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Load Data","commandVersion":0,"state":"finished","results":null,"errorSummary":null,"error":null,"workflows":[],"startTime":0.0,"submitTime":0.0,"finishTime":0.0,"collapsed":false,"bindings":{},"inputWidgets":{},"displayType":"table","width":"auto","height":"auto","xColumns":null,"yColumns":null,"pivotColumns":null,"pivotAggregation":null,"customPlotOptions":{},"commentThread":[],"commentsVisible":false,"parentHierarchy":[],"diffInserts":[],"diffDeletes":[],"globalVars":{},"latestUser":"a user","commandTitle":"","showCommandTitle":false,"hideCommandCode":false,"hideCommandResult":false,"iPythonMetadata":null,"streamStates":{},"nuid":"a2605f36-6472-4dcd-bd10-2fdb68df80cd"},{"version":"CommandV1","origId":3207259124804972,"guid":"2fdb9bba-e665-4257-8cec-ccd3c5d61737","subtype":"command","commandType":"auto","position":2.0,"command":"val taxes2013 = spark.read\n .option(\"header\", \"true\")\n .csv(\"dbfs:/databricks-datasets/data.gov/irs_zip_code_data/data-001/2013_soi_zipcode_agi.csv\")\n\nval markets = spark.read\n .option(\"header\", \"true\")\n .csv(\"dbfs:/databricks-datasets/data.gov/farmers_markets_geographic_data/data-001/market_data.csv\")","commandVersion":0,"state":"finished","results":{"type":"html","data":"<div class=\"ansiout\">taxes2013: org.apache.spark.sql.DataFrame = [STATEFIPS: string, STATE: string ... 112 more fields]\nmarkets: org.apache.spark.sql.DataFrame = [FMID: string, MarketName: string ... 57 more fields]\n</div>","arguments":{},"addedWidgets":{},"removedWidgets":[]},"errorSummary":"Cancelled","error":null,"workflows":[],"startTime":1.487168000603E12,"submitTime":1.487167938054E12,"finishTime":1.487168009031E12,"collapsed":false,"bindings":{},"inputWidgets":{},"displayType":"table","width":"auto","height":"auto","xColumns":null,"yColumns":null,"pivotColumns":null,"pivotAggregation":null,"customPlotOptions":{},"commentThread":[],"commentsVisible":false,"parentHierarchy":[],"diffInserts":[],"diffDeletes":[],"globalVars":{},"latestUser":"a user","commandTitle":"","showCommandTitle":false,"hideCommandCode":false,"hideCommandResult":false,"iPythonMetadata":null,"streamStates":{},"nuid":"a5368a89-1ccb-4eb5-9278-e53f6b8069b2"},{"version":"CommandV1","origId":3207259124804983,"guid":"565d60aa-e548-4f11-bfc7-3b54faf8ea63","subtype":"command","commandType":"auto","position":2.5,"command":"println(taxes2013.columns.length)\ntaxes2013.columns","commandVersion":0,"state":"finished","results":{"type":"html","data":"<div class=\"ansiout\">114\nres0: Array[String] = Array(STATEFIPS, STATE, zipcode, agi_stub, N1, MARS1, MARS2, MARS4, PREP, N2, NUMDEP, A00100, N02650, A02650, N00200, A00200, N00300, A00300, N00600, A00600, N00650, A00650, N00700, A00700, N00900, A00900, N01000, A01000, N01400, A01400, N01700, A01700, SCHF, N02300, A02300, N02500, A02500, N26270, A26270, N02900, A02900, N03220, A03220, N03300, A03300, N03270, A03270, N03150, A03150, N03210, A03210, N03230, A03230, N03240, A03240, N04470, A04470, A00101, N18425, A18425, N18450, A18450, N18500, A18500, N18300, A18300, N19300, A19300, N19700, A19700, N04800, A04800, N05800, A05800, N09600, A09600, N07100, A07100, N07300, A07300, N07180, A07180, N07230, A07230, N07240, A07240, N07220, A07220, N07260, A07260, N09400, A09400, N10600, A10600, N59660, A59660, N59720, A59720, N11070, A11070, N10960, A10960, N06500, A06500, N10300, A10300, N85330, A85330, N85300, A85300, N11901, A11901, N11902, A11902)\n</div>","arguments":{},"addedWidgets":{},"removedWidgets":[]},"errorSummary":null,"error":null,"workflows":[],"startTime":1.487128388492E12,"submitTime":1.487128388474E12,"finishTime":1.487128388691E12,"collapsed":false,"bindings":{},"inputWidgets":{},"displayType":"table","width":"auto","height":"auto","xColumns":null,"yColumns":null,"pivotColumns":null,"pivotAggregation":null,"customPlotOptions":{},"commentThread":[],"commentsVisible":false,"parentHierarchy":[],"diffInserts":[],"diffDeletes":[],"globalVars":{},"latestUser":"a user","commandTitle":"","showCommandTitle":false,"hideCommandCode":false,"hideCommandResult":false,"iPythonMetadata":null,"streamStates":{},"nuid":"a321c2b8-5dbd-420a-aa97-e8355848aa45"},{"version":"CommandV1","origId":3207259124804984,"guid":"b863f2a0-8b8e-4cf3-a0ca-2ea5153e2e37","subtype":"command","commandType":"auto","position":2.75,"command":"println(markets.columns.length)\nmarkets.dtypes","commandVersion":0,"state":"finished","results":{"type":"html","data":"<div class=\"ansiout\">59\nres1: Array[(String, String)] = Array((FMID,StringType), (MarketName,StringType), (Website,StringType), (Facebook,StringType), (Twitter,StringType), (Youtube,StringType), (OtherMedia,StringType), (street,StringType), (city,StringType), (County,StringType), (State,StringType), (zip,StringType), (Season1Date,StringType), (Season1Time,StringType), (Season2Date,StringType), (Season2Time,StringType), (Season3Date,StringType), (Season3Time,StringType), (Season4Date,StringType), (Season4Time,StringType), (x,StringType), (y,StringType), (Location,StringType), (Credit,StringType), (WIC,StringType), (WICcash,StringType), (SFMNP,StringType), (SNAP,StringType), (Organic,StringType), (Bakedgoods,StringType), (Cheese,StringType), (Crafts,StringType), (Flowers,StringType), (Eggs,StringType), (Seafood,StringType), (Herbs,StringType), (Vegetables,StringType), (Honey,StringType), (Jams,StringType), (Maple,StringType), (Meat,StringType), (Nursery,StringType), (Nuts,StringType), (Plants,StringType), (Poultry,StringType), (Prepared,StringType), (Soap,StringType), (Trees,StringType), (Wine,StringType), (Coffee,StringType), (Beans,StringType), (Fruits,StringType), (Grains,StringType), (Juices,StringType), (Mushrooms,StringType), (PetFood,StringType), (Tofu,StringType), (WildHarvested,StringType), (updateTime,StringType))\n</div>","arguments":{},"addedWidgets":{},"removedWidgets":[]},"errorSummary":null,"error":null,"workflows":[],"startTime":1.487128391092E12,"submitTime":1.487128391076E12,"finishTime":1.487128391476E12,"collapsed":false,"bindings":{},"inputWidgets":{},"displayType":"table","width":"auto","height":"auto","xColumns":null,"yColumns":null,"pivotColumns":null,"pivotAggregation":null,"customPlotOptions":{},"commentThread":[],"commentsVisible":false,"parentHierarchy":[],"diffInserts":[],"diffDeletes":[],"globalVars":{},"latestUser":"a user","commandTitle":"","showCommandTitle":false,"hideCommandCode":false,"hideCommandResult":false,"iPythonMetadata":null,"streamStates":{},"nuid":"4ae3c81b-a18a-450e-b109-3622aef51213"},{"version":"CommandV1","origId":1704430907200387,"guid":"52e3891e-31c1-4202-8469-b8342082d891","subtype":"command","commandType":"auto","position":2.875,"command":"%md Because of no schemma, all columns' dtype are StringType.","commandVersion":0,"state":"finished","results":null,"errorSummary":null,"error":null,"workflows":[],"startTime":0.0,"submitTime":0.0,"finishTime":0.0,"collapsed":false,"bindings":{},"inputWidgets":{},"displayType":"table","width":"auto","height":"auto","xColumns":null,"yColumns":null,"pivotColumns":null,"pivotAggregation":null,"customPlotOptions":{},"commentThread":[],"commentsVisible":false,"parentHierarchy":[],"diffInserts":[],"diffDeletes":[],"globalVars":{},"latestUser":"a user","commandTitle":"","showCommandTitle":false,"hideCommandCode":false,"hideCommandResult":false,"iPythonMetadata":null,"streamStates":{},"nuid":"3815a9c0-ba2e-4831-9140-8a2fda87c438"},{"version":"CommandV1","origId":3207259124804973,"guid":"665e07c4-703c-4878-8a10-5a1061df406f","subtype":"command","commandType":"auto","position":3.0,"command":"taxes2013.createOrReplaceTempView(\"taxes2013\")\nmarkets.createOrReplaceTempView(\"markets\")","commandVersion":0,"state":"finished","results":{"type":"html","data":"<div class=\"ansiout\"></div>","arguments":{},"addedWidgets":{},"removedWidgets":[]},"errorSummary":null,"error":null,"workflows":[],"startTime":1.487168030261E12,"submitTime":1.487168030247E12,"finishTime":1.487168030472E12,"collapsed":false,"bindings":{},"inputWidgets":{},"displayType":"table","width":"auto","height":"auto","xColumns":null,"yColumns":null,"pivotColumns":null,"pivotAggregation":null,"customPlotOptions":{},"commentThread":[],"commentsVisible":false,"parentHierarchy":[],"diffInserts":[],"diffDeletes":[],"globalVars":{},"latestUser":"a user","commandTitle":"","showCommandTitle":false,"hideCommandCode":false,"hideCommandResult":false,"iPythonMetadata":null,"streamStates":{},"nuid":"bb21f41a-83bd-4af0-a5a1-81821170ed2c"},{"version":"CommandV1","origId":1704430907200389,"guid":"62ead2df-2fb9-4b77-9edf-adb561341023","subtype":"command","commandType":"auto","position":3.25,"command":"%md\n\n#### at org.apache.spark.sql package\n\ntype DataFrame = Dataset[Row]\n\ntrait Row extends Serializable\n\nclass Dataset[T] extends Serializable\n\nDataset member:\n- def count(): Long\n- def groupBy(cols: Column*): RelationalGroupedDataset\n\n\nclass RelationalGroupedDataset extends AnyRef\n\nRelationalGroupedData member:\n- def count(): DataFrame\n\n### Viewing the following results:\n- number of the unique zip values in markets is 6129.\n- taxes2013's that is 27690.","commandVersion":0,"state":"finished","results":null,"errorSummary":null,"error":null,"workflows":[],"startTime":0.0,"submitTime":0.0,"finishTime":0.0,"collapsed":false,"bindings":{},"inputWidgets":{},"displayType":"table","width":"auto","height":"auto","xColumns":null,"yColumns":null,"pivotColumns":null,"pivotAggregation":null,"customPlotOptions":{},"commentThread":[],"commentsVisible":false,"parentHierarchy":[],"diffInserts":[],"diffDeletes":[],"globalVars":{},"latestUser":"a user","commandTitle":"","showCommandTitle":false,"hideCommandCode":false,"hideCommandResult":false,"iPythonMetadata":null,"streamStates":{},"nuid":"7d86564d-dfe4-4f13-9cae-7302a90cec66"},{"version":"CommandV1","origId":1704430907200390,"guid":"e021e2da-2eca-4ddc-a88c-41e9219b0207","subtype":"command","commandType":"auto","position":3.375,"command":"println(markets.groupBy(\"zip\").count().count())\nprintln(taxes2013.groupBy(\"zipcode\").count().count())","commandVersion":0,"state":"finished","results":{"type":"html","data":"<div class=\"ansiout\">6129\n27690\n</div>","arguments":{},"addedWidgets":{},"removedWidgets":[]},"errorSummary":null,"error":null,"workflows":[],"startTime":1.487168036301E12,"submitTime":1.487168036288E12,"finishTime":1.487168041349E12,"collapsed":false,"bindings":{},"inputWidgets":{},"displayType":"table","width":"auto","height":"auto","xColumns":null,"yColumns":null,"pivotColumns":null,"pivotAggregation":null,"customPlotOptions":{},"commentThread":[],"commentsVisible":false,"parentHierarchy":[],"diffInserts":[],"diffDeletes":[],"globalVars":{},"latestUser":"a user","commandTitle":"","showCommandTitle":false,"hideCommandCode":false,"hideCommandResult":false,"iPythonMetadata":null,"streamStates":{},"nuid":"88718f0b-cb52-466f-bcb8-eef5fe3f9992"},{"version":"CommandV1","origId":3207259124804975,"guid":"57053ddf-7b9a-43fc-9385-43c8eae4ff8b","subtype":"command","commandType":"auto","position":3.5,"command":"%md ## 2. preprocessing\nLike the original, I'll extract the feautures about the zipcode-regional capitality.