问题描述
我想使用 sns.jointplot 来可视化存在两个组的情况下 X 和 Y 之间的关联.然而，在
tips = sns.load_dataset("tips")sns.jointplot("total_bill", "tip", data=tips)没有像 sns.scatterplot 等其他 sns 图中的色调"选项.如何在散点图中以及两个重叠的密度图中为不同的组(例如，hue="smoker")分配不同的颜色.
在 R 中，这可以通过创建具有两个边际密度图的散点图来完成，如
sns 中的等价物是什么?如果这在 sns 中是不可能的，是否有另一个可以用于此的 python 包?
jointplot 是对 sns.JointGrid 的简单包装.如果您创建一个 JointGrid 对象并手动向其中添加绘图，您将对单个绘图有更多的控制.
在这种情况下，你想要的jointplot只是一个scatterplot结合一个kdeplot，你想要做的是传递hue='smoker'(例如)到 scatterplot.
kdeplot 更复杂；seaborn 并不真正支持每个类使用一个 KDE，AFAIK，所以我不得不单独绘制它们(你可以使用 for 循环和更多类).>
因此，您可以这样做:
导入 seaborn 为 sns提示 = sns.load_dataset('提示')grid = sns.JointGrid(x='total_bill', y='tip', data=tips)g = grid.plot_joint(sns.scatterplot，hue='smoker'，data=tips)sns.kdeplot(tips.loc[tips['smoker']=='Yes', 'total_bill'], ax=g.ax_marg_x, legend=False)sns.kdeplot(tips.loc[tips['smoker']=='No','total_bill'],ax=g.ax_marg_x,legend=False)sns.kdeplot(tips.loc[tips['smoker']=='Yes','tip'],ax=g.ax_marg_y,vertical=True,legend=False)sns.kdeplot(tips.loc[tips['smoker']=='No','tip'],ax=g.ax_marg_y,vertical=True,legend=False)I would like to use sns.jointplot to visualise the association between X and Y in the presence of two groups. However, in
tips = sns.load_dataset("tips")
sns.jointplot("total_bill", "tip", data=tips)
there is no "hue" option as in other sns plots such as sns.scatterplot. How could one assign different colours for different groups (e.g. hue="smoker") in both the scatter plot, as well as the two overlapping density plots.
In R this could be done by creating a scatter plot with two marginal density plots as shown in
What is the equivalent in sns? If this is not possible in sns, is there another python package that can be used for this?
jointplot is a simple wrapper around sns.JointGrid. If you create a JointGrid object and add plots to it manually, you will have much more control over the individual plots.
In this case, your desired jointplot is simply a scatterplot combined with a kdeplot, and what you want to do is pass hue='smoker' (for example) to scatterplot.
The kdeplot is more complex; seaborn doesn't really support one KDE for each class, AFAIK, so I was forced to plot them individually (you could use a for loop with more classes).
Accordingly, you can do this:
import seaborn as sns
tips = sns.load_dataset('tips')
grid = sns.JointGrid(x='total_bill', y='tip', data=tips)
g = grid.plot_joint(sns.scatterplot, hue='smoker', data=tips)
sns.kdeplot(tips.loc[tips['smoker']=='Yes', 'total_bill'], ax=g.ax_marg_x, legend=False)
sns.kdeplot(tips.loc[tips['smoker']=='No', 'total_bill'], ax=g.ax_marg_x, legend=False)
sns.kdeplot(tips.loc[tips['smoker']=='Yes', 'tip'], ax=g.ax_marg_y, vertical=True, legend=False)
sns.kdeplot(tips.loc[tips['smoker']=='No', 'tip'], ax=g.ax_marg_y, vertical=True, legend=False)
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