plot_qq
method plot_qq(columns=0, distributions="norm", title=None, legend="lower right", figsize=(900, 600), filename=None, display=True)[source]
Plot a quantile-quantile plot.
Columns are distinguished by color and the distributions are distinguished by marker type. Missing values are ignored.
Parameters |
columns: int, str, segment, sequence or dataframe, default=0
Columns to plot. Selected categorical columns are ignored.
distributions: str or sequence, default="norm"
Names of the
title: str, dict or None, default=Nonescipy.stats distributions to fit to the
columns.
Title for the plot.
legend: str, dict or None, default="lower right"
Legend for the plot. See the user guide for
an extended description of the choices.
figsize: tuple, default=(900, 600)
Figure's size in pixels, format as (x, y).
filename: str, Path or None, default=None
Save the plot using this name. Use "auto" for automatic
naming. The type of the file depends on the provided name
(.html, .png, .pdf, etc...). If
display: bool or None, default=Truefilename has no file type,
the plot is saved as html. If None, the plot is not saved.
Whether to render the plot. If None, it returns the figure.
|
Returns | {#plot_qq-go.Figure or None}
go.Figure or None
Plot object. Only returned if display=None .
|
See Also
Plot a correlation matrix.
Plot column distributions.
Plot pairwise relationships in a dataset.
Example
>>> from atom import ATOMClassifier
>>> from sklearn.datasets import load_breast_cancer
>>> X, y = load_breast_cancer(return_X_y=True, as_frame=True)
>>> atom = ATOMClassifier(X, y, random_state=1)
>>> atom.plot_qq(columns=[5, 6])
>>> atom.plot_qq(columns=0, distributions=["norm", "invgauss", "triang"])