Coconut Documentation

  1. Overview
  2. Compilation
    1. Installation
    2. Usage
      1. Positional Arguments
      2. Optional Arguments
    3. Naming Source Files
    4. Compilation Modes
    5. Compatible Python Versions
    6. Allowable Targets
    7. --strict Mode
    8. IPython/Jupyter Support
      1. Extension
      2. Kernel
  3. Operators
    1. Lambdas
    2. Partial Application
    3. Pipeline
    4. Compose
    5. Chain
    6. Iterator Slicing
    7. Unicode Alternatives
  4. Keywords
    1. data
    2. match
    3. case
    4. Backslash-Escaping
    5. Reserved Variables
  5. Expressions
    1. Lazy Lists
    2. Implicit Partial Application
    3. Set Literals
    4. Imaginary Literals
    5. Underscore Separators
  6. Function Notation
    1. Operator Functions
    2. Shorthand Functions
    3. Infix Functions
    4. Pattern-Matching Functions
  7. Statements
    1. Destructuring Assignment
    2. Decorators
    3. else Statements
    4. except Statements
    5. Variable Lists
    6. Code Passthrough
  8. Built-Ins
    1. addpattern
    2. prepattern
    3. reduce
    4. takewhile
    5. dropwhile
    6. tee
    7. consume
    8. count
    9. map and zip
    10. datamaker
    11. recursive
    12. parallel_map
    13. MatchError
  9. Coconut Utilities
    1. Syntax Highlighting
      1. SublimeText
      2. Pygments
    2. coconut.convenience
      1. parse
      2. setup
      3. cmd
      4. version
      5. CoconutException
    3. coconut.__coconut__


This documentation covers all the technical details of the Coconut Programming Language, and is intended as a reference specification, not a tutorialized introduction. For a full introduction and tutorial of Coconut, see HELP.

Coconut is a variant of Python built for simple, elegant, Pythonic functional programming. Coconut syntax is a strict superset of Python 3 syntax. That means users familiar with Python will already be familiar with most of Coconut.

The Coconut compiler turns Coconut code into Python code. The primary method of accessing the Coconut compiler is through the Coconut command-line utility, which also features an interpreter for real-time compilation. In addition to the command-line utility, Coconut also supports the use of IPython/Jupyter notebooks.

While most of Coconut gets its inspiration simply from trying to make functional programming work in Python, additional inspiration came from Haskell, CoffeeScript, F#, and



Since Coconut is hosted on the Python Package Index, it can be installed easily using pip. Simply install Python, open up a command-line prompt, and enter:

python -m pip install coconut


coconut [-h] [-v] [source] [dest] [-t version] [-s] [-l] [-p] [-a] [-f] [-d] [-r] [-n] [-m] [-i] [-q] [-c code] [--jupyter ...] [--autopep8 ...] [--recursionlimit limit] [--color color] [--verbose]

Positional Arguments

source                path to the coconut file/folder to compile
dest                  destination directory for compiled files (defaults to the source directory)

Optional Arguments

-h, --help              show this help message and exit
-v, --version           print Coconut and Python version information
-t, --target            specify target Python version (defaults to universal)
-s, --strict            enforce code cleanliness standards
-l, --linenumbers       add line number comments for ease of debugging
-p, --package           compile source as part of a package (defaults to only if source is a directory)
-a, --standalone        compile source as standalone files (defaults to only if source is a single file)
-f, --force             force overwriting of compiled Python (otherwise only overwrites when source code or compilation parameters change)
-d, --display           print compiled Python
-r, --run               run compiled Python (often used with --nowrite)
-n, --nowrite           disable writing compiled Python
-m, --minify            compress compiled Python
-i, --interact          force the interpreter to start (otherwise starts if no other command is given)
-q, --quiet             suppress all informational output (combine with --display to write runnable code to stdout)
-c code, --code code    run a line of Coconut passed in as a string (can also be passed into stdin)
--jupyter, --ipython    run Jupyter/IPython with Coconut as the kernel (remaining args passed to Jupyter)
--autopep8 ...          use autopep8 to format compiled code (remaining args passed to autopep8)
--recursionlimit        set maximum recursion depth (default is system dependent)
--tutorial              open the Coconut tutorial in the default web browser
--documentation         open the Coconut documentation in the default web browser
--color color           show all Coconut messages in the given color
--verbose               print verbose debug output

Naming Source Files

Coconut source files should, so the compiler can recognize them, use the extension .coco (preferred), .coc, or .coconut. When Coconut compiles a .coco (or .coc / .coconut) file, it will compile to another file with the same name, except with .py instead of .coco, which will hold the compiled code. If an extension other than .py is desired for the compiled files, such as .pyde for Python Processing, then that extension can be put before .coco in the source file name, and it will be used instead of .py for the compiled files. For example, name.coco will compile to, whereas name.pyde.coco will compile to name.pyde.

Compilation Modes

Files compiled by the coconut command-line utility will vary based on compilation parameters. If an entire directory of files is compiled (which the compiler will search recursively for any folders containing .coco, .coc, or .coconut files), a file will be created to house necessary functions (package mode), whereas if only a single file is compiled, that information will be stored within a header inside the file (standalone mode). Standalone mode is better for single files because it gets rid of the overhead involved in importing, but package mode is better for large packages because it gets rid of the need to run the same Coconut header code again in every file, since it can just be imported from

By default, if the source argument to the command-line utility is a file, it will perform standalone compilation on it, whereas if it is a directory, it will recursively search for all .coco (or .coc / .coconut) files and perform package compilation on them. Thus, in most cases, the mode chosen by Coconut automatically will be the right one. But if it is very important that no additional files like be created, for example, then the command-line utility can also be forced to use a specific mode with the --package (-p) and --standalone (-a) flags.

Compatible Python Versions

While Coconut syntax is based off of Python 3, Coconut code compiled in universal mode (the default --target), and the Coconut compiler, should run on any Python version >= 2.6 on the 2.x branch or >= 3.2 on the 3.x branch.

