Partial Property Updates

New in Dash 2.9. To get the most out of this page, make sure you’ve read about Basic Callbacks in the Dash Fundamentals.

Most of the callback examples we’ve seen in earlier chapters updated an entire property when the callback ran.
In cases where we only want to update a small part of a property, for example, a title on a graph, this is inefficient. It means all the property’s data (the complete figure in this case) is sent back across the network, even though most of the data hasn’t changed.
Improve your app performance by making partial property updates when a full update is not required.

The Dash Patch Class

Partial property updates use the Patch class, which you can import with from dash import Patch.
Instantiate a Patch object in a callback to make partial updates to a callback output.
This Patch object defines the changes that should be made to the property.
Possible changes include assigning a new value, merging a dictionary, and appending an item to a list. The operations are defined by the Patch object within your callback but executed in the browser client in Dash’s frontend.

Updating Title Color with a Partial Update

Here’s an example of using a Patch to update only the graph’s title font color:

from dash import Dash, html, dcc, Input, Output, Patch, callback
import plotly.express as px
import random

app = Dash(__name__)

df = px.data.iris()
fig = px.scatter(
    df, x="sepal_length", y="sepal_width", color="species", title="Updating Title Color"
)

app.layout = html.Div(
    [
        html.Button("Update Graph Color", id="update-color-button-2"),
        dcc.Graph(figure=fig, id="my-fig"),
    ]
)


@callback(Output("my-fig", "figure"), Input("update-color-button-2", "n_clicks"))
def my_callback(n_clicks):
    # Defining a new random color
    red = random.randint(0, 255)
    green = random.randint(0, 255)
    blue = random.randint(0, 255)
    new_color = f"rgb({red}, {green}, {blue})"

    # Creating a Patch object
    patched_figure = Patch()
    patched_figure["layout"]["title"]["font"]["color"] = new_color
    return patched_figure

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

Let’s take a closer look at what’s happening here:

  1. First, we import the Patch class:
from dash import Patch
  1. We define our callback inputs and outputs in the same way as with a standard callback:
@callback(
    Output('my-fig', 'figure'),
    Input('update-color-button-2', 'n_clicks')
)
  1. In our callback, we create a Patch object. Here, we call it patched_figure, but the naming is arbitrary. This patched_figure will define the changes that Dash should make to the figure.
patched_figure = Patch()
  1. Then, we define an assignment change, telling Dash that we want this part of the figure to be the value in new_color.
patched_figure['layout']['title']['font']['color'] = new_color
  1. We return our patched_figure:
return patched_figure
  1. Once the callback returns the Patch object, in the frontend, Dash assigns the value in new_color to ['layout']['title']['font']['color'] on the figure.

In this example, we updated the title’s font color by instructing Dash to update ['layout']['title']['font']['color'] on the figure. For more details on how to explore the property attributes of a Graph Objects Figure see the “Exploring the Structure of Properties” section below.

Here’s how you would update the title font color without partial property updates

 
from dash import Dash, html, dcc, Input, Output, callback
import plotly.express as px
import random

app = Dash(__name__)

app.layout = html.Div(
    [
        html.Button("Update Graph Color", id="update-color-button"),
        dcc.Graph(id="update-color-fig"),
    ]
)


@callback(
    Output("update-color-fig", "figure"),
    Input("update-color-button", "n_clicks"),
)
def my_callback(n_clicks):
    df = px.data.iris()
    fig = px.scatter(
    df, title="Updating Title Color", x="sepal_length", y="sepal_width", color="species"
    )
    if n_clicks:
        red = random.randint(0, 255)
        green = random.randint(0, 255)
        blue = random.randint(0, 255)
        new_color = f"rgb({red}, {green}, {blue})"
        fig.update_layout(
            title_font_color=new_color,
        )
    # Here we return the entire figure, though we've just updated the title font color
    return fig

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

Performance

Let’s look at the difference when doing a partial update instead of a full update. The graph in our earlier example uses a small data set of 150 rows. Here the size difference is relatively small — 9.5 kB for the full update vs 380 B for the partial update.

