Interactive Data Points: Triggering URLs in Python’s Plotly

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💡 Problem Formulation: You’ve crafted a Plotly graph in Python and want to provide an interactive experience where users can click on a data point and be directed to a specified URL. This capability is essential for dashboards and reports that link to external resources for detailed information. For instance, clicking on a bar in a bar chart may open a company’s profile page or clicking a point on a scatter plot might show the latest statistics on a related website.

Method 1: Using Custom Data with Callbacks

This method involves assigning a list of URLs to the ‘customdata’ property of the plotly graph object and then using Dash by Plotly to create a callback that opens a URL when a data point is clicked. Due to its reliance on Dash, it’s primarily suited to web applications.

Here’s an example:

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

app = dash.Dash(__name__)

scatter_data = go.Scatter(
    x=[1, 2, 3],
    y=[4, 1, 2],
    mode='markers',
    customdata=['http://example.com/page1', 'http://example.com/page2', 'http://example.com/page3']
)

@app.callback(
    Output('url', 'href'),
    [Input('scatter-plot', 'clickData')]
)
def open_url(clickData):
    if clickData:
        return clickData['points'][0]['customdata']

app.layout = html.Div([
    dcc.Graph(
        id='scatter-plot',
        figure={'data': [scatter_data]}
    ),
    html.A(id='url', children='Visit page', href='', target='_blank')
])

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

Upon clicking a data point, the browser navigates to the associated URL in a new tab.

This snippet creates a simple Dash web application with a scatter plot. Each marker’s ‘customdata’ property holds a corresponding URL. When a data point is clicked, the callback function ‘open_url’ updates the ‘href’ attribute of an anchor (‘A‘) element, which opens the URL in a new tab.

Method 2: Embedding URLs Directly in the Data Points

Plotly figures can contain embedded URLs within data points by assigning them to the ‘text’ property and setting ‘mode’ to ‘text’. However, this does not make them clickable automatically and might be best for display purposes or combined with other solutions.

Here’s an example:

import plotly.graph_objs as go

points = go.Scatter(
    x=[1, 2, 3],
    y=[4, 1, 2],
    text=['Page 1',
          'Page 2',
          'Page 3'],
    mode='markers+text'
)

fig = go.Figure(data=[points])
fig.show()

The rendered plot shows data points with embedded URLs, but they aren’t clickable.

The code defines a scatter plot with text labels containing HTML anchor tags as URLs. When the plot is rendered, these URLs appear as part of the data point labels. However, this method does not provide clickable links in static images of the plot and is somewhat limited without additional scripting or user interaction.

Method 3: Combining Plotly with a Custom HTML Template

With this method, you can use a custom HTML template to include a script that makes URLs clickable when a data point in a Plotly graph is clicked. This method involves more manual setup and is ideal for those who want to embed Plotly graphs in custom web pages.

Here’s an example:

import plotly.graph_objs as go

scatter_data = go.Scatter(
    x=[1, 2, 3],
    y=[4, 1, 2],
    mode='markers',
    marker=dict(size=20),
    customdata=['http://example.com/page1', 'http://example.com/page2', 'http://example.com/page3']
)

layout = go.Layout(
    clickmode='event+select'
)

fig = go.Figure(data=[scatter_data], layout=layout)
fig.update_layout(template='custom_template')

fig.write_html('scatter_plot.html')

The specified URLs open in a new tab when data points are clicked within the rendered HTML file.

This code creates a Plotly graph using a customized template that includes JavaScript to handle the click event. After applying the ‘custom_template’ with the embedded JS, the final graph is saved as an HTML file. When a marker is clicked, the browser reads the ‘customdata’ property and opens the corresponding URL.

Method 4: Python Widgets in Jupyter Notebooks

When using Jupyter Notebooks, interactive widgets such as IPython.display can be combined with Plotly to trigger a URL in the user’s default web browser. This method is well-integrated with the Jupyter ecosystem and convenient for data science workflows.

Here’s an example:

from plotly.graph_objs import Scatter, Layout
from plotly.offline import iplot
from IPython.display import display, HTML
import plotly.graph_objs as go

points = go.Scatter(
    x=[1, 2, 3],
    y=[4, 1, 2],
    mode='markers',
    customdata=['http://example.com/page1', 'http://example.com/page2', 'http://example.com/page3']
)

layout = Layout(title='Scatter plot with clickable points')

figure = go.Figure(data=[points], layout=layout)

figure.show(config={'staticPlot': False})

def display_click_data(clickData):
    if clickData:
        url = clickData['points'][0]['customdata']
        display(HTML(f'Click here to open: {url}'))

iplot(figure, show_link=False, config={'staticPlot': False})

When a data point is clicked, a clickable link is displayed within the Jupyter Notebook output cell.

The provided code creates an interactive scatter plot within a Jupyter Notebook. Using the ‘customdata’ attribute, each point on the plot is associated with a URL. When a point is clicked, an IPython HTML widget is dynamically updated to provide a clickable link.

Bonus One-Liner Method 5: Direct Plotly Figure Update

If a quick and easy solution is needed, updating the layout of the Plotly figure to open URLs can serve as a one-liner method. This tactic, although less efficient, provides a quick way to prototype.

Here’s an example:

import plotly.graph_objs as go

points = go.Scatter(
    x=[1, 2, 3],
    y=[4, 1, 2],
    text=['http://example.com/page1', 'http://example.com/page2', 'http://example.com/page3'],
    hoverinfo='text',
    mode='markers'
)

layout = go.Layout(
    title='Quick Scatter plot',
    xaxis=dict(title='x-axis'),
    yaxis=dict(title='y-axis')
)

fig = go.Figure(data=[points], layout=layout)
fig.update_traces(marker=dict(size=20))
fig.show()

The output shows the URLs upon hovering over data points.

This method involves setting the ‘text’ attribute of the scatter plot and utilizing ‘hoverinfo’ to display the URL upon hovering over data points. It’s efficient for prototyping but lacks the direct clickable functionality.

Summary/Discussion

  • Method 1: Custom Data with Callbacks. Excellent for interactive web applications using Dash. Requires additional setup but provides a seamless user experience with full control over behavior.
  • Method 2: Embedding URLs Directly. Useful for displaying URLs next to data points but falls short for interactivity as the URLs are not naturally clickable within the graph.
  • Method 3: Custom HTML Template. Flexible and powerful for custom web development, this approach allows for full design control and extensive interactivity potential.
  • Method 4: Jupyter Notebook Widgets. Ideal for exploratory analysis and teaching purposes in a notebook environment. While comfortable in Jupyter, it might not be suited for production-level solutions.
  • Bonus Method 5: Direct Figure Update. A quick method suitable for prototyping but not recommended for final implementations as it lacks direct click interaction.