COMPAT: add refcheck kwarg and percolate out to app-level for PyPy

Author: mattip

pandas/_libs/hashtable.pyx

@@ -65,13 +65,14 @@ cdef class Factorizer:
         array([ 0,  1, 20])
         """
         labels = self.table.get_labels(values, self.uniques,
-                                       self.count, na_sentinel, check_null)
+                                       self.count, na_sentinel, 
+                                       check_null, refcheck=False)
         mask = (labels == na_sentinel)
         # sort on
         if sort:
             if labels.dtype != np.intp:
                 labels = labels.astype(np.intp)
-            sorter = self.uniques.to_array().argsort()
+            sorter = self.uniques.to_array(refcheck=False).argsort()
             reverse_indexer = np.empty(len(sorter), dtype=np.intp)
             reverse_indexer.put(sorter, np.arange(len(sorter)))
             labels = reverse_indexer.take(labels, mode='clip')

pandas/_libs/hashtable_class_helper.pxi.in

@@ -573,7 +573,7 @@ cdef class StringHashTable(HashTable):
         # uniques
         uniques = ObjectVector()
         for i in range(count):
-            uniques.append(values[uindexer[i]])
+            uniques.append(values[uindexer[i]], refcheck=False)
         return uniques.to_array(refcheck=False)
 
     def factorize(self, ndarray[object] values):
@@ -657,7 +657,6 @@ cdef class StringHashTable(HashTable):
             char **vecs
             khiter_t k
 
-        # refcheck ignored, for compatibility only
 
         # these by-definition *must* be strings
         labels = np.zeros(n, dtype=np.int64)
@@ -697,7 +696,7 @@ cdef class StringHashTable(HashTable):
 
         # uniques
         for i in range(count):
-            uniques.append(values[uindexer[i]])
+            uniques.append(values[uindexer[i]], refcheck=refcheck)
 
         return np.asarray(labels)
 
@@ -811,10 +810,10 @@ cdef class PyObjectHashTable(HashTable):
                 k = kh_get_pymap(self.table, <PyObject*>val)
                 if k == self.table.n_buckets:
                     kh_put_pymap(self.table, <PyObject*>val, &ret)
-                    uniques.append(val)
+                    uniques.append(val, refcheck=False)
             elif not seen_na:
                 seen_na = 1
-                uniques.append(nan)
+                uniques.append(nan, refcheck=False)
 
         return uniques.to_array(refcheck=False)
 
@@ -829,8 +828,6 @@ cdef class PyObjectHashTable(HashTable):
             object val
             khiter_t k
 
-        # refcheck ignored, for compatibility only
-
         labels = np.empty(n, dtype=np.int64)
 
         for i in range(n):
@@ -848,7 +845,7 @@ cdef class PyObjectHashTable(HashTable):
             else:
                 k = kh_put_pymap(self.table, <PyObject*>val, &ret)
                 self.table.vals[k] = count
-                uniques.append(val)
+                uniques.append(val, refcheck=refcheck)
                 labels[i] = count
                 count += 1
 

pandas/core/reshape/merge.py

@@ -1407,7 +1407,7 @@ def _factorize_keys(lk, rk, sort=True):
     count = rizer.get_count()
 
     if sort:
-        uniques = rizer.uniques.to_array()
+        uniques = rizer.uniques.to_array(refcheck=False)
         llab, rlab = _sort_labels(uniques, llab, rlab)
 
     # NA group

pandas/tests/test_algos.py

@@ -1069,8 +1069,8 @@ def _test_vector_resize(htable, uniques, dtype, nvals):
             # get_labels appends to the vector
             htable.get_labels(vals[:nvals], uniques, 0, -1)
             # to_array resizes the vector
-            uniques.to_array()
-            htable.get_labels(vals, uniques, 0, -1)
+            uniques.to_array(refcheck=False)
+            htable.get_labels(vals, uniques, 0, -1, refcheck=False)
 
         test_cases = [
             (hashtable.PyObjectHashTable, hashtable.ObjectVector, 'object'),