plotnine.geom_count

geom_count(
    mapping=None,
    data=None,
    *,
    stat="sum",
    position="identity",
    na_rm=False,
    inherit_aes=True,
    show_legend=None,
    raster=False,
    **kwargs
)

Plot overlapping points

This is a variant geom_point that counts the number of observations at each location, then maps the count to point area. It useful when you have discrete data and overplotting.

Parameters

mapping : aes = None

Aesthetic mappings created with aes. If specified and inherit_aes=True, it is combined with the default mapping for the plot. You must supply mapping if there is no plot mapping.

Aesthetic	Default value
x
y
alpha	1
color	'black'
fill	None
group
shape	'o'
size	1.5
stroke	0.5

The bold aesthetics are required.

data : DataFrame = None

The data to be displayed in this layer. If None, the data from from the ggplot() call is used. If specified, it overrides the data from the ggplot() call.

stat : str | stat = "sum"

The statistical transformation to use on the data for this layer. If it is a string, it must be the registered and known to Plotnine.

position : str | position = "identity"

Position adjustment. If it is a string, it must be registered and known to Plotnine.

na_rm : bool = False

If False, removes missing values with a warning. If True silently removes missing values.

inherit_aes : bool = True

If False, overrides the default aesthetics.

show_legend : bool | dict = None

Whether this layer should be included in the legends. None the default, includes any aesthetics that are mapped. If a bool, False never includes and True always includes. A dict can be used to exclude specific aesthetis of the layer from showing in the legend. e.g show_legend={'color': False}, any other aesthetic are included by default.

raster : bool = False

If True, draw onto this layer a raster (bitmap) object even ifthe final image is in vector format.

**kwargs : Any = {}

Aesthetics or parameters used by the stat.

Examples

from plotnine import ggplot, aes, geom_count, scale_size_continuous
from plotnine.data import diamonds

Categorized Data Plot

geom_count() makes the point size proportional to the number of points at a location

diamonds.head()

	carat	cut	color	clarity	depth	table	price	x	y	z
0	0.23	Ideal	E	SI2	61.5	55.0	326	3.95	3.98	2.43
1	0.21	Premium	E	SI1	59.8	61.0	326	3.89	3.84	2.31
2	0.23	Good	E	VS1	56.9	65.0	327	4.05	4.07	2.31
3	0.29	Premium	I	VS2	62.4	58.0	334	4.20	4.23	2.63
4	0.31	Good	J	SI2	63.3	58.0	335	4.34	4.35	2.75

(
    ggplot(diamonds)
    + geom_count(aes(x="cut", y="color"))
)

We can adjust the size range of the points with scale_size_continuous

(
    ggplot(diamonds)
    + geom_count(aes(x="cut", y="color"))
    + scale_size_continuous(range=[1, 20])
)

Source: Categorized Data Plot