Thanks to Dash HoloViews, the full power of the link_selections transformation can be used in Dash. Here is an example o

It’s not just good advice for parents. Right now — especially as the holidays approach — we’re all experiencing some heightened feelings, including some we may not know how to process or explain. And as we look for ways to stay connected to the people we love from a distance, applying the same principle of observations before questions can encourage much-needed emotional vulnerability.

It didn’t last long, thankfully, but I’ve spent the time since then wondering why a question as innocuous as “How was your day?” would prompt such a negative reaction. I recently found what seemed to be an answer in an Instagram post about how to get kids to talk about their feelings: Seattle-based therapist Lindsay Braman explained that such an open-ended prompt can increase anxiety, especially when you’re asking someone (like a five-year-old) to share emotions they don’t know how to explain.

It’s even possible to combine the link_selections and datashade transformations to create linked visualizations across large datasets. This example shows how the two previous examples can be combined to support linking selections across a histogram and a datashaded scatter plot of 1.5 million points.

The drop-off locations are rendered using Datashader on top of a Mapbox map, and is accompanied by a histogram of fare amounts. The datashaded scatter plot automatically resamples in response to zoom and pan events, and selections on both plots are linked.

While the Plotly.js WebGL accelerated scatter trace can handle hundreds of thousands of points, Datashader can handle tens to hundreds of millions. The difference is that rather than passing the entire data set from the Python server to the browser for rendering, Datashader rasterizes the data set to a heatmap or image, and only transfers this heatmap or image to the browser for rendering.

The ability to develop exploratory data analysis workflows using pandas, and then later GPU accelerate them with almost no code changes is one of the most exciting recent developments in the PyData ecosystem.

Many HoloViews operations, including datashade and link_selections, can be accelerated on modern NVIDIA GPUs using technologies from the RAPIDS ecosystem. All of the previous examples can be GPU-accelerated simply by replacing the pandas DataFrame with a cuDF DataFrame.

To effectively use Datashader in an interactive context, it’s necessary to re-render the data set each time the figure viewport changes. This can already be accomplished in Dash by installing a callback function that listens for changes to the relayoutData prop, but coordinating the full update process is not straightforward.

HoloViews allows most 2-dimensional visualization types to be overlaid on top of a map. Here is a more refined example of displaying the 10 million row NYC Taxi data set that is provided by the PyViz project (https://examples.pyviz.org/nyc_taxi/nyc_taxi.html).

Observations, on the other hand, cultivate connection by showing you’re paying attention, Braman explained in her post; that’s why she encourages parents to lead with statements like “You seem frustrated,” or “You’re full of smiles.”

Notice how the box selection tool can be used to select regions of space in each figure and how the selection of the corresponding points is displayed in both figures. Also, notice how the datashaded scatter plot dynamically resamples after zoom and pan operations.

Last year, when my son started kindergarten, my mom friends warned me that he might come home irritable the first few weeks. They were right: My normally happy-go-lucky five-year-old would trudge off the bus, throw his backpack on the entryway floor, and turn on the TV without a word (except to demand a bowl of Goldfish). When I asked about his day at dinner or at bedtime, he’d shut me down and change the subject.

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r and a thrower-outer. My husband is a pile-maker and a saver-in-case-you-might-need-it-somedayer. My favorite gizmo is my label maker. His is… something that is probably under a pile of things in a corner. Despite this, we have a happy marriage. (Because he is probably reading this: I love you, John!) Still, there is a gulf between us regarding the management of stuff. I can’t see how he could object to my neatly labeled, orderly array of things; my alphabetized spices; my photo-labeled clear shoe boxes; my colorized closet, but I suppose in a moment of weakness, he might dismiss me as being a bit anal. On the other hand, I despair at his mounds of junk.

The ability to develop exploratory data analysis workflows using pandas, and then later GPU accelerate them with almost no code changes is one of the most exciting recent developments in the PyData ecosystem.

With HoloViews, the datashade transformation can simply be applied to a scatter element without the need to write any callbacks defining how to re-evaluate the datashading operation in response to viewport changes.