BLD: use tempita for hashtable (pandas-dev#13815)

Author: sinhrks

pandas/core/algorithms.py

@@ -413,7 +413,7 @@ def _value_counts_arraylike(values, dropna=True):
             freq = values.freq
 
         values = values.view(np.int64)
-        keys, counts = htable.value_count_scalar64(values, dropna)
+        keys, counts = htable.value_count_int64(values, dropna)
 
         if dropna:
             msk = keys != iNaT
@@ -434,10 +434,10 @@ def _value_counts_arraylike(values, dropna=True):
 
     elif is_integer_dtype(dtype):
         values = _ensure_int64(values)
-        keys, counts = htable.value_count_scalar64(values, dropna)
+        keys, counts = htable.value_count_int64(values, dropna)
     elif is_float_dtype(dtype):
         values = _ensure_float64(values)
-        keys, counts = htable.value_count_scalar64(values, dropna)
+        keys, counts = htable.value_count_float64(values, dropna)
     else:
         values = _ensure_object(values)
         mask = isnull(values)

pandas/core/groupby.py

@@ -4399,7 +4399,7 @@ def _groupby_indices(values):
         # bit better than factorizing again
         reverse = dict(enumerate(values.categories))
         codes = values.codes.astype('int64')
-        _, counts = _hash.value_count_scalar64(codes, False)
+        _, counts = _hash.value_count_int64(codes, False)
     else:
         reverse, codes, counts = _algos.group_labels(
             _values_from_object(_ensure_object(values)))