diff --git a/readme.md b/readme.md
index 10626cb..d45e80c 100644
--- a/readme.md
+++ b/readme.md
@@ -1,10 +1,8 @@
-# Functional Programming Jargon
+# Functional Programming Jargon in Julia
 
 Functional programming (FP) provides many advantages, and its popularity has been increasing as a result. However, each programming paradigm comes with its own unique jargon and FP is no exception. By providing a glossary, we hope to make learning FP easier.
 
-This is a fork of [Functional Programming Jargon](https://github.com/jmesyou/functional-programming-jargon). Examples are presented in Python3.
-
-This document attempts to adhere to [PEP8](https://www.python.org/dev/peps/pep-0008/) as best as possible.
+This is a fork of [Functional Programming Jargon](https://github.com/hemanth/functional-programming-jargon). Examples are presented in [Julia](https://www.julialang.org).
 
 This document is WIP and pull requests are welcome!
 
@@ -67,9 +65,6 @@ __Table of Contents__
   * [Product type](#product-type)
 * [Option](#option)
 * [Function](#function)
-* [Partial function](#partial-function)
-* [Functional Programming Libraries in Python](#functional-programming-libraries-in-javascript)
-
 
 <!-- /RM -->
 
@@ -77,31 +72,27 @@ __Table of Contents__
 
 The number of arguments a function takes. From words like unary, binary, ternary, etc. This word has the distinction of being composed of two suffixes, "-ary" and "-ity." Addition, for example, takes two arguments, and so it is defined as a binary function or a function with an arity of two. Such a function may sometimes be called "dyadic" by people who prefer Greek roots to Latin. Likewise, a function that takes a variable number of arguments is called "variadic," whereas a binary function must be given two and only two arguments, currying and partial application notwithstanding (see below).
 
-```python
-from inspect import signature 
-
-add = lambda a, b: a + b
+```julia
+julia> myadd(x,y) = x + y
 
-arity = len(signature(add).parameters)
-print(arity) # 2
+julia> arity = first(methods(myadd)).nargs - 1
+2
 
-# The arity of add is 2
+# The arity of `myadd` is 2
 ```
 
 ## Higher-Order Functions (HOF)
 
 A function which takes a function as an argument and/or returns a function.
 
-```python
-filter = lambda predicate, xs: [x for x in xs if predicate(xs)] 
-```
+```julia
+julia> filter(iseven, 1:5)
+2-element Vector{Int64}:
+ 2
+ 4
 
-```python
-is_a = lambda T: lambda x: type(x) is T
-```
-
-```python
-filter(is_a(int), [0, '1', 2, None]) # [0, 2]
+julia> sum(abs, [-1, -2, 3])
+6
 ```
 
 ## Closure
@@ -113,20 +104,17 @@ A closure is a scope which captures local variables of a function for access eve
 ie. they allow referencing a scope after the block in which the variables were declared has finished executing.
 
 
-```python
-add_to = lambda x: lambda y: x + y
-add_to_five = add_to(5)
-add_to_five(3) # returns 8
-```
-The function ```add_to()``` returns a function(internally called ```add()```), lets store it in a variable called ```add_to_five``` with a curried call having parameter 5.
-
-Ideally, when the function ```add_to``` finishes execution, its scope, with local variables add, x, y should not be accessible. But, it returns 8 on calling ```add_to_five()```. This means that the state of the function ```add_to``` is saved even after the block of code has finished executing, otherwise there is no way of knowing that ```add_to``` was called as ```add_to(5)``` and the value of x was set to 5.
+```julia
+# this function returns an anonymous function that **closes over** `init`
+julia> add_to(init) = x -> x + init
 
-Lexical scoping is the reason why it is able to find the values of x and add - the private variables of the parent which has finished executing. This value is called a Closure.
+julia> add_to_five = add_to(5)
 
-The stack along with the lexical scope of the function is stored in form of reference to the parent. This prevents the closure and the underlying variables from being garbage collected(since there is at least one live reference to it).
+julia> add_to_five(3)
+8
+```
 
-Lambda Vs Closure: A lambda is essentially a function that is defined inline rather than the standard method of declaring functions. Lambdas can frequently be passed around as objects.
+Anonymous function Vs Closure: A anonymous is essentially a function that is defined inline rather than the standard method of declaring functions. Lambdas can frequently be passed around as objects.
 
 A closure is a function that encloses its surrounding state by referencing fields external to its body. The enclosed state remains across invocations of the closure.
 
@@ -138,29 +126,29 @@ __Further reading/Sources__
 
 Partially applying a function means creating a new function by pre-filling some of the arguments to the original function.
 
+```julia
+julia> add3(a, b, c) = a + b + c
+add3 (generic function with 1 method)
 
-```python
-# Helper to create partially applied functions
-# Takes a function and some arguments
-partial = lambda f, *args: 
-  # returns a function that takes the rest of the arguments
-  lambda *more_args:
-    # and calls the original function with all of them
-    f(args, more_args)
-# Something to apply
-add3 = lambda a, b, c: a + b + c
-# Partially applying `2` and `3` to `add3` gives you a one-argument function
-five_plus = partial(add3, 2, 3) # (c) => 2 + 3 + c
-
-five_plus(4) # 9
-```
+julia> partial(f, args...) = (x...) -> f(args..., x...)
 
