Building Dashboards with Dash and Plotly
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Turn Plotly charts into interactive Dash dashboards in Python, styled with HTML and CSS and driven by callbacks, linked graphs and filterable tables.
Dash turns Plotly charts into interactive web dashboards built in Python. Working with the sales data of a global e-commerce business, you arrange graphs with HTML, style them with CSS, and wire up callbacks so dropdowns, date pickers, hover and click events, and filterable tables change what each viewer sees on the page.
What You Will Achieve
- Turn existing Plotly figures into a Dash app that shows several sales graphs on one page.
- Control the size, color and position of dashboard objects with HTML elements and CSS, including a dark theme.
- Let viewers filter a dashboard by country, date or free text through dropdowns, date pickers and input fields wired to callbacks.
- Make a hover or click on one graph refresh another, and add AG Grid tables that viewers can sort, filter and page through.
Program Overview
Dash App Structure and HTML Layout
Revisits Plotly figures, then builds a first Dash app and combines several graphs into a draft sales dashboard.
- Plotly graphs and the core Dash app code
- Positioning Dash components with HTML
- A draft multi-graph sales dashboard
Styling with HTML and CSS
Uses HTML elements and CSS rules to change how dashboard objects look and where they sit.
- Logos, notes and HTML lists
- CSS basics and advanced placement
- Dark mode and object layout
Callbacks, Components and Advanced Interactivity
Makes dashboards self-service through interactive components, linked graphs, chained callbacks and Dash AG Grid tables.
- Interactive components and callbacks
- Reusable components and DRY styling
- Chained callbacks and between-element interactivity
- Dash AG Grid tables and pagination
Certificate Awarded (Paid)
Complete the course to earn it.
Job Outlook
Interactive Python dashboards are used by data analysts and Python developers who report on sales, customers and other business data to colleagues who want to explore the figures themselves.
Good for: Analysts who chart data in Python and want colleagues to explore results through filters, dropdowns and linked graphs.
Keep in mind: Every exercise runs on the platform and builds on one global e-commerce sales dataset, so the examples center on sales by country, date and customer location.
Bottom line: A focused course that grows one sales dashboard, chapter by chapter, into a styled, filterable app with linked graphs and AG Grid tables.
Frequently Asked Questions
Can one graph in the dashboard change another?
Yes. The final chapter updates one graph when a viewer hovers over or clicks on another, chains callbacks to offer conditional dropdown options, and lets Dash AG Grid tables trigger changes in other graphs of the sales dashboard.
Do I have to install Dash or Python for the exercises?
No installation is needed for the exercises. They run directly on the course platform, so you can write and test your Dash code there without setting up any software on your own machine.
What role do callbacks play in the dashboards?
Callbacks connect an interactive component, such as a dropdown, date picker, slider or text field, to an element on the page, so that element updates when a viewer makes a choice. Later lessons chain callbacks together and use hover and click events on one graph to change another.
What is Dash AG Grid used for in the course?
Dash AG Grid builds tables of key stats that viewers can filter, sort and paginate. The lessons also style these tables and use them to trigger changes in other graphs of the sales dashboard.
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