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262 changes: 262 additions & 0 deletions .ipynb_checkpoints/lab-python-flow-control-checkpoint.ipynb
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{
"cells": [
{
"cell_type": "markdown",
"id": "d3bfc191-8885-42ee-b0a0-bbab867c6f9f",
"metadata": {
"tags": []
},
"source": [
"# Lab | Flow Control"
]
},
{
"cell_type": "markdown",
"id": "3851fcd1-cf98-4653-9c89-e003b7ec9400",
"metadata": {},
"source": [
"## Exercise: Managing Customer Orders Optimized\n",
"\n",
"In the last lab, you were starting an online store that sells various products. To ensure smooth operations, you developed a program that manages customer orders and inventory.\n",
"\n",
"You did so without using flow control. Let's go a step further and improve this code.\n",
"\n",
"Follow the steps below to complete the exercise:\n",
"\n",
"1. Look at your code from the lab data structures, and improve repeated code with loops.\n",
"\n",
"2. Instead of asking the user to input the name of three products that a customer wants to order, do the following:\n",
" \n",
" a. Prompt the user to enter the name of a product that a customer wants to order.\n",
" \n",
" b. Add the product name to the \"customer_orders\" set.\n",
" \n",
" c. Ask the user if they want to add another product (yes/no).\n",
" \n",
" d. Continue the loop until the user does not want to add another product.\n",
"\n",
"3. Instead of updating the inventory by subtracting 1 from the quantity of each product, only do it for the products that were ordered (those in \"customer_orders\")."
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "1abf2b09-6690-45bd-a7af-861b7c37861e",
"metadata": {},
"outputs": [],
"source": [
"products = [\"tshirt\", \"mug\", \"hat\", \"book\", \"keychain\"]\n",
"inventory = {}"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "a387e2f0-b788-47e0-b765-d256b9bcb50f",
"metadata": {},
"outputs": [
{
"name": "stdin",
"output_type": "stream",
"text": [
"Enter quantity for tshirt: 22\n",
"Enter quantity for mug: 15\n",
"Enter quantity for hat: 11\n",
"Enter quantity for book: 7\n",
"Enter quantity for keychain: 3\n"
]
}
],
"source": [
"for product in products:\n",
" quantity = input(f\"Enter quantity for {product}: \")\n",
" inventory[product] = int(quantity)"
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "bdea4509-61ab-4ef3-bfba-3cb57ff820b6",
"metadata": {},
"outputs": [],
"source": [
"customer_orders = set()"
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "b7738418-1d35-40bc-abd8-fd58ffea2a99",
"metadata": {},
"outputs": [
{
"name": "stdin",
"output_type": "stream",
"text": [
"Enter product to order: hat\n",
"Do you want to add another product? (yes/no): yes\n",
"Enter product to order: book\n",
"Do you want to add another product? (yes/no): yes\n",
"Enter product to order: keychain\n",
"Do you want to add another product? (yes/no): no\n"
]
}
],
"source": [
"another = \"yes\"\n",
"while another == \"yes\":\n",
" order = input(\"Enter product to order: \")\n",
" customer_orders.add(order)\n",
" another = input(\"Do you want to add another product? (yes/no): \").lower()"
]
},
{
"cell_type": "code",
"execution_count": 15,
"id": "061bc2f5-e924-49c1-94bb-e8e7404c3e2a",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'book', 'hat', 'keychain'}"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"customer_orders"
]
},
{
"cell_type": "code",
"execution_count": 16,
"id": "4cca7c79-7608-486c-8a11-80787cb54774",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(3, 60.0)"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"total_products_ordered = len(customer_orders)\n",
"percentage_ordered = (len(customer_orders) / len(products)) * 100\n",
"order_status = (total_products_ordered, percentage_ordered)\n",
"order_status"
]
},
{
"cell_type": "code",
"execution_count": 17,
"id": "d327f508-9749-44ea-b792-3b02fbc9e31b",
"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:\")\n",
"print(f\"Total Products Ordered: {total_products_ordered}\")\n",
"print(f\"Percentage of Products Ordered: {percentage_ordered}%\")"
]
},
{
"cell_type": "code",
"execution_count": 18,
"id": "eab5aa66-9251-4c3e-aa9b-8670ecb14cdb",
"metadata": {},
"outputs": [],
"source": [
"for product in customer_orders:\n",
" inventory[product] -= 1"
]
},
{
"cell_type": "code",
"execution_count": 19,
"id": "53c76ab2-ca9e-41be-a583-a1245fed1a61",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'tshirt': 22, 'mug': 15, 'hat': 10, 'book': 6, 'keychain': 2}\n"
]
}
],
"source": [
"print(inventory)"
]
},
{
"cell_type": "code",
"execution_count": 20,
"id": "79a7d331-63f8-4338-ae9f-29559175d3e9",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"tshirt: 22\n",
"mug: 15\n",
"hat: 10\n",
"book: 6\n",
"keychain: 2\n"
]
}
],
"source": [
"for product in inventory:\n",
" print(f\"{product}: {inventory[product]}\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "4516bdb6-af15-4145-bdf7-f6e91945896b",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python [conda env:base] *",
"language": "python",
"name": "conda-base-py"
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"codemirror_mode": {
"name": "ipython",
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},
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"nbformat": 4,
"nbformat_minor": 5
}
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