Module enabling a sh like infix syntax (using pipes).
As an example, here is the solution for the 2nd Euler Project exercise:
Find the sum of all the even-valued terms in Fibonacci which do not exceed four million.
Given fib a generator of Fibonacci numbers:
euler2 = (fib() | where(lambda x: x % 2 == 0)
| take_while(lambda x: x < 4000000)
| add)To install the library, you can just run the following command:
# Linux/macOS
python3 -m pip install pipe
# Windows
py -3 -m pip install pipeTo install the development version, do the following:
$ git clone https://github.com/JulienPalard/Pipe
$ cd Pipe
$ python3 -m pip install .In pipe 1.x a lot of functions were returning iterables and a lot other functions were returning non-iterables, causing confusion. The one returning non-iterables could only be used as the last function of a pipe expression, so they are in fact useless:
range(100) | where(lambda x: x % 2 == 0) | addcan be rewritten with no less readability as:
sum(range(100) | where(lambda x: x % 2 == 0))so all pipes returning non-iterables are now deprecated and will be removed in pipe 2.0.
- A Pipe: a Pipe is a 'pipeable' function, something that you can pipe to, In the code '[1, 2, 3] | add' add is a Pipe
- A Pipe function: A standard function returning a Pipe so it can be used like a normal Pipe but called like in : [1, 2, 3] | concat("#")
I don't like import * but for the following examples in an REPL it
will be OK, so:
>>> from pipe import *The basic syntax is to use a Pipe like in a shell:
>>> sum(range(100) | select(lambda x: x ** 2) | where(lambda x: x < 100))
285Some pipes take an argument, some do not need one:
>>> sum([1, 2, 3, 4] | where(lambda x: x % 2 == 0))
6
>>> sum([1, [2, 3], 4] | traverse)
10A Pipe as a function is nothing more than a function returning a specialized Pipe.
You can construct your pipes using Pipe class initialized with lambdas like:
stdout = Pipe(lambda x: sys.stdout.write(str(x)))
select = Pipe(lambda iterable, pred: (pred(x) for x in iterable))Or using decorators:
@Pipe
def stdout(x):
sys.stdout.write(str(x))Alphabetical list of available pipes; when several names are listed for a given pipe, these are aliases.
chain
Chain a sequence of iterables:
>>> list([[1, 2], [3, 4], [5]] | chain)
[1, 2, 3, 4, 5]
Warning : chain only unfold iterable containing ONLY iterables:
[1, 2, [3]] | chain
Gives a TypeError: chain argument #1 must support iteration
Consider using traverse.
chain_with()
Like itertools.chain, yields elements of the given iterable,
then yields elements of its parameters
>>> list((1, 2, 3) | chain_with([4, 5], [6]))
[1, 2, 3, 4, 5, 6]
dedup()
Deduplicate values, using the given key function if provided (or else
the identity)
>>> list([1, 1, 2, 2, 3, 3, 1, 2, 3] | dedup)
[1, 2, 3]
>>> list([1, 1, 2, 2, 3, 3, 1, 2, 3] | dedup(key=lambda n:n % 2))
[1, 2]
filter()
alias for where(), see where()
groupby()
Like itertools.groupby(sorted(iterable, key = keyfunc), keyfunc)
(1, 2, 3, 4, 5, 6, 7, 8, 9) \
| groupby(lambda x: "Odd" if i % 2 else "Even")
| select(lambda x: "%s : %s" % (x[0], (x[1] | concat(', '))))
| concat(' / ')
'Odd : 1, 3, 5, 7, 9 / Even : 2, 4, 6, 8'
islice()
Just the itertools.islice
>>> list((1, 2, 3, 4, 5, 6, 7, 8, 9) | islice(2, 8, 2))
[3, 5, 7]
izip()
Just the itertools.izip
>>> list((1, 2, 3, 4, 5, 6, 7, 8, 9)
... | izip([9, 8, 7, 6, 5, 4, 3, 2, 1]))
[(1, 9), (2, 8), (3, 7), (4, 6), (5, 5), (6, 4), (7, 3), (8, 2), (9, 1)]
lstrip
Like Python's lstrip-method for str.
>>> 'abc ' | lstrip
'abc '
>>> '.,[abc] ] ' | lstrip('.,[] ')
'abc] ] '
map(), select()
Apply a conversion expression given as parameter
to each element of the given iterable
>>> list([1, 2, 3] | map(lambda x: x * x))
[1, 4, 9]
permutations()
Returns all possible permutations
>>> list('ABC' | permutations(2))
[('A', 'B'), ('A', 'C'), ('B', 'A'), ('B', 'C'), ('C', 'A'), ('C', 'B')]
>>> list(range(3) | permutations)
[(0, 1, 2), (0, 2, 1), (1, 0, 2), (1, 2, 0), (2, 0, 1), (2, 1, 0)]
reverse
Like Python's built-in "reversed" primitive.
>>> list([1, 2, 3] | reverse)
[3, 2, 1]
rstrip
Like Python's rstrip-method for str.
