{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is a test for the issue [#1168](https://github.com/jupyter/docker-stacks/issues/1168)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pyspark.sql import SparkSession\n",
    "from pyspark.sql.functions import pandas_udf\n",
    "\n",
    "# Spark session & context\n",
    "spark = SparkSession.builder.master('local').getOrCreate()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+---+---+\n",
      "| id|age|\n",
      "+---+---+\n",
      "|  1| 21|\n",
      "+---+---+\n",
      "\n"
     ]
    }
   ],
   "source": [
    "df = spark.createDataFrame([(1, 21), (2, 30)], (\"id\", \"age\"))\n",
    "def filter_func(iterator):\n",
    "    for pdf in iterator:\n",
    "        yield pdf[pdf.id == 1]\n",
    "\n",
    "df.mapInPandas(filter_func, df.schema).show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.6"
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 },
 "nbformat": 4,
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