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Week_1_day_1_lab work. #577

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77120e6
Day 1 in Week 1 lab work
Oct 6, 2025
c1b37b5
data-structure-day1-assignment
Oct 9, 2025
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235 changes: 234 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 +50,239 @@
"\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": [],
"source": [
"products=[\"t-shirt\", \"mug\", \"hat\", \"book\", \"keychain\"]\n",
"inventory={}\n",
"user_input_t_shirt=int(input(\"Enter quantity of t-shirts: \"))\n",
"user_input_mug=int(input(\"Enter quantity of mug: \"))\n",
"user_input_hat=int(input(\"Enter quantity of hat: \"))\n",
"user_input_book=int(input(\"Enter quantity of book: \"))\n",
"user_input_keychain=int(input(\"Enter quantity of keychain: \"))\n",
"inventory={\"t-shirt\":user_input_t_shirt, \"mug\":user_input_mug, \"hat\":user_input_hat, \"book\":user_input_book, \"keychain\":user_input_keychain}\n",
"inventory\n"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stdin",
"output_type": "stream",
"text": [
"Enter your preference from the product list: hat\n",
"Enter your preference from the product list: book\n",
"Enter your preference from the product list: mug\n"
]
},
{
"data": {
"text/plain": [
"{'book', 'hat', 'mug'}"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"customer_orders={}\n",
"customer_preference_1=input(\"Enter your preference from the product list: \")\n",
"customer_preference_2=input(\"Enter your preference from the product list: \")\n",
"customer_preference_3=input(\"Enter your preference from the product list: \")\n",
"customer_orders={customer_preference_1, customer_preference_2, customer_preference_3}\n",
"customer_orders"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"3"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"total_products_ordered=len(customer_orders)\n",
"total_products_ordered"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'inventory' is not defined",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)",
"Cell \u001b[1;32mIn[1], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m total_products_available\u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlen\u001b[39m(inventory)\n\u001b[0;32m 2\u001b[0m percentage_ordered\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mround\u001b[39m((total_products_ordered\u001b[38;5;241m/\u001b[39mtotal_products_available)\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m100\u001b[39m)\n\u001b[0;32m 3\u001b[0m percentage_ordered\n",
"\u001b[1;31mNameError\u001b[0m: name 'inventory' is not defined"
]
}
],
"source": [
"total_products_available= len(inventory)\n",
"percentage_ordered=round((total_products_ordered/total_products_available)*100)\n",
"percentage_ordered\n"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Order Statistics: Total products ordered:3 Percentage of products ordered:15%\n"
]
}
],
"source": [
"print(f\"Order Statistics: Total products ordered:{total_products_ordered} Percentage of products ordered:{percentage_ordered}%\")"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"quantity_subtracted = 1\n",
"inventory[\"t-shirt\"]-=1\n",
"inventory[\"mug\"]-=1\n",
"inventory[\"hat\"]-=1\n",
"inventory[\"book\"]-=1\n",
"inventory[\"keychain\"]-=1"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"1"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"inventory[\"t-shirt\"]"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"2"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"inventory[\"mug\"]"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"3"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"inventory[\"hat\"]"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"4"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"inventory[\"book\"]"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"5"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"inventory[\"keychain\"]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
Expand All @@ -68,7 +301,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.13"
"version": "3.12.7"
}
},
"nbformat": 4,
Expand Down