Pattern-Matching Callbacks

To get the most out of this page, make sure you’ve read about Basic Callbacks in the Dash Fundamentals.

The pattern-matching callback selectors MATCH, ALL, & ALLSMALLER allow you to write callbacks that respond to or update an arbitrary or dynamic number of components.

Simple Example with ALL

This example uses partial property updates, introduced in Dash 2.9. For an example that works with earlier versions of Dash, see Simple ALL Example Without Partial Updates at the end of this example.

This example renders an arbitrary number of dcc.Dropdown elements and the callback is fired whenever any of the dcc.Dropdown elements change. Try adding a few dropdowns and selecting their values to see how the app updates.

This example has not been ported to Julia yet - showing the Python version instead.

Visit the old docs site for Julia at: https://community.plotly.com/c/dash/julia/20

from dash import Dash, dcc, html, Input, Output, ALL, Patch, callback

app = Dash(__name__)

app.layout = html.Div(
    [
        html.Button("Add Filter", id="add-filter-btn", n_clicks=0),
        html.Div(id="dropdown-container-div", children=[]),
        html.Div(id="dropdown-container-output-div"),
    ]
)


@callback(
    Output("dropdown-container-div", "children"), Input("add-filter-btn", "n_clicks")
)
def display_dropdowns(n_clicks):
    patched_children = Patch()
    new_dropdown = dcc.Dropdown(
        ["NYC", "MTL", "LA", "TOKYO"],
        id={"type": "city-filter-dropdown", "index": n_clicks},
    )
    patched_children.append(new_dropdown)
    return patched_children


@callback(
    Output("dropdown-container-output-div", "children"),
    Input({"type": "city-filter-dropdown", "index": ALL}, "value"),
)
def display_output(values):
    return html.Div(
        [html.Div(f"Dropdown {i + 1} = {value}") for (i, value) in enumerate(values)]
    )


if __name__ == "__main__":
    app.run(debug=True)

Some notes about this example:
- Notice how the id in dcc.Dropdown is a dictionary rather than a string.
This is a new feature that we enabled for pattern-matching callbacks (previously, IDs had to be strings).
- In our second callback, we have Input({'type': 'city-filter-dropdown', 'index': ALL}, 'value'). This means “match any input that has an ID dictionary where 'type' is 'city-filter-dropdown' and 'index' is anything. Whenever the value property of any of the dropdowns change, send all of their values to the callback.”
- The keys & values of the ID dictionary (type, index, city-filter-dropdown) are arbitrary. This could’ve be named {'foo': 'bar', 'baz': n_clicks}.
- However, for readability, we recommend using keys like type, index, or id. type can be used to refer to the class or set dynamic components and
index or id could be used to refer which component you are matching within that set. In this example, we just have a single set of dynamic
components but you may have multiple sets of dynamic components in more complex apps or if you are using MATCH (see below).
- In fact, in this example, we didn’t actually need 'type': 'city-filter-dropdown'. The same callback would have worked with Input({'index': ALL}, 'value').
We included 'type': 'city-filter-dropdown' as an extra specifier in case you create multiple sets of dynamic components.
- The component properties themselves (e.g. value) cannot be matched by a pattern, only the IDs are dynamic.
- This example uses Patch to make a partial update to the 'children' property of 'dropdown-container-div'. We append a dropdown each time the first callback runs.

Simple ALL Example Without Partial Updates

This example is similar to the Simple Example with ALL above, but uses State to access the list of currently displayed dropdowns. In the first callback, we then append to this list of dropdowns and return it as the output.

This example has not been ported to Julia yet - showing the Python version instead.

Visit the old docs site for Julia at: https://community.plotly.com/c/dash/julia/20

from dash import Dash, dcc, html, Input, Output, State, ALL, callback

app = Dash(__name__, suppress_callback_exceptions=True)

app.layout = html.Div([
    html.Button("Add Filter", id="add-filter", n_clicks=0),
    html.Div(id='dropdown-container', children=[]),
    html.Div(id='dropdown-container-output')
])

@callback(
    Output('dropdown-container', 'children'),
    Input('add-filter', 'n_clicks'),
    State('dropdown-container', 'children'))
def display_dropdowns(n_clicks, children):
    new_dropdown = dcc.Dropdown(
        ['NYC', 'MTL', 'LA', 'TOKYO'],
        id={
            'type': 'filter-dropdown',
            'index': n_clicks
        }
    )
    children.append(new_dropdown)
    return children

