# How to convert wrapped matrix type of KeyedArray
**URL:** https://discourse.julialang.org/t/how-to-convert-wrapped-matrix-type-of-keyedarray/93149
**Category:** General Usage
**Tags:** question, matrices
**Created:** [January 18, 2023, 12:07pm UTC](https://discourse.julialang.org/t/how-to-convert-wrapped-matrix-type-of-keyedarray/93149 "2023-01-18T12:07:52Z")
**Posts on this page:** 4
**Page:** 1
### Author:  [@jtackm](https://discourse.julialang.org/u/jtackm)
#### Post date: [January 18, 2023, 12:07pm UTC](https://discourse.julialang.org/t/how-to-convert-wrapped-matrix-type-of-keyedarray/93149/1 "2023-01-18T12:07:53Z")
Say I have a KeyedArray wrapping a dense matrix:
```julia
using AxisKeys, Random
n = 100
A = KeyedArray(rand(0:1, n, n), dim1=["a$i" for i in 1:n], dim2=["b$i" for i in 1:n])
2-dimensional KeyedArray(NamedDimsArray(...)) with keys:
↓ dim1 ∈ 100-element Vector{String}
→ dim2 ∈ 100-element Vector{String}
And data, 100×100 Matrix{Int64}:
("b1") ("b2") ("b3") … ("b97") ("b98") ("b99") ("b100")
("a1") 0 0 0 0 1 1 1
("a2") 1 0 0 1 0 1 0
("a3") 1 1 0 1 0 0 0
("a4") 0 1 0 1 1 0 0
("a5") 1 1 1 … 0 0 0 0
("a6") 0 1 0 0 1 1 1
⋮ ⋱ ⋮
("a94") 1 1 0 1 0 1 1
("a95") 0 0 1 0 1 1 1
("a96") 1 0 1 … 0 1 0 0
("a97") 0 0 1 0 0 1 0
("a98") 1 1 1 1 0 0 0
("a99") 0 0 0 0 1 0 0
("a100") 0 1 1 0 1 1 1
```
What is the easiest way to convert the underlying matrix type (say from dense to sparse) without changing axis information? My best solution so far is quite unwieldy:
```julia
using SparseArrays
A_sparse = KeyedArray(sparse(A); (dimnames(A) .=> axiskeys(A))...)
2-dimensional KeyedArray(NamedDimsArray(...)) with keys:
↓ dim1 ∈ 100-element Vector{String}
→ dim2 ∈ 100-element Vector{String}
And data, 100×100 SparseMatrixCSC{Int64, Int64} with 5028 stored entries:
("b1") ("b2") ("b3") … ("b97") ("b98") ("b99") ("b100")
("a1") 0 0 0 0 1 1 1
("a2") 1 0 0 1 0 1 0
("a3") 1 1 0 1 0 0 0
("a4") 0 1 0 1 1 0 0
("a5") 1 1 1 … 0 0 0 0
("a6") 0 1 0 0 1 1 1
⋮ ⋱ ⋮
("a94") 1 1 0 1 0 1 1
("a95") 0 0 1 0 1 1 1
("a96") 1 0 1 … 0 1 0 0
("a97") 0 0 1 0 0 1 0
("a98") 1 1 1 1 0 0 0
("a99") 0 0 0 0 1 0 0
("a100") 0 1 1 0 1 1 1
```
Of course I could define a function, but I’m thinking there must be a build-in option I’m missing.
---
### Author:  [@Dan](https://discourse.julialang.org/u/Dan)
#### Post date: [January 18, 2023, 12:52pm UTC](https://discourse.julialang.org/t/how-to-convert-wrapped-matrix-type-of-keyedarray/93149/2 "2023-01-18T12:52:40Z")
Does:
```julia
refill(na::NamedDimsArray{X},m) where X =
NamedDimsArray{X,eltype(m),ndims(m),typeof(m)}(m)
A_sparse2 = KeyedArray(refill(parent(A), sparse(A)), axiskeys(A))
```
seem less unwieldy?
Another function can be added:
```julia
refill(ka::KeyedArray,m) =
( na = refill(parent(ka),m) ; KeyedArray(na, axiskeys(ka)) )
A_sparse3 = refill(A, sparse(A))
```
and verifying:
```julia
julia> A_sparse == A_sparse2 == A_sparse3
true
```
---
### Author:  [@aplavin](https://discourse.julialang.org/u/aplavin)
#### Post date: [January 18, 2023, 4:01pm UTC](https://discourse.julialang.org/t/how-to-convert-wrapped-matrix-type-of-keyedarray/93149/3 "2023-01-18T16:01:44Z")
With `Accessors`, you can conveniently apply `sparse` to `AxisKeys.keyless_unname(A)`, and store the result back:
```julia
julia> using AccessorsExtra
julia> A_sparse = @modify(sparse, AxisKeys.keyless_unname(A))
2-dimensional KeyedArray(NamedDimsArray(...)) with keys:
↓ dim1 ∈ 100-element Vector{String}
→ dim2 ∈ 100-element Vector{String}
And data, 100×100 SparseMatrixCSC{Int64, Int64} with 5001 stored entries:
("b1") ("b2") ("b3") … ("b98") ("b99") ("b100")
("a1") 0 0 1 0 0 0
...
```
* * *
Btw, even without extra packages, your `KeyedArray(sparse(A); (dimnames(A) .=> axiskeys(A))...)` can be simplified to `KeyedArray(sparse(A); named_axiskeys(A)...)`.
---
### Author:  [@jtackm](https://discourse.julialang.org/u/jtackm)
#### Post date: [January 19, 2023, 3:09pm UTC](https://discourse.julialang.org/t/how-to-convert-wrapped-matrix-type-of-keyedarray/93149/4 "2023-01-19T15:09:18Z")
Thanks for the suggestions! I didn’t know about `named_axiskeys`, that is pretty much what I had in mind 🙂