This package provides load and save support for Feather files under the FileIO.jl package.
Use Pkg.add("FeatherFiles") in Julia to install FeatherFiles and its dependencies.
Load a feather file
To read a feather file into a
DataFrame, use the following julia code:
using FeatherFiles, DataFrames df = DataFrame(load("data.feather"))
The call to
load returns a
struct that is an IterableTable.jl, so it can be passed to any function that can handle iterable tables, i.e. all the sinks in IterableTable.jl. Here are some examples of materializing a feather file into data structures that are not a
using FeatherFiles, DataTables, IndexedTables, TimeSeries, Temporal, Gadfly # Load into a DataTable dt = DataTable(load("data.feather")) # Load into an IndexedTable it = IndexedTable(load("data.feather")) # Load into a TimeArray ta = TimeArray(load("data.feather")) # Load into a TS ts = TS(load("data.feather")) # Plot directly with Gadfly plot(load("data.feather"), x=:a, y=:b, Geom.line)
Save a feather file
The following code saves any iterable table as a feather file:
using FeatherFiles save("output.feather", it)
This will work as long as
it is any of the types supported as sources in IterableTables.jl.
Using the pipe syntax
save also support the pipe syntax. For example, to load a feather file into a
DataFrame, one can use the following code:
using FeatherFiles, DataFrame df = load("data.feather") |> DataFrame
To save an iterable table, one can use the following form:
using FeatherFiles, DataFrame df = # Aquire a DataFrame somehow df |> save("output.feather")
The pipe syntax is especially useful when combining it with Query.jl queries, for example one can easily load a feather file, pipe it into a query, then pipe it to the
save function to store the results in a new file.