How to plot feature importance with feature names from GridSearchCV XGBoost results in Python

Question

So I have some code as follows:

best_score_param_estimator_gs = []
# XGBoost Model
xg_model = xg.XGBClassifier(use_label_encoder=False,
objective='binary:logistic',
verbosity=1,
seed=42
)

params = {
    'learning_rate': [.01],
    'n_estimators': [550],
    'gamma': [5],
    'subsample': [0.65],
    'colsample_bytree': [1.0],
    'max_depth': [6]
}


skf = StratifiedKFold(n_splits=2, shuffle = True, random_state = 1001)
    
# AUC and accuracy as score
scoring = {'AUC':'roc_auc', 'Accuracy':metrics.make_scorer(metrics.accuracy_score)}

# Run grid search
global grid
grid = GridSearchCV(xg_model, param_grid=params, scoring=scoring, refit='AUC', n_jobs=6, 
    cv=skf.split(X_train,y_train), verbose=1)
model = grid.fit(X_train, y_train)

And with this, I get a model ; I would like to plot the feature importance of this model in descending order of the feature importance.

model.best_estimator_.feature_importances_

USing this, I can get an array of all the importances, but how to I make a plot (matplotlib) ALONG WITH their feature names?

This "works" but I don't know which is which

plt.bar(range(len(best_estimator_xgbc.feature_importances_)), best_estimator_xgbc.feature_importances_)