69 lines
2.5 KiB
Kotlin
Vendored
69 lines
2.5 KiB
Kotlin
Vendored
import org.jetbrains.kotlinx.dataframe.*
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import org.jetbrains.kotlinx.dataframe.annotations.*
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import org.jetbrains.kotlinx.dataframe.api.*
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import org.jetbrains.kotlinx.dataframe.io.*
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fun box(): String {
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// multiple columns
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val personsDf = dataFrameOf(
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"name",
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"age",
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"city",
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"weight",
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"height",
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"yearsToRetirement",
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"workExperienceYears",
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"dependentsCount",
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"annualIncome"
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)(
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"Alice", 15, "London", 99.5, "1.85", 50, 0.toShort(), 0.toByte(), 0L,
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"Bob", 20, "Paris", 140.0, "1.35", 45, 2.toShort(), 0.toByte(), 12000L,
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"Charlie", 100, "Dubai", 75.0, "1.95", 0, 70.toShort(), 0.toByte(), 0L,
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"Rose", 1, "Moscow", 45.33, "0.79", 64, 0.toShort(), 2.toByte(), 0L,
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"Dylan", 35, "London", 23.4, "1.83", 30, 15.toShort(), 1.toByte(), 90000L,
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"Eve", 40, "Paris", 56.72, "1.85", 25, 18.toShort(), 3.toByte(), 125000L,
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"Frank", 55, "Dubai", 78.9, "1.35", 10, 35.toShort(), 2.toByte(), 145000L,
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"Grace", 29, "Moscow", 67.8, "1.65", 36, 5.toShort(), 1.toByte(), 70000L,
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"Hank", 60, "Paris", 80.22, "1.75", 5, 40.toShort(), 4.toByte(), 200000L,
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"Isla", 22, "London", 75.1, "1.85", 43, 1.toShort(), 0.toByte(), 30000L,
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)
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// scenario #0: all numerical columns
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val res0 = personsDf.max()
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res0.df().compareSchemas()
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val max01: Int? = res0.age
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val max02: Double? = res0.weight
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val max03: Int? = res0.yearsToRetirement
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val max04: Short? = res0.workExperienceYears
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val max05: Byte? = res0.dependentsCount
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val max06: Long? = res0.annualIncome
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val max07: String? = res0.name
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val max08: String? = res0.city
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val max09: String? = res0.height
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// scenario #1: particular column
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val res1 = personsDf.maxFor { age }
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res1.df().compareSchemas()
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val max11: Int? = res1.age
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// scenario #1.1: particular column with converted type
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val res11 = personsDf.maxFor { dependentsCount }
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res11.df().compareSchemas()
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val max111: Byte? = res11.dependentsCount
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// scenario #2: max of values per columns separately
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val res3 = personsDf.maxFor<_, String> { name and weight and workExperienceYears and dependentsCount and annualIncome }
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res3.df().compareSchemas()
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val max31: String? = res3.name
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val max32: Double? = res3.weight
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val max33: Short? = res3.workExperienceYears
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val max34: Byte? = res3.dependentsCount
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val max35: Long? = res3.annualIncome
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return "OK"
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}
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