seaborn.PairGrid

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2023-12-01
class seaborn.PairGrid(data, hue=None, hue_order=None, palette=None, hue_kws=None, vars=None, x_vars=None, y_vars=None, diag_sharey=True, height=2.5, aspect=1, despine=True, dropna=True, size=None)

Subplot grid for plotting pairwise relationships in a dataset.

This class maps each variable in a dataset onto a column and row in a grid of multiple axes. Different axes-level plotting functions can be used to draw bivariate plots in the upper and lower triangles, and the the marginal distribution of each variable can be shown on the diagonal.

It can also represent an additional level of conditionalization with the hue parameter, which plots different subets of data in different colors. This uses color to resolve elements on a third dimension, but only draws subsets on top of each other and will not tailor the hue parameter for the specific visualization the way that axes-level functions that accept hue will.

See the PairGrid.Subplot grid for plotting conditional relationships.

Examples

Draw a scatterplot for each pairwise relationship:

>>> import matplotlib.pyplot as plt
>>> import seaborn as sns; sns.set()
>>> iris = sns.load_dataset("iris")
>>> g = sns.PairGrid(iris)
>>> g = g.map(plt.scatter)

http://seaborn.pydata.org/_images/seaborn-PairGrid-1.png

Show a univariate distribution on the diagonal:

>>> g = sns.PairGrid(iris)
>>> g = g.map_diag(plt.hist)
>>> g = g.map_offdiag(plt.scatter)

http://seaborn.pydata.org/_images/seaborn-PairGrid-2.png

(It’s not actually necessary to catch the return value every time, as it is the same object, but it makes it easier to deal with the doctests).

Color the points using a categorical variable:

>>> g = sns.PairGrid(iris, hue="species")
>>> g = g.map_diag(plt.hist)
>>> g = g.map_offdiag(plt.scatter)
>>> g = g.add_legend()

http://seaborn.pydata.org/_images/seaborn-PairGrid-3.png

Use a different style to show multiple histograms:

>>> g = sns.PairGrid(iris, hue="species")
>>> g = g.map_diag(plt.hist, histtype="step", linewidth=3)
>>> g = g.map_offdiag(plt.scatter)
>>> g = g.add_legend()

http://seaborn.pydata.org/_images/seaborn-PairGrid-4.png

Plot a subset of variables

>>> g = sns.PairGrid(iris, vars=["sepal_length", "sepal_width"])
>>> g = g.map(plt.scatter)

http://seaborn.pydata.org/_images/seaborn-PairGrid-5.png

Pass additional keyword arguments to the functions

>>> g = sns.PairGrid(iris)
>>> g = g.map_diag(plt.hist, edgecolor="w")
>>> g = g.map_offdiag(plt.scatter, edgecolor="w", s=40)

http://seaborn.pydata.org/_images/seaborn-PairGrid-6.png

Use different variables for the rows and columns:

>>> g = sns.PairGrid(iris,
...                  x_vars=["sepal_length", "sepal_width"],
...                  y_vars=["petal_length", "petal_width"])
>>> g = g.map(plt.scatter)

http://seaborn.pydata.org/_images/seaborn-PairGrid-7.png

Use different functions on the upper and lower triangles:

>>> g = sns.PairGrid(iris)
>>> g = g.map_upper(plt.scatter)
>>> g = g.map_lower(sns.kdeplot, cmap="Blues_d")
>>> g = g.map_diag(sns.kdeplot, lw=3, legend=False)

http://seaborn.pydata.org/_images/seaborn-PairGrid-8.png

Use different colors and markers for each categorical level:

>>> g = sns.PairGrid(iris, hue="species", palette="Set2",
...                  hue_kws={"marker": ["o", "s", "D"]})
>>> g = g.map(plt.scatter, linewidths=1, edgecolor="w", s=40)
>>> g = g.add_legend()

http://seaborn.pydata.org/_images/seaborn-PairGrid-9.png

Methods

| __init__(data[, hue, hue_order, palette, …]) | Initialize the plot figure and PairGrid object. | | add_legend([legend_data, title, label_order]) | Draw a legend, maybe placing it outside axes and resizing the figure. | | map(func, **kwargs) | Plot with the same function in every subplot. | | map_diag(func, **kwargs) | Plot with a univariate function on each diagonal subplot. | | map_lower(func, **kwargs) | Plot with a bivariate function on the lower diagonal subplots. | | map_offdiag(func, **kwargs) | Plot with a bivariate function on the off-diagonal subplots. | | map_upper(func, **kwargs) | Plot with a bivariate function on the upper diagonal subplots. | | savefig(*args, **kwargs) | Save the figure. | | set(**kwargs) | Set attributes on each subplot Axes. |