jupyter/notebook/pyspark-check-install.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Test notebook\n",
    "It should run without errors when all Worker nodes contains python deps. From [OneHotEncoderEstimator](https://spark.apache.org/docs/latest/ml-features#onehotencoderestimator)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import findspark\n",
    "findspark.init()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pyspark.sql import SparkSession\n",
    "spark = SparkSession.builder.appName(\"OneHotEncoderEstimator\").getOrCreate()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pyspark.ml.feature import OneHotEncoderEstimator\n",
    "\n",
    "df = spark.createDataFrame([\n",
    "    (0.0, 1.0),\n",
    "    (1.0, 0.0),\n",
    "    (2.0, 1.0),\n",
    "    (0.0, 2.0),\n",
    "    (0.0, 1.0),\n",
    "    (2.0, 0.0)\n",
    "], [\"categoryIndex1\", \"categoryIndex2\"])\n",
    "\n",
    "encoder = OneHotEncoderEstimator(inputCols=[\"categoryIndex1\", \"categoryIndex2\"],\n",
    "                                 outputCols=[\"categoryVec1\", \"categoryVec2\"])\n",
    "model = encoder.fit(df)\n",
    "encoded = model.transform(df)\n",
    "encoded.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "spark.stop()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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