Kotlin DataFrame aims to reconcile Kotlin's static typing with the dynamic nature of data by utilizing both the full power of the Kotlin language and the opportunities provided by intermittent code execution in Jupyter notebooks and REPL.
- Hierarchical — represents hierarchical data structures, such as JSON or a tree of JVM objects.
- Functional — data processing pipeline is organized in a chain of
DataFrame
transformation operations. Every operation returns a new instance ofDataFrame
reusing underlying storage wherever it's possible. - Readable — data transformation operations are defined in DSL close to natural language.
- Practical — provides simple solutions for common problems and the ability to perform complex tasks.
- Minimalistic — simple, yet powerful data model of three column kinds.
- Interoperable — convertable with Kotlin data classes and collections.
- Generic — can store objects of any type, not only numbers or strings.
- Typesafe — on-the-fly generation of extension properties for type safe data access with Kotlin-style care for null safety.
- Polymorphic — type compatibility derives from column schema compatibility. You can define a function that requires a special subset of columns in a dataframe but doesn't care about other columns.
Integrates with Kotlin kernel for Jupyter. Inspired by krangl, Kotlin Collections and pandas
Explore documentation for details.
You could find the following articles there:
- Get started with Kotlin DataFrame
- Working with Data Schemas
- Full list of all supported operations
- Rendering to HTML
Check out new features in development for the next release in this notebook
The DataFrame compiler plugin reached public preview state! Here's a demo project that works with IntelliJ IDEA 2024.2.
implementation("org.jetbrains.kotlinx:dataframe:0.13.1")
Optional Gradle plugin for enhanced type safety and schema generation https://kotlin.github.io/dataframe/schemasgradle.html
id("org.jetbrains.kotlinx.dataframe") version "0.13.1"
Check out the custom setup page if you don't need some of the formats as dependencies, for Groovy, and for configurations specific to Android projects.
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()
Requires Gradle plugin to work
id("org.jetbrains.kotlinx.dataframe") version "0.13.1"
Plugin generates extension properties API for provided sample of data. Column names and their types become discoverable in completion.
// 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 }
}
Install the Kotlin kernel for Jupyter
Import the stable dataframe
version into a notebook:
%use dataframe
or a specific version:
%use dataframe(<version>)
val df = DataFrame.read("https://raw.githubusercontent.com/Kotlin/dataframe/master/data/jetbrains_repositories.csv")
df // the last expression in the cell is displayed
When a cell with a variable declaration is executed, in the next cell DataFrame
provides extension properties based on its data
df.filter { stargazers_count > 50 }
DataFrame
is a list of columns with equal sizes and distinct names.DataColumn
is a named list of values. Can be one of three kinds:ValueColumn
— contains dataColumnGroup
— contains columnsFrameColumn
— contains dataframes
Let us show you how data cleaning and aggregation pipelines could look like with DataFrame.
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.
Explore more examples here.
This table shows the mapping between main library component versions and minimum supported Java versions.
Kotlin DataFrame Version | Minimum Java Version | Kotlin Version | Kotlin Jupyter Version | OpenAPI version | Apache Arrow version |
---|---|---|---|---|---|
0.10.0 | 8 | 1.8.20 | 0.11.0-358 | 3.0.0 | 11.0.0 |
0.10.1 | 8 | 1.8.20 | 0.11.0-358 | 3.0.0 | 11.0.0 |
0.11.0 | 8 | 1.8.20 | 0.11.0-358 | 3.0.0 | 11.0.0 |
0.11.1 | 8 | 1.8.20 | 0.11.0-358 | 3.0.0 | 11.0.0 |
0.12.0 | 8 | 1.9.0 | 0.11.0-358 | 3.0.0 | 11.0.0 |
0.12.1 | 8 | 1.9.0 | 0.11.0-358 | 3.0.0 | 11.0.0 |
0.13.1 | 8 | 1.9.22 | 0.12.0-139 | 3.0.0 | 15.0.0 |
This project and the corresponding community are governed by the JetBrains Open Source and Community Code of Conduct. Please make sure you read it.
Kotlin DataFrame is licensed under the Apache 2.0 License.