@callback(
    Output('dropdown-container-output', 'children'),
    Input({'type': 'filter-dropdown', 'index': ALL}, 'value')
)
def display_output(values):
    return html.Div([
        html.Div('Dropdown {} = {}'.format(i + 1, value))
        for (i, value) in enumerate(values)
    ])


if __name__ == '__main__':
    app.run(debug=True)

Simple Example with MATCH

This example uses partial property updates, introduced in Dash 2.9. For an example that works with earlier versions of Dash, see Simple MATCH Example Without Partial Updates at the end of this example.

Like ALL, MATCH will fire the callback when any of the component’s properties change. However, instead of passing all of the values into the callback, MATCH will pass just a single value into the callback. Instead of updating a single output, it will update the dynamic output that is “matched” with.

This example has not been ported to Julia yet - showing the Python version instead.

Visit the old docs site for Julia at: https://community.plotly.com/c/dash/julia/20

from dash import Dash, dcc, html, Input, Output, State, MATCH, Patch, callback

app = Dash(__name__)

app.layout = html.Div([
    html.Button("Add Filter", id="dynamic-add-filter-btn", n_clicks=0),
    html.Div(id='dynamic-dropdown-container-div', children=[]),
])

@callback(
    Output('dynamic-dropdown-container-div', 'children'),
    Input('dynamic-add-filter-btn', 'n_clicks')
    )
def display_dropdowns(n_clicks):
    patched_children = Patch()

    new_element = html.Div([
        dcc.Dropdown(
            ['NYC', 'MTL', 'LA', 'TOKYO'],
            id={
                'type': 'city-dynamic-dropdown',
                'index': n_clicks
            }
        ),
        html.Div(
            id={
                'type': 'city-dynamic-output',
                'index': n_clicks
            }
        )
    ])
    patched_children.append(new_element)
    return patched_children


@callback(
    Output({'type': 'city-dynamic-output', 'index': MATCH}, 'children'),
    Input({'type': 'city-dynamic-dropdown', 'index': MATCH}, 'value'),
    State({'type': 'city-dynamic-dropdown', 'index': MATCH}, 'id'),
)
def display_output(value, id):
    return html.Div(f"Dropdown {id['index']} = {value}")


if __name__ == '__main__':
    app.run(debug=True)

Notes about this example:
- The display_dropdowns callback returns two elements with the same index: a dropdown and a div.
- The second callback uses the MATCH selector. With this selector, we’re asking Dash to:

  1. Fire the callback whenever the value property of any component with the id 'type': 'dynamic-dropdown' changes:
    Input({'type': 'dynamic-dropdown', 'index': MATCH}, 'value')
  2. Update the component with the id 'type': 'dynamic-output' and the index that matches the same index of the input:
    Output({'type': 'dynamic-output', 'index': MATCH}, 'children')
  3. Pass along the id of the dropdown into the callback:
    State({'type': 'dynamic-dropdown', 'index': MATCH}, 'id')
    - With the MATCH selector, only a single value is passed into the callback for each Input or State. This is unlike the previous example with the
    ALL selector where Dash passed all of the values into the callback.
    - Notice how it’s important to design IDs dictionaries that “line up” the inputs with outputs. The MATCH contract is that Dash will update whichever output has the same dynamic ID as the id. In this case, the “dynamic ID” is the value of the index and we’ve designed our layout to return dropdowns & divs with identical values of index.
    - In some cases, it may be important to know which dynamic component changed. As above, you can access this by setting id as State in the callback.
    - You can also use dash.callback_context to access the inputs and state and to know which input changed. outputs_list is particularly useful with
    MATCH because it can tell you which dynamic component this particular invocation of the callback is responsible for updating. Here is what that data might look like with two dropdowns rendered on the page after we change the first dropdown.
    - dash.callback_context.triggered_prop_ids (available from Dash 2.4) returns a dictionary of inputs that triggered the callback. Each key is a <component_id>.<component_property> and the corresponding value is the <component_id>. In this example, we can see that the id of the component that triggered the callback was {'index': 0, 'type': 'dynamic-dropdown'}and the property was value:
    { '{"index":0,"type":"dynamic-dropdown"}.value': { "index": 0, "type": "dynamic-dropdown", } }

Simple MATCH Example Without Partial Updates

This example has not been ported to Julia yet - showing the Python version instead.

