Map, Filter & Reduce

These are higher-order functions from functional programming that provide concise ways to process iterables. They are often used with anonymous lambda functions.

map(): Applies a function to every item in an iterable and returns a new iterable with the transformed results. It’s used for transformation.
```
nums = [1, 2, 3, 4]
doubled_nums = list(map(lambda x: x*2, nums))
print(doubled_nums)  # Output: [2, 4, 6, 8]
```
filter(): Creates a new iterable with items from the original iterable that satisfy a certain condition (i.e., the function returns True). It’s used for selection.
```
nums = [1, 2, 3, 4]
even_nums = list(filter(lambda x: x % 2 == 0, nums))
print(even_nums)  # Output: [2, 4]
```
reduce(): Applies a function cumulatively to the items of an iterable, reducing it to a single value. This function is found in the functools module. It’s used for aggregation.
```
from functools import reduce
nums = [1, 2, 3, 4]
sum_nums = reduce(lambda x, y: x + y, nums)
print(sum_nums)  # Output: 10
```