向 seaborn 二维直方图添加正态分布

Question

Is it possible to take a histogram from seaborn and add a normal distribution?

Say I had something like this scatter plot and histogram from the documentation.

import seaborn as sns
penguins = sns.load_dataset("penguins")
sns.jointplot(data=penguins, x="bill_length_mm", y="bill_depth_mm");
plt.savefig('deletethis.png', bbox_inches='tight')

Can i superimpose a distribution on the sides like the image below?

import matplotlib.pyplot as plt
import numpy as np
from scipy.stats import norm

x = np.random.normal(size=100000)

# Plot histogram in one-dimension
plt.hist(x,bins=80,density=True)
xvals = np.arange(-4,4,0.01)
plt.plot(xvals, norm.pdf(xvals),label='$N(0,1)$')
plt.legend();

Answer 1

The following gives a Kernel Density Estimate which displays the distribution (and if it is normal):

g = sns.JointGrid(data=penguins, x="bill_length_mm", y="bill_depth_mm")
g.plot_joint(sns.scatterplot, s=100, alpha=.5)
g.plot_marginals(sns.histplot, kde=True)

The following superimposes a normal distribution on the histograms in the axes.

import seaborn as sns
import numpy as np
import pandas as pd
from scipy.stats import norm

df1 = penguins.loc[:,["bill_length_mm", "bill_depth_mm"]]

axs = sns.jointplot("bill_length_mm", "bill_depth_mm", data=df1)
axs.ax_joint.scatter("bill_length_mm", "bill_depth_mm", data=df1, c='r', marker='x')

axs.ax_marg_x.cla()
axs.ax_marg_y.cla()
sns.distplot(df1.bill_length_mm, ax=axs.ax_marg_x, fit=norm)
sns.distplot(df1.bill_depth_mm, ax=axs.ax_marg_y, vertical=True, fit=norm)