Functional Programming Tools

FP is a style where you treat computation as the evaluation of mathematical functions. It avoids changing-state and mutable data. Python supports FP features that make data processing pipelines concise.

Python
# Imperative (Loop)
results = []
for x in data:
    if x > 5:
        results.append(x * 2)

# Functional (Expression)
results = list(map(lambda x: x*2, filter(lambda x: x>5, data)))

`map(function, iterable)` applies a function to every item in an iterable. It returns a map object (iterator), so wrap it in `list()` to see the result immediately.

Python
prices = [100, 200, 300]
discounted= list(map(lambda p: p * 0.9, prices))
print(discounted)  # [90.0, 180.0, 270.0]

`filter(function, iterable)` returns only the items for which the function returns True. It's like a bouncer for your data list.

Python
scores = [85, 42, 90, 33, 76]
passing = list(filter(lambda s: s >= 50, scores))
print(passing)  # [85, 90, 76]

`reduce(function, iterable)` executes a rolling computation to reduce the list to a single value. It's powerful but must be imported from `functools`.

Python
from functools import reduce

nums = [1, 2, 3, 4]
# (((1 + 2) + 3) + 4)
total = reduce(lambda a, b: a + b, nums)
print(total)  # 10