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325 changes: 322 additions & 3 deletions lab-python-data-structures.ipynb
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Original file line number Diff line number Diff line change
Expand Up @@ -50,13 +50,332 @@
"\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": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"list"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#1. 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\"]\n",
"type (products)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"dict"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#2. Create an empty dictionary called `inventory`.\n",
"inventory = {}\n",
"type (inventory)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stdin",
"output_type": "stream",
"text": [
"Enter the number of t-shirts: 4\n",
"Enter the number of mugs: 5\n",
"Enter the number of hats: 6\n",
"Enter the number of books: 7\n",
"Enter the number of keychains: 8\n"
]
}
],
"source": [
"#3. Ask the user to input the quantity of each product available in the inventory. Use the product names from the\n",
"#`products` list as keys in the `inventory` dictionary and assign the respective quantities as values.\n",
"\n",
"tshirt_quantity= int(input(\"Enter the number of t-shirts: \"))\n",
"mug_quantity= int(input(\"Enter the number of mugs: \"))\n",
"hat_quantity= int(input(\"Enter the number of hats: \"))\n",
"book_quantity= int(input(\"Enter the number of books: \"))\n",
"keychain_quantity= int(input(\"Enter the number of keychains: \"))\n",
"\n",
"inventory = {\"t-shirt\":tshirt_quantity, \"mug\":mug_quantity, \"hat\":hat_quantity, \"book\":book_quantity, \"keychain\":keychain_quantity}\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"dict"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"type(inventory)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"#4. Create an empty set called `customer_orders`.\n",
"\n",
"customer_orders= {}"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"name": "stdin",
"output_type": "stream",
"text": [
"What's the first product that you'd like to order: mug\n",
"What's the second product that you'd like to order: book\n",
"What's the third product that you'd like to order: hat\n"
]
}
],
"source": [
"#5. Ask the user to input the name of three products that a customer wants to order \n",
"#(from those in the products list, meaning three products out of \"t-shirt\", \"mug\", \"hat\", \"book\" or \"keychain\". \n",
"#Add each product name to the `customer_orders` set.\n",
"\n",
"order1=input(\"What's the first product that you'd like to order: \")\n",
"order2=input(\"What's the second product that you'd like to order: \")\n",
"order3=input(\"What's the third product that you'd like to order: \")\n",
"\n",
"customer_orders= {order1,order2,order3}\n"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"set"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"type(customer_orders)\n"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'book', 'hat', 'mug'}\n"
]
}
],
"source": [
"#6. Print the products in the `customer_orders` set.\n",
"\n",
"print (customer_orders)"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"3\n"
]
}
],
"source": [
"#7. Calculate the following order statistics: \n",
"#Total Products Ordered: The total number of products in the `customer_orders` set.\n",
"\n",
"number_of_orders=len(customer_orders)\n",
"print(number_of_orders)\n",
"\n",
" "
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"60.0 %\n"
]
}
],
"source": [
"# Calculate the following order statistics: \n",
"# Percentage of Products Ordered: The percentage of products ordered compared to the total available products.\n",
"\n",
"percentage_of_orders= number_of_orders/5*100\n",
"print((percentage_of_orders) , '%')\n"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"tuple"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Store these statistics in a tuple called `order_status`.\n",
"\n",
"order_status=(number_of_orders,percentage_of_orders)\n",
"type(order_status)"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Order Statistics: Total Products Ordered:3, Percentage of Products Ordered:60.0 %\n"
]
}
],
"source": [
"#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",
"text= f\"Order Statistics: Total Products Ordered:{number_of_orders}, Percentage of Products Ordered:{percentage_of_orders} %\"\n",
"print(text)\n"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [],
"source": [
"#9. Update the inventory by subtracting 1 from the quantity of each product. \n",
"#Modify the `inventory` dictionary accordingly.\n",
"\n",
"inventory = {\"t-shirt\":tshirt_quantity-1, \"mug\":mug_quantity-1, \"hat\":hat_quantity-1, \"book\":book_quantity-1, \"keychain\":keychain_quantity-1}\n"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'t-shirt': 3, 'mug': 4, 'hat': 5, 'book': 6, 'keychain': 7}\n"
]
}
],
"source": [
"print(inventory)\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#10. Print the updated inventory, displaying the quantity of each product on separate lines.\n",
"print(\"Update Inventory:\")\n",
"print(\"T-shirts:\", inventory[\"t-shirt\"])\n",
"print(\"Mugs:\",inventory[\"mug\"])\n",
"print(\"Hats:\",inventory[\"hat\"])\n",
"print(\"Books:\",inventory[\"book\"])\n",
"print(\"Keychains:\",inventory[\"keychain\"])\n",
"\n",
"\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"display_name": "Python [conda env:Anaconda]",
"language": "python",
"name": "python3"
"name": "conda-env-Anaconda-py"
},
"language_info": {
"codemirror_mode": {
Expand All @@ -68,7 +387,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.13"
"version": "3.13.9"
}
},
"nbformat": 4,
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