In the past, creating analytical web applications was a task for seasoned developers that required knowledge of multiple programming languages and frameworks. That’s no longer the case. Nowadays, you can make data visualization interfaces using pure Python. One popular tool for this is Dash.
Dash gives data scientists the ability to showcase their results in interactive web applications. You don’t need to be an expert in web development. In an afternoon, you can build and deploy a Dash app to share with others.
In this tutorial, you’ll learn how to:
- Create a Dash application
- Use Dash core components and HTML components
- Customize the style of your Dash application
- Use callbacks to build interactive applications
- Deploy your application on Heroku
You can download the source code, data, and resources for the sample application you’ll make in this tutorial by clicking the link below:
Get the Source Code: Click here to get the source code you’ll use to learn about creating data visualization interfaces in Python with Dash in this tutorial.
What Is Dash?
Dash is an open source framework for building data visualization interfaces. Released in 2017 as a Python library, it’s grown to include implementations for R and Julia. Dash helps data scientists build analytical web applications without requiring advanced web development knowledge.
Three technologies constitute the core of Dash:
- Flask supplies the web server functionality.
- React.js renders the user interface of the web page.
- Plotly.js generates the charts used in your application.
But you don’t have to worry about making all these technologies work together. Dash will do that for you. You just need to write Python, R, or Julia and sprinkle it with a bit of CSS.
Plotly, a Canada-based company, built Dash and supports its development. You may know the company from the popular graphing libraries that share its name. Plotly (the company) open-sourced Dash and released it under an MIT license, so you can use Dash at no cost.
Plotly also offers a commercial companion to Dash called Dash Enterprise. This paid service provides companies with support services such as hosting, deploying, and handling authentication on Dash applications. But these features live outside of Dash’s open source ecosystem.
Dash will help you build dashboards quickly. If you’re used to analyzing data or building data visualizations using Python, then Dash will be a useful addition to your toolbox. Here are a few examples of what you can make with Dash:
- A dashboard to analyze trading positions in real-time
- A visualization of millions of Uber rides
- An interactive financial report
This is just a tiny sample. If you’d like to see other interesting use cases, then go check the Dash App Gallery.
Note: You don’t need advanced knowledge of web development to follow this tutorial, but some familiarity with HTML and CSS won’t hurt.
The rest of this tutorial assumes you know the basics of the following topics:
- Python graphing libraries such as Plotly, Bokeh, or Matplotlib
- HTML and the structure of an HTML file
- CSS and style sheets
If you feel comfortable with the requirements and want to learn how to use Dash in your next project, then continue to the following section!
Get Started With Dash in Python
In this tutorial, you’ll go through the end-to-end process of building a dashboard using Dash. If you follow along with the examples, then you’ll go from a bare-bones dashboard on your local machine to a styled dashboard deployed on Heroku.
To build the dashboard, you’ll use a dataset of sales and prices of avocados in the United States between 2015 and 2018. This dataset was compiled by Justin Kiggins using data from the Hass Avocado Board.
How to Set Up Your Local Environment
To develop your app, you’ll need a new directory to store your code and data and a clean Python 3 virtual environment. To create those, follow the instructions below, choosing the version that matches your operating system.
If you’re using Windows, then open a command prompt and execute these commands:
c:\> mkdir avocado_analytics && cd avocado_analytics
c:\> c:\path\to\python\launcher\python -m venv venv
c:\> venv\Scripts\activate.bat
Read the full article at https://realpython.com/python-dash/ »
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