Develop Data Visualization Interfaces in Python With Dash

by Dylan Castillo Jan 06, 2021 data-science intermediate

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:

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:

  1. Flask supplies the web server functionality.
  2. React.js renders the user interface of the web page.
  3. 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:

This is just a tiny sample. If you’d like to see other interesting use cases, then go check the Dash App Gallery.

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

The first command creates a directory for your project and moves your current location there. The second command creates a virtual environment in that location. The last command activates the virtual environment. Make sure to replace the path in the second command with the path of your Python 3 launcher.

If you’re using macOS or Linux, then follow these steps from a terminal:

$ mkdir avocado_analytics && cd avocado_analytics
$ python3 -m venv venv
$ source venv/bin/activate

The first two commands perform the following actions:

  1. Create a directory called avocado_analytics
  2. Move your current location to the avocado_analytics directory
  3. Create a clean virtual environment called venv inside that directory

The last command activates the virtual environment you just created.

Next, you need to install the required libraries. You can do that using pip inside your virtual environment. Install the libraries as follows:

(venv) $ python -m pip install dash==1.13.3 pandas==1.0.5

This command will install Dash and pandas in your virtual environment. You’ll use specific versions of these packages to make sure that you have the same environment as the one used throughout this tutorial. In addition to Dash, pandas will help you handle reading and wrangling the data you’ll use in your app.

Finally, you need some data to feed into your dashboard. You can download the data as well as the code you see throughout this tutorial by clicking the link below:

Save the data as avocado.csv in the root directory of the project. By now, you should have a virtual environment with the required libraries and the data in the root folder of your project. Your project’s structure should look like this:

├── venv/
└── avocado.csv

You’re good to go! Next, you’ll build your first Dash application.

How to Build a Dash Application

For development purposes, it’s useful to think of the process of building a Dash application in two steps:

  1. Define the looks of your application using the app’s layout.
  2. Use callbacks to determine which parts of your app are interactive and what they react to.

In this section, you’ll learn about the layout, and in a later section, you’ll learn how to make your dashboard interactive. You’ll start by setting up everything you need to initialize your application and then you’ll define the layout of your app.

Initializing Your Dash Application

Create an empty file named in the root directory of your project, then review the code of in this section. To make it easier for you to copy the full code, you’ll find the entire contents of at the end of this section.

Here are the first few lines of

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