Recalculate and visualize percentile lines in each subplot [duplicate]

Solution 1:

• Using FacetGrid directly is deprecated. Instead, use other figure-level methods like seaborn.displot
  • seaborn.FacetGrid.map works with figure-level methods.
  • seaborn: Building structured multi-plot grids
• 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
  • Draw a line at specific position/annotate a Facetgrid in seaborn
  • Overlay a vertical line on seaborn scatterplot with multiple subplots
  • How to add additional plots to a seaborn FacetGrid and specify colors

Solution 2:

Here you can use sns.FacetGrid.facet_data to iterate the indexes of the subplots and the underlying data.

This is close to how sns.FacetGrid.map works under the hood. sns.FacetGrid.facet_data is a generator that yields a tuple (i, j, k) of row, col, hue index and the data which is a DataFrame that is a subset of the full data corresponding to each facet.

import seaborn as sns
import pandas as pd


pen = sns.load_dataset("penguins")

# Set our x_var for later use
x_var = "body_mass_g"

g = sns.displot(
    data=pen,
    x=x_var,
    col="species",
    facet_kws=dict(sharey=False, sharex=False),
)

for (row, col, hue_idx), data in g.facet_data():
    # Skip empty data
    if not data.values.size:
        continue

    # Get the ax for `row` and `col`
    ax = g.facet_axis(row, col)
    # Set the `vline`s using the var `x_var`
    ax.axvline(data[x_var].mean(), c="k", ls="-", lw=2.5)
    ax.axvline(data[x_var].median(), c="orange", ls="--", lw=2.5)

Which outputs: