API: engine kw to .plot to enable selectable backends

We have had some conversations in the past regarding an .plot(....., engine=) kw. in #8018 This would allow pandas to redirect the plotting to a user selectable back-end, keeping matplotlib as the default.

see chartpy here, for a way to selectively enable matplotlib, bokeh and plotly.

and generically via altair.

Missing from here is when to re-direct to seaborn.

So this issue is for discussion:

1. should we do this
2. implementation method and minimalist dependencies to actually do this (ideally pandas would add NO dependences itself, just import for a particular engine, raising if its not available).
3. maybe should spin off much of the current pandas code into a separate repo (pandas-plot)? and this actually should be the default (rather than matplotlib), which is of course the dependency. This might allow simply removing the vast majority of the custom plotting code.

[trxcllnt]

@rgbkrk heads up, our Arrow lib is now part of the official Apache/Arrow project. The package name on npm will stay the same, intending to release 0.1.3 in the next few days 🎉

[ellisonbg]

Sorry I wasn’t clear - I would keep the data separate and only specify the visual encodings, marks, etc in the JSON. The actual data transfer could be done with either arrow or full pandas data frames. The rendering libraries could deal with the combination of JSON viz spec + DataFrame

On Tue, Oct 3, 2017 at 1:50 PM, Peter Wang [e-mail] wrote:

@ellisonbg https://github.com/ellisonbg you bring up an interesting point, that if the Pandas devs want to “own plotting”, then outputting a JSON-based visualization spec would be the most flexible and accurate approach to doing that. However, the roundtrip through JSON-land is nontrivial - not merely from a logical mapping perspective, but also from the perspective of performance. In the most common case, directly calling matplotlib on a large dataframe is extremely fast. Similarly, there’s no reason why Datashader or Bokeh server can’t also be similarly fast on large dataframes. However, round-tripping those datasets through an encode/decode process to JSON would be quite painful. (And that’s not even considering the use cases of e.g. GeoPandas, with tons of shape geometry data.)

My understanding is that the Pandas devs already have a plotting API on the plot object, namely, the [‘area’, ‘bar’, ‘barh’, ‘box’, ‘density’, ‘hexbin’, ‘hist’, ‘line’, ‘pie’, ‘scatter’] methods, which defines their expectations of the API that the plotting backends must adhere to. At that point, it’s on the viz library developers to properly implement those functions.

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