diff --git a/python-package/xgboost/core.py b/python-package/xgboost/core.py
index a33fe447b..b15672473 100644
--- a/python-package/xgboost/core.py
+++ b/python-package/xgboost/core.py
@@ -904,7 +904,8 @@ class Booster(object):
             preds = preds.astype(np.int32)
         nrow = data.num_row()
         if preds.size != nrow and preds.size % nrow == 0:
-            preds = preds.reshape(nrow, preds.size / nrow)
+            ncol = int(preds.size / nrow)
+            preds = preds.reshape(nrow, ncol)
         return preds
 
     def save_model(self, fname):
diff --git a/python-package/xgboost/training.py b/python-package/xgboost/training.py
index ed1d692b1..d21edd30d 100644
--- a/python-package/xgboost/training.py
+++ b/python-package/xgboost/training.py
@@ -269,7 +269,7 @@ def mknfold(dall, nfold, param, seed, evals=(), fpreproc=None, stratified=False,
 
     if stratified is False and folds is None:
         randidx = np.random.permutation(dall.num_row())
-        kstep = len(randidx) / nfold
+        kstep = int(len(randidx) / nfold)
         idset = [randidx[(i * kstep): min(len(randidx), (i + 1) * kstep)] for i in range(nfold)]
     elif folds is not None:
         idset = [x[1] for x in folds]