diff --git a/Notebook-HengkuanZhang.md b/Notebook-HengkuanZhang.md
index 63ea4addf25899a0e2831183d461772c3b4f9a4b..569ac6a4240389121d4968eb1bbbbba1f62c4599 100644
--- a/Notebook-HengkuanZhang.md
+++ b/Notebook-HengkuanZhang.md
@@ -42,7 +42,88 @@
         Less is more.
         ```
         
+## Mission 2
+
+1.  Jupyter tips and tricks
+
+    1.   Creating and importing a notebook
+
+         Adding a brand new notebook in a given directory
+
+         1.   From the menu: File -> Open. You're now in the Jupyter file manager.
+
+         2.   Navigate to the directory where you want your notebook to be created.
+
+         3.   Then from the top right button: New -> Notebook: Python 3.
+
+         4.   Give your notebook a name from the menu: File -> Rename.
+
+         Importing an already existing notebook
+
+         1.   Download the file on your computer. E.g., for this GitLab hosted notebook, click on Open raw (a small </> within a document icon) and save (Ctrl-S on most browsers) the content (a long JSON text file).
+         
+         2.   Open the Jupyter file manager from the menu File -> Open and navigate to the directory where you want to upload your notebook.
+         
+         3.   Then from the top right button, Upload the previously downloaded notebook and confirm the upload.
+         
+         4.   Open the freshly uploaded notebook through the Jupyter file manager.
+    
+    2.   Running R and Python on a same notebook
+        
+         1.   Loading rpy2:
+              %load_ext rpy2.ipython
+              
+         2.   Using the %R Ipython magic:
+              %%R 
+              summary(cars)
+         
+         3.   Python objects can then even be passed to R as follows (assuming df is a pandas dataframe):
+              %%R -i df
+              plot(df)
+        
+2.   Exo 3
+
+     1.   Sequence plot
+
+          import matplotlib.pyplot as plt
+        
+          import numpy as np
+        
+          x = np.arange(1, 101)  # x轴数据 (1-100)
+        
+          y = np.random.randint(1, 101, 100)  # y轴数据
+        
+          plt.figure(figsize=(8, 5))
+        
+          plt.plot(x, y, 
+           linestyle='-',      # 实线连接
+           color='royalblue',  # 线条颜色
+           linewidth=2)        # 线宽
+        
+          plt.xlabel('X', fontsize=12)
         
+          plt.ylabel('Y', fontsize=12)
         
+          plt.grid(True, linestyle='--', alpha=0.7)  # 添加网格线
+        
+          plt.show()
+        
+     2.   Histogram
+
+          x = np.arange(1, 9) 
+          
+          y = np.random.randint(1, 26, 8)
+
+          plt.figure(figsize=(8, 5))
+          
+          bars = plt.bar(x, y, 
+               color=['#1f77b4', '#ff7f0e', '#2ca02c', '#d62728', '#9467bd'],
+               width=0.6)
+
+          plt.xlabel('x', fontsize=12)
+
+          plt.ylabel('y', fontsize=12)
+          
+          plt.ylim(0, max(y)*1.1)
 
-        
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+          plt.show()    
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