List Comprehensions

inventory = [
    {"id": "P101", "name": "Wireless Mouse", "price": 29.99, "stock": 14, "category": "Electronics"},
    {"id": "P102", "name": "Mechanical Keyboard", "price": 89.99, "stock": 0, "category": "Electronics"},
    {"id": "P103", "name": "USB-C Hub", "price": 45.00, "stock": 5, "category": "Accessories"},
    {"id": "P104", "name": "Desk Lamp", "price": 34.50, "stock": 0, "category": "Furniture"},
    {"id": "P105", "name": "4K Monitor", "price": 320.00, "stock": 8, "category": "Electronics"}
]

# 1. Filter in-stock electronics and calculate discounted price
discounted_electronics = [
    {
        "id": p["id"],
        "name": p["name"],
        "sale_price": round(p["price"] * 0.90, 2)
    }
    for p in inventory
    if p["category"] == "Electronics" and p["stock"] > 0
]

# 2. Build stock lookup table with dict comprehension
stock_map = {p["id"]: p["stock"] for p in inventory}

print("=" * 60)
print(f"{'PROMOTIONAL ELECTRONICS MARKDOWN':^60}")
print("=" * 60)
for item in discounted_electronics:
    print(f"[{item['id']}] {item['name']:<25} -> Sale: ${item['sale_price']:.2f}")

print("-" * 60)
print("Stock Inventory Lookup Dictionary:")
print(stock_map)
print("=" * 60)

import string

documents = {
    "doc1": "Python is fast, elegant, and productive. Python is loved by developers.",
    "doc2": "Data engineers love Python for fast data pipelines.",
    "doc3": "C++ and Rust offer raw speed, while Python provides fast development."
}

# Translation table to delete punctuation
punct_table = str.maketrans("", "", string.punctuation)

# 1. Clean token lists per document using dict comprehension
tokenized_corpus = {
    doc_id: [w for w in text.casefold().translate(punct_table).split() if len(w) > 3]
    for doc_id, text in documents.items()
}

# 2. Extract global unique vocabulary using set comprehension (flattening)
global_vocab = {
    word
    for tokens in tokenized_corpus.values()
    for word in tokens
}

# 3. Construct inverted index using dict comprehension
inverted_index = {
    word: sorted({doc_id for doc_id, tokens in tokenized_corpus.items() if word in tokens})
    for word in sorted(global_vocab)
}

print("=" * 65)
print(f"{'SEARCH ENGINE INVERTED INDEX':^65}")
print("=" * 65)
print(f"Total Unique Keywords Indexed: {len(global_vocab)}")
print("-" * 65)
for word in ["python", "fast", "data", "speed", "rust"]:
    print(f"Keyword '{word:<10}' appears in documents: {inverted_index.get(word, [])}")
print("=" * 65)

users = [
    {"user_id": 1, "name": "Alice", "role": "Admin"},
    {"user_id": 2, "name": "Bob", "role": "Editor"},
    {"user_id": 3, "name": "Carol", "role": "Viewer"}
]

orders = [
    {"order_id": 501, "user_id": 1, "total": 125.50},
    {"order_id": 502, "user_id": 2, "total": 45.00},
    {"order_id": 503, "user_id": 1, "total": 300.00},
    {"order_id": 504, "user_id": 99, "total": 80.00}  # Foreign key orphan
]

# 1. Build O(1) hash map lookup for users
user_index = {u["user_id"]: u for u in users}

# 2. Relational Inner Join using List Comprehension
joined_orders = [
    {
        "order_id": o["order_id"],
        "user_name": user_index[o["user_id"]]["name"],
        "role": user_index[o["user_id"]]["role"],
        "total": o["total"]
    }
    for o in orders
    if o["user_id"] in user_index
]

print("=" * 65)
print(f"{'RELATIONAL JOIN & MATRIX TRANSPOSITION ENGINE':^65}")
print("=" * 65)
print("Joined Order Records:")
for jo in joined_orders:
    print(f"Order #{jo['order_id']} | Buyer: {jo['user_name']:<8} ({jo['role']:<6}) | Total: ${jo['total']:.2f}")

print("-" * 65)

# 3. Matrix Transposition (3 rows x 4 cols -> 4 rows x 3 cols)
matrix = [
    [1, 2, 3, 4],
    [5, 6, 7, 8],
    [9, 10, 11, 12]
]

# Transposition: Outer loop iterates over columns, inner loop extracts elements from rows
transposed = [
    [row[col_idx] for row in matrix]
    for col_idx in range(len(matrix[0]))
]

print("Original Matrix (3x4):")
for r in matrix:
    print(" ", r)

print("Transposed Matrix (4x3):")
for r in transposed:
    print(" ", r)
print("=" * 65)