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)
You could also do this:
> "2014-01-01" %>% as.Date
> 12 %>% sqrt %>% .^2 %>% as.character
To me this is more readable and this extends to use cases beyond the dataframe. Does the python language have support for something similar?
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.
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.
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.
Example Code:
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']
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)
Pipes are a new feature in Pandas 0.16.2.
Example:
import pandas as pd
from sklearn.datasets import load_iris
x = load_iris()
x = pd.DataFrame(x.data, columns=x.feature_names)
def remove_units(df):
df.columns = pd.Index(map(lambda x: x.replace(" (cm)", ""), df.columns))
return df
def length_times_width(df):
df['sepal length*width'] = df['sepal length'] * df['sepal width']
df['petal length*width'] = df['petal length'] * df['petal width']
x.pipe(remove_units).pipe(length_times_width)
x
NB: The Pandas version retains Python's reference semantics. That's why length_times_width doesn't need a return value; it modifies x in place.
Does the python language have support for something similar?
"more functional piping syntax" is this really a more "functional" syntax ? I would say it adds an "infix" syntax to R instead.
That being said, the Python's grammar does not have direct support for infix notation beyond the standard operators.
If you really need something like that, you should take that code from Tomer Filiba as a starting point to implement your own infix notation:
Code sample and comments by Tomer Filiba (http://tomerfiliba.com/blog/Infix-Operators/) :
from functools import partial class Infix(object): def __init__(self, func): self.func = func def __or__(self, other): return self.func(other) def __ror__(self, other): return Infix(partial(self.func, other)) def __call__(self, v1, v2): return self.func(v1, v2)Using instances of this peculiar class, we can now use a new "syntax" for calling functions as infix operators:
>>> @Infix ... def add(x, y): ... return x + y ... >>> 5 |add| 6
PyToolz [doc] allows arbitrarily composable pipes, just they aren't defined with that pipe-operator syntax.
Follow the above link for the quickstart. And here's a video tutorial: http://pyvideo.org/video/2858/functional-programming-in-python-with-pytoolz
In [1]: from toolz import pipe
In [2]: from math import sqrt
In [3]: pipe(12, sqrt, str)
Out[3]: '3.4641016151377544'
If you just want this for personal scripting, you might want to consider using Coconut instead of Python.
Coconut is a superset of Python. You could therefore use Coconut's pipe operator |>, while completely ignoring the rest of the Coconut language.
For example:
def addone(x):
x + 1
3 |> addone
compiles to
# lots of auto-generated header junk
# Compiled Coconut: -----------------------------------------------------------
def addone(x):
return x + 1
(addone)(3)
Building pipe with Infix
As hinted at by Sylvain Leroux, we can use the Infix operator to construct a infix pipe. Let's see how this is accomplished.
First, here is the code from Tomer Filiba
Code sample and comments by Tomer Filiba (http://tomerfiliba.com/blog/Infix-Operators/) :
from functools import partial class Infix(object): def __init__(self, func): self.func = func def __or__(self, other): return self.func(other) def __ror__(self, other): return Infix(partial(self.func, other)) def __call__(self, v1, v2): return self.func(v1, v2)Using instances of this peculiar class, we can now use a new "syntax" for calling functions as infix operators:
>>> @Infix ... def add(x, y): ... return x + y ... >>> 5 |add| 6
The pipe operator passes the preceding object as an argument to the object that follows the pipe, so x %>% f can be transformed into f(x). Consequently, the pipe operator can be defined using Infix as follows:
In [1]: @Infix
...: def pipe(x, f):
...: return f(x)
...:
...:
In [2]: from math import sqrt
In [3]: 12 |pipe| sqrt |pipe| str
Out[3]: '3.4641016151377544'
A note on partial application
The %>% operator from dpylr pushes arguments through the first argument in a function, so
df %>%
filter(x >= 2) %>%
mutate(y = 2*x)
corresponds to
df1 <- filter(df, x >= 2)
df2 <- mutate(df1, y = 2*x)
The easiest way to achieve something similar in Python is to use currying. The toolz library provides a curry decorator function that makes constructing curried functions easy.
In [2]: from toolz import curry
In [3]: from datetime import datetime
In [4]: @curry
def asDate(format, date_string):
return datetime.strptime(date_string, format)
...:
...:
In [5]: "2014-01-01" |pipe| asDate("%Y-%m-%d")
Out[5]: datetime.datetime(2014, 1, 1, 0, 0)
Notice that |pipe| pushes the arguments into the last argument position, that is
x |pipe| f(2)
corresponds to
f(2, x)
When designing curried functions, static arguments (i.e. arguments that might be used for many examples) should be placed earlier in the parameter list.
Note that toolz includes many pre-curried functions, including various functions from the operator module.
In [11]: from toolz.curried import map
In [12]: from toolz.curried.operator import add
In [13]: range(5) |pipe| map(add(2)) |pipe| list
Out[13]: [2, 3, 4, 5, 6]
which roughly corresponds to the following in R
> library(dplyr)
> add2 <- function(x) {x + 2}
> 0:4 %>% sapply(add2)
[1] 2 3 4 5 6
Using other infix delimiters
You can change the symbols that surround the Infix invocation by overriding other Python operator methods. For example, switching __or__ and __ror__ to __mod__ and __rmod__ will change the | operator to the mod operator.
