nonion 0.0.2

Python Functional Programming for Humans.

NOnion

NOnion is a Python package that provides tools for Functional Programming. It aims to eliminate nested function calls such as z(g(f(x))) which remind an onion.

Installing

pip install nonion

Tutorial

NOnion consists of two main concepts:

• Function - a wrapper of any Python Callable,
• Pipeline - a wrapper of any Python Iterable.

Function

In order to create a Function, you simply pass any Callable:

f = Function(lambda x: x + 1)
f(5) # returns 6

You can also create an identity Function:

g = Function()

Notice, that a Function takes exactly single value and returns exactly single value.

compose

A ``Function composition" defined as $( f \circ g )(x) = f(g(x))$ could be done in the following way:

z = f @ g

# alternatively

z = f.compose(g)

You can also use compose several times:

z = f @ g @ f

Instead of wrapping each Callable with a Function, you can wrap only first Callable and use compose on the rest.

def f(x):
  return x + 1

g = Function() @ (lambda x: x * 2) @ f
g(5) # returns 12

The @ (at) operator was used, because it reminds $\circ$ symbol.

then

Function composition sometimes might be hard to read, because you have to read it from right-to-left. In order to achieve better readability, you can use then.

g = Function() / (lambda x: x * 2)  / f
g(5) # returns 11

# alternatively

g = Function().then(lambda x: x * 2).then(f)
g(5) # returns 11

The / (slash) operator was used, because it reminds | (vertical bar) used for piping.

call

Sometimes you want to call a function ``inline'' after several compositions. In this case, you might use:

(Function() / (lambda x: x * 2)  / f)(5) # returns 11

But it might be hard to read. Especially, when you mostly pass lambdas. A better way to call a function is by using:

Function() / (lambda x: x * 2)  / f & 5 # returns 11

The & (ampersand) operator was used, because it looks similar to $ (dollar), which is a Haskell operator.

star (function)

Suppose, that you defined a function with multiple arguments such as:

def f(x, y):
  return x + y * x

And you want to wrap that function using Function. In this case, you have to use star.

Function() @ star(f) & (1, 2) # returns 5

star simply passes arguments to a function using Python * (star) operator.

foreach

You can also call a function for each value in some Iterable in the following way:

ys = Function() / (lambda x: x * 2)  / (lambda x: x + 1) * range(5)

for y in ys:
  print(y)

# 1
# 3
# 5
# 7
# 9
#

The * (star) operator was used, because instead of passing an Iterable to a function, you pass its content as with Python * (star) operator and functions that take *args.

Pipeline

In order to create a Pipeline, you simply pass any Iterable:

xs = Pipeline(range(5))

# notation abuse, do not use that:

xs = Function() / Pipeline & range(5)

You can also create an empty Pipeline:

xs = Pipeline()

Under the hood Pipeline is simply uses iter on a passed Iterable. It means, that if you will pass an Iterable, that could be exhausted, you iterate over Pipeline only once.

xs = Pipeline(range(2))

for x in xs:
  print(x)

# 1
# 2
#

# perfectly fine, because range(x) returns a special object
for x in xs:
  print(x)

# 1
# 2
#

xs = Pipeline(x for x in range(2))

for x in xs:
  print(x)

# 1
# 2
#

# xs already exhausted
for x in xs:
  print(x)

map

map allows you to call a Callable, which takes a single value and returns a single value, on each value of the Pipeline.

ys = Pipeline(range(3)) / (lambda x: x + 1) / (lambda x: (x, x + 1)) / star(lambda x, y: x + y * x)

for y in ys:
  print(y)

# 3
# 8
# 15
#

# alternatively

ys = Pipeline(range(3)).map(lambda x: x + 1).map(lambda x: (x, x + 1)).map(star(lambda x, y: x + y * x))

The / (slash) operator was used, because it reminds | (vertical bar) used for piping.

filter

filter allows you to filter Pipeline values.

ys = Pipeline(range(3)) % (lambda x: x > 1)

for y in ys:
  print(y)

# 2
#

# alternatively

ys = Pipeline(range(3)).filter(lambda x: x > 1)

flatmap

flatmap allows you to call a Callable, which takes a single value and returns an Iterable, on each value of the Pipeline and flatten results into single Pipeline.

ys = Pipeline(range(2)) / (lambda x: x + 1) * (lambda x: (x, x + 1))

for y in ys:
  print(y)

# 1
# 2
# 2
# 3
#

# alternatively

ys = Pipeline(range(2)).map(lambda x: x + 1).flatmap(lambda x: (x, x + 1))

The * (star) operator was used, because intuitively you use Python * (star) operator on each result.

apply

apply allows you to call a Callable, which takes an Iterable and returns an Iterable, on whole Pipeline.

ys = Pipeline(range(2)) / (lambda x: x + 1) // tuple # internally Pipeline now has a tuple

for y in ys:
  print(y)

# 1
# 2
#

# now multiple itertations is possible
for y in ys:
  print(y)

# 1
# 2
#

# alternatively

ys = Pipeline(range(2)).map(lambda x: x + 1).apply(tuple)

collect

collect allows you to call a Callable, which takes an Iterable and returns any single value, on whole Pipeline. The difference between apply and collect is that collect returns the result of a function instead of wrapping it with Pipeline.

ys = Pipeline(range(2)) / (lambda x: x + 1) >> tuple
print(ys)

# (1, 2)
#

# alternatively

ys = Pipeline(range(2)).map(lambda x: x + 1).collect(tuple)

foreach

foreach allows you to call a Callable, which takes a single value, on each value of the Pipeline.

Pipeline(range(2)) / (lambda x: x + 1) & print

# 1
# 2
#

# alternatively

Pipeline(range(2)).map(lambda x: x + 1).foreach(print)