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DataFrameRow: fix iterate, ndims, iterate #1637

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merged 8 commits into from
Dec 18, 2018
Merged

DataFrameRow: fix iterate, ndims, iterate #1637

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7194d5c
DataFrameRow: fix iterate, ndims, iterate
bkamins Dec 14, 2018
0a8769e
add eltype to DataFrameRow
bkamins Dec 14, 2018
da3671d
additional ndims method
bkamins Dec 14, 2018
75b73ac
replace eltype by EltypeUnknown
bkamins Dec 15, 2018
0b1cf5e
Update src/dataframerow/dataframerow.jl
nalimilan Dec 15, 2018
4cd8d9d
Merge branch 'master' into prepare_dataframerow_broadcasting
bkamins Dec 17, 2018
a594aff
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nalimilan Dec 17, 2018
323f2e7
Merge branch 'master' into prepare_dataframerow_broadcasting
bkamins Dec 17, 2018
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14 changes: 6 additions & 8 deletions src/dataframerow/dataframerow.jl
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Original file line number Diff line number Diff line change
Expand Up @@ -80,21 +80,20 @@ index(r::DataFrameRow) = index(parent(r))
Base.size(r::DataFrameRow) = (size(parent(r), 2),)
Base.size(r::DataFrameRow, i) = size(r)[i]
Base.length(r::DataFrameRow) = size(parent(r), 2)
Base.ndims(r::DataFrameRow) = 1
Base.ndims(::Type{DataFrameRow{T}}) where {T<:AbstractDataFrame} = 1
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Compat.lastindex(r::DataFrameRow) = size(parent(r), 2)
Base.lastindex(r::DataFrameRow) = size(parent(r), 2)

function Base.iterate(r::DataFrameRow)
Base.depwarn("iteration over DataFrameRow will return values in the future:" *
"use pairs(r::DataFrameRow) to get the current behavior",
:start)
iterate(r, 1)
end
Base.iterate(r::DataFrameRow) = iterate(r, 1)

function Base.iterate(r::DataFrameRow, st)
st > length(r) && return nothing
return (r[st], st + 1)
end

Base.eltype(r::DataFrameRow) = reduce(typejoin, eltypes(parent(r)))
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Base.convert(::Type{Array}, dfr::DataFrameRow) =
[dfr[i] for j in 1:1, i in 1:length(dfr)]
Base.convert(::Type{Vector}, dfr::DataFrameRow) =
Expand All @@ -104,7 +103,6 @@ Base.convert(::Type{Vector{T}}, dfr::DataFrameRow) where T =
Base.Vector(dfr::DataFrameRow) = convert(Vector, dfr)
Base.Vector{T}(dfr::DataFrameRow) where T = convert(Vector{T}, dfr)


Base.keys(r::DataFrameRow) = names(parent(r))
Base.values(r::DataFrameRow) = ntuple(col -> parent(r)[col][row(r)], length(r))

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3 changes: 0 additions & 3 deletions src/deprecated.jl
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Expand Up @@ -1328,9 +1328,6 @@ import Base: vcat
@deprecate showcols(df::AbstractDataFrame, all::Bool=false, values::Bool=true) describe(df, stats = [:eltype, :nmissing, :first, :last])
@deprecate showcols(io::IO, df::AbstractDataFrame, all::Bool=false, values::Bool=true) show(io, describe(df, stats = [:eltype, :nmissing, :first, :last]), all)

import Base: collect
@deprecate collect(r::DataFrameRow) collect(pairs(r))

import Base: show
@deprecate show(io::IO, df::AbstractDataFrame, allcols::Bool, rowlabel::Symbol, summary::Bool) show(io, df, allcols=allcols, rowlabel=rowlabel, summary=summary)
@deprecate show(io::IO, df::AbstractDataFrame, allcols::Bool, rowlabel::Symbol) show(io, df, allcols=allcols, rowlabel=rowlabel)
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24 changes: 23 additions & 1 deletion test/dataframerow.jl
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Expand Up @@ -89,14 +89,21 @@ module TestDataFrameRow
r = DataFrameRow(df, 1)
@test r[:] == r

# keys, values and iteration
# keys, values and iteration, size
@test keys(r) == names(df)
@test values(r) == (df[1, 1], df[1, 2], df[1, 3], df[1, 4])
@test collect(pairs(r)) == [:a=>df[1, 1], :b=>df[1, 2], :c=>df[1, 3], :d=>df[1, 4]]

@test haskey(r, :a)
@test !haskey(r, :zzz)

@test length(r) == 4
@test ndims(r) == 1
@test ndims(typeof(r)) == 1
@test size(r) == (4,)
@test size(r, 1) == 4
@test_throws BoundsError size(r, 2)

df = DataFrame(a=nothing, b=1)
io = IOBuffer()
show(io, DataFrameRow(df, 1))
Expand All @@ -116,4 +123,19 @@ module TestDataFrameRow
c=["A", "B", "C", "A", "B", missing])
@test copy(DataFrameRow(df, 1)) == (a = 1, b = 2.0, c = "A")
@test isequal(copy(DataFrameRow(df, 2)), (a = 2, b = missing, c = "B"))

# iteration and collect
ref = ["a", "b", "c"]
df = DataFrame(permutedims(ref))
dfr = df[1, :]
@test collect(dfr) == ref
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Also test the type? When you say the eltype is Any, I guess this applies only to heterogeneous column types, right? It would be useful to cover that case too.

(BTW, Vector doesn't promote AFAICT, as it uses a comprehension.)

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I will fix it and test it. Actually it should be narrowest type (as in comprehensions).

What is problematic (and confused me) that in Matrix(df) we use T = reduce(promote_type, eltypes(df)) and probably we should use T = reduce(typejoin, eltypes(df)). Should I fix it?

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I have added eltype for DataFrameRow and now it will be as you wanted (and it is OK to add it given the interface specification).

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See discussion at #1641. Maybe we should use promotion here too.

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As discussed 😄 in #1641, if someone wants a promotion to T one should use Vector{T}(dfr) in my opinion.

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So should we use promote_type here too for consistency with #1641?

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I thought that this was exactly the point we wanted to have a difference here.

Essentially this is the same as:

julia> collect((1, 1.0, true))
3-element Array{Real,1}:
    1
    1.0
 true

julia> [v for v in (1, 1.0, true)]
3-element Array{Real,1}:
    1
    1.0
 true

But I am not too attached to what we have now. But it we want to do the promotion here we should define eltype (and this is exactly what I have removed some 2 commits ago 😄).

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Maybe we should keep collect as-is for consistency with tuples, but have convert use promotion to be consistent with data frames?

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This is what we do. But it is implemented in #1640.

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Ah, right, too many PRs... :-p

@test eltype(collect(dfr)) === String
for (v1, v2) in zip(ref, dfr)
@test v1 == v2
end
for (i, v) in enumerate(dfr)
@test v == ref[i]
end
dfr = DataFrame(a=1, b=true, c=1.0)[1,:]
@test eltype(collect(dfr)) === Real
end