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dataframerow.jl
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dataframerow.jl
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"""
DataFrameRow{<:AbstractDataFrame,<:AbstractIndex}
A view of one row of an `AbstractDataFrame`.
A `DataFrameRow` is returned by `getindex` or `view` functions when one row and a
selection of columns are requested, or when iterating the result
of the call to the [`eachrow`](@ref) function.
The `DataFrameRow` constructor can also be called directly:
```
DataFrameRow(parent::AbstractDataFrame, row::Integer, cols=:)
```
A `DataFrameRow` supports the iteration interface and can therefore be passed to
functions that expect a collection as an argument.
Indexing is one-dimensional like specifying a column of a `DataFrame`.
You can also access the data in a `DataFrameRow` using the `getproperty` and
`setproperty!` functions and convert it to a `NamedTuple` using the `copy` function.
It is possible to create a `DataFrameRow` with duplicate columns.
All such columns will have a reference to the same entry in the parent `DataFrame`.
If the selection of columns in a parent data frame is passed as `:` (a colon)
then `DataFrameRow` will always have all columns from the parent,
even if they are added or removed after its creation.
# Examples
```julia
df = DataFrame(a = repeat([1, 2, 3, 4], outer=[2]),
b = repeat([2, 1], outer=[4]),
c = randn(8))
sdf1 = view(df, 2, :)
sdf2 = @view df[end, [:a]]
sdf3 = eachrow(df)[1]
sdf4 = DataFrameRow(df, 2, 1:2)
sdf5 = DataFrameRow(df, 1)
```
"""
struct DataFrameRow{D<:AbstractDataFrame,S<:AbstractIndex}
df::D
colindex::S
row::Int
@inline DataFrameRow(df::D, colindex::S, row::Union{Signed, Unsigned}) where
{D<:AbstractDataFrame,S<:AbstractIndex} = new{D,S}(df, colindex, row)
end
Base.@propagate_inbounds function DataFrameRow(df::DataFrame, row::Integer, cols)
@boundscheck if !checkindex(Bool, axes(df, 1), row)
throw(BoundsError("attempt to access a data frame with $(nrow(df)) " *
"rows at index $row"))
end
DataFrameRow(df, SubIndex(index(df), cols), row)
end
Base.@propagate_inbounds DataFrameRow(df::DataFrame, row::Bool, cols) =
throw(ArgumentError("invalid row index of type Bool"))
Base.@propagate_inbounds function DataFrameRow(sdf::SubDataFrame, row::Integer, cols)
@boundscheck if !checkindex(Bool, axes(sdf, 1), row)
throw(BoundsError("attempt to access a data frame with $(nrow(sdf)) " *
"rows at index $row"))
end
if index(sdf) isa Index # sdf was created using : as row selector
colindex = SubIndex(index(sdf), cols)
else
colindex = SubIndex(index(parent(sdf)), parentcols(index(sdf), cols))
end
@inbounds DataFrameRow(parent(sdf), colindex, rows(sdf)[row])
end
Base.@propagate_inbounds DataFrameRow(df::SubDataFrame, row::Bool, cols) =
throw(ArgumentError("invalid row index of type Bool"))
Base.@propagate_inbounds DataFrameRow(df::AbstractDataFrame, row::Integer) =
DataFrameRow(df, row, :)
row(r::DataFrameRow) = getfield(r, :row)
Base.parent(r::DataFrameRow) = getfield(r, :df)
Base.parentindices(r::DataFrameRow) = (row(r), parentcols(index(r)))
Base.@propagate_inbounds Base.view(adf::AbstractDataFrame, rowind::Integer,
colinds::Union{Colon, AbstractVector, Regex, Not, Between, All}) =
DataFrameRow(adf, rowind, colinds)
Base.@propagate_inbounds Base.getindex(df::AbstractDataFrame, rowind::Integer,
colinds::Union{AbstractVector, Regex, Not, Between, All}) =
DataFrameRow(df, rowind, colinds)
Base.@propagate_inbounds Base.getindex(df::AbstractDataFrame, rowind::Integer, ::Colon) =
DataFrameRow(df, rowind, :)
Base.@propagate_inbounds Base.getindex(r::DataFrameRow, idx::ColumnIndex) =
parent(r)[row(r), parentcols(index(r), idx)]
Base.@propagate_inbounds Base.getindex(r::DataFrameRow, idxs::Union{AbstractVector, Regex, Not, Between, All}) =
DataFrameRow(parent(r), row(r), parentcols(index(r), idxs))
Base.@propagate_inbounds Base.getindex(r::DataFrameRow, ::Colon) = r
for T in (:AbstractVector, :Regex, :Not, :Between, :All, :Colon)
@eval function Base.setindex!(df::DataFrame,
v::Union{DataFrameRow, NamedTuple, AbstractDict},
row_ind::Integer,
col_inds::$(T))
idxs = index(df)[col_inds]
if length(v) != length(idxs)
throw(DimensionMismatch("$(length(idxs)) columns were selected but the assigned" *
" collection contains $(length(v)) elements"))
end
if v isa AbstractDict
for n in view(_names(df), idxs)
if !haskey(v, n)
throw(ArgumentError("Column :$n not found in source dictionary"))
end
end
elseif !all(((a, b),) -> a == b, zip(view(_names(df), idxs), keys(v)))
mismatched = findall(view(_names(df), idxs) .!= collect(keys(v)))
throw(ArgumentError("Selected column names do not match the names in assigned value in" *
" positions $(join(mismatched, ", ", " and "))"))
end
for (col, val) in pairs(v)
df[row_ind, col] = val
end
return df
end
end
Base.@propagate_inbounds Base.setindex!(r::DataFrameRow, value, idx) =
setindex!(parent(r), value, row(r), parentcols(index(r), idx))
index(r::DataFrameRow) = getfield(r, :colindex)
Base.names(r::DataFrameRow) = _names(parent(r))[parentcols(index(r), :)]
_names(r::DataFrameRow) = view(_names(parent(r)), parentcols(index(r), :))
Base.haskey(r::DataFrameRow, key::Bool) =
throw(ArgumentError("invalid key: $key of type Bool"))
Base.haskey(r::DataFrameRow, key::Integer) = 1 ≤ key ≤ size(r, 1)
function Base.haskey(r::DataFrameRow, key::Symbol)
hasproperty(parent(r), key) || return false
index(r) isa Index && return true
# here index(r) is a SubIndex
pos = index(parent(r))[key]
remap = index(r).remap
length(remap) == 0 && lazyremap!(index(r))
checkbounds(Bool, remap, pos) || return false
remap[pos] > 0
end
Base.getproperty(r::DataFrameRow, idx::Symbol) = getindex(r, idx)
Base.setproperty!(r::DataFrameRow, idx::Symbol, x::Any) = setindex!(r, x, idx)
# Private fields are never exposed since they can conflict with column names
Base.propertynames(r::DataFrameRow, private::Bool=false) = names(r)
Base.view(r::DataFrameRow, col::ColumnIndex) =
view(parent(r)[!, parentcols(index(r), col)], row(r))
Base.view(r::DataFrameRow, cols::Union{AbstractVector, Regex, Not, Between, All}) =
DataFrameRow(parent(r), row(r), parentcols(index(r), cols))
Base.view(r::DataFrameRow, ::Colon) = r
Base.size(r::DataFrameRow) = (length(index(r)),)
Base.size(r::DataFrameRow, i) = size(r)[i]
Base.length(r::DataFrameRow) = size(r, 1)
Base.ndims(r::DataFrameRow) = 1
Base.ndims(::Type{<:DataFrameRow}) = 1
Base.lastindex(r::DataFrameRow) = length(r)
Base.iterate(r::DataFrameRow) = iterate(r, 1)
function Base.iterate(r::DataFrameRow, st)
st > length(r) && return nothing
return (r[st], st + 1)
end
# Computing the element type requires going over all columns,
# so better let collect() do it only if necessary (widening)
Base.IteratorEltype(::Type{<:DataFrameRow}) = Base.EltypeUnknown()
function Base.convert(::Type{Vector}, dfr::DataFrameRow)
df = parent(dfr)
T = reduce(promote_type, (eltype(df[!, i]) for i in parentcols(index(dfr))))
convert(Vector{T}, dfr)
end
Base.convert(::Type{Vector{T}}, dfr::DataFrameRow) where T =
T[dfr[i] for i in 1:length(dfr)]
Base.Vector(dfr::DataFrameRow) = convert(Vector, dfr)
Base.Vector{T}(dfr::DataFrameRow) where T = convert(Vector{T}, dfr)
Base.convert(::Type{Array}, dfr::DataFrameRow) = Vector(dfr)
Base.convert(::Type{Array{T}}, dfr::DataFrameRow) where {T} = Vector{T}(dfr)
Base.Array(dfr::DataFrameRow) = Vector(dfr)
Base.Array{T}(dfr::DataFrameRow) where {T} = Vector{T}(dfr)
Base.keys(r::DataFrameRow) = Tuple(_names(r))
Base.values(r::DataFrameRow) =
ntuple(col -> parent(r)[row(r), parentcols(index(r), col)], length(r))
Base.map(f, r::DataFrameRow, rs::DataFrameRow...) = map(f, copy(r), copy.(rs)...)
Base.get(dfr::DataFrameRow, key::ColumnIndex, default) =
haskey(dfr, key) ? dfr[key] : default
Base.get(f::Base.Callable, dfr::DataFrameRow, key::ColumnIndex) =
haskey(dfr, key) ? dfr[key] : f()
Base.broadcastable(::DataFrameRow) =
throw(ArgumentError("broadcasting over `DataFrameRow`s is reserved"))
"""
copy(dfr::DataFrameRow)
Convert a [`DataFrameRow`](@ref) to a `NamedTuple`.
