BUG: Fix make_sparse mask generation

doc/source/whatsnew/v0.21.0.txt

@@ -544,6 +544,7 @@ Sparse
 
 - Bug in ``SparseSeries`` raises ``AttributeError`` when a dictionary is passed in as data (:issue:`16905`)
 - Bug in :func:`SparseDataFrame.fillna` not filling all NaNs when frame was instantiated from SciPy sparse matrix (:issue:`16112`)
+- Bug in :func:`make_sparse` treating two numeric/boolean data, which have same bits, as same when array ``dtype`` is ``object`` (:issue:`17574`)
 
 
 Reshaping

pandas/core/sparse/array.py

@@ -19,6 +19,7 @@
 from pandas.core.dtypes.common import (
     _ensure_platform_int,
     is_float, is_integer,
+    is_object_dtype,
     is_integer_dtype,
     is_bool_dtype,
     is_list_like,
@@ -789,7 +790,15 @@ def make_sparse(arr, kind='block', fill_value=None):
         if is_string_dtype(arr):
             arr = arr.astype(object)
 
-        mask = arr != fill_value
+        if is_object_dtype(arr.dtype):
+            mask = np.ones(len(arr), dtype=np.bool)
+            fv_type = type(fill_value)
+
+            itr = (type(x) is fv_type for x in arr)
+            cond = np.fromiter(itr, dtype=np.bool)
+            mask[cond] = arr[cond] != fill_value
+        else:
+            mask = arr != fill_value
 
     length = len(arr)
     if length != mask.size:

pandas/tests/sparse/test_array.py

@@ -61,6 +61,15 @@ def test_constructor_object_dtype(self):
         assert arr.dtype == np.object
         assert arr.fill_value == 'A'
 
+        # GH 17574
+        data = [False, 0, 100.0, 0.0]
+        arr = SparseArray(data, dtype=np.object, fill_value=False)
+        assert arr.dtype == np.object
+        assert arr.fill_value is False
+        arr_expected = np.array(data, dtype=np.object)
+        it = (type(x) == type(y) and x == y for x, y in zip(arr, arr_expected))
+        assert np.fromiter(it, dtype=np.bool).all()
+
     def test_constructor_spindex_dtype(self):
         arr = SparseArray(data=[1, 2], sparse_index=IntIndex(4, [1, 2]))
         tm.assert_sp_array_equal(arr, SparseArray([np.nan, 1, 2, np.nan]))