multiple conditions for filter in spark data frames

Question

I have a data frame with four fields. one of the field name is Status and i am trying to use a OR condition in .filter for a dataframe . I tried below queries but no luck.

df2 = df1.filter(("Status=2") || ("Status =3"))

df2 = df1.filter("Status=2" || "Status =3")

Has anyone used this before. I have seen a similar question on stack overflow here . They have used below code for using OR condition. But that code is for pyspark.

from pyspark.sql.functions import col 

numeric_filtered = df.where(
(col('LOW')    != 'null') | 
(col('NORMAL') != 'null') |
(col('HIGH')   != 'null'))
numeric_filtered.show()

Answer 1

Instead of:

df2 = df1.filter("Status=2" || "Status =3")

Try:

df2 = df1.filter($"Status" === 2 || $"Status" === 3)