3 ms·

import pandas as pd import functools ( pd.Series(range(10)) .apply(lambda x: x * 10) .where(lambda x: x % 2 == 0) .

by 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).