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notebook showing kotlin jupyter kernel in google colab. Use http://colab.research.google.com/gist/maxandersen/b3ad22cd39ff95e72a8c54f58580c14d/index.ipynb to open it.
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index.ipynb
{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "view-in-github",
"colab_type": "text"
},
"source": [
"<a href=\"https://colab.research.google.com/gist/maxandersen/b3ad22cd39ff95e72a8c54f58580c14d/index.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "3AbFugV7bL4W"
},
"source": [
"# Kotlin for Google Colab"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "ZPJsQF2ObL4X"
},
"source": [
"This notebook will guide you on how one can use [Kotlin](https://kotlinlang.org/) with [Jupyter notebooks](https://jupyter.org/) on [Google Colab](https://colab.research.google.com)."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "a5hIDu36bL4Y"
},
"source": [
"### Installing kernel"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "GSMWlxUMbL4Y"
},
"source": [
"To install [Kotlin Jupyter kernel](https://github.com/erokhins/kotlin-jupyter) on Google Colab run the following using a Python kernel\n"
]
},
{
"cell_type": "code",
"source": [
"!pip install jbang\n",
"import jbang\n",
"jbang.exec(\"trust add https://github.com/jupyter-java\")\n",
"jbang.exec(\"install-kernel@jupyter-java kotlin\")"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "r_KNlkERcTvp",
"outputId": "ccbf1f1a-07f8-4917-b8c8-8c969b3702ab"
},
"execution_count": 5,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Requirement already satisfied: jbang in /usr/local/lib/python3.10/dist-packages (0.4.0)\n"
]
},
{
"output_type": "execute_result",
"data": {
"text/plain": [
"CompletedProcess(args='curl -Ls https://sh.jbang.dev | bash -s - install-kernel@jupyter-java kotlin', returncode=0, stdout='Kotlin - kotlin (JBang) kernel installed to /root/.local/share/jupyter/kernels/jbang-kotlin/kernel.json\\nKotlin - kotlin (JBang)-ipc kernel installed to /root/.local/share/jupyter/kernels/jbang-kotlin-ipc/kernel.json\\nAdditional file: /root/.local/share/jupyter/kernels/jbang-kotlin-ipc/ipc_proxy_kernel.py\\n\\nBrought to you by https://github.com/jupyter-java\\n', stderr='')"
]
},
"metadata": {},
"execution_count": 5
}
]
},
{
"cell_type": "markdown",
"source": [
"\n",
"When that completes switch to `Kotlin - jbang - ipc` kernel."
],
"metadata": {
"id": "dMisPmOkcXiA"
}
},
{
"cell_type": "markdown",
"metadata": {
"id": "-HXGoUZDbL4Y"
},
"source": [
"### Running cells"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "ZwzQQBjwbL4Z"
},
"source": [
"Here's a simple example with Kotlin code:"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"id": "FkL_Y-L0bL4Z"
},
"outputs": [],
"source": [
"class Greeter(val name: String) {\n",
" fun greet() {\n",
" println(\"Hello, $name!\")\n",
" }\n",
"}"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "pCgfaj-9bL4a",
"outputId": "723198fd-f224-4e85-8ca3-894273006915"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Hello, Jupyter!\n"
]
}
],
"source": [
"Greeter(\"Jupyter\").greet() // Run me"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "gaE9Zrz4bL4a"
},
"source": [
"### Configuring Maven dependencies"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "0F6yXym4bL4b"
},
"source": [
