Show correlation values in pairplot using seaborn in python

Question

I have the below data:

prop_tenure  prop_12m  prop_6m  
0.00         0.00      0.00   
0.00         0.00      0.00   
0.06         0.06      0.10   
0.38         0.38      0.25   
0.61         0.61      0.66   
0.01         0.01      0.02   
0.10         0.10      0.12   
0.04         0.04      0.04   
0.22         0.22      0.22 

and I am doing a pairplot as below:

sns.pairplot(data)
plt.show()

However I would like to display the correlation coefficient among the variables and if possible the skewness and kurtosis of each variable. How do you do that in seaborn?

Answer 1

As far as I'm aware, there is no out of the box function to do this, you'll have to create your own:

from scipy.stats import pearsonr
import matplotlib.pyplot as plt 

def corrfunc(x, y, ax=None, **kws):
    """Plot the correlation coefficient in the top left hand corner of a plot."""
    r, _ = pearsonr(x, y)
    ax = ax or plt.gca()
    ax.annotate(f'ρ = {r:.2f}', xy=(.1, .9), xycoords=ax.transAxes)

Example using your input:

import seaborn as sns; sns.set(style='white')
import pandas as pd

data = {'prop_tenure': [0.0, 0.0, 0.06, 0.38, 0.61, 0.01, 0.10, 0.04, 0.22], 
        'prop_12m':    [0.0, 0.0, 0.06, 0.38, 0.61, 0.01, 0.10, 0.04, 0.22], 
        'prop_6m':     [0.0, 0.0, 0.10, 0.25, 0.66, 0.02, 0.12, 0.04, 0.22]}

df = pd.DataFrame(data)

g = sns.pairplot(df)
g.map_lower(corrfunc)
plt.show()

Answer 2

Just to mention, for seaborn in more recent version (>0.11.0) the answer above doesn't work anymore. But you need to add a hue=None to make it work again.

def corrfunc(x, y, hue=None, ax=None, **kws):
    """Plot the correlation coefficient in the top left hand corner of a plot."""
    r, _ = pearsonr(x, y)
    ax = ax or plt.gca()
    ax.annotate(f'ρ = {r:.2f}', xy=(.1, .9), xycoords=ax.transAxes)

Reference this issue https://github.com/mwaskom/seaborn/issues/2307#issuecomment-702980853