seaborn.JointGrid

class seaborn.JointGrid(x, y, data=None, size=6, ratio=5, space=0.2, dropna=True, xlim=None, ylim=None)

Grid for drawing a bivariate plot with marginal univariate plots.

__init__(x, y, data=None, size=6, ratio=5, space=0.2, dropna=True, xlim=None, ylim=None)

Set up the grid of subplots.

Parameters:

x, y : strings or vectors

Data or names of variables in data.

data : DataFrame, optional

DataFrame when x and y are variable names.

size : numeric

Size of each side of the figure in inches (it will be square).

ratio : numeric

Ratio of joint axes size to marginal axes height.

space : numeric, optional

Space between the joint and marginal axes

dropna : bool, optional

If True, remove observations that are missing from x and y.

{x, y}lim : two-tuples, optional

Axis limits to set before plotting.

See also

jointplot
High-level interface for drawing bivariate plots with several different default plot kinds.

Examples

Initialize the figure but don’t draw any plots onto it:

>>> import seaborn as sns; sns.set(style="ticks", color_codes=True)
>>> tips = sns.load_dataset("tips")
>>> g = sns.JointGrid(x="total_bill", y="tip", data=tips)
../_images/seaborn-JointGrid-1.png

Add plots using default parameters:

>>> g = sns.JointGrid(x="total_bill", y="tip", data=tips)
>>> g = g.plot(sns.regplot, sns.distplot)
../_images/seaborn-JointGrid-2.png

Draw the join and marginal plots separately, which allows finer-level control other parameters:

>>> import matplotlib.pyplot as plt
>>> g = sns.JointGrid(x="total_bill", y="tip", data=tips)
>>> g = g.plot_joint(plt.scatter, color=".5", edgecolor="white")
>>> g = g.plot_marginals(sns.distplot, kde=False, color=".5")
../_images/seaborn-JointGrid-3.png

Draw the two marginal plots separately:

>>> import numpy as np
>>> g = sns.JointGrid(x="total_bill", y="tip", data=tips)
>>> g = g.plot_joint(plt.scatter, color="m", edgecolor="white")
>>> _ = g.ax_marg_x.hist(tips["total_bill"], color="b", alpha=.6,
...                      bins=np.arange(0, 60, 5))
>>> _ = g.ax_marg_y.hist(tips["tip"], color="r", alpha=.6,
...                      orientation="horizontal",
...                      bins=np.arange(0, 12, 1))
../_images/seaborn-JointGrid-4.png

Add an annotation with a statistic summarizing the bivariate relationship:

>>> from scipy import stats
>>> g = sns.JointGrid(x="total_bill", y="tip", data=tips)
>>> g = g.plot_joint(plt.scatter,
...                  color="g", s=40, edgecolor="white")
>>> g = g.plot_marginals(sns.distplot, kde=False, color="g")
>>> g = g.annotate(stats.pearsonr)
../_images/seaborn-JointGrid-5.png

Use a custom function and formatting for the annotation

>>> g = sns.JointGrid(x="total_bill", y="tip", data=tips)
>>> g = g.plot_joint(plt.scatter,
...                  color="g", s=40, edgecolor="white")
>>> g = g.plot_marginals(sns.distplot, kde=False, color="g")
>>> rsquare = lambda a, b: stats.pearsonr(a, b)[0] ** 2
>>> g = g.annotate(rsquare, template="{stat}: {val:.2f}",
...                stat="$R^2$", loc="upper left", fontsize=12)
../_images/seaborn-JointGrid-6.png

Remove the space between the joint and marginal axes:

>>> g = sns.JointGrid(x="total_bill", y="tip", data=tips, space=0)
>>> g = g.plot_joint(sns.kdeplot, cmap="Blues_d")
>>> g = g.plot_marginals(sns.kdeplot, shade=True)
../_images/seaborn-JointGrid-7.png

Draw a smaller plot with relatively larger marginal axes:

>>> g = sns.JointGrid(x="total_bill", y="tip", data=tips,
...                   size=5, ratio=2)
>>> g = g.plot_joint(sns.kdeplot, cmap="Reds_d")
>>> g = g.plot_marginals(sns.kdeplot, color="r", shade=True)
../_images/seaborn-JointGrid-8.png

Set limits on the axes:

>>> g = sns.JointGrid(x="total_bill", y="tip", data=tips,
...                   xlim=(0, 50), ylim=(0, 8))
>>> g = g.plot_joint(sns.kdeplot, cmap="Purples_d")
>>> g = g.plot_marginals(sns.kdeplot, color="m", shade=True)
../_images/seaborn-JointGrid-9.png

Methods

__init__(x, y[, data, size, ratio, space, ...]) Set up the grid of subplots.
annotate(func[, template, stat, loc]) Annotate the plot with a statistic about the relationship.
plot(joint_func, marginal_func[, annot_func]) Shortcut to draw the full plot.
plot_joint(func, **kwargs) Draw a bivariate plot of x and y.
plot_marginals(func, **kwargs) Draw univariate plots for x and y separately.
savefig(*args, **kwargs) Wrap figure.savefig defaulting to tight bounding box.
set_axis_labels([xlabel, ylabel]) Set the axis labels on the bivariate axes.