\n\nBut, I does not zipcode divide by 1/10.\n\nYou have seen above:\n- The unique value of the zipcode of tax2013 is more.\n- Therefore, when comparing with zipcode / 10 = zip / 10,\n- The sum aggregation by the zipcode not contained in the market, will lead the poor corrections.\n\n","commandVersion":0,"state":"finished","results":null,"errorSummary":null,"error":null,"workflows":[],"startTime":0.0,"submitTime":0.0,"finishTime":0.0,"collapsed":false,"bindings":{},"inputWidgets":{},"displayType":"table","width":"auto","height":"auto","xColumns":null,"yColumns":null,"pivotColumns":null,"pivotAggregation":null,"customPlotOptions":{},"commentThread":[],"commentsVisible":false,"parentHierarchy":[],"diffInserts":[],"diffDeletes":[],"globalVars":{},"latestUser":"a user","commandTitle":"","showCommandTitle":false,"hideCommandCode":false,"hideCommandResult":false,"iPythonMetadata":null,"streamStates":{},"nuid":"6821d8e5-7a68-44af-9ecd-2f8906e967a0"},{"version":"CommandV1","origId":3207259124804974,"guid":"b794313c-9e14-405c-af5a-c94a93490f23","subtype":"command","commandType":"auto","position":4.0,"command":"%sql \nDROP TABLE IF EXISTS cleaned_taxes;\n\nCREATE TABLE cleaned_taxes AS\nSELECT state, zipcode, \n int(mars1) as single_returns, \n int(mars2) as joint_returns, \n int(numdep) as numdep, \n double(A02650) as total_income_amount,\n double(A00300) as taxable_interest_amount,\n double(a01000) as net_capital_gains,\n double(a00900) as biz_net_income\nFROM taxes2013","commandVersion":0,"state":"finished","results":{"type":"table","data":[],"arguments":{},"addedWidgets":{},"removedWidgets":[],"schema":[],"overflow":false,"aggData":[],"aggSchema":[],"aggOverflow":false,"aggSeriesLimitReached":false,"aggError":"","aggType":"","plotOptions":null,"isJsonSchema":true,"dbfsResultPath":null},"errorSummary":"Error in SQL statement: AnalysisException: Table or view not found: taxes2013; line 10 pos 5","error":"com.databricks.backend.common.rpc.DatabricksExceptions$SQLExecutionException: org.apache.spark.sql.AnalysisException: Table or view not found: taxes2013; line 10 pos 5\n\tat org.apache.spark.sql.catalyst.analysis.package$AnalysisErrorAt.failAnalysis(package.scala:42)\n\tat org.apache.spark.sql.catalyst.analysis.Analyzer$ResolveRelations$.org$apache$spark$sql$catalyst$analysis$Analyzer$ResolveRelations$$lookupTableFromCatalog(Analyzer.scala:459)\n\tat org.apache.spark.sql.catalyst.analysis.Analyzer$ResolveRelations$$anonfun$apply$9.applyOrElse(Analyzer.scala:478)\n\tat org.apache.spark.sql.catalyst.analysis.Analyzer$ResolveRelations$$anonfun$apply$9.applyOrElse(Analyzer.scala:463)\n\tat org.apache.spark.sql.catalyst.plans.logical.LogicalPlan$$anonfun$resolveOperators$1.apply(LogicalPlan.scala:61)\n\tat org.apache.spark.sql.catalyst.plans.logical.LogicalPlan$$anonfun$resolveOperators$1.apply(LogicalPlan.scala:61)\n\tat org.apache.spark.sql.catalyst.trees.CurrentOrigin$.withOrigin(TreeNode.scala:70)\n\tat org.apache.spark.sql.catalyst.plans.logical.LogicalPlan.resolveOperators(LogicalPlan.scala:60)\n\tat org.apache.spark.sql.catalyst.plans.logical.LogicalPlan$$anonfun$1.apply(LogicalPlan.scala:58)\n\tat org.apache.spark.sql.catalyst.plans.logical.LogicalPlan$$anonfun$1.apply(LogicalPlan.scala:58)\n\tat org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$5.apply(TreeNode.scala:331)\n\tat org.apache.spark.sql.catalyst.trees.TreeNode.mapProductIterator(TreeNode.scala:188)\n\tat org.apache.spark.sql.catalyst.trees.TreeNode.transformChildren(TreeNode.scala:329)\n\tat org.apache.spark.sql.catalyst.plans.logical.LogicalPlan.resolveOperators(LogicalPlan.scala:58)\n\tat org.apache.spark.sql.catalyst.analysis.Analyzer$ResolveRelations$.apply(Analyzer.scala:463)\n\tat org.apache.spark.sql.catalyst.analysis.Analyzer$ResolveRelations$.apply(Analyzer.scala:453)\n\tat org.apache.spark.sql.catalyst.rules.RuleExecutor$$anonfun$execute$1$$anonfun$apply$1.apply(RuleExecutor.scala:85)\n\tat org.apache.spark.sql.catalyst.rules.RuleExecutor$$anonfun$execute$1$$anonfun$apply$1.apply(RuleExecutor.scala:82)\n\tat scala.collection.LinearSeqOptimized$class.foldLeft(LinearSeqOptimized.scala:111)\n\tat scala.collection.immutable.List.foldLeft(List.scala:84)\n\tat org.apache.spark.sql.catalyst.rules.RuleExecutor$$anonfun$execute$1.apply(RuleExecutor.scala:82)\n\tat org.apache.spark.sql.catalyst.rules.RuleExecutor$$anonfun$execute$1.apply(RuleExecutor.scala:74)\n\tat scala.collection.immutable.List.foreach(List.scala:318)\n\tat org.apache.spark.sql.catalyst.rules.RuleExecutor.execute(RuleExecutor.scala:74)\n\tat