diff --git a/2-produce-graph/pie--datacite-type.png b/2-produce-graph/pie--datacite-type.png
index c161e826007b7302b5d614f5da60e505be044ce5..f6d3ac4241e75ab36088151f1ba4398216e02d75 100644
Binary files a/2-produce-graph/pie--datacite-type.png and b/2-produce-graph/pie--datacite-type.png differ
diff --git a/2-produce-graph/pie-data-type.py b/2-produce-graph/pie-data-type.py
index 074b8f73397df72af4dff9f41b60d92656188450..e1b8a46948e83dc2ef90757c6d29358d08118d8d 100644
--- a/2-produce-graph/pie-data-type.py
+++ b/2-produce-graph/pie-data-type.py
@@ -17,7 +17,7 @@ df_type = df["resourceTypeGeneral"].value_counts()
 
 #define a color palette to use
 ### see color palett https://matplotlib.org/stable/users/explain/colors/colormaps.html
-colors = [plt.cm.Pastel1(i) for i in range(len(df_type))]
+colors = [plt.cm.tab20(i) for i in range(len(df_type))]
 random.shuffle(colors) ## so that blue is not more the first item
 
 plt.subplots(figsize=(10, 7))
@@ -25,7 +25,7 @@ plt.subplots(figsize=(10, 7))
 plt.pie(df_type, colors = colors, autopct=lambda p: '{:.0f}%'.format(round(p)) if p > 1 else '', startangle = 160)
 ## auto pct only if value > 1
 
-plt.legend(df_type.index, loc = (0.85, 0.2) )
+plt.legend(df_type.index, loc = (0.80, 0.2), framealpha = 0.95)
 
 plt.title(f"Type of datasets", fontsize = 20, x = 0.5, y = 1.03, alpha = 0.6)
 plt.suptitle(f"n = {len(df)}", fontsize = 11, x = 0.5, y = 0.9, alpha = 0.6)
@@ -33,6 +33,6 @@ plt.tight_layout(h_pad = 0)
 plt.savefig("pie--datacite-type.png")
 print(f"\ngraph produced pie--datacite-type.png")
 
-plt.show()
+# plt.show()
 
 # print(len(df))
\ No newline at end of file