73 lines
2.9 KiB
Kotlin
Vendored
73 lines
2.9 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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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.groupBy { city }.mean()
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res0.compareSchemas()
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val mean01: Double? = res0.age[0]
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val mean02: Double? = res0.weight[0]
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val mean03: Double? = res0.yearsToRetirement[0]
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val mean04: Double? = res0.workExperienceYears[0]
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val mean05: Double? = res0.dependentsCount[0]
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val mean06: Double? = res0.annualIncome[0]
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// scenario #1: particular column
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val res1 = personsDf.groupBy { city }.meanFor { age }
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val mean11: Double? = res1.age[0]
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res1.compareSchemas()
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// scenario #1.1: particular column via mean
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val res11 = personsDf.groupBy { city }.mean { age }
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val mean111: Double? = res11.age[0]
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res11.compareSchemas()
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// scenario #2: particular column with new name - schema changes
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// TODO: not supported scenario
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// val res2 = personsDf.groupBy { city }.mean("age", name = "newAge")
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// val mean21: Double? = res2.newAge[0]
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// scenario #2.1: particular column with new name - schema changes but via columnSelector
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val res21 = personsDf.groupBy { city }.mean("newAge") { age }
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val mean211: Double? = res21.newAge[0]
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res21.compareSchemas()
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// scenario #2.2: two columns with new name - schema changes but via columnSelector
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// TODO: handle multiple columns https://github.com/Kotlin/dataframe/issues/1090
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val res22 = personsDf.groupBy { city }.mean("newAge") { age and yearsToRetirement }
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val mean221: Double? = res22.newAge[0]
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res22.compareSchemas()
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// scenario #3: create new column via expression
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val res3 = personsDf.groupBy { city }.meanOf("newAge") { age * 10 }
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val mean3: Double? = res3.newAge[0]
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res3.compareSchemas()
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return "OK"
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}
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