seaborn.
JointGrid
(x, y, data=None, height=6, ratio=5, space=0.2, dropna=True, xlim=None, ylim=None, size=None)¶Grid for drawing a bivariate plot with marginal univariate plots.
__init__
(x, y, data=None, height=6, ratio=5, space=0.2, dropna=True, xlim=None, ylim=None, size=None)¶Set up the grid of subplots.
Parameters: |
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See also
jointplot
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)
Add plots using default parameters:
>>> g = sns.JointGrid(x="total_bill", y="tip", data=tips)
>>> g = g.plot(sns.regplot, sns.distplot)
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")
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))
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)
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)
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)
Draw a smaller plot with relatively larger marginal axes:
>>> g = sns.JointGrid(x="total_bill", y="tip", data=tips,
... height=5, ratio=2)
>>> g = g.plot_joint(sns.kdeplot, cmap="Reds_d")
>>> g = g.plot_marginals(sns.kdeplot, color="r", shade=True)
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)