Note: The tested against implementations are CPython 2.6, 2.7, 3.2, 3.3, 3.4, 3.5 and PyPy 2.7, 3.2.

As part of Coconut’s cross-compatibility efforts, Coconut adds in new Python 3 built-ins and overwrites Python 2 built-ins to use the Python 3 versions where possible. If access to the Python 2 versions is desired, the old built-ins can be retrieved by prefixing them with py2_. The old built-ins available are:

  • py2_chr
  • py2_filter
  • py2_hex
  • py2_input
  • py2_int
  • py2_map
  • py2_oct
  • py2_open
  • py2_print
  • py2_range
  • py2_raw_input
  • py2_str
  • py2_xrange
  • py2_zip

Additionally, since Coconut also overrides some Python 3 built-ins for optimization purposes, those can be retrieved by prefixing them with py3_. The overwritten built-ins available are:

  • py3_map
  • py3_zip

Finally, while Coconut will try to compile Python-3-specific syntax to its universal equivalent, the follow constructs have no equivalent in Python 2, and require a target of at least 3 to be specified to be used:

  • destructuring assignment with *s (use Coconut pattern-matching instead),
  • function type annotation,
  • the nonlocal keyword,
  • keyword class definition,
  • @ as matrix multiplication (requires --target 3.5),
  • async and await statements (requires --target 3.5), and
  • formatting f strings (requires --target 3.6).

Allowable Targets

If the version of Python that the compiled code will be running on is known ahead of time, a target should be specified with --target. The given target will only affect the compiled code and whether or not certain Python-3-specific syntax is allowed, detailed below. Where Python 3 and Python 2 syntax standards differ, Coconut syntax will always follow Python 3 across all targets. The supported targets are:

  • universal (default) (will work on any of the below),
  • 2, 26 (will work on any Python >= 2.6 but < 3),
  • 27 (will work on any Python >= 2.7 but < 3),
  • 3, 32 (will work on any Python >= 3.2),
  • 33, 34 (will work on any Python >= 3.3),
  • 35 (will work on any Python >= 3.5),
  • 36 (will work on any Python >= 3.6),
  • sys (chooses the specific target corresponding to the current version).

Note: Periods are ignored in target specifications, such that the target 2.7 is equivalent to the target 27.

--strict Mode

If the --strict or -s flag is enabled, Coconut will throw errors on various style problems. These are

  • mixing of tabs and spaces (without --strict Coconut just shows a Warning),
  • use of the Python-style lambda statement,
  • use of u to denote Unicode strings,
  • use of reserved variables (without --strict Coconut just shows a Warning),
  • use of backslash continuations (implicit continuations are preferred), and
  • trailing whitespace at the end of lines.

It is recommended that you use the --strict or -s flag if you are starting a new Coconut project, as it will help you write cleaner code.

IPython/Jupyter Support

If you prefer IPython (the python kernel for the Jupyter framework) to the normal Python shell, Coconut can be used as an IPython extension or Jupyter kernel.


If Coconut is used as an extension, a special magic command will send snippets of code to be evaluated using Coconut instead of IPython, but IPython will still be used as the default. The line magic %load_ext coconut will load Coconut as an extension, adding the %coconut and %%coconut magics. The %coconut line magic will run a line of Coconut with default parameters, and the %%coconut block magic will take command-line arguments on the first line, and run any Coconut code provided in the rest of the cell with those parameters.


If Coconut is used as a kernel, all code in the console or notebook will be sent directly to Coconut instead of Python to be evaluated. The command coconut --jupyter notebook (or coconut --ipython notebook) will launch an IPython/Jupyter notebook using Coconut as the kernel and the command coconut --jupyter console (or coconut --ipython console) will launch an IPython/Jupyter console using Coconut as the kernel. Additionally, the command coconut --jupyter (or coconut --ipython) will add Coconut as a language option inside of all IPython/Jupyter notebooks, even those not launched with Coconut. This command may need to be re-run when a new version of Coconut is installed.



Coconut provides the simple, clean -> operator as an alternative to Python’s lambda statements. The operator has the same precedence as the old statement.


In Python, lambdas are ugly and bulky, requiring the entire word lambda to be written out every time one is constructed. This is fine if in-line functions are very rarely needed, but in functional programming in-line functions are an essential tool.

Python Docs

Lambda forms (lambda expressions) have the same syntactic position as expressions. They are a shorthand to create anonymous functions; the expression (arguments) -> expression yields a function object. The unnamed object behaves like a function object defined with:

def <lambda>(arguments):
    return expression

Note that functions created with lambda forms cannot contain statements or annotations.


dubsums = map((x, y) -> 2*(x+y), range(0, 10), range(10, 20))
dubsums |> list |> print
dubsums = map(lambda x, y: 2*(x+y), range(0, 10), range(10, 20))

Partial Application

Coconut uses a $ sign right after a function’s name but before the open parenthesis used to call the function to denote partial application. It has the same precedence as subscription.


Partial application, or currying, is a mainstay of functional programming, and for good reason: it allows the dynamic customization of functions to fit the needs of where they are being used. Partial application allows a new function to be created out of an old function with some of its arguments pre-specified.

Python Docs

Return a new partial object which when called will behave like func called with the positional arguments args and keyword arguments keywords. If more arguments are supplied to the call, they are appended to args. If additional keyword arguments are supplied, they extend and override keywords. Roughly equivalent to:

def partial(func, *args, **keywords):
    def newfunc(*fargs, **fkeywords):
        newkeywords = keywords.copy()
        return func(*(args + fargs), **newkeywords)
    newfunc.func = func
    newfunc.args = args
    newfunc.keywords = keywords
    return newfunc

The partial object is used for partial function application which “freezes” some portion of a function’s arguments and/or keywords resulting in a new object with a simplified signature.


expnums = map(pow$(2), range(5))
expnums |> list |> print
import functools
expnums = map(functools.partial(pow, 2), range(5))


Coconut uses pipe operators for pipeline-style function application. All the operators have a precedence in-between infix calls and comparisons and are left-associative. All operators also support in-place versions. The different operators are:

(|>)    => pipe forward
(|*>)   => multiple-argument pipe forward
(<|)    => pipe backward
(<*|)   => multiple-argument pipe backward


def sq(x) = x**2
(1, 2) |*> (+) |> sq |> print
import operator
def sq(x): return x**2
print(sq(operator.__add__(1, 2)))


Coconut uses the .. operator for function composition. It has a precedence in-between subscription and exponentiation. The in-place operator is ..=.


fog = f..g
# unlike this simple lambda, .. produces a pickleable object
fog = lambda *args, **kwargs: f(g(*args, **kwargs))


Coconut uses the :: operator for iterator chaining. Coconut’s iterator chaining is done lazily, in that the arguments are not evaluated until they are needed. It has a precedence in-between bitwise or and infix calls. The in-place operator is ::=.


