Collections and Data Structures · The Julia Language
Description: Documentation for The Julia Language.

# Collections and Data Structures

## Iteration

Sequential iteration is implemented by the `iterate` function. The general `for` loop:

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

is translated into:

    next = iterate(iter)
    while next !== nothing
        (i, state) = next
        # body
        next = iterate(iter, state)
    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.

`Base.iterate` — Function

    iterate(iter [, state]) -> Union{Nothing, Tuple{Any, Any}}

Advance the iterator to obtain the next element. If no elements remain, `nothing` should be returned. Otherwise, a 2-tuple of the next element and the new iteration state should be returned.

source
`Base.IteratorSize` — Type

    IteratorSize(itertype::Type) -> IteratorSize

Given the type of an iterator, return 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{N}()` if there is a known length plus a notion of multidimensional shape (as for an array). In this case `N` should give the number of dimensions, and the `axes` 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.

    julia> Base.IteratorSize(1:5)
    Base.HasShape{1}()

    julia> Base.IteratorSize((2,3))
    Base.HasLength()

source
`Base.IteratorEltype` — Type

    IteratorEltype(itertype::Type) -> IteratorEltype

Given the type of an iterator, return 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.

    julia> Base.IteratorEltype(1:5)
    Base.HasEltype()

source

Fully implemented by:

- `AbstractRange`
- `UnitRange`
- `Tuple`
- `Number`
- `AbstractArray`
- `BitSet`
- `IdDict`
- `Dict`
- `WeakKeyDict`
- `EachLine`
- `AbstractString`
- `Set`
- `Pair`
- `NamedTuple`

## Constructors and Types

`Base.AbstractRange` — Type

    AbstractRange{T} <: AbstractVector{T}

Supertype for linear ranges with elements of type `T`. `UnitRange`, `LinRange` and other types are subtypes of this.

All subtypes must define `step`. Thus `LogRange` is not a subtype of `AbstractRange`.

source
`Base.OrdinalRange` — Type

    OrdinalRange{T, S} <: AbstractRange{T}

Supertype for ordinal ranges with elements of type `T` with spacing(s) of type `S`. The steps should be always-exact multiples of `oneunit`, and `T` should be a "discrete" type, which cannot have values smaller than `oneunit`. For example, `Integer` or `Date` types would qualify, whereas `Float64` would not (since this type can represent values smaller than `oneunit(Float64)`. `UnitRange`, `StepRange`, and other types are subtypes of this.

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`Base.AbstractUnitRange` — Type

    AbstractUnitRange{T} <: OrdinalRange{T, T}

Supertype for ranges with a step size of `oneunit(T)` with elements of type `T`. `UnitRange` and other types are subtypes of this.

source
`Base.StepRange` — Type

    StepRange{T, S} <: OrdinalRange{T, S}

Ranges with elements of type `T` with spacing of type `S`. The step between each element is constant, and the range is defined in terms of a `start` and `stop` of type `T` and a `step` of type `S`. Neither `T` nor `S` should be floating point types. The syntax `a:b:c` with `b != 0` and `a`, `b`, and `c` all integers creates a `StepRange`.

Examples

    julia> collect(StepRange(1, Int8(2), 10))
    5-element Vector{Int64}:
     1
     3
     5
     7
     9

    julia> typeof(StepRange(1, Int8(2), 10))
    StepRange{Int64, Int8}

    julia> typeof(1:3:6)
    StepRange{Int64, Int64}

source
`Base.UnitRange` — Type

    UnitRange{T<:Real}

A range parameterized by a `start` and `stop` of type `T`, filled with elements spaced by `1` from `start` until `stop` is exceeded. The syntax `a:b` with `a` and `b` both `Integer`s creates a `UnitRange`.

Examples

    julia> collect(UnitRange(2.3, 5.2))
    3-element Vector{Float64}:
     2.3
     3.3
     4.3

    julia> typeof(1:10)
    UnitRange{Int64}

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`Base.LinRange` — Type

    LinRange{T,L}

A range with `len` linearly spaced elements between its `start` and `stop`. The size of the spacing is controlled by `len`, which must be an `Integer`.

Examples

    julia> LinRange(1.5, 5.5, 9)
    9-element LinRange{Float64, Int64}:
     1.5, 2.0, 2.5, 3.0, 3.5, 4.0, 4.5, 5.0, 5.5

Compared to using `range`, directly constructing a `LinRange` should have less overhead but won't try to correct for floating point errors:

    julia> collect(range(-0.1, 0.3, length=5))
    5-element Vector{Float64}:
     -0.1
      0.0
      0.1
      0.2
      0.3

    julia> collect(LinRange(-0.1, 0.3, 5))
    5-element Vector{Float64}:
     -0.1
     -1.3877787807814457e-17
      0.09999999999999999
      0.19999999999999998
      0.3

See also `Logrange` for logarithmically spaced points.

source

## General Collections

`Base.isempty` — Function

    isempty(condition)

Return `true` if no tasks are waiting on the condition, `false` otherwise.

source

    isempty(collection) -> Bool

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

Warning

`isempty(itr)` may consume the next element of a stateful iterator `itr` unless an appropriate `Base.isdone(itr)` method is defined. Stateful iterators should implement `isdone`, but you may want to avoid using `isempty` when writing generic code which should support any iterator type.

Examples

    julia> isempty([])
    true

    julia> isempty([1 2 3])
    false

source
`Base.isdone` — Function

    isdone(itr, [state]) -> Union{Bool, Missing}

This function provides a fast-path hint for iterator completion. This is useful for stateful iterators that want to avoid having elements consumed if they are not going to be exposed to the user (e.g. when checking for done-ness in `isempty` or `zip`).

Stateful iterators that want to opt into this feature should define an `isdone` method that returns true/false depending on whether the iterator is done or not. Stateless iterators need not implement this function.

If the result is `missing`, callers may go ahead and compute `iterate(x, state) === nothing` to compute a definite answer.

See also `iterate`, `isempty`

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`Base.empty!` — Function

    empty!(c::Channel)

Empty a Channel `c` by calling `empty!` on the internal buffer. Return the empty channel.

source

    empty!(collection) -> collection

Remove all elements from a `collection`.

Examples

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

    julia> empty!(A);

    julia> A
    Dict{String, Int64}()

source
`Base.length` — Function

    length(collection) -> Integer

Return the number of elements in the collection.

Use `lastindex` to get the last valid index of an indexable collection.

See also: `size`, `ndims`, `eachindex`.

Examples

    julia> length(1:5)
    5

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

    julia> length([1 2; 3 4])
    4

source
`Base.checked_length` — Function

    Base.checked_length(r)

Calculates `length(r)`, but may check for overflow errors where applicable when the result doesn't fit into `Union{Integer(eltype(r)),Int}`.

source

Fully implemented by:

- `AbstractRange`
- `UnitRange`
- `Tuple`
- `Number`
- `AbstractArray`
- `BitSet`
- `IdDict`
- `Dict`
- `WeakKeyDict`
- `AbstractString`
- `Set`
- `NamedTuple`

## Iterable Collections

`Base.in` — Function

    in(item, collection) -> Bool
    ∈(item, collection) -> 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. Can equivalently be used with infix syntax:

    item in collection
    item ∈ collection

Return a `Bool` value, except if `item` is `missing` or `collection` contains `missing` but not `item`, in which case `missing` is returned (three-valued logic, matching the behavior of `any` and `==`). Some collections follow a slightly different definition. For example, `Set`s check whether the item `isequal` to one of the elements; `Dict`s 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)`. For the collections mentioned above, the result is always a `Bool`.

When broadcasting with `in.(items, collection)` or `items .∈ collection`, both `items` and `collection` are broadcasted over, which is often not what is intended. For example, if both arguments are vectors (and the dimensions match), the result is a vector indicating whether each value in collection `items` is `in` the value at the corresponding position in `collection`. To get a vector indicating whether each value in `items` is in `collection`, wrap `collection` in a tuple or a `Ref` like this: `in.(items, Ref(collection))` or `items .∈ Ref(collection)`.

See also: `∉`, `insorted`, `contains`, `occursin`, `issubset`.

Examples

    julia> a = 1:3:20
    1:3:19

    julia> 4 in a
    true

    julia> 5 in a
    false

    julia> missing in [1, 2]
    missing

    julia> 1 in [2, missing]
    missing

    julia> 1 in [1, missing]
    true

    julia> missing in Set([1, 2])
    false

    julia> (1=>missing) in Dict(1=>10, 2=>20)
    missing

    julia> [1, 2] .∈ [2, 3]
    2-element BitVector:
     0
     0

    julia> [1, 2] .∈ ([2, 3],)
    2-element BitVector:
     0
     1

source
`Base.:∉` — Function

    ∉(item, collection) -> Bool
    ∌(collection, item) -> Bool

Negation of `∈` and `∋`, i.e. checks that `item` is not in `collection`.

When broadcasting with `items .∉ collection`, both `items` and `collection` are broadcasted over, which is often not what is intended. For example, if both arguments are vectors (and the dimensions match), the result is a vector indicating whether each value in collection `items` is not in the value at the corresponding position in `collection`. To get a vector indicating whether each value in `items` is not in `collection`, wrap `collection` in a tuple or a `Ref` like this: `items .∉ Ref(collection)`.

Examples

    julia> 1 ∉ 2:4
    true

    julia> 1 ∉ 1:3
    false

    julia> [1, 2] .∉ [2, 3]
    2-element BitVector:
     1
     1

    julia> [1, 2] .∉ ([2, 3],)
    2-element BitVector:
     1
     0

source
`Base.hasfastin` — Function

    Base.hasfastin(T)

Determine whether the computation `x ∈ collection` where `collection::T` can be considered as a "fast" operation (typically constant or logarithmic complexity). The definition `hasfastin(x) = hasfastin(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.

The default for `hasfastin(T)` is `true` for subtypes of `AbstractSet`, `AbstractDict` and `AbstractRange` and `false` otherwise.

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`Base.eltype` — Function

    eltype(type)

Determine the type of the elements generated by iterating a collection of the given `type`. For dictionary 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.

See also: `keytype`, `typeof`.

Examples

    julia> eltype(fill(1f0, (2,2)))
    Float32

    julia> eltype(fill(0x1, (2,2)))
    UInt8

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`Base.indexin` — Function

    indexin(a, b)

Return an array containing the first index in `b` for each value in `a` that is a member of `b`. The output array contains `nothing` wherever `a` is not a member of `b`.

See also: `sortperm`, `findfirst`.

Examples

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

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

    julia> indexin(a, b)
    6-element Vector{Union{Nothing, Int64}}:
     1
     2
     3
     2
      nothing
     1

    julia> indexin(b, a)
    3-element Vector{Union{Nothing, Int64}}:
     1
     2
     3

source
`Base.unique` — Function

    unique(A::AbstractArray; dims::Int)

Return unique regions of `A` along dimension `dims`.

Examples

    julia> A = map(isodd, reshape(Vector(1:8), (2,2,2)))
    2×2×2 Array{Bool, 3}:
    [:, :, 1] =
     1  1
     0  0

    [:, :, 2] =
     1  1
     0  0

    julia> unique(A)
    2-element Vector{Bool}:
     1
     0

    julia> unique(A, dims=2)
    2×1×2 Array{Bool, 3}:
    [:, :, 1] =
     1
     0

    [:, :, 2] =
     1
     0

    julia> unique(A, dims=3)
    2×2×1 Array{Bool, 3}:
    [:, :, 1] =
     1  1
     0  0

source

    unique(f, itr)

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

Examples

    julia> unique(x -> x^2, [1, -1, 3, -3, 4])
    3-element Vector{Int64}:
     1
     3
     4

This functionality can also be used to extract the indices of the first occurrences of unique elements in an array:

    julia> a = [3.1, 4.2, 5.3, 3.1, 3.1, 3.1, 4.2, 1.7];

    julia> i = unique(i -> a[i], eachindex(a))
    4-element Vector{Int64}:
     1
     2
     3
     8

    julia> a[i]
    4-element Vector{Float64}:
     3.1
     4.2
     5.3
     1.7

    julia> a[i] == unique(a)
    true

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    unique(itr)

Return an array containing only the unique elements of collection `itr`, as determined by `isequal` and `hash`, in the order that the first of each set of equivalent elements originally appears. The element type of the input is preserved.

See also: `unique!`, `allunique`, `allequal`.

