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solve lab #516

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3e3984d
solve lab
TebasMartinez Jul 8, 2025
e795b3d
correct 9th answer
TebasMartinez Jul 8, 2025
943dad7
correct 7th answer
TebasMartinez Jul 8, 2025
a287af2
correct misspelling
TebasMartinez Jul 9, 2025
784baf1
Update lab-python-data-structures.ipynb
TebasMartinez Jul 9, 2025
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256 changes: 256 additions & 0 deletions .ipynb_checkpoints/lab-python-data-structures-checkpoint.ipynb
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{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"tags": []
},
"source": [
"# Lab | Data Structures "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Exercise: Managing Customer Orders\n",
"\n",
"As part of a business venture, you are starting an online store that sells various products. To ensure smooth operations, you need to develop a program that manages customer orders and inventory.\n",
"\n",
"Follow the steps below to complete the exercise:\n",
"\n",
"1. Define a list called `products` that contains the following items: \"t-shirt\", \"mug\", \"hat\", \"book\", \"keychain\".\n",
"\n",
"2. Create an empty dictionary called `inventory`.\n",
"\n",
"3. Ask the user to input the quantity of each product available in the inventory. Use the product names from the `products` list as keys in the `inventory` dictionary and assign the respective quantities as values.\n",
"\n",
"4. Create an empty set called `customer_orders`.\n",
"\n",
"5. Ask the user to input the name of three products that a customer wants to order (from those in the products list, meaning three products out of \"t-shirt\", \"mug\", \"hat\", \"book\" or \"keychain\". Add each product name to the `customer_orders` set.\n",
"\n",
"6. Print the products in the `customer_orders` set.\n",
"\n",
"7. Calculate the following order statistics:\n",
" - Total Products Ordered: The total number of products in the `customer_orders` set.\n",
" - Percentage of Products Ordered: The percentage of products ordered compared to the total available products.\n",
" \n",
" Store these statistics in a tuple called `order_status`.\n",
"\n",
"8. Print the order statistics using the following format:\n",
" ```\n",
" Order Statistics:\n",
" Total Products Ordered: <total_products_ordered>\n",
" Percentage of Products Ordered: <percentage_ordered>% \n",
" ```\n",
"\n",
"9. Update the inventory by subtracting 1 from the quantity of each product. Modify the `inventory` dictionary accordingly.\n",
"\n",
"10. Print the updated inventory, displaying the quantity of each product on separate lines.\n",
"\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": 14,
"metadata": {},
"outputs": [],
"source": [
"# 1\n",
"products = [\"t-shirt\", \"mug\", \"hat\", \"book\", \"keychain\"]"
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {},
"outputs": [],
"source": [
"# 2\n",
"inventory = {}"
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {},
"outputs": [
{
"name": "stdin",
"output_type": "stream",
"text": [
"Please provide number or t-shirt available: 2\n",
"Please provide number or mug available: 3\n",
"Please provide number or hat available: 4\n",
"Please provide number or book available: 5\n",
"Please provide number or keychain available: 6\n"
]
}
],
"source": [
"# 3\n",
"for product in products:\n",
" inventory[product] = input(f\"Please provide number of {product} available: \")"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [],
"source": [
"# 4\n",
"customer_orders = set()"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [
{
"name": "stdin",
"output_type": "stream",
"text": [
"Please add product number 1 to the order: book\n",
"Please add product number 2 to the order: hat\n",
"Please add product number 3 to the order: keychain\n"
]
}
],
"source": [
"# 5\n",
"for i in range(3):\n",
" customer_orders.add(input(f\"Please add product number {i + 1} to the order: \"))"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The products in the order are: {'keychain', 'book', 'hat'}\n"
]
}
],
"source": [
"# 6\n",
"print(f\"The products in the order are: {customer_orders}\")"
]
},
{
"cell_type": "code",
"execution_count": 48,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The number of products in the order is 3.\n"
]
}
],
"source": [
"# 7\n",
"order_length = len(customer_orders)\n",
"print(f\"The number of products in the order is {order_length}.\")\n",
"\n",
"products_length = len(products)\n",
"\n",
"total_products = 0\n",
"for product in products:\n",
" total_products += int(inventory[product])\n",
"\n",
"perc_ordered = (order_length / total_products) * 100\n",
"\n",
"stats_tuple = (products_length, perc_ordered)"
]
},
{
"cell_type": "code",
"execution_count": 49,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Order Statistics:\n",
"Total Products Ordered: 3\n",
"Percentage of Products Ordered: 17.647058823529413%\n"
]
}
],
"source": [
"# 8\n",
"print(\"Order Statistics:\")\n",
"print(f\"Total Products Ordered: {order_length}\")\n",
"print(f\"Percentage of Products Ordered: {perc_ordered}%\")"
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {},
"outputs": [],
"source": [
"# 9\n",
"for product in customer_orders:\n",
" if product in inventory.keys():\n",
" prod = int(inventory[product])\n",
" prod -= 1\n",
" inventory[product] = str(prod)"
]
},
{
"cell_type": "code",
"execution_count": 47,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"t-shirt: 2\n",
"mug: 3\n",
"hat: 3\n",
"book: 4\n",
"keychain: 5\n"
]
}
],
"source": [
"# 10\n",
"for product in products:\n",
" print(f\"{product}: {inventory[product]}\")"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python [conda env:base] *",
"language": "python",
"name": "conda-base-py"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.12.7"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
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