Hi,
I'd like to append a column of a dataframe to another DF (using Spark
1.5.2):
DataFrame outputDF = unlabelledDF.withColumn("predicted_label",
predictedDF.col("predicted"));
I get the following exception:
java.lang.IllegalArgumentException: requirement failed: DataFrame must have
the same schema as the relation to which is inserted.
DataFrame schema: StructType(StructField(predicted_label,DoubleType,true),
...<other 700 numerical (ByteType/ShortType) columns>
Relation schema: StructType(StructField(predicted_label,DoubleType,true),
...<the same 700 columns>
The interesting part is that the two schemas in the exception are exactly
the same.
The same code with other input data (with fewer, both numerical and
non-numerical column) succeeds.
Any idea why this happens?