"Here's another example, courtsey of [thomasnield/kotlin-statistics](https://github.com/thomasnield/kotlin-statistics), showcasing how to load additional dependencies to the notebook from Maven repos:"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"id": "cmyE_OQ8bL4b"
},
"outputs": [],
"source": [
"@file:Repository(\"https://repo1.maven.org/maven2\")\n",
"@file:DependsOn(\"org.nield:kotlin-statistics:1.2.1\")"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"id": "0P8sxmOXbL4b"
},
"outputs": [],
"source": [
"import java.time.LocalDate\n",
"import java.time.temporal.ChronoUnit\n",
"import org.nield.kotlinstatistics.*\n",
"\n",
"data class Patient(val firstName: String,\n",
" val lastName: String,\n",
" val gender: Gender,\n",
" val birthday: LocalDate,\n",
" val whiteBloodCellCount: Int) {\n",
"\n",
" val age = ChronoUnit.YEARS.between(birthday, LocalDate.now())\n",
"}\n",
"\n",
"val patients = listOf(\n",
" Patient(\"John\", \"Simone\", Gender.MALE, LocalDate.of(1989, 1, 7), 4500),\n",
" Patient(\"Sarah\", \"Marley\", Gender.FEMALE, LocalDate.of(1970, 2, 5), 6700),\n",
" Patient(\"Jessica\", \"Arnold\", Gender.FEMALE, LocalDate.of(1980, 3, 9), 3400),\n",
" Patient(\"Sam\", \"Beasley\", Gender.MALE, LocalDate.of(1981, 4, 17), 8800),\n",
" Patient(\"Dan\", \"Forney\", Gender.MALE, LocalDate.of(1985, 9, 13), 5400),\n",
" Patient(\"Lauren\", \"Michaels\", Gender.FEMALE, LocalDate.of(1975, 8, 21), 5000),\n",
" Patient(\"Michael\", \"Erlich\", Gender.MALE, LocalDate.of(1985, 12, 17), 4100),\n",
" Patient(\"Jason\", \"Miles\", Gender.MALE, LocalDate.of(1991, 11, 1), 3900),\n",
" Patient(\"Rebekah\", \"Earley\", Gender.FEMALE, LocalDate.of(1985, 2, 18), 4600),\n",
" Patient(\"James\", \"Larson\", Gender.MALE, LocalDate.of(1974, 4, 10), 5100),\n",
" Patient(\"Dan\", \"Ulrech\", Gender.MALE, LocalDate.of(1991, 7, 11), 6000),\n",
" Patient(\"Heather\", \"Eisner\", Gender.FEMALE, LocalDate.of(1994, 3, 6), 6000),\n",
" Patient(\"Jasper\", \"Martin\", Gender.MALE, LocalDate.of(1971, 7, 1), 6000)\n",
")\n",
"\n",
"enum class Gender {\n",
" MALE,\n",
" FEMALE\n",
"}\n",
"\n",
"val clusters = patients.multiKMeansCluster(k = 3,\n",
" maxIterations = 10000,\n",
" trialCount = 50,\n",
" xSelector = { it.age.toDouble() },\n",
" ySelector = { it.whiteBloodCellCount.toDouble() }\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "ST1Y9N_jbL4b",
"outputId": "3b5d43ca-e11f-495b-e272-d4ef4299187e"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"CENTROID: 0\n",
"\tPatient(firstName=Sarah, lastName=Marley, gender=FEMALE, birthday=1970-02-05, whiteBloodCellCount=6700)\n",
"\tPatient(firstName=Sam, lastName=Beasley, gender=MALE, birthday=1981-04-17, whiteBloodCellCount=8800)\n",
"CENTROID: 1\n",
"\tPatient(firstName=John, lastName=Simone, gender=MALE, birthday=1989-01-07, whiteBloodCellCount=4500)\n",
"\tPatient(firstName=Jessica, lastName=Arnold, gender=FEMALE, birthday=1980-03-09, whiteBloodCellCount=3400)\n",
"\tPatient(firstName=Michael, lastName=Erlich, gender=MALE, birthday=1985-12-17, whiteBloodCellCount=4100)\n",
"\tPatient(firstName=Jason, lastName=Miles, gender=MALE, birthday=1991-11-01, whiteBloodCellCount=3900)\n",
"\tPatient(firstName=Rebekah, lastName=Earley, gender=FEMALE, birthday=1985-02-18, whiteBloodCellCount=4600)\n",
"CENTROID: 2\n",
