Julia DataFrame, ArgumentError: broadcasting over `DataFrameRow`s is reserved-CodePudding

If I want to add a Vector to each column of a Dataframe, I can broadcast it. If instead I loop over all rows of the same DataFrame and then want to broadcast to all columns, I get the error

ArgumentError: broadcasting over `DataFrameRow`s is reserved

This would be a minimal (non-) working example:

d = DataFrame()

#create the list with column names
columnlist = [:a, :b, :c, :d]

#each column is filled with [0, 0, ...] of length 10
for col in columnlist
    d[!, col] = zeros(10)
end

#then I want to add a "1" to each entry
#this does work
d[!, columnlist] . = ones(10)

#this does produce the error mentioned above
for row in 1:nrow(d)
    dfrow = d[row, :]
    dfrow[columnlist] . = ones(4) #ArgumentError: broadcasting over `DataFrameRow`s is reserved
end

#this also does not work, with the same error
for row in 1:nrow(d)
    d[row, columnlist] . = ones(4) #ArgumentError: broadcasting over `DataFrameRow`s is reserved
end

Is that the expected behaviour, or a bug? Or am I overseeing something stupid? And is there a way how to broadcast to all columns of a DataFrameRow? (I am aware of the fact that Julia is column-major, but as I need to do some heavy interpolation including I/O once for each row, it is faster this way)

I am using Julia 1.7.2 and DataFrames v1.3.4 on Ubuntu 20.04.4.

CodePudding user response：

This is expected. The reason is that Base Julia has not decided yet how to handle broacasting of NamedTuple:

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

julia> nt .  1
ERROR: ArgumentError: broadcasting over dictionaries and `NamedTuple`s is reserved

If you want to use broadcasting the way to do it (till Base Julia decides what to do with NamedTuples) is:

for row in 1:nrow(d)
    d[row:row, columnlist] . = ones(1, 4)
end

or just do not use broadcasting but some other method to get the same result:

for row in 1:nrow(d)
    foreach(col -> d[row, col]  = 1, columnlist)
end

Note: this is an answer to your question. I have not focused on making your code most efficient possible. If you have a performance issue can you please ask another question specifying exactly what is your problem and I will try to help.