how to make histogram for multivariate data in python seaborn?

I have a following data I would like to have a histogram like the following but I could not do it using python. Can anyone please help me how to do it in python?

Group	Summer	Winter	Autumn	Spring
bacteria	20	30	40	20
virus	30	50	20	20
fungi	50	20	40	60

Solution

You can transform the dataframe to long form, and then call sns.histplot() with weights=... and multiple='stack'.

from matplotlib import pyplot as plt
import seaborn as sns
import pandas as pd
import numpy as np
from io import StringIO

data_str = '''Group Summer  Winter  Autumn  Spring
bacteria    20  30  40  20
virus   30  50  20  20
fungi   50  20  40  60'''
df = pd.read_csv(StringIO(data_str), delim_whitespace=True)
df_long = df.melt(id_vars='Group', var_name='Season')
hue_order = ['fungi', 'virus', 'bacteria']
sns.set()
ax = sns.histplot(data=df_long, x='Season', hue='Group', hue_order=hue_order,
                  weights='value', multiple='stack',
                  palette=['orange', 'gold', 'tomato'])
ax.legend(handles=ax.legend_.legendHandles, labels=hue_order, bbox_to_anchor=(1.02, 0.98), loc='upper left')
ax.set_ylabel('Percentage')
plt.tight_layout()
plt.show()

PS: As mentioned in the comments, multiple='fill' is another option. In that case, the bars for each x-value get stretched to fill the total height. This would especially be interesting when the values in the dataframe would be counts (instead of percentages, such as seems to be the case with the example data).

The code could then look like:

from matplotlib.ticker import PercentFormatter

# ... similar data preparation as in the other example

ax = sns.histplot(data=df_long, x='Season', hue='Group', hue_order=hue_order,
                  weights='value', multiple='fill',
                  palette=['orange', 'gold', 'tomato'])
ax.set_ylabel('Percentage')
ax.yaxis.set_major_formatter(PercentFormatter(1))
plt.tight_layout()
plt.show()