Dash Loading Spinners

This library is designed for use with Plotly Dash. The components have all been designed to provide functionality similar to Dash's core Loading component, and will display a loading spinner whilst the underlying children are re-rendering.

The spinners in it have been adapted for use from a number of other existing libraries:

• react-spinners
• react-loader-spinner
• react-css-spinners

The majority of spinner names have been retained from the originals, but some have been amended where there were name clashes.

More details on the components and usage can be found in our documentation.

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Installation

Dash Loading Spinners is available through PyPI, and can be installed with pip:

pip install dash-loading-spinners

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Basic Usage

Once installed, you can make use of the components (in their most basic sense) as follows:

import dash
import dash_loading_spinners as dls

app = dash.Dash()

app.layout = dls.Hash()

if __name__ == "__main__":
    app.run_server()

There are a number of attributes which are common across all spinners. These are:

• id (string; optional): The ID of this component, used to identify dash components in callbacks. The ID needs to be unique across all of the components in an app.
• children (a list of or a singular dash component, string or number; optional): The children of this component.
• show_initially (boolean; default True): Whether the Spinner should show on app start-up before the loading state has been determined. Default True.
• debounce (number; default 0): When using the spinner as a loading spinner, add a time delay (in ms) to the spinner being removed to prevent flickering.
• fullscreen (boolean; optional): Boolean that determines if the loading spinner will be displayed full-screen or not.
• fullscreenClassName (string; optional): CSS class names to apply to the container when in fullscreen.
• fullscreen_style (dict; optional): Defines CSS styles for the container when in fullscreen.

Many spinners additional have properties that can be customised, including colour and size. You can find these on the individual component pages.

Realistically, as part of an application, you will be using them alongside other components and callbacks. Here is an example of what this might look like:

Note: dash-bootstrap-components isn't necessary for dash-loading-spinners to work, but has been included to improve the layout.

# app.py
import dash
import dash_bootstrap_components as dbc
import dash_loading_spinners as dls
from dash import dcc, html, Input, Output

from helpers import get_new_graph

app = dash.Dash(external_stylesheets=[dbc.themes.UNITED])

app.layout = html.Div(
    [
        dbc.Row(
            [
                dbc.Col(
                    html.Div(
                        dbc.Button(
                            "Simulate slow loading component",
                            id="loading-button",
                            className="btn-success",
                            n_clicks=0,
                        )
                    ),
                    md=3,
                ),
                dbc.Col(
                    dls.Hash(
                        dcc.Graph(id="loading-output",),
                        color="#435278",
                        speed_multiplier=2,
                        size=100,
                    ),
                    md=9,
                ),
            ],
        ),
    ]
)


@app.callback(
    Output("loading-output", "figure"), [Input("loading-button", "n_clicks")],
)
def load_output(n):
    # See note below
    return get_new_graph(n)

if __name__ == "__main__":
    app.run_server()

The function get_new_graph in this example is deliberately ambiguous, as it simply acts as a placeholder for any slow-loading component. For completeness, if you wish to use a similar example to the one shown above, you can use the following function:

# helpers.py
import numpy as np
import plotly.graph_objects as go
import time

def get_new_graph(n):
    if n:
        # Simulate slow-loading component
        time.sleep(2)
    # Generate a random scatter plot
    n = (n + 1) * 10
    return go.Figure(
        data=go.Scatter(
            y=np.random.randn(n) * 100,
            mode="markers",
            marker=dict(
                size=16,
                color=np.random.randn(n) * 100,
                colorscale="blues",
                showscale=True,
            ),
        ),
        layout=go.Layout(title="This graph takes ages to re-load"),
    )