fail on float16, but allow np.exp(int8_arrays) II

pandas/core/algorithms.py

@@ -891,6 +891,8 @@ def value_counts(
         else:
             values = _ensure_arraylike(values)
             keys, counts = value_counts_arraylike(values, dropna)
+            if keys.dtype == np.float16:
+                keys = keys.astype(np.float32)
 
             # For backwards compatibility, we let Index do its normal type
             #  inference, _except_ for if if infers from object to bool.

pandas/core/base.py

@@ -1140,6 +1140,8 @@ def factorize(
         codes, uniques = algorithms.factorize(
             self._values, sort=sort, use_na_sentinel=use_na_sentinel
         )
+        if uniques.dtype == np.float16:
+            uniques = uniques.astype(np.float32)
 
         if isinstance(self, ABCIndex):
             # preserve e.g. NumericIndex, preserve MultiIndex

pandas/tests/test_algos.py

@@ -66,7 +66,10 @@ def test_factorize(self, index_or_series_obj, sort):
         constructor = Index
         if isinstance(obj, MultiIndex):
             constructor = MultiIndex.from_tuples
-        expected_uniques = constructor(obj.unique())
+        expected_arr = obj.unique()
+        if expected_arr.dtype == np.float16:
+            expected_arr = expected_arr.astype(np.float32)
+        expected_uniques = constructor(expected_arr)
         if (
             isinstance(obj, Index)
             and expected_uniques.dtype == bool