BUG: Error when specifying int index containing NaN

doc/source/whatsnew/v0.20.0.txt

@@ -765,6 +765,7 @@ Bug Fixes
 - Bug in ``DataFrame.isin`` comparing datetimelike to empty frame (:issue:`15473`)
 
 - Bug in ``Series.where()`` and ``DataFrame.where()`` where array-like conditionals were being rejected (:issue:`15414`)
+- Bug in ``Index`` construction with ``NaN`` elements and integer dtype specified (:issue:`15187`)
 - Bug in ``Series`` construction with a datetimetz (:issue:`14928`)
 - Bug in output formatting of a ``MultiIndex`` when names are integers (:issue:`12223`, :issue:`15262`)
 

pandas/indexes/base.py

@@ -203,6 +203,9 @@ def __new__(cls, data=None, dtype=None, copy=False, name=None,
                         if inferred == 'integer':
                             data = np.array(data, copy=copy, dtype=dtype)
                         elif inferred in ['floating', 'mixed-integer-float']:
+                            if isnull(data).any():
+                                raise ValueError('cannot convert float '
+                                                 'NaN to integer')
 
                             # If we are actually all equal to integers,
                             # then coerce to integer.
@@ -230,8 +233,10 @@ def __new__(cls, data=None, dtype=None, copy=False, name=None,
                     else:
                         data = np.array(data, dtype=dtype, copy=copy)
 
-                except (TypeError, ValueError):
-                    pass
+                except (TypeError, ValueError) as e:
+                    msg = str(e)
+                    if 'cannot convert float' in msg:
+                        raise
 
             # maybe coerce to a sub-class
             from pandas.tseries.period import (PeriodIndex,
@@ -585,7 +590,14 @@ def where(self, cond, other=None):
         if other is None:
             other = self._na_value
         values = np.where(cond, self.values, other)
-        return self._shallow_copy_with_infer(values, dtype=self.dtype)
+
+        dtype = self.dtype
+        if self._is_numeric_dtype and np.any(isnull(values)):
+            # We can't coerce to the numeric dtype of "self" (unless
+            # it's float) if there are NaN values in our output.
+            dtype = None
+
+        return self._shallow_copy_with_infer(values, dtype=dtype)
 
     def ravel(self, order='C'):
         """
@@ -689,7 +701,14 @@ def _coerce_scalar_to_index(self, item):
         ----------
         item : scalar item to coerce
         """
-        return Index([item], dtype=self.dtype, **self._get_attributes_dict())
+        dtype = self.dtype
+
+        if self._is_numeric_dtype and isnull(item):
+            # We can't coerce to the numeric dtype of "self" (unless
+            # it's float) if there are NaN values in our output.
+            dtype = None
+
+        return Index([item], dtype=dtype, **self._get_attributes_dict())
 
     _index_shared_docs['copy'] = """
         Make a copy of this object.  Name and dtype sets those attributes on

pandas/tests/indexes/test_base.py

@@ -199,6 +199,23 @@ def __array__(self, dtype=None):
             result = pd.Index(ArrayLike(array))
             self.assert_index_equal(result, expected)
 
+    def test_constructor_int_dtype_nan(self):
+        # see gh-15187
+        data = [np.nan]
+        msg = "cannot convert"
+
+        with tm.assertRaisesRegexp(ValueError, msg):
+            Index(data, dtype='int64')
+
+        with tm.assertRaisesRegexp(ValueError, msg):
+            Index(data, dtype='uint64')
+
+        # This, however, should not break
+        # because NaN is float.
+        expected = Float64Index(data)
+        result = Index(data, dtype='float')
+        tm.assert_index_equal(result, expected)
+
     def test_index_ctor_infer_nan_nat(self):
         # GH 13467
         exp = pd.Float64Index([np.nan, np.nan])

pandas/tests/indexes/test_numeric.py

@@ -5,7 +5,7 @@
 
 import numpy as np
 
-from pandas import (date_range, Series, Index, Float64Index,
+from pandas import (date_range, notnull, Series, Index, Float64Index,
                     Int64Index, UInt64Index, RangeIndex)
 
 import pandas.util.testing as tm
@@ -686,6 +686,31 @@ def test_coerce_list(self):
         arr = Index([1, 2, 3, 4], dtype=object)
         tm.assertIsInstance(arr, Index)
 
+    def test_where(self):
+        i = self.create_index()
+        result = i.where(notnull(i))
+        expected = i
+        tm.assert_index_equal(result, expected)
+
+        _nan = i._na_value
+        cond = [False] + [True] * len(i[1:])
+        expected = pd.Index([_nan] + i[1:].tolist())
+
+        result = i.where(cond)
+        tm.assert_index_equal(result, expected)
+
+    def test_where_array_like(self):
+        i = self.create_index()
+
+        _nan = i._na_value
+        cond = [False] + [True] * (len(i) - 1)
+        klasses = [list, tuple, np.array, pd.Series]
+        expected = pd.Index([_nan] + i[1:].tolist())
+
+        for klass in klasses:
+            result = i.where(klass(cond))
+            tm.assert_index_equal(result, expected)
+
     def test_get_indexer(self):
         target = Int64Index(np.arange(10))
         indexer = self.index.get_indexer(target)

pandas/tests/indexes/test_range.py

@@ -8,7 +8,8 @@
 
 import numpy as np
 
-from pandas import (Series, Index, Float64Index, Int64Index, RangeIndex)
+from pandas import (notnull, Series, Index, Float64Index,
+                    Int64Index, RangeIndex)
 from pandas.util.testing import assertRaisesRegexp
 
 import pandas.util.testing as tm
@@ -915,3 +916,28 @@ def test_len_specialised(self):
 
             i = RangeIndex(0, 5, step)
             self.assertEqual(len(i), 0)
+
+    def test_where(self):
+        i = self.create_index()
+        result = i.where(notnull(i))
+        expected = i
+        tm.assert_index_equal(result, expected)
+
+        _nan = i._na_value
+        cond = [False] + [True] * len(i[1:])
+        expected = pd.Index([_nan] + i[1:].tolist())
+
+        result = i.where(cond)
+        tm.assert_index_equal(result, expected)
+
+    def test_where_array_like(self):
+        i = self.create_index()
+
+        _nan = i._na_value
+        cond = [False] + [True] * (len(i) - 1)
+        klasses = [list, tuple, np.array, pd.Series]
+        expected = pd.Index([_nan] + i[1:].tolist())
+
+        for klass in klasses:
+            result = i.where(klass(cond))
+            tm.assert_index_equal(result, expected)