Collections and Data Structures

Iteration

Sequential iteration is implemented by the methods start(), done(), and next(). The general for loop:

for i = I   # or  "for i in I"
    # body
end

is translated into:

state = start(I)
while !done(I, state)
    (i, state) = next(I, state)
    # body
end

The state object may be anything, and should be chosen appropriately for each iterable type. See the manual section on the iteration interface for more details about defining a custom iterable type.

start(iter) → state

Get initial iteration state for an iterable object.

done(iter, state) → Bool

Test whether we are done iterating.

next(iter, state) → item, state

For a given iterable object and iteration state, return the current item and the next iteration state.

zip(iters...)

For a set of iterable objects, returns an iterable of tuples, where the ith tuple contains the ith component of each input iterable.

Note that zip() is its own inverse: collect(zip(zip(a...)...)) == collect(a).

julia> a = 1:5
1:5

julia> b = ["e","d","b","c","a"]
5-element Array{String,1}:
 "e"
 "d"
 "b"
 "c"
 "a"

julia> c = zip(a,b)
Base.Zip2{UnitRange{Int64},Array{String,1}}(1:5,String["e","d","b","c","a"])

julia> length(c)
5

julia> first(c)
(1,"e")

enumerate(iter)

An iterator that yields (i, x) where i is a counter starting at 1, and x is the ith value from the given iterator. It’s useful when you need not only the values x over which you are iterating, but also the number of iterations so far. Note that i may not be valid for indexing iter; it’s also possible that x != iter[i], if iter has indices that do not start at 1.

julia> a = ["a", "b", "c"];

julia> for (index, value) in enumerate(a)
           println("$index $value")
       end
1 a
2 b
3 c

rest(iter, state)

An iterator that yields the same elements as iter, but starting at the given state.

countfrom(start=1, step=1)

An iterator that counts forever, starting at start and incrementing by step.

take(iter, n)

An iterator that generates at most the first n elements of iter.

julia> a = 1:2:11
1:2:11

julia> collect(a)
6-element Array{Int64,1}:
  1
  3
  5
  7
  9
 11

julia> collect(take(a,3))
3-element Array{Int64,1}:
 1
 3
 5

drop(iter, n)

An iterator that generates all but the first n elements of iter.

julia> a = 1:2:11
1:2:11

julia> collect(a)
6-element Array{Int64,1}:
  1
  3
  5
  7
  9
 11

julia> collect(drop(a,4))
2-element Array{Int64,1}:
  9
 11

cycle(iter)

An iterator that cycles through iter forever.

repeated(x[, n::Int])

An iterator that generates the value x forever. If n is specified, generates x that many times (equivalent to take(repeated(x), n)).

iteratorsize(itertype::Type) → IteratorSize

Given the type of an iterator, returns one of the following values:

• SizeUnknown() if the length (number of elements) cannot be determined in advance.
• HasLength() if there is a fixed, finite length.
• HasShape() if there is a known length plus a notion of multidimensional shape (as for an array). In this case the size function is valid for the iterator.
• IsInfinite() if the iterator yields values forever.

The default value (for iterators that do not define this function) is HasLength(). This means that most iterators are assumed to implement length.

This trait is generally used to select between algorithms that pre-allocate space for their result, and algorithms that resize their result incrementally.

iteratoreltype(itertype::Type) → IteratorEltype

Given the type of an iterator, returns one of the following values:

• EltypeUnknown() if the type of elements yielded by the iterator is not known in advance.
• HasEltype() if the element type is known, and eltype would return a meaningful value.

HasEltype() is the default, since iterators are assumed to implement eltype.

This trait is generally used to select between algorithms that pre-allocate a specific type of result, and algorithms that pick a result type based on the types of yielded values.

