Is it possible to design dashboards in python? let's find out!


By Shariq Ansari

Why have dashboards become so important today? In today’s data-driven environment, there are dozens of dynamic factors in your business that impact your overall performance. Dashboards summarize these events with easy-to-understand, real-time data visuals. Users need real-time results that are obtained by extracting value from all the data collected (KPIs). 

Every business can benefit from having a dashboard that aligns with their objectives, and at WonderBiz we have built Industrial Dashboards for our clients from around the world. With every client specifying their own set of technology stacks and objectives, we've along the way delivered dashboards using multiple languages, including one that's underutilized, i.e., Python. 

Python tends to get typecast as the go-to language for Machine Learning today, and understandably so, because of its ever-growing libraries sculpted for Data Sciences. One of its libraries, Plotly, has a hidden feature that many are unaware about, and that's Dash.
Dash is a productive Python framework for building web analytic applications.

Written on top of Flask, Plotly.js, and React.js, Dash is ideal for building data visualization apps with highly custom user interfaces in pure Python. It's particularly suited for anyone who works with data in Python.

Through a couple of simple patterns, Dash can keep aside all of the technologies and protocols that are required to build an interactive web-based dashboard. It's simple enough that you can bind a user interface around your Python code in an afternoon.

Dash apps are rendered in the web browser. You can deploy your apps to servers and then share them through URLs. Since Dash apps are viewed in the web browser, Dash is inherently cross-platform and mobile ready.
Traditional BI dashboards no longer cut it in today's AI and ML driven world. Production-grade, low-code apps are needed for the complex analytics of emerging industries such as autonomous vehicles, renewable energy, quantum computing, novel therapeutics, and more. And Dash is able to achieve this without the developer having to acquire extensive knowledge about front-end development.

We utilized Dash for an Industrial Predictive Maintenance POC to find out the Remaining Useful Life (RUL) for Ball Bearings in electric motors. And Dash's compatibility to provide real-time updates through multiple graphical representations in a very short period of time was what surprised us. Our ML Engineers were able to construct the dashboard as per their requirements and experiment with the outcome comfortably, cutting down the need to involve the Front-End team. 

The POC, especially the graphical representations, impressed our client as we were able to convey Bearing statistics in a user friendly form, that any decision maker could easily understand. 

With the Python community growing, and Plotly expanding functionalities in Dash through its Enterprise version, we can expect Python to play a big role in Business Intelligence Dashboard creation as companies would prefer switching to a single language for both Data Analysis and Representation for a seamless experience.
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