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Completed lab exercises #774

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192 changes: 191 additions & 1 deletion lab-python-data-structures.ipynb
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Original file line number Diff line number Diff line change
Expand Up @@ -50,6 +50,196 @@
"\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": 2,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"['t-shirt', 'mug', 'hat', 'book', 'keychain']\n"
]
}
],
"source": [
"products = [\"t-shirt\", \"mug\", \"hat\", \"book\", \"keychain\"]\n",
"print(products) "
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"inventory = {}"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdin",
"output_type": "stream",
"text": [
"Enter quantity for t-shirt: 2\n",
"Enter quantity for mug: 1\n",
"Enter quantity for hat: 1\n",
"Enter quantity for book: 2\n",
"Enter quantity for keychain: 3\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'t-shirt': 2, 'mug': 1, 'hat': 1, 'book': 2, 'keychain': 3}\n"
]
}
],
"source": [
"inventory[\"t-shirt\"] = int(input(\"Enter quantity for t-shirt: \"))\n",
"inventory[\"mug\"] = int(input(\"Enter quantity for mug: \"))\n",
"inventory[\"hat\"] = int(input(\"Enter quantity for hat: \"))\n",
"inventory[\"book\"] = int(input(\"Enter quantity for book: \"))\n",
"inventory[\"keychain\"] = int(input(\"Enter quantity for keychain: \"))\n",
"\n",
"print(inventory)"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [],
"source": [
"customer_orders = set()"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"name": "stdin",
"output_type": "stream",
"text": [
"Enter product 1: hat\n",
"Enter product 2: mug\n",
"Enter product 3: keychain\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Customer orders: {'mug', 'hat', 'keychain'}\n"
]
}
],
"source": [
"customer_orders = set()\n",
"\n",
"order1 = input(\"Enter product 1: \")\n",
"customer_orders.add(order1)\n",
"\n",
"\n",
"order2 = input(\"Enter product 2: \")\n",
"customer_orders.add(order2)\n",
"\n",
"\n",
"order3 = input(\"Enter product 3: \")\n",
"customer_orders.add(order3)\n",
"\n",
"print(\"Customer orders:\", customer_orders)"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Order Statistics: \n",
"Total Products Ordered: 3\n",
"Percentage of Products Ordered: 60.0 %\n"
]
}
],
"source": [
"total_products_ordered = len(customer_orders)\n",
"percentage_ordered = (total_products_ordered / len(products)) * 100\n",
"\n",
"order_status = (total_products_ordered, percentage_ordered)\n",
"\n",
"print(\"Order Statistics: \")\n",
"print(\"Total Products Ordered: \", order_status[0])\n",
"print(\"Percentage of Products Ordered: \", order_status[1], \"%\")"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [],
"source": [
"inventory[order1] = inventory[order1] - 1\n",
"inventory[order2] = inventory[order2] - 1\n",
"inventory[order3] = inventory[order3] - 1"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Updated Inventory: \n",
"t-shirt: 2\n",
"mug: 0\n",
"hat: 0\n",
"book: 2\n",
"keychain: 2\n",
"keychain: 2\n"
]
}
],
"source": [
"print(\"Updated Inventory: \")\n",
"print(\"t-shirt:\",inventory[\"t-shirt\"])\n",
"print(\"mug:\", inventory[\"mug\"])\n",
"print(\"hat:\", inventory[\"hat\"])\n",
"print(\"book:\", inventory[\"book\"])\n",
"print(\"keychain:\", inventory[\"keychain\"]) \n",
"print(\"keychain:\", inventory[\"keychain\"])\n",
" "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
Expand All @@ -68,7 +258,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.13"
"version": "3.14.6"
}
},
"nbformat": 4,
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