A useful tool to make working with iterators as easy as working with sequences is the ability to lazily combine multiple iterators together. This operation is called chain, and is equivalent to addition with sequences, except that nothing gets evaluated until it is needed.

Python Docs

Make an iterator that returns elements from the first iterable until it is exhausted, then proceeds to the next iterable, until all of the iterables are exhausted. Used for treating consecutive sequences as a single sequence. Chained inputs are evaluated lazily. Roughly equivalent to:

def chain(*iterables):
    # chain('ABC', 'DEF') --> A B C D E F
    for it in iterables:
        for element in it:
            yield element


def N(n=0):
    return (0,) :: N(n+1) # no infinite loop because :: is lazy

(range(-10, 0) :: N())$[5:15] |> list |> print

Can’t be done without a complicated iterator comprehension in place of the lazy chaining. See the compiled code for the Python syntax.

Iterator Slicing

Coconut uses a $ sign right after an iterator before a slice to perform iterator slicing. Coconut’s iterator slicing works much the same as Python’s sequence slicing, and looks much the same as Coconut’s partial application, but with brackets instead of parentheses. It has the same precedence as subscription.

Iterator slicing works just like sequence slicing, including support for negative indices and slices, and support for slice objects in the same way as can be done with normal slicing. Iterator slicing makes no guarantee, however, that the original iterator passed to it be preserved (to preserve the iterator, use Coconut’s tee function).

Coconut’s iterator slicing is very similar to Python’s itertools.islice, but unlike itertools.islice, Coconut’s iterator slicing supports negative indices, and is optimized to play nicely with custom or built-in sequence types as well as Coconut’s map, zip, range, and count objects, only computing the elements of each that are actually necessary to extract the desired slice. This behavior can also be extended to custom objects if they define their __getitem__ method lazily and set __coconut_is_lazy__ to True.


map((x)->x*2, range(10**100))$[-1] |> print

Can’t be done without a complicated iterator slicing function and inspection of custom objects. The necessary definitions in Python can be found in the Coconut header.

Unicode Alternatives

Coconut supports Unicode alternatives to many different operator symbols. The Unicode alternatives are relatively straightforward, and chosen to reflect the look and/or meaning of the original symbol.

Full List

 (\u2192)                  => "->"
 (\u21a6)                  => "|>"
*↦ (*\u21a6)                => "|*>"
 (\u21a4)                  => "<|"
↤* (\u21a4*)                => "<*|"
 (\u22c5)                  => "*"
 (\u2191)                  => "**"
÷ (\xf7)                    => "/"
÷/ (\xf7/)                  => "//"
 (\u2212)                  => "-" (only subtraction)
 (\u207b)                  => "-" (only negation)
¬ (\xac)                    => "~"
 (\u2260) or ¬= (\xac=)    => "!="
 (\u2264)                  => "<="
 (\u2265)                  => ">="
 (\u2227) or  (\u2229)    => "&"
 (\u2228) or  (\u222a)    => "|"
 (\u22bb) or  (\u2295)    => "^"
« (\xab)                    => "<<"
» (\xbb)                    => ">>"
 (\u2026)                  => "..."
× (\xd7)                    => "@" (only matrix multiplication)



The syntax for data blocks is a cross between the syntax for functions and the syntax for classes. The first line looks like a function definition, but the rest of the body looks like a class, usually containing method definitions. This is because while data blocks actually end up as classes in Python, Coconut automatically creates a special, immutable constructor based on the given arguments.

Coconut data blocks create immutable classes derived from collections.namedtuple and made immutable with __slots__. Coconut data statement syntax looks like:

data <name>(<args>):

<name> is the name of the new data type, <args> are the arguments to its constructor as well as the names of its attributes, and <body> contains the data type’s methods.

Subclassing data types can be done easily by inheriting from them in a normal Python class, although to make the new subclass immutable, the line

__slots__ = ()

will need to be added to the subclass before any method or attribute definitions.


A mainstay of functional programming that Coconut improves in Python is the use of values, or immutable data types. Immutable data can be very useful because it guarantees that once you have some data it won’t change, but in Python creating custom immutable data types is difficult. Coconut makes it very easy by providing data blocks.

Python Docs

Returns a new tuple subclass. The new subclass is used to create tuple-like objects that have fields accessible by attribute lookup as well as being indexable and iterable. Instances of the subclass also have a helpful docstring (with type names and field names) and a helpful __repr__() method which lists the tuple contents in a name=value format.

Any valid Python identifier may be used for a field name except for names starting with an underscore. Valid identifiers consist of letters, digits, and underscores but do not start with a digit or underscore and cannot be a keyword such as class, for, return, global, pass, or raise.

Named tuple instances do not have per-instance dictionaries, so they are lightweight and require no more memory than regular tuples.


data vector(x, y):
    def __abs__(self):
        return (self.x**2 + self.y**2)**.5

v = vector(3, 4)
v |> print # all data types come with a built-in __repr__
v |> abs |> print
v.x = 2 # this will fail because data objects are immutable
import collections
class vector(collections.namedtuple("vector", "x, y")):
    __slots__ = ()
    def __abs__(self):
        return (self.x**2 + self.y**2)**.5

v = vector(3, 4)
v.x = 2


Coconut provides fully-featured, functional pattern-matching through its match statements.