Fully implemented by:

• Range
• UnitRange
• Tuple
• Number
• AbstractArray
• IntSet
• ObjectIdDict
• Dict
• WeakKeyDict
• EachLine
• AbstractString
• Set
• Task

General Collections

isempty(collection) → Bool

Determine whether a collection is empty (has no elements).

julia> isempty([])
true

julia> isempty([1 2 3])
false

empty!(collection) → collection

Remove all elements from a collection.

length(collection) → Integer

For ordered, indexable collections, the maximum index i for which getindex(collection, i) is valid. For unordered collections, the number of elements.

endof(collection) → Integer

Returns the last index of the collection.

julia> endof([1,2,4])
3

Fully implemented by:

• Range
• UnitRange
• Tuple
• Number
• AbstractArray
• IntSet
• Dict
• WeakKeyDict
• AbstractString
• Set

Iterable Collections

in(item, collection) → Bool

∈(item, collection) → Bool

∋(collection, item) → Bool

∉(item, collection) → Bool

∌(collection, item) → Bool

Determine whether an item is in the given collection, in the sense that it is == to one of the values generated by iterating over the collection. Some collections need a slightly different definition; for example Sets check whether the item isequal() to one of the elements. Dicts look for (key,value) pairs, and the key is compared using isequal(). To test for the presence of a key in a dictionary, use haskey() or k in keys(dict).

eltype(type)

Determine the type of the elements generated by iterating a collection of the given type. For associative collection types, this will be a Pair{KeyType,ValType}. The definition eltype(x) = eltype(typeof(x)) is provided for convenience so that instances can be passed instead of types. However the form that accepts a type argument should be defined for new types.

indexin(a, b)

Returns a vector containing the highest index in b for each value in a that is a member of b . The output vector contains 0 wherever a is not a member of b.

julia> a = ['a', 'b', 'c', 'b', 'd', 'a'];

julia> b = ['a','b','c'];

julia> indexin(a,b)
6-element Array{Int64,1}:
 1
 2
 3
 2
 0
 1

julia> indexin(b,a)
3-element Array{Int64,1}:
 6
 4
 3

findin(a, b)

Returns the indices of elements in collection a that appear in collection b.

julia> a = collect(1:3:15)
5-element Array{Int64,1}:
  1
  4
  7
 10
 13

julia> b = collect(2:4:10)
3-element Array{Int64,1}:
  2
  6
 10

julia> findin(a,b) # 10 is the only common element
1-element Array{Int64,1}:
 4

unique(itr[, dim])

Returns an array containing only the unique elements of the iterable itr, in the order that the first of each set of equivalent elements originally appears. If dim is specified, returns unique regions of the array itr along dim.

unique(itr)

Returns an array containing one value from itr for each unique value, as determined by isequal.

unique(f, itr)

Returns an array containing one value from itr for each unique value produced by f applied to elements of itr.

allunique(itr)

Return true if all values from itr are distinct when compared with isequal.

reduce(op, v0, itr)

Reduce the given collection ìtr with the given binary operator op. v0 must be a neutral element for op that will be returned for empty collections. It is unspecified whether v0 is used for non-empty collections.

Reductions for certain commonly-used operators have special implementations which should be used instead: maximum(itr), minimum(itr), sum(itr), prod(itr), any(itr), all(itr).

The associativity of the reduction is implementation dependent. This means that you can’t use non-associative operations like - because it is undefined whether reduce(-,[1,2,3]) should be evaluated as (1-2)-3 or 1-(2-3). Use foldl or foldr instead for guaranteed left or right associativity.

Some operations accumulate error, and parallelism will also be easier if the reduction can be executed in groups. Future versions of Julia might change the algorithm. Note that the elements are not reordered if you use an ordered collection.

reduce(op, itr)

Like reduce(op, v0, itr). This cannot be used with empty collections, except for some special cases (e.g. when op is one of +, *, max, min, &, |) when Julia can determine the neutral element of op.

foldl(op, v0, itr)

Like reduce(), but with guaranteed left associativity. v0 will be used exactly once.

foldl(op, itr)

Like foldl(op, v0, itr), but using the first element of itr as v0. In general, this cannot be used with empty collections (see reduce(op, itr)).

foldr(op, v0, itr)

Like reduce(), but with guaranteed right associativity. v0 will be used exactly once.

foldr(op, itr)

Like foldr(op, v0, itr), but using the last element of itr as v0. In general, this cannot be used with empty collections (see reduce(op, itr)).

maximum(itr)

