ExcelFiles

This package provides load support for Excel files under the FileIO.jl package.

Use Pkg.add("ExcelFiles") in Julia to install ExcelFiles and its dependencies.

To read a Excel file into a DataFrame, use the following julia code:

using ExcelFiles, DataFrames

df = DataFrame(load("data.xlsx", "Sheet1"))

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 an Excel file into data structures that are not a DataFrame:

using ExcelFiles, DataTables, IndexedTables, TimeSeries, Temporal, Gadfly

# Load into a DataTable
dt = DataTable(load("data.xlsx", "Sheet1"))

# Load into an IndexedTable
it = IndexedTable(load("data.xlsx", "Sheet1"))

# Load into a TimeArray
ta = TimeArray(load("data.xlsx", "Sheet1"))

# Load into a TS
ts = TS(load("data.xlsx", "Sheet1"))

# Plot directly with Gadfly
plot(load("data.xlsx", "Sheet1"), x=:a, y=:b, Geom.line)

The load function also takes a number of parameters:

function load(f::FileIO.File{FileIO.format"Excel"}, range; keywords...)

• range: either the name of the sheet in the Excel file to read, or a full Excel range specification (i.e. "Sheetname!A1:B2").
• The keywords arguments are the same as in ExcelReaders.jl (which is used under the hood to read Excel files). When range is a sheet name, the keyword arguments for the readxlsheet function from ExcelReaders.jl apply, if range is a range specification, the keyword arguments for the readxl function apply.

The following code saves any iterable table as an excel file:

using ExcelFiles

save("output.xlsx", it)

This will work as long as it is any of the types supported as sources in IterableTables.jl.

load also support the pipe syntax. For example, to load an Excel file into a DataFrame, one can use the following code:

using ExcelFiles, DataFrame

df = load("data.xlsx", "Sheet1") |> DataFrame

To save an iterable table, one can use the following form:

using ExcelFiles, DataFrame

df = # Aquire a DataFrame somehow

df |> save("output.xlsx")

The pipe syntax is especially useful when combining it with Query.jl queries, for example one can easily load an Excel file, pipe it into a query, then pipe it to the save function to store the results in a new file.