blob: 26376aef9fb26cf2e161dba1fe31e095c6e9d561 [file]
import org.jetbrains.kotlinx.dataframe.*
import org.jetbrains.kotlinx.dataframe.annotations.*
import org.jetbrains.kotlinx.dataframe.api.*
import org.jetbrains.kotlinx.dataframe.io.*
import java.math.BigInteger
fun box(): String {
// multiple columns
val personsDf = dataFrameOf(
"name", "age", "city", "weight", "height", "yearsToRetirement", "workExperienceYears", "dependentsCount", "annualIncome", "bigNumber",
)(
"Alice", 15, "London", 99.5, "1.85", 50f, 0.toShort(), 0.toByte(), 0L, BigInteger.valueOf(23),
"Bob", 20, "Paris", 140.0, "1.35", 45f, 2.toShort(), 0.toByte(), 12000L, BigInteger.valueOf(12),
"Charlie", 100, "Dubai", 75.0, "1.95", 0f, 70.toShort(), 0.toByte(), 0L, BigInteger.valueOf(68798),
"Rose", 1, "Moscow", 45.33, "0.79", 64f, 0.toShort(), 2.toByte(), 0L, BigInteger.valueOf(46556),
"Dylan", 35, "London", 23.4, "1.83", 30f, 15.toShort(), 1.toByte(), 90000L, BigInteger.valueOf(235),
"Eve", 40, "Paris", 56.72, "1.85", 25f, 18.toShort(), 3.toByte(), 125000L, BigInteger.valueOf(-23534),
"Frank", 55, "Dubai", 78.9, "1.35", 10f, 35.toShort(), 2.toByte(), 145000L, BigInteger.valueOf(235),
"Grace", 29, "Moscow", 67.8, "1.65", 36f, 5.toShort(), 1.toByte(), 70000L, BigInteger.valueOf(0),
"Hank", 60, "Paris", 80.22, "1.75", 5f, 40.toShort(), 4.toByte(), 200000L, BigInteger.valueOf(-4),
"Isla", 22, "London", 75.1, "1.85", 43f, 1.toShort(), 0.toByte(), 30000L, BigInteger.valueOf(2345),
)
// scenario #0: all numerical columns
personsDf.max().let { row ->
row.df().compareSchemas()
val max01: Int? = row.age
val max02: Double? = row.weight
val max03: Float? = row.yearsToRetirement
val max04: Short? = row.workExperienceYears
val max05: Byte? = row.dependentsCount
val max06: Long? = row.annualIncome
val max07: String? = row.name
val max08: String? = row.city
val max09: String? = row.height
}
// scenario #1: particular column
personsDf.maxFor { age }.let { row ->
row.df().compareSchemas()
val max11: Int? = row.age
}
// scenario #1.1: particular column with converted type
personsDf.maxFor { dependentsCount }.let { row ->
row.df().compareSchemas()
val max111: Byte? = row.dependentsCount
}
// scenario #2: max of values per columns separately
personsDf.maxFor { name and weight and workExperienceYears and dependentsCount and annualIncome }.let { row ->
row.df().compareSchemas()
val max31: String? = row.name
val max32: Double? = row.weight
val max33: Short? = row.workExperienceYears
val max34: Byte? = row.dependentsCount
val max35: Long? = row.annualIncome
}
return "OK"
}