Create a list of values that you don't want anf filter oon those >>> DF = spark.range(10) >>> DF DataFrame[id: bigint] >>> >>> array = [1, 2, 3, 8] # don't want these >>> DF.filter(DF.id.isin(array) == False).show() +---+ | id| +---+ | 0| | 4| | 5| | 6| | 7| | 9| +---+
or use binary NOT operator: >>> DF.filter(*~*DF.id.isin(array)).show() +---+ | id| +---+ | 0| | 4| | 5| | 6| | 7| | 9| +---+ HTH view my Linkedin profile <https://www.linkedin.com/in/mich-talebzadeh-ph-d-5205b2/> *Disclaimer:* Use it at your own risk. Any and all responsibility for any loss, damage or destruction of data or any other property which may arise from relying on this email's technical content is explicitly disclaimed. The author will in no case be liable for any monetary damages arising from such loss, damage or destruction. On Sat, 1 Jan 2022 at 20:59, Bitfox <bit...@bitfox.top> wrote: > Using the dataframe API I need to implement a batch filter: > > DF. select(..).where(col(..) != ‘a’ and col(..) != ‘b’ and …) > > There are a lot of keywords should be filtered for the same column in > where statement. > > How can I make it more smater? UDF or others? > > Thanks & Happy new Year! > Bitfox >
