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.. currentmodule:: altair
.. _user-guide-resolve:
Scale and Guide Resolution
--------------------------
When creating compound charts (see :ref:`user-guide-compound`), altair defaults
to using shared chart scales and guides (e.g. axes, legends, etc.).
This default can be adjusted using the :meth:`Chart.resolve_scale`,
:meth:`Chart.resolve_axis`, and :meth:`Chart.resolve_legend` functions.
For example, suppose you would like to concatenate two charts with separate
color scales; the default behavior is for the color scale to be created for
a union of the two color encoding domains:
.. altair-plot::
import altair as alt
from vega_datasets import data
source = data.cars()
base = alt.Chart(source).mark_point().encode(
x='Horsepower:Q',
y='Miles_per_Gallon:Q'
).properties(
width=200,
height=200
)
alt.concat(
base.encode(color='Origin:N'),
base.encode(color='Cylinders:O')
)
This default can be changed by setting the scale resolution for the color to
``"independent"`` (rather than the default, ``"shared"``):
.. altair-plot::
alt.concat(
base.encode(color='Origin:N'),
base.encode(color='Cylinders:O')
).resolve_scale(
color='independent'
)
Dual Y Axis
~~~~~~~~~~~
A common technique for combining chart containing different measures is using a
dual y axis. There are two strategies to achieve this result using altair. The
first is to manually specify the mark color and associated axis title color of
each layer.
.. altair-plot::
import altair as alt
from vega_datasets import data
source = data.cars()
base = alt.Chart(source).encode(x='year(Year):T')
line_A = base.mark_line(color='#5276A7').encode(
alt.Y('average(Horsepower):Q').axis(titleColor='#5276A7')
)
line_B = base.mark_line(color='#F18727').encode(
alt.Y('average(Miles_per_Gallon):Q').axis(titleColor='#F18727')
)
alt.layer(line_A, line_B).resolve_scale(y='independent')
In this case the axis colors act as a pseudo-legend. Alternatively if you want
a legend the :ref:`user-guide-filter-transform` and
:ref:`user-guide-fold-transform` must be applied. Legends are only created in
Vega-Lite to represent an encoding.
.. altair-plot::
base = alt.Chart(source).mark_line().transform_fold(
['Horsepower', 'Miles_per_Gallon'],
as_=['Measure', 'Value']
).encode(
alt.Color('Measure:N'),
alt.X('year(Year):T')
)
line_A = base.transform_filter(
alt.datum.Measure == 'Horsepower'
).encode(
alt.Y('average(Value):Q').title('Horsepower')
)
line_B = base.transform_filter(
alt.datum.Measure == 'Miles_per_Gallon'
).encode(
alt.Y('average(Value):Q').title('Miles_per_Gallon')
)
alt.layer(line_A, line_B).resolve_scale(y='independent')
Note that dual axis charts might be misleading about
relationships in your data. For further reading on the topic see `The case against dual axis
charts <https://blog.datawrapper.de/dualaxis/>`__ by Lisa Charlotte Rost.
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