How to get the SHAP values of each feature?

I am currently working with the SHAP library, I already generated my charts with the avg contribution of each feature, however I would like to know the exact value that is plotted on the chart

import numpy as np
import pandas as pd  
from sklearn.linear_model import LinearRegression
from sklearn.datasets import load_boston
import shap


boston = load_boston()
regr = pd.DataFrame(boston.data)
regr.columns = boston.feature_names
regr['MEDV'] = boston.target

X = regr.drop('MEDV', axis = 1)
Y = regr['MEDV']

fit = LinearRegression().fit(X, Y)

explainer = shap.LinearExplainer(fit, X, feature_dependence = 'independent')
# I used 'independent' because the result is consistent with the ordinary 
# shapely values where `correlated' is not

shap_values = explainer.shap_values(X)

shap.summary_plot(shap_values, X, plot_type = 'bar')

enter image description here

How can I get the exact values that are depicted of the chart?

Solution

pd.DataFrame((zip(X.columns[np.argsort(np.abs(shap_values).mean(0))],
np.abs(shap_values).mean(0))), columns=["feature", "importance" ]).sort_values(by="importance", ascending=False)

Reference GitHub