functional-piped 0.0.1

Write iterator.map(func) instead of map(func, iterator)

functional-piped

Python has native support for some functional programming functions such as map and filter. This library allows you to use them in a "piped" way, i.e. s(iterable).map(func) instead of map(func, iterable), because in any slightly more complex scenarios, the former is much more readable.

For example,

(s(iterable)
    .map(func0)
    .filter(func1)
    .to(list))

makes much more sense than

list(
    filter(
        func1,
        map(func0, iterable)
    )
)

Installation

pip install functional-piped

Usage

>>> from funcpipe import Stream as s

Then you can use .map, .filter, .reduce, and .foreach to manipulate your iterable in a functional programming way. If the result is still an iterable, you can use .to() to collect it into any data type

>>> s([1, 2, 3]).map(lambda x: x + 1).to(list)
[2, 3, 4]

>>> s([1, 2, 3]).map(lambda x: x + 1).filter(lambda x: x % 2).to(list)
[2]

>>> s([1, 2, 3]).map(lambda x: x + 1).to(set)
{2, 3, 4}

>>> (s([1, 2, 3])
...     .map(lambda x: x + 1)            # [2, 3, 4]
...     .filter(lambda x: x % 2 == 0)    # [2, 4]
...     .reduce(lambda x, y: x + y))     # 2 + 4 = 6
6

>>> s([1, 2, 3]).foreach(print)
1
2
3

Iterable Reusability

s(obj) behaves exactly the same as obj in terms of "reusability" when calling iterator/iterable related methods.

If obj is an iterable, not iterator:

>>> obj = [1, 2, 3]
>>> stream = s(obj)

>>> stream.map(lambda x: x + 1).to(list)
[2, 3, 4]

>>> stream.map(lambda x: x + 1).to(list)
[2, 3, 4]

If obj is an iterator:

>>> obj = range(1, 4)
>>> stream = s(obj)

>>> stream.map(lambda x: x + 1).to(list)
[2, 3, 4]

>>> stream.map(lambda x: x + 1).to(list)
[]