如何在 Plotly 中制作多面图有自己的 YAxes 刻度标签？

Question

当我使用 Plotly express 绘制具有不同范围的不同参数时 - 在下面的示例中，BloodPressureHigh、Height(cm)、Weight(kg) 和 BloodPressureLow - 使用 facet_col 参数，我无法得到结果plot 以显示每个多面图的唯一 YTicks。 fig 对象是否有一种简单的方法可以在生成的多面图中显示每组 YTicks？否则，正如您在生成的图像中看到的那样，不清楚每个箱线图是否在其自己独特的 Y 轴上。

import plotly.express as px
import pandas as pd

temp = [
    {"Clinic": "A", "Subject": "Bill", "Height(cm)": 182, "Weight(kg)": 101, "BloodPressureHigh": 128, "BloodPressureLow": 90},
    {"Clinic": "A", "Subject": "Susie", "Height(cm)": 142, "Weight(kg)": 67, "BloodPressureHigh": 120, "BloodPressureLow": 70},
    {"Clinic": "B", "Subject": "John", "Height(cm)": 202, "Weight(kg)": 89, "BloodPressureHigh": 118, "BloodPressureLow": 85},
    {"Clinic": "B", "Subject": "Stacy", "Height(cm)": 156, "Weight(kg)": 78, "BloodPressureHigh": 114, "BloodPressureLow": 76},
    {"Clinic": "B", "Subject": "Lisa", "Height(cm)": 164, "Weight(kg)": 59, "BloodPressureHigh": 112, "BloodPressureLow": 74} 
]
df = pd.DataFrame(temp)

# Melt the dataframe so I can use plotly express to plot distributions of all variables
df_melted = df.melt(id_vars=["Clinic", "Subject"])
# Plot distributions, with different parameters in different columns
fig = px.box(df_melted, x="Clinic", y="value", 
       facet_col="variable", boxmode="overlay"
)
# Update the YAxes so that the faceted column plots no longer share common YLimits
fig.update_yaxes(matches=None)
# Last step needed: Add tick labels to each yaxis so that the difference in YLimits is clear?

Answer 1

这对你有帮助吗？

fig = px.box(df_melted, x="Clinic", y="value", 
             facet_col="variable", boxmode="overlay")

fig.update_yaxes(matches=None)
fig.for_each_yaxis(lambda yaxis: yaxis.update(showticklabels=True))
    
fig.show()