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A library that gives you functional superpowers.

Project Description

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:alt: Code Climate

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Sidekick is a library that gives you functional superpowers. It implements a
few utility functions and types that make functional programming
more pleasant in Python. It is designed be self-contained and it wraps most
functionality in the toolz library. Of course you can also use it in
conjunction with other functional programming libraries such as funcy, and

If you are lazy, simply import everything and start to play ;)

>>> from sidekick import *

Function composition

Heavily functional idioms in Python quickly escalates to an unholy mess:

>>> import os
>>> print(
... '\n'.join(
... map(
... lambda x: '%s) %s' % (x[0] + 1, x[1]),
... enumerate(
... sorted(
... filter(
... lambda x: x.endswith('.py'),
... os.listdir(os.getcwd()),
... ),
... key=str.casefold,
... )
... )
... )
... )
... )

We formatted for maximum legibility, but even so that are so many nested
functions that it is not even funny. The code above reads all files from
the current directory, keeps only the `.py` files, sort them alphabetically
(after normalizing case), and finally prints a list of files on the screen.

We can do better with sidekick:

>>> print(
... os.getcwd() | fn
... >> os.listdir
... >> filter(_.endswith('.py'))
... >> order_by(str.casefold)
... >> enumerate
... >> map(lambda x: '%s) %s' % (x[0] + 1, x[1]))
... >> '\n'.join
... )

Let us unpack all those commands.

**Function pipelines**

The function pipeline operator ``>>`` is used to compose
functions to form a pipeline where each function passes its results to be
consumed by the next one. Hence,

>>> pipeline = f1 >> f2 >> f3 >> ... # doctest: +SKIP

is a function pipeline that calls ``f1()``, than pass the result to ``f2()``,
which goes to ``f3()``, and so on. The code above is equivalent to the nested
function definition:

>>> pipeline = lambda x: ...(f3(f2(f1(x)))) # doctest: +SKIP

The pipeline syntax obviously do not work with regular functions. The
trick is to use fn() magic object either to create functions that accept
composition or to mark the beginning of a pipeline:

.. ignore-next-block
.. code-block:: python

f1 = fn(real_f1) # f1 now understands the pipeline!
pipeline = f1 >> f2 >> f3 >> ...
pipeline = fn >> f1 >> f2 >> f3 >> ... # this also works!

If you are still not so sure how the pipeline works, consider the more
self-contained example:

>>> import math
>>> sqrt = fn(math.sqrt)
>>> safe_sqrt = abs >> sqrt
>>> safe_sqrt(-4)

In the code above, the argument is passed first to the abs() function and then
is redirected it to the sqrt(). The arguments flow in the same direction that
the flow operators ('>>' and '<<') points to.

**Filter operator**

Once a pipeline is created, we can feed arguments to it either by calling
the resulting function or by using the filter (pipe) operator. A filter takes
the value on the left hand side and passes to the function in the right hand

>>> 4 | sqrt

This is equivalent to the more traditional ``sqrt(4)``. Filters can be chained
and mixed with function pipelines

>>> 16 | sqrt | sqrt
>>> 16 | sqrt >> sqrt

Filters have a lower precedence than pipelines. This means that the expression
``x | f1 >> f2 | f3`` is interpreted as ``x | (f1 >> f2) | f3``. That is, it
takes x, passes to the pipeline constructed by composing f1 with f2 and then
finally passes the result to f3.


Let us recap. Remember the code we started with:

.. ignore-next-block

>>> print(
... os.getcwd() | fn
... >> os.listdir
... >> filter(_.endswith('.py'))
... >> order_by(str.casefold)
... >> enumerate
... >> map(lambda x: '%s) %s' % (x[0] + 1, x[1]))
... >> '\n'.join
... )

This should not be so foreign anymore. This line of code reads the current
working dir returned by os.getcwd() than passes it through a series of

1. List the files
2. Select files with the '.py' extension using a quick lambda (more later...)
3. Sort files by name using casefold to normalize
4. Enumerate the sorted list
5. Maps all items to be a string in the ``"idx) filename'`` format.
6. Join the list of files with new lines
7. Finally, pass the result to the print function.

