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269 changes: 255 additions & 14 deletions lab-python-functions.ipynb
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Original file line number Diff line number Diff line change
Expand Up @@ -20,34 +20,275 @@
"In the previous exercise, you improved the code for managing customer orders by using loops and flow control. Now, let's take it a step further and refactor the code by introducing functions.\n",
"\n",
"Follow the steps below to complete the exercise:\n",
"7. Call the functions in the appropriate sequence to execute the program and manage customer orders.\n",
"\n",
"1. Define a function named `initialize_inventory` that takes `products` as a parameter. Inside the function, implement the code for initializing the inventory dictionary using a loop and user input.\n",
"\n",
"2. Define a function named `get_customer_orders` that takes no parameters. Inside the function, implement the code for prompting the user to enter the product names using a loop. The function should return the `customer_orders` set.\n",
"Hints for functions:\n",
"\n",
"3. Define a function named `update_inventory` that takes `customer_orders` and `inventory` as parameters. Inside the function, implement the code for updating the inventory dictionary based on the customer orders.\n",
"- Consider the input parameters required for each function and their return values.\n",
"- Utilize function parameters and return values to transfer data between functions.\n",
"- Test your functions individually to ensure they work correctly.\n",
"\n",
"4. Define a function named `calculate_order_statistics` that takes `customer_orders` and `products` as parameters. Inside the function, implement the code for calculating the order statistics (total products ordered, and percentage of unique products ordered). The function should return these values.\n",
"\n"
]
},
{
"cell_type": "markdown",
"id": "1b7f10d7",
"metadata": {},
"source": [
"1. Define a function named `initialize_inventory` that takes `products` as a parameter. Inside the function, implement the code for initializing the inventory dictionary using a loop and user input."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "ba8a897e",
"metadata": {},
"outputs": [],
"source": [
"inventory = {'mug':0, 'hat':0, 'keychain':0, 'book':0, 't-shirt':0}\n",
"\n",
"5. Define a function named `print_order_statistics` that takes `order_statistics` as a parameter. Inside the function, implement the code for printing the order statistics.\n",
"def initialize_inventory(products):\n",
" for item in inventory:\n",
" products = input (\"Enter a value\")\n",
" if products in inventory:\n",
" value = (int(input(\"Enter a value\")))\n",
" inventory[products]=value\n",
" \n",
" \n",
" return products\n",
"\n",
"6. Define a function named `print_updated_inventory` that takes `inventory` as a parameter. Inside the function, implement the code for printing the updated inventory.\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 138,
"id": "1df42982",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'mug': 1, 'hat': 2, 'keychain': 3, 'book': 4, 't-shirt': 5}\n"
]
}
],
"source": [
"products = inventory\n",
"initialize_inventory (products)\n",
"print(products)\n",
"\n",
"7. Call the functions in the appropriate sequence to execute the program and manage customer orders.\n",
"\n",
"Hints for functions:\n",
"\n",
"- Consider the input parameters required for each function and their return values.\n",
"- Utilize function parameters and return values to transfer data between functions.\n",
"- Test your functions individually to ensure they work correctly.\n",
"\n"
]
},
{
"cell_type": "markdown",
"id": "6610d470",
"metadata": {},
"source": [
"2. Define a function named `get_customer_orders` that takes no parameters. Inside the function, implement the code for prompting the user to enter the product names using a loop. The function should return the `customer_orders` set.\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "4300e4a8",
"metadata": {},
"outputs": [],
"source": [
"customer_orders = set()\n",
"\n",
"def get_customers_orders(): \n",
" \n",
" continue_buying = \"Yes\"\n",
" \n",
" while continue_buying == \"Yes\":\n",
" product = input(\"What do you want to buy?: \")\n",
" customer_orders.add(product)\n",
" continue_buying = input(\"Do you want to continue buying? (Yes/No): \")\n",
" \n",
" return customer_orders\n",
" \n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 162,
"id": "70b11c12",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'mug', 't-shirt'}\n",
"Thanks for your purchase.\n"
]
}
],
"source": [
"get_customers_orders()\n",
"print(customer_orders)\n",
"print(\"Thanks for your purchase.\")"
]
},
{
"cell_type": "markdown",
"id": "20638e0d",
"metadata": {},
"source": [
"3. Define a function named `update_inventory` that takes `customer_orders` and `inventory` as parameters. Inside the function, implement the code for updating the inventory dictionary based on the customer orders."
]
},
{
"cell_type": "code",
"execution_count": 81,
"id": "9f784305",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'mug': 0, 'hat': 2, 'keychain': 3, 'book': 4, 't-shirt': 4}\n"
]
}
],
"source": [
"inventory = {'mug': 1, 'hat': 2, 'keychain': 3, 'book': 4, 't-shirt': 5}\n",
"customer_orders = ('mug', 't-shirt')\n",
"\n",
"def update_inventory(customer_orders, inventory):\n",
" \n",
" for item in customer_orders:\n",
" if item in inventory:\n",
" inventory[item]-=1\n",
"\n",
" return customer_orders, inventory\n",
"\n",
"update_inventory(customer_orders, inventory)\n",
"print(inventory)\n",
"\n",
" "
]
},
{
"cell_type": "markdown",
"id": "b7a40557",
"metadata": {},
"source": [
"4. Define a function named `calculate_order_statistics` that takes `customer_orders` and `products` as parameters. Inside the function, implement the code for calculating the order statistics (total products ordered, and percentage of unique products ordered (Number of different products)). The function should return these values."
]
},
{
"cell_type": "code",
"execution_count": 86,
"id": "5489e00c",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(2, 40.0)"
]
},
"execution_count": 86,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"inventory = {'mug': 1, 'hat': 2, 'keychain': 3, 'book': 4, 't-shirt': 5}\n",
"customer_orders = ('mug', 't-shirt')\n",
"\n",
"def calculate_order_statistics(customer_orders, inventory):\n",
" total_products_ordered = len(customer_orders)\n",
" total_products_inventory = len(inventory)\n",
" percent_unique_products = (total_products_ordered/total_products_inventory)*100\n",
" \n",
" return total_products_ordered, percent_unique_products\n",
"\n",
"calculate_order_statistics(customer_orders, inventory)\n"
]
},
{
"cell_type": "markdown",
"id": "35a81169",
"metadata": {},
"source": [
"5. Define a function named `print_order_statistics` that takes `order_statistics` as a parameter. Inside the function, implement the code for printing the order statistics."
]
},
{
"cell_type": "code",
"execution_count": 97,
"id": "61448a34",
"metadata": {},
"outputs": [],
"source": [
"def print_order_statistics(order_statistics):\n",
" return order_statistics"
]
},
{
"cell_type": "code",
"execution_count": 99,
"id": "69891f1d",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"2"
]
},
"execution_count": 99,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"total_products_ordered = order_statistics\n",
"print_order_statistics(total_products_ordered)"
]
},
{
"cell_type": "markdown",
"id": "9dc024e1",
"metadata": {},
"source": [
"6. Define a function named `print_updated_inventory` that takes `inventory` as a parameter. Inside the function, implement the code for printing the updated inventory."
]
},
{
"cell_type": "code",
"execution_count": 83,
"id": "87f8554a",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'mug': 0, 'hat': 2, 'keychain': 3, 'book': 4, 't-shirt': 4}\n"
]
}
],
"source": [
"def print_updated_inventory(inventory):\n",
" return inventory\n",
"print(inventory)\n"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"display_name": "base",
"language": "python",
"name": "python3"
},
Expand All @@ -61,7 +302,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.13"
"version": "3.13.5"
}
},
"nbformat": 4,
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