Examples

    julia> unique([1, 2, 6, 2])
    3-element Vector{Int64}:
     1
     2
     6

    julia> unique(Real[1, 1.0, 2])
    2-element Vector{Real}:
     1
     2

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`Base.unique!` — Function

    unique!(A::AbstractVector)

Remove duplicate items as determined by `isequal` and `hash`, then return the modified `A`. `unique!` will return the elements of `A` in the order that they occur. If you do not care about the order of the returned data, then calling `(sort!(A); unique!(A))` will be much more efficient as long as the elements of `A` can be sorted.

Examples

    julia> unique!([1, 1, 1])
    1-element Vector{Int64}:
     1

    julia> A = [7, 3, 2, 3, 7, 5];

    julia> unique!(A)
    4-element Vector{Int64}:
     7
     3
     2
     5

    julia> B = [7, 6, 42, 6, 7, 42];

    julia> sort!(B);  # unique! is able to process sorted data much more efficiently.

    julia> unique!(B)
    3-element Vector{Int64}:
      6
      7
     42

source

    unique!(f, A::AbstractVector)

Selects one value from `A` for each unique value produced by `f` applied to elements of `A`, then return the modified A.

Julia 1.1

This method is available as of Julia 1.1.

Examples

    julia> unique!(x -> x^2, [1, -1, 3, -3, 4])
    3-element Vector{Int64}:
     1
     3
     4

    julia> unique!(n -> n%3, [5, 1, 8, 9, 3, 4, 10, 7, 2, 6])
    3-element Vector{Int64}:
     5
     1
     9

    julia> unique!(iseven, [2, 3, 5, 7, 9])
    2-element Vector{Int64}:
     2
     3

source
`Base.allunique` — Function

    allunique(itr) -> Bool
    allunique(f, itr) -> Bool

Return `true` if all values from `itr` are distinct when compared with `isequal`. Or if all of `[f(x) for x in itr]` are distinct, for the second method.

Note that `allunique(f, itr)` may call `f` fewer than `length(itr)` times. The precise number of calls is regarded as an implementation detail.

`allunique` may use a specialized implementation when the input is sorted.

See also: `unique`, `issorted`, `allequal`.

Julia 1.11

The method `allunique(f, itr)` requires at least Julia 1.11.

Examples

    julia> allunique([1, 2, 3])
    true

    julia> allunique([1, 2, 1, 2])
    false

    julia> allunique(Real[1, 1.0, 2])
    false

    julia> allunique([NaN, 2.0, NaN, 4.0])
    false

    julia> allunique(abs, [1, -1, 2])
    false

source
`Base.allequal` — Function

    allequal(itr) -> Bool
    allequal(f, itr) -> Bool

Return `true` if all values from `itr` are equal when compared with `isequal`. Or if all of `[f(x) for x in itr]` are equal, for the second method.

Note that `allequal(f, itr)` may call `f` fewer than `length(itr)` times. The precise number of calls is regarded as an implementation detail.

See also: `unique`, `allunique`.

Julia 1.8

The `allequal` function requires at least Julia 1.8.

Julia 1.11

The method `allequal(f, itr)` requires at least Julia 1.11.

Examples

    julia> allequal([])
    true

    julia> allequal([1])
    true

    julia> allequal([1, 1])
    true

    julia> allequal([1, 2])
    false

    julia> allequal(Dict(:a => 1, :b => 1))
    false

    julia> allequal(abs2, [1, -1])
    true

source
`Base.reduce` — Method

    reduce(op, itr; [init])

Reduce the given collection `itr` with the given binary operator `op`. If provided, the initial value `init` must be a neutral element for `op` that will be returned for empty collections. It is unspecified whether `init` is used for non-empty collections.

For empty collections, providing `init` will be necessary, except for some special cases (e.g. when `op` is one of `+`, `*`, `max`, `min`, `&`, `|`) when Julia can determine the neutral element of `op`.

Reductions for certain commonly-used operators may have special implementations, and should be used instead: `maximum``(itr)`, `minimum``(itr)`, `sum``(itr)`, `prod``(itr)`, `any``(itr)`, `all``(itr)`. There are efficient methods for concatenating certain arrays of arrays by calling `reduce(``vcat``, arr)` or `reduce(``hcat``, arr)`.

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. Parallelism will 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.

Examples

    julia> reduce(*, [2; 3; 4])
    24

    julia> reduce(*, Int[]; init=1)
    1

source
`Base.reduce` — Method

    reduce(f, A::AbstractArray; dims=:, [init])

Reduce 2-argument function `f` along dimensions of `A`. `dims` is a vector specifying the dimensions to reduce, and the keyword argument `init` is the initial value to use in the reductions. For `+`, `*`, `max` and `min` the `init` argument is optional.

The associativity of the reduction is implementation-dependent; if you need a particular associativity, e.g. left-to-right, you should write your own loop or consider using `foldl` or `foldr`. See documentation for `reduce`.

Examples

    julia> a = reshape(Vector(1:16), (4,4))
    4×4 Matrix{Int64}:
     1  5   9  13
     2  6  10  14
     3  7  11  15
     4  8  12  16

    julia> reduce(max, a, dims=2)
    4×1 Matrix{Int64}:
     13
     14
     15
     16

    julia> reduce(max, a, dims=1)
    1×4 Matrix{Int64}:
     4  8  12  16

source
`Base.foldl` — Method

    foldl(op, itr; [init])

Like `reduce`, but with guaranteed left associativity. If provided, the keyword argument `init` will be used exactly once. In general, it will be necessary to provide `init` to work with empty collections.

See also `mapfoldl`, `foldr`, `accumulate`.

Examples

    julia> foldl(=>, 1:4)
    ((1 => 2) => 3) => 4

    julia> foldl(=>, 1:4; init=0)
    (((0 => 1) => 2) => 3) => 4

    julia> accumulate(=>, (1,2,3,4))
    (1, 1 => 2, (1 => 2) => 3, ((1 => 2) => 3) => 4)

source
`Base.foldr` — Method

    foldr(op, itr; [init])

Like `reduce`, but with guaranteed right associativity. If provided, the keyword argument `init` will be used exactly once. In general, it will be necessary to provide `init` to work with empty collections.

Examples

    julia> foldr(=>, 1:4)
    1 => (2 => (3 => 4))

    julia> foldr(=>, 1:4; init=0)
    1 => (2 => (3 => (4 => 0)))

source
`Base.maximum` — Function

    maximum(f, A::AbstractArray; dims)

Compute the maximum value by calling the function `f` on each element of an array over the given dimensions.

Examples

    julia> A = [1 2; 3 4]
    2×2 Matrix{Int64}:
     1  2
     3  4

    julia> maximum(abs2, A, dims=1)
    1×2 Matrix{Int64}:
     9  16

    julia> maximum(abs2, A, dims=2)
    2×1 Matrix{Int64}:
      4
     16

source

    maximum(A::AbstractArray; dims)

Compute the maximum value of an array over the given dimensions. See also the `max(a,b)` function to take the maximum of two or more arguments, which can be applied elementwise to arrays via `max.(a,b)`.

See also: `maximum!`, `extrema`, `findmax`, `argmax`.

Examples

    julia> A = [1 2; 3 4]
    2×2 Matrix{Int64}:
     1  2
     3  4

    julia> maximum(A, dims=1)
    1×2 Matrix{Int64}:
     3  4

    julia> maximum(A, dims=2)
    2×1 Matrix{Int64}:
     2
     4

source

    maximum(itr; [init])

Return the largest element in a collection.

The value returned for empty `itr` can be specified by `init`. It must be a neutral element for `max` (i.e. which is less than or equal to any other element) as it is unspecified whether `init` is used for non-empty collections.

Julia 1.6

Keyword argument `init` requires Julia 1.6 or later.

Examples

    julia> maximum(-20.5:10)
    9.5

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

    julia> maximum(())
    ERROR: ArgumentError: reducing over an empty collection is not allowed; consider supplying `init` to the reducer
    Stacktrace:
    [...]

    julia> maximum((); init=-Inf)
    -Inf

source

    maximum(f, itr; [init])

Return the largest result of calling function `f` on each element of `itr`.

The value returned for empty `itr` can be specified by `init`. It must be a neutral element for `max` (i.e. which is less than or equal to any other element) as it is unspecified whether `init` is used for non-empty collections.

Julia 1.6

Keyword argument `init` requires Julia 1.6 or later.

Examples

    julia> maximum(length, ["Julion", "Julia", "Jule"])
    6

    julia> maximum(length, []; init=-1)
    -1

    julia> maximum(sin, Real[]; init=-1.0)  # good, since output of sin is >= -1
    -1.0

source
`Base.maximum!` — Function

    maximum!(r, A)

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

Warning

Behavior can be unexpected when any mutated argument shares memory with any other argument.

Examples

    julia> A = [1 2; 3 4]
    2×2 Matrix{Int64}:
     1  2
     3  4

    julia> maximum!([1; 1], A)
    2-element Vector{Int64}:
     2
     4

    julia> maximum!([1 1], A)
    1×2 Matrix{Int64}:
     3  4

source
`Base.minimum` — Function

    minimum(f, A::AbstractArray; dims)

Compute the minimum value by calling the function `f` on each element of an array over the given dimensions.

Examples

    julia> A = [1 2; 3 4]
    2×2 Matrix{Int64}:
     1  2
     3  4

    julia> minimum(abs2, A, dims=1)
    1×2 Matrix{Int64}:
     1  4

    julia> minimum(abs2, A, dims=2)
    2×1 Matrix{Int64}:
     1
     9

source

    minimum(A::AbstractArray; dims)

Compute the minimum value of an array over the given dimensions. See also the `min(a,b)` function to take the minimum of two or more arguments, which can be applied elementwise to arrays via `min.(a,b)`.

See also: `minimum!`, `extrema`, `findmin`, `argmin`.

Examples

    julia> A = [1 2; 3 4]
    2×2 Matrix{Int64}:
     1  2
     3  4

    julia> minimum(A, dims=1)
    1×2 Matrix{Int64}:
     1  2

    julia> minimum(A, dims=2)
    2×1 Matrix{Int64}:
     1
     3

source

    minimum(itr; [init])

Return the smallest element in a collection.

The value returned for empty `itr` can be specified by `init`. It must be a neutral element for `min` (i.e. which is greater than or equal to any other element) as it is unspecified whether `init` is used for non-empty collections.

Julia 1.6

Keyword argument `init` requires Julia 1.6 or later.

Examples

    julia> minimum(-20.5:10)
    -20.5

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

    julia> minimum([])
    ERROR: ArgumentError: reducing over an empty collection is not allowed; consider supplying `init` to the reducer
    Stacktrace:
    [...]

    julia> minimum([]; init=Inf)
    Inf

source

    minimum(f, itr; [init])

Return the smallest result of calling function `f` on each element of `itr`.

The value returned for empty `itr` can be specified by `init`. It must be a neutral element for `min` (i.e. which is greater than or equal to any other element) as it is unspecified whether `init` is used for non-empty collections.

Julia 1.6

Keyword argument `init` requires Julia 1.6 or later.

Examples

    julia> minimum(length, ["Julion", "Julia", "Jule"])
    4

    julia> minimum(length, []; init=typemax(Int64))
    9223372036854775807

    julia> minimum(sin, Real[]; init=1.0)  # good, since output of sin is <= 1
    1.0

source
`Base.minimum!` — Function

    minimum!(r, A)

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

Warning

Behavior can be unexpected when any mutated argument shares memory with any other argument.

Examples

    julia> A = [1 2; 3 4]
    2×2 Matrix{Int64}:
     1  2
     3  4

    julia> minimum!([1; 1], A)
    2-element Vector{Int64}:
     1
     3

    julia> minimum!([1 1], A)
    1×2 Matrix{Int64}:
     1  2

source
`Base.extrema` — Function

    extrema(f, A::AbstractArray; dims) -> Array{Tuple}

Compute the minimum and maximum of `f` applied to each element in the given dimensions of `A`.

Julia 1.2

This method requires Julia 1.2 or later.

source

    extrema(A::AbstractArray; dims) -> Array{Tuple}

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

See also: `minimum`, `maximum`, `extrema!`.

Examples

    julia> A = reshape(Vector(1:2:16), (2,2,2))
    2×2×2 Array{Int64, 3}:
    [:, :, 1] =
     1  5
     3  7

    [:, :, 2] =
      9  13
     11  15

    julia> extrema(A, dims = (1,2))
    1×1×2 Array{Tuple{Int64, Int64}, 3}:
    [:, :, 1] =
     (1, 7)

    [:, :, 2] =
     (9, 15)

source

    extrema(f, itr; [init]) -> (mn, mx)

Compute both the minimum `mn` and maximum `mx` of `f` applied to each element in `itr` and return them as a 2-tuple. Only one pass is made over `itr`.

The value returned for empty `itr` can be specified by `init`. It must be a 2-tuple whose first and second elements are neutral elements for `min` and `max` respectively (i.e. which are greater/less than or equal to any other element). It is used for non-empty collections. Note: it implies that, for empty `itr`, the returned value `(mn, mx)` satisfies `mn ≥ mx` even though for non-empty `itr` it satisfies `mn ≤ mx`. This is a "paradoxical" but yet expected result.

Julia 1.2

This method requires Julia 1.2 or later.

Julia 1.8

Keyword argument `init` requires Julia 1.8 or later.

Examples

    julia> extrema(sin, 0:π)
    (0.0, 0.9092974268256817)

    julia> extrema(sin, Real[]; init = (1.0, -1.0))  # good, since -1 ≤ sin(::Real) ≤ 1
    (1.0, -1.0)

source

    extrema(itr; [init]) -> (mn, mx)

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

The value returned for empty `itr` can be specified by `init`. It must be a 2-tuple whose first and second elements are neutral elements for `min` and `max` respectively (i.e. which are greater/less than or equal to any other element). As a consequence, when `itr` is empty the returned `(mn, mx)` tuple will satisfy `mn ≥ mx`. When `init` is specified it may be used even for non-empty `itr`.

Julia 1.8

Keyword argument `init` requires Julia 1.8 or later.

Examples

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

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

    julia> extrema([]; init = (Inf, -Inf))
    (Inf, -Inf)

source
`Base.extrema!` — Function

    extrema!(r, A)

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

Warning

Behavior can be unexpected when any mutated argument shares memory with any other argument.

Julia 1.8

This method requires Julia 1.8 or later.

Examples

    julia> A = [1 2; 3 4]
    2×2 Matrix{Int64}:
     1  2
     3  4

    julia> extrema!([(1, 1); (1, 1)], A)
    2-element Vector{Tuple{Int64, Int64}}:
     (1, 2)
     (3, 4)

    julia> extrema!([(1, 1);; (1, 1)], A)
    1×2 Matrix{Tuple{Int64, Int64}}:
     (1, 3)  (2, 4)

source
`Base.argmax` — Function

    argmax(A; dims) -> indices

For an array input, return the indices of the maximum elements over the given dimensions. `NaN` is treated as greater than all other values except `missing`.

Examples

    julia> A = [1.0 2; 3 4]
    2×2 Matrix{Float64}:
     1.0  2.0
     3.0  4.0

    julia> argmax(A, dims=1)
    1×2 Matrix{CartesianIndex{2}}:
     CartesianIndex(2, 1)  CartesianIndex(2, 2)

    julia> argmax(A, dims=2)
    2×1 Matrix{CartesianIndex{2}}:
     CartesianIndex(1, 2)
     CartesianIndex(2, 2)

source

    argmax(itr)

Return the index or key of the maximal element in a collection. If there are multiple maximal elements, then the first one will be returned.

The collection must not be empty.

Indices are of the same type as those returned by `keys(itr)` and `pairs(itr)`.

Values are compared with `isless`.

See also: `argmin`, `findmax`.

Examples

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

    julia> argmax([1, 7, 7, 6])
    2

    julia> argmax([1, 7, 7, NaN])
    4

source

    argmax(f, domain)

Return a value `x` from `domain` for which `f(x)` is maximised. If there are multiple maximal values for `f(x)` then the first one will be found.

`domain` must be a non-empty iterable.

Values are compared with `isless`.

Julia 1.7

This method requires Julia 1.7 or later.

See also `argmin`, `findmax`.

Examples

    julia> argmax(abs, -10:5)
    -10

    julia> argmax(cos, 0:π/2:2π)
    0.0

source

    argmax(r::AbstractRange)

Ranges can have multiple maximal elements. In that case `argmax` will return a maximal index, but not necessarily the first one.

source
`Base.argmin` — Function

    argmin(A; dims) -> indices

For an array input, return the indices of the minimum elements over the given dimensions. `NaN` is treated as less than all other values except `missing`.

Examples

    julia> A = [1.0 2; 3 4]
    2×2 Matrix{Float64}:
     1.0  2.0
     3.0  4.0

    julia> argmin(A, dims=1)
    1×2 Matrix{CartesianIndex{2}}:
     CartesianIndex(1, 1)  CartesianIndex(1, 2)

    julia> argmin(A, dims=2)
    2×1 Matrix{CartesianIndex{2}}:
     CartesianIndex(1, 1)
     CartesianIndex(2, 1)

source

    argmin(itr)

Return the index or key of the minimal element in a collection. If there are multiple minimal elements, then the first one will be returned.

The collection must not be empty.

Indices are of the same type as those returned by `keys(itr)` and `pairs(itr)`.

`NaN` is treated as less than all other values except `missing`.

See also: `argmax`, `findmin`.

Examples

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

    julia> argmin([7, 1, 1, 6])
    2

    julia> argmin([7, 1, 1, NaN])
    4

source

    argmin(f, domain)

Return a value `x` from `domain` for which `f(x)` is minimised. If there are multiple minimal values for `f(x)` then the first one will be found.

`domain` must be a non-empty iterable.

`NaN` is treated as less than all other values except `missing`.

Julia 1.7

This method requires Julia 1.7 or later.

See also `argmax`, `findmin`.

Examples

    julia> argmin(sign, -10:5)
    -10

    julia> argmin(x -> -x^3 + x^2 - 10, -5:5)
    5

    julia> argmin(acos, 0:0.1:1)
    1.0

source

    argmin(r::AbstractRange)

Ranges can have multiple minimal elements. In that case `argmin` will return a minimal index, but not necessarily the first one.

source
`Base.findmax` — Function

    findmax(f, A; dims) -> (f(x), index)

For an array input, returns the value in the codomain and index of the corresponding value which maximize `f` over the given dimensions.

Examples

    julia> A = [-1.0 1; -0.5 2]
    2×2 Matrix{Float64}:
     -1.0  1.0
     -0.5  2.0

    julia> findmax(abs2, A, dims=1)
    ([1.0 4.0], CartesianIndex{2}[CartesianIndex(1, 1) CartesianIndex(2, 2)])

    julia> findmax(abs2, A, dims=2)
    ([1.0; 4.0;;], CartesianIndex{2}[CartesianIndex(1, 1); CartesianIndex(2, 2);;])

source

    findmax(A; dims) -> (maxval, index)

For an array input, returns the value and index of the maximum over the given dimensions. `NaN` is treated as greater than all other values except `missing`.

Examples

    julia> A = [1.0 2; 3 4]
    2×2 Matrix{Float64}:
     1.0  2.0
     3.0  4.0

    julia> findmax(A, dims=1)
    ([3.0 4.0], CartesianIndex{2}[CartesianIndex(2, 1) CartesianIndex(2, 2)])

    julia> findmax(A, dims=2)
    ([2.0; 4.0;;], CartesianIndex{2}[CartesianIndex(1, 2); CartesianIndex(2, 2);;])

source

    findmax(itr) -> (x, index)

Return the maximal element of the collection `itr` and its index or key. If there are multiple maximal elements, then the first one will be returned. Values are compared with `isless`.

Indices are of the same type as those returned by `keys(itr)` and `pairs(itr)`.

See also: `findmin`, `argmax`, `maximum`.

Examples

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

    julia> findmax([1, 7, 7, 6])
    (7, 2)

    julia> findmax([1, 7, 7, NaN])
    (NaN, 4)

source

    findmax(f, domain) -> (f(x), index)

Return a pair of a value in the codomain (outputs of `f`) and the index or key of the corresponding value in the `domain` (inputs to `f`) such that `f(x)` is maximised. If there are multiple maximal points, then the first one will be returned.

`domain` must be a non-empty iterable supporting `keys`. Indices are of the same type as those returned by `keys(domain)`.

Values are compared with `isless`.

Julia 1.7

This method requires Julia 1.7 or later.

Examples

    julia> findmax(identity, 5:9)
    (9, 5)

    julia> findmax(-, 1:10)
    (-1, 1)

    julia> findmax(first, [(1, :a), (3, :b), (3, :c)])
    (3, 2)

    julia> findmax(cos, 0:π/2:2π)
    (1.0, 1)

source
`Base.findmin` — Function

    findmin(f, A; dims) -> (f(x), index)

For an array input, returns the value in the codomain and index of the corresponding value which minimize `f` over the given dimensions.

Examples

    julia> A = [-1.0 1; -0.5 2]
    2×2 Matrix{Float64}:
     -1.0  1.0
     -0.5  2.0

    julia> findmin(abs2, A, dims=1)
    ([0.25 1.0], CartesianIndex{2}[CartesianIndex(2, 1) CartesianIndex(1, 2)])

    julia> findmin(abs2, A, dims=2)
    ([1.0; 0.25;;], CartesianIndex{2}[CartesianIndex(1, 1); CartesianIndex(2, 1);;])

source

    findmin(A; dims) -> (minval, index)

For an array input, returns the value and index of the minimum over the given dimensions. `NaN` is treated as less than all other values except `missing`.

Examples

    julia> A = [1.0 2; 3 4]
    2×2 Matrix{Float64}:
     1.0  2.0
     3.0  4.0

    julia> findmin(A, dims=1)
    ([1.0 2.0], CartesianIndex{2}[CartesianIndex(1, 1) CartesianIndex(1, 2)])

    julia> findmin(A, dims=2)
    ([1.0; 3.0;;], CartesianIndex{2}[CartesianIndex(1, 1); CartesianIndex(2, 1);;])

source

    findmin(itr) -> (x, index)

Return the minimal element of the collection `itr` and its index or key. If there are multiple minimal elements, then the first one will be returned. `NaN` is treated as less than all other values except `missing`.

Indices are of the same type as those returned by `keys(itr)` and `pairs(itr)`.

See also: `findmax`, `argmin`, `minimum`.

Examples

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

    julia> findmin([1, 7, 7, 6])
    (1, 1)

    julia> findmin([1, 7, 7, NaN])
    (NaN, 4)

source

    findmin(f, domain) -> (f(x), index)

Return a pair of a value in the codomain (outputs of `f`) and the index or key of the corresponding value in the `domain` (inputs to `f`) such that `f(x)` is minimised. If there are multiple minimal points, then the first one will be returned.

`domain` must be a non-empty iterable.

Indices are of the same type as those returned by `keys(domain)` and `pairs(domain)`.

`NaN` is treated as less than all other values except `missing`.

Julia 1.7

This method requires Julia 1.7 or later.

Examples

    julia> findmin(identity, 5:9)
    (5, 1)

    julia> findmin(-, 1:10)
    (-10, 10)

    julia> findmin(first, [(2, :a), (2, :b), (3, :c)])
    (2, 1)

    julia> findmin(cos, 0:π/2:2π)
    (-1.0, 3)

source
`Base.findmax!` — Function

    findmax!(rval, rind, A) -> (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`. `NaN` is treated as greater than all other values except `missing`.

Warning

Behavior can be unexpected when any mutated argument shares memory with any other argument.

source
`Base.findmin!` — Function

    findmin!(rval, rind, A) -> (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`. `NaN` is treated as less than all other values except `missing`.

Warning

Behavior can be unexpected when any mutated argument shares memory with any other argument.

source
`Base.sum` — Function

    sum(f, A::AbstractArray; dims)

Sum the results of calling function `f` on each element of an array over the given dimensions.

Examples

    julia> A = [1 2; 3 4]
    2×2 Matrix{Int64}:
     1  2
     3  4

    julia> sum(abs2, A, dims=1)
    1×2 Matrix{Int64}:
     10  20

    julia> sum(abs2, A, dims=2)
    2×1 Matrix{Int64}:
      5
     25

source

    sum(A::AbstractArray; dims)

Sum elements of an array over the given dimensions.

Examples

    julia> A = [1 2; 3 4]
    2×2 Matrix{Int64}:
     1  2
     3  4

    julia> sum(A, dims=1)
    1×2 Matrix{Int64}:
     4  6

    julia> sum(A, dims=2)
    2×1 Matrix{Int64}:
     3
     7

source

    sum(itr; [init])

Return the sum of all elements in a collection.

