如何使用 sns.boxplot 或 sns.catplot 添加剖面线到箱线图

I need to add hatches to a categorical box plot. What I have is this:

我需要的是这样的（带有中线）：

我尝试过的是这段代码：

exercise = sns.load_dataset("exercise")
g = sns.catplot(x="time", y="pulse", hue="kind", data=exercise, kind="box")
bars = g.axes[0][0].patches
hatches=['//','..','xx','//','..','xx','//','..','xx']
for pat,bar in zip(hatches,bars):
    bar.set_hatch(pat)

这只会生成第一个数字。第 3-6 行的想法来自这个问题 https://stackoverflow.com/questions/64340226/plot-a-stacked-bar-plot-in-seaborn-with-hatching。但想法得到axes[0][0]第 3 行来自这个问题 https://stackoverflow.com/questions/59250699/how-to-add-percentages-on-top-of-bars-in-seaborn-facetgrid.

因为 FacetGrid 没有补丁或容器等属性，所以很难将单个图中的影线答案适应分类图，所以我无法弄清楚。

其他无效的审查问题：

• 箱线图中方框的面图案 https://stackoverflow.com/q/28740234/7758804

1. 迭代每个子图/FacetGridfor ax in g.axes.flat:.
2. ax.patches包含matplotlib.patches.Rectangle and matplotlib.patches.PathPatch，因此必须使用正确的。

• Caveat: all hues must appear for each group in each Facet, otherwise the patches and hatches will not match.
  • 在这种情况下，可能需要手动或条件代码才能正确确定h, so zip(patches, h) works.
• 测试于python 3.10, pandas 1.4.2, matplotlib 3.5.1, seaborn 0.11.2

import matplotlib as mpl
import seaborn as sns

# load test data
exercise = sns.load_dataset("exercise")

# plot
g = sns.catplot(x="time", y="pulse", hue="kind", data=exercise, col='diet', kind="box")

# hatches must equal the number of hues (3 in this case)
hatches = ['//', '..', 'xx']

# iterate through each subplot / Facet
for ax in g.axes.flat:

    # select the correct patches
    patches = [patch for patch in ax.patches if type(patch) == mpl.patches.PathPatch]
    # the number of patches should be evenly divisible by the number of hatches
    h = hatches * (len(patches) // len(hatches))
    # iterate through the patches for each subplot
    for patch, hatch in zip(patches, h):
        patch.set_hatch(hatch)
        fc = patch.get_facecolor()
        patch.set_edgecolor(fc)
        patch.set_facecolor('none')

• 添加以下内容以更改图例。

for lp, hatch in zip(g.legend.get_patches(), hatches):
    lp.set_hatch(hatch)
    fc = lp.get_facecolor()
    lp.set_edgecolor(fc)
    lp.set_facecolor('none')

• 如果仅使用轴级别sns.boxplot，无需迭代多个轴。

ax = sns.boxplot(x="time", y="pulse", hue="kind", data=exercise)

# select the correct patches
patches = [patch for patch in ax.patches if type(patch) == mpl.patches.PathPatch]
# the number of patches should be evenly divisible by the number of hatches
h = hatches * (len(patches) // len(hatches))
# iterate through the patches for each subplot
for patch, hatch in zip(patches, h):
    patch.set_hatch(hatch)
    fc = patch.get_facecolor()
    patch.set_edgecolor(fc)
    patch.set_facecolor('none')

l = ax.legend()
    
for lp, hatch in zip(l.get_patches(), hatches):
    lp.set_hatch(hatch)
    fc = lp.get_facecolor()
    lp.set_edgecolor(fc)
    lp.set_facecolor('none')

• To keep the facecolor of the box plots:
  1. Remove patch.set_facecolor('none')
  2. 将边缘颜色设置为'k'（黑色）代替fc, patch.set_edgecolor('k').
  • 适用于sns.catplot代码也。

ax = sns.boxplot(x="time", y="pulse", hue="kind", data=exercise)

# select the correct patches
patches = [patch for patch in ax.patches if type(patch) == mpl.patches.PathPatch]
# the number of patches should be evenly divisible by the number of hatches
h = hatches * (len(patches) // len(hatches))
# iterate through the patches for each subplot
for patch, hatch in zip(patches, h):
    patch.set_hatch(hatch)
    patch.set_edgecolor('k')
    
l = ax.legend()
    
for lp, hatch in zip(l.get_patches(), hatches):
    lp.set_hatch(hatch)
    lp.set_edgecolor('k')