> ## Documentation Index
> Fetch the complete documentation index at: https://docs.graphext.com/llms.txt
> Use this file to discover all available pages before exploring further.

# Cross Filters

> Quickly filter and select data

Cross filters are arguably one of the most powerful features in Graphext. It's as
simple as it sounds: it filters your data given some criterion. It's the way in which
we define these criterion that is so powerful.

You can **chain** however many filters you want, hence the 'cross filter' naming. This allows
you to hone in to a very thin slice of your data that responds to only the criterion you've
selected. Changing, removing and iterating on this selection is as simple as a couple clicks,
which makes data exploration extremely fast.

<Frame caption="Graphext filtering a ~19K row dataset in realtime and stacking filters in several columns">
  <video loop autoPlay playsInline muted src="https://mintcdn.com/graphext/agfECH-oCIK1Rorn/images/cross-filters-demo.mp4?fit=max&auto=format&n=agfECH-oCIK1Rorn&q=85&s=a397277fd01feffce772212281f63e9b" data-path="images/cross-filters-demo.mp4" />
</Frame>

To filter a column, you can simply interact with the little chart that's
associated to it.

In the case you reach to a particular selection you may want to preserve, you can do so by
clicking on the little dropdown arrow in the top left corner. This will save the current selection
as a [segment](/concepts/graphext-concepts/segments).

<img src="https://mintcdn.com/graphext/fZjz0m5J_8c25YMy/images/save-cross-filter.webp?fit=max&auto=format&n=fZjz0m5J_8c25YMy&q=85&s=05386687464660249112630ae23c261b" alt="Saving a selection as a Segment" width="1264" height="826" data-path="images/save-cross-filter.webp" />

## Absolute and Relative percentages

Upon filtering, all the other variables react to the filter. They show the relative
percentage of entries that fall into their respective categories, effectively showing
you a real-time distribution of the selected data, but in every other column.

To bring it home, let's see this example.

<Frame
  caption="We've only selected those Airbnb listings whose price lies between 1005 and 34391.
This leaves us with 58 entries out of the 7158 in total. "
>
  <img src="https://mintcdn.com/graphext/agfECH-oCIK1Rorn/images/cross-filter-price.webp?fit=max&auto=format&n=agfECH-oCIK1Rorn&q=85&s=4da89c29ef2928ffb81827148cada8c3" alt="Cross Filter Price" width="2876" height="1890" data-path="images/cross-filter-price.webp" />
</Frame>

### Example: Host Acceptance Rate

Upon filtering the price, we see the histogram for `host_acceptance_rate` changed.
Now, it shows a percentage y-scale. The gray bars that sit *in the background*
correspond to the percentage of entries that lie in that bin, had we **NOT** filtered the data.
We can see that the last bar, which corresponds to an acceptance rate range from 100
to 110, goes to just over 60%. That means that over 60% of all the hosts have an
acceptance rate of 100 or more.

The blue bar *on top* indicates the percentage of entries that lie in that bin,
**out of the current selection**. Around 55% of the 58 rows we have selected
lie in that acceptance rate range. That's around \~31 rows.

### Example: Is Super Host

Another example is the `is_super_host` variable, just under it. This variable is
either true (t) or false (f) indicating if the host is marked as a Super Host.

In the whole dataset, around 63% of the hosts are not Super Hosts (f). However,
in our particular selection, this is accentuated. Around 70% of the entries **we have
selected** are not Super Hosts. This can be of relevance, depending the questions
we are asking.

The opposite also applies: the percentage of hosts in our selection is
less than the percentage of hosts in the entire dataset.

## Significant Variables

Also, the [significant variables](/concepts/graphext-concepts/significant-variables) kick-in, showing
what other variables may be interesting in regards to the selected one.