Collections and Data Structures
Iteration
Sequential iteration is implemented by the methods start()
, done()
, and next()
. The general for
loop:
for i = I # or "for i in I"
# body
end
is translated into:
state = start(I)
while !done(I, state)
(i, state) = next(I, state)
# body
end
The state
object may be anything, and should be chosen appropriately for each iterable type. See the manual section on the iteration interface for more details about defining a custom iterable type.
Base.start
— Function.start(iter) -> state
Get initial iteration state for an iterable object.
Examples
julia> start(1:5)
1
julia> start([1;2;3])
1
julia> start([4;2;3])
1
Base.done
— Function.done(iter, state) -> Bool
Test whether we are done iterating.
Examples
julia> done(1:5, 3)
false
julia> done(1:5, 5)
false
julia> done(1:5, 6)
true
Base.next
— Function.next(iter, state) -> item, state
For a given iterable object and iteration state, return the current item and the next iteration state.
Examples
julia> next(1:5, 3)
(3, 4)
julia> next(1:5, 5)
(5, 6)
Base.iteratorsize
— Function.iteratorsize(itertype::Type) -> IteratorSize
Given the type of an iterator, returns one of the following values:
SizeUnknown()
if the length (number of elements) cannot be determined in advance.HasLength()
if there is a fixed, finite length.HasShape()
if there is a known length plus a notion of multidimensional shape (as for an array). In this case thesize
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()
julia> Base.iteratorsize((2,3))
Base.HasLength()
Base.iteratoreltype
— Function.iteratoreltype(itertype::Type) -> IteratorEltype
Given the type of an iterator, returns one of the following values:
EltypeUnknown()
if the type of elements yielded by the iterator is not known in advance.HasEltype()
if the element type is known, andeltype
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()
Fully implemented by:
Range
UnitRange
Tuple
Number
EachLine
AbstractString
General Collections
Base.isempty
— Function.isempty(collection) -> Bool
Determine whether a collection is empty (has no elements).
Examples
julia> isempty([])
true
julia> isempty([1 2 3])
false
Base.empty!
— Function.empty!(collection) -> collection
Remove all elements from a collection
.
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} with 0 entries
Base.length
— Method.length(collection) -> Integer
For ordered, indexable collections, returns the maximum index i
for which getindex(collection, i)
is valid. For unordered collections, returns the number of elements.
Examples
julia> length(1:5)
5
julia> length([1; 2; 3; 4])
4
Base.endof
— Function.endof(collection) -> Integer
Returns the last index of the collection.
Example
julia> endof([1,2,4])
3
Fully implemented by:
Range
UnitRange
Tuple
Number
AbstractString
Iterable Collections
Base.in
— Function.in(item, collection) -> Bool
∈(item,collection) -> Bool
∋(collection,item) -> Bool
∉(item,collection) -> Bool
∌(collection,item) -> Bool
Determine whether an item is in the given collection, in the sense that it is ==
to one of the values generated by iterating over the collection. Some collections need a slightly different definition; for example 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)
.
julia> a = 1:3:20
1:3:19
julia> 4 in a
true
julia> 5 in a
false
Base.eltype
— Function.eltype(type)
Determine the type of the elements generated by iterating a collection of the given type
. For associative collection types, this will be a Pair{KeyType,ValType}
. The definition eltype(x) = eltype(typeof(x))
is provided for convenience so that instances can be passed instead of types. However the form that accepts a type argument should be defined for new types.
julia> eltype(ones(Float32,2,2))
Float32
julia> eltype(ones(Int8,2,2))
Int8
Base.indexin
— Function.indexin(a, b)
Returns a vector containing the highest index in b
for each value in a
that is a member of b
. The output vector contains 0 wherever a
is not a member of b
.
Examples
julia> a = ['a', 'b', 'c', 'b', 'd', 'a'];
julia> b = ['a','b','c'];
julia> indexin(a,b)
6-element Array{Int64,1}:
1
2
3
2
0
1
julia> indexin(b,a)
3-element Array{Int64,1}:
6
4
3
Base.findin
— Function.findin(a, b)
Returns the indices of elements in collection a
that appear in collection b
.
