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 scipy.stats distributions to fit to the columns. title: str, dict or None, default=None Title for the plot. If None, no title is shown. If str, text for the title. If dict, title configuration. legend: str, dict or None, default="lower right" Legend for the plot. See the user guide for an extended description of the choices. If None: No legend is shown. If str: Position to display the legend. If dict: Legend configuration. 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 filename has no file type, the plot is saved as html. If None, the plot is not saved. display: bool or None, default=True Whether to render the plot. If None, it returns the figure.

Returns

go.Figure or None Plot object. Only returned if display=None.

See Also

plot_correlation

Plot a correlation matrix.

plot_distribution

Plot column distributions.

plot_relationships

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"])