The return type is `Int` for signed integers of less than system word size, and `UInt` for unsigned integers of less than system word size. For all other arguments, a common return type is found to which all arguments are promoted.

The value returned for empty `itr` can be specified by `init`. It must be the additive identity (i.e. zero) as it is unspecified whether `init` is used for non-empty collections.

Julia 1.6

Keyword argument `init` requires Julia 1.6 or later.

See also: `reduce`, `mapreduce`, `count`, `union`.

Examples

    julia> sum(1:20)
    210

    julia> sum(1:20; init = 0.0)
    210.0

source

    sum(f, itr; [init])

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

The return type is `Int` for signed integers of less than system word size, and `UInt` for unsigned integers of less than system word size. For all other arguments, a common return type is found to which all arguments are promoted.

The value returned for empty `itr` can be specified by `init`. It must be the additive identity (i.e. zero) as it is unspecified whether `init` is used for non-empty collections.

Julia 1.6

Keyword argument `init` requires Julia 1.6 or later.

Examples

    julia> sum(abs2, [2; 3; 4])
    29

Note the important difference between `sum(A)` and `reduce(+, A)` for arrays with small integer eltype:

    julia> sum(Int8[100, 28])
    128

    julia> reduce(+, Int8[100, 28])
    -128

In the former case, the integers are widened to system word size and therefore the result is 128. In the latter case, no such widening happens and integer overflow results in -128.

source
`Base.sum!` — Function

    sum!(r, A)

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

Warning

Behavior can be unexpected when any mutated argument shares memory with any other argument.

Examples

    julia> A = [1 2; 3 4]
    2×2 Matrix{Int64}:
     1  2
     3  4

    julia> sum!([1; 1], A)
    2-element Vector{Int64}:
     3
     7

    julia> sum!([1 1], A)
    1×2 Matrix{Int64}:
     4  6

source
`Base.prod` — Function

    prod(f, A::AbstractArray; dims)

Multiply the results of calling the function `f` on each element of an array over the given dimensions.

Examples

    julia> A = [1 2; 3 4]
    2×2 Matrix{Int64}:
     1  2
     3  4

    julia> prod(abs2, A, dims=1)
    1×2 Matrix{Int64}:
     9  64

    julia> prod(abs2, A, dims=2)
    2×1 Matrix{Int64}:
       4
     144

source

    prod(A::AbstractArray; dims)

Multiply elements of an array over the given dimensions.

Examples

    julia> A = [1 2; 3 4]
    2×2 Matrix{Int64}:
     1  2
     3  4

    julia> prod(A, dims=1)
    1×2 Matrix{Int64}:
     3  8

    julia> prod(A, dims=2)
    2×1 Matrix{Int64}:
      2
     12

source

    prod(itr; [init])

Return the product of all elements of a collection.

The return type is `Int` for signed integers of less than system word size, and `UInt` for unsigned integers of less than system word size. For all other arguments, a common return type is found to which all arguments are promoted.

The value returned for empty `itr` can be specified by `init`. It must be the multiplicative identity (i.e. one) as it is unspecified whether `init` is used for non-empty collections.

Julia 1.6

Keyword argument `init` requires Julia 1.6 or later.

See also: `reduce`, `cumprod`, `any`.

Examples

    julia> prod(1:5)
    120

    julia> prod(1:5; init = 1.0)
    120.0

source

    prod(f, itr; [init])

Return the product of `f` applied to each element of `itr`.

The return type is `Int` for signed integers of less than system word size, and `UInt` for unsigned integers of less than system word size. For all other arguments, a common return type is found to which all arguments are promoted.

The value returned for empty `itr` can be specified by `init`. It must be the multiplicative identity (i.e. one) as it is unspecified whether `init` is used for non-empty collections.

Julia 1.6

Keyword argument `init` requires Julia 1.6 or later.

Examples

    julia> prod(abs2, [2; 3; 4])
    576

source
`Base.prod!` — Function

    prod!(r, A)

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

Warning

Behavior can be unexpected when any mutated argument shares memory with any other argument.

Examples

    julia> A = [1 2; 3 4]
    2×2 Matrix{Int64}:
     1  2
     3  4

    julia> prod!([1; 1], A)
    2-element Vector{Int64}:
      2
     12

    julia> prod!([1 1], A)
    1×2 Matrix{Int64}:
     3  8

source
`Base.any` — Method

    any(itr) -> Bool

Test whether any elements of a boolean collection are `true`, returning `true` as soon as the first `true` value in `itr` is encountered (short-circuiting). To short-circuit on `false`, use `all`.

If the input contains `missing` values, return `missing` if all non-missing values are `false` (or equivalently, if the input contains no `true` value), following three-valued logic.

See also: `all`, `count`, `sum`, `|`, `||`.

Examples

    julia> a = [true,false,false,true]
    4-element Vector{Bool}:
     1
     0
     0
     1

    julia> any(a)
    true

    julia> any((println(i); v) for (i, v) in enumerate(a))
    1
    true

    julia> any([missing, true])
    true

    julia> any([false, missing])
    missing

source
`Base.any` — Method

    any(p, itr) -> Bool

Determine whether predicate `p` returns `true` for any elements of `itr`, returning `true` as soon as the first item in `itr` for which `p` returns `true` is encountered (short-circuiting). To short-circuit on `false`, use `all`.

If the input contains `missing` values, return `missing` if all non-missing values are `false` (or equivalently, if the input contains no `true` value), following three-valued logic.

Examples

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

    julia> any(i -> (println(i); i > 3), 1:10)
    1
    2
    3
    4
    true

    julia> any(i -> i > 0, [1, missing])
    true

    julia> any(i -> i > 0, [-1, missing])
    missing

    julia> any(i -> i > 0, [-1, 0])
    false

source
`Base.any!` — Function

    any!(r, A)

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

Warning

Behavior can be unexpected when any mutated argument shares memory with any other argument.

Examples

    julia> A = [true false; true false]
    2×2 Matrix{Bool}:
     1  0
     1  0

    julia> any!([1; 1], A)
    2-element Vector{Int64}:
     1
     1

    julia> any!([1 1], A)
    1×2 Matrix{Int64}:
     1  0

source
`Base.all` — Method

    all(itr) -> Bool

Test whether all elements of a boolean collection are `true`, returning `false` as soon as the first `false` value in `itr` is encountered (short-circuiting). To short-circuit on `true`, use `any`.

If the input contains `missing` values, return `missing` if all non-missing values are `true` (or equivalently, if the input contains no `false` value), following three-valued logic.

See also: `all!`, `any`, `count`, `&`, `&&`, `allunique`.

Examples

    julia> a = [true,false,false,true]
    4-element Vector{Bool}:
     1
     0
     0
     1

    julia> all(a)
    false

    julia> all((println(i); v) for (i, v) in enumerate(a))
    1
    2
    false

    julia> all([missing, false])
    false

    julia> all([true, missing])
    missing

source
`Base.all` — Method

    all(p, itr) -> Bool

Determine whether predicate `p` returns `true` for all elements of `itr`, returning `false` as soon as the first item in `itr` for which `p` returns `false` is encountered (short-circuiting). To short-circuit on `true`, use `any`.

If the input contains `missing` values, return `missing` if all non-missing values are `true` (or equivalently, if the input contains no `false` value), following three-valued logic.

Examples

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

    julia> all(i -> (println(i); i < 3), 1:10)
    1
    2
    3
    false

    julia> all(i -> i > 0, [1, missing])
    missing

    julia> all(i -> i > 0, [-1, missing])
    false

    julia> all(i -> i > 0, [1, 2])
    true

source
`Base.all!` — Function

    all!(r, A)

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

Warning

Behavior can be unexpected when any mutated argument shares memory with any other argument.

Examples

    julia> A = [true false; true false]
    2×2 Matrix{Bool}:
     1  0
     1  0

    julia> all!([1; 1], A)
    2-element Vector{Int64}:
     0
     0

    julia> all!([1 1], A)
    1×2 Matrix{Int64}:
     1  0

source
`Base.count` — Function

    count([f=identity,] A::AbstractArray; dims=:)

Count the number of elements in `A` for which `f` returns `true` over the given dimensions.

Julia 1.5

`dims` keyword was added in Julia 1.5.

Julia 1.6

`init` keyword was added in Julia 1.6.

Examples

    julia> A = [1 2; 3 4]
    2×2 Matrix{Int64}:
     1  2
     3  4

    julia> count(<=(2), A, dims=1)
    1×2 Matrix{Int64}:
     1  1

    julia> count(<=(2), A, dims=2)
    2×1 Matrix{Int64}:
     2
     0

source

    count(
        pattern::Union{AbstractChar,AbstractString,AbstractPattern},
        string::AbstractString;
        overlap::Bool = false,
    )

Return the number of matches for `pattern` in `string`. This is equivalent to calling `length(findall(pattern, string))` but more efficient.

If `overlap=true`, the matching sequences are allowed to overlap indices in the original string, otherwise they must be from disjoint character ranges.

Julia 1.3

This method requires at least Julia 1.3.

Julia 1.7

Using a character as the pattern requires at least Julia 1.7.

Examples

    julia> count('a', "JuliaLang")
    2

    julia> count(r"a(.)a", "cabacabac", overlap=true)
    3

    julia> count(r"a(.)a", "cabacabac")
    2

source

    count([f=identity,] itr; init=0) -> Integer

Count the number of elements in `itr` for which the function `f` returns `true`. If `f` is omitted, count the number of `true` elements in `itr` (which should be a collection of boolean values). `init` optionally specifies the value to start counting from and therefore also determines the output type.

Julia 1.6

`init` keyword was added in Julia 1.6.

See also: `any`, `sum`.

Examples

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

    julia> count([true, false, true, true])
    3

    julia> count(>(3), 1:7, init=0x03)
    0x07

source
`Base.foreach` — Function

    foreach(f, c...) -> Nothing

Call function `f` on each element of iterable `c`. For multiple iterable arguments, `f` is called elementwise, and iteration stops when any iterator is finished.

`foreach` should be used instead of `map` when the results of `f` are not needed, for example in `foreach(println, array)`.

Examples

    julia> tri = 1:3:7; res = Int[];

    julia> foreach(x -> push!(res, x^2), tri)

    julia> res
    3-element Vector{Int64}:
      1
     16
     49

    julia> foreach((x, y) -> println(x, " with ", y), tri, 'a':'z')
    1 with a
    4 with b
    7 with c

source
`Base.map` — Function

    map(f, A::AbstractArray...) -> N-array

When acting on multi-dimensional arrays of the same `ndims`, they must all have the same `axes`, and the answer will too.

See also `broadcast`, which allows mismatched sizes.

Examples

    julia> map(//, [1 2; 3 4], [4 3; 2 1])
    2×2 Matrix{Rational{Int64}}:
     1//4  2//3
     3//2  4//1

    julia> map(+, [1 2; 3 4], zeros(2,1))
    ERROR: DimensionMismatch

    julia> map(+, [1 2; 3 4], [1,10,100,1000], zeros(3,1))  # iterates until 3rd is exhausted
    3-element Vector{Float64}:
       2.0
      13.0
     102.0

source

    map(f, c...) -> collection

Transform collection `c` by applying `f` to each element. For multiple collection arguments, apply `f` elementwise, and stop when any of them is exhausted.

The element type of the result is determined in the same manner as in `collect`.

See also `map!`, `foreach`, `mapreduce`, `mapslices`, `zip`, `Iterators.map`.

Examples

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

    julia> map(+, [1, 2, 3], [10, 20, 30, 400, 5000])
    3-element Vector{Int64}:
     11
     22
     33

source
`Base.map!` — Function

    map!(f, values(dict::AbstractDict))

Modifies `dict` by transforming each value from `val` to `f(val)`. Note that the type of `dict` cannot be changed: if `f(val)` is not an instance of the value type of `dict` then it will be converted to the value type if possible and otherwise raise an error.

Julia 1.2

`map!(f, values(dict::AbstractDict))` requires Julia 1.2 or later.

Examples

    julia> d = Dict(:a => 1, :b => 2)
    Dict{Symbol, Int64} with 2 entries:
      :a => 1
      :b => 2

    julia> map!(v -> v-1, values(d))
    ValueIterator for a Dict{Symbol, Int64} with 2 entries. Values:
      0
      1

source

    map!(function, array)

Like `map`, but stores the result in the same array.