Examples
julia> a = collect(1:3:15)
5-element Array{Int64,1}:
1
4
7
10
13
julia> b = collect(2:4:10)
3-element Array{Int64,1}:
2
6
10
julia> findin(a,b) # 10 is the only common element
1-element Array{Int64,1}:
4
Base.unique
— Function.unique(itr)
Returns an array containing one value from itr
for each unique value, as determined by isequal
.
julia> unique([1; 2; 2; 6])
3-element Array{Int64,1}:
1
2
6
unique(f, itr)
Returns an array containing one value from itr
for each unique value produced by f
applied to elements of itr
.
julia> unique(isodd, [1; 2; 2; 6])
2-element Array{Int64,1}:
1
2
unique(itr[, dim])
Returns an array containing only the unique elements of the iterable itr
, in the order that the first of each set of equivalent elements originally appears. If dim
is specified, returns unique regions of the array itr
along dim
.
julia> A = map(isodd, reshape(collect(1:8), (2,2,2)))
2×2×2 Array{Bool,3}:
[:, :, 1] =
true true
false false
[:, :, 2] =
true true
false false
julia> unique(A)
2-element Array{Bool,1}:
true
false
julia> unique(A, 2)
2×1×2 Array{Bool,3}:
[:, :, 1] =
true
false
[:, :, 2] =
true
false
julia> unique(A, 3)
2×2×1 Array{Bool,3}:
[:, :, 1] =
true true
false false
Base.allunique
— Function.allunique(itr) -> Bool
Return true
if all values from itr
are distinct when compared with isequal
.
julia> a = [1; 2; 3]
3-element Array{Int64,1}:
1
2
3
julia> allunique([a, a])
false
Base.reduce
— Method.reduce(op, v0, itr)
Reduce the given collection ìtr
with the given binary operator op
. v0
must be a neutral element for op
that will be returned for empty collections. It is unspecified whether v0
is used for non-empty collections.
Reductions for certain commonly-used operators have special implementations which should be used instead: maximum(itr)
, minimum(itr)
, sum(itr)
, prod(itr)
, any(itr)
, all(itr)
.
The associativity of the reduction is implementation dependent. This means that you can't use non-associative operations like -
because it is undefined whether reduce(-,[1,2,3])
should be evaluated as (1-2)-3
or 1-(2-3)
. Use foldl
or foldr
instead for guaranteed left or right associativity.
Some operations accumulate error, and parallelism will also be easier if the reduction can be executed in groups. Future versions of Julia might change the algorithm. Note that the elements are not reordered if you use an ordered collection.
Examples
julia> reduce(*, 1, [2; 3; 4])
24
Base.reduce
— Method.reduce(op, itr)
Like reduce(op, v0, itr)
. This cannot be used with empty collections, except for some special cases (e.g. when op
is one of +
, *
, max
, min
, &
, |
) when Julia can determine the neutral element of op
.
julia> reduce(*, [2; 3; 4])
24
Base.foldl
— Method.foldl(op, v0, itr)
Like reduce
, but with guaranteed left associativity. v0
will be used exactly once.
julia> foldl(-, 1, 2:5)
-13
Base.foldl
— Method.foldl(op, itr)
Like foldl(op, v0, itr)
, but using the first element of itr
as v0
. In general, this cannot be used with empty collections (see reduce(op, itr)
).
julia> foldl(-, 2:5)
-10
Base.foldr
— Method.foldr(op, v0, itr)
Like reduce
, but with guaranteed right associativity. v0
will be used exactly once.
julia> foldr(-, 1, 2:5)
-1
Base.foldr
— Method.foldr(op, itr)
Like foldr(op, v0, itr)
, but using the last element of itr
as v0
. In general, this cannot be used with empty collections (see reduce(op, itr)
).
julia> foldr(-, 2:5)
-2
Base.maximum
— Method.maximum(itr)
Returns the largest element in a collection.
julia> maximum(-20.5:10)
9.5
julia> maximum([1,2,3])
3
Base.maximum
— Method.maximum(A, 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)
.
julia> A = [1 2; 3 4]
2×2 Array{Int64,2}:
1 2
3 4
julia> maximum(A, 1)
1×2 Array{Int64,2}:
3 4
julia> maximum(A, 2)
2×1 Array{Int64,2}:
2
4
Base.maximum!
— Function.maximum!(r, A)
Compute the maximum value of A
over the singleton dimensions of r
, and write results to r
.
Examples
julia> A = [1 2; 3 4]
2×2 Array{Int64,2}:
1 2
3 4
julia> maximum!([1; 1], A)
2-element Array{Int64,1}:
2
4
julia> maximum!([1 1], A)
1×2 Array{Int64,2}:
3 4
Base.minimum
— Method.minimum(itr)
Returns the smallest element in a collection.
julia> minimum(-20.5:10)
-20.5
julia> minimum([1,2,3])
1
Base.minimum
— Method.minimum(A, 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)
.
Examples
julia> A = [1 2; 3 4]
2×2 Array{Int64,2}:
1 2
3 4
julia> minimum(A, 1)
1×2 Array{Int64,2}:
1 2
julia> minimum(A, 2)
2×1 Array{Int64,2}:
1
3
Base.minimum!
— Function.minimum!(r, A)
Compute the minimum value of A
over the singleton dimensions of r
, and write results to r
.
Examples
julia> A = [1 2; 3 4]
2×2 Array{Int64,2}:
1 2
3 4
julia> minimum!([1; 1], A)
2-element Array{Int64,1}:
1
3
julia> minimum!([1 1], A)
1×2 Array{Int64,2}:
1 2
Base.extrema
— Method.extrema(itr) -> Tuple
Compute both the minimum and maximum element in a single pass, and return them as a 2-tuple.
julia> extrema(2:10)
(2, 10)
julia> extrema([9,pi,4.5])
(3.141592653589793, 9.0)
Base.extrema
— Method.extrema(A, dims) -> Array{Tuple}
Compute the minimum and maximum elements of an array over the given dimensions.
Example
julia> A = reshape(collect(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, (1,2))
1×1×2 Array{Tuple{Int64,Int64},3}:
[:, :, 1] =
(1, 7)
[:, :, 2] =
(9, 15)
Base.indmax
— Function.indmax(itr) -> Integer
Returns the index of the maximum element in a collection. If there are multiple maximal elements, then the first one will be returned. NaN
values are ignored, unless all elements are NaN
.
The collection must not be empty.
Examples
julia> indmax([8,0.1,-9,pi])
1
julia> indmax([1,7,7,6])
2
julia> indmax([1,7,7,NaN])
2
Base.indmin
— Function.indmin(itr) -> Integer
Returns the index of the minimum element in a collection. If there are multiple minimal elements, then the first one will be returned. NaN
values are ignored, unless all elements are NaN
.
The collection must not be empty.
Examples
julia> indmin([8,0.1,-9,pi])
3
julia> indmin([7,1,1,6])
2
julia> indmin([7,1,1,NaN])
2
Base.findmax
— Method.findmax(itr) -> (x, index)
Returns the maximum element of the collection itr
and its index. If there are multiple maximal elements, then the first one will be returned. NaN
values are ignored, unless all elements are NaN
.
The collection must not be empty.
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])
(7.0, 2)
Base.findmax
— Method.findmax(A, region) -> (maxval, index)
For an array input, returns the value and index of the maximum over the given region.
Examples
julia> A = [1 2; 3 4]
2×2 Array{Int64,2}:
1 2
3 4
julia> findmax(A,1)
([3 4], [2 4])
julia> findmax(A,2)
([2; 4], [3; 4])
Base.findmin
— Method.findmin(itr) -> (x, index)
Returns the minimum element of the collection itr
and its index. If there are multiple minimal elements, then the first one will be returned. NaN
values are ignored, unless all elements are NaN
.
The collection must not be empty.
Examples
julia> findmin([8,0.1,-9,pi])
(-9.0, 3)
julia> findmin([7,1,1,6])
(1, 2)
julia> findmin([7,1,1,NaN])
(1.0, 2)
Base.findmin
— Method.findmin(A, region) -> (minval, index)
For an array input, returns the value and index of the minimum over the given region.
Examples
julia> A = [1 2; 3 4]
2×2 Array{Int64,2}:
1 2
3 4
julia> findmin(A, 1)
([1 2], [1 3])
julia> findmin(A, 2)
([1; 3], [1; 2])
Base.findmax!
— Function.findmax!(rval, rind, A, [init=true]) -> (maxval, index)
Find the maximum of A
and the corresponding linear index along singleton dimensions of rval
and rind
, and store the results in rval
and rind
.
Base.findmin!
— Function.findmin!(rval, rind, A, [init=true]) -> (minval, index)
Find the minimum of A
and the corresponding linear index along singleton dimensions of rval
and rind
, and store the results in rval
and rind
.
Base.sum
— Function.sum(f, itr)
Sum the results of calling function f
on each element of itr
.
julia> sum(abs2, [2; 3; 4])
29
sum(itr)
Returns the sum of all elements in a collection.
julia> sum(1:20)
210
sum(A, dims)
Sum elements of an array over the given dimensions.
Examples
julia> A = [1 2; 3 4]
2×2 Array{Int64,2}:
1 2
3 4
julia> sum(A, 1)
1×2 Array{Int64,2}:
4 6
julia> sum(A, 2)
2×1 Array{Int64,2}:
3
7
Base.sum!
— Function.sum!(r, A)
Sum elements of A
over the singleton dimensions of r
, and write results to r
.
Examples
julia> A = [1 2; 3 4]
2×2 Array{Int64,2}:
1 2
3 4
julia> sum!([1; 1], A)
2-element Array{Int64,1}:
3
7
julia> sum!([1 1], A)
1×2 Array{Int64,2}:
4 6
Base.prod
— Function.prod(f, itr)
Returns the product of f
applied to each element of itr
.
julia> prod(abs2, [2; 3; 4])
576
prod(itr)
Returns the product of all elements of a collection.
julia> prod(1:20)
2432902008176640000
prod(A, dims)
Multiply elements of an array over the given dimensions.
