Lifemap.layer_donuts
Lifemap.layer_donuts(
counts_col,*,
=None,
radius='hide',
leaves=None,
scheme=1,
opacity=True,
popup=None,
label )
Add a donuts layer.
This layer displays the distribution of a categorical variable values among each nodes children. Optionally it can also represent leaves values as a point layer.
It should be applied to data computed with aggregate_freq
.
Parameters
Name | Type | Description | Default |
---|---|---|---|
counts_col | str | DataFrame column containing the counts. | required |
radius | float | None | Donut charts radius, by default None | None |
leaves | Literal["show", "hide"] | If "show" , add a points layer with individual leaves values, by default “hide” |
'hide' |
scheme | str | None | Color scheme for donut charts ans points. It is the name of a categorical Observable Plot color scale, by default None | None |
opacity | float | None | Donut charts and points opacity, by default 1 | 1 |
popup | bool | None | If True, display informations in a popup when a point is clicked, by default False, by default True | True |
label | str | None | Legend title for this layer. If None , the value of counts_col is used. |
None |
Returns
Name | Type | Description |
---|---|---|
Lifemap | A Lifemap visualization object. |
Raises
Name | Type | Description |
---|---|---|
ValueError | If leaves is not one of the allowed values. |
Examples
>>> import polars as pl
>>> from pylifemap import Lifemap, aggregate_freq
>>> d = pl.DataFrame(
... {"taxid": [
... 9685,
... 9615,
... 9994,
... 2467430,
... 2514524,
... 2038938,
... 1021470,
... 1415565,
... 1928562,
... 1397240,
... 230741,
...
... ],"category": ["a", "b", "b", "a", "a", "c", "a", "b", "b", "a", "b"],
...
... }
... )>>> d = aggregate_freq(d, column="category")
>>> (Lifemap(d).layer_donuts(counts_col="category", leaves="hide").show())
See also
: aggregation of the values counts of a categorical variable.