I just wanted to highlight some of the rough edges around using vectors in columns in dataframes.
If there is a null in a dataframe column containing vectors pyspark ml models like logistic regression will completely fail. However from what i've read there is no good way to fill in these nulls with empty vectors. Its not possible to create a literal vector column expressiong and coalesce it with the column from pyspark. so we're left with writing a python udf which does this coalesce, this is really inefficient on large datasets and becomes a bottleneck for ml pipelines working with real world data. I'd like to know how other users are dealing with this and what plans there are to extend vector support for dataframes. Thanks!, Franklyn