org.apache.spark.sql.execution.QueryExecution.analyzed$lzycompute(QueryExecution.scala:64)\n\tat org.apache.spark.sql.execution.QueryExecution.analyzed(QueryExecution.scala:62)\n\tat org.apache.spark.sql.execution.QueryExecution.assertAnalyzed(QueryExecution.scala:50)\n\tat org.apache.spark.sql.execution.datasources.AnalyzeCreateTable$$anonfun$apply$2$$anonfun$5.apply(rules.scala:104)\n\tat org.apache.spark.sql.execution.datasources.AnalyzeCreateTable$$anonfun$apply$2$$anonfun$5.apply(rules.scala:101)\n\tat scala.Option.map(Option.scala:145)\n\tat org.apache.spark.sql.execution.datasources.AnalyzeCreateTable$$anonfun$apply$2.applyOrElse(rules.scala:101)\n\tat org.apache.spark.sql.execution.datasources.AnalyzeCreateTable$$anonfun$apply$2.applyOrElse(rules.scala:75)\n\tat org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$3.apply(TreeNode.scala:288)\n\tat org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$3.apply(TreeNode.scala:288)\n\tat org.apache.spark.sql.catalyst.trees.CurrentOrigin$.withOrigin(TreeNode.scala:70)\n\tat org.apache.spark.sql.catalyst.trees.TreeNode.transformDown(TreeNode.scala:287)\n\tat org.apache.spark.sql.catalyst.trees.TreeNode.transform(TreeNode.scala:277)\n\tat org.apache.spark.sql.execution.datasources.AnalyzeCreateTable.apply(rules.scala:75)\n\tat org.apache.spark.sql.execution.datasources.AnalyzeCreateTable.apply(rules.scala:73)\n\tat org.apache.spark.sql.catalyst.rules.RuleExecutor$$anonfun$execute$1$$anonfun$apply$1.apply(RuleExecutor.scala:85)\n\tat org.apache.spark.sql.catalyst.rules.RuleExecutor$$anonfun$execute$1$$anonfun$apply$1.apply(RuleExecutor.scala:82)\n\tat scala.collection.LinearSeqOptimized$class.foldLeft(LinearSeqOptimized.scala:111)\n\tat scala.collection.immutable.List.foldLeft(List.scala:84)\n\tat org.apache.spark.sql.catalyst.rules.RuleExecutor$$anonfun$execute$1.apply(RuleExecutor.scala:82)\n\tat org.apache.spark.sql.catalyst.rules.RuleExecutor$$anonfun$execute$1.apply(RuleExecutor.scala:74)\n\tat scala.collection.immutable.List.foreach(List.scala:318)\n\tat org.apache.spark.sql.catalyst.rules.RuleExecutor.execute(RuleExecutor.scala:74)\n\tat org.apache.spark.sql.execution.QueryExecution.analyzed$lzycompute(QueryExecution.scala:64)\n\tat org.apache.spark.sql.execution.QueryExecution.analyzed(QueryExecution.scala:62)\n\tat org.apache.spark.sql.execution.QueryExecution.assertAnalyzed(QueryExecution.scala:50)\n\tat org.apache.spark.sql.Dataset$.ofRows(Dataset.scala:63)\n\tat org.apache.spark.sql.SparkSession.sql(SparkSession.scala:593)\n\tat org.apache.spark.sql.SQLContext.sql(SQLContext.scala:699)\n\tat com.databricks.backend.daemon.driver.SQLDriverLocal$$anonfun$1.apply(SQLDriverLocal.scala:82)\n\tat com.databricks.backend.daemon.driver.SQLDriverLocal$$anonfun$1.apply(SQLDriverLocal.scala:28)\n\tat scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:244)\n\tat scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:244)\n\tat scala.collection.immutable.List.foreach(List.scala:318)\n\tat scala.collection.TraversableLike$class.map(TraversableLike.scala:244)\n\tat scala.collection.AbstractTraversable.map(Traversable.scala:105)\n\tat com.databricks.backend.daemon.driver.SQLDriverLocal.executeSql(SQLDriverLocal.scala:28)\n\tat com.databricks.backend.daemon.driver.SQLDriverLocal.repl(SQLDriverLocal.scala:128)\n\tat com.databricks.backend.daemon.driver.DriverLocal$$anonfun$execute$1.apply(DriverLocal.scala:196)\n\tat com.databricks.backend.daemon.driver.DriverLocal$$anonfun$execute$1.apply(DriverLocal.scala:185)\n\tat com.databricks.logging.UsageLogging$$anonfun$withAttributionContext$1.apply(UsageLogging.scala:145)\n\tat scala.util.DynamicVariable.withValue(DynamicVariable.scala:57)\n\tat com.databricks.logging.UsageLogging$class.withAttributionContext(UsageLogging.scala:140)\n\tat com.databricks.backend.daemon.driver.DriverLocal.withAttributionContext(DriverLocal.scala:35)\n\tat com.databricks.logging.UsageLogging$class.withAttributionTags(UsageLogging.scala:178)\n\tat com.databricks.backend.daemon.driver.DriverLocal.withAttributionTags(DriverLocal.scala:35)\n\tat com.databricks.backend.daemon.driver.DriverLocal.execute(DriverLocal.scala:185)\n\tat com.databricks.backend.daemon.driver.DriverWrapper$$anonfun$tryExecutingCommand$2.apply(DriverWrapper.scala:589)\n\tat com.databricks.backend.daemon.driver.DriverWrapper$$anonfun$tryExecutingCommand$2.apply(DriverWrapper.scala:589)\n\tat scala.util.Try$.apply(Try.scala:161)\n\tat com.databricks.backend.daemon.driver.DriverWrapper.tryExecutingCommand(DriverWrapper.scala:584)\n\tat com.databricks.backend.daemon.driver.DriverWrapper.executeCommand(DriverWrapper.scala:488)\n\tat com.databricks.backend.daemon.driver.DriverWrapper.runInnerLoop(DriverWrapper.scala:391)\n\tat com.databricks.backend.daemon.driver.DriverWrapper.runInner(DriverWrapper.scala:348)\n\tat com.databricks.backend.daemon.driver.DriverWrapper.run(DriverWrapper.scala:215)\n\tat java.lang.Thread.run(Thread.java:745)\n\n\tat com.databricks.backend.daemon.driver.SQLDriverLocal.executeSql(SQLDriverLocal.scala:116)\n\tat com.databricks.backend.daemon.driver.SQLDriverLocal.repl(SQLDriverLocal.scala:128)\n\tat