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Update README.md #1209

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85 changes: 3 additions & 82 deletions README.md
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Expand Up @@ -30,6 +30,7 @@ You could find the following articles there:

* [Get started with Kotlin DataFrame](https://kotlin.github.io/dataframe/gettingstarted.html)
* [Working with Data Schemas](https://kotlin.github.io/dataframe/schemas.html)
* [Setup compiler plugin in Gradle project](https://kotlin.github.io/dataframe/compiler-plugin.html)
* [Full list of all supported operations](https://kotlin.github.io/dataframe/operations.html)
* [Reading from SQL databases](https://kotlin.github.io/dataframe/readsqldatabases.html)
* [Reading/writing from/to different file formats like JSON, CSV, Apache Arrow](https://kotlin.github.io/dataframe/read.html)
Expand All @@ -52,7 +53,7 @@ implementation("org.jetbrains.kotlinx:dataframe:1.0.0-Beta2")
Check out the [custom setup page](https://kotlin.github.io/dataframe/gettingstartedgradleadvanced.html) if you don't need some of the formats as dependencies,
for Groovy, and for configurations specific to Android projects.

## Getting started
## Code example

```kotlin
import org.jetbrains.kotlinx.dataframe.*
Expand All @@ -78,87 +79,7 @@ Follow this [guide](https://kotlin.github.io/dataframe/gettingstartedkotlinnoteb
* `ColumnGroup` — contains columns
* `FrameColumn` — contains dataframes

## Syntax example

Let us show you how data cleaning and aggregation pipelines could look like with DataFrame.

**Create:**
```kotlin
// 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:**
```kotlin
// 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:**
```kotlin
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**](https://datalore.jetbrains.com/view/notebook/vq5j45KWkYiSQnACA2Ymij) to get a better visual impression of what happens and what the hierarchical dataframe structure looks like.

Explore [**more examples here**](examples).
Explore [**more examples here**](https://kotlin.github.io/dataframe/guides-and-examples.html).

## Kotlin, Kotlin Jupyter, Arrow, and JDK versions

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