I have a dataframe in Spark 1.6 and want to select just some columns out of it. The column names are like:
colA, colB, colC, colD, colE, colF-0, colF-1, colF-2
I know I can do like this to select specific columns:
df.select("colA", "colB", "colE")
but how to select, say "colA", "colB" and all the colF-* columns at once? Is there a way like in Pandas?
First grab the column names with df.columns, then filter down to just the column names you want .filter(_.startsWith("colF")). This gives you an array of Strings. But the select takes select(String, String*). Luckily select for columns is select(Column*), so finally convert the Strings into Columns with .map(df(_)), and finally turn the Array of Columns into a var arg with : _*.
df.select(df.columns.filter(_.startsWith("colF")).map(df(_)) : _*).show
This filter could be made more complex (same as Pandas). It is however a rather ugly solution (IMO):
df.select(df.columns.filter(x => (x.equals("colA") || x.startsWith("colF"))).map(df(_)) : _*).show
If the list of other columns is fixed you could also merge a fixed array of columns names with filtered array.
df.select((Array("colA", "colB") ++ df.columns.filter(_.startsWith("colF"))).map(df(_)) : _*).show
I wrote a function that does that. Read the comments to see how it works.
/**
* Given a sequence of prefixes, select suitable columns from [[DataFrame]]
* @param columnPrefixes Sequence of prefixes
* @param dF Incoming [[DataFrame]]
* @return [[DataFrame]] with prefixed columns selected
*/
def selectPrefixedColumns(columnPrefixes: Seq[String], dF: DataFrame): DataFrame = {
// Find out if given column name matches any of the provided prefixes
def colNameStartsWith: String => Boolean = (colName: String) =>
columnsPrefix.map(prefix => colName.startsWith(prefix)).reduce(_ || _)
// Filter columns list by checking against given prefixes sequence
val columns = dF.columns.filter(colNameStartsWith)
// Select filtered columns list
dF.select(columns.head, columns.tail:_*)
}
