Run Scala in Google Colab with Almond

run_scala_in_colab_with_almond.ipynb

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  "metadata": {
    "colab": {
      "provenance": [],
      "collapsed_sections": [],
      "toc_visible": true,
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Scala",
      "name": "scala"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/gist/kirisakow/69e251071e594979756d11b1a8251229/run_scala_in_colab_with_almond.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Run Scala in Google Colab with Almond\n\n",
        "### <u>**Deprecation warning:**</u> Google Colab interface has seemingly undergone changes and does not allow to use side kernels the way it used to be. I myself have stopped using Google Colab and have been using Docker images and containers instead. To run Scala in Jupyter Notebook as a Docker container, you can use this [guide of mine](https://github.com/kirisakow/scala-jupyter-container) based on `jupyter/all-spark-notebook` Docker image, the latest Almond and Scala. Happy coding!"
      ],
      "metadata": {
        "id": "tnRm0YwmdLhl"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Important prerequisite 1 / 3\n",
        "\n",
        "Open your Colab Notebook with a text editor and make sure the `kernelspec` key is set to work with Scala, like so:\n",
        "\n",
        "```json\n",
        "{\n",
        "  ⋮\n",
        "  \"kernelspec\": {\n",
        "    \"display_name\": \"Scala\",\n",
        "    \"name\": \"scala\"\n",
        "  }\n",
        "  ⋮\n",
        "}\n",
        "```"
      ],
      "metadata": {
        "id": "UMudsO4-dQ03"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QVJoUDPtb9gX"
      },
      "source": [
        "## Important prerequisite 2 / 3\n",
        "\n",
        "Run the cell below to [install the Almond kernel](https://almond.sh/docs/quick-start-install) into the global Jupyter kernels:"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "! curl -sS -Lo coursier https://git.io/coursier-cli\n",
        "! chmod +x coursier\n",
        "! ./coursier launch --fork almond -- --install 1>/dev/null 2>&1\n",
        "! rm -f coursier"
      ],
      "metadata": {
        "id": "j-1b2BcOm6py"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Important prerequisite 3 / 3\n",
        "\n",
        "Reload Google Colab page for Scala to activate."
      ],
      "metadata": {
        "id": "wyH-FiPgxfIL"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now you can work in Scala:"
      ],
      "metadata": {
        "id": "5hDyl5WedYRK"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "println(scala.util.Properties.versionString)"
      ],
      "metadata": {
        "outputId": "e6fd6754-a061-4138-a880-320c9a7af1c6",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ujAul9CtDJ2A"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "version 2.13.8\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "for (day <- 1 to 16) {\n",
        "  if (0 < day % 7 && day % 7 < 6) println(f\"$day%02d: work day\")\n",
        "  else println(f\"$day%02d: week end!!!\")\n",
        "}"
      ],
      "metadata": {
        "id": "yyo4HCFCwzWP",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8eaebd8b-6b9a-4540-c95c-577cdaceb1e0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "01: work day\n",
            "02: work day\n",
            "03: work day\n",
