plotnine.geoms.geom_count

class plotnine.geoms.geom_count(mapping: Aes | None = None, data: DataLike | None = None, **kwargs: Any)

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.

Usage

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

Only the data and mapping can be positional, the rest must be keyword arguments. **kwargs can be aesthetics (or parameters) used by the stat.

Parameters:

mappingaes, optional

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.

datadataframe, optional

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.

statstr or stat, optional (default: 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.

positionstr or position, optional (default: position_identity)

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

na_rmbool, optional (default: False)

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

inherit_aesbool, optional (default: True)

If False, overrides the default aesthetics.

show_legendbool or dict, optional (default: 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.

rasterbool, optional (default: False)

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

Examples

import pandas as pd
import numpy as np

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'))

<Figure Size: (640 x 480)>

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])
)

<Figure Size: (640 x 480)>