I was wondering if it is possible to create a Seaborn count plot, but instead of actual counts on the y-axis, show the relative frequency (percentage) within its group (as specified with the hue parameter).

I sort of fixed this with the following approach, but I can't imagine this is the easiest approach:

# Plot percentage of occupation per income class
grouped = df.groupby(['income'], sort=False)
occupation_counts = grouped['occupation'].value_counts(normalize=True, sort=False)

occupation_data = [
    {'occupation': occupation, 'income': income, 'percentage': percentage*100} for 
    (income, occupation), percentage in dict(occupation_counts).items()
]

df_occupation = pd.DataFrame(occupation_data)

p = sns.barplot(x="occupation", y="percentage", hue="income", data=df_occupation)
_ = plt.setp(p.get_xticklabels(), rotation=90)  # Rotate labels

Result:

I'm using the well known adult data set from the UCI machine learning repository. The pandas dataframe is created like this:

# Read the adult dataset
df = pd.read_csv(
    "data/adult.data",
    engine='c',
    lineterminator='\n',

    names=['age', 'workclass', 'fnlwgt', 'education', 'education_num',
           'marital_status', 'occupation', 'relationship', 'race', 'sex',
           'capital_gain', 'capital_loss', 'hours_per_week',
           'native_country', 'income'],
    header=None,
    skipinitialspace=True,
    na_values="?"
)

This question is sort of related, but does not make use of the hue parameter. And in my case I cannot just change the labels on the y-axis, because the height of the bar must depend on the group.

With newer versions of seaborn you can do following:

import numpy as np
import pandas as pd
import seaborn as sns
sns.set(color_codes=True)

df = sns.load_dataset('titanic')
df.head()

x,y = 'class', 'survived'

(df
.groupby(x)[y]
.value_counts(normalize=True)
.mul(100)
.rename('percent')
.reset_index()
.pipe((sns.catplot,'data'), x=x,y='percent',hue=y,kind='bar'))

output

Update: Also show percentages on top of barplots

If you also want percentages, you can do following:

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

df = sns.load_dataset('titanic')
df.head()

x,y = 'class', 'survived'

df1 = df.groupby(x)[y].value_counts(normalize=True)
df1 = df1.mul(100)
df1 = df1.rename('percent').reset_index()

g = sns.catplot(x=x,y='percent',hue=y,kind='bar',data=df1)
g.ax.set_ylim(0,100)

for p in g.ax.patches:
    txt = str(p.get_height().round(2)) + '%'
    txt_x = p.get_x() 
    txt_y = p.get_height()
    g.ax.text(txt_x,txt_y,txt)