diff --git a/4_decorators.py b/4_decorators.py
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--- /dev/null
+++ b/4_decorators.py
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+"""
+Now that we have covered the basics of a function and variables, we will quickly cover decorators.
+Decorators are extremely useful and are used to wrap existing functions in another function, you can use this 
+for many different things, a good example is permission checks.
+
+Key Concepts:
+    **wrappers** are used to nest functions in another generalized function, this can be used for various things e.g (logging info)
+    **args** we use the "*args* paramater to capture all positional based arguements, these come before keyword arguements.
+     it sounds more complicated than it is, e.g
+     myfunction(arg1, arg2, arg3)
+    **kwargs** are arguements defined by a key rather than their position. e.g
+     myfunction(keyword1="foo", keyword2="bar")
+    **__name__** is just another magic/dunder method that returns the function's name.
+"""
+
+import time
+
+def timed(function: any) -> any: #initial function takes in another function as a arguement
+    
+    def wrapper(*args: any, **kwargs: any) -> any: # the wrapper, takes in the arguements and keyword arguements from the function
+        before = time.time()
+        output = function(*args, **kwargs)
+        after = time.time()
+        print(f"{function.__name__} with output of {output} took {after-before}s to execute.")
+        return output # ensure the output of the function is passed back
+    
+    return wrapper 
+
+@timed
+def exponential_function(n: int) -> int:  # O(n^2) time complexity due to nested loops
+    output = 0
+    for i in range(n):
+        for j in range(i):
+            output += j
+    return output
+
+# Test the decorated function
+print(exponential_function(1000))  # This will take some time to compute
+exponential_function(10000)  # This will take noticeably more time to compute
+
diff --git a/5_pandas.py b/5_pandas.py
new file mode 100644
index 0000000..e69de29