"\tPatient(firstName=Dan, lastName=Forney, gender=MALE, birthday=1985-09-13, whiteBloodCellCount=5400)\n",
"\tPatient(firstName=Lauren, lastName=Michaels, gender=FEMALE, birthday=1975-08-21, whiteBloodCellCount=5000)\n",
"\tPatient(firstName=James, lastName=Larson, gender=MALE, birthday=1974-04-10, whiteBloodCellCount=5100)\n",
"\tPatient(firstName=Dan, lastName=Ulrech, gender=MALE, birthday=1991-07-11, whiteBloodCellCount=6000)\n",
"\tPatient(firstName=Heather, lastName=Eisner, gender=FEMALE, birthday=1994-03-06, whiteBloodCellCount=6000)\n",
"\tPatient(firstName=Jasper, lastName=Martin, gender=MALE, birthday=1971-07-01, whiteBloodCellCount=6000)\n"
]
}
],
"source": [
"clusters.forEachIndexed { index, item ->\n",
" println(\"CENTROID: $index\")\n",
" item.points.forEach {\n",
" println(\"\\t$it\")\n",
" }\n",
"}"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "YT1KZj71bL4b"
},
"source": [
"### Configuring the built-in via magics"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "MdcsHQ_mbL4b"
},
"source": [
"For a more straightforward, the Kotlin kernel pre-configures certain libraries, and allows the notebook user to load them via special commands, also known as [magics](https://ipython.readthedocs.io/en/stable/interactive/magics.html). To pre-configure libraries for a notebook, one must comma-separate their names prepened with `%use`. Here's how it works:"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "cpOpU_77bL4b",
"outputId": "a31c80b9-c58f-4f19-b642-e75f06e3968c"
},
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"The problem is found in one of the loaded libraries: check library imports, dependencies and repositories\n",
"Error compiling code:\n",
"@file:DependsOn(\"com.github.thomasnield:kotlin-statistics:-SNAPSHOT\")\n",
"import org.nield.kotlinstatistics.*\n",
"\n",
"\n",
"Errors:\n",
"Failed to resolve [com.github.thomasnield:kotlin-statistics:-SNAPSHOT]:\n",
"File 'com.github.thomasnield:kotlin-statistics:-SNAPSHOT' not found\n",
"ArtifactResolutionException: Could not transfer artifact com.github.thomasnield:kotlin-statistics:pom:-SNAPSHOT from/to https___jitpack.io_ (https://jitpack.io/): authentication failed for https://jitpack.io/com/github/thomasnield/kotlin-statistics/-SNAPSHOT/kotlin-statistics--SNAPSHOT.pom, status: 401 Unauthorized\n",
"\n",
"org.jetbrains.kotlinx.jupyter.exceptions.ReplLibraryException: The problem is found in one of the loaded libraries: check library imports, dependencies and repositories\n",
"\tat org.jetbrains.kotlinx.jupyter.exceptions.ReplLibraryExceptionKt.rethrowAsLibraryException(ReplLibraryException.kt:32)\n",
"\tat org.jetbrains.kotlinx.jupyter.repl.impl.CellExecutorImpl$ExecutionContext.doAddLibraries(CellExecutorImpl.kt:154)\n",
"\tat org.jetbrains.kotlinx.jupyter.repl.impl.CellExecutorImpl$ExecutionContext.addLibraries(CellExecutorImpl.kt:189)\n",
"\tat org.jetbrains.kotlinx.jupyter.api.KotlinKernelHost$DefaultImpls.addLibrary(KotlinKernelHost.kt:42)\n",
"\tat org.jetbrains.kotlinx.jupyter.repl.impl.CellExecutorImpl$ExecutionContext.addLibrary(CellExecutorImpl.kt:134)\n",
"\tat org.jetbrains.kotlinx.jupyter.repl.impl.CellExecutorImpl.execute(CellExecutorImpl.kt:62)\n",
"\tat org.jetbrains.kotlinx.jupyter.repl.CellExecutor$DefaultImpls.execute$default(CellExecutor.kt:14)\n",
"\tat org.jetbrains.kotlinx.jupyter.ReplForJupyterImpl$evalEx$1.invoke(repl.kt:500)\n",
