To to_dict, or to not aim to support Altair for now? #133

    elif isinstance(data, pd.DataFrame):
        data = sanitize_dataframe(data)
        return data.to_json(orient="records", double_precision=15)
    elif isinstance(data, dict):
        if "values" not in data:
            raise KeyError("values expected in data dict, but not present.")
        return json.dumps(data["values"], sort_keys=True)
    elif hasattr(data, "__dataframe__"):
        # experimental interchange dataframe support
        pi = import_pyarrow_interchange()
        pa_table = pi.from_dataframe(data)
        return json.dumps(pa_table.to_pylist())

from vega_datasets import data
import pyarrow.interchange as pi
import polars as pl

cars = data.cars().head(2)
cars_pl = pl.from_pandas(cars)
print(cars.to_dict(orient='records'))

pa_table = pi.from_dataframe(cars_pl)
print(pa_table.to_pylist())

[{'Name': 'chevrolet chevelle malibu', 'Miles_per_Gallon': 18.0, 'Cylinders': 8, 'Displacement': 307.0, 'Horsepower': 130.0, 'Weight_in_lbs': 3504, 'Acceleration': 12.0, 'Year': Timestamp('1970-01-01 00:00:00'), 'Origin': 'USA'}, {'Name': 'buick skylark 320', 'Miles_per_Gallon': 15.0, 'Cylinders': 8, 'Displacement': 350.0, 'Horsepower': 165.0, 'Weight_in_lbs': 3693, 'Acceleration': 11.5, 'Year': Timestamp('1970-01-01 00:00:00'), 'Origin': 'USA'}]

[{'Name': 'chevrolet chevelle malibu', 'Miles_per_Gallon': 18.0, 'Cylinders': 8, 'Displacement': 307.0, 'Horsepower': 130.0, 'Weight_in_lbs': 3504, 'Acceleration': 12.0, 'Year': Timestamp('1970-01-01 00:00:00'), 'Origin': 'USA'}, {'Name': 'buick skylark 320', 'Miles_per_Gallon': 15.0, 'Cylinders': 8, 'Displacement': 350.0, 'Horsepower': 165.0, 'Weight_in_lbs': 3693, 'Acceleration': 11.5, 'Year': Timestamp('1970-01-01 00:00:00'), 'Origin': 'USA'}]