Examples
julia> A = [1 2; 3 4]
2×2 Array{Int64,2}:
1 2
3 4
julia> prod(A, 1)
1×2 Array{Int64,2}:
3 8
julia> prod(A, 2)
2×1 Array{Int64,2}:
2
12
Base.prod!
— Function.prod!(r, A)
Multiply elements of A
over the singleton dimensions of r
, and write results to r
.
Examples
julia> A = [1 2; 3 4]
2×2 Array{Int64,2}:
1 2
3 4
julia> prod!([1; 1], A)
2-element Array{Int64,1}:
2
12
julia> prod!([1 1], A)
1×2 Array{Int64,2}:
3 8
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).
julia> a = [true,false,false,true]
4-element Array{Bool,1}:
true
false
false
true
julia> any(a)
true
julia> any((println(i); v) for (i, v) in enumerate(a))
1
true
Base.any
— Method.any(A, dims)
Test whether any values along the given dimensions of an array are true
.
Examples
julia> A = [true false; true false]
2×2 Array{Bool,2}:
true false
true false
julia> any(A, 1)
1×2 Array{Bool,2}:
true false
julia> any(A, 2)
2×1 Array{Bool,2}:
true
true
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
.
Examples
julia> A = [true false; true false]
2×2 Array{Bool,2}:
true false
true false
julia> any!([1; 1], A)
2-element Array{Int64,1}:
1
1
julia> any!([1 1], A)
1×2 Array{Int64,2}:
1 0
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).
julia> a = [true,false,false,true]
4-element Array{Bool,1}:
true
false
false
true
julia> all(a)
false
julia> all((println(i); v) for (i, v) in enumerate(a))
1
2
false
Base.all
— Method.all(A, dims)
Test whether all values along the given dimensions of an array are true
.
Examples
julia> A = [true false; true true]
2×2 Array{Bool,2}:
true false
true true
julia> all(A, 1)
1×2 Array{Bool,2}:
true false
julia> all(A, 2)
2×1 Array{Bool,2}:
false
true
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
.
Examples
julia> A = [true false; true false]
2×2 Array{Bool,2}:
true false
true false
julia> all!([1; 1], A)
2-element Array{Int64,1}:
0
0
julia> all!([1 1], A)
1×2 Array{Int64,2}:
1 0
Base.count
— Function.count(p, itr) -> Integer
count(itr) -> Integer
Count the number of elements in itr
for which predicate p
returns true
. If p
is omitted, counts the number of true
elements in itr
(which should be a collection of boolean values).
julia> count(i->(4<=i<=6), [2,3,4,5,6])
3
julia> count([true, false, true, true])
3
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).
julia> any(i->(4<=i<=6), [3,5,7])
true
julia> any(i -> (println(i); i > 3), 1:10)
1
2
3
4
true
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).
julia> all(i->(4<=i<=6), [4,5,6])
true
julia> all(i -> (println(i); i < 3), 1:10)
1
2
3
false
Base.foreach
— Function.foreach(f, c...) -> Void
Call function f
on each element of iterable c
. For multiple iterable arguments, f
is called elementwise. foreach
should be used instead of map
when the results of f
are not needed, for example in foreach(println, array)
.
Example
julia> a = 1:3:7;
julia> foreach(x -> println(x^2), a)
1
16
49
Base.map
— Function.map(f, c...) -> collection
Transform collection c
by applying f
to each element. For multiple collection arguments, apply f
elementwise.
Examples
julia> map(x -> x * 2, [1, 2, 3])
3-element Array{Int64,1}:
2
4
6
julia> map(+, [1, 2, 3], [10, 20, 30])
3-element Array{Int64,1}:
11
22
33
map(f, x::Nullable)
Return f
applied to the value of x
if it has one, as a Nullable
. If x
is null, then return a null value of type Nullable{S}
. S
is guaranteed to be either Union{}
or a concrete type. Whichever of these is chosen is an implementation detail, but typically the choice that maximizes performance would be used. If x
has a value, then the return type is guaranteed to be of type Nullable{typeof(f(x))}
.
Base.map!
— Function.map!(function, destination, collection...)
Like map
, but stores the result in destination
rather than a new collection. destination
must be at least as large as the first collection.
Example
julia> x = zeros(3);
julia> map!(x -> x * 2, x, [1, 2, 3]);
julia> x
3-element Array{Float64,1}:
2.0
4.0
6.0
Base.mapreduce
— Method.mapreduce(f, op, v0, itr)
Apply function f
to each element in itr
, and then reduce the result using the binary function op
. v0
must be a neutral element for op
that will be returned for empty collections. It is unspecified whether v0
is used for non-empty collections.
mapreduce
is functionally equivalent to calling reduce(op, v0, map(f, itr))
, but will in general execute faster since no intermediate collection needs to be created. See documentation for reduce
and map
.
julia> mapreduce(x->x^2, +, [1:3;]) # == 1 + 4 + 9
14
The associativity of the reduction is implementation-dependent. Additionally, some implementations may reuse the return value of f
for elements that appear multiple times in itr
. Use mapfoldl
or mapfoldr
instead for guaranteed left or right associativity and invocation of f
for every value.
