2251 words

11 minutes

Using Python for Real-Time Data Visualization with WebSockets and Plotly

2025-06-29

Tutorial

Python

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WebSockets

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Plotly

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Real-Time

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Data Visualization

Python for Real-Time Data Visualization with WebSockets and Plotly#

Real-time data visualization involves the dynamic updating of charts and graphs as new data arrives. This is critical in applications requiring immediate insights, such as monitoring financial markets, tracking sensor readings, analyzing live application performance, or visualizing IoT data streams. Achieving this dynamic update in a web browser often requires a communication protocol capable of persistent, low-latency data transfer from server to client. Traditional HTTP requests, which are client-initiated and connectionless after the response, are ill-suited for server-pushed real-time updates. This is where WebSockets provide a significant advantage.

WebSockets establish a full-duplex, persistent connection between a client (typically a web browser) and a server. Once the connection is open, data can be sent in either direction at any time without the overhead of initiating a new connection for each message. This makes WebSockets ideal for scenarios where the server needs to push data to multiple clients as it becomes available.

Plotly is an open-source library for creating interactive, publication-quality graphs. Plotly graphs can be displayed in various environments, including web browsers. The core of Plotly’s web capabilities lies in plotly.js, a JavaScript library that renders and manages the interactive aspects of the charts directly in the browser. Python’s plotly library allows data scientists and developers to generate the necessary JSON structure for these plots using Python code. Combining Python’s data processing capabilities with WebSockets for data delivery and Plotly for frontend rendering enables powerful real-time data visualization applications.

This article explores the synergy of Python, WebSockets, and Plotly for building systems that visualize live data streams in a web browser.

Essential Concepts for Real-Time Visualization#

Building a real-time data visualization system using this stack involves understanding the role of each component and how they interact.

Real-Time Data Streams#

Real-time data refers to data that is continuously generated and requires processing and visualization with minimal latency. Examples include:

Stock price updates
Sensor readings (temperature, pressure, etc.)
Log entries from a live system
Geolocation data
Social media feeds

Visualizing this data as it arrives allows for immediate analysis, anomaly detection, and decision-making.

WebSockets Protocol#

Unlike HTTP’s request-response model, WebSockets provide a stateful connection. Key features include:

Persistent Connection: The connection remains open after the initial handshake, allowing for continuous data exchange.
Full-Duplex Communication: Data can flow simultaneously from client to server and server to client.
Lower Overhead: After the initial handshake, data frames are smaller than typical HTTP request/response headers, reducing overhead, especially for frequent small messages.
Server Push: The server can push data to the client without the client explicitly requesting it. This is fundamental for real-time updates.

The WebSocket handshake typically occurs over HTTP/1.1, upgrading the connection to the WebSocket protocol (ws or wss for secure connections).

Python as the Backend#

Python serves as the data source and the WebSocket server. Its strengths in data processing, scientific computing (with libraries like NumPy and Pandas), and its rich ecosystem of libraries for building network applications make it an excellent choice.

Data Generation/Acquisition: Python scripts can simulate data, connect to databases, read from APIs, or interface with hardware to acquire real-time data.
WebSocket Server Implementation: Libraries such as websockets, aiohttp, Flask-SocketIO, or Django Channels facilitate creating WebSocket servers in Python. These libraries handle the WebSocket protocol details, managing connections and sending data frames.

Plotly for Interactive Frontend Visualization#

Plotly provides the means to render and manage the charts in the web browser.

plotly.js: The core JavaScript library that runs in the browser. It takes a JSON object describing a plot and renders an interactive SVG or WebGL chart. It also provides methods for dynamically updating the plot data and layout without redrawing the entire chart.
Python plotly Library: Used on the server (or in a Python-based web framework like Dash) to generate the JSON specification for plotly.js. While the initial plot structure might be defined in Python, real-time updates are typically handled by sending just the new data points over the WebSocket and using plotly.js methods to append or update the existing chart in the browser.

Building a Real-Time Visualization System: Step-by-Step#

Constructing a system requires coordinating actions on both the backend (Python server) and the frontend (HTML/JavaScript client).

Step 1: Setting up the Python Backend (Data Source & WebSocket Server)#

The Python backend’s responsibility is to generate or receive real-time data and then send it over a WebSocket connection to connected clients.

Required Libraries:#

websockets: A simple library for building WebSocket servers and clients with asyncio.
json: For serializing data into JSON format, easily consumable by JavaScript.

Backend Structure:#

Define a data source: This could be a function generating simulated data, reading from a queue, or fetching from an external API.
Implement a WebSocket server: Use websockets to listen for incoming connections.
Handle client connections: When a client connects, the server should enter a loop to continuously send data.
Send data: Package the data (e.g., as a dictionary) and send it as a JSON string over the WebSocket.
Manage multiple clients: The server should be able to handle multiple simultaneous connections, sending data to all connected clients.