Returns the largest element in a collection.

julia> maximum(-20.5:10)
9.5

julia> maximum([1,2,3])
3

maximum(A, dims)

Compute the maximum value of an array over the given dimensions.

maximum!(r, A)

Compute the maximum value of A over the singleton dimensions of r, and write results to r.

minimum(itr)

Returns the smallest element in a collection.

julia> minimum(-20.5:10)
-20.5

julia> minimum([1,2,3])
1

minimum(A, dims)

Compute the minimum value of an array over the given dimensions.

minimum!(r, A)

Compute the minimum value of A over the singleton dimensions of r, and write results to r.

extrema(itr) → Tuple

Compute both the minimum and maximum element in a single pass, and return them as a 2-tuple.

julia> extrema(2:10)
(2,10)

julia> extrema([9,pi,4.5])
(3.141592653589793,9.0)

extrema(A, dims) → Array{Tuple}

Compute the minimum and maximum elements of an array over the given dimensions.

indmax(itr) → Integer

Returns the index of the maximum element in a collection. The collection must not be empty.

julia> indmax([8,0.1,-9,pi])
1

indmin(itr) → Integer

Returns the index of the minimum element in a collection. The collection must not be empty.

julia> indmin([8,0.1,-9,pi])
3

findmax(itr) → (x, index)

Returns the maximum element and its index. The collection must not be empty.

julia> findmax([8,0.1,-9,pi])
(8.0,1)

findmax(A, region) → (maxval, index)

For an array input, returns the value and index of the maximum over the given region.

findmin(itr) → (x, index)

Returns the minimum element and its index. The collection must not be empty.

julia> findmin([8,0.1,-9,pi])
(-9.0,3)

findmin(A, region) → (minval, index)

For an array input, returns the value and index of the minimum over the given region.

findmax!(rval, rind, A[, init=true]) → (maxval, index)

Find the maximum of A and the corresponding linear index along singleton dimensions of rval and rind, and store the results in rval and rind.

findmin!(rval, rind, A[, init=true]) → (minval, index)

Find the minimum of A and the corresponding linear index along singleton dimensions of rval and rind, and store the results in rval and rind.

maxabs(itr)

Compute the maximum absolute value of a collection of values.

julia> maxabs([-1, 3, 4*im])
4.0

maxabs(A, dims)

Compute the maximum absolute values over given dimensions.

maxabs!(r, A)

Compute the maximum absolute values over the singleton dimensions of r, and write values to r.

minabs(itr)

Compute the minimum absolute value of a collection of values.

julia> minabs([-1, 3, 4*im])
1.0

minabs(A, dims)

Compute the minimum absolute values over given dimensions.

minabs!(r, A)

Compute the minimum absolute values over the singleton dimensions of r, and write values to r.

sum(itr)

Returns the sum of all elements in a collection.

sum(A, dims)

Sum elements of an array over the given dimensions.

sum!(r, A)

Sum elements of A over the singleton dimensions of r, and write results to r.

sum(f, itr)

Sum the results of calling function f on each element of itr.

sumabs(itr)

Sum absolute values of all elements in a collection. This is equivalent to sum(abs(itr)) but faster.

sumabs(A, dims)

Sum absolute values of elements of an array over the given dimensions.

sumabs!(r, A)

Sum absolute values of elements of A over the singleton dimensions of r, and write results to r.

sumabs2(itr)

Sum squared absolute values of all elements in a collection. This is equivalent to sum(abs2(itr)) but faster.

sumabs2(A, dims)

Sum squared absolute values of elements of an array over the given dimensions.

sumabs2!(r, A)

Sum squared absolute values of elements of A over the singleton dimensions of r, and write results to r.

prod(itr)

Returns the product of all elements of a collection.

prod(A, dims)

Multiply elements of an array over the given dimensions.

prod!(r, A)

Multiply elements of A over the singleton dimensions of r, and write results to r.

any(itr) → Bool

Test whether any elements of a boolean collection are true.

any(A, dims)

Test whether any values along the given dimensions of an array are true.

any!(r, A)

Test whether any values in A along the singleton dimensions of r are true, and write results to r.

