In R (thanks to magrittr) 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?
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.
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.
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)
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)
print(1 |> isinstance(int))... TypeError: isinstance expected 2 arguments, got 1 –
Frothy print(1 |> isinstance$(int)), or preferably, 1 |> isinstance$(int) |> print. –
Lynching 1 |> print$(2) calls print(2, 1) since $ maps to Python partials. but I want print(1, 2) which matches UFCS and magrittr. Motivation: 1 |> add(2) |> divide(6) should be 0.5, and I should not need parentheses. –
Frothy 1 |> isinstance$(?, int) |> print. For your other examples: 1 |> print$(?, 2), 1 |> (+)$(?, 2) |> (/)$(?, 6). I don't think you can avoid parentheses for partial application. –
Lynching |> and (+)$(?, 2) is, I've come to the conclusion that the programming-language and math establishment does not want me to use this type of syntax, and makes it even uglier than resorting to a set of parentheses. I would use it if it had better syntax (eg. Dlang has UFCS but IDK about arithmetic functions, or if Python had a .. pipe operator). –
Frothy 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
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)
3 | p( f( x, . ) ) ? In R this would be : 3 %>% f(x, .) –
Vinculum px, which is similar to . in R. Though, you should be careful that px should be passed to p(), not to f(). Example: 3 | p(f, x, px) –
Bumkin 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)
dfply and dplython are the same packages. Is there any difference between them? @Navada –
Use dfply, dplython, plydata packages are python ports of the dplyr package so they are going to be pretty similar in syntax. –
Navada dfply is the only one even remotely recently touched: and even that one has zero closed issues or commits for 18 months as of March 2021. I ping'ed the project to see if they have any plans to "wake up" –
Carlisle 3 >> np.sqrt gives an error, but in R it is 3 %>% sqrt –
Vinculum %>% in python so this is likely going to be the closest python equivalent –
Carlisle There is no need for 3rd party libraries or confusing operator trickery to implement a pipe function - you can get the basics going quite easily yourself.
Lets start by defining what a pipe function actually is. At its heart, it is just a way to express a series of function calls in logical order, rather than the standard 'inside out' order.
For example, lets look at these functions:
def one(value):
return value
def two(value):
return 2*value
def three(value):
return 3*value
Not very interesting, but assume interesting things are happening to value. We want to call them in order, passing the output of each to the next. In vanilla python that would be:
result = three(two(one(1)))
It is not incredibly readable and for more complex pipelines its gonna get worse. So, here is a simple pipe function which takes an initial argument, and the series of functions to apply it to:
def pipe(first, *args):
for fn in args:
first = fn(first)
return first
Lets call it:
result = pipe(1, one, two, three)
That looks like very readable 'pipe' syntax to me :). I don't see how it is any less readable than overloading operators or anything like that. In fact, I would argue that it is more readable python code
Here is the humble pipe solving the OP's examples:
from math import sqrt
from datetime import datetime
def as_date(s):
return datetime.strptime(s, '%Y-%m-%d')
def as_character(value):
# Do whatever as.character does
return value
pipe("2014-01-01", as_date)
pipe(12, sqrt, lambda x: x**2, as_character)
list(list(list(... ))) to the code . Nearly Impossible to read. Backwards reading is this also. Try fluentpy or infixpy –
Carlisle list is necessary? the solution here does not return a list at all. Likewise, the pipe function does not read backwards - it is left to right. If you want right to left, you are looking for a compose function. Equally easy to do with the same principles –
Commodus list is needed to materialize the results of a collection. Multiple map, filter etc's require one list per each thus the pollution of the code . The pipe is a third party library so your comment does not apply to it –
Carlisle 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, ...).
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'
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.
_ is not 100% flexible: it doesn't not support all Python operators. Additionaly, if you plan on using _ in an interactive session, you should import it under another name (e.g. from fn import _ as var), because most (if not all) interactive Python shells use _ to represent the last unassigned returned value, thus shadowing the imported object. –
Messieurs There is very nice pipe module here https://pypi.org/project/pipe/
It overloads | operator and provide a lot of pipe-functions like add, first, where, tail etc.
>>> [1, 2, 3, 4] | where(lambda x: x % 2 == 0) | add
6
>>> sum([1, [2, 3], 4] | traverse)
10
Plus it's very easy to write own pipe-functions
@Pipe
def p_sqrt(x):
return sqrt(x)
@Pipe
def p_pr(x):
print(x)
9 | p_sqrt | p_pr
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']
The pipe functionality can be achieved by composing pandas methods with the dot. Here is an example below.
Load a sample data frame:
import seaborn
iris = seaborn.load_dataset("iris")
type(iris)
# <class 'pandas.core.frame.DataFrame'>
Illustrate the composition of pandas methods with the dot:
(iris.query("species == 'setosa'")
.sort_values("petal_width")
.head())
You can add new methods to panda data frame if needed (as done here for example):
pandas.DataFrame.new_method = new_method
Just use cool.
First, run python -m pip install cool.
Then, run python.
from cool import F
range(10) | F(filter, lambda x: x % 2) | F(sum) == 25
You can read https://github.com/abersheeran/cool to get more usages.
My two cents inspired by http://tomerfiliba.com/blog/Infix-Operators/
class FuncPipe:
class Arg:
def __init__(self, arg):
self.arg = arg
def __or__(self, func):
return func(self.arg)
def __ror__(self, arg):
return self.Arg(arg)
pipe = FuncPipe()
Then
1 |pipe| \
(lambda x: return x+1) |pipe| \
(lambda x: return 2*x)
returns
4
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crime_by_state %>% filter(State=="New York", Year==2005) ...from the end of How dplyr replaced my most common R idioms. – Grate%>%is syntactic sugar. I don't think there's an equivalent shortcut in Python, preferring instead to use nested function calls. – Shannonshanny