{
 "cells": [
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Row(movieId='1', tagId='1', relevance='0.025000000000000022'),\n",
       " Row(movieId='1', tagId='2', relevance='0.025000000000000022'),\n",
       " Row(movieId='1', tagId='3', relevance='0.057750000000000024'),\n",
       " Row(movieId='1', tagId='4', relevance='0.09675'),\n",
       " Row(movieId='1', tagId='5', relevance='0.14675')]"
      ]
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     "execution_count": 5,
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    "scores = spark.read.csv(\"/Users/blairhudson/Downloads/ml-20m/genome-scores.csv\",header=True)\n",
    "scores.take(5)\n",
    "sqlContext.registerFunction(\"stringLengthString\", lambda x: len(x))\n",
    "\n"
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   "cell_type": "code",
   "execution_count": 7,
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    {
     "data": {
      "text/plain": [
       "[Row(stringLengthString(test)='4')]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
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    "sqlContext.registerFunction(\"stringLengthString\", lambda x: len(x))\n",
    "sqlContext.sql(\"SELECT stringLengthString('test')\").collect()"
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   "source": []
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