[英]Curve the Kernel Density Estimate (KDE) in seaborn displot
When I try to plot my data in the form of histogram using seaborn displot:当我尝试使用 seaborn 显示直方图形式的 plot 数据时:
plot = sns.displot(
data=z, kde=True, kind="hist", bins=3000, legend=True, aspect=1.8
).set(title='Error Distribution')
The curve for KDE is plotted in the form of straight lines instead of curves like here: KDE 的曲线以直线的形式绘制,而不是像这里这样的曲线: Is there a way to make the KDE lines cover all the bins of the histogram in a curved manner?有没有办法让 KDE 线以弯曲的方式覆盖直方图的所有 bin?
Instead of zooming in, you could use the bins to restrict to a certain range (via binrange=...
).您可以使用 bin 将其限制在某个范围内(通过binrange=...
),而不是放大。 To limit the range of the kde, you can use the clip
keyword.要限制 kde 的范围,可以使用clip
关键字。 Here is an example, first without setting the range:这是一个示例,首先没有设置范围:
from matplotlib import pyplot as plt
import seaborn as sns
import pandas as pd
import numpy as np
# first, create some test data
slatm = np.random.normal(-.9, .4, size=(10000, 10)).max(axis=1)
split = np.random.normal(-.1, .1, size=(10000, 10)).max(axis=1)
split[0] = 200 # ad an extreme far value to the dataset
z = pd.DataFrame({'slatm': slatm, 'split': split})
g = sns.displot(data=z, kde=True, kind="hist", bins=3000, legend=True, aspect=1.8)
g.set(title='Error Distribution')
g.ax.set_xlim(-1, 0.5) # zoom in via the x limits
Here is how it would look with limiting the ranges for the histogram and the kde:以下是限制直方图和 kde 范围的情况:
min_x, max_x = -1, 0.5
g = sns.displot(data=z, kde=True, kind="hist", bins=30, binrange=(min_x, max_x), legend=True, aspect=1.8,
kde_kws={'clip': (min_x, max_x)})
g.set(title='Error Distribution')
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