Full Output Size

<img>

Patch Output Size

<img>

However, in a graph with a much larger data set, the size of a full output increases, whereas the size of the response for our partial update to the title stays the same. Here’s the response size of the same example, but using a data set of 45000 rows in the graph. The full output size is 368 kB, while the partial update stays the same at 380 B.

Full Output Size

<img>

Patch Output Size

<img>

Partial Update Methods

There are multiple ways you can use Patch objects to make partial updates.

Although all the methods outlined below are always available when using a Patch object, they won’t work with every property or attribute type. For, example, prepend, extend, and append work with lists while update works with dictionaries.
See the Exploring the Structure of Properties section below for more details on understanding the property you are updating.

Assign

In the example above, we use assignment with the Patch object. We assign new_color like this:

patched_figure['layout']['title']['font']['color'] = new_color

You can also use dot notation. So you could assign new_color like this:

patched_figure.layout.title.font.color = new_color

To use dot notation to assign to a property attribute, the attribute name must be a valid Python identifier.

Examples of Assigning New Data

In this example, we update the values on the y-axis based on the dropdown selection.

from dash import Dash, html, dcc, Input, Output, Patch, callback
import plotly.express as px

app = Dash(__name__)

# Get data
df = px.data.election()[:20]
# Create figure based on data
fig = px.bar(df, x="district")

app.layout = html.Div(
    [
        dcc.Dropdown(
            ["Coderre", "Joly", "Bergeron"], id="candidate-select", value="Joly"
        ),
        dcc.Graph(figure=fig, id="new-data-graph"),
    ]
)


@callback(Output("new-data-graph", "figure"), Input("candidate-select", "value"))
def update_figure(value):
    patched_fig = Patch()
    patched_fig["data"][0]["y"] = df[value].values
    return patched_fig


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

Here’s another example. Here we update the marker color based on the selected values in a dropdown. In this example, we check which values the user has selected and then return a list with the color of those data points as red and any other data points as blue.

from dash import Dash, dcc, html, Input, Output, Patch, callback
import plotly.express as px

app = Dash(__name__)

# Getting our data
df = px.data.gapminder()
df = df.loc[df.year == 2002].reset_index()

# Creating our figure
fig = px.scatter(x=df.lifeExp, y=df.gdpPercap, hover_name=df.country)
fig.update_traces(marker=dict(color="blue"))

app.layout = html.Div(
    [
        html.H4("Updating Point Colors"),
        dcc.Dropdown(id="dropdown", options=df.country.unique(), multi=True),
        dcc.Graph(id="graph-update-example", figure=fig),
    ]
)


@callback(
    Output("graph-update-example", "figure"), Input("dropdown", "value"), prevent_initial_call=True
)
def update_markers(countries):
    country_count = list(df[df.country.isin(countries)].index)
    patched_figure = Patch()
    updated_markers = [
        "red" if i in country_count else "blue" for i in range(len(df) + 1)
    ]
    patched_figure['data'][0]['marker']['color'] = updated_markers
    return patched_figure

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

Updating Point Colors

Append

Patch has an append method you can use to add to property attributes that are lists. It works like appending to a list in Python, adding the item to the end. It is useful for adding to a component’s children and for adding data to axes on a figure.