-You can also use `functools.partial` to partially apply a function in Python:
+julia> five_plus = partial(add3, 2, 3)
 
-```python
-from functools import partial 
+julia> five_plus(4)
+9
 
-add_more = partial(add3, 2, 3) # (c) => 2 + 3 + c
+julia> one_plus_plus = partial(add3, 1)
+#1 (generic function with 1 method)
+
+julia> one_plus_plus(2,3)
+6
+
+#special case of 2-arguments original function
+julia> six_plus = Base.Fix1(+, 6)
+(::Base.Fix1{typeof(+), Int64}) (generic function with 1 method)
+
+julia> six_plus(7)
+13
 ```
 
 Partial application helps create simpler functions from more complex ones by baking in data when you have it. [Curried](#currying) functions are automatically partially applied.
@@ -172,32 +160,36 @@ The process of converting a function that takes multiple arguments into a functi
 
 Each time the function is called it only accepts one argument and returns a function that takes one argument until all arguments are passed.
 
-```python
-sum = lambda a, b: a + b 
-
-curried_sum = lambda a: lambda b: a + b
+```julia
+julia> curried_add(a) = b -> a + b
 
-curried_sum(40)(2) # 42.
+julia> curried_add(40)(2)
+42
 
-add2 = curried_sum(2) # (b) => 2 + b
+julia> add2 = curried_add(2)
 
-add2(10) # 12
+julia> add2(10)
+12
 ```
 
 ## Auto Currying
 Transforming a function that takes multiple arguments into one that if given less than its correct number of arguments returns a function that takes the rest. When the function gets the correct number of arguments it is then evaluated.
 
-The `toolz` module has an currying decorator which works this way
+The [Curries.jl](https://github.com/MasonProtter/Currier.jl) package has an currying decorator which works this way.
 
 ```python
-from toolz import curry 
+julia> using Currier
 
-@curry
-def add(x, y): return x + y 
+julia> @curried add3(x, y, z) = x + y + z
 
-add(1, 2) # 3
-add(1)    # (y) => 1 + y
-add(1)(2) # 3
+julia> add3(1,2,3)
+6
+
+julia> add3(1,2)(3)
+6
+
+julia> add3(1)(2)(3)
+6
 ```
 
 __Further reading__
@@ -208,68 +200,71 @@ __Further reading__
 
 The act of putting two functions together to form a third function where the output of one function is the input of the other.
 
-```python
-import math 
+```julia
+# `∘` can be typed with \circ<tab>
+julia> floor_and_string = string ∘ floor
 
-compose = lambda f, g: lambda a: f(g(a)) # Definition
-floor_and_str = compose(str, Math.floor) # Usage
-floor_and_string(121.212121) # '121'
+julia> floor_and_string(121.212121)
+"121.0"
 ```
 
 ## Continuation
 
 At any given point in a program, the part of the code that's yet to be executed is known as a continuation.
 
-```python
-print_as_string = lambda num: print(num)
+```julia
+julia> print_as_string(num) = println("Given $num")
 
-# this was previously defined as multi-line function in Javascript
-# however, Python only supports single expression lambdas
-add_one_and_continue = lambda num, cc: cc(num + 1) 
+julia> function add_one_and_continue(num, cc)
+           result = num + 1
+           cc(result)
+       end
 
-add_one_and_continue(2, print_as_string) # 'Given 3'
-```
-
-Continuations are often seen in asynchronous programming when the program needs to wait to receive data before it can continue. The response is often passed off to the rest of the program, which is the continuation, once it's been received.
-
-```python
-continue_program_with = lambda data: ... # Continues program with data
-
-read_file_async('path/to/file', lambda err, response: raise err if err else continue_program_with(response))
+julia> add_one_and_continue(2, print_as_string)
+Given 3
 ```
 
 ## Purity
 
 A function is pure if the return value is only determined by its
-input values, and does not produce side effects.
+input values, and does not produce side effects. Be very careful when
+using [@pure](https://discourse.julialang.org/t/pure-macro/3871) in Julia.
 
-```python
-greet = lambda name: 'Hi, {}'.format(name)
+```julia
+julia> greet(name) = "Hi, $name"
 
-greet('Brianne') # 'Hi, Brianne'
+julia> greet("Brianne")
+"Hi, Brianne"
 ```
 
 As opposed to each of the following:
 
-```python
-name = 'Brianne'
+```julia
+julia> name = "Brianne"
+"Brianne"
 
-greet = lambda: 'Hi, {}'.format(name)
+julia> greet() = "Hi, $name"
+greet (generic function with 2 methods)
 
-greet() # "Hi, Brianne"
+julia> greet()
+"Hi, Brianne"
 ```
 
 The above example's output is based on data stored outside of the function...
 
-```python
-greeting = None
+```julia
+julia> greeting = nothing
+
+julia> function greet(name)
+         global greeting
+         greeting = "Hi, $name"
+         return nothing
+       end
 
-def greet(name):
-  global greeting
-  greeting = 'Hi, {}'.format(name)
+julia> greet("Brianne")
 
-greet('Brianne')
-greeting # "Hi, Brianne"
+julia> greeting
+"Hi, Brianne"
 ```
 
 ... and this one modifies state outside of the function.
@@ -278,78 +273,80 @@ greeting # "Hi, Brianne"
 
 A function or expression is said to have a side effect if apart from returning a value, it interacts with (reads from or writes to) external mutable state.
 
-```python
-different_ever_time = list()
-```
-
-```python
-print('IO is a side effect!')
+```julia
+println("IO is a side effect!")
 ```
 
 ## Idempotent
 
 A function is idempotent if reapplying it to its result does not produce a different result.
 
+```julia
+identity(identity(10))
 ```
-f(f(x)) ≍ f(x)
-```
-
-```python
-import math
 
-math.abs(math.abs(10))
-```
-
-```python
-sorted(sorted(sorted([2, 1])))
+```julia
+# this relys on sort algorithm being stable
+sort(sort(sort([2, 1])))
 ```
 
 ## Point-Free Style
 
 Writing functions where the definition does not explicitly identify the arguments used. This style usually requires [currying](#currying) or other [Higher-Order functions](#higher-order-functions-hof). A.K.A Tacit programming.
 
