1. matplotlib实现

1.1 效果

效果：（二维正态分布的散点图+每个轴的直方图）
在这里插入图片描述

1.2 代码

import numpy as np
import matplotlib.pyplot as plt

# <https://blog.csdn.net/Castlehe/article/details/127555925>
# 6. 正态(normal)/高斯(gaussian)分布distribution 3σ原则
# μ-σ~μ+σ=0.6826，（0-1~0+1   点落在-1~1之间的概率是68.26%）
# μ-2σ~μ+2σ=0.9544    (点落在-2~2之间的概率是95.44%）
# μ-3σ~μ+3σ=0.9974    (点落在-3~3之间的概率是99.74%）

x = np.random.randn(2000)
y = np.random.randn(2000)

def scatter_hist(x, y, ax, ax_histx, ax_histy):
    # no labels
    ax_histx.tick_params(axis="x", labelbottom=False)
    ax_histy.tick_params(axis="y", labelleft=False)
	
	# (0,0) center
    ax.spines['left'].set_position('center')
    ax.spines['right'].set_color('none')
    ax.spines['bottom'].set_position('center')
    ax.spines['top'].set_color('none')
    ax.xaxis.set_ticks_position('bottom')
    ax.yaxis.set_ticks_position('left')
    # x, y scale equal
    ax.set_aspect('equal', adjustable='box')
	
	# scatter plot
    ax.scatter(x, y)

    # now determine nice limits by hand:
    binwidth = 0.25
    xymax = max(np.max(np.abs(x)), np.max(np.abs(y)))
    lim = (int(xymax/binwidth) + 1) * binwidth
	
	# hist plot
    bins = np.arange(-lim, lim + binwidth, binwidth)
    ax_histx.hist(x, bins=bins, color="tab:cyan")
    ax_histy.hist(y, bins=bins, orientation='horizontal', color="y")


fig = plt.figure(figsize=(6, 6))
# fig.add_gridspec 
gs = fig.add_gridspec(2, 2,  width_ratios=(4, 1), height_ratios=(1, 4),
                      left=0.1, right=0.9, bottom=0.1, top=0.9,
                      wspace=0.05, hspace=0.05)
ax = fig.add_subplot(gs[1, 0])
ax_histx = fig.add_subplot(gs[0, 0], sharex=ax)
ax_histy = fig.add_subplot(gs[1, 1], sharey=ax)
scatter_hist(x, y, ax, ax_histx, ax_histy)
plt.show()

参考：Scatter plot with histograms

2. seaborn实现

2.1 效果

在这里插入图片描述

2.2 代码

import seaborn as sns
import numpy as np
import matplotlib.pyplot as plt
x = np.random.randn(2000)
y = np.random.randn(2000)

sns.jointplot(x=x, y=y)
plt.show()