Resolved: Ordering a stacked histplot based on total counts

df_grouped = df.groupby(['A', 'B'])['A'].count().sort_values(ascending=False)
df_grouped = pd.DataFrame(df_grouped)
new_index = pd.MultiIndex.from_tuples(df_grouped.index)
df_grouped.index = new_index
df_grouped.reset_index(inplace=True)
df_grouped.columns = ['A', 'B', 'count']

fig, ax = plt.subplots(1, 1, figsize=(10,5))
sns.histplot(x='A', hue='B', data=df_grouped, ax=ax, multiple='stack', weights='count')

df['total_count'] = df.groupby(by='A')['count'].transform('sum')
df = df.sort_values(by=['total_count'], ascending=False)

import numpy as np  # used for creating sample data
import pandas as pd

# sample dataframe representing raw data
np.random.seed(365)
rows = 1100
data = {'A': np.random.choice([f'A_{v}' for v in range(1, 6)], size=rows, p=[.35, .05, .25, .15, .2]),
        'B': np.random.choice([f'B_{v}' for v in range(1, 6)], size=rows, p=[.2, .35, .05, .15, .25])}
df = pd.DataFrame(data)

# 1. frequency counts
dfc = pd.crosstab(df.A, df.B)

# 2. add total column
dfc['tot_A'] = dfc.sum(axis=1)

# 3. sort
dfc = dfc.sort_values('tot_A', axis=0, ascending=False)

# 4. plot the columns except `tot_A`
dfc.iloc[:, :-1].plot(kind='bar', stacked=True, figsize=(10, 5), rot=0, width=1, ec='k')

     A    B
0  A_5  B_5
1  A_3  B_1
2  A_4  B_5
3  A_3  B_4
4  A_3  B_5

B    B_1  B_2  B_3  B_4  B_5  tot_A
A                                  
A_1   86  131   15   55   90    377
A_3   47   90    9   33   61    240
A_5   37   83   13   33   56    222
A_4   43   65    9   27   50    194
A_2   16   21    1    5   24     67