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Lab Done #591

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ca8b5f9
Lab Done
prodduturisindhurdy Oct 7, 2025
3cf4bf4
Lab Done
prodduturisindhurdy Oct 7, 2025
ed5687b
Lab Done
prodduturisindhurdy Oct 7, 2025
750f895
Solved Done
prodduturisindhurdy Oct 7, 2025
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205 changes: 203 additions & 2 deletions lab-python-data-structures.ipynb
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Original file line number Diff line number Diff line change
Expand Up @@ -50,11 +50,212 @@
"\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": null,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"['t-shirts', 'mug', 'hat', 'book', 'keychain']"
]
},
"execution_count": 1,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"products=[\"t-shirt\",\"mug\",\"hat\",\"book\",\"keychain\"]\n",
"products"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"inventory={}"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"inventory[\"t-shirt\"]=int(input(\"enter the quantity of t-shirt\"))\n",
"inventory[\"mug\"]=int(input(\"enter the quantity of mug\"))\n",
"inventory[\"hat\"]=int(input(\"enter the quantity of hat\"))\n",
"inventory[\"book\"]=int(input(\"enter the quantity of book\"))\n",
"inventory[\"keychain\"]=int(input(\"enter the quantity of keychain\"))"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
"customer_orders=set()"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [],
"source": [
"order1=input(\"enter your order\")\n",
"order2=input(\"enter your order\")\n",
"order3=input(\"enter your order\")\n",
"if order1 in products:\n",
" customer_orders.add(order1)\n",
"else:\n",
" print(\"order1 not found\")\n",
"if order2 in products:\n",
" customer_orders.add(order2)\n",
"else:\n",
" print(\"order2 not found\")\n",
"if order3 in products:\n",
" customer_orders.add(order3)\n",
"else:\n",
" print(\"order3 not found\")\n"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'hat', 'mug', 'book'}\n"
]
}
],
"source": [
"print(customer_orders)"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"3\n",
"60.0\n",
"(3, 60.0)\n"
]
}
],
"source": [
"total_products_order=len(customer_orders)\n",
"print(total_products_order)\n",
"percentage_ordered=(total_products_order / len(products)) * 100\n",
"print(percentage_ordered)\n",
"order_status=(total_products_order,percentage_ordered)\n",
"print(order_status)"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Order Statistics:\n",
"Total Products Ordered: 3\n",
"Percentage of Products Ordered: 60.0\n"
]
}
],
"source": [
"print(\"Order Statistics:\")\n",
"print(\"Total Products Ordered:\", order_status[0])\n",
"print(\"Percentage of Products Ordered:\", order_status[1])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'t-shirts': 9, 'mug': 19, 'hat': 29, 'book': 39, 'keychain': 49}"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"inventory[\"t-shirt\"] -=1\n",
"inventory[\"mug\"] -=1\n",
"inventory[\"hat\"] -=1\n",
"inventory[\"book\"] -=1\n",
"inventory[\"keychain\"] -=1\n",
"inventory"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"t-shirts: 9\n",
"mug: 19\n",
"hat: 29\n",
"book: 39\n",
"keychain: 49\n"
]
}
],
"source": [
"print(\"t-shirt:\",inventory[\"t-shirt\"] )\n",
"print(\"mug:\",inventory[\"mug\"] )\n",
"print(\"hat:\",inventory[\"hat\"] )\n",
"print(\"book:\",inventory[\"book\"] )\n",
"print(\"keychain:\",inventory[\"keychain\"] )"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
Expand All @@ -68,7 +269,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.13"
"version": "3.11.9"
}
},
"nbformat": 4,
Expand Down