kosiew commented on code in PR #25857:
URL: https://github.com/apache/datafusion/pull/25857#discussion_r4334087704
##########
datafusion/core/tests/dataframe/mod.rs:
##########
@@ -7029,6 +7029,106 @@ async fn test_fill_nan_uncastable_value() -> Result<()>
{
Ok(())
}
+#[rstest]
+#[tokio::test]
+async fn test_fill_special_column_names(
+ #[values(false, true)] fill_nan: bool,
+ #[values("Name", "a.b")] name: &str,
+ #[values(false, true)] uncastable: bool,
+) -> Result<()> {
+ let fill = if fill_nan {
+ DataFrame::fill_nan
+ } else {
+ DataFrame::fill_null
+ };
+ let missing = if fill_nan { Some(f64::NAN) } else { None };
+ let other_name = if name == "Name" { "a.b" } else { "Name" };
+ let schema = Arc::new(Schema::new(vec![
+ Field::new(name, DataType::Float64, true),
+ Field::new(other_name, DataType::Utf8, true),
+ ]));
+ let batch = RecordBatch::try_new(
+ schema,
+ vec![
+ Arc::new(Float64Array::from(vec![Some(1.0), missing, Some(3.0)])),
+ Arc::new(StringArray::from(vec![Some("a"), None, Some("c")])),
+ ],
+ )?;
+ let ctx = SessionContext::new();
+ let df = ctx.read_batch(batch.clone())?;
+ let value = if uncastable {
+ ScalarValue::from("abc")
+ } else {
+ ScalarValue::from(0.0)
+ };
+
+ let results = fill(&df, &value, &[name])?.collect().await?;
+ let expected = if uncastable {
+ // An uncastable replacement leaves the selected column unchanged.
+ batch
+ } else {
+ RecordBatch::try_new(
+ batch.schema(),
+ vec![
+ Arc::new(Float64Array::from(vec![1.0, 0.0, 3.0])),
+ Arc::clone(batch.column(1)),
+ ],
+ )?
+ };
+ // Both selected and untouched names must be preserved literally.
+ assert_eq!(batches_to_string(&results), batches_to_string(&[expected]));
Review Comment:
Consider asserting the collected schema's field names and types directly.
The formatted comparison checks headers and values but cannot distinguish
numeric types with identical output; no need to require unchanged nullability.
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