-```python
+```julia
 # Given
-map = lambda fn: lambda xs: [fn(x) for x in xs]
-add = lambda a: lambda b: a + b
+julia> mymap = fn -> vec -> map(fn, vec)
+julia> myadd = a -> b -> a + b
 
 # Then
-
 # Not points-free - `numbers` is an explicit argument
-increment_all = lambda numbers: map(add(1))(numbers)
+julia> increment_all = numbers -> mymap(myadd(1))(numbers)
+
+julia> increment_all([4,7,8])
+3-element Vector{Int64}:
+ 5
+ 8
+ 9
 
 # Points-free - The list is an implicit argument
-increment_all2 = map(add(1))
+julia> increment_all2 = mymap(myadd(1))
+
+julia> increment_all([4,7,8])
+3-element Vector{Int64}:
+ 5
+ 8
+ 9
 ```
 
 `increment_all` identifies and uses the parameter `numbers`, so it is not points-free.  `increment_all2` is written just by combining functions and values, making no mention of its arguments.  It __is__ points-free.
 
-Points-free function definitions look just like normal assignments without `def` or `lambda`.
+Points-free function definitions look just like normal assignments without `function ...` or `blah -> `.
 
 ## Predicate
 A predicate is a function that returns true or false for a given value. A common use of a predicate is as the callback for array filter.
 
-```python
-predicate = lambda a: a > 2
-
-filter(predicate, [1, 2, 3, 4]) # [3, 4]
+```julia
+julia> filter(>(2), [1, 2, 3, 4])
+2-element Vector{Int64}:
+ 3
+ 4
 ```
 
 ## Contracts
 
 A contract specifies the obligations and guarantees of the behavior from a function or expression at runtime. This acts as a set of rules that are expected from the input and output of a function or expression, and errors are generally reported whenever a contract is violated.
 
-```python
-def throw(ex):
-  raise ex
+```julia
+julia> add1(num::Integer) = num + 1
 
-# Define our contract : int -> boolean
-contract = lambda value: True if type(value) is int else throw(Exception('Contract violated: expected int -> boolean'))
+julia> add1(num) = throw(ArgumentError("Contract violated: expected `num::Integer`"))
 
-add1 = lambda num: contract(num) and num + 1
+julia> add1(2)
+3
 
-add1(2) # 3
-add1('some string') # Contract violated: expected int -> boolean
+julia> add1("blah")
+ERROR: ArgumentError: Contract violated: expected `num::Integer`
 ```
 
 ## Category
@@ -379,34 +376,25 @@ __Further reading__
 
 Anything that can be assigned to a variable.
 
-```python
-from collections import namedtuple
-Person = namedtuple('Person', 'name age')
+```julia
+('John', 30)
+(name = 'Person', age = 30)
 5
-Person('John', 30)
-lambda a: a
-[1]
-None
+a -> a^2
+[1.0]
+missing
 ```
 
 ## Constant
 
 A variable that cannot be reassigned once defined.
 
-```python
-
-five = 5
-john = Person('John', 30)
+```julia
+const five = 5
 ```
 
 Constants are [referentially transparent](#referential-transparency). That is, they can be replaced with the values that they represent without affecting the result.
 
-With the above two constants the following expression will always return `true`.
-
-```python
-john.age + five == Person('John' 30).age + 5
-```
-
 ## Functor
 
 An object that implements a `map` function which, while running over each value in the object to produce a new object, adheres to two rules:
@@ -424,37 +412,59 @@ object.map(compose(f, g)) ≍ object.map(g).map(f)
 
 (`f`, `g` are arbitrary functions)
 
-Iterables in Python are functors since they abide by the two functor rules:
+In Julia `map` takes the first argument as the function:
 
-```python
+```julia
+julia> map(identity, [1,2,3])
+3-element Vector{Int64}:
+ 1
+ 2
+ 3
 
-map(lambda x: x, [1, 2, 3]) # = [1, 2, 3]
-map(lambda x: x, (1, 2, 3)) # = (1, 2, 3)
+julia> map(identity, (1,2,3))
+(1, 2, 3)
 ```
 
 and
 
-```python
-f = lambda x: x + 1
-g = lambda x: x * 2
+```julia
+julia> f(x) = x+1
 
-map(lambda x: f(g(x)), [1, 2, 3]) # = [3, 5, 7]
-map(f, map(g, [1, 2, 3]))         # = [3, 5, 7]
+julia> g(x) = x*2
+
+julia> map(f, map(g, [1,2,3]))
+3-element Vector{Int64}:
+ 3
+ 5
+ 7
+
+julia> map(f∘g, [1,2,3])
+3-element Vector{Int64}:
+ 3
+ 5
+ 7
 ```
 
 ## Pointed Functor
 An object with an `of` function that puts _any_ number of values into it. The following property
-`of(f(x)) == of(x).map(f)` must also hold for any pointed functor.
+`of(f(x)) == of(x).map(f)` must also hold for any pointed functor. [Better explaination](https://stackoverflow.com/questions/39179830/how-to-use-pointed-functor-properly)
 
-We create a custom class `Array` which mimics the original Javascript for a pointed functor.
+```julia 
+# here we think of `Vector` and `Base.map` together as a functor:
 
-```python 
+julia> of(t::AbstractVector{T}, vals...) where T = T[vals...]
 
-class Array(list):
+julia> of([], 3)
+1-element Vector{Any}:
+ 3
 
-  of = lambda *args: Array([a for a in args])
+julia> of(Int[], 3)
+1-element Vector{Int64}:
+ 3
 
-Array.of(1) # [1]
+julia> of(Float64[], 3)
+1-element Vector{Float64}:
+ 3.0
 ```
 
 ## Lift
@@ -463,48 +473,43 @@ Lifting is when you take a value and put it into an object like a [functor](#poi
 
 Some implementations have a function called `lift`, or `liftA2` to make it easier to run functions on functors.
 
