Scatter

daspi.plotlib.plotter.CategoricalObservation(source, target, feature='', show_line=True, show_scatter=True, 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

TransformPlotter for visualizing the observation order, but for categorical features.

This class is designed to create a scatter, line, or a combination of these for categorical features. However, the individual points are displayed for each feature category, ordered by observation order, within the available categorical range.

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: ''
show_line	Flag indicating whether to draw a line between the individual points, by default True TYPE: bool DEFAULT: True
show_scatter	Flag indicating whether to show the individual points, by default True. TYPE: bool DEFAULT: True
width	The width of the scatter range, 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 CategoricalObservation

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)))))
observation = CategoricalObservation(
    source=df, target='y', feature='x', ax=ax,
    show_line=True, show_scatter=True,)
observation()
observation.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.CategoricalObservation,
        show_line=True,
        show_scatter=True,
    ).label() # neded to label feature ticks

width = width instance-attribute

The width of the categorical range.

show_line = show_line instance-attribute

Whether to draw a line between the individual points.

show_scatter = show_scatter instance-attribute

Whetter to draw the scatter points.

kw_default property

Default keyword arguments for plotting (read-only)

observation(loc, size)

Creates a uniform sequence of numbers with the number size from loc minus half the width to loc plus half the width.

PARAMETER	DESCRIPTION
loc	Center position (feature axis) of the scatter values. TYPE: float
size	Amount of valaues to generate TYPE: int

RETURNS	DESCRIPTION
1D array	Evenly distributed values.

transform(feature_data, target_data)

Normally randomize the target data for each feature value in the feature axis direction.

PARAMETER	DESCRIPTION
feature_data	Base location (offset) of feature axis coming from `feature_grouped' generator. TYPE: int | float
target_data	feature grouped target data used for transformation, coming from `feature_grouped' generator. TYPE: pandas Series

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