Jefffrey commented on code in PR #5343:
URL: https://github.com/apache/arrow-datafusion/pull/5343#discussion_r1119909964
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docs/source/user-guide/example-usage.md:
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@@ -118,8 +118,8 @@ async fn main() -> datafusion::error::Result<()> {
let ctx = SessionContext::new();
let df = ctx.read_csv("tests/data/capitalized_example.csv",
CsvReadOptions::new()).await?;
- let df = df.filter(col("A").lt_eq(col("c")))?
- .aggregate(vec![col("A")], vec![min(col("b"))])?
+ let df = df.filter(col("\"A\"").lt_eq(col("c")))?
+ .aggregate(vec![col("\"A\"")], vec![min(col("b"))])?
.limit(0, Some(100))?;
Review Comment:
Yeah that would have the benefit of being less breaking and being a bit more
explicit in what is happening. `parse_col` would make it more explicit though
I'm not really a fan of that name, feels a bit clunky.
For what its worth Spark does seem to be able to parse the input to their
`col` function, instead of taking it wholly unqualified I believe
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