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Consider another example for readme page #1210

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@koperagen

Description

@koperagen

I removed this from readme because 1. it uses outdated syntax 2. maxOrNull doesn't work now 3. it looks out of place, there's a lot of content before it

Create:

// 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()

Clean:

// 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 }

Aggregate:

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"
    }

Check it out on Datalore to get a better visual impression of what happens and what the hierarchical dataframe structure looks like.

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