index.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Kotlin for Jupyter Notebooks"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/cheptsov/kotlin-jupyter-demo/master?filepath=index.ipynb)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This notebook will guide you on how one can use [Kotlin](https://kotlinlang.org/) with [Jupyter notebooks](https://jupyter.org/)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Installing kernel"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Currently, [Kotlin Jupyter kernel](https://github.com/erokhins/kotlin-jupyter) can be installed only via [conda](https://conda.io/en/latest/):\n",
    "\n",
    "```bash\n",
    "conda install kotlin-jupyter-kernel -c jetbrains\n",
    "```\n",
    "\n",
    "Later it will be also possible to install it via `pip install`.\n",
    "\n",
    "Note, Kotlin Jupyter requires Java 8 to be installed:\n",
    "\n",
    "```bash\n",
    "apt-get install openjdk-8-jre\n",
    "```\n",
    "\n",
    "Once these requirements are satisfied, feel free to run `jupyter notebook` and switch to `Kotlin` kernel."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Running cells"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here's a simple example with Kotlin code:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Greeter(val name: String) {\n",
    "    fun greet() {\n",
    "        println(\"Hello, $name!\")\n",
    "    }\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Hello, Jupyter!\n"
     ]
    }
   ],
   "source": [
    "Greeter(\"Jupyter\").greet() // Run me"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Configuring Maven dependencies"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "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": {},
   "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": {},
   "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": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CENTROID: 0\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",
      "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=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"
     ]
    }
   ],
   "source": [
    "clusters.forEachIndexed { index, item ->\n",
    "    println(\"CENTROID: $index\")\n",
    "    item.points.forEach {\n",
    "        println(\"\\t$it\")\n",
    "    }\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Configuring the built-in  via magics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "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": {},
   "outputs": [],
   "source": [
    "%use kotlin-statistics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "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": {},
   "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": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div id=\"0maYuj\"></div>\n",
       "                   <script type=\"text/javascript\">\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://dl.bintray.com/jetbrains/lets-plot/lets-plot-1.0.0rc1.min.js\";\n",
       "                           script.onload = function() {\n",
       "                               window.letsPlotCall = function(f) {f();};\n",
       "                               window.letsPlotCallQueue.forEach(function(f) {f();});\n",
       "                               window.letsPlotCallQueue = [];\n",
       "                               \n",
       "                   var div = document.createElement(\"div\");\n",
       "                   div.style.color = 'darkblue';\n",
       "                   div.textContent = 'Lets-Plot JS successfully loaded.';\n",
