![]() Can have a numeric dtype but will always be treated as categorical. Grouping variable that will produce points with different markers. Style:name of variables in data or vector data, optional Can be either categorical or numeric, although size mapping will behave differently in latter case. Grouping variable that will produce points with different sizes. ![]() Size:name of variables in data or vector data, optional Can be either categorical or numeric, although color mapping will behave differently in latter case. Grouping variable that will produce points with different colors. Hue:name of variables in data or vector data, optional Can pass data directly or reference columns in data. 参数: x, y:names of variables in data or vector data, optional both hue and style for the same variable) can be helpful for making graphics more accessible. It is possible to show up to three dimensions independently by using all three semantic types, but this style of plot can be hard to interpret and is often ineffective. These parameters control what visual semantics are used to identify the different subsets. The relationship between x and y can be shown for different subsets of the data using the hue, size, and style parameters. Seaborn.scatterplot seaborn.scatterplot(x=None, y=None, hue=None, style=None, size=None, data=None, palette=None, hue_order=None, hue_norm=None, sizes=None, size_order=None, size_norm=None, markers=True, style_order=None, x_bins=None, y_bins=None, units=None, estimator=None, ci=95, n_boot=1000, alpha='auto', x_jitter=None, y_jitter=None, legend='brief', ax=None, **kwargs)ĭraw a scatter plot with possibility of several semantic groupings.
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