PERF: improve perf of array_equivalent_object (GH8512)

pandas/core/common.py

@@ -411,12 +411,13 @@ def array_equivalent(left, right, strict_nan=False):
     if left.shape != right.shape: return False
 
     # Object arrays can contain None, NaN and NaT.
-    if issubclass(left.dtype.type, np.object_):
+    if issubclass(left.dtype.type, np.object_) or issubclass(right.dtype.type, np.object_):
 
         if not strict_nan:
             # pd.isnull considers NaN and None to be equivalent.
-            return lib.array_equivalent_object(left.ravel(), right.ravel())
-
+            return lib.array_equivalent_object(_ensure_object(left.ravel()),
+                                               _ensure_object(right.ravel()))
+
         for left_value, right_value in zip(left, right):
             if left_value is tslib.NaT and right_value is not tslib.NaT:
                 return False

pandas/lib.pyx

@@ -330,26 +330,6 @@ def list_to_object_array(list obj):
     return arr
 
 
-@cython.wraparound(False)
-@cython.boundscheck(False)
-def array_equivalent_object(ndarray left, ndarray right):
-    cdef Py_ssize_t i, n
-    cdef object lobj, robj
-
-    n = len(left)
-    for i from 0 <= i < n:
-        lobj = left[i]
-        robj = right[i]
-
-        # we are either not equal or both nan
-        # I think None == None will be true here
-        if lobj != robj:
-            if checknull(lobj) and checknull(robj):
-                continue
-            return False
-    return True
-
-
 @cython.wraparound(False)
 @cython.boundscheck(False)
 def fast_unique(ndarray[object] values):
@@ -692,6 +672,31 @@ def scalar_compare(ndarray[object] values, object val, object op):
 
     return result.view(bool)
 
+@cython.wraparound(False)
+@cython.boundscheck(False)
+def array_equivalent_object(ndarray[object] left, ndarray[object] right):
+    """ perform an element by element comparion on 1-d object arrays
+        taking into account nan positions """
+    cdef Py_ssize_t i, n
+    cdef object x, y
+
+    n = len(left)
+    for i from 0 <= i < n:
+        x = left[i]
+        y = right[i]
+
+        # we are either not equal or both nan
+        # I think None == None will be true here
+        if cpython.PyObject_RichCompareBool(x, y, cpython.Py_EQ):
+            continue
+        elif _checknull(x) and _checknull(y):
+            continue
+        else:
+            return False
+
+    return True
+
+
 @cython.wraparound(False)
 @cython.boundscheck(False)
 def vec_compare(ndarray[object] left, ndarray[object] right, object op):