diff --git a/lab-python-data-structures.ipynb b/lab-python-data-structures.ipynb index 5b3ce9e0..23999e29 100644 --- a/lab-python-data-structures.ipynb +++ b/lab-python-data-structures.ipynb @@ -50,13 +50,263 @@ "\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": 29, + "metadata": {}, + "outputs": [], + "source": [ + "#Define a list called products that contains the following items: \"t-shirt\", \"mug\", \"hat\", \"book\", \"keychain\".\n", + "\n", + "products = [\"t-shirt\", \"mug\", \"hat\", \"book\", \"keychain\"]" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [], + "source": [ + "#Create an empty dictionary called inventory\n", + "\n", + "inventory = {}" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "name": "stdin", + "output_type": "stream", + "text": [ + "What is the quantity of t-shirts?: 5\n", + "What is the quantity of mugs?: 5\n", + "What is the quantity of hats?: 5\n", + "What is the quantity of books?: 5\n", + "What is the quantity of keychains?: 5\n" + ] + } + ], + "source": [ + "# Ask the user to input the quantity of each product available in the inventory. Use the product names from the products list as keys\n", + "# in the inventory dictionary and assign the respective quantities as values.\n", + "\n", + "for product in products:\n", + " quantity = int(input(f\"What is the quantity of {product}s?: \"))\n", + " inventory[product] = quantity" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [], + "source": [ + "# Create empty set\n", + "\n", + "customer_orders = set()" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [ + { + "name": "stdin", + "output_type": "stream", + "text": [ + "Enter a product: hat\n", + "Enter a product: muh\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Product not in list.\n" + ] + }, + { + "name": "stdin", + "output_type": "stream", + "text": [ + "Enter a product: mug\n", + "Enter a product: keychain\n" + ] + } + ], + "source": [ + "# Ask the user to input the name of three products that a customer wants to order (from those in products list).\n", + "# Add each product name to the customer_orders set.\n", + "\n", + "counter = 3\n", + "\n", + "while counter > 0:\n", + " \n", + " three_products = input(\"Enter a product: \")\n", + "\n", + " if three_products in products:\n", + " customer_orders.add(three_products)\n", + " counter -=1\n", + " else:\n", + " print(\"Product not in list.\")" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'hat', 'keychain', 'mug'}\n" + ] + } + ], + "source": [ + "# Print the products in the customer_orders set.\n", + "\n", + "print(customer_orders)" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "15\n" + ] + } + ], + "source": [ + "# Calculate the following order statistics:\n", + "# Total Products Ordered: The total number of products in the customer_orders set.\n", + "\n", + "total_products_ordered = sum(inventory[product] for product in customer_orders)\n", + "print(total_products_ordered)" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [], + "source": [ + "# Percentage of products ordered: The percentage of products ordered compared to the total available products.\n", + "\n", + "total_available_products = sum(inventory.values())\n", + "percentage_products = (total_products_ordered / total_available_products) * 100" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(15, 60.0)\n" + ] + } + ], + "source": [ + "# Store these statistics in a tuple called order_status.\n", + "\n", + "order_status = (total_products_ordered, percentage_products)\n", + "\n", + "print(order_status)" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Order statistics:\n", + "Total products ordered: 15\n", + "Percentage of products ordered: 60.0\n" + ] + } + ], + "source": [ + "# Print the order statistics using the following format:\n", + "# Order statistics:\n", + "# Total products ordered: \n", + "# Percentage of products ordered: \n", + "\n", + "print(\"Order statistics:\")\n", + "print(\"Total products ordered: \", total_products_ordered)\n", + "print(\"Percentage of products ordered: \", percentage_products)" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'t-shirt': 4, 'mug': 4, 'hat': 4, 'book': 4, 'keychain': 4}\n" + ] + } + ], + "source": [ + "# Update the inventory by subtracting 1 from the quantity of each product. Modify the inventory dictionary accordingly.\n", + "\n", + "for product in inventory:\n", + " inventory[product] -= 1\n", + " \n", + "print(inventory)" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "t-shirt : 4\n", + "mug : 4\n", + "hat : 4\n", + "book : 4\n", + "keychain : 4\n" + ] + } + ], + "source": [ + "# Print the updated inventory , displaying the quantity of each product on separate lines.\n", + "\n", + "for product, quantity in inventory.items():\n", + " print(product,\":\", quantity)" + ] } ], "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 +318,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.13" + "version": "3.13.5" } }, "nbformat": 4,