Point#

point mark represents each data point with a symbol. Point marks are commonly used in visualizations like scatter plots.

Point Mark Properties#

Click to show code
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 standard mark properties and the following special properties:

Click to show table

Property

Type

Description

shape

anyOf(anyOf(SymbolShape, string), ExprRef)

Shape of the point marks. Supported values include:

  • plotting shapes: "circle", "square", "cross", "diamond", "triangle-up", "triangle-down", "triangle-right", or "triangle-left".

  • the line symbol "stroke"

  • centered directional shapes "arrow", "wedge", or "triangle"

  • a custom SVG path string (For correct sizing, custom shape paths should be defined within a square bounding box with coordinates ranging from -1 to 1 along both the x and y dimensions.)

Default value: "circle"

size

anyOf(number, ExprRef)

Default size for marks.

  • For point/circle/square, this represents the pixel area of the marks. Note that this value sets the area of the symbol; the side lengths will increase with the square root of this value.

  • For bar, this represents the band size of the bar, in pixels.

  • For text, this represents the font size, in pixels.

Default value:

  • 30 for point, circle, square marks; width/height’s step

  • 2 for bar marks with discrete dimensions;

  • 5 for bar marks with continuous dimensions;

  • 11 for text marks.

Examples#

Dot Plot#

Mapping a field to either only x or only y of point marks creates a dot 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.

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.

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.

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.

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:

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".

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.

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
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