diff --git a/README.md b/README.md
index b66b7a56..c2e5ccbe 100644
--- a/README.md
+++ b/README.md
@@ -36,7 +36,7 @@ Before this starting this lab, you should have learnt about:
 
 ## Introduction
 
-Welcome to your first data analytics bootcamp lab! In this lab, you will have the opportunity to dive into one of the fundamental building blocks of Python programming: data structures.
+Welcome to your first data analytics bootcamp lab!!! In this lab, you will have the opportunity to dive into one of the fundamental building blocks of Python programming: data structures.
 
 As you may already know, data structures are collections of values that can be used to organize and manipulate data more efficiently, such as lists, dictionaries, sets, and tuples.
 
diff --git a/lab-python-data-structures.ipynb b/lab-python-data-structures.ipynb
index 5b3ce9e0..f04a58bd 100644
--- a/lab-python-data-structures.ipynb
+++ b/lab-python-data-structures.ipynb
@@ -50,6 +50,155 @@
     "\n",
     "Solve the exercise by implementing the steps using the Python concepts of lists, dictionaries, sets, and basic input/output operations. "
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdin",
+     "output_type": "stream",
+     "text": [
+      " Enter the inventory quantity of t-shirt:  4\n",
+      " Enter the inventory quantity of mug:  3\n",
+      " Enter the inventory quantity of hat:  4\n",
+      " Enter the inventory quantity of book:  3\n",
+      " Enter the inventory quantity of keychain:  4\n",
+      "Enter 3 products to order: ss\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "'ss'"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "products=[\"t-shirt\", \"mug\", \"hat\", \"book\", \"keychain\"] #1\n",
+    "\n",
+    "inventory={} #2\n",
+    "\n",
+    "for x in products: #3\n",
+    "    qnt =input (f\" Enter the inventory quantity of {x}: \")\n",
+    "    inventory[x] = int(qnt)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdin",
+     "output_type": "stream",
+     "text": [
+      "Enter a product (t-shirt, mug, hat, book, keychain):  hat\n",
+      "Enter a product (t-shirt, mug, hat, book, keychain):  mug\n",
+      "Enter a product (t-shirt, mug, hat, book, keychain):  book\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'mug', 'book', 'hat'}\n"
+     ]
+    }
+   ],
+   "source": [
+    "customer_orders = set() #4\n",
+    "\n",
+    "for i in range(3): #5\n",
+    "    product = input(\"Enter a product (t-shirt, mug, hat, book, keychain): \")\n",
+    "    if product in products:\n",
+    "        customer_orders.add(product)\n",
+    "    else:\n",
+    "        print(\"Invalid product. Please enter one of the listed products.\")\n",
+    "        \n",
+    "print (customer_orders) #6\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(3, 60.0)\n"
+     ]
+    }
+   ],
+   "source": [
+    "\n",
+    "total_products_ordered = len(customer_orders) #7\n",
+    "\n",
+    "percentage_products_ordered = (total_products_ordered / len(products)) * 100  \n",
+    "\n",
+    "order_status = (total_products_ordered, percentage_products_ordered)\n",
+    "\n",
+    "print(order_status)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Order Statistics:\n",
+      "Total Products Ordered: 3\n",
+      "Percentage of Products Ordered: 60.0%\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"Order Statistics:\") #8\n",
+    "print(f\"Total Products Ordered: {total_products_ordered}\")\n",
+    "print(f\"Percentage of Products Ordered: {percentage_products_ordered}%\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "t-shirt 3\n",
+      "mug 2\n",
+      "hat 3\n",
+      "book 2\n",
+      "keychain 3\n"
+     ]
+    }
+   ],
+   "source": [
+    "for product in inventory: #9\n",
+    "    inventory[product] -= 1\n",
+    "    print(product, inventory[product])#10"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
@@ -68,7 +217,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.13"
+   "version": "3.9.6"
   }
  },
  "nbformat": 4,