how to select all columns that starts with a common label

Question

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?

Answer 1

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

Answer 2

Python (tested in Azure Databricks)

selected_columns = [column for column in df.columns if column.startswith("colF")]
df2 = df.select(selected_columns)