JOjoeman1000·May 17, 2023
This is very similar to DataFramesMeta:
https://github.com/JuliaData/DataFramesMeta.jl
In Pandas vs. Julia – cheat sheet and comparisonJuliaData
DataFramesMeta.jl
Julia2022–2023
JuliaData / DataFramesMeta.jl
DataFramesMeta.jl
Metaprogramming tools for DataFrames
2Distinct people
2Mentions
0Explicit recommenders
495GitHub stars
What to know before exploring
What it is
Metaprogramming tools for DataFrames
GitHub
Where it is used
No documented product use has been verified yet.
What people are saying
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This week on Hacker News
Nov 7–Nov 14 UTC · Direct repository links and separately labeled related project stories. Indexed through 11/14/2026, 12:54:37 PM UTC.
No direct repository links were found in the indexed HN discussions over the past seven days. Older discussions are available below.
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HN mentions by month2 dated mentions
Observed mentions: 2022-01-26 14:53:32 UTC to 2023-05-17 21:53:39 UTC.
Counts come from the repository’s dated source evidence. Index cutoff: 2026-11-02 08:16:59 UTC.
View mention counts and date ranges
| Period | Start (UTC) | End (exclusive, UTC) | Mentions | Submissions | Comments |
|---|---|---|---|---|---|
| Jan 2022 | 2022-01-01 00:00:00 UTC | 2022-02-01 00:00:00 UTC | 1 | 0 | 1 |
| Feb 2022 | 2022-02-01 00:00:00 UTC | 2022-03-01 00:00:00 UTC | 0 | 0 | 0 |
| Mar 2022 | 2022-03-01 00:00:00 UTC | 2022-04-01 00:00:00 UTC | 0 | 0 | 0 |
| Apr 2022 | 2022-04-01 00:00:00 UTC | 2022-05-01 00:00:00 UTC | 0 | 0 | 0 |
| May 2022 | 2022-05-01 00:00:00 UTC | 2022-06-01 00:00:00 UTC | 0 | 0 | 0 |
| Jun 2022 | 2022-06-01 00:00:00 UTC | 2022-07-01 00:00:00 UTC | 0 | 0 | 0 |
| Jul 2022 | 2022-07-01 00:00:00 UTC | 2022-08-01 00:00:00 UTC | 0 | 0 | 0 |
| Aug 2022 | 2022-08-01 00:00:00 UTC | 2022-09-01 00:00:00 UTC | 0 | 0 | 0 |
| Sep 2022 | 2022-09-01 00:00:00 UTC | 2022-10-01 00:00:00 UTC | 0 | 0 | 0 |
| Oct 2022 | 2022-10-01 00:00:00 UTC | 2022-11-01 00:00:00 UTC | 0 | 0 | 0 |
| Nov 2022 | 2022-11-01 00:00:00 UTC | 2022-12-01 00:00:00 UTC | 0 | 0 | 0 |
| Dec 2022 | 2022-12-01 00:00:00 UTC | 2023-01-01 00:00:00 UTC | 0 | 0 | 0 |
| Jan 2023 | 2023-01-01 00:00:00 UTC | 2023-02-01 00:00:00 UTC | 0 | 0 | 0 |
| Feb 2023 | 2023-02-01 00:00:00 UTC | 2023-03-01 00:00:00 UTC | 0 | 0 | 0 |
| Mar 2023 | 2023-03-01 00:00:00 UTC | 2023-04-01 00:00:00 UTC | 0 | 0 | 0 |
| Apr 2023 | 2023-04-01 00:00:00 UTC | 2023-05-01 00:00:00 UTC | 0 | 0 | 0 |
| May 2023 | 2023-05-01 00:00:00 UTC | 2023-06-01 00:00:00 UTC | 1 | 0 | 1 |
What Hacker News says
2 sourcesLet's say you have a data frame
df = tibble(a = c(1, 2))
and you want to use a dplyr verb to modify it
mutate(df, b = a + 1)
the `a` in the above expression refers to the column in `df`, but this means it's hard to reference a variable in the outer scope named `a`. Furthermore, if you have a string referring to the column name `"a"`, you can't simply write
mutate(df, b = a_var + 1)
Contrast this with DataFramesMeta.jl, which is a dply-like library for Julia, written with macros.
df = DataFrame(a = [1, 2])
@transform df :b = :a .+ 1
Because of the use of Symbols, there is no ambiguity about scopes. To work with a variable referring to column `a` you can write
a_str = "a"
@transform df :b = $a_str .+ 1
I won't pretend this isn't more complicated or harder to learn. Some of the complexity is due to Julia's h
Read the rest
igh performance limiting non-standard evaluation in subtle ways. But a core strength of Julia's macros is that it's easy to inspect these expressions and understand exactly what's going on, with `@macroexpand` as shown in the blog post.
DataFramesMeta.jl repo: https://github.com/JuliaData/DataFramesMeta.jl