diff --git a/python-package/xgboost/core.py b/python-package/xgboost/core.py
index f99df9216..8e282cae9 100644
--- a/python-package/xgboost/core.py
+++ b/python-package/xgboost/core.py
@@ -1336,17 +1336,23 @@ class Booster(object):
         """Get feature importance of each feature.
         Importance type can be defined as:
             'weight' - the number of times a feature is used to split the data across all trees.
-            'gain' - the average gain of the feature when it is used in trees
-            'cover' - the average coverage of the feature when it is used in trees
+            'gain' - the average gain across all splits the feature is used in.
+            'cover' - the average coverage across all splits the feature is used in.
+            'total_gain' - the total gain across all splits the feature is used in.
+            'total_cover' - the total coverage across all splits the feature is used in.
 
         Parameters
         ----------
         fmap: str (optional)
-           The name of feature map file
+           The name of feature map file.
+        importance_type: str, default 'weight'
+            One of the importance types defined above.
         """
 
-        if importance_type not in ['weight', 'gain', 'cover']:
-            msg = "importance_type mismatch, got '{}', expected 'weight', 'gain', or 'cover'"
+        allowed_importance_types = ['weight', 'gain', 'cover', 'total_gain', 'total_cover']
+        if importance_type not in allowed_importance_types:
+            msg = ("importance_type mismatch, got '{}', expected one of " +
+                   repr(allowed_importance_types))
             raise ValueError(msg.format(importance_type))
 
         # if it's weight, then omap stores the number of missing values
@@ -1375,6 +1381,14 @@ class Booster(object):
             return fmap
 
         else:
+            average_over_splits = True
+            if importance_type == 'total_gain':
+                importance_type = 'gain'
+                average_over_splits = False
+            elif importance_type == 'total_cover':
+                importance_type = 'cover'
+                average_over_splits = False
+
             trees = self.get_dump(fmap, with_stats=True)
 
             importance_type += '='
@@ -1406,8 +1420,9 @@ class Booster(object):
                         gmap[fid] += g
 
             # calculate average value (gain/cover) for each feature
-            for fid in gmap:
-                gmap[fid] = gmap[fid] / fmap[fid]
+            if average_over_splits:
+                for fid in gmap:
+                    gmap[fid] = gmap[fid] / fmap[fid]
 
             return gmap
 
diff --git a/tests/python/test_shap.py b/tests/python/test_shap.py
index e4256dcc8..3afe1630f 100644
--- a/tests/python/test_shap.py
+++ b/tests/python/test_shap.py
@@ -33,10 +33,14 @@ class TestSHAP(unittest.TestCase):
         scores2 = bst.get_score(importance_type='weight')
         scores3 = bst.get_score(importance_type='cover')
         scores4 = bst.get_score(importance_type='gain')
+        scores5 = bst.get_score(importance_type='total_cover')
+        scores6 = bst.get_score(importance_type='total_gain')
         assert len(scores1) == len(features)
         assert len(scores2) == len(features)
         assert len(scores3) == len(features)
         assert len(scores4) == len(features)
+        assert len(scores5) == len(features)
+        assert len(scores6) == len(features)
 
         # check backwards compatibility of get_fscore
         fscores = bst.get_fscore()