Understand how simple data visualizations, like a pie chart, can reveal powerful insights hidden within raw data. Learn why Dash is a preferred framework over Jupyter Notebook for building interactive, shareable data visualizations.
Key Insights
- Data visualization helps identify patterns and trends—such as Apple's dominance in share volume—which may be difficult to detect in raw datasets.
- Dash is favored over Jupyter Notebook for creating interactive, web-based visualizations that can be deployed and accessed globally.
- Noble Desktop's training shows how Dash enables developers to build composable and scalable visual apps, extending beyond individual use to broader public engagement.
Note: These materials offer prospective students a preview of how our classes are structured. Students enrolled in this course will receive access to the full set of materials, including video lectures, project-based assignments, and instructor feedback.
Let's talk for a moment about why we just did what we did and why we use Dash to do it. So why did we make a pie chart? Why does anyone make a pie chart? What's the point of a pie chart? It's data visualization. And data visualization is very, very powerful.
It's a tool for understanding and communicating that data to others. And to ourselves, a lot of the time, it helps us to gain insight as well. It allows us to see patterns, trends, and outliers in our data that might not be immediately obvious just from looking at some numbers in a table.
By using these visual elements, like charts, graphs, and maps, we can create a more accessible way to see and understand this data. Even just in this little example we threw together here, we can see how data visualization can help us to understand our data better. I don't know if you noticed looking at the data how much bigger Apple is on this one day's volume than the other ones.
Not only is it far bigger, about twice as big as the next nearest, but it's also a huge share of the overall top five. Over a third of the total shares sold belong to Apple. That's easy to miss looking at the raw data.
It's a great example of how even the most simple, not juiced up, not prettied up, just a very simple chart with just a few lines of code can really help us understand our data better. And I like to say that data visualization is telling stories with data. And even just a simple pie chart tells a story of dominance, of Apple being far larger than the other companies and of taking up a huge share of the overall market.
So why don't we build this with Dash? Because we could have built this in something like Jupyter Notebook with Pipelot. Here's the advantage that it has. Jupyter Notebook is fantastic.
I really like Jupyter Notebook and it's perfect for its use case, but its use case is much more often prototyping, making simple versions of things, making versions that are programmer facing, making a notebook for yourself or for other data scientists. But Dash allows us to really put it out there for the world in a way that Jupyter Notebooks really can't compare to. For one thing, later we'll be creating very interactive visualizations.
This one isn't particularly interactive, but web apps are very interactive as a platform. And so allowing us to make interactive web apps is very powerful. It allows us to create more engaging and informative visualizations when the user can interact with the visualization.
It also allows us to deploy. Now this is not deployed, it's only run locally. You can't go to this website right now and see my top five stocks by volume here.
But later you are going to be able to deploy it to a server and have anybody be able to, anywhere in the world, be able to view your visualization. And finally, in a way that Jupyter Notebook is not, Dash apps are able to build off of each other, to incorporate other things within, to be composable, that we can combine different pieces of visualization into a bigger whole.