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.. currentmodule:: altair
.. _user-guide-point-marks:
Point
~~~~~
``point`` mark represents each data point with a symbol. Point marks are commonly used in visualizations like scatter plots.
Point Mark Properties
---------------------
.. altair-plot::
:hide-code:
:div_class: properties-example
import altair as alt
from vega_datasets import data
source = data.cars()
shape_select = alt.binding_select(
options=[
"arrow",
"circle",
"square",
"cross",
"diamond",
"triangle",
"triangle-up",
"triangle-down",
"triangle-right",
"triangle-left",
"wedge",
"stroke",
"M-1,-1H1V1H-1Z",
"M0,.5L.6,.8L.5,.1L1,-.3L.3,-.4L0,-1L-.3,-.4L-1,-.3L-.5,.1L-.6,.8L0,.5Z",
],
name="shape",
)
shape_var = alt.param(bind=shape_select, value="circle")
angle_slider = alt.binding_range(min=-360, max=360, step=1, name="angle")
angle_var = alt.param(bind=angle_slider, value=0)
size_slider = alt.binding_range(min=0, max=500, step=10, name="size")
size_var = alt.param(bind=size_slider, value=50)
strokeWidth_slider = alt.binding_range(min=0, max=10, step=0.5, name="strokeWidth")
strokeWidth_var = alt.param(bind=strokeWidth_slider, value=2)
alt.Chart(source).mark_point(
shape=shape_var,
angle=angle_var,
size=size_var,
strokeWidth=strokeWidth_var,
).encode(x="Horsepower:Q", y="Miles_per_Gallon:Q").add_params(
shape_var, angle_var, size_var, strokeWidth_var
)
A ``point`` mark definition can contain any :ref:`standard mark properties <mark-properties>`
and the following special properties:
.. altair-object-table:: altair.MarkDef
:properties: shape size
Examples
--------
Dot Plot
^^^^^^^^
Mapping a field to either only ``x`` or only ``y`` of point marks creates a dot plot.
.. altair-plot::
import altair as alt
from vega_datasets import data
source = data.movies()
alt.Chart(source).mark_point().encode(
x="IMDB_Rating:Q"
)
Scatter Plot
^^^^^^^^^^^^
Mapping fields to both the ``x`` and ``y`` channels creates a scatter plot.
.. altair-plot::
import altair as alt
from vega_datasets import data
source = data.cars()
alt.Chart(source).mark_point().encode(
x="Horsepower:Q",
y="Miles_per_Gallon:Q",
)
By default, ``point`` marks only have borders and are transparent inside. You can create a filled point by setting ``filled`` to ``True``.
.. altair-plot::
import altair as alt
from vega_datasets import data
source = data.cars()
alt.Chart(source).mark_point(filled=True).encode(
x="Horsepower:Q",
y="Miles_per_Gallon:Q",
)
Bubble Plot
^^^^^^^^^^^
By mapping a third field to the ``size`` channel in the scatter plot, we can create a bubble plot instead.
.. altair-plot::
import altair as alt
from vega_datasets import data
source = data.cars()
alt.Chart(source).mark_point().encode(
x="Horsepower:Q",
y="Miles_per_Gallon:Q",
size="Acceleration:Q",
)
Scatter Plot with Color and/or Shape
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
Fields can also be encoded in the scatter plot using the ``color`` or ``shape`` channels. For example, this specification encodes the field ``Origin`` with both ``color`` and ``shape``.
.. altair-plot::
import altair as alt
from vega_datasets import data
source = data.cars()
alt.Chart(source).mark_point().encode(
alt.X("Miles_per_Gallon:Q").scale(zero=False),
alt.Y("Horsepower:Q").scale(zero=False),
color="Origin:N",
shape="Origin:N",
)
Dot Plot with Jittering
^^^^^^^^^^^^^^^^^^^^^^^
To jitter points on a discrete scale, you can add a random offset:
.. altair-plot::
import altair as alt
from vega_datasets import data
source = data.cars()
alt.Chart(source).mark_point().encode(
x="Horsepower:Q",
y="Cylinders:O",
yOffset="random:Q",
).transform_calculate(
random="random()"
).properties(
height=alt.Step(50)
)
Wind Vector Example
^^^^^^^^^^^^^^^^^^^
We can also use point mark with ``wedge`` as ``shape`` and ``angle`` encoding to create a wind vector map. Other shape options are:
``"circle"``, ``"square"``, ``"cross"``, ``"diamond"``, ``"triangle-up"``, ``"triangle-down"``, ``"triangle-right"``, ``"triangle-left"``, ``"stroke"``, ``"arrow"``, and ``"triangle"``.
.. altair-plot::
import altair as alt
from vega_datasets import data
source = data.windvectors()
alt.Chart(source).mark_point(shape="wedge", filled=True).encode(
latitude="latitude",
longitude="longitude",
color=alt.Color("dir").scale(domain=[0, 360], scheme="rainbow").legend(None),
angle=alt.Angle("dir").scale(domain=[0, 360], range=[180, 540]),
size=alt.Size("speed").scale(rangeMax=500),
).project("equalEarth")
Geo Point
^^^^^^^^^
By mapping geographic coordinate data to ``longitude`` and ``latitude`` channels of a corresponding projection, we can visualize geographic points. The example below shows major airports in the US.
.. altair-plot::
import altair as alt
from vega_datasets import data
airports = data.airports()
states = alt.topo_feature(data.us_10m.url, feature="states")
# US states background
background = alt.Chart(states).mark_geoshape(
fill="lightgray",
stroke="white"
).properties(
width=500,
height=300,
).project("albersUsa")
# airport positions on background
points = alt.Chart(airports).mark_circle(
size=10,
color="steelblue",
).encode(
longitude="longitude:Q",
latitude="latitude:Q",
tooltip=["name", "city", "state"],
)
background + points
|
