implementation("org.jetbrains.kotlinx:dataframe:0.14.1")

id("org.jetbrains.kotlinx.dataframe") version "0.14.1"

import org.jetbrains.kotlinx.dataframe.*
import org.jetbrains.kotlinx.dataframe.api.*
import org.jetbrains.kotlinx.dataframe.io.*

val df = DataFrame.read("https://raw.githubusercontent.com/Kotlin/dataframe/master/data/jetbrains_repositories.csv")
df["full_name"][0] // Indexing https://kotlin.github.io/dataframe/access.html

df.filter { "stargazers_count"<Int>() > 50 }.print() 

id("org.jetbrains.kotlinx.dataframe") version "0.14.1"

// Make sure to place the file annotation above the package directive
@file:ImportDataSchema(
    "Repository",
    "https://raw.githubusercontent.com/Kotlin/dataframe/master/data/jetbrains_repositories.csv",
)

package example

import org.jetbrains.kotlinx.dataframe.annotations.ImportDataSchema
import org.jetbrains.kotlinx.dataframe.api.*

fun main() {
    // execute `assemble` to generate extension properties API
    val df = Repository.readCSV()
    df.fullName[0]
    
    df.filter { stargazersCount > 50 }
}

val df = DataFrame.read("https://raw.githubusercontent.com/Kotlin/dataframe/master/data/jetbrains_repositories.csv")
df // the last expression in the cell is displayed

df.filter { stargazers_count > 50 }

// create columns
val fromTo by columnOf("LoNDon_paris", "MAdrid_miLAN", "londON_StockhOlm", "Budapest_PaRis", "Brussels_londOn")
val flightNumber by columnOf(10045.0, Double.NaN, 10065.0, Double.NaN, 10085.0)
val recentDelays by columnOf("23,47", null, "24, 43, 87", "13", "67, 32")
val airline by columnOf("KLM(!)", "{Air France} (12)", "(British Airways. )", "12. Air France", "'Swiss Air'")

// create dataframe
val df = dataFrameOf(fromTo, flightNumber, recentDelays, airline)

// print dataframe
df.print()

// typed accessors for columns
// that will appear during
// dataframe transformation
val origin by column<String>()
val destination by column<String>()

val clean = df
    // fill missing flight numbers
    .fillNA { flightNumber }.with { prev()!!.flightNumber + 10 }

    // convert flight numbers to int
    .convert { flightNumber }.toInt()

    // clean 'airline' column
    .update { airline }.with { "([a-zA-Z\\s]+)".toRegex().find(it)?.value ?: "" }

    // split 'fromTo' column into 'origin' and 'destination'
    .split { fromTo }.by("_").into(origin, destination)

    // clean 'origin' and 'destination' columns
    .update { origin and destination }.with { it.lowercase().replaceFirstChar(Char::uppercase) }

    // split lists of delays in 'recentDelays' into separate columns
    // 'delay1', 'delay2'... and nest them inside original column `recentDelays`
    .split { recentDelays }.inward { "delay$it" }

    // convert string values in `delay1`, `delay2` into ints
    .parse { recentDelays }

clean
    // group by the flight origin renamed into "from"
    .groupBy { origin named "from" }.aggregate {
        // we are in the context of a single data group

        // total number of flights from origin
        count() into "count"

        // list of flight numbers
        flightNumber into "flight numbers"

        // counts of flights per airline
        airline.valueCounts() into "airlines"

        // max delay across all delays in `delay1` and `delay2`
        recentDelays.maxOrNull { delay1 and delay2 } into "major delay"

        // separate lists of recent delays for `delay1`, `delay2` and `delay3`
        recentDelays.implode(dropNA = true) into "recent delays"

        // total delay per destination
        pivot { destination }.sum { recentDelays.colsOf<Int?>() } into "total delays to"
    }