Is it possible to filter Spark DataFrames to return all rows where a column value is in a list using pyspark?

Question

How can I return only the rows of a Spark DataFrame where the values for a column are within a specified list?

Here's my Python pandas way of doing this operation:

df_start = df[df['name'].isin(['App Opened', 'App Launched'])].copy()

I saw this SO scala implementation and tried several permutations, but couldn't get it to work.

Here's one failed attempt to do it using pyspark:

df_start = df_spark.filter(col("name") isin ['App Opened', 'App Launched'])

Output:

Traceback (most recent call last):
  File "/tmp/zeppelin_pyspark-6660042787423349557.py", line 253, in <module>
    code = compile('\n'.join(final_code), '<stdin>', 'exec', ast.PyCF_ONLY_AST, 1)
  File "<stdin>", line 18
    df_start = df_spark.filter(col("name") isin ['App Opened', 'App Launched'])
                                               ^
SyntaxError: invalid syntax

Another attempt:

df_start = df_spark.filter(col("name").isin(['App Opened', 'App Launched']))

Output:

Traceback (most recent call last):
  File "/tmp/zeppelin_pyspark-6660042787423349557.py", line 267, in <module>
    raise Exception(traceback.format_exc())
Exception: Traceback (most recent call last):
  File "/tmp/zeppelin_pyspark-6660042787423349557.py", line 260, in <module>
    exec(code)
  File "<stdin>", line 18, in <module>
NameError: name 'col' is not defined

Answer 1

As dmdmdmdmdmd pointed out in the comments, the second method didn't work because col needed to be imported:

from pyspark.sql.functions import col
df_start = df_spark.filter(col("name").isin(['App Opened', 'App Launched']))

Here's another way of accomplishing the filter:

df_start = df_spark.filter(df_spark.name.isin(['App Opened', 'App Launched']))