"\tat org.jetbrains.kotlinx.jupyter.ReplForJupyterImpl$evalEx$1.invoke(repl.kt:478)\n",
"\tat org.jetbrains.kotlinx.jupyter.ReplForJupyterImpl.withEvalContext(repl.kt:441)\n",
"\tat org.jetbrains.kotlinx.jupyter.ReplForJupyterImpl.evalEx(repl.kt:478)\n",
"\tat org.jetbrains.kotlinx.jupyter.messaging.ProtocolKt$shellMessagesHandler$2$res$1.invoke(protocol.kt:320)\n",
"\tat org.jetbrains.kotlinx.jupyter.messaging.ProtocolKt$shellMessagesHandler$2$res$1.invoke(protocol.kt:314)\n",
"\tat org.jetbrains.kotlinx.jupyter.JupyterExecutorImpl$runExecution$execThread$1.invoke(execution.kt:38)\n",
"\tat org.jetbrains.kotlinx.jupyter.JupyterExecutorImpl$runExecution$execThread$1.invoke(execution.kt:33)\n",
"\tat kotlin.concurrent.ThreadsKt$thread$thread$1.run(Thread.kt:30)\n",
"Caused by: org.jetbrains.kotlinx.jupyter.exceptions.ReplCompilerException: Failed to resolve [com.github.thomasnield:kotlin-statistics:-SNAPSHOT]:\n",
"File 'com.github.thomasnield:kotlin-statistics:-SNAPSHOT' not found\n",
"ArtifactResolutionException: Could not transfer artifact com.github.thomasnield:kotlin-statistics:pom:-SNAPSHOT from/to https___jitpack.io_ (https://jitpack.io/): authentication failed for https://jitpack.io/com/github/thomasnield/kotlin-statistics/-SNAPSHOT/kotlin-statistics--SNAPSHOT.pom, status: 401 Unauthorized\n",
"\tat org.jetbrains.kotlinx.jupyter.repl.impl.JupyterCompilerImpl.compileSync(JupyterCompilerImpl.kt:182)\n",
"\tat org.jetbrains.kotlinx.jupyter.repl.impl.InternalEvaluatorImpl.eval(InternalEvaluatorImpl.kt:102)\n",
"\tat org.jetbrains.kotlinx.jupyter.repl.impl.CellExecutorImpl$execute$1$result$1.invoke(CellExecutorImpl.kt:75)\n",
"\tat org.jetbrains.kotlinx.jupyter.repl.impl.CellExecutorImpl$execute$1$result$1.invoke(CellExecutorImpl.kt:73)\n",
"\tat org.jetbrains.kotlinx.jupyter.ReplForJupyterImpl.withHost(repl.kt:690)\n",
"\tat org.jetbrains.kotlinx.jupyter.repl.impl.CellExecutorImpl.execute(CellExecutorImpl.kt:73)\n",
"\tat org.jetbrains.kotlinx.jupyter.repl.CellExecutor$DefaultImpls.execute$default(CellExecutor.kt:14)\n",
"\tat org.jetbrains.kotlinx.jupyter.repl.impl.CellExecutorImpl$ExecutionContext.execute(CellExecutorImpl.kt:227)\n",
"\tat org.jetbrains.kotlinx.jupyter.api.libraries.CodeExecutionCallback.invoke(CodeExecutionCallback.kt:8)\n",
"\tat org.jetbrains.kotlinx.jupyter.api.libraries.CodeExecutionCallback.invoke(CodeExecutionCallback.kt:6)\n",
"\tat org.jetbrains.kotlinx.jupyter.repl.impl.CellExecutorImpl$ExecutionContext.execute(CellExecutorImpl.kt:254)\n",
"\tat org.jetbrains.kotlinx.jupyter.repl.impl.CellExecutorImpl$ExecutionContext.runChild(CellExecutorImpl.kt:147)\n",
"\tat org.jetbrains.kotlinx.jupyter.repl.impl.CellExecutorImpl$ExecutionContext.runChild(CellExecutorImpl.kt:143)\n",
"\tat org.jetbrains.kotlinx.jupyter.repl.impl.CellExecutorImpl$ExecutionContext.access$runChild(CellExecutorImpl.kt:134)\n",
"\tat org.jetbrains.kotlinx.jupyter.repl.impl.CellExecutorImpl$ExecutionContext$doAddLibraries$1.invoke(CellExecutorImpl.kt:155)\n",
"\tat org.jetbrains.kotlinx.jupyter.repl.impl.CellExecutorImpl$ExecutionContext$doAddLibraries$1.invoke(CellExecutorImpl.kt:154)\n",
"\tat org.jetbrains.kotlinx.jupyter.exceptions.ReplLibraryExceptionKt.rethrowAsLibraryException(ReplLibraryException.kt:30)\n",
"\t... 15 more\n",
"\n"
]
}
],
"source": [
