# Alternative to map() for grouped dataframe?
**URL:** https://discourse.julialang.org/t/alternative-to-map-for-grouped-dataframe/134038
**Category:** General Usage
**Tags:** dataframes
**Created:** [November 22, 2025, 1:00am UTC](https://discourse.julialang.org/t/alternative-to-map-for-grouped-dataframe/134038 "2025-11-22T01:00:21Z")
**Posts on this page:** 10
**Page:** 1
### Author:  [@Quantodeluz](https://discourse.julialang.org/u/Quantodeluz)
#### Post date: [November 22, 2025, 1:00am UTC](https://discourse.julialang.org/t/alternative-to-map-for-grouped-dataframe/134038/1 "2025-11-22T01:00:21Z")
I’m trying to do one quadratic fit per subdataframe on a grouped dataframe (using two of it’s columns obviously), but i can’t use `map()` to do it because it’s reserved, and doing a list comprehension throws an error because i’m working with views. How should i do this?
---
### Author:  [@jling](https://discourse.julialang.org/u/jling)
#### Post date: [November 22, 2025, 2:30am UTC](https://discourse.julialang.org/t/alternative-to-map-for-grouped-dataframe/134038/2 "2025-11-22T02:30:14Z")
it would be easier if you post a semi-runnable snippet of what you’re trying to do
---
### Author:  [@Quantodeluz](https://discourse.julialang.org/u/Quantodeluz)
#### Post date: [November 22, 2025, 3:21am UTC](https://discourse.julialang.org/t/alternative-to-map-for-grouped-dataframe/134038/3 "2025-11-22T03:21:40Z")
I thought that would be too difficult for some reason… heh
Working on a MWE right now
---
### Author:  [@Quantodeluz](https://discourse.julialang.org/u/Quantodeluz)
#### Post date: [November 22, 2025, 3:31am UTC](https://discourse.julialang.org/t/alternative-to-map-for-grouped-dataframe/134038/4 "2025-11-22T03:31:27Z")
```julia-auto
using DataFrames
using EasyFit
data = DataFrame(throw = repeat(1:5, inner=10), t = repeat(1:10, 5)), x = repeat((1:10).^2, 5)
data_gdf = groupby(data, :throw)
fits = map(data_gdf) do sdf
time, distance = sdf[!, :t], sdf[!, :x]
fitquad(time, distance)
end
```
Output:
```ps
ArgumentError: using map over `GroupedDataFrame`s is reserved
```
---
### Author:  [@technocrat](https://discourse.julialang.org/u/technocrat)
#### Post date: [November 22, 2025, 5:02am UTC](https://discourse.julialang.org/t/alternative-to-map-for-grouped-dataframe/134038/5 "2025-11-22T05:02:03Z")
`DataFrames` no longer allows `map` over `GroupedDataFrames.` Also, the data in the MWE wasn’t usable (ERROR: Could not obtain any successful fit, probably the data is not well posed).
So, use a comprehension.
```julia-auto
using DataFrames, EasyFit, Random
Random.seed!(1) # for reproducibility
a = repeat(1:5, inner=20) # 5 groups, 20 points each
t = repeat(range(0, 10; length=20), 5) # same t grid per group
# true quadratic y = 2t^2 - 3t + 1 plus small noise
x = 2 .* t.^2 .- 3 .* t .+ 1 .+ 0.1 .* randn(length(t))
df = DataFrame(a = a, t = t, x = x)
data_gdf = groupby(df, :a)
fits = [fitquad(sdf.t, sdf.x) for sdf in data_gdf]
summary_fits = [(a = fit.a, b = fit.b, c = fit.c) for fit in fits]
5-element Vector{@NamedTuple{a::Float64, b::Float64, c::Float64}}:
(a = 2.000993698655384, b = -3.016720982558867, c = 1.0089476616480098)
(a = 1.9991770713827897, b = -2.994078426802373, c = 0.9818210102989037)
(a = 1.9994283879614307, b = -2.998494794197106, c = 1.0155535104300808)
(a = 2.0023161744639304, b = -3.029790779480457, c = 1.095258850137451)
(a = 2.002728192866932, b = -3.0281152790428805, c = 1.0897536120125266)
```
---
### Author:  [@icweaver](https://discourse.julialang.org/u/icweaver)
#### Post date: [November 22, 2025, 10:04am UTC](https://discourse.julialang.org/t/alternative-to-map-for-grouped-dataframe/134038/6 "2025-11-22T10:04:38Z")
I think `map` supports `pairs`, so things like this should work:
```julia-repl
julia> fits = map(pairs(data_gdf)) do (k, sdf)
fitquad(sdf.t, sdf.x)
end;
julia> summary_fits = [(a = fit.a, b = fit.b, c = fit.c) for fit in fits]
```
I’d probs just go for it in one shot with either base DataFrames.jl:
```julia-repl
julia> summary_fits = combine(groupby(df, :a)) do sdf
fits = fitquad(sdf.t, sdf.x)
(fits_a = fits.a, fits_b = fits.b, fits_c = fits.c)
end
5×4 DataFrame
Row │ a fits_a fits_b fits_c
│ Int64 Float64 Float64 Float64
─────┼────────────────────────────────────
1 │ 1 2.00099 -3.01672 1.00895
2 │ 2 1.99918 -2.99408 0.981821
3 │ 3 1.99943 -2.99849 1.01555
4 │ 4 2.00232 -3.02979 1.09526
5 │ 5 2.00273 -3.02812 1.08975
```
or DataFramesMeta.jl though:
```julia-repl
julia> summary_fits = @by df :a @astable begin
fits = fitquad(:t, :x)
:fits_a = fits.a
:fits_b = fits.b
:fits_c = fits.c
end
5×4 DataFrame
Row │ a fits_a fits_b fits_c
│ Int64 Float64 Float64 Float64
─────┼────────────────────────────────────
1 │ 1 2.00099 -3.01672 1.00895
2 │ 2 1.99918 -2.99408 0.981821
3 │ 3 1.99943 -2.99849 1.01555
4 │ 4 2.00232 -3.02979 1.09526
5 │ 5 2.00273 -3.02812 1.08975
```
---
### Author:  [@pdeffebach](https://discourse.julialang.org/u/pdeffebach)
#### Post date: [November 22, 2025, 12:27pm UTC](https://discourse.julialang.org/t/alternative-to-map-for-grouped-dataframe/134038/7 "2025-11-22T12:27:38Z")
I would help but I get an error with `easyfit`
```julia-auto
julia> fits = [fitquad(sdf.t, sdf.x) for sdf in data_gdf]
5-element Vector{EasyFit.Quadratic{Float64, Float64, Float64, Float64, Float64}}:
Error showing value of type Vector{EasyFit.Quadratic{Float64, Float64, Float64, Float64, Float64}}:
SYSTEM (REPL): showing an error caused an error
ERROR: 1-element ExceptionStack:
UndefVarError: `f` not defined in `EasyFit`
Suggestion: check for spelling errors or missing imports.
