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Solved lab data structures enia #279

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enialahcene Sep 22, 2024
05764e4
Solved lab
enialahcene Sep 22, 2024
30c8e24
Solved lab
enialahcene Sep 22, 2024
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176 changes: 175 additions & 1 deletion lab-python-data-structures.ipynb
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Original file line number Diff line number Diff line change
Expand Up @@ -50,6 +50,180 @@
"\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": 61,
"metadata": {},
"outputs": [],
"source": [
"products = [\"t-shirt\", \"mug\", \"hat\", \"book\", \"keychain\"]"
]
},
{
"cell_type": "code",
"execution_count": 62,
"metadata": {},
"outputs": [],
"source": [
"inventory = {}"
]
},
{
"cell_type": "code",
"execution_count": 63,
"metadata": {},
"outputs": [],
"source": [
"inventory[products[0]] = int(input(f\"Give me the quantity of {products[0]}: \"))\n",
"inventory[products[1]] = int(input(f\"Give me the quantity of {products[1]}: \"))\n",
"inventory[products[2]] = int(input(f\"Give me the quantity of {products[2]}: \"))\n",
"inventory[products[3]] = int(input(f\"Give me the quantity of {products[3]}: \"))\n",
"inventory[products[4]] = int(input(f\"Give me the quantity of {products[4]}: \"))"
]
},
{
"cell_type": "code",
"execution_count": 64,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'t-shirt': 2, 'mug': 3, 'hat': 4, 'book': 1, 'keychain': 5}\n"
]
}
],
"source": [
"print(inventory)"
]
},
{
"cell_type": "code",
"execution_count": 65,
"metadata": {},
"outputs": [],
"source": [
"customer_orders = set()"
]
},
{
"cell_type": "code",
"execution_count": 66,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'mug', 'hat', 'book'}\n"
]
}
],
"source": [
"product1 = input(f\"Give me the name of a product you want to oredered it. Choose one of this list {products}\")\n",
"product2 = input(f\"Give me the name of a product you want to oredered it. Choose one of this list {products}\")\n",
"product3 = input(f\"Give me the name of a product you want to oredered it. Choose one of this list {products}\")\n",
"\n",
"customer_orders.add(product1)\n",
"customer_orders.add(product2)\n",
"customer_orders.add(product3)\n",
"\n",
"print(customer_orders)\n"
]
},
{
"cell_type": "code",
"execution_count": 67,
"metadata": {},
"outputs": [],
"source": [
"num_products = len(customer_orders)\n",
"percentage = num_products/len(products)*100\n",
"\n",
"order_status = (num_products,percentage)\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 68,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
" Order Statistics:\n",
" Total Products Ordered: 3\n",
" Percentage of Products Ordered: 60.0 \n",
" \n"
]
}
],
"source": [
"print(f'''\n",
" Order Statistics:\n",
" Total Products Ordered: {num_products}\n",
" Percentage of Products Ordered: {percentage} \n",
" ''')"
]
},
{
"cell_type": "code",
"execution_count": 74,
"metadata": {},
"outputs": [],
"source": [
"for product in customer_orders:\n",
" if inventory[product] > 0:\n",
" inventory[product] -= 1\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 70,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" Updated inventory: \n",
"\n",
" t-shirt: 2 \n",
"\n",
" mug: 2 \n",
"\n",
" hat: 3 \n",
"\n",
" book: 0 \n",
"\n",
" keychain: 5 \n",
"\n",
" \n"
]
}
],
"source": [
"print(f''' Updated inventory: \\n\n",
" {products[0]}: {inventory[products[0]]} \\n\n",
" {products[1]}: {inventory[products[1]]} \\n\n",
" {products[2]}: {inventory[products[2]]} \\n\n",
" {products[3]}: {inventory[products[3]]} \\n\n",
" {products[4]}: {inventory[products[4]]} \\n\n",
" ''' )\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
Expand All @@ -68,7 +242,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.13"
"version": "3.12.6"
}
},
"nbformat": 4,
Expand Down