Beeswarm

daspi.plotlib.plotter.Beeswarm(source, target, feature='', n_bins=None, width=CATEGORY.FEATURE_SPACE, skip_na=None, target_on_y=True, color=None, marker=None, ax=None, visible_spines=None, hide_axis=None, **kwds)

Bases: TransformPlotter

A class to create and display a basic bee swarm plot.

This class includes methods that organize the input data into bins according to a specified number of bins (or a default value if none is provided). It calculates the upper limits for each bin and positions the data points within these bins to achieve a horizontal distribution in the plot, ensuring as little overlap as possible among the points.

PARAMETER	DESCRIPTION
source	Pandas long format DataFrame containing the data source for the plot. TYPE: pandas DataFrame
target	Column name of the target variable for the plot. TYPE: str
feature	Column name of the feature variable for the plot, by default '' TYPE: str DEFAULT: ''
n_bins	The number of bins to divide the data into. If not specified, it is calculated as the length of the data divided by 6. Defaults to None TYPE: int | None DEFAULT: None
width	The width of the beeswarm, by default CATEGORY.FEATURE_SPACE. TYPE: float DEFAULT: FEATURE_SPACE
skip_na	Flag indicating whether to skip missing values in the feature grouped data, by default None - None, no missing values are skipped - all', grouped data is skipped if all values are missing - any', grouped data is skipped if any value is missing TYPE: Literal['none', 'all', 'any'] DEFAULT: None
target_on_y	Flag indicating whether the target variable is plotted on the y-axis, by default True TYPE: bool DEFAULT: True
color	Color to be used to draw the artists. If None, the first color is taken from the color cycle, by default None. TYPE: str | None DEFAULT: None
marker	The marker style for the scatter plot. Available markers see: https://matplotlib.org/stable/api/markers_api.html, by default None TYPE: str | None DEFAULT: None
ax	The axes object for the plot. If None, the current axes is fetched using plt.gca(). If no axes are available, a new one is created. Defaults to None. TYPE: Axes | None DEFAULT: None
visible_spines	Specifies which spines are visible, the others are hidden. If 'none', no spines are visible. If None, the spines are drawn according to the stylesheet. Defaults to None. TYPE: Literal['target', 'feature', 'none'] | None DEFAULT: None
hide_axis	Specifies which axes should be hidden. If None, both axes are displayed. Defaults to None. TYPE: Literal['target', 'feature', 'both'] | None DEFAULT: None
**kwds	Those arguments have no effect. Only serves to catch further arguments that have no use here (occurs when this class is used within chart objects). DEFAULT: {}

Examples:

Apply to an existing Axes object:

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from daspi import Beeswarm

fig, ax = plt.subplots()
df = pd.DataFrame(dict(
    x = ['first'] * 50 + ['second'] * 50 + ['third'] * 50,
    y = (
        list(np.random.normal(loc=3, scale=1, size=50))
        + list(np.random.normal(loc=4, scale=1, size=50))
        + list(np.random.normal(loc=2, scale=1, size=50)))))
beeswarm = Beeswarm(
    source=df, target='y', feature='x', ax=ax)
beeswarm()
beeswarm.label_feature_ticks()

Apply using the plot method of a DaSPi Chart object:

import numpy as np
import daspi as dsp
import pandas as pd

df = pd.DataFrame(dict(
    x = ['first'] * 50 + ['second'] * 50 + ['third'] * 50,
    y = (
        list(np.random.normal(loc=3, scale=1, size=50))
        + list(np.random.normal(loc=4, scale=1, size=50))
        + list(np.random.normal(loc=2, scale=1, size=50)))))
chart = dsp.SingleChart(
        source=df,
        target='y',
        feature='x',
        categorical_feature=True # neded to label feature ticks
    ).plot(
        dsp.Beeswarm,
    ).label() # neded to label feature ticks

Source

This code is based on the following source: https://python-graph-gallery.com/509-introduction-to-swarm-plot-in-matplotlib/

n_bins = n_bins instance-attribute

The number of bins to divide the data into

width = width instance-attribute

The maximum width of the beeswarm.

kw_default property

Default keyword arguments for plotting (read-only)

transform(feature_data, target_data)

Generates the spread values for the beeswarm plot by arranging the target data into bins.

The method divides the input data into bins based on the specified number of bins and calculates the spread of values within each bin to create a horizontal distribution.

PARAMETER	DESCRIPTION
feature_data	The center position on the feature axis where the beeswarm values will be centered. TYPE: float
target_data	feature grouped target data, coming from `feature_grouped' generator. TYPE: pandas Series

RETURNS	DESCRIPTION
data	The transformed data source for the plot. TYPE: pandas DataFrame