Comprehensions and lambda

List, dict, and set comprehensions are Python's signature feature for transforming collections. They're more compact than loops, more idiomatic, and slightly faster. Get fluent with them and your code will look like the Python you read in textbooks and on Stack Overflow.

List comprehension — the basic shape

# Loop version
squared = []
for x in [1, 2, 3, 4, 5]:
    squared.append(x ** 2)

# Comprehension version
squared = [x ** 2 for x in [1, 2, 3, 4, 5]]
# [1, 4, 9, 16, 25]

Filtering with if

rates = [0.07, 0.10, 0.12, 0.15, 0.08]

# Above-average rates only
high = [r for r in rates if r > 0.10]
# [0.12, 0.15]

# Tag each rate as 'high' or 'low'
tagged = ['high' if r > 0.10 else 'low' for r in rates]
# Note: the if/else here is the ternary expression, not a filter

Dict and set comprehensions

# Dict — map keys to derived values
banks = ['KCB', 'Equity', 'Coop']
lengths = {b: len(b) for b in banks}
# {'KCB': 3, 'Equity': 6, 'Coop': 4}

# Set — collect unique results
first_letters = {b[0] for b in banks}
# {'K', 'E', 'C'}

Nested comprehensions

You can write nested comprehensions, but they get hard to read fast. The rule: if the comprehension reads as a single English sentence, use it. If you have to mentally parse the order of fors and ifs, use a normal loop.

matrix = [[1, 2, 3], [4, 5, 6], [7, 8, 9]]
flat = [x for row in matrix for x in row]
# [1, 2, 3, 4, 5, 6, 7, 8, 9]

Generator expressions

If you replace the outer brackets with parentheses, you get a generator expression — a lazy version of the comprehension that produces values on demand instead of materialising a list. Useful for large data: sum(x ** 2 for x in range(10**6)) doesn't build the squared list, just streams through it.