Infix piping operator
Project description
ipo: Infix piping operator
Demo
from functools import partial as p
from ipo import ipo, head
# Ipo makes data flows much easier to follow.
ipo([5, 2, 3, 1, 4]) | sorted | list | print # [1, 2, 3, 4, 5]
ipo([5, 2, 3, 1, 4]) | p(head, 3) | list | print # [5, 2, 3]
ipo([5, 2, 3, 1, 4]) | sorted | p(head, 3) | list | print # [1, 2, 3]
ipo([5, 2, 3, 1, 4]) | p(head, 3) | sorted | list | print # [2, 3, 5]
# Much more readable than the original!
# print(list(sorted(itertools.islice([5, 2, 3, 1, 4], 3))))
from functools import partial as p
from itertools import starmap
from ipo import from_csv, skip, to_csv, before, write
# Ipo is ideally suited for working with CSV data.
ipo("""
#Voltage,Current
12,1
8,2
220,6
""".strip().split()) | from_csv | p(skip, 1) | \
p(starmap, lambda v, a: (v, a, float(v) * float(a))) | \
to_csv | p(before, ["#Voltage,Current,Power"]) | write
# #Voltage,Current,Power
# 12,1,12.0
# 8,2,16.0
# 220,6,1320.0
from functools import partial as p
from ipo import read, write
# Most ipo chains that work over iterable data are lazy by default. You can load huge files,
# process them and write the results to another file.
with open("bigfile.txt") as f_in, open("processed.txt") as f_out:
read(file=f_in) | p(map, some_preprocessing) | \
p(map, some_advanced_function) | \
p(map, some_formatting) | p(write, file=f_out)
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