Example with X and Y Axes of a Graph

By appending to the X and Y data arrays of a graph, we can add additional data points. In this example, we start with a graph with no data and append to both the X and Y axes each time the button is selected.

from dash import Dash, html, dcc, Input, Output, Patch, callback
import plotly.graph_objects as go

import datetime
import random

app = Dash(__name__)

fig = go.Figure()

app.layout = html.Div(
    [
        html.Button("Append", id="append-new-val"),
        dcc.Graph(figure=fig, id="append-example-graph"),
    ]
)

@callback(
    Output("append-example-graph", "figure"),
    Input("append-new-val", "n_clicks"),
    prevent_initial_call=True,
)
def add_data_to_fig(n_clicks):
    current_time = datetime.datetime.now()
    random_value = random.randrange(1, 30, 1)
    patched_figure = Patch()
    patched_figure["data"][0]["x"].append(current_time)
    patched_figure["data"][0]["y"].append(random_value)
    return patched_figure


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

Example of Append with Pattern-Matching Callbacks

With Pattern-Matching Callbacks, we can add content dynamically to our layout. Often, we’ll want to add to one component’s children. In this example, the children of 'dropdown-container-2' starts out as an empty list. Each time the display_dropdowns callback runs, a new dropdown is appended to the children of 'dropdown-container-2' using a Patch object.

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

app = Dash(__name__, suppress_callback_exceptions=True)

app.layout = html.Div(
    [
        html.Button("Add Filter", id="add-filter-2", n_clicks=0),
        html.Div(id="dropdown-container-2", children=[]),
        html.Div(id="dropdown-container-output-2"),
    ]
)


@callback(
    Output("dropdown-container-2", "children"),
    Input("add-filter-2", "n_clicks"),
)
def display_dropdowns(n_clicks):
    patched_children = Patch()
    new_dropdown = dcc.Dropdown(
        ["NYC", "MTL", "LA", "TOKYO"],
        id={"type": "filter-dropdown-2", "index": n_clicks},
    )
    patched_children.append(new_dropdown)
    return patched_children


@callback(
    Output("dropdown-container-output-2", "children"),
    Input({"type": "filter-dropdown-2", "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)

For additional examples, see the Pattern-Matching Callbacks page.

Prepend

The prepend method adds the provided value to the start of the list. Here we prepend to both the X and Y axes.

from dash import Dash, html, dcc, Input, Output, Patch, callback
import plotly.express as px

import random

app = Dash(__name__)

# Creating some starter x and y data
x_values = [2019, 2020, 2021, 2022, 2023]
y_values = [random.randrange(1, 30, 1) for i in range(5)]

fig = px.bar(x=x_values, y=y_values)

app.layout = html.Div(
    [
        html.Button("Prepend", id="prepend-new-val"),
        dcc.Graph(figure=fig, id="prepend-example-graph"),
    ]
)


@callback(
    Output("prepend-example-graph", "figure"),
    Input("prepend-new-val", "n_clicks"),
    prevent_initial_call=True,
)
def add_data_to_fig(n_clicks):
    random_value = random.randrange(1, 30, 1)
    patched_figure = Patch()
    patched_figure["data"][0]["x"].prepend(2019 - n_clicks)
    patched_figure["data"][0]["y"].prepend(random_value)
    return patched_figure


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

Extend

The extend method works like extending a list in Python. Use it by providing an iterable whose values will be added to the end of the list.

from dash import Dash, html, dcc, Input, Output, Patch, callback
from plotly import graph_objects as go

app = Dash(__name__)

x = ["Product A", "Product B", "Product C"]
y = [20, 14, 23]

additional_products_x = ["Product D", "Product E", "Product F"]
additional_products_y = [10, 24, 8]

fig = go.Figure(data=[go.Bar(x=x, y=y)])

app.layout = html.Div(
    [
        html.Button("Update products", id="additional-products"),
        dcc.Graph(figure=fig, id="extend-example"),
    ]
)


@callback(
    Output("extend-example", "figure"),
    Input("additional-products", "n_clicks"),
    prevent_initial_call=True,
)
def add_data_to_fig(n_clicks):
    if n_clicks % 2 != 0:
        patched_figure = Patch()
        patched_figure["data"][0]["x"].extend(additional_products_x)
        patched_figure["data"][0]["y"].extend(additional_products_y)
        return patched_figure
    else:
        return fig

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

Maximum Number of Values

Each time you extend a list, it adds to the existing data. It’s not currently possible to limit the maximum number of values in the list. This is something we hope to support in the future. To do this currently, you’ll need to use assignment and replace the entire list with a new one with the exact number of values you want.

