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216 changes: 213 additions & 3 deletions lab-python-functions.ipynb
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Original file line number Diff line number Diff line change
Expand Up @@ -43,13 +43,223 @@
"\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "aaf1c5c1-a090-4987-b099-5fefc888a40c",
"metadata": {},
"outputs": [],
"source": [
"def initialize_inventory (products):\n",
" inventory={}\n",
" for item in products:\n",
" inventory[item]=int(input(f\"quantity of {item} available in the inventory: \"))\n",
" \n",
" return inventory"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "2fe60b8a-cab8-4836-b19f-6f7811294dc5",
"metadata": {},
"outputs": [],
"source": [
"def get_customer_orders():\n",
" customer_orders=set() \n",
" add_another_product=\"yes\"\n",
" \n",
" while add_another_product== \"yes\" :\n",
" x=input(\"input order :\")\n",
" customer_orders.add(x)\n",
" add_another_product=input(\"if you want to add another product (yes/no):\")\n",
" \n",
" return customer_orders"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "05ac0ed1-3263-4ca4-878e-fb87ddf8e218",
"metadata": {},
"outputs": [],
"source": [
"def update_inventory (customer_orders, inventory ):\n",
" for item in customer_orders:\n",
" inventory[item]=inventory[item]-1\n",
" "
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "4e6d5357-6e82-4f44-b1b5-f1ab6e9838ac",
"metadata": {},
"outputs": [],
"source": [
"def calculate_order_statistics (customer_orders, products):\n",
" total_products_ordered=len(customer_orders)\n",
" percentage_ordered=len(customer_orders)/len(products)*100\n",
" order_statistics=(total_products_ordered,percentage_ordered)\n",
" return order_statistics"
]
},
{
"cell_type": "code",
"execution_count": 16,
"id": "75fdd038-5d04-4ab9-b2ce-7c125c642161",
"metadata": {},
"outputs": [],
"source": [
"def print_order_statistics (order_statistics ):\n",
" print(\"Order Statistics:\")\n",
" print(f\"Total Products Ordered:{order_statistics[0] }\")\n",
" print(f\"Percentage of Products Ordered:{order_statistics[1]}\")\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 19,
"id": "bbea8abd-b8eb-4765-9cd2-4ef7384b31fb",
"metadata": {},
"outputs": [],
"source": [
"def print_updated_inventory(inventory ):\n",
" print (inventory)\n",
" print(inventory.items())\n",
" print(inventory.keys())\n",
" print(inventory.values())\n",
" for item in inventory.items() :\n",
" print(item)\n",
" "
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "a35ff804-b525-4aee-b161-fd93bf55a282",
"metadata": {},
"outputs": [
{
"name": "stdin",
"output_type": "stream",
"text": [
"quantity of t-shirt available in the inventory: 20\n",
"quantity of mug available in the inventory: 34\n",
"quantity of hat available in the inventory: 46\n",
"quantity of book available in the inventory: 52\n",
"quantity of keychain available in the inventory: 63\n"
]
}
],
"source": [
"products=[\"t-shirt\", \"mug\", \"hat\", \"book\", \"keychain\"]\n",
"inventory=initialize_inventory (products)"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "7afd25dc-b382-4d4f-901a-f78a77c906cb",
"metadata": {},
"outputs": [
{
"name": "stdin",
"output_type": "stream",
"text": [
"input order : mug\n",
"if you want to add another product (yes/no): yes\n",
"input order : hat\n",
"if you want to add another product (yes/no): no\n"
]
}
],
"source": [
"customer_orders=get_customer_orders()"
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "752eff20-64f9-42a1-ad78-ae657464d5b8",
"metadata": {},
"outputs": [],
"source": [
"update_inventory (customer_orders, inventory )"
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "6ac4887d-25d8-4960-8c27-4c663f4fb39e",
"metadata": {},
"outputs": [],
"source": [
"order_status=calculate_order_statistics (customer_orders, products)"
]
},
{
"cell_type": "code",
"execution_count": 17,
"id": "df70ee9b-4071-44ec-b7f8-fee1b32bb101",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Order Statistics:\n",
"Total Products Ordered:2\n",
"Percentage of Products Ordered:40.0\n"
]
}
],
"source": [
"print_order_statistics (order_status)"
]
},
{
"cell_type": "code",
"execution_count": 20,
"id": "34e7ad70-a217-41c5-abba-079db6368835",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'t-shirt': 20, 'mug': 32, 'hat': 44, 'book': 52, 'keychain': 63}\n",
"dict_items([('t-shirt', 20), ('mug', 32), ('hat', 44), ('book', 52), ('keychain', 63)])\n",
"dict_keys(['t-shirt', 'mug', 'hat', 'book', 'keychain'])\n",
"dict_values([20, 32, 44, 52, 63])\n",
"('t-shirt', 20)\n",
"('mug', 32)\n",
"('hat', 44)\n",
"('book', 52)\n",
"('keychain', 63)\n"
]
}
],
"source": [
"print_updated_inventory(inventory )"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "8f9e85e1-1599-4df2-8550-2f84797c3d39",
"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": {
Expand All @@ -61,7 +271,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.13"
"version": "3.13.9"
}
},
"nbformat": 4,
Expand Down