Box-Plot with outlier jitter

What you see in that picture is a workaround for what I really would like to have. When searching the web you often got the combine-boxplot-with-swarmplot-solution. It would IMHO improve seaborn if this could be done via seaborn without a workaround.

The problems with that example are

1. The outliers are drawn twice (green and red circles). Only draw the jittered outliers (the green ones).
2. The none-outliers are also drawn. There is no need for them.

This is an MWE to reproduce that picture.

#!/usr/bin/env python3
import random
import pandas
import seaborn as sns
import matplotlib.pyplot as plt
sns.set_theme()

random.seed(0)

df = pandas.DataFrame({
    'Vals': random.choices(range(200), k=200)})
df_outliers = pandas.DataFrame({
    'Vals': random.choices(range(400, 700), k=20)})

df = pandas.concat([df, df_outliers], axis=0)

flierprops = {
    'marker': 'o',
    'markeredgecolor': 'red',
    'markerfacecolor': 'none'
}

# Usual boxplot
ax = sns.boxplot(y='Vals', data=df, flierprops=flierprops)

# Add jitter with the swarmplot function
ax = sns.swarmplot(y='Vals', data=df, linewidth=.75, color='none', edgecolor='green')
plt.show()

[jhncls]

Here is a hacky way to work with a swarmplot instead of a stripplot for the outliers:

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

sns.set_theme()

df = pd.DataFrame({'Vals': np.concatenate([np.random.randint(0, 200, size=1000),
                                           np.random.randint(400, 700, size=100),
                                           np.arange(600, 620)])})
df['x'] = np.random.randint(0, 3, len(df))

ax = sns.boxplot(x='x', y='Vals', data=df, orient='v')

xpos = np.array([])
ypos = np.array([])
for line in ax.lines:
     if line.get_linestyle() == 'None':
          xpos = np.append(xpos, line.get_xdata())
          ypos = np.append(ypos, line.get_ydata())
          line.remove()
sns.swarmplot(x=xpos, y=ypos, ax=ax, color='red', orient='v')

plt.tight_layout()
plt.show()

[mwaskom]

Yes, you’d also need a stat transform that filters to/out outliers. (And a swarm mark since that’s apparently what’s actually desired here, not jitter).

Boxplots are annoying in that they’re a “standard” plot type but they’re actually quite complicated to make and open the door to all sorts of API complexity.