"%use kotlin-statistics"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "T1Bw3GH0bL4b"
},
"source": [
"When such a cell is executed, the kernel, makes sure the corresponding Maven repo is configured, the library is loaded, necessary import statements are added (e.g. in that case `import org.nield.kotlinstatistics.*` won't be needed), and necessary renderers are configured. The supported magics now include: [`%%kotlin-statistics`](https://github.com/thomasnield/kotlin-statistics), [`klaxon`](https://github.com/cbeust/klaxon), [`krangl`](https://github.com/holgerbrandl/krangl), [`kravis`](https://github.com/holgerbrandl/kravis), and [`lets-plot`](https://github.com/jetbrains/datalore-plot)."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "PGqmZHGSbL4b"
},
"source": [
"Here's another example, showcasing [`krangl`](https://github.com/holgerbrandl/krangl), and [`lets-plot`](https://github.com/jetbrains/datalore-plot) libraries:"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 17
},
"id": "PrDaMt4wbL4b",
"outputId": "228ee9e5-7a04-4f27-9989-de69479c5231"
},
"outputs": [
{
"output_type": "display_data",
"data": {
"text/html": [
" <div id=\"DeJ5Hw\"></div>\n",
" <script type=\"text/javascript\" data-lets-plot-script=\"library\">\n",
" if(!window.letsPlotCallQueue) {\n",
" window.letsPlotCallQueue = [];\n",
" }; \n",
" window.letsPlotCall = function(f) {\n",
" window.letsPlotCallQueue.push(f);\n",
" };\n",
" (function() {\n",
" var script = document.createElement(\"script\");\n",
" script.type = \"text/javascript\";\n",
" script.src = \"https://cdn.jsdelivr.net/gh/JetBrains/lets-plot@v4.1.0/js-package/distr/lets-plot.min.js\";\n",
" script.onload = function() {\n",
" window.letsPlotCall = function(f) {f();};\n",
" window.letsPlotCallQueue.forEach(function(f) {f();});\n",
" window.letsPlotCallQueue = [];\n",
" \n",
" \n",
" };\n",
" script.onerror = function(event) {\n",
" window.letsPlotCall = function(f) {};\n",
" window.letsPlotCallQueue = [];\n",
" var div = document.createElement(\"div\");\n",
" div.style.color = 'darkred';\n",
" div.textContent = 'Error loading Lets-Plot JS';\n",
" document.getElementById(\"DeJ5Hw\").appendChild(div);\n",
" };\n",
" var e = document.getElementById(\"DeJ5Hw\");\n",
" e.appendChild(script);\n",
" })();\n",
" </script>"
]
},
"metadata": {}
}
],
"source": [
"%use lets-plot, krangl"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 177
},
"id": "jrW0xodEbL4b",
"outputId": "4ff4c727-5c14-4b24-8bfa-dedbd7fce7f3"
},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/html": [
"<html><body><table><tr><th style=\"text-align:left\">sepal_length</th><th style=\"text-align:left\">sepal_width</th><th style=\"text-align:left\">petal_length</th><th style=\"text-align:left\">petal_width</th><th style=\"text-align:left\">species</th></tr><tr><td style=\"text-align:left\" title=\"5.1\">5.1</td><td style=\"text-align:left\" title=\"3.5\">3.5</td><td style=\"text-align:left\" title=\"1.4\">1.4</td><td style=\"text-align:left\" title=\"0.2\">0.2</td><td style=\"text-align:left\" title=\"Iris-setosa\">Iris-setosa</td></tr><tr><td style=\"text-align:left\" title=\"4.9\">4.9</td><td style=\"text-align:left\" title=\"3.0\">3.0</td><td style=\"text-align:left\" title=\"1.4\">1.4</td><td style=\"text-align:left\" title=\"0.2\">0.2</td><td