Base.mapreduce
— Method.mapreduce(f, op, itr)
Like mapreduce(f, op, v0, itr)
. In general, this cannot be used with empty collections (see reduce(op, itr)
).
Base.mapfoldl
— Method.mapfoldl(f, op, v0, itr)
Like mapreduce
, but with guaranteed left associativity, as in foldl
. v0
will be used exactly once.
Base.mapfoldl
— Method.mapfoldl(f, op, itr)
Like mapfoldl(f, op, v0, itr)
, but using the first element of itr
as v0
. In general, this cannot be used with empty collections (see reduce(op, itr)
).
Base.mapfoldr
— Method.mapfoldr(f, op, v0, itr)
Like mapreduce
, but with guaranteed right associativity, as in foldr
. v0
will be used exactly once.
Base.mapfoldr
— Method.mapfoldr(f, op, itr)
Like mapfoldr(f, op, v0, itr)
, but using the first element of itr
as v0
. In general, this cannot be used with empty collections (see reduce(op, itr)
).
Base.first
— Function.first(coll)
Get the first element of an iterable collection. Returns the start point of a Range
even if it is empty.
julia> first(2:2:10)
2
julia> first([1; 2; 3; 4])
1
Base.last
— Function.last(coll)
Get the last element of an ordered collection, if it can be computed in O(1) time. This is accomplished by calling endof
to get the last index. Returns the end point of a Range
even if it is empty.
julia> last(1:2:10)
9
julia> last([1; 2; 3; 4])
4
Base.step
— Function.step(r)
Get the step size of a Range
object.
julia> step(1:10)
1
julia> step(1:2:10)
2
julia> step(2.5:0.3:10.9)
0.3
julia> step(linspace(2.5,10.9,85))
0.1
Base.collect
— Method.collect(collection)
Return an Array
of all items in a collection or iterator. For associative collections, returns Pair{KeyType, ValType}
. If the argument is array-like or is an iterator with the HasShape()
trait, the result will have the same shape and number of dimensions as the argument.
Example
julia> collect(1:2:13)
7-element Array{Int64,1}:
1
3
5
7
9
11
13
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
.
julia> collect(Float64, 1:2:5)
3-element Array{Float64,1}:
1.0
3.0
5.0
Base.issubset
— Method.issubset(a, b)
⊆(a,b) -> Bool
⊈(a,b) -> Bool
⊊(a,b) -> Bool
Determine whether every element of a
is also in b
, using in
.
Examples
julia> issubset([1, 2], [1, 2, 3])
true
julia> issubset([1, 2, 3], [1, 2])
false
Base.filter
— Function.filter(function, collection)
Return a copy of collection
, removing elements for which function
is false
. For associative collections, the function is passed two arguments (key and value).
Examples
julia> a = 1:10
1:10
julia> filter(isodd, a)
5-element Array{Int64,1}:
1
3
5
7
9
julia> d = Dict(1=>"a", 2=>"b")
Dict{Int64,String} with 2 entries:
2 => "b"
1 => "a"
julia> filter((x,y)->isodd(x), d)
Dict{Int64,String} with 1 entry:
1 => "a"
filter(p, x::Nullable)
Return null if either x
is null or p(get(x))
is false, and x
otherwise.
Base.filter!
— Function.filter!(function, collection)
Update collection
, removing elements for which function
is false
. For associative collections, the function is passed two arguments (key and value).
Example
julia> filter!(isodd, collect(1:10))
5-element Array{Int64,1}:
1
3
5
7
9
Indexable Collections
Base.getindex
— Method.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, ...)
.
Example
julia> A = Dict("a" => 1, "b" => 2)
Dict{String,Int64} with 2 entries:
"b" => 2
"a" => 1
julia> getindex(A, "a")
1
Base.setindex!
— Method.setindex!(collection, value, key...)
Store the given value at the given key or index within a collection. The syntax a[i,j,...] = x
is converted by the compiler to (setindex!(a, x, i, j, ...); x)
.
Fully implemented by:
SubArray
AbstractString
Partially implemented by:
Range
UnitRange
Tuple
Associative Collections
Dict
is the standard associative collection. Its implementation uses hash()
as the hashing function for the key, and isequal()
to determine equality. Define these two functions for custom types to override how they are stored in a hash table.
ObjectIdDict
is a special hash table where the keys are always object identities.
WeakKeyDict
is a hash table implementation where the keys are weak references to objects, and thus may be garbage collected even when referenced in a hash table.
