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.. currentmodule:: altair
.. _user-guide-area-marks:
Area
~~~~~~~~~~
``area`` represent multple data element as a single area shape.
Area marks are often used to show change over time, using either a single area or stacked areas.
Area Mark Properties
--------------------
.. altair-plot::
:hide-code:
:div_class: properties-example
import altair as alt
import pandas as pd
interpolate_select = alt.binding_select(
options=[
"basis",
"cardinal",
"catmull-rom",
"linear",
"monotone",
"natural",
"step",
"step-after",
"step-before",
],
name="interpolate",
)
interpolate_var = alt.param(bind=interpolate_select, value="linear")
tension_slider = alt.binding_range(min=0, max=1, step=0.05, name="tension")
tension_var = alt.param(bind=tension_slider, value=0)
source = pd.DataFrame({"u": [1, 2, 3, 4, 5, 6], "v": [28, 55, 42, 34, 36, 38]})
alt.Chart(source).mark_area(interpolate=interpolate_var, tension=tension_var).encode(
x="u", y="v"
).add_params(interpolate_var, tension_var)
An ``area`` mark definition can contain any :ref:`standard mark properties <mark-properties>`
and the following line interpolation as well as line and point overlay properties:
.. altair-object-table:: altair.MarkDef
:properties: align baseline orient interpolate tension line point
Examples
--------
Area Chart
^^^^^^^^^^
Using ``area`` mark with one temporal or ordinal field (typically on ``x``) and
one quantitative field (typically on ``y``) produces an area chart. For example,
the following area chart shows a number of unemployment people in the US over time.
.. altair-plot::
import altair as alt
from vega_datasets import data
source = data.unemployment_across_industries.url
alt.Chart(source).mark_area().encode(
x="yearmonth(date):T",
y="sum(count):Q",
).properties(width=300, height=200)
Area Chart with Overlaying Lines and Point Markers
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
By setting ``line`` and ``point`` properties of the mark definition
to ``true`` or an object defining a property of the overlaying point marks, we can overlay line and point markers on top of area.
.. altair-plot::
import altair as alt
from vega_datasets import data
from altair.expr import datum
source = data.stocks.url
alt.Chart(source).mark_area(line=True, point=True).encode(
x="date:T",
y="price:Q",
).transform_filter(
alt.datum.symbol == "GOOG"
)
Instead of using a single color as the fill color of the area, we can set it to a gradient.
In this example, we are also customizing the overlay. For more information about gradient options see the `Vega-Lite Gradient documentation
<https://vega.github.io/vega-lite/docs/gradient.html>`_.
.. altair-plot::
import altair as alt
from vega_datasets import data
source = data.stocks()
alt.Chart(source).transform_filter(alt.datum.symbol == "GOOG").mark_area(
line={"color": "darkgreen"},
color=alt.Gradient(
gradient="linear",
stops=[
alt.GradientStop(color="white", offset=0),
alt.GradientStop(color="darkgreen", offset=1),
],
x1=1,
x2=1,
y1=1,
y2=0,
),
).encode(
alt.X("date:T"),
alt.Y("price:Q"),
)
Stacked Area Chart
^^^^^^^^^^^^^^^^^^
Adding a color field to area chart creates stacked area chart by default. For example, here we split the area chart by industry.
.. altair-plot::
import altair as alt
from vega_datasets import data
source = data.unemployment_across_industries.url
alt.Chart(source).mark_area().encode(
alt.X("yearmonth(date):T").axis(format="%Y", domain=False, tickSize=0),
alt.Y("sum(count):Q"),
alt.Color("series:N").scale(scheme="category20b"),
)
Normalized Stacked Area Chart
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
You can also create a normalized stacked area chart by setting ``stack`` to ``"normalize"`` in the encoding channel. Here we can easily see the percentage of unemployment across industries.
.. altair-plot::
import altair as alt
from vega_datasets import data
source = data.unemployment_across_industries.url
alt.Chart(source).mark_area().encode(
alt.X("yearmonth(date):T").axis(format="%Y", domain=False, tickSize=0),
alt.Y("sum(count):Q").stack("normalize"),
alt.Color("series:N").scale(scheme="category20b"),
)
Steamgraph
^^^^^^^^^^^
We can also shift the stacked area chart's baseline to center and produces a streamgraph by setting ``stack`` to ``"center"`` in the encoding channel.
Adding the ``interactive`` method allows for zooming and panning the x-scale.
.. altair-plot::
import altair as alt
from vega_datasets import data
source = data.unemployment_across_industries.url
alt.Chart(source).mark_area().encode(
alt.X("yearmonth(date):T").axis(format="%Y", domain=False, tickSize=0),
alt.Y("sum(count):Q").stack("center").axis(None),
alt.Color("series:N").scale(scheme="category20b"),
).interactive()
Ranged Area
^^^^^^^^^^^
Specifying ``X2`` or ``Y2`` for the quantitative axis of area marks produce ranged areas. For example, we can use ranged area to highlight the mininium and maximum measured temperatures over time, aggregated by ``monthdate``.
.. altair-plot::
import altair as alt
from vega_datasets import data
source = data.seattle_weather()
alt.Chart(source).mark_area(opacity=0.7).encode(
alt.X("monthdate(date):T").title("Date"),
alt.Y("mean(temp_max):Q").title("Daily Temperature Range (C)"),
alt.Y2("mean(temp_min):Q"),
).properties(width=600, height=300)
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