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..cb797e78 --- /dev/null +++ b/.ipynb_checkpoints/lab-python-data-structures-checkpoint.ipynb @@ -0,0 +1,279 @@ +{ + "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": [], + "source": [ + "products = [\"tshirt\", \"mug\", \"hat\", \"book\", \"keychain\"]\n", + "inventory = {}" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "name": "stdin", + "output_type": "stream", + "text": [ + "Enter quantity for tshirt: 20\n", + "Enter quantity for mug: 10\n", + "Enter quantity for hat: 5\n", + "Enter quantity for book: 2\n", + "Enter quantity for keychain: 1\n" + ] + } + ], + "source": [ + "for product in products:\n", + " quantity = input(f\"Enter quantity for {product}: \")\n", + " inventory[product] = int(quantity)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "customer_orders = set()" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "set()" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "customer_orders" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdin", + "output_type": "stream", + "text": [ + "Enter a product to order: keychain\n", + "Enter a product to order: book\n", + "Enter a product to order: hat\n" + ] + } + ], + "source": [ + "for i in range(3):\n", + " order = input(\"Enter a product to order: \")\n", + " customer_orders.add(order)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'book', 'hat', 'keychain'}" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "customer_orders" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(3, 60.0)" + ] + }, + "execution_count": 17, + "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": 22, + "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": 30, + "metadata": {}, + "outputs": [], + "source": [ + "for product in inventory:\n", + " inventory[product] -= 1" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'tshirt': 19, 'mug': 9, 'hat': 4, 'book': 1, 'keychain': 0}\n" + ] + } + ], + "source": [ + "print(inventory)" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "tshirt: 19\n", + "mug: 9\n", + "hat: 4\n", + "book: 1\n", + "keychain: 0\n" + ] + } + ], + "source": [ + "for product in inventory:\n", + " print(f\"{product}: {inventory[product]}\")" + ] + }, + { + "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": "3.13.9" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/lab-python-data-structures.ipynb b/lab-python-data-structures.ipynb index 5b3ce9e0..cb797e78 100644 --- a/lab-python-data-structures.ipynb +++ b/lab-python-data-structures.ipynb @@ -50,13 +50,216 @@ "\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": [ + "products = [\"tshirt\", \"mug\", \"hat\", \"book\", \"keychain\"]\n", + "inventory = {}" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "name": "stdin", + "output_type": "stream", + "text": [ + "Enter quantity for tshirt: 20\n", + "Enter quantity for mug: 10\n", + "Enter quantity for hat: 5\n", + "Enter quantity for book: 2\n", + "Enter quantity for keychain: 1\n" + ] + } + ], + "source": [ + "for product in products:\n", + " quantity = input(f\"Enter quantity for {product}: \")\n", + " inventory[product] = int(quantity)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "customer_orders = set()" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "set()" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "customer_orders" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdin", + "output_type": "stream", + "text": [ + "Enter a product to order: keychain\n", + "Enter a product to order: book\n", + "Enter a product to order: hat\n" + ] + } + ], + "source": [ + "for i in range(3):\n", + " order = input(\"Enter a product to order: \")\n", + " customer_orders.add(order)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'book', 'hat', 'keychain'}" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "customer_orders" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(3, 60.0)" + ] + }, + "execution_count": 17, + "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": 22, + "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": 30, + "metadata": {}, + "outputs": [], + "source": [ + "for product in inventory:\n", + " inventory[product] -= 1" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'tshirt': 19, 'mug': 9, 'hat': 4, 'book': 1, 'keychain': 0}\n" + ] + } + ], + "source": [ + "print(inventory)" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "tshirt: 19\n", + "mug: 9\n", + "hat: 4\n", + "book: 1\n", + "keychain: 0\n" + ] + } + ], + "source": [ + "for product in inventory:\n", + " print(f\"{product}: {inventory[product]}\")" + ] + }, + { + "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 +271,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.13" + "version": "3.13.9" } }, "nbformat": 4,