com.databricks.backend.daemon.driver.DriverLocal$$anonfun$execute$1.apply(DriverLocal.scala:196)\n\tat com.databricks.backend.daemon.driver.DriverLocal$$anonfun$execute$1.apply(DriverLocal.scala:185)\n\tat com.databricks.logging.UsageLogging$$anonfun$withAttributionContext$1.apply(UsageLogging.scala:145)\n\tat scala.util.DynamicVariable.withValue(DynamicVariable.scala:57)\n\tat com.databricks.logging.UsageLogging$class.withAttributionContext(UsageLogging.scala:140)\n\tat com.databricks.backend.daemon.driver.DriverLocal.withAttributionContext(DriverLocal.scala:35)\n\tat com.databricks.logging.UsageLogging$class.withAttributionTags(UsageLogging.scala:178)\n\tat com.databricks.backend.daemon.driver.DriverLocal.withAttributionTags(DriverLocal.scala:35)\n\tat com.databricks.backend.daemon.driver.DriverLocal.execute(DriverLocal.scala:185)\n\tat com.databricks.backend.daemon.driver.DriverWrapper$$anonfun$tryExecutingCommand$2.apply(DriverWrapper.scala:589)\n\tat com.databricks.backend.daemon.driver.DriverWrapper$$anonfun$tryExecutingCommand$2.apply(DriverWrapper.scala:589)\n\tat scala.util.Try$.apply(Try.scala:161)\n\tat com.databricks.backend.daemon.driver.DriverWrapper.tryExecutingCommand(DriverWrapper.scala:584)\n\tat com.databricks.backend.daemon.driver.DriverWrapper.executeCommand(DriverWrapper.scala:488)\n\tat com.databricks.backend.daemon.driver.DriverWrapper.runInnerLoop(DriverWrapper.scala:391)\n\tat com.databricks.backend.daemon.driver.DriverWrapper.runInner(DriverWrapper.scala:348)\n\tat com.databricks.backend.daemon.driver.DriverWrapper.run(DriverWrapper.scala:215)\n\tat java.lang.Thread.run(Thread.java:745)\n","workflows":[],"startTime":1.487168045906E12,"submitTime":1.487168045865E12,"finishTime":1.48716805388E12,"collapsed":false,"bindings":{},"inputWidgets":{},"displayType":"table","width":"auto","height":"auto","xColumns":null,"yColumns":null,"pivotColumns":null,"pivotAggregation":null,"customPlotOptions":{},"commentThread":[],"commentsVisible":false,"parentHierarchy":[],"diffInserts":[],"diffDeletes":[],"globalVars":{},"latestUser":"a 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So, Additional Sum operation over those can't.\n\nI'll try to do that with sql cell.","commandVersion":0,"state":"finished","results":null,"errorSummary":null,"error":null,"workflows":[],"startTime":0.0,"submitTime":0.0,"finishTime":0.0,"collapsed":false,"bindings":{},"inputWidgets":{},"displayType":"table","width":"auto","height":"auto","xColumns":null,"yColumns":null,"pivotColumns":null,"pivotAggregation":null,"customPlotOptions":{},"commentThread":[],"commentsVisible":false,"parentHierarchy":[],"diffInserts":[],"diffDeletes":[],"globalVars":{},"latestUser":"a user","commandTitle":"","showCommandTitle":false,"hideCommandCode":false,"hideCommandResult":false,"iPythonMetadata":null,"streamStates":{},"nuid":"436d7e96-1584-421d-a914-bc6f604a4a62"},{"version":"CommandV1","origId":1704430907200396,"guid":"d938b12b-5f4c-4910-a74b-c0144b02cec2","subtype":"command","commandType":"auto","position":9.75,"command":"%sql\nDROP TABLE IF EXISTS joined;\n\nCREATE TABLE joined AS\nSELECT a.*, count, int(zip/10) as zip\nFROM cleaned_taxes a, cleaned_markets b\nWHERE a.zipcode == b.zip;\n\nSELECT * FROM joined LIMIT 50;","commandVersion":0,"state":"finished","results":{"type":"table","data":[["AL","35010",2040.0,560.0,3280.0,55178.0,394.0,25.0,1962.0,1.0,3501.0],["AL","35010",660.0,650.0,2280.0,74726.0,313.0,178.0,1202.0,1.0,3501.0],["AL","35010",210.0,560.0,680.0,56040.0,359.0,339.0,1335.0,1.0,3501.0],["AL","35010",50.0,460.0,450.0,45809.0,288.0,795.0,1371.0,1.0,3501.0],["AL","35010",40.0,480.0,430.0,68277.0,661.0,1683.0,1635.0,1.0,3501.0],["AL","35010",0.0,140.0,110.0,82076.0,2230.0,6124.0,4607.0,1.0,3501.0],["AL","35022",1970.0,430.0,1860.0,42500.0,275.0,-61.0,2160.0,1.0,3502.0],["AL","35022",970.0,580.0,1570.0,85238.0,327.0,69.0,1081.0,1.0,3502.0],["AL","35022",500.0,630.0,930.0,91454.0,366.0,195.0,771.0,1.0,3502.0],["AL","35022",190.0,670.0,760.0,86327.0,261.0,388.0,913.0,1.0,3502.0],["AL","35022",120.0,1140.0,1110.0,172554.0,533.0,1065.0,2536.0,1.0,3502.0],["AL","35022",0.0,140.0,120.0,43466.0,137.0,2404.0,1371.0,1.0,3502.0],["AL","35040",1380.0,350.0,1330.0,30584.0,193.0,31.0,2415.0,1.0,3504.0],["AL","35040",850.0,580.0,1520.0,77085.0,155.0,130.0,1177.0,1.0,3504.0],["AL","35040",360.0,730.0,1050.0,80393.0,300.0,223.0,1276.0,1.0,3504.0],["AL","35040",80.0,600.0,740.0,64823.0,273.0,133.0,1102.0,1.0,3504.0],["AL","35040",50.0,570.0,640.0,82717.0,210.0,766.0,3121.0,1.0,3504.0],["AL","35040",0.0,60.0,40.0,24266.0,227.0,595.0,0.0,1.0,3504.0],["AL","35042",490.0,160.0,670.0,12988.0,145.0,12.0,721.0,1.0,3504.0],["AL","35042",200.0,220.0,450.0,20034.0,75.0,65.0,374.0,1.0,3504.0],["AL","35042",80.0,200.0,270.0,19866.0,98.0,44.0,431.0,1.0,3504.0],["AL","35042",30.0,160.0,190.0,16043.0,68.0,151.0,90.0,1.0,3504.0],["AL","35042",0.0,180.0,220.0,27572.0,96.0,627.0,515.0,1.0,3504.0],["AL","35042",0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,3504.0],["AL","35051",880.0,240.0,740.0,18327.0,161.0,10.0,1423.0,1.0,3505.0],["AL","35051",320.0,310.0,610.0,28657.0,116.0,57.0,620.0,1.0,3505.0],["AL","35051",100.0,290.0,360.0,26929.0,102.0,75.0,659.0,1.0,3505.0],["AL","35051",40.0,270.0,310.0,28500.0,214.0,126.0,950.0,1.0,