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filter stop words from text column – spark SQL

I’m using spark SQL and have a data frame with user IDs & reviews of products. I need to filter stop words from the reviews, and I have a text file with stop words to filter.

I managed to split the reviews to lists of strings, but don’t know how to filter.

this is what I tried to do:

from pyspark.sql.functions import col

stopWords ='/FileStore/tables/english.txt')"reviewText")," "))

df.filter(col("reviewText") == stopWords)



You are a little vague in that you do not allude to the flatMap approach, which is more common.

Here an alternative just examining the dataframe column.

import pyspark.sql.functions as F
from pyspark.sql.functions import regexp_extract 

stopWordsIn ='/FileStore/tables/sw.txt').rdd.flatMap(lambda line: line.value.split(" "))
stopWords = stopWordsIn.collect()
words ='/FileStore/tables/df.txt')
words = words.withColumn('value_1', F.lower(F.regexp_replace('value', "[^0-9a-zA-Z^ ]+", "")))
words = words.withColumn('value_2', F.regexp_replace('value_1', '\b(' + '|'.join(stopWords) + ')\b', ''))

returns – and filter out the columns you do not want.

['a', 'in', 'the']

|         value|      value_1|      value_2|
|      A quick2|     A quick2|       quick2|
|brown fox was#|brown fox was|brown fox was|
| in the house.| in the house|        house|

You see the stop words and the fact that I converted all to lower case and stripped some stuff out.