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?
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Answer
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()