`PlotlyJSONEncoder` always casts values to float64 due to using `tolist()`

Regarding the numpy floating point precision and that PlotlyJSONEncoder always casts those to float64 due to using tolist()...

This had always bugged me, as it resulted in much larger exports (i.e. html / ipynb file sizes) than necessary (when float16 or float32 is sufficient) and affected not only coordinate data, but also marker sizes, meta info, etc.

Just in case the plotly.py devs or others are interested: I had found a way to avoid this number inflation by modifying (& monkey patching) the encode_as_list method:

@staticmethod
def encode_as_list_patch(obj):
    """Attempt to use `tolist` method to convert to normal Python list."""
    if hasattr(obj, "tolist"):

        numpy = get_module("numpy")
        try:
            if isinstance(obj, numpy.ndarray) \
               and obj.dtype == numpy.float32 or obj.dtype == numpy.float16 \
               and obj.flags.contiguous:
                return [float('%s' % x) for x in obj]
        except AttributeError:
            raise NotEncodable

        return obj.tolist()
    else:
        raise NotEncodable

It's about 30-50x slower than .tolist(), but - being in the order of a few μs - still much faster than the json encoding, with the benefit of ~3x smaller exports.

I always wanted to report this, and this PR revived the topic. Could this be relevant for a new issue (especially since orjson will not become the default)?

FYI: for reference, a quick search revealed that a patch of encode_as_list was already suggested before: https://github.com/plotly/plotly.py/issues/1842#issuecomment-549401190, in the context of treating inf & NaN, which got brought up again in https://github.com/plotly/plotly.py/pull/2880#issuecomment-726860782.

Originally posted by @mherrmann3 in https://github.com/plotly/plotly.py/issues/2955#issuecomment-856651213

Start in packages/python/plotly/_plotly_utils/utils.py at PlotlyJSONEncoder.encode_as_list, then review the linked discussion about NumPy float16 and float32 handling. Determine how preserving their precision affects serialization and export size without changing other values; done means the behavior is validated for the relevant NumPy types and existing encoding behavior remains intact.