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294 changes: 290 additions & 4 deletions lab-python-data-structures.ipynb
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Original file line number Diff line number Diff line change
Expand Up @@ -34,7 +34,7 @@
"7. Calculate the following order statistics:\n",
" - Total Products Ordered: The total number of products in the `customer_orders` set.\n",
" - Percentage of Products Ordered: The percentage of products ordered compared to the total available products.\n",
" \n",
" percentage_ordered \n",
" Store these statistics in a tuple called `order_status`.\n",
"\n",
"8. Print the order statistics using the following format:\n",
Expand All @@ -50,13 +50,299 @@
"\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": 1,
"metadata": {},
"outputs": [],
"source": [
"products=[\"t-shirt\", \"mug\", \"hat\", \"book\", \"keychain\"]"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"inventory={}"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdin",
"output_type": "stream",
"text": [
"quantity of t-shirt available in the inventory: 22\n"
]
}
],
"source": [
"inventory[\"t-shirt\"]=int(input(\"quantity of t-shirt available in the inventory: \"))"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdin",
"output_type": "stream",
"text": [
"quantity of mug available in the inventory: 32\n"
]
}
],
"source": [
"inventory[\"mug\"]=int(input(\"quantity of mug available in the inventory: \"))"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdin",
"output_type": "stream",
"text": [
"quantity of hat available in the inventory: 45\n"
]
}
],
"source": [
"inventory[\"hat\"]=int(input(\"quantity of hat available in the inventory: \"))"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdin",
"output_type": "stream",
"text": [
"quantity of book available in the inventory: 56\n"
]
}
],
"source": [
"inventory[\"book\"]=int(input(\"quantity of book available in the inventory: \"))"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stdin",
"output_type": "stream",
"text": [
"quantity of keychain available in the inventory: 72\n"
]
}
],
"source": [
"inventory[\"keychain\"]=int(input(\"quantity of keychain available in the inventory: \"))"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"customer_orders=set() \n",
"\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"name": "stdin",
"output_type": "stream",
"text": [
"input order1: mug\n",
"input order2: hat\n",
"input order3: book\n"
]
}
],
"source": [
"x=input(\"input order1: \")\n",
"y=input(\"input order2: \")\n",
"z=input(\"input order3: \")\n",
"customer_orders.add(x)\n",
"customer_orders.add(y)\n",
"customer_orders.add(z)\n"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'book', 'hat', 'mug'}\n"
]
}
],
"source": [
"print(customer_orders)\n"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"3\n"
]
}
],
"source": [
"total_products_ordered=len(customer_orders)\n",
"print(total_products_ordered)"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
"percentage_ordered=len(customer_orders)/len(products)*100"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [],
"source": [
"order_status=(total_products_ordered,percentage_ordered)"
]
},
{
"cell_type": "code",
"execution_count": 14,
"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: \"+ str(total_products_ordered))\n",
"print(\"Percentage of Products Ordered: \"+ str(percentage_ordered))"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [],
"source": [
"for item in customer_orders:\n",
" inventory[item]=inventory[item]-1\n",
" "
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'t-shirt': 22, 'mug': 31, 'hat': 44, 'book': 55, 'keychain': 72}\n"
]
}
],
"source": [
"print (inventory)"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"('t-shirt', 22)\n",
"('mug', 31)\n",
"('hat', 44)\n",
"('book', 55)\n",
"('keychain', 72)\n"
]
}
],
"source": [
"for item in inventory.items() :\n",
" print(item)"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"dict_items([('t-shirt', 22), ('mug', 31), ('hat', 44), ('book', 55), ('keychain', 72)])\n"
]
}
],
"source": [
"print(inventory.items())"
]
},
{
"cell_type": "code",
"execution_count": null,
"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 @@ -68,7 +354,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.13"
"version": "3.13.9"
}
},
"nbformat": 4,
Expand Down