Example with a Dataframe

Here’s another example of using extend. In this example, we add rows from a dataframe to a Datatable‘s data on each click (after 10 clicks we stop updating the table).

from dash import Dash, html, Input, Output, Patch, dash_table, no_update, callback
import plotly.express as px

app = Dash(__name__)

df = px.data.iris()

app.layout = html.Div(
    [
        html.Button("Add Rows", id="add-data-rows"),
        dash_table.DataTable(
            data=df.to_dict("records"),
            columns=[{"name": i, "id": i} for i in df.columns],
            page_size=10,
            id="df-table",
        ),
    ]
)


@callback(
    Output("df-table", "data"),
    Input("add-data-rows", "n_clicks"),
    prevent_initial_call=True,
)
def add_data_to_fig(n_clicks):
    if n_clicks < 10:
        patched_table = Patch()
        patched_table.extend(df.to_dict("records"))
        return patched_table
    else:
        return no_update

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

Insert

Use insert to add to a list at a specific index. The insert method takes two arguments: the index to insert at and the data to add. In this example, we add “Product B” after “Product A” by inserting its X and Y data at index 1.

from dash import Dash, html, dcc, Input, Output, Patch, callback
from plotly import graph_objects as go

app = Dash(__name__)

x = ["Product A", "Product C", "Product D", "Product E",]
y = [20, 14, 23, 8]

fig = go.Figure(data=[go.Bar(x=x, y=y)])

app.layout = html.Div(
    [
        html.Button("Update products", id="additional-product-insert"),
        dcc.Graph(figure=fig, id="insert-example"),
    ]
)


@callback(
    Output("insert-example", "figure"),
    Input("additional-product-insert", "n_clicks"),
    prevent_initial_call=True,
)
def add_data_to_fig(n_clicks):
    if n_clicks % 2 != 0:
        patched_figure = Patch()
        patched_figure["data"][0]["x"].insert(1, "Product B")
        patched_figure["data"][0]["y"].insert(1, 10)
        return patched_figure
    else:
        return fig

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

Reverse

Using reverse you can reverse the order of items in a list. Here, we reverse the data on the X and Y axes of the figure.

from dash import Dash, html, dcc, Input, Output, Patch, callback
from plotly import graph_objects as go

app = Dash(__name__)

x = ["Product A", "Product C", "Product D", "Product E",]
y = [20, 14, 23, 8]

fig = go.Figure(data=[go.Bar(x=x, y=y)])

app.layout = html.Div(
    [
        html.Button("Reverse Items", id="reverse-button"),
        dcc.Graph(figure=fig, id="reverse-example"),
    ]
)


@callback(
    Output("reverse-example", "figure"),
    Input("reverse-button", "n_clicks")
)
def add_data_to_fig(n_clicks):
    patched_figure = Patch()
    patched_figure["data"][0]["x"].reverse()
    patched_figure["data"][0]["y"].reverse()
    return patched_figure

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

Clear

Use clear to remove all items in a list. annotations is a list of dictionaries on the graph’s layout. In this example, we clear the list when the button is selected. The next time the button is selected we add the annotations back.

from dash import Dash, html, dcc, Input, Output, Patch, callback
import plotly.graph_objects as go

app = Dash(__name__)

fig = go.Figure(
    [
        go.Scatter(x=[0, 1, 2, 3, 4, 5, 6, 7, 8], y=[0, 1, 3, 2, 4, 3, 4, 6, 5]),
        go.Scatter(x=[0, 1, 2, 3, 4, 5, 6, 7, 8], y=[0, 4, 5, 1, 2, 2, 3, 4, 2]),
    ],
    go.Layout(
        dict(
            annotations=[
                dict(
                    x=2,
                    y=5,
                    text="Text annotation with arrow",
                    showarrow=True,
                    arrowhead=1,
                ),
                dict(
                    x=4,
                    y=4,
                    text="Text annotation without arrow",
                    showarrow=False,
                    yshift=10,
                ),
            ]
        ),
        showlegend=False,
    ),
)

app.layout = html.Div(
    [
        html.Button("Show/Clear Annotations", id="clear-button"),
        dcc.Graph(id="clear-example", figure=fig),
    ]
)