3505.0],["AL","35051",20.0,320.0,340.0,46900.0,502.0,798.0,3102.0,1.0,3505.0],["AL","35051",0.0,80.0,90.0,37174.0,207.0,529.0,0.0,1.0,3505.0],["AL","35071",1560.0,360.0,820.0,29835.0,488.0,64.0,2001.0,1.0,3507.0],["AL","35071",800.0,560.0,1080.0,66749.0,498.0,135.0,1255.0,1.0,3507.0],["AL","35071",350.0,610.0,820.0,70433.0,425.0,89.0,1384.0,1.0,3507.0],["AL","35071",110.0,670.0,700.0,72528.0,301.0,200.0,1031.0,1.0,3507.0],["AL","35071",70.0,930.0,970.0,135809.0,630.0,1447.0,2437.0,1.0,3507.0],["AL","35071",0.0,140.0,150.0,54471.0,246.0,1343.0,2382.0,1.0,3507.0],["AL","35080",1380.0,250.0,670.0,22895.0,161.0,42.0,1959.0,1.0,3508.0],["AL","35080",680.0,390.0,890.0,52869.0,165.0,118.0,1623.0,1.0,3508.0],["AL","35080",330.0,680.0,1100.0,75385.0,241.0,331.0,1685.0,1.0,3508.0],["AL","35080",140.0,790.0,1160.0,88494.0,203.0,324.0,2185.0,1.0,3508.0],["AL","35080",90.0,1360.0,1950.0,198557.0,679.0,1105.0,3480.0,1.0,3508.0],["AL","35080",0.0,170.0,250.0,59400.0,129.0,1845.0,1180.0,1.0,3508.0],["AL","35094",1450.0,420.0,1450.0,32350.0,264.0,1.0,2600.0,1.0,3509.0],["AL","35094",710.0,530.0,1160.0,60032.0,218.0,119.0,1268.0,1.0,3509.0],["AL","35094",260.0,560.0,640.0,58457.0,247.0,313.0,1086.0,1.0,3509.0],["AL","35094",80.0,480.0,480.0,51321.0,203.0,190.0,1032.0,1.0,3509.0],["AL","35094",60.0,540.0,480.0,80970.0,493.0,1386.0,2140.0,1.0,3509.0],["AL","35094",0.0,100.0,100.0,51655.0,813.0,2037.0,2742.0,1.0,3509.0],["AL","35115",1390.0,420.0,1530.0,31069.0,201.0,5.0,3197.0,1.0,3511.0],["AL","35115",490.0,480.0,1100.0,45970.0,152.0,135.0,1342.0,1.0,3511.0]],"arguments":{},"addedWidgets":{},"removedWidgets":[],"schema":[{"name":"state","type":"\"string\"","metadata":"{}"},{"name":"zipcode","type":"\"string\"","metadata":"{}"},{"name":"single_returns","type":"\"integer\"","metadata":"{}"},{"name":"joint_returns","type":"\"integer\"","metadata":"{}"},{"name":"numdep","type":"\"integer\"","metadata":"{}"},{"name":"total_income_amount","type":"\"double\"","metadata":"{}"},{"name":"taxable_interest_amount","type":"\"double\"","metadata":"{}"},{"name":"net_capital_gains","type":"\"double\"","metadata":"{}"},{"name":"biz_net_income","type":"\"double\"","metadata":"{}"},{"name":"count","type":"\"long\"","metadata":"{}"},{"name":"zip","type":"\"integer\"","metadata":"{}"}],"overflow":false,"aggData":[],"aggSchema":[],"aggOverflow":false,"aggSeriesLimitReached":false,"aggError":"","aggType":"","plotOptions":null,"isJsonSchema":true,"dbfsResultPath":null},"errorSummary":"Error in SQL statement: ParseException: \nextraneous input 'DESCRIBE' expecting {<EOF>, '.', '[', 'GROUP', 'ORDER', 'HAVING', 'LIMIT', 'OR', 'AND', 'IN', NOT, 'BETWEEN', 'LIKE', RLIKE, 'IS', 'WINDOW', 'UNION', 'EXCEPT', 'MINUS', 'INTERSECT', EQ, '<=>', '<>', '!=', '<', LTE, '>', GTE, '+', '-', '*', '/', '%', 'DIV', '&', '|', '^', 'SORT', 'CLUSTER', 'DISTRIBUTE'}(line 6, pos 0)\n\n== SQL ==\nCREATE TABLE prepped AS\nSELECT a.*, b.count\nFROM cleaned_taxes a, cleaned_markets b\nWHERE a.zipcode = b.zip\n\nDESCRIBE prepped\n^^^\n","error":"com.databricks.backend.common.rpc.DatabricksExceptions$SQLExecutionException: org.apache.spark.sql.catalyst.parser.ParseException: \nextraneous input 'DESCRIBE' expecting {<EOF>, '.', '[', 'GROUP', 'ORDER', 'HAVING', 'LIMIT', 'OR', 'AND', 'IN', NOT, 'BETWEEN', 'LIKE', RLIKE, 'IS', 'WINDOW', 'UNION', 'EXCEPT', 'MINUS', 'INTERSECT', EQ, '<=>', '<>', '!=', '<', LTE, '>', GTE, '+', '-', '*', '/', '%', 'DIV', '&', '|', '^', 'SORT', 'CLUSTER', 'DISTRIBUTE'}(line 6, pos 0)\n\n== SQL ==\nCREATE TABLE prepped AS\nSELECT a.*, b.count\nFROM cleaned_taxes a, cleaned_markets b\nWHERE a.zipcode = b.zip\n\nDESCRIBE prepped\n^^^\n\n\tat org.apache.spark.sql.catalyst.parser.ParseException.withCommand(ParseDriver.scala:196)\n\tat org.apache.spark.sql.catalyst.parser.AbstractSqlParser.parse(ParseDriver.scala:98)\n\tat org.apache.spark.sql.execution.SparkSqlParser.parse(SparkSqlParser.scala:45)\n\tat org.apache.spark.sql.catalyst.parser.AbstractSqlParser.parsePlan(ParseDriver.scala:52)\n\tat org.apache.spark.sql.SparkSession.sql(SparkSession.scala:593)\n\tat org.apache.spark.sql.SQLContext.sql(SQLContext.scala:699)\n\tat com.databricks.backend.daemon.driver.SQLDriverLocal$$anonfun$1.apply(SQLDriverLocal.scala:82)\n\tat com.databricks.backend.daemon.driver.SQLDriverLocal$$anonfun$1.apply(SQLDriverLocal.scala:28)\n\tat scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:244)\n\tat scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:244)\n\tat scala.collection.immutable.List.foreach(List.scala:318)\n\tat scala.collection.TraversableLike$class.map(TraversableLike.scala:244)\n\tat scala.collection.AbstractTraversable.map(Traversable.scala:105)\n\tat com.databricks.backend.daemon.driver.SQLDriverLocal.executeSql(SQLDriverLocal.scala:28)\n\tat com.databricks.backend.daemon.driver.SQLDriverLocal.repl(SQLDriverLocal.scala:128)\n\tat com.databricks.backend.daemon.driver.DriverLocal$$anonfun$execute$1.apply(DriverLocal.scala:196)\n\tat com.databricks.backend.daemon.driver.DriverLocal$$anonfun$execute$1.apply(DriverLocal.scala:185)\n\tat com.databricks.logging.UsageLogging$$anonfun$withAttributionContext$1.apply(UsageLogging.scala:145)\n\tat scala.util.DynamicVariable.withValue(DynamicVariable.scala:57)\n\tat