There are plenty of Julia books, and the main author of DataFrames.jl has at least two, here’s a relevant one:
He also has free tutorials here:
and writes frequently on this on the Julia blog.
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I got started with Julia from The Dabbling Doggo. His beginners’ playlist is fire.
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A lot of work has happened in TSFrames.jl and it supports a lot of important functionality for handling timeseries data already. Work is going on to build more features and improving existing interfaces. Things which already work:
- Support for heterogenuous data types in columns.
- Frequency conversion/downsampling.
- R zoo/xts like applying a method over a period.
- Rolling functions over date time periods.
- Multi-table joins.
- Tables.jl interfaces to support seamless conversion of tabular data from other sources (CSV, etc.).
Because TSFrames uses a DataFrame to store the data, a TSFrame object can access the underlying DataFrame using the coredata property to enable any other complex data manipulations.
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Someone mentioned upsampling above. There is the resample function from DSP.jl package.
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*straightforward.
thank you for all the responses.
as a matter of interest, i’m curious if anyone knows any benchmark comparisons between q / kdp+ and Julia?
Since you can’t publicly share q/kdb+ benchmark without getting permission first(which says quite a lot) so probably not. kdb+ is a database so you can’t compare it with Julia either. And don’t be bothered with Julia vs q. Julia is plenty fast.
FYI, TSFrames.jl has a couple of guides which you might find useful apart from the package README.md which shows basic functionality.