Match statements follow the basic syntax match <pattern> in <value>. The match statement will attempt to match the value against the pattern, and if successful, bind any variables in the pattern to whatever is in the same position in the value, and execute the code below the match statement. Match statements also support, in their basic syntax, an if <cond> that will check the condition after executing the match before executing the code below, and an else statement afterwards that will only be executed if the match statement is not. What is allowed in the match statement’s pattern has no equivalent in Python, and thus the specifications below are provided to explain it.

Syntax Specification

Coconut match statement syntax is

match <pattern> in <value> [if <cond>]:

where <value> is the item to match against, <cond> is an optional additional check, and <body> is simply code that is executed if the header above it succeeds. <pattern> follows its own, special syntax, defined roughly like so:

pattern ::= (
    "(" pattern ")"                 # parentheses
    | "None" | "True" | "False"     # constants
    | "=" NAME                      # check
    | NUMBER                        # numbers
    | STRING                        # strings
    | [pattern "as"] NAME           # capture
    | NAME "(" patterns ")"         # data types
    | "(" patterns ")"              # sequences can be in tuple form
    | "[" patterns "]"              #  or in list form
    | "(|" patterns "|)"            # lazy lists
    | "{" pattern_pairs "}"         # dictionaries
    | ["s"] "{" pattern_consts "}"  # sets
    | (                             # head-tail splits
        "(" patterns ")"
        | "[" patterns "]"
      ) "+" pattern
    | pattern "+" (                 # init-last splits
        "(" patterns ")"
        | "[" patterns "]"
    | (                             # head-last splits
        "(" patterns ")"
        | "[" patterns "]"
      ) "+" pattern "+" (
        "(" patterns ")"                # this match must be the same
        | "[" patterns "]"              #  construct as the first match
    | (                             # iterator splits
        "(" patterns ")"
        | "[" patterns "]"
        | "(|" patterns "|)"            # lazy lists
      ) "::" pattern
    | pattern "is" exprs            # type-checking
    | pattern "and" pattern         # match all
    | pattern "or" pattern          # match any

Semantic Specification

match statements will take their pattern and attempt to “match” against it, performing the checks and deconstructions on the arguments as specified by the pattern. The different constructs that can be specified in a pattern, and their function, are:

  • Constants, Numbers, and Strings: will only match to the same constant, number, or string in the same position in the arguments.
  • Variables: will match to anything, and will be bound to whatever they match to, with some exceptions:
    • If the same variable is used multiple times, a check will be performed that each use match to the same value.
    • If the variable name _ is used, nothing will be bound and everything will always match to it.
  • Explicit Bindings (<pattern> as <var>): will bind <var> to <pattern>.
  • Checks (=<var>): will check that whatever is in that position is equal to the previously defined variable <var>.
  • Type Checks (<var> is <types>): will check that whatever is in that position is of type(s) <types> before binding the <var>.
  • Data Types (<name>(<args>)): will check that whatever is in that position is of data type <name> and will match the attributes to <args>.
  • Lists ([<patterns>]), Tuples ((<patterns>)), or Lazy lists ((|<patterns>|)): will only match a sequence ( of the same length, and will check the contents against <patterns>.
  • Dicts ({<pairs>}): will only match a mapping ( of the same length, and will check the contents against <pairs>.
  • Sets ({<constants>}): will only match a set ( of the same length and contents.
  • Head-Tail Splits (<list/tuple> + <var>): will match the beginning of the sequence against the <list/tuple>, then bind the rest to <var>, and make it the type of the construct used.
  • Init-Last Splits (<var> + <list/tuple>): exactly the same as head-tail splits, but on the end instead of the beginning of the sequence.
  • Head-Last Splits (<list/tuple> + <var> + <list/tuple>): the combination of a head-tail and an init-last split.
  • Iterator Splits (<list/tuple/lazy list> :: <var>, or <lazy list>): will match the beginning of an iterable ( against the <list/tuple/lazy list>, then bind the rest to <var> or check that the iterable is done.

When checking whether or not an object can be matched against in a particular fashion, Coconut makes use of Python’s abstract base classes. Therefore, to enable proper matching for a custom object, register it with the proper abstract base classes.


def factorial(value):
    match 0 in value:
        return 1
    else: match n is int in value if n > 0: # possible because of Coconut's
        return n * factorial(n-1)           #   enhanced else statements
        raise TypeError("invalid argument to factorial of: "+repr(value))

3 |> factorial |> print

Showcases else statements, which work much like else statements in Python: the code under an else statement is only executed if the corresponding match fails.

data point(x, y):
    def transform(self, other):
        match point(x, y) in other:
            return point(self.x + x, self.y + y)
            raise TypeError("arg to transform must be a point")
    def __eq__(self, other):
        match point(=self.x, =self.y) in other:
            return True
            return False

point(1,2) |> point(3,4).transform |> print
point(1,2) |> point(1,2).__eq__ |> print

Showcases matching to data types. Values defined by the user with the data statement can be matched against and their contents accessed by specifically referencing arguments to the data type’s constructor.

data empty(): pass
data leaf(n): pass
data node(l, r): pass
tree = (empty, leaf, node)

def depth(t):
    match tree() in t:
        return 0
    match tree(n) in t:
        return 1
    match tree(l, r) in t:
        return 1 + max([depth(l), depth(r)])

empty() |> depth |> print
leaf(5) |> depth |> print
node(leaf(2), node(empty(), leaf(3))) |> depth |> print

Showcases how the combination of data types and match statements can be used to powerful effect to replicate the usage of algebraic data types in other functional programming languages.

def duplicate_first(value):
    match [x] + xs as l in value:
        return [x] + l
        raise TypeError()

[1,2,3] |> duplicate_first |> print

Showcases head-tail splitting, one of the most common uses of pattern-matching, where a + <var> (or :: <var> for any iterable) at the end of a list or tuple literal can be used to match the rest of the sequence.