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)
.
Associative collections 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.Dict
— Type.Dict([itr])
Dict{K,V}()
constructs a hash table with keys of type K
and values of type V
.
Given a single iterable argument, constructs a Dict
whose key-value pairs are taken from 2-tuples (key,value)
generated by the argument.
julia> Dict([("A", 1), ("B", 2)])
Dict{String,Int64} with 2 entries:
"B" => 2
"A" => 1
Alternatively, a sequence of pair arguments may be passed.
julia> Dict("A"=>1, "B"=>2)
Dict{String,Int64} with 2 entries:
"B" => 2
"A" => 1
Base.ObjectIdDict
— Type.ObjectIdDict([itr])
ObjectIdDict()
constructs a hash table where the keys are (always) object identities. Unlike Dict
it is not parameterized on its key and value type and thus its eltype
is always Pair{Any,Any}
.
See Dict
for further help.
Base.WeakKeyDict
— Type.WeakKeyDict([itr])
WeakKeyDict()
constructs a hash table where the keys are weak references to objects, and thus may be garbage collected even when referenced in a hash table.
See Dict
for further help.
Base.haskey
— Function.haskey(collection, key) -> Bool
Determine whether a collection has a mapping for a given key.
julia> a = Dict('a'=>2, 'b'=>3)
Dict{Char,Int64} with 2 entries:
'b' => 3
'a' => 2
julia> haskey(a,'a')
true
julia> haskey(a,'c')
false
Base.get
— Method.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.
Examples
julia> d = Dict("a"=>1, "b"=>2);
julia> get(d, "a", 3)
1
julia> get(d, "c", 3)
3
Base.get
— Function.get(f::Function, collection, key)
Return the value stored for the given key, or if no mapping for the key is present, return f()
. Use get!
to also store the default value in the dictionary.
This is intended to be called using do
block syntax
get(dict, key) do
# default value calculated here
time()
end
Base.get!
— Method.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
Base.get!
— Method.get!(f::Function, collection, key)
Return the value stored for the given key, or if no mapping for the key is present, store key => f()
, and return f()
.
This is intended to be called using do
block syntax:
get!(dict, key) do
# default value calculated here
time()
end
Base.getkey
— Function.getkey(collection, key, default)
Return the key matching argument key
if one exists in collection
, otherwise return default
.
julia> a = Dict('a'=>2, 'b'=>3)
Dict{Char,Int64} with 2 entries:
'b' => 3
'a' => 2
julia> getkey(a,'a',1)
'a': ASCII/Unicode U+0061 (category Ll: Letter, lowercase)
julia> getkey(a,'d','a')
'a': ASCII/Unicode U+0061 (category Ll: Letter, lowercase)
Base.delete!
— Function.delete!(collection, key)
Delete the mapping for the given key in a collection, and return the collection.
Example
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
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:
[1] pop!(::Dict{String,Int64}, ::String) at ./dict.jl:539
julia> pop!(d, "e", 4)
4
Base.keys
— Function.keys(a::Associative)
Return an iterator over all keys in a collection. collect(keys(a))
returns an array of keys. Since the keys are stored internally in a hash table, the order in which they are returned may vary. But keys(a)
and values(a)
both iterate a
and return the elements in the same order.
julia> a = Dict('a'=>2, 'b'=>3)
Dict{Char,Int64} with 2 entries:
'b' => 3
'a' => 2
julia> collect(keys(a))
2-element Array{Char,1}:
'b'
'a'
Base.values
— Function.values(a::Associative)
Return an iterator over all values in a collection. collect(values(a))
returns an array of values. Since the values are stored internally in a hash table, the order in which they are returned may vary. But keys(a)
and values(a)
both iterate a
and return the elements in the same order.
julia> a = Dict('a'=>2, 'b'=>3)
Dict{Char,Int64} with 2 entries:
'b' => 3
'a' => 2
julia> collect(values(a))
2-element Array{Int64,1}:
3
2
Base.merge
— Function.merge(d::Associative, others::Associative...)
Construct a merged collection from the given collections. If necessary, the types of the resulting collection will be promoted to accommodate the types of the merged collections. If the same key is present in another collection, the value for that key will be the value it has in the last collection listed.
julia> a = Dict("foo" => 0.0, "bar" => 42.0)
Dict{String,Float64} with 2 entries:
"bar" => 42.0
"foo" => 0.0
julia> b = Dict("baz" => 17, "bar" => 4711)
Dict{String,Int64} with 2 entries:
"bar" => 4711
"baz" => 17
julia> merge(a, b)
Dict{String,Float64} with 3 entries:
"bar" => 4711.0
"baz" => 17.0
"foo" => 0.0
julia> merge(b, a)
Dict{String,Float64} with 3 entries:
"bar" => 42.0
"baz" => 17.0
"foo" => 0.0
merge(combine, d::Associative, others::Associative...)
