fastplotlib(label: str, data: dict, size: float = 1.0) -> str:

The fastplotlib function renders high-performance scatter plots using GPU acceleration via Fastplotlib. It is ideal for visualizing large datasets directly from the backend with minimal frontend overhead.

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Parameters

  • label (str): A title or alt-text label describing the chart.
  • data (dict): A dictionary containing the following keys:
    • x (list or np.ndarray): X-axis values.
    • y (list or np.ndarray): Y-axis values.
    • color (optional list): Labels used to assign different colors. The component cycles through a fixed palette.
  • size (float): (Optional) The width of the component in a row. Defaults to 1.0 (full row). See the Layout Guide for details.

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Returns

  • str: A unique component ID referencing the rendered image.

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Usage Example

from preswald import get_df, connect, fastplotlib

connect()
df = get_df("iris_csv")

fastplotlib(
    label="Iris Dataset (Fastplotlib)",
    data={
        "x": df["sepal.length"],
        "y": df["petal.width"],
        "color": df["variety"]
    },
    size=1.0
)

This will render a GPU-accelerated scatter plot, cycling colors by species. It is exported server-side and displayed as an image in the frontend.

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Notes

  • Rendering happens offscreen via Fastplotlib’s WGPU backend, so this is ideal for large or compute-heavy charts.
  • Colors are currently cycled from a fixed palette. For more control over color schemes, we recommend extending the component in the future with support for RGBA arrays or named colors.

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