Julia 1.12

This method requires Julia 1.12 or later. To support previous versions too, use the equivalent `map!(function, array, array)`.

Examples

    julia> a = [1 2 3; 4 5 6];

    julia> map!(x -> x^3, a);

    julia> a
    2×3 Matrix{Int64}:
      1    8   27
     64  125  216

source

    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 smallest collection.

Warning

Behavior can be unexpected when any mutated argument shares memory with any other argument.

See also: `map`, `foreach`, `zip`, `copyto!`.

Examples

    julia> a = zeros(3);

    julia> map!(x -> x * 2, a, [1, 2, 3]);

    julia> a
    3-element Vector{Float64}:
     2.0
     4.0
     6.0

    julia> map!(+, zeros(Int, 5), 100:999, 1:3)
    5-element Vector{Int64}:
     101
     103
     105
       0
       0

source
`Base.mapreduce` — Method

    mapreduce(f, op, itrs...; [init])

Apply function `f` to each element(s) in `itrs`, and then reduce the result using the binary function `op`. If provided, `init` must be a neutral element for `op` that will be returned for empty collections. It is unspecified whether `init` is used for non-empty collections. In general, it will be necessary to provide `init` to work with empty collections.

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

Julia 1.2

`mapreduce` with multiple iterators requires Julia 1.2 or later.

Examples

    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.

source
`Base.mapfoldl` — Method

    mapfoldl(f, op, itr; [init])

Like `mapreduce`, but with guaranteed left associativity, as in `foldl`. If provided, the keyword argument `init` will be used exactly once. In general, it will be necessary to provide `init` to work with empty collections.

source
`Base.mapfoldr` — Method

    mapfoldr(f, op, itr; [init])

Like `mapreduce`, but with guaranteed right associativity, as in `foldr`. If provided, the keyword argument `init` will be used exactly once. In general, it will be necessary to provide `init` to work with empty collections.

source
`Base.first` — Function

    first(s::AbstractString, n::Integer)

Get a string consisting of the first `n` characters of `s`.

Examples

    julia> first("∀ϵ≠0: ϵ²>0", 0)
    ""

    julia> first("∀ϵ≠0: ϵ²>0", 1)
    "∀"

    julia> first("∀ϵ≠0: ϵ²>0", 3)
    "∀ϵ≠"

source

    first(itr, n::Integer)

Get the first `n` elements of the iterable collection `itr`, or fewer elements if `itr` is not long enough.

See also: `startswith`, `Iterators.take`.

Julia 1.6

This method requires at least Julia 1.6.

Examples

    julia> first(["foo", "bar", "qux"], 2)
    2-element Vector{String}:
     "foo"
     "bar"

    julia> first(1:6, 10)
    1:6

    julia> first(Bool[], 1)
    Bool[]

source

    first(coll)

Get the first element of an iterable collection. Return the start point of an `AbstractRange` even if it is empty.

See also: `only`, `firstindex`, `last`.

Examples

    julia> first(2:2:10)
    2

    julia> first([1; 2; 3; 4])
    1

source
`Base.last` — Function

    last(s::AbstractString, n::Integer)

Get a string consisting of the last `n` characters of `s`.

Examples

    julia> last("∀ϵ≠0: ϵ²>0", 0)
    ""

    julia> last("∀ϵ≠0: ϵ²>0", 1)
    "0"

    julia> last("∀ϵ≠0: ϵ²>0", 3)
    "²>0"

source

    last(itr, n::Integer)

Get the last `n` elements of the iterable collection `itr`, or fewer elements if `itr` is not long enough.

Julia 1.6

This method requires at least Julia 1.6.

Examples

    julia> last(["foo", "bar", "qux"], 2)
    2-element Vector{String}:
     "bar"
     "qux"

    julia> last(1:6, 10)
    1:6

    julia> last(Float64[], 1)
    Float64[]

source

    last(coll)

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

See also `first`, `endswith`.

Examples

    julia> last(1:2:10)
    9

    julia> last([1; 2; 3; 4])
    4

source
`Base.front` — Function

    front(x::Tuple)::Tuple

Return a `Tuple` consisting of all but the last component of `x`.

See also: `first`, `tail`.

Examples

    julia> Base.front((1,2,3))
    (1, 2)

    julia> Base.front(())
    ERROR: ArgumentError: Cannot call front on an empty tuple.

source
`Base.tail` — Function

    tail(x::Tuple)::Tuple

Return a `Tuple` consisting of all but the first component of `x`.

See also: `front`, `rest`, `first`, `Iterators.peel`.

Examples

    julia> Base.tail((1,2,3))
    (2, 3)

    julia> Base.tail(())
    ERROR: ArgumentError: Cannot call tail on an empty tuple.

source
`Base.step` — Function

    step(r)

Get the step size of an `AbstractRange` object.

Examples

    julia> step(1:10)
    1

    julia> step(1:2:10)
    2

    julia> step(2.5:0.3:10.9)
    0.3

    julia> step(range(2.5, stop=10.9, length=85))
    0.1

source
`Base.collect` — Method

    collect(iterator)

Return an `Array` of all items in a collection or iterator. For dictionaries, returns a `Vector` of `key=>value` Pairs. 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.

Used by comprehensions to turn a generator expression into an `Array`. Thus, on generators, the square-brackets notation may be used instead of calling `collect`, see second example.

The element type of the returned array is based on the types of the values collected. However, if the iterator is empty then the element type of the returned (empty) array is determined by type inference.

Examples

Collect items from a `UnitRange{Int64}` collection:

    julia> collect(1:3)
    3-element Vector{Int64}:
     1
     2
     3

Collect items from a generator (same output as `[x^2 for x in 1:3]`):

    julia> collect(x^2 for x in 1:3)
    3-element Vector{Int64}:
     1
     4
     9

Collecting an empty iterator where the result type depends on type inference:

    julia> [rand(Bool) ? 1 : missing for _ in []]
    Union{Missing, Int64}[]

When the iterator is non-empty, the result type depends only on values:

    julia> [rand(Bool) ? 1 : missing for _ in [""]]
    1-element Vector{Int64}:
     1

source
`Base.collect` — Method

    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`.

Examples

    julia> collect(Float64, 1:2:5)
    3-element Vector{Float64}:
     1.0
     3.0
     5.0

source
`Base.filter` — Function

    filter(f, itr::SkipMissing{<:AbstractArray})

Return a vector similar to the array wrapped by the given `SkipMissing` iterator but with all missing elements and those for which `f` returns `false` removed.

Julia 1.2

This method requires Julia 1.2 or later.

Examples

    julia> x = [1 2; missing 4]
    2×2 Matrix{Union{Missing, Int64}}:
     1         2
      missing  4

    julia> filter(isodd, skipmissing(x))
    1-element Vector{Int64}:
     1

source

    filter(f, d::AbstractDict)

Return a copy of `d`, removing elements for which `f` is `false`. The function `f` is passed `key=>value` pairs.

Examples

    julia> d = Dict(1=>"a", 2=>"b")
    Dict{Int64, String} with 2 entries:
      2 => "b"
      1 => "a"

    julia> filter(p->isodd(p.first), d)
    Dict{Int64, String} with 1 entry:
      1 => "a"

source

    filter(f)

Create a function that filters its arguments with function `f` using `filter`, i.e. a function equivalent to `x -> filter(f, x)`.

The returned function is of type `Base.Fix1{typeof(filter)}`, which can be used to implement specialized methods.

Examples

    julia> (1, 2, Inf, 4, NaN, 6) |> filter(isfinite)
    (1, 2, 4, 6)

    julia> map(filter(iseven), [1:3, 2:4, 3:5])
    3-element Vector{Vector{Int64}}:
     [2]
     [2, 4]
     [4]

Julia 1.9

This method requires at least Julia 1.9.

source

    filter(f, a)

Return a copy of collection `a`, removing elements for which `f` is `false`. The function `f` is passed one argument.

Julia 1.4

Support for `a` as a tuple requires at least Julia 1.4.

See also: `filter!`, `Iterators.filter`.

Examples

    julia> a = 1:10
    1:10

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

source
`Base.filter!` — Function

    filter!(f, d::AbstractDict)

Update `d`, removing elements for which `f` is `false`. The function `f` is passed `key=>value` pairs.

Examples

    julia> d = Dict(1=>"a", 2=>"b", 3=>"c")
    Dict{Int64, String} with 3 entries:
      2 => "b"
      3 => "c"
      1 => "a"

    julia> filter!(p->isodd(p.first), d)
    Dict{Int64, String} with 2 entries:
      3 => "c"
      1 => "a"

source

    filter!(f, a)

Update collection `a`, removing elements for which `f` is `false`. The function `f` is passed one argument.

Examples

    julia> filter!(isodd, Vector(1:10))
    5-element Vector{Int64}:
     1
     3
     5
     7
     9

source
`Base.replace` — Method

    replace(A, old_new::Pair...; [count::Integer])

Return a copy of collection `A` where, for each pair `old=>new` in `old_new`, all occurrences of `old` are replaced by `new`. Equality is determined using `isequal`. If `count` is specified, then replace at most `count` occurrences in total.

The element type of the result is chosen using promotion (see `promote_type`) based on the element type of `A` and on the types of the `new` values in pairs. If `count` is omitted and the element type of `A` is a `Union`, the element type of the result will not include singleton types which are replaced with values of a different type: for example, `Union{T,Missing}` will become `T` if `missing` is replaced.

See also `replace!`, `splice!`, `delete!`, `insert!`.

Julia 1.7

Version 1.7 is required to replace elements of a `Tuple`.

Examples

    julia> replace([1, 2, 1, 3], 1=>0, 2=>4, count=2)
    4-element Vector{Int64}:
     0
     4
     1
     3

    julia> replace([1, missing], missing=>0)
    2-element Vector{Int64}:
     1
     0

source
`Base.replace` — Method

    replace(new::Union{Function, Type}, A; [count::Integer])

Return a copy of `A` where each value `x` in `A` is replaced by `new(x)`. If `count` is specified, then replace at most `count` values in total (replacements being defined as `new(x) !== x`).

Julia 1.7

Version 1.7 is required to replace elements of a `Tuple`.

Examples

    julia> replace(x -> isodd(x) ? 2x : x, [1, 2, 3, 4])
    4-element Vector{Int64}:
     2
     2
     6
     4

    julia> replace(Dict(1=>2, 3=>4)) do kv
               first(kv) < 3 ? first(kv)=>3 : kv
           end
    Dict{Int64, Int64} with 2 entries:
      3 => 4
      1 => 3

source
`Base.replace!` — Function

    replace!(new::Union{Function, Type}, A; [count::Integer])

Replace each element `x` in collection `A` by `new(x)`. If `count` is specified, then replace at most `count` values in total (replacements being defined as `new(x) !== x`).

Examples

    julia> replace!(x -> isodd(x) ? 2x : x, [1, 2, 3, 4])
    4-element Vector{Int64}:
     2
     2
     6
     4

    julia> replace!(Dict(1=>2, 3=>4)) do kv
               first(kv) < 3 ? first(kv)=>3 : kv
           end
    Dict{Int64, Int64} with 2 entries:
      3 => 4
      1 => 3

    julia> replace!(x->2x, Set([3, 6]))
    Set{Int64} with 2 elements:
      6
      12

source

    replace!(A, old_new::Pair...; [count::Integer])

For each pair `old=>new` in `old_new`, replace all occurrences of `old` in collection `A` by `new`. Equality is determined using `isequal`. If `count` is specified, then replace at most `count` occurrences in total. See also `replace`.

Examples

    julia> replace!([1, 2, 1, 3], 1=>0, 2=>4, count=2)
    4-element Vector{Int64}:
     0
     4
     1
     3

    julia> replace!(Set([1, 2, 3]), 1=>0)
    Set{Int64} with 3 elements:
      0
      2
      3

source
`Base.rest` — Function

    Base.rest(collection[, itr_state])

Generic function for taking the tail of `collection`, starting from a specific iteration state `itr_state`. Return a `Tuple`, if `collection` itself is a `Tuple`, a subtype of `AbstractVector`, if `collection` is an `AbstractArray`, a subtype of `AbstractString` if `collection` is an `AbstractString`, and an arbitrary iterator, falling back to `Iterators.rest(collection[, itr_state])`, otherwise.

Can be overloaded for user-defined collection types to customize the behavior of slurping in assignments in final position, like `a, b... = collection`.

Julia 1.6

`Base.rest` requires at least Julia 1.6.

