Annotate markers values on Seaborn line plot (sns)

Question

is there a way to annotate the markers value on a Seaborn line plot?

This is my actual line plot:

a4_dims = (20, 10)
fig, ax = plt.subplots(figsize=a4_dims)
p1 = sns.lineplot(x='NBags', y='value', hue='variable', style="variable", markers=True, dashes=False, 
             data=pd.melt(df_knn1, ['NBags']))

And This is what I want:

Where the numeric values on each marker is the value of the marker itself on the line.

Solution

Yes, it is possible with ax.text(...), by default the annotated text color is black. If you want to color group the labels, then a possible way is to do a groupby ax.text(...) loop (with your predefined color palette) as follows,

Import libraries and create a sample data frame

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

np.random.seed(12345)
x = np.random.rand(15,3)
y = np.random.binomial(2, 0.5, (15,1))
z = np.concatenate((x,y),axis=1)

df = pd.DataFrame(z,columns=['x','y','mark_value','label'])
df['label'] = df['label'].astype(int)
print(df)
#            x         y  mark_value  label
# 0   0.929616  0.316376    0.183919      1
# 1   0.204560  0.567725    0.595545      1
# 2   0.964515  0.653177    0.748907      1
# 3   0.653570  0.747715    0.961307      1
# 4   0.008388  0.106444    0.298704      0
# 5   0.656411  0.809813    0.872176      1
# 6   0.964648  0.723685    0.642475      0
# 7   0.717454  0.467599    0.325585      2
# 8   0.439645  0.729689    0.994015      2
# 9   0.676874  0.790823    0.170914      1
# 10  0.026849  0.800370    0.903723      0
# 11  0.024676  0.491747    0.526255      1
# 12  0.596366  0.051958    0.895090      1
# 13  0.728266  0.818350    0.500223      2
# 14  0.810189  0.095969    0.218950      2

Code for plotting

a4_dims = (20, 10)
fig, ax = plt.subplots(figsize=a4_dims)

palette = ['r','b','g']

p1 = sns.lineplot(x='x', y='y', hue='label', style='label', markers=True, dashes=False, 
             data=df, palette=palette)

for item, color in zip(df.groupby('label'),palette):
    #item[1] is a grouped data frame
    for x,y,m in item[1][['x','y','mark_value']].values:
        ax.text(x,y,f'{m:.2f}',color=color)

Output