Reading the Spark documentation I found an easier solution.
Since version 1.4 of spark there is a function drop(col)
which can be used in pyspark on a dataframe.
You can use it in two ways
df.drop('age')
df.drop(df.age)
Reading the Spark documentation I found an easier solution.
Since version 1.4 of spark there is a function drop(col)
which can be used in pyspark on a dataframe.
You can use it in two ways
df.drop('age')
df.drop(df.age)