diff --git a/example/gluon/data.py b/example/gluon/data.py
index 5a7f9e6ca091..6aa531648613 100644
--- a/example/gluon/data.py
+++ b/example/gluon/data.py
@@ -19,6 +19,7 @@
 """ data iterator for mnist """
 import os
 import random
+import tarfile
 import logging
 logging.basicConfig(level=logging.INFO)
 
@@ -111,7 +112,7 @@ def transform(image, label):
         # center and crop an area of size (224,224)
         cropped, crop_info = mx.image.center_crop(resized, 224)
         # transpose the channels to be (3,224,224)
-        transposed = nd.transpose(cropped, (2, 0, 1))
+        transposed = mx.nd.transpose(cropped, (2, 0, 1))
         image = mx.nd.cast(image, dtype)
         return image, label
 
@@ -119,8 +120,8 @@ def transform(image, label):
     dataset_train = ImageFolderDataset(root=training_path, transform=transform)
     dataset_test = ImageFolderDataset(root=testing_path, transform=transform)
 
-    train_data = gluon.data.DataLoader(dataset_train, batch_size, shuffle=True, num_workers=num_workers)
-    test_data = gluon.data.DataLoader(dataset_test, batch_size, shuffle=False, num_workers=num_workers)
+    train_data = mx.gluon.data.DataLoader(dataset_train, batch_size, shuffle=True, num_workers=num_workers)
+    test_data = mx.gluon.data.DataLoader(dataset_test, batch_size, shuffle=False, num_workers=num_workers)
     return DataLoaderIter(train_data), DataLoaderIter(test_data)
 
 class DummyIter(mx.io.DataIter):
diff --git a/example/kaggle-ndsb1/gen_img_list.py b/example/kaggle-ndsb1/gen_img_list.py
index adfc4fe09d68..8b275508ed1b 100644
--- a/example/kaggle-ndsb1/gen_img_list.py
+++ b/example/kaggle-ndsb1/gen_img_list.py
@@ -18,7 +18,6 @@
 from __future__ import print_function
 import csv
 import os
-import sys
 import random
 import numpy as np
 import argparse
@@ -57,7 +56,7 @@
 img_lst = []
 cnt = 0
 if args.train:
-    for i in xrange(len(head)):
+    for i in range(len(head)):
         path = args.image_folder + head[i]
         lst = os.listdir(args.image_folder + head[i])
         for img in lst:
@@ -104,5 +103,3 @@
         tr_fo.writerow(item)
     for item in va_lst:
         va_fo.writerow(item)
-
-
diff --git a/example/reinforcement-learning/ddpg/ddpg.py b/example/reinforcement-learning/ddpg/ddpg.py
index aa34e4d92804..14c179575547 100644
--- a/example/reinforcement-learning/ddpg/ddpg.py
+++ b/example/reinforcement-learning/ddpg/ddpg.py
@@ -190,7 +190,7 @@ def train(self):
         end = False
         obs = self.env.reset()
 
-        for epoch in xrange(self.n_epochs):
+        for epoch in range(self.n_epochs):
             logger.push_prefix("epoch #%d | " % epoch)
             logger.log("Training started")
             for epoch_itr in pyprind.prog_bar(range(self.epoch_length)):
@@ -220,7 +220,7 @@ def train(self):
                 obs = nxt
 
                 if memory.size >= self.memory_start_size:
-                    for update_time in xrange(self.n_updates_per_sample):
+                    for update_time in range(self.n_updates_per_sample):
                         batch = memory.get_batch(self.batch_size)
                         self.do_update(itr, batch)
 
diff --git a/example/ssd/dataset/pycocotools/coco.py b/example/ssd/dataset/pycocotools/coco.py
index a8939f64a38c..19a7b8b7f64d 100755
--- a/example/ssd/dataset/pycocotools/coco.py
+++ b/example/ssd/dataset/pycocotools/coco.py
@@ -55,12 +55,12 @@
 # from . import mask as maskUtils
 import os
 from collections import defaultdict
-import sys
-PYTHON_VERSION = sys.version_info[0]
-if PYTHON_VERSION == 2:
-    from urllib import urlretrieve
-elif PYTHON_VERSION == 3:
+from mxnet.base import string_types
+try:
     from urllib.request import urlretrieve
+except ImportError:
+    from urllib import urlretrieve
+
 
 class COCO:
     def __init__(self, annotation_file=None):
@@ -302,7 +302,7 @@ def loadRes(self, resFile):
 
         print('Loading and preparing results...')
         tic = time.time()
-        if type(resFile) == str or type(resFile) == unicode:
+        if type(resFile) in string_types:
             anns = json.load(open(resFile))
         elif type(resFile) == np.ndarray:
             anns = self.loadNumpyAnnotations(resFile)