Can’t be done without a long series of checks for each match statement. See the compiled code for the Python syntax.


Coconut’s case statement is an extension of Coconut’s match statement for performing multiple match statements against the same value, where only one of them should succeed. Unlike lone match statements, only one match statement inside of a case block will ever succeed, and thus more general matches should be put below more specific ones.

Each pattern in a case block is checked until a match is found, and then the corresponding body is executed, and the case block terminated. The syntax for case blocks is

case <value>:
    match <pattern> [if <cond>]:
    match <pattern> [if <cond>]:

where <pattern> is any match pattern, <value> is the item to match against, <cond> is an optional additional check, and <body> is simply code that is executed if the header above it succeeds. Note the absence of an in in the match statements: that’s because the <value> in case <value> is taking its place.


def classify_sequence(value):
    out = ""        # unlike with normal matches, only one of the patterns
    case value:     #  will match, and out will only get appended to once
        match ():
            out += "empty"
        match (_,):
            out += "singleton"
        match (x,x):
            out += "duplicate pair of "+str(x)
        match (_,_):
            out += "pair"
        match _ is (tuple, list):
            out += "sequence"
        raise TypeError()
    return out

[] |> classify_sequence |> print
() |> classify_sequence |> print
[1] |> classify_sequence |> print
(1,1) |> classify_sequence |> print
(1,2) |> classify_sequence |> print
(1,1,1) |> classify_sequence |> print

Can’t be done without a long series of checks for each match statement. See the compiled code for the Python syntax.


In Coconut, the keywords data, match, case, async (keyword in Python 3.5), and await (keyword in Python 3.5) are also valid variable names. While Coconut can disambiguate these two use cases, when using one of these keywords as a variable name, a backslash is allowed in front to be explicit about using a keyword as a variable name.


\data = 5
data = 5

Reserved Variables

In Coconut, all variable names starting with _coconut are reserved. The Coconut compiler will modify and reference these variables with the assumption that the code being compiled does not modify them in any way. If your code does modify any such variables, your code is unlikely to work properly.


Lazy Lists

Coconut supports the creation of lazy lists, where the contents in the list will be treated as an iterator and not evaluated until they are needed. Lazy lists can be created in Coconut simply by simply surrounding a comma-seperated list of items with (| and |) (so-called “banana brackets”) instead of [ and ] for a list or ( and ) for a tuple.

Lazy lists use the same machinery as iterator chaining to make themselves lazy, and thus the lazy list (| x, y |) is equivalent to the iterator chaining expression (x,) :: (y,), although the lazy list won’t construct the intermediate tuples.


Lazy lists, where sequences are only evaluated when their contents are requested, are a mainstay of functional programming, allowing for dynamic evaluation of the list’s contents.


(| print("hello,"), print("world!") |) |> consume

Can’t be done without a complicated iterator comprehension in place of the lazy list. See the compiled code for the Python syntax.

Implicit Partial Application

Coconut supports a number of different syntactical aliases for common partial application use cases. These are:

.name       =>      operator.attrgetter("name")
obj.        =>      getattr$(obj)
func$       =>      ($)$(func)
seq[]       =>      operator.__getitem__$(seq)
iter$[]     =>      # the equivalent of seq[] for iterators


1 |> "123"[]
mod$ <| 5 <| 3
mod(5, 3)

Set Literals

Coconut allows an optional s to be prepended in front of Python set literals. While in most cases this does nothing, in the case of the empty set it lets Coconut know that it is an empty set and not an empty dictionary. Additionally, an f is also supported, in which case a Python frozenset will be generated instead of a normal set.


empty_frozen_set = f{}
empty_frozen_set = frozenset()

Imaginary Literals

In addition to Python’s <num>j or <num>J notation for imaginary literals, Coconut also supports <num>i or <num>I, to make imaginary literals more readable if used in a mathematical context.

Python Docs

Imaginary literals are described by the following lexical definitions:

imagnumber ::= (floatnumber | intpart) ("j" | "J" | "i" | "I")

An imaginary literal yields a complex number with a real part of 0.0. Complex numbers are represented as a pair of floating point numbers and have the same restrictions on their range. To create a complex number with a nonzero real part, add a floating point number to it, e.g., (3+4i). Some examples of imaginary literals:

3.14i   10.i    10i     .001i   1e100i  3.14e-10i


3 + 4i |> abs |> print
print(abs(3 + 4j))

Underscore Separators

Coconut allows for one underscore between digits and after base specifiers in numeric literals. These underscores are ignored and should only be used to increase code readability.



Function Notation

Operator Functions

Coconut uses a simple operator function short-hand: surround an operator with parentheses to retrieve its function. Similarly to iterator comprehensions, if the operator function is the only argument to a function, the parentheses of the function call can also serve as the parentheses for the operator function.


A very common thing to do in functional programming is to make use of function versions of built-in operators: currying them, composing them, and piping them. To make this easy, Coconut provides a short-hand syntax to access operator functions.

Full List

(|>)        => # pipe forward
(|*>)       => # multi-arg pipe forward
(<|)        => # pipe backward
(|*>)       => # multi-arg pipe backward
(..)        => # function composition
(.)         => (getattr)
(::)        => (itertools.chain) # will not evaluate its arguments lazily
($)         => (functools.partial)
(+)         => (operator.__add__)
(-)         => # 1 arg: operator.__neg__, 2 args: operator.__sub__
(*)         => (operator.__mul__)
(**)        => (operator.__pow__)
(/)         => (operator.__truediv__)
(//)        => (operator.__floordiv__)
(%)         => (operator.__mod__)
(&)         => (operator.__and__)
(^)         => (operator.__xor__)
(|)         => (operator.__or__)
(<<)        => (operator.__lshift__)
(>>)        => (operator.__rshift__)
(<)         => (operator.__lt__)
(>)         => (operator.__gt__)
(==)        => (operator.__eq__)
(<=)        => (operator.__le__)
(>=)        => (operator.__ge__)
(!=)        => (operator.__ne__)
(~)         => (operator.__inv__)
(@)         => (operator.__matmul__)
(not)       => (operator.__not__)
(and)       => # boolean and
(or)        => # boolean or
(is)        => (operator.is_)
(in)        => (operator.__contains__)


(range(0, 5), range(5, 10)) |*> map$(+) |> list |> print
import operator
print(list(map(operator.__add__, range(0, 5), range(5, 10))))

Shorthand Functions

Coconut allows for shorthand in-line function definition, where the body of the function is assigned directly to the function call. The syntax for shorthand function definition is

def <name>(<args>) = <expr>

where <name> is the name of the function, <args> are the functions arguments, and <expr> evaluates the value that the function should return.