-```python
-# OSlash applicative library https://github.com/dbrattli/oslash
-from oslash import List, Applicative # we import the Applicative 
+```julia
+julia> liftA2(fn) = (a, b) -> fn.(a, b) # cheating, rely on broadcast to figure out how to apply `fn`
+liftA2 (generic function with 1 method)
 
-liftA2 = lambda f: lambda a, b:  a.map(f) * b # a, b is of type Applicative where (*) is the apply function.
+julia> mult(a, b) = a * b
+mult (generic function with 1 method)
 
-mult = lambda a: lambda b: a * b # (*) is just regular multiplication
+julia> lifted_mult = liftA2(mult) # this function now works on functors (arrays)
+#5 (generic function with 1 method)
 
-lifted_mult = liftA2(mult) # this function now works on functors like oslash.List
+julia> lifted_mult([1, 2], [3]) # [3, 6]
+2-element Vector{Int64}:
+ 3
+ 6
 
-lifted_mult(List([1, 2]), List([3])) # [3, 6]
-liftA2(lambda a: lambda b: a + b)(List([1, 2]), List([3, 4])) # [4, 5, 5, 6]
+julia> lifted_mult([1, 2], 3)
+2-element Vector{Int64}:
+ 3
+ 6
 
-# or using oslash, it can alternatively be written as
-
-List([1, 2]).lift_a2(mult, List([3]))
-List([1, 2]).lift_a2(lambda a: lambda b: a + b, List([3, 4]))
+julia> lifted_mult([1, 2], [3,4])
+2-element Vector{Int64}:
+ 3
+ 8
 ```
 
-Lifting a one-argument function and applying it does the same thing as `map`.
-
-```python
-increment = lambda x: x + 1
-
-lift(increment)(List([2])) # [3]
-List([2]).map(increment)  # [3]
-```
-
-
 ## Referential Transparency
 
 An expression that can be replaced with its value without changing the
 behavior of the program is said to be referentially transparent.
 
-Say we have function greet:
-
 ```python
-greet = lambda: 'Hello World!'
+greet() = "Hello World!"
 ```
 
 Any invocation of `greet()` can be replaced with `Hello World!` hence greet is
-referentially transparent.
+referentially transparent. In Julia this is trivially true for any `immutable` object.
 
 ##  Equational Reasoning
 
@@ -514,22 +519,27 @@ When an application is composed of expressions and devoid of side effects, truth
 
 An anonymous function that can be treated like a value.
 
-```python 
-def f(a):
-  return a + 1
+```julia 
+julia> f(a) = a + 1
+f (generic function with 1 method)
 
-lambda a: a + 1
+julia> a -> a + 1
+#3 (generic function with 1 method)
 ```
 Lambdas are often passed as arguments to Higher-Order functions.
 
-```python
-List([1, 2]).map(lambda x: x + 1) # [2, 3]
+```julia
+julia> filter(x -> x^2 < 10, 1:5)
+3-element Vector{Int64}:
+ 1
+ 2
+ 3
 ```
 
 You can assign a lambda to a variable.
-
-```python
-add1 = lambda a: a + 1
+```julia
+julia> addone = a -> a + 1
+#1 (generic function with 1 method)
 ```
 
 ## Lambda Calculus
@@ -539,16 +549,13 @@ A branch of mathematics that uses functions to create a [universal model of comp
 
 Lazy evaluation is a call-by-need evaluation mechanism that delays the evaluation of an expression until its value is needed. In functional languages, this allows for structures like infinite lists, which would not normally be available in an imperative language where the sequencing of commands is significant.
 
-```python
-import random
-def rand(): 
-  while True:
-    yield random.randint(1,101)
-```
+```julia
+julia> struct myrand end
 
-```python
-randIter = rand()
-next(randIter) # Each execution gives a random value, expression is evaluated on need.
+julia> Base.iterate(::myrand, state=nothing) = rand()
+
+julia> first(myrand())
+0.1381937787185451
 ```
 
 ## Monoid
@@ -557,72 +564,72 @@ An object with a function that "combines" that object with another of the same t
 
 One simple monoid is the addition of numbers:
 
-```python
-1 + 1 # 2
+```julia
+julia> 1 + 1
+2
 ```
 In this case number is the object and `+` is the function.
 
 An "identity" value must also exist that when combined with a value doesn't change it.
 
-The identity value for addition is `0`.
-```python
-1 + 0 # 1
+The additive identity value for addition is `0`, can also be obtained by `zero()`:
+```julia
+julia> 1 + zero(1)
+1
 ```
 
 It's also required that the grouping of operations will not affect the result (associativity):
-
-```python
-1 + (2 + 3) == (1 + 2) + 3 # true
+```julia
+julia> 1 + (2 + 3) == (1 + 2) + 3
+true
 ```
 
 List concatenation also forms a monoid:
-
-```python
-[1, 2] + [3, 4] # [1, 2, 3, 4]
+```julia
+julia> vcat([1, 2], [3, 4])
+4-element Vector{Int64}:
+ 1
+ 2
+ 3
+ 4
 ```
 
-The identity value is empty array `[]`
-
-```python
-[1, 2] + [] # [1, 2]
+The identity value is empty Int array `Int[]` (can be obtained by `empty()`)
+```julia
+julia> vcat([1, 2], empty([1,2]))
+2-element Vector{Int64}:
+ 1
+ 2
 ```
 
 If identity and compose functions are provided, functions themselves form a monoid:
 
