after 1 hour of inactivity)
• base grid view of pandas dataframe & series objects
  • sorting, filtering, formatting, column locking
• histograms
• correlation breakdowns
• coverage analysis