Book Image

Mastering Julia

Book Image

Mastering Julia

Overview of this book

Table of Contents (17 chapters)
Mastering Julia
Credits
About the Author
About the Reviewers
www.PacktPub.com
Preface
Index

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
...