all(itr) → Bool

Test whether all elements of a boolean collection are true.

all(A, dims)

Test whether all values along the given dimensions of an array are true.

all!(r, A)

Test whether all values in A along the singleton dimensions of r are true, and write results to r.

count(p, itr) → Integer

Count the number of elements in itr for which predicate p returns true.

julia> count(i->(4<=i<=6), [2,3,4,5,6])
3

any(p, itr) → Bool

Determine whether predicate p returns true for any elements of itr.

julia> any(i->(4<=i<=6), [3,5,7])
true

all(p, itr) → Bool

Determine whether predicate p returns true for all elements of itr.

julia> all(i->(4<=i<=6), [4,5,6])
true

foreach(f, c...) → Void

Call function f on each element of iterable c. For multiple iterable arguments, f is called elementwise. foreach should be used instead of map when the results of f are not needed, for example in foreach(println, array).

julia> a = 1:3:7;

julia> foreach(x->println(x^2),a)
1
16
49

map(f, c...) → collection

Transform collection c by applying f to each element. For multiple collection arguments, apply f elementwise.

julia> map((x) -> x * 2, [1, 2, 3])
3-element Array{Int64,1}:
 2
 4
 6

julia> map(+, [1, 2, 3], [10, 20, 30])
3-element Array{Int64,1}:
 11
 22
 33

map!(function, collection)

In-place version of map().

map!(function, destination, collection...)

Like map(), but stores the result in destination rather than a new collection. destination must be at least as large as the first collection.

mapreduce(f, op, v0, itr)

Apply function f to each element in itr, and then reduce the result using the binary function op. v0 must be a neutral element for op that will be returned for empty collections. It is unspecified whether v0 is used for non-empty collections.

mapreduce() is functionally equivalent to calling reduce(op, v0, map(f, itr)), but will in general execute faster since no intermediate collection needs to be created. See documentation for reduce() and map().

julia> mapreduce(x->x^2, +, [1:3;]) # == 1 + 4 + 9
14

The associativity of the reduction is implementation-dependent. Additionally, some implementations may reuse the return value of f for elements that appear multiple times in itr. Use mapfoldl() or mapfoldr() instead for guaranteed left or right associativity and invocation of f for every value.

mapreduce(f, op, itr)

Like mapreduce(f, op, v0, itr). In general, this cannot be used with empty collections (see reduce(op, itr)).

mapfoldl(f, op, v0, itr)

Like mapreduce(), but with guaranteed left associativity. v0 will be used exactly once.

mapfoldl(f, op, itr)

Like mapfoldl(f, op, v0, itr), but using the first element of itr as v0. In general, this cannot be used with empty collections (see reduce(op, itr)).

mapfoldr(f, op, v0, itr)

Like mapreduce(), but with guaranteed right associativity. v0 will be used exactly once.

mapfoldr(f, op, itr)

Like mapfoldr(f, op, v0, itr), but using the first element of itr as v0. In general, this cannot be used with empty collections (see reduce(op, itr)).

first(coll)

Get the first element of an iterable collection. Returns the start point of a Range even if it is empty.

last(coll)

Get the last element of an ordered collection, if it can be computed in O(1) time. This is accomplished by calling endof() to get the last index. Returns the end point of a Range even if it is empty.

step(r)

Get the step size of a Range object.

julia> step(1:10)
1

julia> step(1:2:10)
2

julia> step(2.5:0.3:10.9)
0.3

julia> step(linspace(2.5,10.9,85))
0.1

collect(collection)

Return an Array of all items in a collection or iterator. For associative collections, returns Pair{KeyType, ValType}. If the argument is array-like or is an iterator with the HasShape() trait, the result will have the same shape and number of dimensions as the argument.

collect(element_type, collection)

Return an Array with the given element type of all items in a collection or iterable. The result has the same shape and number of dimensions as collection.

issubset(a, b)

⊆(a, b) → Bool

⊈(a, b) → Bool

⊊(a, b) → Bool

Determine whether every element of a is also in b, using in().

filter(function, collection)

Return a copy of collection, removing elements for which function is false. For associative collections, the function is passed two arguments (key and value).

julia> a = 1:10
1:10

julia> filter(isodd, a)
5-element Array{Int64,1}:
 1
 3
 5
 7
 9

filter!(function, collection)

Update collection, removing elements for which function is false. For associative collections, the function is passed two arguments (key and value).