-```python
-identity = lambda a: a
-compose = lambda f, g: lambda x: f(g(x))
-```
-`foo` is any function that takes one argument.
-```
-compose(foo, identity) ≍ compose(identity, foo) ≍ foo
+```julia
+# `foo` is a 1-argument function
+foo∘identity ≍ identity∘foo ≍ foo
 ```
 
 ## Monad
 
 A monad is an object with [`of`](#pointed-functor) and `chain` functions. `chain` is like [`map`](#functor) except it un-nests the resulting nested object.
 
-```python
-# pymonad Monad library https://bitbucket.org/jason_delaat/pymonad
-from pymonad.List import *
-from functools import reduce
-#implementation
-class Array(list):
-
-  def of(*args): 
-    return Array([a for a in args])
-
-  def chain(self, f):
-    return reduce(lambda acc, it: acc + f(it), self[:], [])
+```julia
+julia> of(t::AbstractVector{T}, vals...) where T = T[vals...]
 
-  def map(self, f):
-    return [f(x) for x in self[:]]
+julia> chain(fn, v) = mapreduce(fn, vcat, v)
 
-# Usage
-Array.of('cat,dog', 'fish,bird').chain(lambda a: a.split(',')) # ['cat', 'dog', 'fish', 'bird']
+julia> chain(split, of([], "cat dot", "fish bird"))
+4-element Vector{SubString{String}}:
+ "cat"
+ "dot"
+ "fish"
+ "bird"
 
 # Contrast to map
-Array.of('cat,dog', 'fish,bird').map(lambda a: a.split(',')) # [['cat', 'dog'], ['fish', 'bird']]
+julia> map(split, of([], "cat dot", "fish bird"))
+2-element Vector{Vector{SubString{String}}}:
+ ["cat", "dot"]
+ ["fish", "bird"]
 ```
 
 `of` is also known as `return` in other functional languages.
@@ -630,57 +637,38 @@ Array.of('cat,dog', 'fish,bird').map(lambda a: a.split(',')) # [['cat', 'dog'],
 
 ## Comonad
 
-An object that has `extract` and `extend` functions.
+An object that has `extract` and `extend` functions. `Ref` is a natural condidate:
 
-```python
-class CoIdentity:
-
-  def __init__(self, v):
-    self.val = v
-
-  def extract(self):
-    return self.val
-
-  def extend(f):
-    return CoIdentity(f(self))
-```
+```julia
+julia> extract(x::Ref) = x[]
 
-Extract takes a value out of a functor.
+julia> extend(x::T, f) where T<:Ref = T(f(x[]))
 
-```python
-CoIdentity(1).extract() # 1
+julia> extract(Ref(1.0))
+1.0
 ```
 
 Extend runs a function on the comonad. The function should return the same type as the comonad.
-
-```python
-CoIdentity(1).extend(lambda co: co.extract() + 1) # CoIdentity(2)
+```julia
+julia> extend(Ref(1.0), x->3) # notice the type is preserved correctly
+Base.RefValue{Float64}(3.0)
 ```
 
 ## Applicative Functor
 
 An applicative functor is an object with an `ap` function. `ap` applies a function in the object to a value in another object of the same type.
 
-```python
-from functools import reduce
-# Implementation
-
-class Array(list):
-
-  def of(*args): 
-    return Array([a for a in args])
+```julia
+julia> ap(fn, val) = fn(val)
+ap (generic function with 1 method)
 
-  def chain(self, f):
-    return reduce(lambda acc, it: acc + f(it), self[:], [])
-
-  def map(self, f):
-    return [f(x) for x in self[:]]
-
-  def ap(self, xs):
-    return reduce(lambda acc, f: acc + (xs.map(f)), self[:], [])
+julia> ap(fns::Vector, vals) = map(ap, fns, vals)
+ap (generic function with 2 methods)
 
 # Example usage
-Array([lambda a: a + 1]).ap(Array([1])) # [2]
+julia> ap([a->a+1], [1])
+1-element Vector{Int64}:
+ 2
 ```
 
 This is useful if you have two objects and you want to apply a binary function to their contents.
@@ -691,15 +679,18 @@ arg1 = [1, 3]
 arg2 = [4, 5]
 
 # combining function - must be curried for this to work
-add = lambda x: lambda y: x + y
+julia> add(x) = y -> x + y
 
-partially_applied_adds = [add].ap(arg1) # [(y) => 1 + y, (y) => 3 + y]
+julia> partially_applied_adds = map(add, arg1)
 ```
 
 This gives you an array of functions that you can call `ap` on to get the result:
 
-```python
-partially_applied_adds.ap(arg2) # [5, 6, 7, 8]
+```julia
+julia> ap(partially_applied_adds, arg2)
+2-element Vector{Int64}:
+ 5
+ 8
 ```
 
 ## Morphism
@@ -710,12 +701,13 @@ A transformation function.
 
 A function where the input type is the same as the output.
 
-```python
-# uppercase :: String -> String
-uppercase = lambda s: s.upper() 
+```julia
+# String -> String
+julia> uppercase("Aasd")
+"AASD"
 
-# decrement :: Number -> Number
-decrement = lambda x: x - 1
+# Number -> Number
+julia> decrement(num::T)::T = num-1
 ```
 
 ### Isomorphism
@@ -724,65 +716,58 @@ A pair of transformations between 2 types of objects that is structural in natur
 
 For example, 2D coordinates could be stored as an array `[2,3]` or object `{x: 2, y: 3}`.
 
-```python
-# Providing functions to convert in both directions makes them isomorphic.
-from collections import namedtuple
-
-Coords = namedtuple('Coords', 'x y')
-pair_to_coords = lambda pair: Coords(pair[0], pair[1])
-
-coords_to_pair = lambda coords: [coords.x, coords.y]
+```julia
+julia> coords_to_pair(coords) = (coords.x, coords.y)
 
-coords_to_pair(pair_to_coords([1, 2])) # [1, 2]
+julia> pair_to_coords(pair) = (x=pair[1], y=pair[2])
 
-pair_to_coords(coords_to_pair(Coords(1, 2))) # Coords(x=1, y=2)
+julia> pair_to_coords(coords_to_pair((x=1, y=2)))
+(x = 1, y = 2)
 ```
 
 ### Homomorphism
 
-A homomorphism is just a structure preserving map. In fact, a functor is just a homomorphism between categories as it preserves the original category's structure under the mapping.
-
-```python
-from pymonad import *
-f * A.unit(x) == A.unit(f(x))
+A homomorphism is just a structure preserving map. Weaker than isomorphism in that it doesn't require `bijectivity`.
+In fact, a functor is just a homomorphism between categories as it preserves the original category's structure under the mapping.
 
-(lambda x: x.upper()) * (Either.unit("oreos")) == Either.unit("oreos".upper())
+Julia's `map` satisfy this (one-to-one/injective) unless the function is not "pure":
+```julia
+map(uppercase, ["oreos"]) == [uppercase("oreos")]
 ```
 
 ### Catamorphism
 
 A `reduce_right` function that applies a function against an accumulator and each value of the array (from right-to-left) to reduce it to a single value.
 
