Data arrays and data frames

Users of R will be aware of the success of data frames when employed in analyzing datasets, a success which has been mirrored by Python with the pandas package. Julia too adds data frame support through use of a package DataFrames, which is available on GitHub, in the usual way.

The package extends Julia's base by introducing three basic types:

• NA: An indicator that a data value is missing

• DataArray: An extension to the Array type that can contain missing values

• DataFrame: A data structure for representing tabular datasets

It is such a large topic that we will be looking at data frames in some depth when we consider statistical computing in Chapter 4, Interoperability.

However, to get a flavor of processing data with these packages:

julia> Pkg.add("DataFrames")
# if not already done so, adding DataFrames will add the DataArray and Blocks framework too.
julia> using DataFrames
julia> d0 = @data([1.,3.,2.,NA,6.])
5-element DataArray{Float64,1}:
 1.0
 3.0
...