How can I add hatching for specific bars in sns.catplot?

I use seaborn to make a categorical barplot of a df containing Pearson correlation R-values for 17 vegetation classes, 3 carbon species and 4 regions. I try to recreate a smaller sample df here:

import pandas as pd
import seaborn as sns
import random
import numpy as np


df = pd.DataFrame({

    'veg class':12*['Tree bl dc','Shrubland','Grassland'],
    'Pearson R':np.random.uniform(0,1, 36),
    'Pearson p':np.random.uniform(0,0.1, 36),
    'carbon':4*['CO2','CO2','CO2', 'CO', 'CO', 'CO', 'CO2 corr', 'CO2 corr', 'CO2 corr'],
    'spatial':9*['SH'] + 9*['larger AU region'] + 9*['AU'] + 9*['SE-AU'] 

    })

#In my original df, the number of vegetation classes where R-values are
#available is not the same for all spatial scales, so I drop random rows
#to make it more similar:
df.drop([11,14,17,20,23,26,28,29,31,32,34,35], inplace=True)

#I added colums indicating where hatching should be
#boolean:
df['significant'] = 1
df.loc[df['Pearson p'] > 0.05, 'significant'] = 0
#string:
df['hatch'] = ''
df.loc[df['Pearson p'] > 0.05, 'hatch'] = 'x'

df.head()

This is my plotting routine:

sns.set(font_scale=2.1)

#Draw a nested barplot by veg class
g = sns.catplot(
data=df, kind="bar", row="spatial",
x="veg class", y="Pearson R", hue="carbon",
    ci=None, palette="YlOrBr", aspect=5
    )

g.despine(left=True)
g.set_titles("{row_name}")
g.set_axis_labels("", "Pearson R")
g.set(xlabel=None)
g.legend.set_title("")
g.set_xticklabels(rotation = 60)

(The plot looks as follows: seaborn categorical barplot)

The plot is exactly how I would like it, except that now I would like to add hatching (or any kind of distinction) for all bars where the Pearson R value is insignificant, i.e. where the p value is larger than 0.05. I found this stackoverflow entry, but my problem differs from this, as the plots that should be hatched are not in repetitive order.

Any hints will be highly appreciated!

Answer

To determine the height of individual bars and hatching, we get a container for each graph unit, get the height of that individual container, determine it with a specified threshold, and then set the hatching and color. Please add the following code at the end.

for ax in g.axes.flat:
    for k in range(len(ax.containers)):
        h = ax.patches[k].get_height()
        if h >= 0.8:
            ax.patches[k].set_hatch('*')
            ax.patches[k].set_edgecolor('k')

Edit: The data has been updated to match the actual data, and the code has been modified accordingly. Also, the logic is conditional on the value of the hatching column.

for i,ax in enumerate(g.axes.flat):
    s = ax.get_title()
    dff = df.query('spatial == @s')
    dff = dff.sort_values('veg class', ascending=False)
    ha = dff['hatch'].tolist()
    p = dff['Pearson R'].tolist()
    print(ha)
    for k in range(len(dff)):
        if ha[k] == 'x':
            ax.patches[k].set_hatch('*')
            ax.patches[k].set_edgecolor('k')