Indexable Collections

getindex(collection, key...)

Retrieve the value(s) stored at the given key or index within a collection. The syntax a[i,j,...] is converted by the compiler to getindex(a, i, j, ...).

setindex!(collection, value, key...)

Store the given value at the given key or index within a collection. The syntax a[i,j,...] = x is converted by the compiler to (setindex!(a, x, i, j, ...); x).

Fully implemented by:

• Array
• BitArray
• AbstractArray
• SubArray
• ObjectIdDict
• Dict
• WeakKeyDict
• AbstractString

Partially implemented by:

• Range
• UnitRange
• Tuple

Associative Collections

Dict is the standard associative collection. Its implementation uses hash() as the hashing function for the key, and isequal() to determine equality. Define these two functions for custom types to override how they are stored in a hash table.

ObjectIdDict is a special hash table where the keys are always object identities.

WeakKeyDict is a hash table implementation where the keys are weak references to objects, and thus may be garbage collected even when referenced in a hash table.

Dicts can be created by passing pair objects constructed with =>() to a Dict constructor: Dict("A"=>1, "B"=>2). This call will attempt to infer type information from the keys and values (i.e. this example creates a Dict{String, Int64}). To explicitly specify types use the syntax Dict{KeyType,ValueType}(...). For example, Dict{String,Int32}("A"=>1, "B"=>2).

Dicts may also be created with generators. For example, Dict(i => f(i) for i = 1:10).

Given a dictionary D, the syntax D[x] returns the value of key x (if it exists) or throws an error, and D[x] = y stores the key-value pair x => y in D (replacing any existing value for the key x). Multiple arguments to D[...] are converted to tuples; for example, the syntax D[x,y] is equivalent to D[(x,y)], i.e. it refers to the value keyed by the tuple (x,y).

Dict([itr])

Dict{K,V}() constructs a hash table with keys of type K and values of type V.

Given a single iterable argument, constructs a Dict whose key-value pairs are taken from 2-tuples (key,value) generated by the argument.

julia> Dict([("A", 1), ("B", 2)])
Dict{String,Int64} with 2 entries:
  "B" => 2
  "A" => 1

Alternatively, a sequence of pair arguments may be passed.

julia> Dict("A"=>1, "B"=>2)
Dict{String,Int64} with 2 entries:
  "B" => 2
  "A" => 1

haskey(collection, key) → Bool

Determine whether a collection has a mapping for a given key.

get(collection, key, default)

Return the value stored for the given key, or the given default value if no mapping for the key is present.

get(f::Function, collection, key)

Return the value stored for the given key, or if no mapping for the key is present, return f(). Use get!() to also store the default value in the dictionary.

This is intended to be called using do block syntax

get(dict, key) do
    # default value calculated here
    time()
end

get!(collection, key, default)

Return the value stored for the given key, or if no mapping for the key is present, store key => default, and return default.

get!(f::Function, collection, key)

Return the value stored for the given key, or if no mapping for the key is present, store key => f(), and return f().

This is intended to be called using do block syntax:

get!(dict, key) do
    # default value calculated here
    time()
end

getkey(collection, key, default)

Return the key matching argument key if one exists in collection, otherwise return default.

delete!(collection, key)

Delete the mapping for the given key in a collection, and return the collection.

pop!(collection, key[, default])

Delete and return the mapping for key if it exists in collection, otherwise return default, or throw an error if default is not specified.

keys(collection)

Return an iterator over all keys in a collection. collect(keys(d)) returns an array of keys.

values(collection)

Return an iterator over all values in a collection. collect(values(d)) returns an array of values.

merge(collection, others...)

