>>> a DataFrame[id: bigint, julian_date: string, user_id: bigint] >>> b DataFrame[id: bigint, quan_created_money: decimal(10,0), quan_created_cnt: bigi
An easy way to do this is to user "select" and realize you can get a list of all columns for the dataframe, df, with df.columns
select
columns
dataframe
df
df.columns
drop_list = ['a column', 'another column', ...] df.select([column for column in df.columns if column not in drop_list])