Btw, I saw you are using PrettyTables.jl to print the data! Please, feel free to ping me if you need some feature or, specially, if I break something PrettyTables.jl is passing for a huge rewrite that will greatly increase its performance (time to print the first table is down by almost 50%). I am trying as hard as I can to avoid breaking changes, but it can happen. I will remember to check the interoperability with your package before I release v2.0.
I think the new release will fix this problem in your comments:
# Print the table with the selected options.
# currently pretty_table is very slow for large tables, the workaround is to use only few rows
Of course it will always be slow when printing the entire very big table. But it should now be very fast printing any table when cropping is enabled:
It’s quite a while I am monitoring julia’s dataframes package but anytime I wanted to use it for a project I hit lack-of-features wall. as examples I frequently need to pivot_long_to_wide or visa versa but nothing was available in dataframes. also functionality which I often need is to non-equi join dataframes which it wasn’t there. but I’m happy to see both of them in this announcement. to be frank, for me a practical solution with more features is more important than obsession with speed or abstractness.
Many thanks for the “home made” algo and implementation. IMHO it is good to the community.
Currently no need to worry about changing several Base functions when missing involved because given the long time of testing (and discussion etc.) DataFrames.jl will still be #1 choice for most (potential) users.
I’ll set aside some free time to learn/test this new package. So thanks again.
The main problem with piracy isn’t really that it is surprising to the immediate users, since they can always read the docs (assuming it is mentioned there); the problem is that it is surprising to users who don’t know they are using the package when it is a dependency of a dependency of a dependency etc. Currently, if any package in a user’s full dependency tree uses InMemoryDatasets, then the behavior of Base functions changes everywhere – and they very well might not know they are depending on InMemoryDatasets! (Consider the user who hasn’t seen this Discourse thread, for example). This could cause all sorts of bugs, since other code (and other packages) will expect those functions to not have been pirated. In other words, it’s non-composable.
I think this makes sense given the wide-ranging consequences of type piracy, especially if we had a reliable and robust check. File an issue in RegistryCI?
The DataFrames.jl package is one of the few packages in Julia that I frequently use, and honestly is the reason that I started using Julia in the first place. I am very glad to see now there are two packages in Julia that fit my usage.
I occasionally answers questions related to DataFrames in this forum and probably I will use your package in future answers when it is appropriate. Actually, right now, I’m reading your rather comprehensive package documentation and simply enjoying it.
I admire your courage to start working on such a general type of package with those many competitors out there.
PS, the benchmarks are outstanding and I want to thank you personally for beating polars and data.table in one shot!
Thanks for you support. PrettyTables.jl is great! I am looking forward for the next big release. I have just one issue which you may want to help: printing view of a large data set. In current release it is very slow and I ended with a silly way as workaround.
Performance benchmarks of InMemoryDatasets look great! Would also be useful to extend the size range to small tables to see what packages are feasible for use in tight loops.
Also, nice to see different API syntaxes tried out. I personally still like Base Julia map/filter/... more, but choice is good anyway.
I’m curious about the design decision to create both a new data structure, and functions that work on it (and only it). Why not define functions for one of the already existing table types instead, StructArray/TypedTable/...? Many of them are column-based and the same performance could be achieved.
The same question could potentially be asked about DataFrames.jl, but the landscape was quite different when they were created, and this may be a piece of historical baggage (or may be not).
IMD is designed for data scientists and contains a set of functions which are very useful for data manipulation, wrangling, etc. In general, I like the syntax of base functions, but when I am working with tabular data some of those syntaxes are not intuitive and IMD has taken the bold approach to modify them to fit to a data manipulation workflow, e.g. see transpose function.