Example Backend Snippet (Conceptual):#

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import asyncio
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import websockets
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import json
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import random
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import datetime
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# --- Data Source (Example: Simulate a sine wave with noise) ---
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async def generate_data():
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    while True:
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        # Simulate new data point: timestamp and a value
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        timestamp = datetime.datetime.now().timestamp() * 1000 # milliseconds for JS
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        value = 50 + 20 * (asyncio.get_event_loop().time() % 10) + random.uniform(-5, 5)
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        data_point = {'time': timestamp, 'value': value}
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        yield json.dumps(data_point) # Yield JSON string
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        await asyncio.sleep(0.5) # Generate data every 0.5 seconds
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# --- WebSocket Server ---
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async def time_series_server(websocket, path):
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    print(f"Client connected from {websocket.remote_address}")
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    try:
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        async for data_message in generate_data():
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            await websocket.send(data_message)
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    except websockets.exceptions.ConnectionClosed:
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        print(f"Client disconnected from {websocket.remote_address}")
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    except Exception as e:
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        print(f"An error occurred: {e}")
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# --- Run the server ---
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async def main():
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    # Start the WebSocket server on localhost, port 8765
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    server = await websockets.serve(time_series_server, "localhost", 8765)
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    print("WebSocket server started on ws://localhost:8765")
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    await server.wait_closed()
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if __name__ == "__main__":
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    asyncio.run(main())

This snippet demonstrates a basic data stream and a server pushing JSON. In a real application, data would come from an external source.

Step 2: Setting up the Frontend (HTML, JavaScript, Plotly.js)#

The frontend resides in a web browser. It needs to:

Include the plotly.js library.
Establish a WebSocket connection to the Python backend.
Initialize a Plotly chart on the page.
Listen for incoming data messages from the WebSocket.
Update the Plotly chart with the received data.

Required Libraries/Resources:#

plotly.js: Include via a CDN or host locally.

Frontend Structure:#

HTML Page: Create an HTML file with a div element to host the Plotly graph. Include <script> tags for plotly.js and custom JavaScript.
JavaScript:
- Define the URL of the WebSocket server.
- Create a WebSocket object to connect.
- Implement onopen, onmessage, onerror, and onclose event handlers.
- In onmessage, parse the incoming JSON data.
- Initialize the Plotly chart (e.g., a scatter plot or line chart).
- Use Plotly.extendTraces() to efficiently add new data points to the existing chart without redrawing the whole plot, which is crucial for performance in real-time updates.

Example Frontend Snippet (Conceptual):#

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<!DOCTYPE html>
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<html>
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<head>
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    <title>Real-Time Data Visualization</title>
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    <!-- Include Plotly.js -->
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    <script src="https://cdn.plot.ly/plotly-2.27.0.min.js"></script>
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    <style>
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        #realtime-graph {
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            width: 80%;
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            height: 400px;
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            margin: 0 auto;
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        }
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    </style>
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</head>
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<body>
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    <h1>Live Data Stream</h1>
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    <div id="realtime-graph"></div>
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    <script>
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        // --- WebSocket Setup ---
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        const websocketUrl = 'ws://localhost:8765'; // Must match Python server address
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        const websocket = new WebSocket(websocketUrl);
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        // --- Plotly Setup ---
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        const graphDiv = document.getElementById('realtime-graph');
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        let initialData = [{
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            x: [], // Time/Timestamp axis
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            y: [], // Value axis
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            mode: 'lines',
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            name: 'Live Data'
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        }];
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        let layout = {
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            title: 'Live Data Plot',
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            xaxis: { title: 'Time', type: 'date' }, // Use 'date' type for timestamps
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            yaxis: { title: 'Value' },
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            margin: { t: 50, b: 50, l: 50, r: 50 }
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        };
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        // Initialize the plot with empty data
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        Plotly.newPlot(graphDiv, initialData, layout);
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        // --- WebSocket Event Handlers ---
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        websocket.onopen = function(event) {
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            console.log("WebSocket connection opened");
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        };
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        websocket.onmessage = function(event) {
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            // Parse the incoming JSON data
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            const data = JSON.parse(event.data);
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            console.log("Received data:", data);
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            // Prepare the data for Plotly update
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            const update = {
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                x: [[new Date(data.time)]], // Wrap in arrays for extendTraces, convert timestamp to Date object
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                y: [[data.value]]
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            };
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            // Update the Plotly chart
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            // extendTraces adds new points efficiently
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            Plotly.extendTraces(graphDiv, update, [0], 100); // trace index 0, keep last 100 points
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        };
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        websocket.onerror = function(event) {
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            console.error("WebSocket error observed:", event);
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        };
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        websocket.onclose = function(event) {
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            if (event.wasClean) {
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                console.log(`WebSocket connection closed cleanly, code=${event.code} reason=${event.reason}`);
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            } else {
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                console.error('WebSocket connection died');
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            }
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        };
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        // Optional: Close WebSocket when the page is closed
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        window.onbeforeunload = function() {
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            websocket.close();
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        };
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    </script>
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</body>
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</html>

This snippet shows how to connect via WebSocket, receive JSON, parse it, and use Plotly.extendTraces to update the chart.

Step 3: Connecting Backend and Frontend#

With the backend server running and the frontend HTML file open in a browser (served via a simple HTTP server or opened directly, though serving is better for security/features), the JavaScript client attempts to establish a WebSocket connection to the specified websocketUrl.

