A kernel density estimate (KDE) plot is a method for visualizing the distribution of observations in a dataset, analogous to a histogram. KDE represents the data using a continuous probability …

KDE plots have many advantages. Important features of the data are easy to discern (central tendency, bimodality, skew), and they afford easy comparisons between subsets.

Multiple bivariate KDE plots # seaborn components used: set_theme(), load_dataset(), kdeplot()

Overlapping densities (‘ridge plot’) # seaborn components used: set_theme(), cubehelix_palette(), FacetGrid

import seaborn as sns sns.set_theme(style="ticks") # Load the penguins dataset penguins = sns.load_dataset("penguins") # Show the joint distribution using kernel density estimation g = …

seaborn.objects.KDE # class seaborn.objects.KDE(bw_adjust=1, bw_method='scott', common_norm=True, common_grid=True, gridsize=200, cut=3, cumulative=False) # Compute …

A kernel density estimate (KDE) plot is a method for visualizing the distribution of observations in a dataset, analagous to a histogram. KDE represents the data using a continuous probability …

Plot pairwise relationships in a dataset. By default, this function will create a grid of Axes such that each numeric variable in data will by shared across the y-axes across a single row and the x …

Figure-level interface for drawing distribution plots onto a FacetGrid. This function provides access to several approaches for visualizing the univariate or bivariate distribution of data, including …

Draw a plot of two variables with bivariate and univariate graphs. This function provides a convenient interface to the JointGrid class, with several canned plot kinds.