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.. currentmodule:: altair
.. _user-guide-errorband-marks:
Error Band
~~~~~~~~~~
An error band summarizes an error range of quantitative values using a set of summary statistics, representing by area. Error band in Altair can either be used to aggregate raw data or directly visualize aggregated data.
To create an error band, use ``mark_errorband``.
Error Band Mark Properties
^^^^^^^^^^^^^^^^^^^^^^^^^^
An ``errorband`` mark definition can contain the following properties:
.. altair-object-table:: altair.ErrorBandDef
:properties: extent orient color opacity interpolate tension
Besides the properties listed above, ``band`` and ``borders`` can be used to specify
the underlying mark properties for different parts of the error band as well.
Comparing the usage of Error Band to the usage of Error Bar
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
All the properties and usage of error band are identical to error bar’s, except the ``band`` and ``borders`` that replace the error bar’s ``rule`` and ``ticks``.
**Error Band**
.. altair-plot::
import altair as alt
from vega_datasets import data
source = data.cars.url
alt.Chart(source).mark_errorband(extent="ci", borders=True).encode(
x="year(Year)",
y=alt.Y(
"Miles_per_Gallon:Q",
scale=alt.Scale(zero=False),
title="Miles per Gallon (95% CIs)",
),
)
**Error Bar**
.. altair-plot::
import altair as alt
from vega_datasets import data
source = data.cars.url
alt.Chart(source).mark_errorbar(extent="ci", ticks=True).encode(
x="year(Year)",
y=alt.Y(
"Miles_per_Gallon:Q",
scale=alt.Scale(zero=False),
title="Miles per Gallon (95% CIs)",
),
)
Using Error Band to Aggregate Raw Data
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
If the data is not aggregated yet, Altair will aggregate the data based on the ``extent`` properties in the mark definition as done in the error band showing confidence interval above. All other ``extent`` values are defined in Error Bar.
Using Error Band to Visualize Aggregated Data
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
1. Data is aggregated with low and high values of the error band
If the data is already pre-aggregated with low and high values of the error band, you can directly specify ``x`` and ``x2`` (or ``y`` and ``y2``) to use error band as a ranged mark.
.. altair-plot::
import altair as alt
import pandas as pd
source = pd.DataFrame(
{
"ci1": [23.5007, 25.8214, 26.4472, 27.7074],
"ci0": [19.6912, 20.8554, 21.9749, 22.6203],
"center": [21.5735, 23.3750, 24.0611, 25.0931],
"Year": [189302400000, 220924800000, 252460800000, 283996800000],
}
)
band = alt.Chart(source).mark_errorband().encode(
alt.Y(
"ci1:Q",
scale=alt.Scale(zero=False),
title="Mean of Miles per Gallon (95% CIs)"
),
alt.Y2("ci0:Q"),
alt.X("year(Year)"),
)
line = alt.Chart(source).mark_line().encode(
alt.Y("center:Q"),
alt.X("year(Year)")
)
band + line
2. Data is aggregated with center and error value(s)
If the data is already pre-aggregated with center and error values of the error band, you can use ``x/y``, ``x/yError``, and ``x/yError2`` as defined in Error Bar.
Dimension
^^^^^^^^^
Altair supports both 1D and 2D error bands:
A **1D error band** shows the error range of a continuous field; it can be used to show the global error range of the whole plot.
.. altair-plot::
import altair as alt
from vega_datasets import data
source = data.cars.url
band = alt.Chart(source).mark_errorband(extent="stdev").encode(
alt.Y("Miles_per_Gallon:Q").title("Miles per Gallon")
)
points = alt.Chart(source).mark_point().encode(
x="Horsepower:Q",
y="Miles_per_Gallon:Q",
)
band + points
A **2D error** band shows the error range of a continuous field for each dimension value such as year.
.. altair-plot::
import altair as alt
from vega_datasets import data
source = data.cars()
line = alt.Chart(source).mark_line().encode(
x="Year",
y="mean(Miles_per_Gallon)"
)
band = alt.Chart(source).mark_errorband(extent="ci").encode(
x="Year",
y=alt.Y("Miles_per_Gallon").title("Miles/Gallon"),
)
band + line
Color and Opacity Encoding Channels
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
You can customize the color and opacity of the bands by using the ``color`` and ``opacity`` encoding channels.
Here is an example of a ``errorband`` with the ``color`` encoding channel set to ``alt.value('black')``.
.. altair-plot::
import altair as alt
from vega_datasets import data
source = data.cars.url
alt.Chart(source).mark_errorband(extent="ci", borders=True).encode(
x="year(Year)",
y=alt.Y("Miles_per_Gallon:Q")
.scale(zero=False)
.title("Miles per Gallon (95% CIs)"),
color=alt.value("black")
)
|
