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Solved the first lab #507

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219 changes: 219 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": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"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": 37,
"metadata": {},
"outputs": [],
"source": [
"# Creating a list\n",
"products = [\"t-shirt\", \"mug\",\"hat\", \"book\", \"keychain\"]\n",
"# Creating an empty dictionary\n",
"inventory = {}"
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {},
"outputs": [
{
"name": "stdin",
"output_type": "stream",
"text": [
"Enter key: t-shirt\n",
"Enter value: 4\n",
"Enter key: mug\n",
"Enter value: 3\n",
"Enter key: hat\n",
"Enter value: 2\n",
"Enter key: book\n",
"Enter value: 5\n",
"Enter key: keychain\n",
"Enter value: 2\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'t-shirt': 4, 'mug': 3, 'hat': 2, 'book': 5, 'keychain': 2}\n"
]
}
],
"source": [
"# Input the quanitiy of each product and show the content of the inventory\n",
"inventory = {input(\"Enter key: \"): int(input(\"Enter value: \")) for product in products}\n",
"print (inventory)"
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stdin",
"output_type": "stream",
"text": [
"enter the product1 (chose from ['t-shirt', 'mug', 'hat', 'book', 'keychain']): mug\n",
"enter the product2 (chose from ['t-shirt', 'mug', 'hat', 'book', 'keychain']): hat\n",
"enter the product3 (chose from ['t-shirt', 'mug', 'hat', 'book', 'keychain']): book\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"customer orders are: {'book', 'mug', 'hat'}\n"
]
}
],
"source": [
"# Input the name of the products and add them to customer_orders set\n",
"customer_orders= set()\n",
"for i in range(3):\n",
" value = input(f\"enter the product{i + 1} (chose from {products}): \")\n",
" customer_orders.add(value)\n",
"print (\"customer orders are: \", customer_orders) "
]
},
{
"cell_type": "code",
"execution_count": 69,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Order Statistics\n",
"Total Products Ordered: 3\n",
"Percentage of Products Ordered: 27.27272727272727 %\n"
]
}
],
"source": [
"# Calculate the order statistics and print them:\n",
"total_products_ordered=int(len(customer_orders))\n",
"percentage_of_products_ordered = (total_products_ordered/sum(inventory.values())) * 100\n",
"order_status=(total_products_ordered,percentage_of_products_ordered)\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": 71,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Updated t-shirt: 2 left\n",
"Updated mug: 1 left\n",
"Updated hat: 0 left\n",
"Updated book: 3 left\n",
"Updated keychain: 0 left\n"
]
}
],
"source": [
"# Update the inventory\n",
"for quantity in inventory:\n",
" inventory[quantity] = inventory[quantity]-1\n",
" print (f\"Updated {quantity}: {inventory[quantity]} left\")\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"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
}
149 changes: 146 additions & 3 deletions lab-python-data-structures.ipynb
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Original file line number Diff line number Diff line change
Expand Up @@ -9,6 +9,13 @@
"# Lab | Data Structures "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
Expand Down Expand Up @@ -50,13 +57,149 @@
"\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": 37,
"metadata": {},
"outputs": [],
"source": [
"# Creating a list\n",
"products = [\"t-shirt\", \"mug\",\"hat\", \"book\", \"keychain\"]\n",
"# Creating an empty dictionary\n",
"inventory = {}"
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {},
"outputs": [
{
"name": "stdin",
"output_type": "stream",
"text": [
"Enter key: t-shirt\n",
"Enter value: 4\n",
"Enter key: mug\n",
"Enter value: 3\n",
"Enter key: hat\n",
"Enter value: 2\n",
"Enter key: book\n",
"Enter value: 5\n",
"Enter key: keychain\n",
"Enter value: 2\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'t-shirt': 4, 'mug': 3, 'hat': 2, 'book': 5, 'keychain': 2}\n"
]
}
],
"source": [
"# Input the quanitiy of each product and show the content of the inventory\n",
"inventory = {input(\"Enter key: \"): int(input(\"Enter value: \")) for product in products}\n",
"print (inventory)"
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stdin",
"output_type": "stream",
"text": [
"enter the product1 (chose from ['t-shirt', 'mug', 'hat', 'book', 'keychain']): mug\n",
"enter the product2 (chose from ['t-shirt', 'mug', 'hat', 'book', 'keychain']): hat\n",
"enter the product3 (chose from ['t-shirt', 'mug', 'hat', 'book', 'keychain']): book\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"customer orders are: {'book', 'mug', 'hat'}\n"
]
}
],
"source": [
"# Input the name of the products and add them to customer_orders set\n",
"customer_orders= set()\n",
"for i in range(3):\n",
" value = input(f\"enter the product{i + 1} (chose from {products}): \")\n",
" customer_orders.add(value)\n",
"print (\"customer orders are: \", customer_orders) "
]
},
{
"cell_type": "code",
"execution_count": 69,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Order Statistics\n",
"Total Products Ordered: 3\n",
"Percentage of Products Ordered: 27.27272727272727 %\n"
]
}
],
"source": [
"# Calculate the order statistics and print them:\n",
"total_products_ordered=int(len(customer_orders))\n",
"percentage_of_products_ordered = (total_products_ordered/sum(inventory.values())) * 100\n",
"order_status=(total_products_ordered,percentage_of_products_ordered)\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": 71,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Updated t-shirt: 2 left\n",
"Updated mug: 1 left\n",
"Updated hat: 0 left\n",
"Updated book: 3 left\n",
"Updated keychain: 0 left\n"
]
}
],
"source": [
"# Update the inventory\n",
"for quantity in inventory:\n",
" inventory[quantity] = inventory[quantity]-1\n",
" print (f\"Updated {quantity}: {inventory[quantity]} left\")\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"display_name": "Python [conda env:base] *",
"language": "python",
"name": "python3"
"name": "conda-base-py"
},
"language_info": {
"codemirror_mode": {
Expand All @@ -68,7 +211,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.13"
"version": "3.12.7"
}
},
"nbformat": 4,
Expand Down