Visit the old docs site for Julia at: https://community.plotly.com/c/dash/julia/20

from dash import Dash, dcc, html, Input, Output, State, MATCH, callback

app = Dash(__name__, suppress_callback_exceptions=True)

app.layout = html.Div([
    html.Button('Add Filter', id='dynamic-add-filter', n_clicks=0),
    html.Div(id='dynamic-dropdown-container', children=[]),
])

@callback(
    Output('dynamic-dropdown-container', 'children'),
    Input('dynamic-add-filter', 'n_clicks'),
    State('dynamic-dropdown-container', 'children'))
def display_dropdowns(n_clicks, children):
    new_element = html.Div([
        dcc.Dropdown(
            ['NYC', 'MTL', 'LA', 'TOKYO'],
            id={
                'type': 'dynamic-dropdown',
                'index': n_clicks
            }
        ),
        html.Div(
            id={
                'type': 'dynamic-output',
                'index': n_clicks
            }
        )
    ])
    children.append(new_element)
    return children


@callback(
    Output({'type': 'dynamic-output', 'index': MATCH}, 'children'),
    Input({'type': 'dynamic-dropdown', 'index': MATCH}, 'value'),
    State({'type': 'dynamic-dropdown', 'index': MATCH}, 'id'),
)
def display_output(value, id):
    return html.Div('Dropdown {} = {}'.format(id['index'], value))


if __name__ == '__main__':
    app.run(debug=True)

Simple Example with ALLSMALLER

This example uses partial property updates, introduced in Dash 2.9. For an example that works with earlier versions of Dash, see Simple ALLSMALLER Example Without Partial Updates at the end of this example.

In the following example, ALLSMALLER is used to pass in the values of all of the dropdowns on the page that have an index smaller than the index corresponding to the div.

The user interface in the example below displays filter results that are increasingly specific in each as we apply each additional dropdown.

ALLSMALLER can only be used in Input and State items, and must be used on a key that has MATCH in the Output item(s).

ALLSMALLER it isn’t always necessary (you can usually use ALL and filter out the indices in your callback) but it will make your logic simpler.

This example has not been ported to Julia yet - showing the Python version instead.

Visit the old docs site for Julia at: https://community.plotly.com/c/dash/julia/20

from dash import Dash, dcc, html, Input, Output, Patch, MATCH, ALLSMALLER, callback
import pandas as pd

df = pd.read_csv('https://raw.githubusercontent.com/plotly/datasets/master/gapminder2007.csv')

app = Dash(__name__, suppress_callback_exceptions=True)

app.layout = html.Div([
    html.Button('Add Filter', id='add-filter-ex3-btn', n_clicks=0),
    html.Div(id='container-ex3-div', children=[]),
])

@callback(
    Output('container-ex3-div', 'children'),
    Input('add-filter-ex3-btn', 'n_clicks'))
def display_dropdowns(n_clicks):
    patched_children = Patch()
    patched_children.append(html.Div([
        dcc.Dropdown(
            df['country'].unique(),
            df['country'].unique()[n_clicks],
            id={
                'type': 'filter-dd-ex3',
                'index': n_clicks
            }
        ),
        html.Div(id={
            'type': 'output-div-ex3',
            'index': n_clicks
        })
    ]))
    return patched_children


@callback(
    Output({'type': 'output-div-ex3', 'index': MATCH}, 'children'),
    Input({'type': 'filter-dd-ex3', 'index': MATCH}, 'value'),
    Input({'type': 'filter-dd-ex3', 'index': ALLSMALLER}, 'value'),
)
def display_output(matching_value, previous_values):
    previous_values_in_reversed_order = previous_values[::-1]
    all_values = [matching_value] + previous_values_in_reversed_order

    dff = df[df['country'].str.contains('|'.join(all_values))]
    avgLifeExp = dff['lifeExp'].mean()

    # Return a slightly different string depending on number of values
    if len(all_values) == 1:
        return html.Div('{:.2f} is the life expectancy of {}'.format(
            avgLifeExp, matching_value
        ))
    elif len(all_values) == 2:
        return html.Div('{:.2f} is the average life expectancy of {}'.format(
            avgLifeExp, ' and '.join(all_values)
        ))
    else:
        return html.Div('{:.2f} is the average life expectancy of {}, and {}'.format(
            avgLifeExp, ', '.join(all_values[:-1]), all_values[-1]
        ))

if __name__ == '__main__':
    app.run(debug=True)

ALLSMALLER Example Without Partial Updates

This example has not been ported to Julia yet - showing the Python version instead.

