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A companion that gives you functional superpowers

Project description

Sidekick is a library that gives you functional superpowers.

Sidekick 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 Pyrsistent.

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:

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 side:

>>> 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:

>>> 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 transformations:

  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 definitions.

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:

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 application:

>>> 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 application:

>>> 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 positions

>>> 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:

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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