CODEWITHSUNDEEP
pythonloopsdataframepysparknull
jgtrz
jgtrz

Reputation: 375

pySpark replacing nulls in specific columns

My code below works however it is replacing all nulls in the dataframe to "nI". I only want to replace nulls for the columns that are being renamed. I want to do this without hardcoding any column names. 

  df =datasetMatchedDomains

  for i in TRUE_matchedAttributeName_List.keys():
      df = df.withColumnRenamed(i,TRUE_matchedAttributeName_List[i]);

  df_final=df.na.fill('NI')
  display(df_final)
else:
  print("clean")

Upvotes: 2

Views: 140

Answers (1)

kites
kites

Reputation: 1405

you can mention the subset of columns you want in df.na. You can find more info about here

Here is an example

 df = sc.parallelize([
("portfolio1",None ,"star1"), (None, "Lease", "star2"), ("portfolio2",None, "star3")]).toDF(["a", "b", "c"])

 df.show()

 +----------+-----+-----+
 |         a|    b|    c|
 +----------+-----+-----+
 |portfolio1| null|star1|
 |      null|Lease|star2|
 |portfolio2| null|star3|
 +----------+-----+-----+

TRUE_matchedAttributeName = {'a':'a1'}

subset=[]
for i in TRUE_matchedAttributeName.keys():
   subset.append(TRUE_matchedAttributeName[i])
   df = df.withColumnRenamed(i, TRUE_matchedAttributeName[i])

df.fillna('source not implemented', subset=subset).show(truncate=False)

+----------------------+-----+-----+
|a1                    |b    |c    |
+----------------------+-----+-----+
|portfolio1            |null |star1|
|source not implemented|Lease|star2|
|portfolio2            |null |star3|
+----------------------+-----+-----+

Upvotes: 1

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