-```python
-from functools import reduce
-class Array(list):
-
-  def reduce_right(self, f, init):
-    return reduce(f, self[::-1], init)
+```julia
+julia> sum_by_reduce(vec) = foldr(+, vec)
 
-sum = lambda xs: xs.reduce_right(lambda acc, x: acc + x, 0)
-
-sum(Array([1, 2, 3, 4, 5])) # 15
+julia> sum_by_reduce([1,2,3])
+6
 ```
 
 ### Anamorphism
 
 An `unfold` function. An `unfold` is the opposite of `fold` (`reduce`). It generates a list from a single value.
 
-```python
-
-def unfold(f, seed):
-  def go(f, seed, acc):
-    res = f(seed)
-    return go(f, res[1], acc + [res[0]]) if res else acc
-
-  return go(f, seed, [])
-```
+```julia
+julia> function unfold(f, seed)
+         function go(f, seed, acc)
+           res = f(seed)
+           return isnothing(res) ? acc : go(f, res[2], push!(acc, res[1]))
+           end
+         return go(f, seed, [])
+         end
+unfold (generic function with 1 method)
 
-```python
-count_down = lambda n: unfold(lambda n: None if n <= 0 else (n, n - 1), n)
+julia> countDown(n) = unfold(n->ifelse(n<=0, nothing, (n, n-1)), 3)
+countDown (generic function with 1 method)
 
-count_down(5) # [5, 4, 3, 2, 1]
+julia> countDown(3)
+3-element Vector{Any}:
+ 3
+ 2
+ 1
 ```
 
 ### Hylomorphism
@@ -795,23 +780,26 @@ A function just like `reduce_right`. However, there's a difference:
 
 In paramorphism, your reducer's arguments are the current value, the reduction of all previous values, and the list of values that formed that reduction.
 
-```python
-
-def para(reducer, accumulator, elements):
-  if not len(elements):
-    return accumulator
+```julia
 
-  head = elements[0]
-  tail = elements[1:]
+julia> function para(reducer, accumulator, elements)
+         isempty(elements) && return accumulator
+         head, tail... = elements
+         return reducer(head, tail, para(reducer, accumulator, tail))
+       end
 
-  return reducer(head, tail, para(reducer, accumulator, tail))
+julia> suffixes(lst) = para((x, xs, sxs) -> [[xs]; sxs], [], lst)
 
-suffixes = lambda lst: para(lambda x, xs, suffxs: [xs, *suffxs], [], lst)
-
-suffixes([1, 2, 3, 4, 5]) # [[2, 3, 4, 5], [3, 4, 5], [4, 5], [5], []]
+julia> suffixes([1,2,3,4,5])
+5-element Vector{Any}:
+ [2, 3, 4, 5]
+ [3, 4, 5]
+ [4, 5]
+ [5]
+ Int64[]
 ```
 
-The third parameter in the reducer (in the above example, `[x, *xs]`) is kind of like having a history of what got you to your current acc value.
+The third parameter in the reducer (in the above example, `[[xs]; sxs]`) is kind of like having a history of what got you to your current acc value.
 
 ### Apomorphism
 
@@ -821,28 +809,18 @@ it's the opposite of paramorphism, just as anamorphism is the opposite of catamo
 
 An object that has an `equals` function which can be used to compare other objects of the same type.
 
-Make array a setoid:
-
-```python 
-
-class Array(list):
-
-  def equals(self, other):
-    if len(self) != len(other):
-      return False 
-    else:
-      return reduce(lambda ident, pair: ident and (pair[0] == pair[1]), zip(self[:], other[:]), True)
-
-Array([1, 2]).equals(Array([1, 2])) # true
-Array([1, 2]).equals(Array([0])) # false
-```
+In `Julia`, just use `isequal(obj1, obj2)`
 
 ## Semigroup
 
 An object that has a `concat` function that combines it with another object of the same type.
 
-```python
-[1] + [2] # [1, 2]
+```julia
+# column-major
+julia> vcat([1], [2])
+2-element Vector{Int64}:
+ 1
+ 2
 ```
 
 ## Foldable
@@ -850,46 +828,46 @@ An object that has a `concat` function that combines it with another object of t
 An object that has a `reduce` function that applies a function against an accumulator and each element in the array (from left to right) to reduce it to a single value.
 
 ```python
-sum = lambda lst: reduce(lambda acc, x: acc + x, lst, 0)
-sum([1, 2, 3]) # 6
+julia> sum_by_reduce(vec) = reduce(+, vec)
+
+julia> sum_by_reduce([1,2,3])
+6
 ```
 
 ## Lens ##
 A lens is a structure (often an object or function) that pairs a getter and a non-mutating setter for some other data
 structure.
 
-```python
-from lenses import lens
-# Using [python-lenses](https://python-lenses.readthedocs.io/en/latest/tutorial/methods.html)
-
-name_lens = lens['name'] # we create an unbound lens which accesses the value associate with the 'name' key of a dict
-```
+A popular implementation is [Accessors.jl](https://github.com/JuliaObjects/Accessors.jl)
 
-Having the pair of get and set for a given data structure enables a few key features.
+```julia
+julia> using Accessors
 
-```python
-person = {'name': 'Gertrude Blanch'}
+# this is immutable
+julia> struct T
+            a
+            b
+        end
 
-# invoke the getter
-name_lens.get()(person) # 'Gertrude Blanch'
+julia> obj = T("AA", "BB");
 
-# invoke the setter
-name_lens.set('Shafi Goldwasser')(person) # {'name': 'Shafi Goldwasser'}
+julia> obj.a = "aa"
+ERROR: setfield!: immutable struct of type T cannot be changed
 
-# run a function on the value in the structure
-name_lens.modify(lambda x: x.upper())(person) # {'name': 'GERTRUDE BLANCH'}
-```
+julia> lens = @optic _.a
+(@optic _.a)
 
-Lenses are also composable. This allows easy immutable updates to deeply nested data.
+julia> lens(obj)
+"AA"
 
-```python
-# This lens focuses on the first item in a non-empty array
-first_lens = lens[0]
+julia> set(obj, lens, 2)
+T(2, "BB")
 
-people = [{'name': 'Gertrude Blanch'}, {'name': 'Shafi Goldwasser'}]
+julia> obj # the object was not mutated, instead an updated copy was created
+T("AA", "BB")
 
-# Despite what you may assume, lenses compose left-to-right.
-(first_lens & name_lens).modify(lambda x: x.upper())(people) # [{'name': 'GERTRUDE BLANCH'}, {'name': 'Shafi Goldwasser'}]
+julia> modify(lowercase, obj, lens)
+T("aa", "BB")
 ```
 
 ## Type Signatures
@@ -898,30 +876,17 @@ Often functions in JavaScript will include comments that indicate the types of t
 
 There's quite a bit of variance across the community but they often follow the following patterns:
 
-```python
-# function :: a -> b -> c
-
+```julia
 # add :: int -> int -> int
-# alternatively could be float -> float -> float
-add = lambda y: lambda x: x + y
+myadd1 = (x::Int -> y::Int -> x + y)::Int
 
 # increment :: int -> int
-increment = lambda x: x + 1
-```
-
-If a function accepts another function as an argument it is wrapped in parentheses.
-
-```python
-# call :: (a -> b) -> a -> b
-call = lambda f: lambda x: f(x)
+increment(x::Int)::Int = x + 1
 ```
 
-The letters `a`, `b`, `c`, `d` are used to signify that the argument can be of any type. The following version of `map` takes a function that transforms a value of some type `a` into another type `b`, an array of values of type `a`, and returns an array of values of type `b`.
-
-```python
-# map :: (a -> b) -> [a] -> [b]
-map(f, lst)
-```
+Due to JIT, it's better to NOT over-specificy types, because it only reduces the genrality of your function
+without any speed gain, also see [Multiple Dispatch](https://docs.julialang.org/en/v1/manual/methods/#Parametric-Methods) for
+more idiomatic way to write parametric methods in Julia.
 
 __Further reading__
 * [Ramda's type signatures](https://github.com/ramda/ramda/wiki/Type-Signatures)
@@ -934,30 +899,46 @@ A composite type made from putting other types together. Two common classes of a
 ### Sum type
 A Sum type is the combination of two types together into another one. It is called sum because the number of possible values in the result type is the sum of the input types.
 
-JavaScript doesn't have types like this (neither does python) but we can use `Set`s to pretend:
+In Julia, it's almost the same as [Type Unions](https://docs.julialang.org/en/v1/manual/types/#Type-Unions), albeit some implementation details
+and lack of pattern matching, but because Julia is a dynamic language (i.e. behavior always correct when you take a value out of a union typed
+container), the line is blurred.
+
 ```python
-# imagine that rather than sets here we have types that can only have these values
-bools = set([True, False])
-half_true = set(['half-true'])
+julia> a = Union{Float64, Int}[1,2,3]
+3-element Vector{Union{Float64, Int64}}:
+ 1
+ 2
+ 3
+
+julia> typeof.(a)
+3-element Vector{DataType}:
+ Int64
+ Int64
+ Int64
 
-# The weakLogic type contains the sum of the values from bools and halfTrue
-weak_logic_values = bools.union(half_true)
+julia> a = Union{Float64, Int}[1,2,3.0]
+3-element Vector{Union{Float64, Int64}}:
+ 1
+ 2
+ 3.0
+
+julia> typeof.(a)
+3-element Vector{DataType}:
+ Int64
+ Int64
+ Float64
 ```
 
 Sum types are sometimes called union types, discriminated unions, or tagged unions.
 
-The [sumtypes](https://github.com/radix/sumtypes/) library in Python helps with defining and using union types.
+See an implementation [SumTypes.jl](https://github.com/MasonProtter/SumTypes.jl).
 
 ### Product type
 
 A **product** type combines types together in a way you're probably more familiar with:
 
-```python
-from collections import namedtuple
-
-Point = namedtuple('Point', 'x y')
-# point :: (Number, Number) -> {x: Number, y: Number}
-point = lambda pair: Point(x, y)
+```julia
+point(p) = (x=p[1], y=p[2])
 ```
 It's called a product because the total possible values of the data structure is the product of the different values. Many languages have a tuple type which is the simplest formulation of a product type.
 
@@ -968,144 +949,30 @@ Option is a [sum type](#sum-type) with two cases often called `Some` and `None`.
 
 Option is useful for composing functions that might not return a value.
 
-```python
-# Naive definition
-
-class _Some:
-  
-  def __init__(self, v):
-    self.val = v
+```julia
+help?> Some
+search: Some something @something @showtime VersionNumber test_colorscheme DimensionMismatch sortperm
 
-  def map(self, f):
-    return _Some(f(self.val))
+  Some{T}
 
-  def chain(self, f):
-    return f(self.val)
-  
-# None is a keyword in python
-class _None:
 
-  def map(self, f):
-    return self
+  A wrapper type used in `Union{Some{T}, Nothing}` to distinguish between the absence of a value (`nothing`)
+  and the presence of a `nothing` value (i.e. `Some(nothing)`).
 
-  def chain(self, f):
-    return self
-
-# maybe_prop :: String -> (String => a) -> Option a
-maybe_prop = lambda key, obj: _None() if key not in obj else Some(obj[key])
+  Use `something` to access the value wrapped by a Some object.
 ```
-Use `chain` to sequence functions that return `Option`s
-```python
-
-# get_item :: Cart -> Option CartItem
-get_item = lambda cart: maybe_prop('item', cart)
-
-# get_price :: Cart -> Option Float
-get_price = lambda item: maybe_prop('price', item)
-
-# get_nested_price :: Cart -> Option a
-get_nested_price = lambda cart: get_item(cart).chain(get_price)
-
-get_nested_price({}) # _None()
-get_nested_price({"item": {"foo": 1}}) # _None()
-get_nested_price({"item": {"price": 9.99}}) # _Some(9.99)
-```
-
-`Option` is also known as `Maybe`. `Some` is sometimes called `Just`. `None` is sometimes called `Nothing`.
+`Option` is also known as `Maybe`. `Some` is sometimes called `Just`. `None` is sometimes called `Nothing` (as in Julia).
 
 ## Function
 A **function** `f :: A => B` is an expression - often called arrow or lambda expression - with **exactly one (immutable)** parameter of type `A` and **exactly one** return value of type `B`. That value depends entirely on the argument, making functions context-independant, or [referentially transparent](#referential-transparency). What is implied here is that a function must not produce any hidden [side effects](#side-effects) - a function is always [pure](#purity), by definition. These properties make functions pleasant to work with: they are entirely deterministic and therefore predictable. Functions enable working with code as data, abstracting over behaviour:
 
-```python
-# times2 :: Number -> Number
-times2 = lambda n: n * 2
+```julia
+julia> times2(n::Number) = (n * 2)::Number
+times2 (generic function with 1 method)
 
-map(times2, [1, 2, 3] # [2, 4, 6]
+julia> map(times2, [1, 2, 3])
+3-element Vector{Int64}:
+ 2
+ 4
+ 6
 ```
-
-## Partial function
-A partial function is a [function](#function) which is not defined for all arguments - it might return an unexpected result or may never terminate. Partial functions add cognitive overhead, they are harder to reason about and can lead to runtime errors. Some examples:
-```python
-from functools import partial
-# example 1: sum of the list
-# sum :: [int] -> int
-sum = partial(reduce, lambda a, b: a + b, arr)
-sum([1, 2, 3]) # 6
-sum([])        # TypeError: reduce() of empty sequence with no initial value
-
-# example 2: get the first item in list
-# first :: [a] -> a
-first = lambda a: a[0]
-first([42]) # 42
-first([])   # IndexError
-# or even worse:
-first([[42]])[0] # 42
-first([])[0]     # Uncaught TypeError: an IndexError is throw instead
-
-# example 3: repeat function N times
-# times :: int -> (int -> int) -> int
-times = lambda n: lambda fn: n and (fn(n), times(n - 1)(fn))
-times(3)(print)
-# 3
-# 2
-# 1
-# out: (None, (None, (None, 0)))
-times(-1)(print)
-# RecursionError: maximum recursion depth exceeded while calling a Python object
-```
-
-### Dealing with partial functions
-Partial functions are dangerous as they need to be treated with great caution. You might get an unexpected (wrong) result or run into runtime errors. Sometimes a partial function might not return at all. Being aware of and treating all these edge cases accordingly can become very tedious.
-Fortunately a partial function can be converted to a regular (or total) one. We can provide default values or use guards to deal with inputs for which the (previously) partial function is undefined. Utilizing the [`Option`](#Option) type, we can yield either `Some(value)` or `None` where we would otherwise have behaved unexpectedly:
-```python
-
-# re-order function arguments for easier partial function application
-_reduce = lambda fn, init, seq: reduce(fn, seq, init) 
-# example 1: sum of the list
-# we can provide default value so it will always return result
-# sum :: [int] -> int
-
-sum = partial(_reduce, lambda a, b: a + b, 0)
-sum([1, 2, 3]) # 6
-sum([]) # 0
-
-# example 2: get the first item in list
-# change result to Option
-# first :: [A] -> Option A
-first = lambda a: _Some(a[0]) if len(a) else _None()
-first([42]).map(print) # 42
-first([]).map(print) # print won't execute at all
-# our previous worst case
-first([[42]]).map(lambda a: print(a[0])) # 42
-first([]).map(lambda a: print(a[0])) # won't execute, so we won't have error here
-# more of that, you will know by function return type (Option)
-# that you should use `.map` method to access the data and you will never forget
-# to check your input because such check become built-in into the function
-
-# example 3: repeat function N times
-# we should make function always terminate by changing conditions:
-# times :: int -> (int -> int) -> int
-times = lambda n: lambda fn: n > 0 and (fn(n), times(n - 1)(fn))
-times(3)(print)
-# 3
-# 2
-# 1
-times(-1)(print)
-# won't execute anything
-```
-Making your partial functions total ones, these kinds of runtime errors can be prevented. Always returning a value will also make for code that is both easier to maintain as well as to reason about.
-
-## Functional Programming Libraries in Python
-
-### In This Doc
-  * [functools](https://docs.python.org/3/library/functools.html)
-  * [oslash](https://github.com/dbrattli/oslash)
-  * [python-lenses](https://github.com/ingolemo/python-lenses)
-  * [pymonad](https://bitbucket.org/jason_delaat/pymonad)
-  * [toolz](https://github.com/pytoolz/toolz)
-
-A comprehensive curated list of functional programming libraries for Python can be found [here](https://github.com/sfermigier/awesome-functional-python)
-
----
-
-__P.S:__ This repo is successful due to the wonderful [contributions](https://github.com/hemanth/functional-programming-jargon/graphs/contributors)!