See also: `first`, `Iterators.rest`, `Base.split_rest`.

Examples

    julia> a = [1 2; 3 4]
    2×2 Matrix{Int64}:
     1  2
     3  4

    julia> first, state = iterate(a)
    (1, 2)

    julia> first, Base.rest(a, state)
    (1, [3, 2, 4])

source
`Base.split_rest` — Function

    Base.split_rest(collection, n::Int[, itr_state]) -> (rest_but_n, last_n)

Generic function for splitting the tail of `collection`, starting from a specific iteration state `itr_state`. Returns a tuple of two new collections. The first one contains all elements of the tail but the `n` last ones, which make up the second collection.

The type of the first collection generally follows that of `Base.rest`, except that the fallback case is not lazy, but is collected eagerly into a vector.

Can be overloaded for user-defined collection types to customize the behavior of slurping in assignments in non-final position, like `a, b..., c = collection`.

Julia 1.9

`Base.split_rest` requires at least Julia 1.9.

See also: `Base.rest`.

Examples

    julia> a = [1 2; 3 4]
    2×2 Matrix{Int64}:
     1  2
     3  4

    julia> first, state = iterate(a)
    (1, 2)

    julia> first, Base.split_rest(a, 1, state)
    (1, ([3, 2], [4]))

source

## Indexable Collections

`Base.getindex` — Function

    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, ...)`.

See also `get`, `keys`, `eachindex`.

Examples

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

    julia> getindex(A, "a")
    1

source
`Base.setindex!` — Function

    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)`.

Examples

    julia> a = Dict("a"=>1)
    Dict{String, Int64} with 1 entry:
      "a" => 1

    julia> setindex!(a, 2, "b")
    Dict{String, Int64} with 2 entries:
      "b" => 2
      "a" => 1

source
`Base.firstindex` — Function

    firstindex(collection) -> Integer
    firstindex(collection, d) -> Integer

Return the first index of `collection`. If `d` is given, return the first index of `collection` along dimension `d`.

The syntaxes `A[begin]` and `A[1, begin]` lower to `A[firstindex(A)]` and `A[1, firstindex(A, 2)]`, respectively.

See also: `first`, `axes`, `lastindex`, `nextind`.

Examples

    julia> firstindex([1,2,4])
    1

    julia> firstindex(rand(3,4,5), 2)
    1

source
`Base.lastindex` — Function

    lastindex(collection) -> Integer
    lastindex(collection, d) -> Integer

Return the last index of `collection`. If `d` is given, return the last index of `collection` along dimension `d`.

The syntaxes `A[end]` and `A[end, end]` lower to `A[lastindex(A)]` and `A[lastindex(A, 1), lastindex(A, 2)]`, respectively.

See also: `axes`, `firstindex`, `eachindex`, `prevind`.

Examples

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

    julia> lastindex(rand(3,4,5), 2)
    4

source

Fully implemented by:

- `Array`
- `BitArray`
- `AbstractArray`
- `SubArray`

Partially implemented by:

- `AbstractRange`
- `UnitRange`
- `Tuple`
- `AbstractString`
- `Dict`
- `IdDict`
- `WeakKeyDict`
- `NamedTuple`

## Dictionaries

`Dict` is the standard dictionary. 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.

`IdDict` 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. Like `Dict` it uses `hash` for hashing and `isequal` for equality, unlike `Dict` it does not convert keys on insertion.

`Dict`s 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)`.

Dictionaries 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)`.

`Base.AbstractDict` — Type

    AbstractDict{K, V}

Supertype for dictionary-like types with keys of type `K` and values of type `V`. `Dict`, `IdDict` and other types are subtypes of this. An `AbstractDict{K, V}` should be an iterator of `Pair{K, V}`.

source
`Base.Dict` — Type

    Dict([itr])

`Dict{K,V}()` constructs a hash table with keys of type `K` and values of type `V`. Keys are compared with `isequal` and hashed with `hash`.

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

Examples

    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

Warning

Keys are allowed to be mutable, but if you do mutate stored keys, the hash table may become internally inconsistent, in which case the `Dict` will not work properly. `IdDict` can be an alternative if you need to mutate keys.

source
`Base.IdDict` — Type

    IdDict([itr])

`IdDict{K,V}()` constructs a hash table using `objectid` as hash and `===` as equality with keys of type `K` and values of type `V`. See `Dict` for further help and `IdSet` for the set version of this.

In the example below, the `Dict` keys are all `isequal` and therefore get hashed the same, so they get overwritten. The `IdDict` hashes by object-id, and thus preserves the 3 different keys.

Examples

    julia> Dict(true => "yes", 1 => "no", 1.0 => "maybe")
    Dict{Real, String} with 1 entry:
      1.0 => "maybe"

    julia> IdDict(true => "yes", 1 => "no", 1.0 => "maybe")
    IdDict{Any, String} with 3 entries:
      true => "yes"
      1.0  => "maybe"
      1    => "no"

source
`Base.WeakKeyDict` — Type

    WeakKeyDict([itr])

`WeakKeyDict()` constructs a hash table where the keys are weak references to objects which may be garbage collected even when referenced in a hash table.

See `Dict` for further help. Note, unlike `Dict`, `WeakKeyDict` does not convert keys on insertion, as this would imply the key object was unreferenced anywhere before insertion.

See also `WeakRef`.

source
`Base.ImmutableDict` — Type

    ImmutableDict

`ImmutableDict` is a dictionary implemented as an immutable linked list, which is optimal for small dictionaries that are constructed over many individual insertions. Note that it is not possible to remove a value, although it can be partially overridden and hidden by inserting a new value with the same key.

    ImmutableDict(KV::Pair)

Create a new entry in the `ImmutableDict` for a `key => value` pair

- use `(key => value) in dict` to see if this particular combination is in the properties set
- use `get(dict, key, default)` to retrieve the most recent value for a particular key

source
`Base.PersistentDict` — Type

    PersistentDict

`PersistentDict` is a dictionary implemented as an hash array mapped trie, which is optimal for situations where you need persistence, each operation returns a new dictionary separate from the previous one, but the underlying implementation is space-efficient and may share storage across multiple separate dictionaries.

Note

It behaves like an IdDict.

    PersistentDict(KV::Pair)

Examples

    julia> dict = Base.PersistentDict(:a=>1)
    Base.PersistentDict{Symbol, Int64} with 1 entry:
      :a => 1

    julia> dict2 = Base.delete(dict, :a)
    Base.PersistentDict{Symbol, Int64}()

    julia> dict3 = Base.PersistentDict(dict, :a=>2)
    Base.PersistentDict{Symbol, Int64} with 1 entry:
      :a => 2

source
`Base.haskey` — Function

    haskey(collection, key) -> Bool

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

Examples

    julia> D = Dict('a'=>2, 'b'=>3)
    Dict{Char, Int64} with 2 entries:
      'a' => 2
      'b' => 3

    julia> haskey(D, 'a')
    true

    julia> haskey(D, 'c')
    false

source
`Base.get` — Function

    get(f::Union{Function, Type}, 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

source

    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.

Julia 1.7

For tuples and numbers, this function requires at least Julia 1.7.

Examples

    julia> d = Dict("a"=>1, "b"=>2);

    julia> get(d, "a", 3)
    1

    julia> get(d, "c", 3)
    3

source
`Base.get!` — Function

    get!(f::Union{Function, Type}, 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.

Examples

    julia> squares = Dict{Int, Int}();

    julia> function get_square!(d, i)
               get!(d, i) do
                   i^2
               end
           end
    get_square! (generic function with 1 method)

    julia> get_square!(squares, 2)
    4

    julia> squares
    Dict{Int64, Int64} with 1 entry:
      2 => 4

source

    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`.

Examples

    julia> d = Dict("a"=>1, "b"=>2, "c"=>3);

    julia> get!(d, "a", 5)
    1

    julia> get!(d, "d", 4)
    4

    julia> d
    Dict{String, Int64} with 4 entries:
      "c" => 3
      "b" => 2
      "a" => 1
      "d" => 4

source
`Base.getkey` — Function

    getkey(collection, key, default)

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

Examples

    julia> D = Dict('a'=>2, 'b'=>3)
    Dict{Char, Int64} with 2 entries:
      'a' => 2
      'b' => 3

    julia> getkey(D, 'a', 1)
    'a': ASCII/Unicode U+0061 (category Ll: Letter, lowercase)

    julia> getkey(D, 'd', 'a')
    'a': ASCII/Unicode U+0061 (category Ll: Letter, lowercase)

source
`Base.delete!` — Function

    delete!(collection, key)

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

Examples

    julia> d = Dict("a"=>1, "b"=>2)
    Dict{String, Int64} with 2 entries:
      "b" => 2
      "a" => 1

    julia> delete!(d, "b")
    Dict{String, Int64} with 1 entry:
      "a" => 1

    julia> delete!(d, "b") # d is left unchanged
    Dict{String, Int64} with 1 entry:
      "a" => 1

source
`Base.pop!` — Method

    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.

Examples

    julia> d = Dict("a"=>1, "b"=>2, "c"=>3);

    julia> pop!(d, "a")
    1

    julia> pop!(d, "d")
    ERROR: KeyError: key "d" not found
    Stacktrace:
    [...]

    julia> pop!(d, "e", 4)
    4

source
`Base.keys` — Function

    keys(iterator)

For an iterator or collection that has keys and values (e.g. arrays and dictionaries), return an iterator over the keys.

source
`Base.values` — Function

    values(a::AbstractDict)

Return an iterator over all values in a collection. `collect(values(a))` returns an array of values. When the values are stored internally in a hash table, as is the case for `Dict`, the order in which they are returned may vary. But `keys(a)`, `values(a)` and `pairs(a)` all iterate `a` and return the elements in the same order.

Examples

    julia> D = Dict('a'=>2, 'b'=>3)
    Dict{Char, Int64} with 2 entries:
      'a' => 2
      'b' => 3

    julia> collect(values(D))
    2-element Vector{Int64}:
     2
     3

source

    values(iterator)

For an iterator or collection that has keys and values, return an iterator over the values. This function simply returns its argument by default, since the elements of a general iterator are normally considered its "values".

Examples

    julia> d = Dict("a"=>1, "b"=>2);

    julia> values(d)
    ValueIterator for a Dict{String, Int64} with 2 entries. Values:
      2
      1

    julia> values([2])
    1-element Vector{Int64}:
     2

source
`Base.pairs` — Function

    pairs(IndexLinear(), A)
    pairs(IndexCartesian(), A)
    pairs(IndexStyle(A), A)

An iterator that accesses each element of the array `A`, returning `i => x`, where `i` is the index for the element and `x = A[i]`. Identical to `pairs(A)`, except that the style of index can be selected. Also similar to `enumerate(A)`, except `i` will be a valid index for `A`, while `enumerate` always counts from 1 regardless of the indices of `A`.

Specifying `IndexLinear()` ensures that `i` will be an integer; specifying `IndexCartesian()` ensures that `i` will be a `Base.CartesianIndex`; specifying `IndexStyle(A)` chooses whichever has been defined as the native indexing style for array `A`.

Mutation of the bounds of the underlying array will invalidate this iterator.

Examples

    julia> A = ["a" "d"; "b" "e"; "c" "f"];

    julia> for (index, value) in pairs(IndexStyle(A), A)
               println("$index $value")
           end
    1 a
    2 b
    3 c
    4 d
    5 e
    6 f

    julia> S = view(A, 1:2, :);

    julia> for (index, value) in pairs(IndexStyle(S), S)
               println("$index $value")
           end
    CartesianIndex(1, 1) a
    CartesianIndex(2, 1) b
    CartesianIndex(1, 2) d
    CartesianIndex(2, 2) e

See also `IndexStyle`, `axes`.

source

    pairs(collection)

Return an iterator over `key => value` pairs for any collection that maps a set of keys to a set of values. This includes arrays, where the keys are the array indices. When the entries are stored internally in a hash table, as is the case for `Dict`, the order in which they are returned may vary. But `keys(a)`, `values(a)` and `pairs(a)` all iterate `a` and return the elements in the same order.

