Reading csv files with quoted fields containing embedded commas

Question

I am reading a csv file in Pyspark as follows:

df_raw=spark.read.option("header","true").csv(csv_path)

However, the data file has quoted fields with embedded commas in them which should not be treated as commas. How can I handle this in Pyspark ? I know pandas can handle this, but can Spark ? The version I am using is Spark 2.0.0.

Here is an example which works in Pandas but fails using Spark:

In [1]: import pandas as pd

In [2]: pdf = pd.read_csv('malformed_data.csv')

In [3]: sdf=spark.read.format("org.apache.spark.csv").csv('malformed_data.csv',header=True)

In [4]: pdf[['col12','col13','col14']]
Out[4]:
                    col12                                             col13  \
0  32 XIY "W"   JK, RE LK  SOMETHINGLIKEAPHENOMENON#YOUGOTSOUL~BRINGDANOISE
1                     NaN                     OUTKAST#THROOTS~WUTANG#RUNDMC

   col14
0   23.0
1    0.0

In [5]: sdf.select("col12","col13",'col14').show()
+------------------+--------------------+--------------------+
|             col12|               col13|               col14|
+------------------+--------------------+--------------------+
|"32 XIY ""W""   JK|              RE LK"|SOMETHINGLIKEAPHE...|
|              null|OUTKAST#THROOTS~W...|                 0.0|
+------------------+--------------------+--------------------+

The contents of the file :

    col1,col2,col3,col4,col5,col6,col7,col8,col9,col10,col11,col12,col13,col14,col15,col16,col17,col18,col19
80015360210876000,11.22,X,4076710258,,,sxsw,,"32 YIU ""A""",S5,,"32 XIY ""W""   JK, RE LK",SOMETHINGLIKEAPHENOMENON#YOUGOTSOUL~BRINGDANOISE,23.0,cyclingstats,2012-25-19,432,2023-05-17,CODERED
61670000229561918,137.12,U,8234971771,,,woodstock,,,T4,,,OUTKAST#THROOTS~WUTANG#RUNDMC,0.0,runstats,2013-21-22,1333,2019-11-23,CODEBLUE

Answer 1

I noticed that your problematic line has escaping that uses double quotes themselves:

"32 XIY ""W"" JK, RE LK"

which should be interpreter just as

32 XIY "W" JK, RE LK

As described in RFC-4180, page 2 -

7. If double-quotes are used to enclose fields, then a double-quote appearing inside a field must be escaped by preceding it with another double quote

That's what Excel does, for example, by default.

Although in Spark (as of Spark 2.1), escaping is done by default through non-RFC way, using backslah (\). To fix this you have to explicitly tell Spark to use doublequote to use for as an escape character:

.option("quote", "\"")
.option("escape", "\"")

This may explain that a comma character wasn't interpreted as it was inside a quoted column.

Options for Spark csv format are not documented well on Apache Spark site, but here's a bit older documentation which I still find useful quite often:

https://github.com/databricks/spark-csv

Update Aug 2018: Spark 3.0 might change this behavior to be RFC-compliant. See SPARK-22236 for details.