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JuliaStats/DataFramesMeta.jl

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JuliaStats / DataFramesMeta.jl

Julia

Metaprogramming tools for DataFrames


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DataFramesMeta.jl

DataFramesMeta DataFramesMeta Coveralls Travis AppVeyor

Metaprogramming tools for DataFrames and Associative objects. These macros improve performance and provide more convenient syntax.

Features

@with

@with allows DataFrame columns to be referenced as symbols like :colX in expressions. If an expression is wrapped in ^(expr), expr gets passed through untouched. If an expression is wrapped in _I_(expr), the column is referenced by the variable expr rather than a symbol. Here are some examples:

using DataArrays, DataFrames
using DataFramesMeta

df = DataFrame(x = 1:3, y = [2, 1, 2])
x = [2, 1, 0]

@with(df, :y + 1)
@with(df, :x + x)  # the two x's are different

x = @with df begin
    res = 0.0
    for i in 1:length(:x)
        res += :x[i] * :y[i]
    end
    res
end

@with(df, df[:x .> 1, ^(:y)]) # The ^ means leave the :y alone

colref = :x
@with(df, :y + _I_(colref)) # Equivalent to df[:y] + df[colref]

This works for Associative types, too:

y = 3
d = Dict(:s => 3, :y => 44, :d => 5)

@with(d, :s + :y + y)

@with is the fundamental macro used by the other metaprogramming utilities.

@with creates a function, so scope within @with is a hard scope, as with do-blocks or other function definitions. Variables in the parent can be read. Writing to variables in the parent scope differs depending on the type of scope of the parent. If the parent scope is a global scope, then a variable cannot be assigned without using the global keyword. If the parent scope is a local scope (inside a function or let block for example), the global keyword is not needed to assign to that parent scope.

@where

Select row subsets.

@where(df, :x .> 1)
@where(df, :x .> x)
@where(df, :x .> x, :y .== 3)  # the two expressions are "and-ed"

@select

Column selections and transformations. Also works with Associative types.

@select(df, :x, :y, :z)
@select(df, x2 = 2 * :x, :y, :z)

@transform

Add additional columns based on keyword arguments. This is available in both function and macro versions with the macro version allowing direct reference to columns using the colon syntax:

transform(df, newCol = cos(df[:x]), anotherCol = df[:x]^2 + 3*df[:x] + 4)
@transform(df, newCol = cos(:x), anotherCol = :x^2 + 3*:x + 4)

@transform works for Associative types, too.

@byrow!

Act on a DataFrame row-by-row. Includes support for control flow and begin end blocks. Since the "environment" induced by @byrow! df is implicitly a single row of df, one uses regular operators and comparisons instead of their elementwise counterparts as in @with.

@byrow! df if :A > :B; :A = :B * :C end
let x = 0.0
    @byrow! df begin
        if :A < :B
            x += :B * :C
        end
    end
    x
end

Note that the let block is required here to create a scope to allow assignment of x within @byrow!.

byrow! also supports special syntax for allocating new columns to make byrow! more useful for data transformations. The syntax @newcol x::Array{Int} allocates a new column :x with an Array container with eltype Int. Note that the returned AbstractDataFrame includes these new columns, but the original df is not affected. Here is an example where two new columns are added:

df = DataFrame(A = 1:3, B = [2, 1, 2])
df2 = @byrow! df begin
    @newcol colX::Array{Float64}
    @newcol colY::DataArray{Int}
    :colX = :B == 2 ? pi * :A : :B
    if :A > 1 
        :colY = :A * :B
    end
end

LINQ-Style Queries and Transforms

A number of functions for operations on DataFrames have been defined. Here is a table of equivalents for Hadley's dplyr and common LINQ functions.

Julia             dplyr            LINQ
---------------------------------------------
@where            filter           Where
@transform        mutate           Select (?)
@by                                GroupBy
@groupby          group_by
@based_on         summarise/do
@orderby          arrange          OrderBy
@select           select           Select

Chaining operations is a useful way to manipulate data. There are several ways to do this. This is still in flux in base Julia (https://github.com/JuliaLang/julia/issues/5571). Here is one option from Lazy.jl by Mike Innes:

x_thread = @> begin
    df
    @transform(y = 10 * :x)
    @where(:a .> 2)
    @by(:b, meanX = mean(:x), meanY = mean(:y))
    @orderby(:meanX)
    @select(:meanX, :meanY, var = :b)
end

Alternative LINQ macro

As another experiment, there is also a @linq macro that supports chaining and all of the functionality defined in other macros. Here is an example of @linq:

x_thread = @linq df |>
    transform(y = 10 * :x) |>
    where(:a .> 2) |>
    by(:b, meanX = mean(:x), meanY = mean(:y)) |>
    orderby(:meanX) |>
    select(:meanX, :meanY, var = :b)

Relative to the use of individual macros, chaining looks cleaner and more obvious with less noise from @ symbols. This approach also avoids filling up the limited macro name space. The main downside is that more magic happens under the hood.