Compare it to a more idiomatic Python code::

dir = os.getcwd()
files = os.listdir(files)
py_files = (f for f in files if f.endswith('.py'))
py_files = sorted(py_files, key=str.casefold)
lines = ['%s) %s' % item for item in enumerate(files)]

It all comes to personal taste, but one cannot deny the functional version
is more compact since it do not require all those temporary variable

Partial application

The fn object can be used as a decorator to give regular functions
superpowers. We already mentioned the pipeline and filter operators. Let us see
what else it can give us.

Consider the function:

.. code-block:: python

def g(x, y, z):
return (x, y, z)

The function ``g`` can now be used as a filter or as a part of a pipeline.
Like normal Python functions, fn-functions also use parenthesis to make call.
If a function is called with square brackets, however, it makes a partial

>>> g2 = g[1, 2]
>>> g2(3)
(1, 2, 3)

By default, partial application respect a auto-currying semantics. We decided to
not make currying the default behavior for standard function calls since
currying can be confusing on languages that support a variable number
of arguments such as Python. If you never heard this name, autocurrying is the
process in which a function that do not receive all required arguments simply
return another function that receives the missing ones. It is an attempt to
mimick the behavior of curried programming languages define only single-argument
functions (in those languages, e.g., Haskell, a function of two variables is
a function of a single variable that returns another function of one variable).

fn-functions also suports a more explicit and flexible mode of partial function

>>> gpart = g.partial(1, y=2)

Finally, both partial and the square-brackets notation understands the special
placeholder object ``_`` as a declaration for the position in which a single
free argument should be used

>>> g[1, 2](3) == g[_, 2, 3](1) == g[1, _, 3](2)

If the placeholder is repeated, the same argument is passed to all used

>>> g[_, _, _](1)
(1, 1, 1)

The fn object offers a few additional goodies. The first is the ``method``
attribute, that declares a function to be autocurrying::

>>> g = fn.curried(lambda x, y, z: x + y + z)
>>> g(1, 2, 3) == g(1, 2)(3) == g(1)(2)(3) == 6

Secondly, the fn object itself accepts the bracket notation and can be used
to define partial application directly when the function is created::

.. skip-next-block
.. code-block:: python

g_ = lambda x, y, z: x + y + z
fn[g] # the same as fn(g)
fn[g, 1] # the same as fn(g)[1]
fn[g, _, 2, 3] # the same as fn(g)[_, 2, 3] (you get the idea!)

Quick lambdas

The previous section introduced the placeholder object ``_``. It exists in order
to create quick lambdas for use in functional code. Functional code relies on
lots of short anonymous functions and seems that nobody likes Python
lambda's syntax: it is ugly, a bit too verbose and not particularly readable.
Even Javascript did it right with ES6, so why wouldn't we?

Sidekick provides a quick way to define lambdas using the placeholder object.
Just create an arbitrary Python expression and wrap it with the fn() object.

>>> inc = fn(_ + 1)
>>> total_cost = fn(_.num_items * _.price)

In the future, we may create additional placeholders such as ``__`` and ``___``
to define functions with multiple arguments. For now, use a lambda.


Predicates are functions that receive a single argument and return a boolean.
They are used in many contexts, usually to select elements in an collection.
Consider Python's builtin filter function:

>>> names = ['foo', 'bar', 'ham']

Sidekick extends the builtin filter function to accept placeholder expressions
and curring.

>>> filtered = filter(_.startswith('f'), names)

The result is a filter object, which we convert to a list using the magic ``| L``
pipe notation:

>>> filtered | L

In sidekick we can explicitly tell that a quick lambda or a function is a
predicate by wrapping it with the predicate function:

>>> startswith_f = predicate(_.startswith('f'))
>>> filter(startswith_f, names) | L

For now it is just the same as using a regular function. Predicate functions,
however, compose nicely under boolean expressions. This makes it easier to
create complex predicates instead of relying on awkward lambda functions:

>>> startswith_b = predicate(_.startswith('b'))
>>> filter(startswith_f | startswith_b, names) | L
['foo', 'bar']

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