The Python server listens on localhost:8765.
The JavaScript client attempts to connect to ws://localhost:8765.
Upon successful connection, the Python server starts sending data messages (JSON strings) periodically.
The JavaScript client’s onmessage function receives these strings, parses the JSON, and uses Plotly.extendTraces to add the new data points to the plot, creating the illusion of a live, continuously updating chart.
Plotly.extendTraces is key for performance, especially with high-frequency data, as it only updates the necessary parts of the graph. The third argument [0] indicates which trace(s) to update (the first trace in the initialData array). The fourth argument 100 sets a maximum number of points to display, keeping the visualization manageable.

Practical Implementation Details and Considerations#

Implementing such a system involves more than just the core push/update loop.

Data Format: Standardizing the data format (e.g., JSON) is crucial for interoperability between Python and JavaScript. The JSON structure should be consistent for ease of parsing on the client side.
Error Handling: Implement robust error handling on both the server (handling disconnected clients, data source issues) and the client (handling connection errors, invalid data formats).
Performance & Scaling:
- Backend: For a large number of clients or very high data rates, the Python server needs to be asynchronous (like the websockets example using asyncio) or use a framework optimized for handling many connections (like Flask-SocketIO or Django Channels with their channel layers).
- Frontend: Plotly.extendTraces is efficient, but rendering too many points can still slow down the browser. Limiting the number of points displayed (as shown in the example), downsampling data, or aggregating data on the server side might be necessary for long-running or high-volume streams.
Alternative Frameworks:
- Flask-SocketIO: Integrates SocketIO (a library that uses WebSockets when possible, falling back to other techniques like long polling) with the Flask web framework. Useful if the real-time visualization is part of a larger web application.
- Django Channels: Provides asynchronous capabilities and WebSocket support for Django projects.
- Dash: Built on Flask, React, and Plotly. It offers a higher-level abstraction for building analytical web applications directly in Python. While it doesn’t expose WebSockets directly to the user, it handles the live updates internally, simplifying development for many use cases. However, understanding the underlying WebSocket mechanism is still valuable.
Security: For production systems, use wss (WebSocket Secure) and implement proper authentication and authorization to control access to data streams.
Deployment: Deploying the Python server requires a platform capable of running asynchronous applications and handling persistent connections (e.g., using Gunicorn with a suitable worker class, Daphne for Django Channels, or cloud-managed WebSocket services).

Real-World Application Example: Live Sensor Monitoring#

Consider a system monitoring temperature sensors in a factory. Each sensor periodically sends readings to a central data collection point.

Python Backend: A Python application receives sensor data (e.g., via MQTT, Kafka, or direct TCP/IP). This application also runs a WebSocket server. As new temperature readings arrive, the Python backend processes them (e.g., averaging, checking thresholds) and pushes the latest reading for each sensor, packaged as JSON ({'sensor_id': 'temp_01', 'timestamp': ..., 'temperature': ...}), over the WebSocket connection to connected clients.
Frontend (Web Browser): An HTML page loads Plotly.js and custom JavaScript. The JavaScript connects to the Python WebSocket server. It initializes separate line plots for each sensor or a single plot with multiple traces. When a message arrives via WebSocket, the JavaScript identifies the sensor ID and uses Plotly.extendTraces to update the corresponding trace with the new timestamp and temperature value. This provides operators with a live view of factory temperatures, allowing them to react quickly to anomalies.

This example highlights how Python handles data acquisition and distribution, WebSockets ensure timely delivery, and Plotly renders an interactive, continuously updating dashboard in the browser.

Benefits and Limitations#

Feature	Benefits	Limitations
WebSockets	Low latency, efficient for frequent updates, server push capability.	Requires backend support, initial handshake overhead, stateful connection management.
Python	Rich data processing ecosystem, strong library support for networking.	Global Interpreter Lock (GIL) can limit raw CPU parallelism (though async libraries mitigate this for I/O).
Plotly	Interactive, web-based charts, high quality, `extendTraces` for efficient updates.	Relies on browser performance, complex charts can be resource-intensive.
Combined System	Powerful live dashboards, leverages Python data handling, web-accessible.	Development complexity increases with data volume/clients, requires coordinating backend and frontend logic.

Key Takeaways#

Real-time data visualization in a web browser requires a protocol capable of server-push, such as WebSockets.
WebSockets provide a persistent, full-duplex connection enabling efficient data transfer from a Python backend to a web frontend.
Python is well-suited for implementing the backend, handling data acquisition and running the WebSocket server using libraries like websockets or Flask-SocketIO.
Plotly, specifically plotly.js running in the browser, is used to render interactive charts and includes methods like Plotly.extendTraces optimized for efficiently adding new data points to existing graphs.
Building the system involves creating a Python script that sends data over a WebSocket and a JavaScript script in an HTML page that receives data and updates the Plotly chart.
Careful consideration of data format, error handling, performance, and security is necessary for robust real-world applications.
Alternative frameworks like Dash can simplify the development process for certain types of Python-based web visualization applications by abstracting away some of the backend-frontend communication details.