How to stack only one bar in sns countplot

Solution for How to stack only one bar in sns countplot
is Given Below:

Perhaps some of you can help me with the following graph…

I have a dataframe containing the survey data of people who traveled during the last year (Yes, No) and if Yes which transporation they used (Airplane, Car, Train)

import pandas as pd
import numpy as np
data = {'Travel':  ['Yes', 'No', 'Yes', 'Yes', 'Yes', 'Yes', 'No'],
    'Transporation': ['Airplaine', np.nan, 'Car', 'Train', 'Train', 'Car', 'np.nan']
    }
df = pd.DataFrame (data, columns = ['Travel','Transporation'])

  Travel Transporation
0    Yes     Airplaine
1     No           NaN
2    Yes           Car
3    Yes         Train
4    Yes         Train
5    Yes           Car
6     No           NaN

I plot the countplot of the first question and add the relative percentage of respondents who answered Yes and No.

import seaborn as sns

ax = sns.countplot(y='Travel', data=df, palette=['green',"red"])
ax.set_yticklabels(ax.get_yticklabels(), rotation=45)
ax.set_title('Travel last year')
ax.set_ylabel('')
total = df.shape[0]
for p in ax.patches:
    percentage="{:.1f}%".format(100 * p.get_width()/total)
    x = p.get_x() + p.get_width()# / 2 - 0.05
    y = p.get_y() + p.get_height() / 2 - 0.05
    ax.annotate(percentage, (x, y), size = 12)
plt.show()

enter image description here

In the same graph, I would like to make the bar indicating the Yes a stacked bar indicating which transportation the people who answered yes used.
The final graph should be something like this:

enter image description here

The simpliest way I know is to group the pandas dataframe as:

df_plot = df.fillna('_Hidden').replace('np.nan', '_Hidden').groupby(['Travel', 'Transporation']).size().reset_index().pivot(columns="Transporation", index = 'Travel', values = 0)

Then you can plot with:

ax = df_plot.plot(kind = 'barh', stacked = True)

Finally you can add the percentages:

total = df.shape[0]
yes = len(df[df['Travel'] == 'Yes'])/total
no = len(df[df['Travel'] == 'No'])/total
for p in ax.patches:
    width, height = p.get_width(), p.get_height()
    x, y = p.get_xy()
    x = x + width
    y = y + height / 2 - 0.05

    if x/total == yes:
        ax.annotate(f'{round(100*yes, 1)}%', (x, y), size = 12)
    if x/total == no:
        ax.annotate(f'{round(100*no, 1)}%', (x, y), size = 12)

    if width != 0:
        x, y = p.get_xy()
        if y > 0:
            ax.text(x + width/2,
                    y + height/2,
                    '{:.0f} %'.format(100*width/(yes*total)),
                    horizontalalignment="center",
                    verticalalignment="center")

Complete Code

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt

data = {'Travel': ['Yes', 'No', 'Yes', 'Yes', 'Yes', 'Yes', 'No'],
        'Transporation': ['Airplaine', np.nan, 'Car', 'Train', 'Train', 'Car', 'np.nan']}
df = pd.DataFrame (data, columns = ['Travel','Transporation'])

df_plot = df.fillna('_Hidden').replace('np.nan', '_Hidden').groupby(['Travel', 'Transporation']).size().reset_index().pivot(columns="Transporation", index = 'Travel', values = 0)
ax = df_plot.plot(kind = 'barh', stacked = True)
ax.legend(['Airplaine', 'Car', 'Train'])
ax.set_yticklabels(ax.get_yticklabels(), rotation = 45)
ax.set_title('Travel last year')
ax.set_ylabel('')

total = df.shape[0]
yes = len(df[df['Travel'] == 'Yes'])/total
no = len(df[df['Travel'] == 'No'])/total
for p in ax.patches:
    width, height = p.get_width(), p.get_height()
    x, y = p.get_xy()
    x = x + width
    y = y + height / 2 - 0.05

    if x/total == yes:
        ax.annotate(f'{round(100*yes, 1)}%', (x, y), size = 12)
    if x/total == no:
        ax.annotate(f'{round(100*no, 1)}%', (x, y), size = 12)

    if width != 0:
        x, y = p.get_xy()
        if y > 0:
            ax.text(x + width/2,
                    y + height/2,
                    '{:.0f} %'.format(100*width/(yes*total)),
                    horizontalalignment="center",
                    verticalalignment="center")

plt.show()

enter image description here