Add performance doc

[LiamConnors]

Documentation PR

• I've seen the doc/README.md file
• This change runs in the current version of Plotly on PyPI and targets the doc-prod branch OR it targets the master branch
• If this PR modifies the first example in a page or adds a new one, it is a px example if at all possible
• Every new/modified example has a descriptive title and motivating sentence or paragraph
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• Every new/modified example is optimized for short line count and focuses on the Plotly/visualization-related aspects of the example rather than the computation required to produce the data being visualized
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• The random seed is set if using randomly-generated data in new/modified examples
• New/modified remote datasets are loaded from https://plotly.github.io/datasets and added to https://github.com/plotly/datasets
• Large computations are avoided in the new/modified examples in favour of loading remote datasets that represent the output of such computations
• Imports are plotly.graph_objects as go / plotly.express as px / plotly.io as pio
• Data frames are always called df
• fig = <something> call is high up in each new/modified example (either px.<something> or make_subplots or go.Figure)
• Liberal use is made of fig.add_* and fig.update_* rather than go.Figure(data=..., layout=...) in every new/modified example
• Specific adders and updaters like fig.add_shape and fig.update_xaxes are used instead of big fig.update_layout calls in every new/modified example
• fig.show() is at the end of each new/modified example
• plotly.plot() and plotly.iplot() are not used in any new/modified example
• Hex codes for colors are not used in any new/modified example in favour of these nice ones

Code PR

• I have read through the contributing notes and understand the structure of the package. In particular, if my PR modifies code of plotly.graph_objects, my modifications concern the codegen files and not generated files.
• I have added tests (if submitting a new feature or correcting a bug) or
  modified existing tests.
• For a new feature, I have added documentation examples in an existing or
  new tutorial notebook (please see the doc checklist as well).
• I have added a CHANGELOG entry if fixing/changing/adding anything substantial.
• For a new feature or a change in behaviour, I have updated the relevant docstrings in the code to describe the feature or behaviour (please see the doc checklist as well).

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[LiamConnors]

@marthacryan @archmoj could I get your feedback on this first draft?
cc @ndrezn

[marthacryan]

I wonder if we should even bring up base64 here. Maybe it could just say something like "performance improvements related to using pandas and numpy arrays"?

[marthacryan]

Along the same lines, maybe we could rename this file

[LiamConnors]

You're right. That info probably isn't that useful for the user. And I'll rename the file too. Thanks @marthacryan

[LiamConnors]

I made those updates. There's still a mention here. Is it base64-encoded typed arrays support in Plotly.js specifically that allows for the improved performance?

Plotly.py uses Plotly.js for rendering, which supports base64-encoded typed arrays. In Plotly.py, NumPy array and NumPy-convertible arrays are base64 encoded before being passed to Plotly.js for rendering.

[LiamConnors]

@marthacryan do you have any additional feedback on this one?

[marthacryan]

Maybe we should rename the file as well to be called performance. Also, it was sort of hard to find the numpy section because the webgl part was so long. I wonder if we could split them into separate pages that are both linked from the performance page? Or is there some kind of table of contents or link that could be added to the top of the doc to make it easy to jump to the section on numpy arrays?

[LiamConnors]

Maybe we should rename the file as well to be called performance. Also, it was sort of hard to find the numpy section because the webgl part was so long. I wonder if we could split them into separate pages that are both linked from the performance page? Or is there some kind of table of contents or link that could be added to the top of the doc to make it easy to jump to the section on numpy arrays?

Thanks @marthacryan. When it is in the docs, there will be sidebar with the TOC:

It's a good point about the Webgl section though. Some of the examples I think could go. Taking a look.

[ndrezn]

Would be great to combine https://plotly.com/python/datashader/ into this page too!