Construct a merged collection from the given collections. If necessary, the types of the resulting collection will be promoted to accommodate the types of the merged collections. If the same key is present in another collection, the value for that key will be the value it has in the last collection listed.

julia> a = Dict("foo" => 0.0, "bar" => 42.0)
Dict{String,Float64} with 2 entries:
  "bar" => 42.0
  "foo" => 0.0

julia> b = Dict("baz" => 17, "bar" => 4711)
Dict{String,Int64} with 2 entries:
  "bar" => 4711
  "baz" => 17

julia> merge(a, b)
Dict{String,Float64} with 3 entries:
  "bar" => 4711.0
  "baz" => 17.0
  "foo" => 0.0

julia> merge(b, a)
Dict{String,Float64} with 3 entries:
  "bar" => 42.0
  "baz" => 17.0
  "foo" => 0.0

merge!(collection, others...)

Update collection with pairs from the other collections.

sizehint!(s, n)

Suggest that collection s reserve capacity for at least n elements. This can improve performance.

keytype(type)

Get the key type of an associative collection type. Behaves similarly to eltype.

valtype(type)

Get the value type of an associative collection type. Behaves similarly to eltype.

Fully implemented by:

• ObjectIdDict
• Dict
• WeakKeyDict

Partially implemented by:

• IntSet
• Set
• EnvHash
• Array
• BitArray

Set-Like Collections

Set([itr])

Construct a Set of the values generated by the given iterable object, or an empty set. Should be used instead of IntSet for sparse integer sets, or for sets of arbitrary objects.

IntSet([itr])

Construct a sorted set of positive Ints generated by the given iterable object, or an empty set. Implemented as a bit string, and therefore designed for dense integer sets. Only Ints greater than 0 can be stored. If the set will be sparse (for example holding a few very large integers), use Set instead.

union(s1, s2...)

∪(s1, s2...)

Construct the union of two or more sets. Maintains order with arrays.

union!(s, iterable)

Union each element of iterable into set s in-place.

intersect(s1, s2...)

∩(s1, s2)

Construct the intersection of two or more sets. Maintains order and multiplicity of the first argument for arrays and ranges.

setdiff(a, b)

Construct the set of elements in a but not b. Maintains order with arrays. Note that both arguments must be collections, and both will be iterated over. In particular, setdiff(set,element) where element is a potential member of set, will not work in general.

julia> setdiff([1,2,3],[3,4,5])
2-element Array{Int64,1}:
 1
 2

setdiff!(s, iterable)

Remove each element of iterable from set s in-place.

symdiff(a, b, rest...)

Construct the symmetric difference of elements in the passed in sets or arrays. Maintains order with arrays.

julia> symdiff([1,2,3],[3,4,5],[4,5,6])
3-element Array{Int64,1}:
 1
 2
 6

symdiff!(s, n)

The set s is destructively modified to toggle the inclusion of integer n.

symdiff!(s, itr)

For each element in itr, destructively toggle its inclusion in set s.

symdiff!(s1, s2)

Construct the symmetric difference of sets s1 and s2, storing the result in s1.

intersect!(s1, s2)

Intersects sets s1 and s2 and overwrites the set s1 with the result. If needed, s1 will be expanded to the size of s2.

issubset(A, S) → Bool

⊆(A, S) → Bool

Return true if A is a subset of or equal to S.

Fully implemented by:

• IntSet
• Set

Partially implemented by:

• Array

Dequeues

push!(collection, items...) → collection

Insert one or more items at the end of collection.

julia> push!([1, 2, 3], 4, 5, 6)
6-element Array{Int64,1}:
 1
 2
 3
 4
 5
 6

Use append!() to add all the elements of another collection to collection. The result of the preceding example is equivalent to append!([1, 2, 3], [4, 5, 6]).

pop!(collection) → item

Remove the last item in collection and return it.

julia> A=[1, 2, 3, 4, 5, 6]
6-element Array{Int64,1}:
 1
 2
 3
 4
 5
 6

julia> pop!(A)
6

julia> A
5-element Array{Int64,1}:
 1
 2
 3
 4
 5

unshift!(collection, items...) → collection

Insert one or more items at the beginning of collection.

julia> unshift!([1, 2, 3, 4], 5, 6)
6-element Array{Int64,1}:
 5
 6
 1
 2
 3
 4

shift!(collection) → item

Remove the first item from collection.

