Steps and Tips for Dash Design with Plotly Dash
In the field of data visualization, dashboards are a very useful tool that can present data to users in an easy to understand and interactive way. Plotly Dash is an open source Python-based framework that helps you build interactive dashboards quickly and flexibly. This article will introduce the steps and tips for creating a dash using Plotly Dash, and attach a code example to demonstrate each step.
step
1. Import the necessary libraries
First, you need to import the necessary libraries. Plotly Dash depends ondashanddash_core_components、dash_html_componentsThese two modules.
import dash import dash_core_components as dcc import dash_html_components as html
2. Create an application
Next, create a Dash application.
app = (__name__)
3. Design layout
Use HTML and Dash components to design your dash layout.
= (children=[
html.H1('My Data Dashboard'),
(
id='example-graph',
figure={
'data': [
{'x': [1, 2, 3], 'y': [4, 1, 2], 'type': 'bar', 'name': 'SF'},
{'x': [1, 2, 3], 'y': [2, 4, 5], 'type': 'bar', 'name': u'Montréal'},
],
'layout': {
'title': 'His chart example'
}
}
)
])
4. Run the application
Finally, run your Dash application.
if __name__ == '__main__':
app.run_server(debug=True)
Skill
1. Use Markdown
You can use Markdown in your dashboard to add text, titles, and format instructions to make the dashboard easier to understand.
([
('''
### Data Visualization This is an example of a dashboard for data visualization.
''')
])
2. Add interactive elements
By adding interactive elements (download menus, sliders, etc.), users can customize the display of data.
(
id='dropdown',
options=[
{'label': 'New York City', 'value': 'NYC'},
{'label': 'Montréal', 'value': 'MTL'},
{'label': 'San Francisco', 'value': 'SF'}
],
value='NYC'
)
3. Deploy to the server
Deploy your dashboard to the server so that others can access and interact with it.
$ python
Complete example
import dash
import dash_core_components as dcc
import dash_html_components as html
app = (__name__)
= (children=[
html.H1('My Data Dashboard'),
('''
### Data Visualization This is an example of a dashboard for data visualization.
'''),
(
id='example-graph',
figure={
'data': [
{'x': [1, 2, 3], 'y': [4, 1, 2], 'type': 'bar', 'name': 'SF'},
{'x': [1, 2, 3], 'y': [2, 4, 5], 'type': 'bar', 'name': u'Montréal'},
],
'layout': {
'title': 'His chart example'
}
}
),
(
id='dropdown',
options=[
{'label': 'New York City', 'value': 'NYC'},
{'label': 'Montréal', 'value': 'MTL'},
{'label': 'San Francisco', 'value': 'SF'}
],
value='NYC'
)
])
if __name__ == '__main__':
app.run_server(debug=True)
With the above steps and tips, you can use Plotly Dash to build your own interactive dashboard and showcase your data and insights.
Advanced Tips
1. Use callback function
Using Dash's callback function, you can update charts or layouts according to user interaction actions.
@(
('example-graph', 'figure'),
[('dropdown', 'value')])
def update_graph(selected_value):
# Update chart data according to the selection of the drop-down menu if selected_value == 'NYC':
data = [{'x': [1, 2, 3], 'y': [4, 1, 2], 'type': 'bar', 'name': 'NYC'}]
elif selected_value == 'MTL':
data = [{'x': [1, 2, 3], 'y': [2, 4, 5], 'type': 'bar', 'name': 'MTL'}]
else:
data = [{'x': [1, 2, 3], 'y': [3, 2, 4], 'type': 'bar', 'name': 'SF'}]
return {
'data': data,
'layout': {
'title': 'Updated bar chart example'
}
}
2. Custom styles
Custom CSS styles can make your dashboard more beautiful and easy to use.
.append_css({
'external_url': ''
})
3. Multi-page application
Dash supports building multi-page applications, allowing you to organize different types of data and visual content into different pages.
= ([
(id='url', refresh=False),
(id='page-content')
])
@(('page-content', 'children'),
[('url', 'pathname')])
def display_page(pathname):
if pathname == '/page-1':
return layout_page_1
elif pathname == '/page-2':
return layout_page_2
else:
return '404 - Page Not Found'
Deploy to the server
Deploy your dashboard to the server so that others can access and interact with it. You can choose to use a variety of cloud service providers (such as AWS, Google Cloud, Azure, etc.) or deploy to on-premises servers.
Deployment with Heroku
Heroku is a popular cloud platform that allows easy deployment of Python applications. Here are the brief steps to deploy to Heroku:
Create a name calledProcfilefile and add the following content:
web: gunicorn app:server
Create a name calledfile listing all dependencies required by your application:
dash gunicorn
Use Git to push your application code to a new GitHub repository.
Create a free Heroku account and create a new application on Heroku.
Associate your GitHub repository with the Heroku application and deploy it.
Visit the URL of your Heroku application to view the deployed Dash dash.
Deployment with Docker
If you prefer to deploy your applications using Docker containers, you can also easily package your Dash application as a Docker image and deploy it to various containerized platforms such as Kubernetes or Docker Swarm.
Create a name calledDockerfilefile and add the following content:
FROM python:3.8-slim WORKDIR /app COPY RUN pip install -r COPY . . CMD ["gunicorn", "app:server", "-b", "0.0.0.0:8050"]
Building a Docker image:
docker build -t my-dash-app .
Run the Docker container:
docker run -p 8050:8050 my-dash-app
Visitlocalhost:8050, you can view the deployed Dash dash.
Summarize
In this article, we dive into the steps and some tips for creating a dash using Plotly Dash. We started by importing the necessary libraries, created a basic Dash application, and designed the layout of the dash. Next, we introduced some tips, such as adding text using Markdown, using callback functions to implement interaction, custom styles, and building multi-page applications.
We then discussed how to deploy your dashboard to the server so that others can access and interact over the internet. We provide two common deployment methods: deploy with Heroku and packaged as containers with Docker. Finally, we highlight the security and stability issues that need to be paid attention to during the deployment process.
With the guidance in this article, you can start building your own data dash with Plotly Dash and deploying it to your server to showcase data and insights and share them with others. Wish you success in dashboard design and deployment!
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