Reading csv files with quoted fields containing em

问题:

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

回答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 often find useful:

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

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

回答2:

For anyone doing this in Scala: Tagar's answer nearly worked for me (thank you!); all I had to do was escape the double quote when setting my option param:

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

I'm using Spark 2.3, so I can confirm Tagar's solution still seems to work the same under the new release.

回答3:

Delimiter(comma) specified inside quotes will be ignored by default. Spark SQL does have inbuilt CSV reader in Spark 2.0.

df = session.read
  .option("header", "true")
  .csv("csv/file/path")

more about CSV reader here - .