Segments can be thought of as a group of cross filters. These allow
us to save an arbitrary amount of filters on several columns that may
make semantical sense.
Demographics segment in the Titanic dataset, giving a broader perspective on the age and gender distribution of people.
First, make any selection you are interested in, and then head to the Save selection menu, just under the big figure
showing the currently selected rows, in the top left corner of the screen.
If you already created a segment, it will appear in the menu. Choose it to save the current selection to the
existing segment. Otherwhise, create a new segment clicking the “New Segmentation” button.Here in the video we save 3 groups of gender-age demographics for easier access: men in the age 0–25 age range, 25–50 and 50–90.These are saved in new, specific categories inside a new variable that can be used to select that data in one single click,
or even train a machine learning model.
For example, you could save demographic data in a more approachable way. In the
titanic dataset, we have age and gender in two different variables. Assuming we wanted
to have a coarse perspective on these two factors, like Young vs Old people, and
Men vs Women, we could create a “Demographics” segment.
By creating these four segments, we now have a very quick way to
reach for the “Young Men” category, which would involve selecting
all Male passengers under the age of 25.This is a simple example, but we could compose an arbitrarily complex
filter, which would make reaching for these specific rows much easier.
As we can see, this process just involves creating a new multivalued column
that assigns the name of the segment to the selected rows. If the row was present
in the initial filter, the row gets that category in the column.Some rows may end up in several filters simultaneously, hence the multivalued column that can store
an array many different values.