from catboost import CatBoostClassifier
model = CatBoostClassifier(iterations=300)
model.fit(X, y,cat_features=cat_features)

pool1 = Pool(data=X, label=y, cat_features=cat_features)
shap_values = model.get_feature_importance(data=pool1, fstr_type='ShapValues', verbose=10000)

shap_values.shape
Output: (32769, 10)
X.shape
Output: (32769, 9)

shap.initjs()
shap.force_plot(shap_values[0,:-1], X.iloc[0,:])

shap.initjs()
shap.summary_plot(shap_values[:,:-1], X)

explainer = shap.TreeExplainer(model,data=pool1)
#Also tried:
explainer = shap.TreeExplainer(model,data=X)