Interactive Dashboards and Data Apps with Plotly and Dash

In today's data-driven world, it's more important than ever to be able to visualize and interact with your data in a meaningful way. Dashboards and data apps are a great way to do this, as they allow you to create interactive visualizations that can be easily shared with others.

Interactive Dashboards and Data Apps with Plotly and Dash: Harness the power of a fully fledged frontend web framework in Python – no JavaScript required

by Elias Dabbas

4.3 out of 5

Language	:	English
File size	:	30746 KB
Text-to-Speech	:	Enabled
Screen Reader	:	Supported
Enhanced typesetting	:	Enabled
Print length	:	364 pages

FREE

In this guide, we'll show you how to build interactive dashboards and data apps using Plotly and Dash. We'll cover everything from data preparation to deployment, so that you can create powerful visualizations that bring your data to life.

What is Plotly?

Plotly is a powerful open-source graphing library that allows you to create interactive visualizations in Python. Plotly graphs are highly customizable and can be easily embedded in web applications.

What is Dash?

Dash is a Python framework for building analytical web applications. Dash apps are built with Python code and can be deployed to the web with a single command.

Getting Started

To get started, you'll need to install Plotly and Dash. You can do this using the following commands:

pip install plotly pip install dash

Once you have installed Plotly and Dash, you can create your first Dash app by running the following command:

dash generate app

This will create a new directory called `app` that contains all of the files you need to create a Dash app.

Data Preparation

The first step in creating a dashboard is to prepare your data. This involves cleaning your data, removing any errors, and converting it into a format that is suitable for visualization.

There are a number of different ways to prepare your data for visualization. You can use a spreadsheet program like Microsoft Excel or Google Sheets, or you can use a data analysis library like Pandas.

Once you have prepared your data, you can start creating your visualizations.

Creating Visualizations

Plotly offers a wide range of different chart types, including line charts, bar charts, scatter plots, and 3D surfaces. You can create a Plotly chart by passing your data and a set of options to the `plotly.graph_objs.Figure` class.

For example, the following code creates a line chart:

import plotly.graph_objs as go

data = [go.Scatter(x=[1, 2, 3], y=[4, 5, 6])]

layout = go.Layout( title="Line Chart", xaxis=dict(title="X-Axis"),yaxis=dict(title="Y-Axis") )

fig = go.Figure(data=data, layout=layout)

You can then display your Plotly chart in a Dash app by using the `dcc.Graph` component. The `dcc.Graph` component takes a Plotly figure as its `figure` property.

For example, the following code creates a Dash app that displays a line chart:

import dash import dash_core_components as dcc import dash_html_components as html

app = dash.Dash(__name__)

app.layout = html.Div([ dcc.Graph( id='line-chart', figure=fig ) ])

if __name__ =='__main__': app.run_server(debug=True)

Interactivity

One of the great things about Dash is that it allows you to create interactive visualizations. You can add interactivity to your visualizations by using the `dash_core_components` and `dash_html_components` libraries.

For example, the following code creates a Dash app that allows the user to zoom and pan a line chart:

import dash import dash_core_components as dcc import dash_html_components as html

app = dash.Dash(__name__)

app.layout = html.Div([ dcc.Graph( id='line-chart', figure=fig, config={ 'scrollZoom': True, 'pan': True, 'toImageButtonOptions': { 'format': 'svg', 'filename': 'custom_image', 'height': 500, 'width': 1000, 'scale': 1 }}) ])

if __name__ =='__main__': app.run_server(debug=True)

Deployment

Once you have created your Dash app, you can deploy it to the web so that others can access it. There are a number of different ways to deploy a Dash app, including using a cloud hosting provider or deploying it to your own server.

For more information on deploying Dash apps, see the Dash documentation.

In this guide, we've shown you how to build interactive dashboards and data apps using Plotly and Dash. Plotly and Dash are powerful tools that can help you create visualizations that are both informative and engaging.

We encourage you to experiment with Plotly and Dash to see what you can create. The possibilities are endless!

Interactive Dashboards and Data Apps with Plotly and Dash: Harness the power of a fully fledged frontend web framework in Python – no JavaScript required

by Elias Dabbas

4.3 out of 5

Language	:	English
File size	:	30746 KB
Text-to-Speech	:	Enabled
Screen Reader	:	Supported
Enhanced typesetting	:	Enabled
Print length	:	364 pages

FREE