Note: Shorthand function definition can be combined with infix and pattern-matching function definition.


Coconut’s shorthand function definition is as easy to write as assignment to a lambda, but will appear named in tracebacks, as it compiles to normal Python function definition.


def binexp(x) = 2**x
5 |> binexp |> print
def binexp(x): return 2**x

Infix Functions

Coconut allows for infix function calling, where a function is surrounded by backticks and then can have arguments placed in front of or behind it. Backtick calling has a precedence in-between chaining and piping.

Coconut also supports infix function definition to make defining functions that are intended for infix usage simpler. The syntax for infix function definition is

def <arg> `<name>` <arg>:

where <name> is the name of the function, the <arg>s are the function arguments, and <body> is the body of the function. If an <arg> includes a default, the <arg> must be surrounded in parentheses.

Note: Infix function definition can be combined with shorthand and pattern-matching function definition.


A common idiom in functional programming is to write functions that are intended to behave somewhat like operators, and to call and define them by placing them between their arguments. Coconut’s infix syntax makes this possible.


def a `mod` b = a % b
(x `mod` 2) `print`
def mod(a, b): return a % b
print(mod(x, 2))

Pattern-Matching Functions

Coconut supports pattern-matching / destructuring assignment syntax inside of function definition. The syntax for pattern-matching function definition is

[match] def <name>(<pattern>, <pattern>, ... [if <cond>]):

where <name> is the name of the function, <cond> is an optional additional check, <body> is the body of the function, and <pattern> is defined by Coconut’s match statement. The match keyword at the beginning is optional, but is sometimes necessary to disambiguate pattern-matching function definition from normal function definition, which will always take precedence. Coconut’s pattern-matching function definition is equivalent to a match statement that looks like:

def <name>(*args):
    match (<pattern>, <pattern>, ...) in args:
        err = MatchError(<error message>)
        err.pattern = "def <name>(<pattern>, <pattern>, ...):"
        err.value = args
        raise err

If pattern-matching function definition fails, it will raise a [MatchError]((#matcherror) object just like destructuring assignment.

Note: Pattern-matching function definition can be combined with shorthand and infix function definition.


def last_two(_ + [a, b]):
    return a, b
def xydict_to_xytuple({"x":x is int, "y":y is int}):
    return x, y

range(5) |> last_two |> print
{"x":1, "y":2} |> xydict_to_xytuple |> print

Can’t be done without a long series of checks at the top of the function. See the compiled code for the Python syntax.


Destructuring Assignment

Coconut supports significantly enhanced destructuring assignment, similar to Python’s tuple/list destructuring, but much more powerful. The syntax for Coconut’s destructuring assignment is

[match] <pattern> = <value>

where <value> is any expression and <pattern> is defined by Coconut’s match statement. The match keyword at the beginning is optional, but is sometimes necessary to disambiguate destructuring assignment from normal assignment, which will always take precedence. Coconut’s destructuring assignment is equivalent to a match statement that follows the syntax:

match <pattern> in <value>:
    err = MatchError(<error message>)
    err.pattern = "<pattern>"
    err.value = <value>
    raise err

If a destructuring assignment statement fails, then instead of continuing on as if a match block had failed, a MatchError object will be raised describing the failure.


def last_two(l):
    _ + [a, b] = l
    return a, b

[0,1,2,3] |> last_two |> print

Can’t be done without a long series of checks in place of the destructuring assignment statement. See the compiled code for the Python syntax.


Unlike Python, which only supports a single variable or function call in a decorator, Coconut supports any expression.


@ wrapper1 .. wrapper2 $(arg)
def func(x) = x**2
def wrapper(func):
    return wrapper1(wrapper2(arg, func))
def func(x):
    return x**2

else Statements

Coconut supports the compound statements try, if, and match on the end of an else statement like any simple statement would be. This is most useful for mixing match and if statements together, but also allows for compound try statements.


except MyError:
else: try:
except MyError:
except MyError:
    except MyError:

except Statements

Python 3 requires that if multiple exceptions are to be caught, they must be placed inside of parentheses, so as to disallow Python 2’s use of a comma instead of as. Coconut allows commas in except statements to translate to catching multiple exceptions without the need for parentheses.


except SyntaxError, ValueError as err:
except (SyntaxError, ValueError) as err:

Variable Lists

Coconut allows for the more elegant parenthetical continuation instead of the less elegant backslash continuation in import, del, global, and nonlocal statements.


global (really_long_global_variable_name_the_first_one,
global really_long_global_variable_name_the_first_one, \

Code Passthrough

Coconut supports the ability to pass arbitrary code through the compiler without being touched, for compatibility with other variants of Python, such as Cython or Mython. Anything placed between \( and the corresponding close parenthesis will be passed through, as well as any line starting with \\, which will have the additional effect of allowing indentation under it.


\\cdef f(x):
    return x |> g
cdef f(x):
    return g(x)



Takes one argument that is a pattern-matching function, and returns a decorator that adds the patterns in the existing function to the new function being decorated, where the existing patterns are checked first, then the new. Equivalent to:

def addpattern(base_func):
    """Decorator to add a new case to a pattern-matching function, where the new case is checked last."""
    def pattern_adder(func):
        def add_pattern_func(*args, **kwargs):
                return base_func(*args, **kwargs)
            except MatchError:
                return func(*args, **kwargs)
        return add_pattern_func
    return pattern_adder


def factorial(0) = 1

def factorial(n) = n * factorial(n - 1)

Can’t be done without a complicated decorator definition and a long series of checks for each pattern-matching. See the compiled code for the Python syntax.


Takes one argument that is a pattern-matching function, and returns a decorator that adds the patterns in the existing function to the new function being decorated, where the new patterns are checked first, then the existing. Equivalent to:

def prepattern(base_func):
    """Decorator to add a new case to a pattern-matching function, where the new case is checked first."""
    def pattern_prepender(func):
        def pre_pattern_func(*args, **kwargs):
                return func(*args, **kwargs)
            except MatchError:
                return base_func(*args, **kwargs)
        return pre_pattern_func
    return pattern_prepender


def factorial(n) = n * factorial(n - 1)

def factorial(0) = 1

Can’t be done without a complicated decorator definition and a long series of checks for each pattern-matching. See the compiled code for the Python syntax.


Coconut re-introduces Python 2’s reduce built-in, using the functools.reduce version.

Python Docs

reduce(function, iterable[, initializer])

Apply function of two arguments cumulatively to the items of sequence, from left to right, so as to reduce the sequence to a single value. For example, reduce((x, y) -> x+y, [1, 2, 3, 4, 5]) calculates ((((1+2)+3)+4)+5). The left argument, x, is the accumulated value and the right argument, y, is the update value from the sequence. If the optional initializer is present, it is placed before the items of the sequence in the calculation, and serves as a default when the sequence is empty. If initializer is not given and sequence contains only one item, the first item is returned.


prod = reduce$(*)
range(1, 10) |> prod |> print
import operator
import functools
prod = functools.partial(functools.reduce, operator.__mul__)
print(prod(range(1, 10)))


Coconut provides itertools.takewhile as a built-in under the name takewhile.

Python Docs

takewhile(predicate, iterable)

Make an iterator that returns elements from the iterable as long as the predicate is true. Equivalent to:

def takewhile(predicate, iterable):
    # takewhile(lambda x: x<5, [1,4,6,4,1]) --> 1 4
    for x in iterable:
        if predicate(x):
            yield x


negatives = takewhile(numiter, (x) -> x<0)
import itertools
negatives = itertools.takewhile(numiter, lambda x: x<0)


Coconut provides itertools.dropwhile as a built-in under the name dropwhile.

Python Docs

dropwhile(predicate, iterable)

Make an iterator that drops elements from the iterable as long as the predicate is true; afterwards, returns every element. Note: the iterator does not produce any output until the predicate first becomes false, so it may have a lengthy start-up time. Equivalent to:

def dropwhile(predicate, iterable):
    # dropwhile(lambda x: x<5, [1,4,6,4,1]) --> 6 4 1
    iterable = iter(iterable)
    for x in iterable:
        if not predicate(x):
            yield x
    for x in iterable:
        yield x


positives = dropwhile(numiter, (x) -> x<0)
import itertools
positives = itertools.dropwhile(numiter, lambda x: x<0)


Coconut provides itertools.tee as a built-in under the name tee.

Python Docs

tee(iterable, n=2)

Return n independent iterators from a single iterable. Equivalent to:

def tee(iterable, n=2):
    it = iter(iterable)
    deques = [collections.deque() for i in range(n)]
    def gen(mydeque):
        while True:
            if not mydeque:             # when the local deque is empty
                newval = next(it)       # fetch a new value and
                for d in deques:        # load it to all the deques
            yield mydeque.popleft()
    return tuple(gen(d) for d in deques)

Once tee() has made a split, the original iterable should not be used anywhere else; otherwise, the iterable could get advanced without the tee objects being informed.

This itertool may require significant auxiliary storage (depending on how much temporary data needs to be stored). In general, if one iterator uses most or all of the data before another iterator starts, it is faster to use list() instead of tee().


original, temp = tee(original)
sliced = temp$[5:]
import itertools
original, temp = itertools.tee(original)
sliced = itertools.islice(temp, 5, None)


Coconut provides the consume function to efficiently exhaust an iterator and thus perform any lazy evaluation contained within it. consume takes one optional argument, keep_last, that defaults to 0 and specifies how many, if any, items from the end to return as an iterable (None will keep all elements). Equivalent to:

def consume(iterable, keep_last=0):
    """Fully exhaust iterable and return the last keep_last elements."""
    return collections.deque(iterable, maxlen=keep_last) # fastest way to exhaust an iterator


In the process of lazily applying operations to iterators, eventually a point is reached where evaluation of the iterator is necessary. To do this efficiently, Coconut provides the consume function, which will fully exhaust the iterator given to it.


range(10) |> map$((x) -> x**2) |> map$(print) |> consume
collections.deque(map(print, map(lambda x: x**2, range(10))), maxlen=0)


Coconut provides a modified version of itertools.count that supports in, normal slicing, optimized iterator slicing, count and index sequence methods, repr, and _start and _step attributes as a built-in under the name count.

Python Docs

count(start=0, step=1)

Make an iterator that returns evenly spaced values starting with number start. Often used as an argument to map() to generate consecutive data points. Also, used with zip() to add sequence numbers. Roughly equivalent to:

def count(start=0, step=1):
    # count(10) --> 10 11 12 13 14 ...
    # count(2.5, 0.5) -> 2.5 3.0 3.5 ...
    n = start
    while True:
        yield n
        n += step


count()$[10**100] |> print

Can’t be done quickly without Coconut’s iterator slicing, which requires many complicated pieces. The necessary definitions in Python can be found in the Coconut header.

map and zip

Coconut’s map and zip objects are enhanced versions of their Python equivalents that support normal slicing, optimized iterator slicing (through __coconut_is_lazy__), reversed, len, repr, and have added attributes which subclasses can make use of to get at the original arguments to the object (map supports _func and _iters attributes and zip supports the _iters attribute).


map((+), range(5), range(6)) |> len |> print

Can’t be done without defining a custom map type. The full definition of map can be found in the Coconut header.


