diff --git a/quapy/plot.py b/quapy/plot.py
index 7b2145f..2e41413 100644
--- a/quapy/plot.py
+++ b/quapy/plot.py
@@ -4,7 +4,7 @@ from matplotlib.cm import get_cmap
 import numpy as np
 from matplotlib import cm
 from scipy.stats import ttest_ind_from_stats
-from matplotlib.ticker import StrMethodFormatter, NullFormatter
+from matplotlib.ticker import ScalarFormatter
 
 import quapy as qp
 
@@ -270,11 +270,11 @@ def error_by_drift(method_names, true_prevs, estim_prevs, tr_prevs,
         tr_test_drifts = data[method]['x']
         method_drifts = data[method]['y']
         if logscale:
-            #method_drifts=np.log(1+method_drifts)
-            plt.yscale("log")
-            ax.yaxis.set_major_formatter(StrMethodFormatter('{x:.2f}'))
-            ax.yaxis.set_minor_formatter(StrMethodFormatter('{x:.2f}'))
-            
+            ax.set_yscale("log")
+            ax.yaxis.set_major_formatter(ScalarFormatter())
+            ax.yaxis.set_minor_formatter(ScalarFormatter())
+            ax.yaxis.get_major_formatter().set_scientific(False)
+            ax.yaxis.get_minor_formatter().set_scientific(False)
 
         inds = np.digitize(tr_test_drifts, bins, right=True)
 
@@ -305,8 +305,14 @@ def error_by_drift(method_names, true_prevs, estim_prevs, tr_prevs,
             ax.fill_between(xs, ys-ystds, ys+ystds, alpha=0.25)
 
     if show_density:
-        ax.bar([ind * binwidth-binwidth/2 for ind in range(len(bins))],
+        ax2 = ax.twinx()
+        ax2.bar([ind * binwidth-binwidth/2 for ind in range(len(bins))],
                max_y*npoints/np.max(npoints), alpha=0.15, color='g', width=binwidth, label='density')
+        #ax2.set_ylabel("bar data")
+        ax2.set_ylim(0,1)
+        ax2.spines['right'].set_color('g')
+        ax2.tick_params(axis='y', colors='g')
+        #ax2.yaxis.set_visible(False)
     
     ax.set(xlabel=f'Distribution shift between training set and test sample',
            ylabel=f'{error_name.upper()} (true distribution, predicted distribution)',
@@ -321,8 +327,8 @@ def error_by_drift(method_names, true_prevs, estim_prevs, tr_prevs,
     ax.set_xlim(min_x, max_x)
     
     if show_legend:
-        ax.legend(loc='center left', bbox_to_anchor=(1, 0.5))
-
+        fig.legend(loc='right')
+      
     _save_or_show(savepath)