@callback(Output("clear-example", "figure"), Input("clear-button", "n_clicks"))
def add_data_to_fig(n_clicks):
    patched_figure = Patch()
    if n_clicks and n_clicks % 2 != 0:
        patched_figure["layout"]["annotations"].clear()
    else:
        patched_figure["layout"]["annotations"].extend(
            [
                dict(
                    x=2,
                    y=5,
                    text="Text annotation with arrow",
                    showarrow=True,
                    arrowhead=1,
                ),
                dict(
                    x=4,
                    y=4,
                    text="Text annotation without arrow",
                    showarrow=False,
                    yshift=10,
                ),
            ]
        )
    return patched_figure


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

Update

For a property attribute that is a dictionary, you can use the update method to merge another dictionary into it. In this example, the RadioItems component’s options are created as a dictionary. The initial dictionary has three key-value pairs, and when the button is selected, three additional key-value pairs are merged into it.

Note: update performs a single-level merge, not a deep merge.

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

app = Dash(__name__)

app.layout = html.Div(
    [
        html.Button("Add options", id="add-options"),
        dcc.RadioItems(
            options={
                "New York City": "New York City",
                "Montreal": "Montreal",
                "San Francisco": "San Francisco",
            },
            value="Montreal",
            id="city-dd",
        ),
        html.Div(id="city-output-container"),
    ]
)


@callback(Output("city-output-container", "children"), Input("city-dd", "value"))
def update_output(value):
    return f"You have selected {value}"


@callback(
    Output("city-dd", "options"),
    Input("add-options", "n_clicks"),
    prevent_initial_call=True,
)
def update_output(n_clicks):
    patched_dropdown = Patch()
    european_cities = {"Paris": "Paris", "London": "London", "Berlin": "Berlin"}
    patched_dropdown.update(european_cities)
    return patched_dropdown

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

Delete

You can also make partial updates that delete parts of a property’s data. In this example, we delete the first row of the table by deleting the element in data at index 0:

from dash import Dash, dash_table, html, Input, Output, Patch, callback
import pandas as pd

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

app = Dash(__name__)

app.layout = html.Div(
    [
        html.Button("Delete first row", id="delete-button-1"),
        dash_table.DataTable(
            df.to_dict("records"),
            [{"name": i, "id": i} for i in df.columns],
            id="table-example-for-delete-1",
        ),
    ]
)

# Deleting row at index 0 in the data when the delete button is clicked
@callback(
    Output("table-example-for-delete-1", "data"),
    Input("delete-button-1", "n_clicks")
)
def delete_records(n_clicks):
    patched_table = Patch()
    del patched_table[0]
    return patched_table

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

Remove

You can also make partial updates that remove parts of a property’s data that matches a given value. In this example, when the button is selected, we remove all items in the Checklist that are in the list canandian_cities.

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

app = Dash(__name__)

app.layout = html.Div(
    [
        html.Button("Remove items", id="remove-button"),
        dcc.Checklist(id="checklist-remove-items"),
    ]
)

@callback(
    Output("checklist-remove-items", "options"),
    Input("remove-button", "n_clicks"),
)
def remove_records(n_clicks):
    if not n_clicks:
        return [
            "Boston",
            "Montreal",
            "New York",
            "Toronto",
            "San Francisco",
            "Vancouver",
        ]
    else:
        canadian_cities = ["Montreal", "Toronto", "Vancouver"]
        patched_list = Patch()
        for x in canadian_cities:
            patched_list.remove(x)
        return patched_list


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

Math Operations

Patch also supports math operations, for example, to increment, decrement, multiply, and divide values.
In this example, we increment the value on the y-axis when a bar is selected.

