8 ms·

shameless plug: I maintain a small library to do functional pipes. You can write: ( range(10) | Map(lambda x: x * 10) | Filter(lam

by tandav 3y ago

shameless plug: I maintain a small library to do functional pipes. You can write: ( range(10) | Map(lambda x: x * 10) | Filter(lambda x: x % 2 == 0) | Reduce(lambda a, b: a + b) ) instead of: x = range(10) x = map(lambda x: x * 10, x) x = filter(lambda x: x % 2 == 0, x) x = reduce(lambda a, b: a + b, x) and more. https://tandav.github.io/pipe21/ https://tandav.github.io/pipe21/

sampo 3y ago
import pandas as pd import functools ( pd.Series(range(10)) .apply(lambda x: x * 10) .where(lambda x: x % 2 == 0) .pipe(lambda s: functools.reduce(lambda x, y : x + y, s)) )
- melx 3y ago
  Readability isn't the best. Also what you present here is method chaining and not functional pipes.
  - heyoni 3y ago
    Is the difference mostly syntactic?
    - archarios 3y ago
      with dot chaining you are restricted to only the functions that the API you're working with provides. With functional pipes you can use whatever functions you want.
      - sampo 3y ago
        In this case, the API provides .pipe, with which you can then use whatever functions you want.
  - gnulinux 3y ago
    What difference does it make? They're conceptually the same thing. You're mapping immutable data to map/reduce/filter like pure functions to get new data.
    - melx 3y ago
      Improved code readability. The pipe operator was a game changer for me even after 20 years of programming (I know I was probably touching wrong things).
etra0 3y ago
This looks cool! I always wanted something similar for pandas, because I found it very elegant in dplyr in R. AFAIK they do have a `.pipe` method now but it could definitely be better in the future.
gnulinux 3y ago
Hmm, I've never seen code like the latter so it's unclear what problem this solves. I'd just write the pandas code snippet given below. Possibly with polars to make it lazy.
- mtreis86 3y ago
  It is a fairly common pattern in low level stuff like hashing algorithms or cryptography.
- tandav 3y ago
  My library solves 2 problems. 1. It does not require to wrap your iterable into some wrapper to use functional methods. It takes an iterable/object and returns another iterable/object. You don't have to unwrap it after transformations. 2. it uses oneliners (library is 80LOC single file) for most of the methods. You can just copy-paste it to use instead of install and import. E.g map, filter, reduce is just: class B: def __init__(self, f): self.f = f class Pipe (B): __ror__ = lambda self, x: self.f(x) class Map (B): __ror__ = lambda self, x: map (self.f, x) class Filter(B): __ror__ = lambda self, x: filter(self.f, x) class Reduce(B): __ror__ = lambda self, it: functools.reduce(self.f, it, *self.args)
archarios 3y ago
if folks like this and use JS, there is a very similar library for that ecosystem: https://ramdajs.com/ https://ramdajs.com/

by tandav 3y ago

sampo 3y ago

import pandas as pd import functools ( pd.Series(range(10)) .apply(lambda x: x * 10) .where(lambda x: x % 2 == 0) .pipe(lambda s: functools.reduce(lambda x, y : x + y, s)) )

melx 3y ago
Readability isn't the best. Also what you present here is method chaining and not functional pipes.
- heyoni 3y ago
  Is the difference mostly syntactic?
  - archarios 3y ago
    with dot chaining you are restricted to only the functions that the API you're working with provides. With functional pipes you can use whatever functions you want.
    - sampo 3y ago
      In this case, the API provides .pipe, with which you can then use whatever functions you want.
- gnulinux 3y ago
  What difference does it make? They're conceptually the same thing. You're mapping immutable data to map/reduce/filter like pure functions to get new data.
  - melx 3y ago
    Improved code readability. The pipe operator was a game changer for me even after 20 years of programming (I know I was probably touching wrong things).

etra0 3y ago

This looks cool! I always wanted something similar for pandas, because I found it very elegant in dplyr in R. AFAIK they do have a `.pipe` method now but it could definitely be better in the future.

gnulinux 3y ago

Hmm, I've never seen code like the latter so it's unclear what problem this solves. I'd just write the pandas code snippet given below. Possibly with polars to make it lazy.

mtreis86 3y ago
It is a fairly common pattern in low level stuff like hashing algorithms or cryptography.
tandav 3y ago
My library solves 2 problems. 1. It does not require to wrap your iterable into some wrapper to use functional methods. It takes an iterable/object and returns another iterable/object. You don't have to unwrap it after transformations. 2. it uses oneliners (library is 80LOC single file) for most of the methods. You can just copy-paste it to use instead of install and import. E.g map, filter, reduce is just: class B: def __init__(self, f): self.f = f class Pipe (B): __ror__ = lambda self, x: self.f(x) class Map (B): __ror__ = lambda self, x: map (self.f, x) class Filter(B): __ror__ = lambda self, x: filter(self.f, x) class Reduce(B): __ror__ = lambda self, it: functools.reduce(self.f, it, *self.args)

archarios 3y ago

if folks like this and use JS, there is a very similar library for that ecosystem: https://ramdajs.com/ https://ramdajs.com/