style=\"text-align:left\" title=\"Iris-setosa\">Iris-setosa</td></tr><tr><td style=\"text-align:left\" title=\"4.7\">4.7</td><td style=\"text-align:left\" title=\"3.2\">3.2</td><td style=\"text-align:left\" title=\"1.3\">1.3</td><td style=\"text-align:left\" title=\"0.2\">0.2</td><td style=\"text-align:left\" title=\"Iris-setosa\">Iris-setosa</td></tr><tr><td style=\"text-align:left\" title=\"4.6\">4.6</td><td style=\"text-align:left\" title=\"3.1\">3.1</td><td style=\"text-align:left\" title=\"1.5\">1.5</td><td style=\"text-align:left\" title=\"0.2\">0.2</td><td style=\"text-align:left\" title=\"Iris-setosa\">Iris-setosa</td></tr><tr><td style=\"text-align:left\" title=\"5.0\">5.0</td><td style=\"text-align:left\" title=\"3.6\">3.6</td><td style=\"text-align:left\" title=\"1.4\">1.4</td><td style=\"text-align:left\" title=\"0.2\">0.2</td><td style=\"text-align:left\" title=\"Iris-setosa\">Iris-setosa</td></tr></table><p>Shape: 5 x 5. \n",
"</p></body></html>"
]
},
"metadata": {},
"execution_count": 13
}
],
"source": [
"val df = DataFrame.readCSV(\"https://raw.githubusercontent.com/cheptsov/kotlin-jupyter-demo/master/data/iris.csv\")\n",
"df.head()"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"scrolled": true,
"colab": {
"base_uri": "https://localhost:8080/",
"height": 134
},
"id": "LAOThax5bL4c",
"outputId": "c8e8884b-27a7-4738-f3c5-0fde8a6c62fd"
},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/html": [
"<html><body><table><tr><th style=\"text-align:left\">species</th><th style=\"text-align:left\">n</th></tr><tr><td style=\"text-align:left\" title=\"Iris-setosa\">Iris-setosa</td><td style=\"text-align:left\" title=\"50\">50</td></tr><tr><td style=\"text-align:left\" title=\"Iris-versicolor\">Iris-versicolor</td><td style=\"text-align:left\" title=\"50\">50</td></tr><tr><td style=\"text-align:left\" title=\"Iris-virginica\">Iris-virginica</td><td style=\"text-align:left\" title=\"50\">50</td></tr></table><p>Shape: 3 x 2. \n",
"</p></body></html>"
]
},
"metadata": {},
"execution_count": 14
}
],
"source": [
"df.groupBy(\"species\").count()"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"scrolled": false,
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "9DykR4-5bL4c",
"outputId": "aa4e85a7-711a-453e-aa1f-013ca33f47a8"
},
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"Line_23.jupyter.kts (1:14 - 24) Unresolved reference: geom_point\n",
"Line_23.jupyter.kts (3:9 - 55) Type mismatch: inferred type is ColumnFormula but Pair<TypeVariable(K), TypeVariable(V)> was expected\n",
"Line_23.jupyter.kts (3:35 - 44) Unresolved reference: asDoubles\n",
"Line_23.jupyter.kts (4:9 - 54) Type mismatch: inferred type is ColumnFormula but Pair<TypeVariable(K), TypeVariable(V)> was expected\n",
"Line_23.jupyter.kts (4:34 - 43) Unresolved reference: asDoubles\n",
"Line_23.jupyter.kts (5:9 - 54) Type mismatch: inferred type is ColumnFormula but Pair<TypeVariable(K), TypeVariable(V)> was expected\n",
"Line_23.jupyter.kts (5:34 - 43) Unresolved reference: asStrings\n",
"Line_23.jupyter.kts (9:5 - 6) Unresolved reference: x\n",
"Line_23.jupyter.kts (10:5 - 6) Unresolved reference: y\n",
"Line_23.jupyter.kts (11:5 - 10) Unresolved reference: color"
]
}
],
"source": [
"val points = geom_point(\n",
" data = mapOf(\n",
" \"x\" to df[\"sepal_length\"].asDoubles().toList(),\n",
" \"y\" to df[\"sepal_width\"].asDoubles().toList(),\n",
