When memory constraints are not an issue, decoding an entire CSV file into a single collection, such as a List or
Vector, can be useful. kantan.csv offers simple primitives for just this purpose.
Let’s take the cars example from wikipedia, which we’ll get from this project’s resources:
val rawData: java.net.URL = getClass.getResource("/wikipedia.csv")
This is what this data looks like:
scala.io.Source.fromURL(rawData).mkString
// res0: String = """Year,Make,Model,Description,Price
// 1997,Ford,E350,"ac, abs, moon",3000.00
// 1999,Chevy,"Venture ""Extended Edition""","",4900.00
// 1999,Chevy,"Venture ""Extended Edition, Very Large""",,5000.00
// 1996,Jeep,Grand Cherokee,"MUST SELL!
// air, moon roof, loaded",4799.00"""
Our goal here is to load this entire resource as a List. In order to do that, we must be able to decode each
row as a case class. This is exactly what we did in a previous tutorial:
import kantan.csv._
import kantan.csv.ops._
import kantan.csv.generic._
case class Car(year: Int, make: String, model: String, desc: Option[String], price: Float)
Now that we have everything we need to decode the CSV data, here’s how to turn it into a List:
rawData.readCsv[List, Car](rfc.withHeader)
// res1: List[ReadResult[Car]] = List(
// Right(
// value = Car(
// year = 1997,
// make = "Ford",
// model = "E350",
// desc = Some(value = "ac, abs, moon"),
// price = 3000.0F
// )
// ),
// Right(
// value = Car(
// year = 1999,
// make = "Chevy",
// model = "Venture \"Extended Edition\"",
// desc = None,
// price = 4900.0F
// )
// ),
// Right(
// value = Car(
// year = 1999,
// make = "Chevy",
// model = "Venture \"Extended Edition, Very Large\"",
// desc = None,
// price = 5000.0F
// )
// ),
// Right(
// value = Car(
// year = 1996,
// make = "Jeep",
// model = "Grand Cherokee",
// desc = Some(
// value = """MUST SELL!
// air, moon roof, loaded"""
// ),
// price = 4799.0F
// )
// )
// )
This readCsv method takes two type parameters: the type of the collection in which to store each row, and the type
as which to decode each row. In our example, we requested a List of Car, but we could just easily have asked
for a Set or a Vector - it could, in fact, have been anything that has a CanBuildFrom instance.
The two value parameters should be familiar by now: the first one is the column separator, the second one a flag to let kantan.csv know whether to skip the first row or not.
Note that in our example, results are wrapped in a ReadResult. This allows readCsv to be safe - it does not throw
exceptions, preferring to encode errors in the return type instead. Should you rather not have to deal with errors and
let your code crash when they happen, you might prefer unsafeReadCsv.
If you want to learn more about: