BUG: fix isin for 32bit platform issues

Author: jreback

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))