diff --git a/sklearn/metrics/_ranking.py b/sklearn/metrics/_ranking.py
index 6aab05a71707d..e94ef49a78c33 100644
--- a/sklearn/metrics/_ranking.py
+++ b/sklearn/metrics/_ranking.py
@@ -678,7 +678,7 @@ def precision_recall_curve(y_true, probas_pred, *, pos_label=None,
 
     precision = tps / (tps + fps)
     precision[np.isnan(precision)] = 0
-    recall = tps / tps[-1]
+    recall = np.ones(tps.size) if tps[-1] == 0 else tps / tps[-1]
 
     # stop when full recall attained
     # and reverse the outputs so recall is decreasing
diff --git a/sklearn/metrics/tests/test_ranking.py b/sklearn/metrics/tests/test_ranking.py
index a66ff9525c28c..f662e441c1812 100644
--- a/sklearn/metrics/tests/test_ranking.py
+++ b/sklearn/metrics/tests/test_ranking.py
@@ -807,6 +807,11 @@ def test_precision_recall_curve_toydata():
             precision_recall_curve(y_true, y_score)
         with pytest.raises(Exception):
             average_precision_score(y_true, y_score)
+        # p, r, _ = precision_recall_curve(y_true, y_score)
+        # auc_prc = average_precision_score(y_true, y_score)
+        # assert_array_almost_equal(p, [0, 1])
+        # assert_array_almost_equal(r, [1, 0.])
+        # assert_almost_equal(auc_prc, 0.)
 
         y_true = [1, 1]
         y_score = [0.25, 0.75]
@@ -822,6 +827,10 @@ def test_precision_recall_curve_toydata():
             average_precision_score(y_true, y_score, average="macro")
         with pytest.raises(Exception):
             average_precision_score(y_true, y_score, average="weighted")
+        # assert_almost_equal(average_precision_score(y_true, y_score,
+        #                     average="macro"), 0.5)
+        # assert_almost_equal(average_precision_score(y_true, y_score,
+        #                     average="weighted"), 1.)
         assert_almost_equal(average_precision_score(y_true, y_score,
                             average="samples"), 1.)
         assert_almost_equal(average_precision_score(y_true, y_score,
@@ -833,6 +842,10 @@ def test_precision_recall_curve_toydata():
             average_precision_score(y_true, y_score, average="macro")
         with pytest.raises(Exception):
             average_precision_score(y_true, y_score, average="weighted")
+        # assert_almost_equal(average_precision_score(y_true, y_score,
+        #                     average="macro"), 0.5)
+        # assert_almost_equal(average_precision_score(y_true, y_score,
+        #                     average="weighted"), 1.)
         assert_almost_equal(average_precision_score(y_true, y_score,
                             average="samples"), 0.75)
         assert_almost_equal(average_precision_score(y_true, y_score,
@@ -860,12 +873,36 @@ def test_precision_recall_curve_toydata():
         assert_almost_equal(average_precision_score(y_true, y_score,
                             average="micro"), 0.5)
 
+<<<<<<< HEAD
     with np.errstate(all="ignore"):
         # if one class is never present weighted should not be NaN
         y_true = np.array([[0, 0], [0, 1]])
         y_score = np.array([[0, 0], [0, 1]])
         assert_almost_equal(average_precision_score(y_true, y_score,
                             average="weighted"), 1)
+=======
+        y_true = np.array([[0, 0], [0, 0]])
+        y_score = np.array([[0, 1], [0, 1]])
+        assert_almost_equal(average_precision_score(y_true, y_score,
+                            average="macro"), 0.)
+        assert_almost_equal(average_precision_score(y_true, y_score,
+                            average="weighted"), 0.)
+        assert_almost_equal(average_precision_score(y_true, y_score,
+                            average="samples"), 0.)
+        assert_almost_equal(average_precision_score(y_true, y_score,
+                            average="micro"), 0.)
+
+        y_true = np.array([[1, 1], [1, 1]])
+        y_score = np.array([[0, 1], [0, 1]])
+        assert_almost_equal(average_precision_score(y_true, y_score,
+                            average="macro"), 1.)
+        assert_almost_equal(average_precision_score(y_true, y_score,
+                            average="weighted"), 1.)
+        assert_almost_equal(average_precision_score(y_true, y_score,
+                            average="samples"), 1.)
+        assert_almost_equal(average_precision_score(y_true, y_score,
+                            average="micro"), 1.)
+>>>>>>> fixed bug for precision recall curve when all labels are negative
 
 
 def test_average_precision_constant_values():