Plotly Dash is an open-source Python library for creating interactive web applications. It is built on top of the popular Plotly library and enables users to create highly customizable and interactive data visualizations. Dash is used by data scientists, developers, and analysts to create powerful data-driven applications.
Plotly Dash is a library for creating interactive web applications with Python. It is built on top of the popular Plotly library and enables users to create highly customizable and interactive data visualizations. Dash is used by data scientists, developers, and analysts to create powerful data-driven applications. Dash applications are composed of two parts: the web application front-end and the Python code that runs the application. The web application front-end is written in HTML, CSS, and JavaScript and is responsible for displaying the data visualizations. The Python code is responsible for creating the data visualizations and is written in Python.
Dash applications are highly customizable and interactive. Dash allows users to create data visualizations with various interactive features such as zooming, panning, and hovering. Dash also provides a wide variety of charts and plots, including line charts, bar charts, scatter plots, and more.
Plotly Dash provides a wide variety of features for creating interactive web applications with Python. Some of the features include:
Here is an example of a simple Dash application that displays a line chart.
import dash
import dash_core_components as dcc
import dash_html_components as html
import plotly.graph_objects as go
app = dash.Dash()
app.layout = html.Div([
dcc.Graph(
figure=go.Figure(
data=[
go.Scatter(
x=[1, 2, 3],
y=[4, 5, 6],
mode='lines'
)
]
)
)
])
if __name__ == '__main__':
app.run_server(debug=True)
Pros:
Cons:
Plotly Dash is related to other libraries such as Plotly, Bokeh, and Matplotlib. These libraries are all used for creating data visualizations in Python. Plotly and Dash are both built on top of the popular Plotly library and enable users to create highly customizable and interactive data visualizations. Bokeh is a library for creating interactive data visualizations with Python and is used for creating interactive web applications. Matplotlib is a library for creating static data visualizations with Python and is used for creating static data visualizations.