Each pair of numeric columns in the DataFrame is plotted against each other,
resulting in a matrix of scatter plots. The diagonal plots can display either
histograms or Kernel Density Estimation (KDE) plots for each variable.
Parameters:
frameDataFrame
The data to be plotted.
alphafloat, optional
Amount of transparency applied.
figsize(float,float), optional
A tuple (width, height) in inches.
axMatplotlib axis object, optional
An existing Matplotlib axis object for the plots. If None, a new axis is
created.
gridbool, optional
Setting this to True will show the grid.
diagonal{‘hist’, ‘kde’}
Pick between ‘kde’ and ‘hist’ for either Kernel Density Estimation or
Histogram plot in the diagonal.
markerstr, optional
Matplotlib marker type, default ‘.’.
density_kwdskeywords
Keyword arguments to be passed to kernel density estimate plot.
hist_kwdskeywords
Keyword arguments to be passed to hist function.
range_paddingfloat, default 0.05
Relative extension of axis range in x and y with respect to
(x_max - x_min) or (y_max - y_min).
**kwargs
Keyword arguments to be passed to scatter function.
Returns:
numpy.ndarray
A matrix of scatter plots.
See also
plotting.parallel_coordinates
Plots parallel coordinates for multivariate data.
plotting.andrews_curves
Generates Andrews curves for visualizing clusters of multivariate data.
plotting.radviz
Creates a RadViz visualization.
plotting.bootstrap_plot
Visualizes uncertainty in data via bootstrap sampling.