More interpretable, especially when drawing multiple distributions.
Relative to a histogram, KDE can produce a plot that is less cluttered and The approach is explained further in the user guide. Represents the data using a continuous probability density curve in one or
Plot univariate or bivariate distributions using kernel density estimation.Ī kernel density estimate (KDE) plot is a method for visualizing theĭistribution of observations in a dataset, analogous to a histogram. kdeplot ( data = None, *, x = None, y = None, hue = None, weights = None, palette = None, hue_order = None, hue_norm = None, color = None, fill = None, multiple = 'layer', common_norm = True, common_grid = False, cumulative = False, bw_method = 'scott', bw_adjust = 1, warn_singular = True, log_scale = None, levels = 10, thresh = 0.05, gridsize = 200, cut = 3, clip = None, legend = True, cbar = False, cbar_ax = None, cbar_kws = None, ax = None, ** kwargs ) #
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