diff --git a/README.md b/README.md
index 2df58f4..0f60a76 100644
--- a/README.md
+++ b/README.md
@@ -65,8 +65,8 @@ Now we can call `exact_cover`:
     >>> import numpy as np
     >>> import exact_cover as ec
     >>> S = np.array([[1,0,0,1,0],[1,1,1,0,0],[0,1,1,0,0],[0,0,0,0,1]], dtype=bool)
-    >>> ec.get_exact_cover(S)
-    array([0, 2, 3])
+    >>> print(ec.get_exact_cover(S))
+    [0 2 3]
 
 This is telling us that the 0th row (i.e. A), the 2nd row (i.e. C),
 and the 3rd row (i.e. D) together form an exact cover.
diff --git a/examples.md b/examples.md
index 2cb04c3..453c044 100644
--- a/examples.md
+++ b/examples.md
@@ -10,11 +10,11 @@ Input and output to the package are using the numpy array type:
 
 This example is explained in README.md
     >>> S = np.array([[1,0,0,1,0],[1,1,1,0,0],[0,1,1,0,0],[0,0,0,0,1]], dtype=np.int32)
-    >>> ec.get_exact_cover(S)
-    array([0, 2, 3])
+    >>> print(ec.get_exact_cover(S))
+    [0 2 3]
 
 The numpy type to use is defined in the package for convenience and compatibility:
     >>> from exact_cover.io import DTYPE_FOR_ARRAY
     >>> T = np.array([[1,0,0,1,0],[1,1,1,0,0],[0,1,1,0,0],[0,0,0,0,1]], dtype=DTYPE_FOR_ARRAY)
-    >>> ec.get_exact_cover(T)
-    array([0, 2, 3])
+    >>> print(ec.get_exact_cover(T))
+    [0 2 3]