diff --git a/privacy/analysis/rdp_accountant.py b/privacy/analysis/rdp_accountant.py
index 8c63dfe..b71edf8 100644
--- a/privacy/analysis/rdp_accountant.py
+++ b/privacy/analysis/rdp_accountant.py
@@ -45,11 +45,7 @@ import sys
 
 import numpy as np
 from scipy import special
-
-try:
-  long
-except NameError:
-  long = int
+import six
 
 ########################
 # LOG-SPACE ARITHMETIC #
@@ -91,7 +87,7 @@ def _log_print(logx):
 
 def _compute_log_a_int(q, sigma, alpha):
   """Compute log(A_alpha) for integer alpha. 0 < q < 1."""
-  assert isinstance(alpha, (int, long))
+  assert isinstance(alpha, six.integer_types)
 
   # Initialize with 0 in the log space.
   log_a = -np.inf
diff --git a/privacy/optimizers/gaussian_query_test.py b/privacy/optimizers/gaussian_query_test.py
index 37b8789..7de0e51 100644
--- a/privacy/optimizers/gaussian_query_test.py
+++ b/privacy/optimizers/gaussian_query_test.py
@@ -24,11 +24,6 @@ import tensorflow as tf
 
 from privacy.optimizers import gaussian_query
 
-try:
-    xrange
-except NameError:
-    xrange = range
-
 
 def _run_query(query, records):
   """Executes query on the given set of records as a single sample.
@@ -114,7 +109,7 @@ class GaussianQueryTest(tf.test.TestCase, parameterized.TestCase):
       query_result = _run_query(query, [record1, record2])
 
       noised_averages = []
-      for _ in xrange(1000):
+      for _ in range(1000):
         noised_averages.append(sess.run(query_result))
 
       result_stddev = np.std(noised_averages)
diff --git a/privacy/optimizers/nested_query_test.py b/privacy/optimizers/nested_query_test.py
index add2fe1..9ec643b 100644
--- a/privacy/optimizers/nested_query_test.py
+++ b/privacy/optimizers/nested_query_test.py
@@ -30,11 +30,6 @@ nest = tf.contrib.framework.nest
 
 _basic_query = gaussian_query.GaussianSumQuery(1.0, 0.0)
 
-try:
-    xrange
-except NameError:
-    xrange = range
-
 
 def _run_query(query, records):
   """Executes query on the given set of records as a single sample.
@@ -145,7 +140,7 @@ class NestedQueryTest(tf.test.TestCase, parameterized.TestCase):
       query_result = _run_query(query, [record1, record2])
 
       noised_averages = []
-      for _ in xrange(1000):
+      for _ in range(1000):
         noised_averages.append(nest.flatten(sess.run(query_result)))
 
       result_stddev = np.std(noised_averages, 0)