Dictionary Comprehensions in Python

# Traditional loop
words   = ["apple", "banana", "cherry"]
lengths = {}
for w in words:
    lengths[w] = len(w)
print(lengths)

# Dictionary comprehension — same result, one line
lengths = {w: len(w) for w in words}
print(lengths)

# Map numbers to their squares
squares = {n: n ** 2 for n in range(1, 8)}
print(squares)

# Map numbers to their cubes
cubes = {n: n ** 3 for n in range(1, 6)}
print(cubes)

# Build a lookup: character → ASCII code
ascii_map = {c: ord(c) for c in "PYTHON"}
print(ascii_map)

# Build a lookup: user ID → username from a list of tuples
users = [(1, "alice"), (2, "bob"), (3, "carol")]
id_to_name = {uid: name for uid, name in users}
print(id_to_name)

costs = {"shirt": 12.00, "hat": 8.50, "bag": 22.00, "scarf": 6.00}

# Apply 40% markup to every price
retail = {item: round(price * 1.40, 2) for item, price in costs.items()}
print("Retail:", retail)

# Uppercase all keys
upper_keys = {k.upper(): v for k, v in costs.items()}
print("Upper:", upper_keys)

# Round all values to nearest pound
rounded = {k: round(v) for k, v in costs.items()}
print("Rounded:", rounded)

# Transform both key and value simultaneously
formatted = {k.title(): f"£{v:.2f}" for k, v in costs.items()}
print("Formatted:", formatted)

scores = {"Alice": 88, "Bob": 53, "Carol": 76, "Dave": 45, "Eve": 92}

# Keep only students who passed (≥60)
passed = {name: score for name, score in scores.items() if score >= 60}
print("Passed:", passed)

# Keep only students who failed
failed = {name: score for name, score in scores.items() if score < 60}
print("Failed:", failed)

# Filter by key — keep only names starting with a vowel
vowel_names = {k: v for k, v in scores.items() if k[0].lower() in "aeiou"}
print("Vowel names:", vowel_names)

# Filter an inventory — keep only affordable in-stock items
inventory = {
    "pen":      {"price": 1.50, "stock": 200},
    "desk":     {"price": 350,  "stock": 0},
    "notebook": {"price": 4.99, "stock": 50},
    "lamp":     {"price": 24.99,"stock": 0},
    "ruler":    {"price": 0.99, "stock": 100},
}
available = {
    item: info["price"]
    for item, info in inventory.items()
    if info["stock"] > 0 and info["price"] < 10
}
print("Affordable & in stock:", available)

products = ["coffee", "tea", "juice", "water"]
prices   = [3.50,    2.00,  4.25,   1.00  ]

# Basic pairing — comprehension or dict(zip()) both work
menu = {item: price for item, price in zip(products, prices)}
print("Menu:", menu)

# Comprehension adds power — transform while pairing
menu_formatted = {item.title(): f"${price:.2f}" for item, price in zip(products, prices)}
print("Formatted menu:", menu_formatted)

# Build from CSV header + data row
header = ["name",   "dept",        "salary"]
data   = ["Priya",  "Engineering", 90000  ]
record = {field: value for field, value in zip(header, data)}
print("Record:", record)

# Enumerate gives index + item — useful as a lookup
words = ["python", "data", "science"]
index_map = {word: i for i, word in enumerate(words)}
print("Index map:", index_map)

# Invert a dictionary — swap keys and values
country_code = {"US": "United States", "CA": "Canada", "MX": "Mexico", "IN": "India"}
code_country = {v: k for k, v in country_code.items()}
print(code_country)

# Real use — build a reverse word index
word_index = {"python": 0, "data": 1, "science": 2}
index_word = {v: k for k, v in word_index.items()}
print(index_word)

# Warning: if values are not unique, only the LAST key survives
grades = {"Alice": "A", "Bob": "B", "Carol": "A"}   # Alice and Carol share "A"
inverted = {v: k for k, v in grades.items()}
print(inverted)   # "A" maps to Carol (Bob's "A" overwrites Alice's)

scores = {"Alice": 88, "Bob": 53, "Carol": 76, "Dave": 45, "Eve": 92}

# Label each student as pass or fail
results = {name: "pass" if score >= 60 else "fail"
           for name, score in scores.items()}
print(results)

# Assign a letter grade
grades = {
    name: "A" if score >= 90 else ("B" if score >= 75 else ("C" if score >= 60 else "F"))
    for name, score in scores.items()
}
print(grades)

# Apply tiered discount based on quantity
orders = {"pen": 50, "notebook": 8, "desk": 1, "chair": 3}
discounts = {item: 0.20 if qty >= 20 else (0.10 if qty >= 5 else 0.0)
             for item, qty in orders.items()}
print(discounts)

# Build a frequency map — count occurrences of each item
words = ["apple", "banana", "apple", "cherry", "banana", "apple"]
freq = {word: words.count(word) for word in set(words)}
print("Frequency:", freq)

# Normalise a dictionary — scale all values to 0–1 range
raw = {"speed": 120, "accuracy": 95, "memory": 80}
max_val = max(raw.values())
normalised = {k: round(v / max_val, 3) for k, v in raw.items()}
print("Normalised:", normalised)

# Process a list of API records into a lookup dict
api_records = [
    {"id": "u1", "name": "Priya",  "active": True},
    {"id": "u2", "name": "Kiran",  "active": False},
    {"id": "u3", "name": "Arjun",  "active": True},
]
# Build lookup: id → name, active users only
active_lookup = {r["id"]: r["name"] for r in api_records if r["active"]}
print("Active users:", active_lookup)

# Build a grade table: student → subject → default score
students = ["Alice", "Bob", "Carol"]
subjects = ["math", "science", "english"]

grade_table = {s: {sub: 0 for sub in subjects} for s in students}
for student, grades in grade_table.items():
    print(f"{student}: {grades}")

# Build a multiplication table
times_table = {r: {c: r * c for c in range(1, 6)} for r in range(1, 4)}
for row, cols in times_table.items():
    print(f"Row {row}: {cols}")