From 302c7b0e65d52ad875d3ffe85448ca245335a4d7 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Bogumi=C5=82=20Kami=C5=84ski?= Date: Thu, 14 Dec 2023 20:33:20 +0100 Subject: [PATCH] Apply suggestions from code review Co-authored-by: Jacob Quinn Co-authored-by: Milan Bouchet-Valat --- docs/src/man/basics.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/docs/src/man/basics.md b/docs/src/man/basics.md index a6cac7bcc..d1962262b 100644 --- a/docs/src/man/basics.md +++ b/docs/src/man/basics.md @@ -186,7 +186,7 @@ julia> source = [(type="circle", radius=10), (type="square", side=20)] ``` If you want to create a data frame from such data containing all columns present in at least -one of the source observations, and holding `missing` entry if some column is not present then +one of the source observations, with a `missing` entry if some column is not present then you can use `Tables.dictcolumntable` function to help you create the desired data frame: ``` @@ -199,7 +199,7 @@ julia> DataFrame(Tables.dictcolumntable(source)) 2 │ square missing 20 ``` -The `Tables.dictcolumntable` role is to make sure that the `DataFrame` constructor gets information +The role of `Tables.dictcolumntable` is to make sure that the `DataFrame` constructor gets information about all columns present in the source data and properly instantiates them. If we did not use this function the `DataFrame` constructor would assume that the first row of data contains the set of columns present in the source, which would lead to an error in our example: