julia> Pkg.clone("https://github.com/milktrader/Series.jl")Series is a simple, lightweight package that leverages Julia's Array
methods to provide functionality important for working with serialized data.
The primary mechanism to achieve this is the SeriesPair type, a tuple-like type
with semantics that makes it easy to reason about serialized applications.
SeriesPair is immutable and includes two fields, index and value. When combined
into an array, the resulting object becomes an Array{SeriesPair{T,V},1}, which
enforces that each index in the array is the same type, and each value in the array
is the same type.
Series is an intermediary tool for working with Arrays of base-defined types
(e.g. Float64, Bool), and heterogeneous DataFrames. Conversion and promotion methods
are planned to allow transition between the data structures.
To enforce the goal of simplicity, Series only supports single column values. Multiple columns can be attained by transitioning to an array of SeriesPair values, or to DataFrames.
julia> using Series
# generic index
julia> foo = SeriesArray([1:3], 5.*[1:3.])
3-element Array{SeriesPair{Int64,Float64},1}:
1 5.0
2 10.0
3 15.0
julia> bar = SeriesArray([2:4], 10.*[2:4.])
3-element Array{SeriesPair{Int64,Float64},1}:
2 20.0
3 30.0
4 40.0
julia> Array(foo, bar)
4x2 Array{Float64,2}:
5.0 NaN
10.0 20.0
15.0 30.0
NaN 40.0
julia> ans |> removenan
2x2 Array{Float64,2}:
10.0 20.0
15.0 30.0
# using Datetime object as index
julia> op = readseries(Pkg.dir("Series/test/data/spx.csv"));
julia> cl = readseries(Pkg.dir("Series/test/data/spx.csv"), value=5);
julia> cl ./ op |> head
1-element Array{SeriesPair{Date{ISOCalendar},Float64},1}:
1970-01-02 1.0102107321312188
julia> op |> percentchange |> removenan |> head
1-element Array{SeriesPair{Date{ISOCalendar},Float64},1}:
1970-01-05 0.010210732131218946
julia> lag(cl)[1:2]
2-element Array{SeriesPair{Date{ISOCalendar},Float64},1}:
1970-01-02 NaN
1970-01-05 93.0
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