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TABLE

Create a Plotly Table visualization for a given input DataContainer.Params:default : OrderedTriple|OrderedPair|DataFrame|Vector|Scalarthe DataContainer to be visualizedReturns:out : Plotlythe DataContainer containing the Plotly Table visualization
Python Code
import pandas as pd
import plotly.graph_objects as go
from flojoy import DataFrame, OrderedPair, OrderedTriple, Plotly, Scalar, Vector, flojoy
from blocks.DATA.VISUALIZATION.template import plot_layout


@flojoy
def TABLE(default: OrderedTriple | OrderedPair | DataFrame | Vector) -> Plotly:
    """Create a Plotly Table visualization for a given input DataContainer.

    Parameters
    ----------
    default : OrderedTriple|OrderedPair|DataFrame|Vector|Scalar
        the DataContainer to be visualized

    Returns
    -------
    Plotly
        the DataContainer containing the Plotly Table visualization
    """

    layout = plot_layout(title="TABLE")
    fig = go.Figure(layout=layout)

    match default:
        case OrderedPair():
            x = default.x
            y = default.y
            fig.add_trace(
                go.Table(
                    header=dict(values=["x", "y"], align="center"),
                    cells=dict(values=[x, y], align="center"),
                )
            )
        case OrderedTriple():
            x = default.x
            y = default.y
            z = default.z
            fig.add_trace(
                go.Table(
                    header=dict(values=["x", "y", "z"], align="center"),
                    cells=dict(values=[x, y, z], align="center"),
                )
            )
        case Vector():
            v = default.v
            fig.add_trace(
                go.Table(
                    header=dict(values=["v"], align="center"),
                    cells=dict(values=[v], align="center"),
                )
            )
        case Scalar():
            c = default.c
            fig.add_trace(
                go.Table(
                    header=dict(
                        values=["Scalar"],
                        align="center",
                        font=dict(size=1),
                        height=0,
                    ),
                    cells=dict(
                        values=[[c]],
                        align="center",
                        font=dict(size=25),
                    ),
                )
            )
        case DataFrame():
            df = pd.DataFrame(default.m)
            fig.add_trace(
                go.Table(
                    header=dict(values=list(df.columns), align="center"),
                    cells=dict(
                        values=[df[col] for col in df.columns],
                        align="center",
                    ),
                )
            )
    return Plotly(fig=fig)

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Example

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React Flow mini map

In this example, we start by using the LINSPACE node to generate a Vector object of the DataContainer class.

Next, we employ the PLOTLY_DATASET node to create a DataFrame object of the DataContainer class.

To convert the DataFrame into an OrderedTriple object of the DataContainer class, we utilize the DF_2_ORDEREDTRIPLE node. The resulting OrderedTriple object contains three arrays: x, y, and z.

Finally, we visualize the data from these three nodes using the TABLE node, which generates a Plotly Table visualization for each of the input nodes.