```
---
### Author:  [@Quantodeluz](https://discourse.julialang.org/u/Quantodeluz)
#### Post date: [November 22, 2025, 5:48pm UTC](https://discourse.julialang.org/t/alternative-to-map-for-grouped-dataframe/134038/8 "2025-11-22T17:48:54Z")
```julia-auto
MethodError: no method matching fitquadratic(::SubArray{Int64, 1, Vector{Int64}, Tuple{Vector{Int64}}, false}, ::SubArray{Union{Missing, Float64}, 1, Vector{Union{Missing, Float64}}, Tuple{Vector{Int64}}, false})
The function `fitquadratic` exists, but no method is defined for this combination of argument types.
```
---
### Author:  [@drizk1](https://discourse.julialang.org/u/drizk1)
#### Post date: [November 22, 2025, 8:46pm UTC](https://discourse.julialang.org/t/alternative-to-map-for-grouped-dataframe/134038/9 "2025-11-22T20:46:57Z")
i think this error has to do with the dataframe display. because if you unnest the column or use `;` to suppress the output the error should go away
```julia
UndefVarError: `f` not defined in `EasyFit`
```
in addition the the many solutions above, you could use TidierData.jl (the main branch at this time, still unreleased) and `@unnest_wider`
```julia-auto
julia> @chain df begin
@group_by(a)
@summarize(model = fitquad(t, x))
@unnest_wider(model)
end
5×9 DataFrame
Row │ a model_a model_b model_c model_R2 model_x ⋯
│ Int64 Float64 Float64 Float64 Float64 Array… ⋯
─────┼─────────────────────────────────────────────────────────────────────────────────
1 │ 1 2.00099 -3.01672 1.00895 0.999996 [0.0, 0.10101, 0.20202, 0.30303, ⋯
2 │ 2 1.99918 -2.99408 0.981821 0.999996 [0.0, 0.10101, 0.20202, 0.30303,
3 │ 3 1.99943 -2.99849 1.01555 0.999998 [0.0, 0.10101, 0.20202, 0.30303,
4 │ 4 2.00232 -3.02979 1.09526 0.999997 [0.0, 0.10101, 0.20202, 0.30303,
5 │ 5 2.00273 -3.02812 1.08975 0.999998 [0.0, 0.10101, 0.20202, 0.30303, ⋯
4 columns omitted
```
---
### Author:  [@ararslan](https://discourse.julialang.org/u/ararslan)
#### Post date: [November 22, 2025, 9:42pm UTC](https://discourse.julialang.org/t/alternative-to-map-for-grouped-dataframe/134038/10 "2025-11-22T21:42:04Z")
This is because EasyFit apparently does not support arrays that allow missing values, even if they do not contain any, due to type parameter constraints. The problem is not that it’s a view, as you’d said in the top post. You can do [`dropmissing!`](https://dataframes.juliadata.org/stable/lib/functions/#DataFrames.dropmissing!) to remove any missing values before passing the data to `fitquad`, or just [`disallowmissing!`](https://dataframes.juliadata.org/stable/lib/functions/#DataFrames.disallowmissing!) if you don’t already have missing values.
For the sake of a reproducible example, adding an `allowmissing!(df, :t, :x)` to a similar setup to what @technocrat provided above:
```julia-repl
julia> disallowmissing!(df);
julia> combine(groupby(df, :throw), [:t, :x] => function (t, x)
fit = fitquad(t, x)
return (; fit.a, fit.b, fit.c)
end => AsTable)
5×4 DataFrame
Row │ throw a b c
│ Int64 Float64 Float64 Float64
─────┼────────────────────────────────────
1 │ 1 2.00099 -3.01672 1.00895
2 │ 2 1.99918 -2.99408 0.981821
3 │ 3 1.99943 -2.99849 1.01555
4 │ 4 2.00232 -3.02979 1.09526
5 │ 5 2.00273 -3.02812 1.08975
```