>>> ' abc ' | rstrip
' abc'
>>> '.,[abc] ] ' | rstrip('.,[] ')
'.,[abc'
select()
alias for map(), see map()
skip()
Skips the given quantity of elements from the given iterable, then yields
>>> list((1, 2, 3, 4, 5) | skip(2))
[3, 4, 5]
skip_while()
Like itertools.dropwhile, skips elements of the given iterable
while the predicate is true, then yields others:
>>> list([1, 2, 3, 4] | skip_while(lambda x: x < 3))
[3, 4]
sort()
Like Python's built-in "sorted" primitive. Allows cmp (Python 2.x
only), key, and reverse arguments. By default sorts using the
identity function as the key.
>>> ''.join("python" | sort)
'hnopty'
>>> list([5, -4, 3, -2, 1] | sort(key=abs))
[1, -2, 3, -4, 5]
strip
Like Python's strip-method for str.
>>> ' abc ' | strip
'abc'
>>> '.,[abc] ] ' | strip('.,[] ')
'abc'
t
Like Haskell's operator ":"
>>> list(0 | t(1) | t(2)) == list(range(3))
True
tail()
Yields the given quantity of the last elements of the given iterable.
>>> list((1, 2, 3, 4, 5) | tail(3))
[3, 4, 5]
take()
Yields the given quantity of elements from the given iterable, like head
in shell script.
>>> list((1, 2, 3, 4, 5) | take(2))
[1, 2]
take_while()
Like itertools.takewhile, yields elements of the
given iterable while the predicate is true:
>>> list([1, 2, 3, 4] | take_while(lambda x: x < 3))
[1, 2]
tee
tee outputs to the standard output and yield unchanged items, useful for
debugging
>>> sum([1, 2, 3, 4, 5] | tee)
1
2
3
4
5
15
transpose()
Transposes the rows and columns of a matrix
>>> [[1, 2, 3], [4, 5, 6], [7, 8, 9]] | transpose
[(1, 4, 7), (2, 5, 8), (3, 6, 9)]
traverse
Recursively unfold iterables:
>>> list([[1, 2], [[[3], [[4]]], [5]]] | traverse)
[1, 2, 3, 4, 5]
>>> squares = (i * i for i in range(3))
>>> list([[0, 1, 2], squares] | traverse)
[0, 1, 2, 0, 1, 4]
uniq()
Like dedup() but only deduplicate consecutive values, using the given
key function if provided (or else the identity)
>>> list([1, 1, 2, 2, 3, 3, 1, 2, 3] | uniq)
[1, 2, 3, 1, 2, 3]
>>> list([1, 1, 2, 2, 3, 3, 1, 2, 3] | uniq(key=lambda n:n % 2))
[1, 2, 3, 2, 3]
where(), filter()
Only yields the matching items of the given iterable:
>>> list([1, 2, 3] | where(lambda x: x % 2 == 0))
[2]
Find the sum of all the multiples of 3 or 5 below 1000.
euler1 = (
sum(itertools.count() | select(lambda x: x * 3) | take_while(lambda x: x < 1000))
+ sum(itertools.count() | select(lambda x: x * 5) | take_while(lambda x: x < 1000))
- sum(itertools.count() | select(lambda x: x * 15) | take_while(lambda x: x < 1000))
)
assert euler1 == 233168Find the sum of all the even-valued terms in Fibonacci which do not exceed four million.
euler2 = sum(fib() | where(lambda x: x % 2 == 0) | take_while(lambda x: x < 4000000))
assert euler2 == 4613732Find the difference between the sum of the squares of the first one hundred natural numbers and the square of the sum.
euler6 = sum(itertools.count(1) | take(100)) ** 2 - sum(
itertools.count(1) | take(100) | select(lambda x: x ** 2)
)
assert euler6 == 25164150Using this module, you get lazy evaluation at two levels:
- the object obtained by piping is a generator and will be evaluated only if needed,
- within a series of pipe commands, only the elements that are actually needed will be evaluated.
To illustrate:
from itertools import count
from pipe import select, where, take
def dummy_func(x):
print(f"processing at value {x}")
return x
print("----- test using a generator as input -----")
print(f"we are feeding in a: {type(count(100))}")
res_with_count = (count(100) | select(dummy_func)
| where(lambda x: x % 2 == 0)
| take(2))
print(f"the resulting object is: {res_with_count}")
print(f"when we force evaluation we get:")
print(f"{list(res_with_count)}")
print("----- test using a list as input -----")
list_to_100 = list(range(100))
print(f"we are feeding in a: {type(list_to_100)} which has length {len(list_to_100)}")
res_with_list = (list_to_100 | select(dummy_func)
| where(lambda x: x % 2 == 0)
| take(2))
print(f"the resulting object is: {res_with_list}")
print(f"when we force evaluation we get:")
print(f"{list(res_with_list)}")Which prints:
----- test using a generator as input -----
we are feeding in a: <class 'itertools.count'>
the resulting object is: <generator object take at 0x7fefb5e70c10>
when we force evaluation we get:
processing at value 100
processing at value 101
processing at value 102
processing at value 103
processing at value 104
[100, 102]
----- test using a list as input -----
we are feeding in a: <class 'list'> which has length 100
the resulting object is: <generator object take at 0x7fefb5e70dd0>
when we force evaluation we get:
processing at value 0
processing at value 1
processing at value 2
processing at value 3
processing at value 4
[0, 2]
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