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 missingDataArray
: An extension to theArray
type that can contain missing valuesDataFrame
: 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 ...