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https://github.com/vopaaz/functional-piped
Write s(iterator).map(func) instead of map(func, iterator)
https://github.com/vopaaz/functional-piped
python python3
Last synced: 2 months ago
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Write s(iterator).map(func) instead of map(func, iterator)
- Host: GitHub
- URL: https://github.com/vopaaz/functional-piped
- Owner: Vopaaz
- License: mit
- Created: 2023-09-07T02:39:53.000Z (over 1 year ago)
- Default Branch: master
- Last Pushed: 2024-04-16T05:02:18.000Z (8 months ago)
- Last Synced: 2024-09-17T04:09:25.441Z (3 months ago)
- Topics: python, python3
- Language: Python
- Homepage:
- Size: 23.4 KB
- Stars: 2
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# 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,
```python
(s(iterable)
.map(func0)
.filter(func1)
.to(list))
```
makes much more sense than
```python
list(
filter(
func1,
map(func0, iterable)
)
)
```
## Installation
`pip install functional-piped`
## Usage
```python
>>> from funcpipe import Stream as s
```
Then you can use `.map`, `.filter`, `.reduce`, `.foreach`, and `.zip`
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
```python
>>> 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
```
### Using `.zip` and `.star`
One common use case in Python is
```python
a_list = [...]
b_list = [...]
for a, b in zip(a_list, b_list):
...
```
To write equivalent code, you can use `s(a_list).zip(b_list)` to zip the two lists together,
and use the `.star` attribute to apply `.foreach` to the unpacked zipped values
```python
>>> s([1, 2]).zip([3, 4]).to(list)
[(1, 3), (2, 4)]
>>> s([1, 2]).zip([3, 4]).star.foreach(lambda x, y: print(f"{x} + {y}"))
1 + 3
2 + 4
```
The same thing applies to map and filter:
```python
>>> s([1, 2]).zip([3, 4]).star.map(lambda x, y: x + y).to(list)
[4, 6]
```
If you don't use `.star`, the callback of each function will receive a tuple:
```python
>>> s([1, 2]).zip([3, 4]).map(lambda x: x[0] + x[1]).to(list)
[4, 6]
```
Naming of `.star` comes from the `itertools.starmap`, but this concept also
applies to `.filter` and `.foreach`, so instead of creating a `.starmap` method for `Stream`, we use the above mentioned syntax there.
## 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:
```python
>>> 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:
```python
>>> obj = iter(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)
[]
```