"""
Base.copy(r::DataFrameRow) = NamedTuple{Tuple(keys(r))}(values(r))
# hash column element
Base.@propagate_inbounds hash_colel(v::AbstractArray, i, h::UInt = zero(UInt)) =
hash(v[i], h)
Base.@propagate_inbounds function hash_colel(v::AbstractCategoricalArray, i,
h::UInt = zero(UInt))
ref = v.refs[i]
if eltype(v) >: Missing && ref == 0
hash(missing, h)
else
hash(CategoricalArrays.index(v.pool)[ref], h)
end
end
# hash of DataFrame rows based on its values
# so that duplicate rows would have the same hash
# table columns are passed as a tuple of vectors to ensure type specialization
rowhash(cols::Tuple{AbstractVector}, r::Int, h::UInt = zero(UInt))::UInt =
hash_colel(cols[1], r, h)
function rowhash(cols::Tuple{Vararg{AbstractVector}}, r::Int, h::UInt = zero(UInt))::UInt
h = hash_colel(cols[1], r, h)
rowhash(Base.tail(cols), r, h)
end
Base.hash(r::DataFrameRow, h::UInt = zero(UInt)) =
rowhash(ntuple(col -> parent(r)[!, parentcols(index(r), col)], length(r)), row(r), h)
function Base.:(==)(r1::DataFrameRow, r2::DataFrameRow)
if parent(r1) === parent(r2)
parentcols(index(r1)) == parentcols(index(r2)) || return false
row(r1) == row(r2) && return true
else
_names(r1) == _names(r2) || return false
end
all(((a, b),) -> a == b, zip(r1, r2))
end
function Base.isequal(r1::DataFrameRow, r2::DataFrameRow)
if parent(r1) === parent(r2)
parentcols(index(r1)) == parentcols(index(r2)) || return false
row(r1) == row(r2) && return true
else
_names(r1) == _names(r2) || return false
end
all(((a, b),) -> isequal(a, b), zip(r1, r2))
end
# lexicographic ordering on DataFrame rows, missing > !missing
function Base.isless(r1::DataFrameRow, r2::DataFrameRow)
length(r1) == length(r2) ||
throw(ArgumentError("compared DataFrameRows must have the same number " *
"of columns (got $(length(r1)) and $(length(r2)))"))
for (a,b) in zip(r1, r2)
isequal(a, b) || return isless(a, b)
end
return false
end
function DataFrame(dfr::DataFrameRow)
row, cols = parentindices(dfr)
parent(dfr)[row:row, cols]
end
@noinline pushhelper!(x, r) = push!(x, x[r])
function Base.push!(df::DataFrame, dfr::DataFrameRow; cols::Symbol=:setequal,
columns::Union{Nothing,Symbol}=nothing)
if columns !== nothing
cols = columns
Base.depwarn("`columns` keyword argument is deprecated. Use `cols` instead.", :push!)
end
possible_cols = (:orderequal, :setequal, :intersect, :subset)
if !(cols in possible_cols)
throw(ArgumentError("`cols` keyword argument must be any of :" * join(possible_cols, ", :")))
end
nrows, ncols = size(df)
targetrows = nrows + 1
if ncols == 0
for (n, v) in pairs(dfr)
setproperty!(df, n, fill!(Tables.allocatecolumn(typeof(v), 1), v))
end
return df
end
current_col = 0
try
if parent(dfr) === df && index(dfr) isa Index
# in this case we are sure that all we do is safe
r = row(dfr)
for col in _columns(df)
# use a barrier function to improve performance
pushhelper!(col, r)
end
else
# DataFrameRow can contain duplicate columns and we disallow this
# corner case when push!-ing
# Only check for equal lengths, as an error will be thrown below if some names don't match
if cols === :orderequal
if _names(df) != _names(dfr)
msg = "when `cols=:equal` pushed row must have the same column " *
"names and in the same order as the target data frame"
throw(ArgumentError(msg))
end
elseif cols === :setequal || cols === :equal
if cols === :equal
Base.depwarn("`cols=:equal` is deprecated." *
"Use `:setequal` instead.", :push!)
end
msg = "Number of columns of `DataFrameRow` does not match that of " *
"target data frame (got $(length(dfr)) and $ncols)."
ncols == length(dfr) || throw(ArgumentError(msg))
end
for (col, nm) in zip(_columns(df), _names(df))
current_col += 1
if cols === :subset
val = get(dfr, nm, missing)
else
val = dfr[nm]
end
push!(col, val)
end
end
for col in _columns(df)
@assert length(col) == targetrows
end
catch err
for col in _columns(df)
resize!(col, nrows)
end
if current_col > 0
@error "Error adding value to column :$(names(df)[current_col])."
end
rethrow(err)
end
df
end