`dabl`.plot¶

dabl.plot(X, y=None, target_col=None, type_hints=None, scatter_alpha='auto', scatter_size='auto', drop_outliers=True, verbose=10, plot_pairwise=True, **kwargs)[source]¶

Automatic plots for classification and regression.

Determines whether the target is categorical or continuous and plots the target distribution. Then calls the relevant plotting functions accordingly.

See the functions in the “see also” section for more parameters that can be passed as kwargs.

Parameters:

XDataFrame: Input features. If target_col is specified, X also includes the target.
ySeries or numpy array, optional.: Target. You need to specify either y or target_col.
target_colstring or int, optional: Column name of target if included in X.
type_hintsdict or None: If dict, provide type information for columns. Keys are column names, values are types as provided by detect_types.
scatter_alphafloat, default=’auto’: Alpha values for scatter plots. ‘auto’ is dirty hacks.
scatter_sizefloat, default=’auto’.: Marker size for scatter plots. ‘auto’ is dirty hacks.
plot_pairwisebool, default=True: Whether to include pairwise scatterplots for classification. These can be somewhat expensive to compute.
verboseint, default=10: Controls the verbosity (output).
drop_outliersbool, default=True: Whether to drop outliers in the target column for regression.

dabl.plot¶

`dabl`.plot¶