BUG: various 32bit compat issues

doc/source/whatsnew/v0.20.0.txt

@@ -824,6 +824,7 @@ Bug Fixes
 - Bug in ``pd.qcut()`` with a single quantile and an array with identical values (:issue:`15431`)
 - Compat with SciPy 0.19.0 for testing on ``.interpolate()`` (:issue:`15662`)
 
+- Compat for 32-bit platforms for ``.qcut/cut``; bins will now be ``in64`` dtype (:issue:`14866`)
 
 - Bug in the display of ``.info()`` where a qualifier (+) would always be displayed with a ``MultiIndex`` that contains only non-strings (:issue:`15245`)
 - Bug in ``.replace()`` may result in incorrect dtypes. (:issue:`12747`, :issue:`15765`)

pandas/core/algorithms.py

@@ -169,33 +169,45 @@ def isin(comps, values):
         raise TypeError("only list-like objects are allowed to be passed"
                         " to isin(), you passed a "
                         "[{0}]".format(type(comps).__name__))
-    comps = np.asarray(comps)
     if not is_list_like(values):
         raise TypeError("only list-like objects are allowed to be passed"
                         " to isin(), you passed a "
                         "[{0}]".format(type(values).__name__))
-    if not isinstance(values, np.ndarray):
-        values = list(values)
+
+    from pandas import DatetimeIndex, PeriodIndex
+
+    if not isinstance(values, (ABCIndex, ABCSeries, np.ndarray)):
+        values = np.array(list(values), dtype='object')
+
+    if needs_i8_conversion(comps):
+        if is_period_dtype(values):
+            comps = PeriodIndex(comps)
+            values = PeriodIndex(values)
+        else:
+            comps = DatetimeIndex(comps)
+            values = DatetimeIndex(values)
+
+        values = values.asi8
+        comps = comps.asi8
+    elif is_bool_dtype(comps):
+
+        try:
+            comps = np.asarray(comps).view('uint8')
+            values = np.asarray(values).view('uint8')
+        except TypeError:
+            # object array conversion will fail
+            pass
+    else:
+        comps = np.asarray(comps)
+        values = np.asarray(values)
 
     # GH11232
     # work-around for numpy < 1.8 and comparisions on py3
     # faster for larger cases to use np.in1d
     if (_np_version_under1p8 and compat.PY3) or len(comps) > 1000000:
         f = lambda x, y: np.in1d(x, np.asarray(list(y)))
-    else:
-        f = lambda x, y: lib.ismember_int64(x, set(y))
-
-    # may need i8 conversion for proper membership testing
-    if is_datetime64_dtype(comps):
-        from pandas.tseries.tools import to_datetime
-        values = to_datetime(values)._values.view('i8')
-        comps = comps.view('i8')
-    elif is_timedelta64_dtype(comps):
-        from pandas.tseries.timedeltas import to_timedelta
-        values = to_timedelta(values)._values.view('i8')
-        comps = comps.view('i8')
     elif is_int64_dtype(comps):
-        pass
+        f = lambda x, y: lib.ismember_int64(x, set(y))
     else:
         f = lambda x, y: lib.ismember(x, set(values))
 

pandas/tests/indexes/test_multi.py

@@ -1359,7 +1359,7 @@ def test_hash_collisions(self):
                                         names=['one', 'two'])
         result = index.get_indexer(index.values)
         self.assert_numpy_array_equal(result,
-                                      np.arange(len(index), dtype='int64'))
+                                      np.arange(len(index), dtype='intp'))
 
         for i in [0, 1, len(index) - 2, len(index) - 1]:
             result = index.get_loc(index[i])

pandas/tests/tools/test_tile.py

@@ -19,8 +19,8 @@ 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])
-        tm.assert_numpy_array_equal(result, desired,
+        expected = np.array([1, 1, 1, 1, 1])
+        tm.assert_numpy_array_equal(result, expected,
                                     check_dtype=False)
 
     def test_bins(self):

pandas/tools/tile.py

@@ -4,7 +4,7 @@
 
 from pandas.types.missing import isnull
 from pandas.types.common import (is_float, is_integer,
-                                 is_scalar)
+                                 is_scalar, _ensure_int64)
 
 from pandas.core.api import Series
 from pandas.core.categorical import Categorical
@@ -215,7 +215,7 @@ def _bins_to_cuts(x, bins, right=True, labels=None,
             bins = unique_bins
 
     side = 'left' if right else 'right'
-    ids = bins.searchsorted(x, side=side)
+    ids = _ensure_int64(bins.searchsorted(x, side=side))
 
     if include_lowest:
         ids[x == bins[0]] = 1