Yet another take on piping in Julia.
Even with multiple existing implementations of the piping concept in general, I didn’t find one that is convenient enough [for my usecases] while still remains general. Additionally, I became curious in how metaprogramming works, so developing
DataPipes helped me understand a lot of that.
DataPipes inteface is designed with common data processing functions (e.g.,
filter ) in mind, but is not specifically tied to them and can be used for all kinds of pipelines. This package is extensively tested, and I almost always use it myself for data manipulation.
- Gets rid of basically all the boilerplate for functions that follow the common Julia argument order
- Can be plugged in as a step of a vanilla pipeline
- Can define a function instead of immediately applying it
- Can easily export the result of an intermediate step
If I missed another implementation that also ticks these points, please let me know.
DataPipes tries to minimally modify regular Julia syntax and aims to stay composable both with other instruments (e.g. vanilla pipelines) and with itself (nested pipes).
See usage examples in README.
DataPipes is submitted to the
General registry, and meanwhile can be installed from