julia> A = [1, 2, 3, 4, 5, 6]
6-element Array{Int64,1}:
 1
 2
 3
 4
 5
 6

julia> shift!(A)
1

julia> A
5-element Array{Int64,1}:
 2
 3
 4
 5
 6

insert!(collection, index, item)

Insert an item into collection at the given index. index is the index of item in the resulting collection.

julia> insert!([6, 5, 4, 2, 1], 4, 3)
6-element Array{Int64,1}:
 6
 5
 4
 3
 2
 1

deleteat!(a::Vector, i::Integer)

Remove the item at the given i and return the modified a. Subsequent items are shifted to fill the resulting gap.

julia> deleteat!([6, 5, 4, 3, 2, 1], 2)
5-element Array{Int64,1}:
 6
 4
 3
 2
 1

deleteat!(a::Vector, inds)

Remove the items at the indices given by inds, and return the modified a. Subsequent items are shifted to fill the resulting gap. inds must be sorted and unique.

julia> deleteat!([6, 5, 4, 3, 2, 1], 1:2:5)
3-element Array{Int64,1}:
 5
 3
 1

julia> deleteat!([6, 5, 4, 3, 2, 1], (2, 2))
ERROR: ArgumentError: indices must be unique and sorted
 in deleteat!(::Array{Int64,1}, ::Tuple{Int64,Int64}) at ./array.jl:614
 ...

splice!(collection, index[, replacement]) → item

Remove the item at the given index, and return the removed item. Subsequent items are shifted down to fill the resulting gap. If specified, replacement values from an ordered collection will be spliced in place of the removed item.

julia> A = [6, 5, 4, 3, 2, 1]; splice!(A, 5)
2

julia> A
5-element Array{Int64,1}:
 6
 5
 4
 3
 1

julia> splice!(A, 5, -1)
1

julia> A
5-element Array{Int64,1}:
  6
  5
  4
  3
 -1

julia> splice!(A, 1, [-1, -2, -3])
6

julia> A
7-element Array{Int64,1}:
 -1
 -2
 -3
  5
  4
  3
 -1

To insert replacement before an index n without removing any items, use splice!(collection, n:n-1, replacement).

splice!(collection, range[, replacement]) → items

Remove items in the specified index range, and return a collection containing the removed items. Subsequent items are shifted down to fill the resulting gap. If specified, replacement values from an ordered collection will be spliced in place of the removed items.

To insert replacement before an index n without removing any items, use splice!(collection, n:n-1, replacement).

julia> splice!(A, 4:3, 2)
0-element Array{Int64,1}

julia> A
8-element Array{Int64,1}:
 -1
 -2
 -3
  2
  5
  4
  3
 -1

resize!(collection, n) → collection

Resize collection to contain n elements. If n is smaller than the current collection length, the first n elements will be retained. If n is larger, the new elements are not guaranteed to be initialized.

julia> resize!([6, 5, 4, 3, 2, 1], 3)
3-element Array{Int64,1}:
 6
 5
 4

julia> resize!([6, 5, 4, 3, 2, 1], 8)
8-element Array{Int64,1}:
 6
 5
 4
 3
 2
 1
 0
 0

append!(collection, collection2) → collection.

Add the elements of collection2 to the end of collection.

julia> append!([1],[2,3])
3-element Array{Int64,1}:
 1
 2
 3

julia> append!([1, 2, 3], [4, 5, 6])
6-element Array{Int64,1}:
 1
 2
 3
 4
 5
 6

Use push!() to add individual items to collection which are not already themselves in another collection. The result is of the preceding example is equivalent to push!([1, 2, 3], 4, 5, 6).

prepend!(collection, items) → collection

Insert the elements of items to the beginning of collection.

julia> prepend!([3],[1,2])
3-element Array{Int64,1}:
 1
 2
 3

Fully implemented by:

• Vector (a.k.a. 1-dimensional Array)
• BitVector (a.k.a. 1-dimensional BitArray)

PriorityQueue

The PriorityQueue type is available from the Collections module. It provides a basic priority queue implementation allowing for arbitrary key and priority types. Multiple identical keys are not permitted, but the priority of existing keys can be changed efficiently.

