I am a bit confused why we need another additional package instead of working with seaborn and implement the changes in there.

In my business, we have a lot of test data on a database, where everyone uses their own python-based solutions for plotting, mostly done in a Jupyter notebook.

Guess what, to compare results and have one dedicated style-guide for the project, you create more complexity than needed.

-> I tried Orange3 for a while, which has a really intuitive way to use, but I miss the direct connection to a DB. Any advice warmly welcome :-)

Hey there, I'm the author of Dexplot. There are many issues I had with seaborn

• Not allowed to set figure size

• No wrapping of tick labels

• No strings for pandas aggregation functions

• No automatic ordering of x/y labels (dexplot provides several options)

• Having to use separate grid functions (catplot, lmplot) for multiple subplots

• Something like 5 different functions for scatterplots. Dexplot has one

• No relative frequency bar charts, which are a fantastic way to explore data. Dexplot provides normalization over any set of variable

• No stacked bar charts

• Seaborn docs have distribution plots (box, violin) in the "categorical" section. A major distinction needs to be made between plots that aggregate, show distributions, and those that plot raw data (like scatterplots)

• Returning of matplotlib axes or seaborn grid objects. Dexplot always returns the matplotlib figure

• Seaborn is essentially dead as far as I can tell with few changes in the last 2-3 years. There are even parameters that continue to be non-functional

In the future, Dexplot will add:

• Many more plotting functions

• Several apps (built from ipywidgets) to explore data. Currently, there is one for viewing colors

• Better automatic figure sizing (it exists now, but will be improved)

• Automatic DPI detection so that matplotlib inches correspond to actual screen inches

Dexplot aims to be very intuitive, easy to use, consistent, and allow easy exploration (the name is a smashing together of data exploration plotting).