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.
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" => 4753.0
"baz" => 17.0
"foo" => 0.0
Base.merge!
— Function.Merge changes into current head
Internal implementation of merge. Returns true
if merge was successful, otherwise false
merge!(repo::GitRepo; kwargs...) -> Bool
Perform a git merge on the repository repo
, merging commits with diverging history into the current branch. Returns true
if the merge succeeded, false
if not.
The keyword arguments are:
committish::AbstractString=""
: Merge the named commit(s) incommittish
.branch::AbstractString=""
: Merge the branchbranch
and all its commits since it diverged from the current branch.fastforward::Bool=false
: Iffastforward
istrue
, only merge if the merge is a fast-forward (the current branch head is an ancestor of the commits to be merged), otherwise refuse to merge and returnfalse
. This is equivalent to the git CLI option--ff-only
.merge_opts::MergeOptions=MergeOptions()
:merge_opts
specifies options for the merge, such as merge strategy in case of conflicts.checkout_opts::CheckoutOptions=CheckoutOptions()
:checkout_opts
specifies options for the checkout step.
Equivalent to git merge [--ff-only] [<committish> | <branch>]
.
If you specify a branch
, this must be done in reference format, since the string will be turned into a GitReference
. For example, if you wanted to merge branch branch_a
, you would call merge!(repo, branch="refs/heads/branch_a")
.
merge!(d::Associative, others::Associative...)
Update collection with pairs from the other collections. See also merge
.
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
merge!(combine, d::Associative, others::Associative...)
Update collection with pairs from the other collections. Values with the same key will be combined using the combiner function.
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 => 6
julia> merge!(-, d1, d1);
julia> d1
Dict{Int64,Int64} with 3 entries:
4 => 0
3 => 0
1 => 0
Base.sizehint!
— Function.sizehint!(s, n)
Suggest that collection s
reserve capacity for at least n
elements. This can improve performance.
Base.keytype
— Function.keytype(type)
Get the key type of an associative collection type. Behaves similarly to eltype
.
julia> keytype(Dict(Int32(1) => "foo"))
Int32
Base.valtype
— Function.valtype(type)
Get the value type of an associative collection type. Behaves similarly to eltype
.
julia> valtype(Dict(Int32(1) => "foo"))
String
Fully implemented by:
Partially implemented by:
Set-Like Collections
Base.Set
— Type.Set([itr])
Construct a Set
of the values generated by the given iterable object, or an empty set. Should be used instead of IntSet
for sparse integer sets, or for sets of arbitrary objects.
Base.IntSet
— Type.IntSet([itr])
Construct a sorted set of positive Int
s generated by the given iterable object, or an empty set. Implemented as a bit string, and therefore designed for dense integer sets. Only Int
s greater than 0 can be stored. If the set will be sparse (for example holding a few very large integers), use Set
instead.
Base.union
— Function.union(s1,s2...)
∪(s1,s2...)
Construct the union of two or more sets. Maintains order with arrays.
Examples
julia> union([1, 2], [3, 4])
4-element Array{Int64,1}:
1
2
3
4
julia> union([1, 2], [2, 4])
3-element Array{Int64,1}:
1
2
4
julia> union([4, 2], [1, 2])
3-element Array{Int64,1}:
4
2
1
Base.union!
— Function.union!(s, iterable)
Union each element of iterable
into set s
in-place.
Base.intersect
— Function.intersect(s1,s2...)
∩(s1,s2)
Construct the intersection of two or more sets. Maintains order and multiplicity of the first argument for arrays and ranges.
Base.setdiff
— Function.setdiff(a, b)
Construct the set of elements in a
but not b
. Maintains order with arrays. Note that both arguments must be collections, and both will be iterated over. In particular, setdiff(set,element)
where element
is a potential member of set
, will not work in general.
Example
julia> setdiff([1,2,3],[3,4,5])
2-element Array{Int64,1}:
1
2
Base.setdiff!
— Function.setdiff!(s, iterable)
Remove each element of iterable
from set s
in-place.
Base.symdiff
— Function.symdiff(a, b, rest...)
Construct the symmetric difference of elements in the passed in sets or arrays. Maintains order with arrays.
Example
julia> symdiff([1,2,3],[3,4,5],[4,5,6])
3-element Array{Int64,1}:
1
2
6
Base.symdiff!
— Method.symdiff!(s, n)
The set s
is destructively modified to toggle the inclusion of integer n
.
Base.symdiff!
— Method.symdiff!(s, itr)
For each element in itr
, destructively toggle its inclusion in set s
.
Base.symdiff!
— Method.symdiff!(s, itr)
For each element in itr
, destructively toggle its inclusion in set s
.
