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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