       "                   document.getElementById(\"0maYuj\").appendChild(div);\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(\"0maYuj\").appendChild(div);\n",
       "                           };\n",
       "                           var e = document.getElementById(\"0maYuj\");\n",
       "                           e.appendChild(script);\n",
       "                       })()\n",
       "                   </script>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%use lets-plot, krangl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": []
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/html": [
       "<table><tr><th>sepal_length</th><th>sepal_width</th><th>petal_length</th><th>petal_width</th><th>species</th></tr><tr><td>5.1</td><td>3.5</td><td>1.4</td><td>0.2</td><td>Iris-setosa</td></tr><tr><td>4.9</td><td>3.0</td><td>1.4</td><td>0.2</td><td>Iris-setosa</td></tr><tr><td>4.7</td><td>3.2</td><td>1.3</td><td>0.2</td><td>Iris-setosa</td></tr><tr><td>4.6</td><td>3.1</td><td>1.5</td><td>0.2</td><td>Iris-setosa</td></tr><tr><td>5.0</td><td>3.6</td><td>1.4</td><td>0.2</td><td>Iris-setosa</td></tr></table>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "val df = DataFrame.readCSV(\"data/iris.csv\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": []
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/html": [
       "<table><tr><th>species</th><th>n</th></tr><tr><td>Iris-setosa</td><td>50</td></tr><tr><td>Iris-versicolor</td><td>50</td></tr><tr><td>Iris-virginica</td><td>50</td></tr></table>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.groupBy(\"species\").count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": []
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/html": [
       "\n",
       "            <div id=\"plot_output_V5pqek\"></div>\n",
       "            <script type=\"text/javascript\">\n",
       "                var plotSpec={\n",
       "'mapping':{\n",
       "},\n",
       "'kind':\"plot\",\n",
       "'scales':[],\n",
       "'layers':[{\n",
       "'stat':\"identity\",\n",
       "'mapping':{\n",
       "'color':\"color\",\n",
       "'x':\"x\",\n",
       "'y':\"y\"\n",
       "},\n",
       "'data':{\n",
       "'color':[\"Iris-setosa\",\"Iris-setosa\",\"Iris-setosa\",\"Iris-setosa\",\"Iris-setosa\",\"Iris-setosa\",\"Iris-setosa\",\"Iris-setosa\",\"Iris-setosa\",\"Iris-setosa\",\"Iris-setosa\",\"Iris-setosa\",\"Iris-setosa\",\"Iris-setosa\",\"Iris-setosa\",\"Iris-setosa\",\"Iris-setosa\",\"Iris-setosa\",\"Iris-setosa\",\"Iris-setosa\",\"Iris-setosa\",\"Iris-setosa\",\"Iris-setosa\",\"Iris-setosa\",\"Iris-setosa\",\"Iris-setosa\",\"Iris-setosa\",\"Iris-setosa\",\"Iris-setosa\",\"Iris-setosa\",\"Iris-setosa\",\"Iris-setosa\",\"Iris-setosa\",\"Iris-setosa\",\"Iris-setosa\",\"Iris-setosa\",\"Iris-setosa\",\"Iris-setosa\",\"Iris-setosa\",\"Iris-setosa\",\"Iris-setosa\",\"Iris-setosa\",\"Iris-setosa\",\"Iris-setosa\",\"Iris-setosa\",\"Iris-setosa\",\"Iris-setosa\",\"Iris-setosa\",\"Iris-setosa\",\"Iris-setosa\",\"Iris-versicolor\",\"Iris-versicolor\",\"Iris-versicolor\",\"Iris-versicolor\",\"Iris-versicolor\",\"Iris-versicolor\",\"Iris-versicolor\",\"Iris-versicolor\",\"Iris-versicolor\",\"Iris-versicolor\",\"Iris-versicolor\",\"Iris-versicolor\",\"Iris-versicolor\",\"Iris-versicolor\",\"Iris-versicolor\",\"Iris-versicolor\",\"Iris-versicolor\",\"Iris-versicolor\",\"Iris-versicolor\",\"Iris-versicolor\",\"Iris-versicolor\",\"Iris-versicolor\",\"Iris-versicolor\",\"Iris-versicolor\",\"Iris-versicolor\",\"Iris-versicolor\",\"Iris-versicolor\",\"Iris-versicolor\",\"Iris-versicolor\",\"Iris-versicolor\",\"Iris-versicolor\",\"Iris-versicolor\",\"Iris-versicolor\",\"Iris