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.. currentmodule:: altair
.. _user-guide-stack-transform:
Stack
~~~~~
The stack transform allows you to compute values associated with stacked versions
of encodings. For example, consider this stacked bar chart:
.. altair-plot::
import altair as alt
from vega_datasets import data
source = data.barley()
alt.Chart(source).mark_bar().encode(
column='year:O',
x='yield:Q',
y='variety:N',
color='site:N'
).properties(width=220)
Implicitly, this data is being grouped and stacked, but what if you would like to
access those stacked values directly?
We can construct that same chart manually using the stack transform:
.. altair-plot::
import altair as alt
from vega_datasets import data
source = data.barley()
alt.Chart(source).transform_stack(
stack='yield',
as_=['yield_1', 'yield_2'],
groupby=['year', 'variety'],
sort=[alt.SortField('site', 'descending')]
).mark_bar().encode(
column='year:O',
x=alt.X('yield_1:Q').title('yield'),
x2='yield_2:Q',
y='variety:N',
color='site:N',
tooltip=['site', 'yield', 'variety']
).properties(width=220)
Notice that the bars are now explicitly drawn between values computed and
specified within the x and x2 encodings.
Transform Options
^^^^^^^^^^^^^^^^^
The :meth:`~Chart.transform_stack` method is built on the :class:`~StackTransform`
class, which has the following options:
.. altair-object-table:: altair.StackTransform
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