diff --git a/Chapter 06/thompson_sampling.py b/Chapter 06/thompson_sampling.py
index 03313f38..7096bc3b 100644
--- a/Chapter 06/thompson_sampling.py	
+++ b/Chapter 06/thompson_sampling.py	
@@ -24,6 +24,8 @@
 total_reward_ts = 0
 numbers_of_rewards_1 = [0] * d
 numbers_of_rewards_0 = [0] * d
+# collect data for plot
+results = []
 for n in range(0, N):
     # Random Selection
     strategy_rs = random.randrange(d)
@@ -45,7 +47,10 @@
         numbers_of_rewards_0[strategy_ts] = numbers_of_rewards_0[strategy_ts] + 1
     strategies_selected_ts.append(strategy_ts)
     total_reward_ts = total_reward_ts + reward_ts
-
+    # collect data for plot every x iterations
+    if n%50==0:
+        results.append(numbers_of_rewards_1.copy())
+        
 # Computing the Relative Return
 relative_return = (total_reward_ts - total_reward_rs) / total_reward_rs * 100
 print("Relative Return: {:.0f} %".format(relative_return))
@@ -56,3 +61,15 @@
 plt.xlabel('Strategy')
 plt.ylabel('Number of times the strategy was selected')
 plt.show()
+
+# Plotting animated plot
+from matplotlib.animation import FuncAnimation
+fig, ax = plt.subplots()
+fig.set_tight_layout(True)
+
+def update(i):
+    return plt.plot(range(9), results[i])
+# FuncAnimation will call the 'update' function for each frame; here
+    # animating over all frames, with an interval of 200ms between frames.
+anim = FuncAnimation(fig, update, frames=np.arange(0, len(results)), interval=200)
+plt.show()