from dash import Dash, html, dcc, Input, Output, Patch, callback
from plotly import graph_objects as go

app = Dash(__name__)

x = ["Product A", "Product B", "Product C"]
y = [20, 14, 23]

fig = go.Figure(data=[go.Bar(x=x, y=y)])

app.layout = html.Div(
    [
        dcc.Graph(figure=fig, id="increment-example-graph"),
    ]
)


@callback(
    Output("increment-example-graph", "figure"),
    Input("increment-example-graph", "clickData"),
    prevent_initial_call=True,
)
def check_selected_data(click_data):
    selected_product = click_data["points"][0]["label"]
    patched_figure = Patch()
    if selected_product == "Product A":
        patched_figure["data"][0]["y"][0] += 1
    elif selected_product == "Product B":
        patched_figure["data"][0]["y"][1] += 1
    elif selected_product == "Product C":
        patched_figure["data"][0]["y"][2] += 1
    return patched_figure


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

Method Summary

python patched_figure = Patch() patched_figure.layout.title = 'New Title'

Combining Patch Methods

You can combine the different Patch methods mentioned above. In this example, we use prepend and append with the same object to update the same attribute.
The operations are applied in the order that they are defined in the callback.

from dash import Dash, html, dcc, Input, Output, Patch, callback
from plotly import graph_objects as go

app = Dash(__name__)

x = ["Product A", "Product B", "Product C"]
y = [20, 14, 23]

fig = go.Figure(data=[go.Bar(x=x, y=y)])

app.layout = html.Div(
    [
        html.Button("Update products", id="add-product-d-e"),
        dcc.Graph(figure=fig, id="prepend-append-example-graph"),
    ]
)


@callback(
    Output("prepend-append-example-graph", "figure"),
    Input("add-product-d-e", "n_clicks"),
    prevent_initial_call=True,
)
def add_data_to_fig(n_clicks):
    if n_clicks % 2 != 0:
        patched_figure = Patch()
        patched_figure["data"][0]["x"].prepend("Product D")
        patched_figure["data"][0]["y"].prepend(34)
        patched_figure["data"][0]["x"].append("Product E")
        patched_figure["data"][0]["y"].append(34)
        return patched_figure
    else:
        return fig

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

Using Patches on Multiple Outputs

You can use multiple Patch objects within a callback. In this example, we create one Patch object, patched_figure to append to the figure data, and we create another Patch object, patched_table, to update the DataTable‘s data property.

from dash import Dash, html, dcc, Input, Output, Patch, dash_table, callback
from plotly import graph_objects as go

app = Dash(__name__)

# Initial data for the figure and datatable
x = ["Product A", "Product B", "Product C"]
y = [20, 14, 23]

table_data = [{"Product": x_value, "Value": y_value} for x_value, y_value in zip(x, y)]

# Additional data for the figure and datatable
additional_products_x = ["Product D", "Product E", "Product F"]
additional_products_y = [10, 24, 8]

fig = go.Figure(data=[go.Bar(x=x, y=y)])

app.layout = html.Div(
    [
        html.Button("Update Products", id="add-additional-products"),
        dcc.Graph(figure=fig, id="multiple-outputs-fig"),
        dash_table.DataTable(data=table_data, id="multiple-outputs-table"),
    ]
)


@callback(
    Output("multiple-outputs-fig", "figure"),
    Output("multiple-outputs-table", "data"),
    Input("add-additional-products", "n_clicks"),
    prevent_initial_call=True,
)
def add_data_to_fig(n_clicks):
    if n_clicks % 2 != 0:
        patched_figure = Patch()
        patched_table = Patch()

        additional_table_data = [
            {"Product": x_value, "Value": y_value}
            for x_value, y_value in zip(additional_products_x, additional_products_y)
        ]

        patched_table.extend(additional_table_data)

        patched_figure["data"][0]["x"].extend(additional_products_x)
        patched_figure["data"][0]["y"].extend(additional_products_y)

        return patched_figure, patched_table
    else:
        return fig, table_data

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

Allowing Duplicate Callback Outputs

Sometimes you’ll want to update the same component-property pair from multiple callback outputs.
For example, you could have one callback output to update the color of a graph, and another callback output to update the data. You can do this by setting allow_duplicate=True on any outputs that are used more than once.