com.databricks.logging.UsageLogging$class.withAttributionContext(UsageLogging.scala:140)\n\tat com.databricks.backend.daemon.driver.DriverLocal.withAttributionContext(DriverLocal.scala:35)\n\tat com.databricks.logging.UsageLogging$class.withAttributionTags(UsageLogging.scala:178)\n\tat com.databricks.backend.daemon.driver.DriverLocal.withAttributionTags(DriverLocal.scala:35)\n\tat com.databricks.backend.daemon.driver.DriverLocal.execute(DriverLocal.scala:185)\n\tat com.databricks.backend.daemon.driver.DriverWrapper$$anonfun$tryExecutingCommand$2.apply(DriverWrapper.scala:589)\n\tat com.databricks.backend.daemon.driver.DriverWrapper$$anonfun$tryExecutingCommand$2.apply(DriverWrapper.scala:589)\n\tat scala.util.Try$.apply(Try.scala:161)\n\tat com.databricks.backend.daemon.driver.DriverWrapper.tryExecutingCommand(DriverWrapper.scala:584)\n\tat com.databricks.backend.daemon.driver.DriverWrapper.executeCommand(DriverWrapper.scala:488)\n\tat com.databricks.backend.daemon.driver.DriverWrapper.runInnerLoop(DriverWrapper.scala:391)\n\tat com.databricks.backend.daemon.driver.DriverWrapper.runInner(DriverWrapper.scala:348)\n\tat com.databricks.backend.daemon.driver.DriverWrapper.run(DriverWrapper.scala:215)\n\tat java.lang.Thread.run(Thread.java:745)\n\n\tat com.databricks.backend.daemon.driver.SQLDriverLocal.executeSql(SQLDriverLocal.scala:116)\n\tat com.databricks.backend.daemon.driver.SQLDriverLocal.repl(SQLDriverLocal.scala:128)\n\tat com.databricks.backend.daemon.driver.DriverLocal$$anonfun$execute$1.apply(DriverLocal.scala:196)\n\tat com.databricks.backend.daemon.driver.DriverLocal$$anonfun$execute$1.apply(DriverLocal.scala:185)\n\tat com.databricks.logging.UsageLogging$$anonfun$withAttributionContext$1.apply(UsageLogging.scala:145)\n\tat scala.util.DynamicVariable.withValue(DynamicVariable.scala:57)\n\tat com.databricks.logging.UsageLogging$class.withAttributionContext(UsageLogging.scala:140)\n\tat com.databricks.backend.daemon.driver.DriverLocal.withAttributionContext(DriverLocal.scala:35)\n\tat com.databricks.logging.UsageLogging$class.withAttributionTags(UsageLogging.scala:178)\n\tat com.databricks.backend.daemon.driver.DriverLocal.withAttributionTags(DriverLocal.scala:35)\n\tat com.databricks.backend.daemon.driver.DriverLocal.execute(DriverLocal.scala:185)\n\tat com.databricks.backend.daemon.driver.DriverWrapper$$anonfun$tryExecutingCommand$2.apply(DriverWrapper.scala:589)\n\tat com.databricks.backend.daemon.driver.DriverWrapper$$anonfun$tryExecutingCommand$2.apply(DriverWrapper.scala:589)\n\tat scala.util.Try$.apply(Try.scala:161)\n\tat com.databricks.backend.daemon.driver.DriverWrapper.tryExecutingCommand(DriverWrapper.scala:584)\n\tat com.databricks.backend.daemon.driver.DriverWrapper.executeCommand(DriverWrapper.scala:488)\n\tat com.databricks.backend.daemon.driver.DriverWrapper.runInnerLoop(DriverWrapper.scala:391)\n\tat com.databricks.backend.daemon.driver.DriverWrapper.runInner(DriverWrapper.scala:348)\n\tat com.databricks.backend.daemon.driver.DriverWrapper.run(DriverWrapper.scala:215)\n\tat java.lang.Thread.run(Thread.java:745)\n","workflows":[],"startTime":1.487168116707E12,"submitTime":1.487168107437E12,"finishTime":1.487168126166E12,"collapsed":false,"bindings":{},"inputWidgets":{},"displayType":"table","width":"810.4","height":"334.4","xColumns":null,"yColumns":null,"pivotColumns":null,"pivotAggregation":null,"customPlotOptions":{},"commentThread":[],"commentsVisible":false,"parentHierarchy":[],"diffInserts":[],"diffDeletes":[],"globalVars":{},"latestUser":"a user","commandTitle":"","showCommandTitle":false,"hideCommandCode":false,"hideCommandResult":false,"iPythonMetadata":null,"streamStates":{},"nuid":"cf9f11dc-25f7-4d99-8f59-750c120b6e9c"},{"version":"CommandV1","origId":1704430907200402,"guid":"c7476727-5231-4304-87e7-a723bd4a0a69","subtype":"command","commandType":"auto","position":9.8125,"command":"%sql\nDESCRIBE joined;","commandVersion":0,"state":"finished","results":{"type":"table","data":[["state","string",null],["zipcode","string",null],["single_returns","int",null],["joint_returns","int",null],["numdep","int",null],["total_income_amount","double",null],["taxable_interest_amount","double",null],["net_capital_gains","double",null],["biz_net_income","double",null],["count","bigint",null],["zip","int",null]],"arguments":{},"addedWidgets":{},"removedWidgets":[],"schema":[{"name":"col_name","type":"\"string\"","metadata":"{\"comment\":\"name of the column\"}"},{"name":"data_type","type":"\"string\"","metadata":"{\"comment\":\"data type of the column\"}"},{"name":"comment","type":"\"string\"","metadata":"{\"comment\":\"comment of the column\"}"}],"overflow":false,"aggData":[],"aggSchema":[],"aggOverflow":false,"aggSeriesLimitReached":false,"aggError":"","aggType":"","plotOptions":null,"isJsonSchema":true,"dbfsResultPath":null},"errorSummary":"Error in SQL statement: AnalysisException: cannot resolve '`FROM`' given input columns: []; line 1 pos 11;\n'GlobalLimit 5\n+- 'LocalLimit 5\n +- 'Project ['FROM AS prepped#20969]\n +- OneRowRelation$\n","error":"com.databricks.backend.common.rpc.DatabricksExceptions$SQLExecutionException: org.apache.spark.sql.AnalysisException: cannot resolve '`FROM`' given input columns: []; line 