How to fill empty cells and any cell which contains only spaces with Null in Spark DataFrame?

Question

I have a dataset which has empty cells, and also cells which contain only spaces (one or more). I want to convert all these cells into Null.

Sample dataset:

data = [("", "CA", " "), ("Julia", "", None),("Robert", "  ", None), ("Tom", "NJ", "   ")]
df = spark.createDataFrame(data,["name", "state", "code"])
df.show() 

I can convert empty cells by:

df = df.select( [F.when(F.col(c)=="", None).otherwise(F.col(c)).alias(c) for c in df.columns] )
df.show() 

And cells with one space:

df = df.select( [F.when(F.col(c)==" ", None).otherwise(F.col(c)).alias(c) for c in df.columns] )
df.show() 

But, I don't want to repeat the above codes for cells with 2, 3, or more spaces.

Is there any way I can convert those cells at once?

Answer 1

you could use trim to remove the spaces which leaves ablankand then check for blanks in all cells.

see example below

data_sdf. 
    selectExpr(*['if(trim({0}) = "", null, {0}) as {0}'.format(c) for c in data_sdf.columns]). 
    show()

# +------+-----+----+
# |  name|state|code|
# +------+-----+----+
# |  null|   CA|null|
# | Julia| null|null|
# |Robert| null|null|
# |   Tom|   NJ|null|
# +------+-----+----+

---

the list comprehension would result in if expression statements for every column

['if(trim({0}) = "", null, {0}) as {0}'.format(c) for c in data_sdf.columns]

# ['if(trim(name) = "", null, name) as name',
#  'if(trim(state) = "", null, state) as state',
#  'if(trim(code) = "", null, code) as code']

Answer 2

You can additional use trim or regex_replace the column before you apply when-otherwise

Trim

df = df.select( [F.when(F.trim(F.col(c))=="", None).otherwise(F.col(c)).alias(c) for c in df.columns] )

Regex Replace

df = df.select( [F.when(F.regexp_replace(col(c), "^s+$", ""))=="", None).otherwise(F.col(c)).alias(c) for c in df.columns] )