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192 changes: 190 additions & 2 deletions lab-python-data-structures.ipynb
Original file line number Diff line number Diff line change
Expand Up @@ -50,11 +50,199 @@
"\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": 1,
"metadata": {},
"outputs": [],
"source": [
"#1\n",
"products = (\"t-shirt\", \"mug\", \"hat\", \"book\", \"keychain\")\n",
"\n",
"\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"#2 \n",
"inventory = { }"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'t-shirt': 45, 'mug': 56, 'hat': 33, 'book': 41, 'keychain': 70}\n"
]
}
],
"source": [
"#3 \n",
"q1 = int(input(\"Quantity of t-shirt: \"))\n",
"inventory['t-shirt'] = q1\n",
"\n",
"q2 = int(input(\"Quantity of mug: \"))\n",
"inventory['mug'] = q2\n",
"\n",
"q3 = int(input(\"Quantity of hat: \"))\n",
"inventory['hat'] = q3\n",
"\n",
"q4 = int(input(\"Quantity of book: \"))\n",
"inventory['book'] = q4\n",
"\n",
"q5 = int(input(\"Quantity of keychain: \"))\n",
"inventory['keychain'] = q5\n",
"\n",
"print(inventory)\n",
"\n",
"#for i range(5)\n",
" #"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"#4.\n",
"customer_orders = set()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'t-shirt', 'hat', 'kechain'}\n"
]
}
],
"source": [
"#5. \n",
"o1 = input(\"Enter Product 1 to Order: \")\n",
"customer_orders.add(o1)\n",
"\n",
"o2 = input(\"Enter Product 2to Order: \")\n",
"customer_orders.add(o2)\n",
"\n",
"o3 = input(\"Enter Product 3 to Order: \")\n",
"customer_orders.add(o3)\n",
"\n",
"#6\n",
"print(customer_orders)"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"3\n",
"60.0 %\n"
]
}
],
"source": [
"#7. total number of products in the customer order set\n",
"print(len(customer_orders))\n",
"\n",
"#Percentage of Products Ordered from the inventory\n",
"percentage_po = len(customer_orders)*100/len(inventory)\n",
"print(percentage_po, '%')\n",
"\n",
"#Store these statistics in a tuple called `order_status`\n",
"\n",
"order_status = (len(customer_orders), percentage_po)\n",
"\n"
]
},
{
"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": [
"#8. Print order status\n",
"\n",
"#print(order_status)\n",
"print('Order Statistics:')\n",
"print('Total Products Ordered:', len(customer_orders))\n",
"print('Percentage of Products Ordered:', (percentage_po),'%')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#9.\n",
"\n",
"for product in inventory:\n",
" inventory[product] = inventory[product] - 1\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'t-shirt': 43, 'mug': 55, 'hat': 31, 'book': 40, 'keychain': 69}\n"
]
}
],
"source": [
"#10. \n",
"\n",
"print(inventory)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
Expand All @@ -68,7 +256,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.13"
"version": "3.13.2"
}
},
"nbformat": 4,
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