In [5]: 12 %pipe% sqrt %pipe% str
Out[5]: '3.4641016151377544'
I missed the |> pipe operator from Elixir so I created a simple function decorator (~ 50 lines of code) that reinterprets the >> Python right shift operator as a very Elixir-like pipe at compile time using the ast library and compile/exec:
from pipeop import pipes
def add3(a, b, c):
return a + b + c
def times(a, b):
return a * b
@pipes
def calc()
print 1 >> add3(2, 3) >> times(4) # prints 24
All it's doing is rewriting a >> b(...) as b(a, ...).
You can use sspipe library. It exposes two objects p and px. Similar to x %>% f(y,z), you can write x | p(f, y, z) and similar to x %>% .^2 you can write x | px**2.
from sspipe import p, px
from math import sqrt
12 | p(sqrt) | px ** 2 | p(str)
Adding my 2c. I personally use package fn for functional style programming. Your example translates into
from fn import F, _
from math import sqrt
(F(sqrt) >> _**2 >> str)(12)
F is a wrapper class with functional-style syntactic sugar for partial application and composition. _ is a Scala-style constructor for anonymous functions (similar to Python's lambda); it represents a variable, hence you can combine several _ objects in one expression to get a function with more arguments (e.g. _ + _ is equivalent to lambda a, b: a + b). F(sqrt) >> _**2 >> str results in a Callable object that can be used as many times as you want.
One alternative solution would be to use the workflow tool dask. Though it's not as syntactically fun as...
var
| do this
| then do that
...it still allows your variable to flow down the chain and using dask gives the added benefit of parallelization where possible.
Here's how I use dask to accomplish a pipe-chain pattern:
import dask
def a(foo):
return foo + 1
def b(foo):
return foo / 2
def c(foo,bar):
return foo + bar
# pattern = 'name_of_behavior': (method_to_call, variables_to_pass_in, variables_can_be_task_names)
workflow = {'a_task':(a,1),
'b_task':(b,'a_task',),
'c_task':(c,99,'b_task'),}
#dask.visualize(workflow) #visualization available.
dask.get(workflow,'c_task')
# returns 100
After having worked with elixir I wanted to use the piping pattern in Python. This isn't exactly the same pattern, but it's similar and like I said, comes with added benefits of parallelization; if you tell dask to get a task in your workflow which isn't dependant upon others to run first, they'll run in parallel.
If you wanted easier syntax you could wrap it in something that would take care of the naming of the tasks for you. Of course in this situation you'd need all functions to take the pipe as the first argument, and you'd lose any benefit of parallization. But if you're ok with that you could do something like this:
def dask_pipe(initial_var, functions_args):
'''
call the dask_pipe with an init_var, and a list of functions
workflow, last_task = dask_pipe(initial_var, {function_1:[], function_2:[arg1, arg2]})
workflow, last_task = dask_pipe(initial_var, [function_1, function_2])
dask.get(workflow, last_task)
'''
workflow = {}
if isinstance(functions_args, list):
for ix, function in enumerate(functions_args):
if ix == 0:
workflow['task_' + str(ix)] = (function, initial_var)
else:
workflow['task_' + str(ix)] = (function, 'task_' + str(ix - 1))
return workflow, 'task_' + str(ix)
elif isinstance(functions_args, dict):
for ix, (function, args) in enumerate(functions_args.items()):
if ix == 0:
workflow['task_' + str(ix)] = (function, initial_var)
else:
workflow['task_' + str(ix)] = (function, 'task_' + str(ix - 1), *args )
return workflow, 'task_' + str(ix)
# piped functions
def foo(df):
return df[['a','b']]
def bar(df, s1, s2):
return df.columns.tolist() + [s1, s2]
def baz(df):
return df.columns.tolist()
# setup
import dask
import pandas as pd
df = pd.DataFrame({'a':[1,2,3],'b':[1,2,3],'c':[1,2,3]})
Now, with this wrapper, you can make a pipe following either of these syntactical patterns:
# wf, lt = dask_pipe(initial_var, [function_1, function_2])
# wf, lt = dask_pipe(initial_var, {function_1:[], function_2:[arg1, arg2]})
like this:
# test 1 - lists for functions only:
workflow, last_task = dask_pipe(df, [foo, baz])
print(dask.get(workflow, last_task)) # returns ['a','b']
# test 2 - dictionary for args:
workflow, last_task = dask_pipe(df, {foo:[], bar:['string1', 'string2']})
print(dask.get(workflow, last_task)) # returns ['a','b','string1','string2']
There is dfply module. You can find more information at
https://github.com/kieferk/dfply
Some examples are:
from dfply import *
diamonds >> group_by('cut') >> row_slice(5)
diamonds >> distinct(X.color)
diamonds >> filter_by(X.cut == 'Ideal', X.color == 'E', X.table < 55, X.price < 500)
diamonds >> mutate(x_plus_y=X.x + X.y, y_div_z=(X.y / X.z)) >> select(columns_from('x')) >> head(3)
来源：https://stackoverflow.com/questions/28252585/functional-pipes-in-python-like-from-rs-magritrr