8c2849a695
### What changes were proposed in this pull request? Do not cast `NaN` to an `Integer`, `Long`, `Short` or `Byte`. This is because casting `NaN` to those types results in a `0` which erroneously replaces `0`s while only `NaN`s should be replaced. ### Why are the changes needed? This Scala code snippet: ``` import scala.math; println(Double.NaN.toLong) ``` returns `0` which is problematic as if you run the following Spark code, `0`s get replaced as well: ``` >>> df = spark.createDataFrame([(1.0, 0), (0.0, 3), (float('nan'), 0)], ("index", "value")) >>> df.show() +-----+-----+ |index|value| +-----+-----+ | 1.0| 0| | 0.0| 3| | NaN| 0| +-----+-----+ >>> df.replace(float('nan'), 2).show() +-----+-----+ |index|value| +-----+-----+ | 1.0| 2| | 0.0| 3| | 2.0| 2| +-----+-----+ ``` ### Does this PR introduce any user-facing change? Yes, after the PR, running the same above code snippet returns the correct expected results: ``` >>> df = spark.createDataFrame([(1.0, 0), (0.0, 3), (float('nan'), 0)], ("index", "value")) >>> df.show() +-----+-----+ |index|value| +-----+-----+ | 1.0| 0| | 0.0| 3| | NaN| 0| +-----+-----+ >>> df.replace(float('nan'), 2).show() +-----+-----+ |index|value| +-----+-----+ | 1.0| 0| | 0.0| 3| | 2.0| 0| +-----+-----+ ``` ### How was this patch tested? Added unit tests to verify replacing `NaN` only affects columns of type `Float` and `Double` Closes #26738 from johnhany97/SPARK-30082. Lead-authored-by: John Ayad <johnhany97@gmail.com> Co-authored-by: John Ayad <jayad@palantir.com> Signed-off-by: Wenchen Fan <wenchen@databricks.com> |
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benchmarks | ||
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v1.2/src | ||
v2.3/src | ||
pom.xml |