Visit the old docs site for Julia at: https://community.plotly.com/c/dash/julia/20

from dash import Dash, dcc, html, Input, Output, State, MATCH, ALLSMALLER, callback
import pandas as pd

df = pd.read_csv('https://raw.githubusercontent.com/plotly/datasets/master/gapminder2007.csv')

app = Dash(__name__, suppress_callback_exceptions=True)

app.layout = html.Div([
    html.Button('Add Filter', id='add-filter-ex3', n_clicks=0),
    html.Div(id='container-ex3', children=[]),
])

@callback(
    Output('container-ex3', 'children'),
    Input('add-filter-ex3', 'n_clicks'),
    State('container-ex3', 'children'))
def display_dropdowns(n_clicks, existing_children):
    existing_children.append(html.Div([
        dcc.Dropdown(
            df['country'].unique(),
            df['country'].unique()[n_clicks],
            id={
                'type': 'filter-dropdown-ex3',
                'index': n_clicks
            }
        ),
        html.Div(id={
            'type': 'output-ex3',
            'index': n_clicks
        })
    ]))
    return existing_children


@callback(
    Output({'type': 'output-ex3', 'index': MATCH}, 'children'),
    Input({'type': 'filter-dropdown-ex3', 'index': MATCH}, 'value'),
    Input({'type': 'filter-dropdown-ex3', 'index': ALLSMALLER}, 'value'),
)
def display_output(matching_value, previous_values):
    previous_values_in_reversed_order = previous_values[::-1]
    all_values = [matching_value] + previous_values_in_reversed_order

    dff = df[df['country'].str.contains('|'.join(all_values))]
    avgLifeExp = dff['lifeExp'].mean()

    # Return a slightly different string depending on number of values
    if len(all_values) == 1:
        return html.Div('{:.2f} is the life expectancy of {}'.format(
            avgLifeExp, matching_value
        ))
    elif len(all_values) == 2:
        return html.Div('{:.2f} is the average life expectancy of {}'.format(
            avgLifeExp, ' and '.join(all_values)
        ))
    else:
        return html.Div('{:.2f} is the average life expectancy of {}, and {}'.format(
            avgLifeExp, ', '.join(all_values[:-1]), all_values[-1]
        ))

if __name__ == '__main__':
    app.run(debug=True)

Todo App

Creating a Todo App is a classic UI exercise in that demonstrates many features in common “create, read, update and delete” (CRUD) applications.

This example uses partial property updates and duplicate callback outputs, introduced in Dash 2.9. For an example that works with earlier versions of Dash, see Todo App Without Partial Updates below.

This example has not been ported to Julia yet - showing the Python version instead.

Visit the old docs site for Julia at: https://community.plotly.com/c/dash/julia/20

from dash import Dash, dcc, html, Input, Output, State, MATCH, ALL, Patch, callback

app = Dash(__name__)

app.layout = html.Div(
    [
        html.Div("Dash To-Do list"),
        dcc.Input(id="new-item-input"),
        html.Button("Add", id="add-btn"),
        html.Button("Clear Done", id="clear-done-btn"),
        html.Div(id="list-container-div"),
        html.Div(id="totals-div"),
    ]
)

# Callback to add new item to list
@callback(
    Output("list-container-div", "children", allow_duplicate=True),
    Output("new-item-input", "value"),
    Input("add-btn", "n_clicks"),
    State("new-item-input", "value"),
    prevent_initial_call=True,
)
def add_item(button_clicked, value):
    patched_list = Patch()

    def new_checklist_item():
        return html.Div(
            [
                dcc.Checklist(
                    options=[{"label": "", "value": "done"}],
                    id={"index": button_clicked, "type": "done"},
                    style={"display": "inline"},
                    labelStyle={"display": "inline"},
                ),
                html.Div(
                    [value],
                    id={"index": button_clicked, "type": "output-str"},
                    style={"display": "inline", "margin": "10px"},
                ),
            ]
        )

    patched_list.append(new_checklist_item())
    return patched_list, ""


# Callback to update item styling
@callback(
    Output({"index": MATCH, "type": "output-str"}, "style"),
    Input({"index": MATCH, "type": "done"}, "value"),
    prevent_initial_call=True,
)
def item_selected(input):
    if not input:
        style = {"display": "inline", "margin": "10px"}
    else:
        style = {
            "display": "inline",
            "margin": "10px",
            "textDecoration": "line-through",
            "color": "#888",
        }
    return style


# Callback to delete items marked as done
@callback(
    Output("list-container-div", "children", allow_duplicate=True),
    Input("clear-done-btn", "n_clicks"),
    State({"index": ALL, "type": "done"}, "value"),
    prevent_initial_call=True,
)
def delete_items(n_clicks, state):
    patched_list = Patch()
    values_to_remove = []
    for i, val in enumerate(state):
        if val:
            values_to_remove.insert(0, i)
    for v in values_to_remove:
        del patched_list[v]
    return patched_list


# Callback to update totals
@callback(
    Output("totals-div", "children"), Input({"index": ALL, "type": "done"}, "value")
)
def show_totals(done):
    count_all = len(done)
    count_done = len([d for d in done if d])
    result = f"{count_done} of {count_all} items completed"
    if count_all:
        result += f" - {int(100 * count_done / count_all)}%"
    return result


if __name__ == "__main__":
    app.run(debug=True)
Dash To-Do list

Todo App Without Partial Updates

This example has not been ported to Julia yet - showing the Python version instead.

Visit the old docs site for Julia at: https://community.plotly.com/c/dash/julia/20

from dash import Dash, html, dcc, Input, Output, State, MATCH, ALL, ctx, callback

app = Dash(__name__)

app.layout = html.Div([
    html.Div('Dash To-Do list'),
    dcc.Input(id="new-item"),
    html.Button("Add", id="add"),
    html.Button("Clear Done", id="clear-done"),
    html.Div(id="list-container"),
    html.Div(id="totals")
])

style_todo = {"display": "inline", "margin": "10px"}
style_done = {"textDecoration": "line-through", "color": "#888"}
style_done.update(style_todo)


@callback(
    [
        Output("list-container", "children"),
        Output("new-item", "value")
    ],
    [
        Input("add", "n_clicks"),
        Input("new-item", "n_submit"),
        Input("clear-done", "n_clicks")
    ],
    [
        State("new-item", "value"),
        State({"index": ALL}, "children"),
        State({"index": ALL, "type": "done"}, "value")
    ]
)
def edit_list(add, add2, clear, new_item, items, items_done):
    triggered = [t["prop_id"] for t in ctx.triggered]
    adding = len([1 for i in triggered if i in ("add.n_clicks", "new-item.n_submit")])
    clearing = len([1 for i in triggered if i == "clear-done.n_clicks"])
    new_spec = [
        (text, done) for text, done in zip(items, items_done)
        if not (clearing and done)
    ]
    if adding:
        new_spec.append((new_item, []))
    new_list = [
        html.Div([
            dcc.Checklist(
                id={"index": i, "type": "done"},
                options=[{"label": "", "value": "done"}],
                value=done,
                style={"display": "inline"},
                labelStyle={"display": "inline"}
            ),
            html.Div(text, id={"index": i}, style=style_done if done else style_todo)
        ], style={"clear": "both"})
        for i, (text, done) in enumerate(new_spec)
    ]
    return [new_list, "" if adding else new_item]


@callback(
    Output({"index": MATCH}, "style"),
    Input({"index": MATCH, "type": "done"}, "value")
)
def mark_done(done):
    return style_done if done else style_todo


@callback(
    Output("totals", "children"),
    Input({"index": ALL, "type": "done"}, "value")
)
def show_totals(done):
    count_all = len(done)
    count_done = len([d for d in done if d])
    result = "{} of {} items completed".format(count_done, count_all)
    if count_all:
        result += " - {}%".format(int(100 * count_done / count_all))
    return result


if __name__ == "__main__":
    app.run(debug=True)