Base.intersect!
— Function.intersect!(s1::IntSet, s2::IntSet)
Intersects sets s1
and s2
and overwrites the set s1
with the result. If needed, s1
will be expanded to the size of s2
.
Base.issubset
— Function.issubset(A, S) -> Bool
⊆(A,S) -> Bool
Return true
if A
is a subset of or equal to S
.
Fully implemented by:
Partially implemented by:
Dequeues
Base.push!
— Function.push!(collection, items...) -> collection
Insert one or more items
at the end of collection
.
Example
julia> push!([1, 2, 3], 4, 5, 6)
6-element Array{Int64,1}:
1
2
3
4
5
6
Use append!
to add all the elements of another collection to collection
. The result of the preceding example is equivalent to append!([1, 2, 3], [4, 5, 6])
.
Base.pop!
— Method.pop!(collection) -> item
Remove the last item in collection
and return it.
Examples
julia> A=[1, 2, 3, 4, 5, 6]
6-element Array{Int64,1}:
1
2
3
4
5
6
julia> pop!(A)
6
julia> A
5-element Array{Int64,1}:
1
2
3
4
5
Base.unshift!
— Function.unshift!(collection, items...) -> collection
Insert one or more items
at the beginning of collection
.
Example
julia> unshift!([1, 2, 3, 4], 5, 6)
6-element Array{Int64,1}:
5
6
1
2
3
4
Base.shift!
— Function.shift!(collection) -> item
Remove the first item
from collection
.
Example
julia> A = [1, 2, 3, 4, 5, 6]
6-element Array{Int64,1}:
1
2
3
4
5
6
julia> shift!(A)
1
julia> A
5-element Array{Int64,1}:
2
3
4
5
6
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
.
Example
julia> insert!([6, 5, 4, 2, 1], 4, 3)
6-element Array{Int64,1}:
6
5
4
3
2
1
Base.deleteat!
— Function.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.
Example
julia> deleteat!([6, 5, 4, 3, 2, 1], 2)
5-element Array{Int64,1}:
6
4
3
2
1
deleteat!(a::Vector, inds)
Remove the items at the indices given by inds
, and return the modified a
. Subsequent items are shifted to fill the resulting gap.
inds
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 Array{Int64,1}:
5
3
1
julia> deleteat!([6, 5, 4, 3, 2, 1], [true, false, true, false, true, false])
3-element Array{Int64,1}:
5
3
1
julia> deleteat!([6, 5, 4, 3, 2, 1], (2, 2))
ERROR: ArgumentError: indices must be unique and sorted
Stacktrace:
[1] _deleteat!(::Array{Int64,1}, ::Tuple{Int64,Int64}) at ./array.jl:926
[2] deleteat!(::Array{Int64,1}, ::Tuple{Int64,Int64}) at ./array.jl:913
Base.splice!
— Function.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.
Examples
julia> A = [6, 5, 4, 3, 2, 1]; splice!(A, 5)
2
julia> A
5-element Array{Int64,1}:
6
5
4
3
1
julia> splice!(A, 5, -1)
1
julia> A
5-element Array{Int64,1}:
6
5
4
3
-1
julia> splice!(A, 1, [-1, -2, -3])
6
julia> A
7-element Array{Int64,1}:
-1
-2
-3
5
4
3
-1
To insert replacement
before an index n
without removing any items, use splice!(collection, n:n-1, replacement)
.
splice!(a::Vector, range, [replacement]) -> items
Remove items in the specified index range, and return a collection containing the removed items. 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 items.
To insert replacement
before an index n
without removing any items, use splice!(collection, n:n-1, replacement)
.
Example
julia> splice!(A, 4:3, 2)
0-element Array{Int64,1}
julia> A
8-element Array{Int64,1}:
-1
-2
-3
2
5
4
3
-1
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 Array{Int64,1}:
6
5
4
julia> a = resize!([6, 5, 4, 3, 2, 1], 8);
julia> length(a)
8
julia> a[1:6]
6-element Array{Int64,1}:
6
5
4
3
2
1
Base.append!
— Function.append!(collection, collection2) -> collection.
Add the elements of collection2
to the end of collection
.
Examples
julia> append!([1],[2,3])
3-element Array{Int64,1}:
1
2
3
julia> append!([1, 2, 3], [4, 5, 6])
6-element Array{Int64,1}:
1
2
3
4
5
6
Use push!
to add individual items to collection
which are not already themselves in another collection. The result is of the preceding example is equivalent to push!([1, 2, 3], 4, 5, 6)
.
Base.prepend!
— Function.prepend!(a::Vector, items) -> collection
Insert the elements of items
to the beginning of a
.
Example
julia> prepend!([3],[1,2])
3-element Array{Int64,1}:
1
2
3
Fully implemented by: