COMPAT: 32-bit compat fixes mainly in testing

closes #13566
closes #13584

Author: jreback

pandas/core/internals.py

@@ -3085,7 +3085,7 @@ def reduction(self, f, axis=0, consolidate=True, transposed=False,
         # compute the orderings of our original data
         if len(self.blocks) > 1:
 
-            indexer = np.empty(len(self.axes[0]), dtype='int64')
+            indexer = np.empty(len(self.axes[0]), dtype=np.intp)
             i = 0
             for b in self.blocks:
                 for j in b.mgr_locs:

pandas/tests/indexes/test_datetimelike.py

@@ -534,9 +534,9 @@ def test_get_loc(self):
         # time indexing
         idx = pd.date_range('2000-01-01', periods=24, freq='H')
         tm.assert_numpy_array_equal(idx.get_loc(time(12)),
-                                    np.array([12], dtype=np.int64))
+                                    np.array([12]), check_dtype=False)
         tm.assert_numpy_array_equal(idx.get_loc(time(12, 30)),
-                                    np.array([], dtype=np.int64))
+                                    np.array([]), check_dtype=False)
         with tm.assertRaises(NotImplementedError):
             idx.get_loc(time(12, 30), method='pad')
 
@@ -587,7 +587,8 @@ def test_time_loc(self):  # GH8667
             ts = pd.Series(np.random.randn(n), index=idx)
             i = np.arange(start, n, step)
 
-            tm.assert_numpy_array_equal(ts.index.get_loc(key), i)
+            tm.assert_numpy_array_equal(ts.index.get_loc(key), i,
+                                        check_dtype=False)
             tm.assert_series_equal(ts[key], ts.iloc[i])
 
             left, right = ts.copy(), ts.copy()

pandas/tests/indexes/test_multi.py

@@ -1750,12 +1750,12 @@ def test_reindex_level(self):
         exp_index2 = self.index.join(idx, level='second', how='left')
 
         self.assertTrue(target.equals(exp_index))
-        exp_indexer = np.array([0, 2, 4], dtype=np.int64)
-        tm.assert_numpy_array_equal(indexer, exp_indexer)
+        exp_indexer = np.array([0, 2, 4])
+        tm.assert_numpy_array_equal(indexer, exp_indexer, check_dtype=False)
 
         self.assertTrue(target2.equals(exp_index2))
-        exp_indexer2 = np.array([0, -1, 0, -1, 0, -1], dtype=np.int64)
-        tm.assert_numpy_array_equal(indexer2, exp_indexer2)
+        exp_indexer2 = np.array([0, -1, 0, -1, 0, -1])
+        tm.assert_numpy_array_equal(indexer2, exp_indexer2, check_dtype=False)
 
         assertRaisesRegexp(TypeError, "Fill method not supported",
                            self.index.reindex, self.index, method='pad',

pandas/tests/series/test_analytics.py

@@ -262,7 +262,7 @@ def test_kurt(self):
                 self.assertTrue((df.kurt() == 0).all())
 
     def test_argsort(self):
-        self._check_accum_op('argsort')
+        self._check_accum_op('argsort', check_dtype=False)
         argsorted = self.ts.argsort()
         self.assertTrue(issubclass(argsorted.dtype.type, np.integer))
 
@@ -289,8 +289,10 @@ def test_argsort_stable(self):
         mexpected = np.argsort(s.values, kind='mergesort')
         qexpected = np.argsort(s.values, kind='quicksort')
 
-        self.assert_series_equal(mindexer, Series(mexpected))
-        self.assert_series_equal(qindexer, Series(qexpected))
+        self.assert_series_equal(mindexer, Series(mexpected),
+                                 check_dtype=False)
+        self.assert_series_equal(qindexer, Series(qexpected),
+                                 check_dtype=False)
         self.assertFalse(np.array_equal(qindexer, mindexer))
 
     def test_cumsum(self):
@@ -487,10 +489,11 @@ def testit():
         except ImportError:
             pass
 
-    def _check_accum_op(self, name):
+    def _check_accum_op(self, name, check_dtype=True):
         func = getattr(np, name)
         self.assert_numpy_array_equal(func(self.ts).values,
-                                      func(np.array(self.ts)))
+                                      func(np.array(self.ts)),
+                                      check_dtype=check_dtype)
 
         # with missing values
         ts = self.ts.copy()
@@ -499,7 +502,8 @@ def _check_accum_op(self, name):
         result = func(ts)[1::2]
         expected = func(np.array(ts.valid()))
 
-        self.assert_numpy_array_equal(result.values, expected)
+        self.assert_numpy_array_equal(result.values, expected,
+                                      check_dtype=False)
 
     def test_compress(self):
         cond = [True, False, True, False, False]
@@ -1360,13 +1364,13 @@ def test_searchsorted_numeric_dtypes_scalar(self):
         self.assertEqual(r, e)
 
         r = s.searchsorted([30])
-        e = np.array([2], dtype=np.int64)
+        e = np.array([2], dtype=np.intp)
         tm.assert_numpy_array_equal(r, e)
 
     def test_searchsorted_numeric_dtypes_vector(self):
         s = Series([1, 2, 90, 1000, 3e9])
         r = s.searchsorted([91, 2e6])
-        e = np.array([3, 4], dtype=np.int64)
+        e = np.array([3, 4], dtype=np.intp)
         tm.assert_numpy_array_equal(r, e)
 
     def test_search_sorted_datetime64_scalar(self):
@@ -1380,14 +1384,14 @@ def test_search_sorted_datetime64_list(self):
         s = Series(pd.date_range('20120101', periods=10, freq='2D'))
         v = [pd.Timestamp('20120102'), pd.Timestamp('20120104')]
         r = s.searchsorted(v)
-        e = np.array([1, 2], dtype=np.int64)
+        e = np.array([1, 2], dtype=np.intp)
         tm.assert_numpy_array_equal(r, e)
 
     def test_searchsorted_sorter(self):
         # GH8490
         s = Series([3, 1, 2])
         r = s.searchsorted([0, 3], sorter=np.argsort(s))
-        e = np.array([0, 2], dtype=np.int64)
+        e = np.array([0, 2], dtype=np.intp)
         tm.assert_numpy_array_equal(r, e)
 
     def test_is_unique(self):

pandas/tests/test_algos.py

@@ -702,12 +702,14 @@ def test_unique_label_indices():
     left = unique_label_indices(a)
     right = np.unique(a, return_index=True)[1]
 
-    tm.assert_numpy_array_equal(left, right)
+    tm.assert_numpy_array_equal(left, right,
+                                check_dtype=False)
 
     a[np.random.choice(len(a), 10)] = -1
     left = unique_label_indices(a)
     right = np.unique(a, return_index=True)[1][1:]
-    tm.assert_numpy_array_equal(left, right)
+    tm.assert_numpy_array_equal(left, right,
+                                check_dtype=False)
 
 
 def test_rank():

pandas/tests/test_categorical.py

@@ -515,17 +515,20 @@ def f():
     def test_argsort(self):
         c = Categorical([5, 3, 1, 4, 2], ordered=True)
 
-        expected = np.array([2, 4, 1, 3, 0], dtype=np.int64)
-        tm.assert_numpy_array_equal(c.argsort(ascending=True), expected)
+        expected = np.array([2, 4, 1, 3, 0])
+        tm.assert_numpy_array_equal(c.argsort(ascending=True), expected,
+                                    check_dtype=False)
 
         expected = expected[::-1]
-        tm.assert_numpy_array_equal(c.argsort(ascending=False), expected)
+        tm.assert_numpy_array_equal(c.argsort(ascending=False), expected,
+                                    check_dtype=False)
 
     def test_numpy_argsort(self):
         c = Categorical([5, 3, 1, 4, 2], ordered=True)
 
-        expected = np.array([2, 4, 1, 3, 0], dtype=np.int64)
-        tm.assert_numpy_array_equal(np.argsort(c), expected)
+        expected = np.array([2, 4, 1, 3, 0])
+        tm.assert_numpy_array_equal(np.argsort(c), expected,
+                                    check_dtype=False)
 
         msg = "the 'kind' parameter is not supported"
         tm.assertRaisesRegexp(ValueError, msg, np.argsort,
@@ -1505,7 +1508,7 @@ def test_searchsorted(self):
         # Single item array
         res = c1.searchsorted(['bread'])
         chk = s1.searchsorted(['bread'])
-        exp = np.array([1], dtype=np.int64)
+        exp = np.array([1], dtype=np.intp)
         self.assert_numpy_array_equal(res, exp)
         self.assert_numpy_array_equal(res, chk)
 
@@ -1514,21 +1517,21 @@ def test_searchsorted(self):
         # np.array.searchsorted()
         res = c1.searchsorted('bread')
         chk = s1.searchsorted('bread')
-        exp = np.array([1], dtype=np.int64)
+        exp = np.array([1], dtype=np.intp)
         self.assert_numpy_array_equal(res, exp)
         self.assert_numpy_array_equal(res, chk)
 
         # Searching for a value that is not present in the Categorical
         res = c1.searchsorted(['bread', 'eggs'])
         chk = s1.searchsorted(['bread', 'eggs'])
-        exp = np.array([1, 4], dtype=np.int64)
+        exp = np.array([1, 4], dtype=np.intp)
         self.assert_numpy_array_equal(res, exp)
         self.assert_numpy_array_equal(res, chk)
 
         # Searching for a value that is not present, to the right
         res = c1.searchsorted(['bread', 'eggs'], side='right')
         chk = s1.searchsorted(['bread', 'eggs'], side='right')
-        exp = np.array([3, 4], dtype=np.int64)  # eggs before milk
+        exp = np.array([3, 4], dtype=np.intp)  # eggs before milk
         self.assert_numpy_array_equal(res, exp)
         self.assert_numpy_array_equal(res, chk)
 
@@ -1538,7 +1541,7 @@ def test_searchsorted(self):
         chk = s2.searchsorted(['bread', 'eggs'], side='right',
                               sorter=[0, 1, 2, 3, 5, 4])
         # eggs after donuts, after switching milk and donuts
-        exp = np.array([3, 5], dtype=np.int64)
+        exp = np.array([3, 5], dtype=np.intp)
         self.assert_numpy_array_equal(res, exp)
         self.assert_numpy_array_equal(res, chk)
 

pandas/tests/test_groupby.py

@@ -5934,49 +5934,49 @@ def test_nargsort(self):
         result = _nargsort(items, kind='mergesort', ascending=True,
                            na_position='last')
         exp = list(range(5, 105)) + list(range(5)) + list(range(105, 110))
-        tm.assert_numpy_array_equal(result, np.array(exp, dtype=np.int64))
+        tm.assert_numpy_array_equal(result, np.array(exp), check_dtype=False)
 
         # mergesort, ascending=True, na_position='first'
         result = _nargsort(items, kind='mergesort', ascending=True,
                            na_position='first')
         exp = list(range(5)) + list(range(105, 110)) + list(range(5, 105))
-        tm.assert_numpy_array_equal(result, np.array(exp, dtype=np.int64))
+        tm.assert_numpy_array_equal(result, np.array(exp), check_dtype=False)
 
         # mergesort, ascending=False, na_position='last'
         result = _nargsort(items, kind='mergesort', ascending=False,
                            na_position='last')
         exp = list(range(104, 4, -1)) + list(range(5)) + list(range(105, 110))
-        tm.assert_numpy_array_equal(result, np.array(exp, dtype=np.int64))
+        tm.assert_numpy_array_equal(result, np.array(exp), check_dtype=False)
 
         # mergesort, ascending=False, na_position='first'
         result = _nargsort(items, kind='mergesort', ascending=False,
                            na_position='first')
         exp = list(range(5)) + list(range(105, 110)) + list(range(104, 4, -1))
-        tm.assert_numpy_array_equal(result, np.array(exp, dtype=np.int64))
+        tm.assert_numpy_array_equal(result, np.array(exp), check_dtype=False)
 
         # mergesort, ascending=True, na_position='last'
         result = _nargsort(items2, kind='mergesort', ascending=True,
                            na_position='last')
         exp = list(range(5, 105)) + list(range(5)) + list(range(105, 110))
-        tm.assert_numpy_array_equal(result, np.array(exp, dtype=np.int64))
+        tm.assert_numpy_array_equal(result, np.array(exp), check_dtype=False)
 
