如何在seaborn分布图中填充曲线下面积

时间:2017-12-05 02:46:17

标签: python python-3.x matplotlib plot seaborn

我有两个变量

x = [1.883830, 7.692308,8.791209, 9.262166]
y = [5.337520, 4.866562, 2.825746, 6.122449]

我想使用包裹在matplotlib中的seaborn来拟合高斯分布。似乎sns.distplot函数是执行此操作的最佳方法,但我无法弄清楚如何填充曲线下方的区域。帮助

fig, ax = plt.subplots(1)
sns.distplot(x,kde_kws={"shade":True}, kde=False, fit=stats.gamma, hist=None, color="red", label="2016", fit_kws={'color':'red'});
sns.distplot(y,kde_kws={"shade":True}, kde=False, fit=stats.gamma, hist=None, color="blue", label="2017", fit_kws={'color':'blue'})

我认为"阴影"论证可能是fit_kws论证的一部分,但我还没有得到这个。

另一种选择是使用ax.fill()

1 个答案:

答案 0 :(得分:7)

是的,与shade不同,fit_kws不支持kde_kws参数。但正如您猜测的那样,我们可以使用ax.fill_between()填充两条曲线下的区域。我们必须从ax对象及其x-y数据中获取线,然后使用它来填充曲线下的区域。这是一个例子。

import numpy as np
import seaborn as sns
import scipy.stats as stats
import matplotlib.pyplot as plt

x = [1.883830, 7.692308,8.791209, 9.262166]
y = [5.337520, 4.866562, 2.825746, 6.122449]
ax = sns.distplot(x, fit_kws={"color":"red"}, kde=False,
        fit=stats.gamma, hist=None, label="label 1");
ax = sns.distplot(y, fit_kws={"color":"blue"}, kde=False,
        fit=stats.gamma, hist=None, label="label 2");

# Get the two lines from the axes to generate shading
l1 = ax.lines[0]
l2 = ax.lines[1]

# Get the xy data from the lines so that we can shade
x1 = l1.get_xydata()[:,0]
y1 = l1.get_xydata()[:,1]
x2 = l2.get_xydata()[:,0]
y2 = l2.get_xydata()[:,1]
ax.fill_between(x1,y1, color="red", alpha=0.3)
ax.fill_between(x2,y2, color="blue", alpha=0.3)

plt.show(block=False)

结果如下所示: enter image description here