Examples

    julia> a = Dict(zip(["a", "b", "c"], [1, 2, 3]))
    Dict{String, Int64} with 3 entries:
      "c" => 3
      "b" => 2
      "a" => 1

    julia> pairs(a)
    Dict{String, Int64} with 3 entries:
      "c" => 3
      "b" => 2
      "a" => 1

    julia> foreach(println, pairs(["a", "b", "c"]))
    1 => "a"
    2 => "b"
    3 => "c"

    julia> (;a=1, b=2, c=3) |> pairs |> collect
    3-element Vector{Pair{Symbol, Int64}}:
     :a => 1
     :b => 2
     :c => 3

    julia> (;a=1, b=2, c=3) |> collect
    3-element Vector{Int64}:
     1
     2
     3

source
`Base.merge` — Function

    merge(initial::StyledStrings.Face, others::StyledStrings.Face...)

Merge the properties of the `initial` face and `others`, with later faces taking priority.

This is used to combine the styles of multiple faces, and to resolve inheritance.

source

    merge(a::NamedTuple, iterable)

Interpret an iterable of key-value pairs as a named tuple, and perform a merge.

    julia> merge((a=1, b=2, c=3), [:b=>4, :d=>5])
    (a = 1, b = 4, c = 3, d = 5)

source

    merge(a::NamedTuple, bs::NamedTuple...)

Construct a new named tuple by merging two or more existing ones, in a left-associative manner. Merging proceeds left-to-right, between pairs of named tuples, and so the order of fields present in both the leftmost and rightmost named tuples take the same position as they are found in the leftmost named tuple. However, values are taken from matching fields in the rightmost named tuple that contains that field. Fields present in only the rightmost named tuple of a pair are appended at the end. A fallback is implemented for when only a single named tuple is supplied, with signature `merge(a::NamedTuple)`.

Julia 1.1

Merging 3 or more `NamedTuple` requires at least Julia 1.1.

Examples

    julia> merge((a=1, b=2, c=3), (b=4, d=5))
    (a = 1, b = 4, c = 3, d = 5)

    julia> merge((a=1, b=2), (b=3, c=(d=1,)), (c=(d=2,),))
    (a = 1, b = 3, c = (d = 2,))

source

    merge(d::AbstractDict, others::AbstractDict...)

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. See also `mergewith` for custom handling of values with the same key.

Examples

    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

source
`Base.mergewith` — Function

    mergewith(combine, d::AbstractDict, others::AbstractDict...)
    mergewith(combine)
    merge(combine, d::AbstractDict, others::AbstractDict...)

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. Values with the same key will be combined using the combiner function. The curried form `mergewith(combine)` returns the function `(args...) -> mergewith(combine, args...)`.

Method `merge(combine::Union{Function,Type}, args...)` as an alias of `mergewith(combine, args...)` is still available for backward compatibility.

Julia 1.5

`mergewith` requires Julia 1.5 or later.

Examples

    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> mergewith(+, a, b)
    Dict{String, Float64} with 3 entries:
      "bar" => 4753.0
      "baz" => 17.0
      "foo" => 0.0

    julia> ans == mergewith(+)(a, b)
    true

    julia> mergewith(-, Dict(), Dict(:a=>1))  # Combining function only used if key is present in both
    Dict{Any, Any} with 1 entry:
      :a => 1

source
`Base.merge!` — Function

    merge!(d::AbstractDict, others::AbstractDict...)

Update collection with pairs from the other collections. See also `merge`.

Examples

    julia> d1 = Dict(1 => 2, 3 => 4);

    julia> d2 = Dict(1 => 4, 4 => 5);

    julia> merge!(d1, d2);

    julia> d1
    Dict{Int64, Int64} with 3 entries:
      4 => 5
      3 => 4
      1 => 4

source
`Base.mergewith!` — Function

    mergewith!(combine, d::AbstractDict, others::AbstractDict...) -> d
    mergewith!(combine)
    merge!(combine, d::AbstractDict, others::AbstractDict...) -> d

Update collection with pairs from the other collections. Values with the same key will be combined using the combiner function. The curried form `mergewith!(combine)` returns the function `(args...) -> mergewith!(combine, args...)`.

Method `merge!(combine::Union{Function,Type}, args...)` as an alias of `mergewith!(combine, args...)` is still available for backward compatibility.

Julia 1.5

`mergewith!` requires Julia 1.5 or later.

Examples

    julia> d1 = Dict(1 => 2, 3 => 4);

    julia> d2 = Dict(1 => 4, 4 => 5);

    julia> mergewith!(+, d1, d2);

    julia> d1
    Dict{Int64, Int64} with 3 entries:
      4 => 5
      3 => 4
      1 => 6

    julia> mergewith!(-, d1, d1);

    julia> d1
    Dict{Int64, Int64} with 3 entries:
      4 => 0
      3 => 0
      1 => 0

    julia> foldl(mergewith!(+), [d1, d2]; init=Dict{Int64, Int64}())
    Dict{Int64, Int64} with 3 entries:
      4 => 5
      3 => 0
      1 => 4

source
`Base.sizehint!` — Function

    sizehint!(s, n; first::Bool=false, shrink::Bool=true) -> s

Suggest that collection `s` reserve capacity for at least `n` elements. That is, if you expect that you're going to have to push a lot of values onto `s`, you can avoid the cost of incremental reallocation by doing it once up front; this can improve performance.

If `first` is `true`, then any additional space is reserved before the start of the collection. This way, subsequent calls to `pushfirst!` (instead of `push!`) may become faster. Supplying this keyword may result in an error if the collection is not ordered or if `pushfirst!` is not supported for this collection.

If `shrink=true` (the default), the collection's capacity may be reduced if its current capacity is greater than `n`.

See also `resize!`.

Notes on the performance model

For types that support `sizehint!`,

1. `push!` and `append!` methods generally may (but are not required to) preallocate extra storage. For types implemented in `Base`, they typically do, using a heuristic optimized for a general use case.

2. `sizehint!` may control this preallocation. Again, it typically does this for types in `Base`.

3. `empty!` is nearly costless (and O(1)) for types that support this kind of preallocation.

Julia 1.11

The `shrink` and `first` arguments were added in Julia 1.11.

source
`Base.keytype` — Function

    keytype(type)

Get the key type of a dictionary type. Behaves similarly to `eltype`.

Examples

    julia> keytype(Dict(Int32(1) => "foo"))
    Int32

source

    keytype(T::Type{<:AbstractArray})
    keytype(A::AbstractArray)

Return the key type of an array. This is equal to the `eltype` of the result of `keys(...)`, and is provided mainly for compatibility with the dictionary interface.

Examples

    julia> keytype([1, 2, 3]) == Int
    true

    julia> keytype([1 2; 3 4])
    CartesianIndex{2}

Julia 1.2

For arrays, this function requires at least Julia 1.2.

source
`Base.valtype` — Function

    valtype(type)

Get the value type of a dictionary type. Behaves similarly to `eltype`.

Examples

    julia> valtype(Dict(Int32(1) => "foo"))
    String

source

    valtype(T::Type{<:AbstractArray})
    valtype(A::AbstractArray)

Return the value type of an array. This is identical to `eltype` and is provided mainly for compatibility with the dictionary interface.

Examples

    julia> valtype(["one", "two", "three"])
    String

Julia 1.2

For arrays, this function requires at least Julia 1.2.

source

Fully implemented by:

- `Dict`
- `IdDict`
- `WeakKeyDict`

Partially implemented by:

- `Set`
- `BitSet`
- `IdSet`
- `EnvDict`
- `Array`
- `BitArray`
- `ImmutableDict`
- `PersistentDict`
- `Iterators.Pairs`

## Set-Like Collections

`Base.AbstractSet` — Type

    AbstractSet{T}

Supertype for set-like types whose elements are of type `T`. `Set`, `BitSet` and other types are subtypes of this.

source
`Base.Set` — Type

    Set{T} <: AbstractSet{T}

`Set`s are mutable containers that provide fast membership testing.

`Set`s have efficient implementations of set operations such as `in`, `union` and `intersect`. Elements in a `Set` are unique, as determined by the elements' definition of `isequal`. The order of elements in a `Set` is an implementation detail and cannot be relied on.

See also: `AbstractSet`, `BitSet`, `Dict`, `push!`, `empty!`, `union!`, `in`, `isequal`

Examples

    julia> s = Set("aaBca")
    Set{Char} with 3 elements:
      'a'
      'c'
      'B'

    julia> push!(s, 'b')
    Set{Char} with 4 elements:
      'a'
      'b'
      'B'
      'c'

    julia> s = Set([NaN, 0.0, 1.0, 2.0]);

    julia> -0.0 in s # isequal(0.0, -0.0) is false
    false

    julia> NaN in s # isequal(NaN, NaN) is true
    true

source
`Base.BitSet` — Type

    BitSet([itr])

Construct a sorted set of `Int`s generated by the given iterable object, or an empty set. Implemented as a bit string, and therefore designed for dense integer sets. If the set will be sparse (for example, holding a few very large integers), use `Set` instead.

source
`Base.IdSet` — Type

    IdSet{T}([itr])
    IdSet()

IdSet{T}() constructs a set (see `Set`) using `===` as equality with values of type `T`.

In the example below, the values are all `isequal` so they get overwritten in the ordinary `Set`. The `IdSet` compares by `===` and so preserves the 3 different values.

Julia 1.11

Exported in Julia 1.11 and later.

Examples

    julia> Set(Any[true, 1, 1.0])
    Set{Any} with 1 element:
      1.0

    julia> IdSet{Any}(Any[true, 1, 1.0])
    IdSet{Any} with 3 elements:
      1.0
      1
      true

source
`Base.union` — Function

    union(s, itrs...)
    ∪(s, itrs...)

Construct an object containing all distinct elements from all of the arguments.

The first argument controls what kind of container is returned. If this is an array, it maintains the order in which elements first appear.

Unicode `∪` can be typed by writing `\cup` then pressing tab in the Julia REPL, and in many editors. This is an infix operator, allowing `s ∪ itr`.

See also `unique`, `intersect`, `isdisjoint`, `vcat`, `Iterators.flatten`.

Examples

    julia> union([1, 2], [3])
    3-element Vector{Int64}:
     1
     2
     3

    julia> union([4 2 3 4 4], 1:3, 3.0)
    4-element Vector{Float64}:
     4.0
     2.0
     3.0
     1.0

    julia> (0, 0.0) ∪ (-0.0, NaN)
    3-element Vector{Real}:
       0
      -0.0
     NaN

    julia> union(Set([1, 2]), 2:3)
    Set{Int64} with 3 elements:
      2
      3
      1

source
`Base.union!` — Function

    union!(s::IntDisjointSet{T}, x::T, y::T)

Merge the subset containing `x` and that containing `y` into one and return the root of the new set.

source

    union!(s::Union{AbstractSet,AbstractVector}, itrs...)

Construct the `union` of passed in sets and overwrite `s` with the result. Maintain order with arrays.

Warning

Behavior can be unexpected when any mutated argument shares memory with any other argument.

Examples

    julia> a = Set([3, 4, 5]);

    julia> union!(a, 1:2:7);

    julia> a
    Set{Int64} with 5 elements:
      5
      4
      7
      3
      1

source
`Base.intersect` — Function

    intersect(s, itrs...)
    ∩(s, itrs...)

Construct the set containing those elements which appear in all of the arguments.

The first argument controls what kind of container is returned. If this is an array, it maintains the order in which elements first appear.

Unicode `∩` can be typed by writing `\cap` then pressing tab in the Julia REPL, and in many editors. This is an infix operator, allowing `s ∩ itr`.

See also `setdiff`, `isdisjoint`, `issubset`, `issetequal`.

Julia 1.8

As of Julia 1.8 intersect returns a result with the eltype of the type-promoted eltypes of the two inputs

Examples

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

    julia> intersect([1, 4, 4, 5, 6], [6, 4, 6, 7, 8])
    2-element Vector{Int64}:
     4
     6

    julia> intersect(1:16, 7:99)
    7:16

    julia> (0, 0.0) ∩ (-0.0, 0)
    1-element Vector{Real}:
     0

    julia> intersect(Set([1, 2]), BitSet([2, 3]), 1.0:10.0)
    Set{Float64} with 1 element:
      2.0

source
`Base.setdiff` — Function

    setdiff(s, itrs...)

Construct the set of elements in `s` but not in any of the iterables in `itrs`. Maintain order with arrays.

See also `setdiff!`, `union` and `intersect`.

Examples

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

source
`Base.setdiff!` — Function

    setdiff!(s, itrs...)

Remove from set `s` (in-place) each element of each iterable from `itrs`. Maintain order with arrays.

Warning

Behavior can be unexpected when any mutated argument shares memory with any other argument.

