diff --git a/CHANGES.txt b/CHANGES.txt
index 66570409c..ae96037d1 100644
--- a/CHANGES.txt
+++ b/CHANGES.txt
@@ -128,6 +128,7 @@ v<0.8.9>, <06/10/2021> -- Add PyTorch AutoEncoder
 v<0.8.9>, <06/11/2021> -- Fix LMDD parameter (#307)
 v<0.9.0>, <06/20/2021> -- Add clone test for models.
 v<0.9.0>, <07/03/2021> -- ROD hot fix (#316).
+v<0.9.0>, <07/04/2021> -- Improve COPOD plot with colunms parameter.
 
 
 
diff --git a/pyod/models/copod.py b/pyod/models/copod.py
index feaef421b..eabf86e11 100644
--- a/pyod/models/copod.py
+++ b/pyod/models/copod.py
@@ -226,6 +226,9 @@ def explain_outlier(self, ind, columns=None, cutoffs=None,
         ind : int
             The index of the data point one wishes to obtain
             a dimensional outlier graph for.
+
+        columns : list
+            Specify a list of features/dimensions for plotting.
         
         cutoffs : list of floats in (0., 1), optional (default=[0.95, 0.99])
             The significance cutoff bands of the dimensional outlier graph.
@@ -272,6 +275,7 @@ def explain_outlier(self, ind, columns=None, cutoffs=None,
             index=ind + 1, label=label))
         plt.legend()
         plt.show()
-        return self.O.loc[ind, columns], self.O.loc[:, columns].quantile(q=cutoffs[0],
-                                                 axis=0), self.O.loc[:, columns].quantile(
+        return self.O.loc[ind, columns], self.O.loc[:, columns].quantile(
+            q=cutoffs[0],
+            axis=0), self.O.loc[:, columns].quantile(
             q=cutoffs[1], axis=0)