This method is extensible. Here is a comparison of the macro and @linq versions of with.

macro with(d, body)
    esc(with_helper(d, body))
end

function linq(::SymbolParameter{:with}, d, body)
    with_helper(d, body)
end

The linq method above registers the expression-replacement method defined for all with() calls. It should return an expression like a macro.

Again, this is experimental. Based on feedback, we may decide to only use @linq or only support the set of linq-like macros.

Operations on GroupedDataFrames

The following operations are now included:

  • where(g, d -> mean(d[:a]) > 0) and @where(g, mean(:a) > 0) -- Filter groups based on the given criteria. Returns a GroupedDataFrame.

  • orderby(g, d -> mean(d[:a])) and @orderby(g, mean(:a)) -- Sort groups based on the given criteria. Returns a GroupedDataFrame.

  • DataFrame(g) -- Convert groups back to a DataFrame with the same group orderings.

  • @based_on(g, z = mean(:a)) -- Summarize results within groups. Returns a DataFrame.

  • transform(g, d -> y = d[:a] - mean(d[:a])) and @transform(g, y = :a - mean(:a)) -- Transform a DataFrame based on operations within a group. Returns a DataFrame.

You can also index on GroupedDataFrames. g[1] is the first group, returned as a SubDataFrame. g[[1,4,5]] or g[[true, false, true, false, false]] return subsets of groups as a GroupedDataFrame. You can also iterate over GroupedDataFrames.

The most general split-apply-combine approach is based on map. map(fun, g) returns a GroupApplied object with keys and vals. This can be used with combine.

Performance

@with works by parsing the expression body for all columns indicated by symbols (e.g. :colA). Then, a function is created that wraps the body and passes the columns as function arguments. This function is then called. Operations are efficient because:

  • A pseudo-anonymous function is defined, so types are stable.
  • Columns are passed as references, eliminating DataFrame indexing.

All of the other macros are based on @with.

CompositeDataFrame

A CompositeDataFrame is a type-stable AbstractDataFrame built using composite types. Each column is a field in a composite type. CompositeDataFrame is an abstract type; each concrete composite type inherits from this. The advantages of this approach are:

  • You can access single columns directly using df.colA. This is type stable, so code should be faster. (There is still the function boundary to worry about.)

  • All indexing operations can be done currently.

Some downsides include:

  • As an abuse of the type system, creating a new type for each change to a CompositeDataFrame may waste memory.

  • You cannot change the structure of a CompositeDataFrame once created. It is nearly like an immutable object. For example to add a column, you need to do something like:

    transform(df, newcol = df.colA + 5)

An advantage of this is that the API becomes more functional. All manipulations of the CompositeDataFrame return a new object. Normally, this doesn't create much more memory.

To create a CompositeDataFrame, use CompositeDataFrame:

n = 10
d = CompositeDataFrame(a = 1:n, b = rand(10), c = DataArray(rand(1:3, n)))

Note that CompositeDataFrame() does not coerce to DataArrays. Ranges and other AbstractVectors are left as is, so convert to DataArray or NullableArray as appropriate.

You can also name the type of the CompositeDataFrame by including that as the first symbol:

n = 10
d = CompositeDataFrame(:MyDF, a = 1:n, b = rand(n), c = DataArray(rand(1:3, n)))

You can also define a CompositeDataFrame manually as follows. If you do this, you are responsible for keeping each column the same length.

immutable MyDF <: AbstractCompositeDataFrame
    a::Vector{Int}
    b::Vector{Float64}
    c::DataVector{Float64}
end

MyDF(n::Integer) = MyDF(zeros(Int, n), zeros(n), DataArray(zeros(n)))
d = MyDF(10)

Note that a CompositeDataFrame is type stable with field access like df.colA but not with getindex indexing like df[:colA]. df[:colA] works, but it is not type stable.

Type-stable access to rows is also provided using row(d, i) or the iterator eachrow(d). Here is an example:

n = 10
d = CompositeDataFrame(:MyDF, a = 1:n, b = rand(10), c = DataArray(rand(1:3, n)))
x = row(d, 5)
x.a    # 5
y = [x.a * x.b for x in eachrow(d)]

In the example above, the call to CompositeDataFrame creates the type MyDF that holds the composite data frame and another type MyDFRow that is used by row and eachrow.

Package Maintenance

Tom Short is the lead maintainer. Any of the JuliaStats collaborators also have write access and can accept pull requests.

Pull requests are welcome. Pull requests should include updated tests. If functionality is changed, docstrings should be added or updated. Generally, follow the guidelines in DataFrames.