-versicolor\",\"Iris-versicolor\",\"Iris-versicolor\",\"Iris-versicolor\",\"Iris-versicolor\",\"Iris-versicolor\",\"Iris-versicolor\",\"Iris-versicolor\",\"Iris-versicolor\",\"Iris-versicolor\",\"Iris-versicolor\",\"Iris-versicolor\",\"Iris-versicolor\",\"Iris-versicolor\",\"Iris-versicolor\",\"Iris-versicolor\",\"Iris-versicolor\",\"Iris-virginica\",\"Iris-virginica\",\"Iris-virginica\",\"Iris-virginica\",\"Iris-virginica\",\"Iris-virginica\",\"Iris-virginica\",\"Iris-virginica\",\"Iris-virginica\",\"Iris-virginica\",\"Iris-virginica\",\"Iris-virginica\",\"Iris-virginica\",\"Iris-virginica\",\"Iris-virginica\",\"Iris-virginica\",\"Iris-virginica\",\"Iris-virginica\",\"Iris-virginica\",\"Iris-virginica\",\"Iris-virginica\",\"Iris-virginica\",\"Iris-virginica\",\"Iris-virginica\",\"Iris-virginica\",\"Iris-virginica\",\"Iris-virginica\",\"Iris-virginica\",\"Iris-virginica\",\"Iris-virginica\",\"Iris-virginica\",\"Iris-virginica\",\"Iris-virginica\",\"Iris-virginica\",\"Iris-virginica\",\"Iris-virginica\",\"Iris-virginica\",\"Iris-virginica\",\"Iris-virginica\",\"Iris-virginica\",\"Iris-virginica\",\"Iris-virginica\",\"Iris-virginica\",\"Iris-virginica\",\"Iris-virginica\",\"Iris-virginica\",\"Iris-virginica\",\"Iris-virginica\",\"Iris-virginica\",\"Iris-virginica\"],\n",
       "'x':[5.1,4.9,4.7,4.6,5.0,5.4,4.6,5.0,4.4,4.9,5.4,4.8,4.8,4.3,5.8,5.7,5.4,5.1,5.7,5.1,5.4,5.1,4.6,5.1,4.8,5.0,5.0,5.2,5.2,4.7,4.8,5.4,5.2,5.5,4.9,5.0,5.5,4.9,4.4,5.1,5.0,4.5,4.4,5.0,5.1,4.8,5.1,4.6,5.3,5.0,7.0,6.4,6.9,5.5,6.5,5.7,6.3,4.9,6.6,5.2,5.0,5.9,6.0,6.1,5.6,6.7,5.6,5.8,6.2,5.6,5.9,6.1,6.3,6.1,6.4,6.6,6.8,6.7,6.0,5.7,5.5,5.5,5.8,6.0,5.4,6.0,6.7,6.3,5.6,5.5,5.5,6.1,5.8,5.0,5.6,5.7,5.7,6.2,5.1,5.7,6.3,5.8,7.1,6.3,6.5,7.6,4.9,7.3,6.7,7.2,6.5,6.4,6.8,5.7,5.8,6.4,6.5,7.7,7.7,6.0,6.9,5.6,7.7,6.3,6.7,7.2,6.2,6.1,6.4,7.2,7.4,7.9,6.4,6.3,6.1,7.7,6.3,6.4,6.0,6.9,6.7,6.9,5.8,6.8,6.7,6.7,6.3,6.5,6.2,5.9],\n",
       "'y':[3.5,3.0,3.2,3.1,3.6,3.9,3.4,3.4,2.9,3.1,3.7,3.4,3.0,3.0,4.0,4.4,3.9,3.5,3.8,3.8,3.4,3.7,3.6,3.3,3.4,3.0,3.4,3.5,3.4,3.2,3.1,3.4,4.1,4.2,3.1,3.2,3.5,3.1,3.0,3.4,3.5,2.3,3.2,3.5,3.8,3.0,3.8,3.2,3.7,3.3,3.2,3.2,3.1,2.3,2.8,2.8,3.3,2.4,2.9,2.7,2.0,3.0,2.2,2.9,2.9,3.1,3.0,2.7,2.2,2.5,3.2,2.8,2.5,2.8,2.9,3.0,2.8,3.0,2.9,2.6,2.4,2.4,2.7,2.7,3.0,3.4,3.1,2.3,3.0,2.5,2.6,3.0,2.6,2.3,2.7,3.0,2.9,2.9,2.5,2.8,3.3,2.7,3.0,2.9,3.0,3.0,2.5,2.9,2.5,3.6,3.2,2.7,3.0,2.5,2.8,3.2,3.0,3.8,2.6,2.2,3.2,2.8,2.8,2.7,3.3,3.2,2.8,3.0,2.8,3.0,2.8,3.8,2.8,2.8,2.6,3.0,3.4,3.1,3.0,3.1,3.1,3.1,2.7,3.2,3.3,3.0,2.5,3.0,3.4,3.0]\n",
       "},\n",
       "'alpha':1.0,\n",
       "'position':\"identity\",\n",
       "'geom':\"point\"\n",
       "}]\n",
       "};\n",
       "\n",
       "                var plotContainer = document.getElementById(\"plot_output_V5pqek\");\n",
       "                LetsPlot.buildPlotFromProcessedSpecs(plotSpec, -1, -1, plotContainer);\n",
       "            </script>\n",
       "        "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "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": {},
   "source": [
    "### Useful libraries"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "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."
   ]
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    "### Documentation and contribution"
   ]
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   "source": [
    "The kernel's source code along with documentation is available on [GitHub](https://github.com/erokhins/kotlin-jupyter)."
   ]
  },
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   "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)."
   ]
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