Understanding Data

# Temperature data for 7 days (in Celsius)
temps = [34, 34, 27, 28, 27, 34, 34]

# 1) Mean (Average)
mean_temp = sum(temps) / len(temps)

# 2) Range = max - min
temp_range = max(temps) - min(temps)

# 3) Population Standard Deviation (divide by n)
#    Steps:
#    - find squared difference of each value from mean
#    - take average of those squared differences
#    - take square root
squared_diffs = []
for x in temps:
    squared_diffs.append((x - mean_temp) ** 2)
variance = sum(squared_diffs) / len(temps)
std_dev = variance ** 0.5

print("Temperatures:", temps)
print("Mean (Average):", round(mean_temp, 2))
print("Range:", temp_range)
print("Standard Deviation:", round(std_dev, 2))

Temperatures: [34, 34, 27, 28, 27, 34, 34]
Mean (Average): 31.14
Range: 7
Standard Deviation: 3.31

# Canteen dataset (sample)
# For fruit juice, we include multiple companies (same quantity)
# to study variation in sale price.

items = [
    # category, item, company, qty_ml, purchase_price, sale_price, units_sold
    ("Fruit Juice", "Juice", "Real",       200, 18, 25, 40),
    ("Fruit Juice", "Juice", "Tropicana",  200, 20, 28, 35),
    ("Fruit Juice", "Juice", "MinuteMaid", 200, 19, 26, 30),

    ("Biscuits", "Parle-G", None, None, 5, 8, 120),
    ("Biscuits", "Oreo",    None, None, 18, 25, 60),

    ("Snack", "Samosa", None, None, 7, 12, 150),
]

# (a) Compare purchase and sale price of fruit juice and biscuits
print("A) Purchase vs Sale (Profit per unit)")
print()
for cat, name, comp, qty, buy, sell, sold in items:
    if cat in ("Fruit Juice", "Biscuits"):
        label = (
            cat + " - " + name
            if comp is None
            else cat + " - " + comp + " (" + str(qty) + "ml)"
        )
        profit = sell - buy
        line = (
            label
            + " Buy=" + str(buy)
            + "  Sell=" + str(sell)
            + "  Profit=" + str(profit)
        )
        print(line)

# (b) Compare sales of fruit juice, biscuits and samosa
# (total units sold per category)
sales_by_cat = {}
for cat, name, comp, qty, buy, sell, sold in items:
    sales_by_cat[cat] = sales_by_cat.get(cat, 0) + sold

print()
print("B) Total units sold by category")
print()
for cat in sorted(sales_by_cat):
    print(
        cat + ": " + str(sales_by_cat[cat]) + " units"
    )

# (c) Variation in sale price of fruit juices
# (different companies, same quantity)
fruit_sales_prices = [
    sell
    for cat, name, comp, qty, buy, sell, sold in items
    if cat == "Fruit Juice"
]

mean_price = sum(fruit_sales_prices) / len(fruit_sales_prices)
price_range = max(fruit_sales_prices) - min(fruit_sales_prices)
variance = (
    sum((x - mean_price) ** 2 for x in fruit_sales_prices)
    / len(fruit_sales_prices)
)
std_dev = variance ** 0.5

print()
print("C) Variation in sale price (Fruit Juice, 200ml)")
print()
print("Sale prices:", fruit_sales_prices)
print("Mean sale price:", round(mean_price, 2))
print("Range:", price_range)
print("Standard Deviation:", round(std_dev, 2))

A) Purchase vs Sale (Profit per unit)

Fruit Juice - Real (200ml) Buy=18  Sell=25  Profit=7
Fruit Juice - Tropicana (200ml) Buy=20  Sell=28  Profit=8
Fruit Juice - MinuteMaid (200ml) Buy=19  Sell=26  Profit=7
Biscuits - Parle-G Buy=5  Sell=8  Profit=3
Biscuits - Oreo Buy=18  Sell=25  Profit=7

B) Total units sold by category

Biscuits: 180 units
Fruit Juice: 105 units
Snack: 150 units

C) Variation in sale price (Fruit Juice, 200ml)

Sale prices: [25, 28, 26]
Mean sale price: 26.33
Range: 3
Standard Deviation: 1.25