Creating a Bar Chart in Dash Callbacks

What will you learn?

In this tutorial, you will delve into the world of Dash and learn how to dynamically create a new table within a function to generate a bar chart as output in a Dash callback. By the end of this guide, you will be equipped with the knowledge to enhance your web applications built with Dash by incorporating interactive charts that respond to user input.

Introduction to Problem and Solution

Dash by Plotly is an exceptional tool that transforms Python code into interactive web applications. One common necessity is updating visual elements like graphs and charts based on user interactions or dynamic data sources. Specifically, creating and showcasing a new table inside a function that outputs as a bar chart in response to an event poses an intriguing challenge.

To effectively address this issue, we need two crucial components: 1. Callback functions for capturing events or inputs from users. 2. Plotly’s graphing library features for creating bar charts.

By defining our callback function to listen to specific inputs from the app’s UI elements, processing incoming data, generating a new Pandas DataFrame (our “table”), and producing a Plotly graph object representing the desired bar chart, we can successfully display dynamic visuals in response to user actions.


import dash
from dash import dcc, html
from dash.dependencies import Input, Output
import as px
import pandas as pd

# Sample DataFrame creation function - replace with actual data source/logic.
def create_data():
    return pd.DataFrame({
        'Categories': ['A', 'B', 'C'],
        'Values': [10, 20, 30]

app = dash.Dash(__name__)

app.layout = html.Div([
    html.Button('Generate Chart', id='generate-chart-btn'),

    Output('bar-chart', 'figure'),
    [Input('generate-chart-btn', 'n_clicks')]
def update_chart(n):
    if n is None:
        raise dash.exceptions.PreventUpdate

    df = create_data()

    fig =, x='Categories', y='Values')

    return fig

if __name__ == '__main__':

# Copyright PHD


In the provided solution: – The layout of a simple Dash application is defined with two elements: A button labeled “Generate Chart” and an empty Graph component for displaying the bar chart. – The @app.callback decorator connects the button click event (Input) with the graph component (Output). Whenever the button is clicked (n_clicks increases), update_chart function is triggered. – Inside update_chart, data generation is simulated using create_data(), representing dynamic data retrieval or calculation. – Utilizing Plotly Express‘s method along with the newly created DataFrame (df) generates an aesthetically pleasing bar chart based on category-value pairs. – Returning fig updates the Graph component with the freshly generated figure each time users interact with our trigger (button).

  1. How do I install Dash?

  2. To install Dash, run:

  3. pip install dash
  4. # Copyright PHD
  5. Can I use callbacks with other types of plots?

  6. Yes! Callbacks are universally compatible across different plot types available in Plotly.

  7. What if my dataset is very large?

  8. For large datasets, consider optimizing dataframe operations or explore server-side processing options.

  9. How do I customize my chart further?

  10. Explore Plotly Express documentation for extensive customization options such as colorschemes and layout adjustments.

  11. Is there support for asynchronous callbacks?

  12. Yes! Recent versions of Dash support async callbacks using Python’s async keyword.


The process of building interactive visualizations within web applications using Python has been significantly streamlined thanks to libraries like Dash. Understanding callback mechanisms coupled with Plotly Express’s robust plotting capabilities makes designing responsive and dynamic charts almost effortless. This not only enhances user experience but also opens up endless possibilities for creative visualization solutions.

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