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170 changes: 167 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 +50,177 @@
"\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": 5,
"metadata": {},
"outputs": [],
"source": [
"#1.\n",
"products=[\"t-shirt\", \"mug\", \"hat\", \"book\", \"keychain\"] #products list"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"#2.\n",
"inventory={} #empty dictionary"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stdin",
"output_type": "stream",
"text": [
"Enter the number of available t-shirt 5\n",
"Enter the number of available mug 9\n",
"Enter the number of available hat 5\n",
"Enter the number of available book 45\n",
"Enter the number of available keychain 58\n"
]
}
],
"source": [
"#3.\n",
"for product in products:\n",
" inventory[product]=input(f\"Enter the number of available {product}\")"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"#4.\n",
"customer_orders=set() #set"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"name": "stdin",
"output_type": "stream",
"text": [
"Enter any product that a customer wants to order from ['t-shirt', 'mug', 'hat', 'book', 'keychain'] mug\n",
"Enter any product that a customer wants to order from ['t-shirt', 'mug', 'hat', 'book', 'keychain'] book\n",
"Enter any product that a customer wants to order from ['t-shirt', 'mug', 'hat', 'book', 'keychain'] keychain\n"
]
}
],
"source": [
"#5.\n",
"for i in range(3):\n",
" customer_orders.add(input(f\"Enter any product that a customer wants to order from {products} \"))\n"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'book', 'mug', 'keychain'}\n"
]
}
],
"source": [
"#6.\n",
"print(customer_orders)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
"#7.\n",
"total_products_ordered=len(customer_orders)\n",
"percentage_ordered =100*total_products_ordered/len(products)\n",
"order_status=(total_products_ordered,percentage_ordered)\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Order Statistics:\n",
"Total Products Ordered: 3\n",
"Percentage of Products Ordered: 60.0% \n"
]
}
],
"source": [
"#8.\n",
"\n",
"print(\"Order Statistics:\")\n",
"print(f\"Total Products Ordered: {order_status[0]}\")\n",
"print(f\"Percentage of Products Ordered: {order_status[1]}% \")"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [],
"source": [
"#9.\n",
"for customer_order in customer_orders:\n",
" if customer_order in inventory and int(inventory[customer_order])>0:\n",
" inventory[customer_order]=str(int(inventory[customer_order])-1)\n"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"t-shirt-5\n",
"mug-8\n",
"hat-5\n",
"book-44\n",
"keychain-57\n"
]
}
],
"source": [
"#10.\n",
"for key,value in inventory.items():\n",
" print(f\"{key}-{value}\")\n",
" "
]
}
],
"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 +232,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.13"
"version": "3.12.7"
}
},
"nbformat": 4,
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