You can also use allow_duplicate to do full updates on one callback output and a partial update on another.
In this example, clicking one button deletes the first row of the data.
The second button sends the full data back to the component.

When using duplicate callback outputs (with allow_duplicate=True), the order in which callbacks that run at the same time are updated is not guaranteed. See Duplicate Callback Outputs.

from dash import Dash, dash_table, html, Input, Output, Patch, callback
import pandas as pd

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

app = Dash(__name__)

app.layout = html.Div(
    [
        html.Button("Delete first row", id="delete-button"),
        html.Button("Reload data", id="reload-button"),
        dash_table.DataTable(
            df.to_dict("records"),
            [{"name": i, "id": i} for i in df.columns],
            id="table-example-for-delete",
        ),
    ]
)

# Returning all records from the dataframe to the component when the reload button is clicked
@callback(
    Output("table-example-for-delete", "data"),
    Input("reload-button", "n_clicks")
)
def reload_data(n_clicks):
    return df.to_dict("records")

# Deleting row at index 0 in the data when the delete button is clicked
@callback(
    Output("table-example-for-delete", "data", allow_duplicate=True),
    Input("delete-button", "n_clicks"),
    prevent_initial_call=True
)
def delete_records(n_clicks):
    patched_table = Patch()
    del patched_table[0]
    return patched_table

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

Exploring the Structure of Properties

To make partial updates to a property, you need to know the structure of the property you want to update.
To understand the structure of any component property, check out the reference docs for it. For example, you’ll find the reference docs for each Dash Core Component at the end of the component’s page. For example, the Dropdown properties.

Graph Objects

As we’ve seen in the examples above, a great use for partial updates is updating individual parts of Plotly.py Graph Objects Figure objects, which you can pass to the figure parameter of a dcc.Graph component. Often you’ll only want to update a small detail on a graph, such as a color, and avoid sending all the graph’s data back to the browser.

To do this successfully, you’ll need to understand the structure of a Figure.

Here is an example of the structure of a simple Figure object.

Figure({
    'data': [{'hovertemplate': 'x=%{x}&lt;br&gt;y=%{y}&lt;extra&gt;&lt;extra&gt;',
              'legendgroup': '',
              'line': {'color': '#636efa', 'dash': 'solid'},
              'marker': {'symbol': 'circle'},
              'mode': 'lines',
              'name': '',
              'orientation': 'v',
              'showlegend': False,
              'type': 'scatter',
              'x': array(['a', 'b', 'c'], dtype=object),
              'xaxis': 'x',
              'y': array([1, 3, 2]),
              'yaxis': 'y'}],
    'layout': {'legend': {'tracegroupgap': 0},
               'template': '...',
               'title': {'font': {'color': 'red'}, 'text': 'sample figure'},
               'xaxis': {'anchor': 'y', 'domain': [0.0, 1.0], 'title': {'text': 'x'}},
               'yaxis': {'anchor': 'x', 'domain': [0.0, 1.0], 'title': {'text': 'y'}}}
})

In our first Patch example above, we assigned a color to the title.

patched_figure['layout']['title']['font']['color'] = new_color

Similarly, we could update the title’s text, which is currently set to sample figure:

patched_figure['layout']['title']['text'] = "my new title text"

When working with a Figure, you can see its structure by printing it.

fig = px.line(x=["a","b","c"], y=[1,3,2], title="sample figure")

fig.update_layout(
    title_font_color="red"
)
print(fig)

See the The Figure Data Structure in Python page in the Graphing Library docs for more details.

Limitations