diff --git a/doc/source/user_guide/basics.rst b/doc/source/user_guide/basics.rst
index 8155aa0ae03fa..d35cc06db9f06 100644
--- a/doc/source/user_guide/basics.rst
+++ b/doc/source/user_guide/basics.rst
@@ -209,6 +209,10 @@ either match on the *index* or *columns* via the **axis** keyword:
    df.sub(column, axis="index")
    df.sub(column, axis=0)
 
+Be careful when using raw Python lists in binary operations with DataFrames.
+Unlike NumPy arrays or Series, lists are not broadcast across rows or columns.
+Instead, pandas attempts to match the entire list against a single axis, which may lead to confusing results such as Series of arrays.
+To ensure proper broadcasting behavior, use a NumPy array or Series with explicit index or shape.
 Furthermore you can align a level of a MultiIndexed DataFrame with a Series.
 
 .. ipython:: python
diff --git a/doc/source/user_guide/dsintro.rst b/doc/source/user_guide/dsintro.rst
index 89981786d60b5..59938a66bb507 100644
--- a/doc/source/user_guide/dsintro.rst
+++ b/doc/source/user_guide/dsintro.rst
@@ -650,6 +650,13 @@ row-wise. For example:
 
    df - df.iloc[0]
 
+When using a Python list in arithmetic operations with a DataFrame, the behavior is not element-wise broadcasting.
+Instead, the list is treated as a single object and the operation is performed column-wise, resulting in unexpected output (e.g. arrays inside each cell).
+
+.. ipython:: python
+
+    df + np.array([1, 2, 3])  
+    
 For explicit control over the matching and broadcasting behavior, see the
 section on :ref:`flexible binary operations <basics.binop>`.