第二个 y 轴和垂直线
Second y axis and vertical line
我正在使用以下代码创建小提琴图:
import seaborn as sns
ax = sns.violinplot(data=df[['SoundProduction','SoundForecast','diff']])
ax.set_ylabel("Sound power level [dB(A)]")
它给了我以下结果:
有什么方法可以在第二个 y 轴上绘制 diff
以便所有三个系列都清晰可见?
此外,有没有办法在两个系列之间画一条垂直线?在这种情况下,一旦它们被绘制在两个不同的轴上,我想要在 SoundForecast
和 diff
之间有一条垂直线。
您可以使用多个子图实现此目的,使用 plt.subplots (see lots more subplot examples).
可以轻松设置这些子图
这允许您以适当的比例显示分布,而不是 "waste" 显示 space。 seaborn 的大多数(全部?)绘图函数都接受 ax=
参数,因此您可以设置绘制绘图的轴。轴之间也有明显的分隔。
import seaborn as sns
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
# generate some random distribution data
n = 800 # samples
prod = 95 + 5 * np.random.beta(0.6, 0.5, size=n); # a bimodal distribution
forecast = prod + 3*np.random.randn(n) # forecast is noisy estimate around the "true" production
diff = prod-forecast # should be with mu 0 sigma 3
df = pd.DataFrame(np.array([prod, forecast, diff]).T, columns=['SoundProduction','SoundForecast','diff']);
# set up two subplots, with one wider than the other
fig, ax = plt.subplots(1,2, num=1, gridspec_kw={'width_ratios':[2,1]})
# plot violin distribution estimates separately so the y-scaling makes sense in each group
sns.violinplot(data=df[['SoundProduction','SoundForecast']], ax=ax[0])
sns.violinplot(data=df[['diff']], ax=ax[1])
我正在使用以下代码创建小提琴图:
import seaborn as sns
ax = sns.violinplot(data=df[['SoundProduction','SoundForecast','diff']])
ax.set_ylabel("Sound power level [dB(A)]")
它给了我以下结果:
有什么方法可以在第二个 y 轴上绘制 diff
以便所有三个系列都清晰可见?
此外,有没有办法在两个系列之间画一条垂直线?在这种情况下,一旦它们被绘制在两个不同的轴上,我想要在 SoundForecast
和 diff
之间有一条垂直线。
您可以使用多个子图实现此目的,使用 plt.subplots (see lots more subplot examples).
可以轻松设置这些子图这允许您以适当的比例显示分布,而不是 "waste" 显示 space。 seaborn 的大多数(全部?)绘图函数都接受 ax=
参数,因此您可以设置绘制绘图的轴。轴之间也有明显的分隔。
import seaborn as sns
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
# generate some random distribution data
n = 800 # samples
prod = 95 + 5 * np.random.beta(0.6, 0.5, size=n); # a bimodal distribution
forecast = prod + 3*np.random.randn(n) # forecast is noisy estimate around the "true" production
diff = prod-forecast # should be with mu 0 sigma 3
df = pd.DataFrame(np.array([prod, forecast, diff]).T, columns=['SoundProduction','SoundForecast','diff']);
# set up two subplots, with one wider than the other
fig, ax = plt.subplots(1,2, num=1, gridspec_kw={'width_ratios':[2,1]})
# plot violin distribution estimates separately so the y-scaling makes sense in each group
sns.violinplot(data=df[['SoundProduction','SoundForecast']], ax=ax[0])
sns.violinplot(data=df[['diff']], ax=ax[1])