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    <p>I have the following snippets. This first solution uses <code>groupBy</code> to group the entrances related to the same station. It does not assume that rows are sorted. Although it reads the file only once, it really does 3 passes (one to read all in memory, one for <code>groupBy</code> and one to create the stations). See at the end for the code for the <code>Row</code> extractor.</p> <pre><code>val stations = { val file = new java.io.File("StationEntrances.csv") val reader = com.github.tototoshi.csv.CSVReader.open(file) val byStation = reader .all // read all in memory .drop(1) // drop header .groupBy { case List(division, line, station, _*) =&gt; (division, line, station) } reader.close byStation.values.toList map { rows =&gt; val entrances = rows map { case Row(_, _, _, _, entrance) =&gt; entrance } rows.head match { case Row(division, line, station, routes, _) =&gt; Station( division, line, station, routes.toList.filter(_ != ""), entrances) } } } </code></pre> <p>This solution assumes that the rows are sorted and should be faster, as it does only one pass and build the result list as it reads the file. </p> <pre><code>val stations2 = { import collection.mutable.ListBuffer def processByChunk(iter: Iterator[Seq[String]], acc: ListBuffer[Station]) : List[Station] = { if (!iter.hasNext) acc.toList else { val head = iter.next val marker = head.take(3) val (rows, rest) = iter.span(_ startsWith marker) val entrances = (head :: rows.toList) map { case Row(_, _, _, _, entrance) =&gt; entrance } val station = head match { case Row(division, line, station, routes, _) =&gt; Station( division, line, station, routes.toList.filter(_ != ""), entrances) } processByChunk(rest, acc += station) } } val file = new java.io.File("StationEntrances.csv") val reader = com.github.tototoshi.csv.CSVReader.open(file) val stations = processByChunk(reader.iterator.drop(1), ListBuffer()) reader.close stations } </code></pre> <p>I have created a dedicated extractor to get the routes/entrances from a given line. I think it makes the code more readable, but also if you are dealing with list, calling <code>fields(0)</code> to <code>fields(25)</code> is not optimal since each call has to traverse the list. The extractor avoids this. For most Java csv parsers, you usually get <code>Array[String]</code>, so that's usually not an issue. Finally, the csv parsing usually doesn't return null strings, so you may want to use <code>if (adaNotes == "") None else Some(adaNotes)</code> instead of <code>Option(adaNotes)</code>.</p> <pre><code>object Row { def unapply(s: Seq[String]) = s match { case List(division, line, station, rest @ _*) =&gt; val (routes, List(ada, adaNotes, freeCrossover, entranceType, entry, exitOnly, entranceStaffing, northSouthStreet, eastWestStreet, corner, latitude, longitude)) = rest splitAt 11 // 11 routes Some(( division, line, station, routes, Entrance( ada.toBoolean, Option(adaNotes), freeCrossover.toBoolean, entranceType, entry == "YES", exitOnly == "YES", entranceStaffing, northSouthStreet, eastWestStreet, corner, latitude.toInt, longitude.toInt))) case _ =&gt; None } } </code></pre>
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