python中的功能管道，例如R的magritrr中的％>％ [英] Functional pipes in python like %>% from R's magritrr
问题描述
在R中(感谢magritrr)，您现在可以通过%>%以更具功能性的管道语法执行操作.这意味着无需编码:
In R (thanks to magritrr) you can now perform operations with a more functional piping syntax via %>%. This means that instead of coding this:
> as.Date("2014-01-01")
> as.character((sqrt(12)^2)
您也可以这样做:
> "2014-01-01" %>% as.Date
> 12 %>% sqrt %>% .^2 %>% as.character
对我来说，这更具可读性，并且扩展到数据框之外的用例. python语言是否支持类似功能?
To me this is more readable and this extends to use cases beyond the dataframe. Does the python language have support for something similar?
推荐答案
一种可行的方法是使用名为 macropy的模块. Macropy允许您将转换应用于所编写的代码.因此，可以将a | b转换为b(a).这有很多优点和缺点.
One possible way of doing this is by using a module called macropy. Macropy allows you to apply transformations to the code that you have written. Thus a | b can be transformed to b(a). This has a number of advantages and disadvantages.
与Sylvain Leroux提到的解决方案相比，主要优点是您不需要为您想要使用的功能创建中缀对象-只需标记要使用转换的代码区域即可.其次，由于转换是在编译时而不是在运行时应用的，因此转换后的代码在运行时不会受到开销-当最初从源代码生成字节代码时，所有工作都将完成.
In comparison to the solution mentioned by Sylvain Leroux, The main advantage is that you do not need to create infix objects for the functions you are interested in using -- just mark the areas of code that you intend to use the transformation. Secondly, since the transformation is applied at compile time, rather than runtime, the transformed code suffers no overhead during runtime -- all the work is done when the byte code is first produced from the source code.
主要缺点是，宏需要某种特定的激活方式才能起作用(稍后将提到).与更快的运行时间相比，源代码的解析在计算上更加复杂，因此程序将需要更长的启动时间.最后，它添加了一种语法样式，这意味着不熟悉macropy的程序员可能会发现您的代码更难理解.
The main disadvantages are that macropy requires a certain way to be activated for it to work (mentioned later). In contrast to a faster runtime, the parsing of the source code is more computationally complex and so the program will take longer to start. Finally, it adds a syntactic style that means programmers who are not familiar with macropy may find your code harder to understand.
run.py
import macropy.activate
# Activates macropy, modules using macropy cannot be imported before this statement
# in the program.
import target
# import the module using macropy
target.py
from fpipe import macros, fpipe
from macropy.quick_lambda import macros, f
# The `from module import macros, ...` must be used for macropy to know which
# macros it should apply to your code.
# Here two macros have been imported `fpipe`, which does what you want
# and `f` which provides a quicker way to write lambdas.
from math import sqrt
# Using the fpipe macro in a single expression.
# The code between the square braces is interpreted as - str(sqrt(12))
print fpipe[12 | sqrt | str] # prints 3.46410161514
# using a decorator
# All code within the function is examined for `x | y` constructs.
x = 1 # global variable
@fpipe
def sum_range_then_square():
"expected value (1 + 2 + 3)**2 -> 36"
y = 4 # local variable
return range(x, y) | sum | f[_**2]
# `f[_**2]` is macropy syntax for -- `lambda x: x**2`, which would also work here
print sum_range_then_square() # prints 36
# using a with block.
# same as a decorator, but for limited blocks.
with fpipe:
print range(4) | sum # prints 6
print 'a b c' | f[_.split()] # prints ['a', 'b', 'c']
最后是完成工作的模块.我称它为功能管道的fpipe，是它的模拟shell语法，用于将输出从一个进程传递到另一个进程.
And finally the module that does the hard work. I've called it fpipe for functional pipe as its emulating shell syntax for passing output from one process to another.
fpipe.py
from macropy.core.macros import *
from macropy.core.quotes import macros, q, ast
macros = Macros()
@macros.decorator
@macros.block
@macros.expr
def fpipe(tree, **kw):
@Walker
def pipe_search(tree, stop, **kw):
"""Search code for bitwise or operators and transform `a | b` to `b(a)`."""
if isinstance(tree, BinOp) and isinstance(tree.op, BitOr):
operand = tree.left
function = tree.right
newtree = q[ast[function](ast[operand])]
return newtree
return pipe_search.recurse(tree)
这篇关于python中的功能管道，例如R的magritrr中的％>％的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！
