Pyspark CSV Incorrect Count

[Tarique]

B1123451020-502,"","{""m"": {""difference"": 60}}","","","",2022-02-12T15:40:00.783Z
B1456741975-266,"","{""m"": {""difference"": 60}}","","","",2022-02-04T17:03:59.566Z
B1789753479-460,"","",",","","",2022-02-18T14:46:57.332Z
B1456741977-123,"","{""m"": {""difference"": 60}}","","","",2022-02-04T17:03:59.566Z

df_inputfile = (spark.read.format("com.databricks.spark.csv")
                                     .option("inferSchema", "true")
                                     .option("header","false")                
                                     .option("quotedstring",'\"')
                                     .option("escape",'\"')
                                     .option("multiline","true")
                                     .option("delimiter",",")
                                     .load('<path to csv>'))
 
print(df_inputfile.count()) # Prints 3
print(df_inputfile.distinct().count()) # Prints 4

I'm trying to read the data above from a CSV file and end up with a wrong count, although the dataframe contains all the expected records. df_inputfile.count() prints 3 although it should have been 4.

It looks like this is happening because of the single comma in the 4th column of the 3rd row. Can someone please explain why?

[Debayan]

Hi, Could you please check the syntax? '\"' ?

[Tarique]

Hi Debayan, there's no syntax error in the code snippet. Using .option("escape",'"') makes no difference to the counts. I still get wrong counts.

[Tarique]

Hi @Kaniz Fatma​ Unfortunately, the suggestion hasn't helped and I've not been able to figure out the reason for the strange results so far.

[Anonymous]

Hi @Tarique Anwer​ 

Hope all is well!

Just wanted to check in if you were able to resolve your issue and would you be happy to share the solution or mark an answer as best? Else please let us know if you need more help. 

We'd love to hear from you.

Thanks!