# What is easiest way to transform an array of objects into a data table?
**URL:** https://discourse.julialang.org/t/what-is-easiest-way-to-transform-an-array-of-objects-into-a-data-table/15310
**Category:** General Usage
**Tags:** question
**Created:** [September 21, 2018, 7:36pm UTC](https://discourse.julialang.org/t/what-is-easiest-way-to-transform-an-array-of-objects-into-a-data-table/15310 "2018-09-21T19:36:30Z")
**Posts on this page:** 6
**Page:** 1
### Author:  [@djsegal](https://discourse.julialang.org/u/djsegal)
#### Post date: [September 21, 2018, 7:36pm UTC](https://discourse.julialang.org/t/what-is-easiest-way-to-transform-an-array-of-objects-into-a-data-table/15310/1 "2018-09-21T19:36:30Z")
Let’s say you have a struct Foo, that has 30 or so named fields:
```julia
struct Foo
a::Int
b::AbstractFloat
...
y::Bool
z::Int
end
```
Now you want to store it (without the use of JLD).
What is the go-to method for transforming a vector of these Foo objects into a table?
* * *
I’ve tried `DataFrames`, `DataTables`, `DataData`, `DataLife`, `DataTaxes`, etc.
But it seems like this extremely simple use-case is never spelled out in laymanese
---
### Author:  [@pdeffebach](https://discourse.julialang.org/u/pdeffebach)
#### Post date: [September 21, 2018, 7:48pm UTC](https://discourse.julialang.org/t/what-is-easiest-way-to-transform-an-array-of-objects-into-a-data-table/15310/2 "2018-09-21T19:48:17Z")
Could you expand? Do you want each Foo object to be a column in a DataFrame, with each row a field, or do you want a vector of Foo objects?
---
### Author:  [@djsegal](https://discourse.julialang.org/u/djsegal)
#### Post date: [September 21, 2018, 7:49pm UTC](https://discourse.julialang.org/t/what-is-easiest-way-to-transform-an-array-of-objects-into-a-data-table/15310/3 "2018-09-21T19:49:17Z")
I would like to have:
- each Foo object in the array as a row in the database
- with the columns as the fields (that are assumed to have a fixed-byte representation)
* * *
**edit:** here’s a quick rundown of how it would look:
| index | a | b | … | y | z |
| --- | --- | --- | --- | --- | --- |
| 1 | 2 | 3.1 | … | true | 1 |
| 2 | 52 | 0.4 | … | false | 2 |
| … | … | … | … | … | … |
| 98 | 22 | 1.5 | … | false | 2 |
| 99 | -1 | 5.7 | … | false | 1 |
---
### Author:  [@pdeffebach](https://discourse.julialang.org/u/pdeffebach)
#### Post date: [September 21, 2018, 7:54pm UTC](https://discourse.julialang.org/t/what-is-easiest-way-to-transform-an-array-of-objects-into-a-data-table/15310/4 "2018-09-21T19:54:05Z")
Does this work?
```julia
function makeVec(x::Foo)
t = fieldnames(typeof(x))
[getproperty(x, field) for field in t]
end
df = DataFrame(a = Int[], b = Int[]... allocate the types and names)
for x in VectorOfFoos
push!(df, makeVec(x)
end
```
You could also do
```julia
df = DataFrame(a = [x.a for x in VectorOfFoos], b = [x.b for x in VectorOfFoos]...)
```
---
### Author:  [@kristoffer.carlsson](https://discourse.julialang.org/u/kristoffer.carlsson)
#### Post date: [September 21, 2018, 7:56pm UTC](https://discourse.julialang.org/t/what-is-easiest-way-to-transform-an-array-of-objects-into-a-data-table/15310/5 "2018-09-21T19:56:55Z")
Using (the yet unreleased StructArrays and the latest DataFrames) something like this is possible
```julia
julia> using DataFrames, StructArrays
julia> struct Foo
a::Int
b::Float64
end
julia> c = [Foo(1, 2.0), Foo(2, 4.0)]
2-element Array{Foo,1}:
Foo(1, 2.0)
Foo(2, 4.0)
julia> DataFrame(StructArray(c))
2×2 DataFrame
│ Row │ a │ b │
│ │ Int64 │ Float64 │
├─────┼───────┼─────────┤
│ 1 │ 1 │ 2.0 │
│ 2 │ 2 │ 4.0 │
```
Edit: Updated the answer since master DataFrames makes this easier.
---
### Author:  [@y4lu](https://discourse.julialang.org/u/y4lu)
#### Post date: [September 22, 2018, 1:01am UTC](https://discourse.julialang.org/t/what-is-easiest-way-to-transform-an-array-of-objects-into-a-data-table/15310/6 "2018-09-22T01:01:39Z")
for shorter structs you might also be able do something like
`reduce(vcat, map(x-> [x.a x.b], arrFoo))`
Add: There might be a performance regression using `mapreduce()`
```julia
struct n; a; b; end;
p = fill(n(rand(1:10), rand()*10), 10^6);
@time mapreduce(x->[x.a x.b], vcat, p);
5.99 seconds
@time reduce(vcat, map(x-> [x.a x.b], p));
~0.85 sec
```