            "04: work day\n",
            "05: work day\n",
            "06: week end!!!\n",
            "07: week end!!!\n",
            "08: work day\n",
            "09: work day\n",
            "10: work day\n",
            "11: work day\n",
            "12: work day\n",
            "13: week end!!!\n",
            "14: week end!!!\n",
            "15: work day\n",
            "16: work day\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import scala.language.postfixOps\n",
        "\n",
        "def sorter(a: Array[Int]): Array[Int] = {\n",
        "  if (a.length <= 1) a\n",
        "  else {\n",
        "    val pivot = a(a.length / 2)\n",
        "    Array.concat(\n",
        "      sorter(a filter (pivot >)),\n",
        "      a filter (pivot ==),\n",
        "      sorter(a filter (pivot <)),\n",
        "    )\n",
        "  }\n",
        "}\n",
        "\n",
        "val arr = Array(3, 5, 6, 8, 2, 9, 7, 7)\n",
        "val sorted_arr = sorter(arr)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Tb3ZeBrlDzIW",
        "outputId": "f6c8835f-54dc-4671-d974-3335a1b7500c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "\u001b[32mimport \u001b[39m\u001b[36mscala.language.postfixOps\n",
              "\n",
              "\u001b[39m\n",
              "defined \u001b[32mfunction\u001b[39m \u001b[36msorter\u001b[39m\n",
              "\u001b[36marr\u001b[39m: \u001b[32mArray\u001b[39m[\u001b[32mInt\u001b[39m] = \u001b[33mArray\u001b[39m(\u001b[32m3\u001b[39m, \u001b[32m5\u001b[39m, \u001b[32m6\u001b[39m, \u001b[32m8\u001b[39m, \u001b[32m2\u001b[39m, \u001b[32m9\u001b[39m, \u001b[32m7\u001b[39m, \u001b[32m7\u001b[39m)\n",
              "\u001b[36msorted_arr\u001b[39m: \u001b[32mArray\u001b[39m[\u001b[32mInt\u001b[39m] = \u001b[33mArray\u001b[39m(\u001b[32m2\u001b[39m, \u001b[32m3\u001b[39m, \u001b[32m5\u001b[39m, \u001b[32m6\u001b[39m, \u001b[32m7\u001b[39m, \u001b[32m7\u001b[39m, \u001b[32m8\u001b[39m, \u001b[32m9\u001b[39m)"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "val l0 = List(1, 2, 3, 4)\n",
        "val l1 = l0.foreach { case i => i + 10 }\n",
        "val l2 = l0.map((i: Int) => i * 2)\n",
        "val l3 = l0.filter((i: Int) => i % 2 == 0)\n",
        "val l4 = l0.zip(l2)\n",
        "val summ = l0.foldLeft(0) { (acc, num) => acc + num }"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LTjSzesAHm2_",
        "outputId": "d5206e19-918c-4256-8011-6cd35e52aa4e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "\u001b[36ml0\u001b[39m: \u001b[32mList\u001b[39m[\u001b[32mInt\u001b[39m] = \u001b[33mList\u001b[39m(\u001b[32m1\u001b[39m, \u001b[32m2\u001b[39m, \u001b[32m3\u001b[39m, \u001b[32m4\u001b[39m)\n",
              "\u001b[36ml2\u001b[39m: \u001b[32mList\u001b[39m[\u001b[32mInt\u001b[39m] = \u001b[33mList\u001b[39m(\u001b[32m2\u001b[39m, \u001b[32m4\u001b[39m, \u001b[32m6\u001b[39m, \u001b[32m8\u001b[39m)\n",
              "\u001b[36ml3\u001b[39m: \u001b[32mList\u001b[39m[\u001b[32mInt\u001b[39m] = \u001b[33mList\u001b[39m(\u001b[32m2\u001b[39m, \u001b[32m4\u001b[39m)\n",
              "\u001b[36ml4\u001b[39m: \u001b[32mList\u001b[39m[(\u001b[32mInt\u001b[39m, \u001b[32mInt\u001b[39m)] = \u001b[33mList\u001b[39m((\u001b[32m1\u001b[39m, \u001b[32m2\u001b[39m), (\u001b[32m2\u001b[39m, \u001b[32m4\u001b[39m), (\u001b[32m3\u001b[39m, \u001b[32m6\u001b[39m), (\u001b[32m4\u001b[39m, \u001b[32m8\u001b[39m))\n",
              "\u001b[36msumm\u001b[39m: \u001b[32mInt\u001b[39m = \u001b[32m10\u001b[39m"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "val names0 = Vector(\"Bob\", \"Fred\", \"Joe\", \"Julia\", \"Kim\")\n",
        "val names1 = for (name <- names0 if name.startsWith(\"J\")) yield name"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1qw4nQm5Lgkk",
        "outputId": "90ae0178-acdf-4bd5-8814-56635a752c7e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "\u001b[36mnames0\u001b[39m: \u001b[32mVector\u001b[39m[\u001b[32mString\u001b[39m] = \u001b[33mVector\u001b[39m(\u001b[32m\"Bob\"\u001b[39m, \u001b[32m\"Fred\"\u001b[39m, \u001b[32m\"Joe\"\u001b[39m, \u001b[32m\"Julia\"\u001b[39m, \u001b[32m\"Kim\"\u001b[39m)\n",
              "\u001b[36mnames1\u001b[39m: \u001b[32mVector\u001b[39m[\u001b[32mString\u001b[39m] = \u001b[33mVector\u001b[39m(\u001b[32m\"Joe\"\u001b[39m, \u001b[32m\"Julia\"\u001b[39m)"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "case class User(name: String, age: Int)\n",
        "\n",
        "val userBase = List(\n",
        "  User(\"Travis\", 28),\n",
        "  User(\"Kelly\", 33),\n",
        "  User(\"Jennifer\", 44),\n",
        "  User(\"Dennis\", 23)\n",
        ")\n",
        "\n",
        "val twentySomethings = for (user <- userBase if user.age >=20 && user.age < 30) yield user.name\n",
        "\n",
        "twentySomethings.foreach(println)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "f8DiIy2pNiu-",
        "outputId": "872233aa-65f5-4c95-9d7e-3b6f2c3e1393"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Travis\n",
            "Dennis\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "defined \u001b[32mclass\u001b[39m \u001b[36mUser\u001b[39m\n",
              "\u001b[36muserBase\u001b[39m: \u001b[32mList\u001b[39m[\u001b[32mUser\u001b[39m] = \u001b[33mList\u001b[39m(\n",
              "  \u001b[33mUser\u001b[39m(name = \u001b[32m\"Travis\"\u001b[39m, age = \u001b[32m28\u001b[39m),\n",
              "  \u001b[33mUser\u001b[39m(name = \u001b[32m\"Kelly\"\u001b[39m, age = \u001b[32m33\u001b[39m),\n",
              "  \u001b[33mUser\u001b[39m(name = \u001b[32m\"Jennifer\"\u001b[39m, age = \u001b[32m44\u001b[39m),\n",
              "  \u001b[33mUser\u001b[39m(name = \u001b[32m\"Dennis\"\u001b[39m, age = \u001b[32m23\u001b[39m)\n",
              ")\n",
              "\u001b[36mtwentySomethings\u001b[39m: \u001b[32mList\u001b[39m[\u001b[32mString\u001b[39m] = \u001b[33mList\u001b[39m(\u001b[32m\"Travis\"\u001b[39m, \u001b[32m\"Dennis\"\u001b[39m)"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "val m = Map(\n",
        "  \"un\" -> 1,\n",
        "  \"deux\" -> 2,\n",
        "  \"trois\" -> 3,\n",
        ")\n",
        "m.foreach {\n",
        "  case (cle, valeur) => println(s\"$cle — $valeur\")\n",
        "}"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bMaPhdBTQQlj",
        "outputId": "b5e8a26f-3b4c-4832-9174-eace7f638f6d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "un — 1\n",
            "deux — 2\n",
            "trois — 3\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "\u001b[36mm\u001b[39m: \u001b[32mMap\u001b[39m[\u001b[32mString\u001b[39m, \u001b[32mInt\u001b[39m] = \u001b[33mMap\u001b[39m(\u001b[32m\"un\"\u001b[39m -> \u001b[32m1\u001b[39m, \u001b[32m\"deux\"\u001b[39m -> \u001b[32m2\u001b[39m, \u001b[32m\"trois\"\u001b[39m -> \u001b[32m3\u001b[39m)"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    }
  ]
}