This is an automated email from the ASF dual-hosted git repository.
bkamins pushed a commit to branch main
in repository https://gitbox.apache.org/repos/asf/arrow-julia.git
The following commit(s) were added to refs/heads/main by this push:
new 787768f Docs: add a note about using `copy()` to get a `DataFrame`
where the columns are regular vectors (#487)
787768f is described below
commit 787768fc60ff09ea52be0c5fb7c40e04be47619b
Author: Dilum Aluthge <[email protected]>
AuthorDate: Sun Oct 22 16:54:34 2023 -0400
Docs: add a note about using `copy()` to get a `DataFrame` where the
columns are regular vectors (#487)
Co-authored-by: Bogumił Kamiński <[email protected]>
---
docs/src/manual.md | 1 +
1 file changed, 1 insertion(+)
diff --git a/docs/src/manual.md b/docs/src/manual.md
index 802d095..cb7e433 100644
--- a/docs/src/manual.md
+++ b/docs/src/manual.md
@@ -66,6 +66,7 @@ So, what can you do with an `Arrow.Table` full of data? Quite
a bit actually!
Because `Arrow.Table` implements the
[Tables.jl](https://juliadata.github.io/Tables.jl/stable/) interface, it opens
up a world of integrations for using arrow data. A few examples include:
* `df = DataFrame(Arrow.Table(file))`: Build a
[`DataFrame`](https://juliadata.github.io/DataFrames.jl/stable/), using the
arrow vectors themselves; this allows utilizing a host of DataFrames.jl
functionality directly on arrow data; grouping, joining, selecting, etc.
+* `df = copy(DataFrame(Arrow.Table(file)))`: Build a
[`DataFrame`](https://juliadata.github.io/DataFrames.jl/stable/), where the
columns are regular in-memory vectors (specifically, `Base.Vector`s and/or
`PooledVector`s). This requires that you have enough memory to load the entire
`DataFrame` into memory.
* `Tables.datavaluerows(Arrow.Table(file)) |> @map(...) |> @filter(...) |>
DataFrame`: use
[`Query.jl`'s](https://www.queryverse.org/Query.jl/stable/standalonequerycommands/)
row-processing utilities to map, group, filter, mutate, etc. directly over
arrow data.
* `Arrow.Table(file) |> SQLite.load!(db, "arrow_table")`: load arrow data
directly into an sqlite database/table, where sql queries can be executed on
the data
* `Arrow.Table(file) |> CSV.write("arrow.csv")`: write arrow data out to a csv
file
