diff --git a/lab-python-data-structures.ipynb b/lab-python-data-structures.ipynb
index 5b3ce9e0..7c1759fa 100644
--- a/lab-python-data-structures.ipynb
+++ b/lab-python-data-structures.ipynb
@@ -50,11 +50,242 @@
     "\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": 61,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# This is Kyle Solving the Lab"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "products= [\"t-shirt\", \"mug\", \"hat\", \"book\", \"keychain\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "inventory={}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "t_shirt_quantity=int(input(\"Enter the quantity of t-shirsts:\"))\n",
+    "mug_quantity=int(input(\"Enter the quantity of mugs:\"))\n",
+    "hat_quantity=int(input(\"Enter the quantity of hats:\"))\n",
+    "book_quantity=int(input(\"Enter the quantity of mugs:\"))\n",
+    "keychain_quantity=int(input(\"Enter the quantity of keychains:\"))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "inventory={\"t-shirt\": t_shirt_quantity, \n",
+    "\"mug\": mug_quantity, \n",
+    "\"hat\": hat_quantity, \n",
+    "\"book\": book_quantity, \n",
+    "\"keychain\": keychain_quantity }"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'t-shirt': 5, 'mug': 5, 'hat': 5, 'book': 5, 'keychain': 5}"
+      ]
+     },
+     "execution_count": 66,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "inventory"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "customer_orders={}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 68,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "order_1=input(\"Please enter a product to order:\")\n",
+    "order_2= input(\"Please enter a product to order:\")\n",
+    "order_3=input(\"Please enter a product to order:\")\n",
+    "order_= input(\"Please enter a product to order:\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 69,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'mug', 'keychain', 'hat'}\n"
+     ]
+    }
+   ],
+   "source": [
+    "customer_orders={order_1, order_2, order_3}\n",
+    "print(customer_orders)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 70,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(3, 60.0)\n"
+     ]
+    }
+   ],
+   "source": [
+    "products_ordered= len(customer_orders)\n",
+    "percentage_ordered= len(customer_orders)/len(products)*100\n",
+    "order_statistics=(products_ordered, percentage_ordered,)\n",
+    "print(order_statistics)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 74,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'t-shirt': 3, 'mug': 3, 'hat': 3, 'book': 3, 'keychain': 3}\n"
+     ]
+    }
+   ],
+   "source": [
+    "inventory[\"t-shirt\"]=inventory[\"t-shirt\"]-1\n",
+    "inventory[\"mug\"]=inventory[\"mug\"]-1\n",
+    "inventory[\"book\"]=inventory[\"book\"]-1\n",
+    "inventory[\"hat\"]=inventory[\"hat\"]-1\n",
+    "inventory[\"keychain\"]=inventory[\"keychain\"]-1\n",
+    "print(inventory)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "Python 3",
    "language": "python",
    "name": "python3"
   },
@@ -68,7 +299,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.13"
+   "version": "3.9.10"
   }
  },
  "nbformat": 4,