Examples

    julia> a = Set([1, 3, 4, 5]);

    julia> setdiff!(a, 1:2:6);

    julia> a
    Set{Int64} with 1 element:
      4

source
`Base.symdiff` — Function

    symdiff(s, itrs...)

Construct the symmetric difference of elements in the passed in sets. When `s` is not an `AbstractSet`, the order is maintained.

See also `symdiff!`, `setdiff`, `union` and `intersect`.

Examples

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

    julia> symdiff([1,2,1], [2, 1, 2])
    Int64[]

source
`Base.symdiff!` — Function

    symdiff!(s::Union{AbstractSet,AbstractVector}, itrs...)

Construct the symmetric difference of the passed in sets, and overwrite `s` with the result. When `s` is an array, the order is maintained. Note that in this case the multiplicity of elements matters.

Warning

Behavior can be unexpected when any mutated argument shares memory with any other argument.

source
`Base.intersect!` — Function

    intersect!(s::Union{AbstractSet,AbstractVector}, itrs...)

Intersect all passed in sets and overwrite `s` with the result. Maintain order with arrays.

Warning

Behavior can be unexpected when any mutated argument shares memory with any other argument.

source
`Base.issubset` — Function

    issubset(a, b) -> Bool
    ⊆(a, b) -> Bool
    ⊇(b, a) -> Bool

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

See also `⊊`, `⊈`, `∩`, `∪`, `contains`.

Examples

    julia> issubset([1, 2], [1, 2, 3])
    true

    julia> [1, 2, 3] ⊆ [1, 2]
    false

    julia> [1, 2, 3] ⊇ [1, 2]
    true

source
`Base.in!` — Function

    in!(x, s::AbstractSet) -> Bool

If `x` is in `s`, return `true`. If not, push `x` into `s` and return `false`. This is equivalent to `in(x, s) ? true : (push!(s, x); false)`, but may have a more efficient implementation.

See also: `in`, `push!`, `Set`

Julia 1.11

This function requires at least 1.11.

Examples

    julia> s = Set{Any}([1, 2, 3]); in!(4, s)
    false

    julia> length(s)
    4

    julia> in!(0x04, s)
    true

    julia> s
    Set{Any} with 4 elements:
      4
      2
      3
      1

source
`Base.:⊈` — Function

    ⊈(a, b) -> Bool
    ⊉(b, a) -> Bool

Negation of `⊆` and `⊇`, i.e. checks that `a` is not a subset of `b`.

See also `issubset` (`⊆`), `⊊`.

Examples

    julia> (1, 2) ⊈ (2, 3)
    true

    julia> (1, 2) ⊈ (1, 2, 3)
    false

source
`Base.:⊊` — Function

    ⊊(a, b) -> Bool
    ⊋(b, a) -> Bool

Determines if `a` is a subset of, but not equal to, `b`.

See also `issubset` (`⊆`), `⊈`.

Examples

    julia> (1, 2) ⊊ (1, 2, 3)
    true

    julia> (1, 2) ⊊ (1, 2)
    false

source
`Base.issetequal` — Function

    issetequal(x)

Create a function that compares its argument to `x` using `issetequal`, i.e. a function equivalent to `y -> issetequal(y, x)`. The returned function is of type `Base.Fix2{typeof(issetequal)}`, which can be used to implement specialized methods.

Julia 1.11

This functionality requires at least Julia 1.11.

source

    issetequal(a, b) -> Bool

Determine whether `a` and `b` have the same elements. Equivalent to `a ⊆ b && b ⊆ a` but more efficient when possible.

See also: `isdisjoint`, `union`.

Examples

    julia> issetequal([1, 2], [1, 2, 3])
    false

    julia> issetequal([1, 2], [2, 1])
    true

source
`Base.isdisjoint` — Function

    isdisjoint(x)

Create a function that compares its argument to `x` using `isdisjoint`, i.e. a function equivalent to `y -> isdisjoint(y, x)`. The returned function is of type `Base.Fix2{typeof(isdisjoint)}`, which can be used to implement specialized methods.

Julia 1.11

This functionality requires at least Julia 1.11.

source

    isdisjoint(a, b) -> Bool

Determine whether the collections `a` and `b` are disjoint. Equivalent to `isempty(a ∩ b)` but more efficient when possible.

See also: `intersect`, `isempty`, `issetequal`.

Julia 1.5

This function requires at least Julia 1.5.

Examples

    julia> isdisjoint([1, 2], [2, 3, 4])
    false

    julia> isdisjoint([3, 1], [2, 4])
    true

source

Fully implemented by:

- `Set`
- `BitSet`
- `IdSet`

Partially implemented by:

- `Array`

## Dequeues

`Base.push!` — Function

    push!(collection, items...) -> collection

Insert one or more `items` in `collection`. If `collection` is an ordered container, the items are inserted at the end (in the given order).

Examples

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

If `collection` is ordered, use `append!` to add all the elements of another collection to it. The result of the preceding example is equivalent to `append!([1, 2, 3], [4, 5, 6])`. For `AbstractSet` objects, `union!` can be used instead.

See `sizehint!` for notes about the performance model.

See also `pushfirst!`.

source
`Base.pop!` — Function

    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.

Examples

    julia> d = Dict("a"=>1, "b"=>2, "c"=>3);

    julia> pop!(d, "a")
    1

    julia> pop!(d, "d")
    ERROR: KeyError: key "d" not found
    Stacktrace:
    [...]

    julia> pop!(d, "e", 4)
    4

source

    pop!(collection) -> item

Remove an item in `collection` and return it. If `collection` is an ordered container, the last item is returned; for unordered containers, an arbitrary element is returned.

See also: `popfirst!`, `popat!`, `delete!`, `deleteat!`, `splice!`, and `push!`.

Examples

    julia> A=[1, 2, 3]
    3-element Vector{Int64}:
     1
     2
     3

    julia> pop!(A)
    3

    julia> A
    2-element Vector{Int64}:
     1
     2

    julia> S = Set([1, 2])
    Set{Int64} with 2 elements:
      2
      1

    julia> pop!(S)
    2

    julia> S
    Set{Int64} with 1 element:
      1

    julia> pop!(Dict(1=>2))
    1 => 2

source
`Base.popat!` — Function

    popat!(a::Vector, i::Integer, [default])

Remove the item at the given `i` and return it. Subsequent items are shifted to fill the resulting gap. When `i` is not a valid index for `a`, return `default`, or throw an error if `default` is not specified.

See also: `pop!`, `popfirst!`, `deleteat!`, `splice!`.

Julia 1.5

This function is available as of Julia 1.5.

Examples

    julia> a = [4, 3, 2, 1]; popat!(a, 2)
    3

    julia> a
    3-element Vector{Int64}:
     4
     2
     1

    julia> popat!(a, 4, missing)
    missing

    julia> popat!(a, 4)
    ERROR: BoundsError: attempt to access 3-element Vector{Int64} at index [4]
    [...]

source
`Base.pushfirst!` — Function

    pushfirst!(collection, items...) -> collection

Insert one or more `items` at the beginning of `collection`.

This function is called `unshift` in many other programming languages.

Examples

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

source
`Base.popfirst!` — Function

    popfirst!(collection) -> item

Remove the first `item` from `collection`.

This function is called `shift` in many other programming languages.

See also: `pop!`, `popat!`, `delete!`.

Examples

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

    julia> popfirst!(A)
    1

    julia> A
    5-element Vector{Int64}:
     2
     3
     4
     5
     6

source
`Base.insert!` — Function

    insert!(a::Vector, index::Integer, item)

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

See also: `push!`, `replace`, `popat!`, `splice!`.

Examples

    julia> insert!(Any[1:6;], 3, "here")
    7-element Vector{Any}:
     1
     2
      "here"
     3
     4
     5
     6

source
`Base.deleteat!` — Function

    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` can be either an iterator or a collection of sorted and unique integer indices, or a boolean vector of the same length as `a` with `true` indicating entries to delete.

Examples

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

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

    julia> deleteat!([6, 5, 4, 3, 2, 1], (2, 2))
    ERROR: ArgumentError: indices must be unique and sorted
    Stacktrace:
    [...]

source

    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.

See also: `keepat!`, `delete!`, `popat!`, `splice!`.

Examples

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

source
`Base.keepat!` — Function

    keepat!(a::Vector, m::AbstractVector{Bool})
    keepat!(a::BitVector, m::AbstractVector{Bool})

The in-place version of logical indexing `a = a[m]`. That is, `keepat!(a, m)` on vectors of equal length `a` and `m` will remove all elements from `a` for which `m` at the corresponding index is `false`.

Examples

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

    julia> keepat!(a, [true, false, true])
    2-element Vector{Symbol}:
     :a
     :c

    julia> a
    2-element Vector{Symbol}:
     :a
     :c

source

    keepat!(a::Vector, inds)
    keepat!(a::BitVector, inds)

Remove the items at all the indices which are not given by `inds`, and return the modified `a`. Items which are kept are shifted to fill the resulting gaps.

Warning

Behavior can be unexpected when any mutated argument shares memory with any other argument.

`inds` must be an iterator of sorted and unique integer indices. See also `deleteat!`.

Julia 1.7

This function is available as of Julia 1.7.

Examples

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

source
`Base.splice!` — Function

    splice!(a::Vector, indices, [replacement]) -> items

Remove items at specified indices, and return a collection containing the removed items. Subsequent items are shifted left to fill the resulting gaps. If specified, replacement values from an ordered collection will be spliced in place of the removed items; in this case, `indices` must be a `AbstractUnitRange`.

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

Warning

Behavior can be unexpected when any mutated argument shares memory with any other argument.

Julia 1.5

Prior to Julia 1.5, `indices` must always be a `UnitRange`.

Julia 1.8

Prior to Julia 1.8, `indices` must be a `UnitRange` if splicing in replacement values.

Examples

    julia> A = [-1, -2, -3, 5, 4, 3, -1]; splice!(A, 4:3, 2)
    Int64[]

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

source

    splice!(a::Vector, index::Integer, [replacement]) -> item

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

See also: `replace`, `delete!`, `deleteat!`, `pop!`, `popat!`.

Examples

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

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

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

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

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

    julia> A
    7-element Vector{Int64}:
     -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)`.

source
`Base.resize!` — Function

    resize!(a::Vector, n::Integer) -> Vector

Resize `a` 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.

Examples

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

    julia> a = resize!([6, 5, 4, 3, 2, 1], 8);

    julia> length(a)
    8

    julia> a[1:6]
    6-element Vector{Int64}:
     6
     5
     4
     3
     2
     1

source
`Base.append!` — Function

    append!(collection, collections...) -> collection.

For an ordered container `collection`, add the elements of each `collections` to the end of it.

Julia 1.6

Specifying multiple collections to be appended requires at least Julia 1.6.

Examples

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

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

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

See `sizehint!` for notes about the performance model.

See also `vcat` for vectors, `union!` for sets, and `prepend!` and `pushfirst!` for the opposite order.

source
`Base.prepend!` — Function

    prepend!(a::Vector, collections...) -> collection

Insert the elements of each `collections` to the beginning of `a`.

When `collections` specifies multiple collections, order is maintained: elements of `collections[1]` will appear leftmost in `a`, and so on.

Julia 1.6

Specifying multiple collections to be prepended requires at least Julia 1.6.

Examples

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

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

source

Fully implemented by:

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

## Utility Collections

`Core.Pair` — Type

    Pair(x, y)
    x => y

Construct a `Pair` object with type `Pair{typeof(x), typeof(y)}`. The elements are stored in the fields `first` and `second`. They can also be accessed via iteration (but a `Pair` is treated as a single "scalar" for broadcasting operations).

See also `Dict`.

Examples

    julia> p = "foo" => 7
    "foo" => 7

    julia> typeof(p)
    Pair{String, Int64}

    julia> p.first
    "foo"

    julia> for x in p
               println(x)
           end
    foo
    7

    julia> replace.(["xops", "oxps"], "x" => "o")
    2-element Vector{String}:
     "oops"
     "oops"

source
`Base.Pairs` — Type

    Base.Pairs(values, keys) <: AbstractDict{eltype(keys), eltype(values)}

Transforms an indexable container into a Dictionary-view of the same data. Modifying the key-space of the underlying data may invalidate this object.

source

Theme

This document was generated with Documenter.jl version 1.17.0 on Thursday 9 April 2026. Using Julia version 1.12.6.