- Using
FacetGrid directly is not recommended. Instead, use other figure-level methods like seaborn.displot
- Tested in
python 3.8.11, pandas 1.3.2, matplotlib 3.4.3, seaborn 0.11.2
Option 1
- Use
plt. instead of ax.
- In the OP, the
vlines are going to ax for the histplot, but here, the figure is created before .map.
penguins = sns.load_dataset("penguins")
g = sns.displot(
data=penguins, x='body_mass_g',
col="species",
facet_kws=dict(sharey=False, sharex=False)
)
def specs(x, **kwargs):
plt.axvline(x.mean(), c="k", ls="-", lw=2.5)
plt.axvline(x.median(), c="orange", ls="--", lw=2.5)
g.map(specs,'body_mass_g' )
Option 2
- This option is more verbose, but more flexible in that it allows for accessing and adding information from a data source other than the one used to create the
displot.
import seaborn as sns
import pandas as pd
# load the data
pen = sns.load_dataset("penguins")
# groupby to get mean and median
pen_g = pen.groupby('species').body_mass_g.agg(['mean', 'median'])
g = sns.displot(
data=pen, x='body_mass_g',
col="species",
facet_kws=dict(sharey=False, sharex=False)
)
# extract and flatten the axes from the figure
axes = g.axes.flatten()
# iterate through each axes
for ax in axes:
# extract the species name
spec = ax.get_title().split(' = ')[1]
# select the data for the species
data = pen_g.loc[spec, :]
# print data as needed or comment out
print(data)
# plot the lines
ax.axvline(x=data['mean'], c="k", ls="-", lw=2.5)
ax.axvline(x=data['median'], c="orange", ls="--", lw=2.5)
Output for both options
Resources
- Also see the following questions/answers for other ways to add information to a seaborn FacetGrid
