Book Image

Interactive Dashboards and Data Apps with Plotly and Dash

By : Elias Dabbas
Book Image

Interactive Dashboards and Data Apps with Plotly and Dash

By: Elias Dabbas

Overview of this book

Plotly's Dash framework is a life-saver for Python developers who want to develop complete data apps and interactive dashboards without JavaScript, but you'll need to have the right guide to make sure you’re getting the most of it. With the help of this book, you'll be able to explore the functionalities of Dash for visualizing data in different ways. Interactive Dashboards and Data Apps with Plotly and Dash will first give you an overview of the Dash ecosystem, its main packages, and the third-party packages crucial for structuring and building different parts of your apps. You'll learn how to create a basic Dash app and add different features to it. Next, you’ll integrate controls such as dropdowns, checkboxes, sliders, date pickers, and more in the app and then link them to charts and other outputs. Depending on the data you are visualizing, you'll also add several types of charts, including scatter plots, line plots, bar charts, histograms, and maps, as well as explore the options available for customizing them. By the end of this book, you'll have developed the skills you need to create and deploy an interactive dashboard, handle complexities and code refactoring, and understand the process of improving your application.
Table of Contents (18 chapters)
1
Section 1: Building a Dash App
6
Section 2: Adding Functionality to Your App with Real Data
11
Section 3: Taking Your App to the Next Level

Utilizing animation frames to add a new layer to your plots

In the last examples, we set the year as a variable and got a snapshot of the desired indicator for that year. Since the years represent sequential values, and can also be used as a grouping variable, we can use the years in the animation_frame parameter and make the chart interactive. This would introduce a new handle underneath the chart, where users can either drag to the desired year or press the play button to watch how the respective indicator progresses throughout the years. It would be a sequence of frames, like watching a video. What this does is that for a selected year, we will get a subset of the DataFrame where the rows in the year column are equal to the selected year. The chart automatically updates with colors corresponding to the values of the year that was chosen.

Here is the updated code to produce an animated chart (by year):

fig = px.choropleth(poverty[poverty['is_country']],|
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