This is a super simple wrapper , let's use python functional programming like Unix pipe!
Inspired by abersheeran/only-pipe , czheo/syntax_sugar_python , pipetools
pip install pipeit
- Statements start with
PIPEand end withENDOR you can even ellipsis them. - There're only two objects(
PIPE&END) and three types(Filter,Map&Reduce) in namespace, so feel free to usefrom pipeit import *. - Convert filter into tuple or capital the first letter, e.g.
map(lambda x:x + 1) => (map , lambda x:x + 1)orMap(lambda x:x + 1), however DO NOT MIX USE THEM. - It'll be 10% ~ 20% faster using the original python functional way than using these wrappers.
v0.2.0 Update:
- Simple code timing means
v0.3.0 Update:
- Easier reading and writing operations
Basic useage:
>>> from pipit import PIPE , END , Map , Filter , Reduce
>>> data = PIPE | range(10) | (map , lambda x:x + 1) | (map , str) | list | END
>>> data
['1', '2', '3', '4', '5', '6', '7', '8', '9', '10']
# (map , lambda x:x + 1) equals to Map(lambda x:x + 1)
>>> func = lambda x: PIPE | range(x) | Map(lambda x:x + 1) | Map(str) | list | END
>>> func(10)
['1', '2', '3', '4', '5', '6', '7', '8', '9', '10']Or you may want a more easy use.
>>> range(10) | Filter(lambda x:x<5) | list
[0, 1, 2, 3, 4]
>>> for _ in range(3) | Map(str):
print(repr(_))
'0'
'1'
'2'Code timer updated in version 0.2.0, you can easily detect the execution time of code blocks or statements.
from pipeit import *
from functools import reduce
foo = list(range(100))
for _ in timeit(1e6): # which means loop for 1m times
bar = foo | Filter(lambda x: x%3) | Map(lambda x: 10*x) | Reduce(lambda x, y: x+y) | int
with timeit(): # a handwritten for loop is required under context manager mode
for _ in range(int(1e6)):
bar = reduce(lambda x, y: x+y, map(lambda x: 10*x, filter(lambda x: x%3, foo)))
# output:
# [line 5][approximate] time cost / loop: 9.8967234μs
# [line 8][exact] time cost: 7.0519098s Better IO functions are updated in version 0.3.0. If you hate typing encoding="utf-8" over and over again, believe me, you'll love them.
Use simple Read()/Write()/ReadB()/WriteB() functions instead of the default practice of with open(). You can specify the encoding format, but they are specified as utf-8 by default. Another advantage of doing this is that you no longer need to worry about accidentally emptying the file by not changing 'w' to 'r'.
from pipeit import *
import json
#
Write("a.txt", "Hello World!")
WriteB("b.json", "[1, 2, 3]".encode("utf-8"))
assert Read("a.txt") == "Hello World!"
assert ReadB("b.txt") == b"[1, 2, 3]"Similarly, you can use pipes to send data to them, or pipes to receive data from them.
# OR
"[1, 2, 3]".encode("utf-8") | WriteB("b.json")
# another typical scenario is to cache data to hard disk and read it back again
html_a = "abc" # requests.get("https://a.example.com").text
html_b = "123" # ...
htmls = {"html_a": html_a, "html_b": html_b}
json.dumps(htmls) | Write("htmls.json")
#
htmls = Read("htmls.json") | json.loads
html_a, html_b = htmls.values()>> and | operands are functionally identical while doing IO.
"Hello World!" >> Write("a.txt")
text = Read("a.txt") >> str
assert text == "Hello World!"
