How to exclude multiple columns in Spark dataframe in Python

In PySpark 2.1.0 method drop supports multiple columns:

PySpark 2.0.2:

DataFrame.drop(col)

PySpark 2.1.0:

DataFrame.drop(*cols)

Example:

df.drop('col1', 'col2')

or using the * operator as

df.drop(*['col1', 'col2'])

Simply with select:

df.select([c for c in df.columns if c not in {'GpuName','GPU1_TwoPartHwID'}])

or if you really want to use drop then reduce should do the trick:

from functools import reduce
from pyspark.sql import DataFrame

reduce(DataFrame.drop, ['GpuName','GPU1_TwoPartHwID'], df)

Note:

(difference in execution time):

There should be no difference when it comes to data processing time. While these methods generate different logical plans physical plans are exactly the same.

There is a difference however when we analyze driver-side code:

• the first method makes only a single JVM call while the second one has to call JVM for each column that has to be excluded
• the first method generates logical plan which is equivalent to physical plan. In the second case it is rewritten.
• finally comprehensions are significantly faster in Python than methods like map or reduce
• Spark 2.x+ supports multiple columns in drop. See SPARK-11884 (Drop multiple columns in the DataFrame API) and SPARK-12204 (Implement drop method for DataFrame in SparkR) for detials.

The right way to do this is:

df.drop(*['col1', 'col2', 'col3'])

The * needs to come outside of the brackets if there are multiple columns to drop.