" \"color\" to df[\"species\"].asStrings().toList()\n",
"\n",
" ), alpha=1.0)\n",
"{\n",
" x = \"x\"\n",
" y = \"y\"\n",
" color = \"color\"\n",
"}\n",
"\n",
"ggplot() + points"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "tnO2488VbL4c"
},
"source": [
"### Useful libraries"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "rXloBquabL4c"
},
"source": [
"* [kotlin-statistics](https://github.com/thomasnield/kotlin-statistics) is a library that provides a set of extension functions to perform exploratory and production statistics. It supports basic numeric list/sequence/array functions (from `sum` to `skewness`), slicing operators (e.g. `countBy`, `simpleRegressionBy`, etc), binning operations, discrete PDF sampling, naive bayes classifier, clustering, linear regression, and more.\n",
"* [kmath](https://github.com/mipt-npm/kmath) is a library inspired by `numpy`; this library supports algebraic structures and operations, array-like structures, math expressions, histograms, streaming operations, wrappers around [commons-math](http://commons.apache.org/proper/commons-math/) and [koma](https://github.com/kyonifer/koma), and more.\n",
"* [krangl](https://github.com/holgerbrandl/krangl) is a library inspired by R's `dplyr` and Python's `pandas`; this library provides functionality for data manipulation using a functional-style API; it allows to filter, transform, aggregate and reshape tabular data.\n",
"* [lets-plot](https://github.com/JetBrains/lets-plot) is a library for declaratively creating plots based tabular data; it is inspired by Python's `ggplot` and [The Grammar of Graphics](https://www.amazon.com/Grammar-Graphics-Statistics-Computing/dp/0387245448/); this library is integrated tightly with the Kotlin kernel; the library is multi-platform and can be used not just with JVM but also from JS and Python.\n",
"* [kravis](https://github.com/holgerbrandl/kravis) is another library inspired by Python's `ggplot` for visualization of tabular data."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "00tvJcovbL4c"
},
"source": [
"### Documentation and contribution"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "R96670u5bL4c"
},
"source": [
"The kernel's source code along with documentation is available on [GitHub](https://github.com/erokhins/kotlin-jupyter)."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "7TTydDODbL4c"
},
"source": [
"The community has already started adopting Kotlin for data science, and this adoption is only growing. It’s very much recommended to watch a [talk](https://www.youtube.com/watch?v=yjVW6uCmVBA) by Holger Brandl (the creator of [krangl](https://github.com/holgerbrandl/krangl), a Kotlin’s analog of Python’s pandas) or another [talk](https://www.youtube.com/watch?v=-zTqtEcnM7A&feature=youtu.be) by Thomas Nield (the creator of [kotlin-statistics](https://github.com/thomasnield/kotlin-statistics)), or read his [article](https://towardsdatascience.com/introduction-to-kotlin-statistics-cdad3be88b5)."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "7Sdg2HYsbL4c"
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Kotlin - kotlin (JBang)-ipc",
"name": "jbang-kotlin-ipc"
},
"colab": {
"provenance": [],
"include_colab_link": true
}
},
"nbformat": 4,
"nbformat_minor": 0
}
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