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BAR

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Create a Plotly Bar visualization for a given input DataContainer. Params: default : OrderedPair|DataFrame|Matrix|Vector the DataContainer to be visualized in a bar chart Returns: out : Plotly the DataContainer containing the Plotly Bar chart visualization
Python Code
import numpy as np
import pandas as pd
import plotly.graph_objects as go
from flojoy import DataFrame, Matrix, OrderedPair, Plotly, Vector, flojoy
from blocks.DATA.VISUALIZATION.template import plot_layout


@flojoy
def BAR(default: OrderedPair | DataFrame | Matrix | Vector) -> Plotly:
    """Create a Plotly Bar visualization for a given input DataContainer.

    Parameters
    ----------
    default : OrderedPair|DataFrame|Matrix|Vector
        the DataContainer to be visualized in a bar chart

    Returns
    -------
    Plotly
        the DataContainer containing the Plotly Bar chart visualization
    """

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

    match default:
        case OrderedPair():
            x = default.x
            if isinstance(default.x, dict):
                dict_keys = list(default.x.keys())
                x = default.x[dict_keys[0]]
            y = default.y
            fig.add_trace(go.Bar(x=x, y=y))
        case DataFrame():
            df = default.m
            first_col = df.iloc[:, 0]
            is_timeseries = False
            if pd.api.types.is_datetime64_any_dtype(first_col):
                is_timeseries = True
            if is_timeseries:
                for col in df.columns:
                    if col != df.columns[0]:
                        fig.add_trace(
                            go.Bar(
                                y=df[col].values,
                                x=first_col,
                                name=col,
                            )
                        )
                fig.update_layout(xaxis_title=df.columns[0])
            else:
                for col in df.columns:
                    if df[col].dtype == "object":
                        counts = df[col].value_counts()
                        fig.add_trace(
                            go.Bar(x=counts.index.tolist(), y=counts.tolist(), name=col)
                        )
                    else:
                        fig.add_trace(go.Bar(x=df.index, y=df[col], name=col))
                fig.update_layout(xaxis_title="DF index", yaxis_title="Y Axis")

        case Matrix():
            m = default.m

            num_rows, num_cols = m.shape

            x_ticks = np.arange(num_cols)

            for i in range(num_rows):
                fig.add_trace(go.Bar(x=x_ticks, y=m[i, :], name=f"Row {i+1}"))

            fig.update_layout(xaxis_title="Column", yaxis_title="Value")
        case Vector():
            y = default.v
            x = np.arange(len(y))
            fig.add_trace(go.Bar(x=x, y=y))

    return Plotly(fig=fig)

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React Flow mini map

In this example weโ€™re simulating data from LINSPACE, TIMESERIES, MATRIX and R_DATASET and visualizing them with BAR node which creates a Plotly Bar visualization for each of the input node.