PriorityQueue(K, V[, ord])

Construct a new PriorityQueue, with keys of type K and values/priorites of type V. If an order is not given, the priority queue is min-ordered using the default comparison for V.

A PriorityQueue acts like a Dict, mapping values to their priorities, with the addition of a dequeue! function to remove the lowest priority element.

julia> a = Base.Collections.PriorityQueue(["a","b","c"],[2,3,1],Base.Order.Forward)
Base.Collections.PriorityQueue{String,Int64,Base.Order.ForwardOrdering} with 3 entries:
  "c" => 1
  "b" => 3
  "a" => 2

enqueue!(pq, k, v)

Insert the a key k into a priority queue pq with priority v.

julia> a = Base.Collections.PriorityQueue(["a","b","c"],[2,3,1],Base.Order.Forward)
Base.Collections.PriorityQueue{String,Int64,Base.Order.ForwardOrdering} with 3 entries:
  "c" => 1
  "b" => 3
  "a" => 2

julia> Base.Collections.enqueue!(a, "d", 4)
Base.Collections.PriorityQueue{String,Int64,Base.Order.ForwardOrdering} with 4 entries:
  "c" => 1
  "b" => 3
  "a" => 2
  "d" => 4

dequeue!(pq)

Remove and return the lowest priority key from a priority queue.

julia> a = Base.Collections.PriorityQueue(["a","b","c"],[2,3,1],Base.Order.Forward)
Base.Collections.PriorityQueue{String,Int64,Base.Order.ForwardOrdering} with 3 entries:
  "c" => 1
  "b" => 3
  "a" => 2

julia> Base.Collections.dequeue!(a)
"c"

julia> a
Base.Collections.PriorityQueue{String,Int64,Base.Order.ForwardOrdering} with 2 entries:
  "b" => 3
  "a" => 2

peek(pq)

Return the lowest priority key from a priority queue without removing that key from the queue.

PriorityQueue also behaves similarly to a Dict in that keys can be inserted and priorities accessed or changed using indexing notation.

julia> # Julia code
       pq = Collections.PriorityQueue();

julia> # Insert keys with associated priorities
       pq["a"] = 10; pq["b"] = 5; pq["c"] = 15; pq
Base.Collections.PriorityQueue{Any,Any,Base.Order.ForwardOrdering} with 3 entries:
  "c" => 15
  "b" => 5
  "a" => 10

julia> # Change the priority of an existing key
       pq["a"] = 0; pq
Base.Collections.PriorityQueue{Any,Any,Base.Order.ForwardOrdering} with 3 entries:
  "c" => 15
  "b" => 5
  "a" => 0

Heap Functions

Along with the PriorityQueue type, the Collections module provides lower level functions for performing binary heap operations on arrays. Each function takes an optional ordering argument. If not given, default ordering is used, so that elements popped from the heap are given in ascending order.

heapify(v, ord::Ordering=Forward)

Returns a new vector in binary heap order, optionally using the given ordering.

julia> a = [1,3,4,5,2];

julia> Base.Collections.heapify(a)
5-element Array{Int64,1}:
 1
 2
 4
 5
 3

julia> Base.Collections.heapify(a, Base.Order.Reverse)
5-element Array{Int64,1}:
 5
 3
 4
 1
 2

heapify!(v, ord::Ordering=Forward)

In-place heapify().

isheap(v, ord::Ordering=Forward)

Return true if an array is heap-ordered according to the given order.

julia> a = [1,2,3]
3-element Array{Int64,1}:
 1
 2
 3

julia> Base.Collections.isheap(a,Base.Order.Forward)
true

julia> Base.Collections.isheap(a,Base.Order.Reverse)
false

heappush!(v, x[, ord])

Given a binary heap-ordered array, push a new element x, preserving the heap property. For efficiency, this function does not check that the array is indeed heap-ordered.

heappop!(v[, ord])

Given a binary heap-ordered array, remove and return the lowest ordered element. For efficiency, this function does not check that the array is indeed heap-ordered.