Categorical observation

daspi.plotlib.plotter.Scatter(source, target, feature, target_on_y=True, color=None, marker=None, size=None, ax=None, visible_spines=None, hide_axis=None, **kwds)

Bases: Plotter

A scatter plotter that extends the Plotter base class.

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 TYPE: str
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
size	The size of the markers in the scatter plot, by default None TYPE: Iterable[int] | 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 Scatter

fig, ax = plt.subplots()
df = pd.DataFrame({'x': list(x*np.pi/50 for x in range(100))})
df['y'] = np.cos(df['x'])
scatter = Scatter(source=df, target='y', feature='x', ax=ax)
scatter(color='red', s=20, marker='s', alpha=0.6)

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({'x': list(x*np.pi/50 for x in range(100))})
df['y'] = np.cos(df['x'])

chart = dsp.SingleChart(
        source=df,
        target='y',
        feature='x'
    ).plot(
        dsp.Scatter,
        kw_call=dict(color='red', s=20, marker='s', alpha=0.6))

size = size instance-attribute

The sizes of the markers in the scatter plot.

kw_default property

Default keyword arguments for plotting (read-only)