Plotly Dash Web App - Simple Charts

Plotly Dash Simple Charts

Plotly Dash is a powerful Python framework that allows you to build interactive web applications using Python. It leverages the capabilities of Plotly.js, Flask, and React.js to create visually appealing and highly functional data visualization apps.

Plotly Dash provides a user-friendly, streamlined approach for creating data visualizations, dashboards, and analytical tools. One of its core features is the ability to create simple charts that are both visually appealing and easy to customize. 

In this tutorial, we'll delve into the basics of creating simple charts with Plotly Dash. By the end of this tutorial, you'll have a solid foundation for building simple data visualization using Plotly Dash.

Install Required Libraries: 

pip install dash plotly

Create the App:

This script sets up a basic Dash app that displays a bar chart with sample 

data. You can customize the data and chart as needed.

import dash

from dash import dcc

from dash import html

import plotly.express as px

import pandas as pd

# Sample Data

df = pd.DataFrame({

    "Fruit": ["Apples", "Oranges", "Bananas", "Grapes"],

    "Amount": [4, 1, 2, 3]

})

# Create a bar chart

fig = px.bar(df, x="Fruit", y="Amount", title="Fruit Amounts")

# Initialize the Dash app

app = dash.Dash(__name__)

# Define the layout

app.layout = html.Div(children=[

    html.H1(children='Simple Bar Chart'),

    dcc.Graph(

        id='example-graph',

        figure=fig

    )

])

if __name__ == '__main__':

    app.run_server(debug=True)

Run the App: 

Save the script as app.py and run it using:

python app.py

View the App: 

Open your web browser and go to http://127.0.0.1:8050/ to view the bar 

chart.

Congratulations on completing your first Plotly Dash tutorial! In this tutorial, we've explored the basics of creating simple bar charts with Plotly Dash. In future tutorials, as you continue your journey with Plotly Dash, You will learn how to customize the appearance of our charts, add titles and labels, and interact with them using hover and click events. You will discover a vast array of chart types and interactive features to explore. Stay tuned for future tutorials where we'll delve into more advanced charting techniques and create even more sophisticated visualizations

See also: 

Plotly Dash: Framework created for Data Scientists / Analysts (Introduction to Dash)

Plotly Dash Learning Roadmap