         # mergesort, ascending=True, na_position='first'
         result = _nargsort(items2, kind='mergesort', ascending=True,
                            na_position='first')
         exp = list(range(5)) + list(range(105, 110)) + list(range(5, 105))
-        tm.assert_numpy_array_equal(result, np.array(exp, dtype=np.int64))
+        tm.assert_numpy_array_equal(result, np.array(exp), check_dtype=False)
 
         # mergesort, ascending=False, na_position='last'
         result = _nargsort(items2, kind='mergesort', ascending=False,
                            na_position='last')
         exp = list(range(104, 4, -1)) + list(range(5)) + list(range(105, 110))
-        tm.assert_numpy_array_equal(result, np.array(exp, dtype=np.int64))
+        tm.assert_numpy_array_equal(result, np.array(exp), check_dtype=False)
 
         # mergesort, ascending=False, na_position='first'
         result = _nargsort(items2, kind='mergesort', ascending=False,
                            na_position='first')
         exp = list(range(5)) + list(range(105, 110)) + list(range(104, 4, -1))
-        tm.assert_numpy_array_equal(result, np.array(exp, dtype=np.int64))
+        tm.assert_numpy_array_equal(result, np.array(exp), check_dtype=False)
 
     def test_datetime_count(self):
         df = DataFrame({'a': [1, 2, 3] * 2,

pandas/tools/merge.py

@@ -436,7 +436,8 @@ def _merger(x, y):
         # if we DO have duplicates, then
         # we cannot guarantee order
 
-        sorter = np.concatenate([groupby.indices[g] for g, _ in groupby])
+        sorter = com._ensure_platform_int(
+            np.concatenate([groupby.indices[g] for g, _ in groupby]))
         if len(result) != len(sorter):
             if check_duplicates:
                 raise AssertionError("invalid reverse grouping")

pandas/tools/tests/test_merge.py

@@ -91,8 +91,8 @@ def test_cython_left_outer_join(self):
         exp_rs = exp_rs.take(exp_ri)
         exp_rs[exp_ri == -1] = -1
 
-        self.assert_numpy_array_equal(ls, exp_ls)
-        self.assert_numpy_array_equal(rs, exp_rs)
+        self.assert_numpy_array_equal(ls, exp_ls, check_dtype=False)
+        self.assert_numpy_array_equal(rs, exp_rs, check_dtype=False)
 
     def test_cython_right_outer_join(self):
         left = a_([0, 1, 2, 1, 2, 0, 0, 1, 2, 3, 3], dtype=np.int64)
@@ -117,8 +117,8 @@ def test_cython_right_outer_join(self):
         exp_rs = exp_rs.take(exp_ri)
         exp_rs[exp_ri == -1] = -1
 
-        self.assert_numpy_array_equal(ls, exp_ls)
-        self.assert_numpy_array_equal(rs, exp_rs)
+        self.assert_numpy_array_equal(ls, exp_ls, check_dtype=False)
+        self.assert_numpy_array_equal(rs, exp_rs, check_dtype=False)
 
     def test_cython_inner_join(self):
         left = a_([0, 1, 2, 1, 2, 0, 0, 1, 2, 3, 3], dtype=np.int64)
@@ -141,8 +141,8 @@ def test_cython_inner_join(self):
         exp_rs = exp_rs.take(exp_ri)
         exp_rs[exp_ri == -1] = -1
 
-        self.assert_numpy_array_equal(ls, exp_ls)
-        self.assert_numpy_array_equal(rs, exp_rs)
+        self.assert_numpy_array_equal(ls, exp_ls, check_dtype=False)
+        self.assert_numpy_array_equal(rs, exp_rs, check_dtype=False)
 
     def test_left_outer_join(self):
         joined_key2 = merge(self.df, self.df2, on='key2')

pandas/tools/tests/test_tile.py

@@ -19,8 +19,9 @@ class TestCut(tm.TestCase):
     def test_simple(self):
         data = np.ones(5)
         result = cut(data, 4, labels=False)
-        desired = np.array([1, 1, 1, 1, 1], dtype=np.int64)
-        tm.assert_numpy_array_equal(result, desired)
+        desired = np.array([1, 1, 1, 1, 1])
+        tm.assert_numpy_array_equal(result, desired,
+                                    check_dtype=False)
 
     def test_bins(self):
         data = np.array([.2, 1.4, 2.5, 6.2, 9.7, 2.1])