Callbacks for Plotly - Introduction

An Introduction to Python Functions and Data Analysis with Pandas

Mastering Callbacks in Plotly Dash

One of the most powerful features of Plotly's Dash is the ability to create callbacks. These allow you to create interactive, dynamic web applications all with Python. In this post, we'll dive into the fundamentals of callbacks in Dash and demonstrate how they can be used to enhance your data visualizations.

What is a Callback?

In the context of Dash, a callback is essentially a Python function that is automatically executed by Dash in response to some kind of input, like a change in a dropdown, slider, or other interactive component.

Callbacks are declared using Python decorators, which are a unique feature of Python that allows us to modify the behavior of a function. When we declare a callback in Dash, we use the @app.callback decorator.

A Basic Callback Example

Let's start with a simple example of a callback. Suppose we have a Dash app with a dropdown that allows the user to select a data category, and we want to update a text output component whenever the dropdown value changes.

    Output('text-output', 'children'),
    Input('data-dropdown', 'value')
def update_text_output(selected_data_category):
    # Assume get_data_summary() is a function that generates a summary of the data
    return get_data_summary(selected_data_category)

In this example, whenever the value of 'data-dropdown' changes, the function update_text_output is called with the new dropdown value, and the result is placed into the 'children' property of the 'text-output' component.

Multiple Inputs and Outputs

One of the key features of Dash is that callbacks can have multiple inputs and outputs. For example, suppose we now have a graph component, and we want to update both the text output and the graph whenever the dropdown value changes. We can do this with a single callback:

    [Output('text-output', 'children'), Output('data-graph', 'figure')],
    Input('data-dropdown', 'value')
def update_page(selected_data_category):
    data_summary = get_data_summary(selected_data_category)
    data_figure = create_data_figure(selected_data_category)  # Assume this is a function that creates a Plotly figure
    return data_summary, data_figure

In this example, both 'text-output' and 'data-graph' are updated whenever 'data-dropdown' changes. The update_page function returns two values, which are placed into 'text-output' and 'data-graph' respectively.

Deploying to Heroku

After creating your dynamic and interactive Dash app, you may want to share it with others. One way to do this is to deploy your app on a platform like Heroku. To do this, you'll need to create a requirements.txt file with all your Python dependencies and a Procfile to specify how to run your app.


Callbacks are at the heart of every Dash application, allowing you to create fully interactive web apps with just Python. With the ability to have multiple inputs and outputs, you can create complex and dynamic interfaces. By deploying your app to Heroku, you can share your interactive visualizations with the world.


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