5 Best Ways to Add Multiple Graphs to a Plotly Dash App on a Single Browser Page

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💡 Problem Formulation: Users often require the capability to display multiple data visualizations simultaneously on a single web page within a Plotly Dash app. This is particularly useful for comparing different datasets, monitoring various metrics, or simply providing a comprehensive dashboard. We aim to tackle this by detailing multiple approaches to inserting several Plotly graphs in a coordinated layout on one browser page, using Python and the Plotly Dash framework.

Method 1: Using Dash HTML Components

This method leverages Dash’s native HTML components to structure your app’s layout, organizing multiple Graph components within HTML containers like Div. One can easily control the placement of graphs by setting the style properties such as width, height, and display to embed multiple graphs in a single page layout.

Here’s an example:

import dash
import dash_core_components as dcc
import dash_html_components as html

app = dash.Dash(__name__)

app.layout = html.Div([
    html.Div([dcc.Graph(id='graph1', figure={'data': [{'x': [1, 2, 3], 'y': [4, 1, 2]}]})]),
    html.Div([dcc.Graph(id='graph2', figure={'data': [{'x': [1, 2, 3], 'y': [1, 4, 1]}]})])
])

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

The output will display two graphs aligned vertically in your browser on the same page.

This snippet creates a Dash app and defines its layout with two Div HTML components, each containing a Graph component. The graphs are arranged vertically by default, laid out one after the other, showcasing a clean division between the two datasets.

Method 2: Using Dash Bootstrap Components

Dash Bootstrap Components is an extension of Dash that facilitates easier styling and layout organization, offering pre-styled components based on Bootstrap. This enables developers to arrange multiple plots in a grid, using rows and columns, resembling the Bootstrap framework’s responsive grid system.

Here’s an example:

import dash
import dash_core_components as dcc
import dash_bootstrap_components as dbc

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

app.layout = dbc.Container([
    dbc.Row([
        dbc.Col(dcc.Graph(figure={'data': [{'x': [1, 2, 3], 'y': [1, 2, 3]}]})),
        dbc.Col(dcc.Graph(figure={'data': [{'x': [1, 2, 3], 'y': [3, 1, 2]}]}))
    ])
])

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

The output is a browser page with two graphs side by side, responsive to screen size changes.

This code uses the Dash Bootstrap Components to create a row with two columns, each containing a graph. This setup is handy for creating layouts that adapt to different screen sizes while placing the graphs side by side for easy comparisons.

Method 3: Using Callbacks for Dynamic Graphs

Callbacks in Dash can dynamically update multiple graphs on a page based on user input. This method involves defining input components and using the callback functions to update the graphs whenever the input changes—offering an interactive experience to the end-user.

Here’s an example:

import dash
from dash.dependencies import Input, Output
import dash_core_components as dcc
import dash_html_components as html

app = dash.Dash(__name__)

app.layout = html.Div([
    dcc.Input(id='input', value='initial value', type='text'),
    html.Div([
        dcc.Graph(id='graph1'),
        dcc.Graph(id='graph2')
    ])
])

@app.callback(
    [Output('graph1', 'figure'), Output('graph2', 'figure')],
    [Input('input', 'value')]
)
def update_graph(input_value):
    data = [{'x': [1, 2, 3], 'y': [int(i) + int(input_value) for i in range(3)]}] 
    figure1 = {'data': data}
    figure2 = {'data': data, 'layout': {'yaxis': {'autorange': 'reversed'}}}
    return figure1, figure2

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

The output will show two graphs that dynamically update with changes in the input field.

This example introduces a text input field and two graphs. The callback function reads the input value and updates both graphs’ data whenever the user types, showcasing the power of Dash to create reactive data visualizations.

Method 4: Nested Layouts with Dash Core Components

Nested layouts with Dash Core components like Div, Graph, and combining them with html.Section and html.Article allow for more complex arrangements of multiple graphs, such as side by side with additional context or descriptions.

Here’s an example:

import dash
import dash_core_components as dcc
import dash_html_components as html

app = dash.Dash(__name__)

app.layout = html.Div([
    html.Article([
        dcc.Graph(id='graph1', figure={'data': [{'x': [1, 2, 3], 'y': [3, 2, 1]}]})
    ]),
    html.Section([
        dcc.Graph(id='graph2', figure={'data': [{'x': [1, 2, 3], 'y': [2, 3, 1]}]})
    ])
])

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

The output is a web page with two graphs, each enclosed in its own semantic HTML5 container.

With this approach, we demonstrate how to use semantic HTML5 elements to organize the layout. This not only makes your code more readable but also aids in structuring the content in a meaningful way, improving accessibility and SEO.

Bonus One-Liner Method 5: Using List Comprehensions

For a quick and concise way to display multiple graphs, Python’s list comprehensions can be used to generate a sequence of Graph components that can be directly placed in the layout definition. This is effective for generating similar graphs from a list of datasets.

Here’s an example:

import dash
import dash_core_components as dcc
import dash_html_components as html

app = dash.Dash(__name__)

data_list = [[[1, 2, 3], [3, 1, 2]], [[1, 2, 3], [2, 3, 1]]]
graphs = [dcc.Graph(figure={'data': [{'x': data[0], 'y': data[1]}]}) for data in data_list]

app.layout = html.Div(graphs)

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

The output will be a series of graphs on a web page, each representing the datasets provided.

This snippet demonstrates how to use list comprehensions to efficiently generate a series of graphs, reducing the verbosity of your code and enabling a dynamic approach to building the layout of your Dash app.

Summary/Discussion

  • Method 1: Using Dash HTML Components. Strengths: Simplicity; native to Dash. Weaknesses: Requires manual CSS styling for complex layouts.
  • Method 2: Using Dash Bootstrap Components. Strengths: Responsive design; pre-built styling. Weaknesses: Additional dependency; Bootstrap familiarity required.
  • Method 3: Using Callbacks for Dynamic Graphs. Strengths: Interactive; dynamic. Weaknesses: Slightly more complex to implement.
  • Method 4: Nested Layouts. Strengths: Structured; improved accessibility and SEO. Weaknesses: More HTML knowledge required.
  • Method 5: Using List Comprehensions. Strengths: Efficient; concise for similar graphs. Weaknesses: Less flexibility for differing layouts.