diff --git a/GPy/plotting/matplot_dep/variational_plots.py b/GPy/plotting/matplot_dep/variational_plots.py
index ca8b1e86..3f20efeb 100644
--- a/GPy/plotting/matplot_dep/variational_plots.py
+++ b/GPy/plotting/matplot_dep/variational_plots.py
@@ -34,7 +34,7 @@ def plot(parameterized, fignum=None, ax=None, colors=None, figsize=(12, 6)):
         else:
             raise ValueError("Need one ax per latent dimension input_dim")
         bg_lines.append(a.plot(means, c='k', alpha=.3))
-        lines.extend(a.plot(x, means.T[i], c=colors.next(), label=r"$\mathbf{{X_{{{}}}}}$".format(i)))
+        lines.extend(a.plot(x, means.T[i], c=next(colors), label=r"$\mathbf{{X_{{{}}}}}$".format(i)))
         fills.append(a.fill_between(x,
                         means.T[i] - 2 * np.sqrt(variances.T[i]),
                         means.T[i] + 2 * np.sqrt(variances.T[i]),
@@ -86,7 +86,7 @@ def plot_SpikeSlab(parameterized, fignum=None, ax=None, colors=None, side_by_sid
         # mean and variance plot
         a = fig.add_subplot(*sub1)
         a.plot(means, c='k', alpha=.3)
-        plots.extend(a.plot(x, means.T[i], c=colors.next(), label=r"$\mathbf{{X_{{{}}}}}$".format(i)))
+        plots.extend(a.plot(x, means.T[i], c=next(colors), label=r"$\mathbf{{X_{{{}}}}}$".format(i)))
         a.fill_between(x,
                         means.T[i] - 2 * np.sqrt(variances.T[i]),
                         means.T[i] + 2 * np.sqrt(variances.T[i]),
diff --git a/GPy/util/pca.py b/GPy/util/pca.py
index edb8bb7d..3bfcacd9 100644
--- a/GPy/util/pca.py
+++ b/GPy/util/pca.py
@@ -131,7 +131,7 @@ class PCA(object):
         kwargs.update(dict(s=s))
         plots = list()
         for i, l in enumerate(ulabels):
-            kwargs.update(dict(color=colors.next(), marker=marker[i % len(marker)]))
+            kwargs.update(dict(color=next(colors), marker=marker[i % len(marker)]))
             plots.append(ax.scatter(*X_[labels == l, :].T, label=str(l), **kwargs))
         ax.set_xlabel(r"PC$_1$")
         ax.set_ylabel(r"PC$_2$")