diff --git a/.ipynb_checkpoints/lab-python-data-structures-checkpoint.ipynb b/.ipynb_checkpoints/lab-python-data-structures-checkpoint.ipynb new file mode 100644 index 00000000..6076853d --- /dev/null +++ b/.ipynb_checkpoints/lab-python-data-structures-checkpoint.ipynb @@ -0,0 +1,149 @@ +{ + "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: \n", + " Percentage of Products 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": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdin", + "output_type": "stream", + "text": [ + "Please, informe the quantity of t-shirt: 5\n", + "Please, informe the quantity of mug: 3\n", + "Please, informe the quantity of hat: 2\n", + "Please, informe the quantity of book: 1\n", + "Please, informe the quantity of keychain: 9\n", + "Please choose a product from the list ['t-shirt', 'mug', 'hat', 'book', 'keychain']: mug\n", + "Please choose a product from the list ['t-shirt', 'mug', 'hat', 'book', 'keychain']: hat\n", + "Please choose a product from the list ['t-shirt', 'mug', 'hat', 'book', 'keychain']: book\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'mug', 'hat', 'book'}\n", + "Order Statistics: \n", + "Total Products Ordered: 3. \n", + "Percentage of Products Ordered: 15.00%.\n", + "{'t-shirt': 5, 'mug': 2, 'hat': 1, 'book': 0, 'keychain': 9}\n" + ] + } + ], + "source": [ + "products = [\"t-shirt\", \"mug\", \"hat\", \"book\", \"keychain\"]\n", + "inventory = {}\n", + "\n", + "for product in products:\n", + " quantity = int(input(f\"Please, informe the quantity of {product}: \"))\n", + " inventory[product] = quantity\n", + "\n", + "customer_orders = set()\n", + "\n", + "for order in range(0,3):\n", + " order = input(f\"Please choose a product from the list {products}: \")\n", + " if order not in products:\n", + " order = input(f\"This is not available, please choose a product from the list {products}: \")\n", + " customer_orders.add(order)\n", + "\n", + "print(customer_orders)\n", + "\n", + "total_products_ordered = len(customer_orders)\n", + "\n", + "total_inventory = sum(inventory.values())\n", + "percentage_products_ordered = (total_products_ordered/total_inventory)*100\n", + "\n", + "order_status = total_products_ordered, percentage_products_ordered\n", + "\n", + "print(f\"\"\"Order Statistics: \n", + "Total Products Ordered: {total_products_ordered}. \n", + "Percentage of Products Ordered: {percentage_products_ordered:.2f}%.\"\"\")\n", + "\n", + "for order in customer_orders:\n", + " inventory[order] -= 1\n", + "\n", + "print(inventory)" + ] + }, + { + "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": "3.13.5" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/lab-python-data-structures.ipynb b/lab-python-data-structures.ipynb index 5b3ce9e0..6076853d 100644 --- a/lab-python-data-structures.ipynb +++ b/lab-python-data-structures.ipynb @@ -50,13 +50,86 @@ "\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": [ + { + "name": "stdin", + "output_type": "stream", + "text": [ + "Please, informe the quantity of t-shirt: 5\n", + "Please, informe the quantity of mug: 3\n", + "Please, informe the quantity of hat: 2\n", + "Please, informe the quantity of book: 1\n", + "Please, informe the quantity of keychain: 9\n", + "Please choose a product from the list ['t-shirt', 'mug', 'hat', 'book', 'keychain']: mug\n", + "Please choose a product from the list ['t-shirt', 'mug', 'hat', 'book', 'keychain']: hat\n", + "Please choose a product from the list ['t-shirt', 'mug', 'hat', 'book', 'keychain']: book\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'mug', 'hat', 'book'}\n", + "Order Statistics: \n", + "Total Products Ordered: 3. \n", + "Percentage of Products Ordered: 15.00%.\n", + "{'t-shirt': 5, 'mug': 2, 'hat': 1, 'book': 0, 'keychain': 9}\n" + ] + } + ], + "source": [ + "products = [\"t-shirt\", \"mug\", \"hat\", \"book\", \"keychain\"]\n", + "inventory = {}\n", + "\n", + "for product in products:\n", + " quantity = int(input(f\"Please, informe the quantity of {product}: \"))\n", + " inventory[product] = quantity\n", + "\n", + "customer_orders = set()\n", + "\n", + "for order in range(0,3):\n", + " order = input(f\"Please choose a product from the list {products}: \")\n", + " if order not in products:\n", + " order = input(f\"This is not available, please choose a product from the list {products}: \")\n", + " customer_orders.add(order)\n", + "\n", + "print(customer_orders)\n", + "\n", + "total_products_ordered = len(customer_orders)\n", + "\n", + "total_inventory = sum(inventory.values())\n", + "percentage_products_ordered = (total_products_ordered/total_inventory)*100\n", + "\n", + "order_status = total_products_ordered, percentage_products_ordered\n", + "\n", + "print(f\"\"\"Order Statistics: \n", + "Total Products Ordered: {total_products_ordered}. \n", + "Percentage of Products Ordered: {percentage_products_ordered:.2f}%.\"\"\")\n", + "\n", + "for order in customer_orders:\n", + " inventory[order] -= 1\n", + "\n", + "print(inventory)" + ] + }, + { + "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": { @@ -68,7 +141,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.13" + "version": "3.13.5" } }, "nbformat": 4,