seaborn.JointGrid

Set up the grid of subplots.

参数：x, y：strings or vectors

data：DataFrame, optional

DataFrame when x and y are variable names.

height：numeric

ratio：numeric

Ratio of joint axes size to marginal axes height.

space：numeric, optional

dropna：bool, optional

{x, y}lim：two-tuples, optional

See also

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)

Add plots using default parameters:

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

>>> import matplotlib.pyplot as plt
>>> 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 = 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:

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.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:

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")

Methods

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