Build beautiful, web-based analytic apps.
The Dash platform empowers Data Science teams to focus on the data and models, while producing and sharing enterprise-ready analytic apps that sit on top of Python and R models. What would typically require a team of back-end developers, front-end developers, and IT can all be done with Dash.
Dash Open Source
Plotly stewards Python's leading data visualization and UI libraries.
With Dash Open Source, Dash apps run on your local laptop or workstation, but cannot be easily accessed by others in your organization.
Scale up with Dash Enterprise when your Dash app is ready for department or company-wide consumption. Or, launch your initiative with Dash Enterprise from the start to unlock developer productivity gains and hands-on acceleration from Plotly's team.
Click below to install Dash Open Source:
Kubernetes platform for rapid Dash app deployment.
Dash Enterprise helps businesses operationalize data science, AI, and ML models. It’s everything you need to deliver your AI or ML initiative at scale.
🚀Easily build, deploy, and manage—no DevOps required.
Speed your time-to-delivery with complete management of your applications.
💄Design like a pro without writing a line of CSS.
Easily arrange, style, and customize your Dash apps.
🕙Share, monitor, report—no CronJobs required.
Generate and store point-in-time reports as an interactive web view or printable PDF.
🔒Stay safe and secure.
Manage sharing and authentication preferences and easily secure your apps with built-in permissions and integrations with SAML and LDAP.
Say goodbye to outdated embedded BI.
Natively embed Dash apps in an existing web application or website without the use of IFrames.
Kubernetes (K8S) & high availability environments.
Minimize production impact of hardware and software failures. Increase accessibility and infinitely scale.
See Dash in action.
Machine Learning Dashboard.
This object-detection app provides useful visualizations about what's happening inside a complex video in real time. The data is generated using MobileNet v1 in Tensorflow, trained on the COCO dataset. The video is displayed using the community-maintained video component.
Oil & Gas dashboard.
This Dash app displays oil production in western New York. Filters at the top of the app update the graphs below. Selecting or hovering over data in one plot will update the other plots ('cross-filtering'). Dash apps are powered by Plotly.js, a fully featured charting library including maps like these, financial charts, scientific graphs, and more.
This app, created for noncompartmental pharmacokinetics, is typically used to analyze data from small animal studies during the lead optimization phase of drug discovery. These studies are used to help predict human dosing and plan safety studies.
Financial Reporting dashboard.
These customized and interactive reports enables your end users to mine relevant insights by interacting with and exploring your data content.
Reports built with Dash, benefit from further out of the box interactive functionality, such as adding dropdown or a search box elements.
Dash helps teams do more.
Before Dash, it would take an entire team of engineers and designers to create interactive analytics apps.
Every aesthetic element of a Dash app is customizable and rendered in the web so you can employ the full power of CSS.
Dash apps require very little boilerplate to get started. A fully-functional analytics app can weigh in at just 40 lines of Python or R code.
Dash links interactive UI controls and displays, like sliders, dropdown menus, and graphs, to your data analytics code, giving you hands-on input for your data views.
“Plotly’s visualization libraries have expanded S&P Global’s Market Intelligence New Product Development capabilities to create and extend our insights to our clients efficiently using highly interactive scientific visualizations to deliver our insights.”
See Dash in action.
Sign up for our next Dash Live Weekly demo session to learn more about our Dash Enterprise offering, including industry applications and all the latest tips and features on how to operationalize your data science models.