Coconut provides the datamaker function to allow direct access to the base constructor of data types created with the Coconut data statement. This is particularly useful when writing alternative constructors for data types by overwriting __new__. Equivalent to:

def datamaker(data_type):
    """Returns base data constructor of data_type."""
    return super(data_type, data_type).__new__$(data_type)


data trilen(h):
    def __new__(cls, a, b):
        return (a**2 + b**2)**0.5 |> datamaker(cls)
import collections
class trilen(collections.namedtuple("trilen", "h")):
    __slots__ = ()
    def __new__(cls, a, b):
        return super(cls, cls).__new__(cls, (a**2 + b**2)**0.5)


Coconut provides a recursive decorator to perform tail recursion optimization on a function written in a tail-recursive style, where it directly returns all calls to itself. Do not use this decorator on a function not written in a tail-recursive style or the function will likely break.


def factorial(n, acc=1):
    case n:
        match 0:
            return acc
        match _ is int if n > 0:
            return factorial(n-1, acc*n)
        raise TypeError("the argument to factorial must be an integer >= 0")

Can’t be done without a long decorator definition. The full definition of the decorator in Python can be found in the Coconut header.


Coconut provides a parallel version of map under the name parallel_map. parallel_map makes use of multiple processes, and is therefore often much faster than map. Use of parallel_map requires concurrent.futures, which exits in the Python 3 standard library, but under Python 2 will require python -m pip install futures to function.

Because parallel_map uses multiple processes for its execution, it is necessary that all of its arguments be pickleable. Only objects defined at the module level, and not lambdas, objects defined inside of a function, or objects defined inside of the interpreter, are pickleable. Furthermore, on Windows, it is necessary that all calls to parallel_map occur inside of an if __name__ == "__main__" guard.

Python Docs

parallel_map(_func, *iterables_)

Equivalent to map(func, *iterables) except func is executed asynchronously and several calls to func may be made concurrently. If a call raises an exception, then that exception will be raised when its value is retrieved from the iterator.


parallel_map(pow$(2), range(100)) |> list |> print
import functools
import concurrent.futures
with concurrent.futures.ProcessPoolExecutor() as executor:
    print(list(, 2), range(100))))


A MatchError is raised when a destructuring assignment statement fails, and thus MatchError is provided as a built-in for catching those errors. MatchError objects support two attributes, pattern, which is a string describing the failed pattern, and value, which is the object that failed to match that pattern.

Coconut Utilities

Syntax Highlighting

There are currently three options for Coconut syntax highlighting:

  1. use SublimeText,
  2. use an editor that supports Pygments, or
  3. just treat Coconut as Python.

Instructions on how to set up syntax highlighting for SublimeText and Pygments are included below. If you don’t like SublimeText and your chosen alternative text editor doesn’t have pygments support, however, it should be sufficient to set up your editor so it interprets all .coco (also .coc and .coconut, although .coco is the preferred extension) files as Python code, as this should highlight most of your code well enough.


Coconut syntax highlighting for SublimeText requires that Package Control, the standard package manager for SublimeText, be installed. Once that is done, simply open the SublimeText command palette by entering Ctrl+Shift+P, enter Package Control: Install Package, and then Coconut. To make sure everything is working properly, open a .coco file, and make sure Coconut appears in the bottom right-hand corner. If something else appears, like Plain Text, click on it, select Open all with current extension as... at the top of the resulting menu, and then select Coconut.


The same pip install coconut command that installs the Coconut command-line utility will also install the coconut Pygments lexer. How to use this lexer depends on the Pygments-enabled application being used, but in general simply enter coconut as the language being highlighted and/or use a valid Coconut file extension (.coco, .coc, or .coconut) and Pygments should be able to figure it out. For example, this documentation is generated with Sphinx, with the syntax highlighting you see created by adding the line

highlight_language = "coconut"

to Coconut’s


It is sometimes useful to be able to use the Coconut compiler from code, instead of from the command line. The recommended way to do this is to use from coconut.convenience import and import whatever convenience functions you’ll be using. Specifications of the different convenience functions are as follows.


coconut.convenience.parse(code, [mode])

Likely the most useful of the convenience functions, parse takes Coconut code as input and outputs the equivalent compiled Python code. The second argument, mode, is used to indicate the context for the parsing. Possible values of mode are:

  • "exec": code for use in exec (the default)
  • "file": a stand-alone file
  • "single": a single line of code
  • "module": a file in a folder or module
  • "block": any number of lines of code
  • "eval": a single expression
  • "debug": lines of code with no header


coconut.convenience.setup(target, strict, minify, linenumbers, quiet, color)**

If --target, --strict, --minify, --linenumbers, --quiet, or --color are desired for parse, the arguments to setup will each set the value of the corresponding flag. The possible values for each flag are:

  • target: None (default), or any allowable target
  • strict: False (default) or True
  • minify: False (default) or True
  • linenumbers: False (default) or True
  • quiet: False (default) or True
  • color: None (default) or str


coconut.convenience.cmd(args, [interact])

Executes the given args as if they were fed to coconut on the command-line, with the exception that unless interact is true or -i is passed, the interpreter will not be started. Additionally, since parse and cmd share the same convenience parsing object, any changes made to the parsing with cmd will work just as if they were made with setup.



Retrieves a string containing information about the Coconut version. The optional argument which is the type of version information desired. Possible values of which are:

  • "num": the numerical version (the default)
  • "name": the version codename
  • "spec": the numerical version with the codename attached
  • "tag": the version tag used in GitHub and documentation URLs
  • "-v": the full string printed by coconut -v


If an error is encountered in a convenience function, a CoconutException instance may be raised. coconut.convenience.CoconutException is provided to allow catching such errors.


It is sometimes useful to be able to access Coconut built-ins from pure Python. To accomplish this, Coconut provides coconut.__coconut__, which behaves exactly like the header file included when Coconut is compiled in package mode.

All Coconut built-ins are accessible from coconut.__coconut__. The recommended way to import them is to use from coconut.__coconut__ import and import whatever built-ins you’ll be using.


from coconut.__coconut__ import recursive

def recursive_func(args):