Violine

daspi.plotlib.plotter.Violine(source, target, feature='', width=CATEGORY.FEATURE_SPACE, margin=0, fill=True, agreements=DEFAULT.AGREEMENTS, target_on_y=True, color=None, ax=None, visible_spines=None, hide_axis=None, **kwds)

Bases: GaussianKDE

Class for creating violine plotters.

This violin plot is composed of a double-sided Gaussian kernel density estimate. The width of the violin is stretched to fill the available width.

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: ''
width	Width of the violine, by default CATEGORY.FEATURE_SPACE. TYPE: float DEFAULT: FEATURE_SPACE
margin	Margin for the sequence as factor of data range (max - min ). If margin is 0, The two ends of the estimated density curve then show the minimum and maximum value. Default is 0. TYPE: float DEFAULT: 0
fill	Flag whether to fill in the curves, by default True TYPE: bool DEFAULT: True
agreements	Specifies the tolerated process variation for calculating quantiles. These quantiles are used to represent the filled area with different opacity, thus highlighting the quantiles.If you want the filled area to be uniform without highlighting the quantiles, provide an empty tuple. This argument is only taken into account if fill is set to True. The agreements can be either integers or floats, determining the process variation tolerance in the following ways: - If integers, the quantiles are determined using the normal distribution (agreement * σ), e.g., agreement = 6 covers ~99.75% of the data. - If floats, values must be between 0 and 1, interpreted as acceptable proportions for the quantiles, e.g., 0.9973 corresponds to ~6σ. - If empty tuple, the filled area is uniform without highlighting the quantiles. Default is DEFAULT.AGREEMENTS = (2, 4, 6), corresponding to (±1σ, ±2σ, ±3σ). TYPE: Tuple[float, ...] or Tuple[int, ...] DEFAULT: AGREEMENTS
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
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	Additional keyword arguments that have no effect and are only used 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 Violine

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)))))
violine = Violine(
    source=df, target='y', feature='x', fill=True, margin=0.3, 
    agreements=(), ax=ax)
violine()
violine.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 the feature tick labels
    ).plot(
        dsp.Violine,
        fill=False,
        margin=0.3
    ).label() # neded to label the feature tick labels

kw_default property

Default keyword arguments for plotting (read-only)