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Tutorial: Visualizations with the Standalone Server

In this tutorial you'll use pls serve to create, view, and manage interactive Python visualizations through a browser interface. By the end, you'll have created several visualizations and know how to browse and manage them.

What You'll Need

  • Panel Live Server installed — see Installation
  • A web browser

Step 1: Start the server

pls serve

You should see:

Panel Live Server running at:

  Add:   http://localhost:5077/add
  Feed:  http://localhost:5077/feed
  Admin: http://localhost:5077/admin

Keep this terminal open for the rest of the tutorial.

Custom port or host

pls serve --port 9999
pls serve --host 0.0.0.0 --port 8080

Step 2: Create your first visualization

Open http://localhost:5077/add in your browser. You'll see a form with a code editor.

Enter this code:

import pandas as pd
import hvplot.pandas

df = pd.DataFrame({
    'Product': ['A', 'B', 'C', 'D'],
    'Sales': [120, 95, 180, 150]
})

df.hvplot.bar(x='Product', y='Sales', title='Sales by Product')

Fill in the form fields:

  • Name: "Product Sales Chart"
  • Description: "An interactive bar chart showing sales by product"
  • Execution Method: jupyter (default)

Click Submit. You'll see a success message with a link to your new visualization.

Success

Each visualization gets its own unique URL you can bookmark or share.

Step 3: View your visualization

Click the link in the success message. You'll be taken to a URL like http://localhost:5077/view?id=abc123. Your interactive bar chart is running live — hover over the bars to see the tooltips.

Step 4: Try a Panel application

The panel execution method lets you serve a full Panel app with reactive widgets. Go back to /add, clear the editor, and enter:

import panel as pn

pn.extension()

slider = pn.widgets.IntSlider(name='x', start=0, end=100, value=10)

pn.Column(
    slider,
    pn.bind(lambda x: f'{x} squared is **{x**2}**', slider)
).servable()
  • Name: "Square Calculator"
  • Description: "A slider that computes its square"
  • Execution Method: panel

Click Submit and open the link. Drag the slider — the result updates in real time.

Step 5: Browse your visualizations

Navigate to http://localhost:5077/feed. You'll see a live-updating list of your recent visualizations with inline previews, names, descriptions, and direct links.

Step 6: Manage your collection

Visit http://localhost:5077/admin for a table view of all your snippets. From here you can search, inspect the code, and delete visualizations you no longer need.

Step 7: Create a visualization via the REST API

Panel Live Server exposes a REST API, useful for automation and scripting. Create script.py:

import requests

response = requests.post(
    "http://localhost:5077/api/snippet",
    json={
        "code": "msg = 'Hello, Panel Live Server!'\nmsg",
        "name": "Hello World",
        "method": "jupyter",
    }
)

data = response.json()
print(f"Status:  {response.status_code}")
print(f"View at: {data['url']}")

Run it:

python script.py

Open the printed URL to see your visualization.

Understanding execution methods

Jupyter (default)

The last expression in the code is automatically captured and displayed — just like a notebook cell:

import pandas as pd
df = pd.DataFrame({'x': [1, 2, 3], 'y': [4, 5, 6]})
df  # This is displayed

Use this for data exploration, quick charts, DataFrames, and any Python object.

Panel

Code that explicitly calls .servable() on Panel components. Multiple objects can be served:

import panel as pn

pn.extension()

pn.Column(
    pn.pane.Markdown("# My Dashboard"),
    pn.widgets.Button(name="Click me"),
).servable()

Use this for complex, interactive applications with multiple components and reactive state.

Checking server status

In a new terminal:

pls status

This queries the health endpoint and reports whether the server is running.

What You've Learned

  • Start pls serve and create visualizations via the web UI
  • Use both the jupyter and panel execution methods
  • Browse visualizations at /feed and manage them at /admin
  • Create visualizations programmatically via the REST API

Next Steps