Real-Time Data Visualization Dashboard with Plotly and Dash

Write a Python program to build a real-time data visualization dashboard using Plotly and Dash.

The task involves creating a real-time data visualization dashboard using Plotly and Dash modules. This dashboard will dynamically update and display data, allowing users to visualize changes as they occur in real-time. It should leverage Plotly for creating interactive and visually appealing graphs, and Dash for building the web-based interface and handling real-time data updates. The aim is to provide a tool that can effectively monitor and display data trends continuously.

Sample Solution:

Python Code :

# Import necessary libraries
import dash
from dash import dcc, html
from dash.dependencies import Input, Output
import plotly.express as px
import pandas as pd
import random

# Function to generate random data
def generate_data():
    # Create a dictionary with timestamps and random values
    return {
        'timestamp': pd.date_range(start='2022-01-01', periods=100, freq='H'),
        'value': [random.randint(0, 100) for _ in range(100)]
    }

# Create a Dash app instance
app = dash.Dash(__name__)

# Define the layout of the app
app.layout = html.Div([
    # Add a header to the dashboard
    html.H1("Real-Time Data Visualization Dashboard"),
    # Add a Graph component to display the plot
    dcc.Graph(id='real-time-plot'),
    # Add an Interval component to update the plot at regular intervals
    dcc.Interval(
        id='interval-component',
        interval=1000,  # Update every 1000 milliseconds (1 second)
        n_intervals=0  # Initial number of intervals
    )
])

# Define a callback function to update the plot with new data
@app.callback(
    Output('real-time-plot', 'figure'),  # Output is the figure of the Graph component
    Input('interval-component', 'n_intervals')  # Input is the number of intervals
)
def update_plot(n):
    # Generate new data
    data = generate_data()
    # Convert the data into a DataFrame
    df = pd.DataFrame(data)
    
    # Create a line plot using Plotly Express
    fig = px.line(df, x='timestamp', y='value', title='Real-Time Data')
    
    # Return the plot
    return fig

# Run the app server
if __name__ == '__main__':
    # Start the Dash app with debug mode enabled
    app.run_server(debug=True)

Output:

Address: 'http://127.0.0.1:8050'
Real time Data Visualization Dashboard.png

Explanation:

• Import Libraries: Necessary libraries such as Dash for the web application, Plotly Express for data visualization, Pandas for data manipulation, and Random for generating random data are imported.
• Generate Random Data: The "generate_data()" function creates a dictionary with timestamps and random values. This data simulates the real-time data to be visualized.
• Create Dash App: An instance of the Dash app is created, which will serve as the web application framework.
• App Layout: The layout of the app is defined using Dash HTML components. It includes:
• A header (H1) for the dashboard title.
• A Graph component to display the plot.
• An Interval component that triggers updates every second.
• Callback Function: The "update_plot()" function is defined as a callback that updates the plot:
• It generates new random data.
• Converts the data into a DataFrame.
• Creates a line plot using Plotly Express.
• Returns the updated plot.
• Run the App: The app server is started with app.run_server(debug=True), allowing the dashboard to be accessed in a web browser with real-time updates enabled.

Python Code Editor :

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