BUG: Make randint backwards compatible with pandas

The 'pandas' library expects Python integers to be
returned, so this commit changes the API so that
the default is 'np.int' which converts to native
Python integer types when a singleton is being
generated with this function.

Closes gh-7284.

2 files changed, 24 insertions, 9 deletions

diff --git a/numpy/random/mtrand/mtrand.pyx b/numpy/random/mtrand/mtrand.pyx
index 35bbcc505..b05d86747 100644
--- a/numpy/random/mtrand/mtrand.pyx
+++ b/numpy/random/mtrand/mtrand.pyx
@@ -1159,7 +1159,7 @@ cdef class RandomState:
         """
         return disc0_array(self.internal_state, rk_long, size, self.lock)
 
-    def randint(self, low, high=None, size=None, dtype='l'):
+    def randint(self, low, high=None, size=None, dtype=int):
         """
         randint(low, high=None, size=None, dtype='l')
 
@@ -1186,7 +1186,7 @@ cdef class RandomState:
             Desired dtype of the result. All dtypes are determined by their
             name, i.e., 'int64', 'int', etc, so byteorder is not available
             and a specific precision may have different C types depending
-            on the platform. The default value is 'l' (C long).
+            on the platform. The default value is 'np.int'.
 
             .. versionadded:: 1.11.0
 
@@ -1234,7 +1234,13 @@ cdef class RandomState:
             raise ValueError("low >= high")
 
         with self.lock:
-            return randfunc(low, high - 1, size, self.state_address)
+            ret = randfunc(low, high - 1, size, self.state_address)
+
+            if size is None:
+                if dtype in (np.bool, np.int, np.long):
+                    return dtype(ret)
+
+            return ret
 
     def bytes(self, npy_intp length):
         """
diff --git a/numpy/random/tests/test_random.py b/numpy/random/tests/test_random.py
index fac287b3f..a07fa52f5 100644
--- a/numpy/random/tests/test_random.py
+++ b/numpy/random/tests/test_random.py
@@ -137,7 +137,7 @@ class TestRandint(TestCase):
     rfunc = np.random.randint
 
     # valid integer/boolean types
-    itype = [np.bool, np.int8, np.uint8, np.int16, np.uint16,
+    itype = [np.bool_, np.int8, np.uint8, np.int16, np.uint16,
              np.int32, np.uint32, np.int64, np.uint64]
 
     def test_unsupported_type(self):
@@ -145,8 +145,8 @@ class TestRandint(TestCase):
 
     def test_bounds_checking(self):
         for dt in self.itype:
-            lbnd = 0 if dt is np.bool else np.iinfo(dt).min
-            ubnd = 2 if dt is np.bool else np.iinfo(dt).max + 1
+            lbnd = 0 if dt is np.bool_ else np.iinfo(dt).min
+            ubnd = 2 if dt is np.bool_ else np.iinfo(dt).max + 1
             assert_raises(ValueError, self.rfunc, lbnd - 1, ubnd, dtype=dt)
             assert_raises(ValueError, self.rfunc, lbnd, ubnd + 1, dtype=dt)
             assert_raises(ValueError, self.rfunc, ubnd, lbnd, dtype=dt)
@@ -154,8 +154,8 @@ class TestRandint(TestCase):
 
     def test_rng_zero_and_extremes(self):
         for dt in self.itype:
-            lbnd = 0 if dt is np.bool else np.iinfo(dt).min
-            ubnd = 2 if dt is np.bool else np.iinfo(dt).max + 1
+            lbnd = 0 if dt is np.bool_ else np.iinfo(dt).min
+            ubnd = 2 if dt is np.bool_ else np.iinfo(dt).max + 1
             tgt = ubnd - 1
             assert_equal(self.rfunc(tgt, tgt + 1, size=1000, dtype=dt), tgt)
             tgt = lbnd
@@ -211,11 +211,20 @@ class TestRandint(TestCase):
     def test_respect_dtype_singleton(self):
         # See gh-7203
         for dt in self.itype:
+            lbnd = 0 if dt is np.bool_ else np.iinfo(dt).min
+            ubnd = 2 if dt is np.bool_ else np.iinfo(dt).max + 1
+
+            sample = self.rfunc(lbnd, ubnd, dtype=dt)
+            self.assertEqual(sample.dtype, np.dtype(dt))
+
+        for dt in (np.bool, np.int, np.long):
             lbnd = 0 if dt is np.bool else np.iinfo(dt).min
             ubnd = 2 if dt is np.bool else np.iinfo(dt).max + 1
 
+            # gh-7284: Ensure that we get Python data types
             sample = self.rfunc(lbnd, ubnd, dtype=dt)
-            self.assertEqual(sample.dtype, np.dtype(dt))
+            self.assertFalse(hasattr(sample, 'dtype'))
+            self.assertEqual(type(sample), dt)
 
 
 class TestRandomDist(TestCase):