1 pos 11;\n'GlobalLimit 5\n+- 'LocalLimit 5\n +- 'Project ['FROM AS prepped#20969]\n +- OneRowRelation$\n\n\tat org.apache.spark.sql.catalyst.analysis.package$AnalysisErrorAt.failAnalysis(package.scala:42)\n\tat org.apache.spark.sql.catalyst.analysis.CheckAnalysis$$anonfun$checkAnalysis$1$$anonfun$apply$2.applyOrElse(CheckAnalysis.scala:77)\n\tat org.apache.spark.sql.catalyst.analysis.CheckAnalysis$$anonfun$checkAnalysis$1$$anonfun$apply$2.applyOrElse(CheckAnalysis.scala:74)\n\tat org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$transformUp$1.apply(TreeNode.scala:310)\n\tat org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$transformUp$1.apply(TreeNode.scala:310)\n\tat org.apache.spark.sql.catalyst.trees.CurrentOrigin$.withOrigin(TreeNode.scala:70)\n\tat org.apache.spark.sql.catalyst.trees.TreeNode.transformUp(TreeNode.scala:309)\n\tat org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$4.apply(TreeNode.scala:307)\n\tat org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$4.apply(TreeNode.scala:307)\n\tat org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$5.apply(TreeNode.scala:331)\n\tat org.apache.spark.sql.catalyst.trees.TreeNode.mapProductIterator(TreeNode.scala:188)\n\tat org.apache.spark.sql.catalyst.trees.TreeNode.transformChildren(TreeNode.scala:329)\n\tat org.apache.spark.sql.catalyst.trees.TreeNode.transformUp(TreeNode.scala:307)\n\tat org.apache.spark.sql.catalyst.plans.QueryPlan.transformExpressionUp$1(QueryPlan.scala:282)\n\tat org.apache.spark.sql.catalyst.plans.QueryPlan.org$apache$spark$sql$catalyst$plans$QueryPlan$$recursiveTransform$2(QueryPlan.scala:292)\n\tat org.apache.spark.sql.catalyst.plans.QueryPlan$$anonfun$org$apache$spark$sql$catalyst$plans$QueryPlan$$recursiveTransform$2$1.apply(QueryPlan.scala:296)\n\tat scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:244)\n\tat scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:244)\n\tat scala.collection.immutable.List.foreach(List.scala:318)\n\tat scala.collection.TraversableLike$class.map(TraversableLike.scala:244)\n\tat scala.collection.AbstractTraversable.map(Traversable.scala:105)\n\tat org.apache.spark.sql.catalyst.plans.QueryPlan.org$apache$spark$sql$catalyst$plans$QueryPlan$$recursiveTransform$2(QueryPlan.scala:296)\n\tat org.apache.spark.sql.catalyst.plans.QueryPlan$$anonfun$7.apply(QueryPlan.scala:301)\n\tat org.apache.spark.sql.catalyst.trees.TreeNode.mapProductIterator(TreeNode.scala:188)\n\tat org.apache.spark.sql.catalyst.plans.QueryPlan.transformExpressionsUp(QueryPlan.scala:301)\n\tat org.apache.spark.sql.catalyst.analysis.CheckAnalysis$$anonfun$checkAnalysis$1.apply(CheckAnalysis.scala:74)\n\tat org.apache.spark.sql.catalyst.analysis.CheckAnalysis$$anonfun$checkAnalysis$1.apply(CheckAnalysis.scala:67)\n\tat org.apache.spark.sql.catalyst.trees.TreeNode.foreachUp(TreeNode.scala:128)\n\tat org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$foreachUp$1.apply(TreeNode.scala:127)\n\tat org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$foreachUp$1.apply(TreeNode.scala:127)\n\tat scala.collection.immutable.List.foreach(List.scala:318)\n\tat org.apache.spark.sql.catalyst.trees.TreeNode.foreachUp(TreeNode.scala:127)\n\tat org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$foreachUp$1.apply(TreeNode.scala:127)\n\tat org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$foreachUp$1.apply(TreeNode.scala:127)\n\tat scala.collection.immutable.List.foreach(List.scala:318)\n\tat org.apache.spark.sql.catalyst.trees.TreeNode.foreachUp(TreeNode.scala:127)\n\tat org.apache.spark.sql.catalyst.analysis.CheckAnalysis$class.checkAnalysis(CheckAnalysis.scala:67)\n\tat org.apache.spark.sql.catalyst.analysis.Analyzer.checkAnalysis(Analyzer.scala:57)\n\tat org.apache.spark.sql.execution.QueryExecution.assertAnalyzed(QueryExecution.scala:48)\n\tat org.apache.spark.sql.Dataset$.ofRows(Dataset.scala:63)\n\tat org.apache.spark.sql.SparkSession.sql(SparkSession.scala:593)\n\tat org.apache.spark.sql.SQLContext.sql(SQLContext.scala:699)\n\tat com.databricks.backend.daemon.driver.SQLDriverLocal$$anonfun$1.apply(SQLDriverLocal.scala:82)\n\tat com.databricks.backend.daemon.driver.SQLDriverLocal$$anonfun$1.apply(SQLDriverLocal.scala:28)\n\tat scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:244)\n\tat scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:244)\n\tat scala.collection.immutable.List.foreach(List.scala:318)\n\tat scala.collection.TraversableLike$class.map(TraversableLike.scala:244)\n\tat scala.collection.AbstractTraversable.map(Traversable.scala:105)\n\tat com.databricks.backend.daemon.driver.SQLDriverLocal.executeSql(SQLDriverLocal.scala:28)\n\tat com.databricks.backend.daemon.driver.SQLDriverLocal.repl(SQLDriverLocal.scala:128)\n\tat com.databricks.backend.daemon.driver.DriverLocal$$anonfun$execute$1.apply(DriverLocal.scala:196)\n\tat com.databricks.backend.daemon.driver.DriverLocal$$anonfun$execute$1.apply(DriverLocal.scala:185)\n\tat com.databricks.logging.UsageLogging$$anonfun$withAttributionContext$1.apply(UsageLogging.scala:145)\n\tat scala.util.DynamicVariable.withValue(DynamicVariable.scala:57)\n\tat com.databricks.logging.UsageLogging$class.withAttributionContext(UsageLogging.scala:140)\n\tat com.databricks.backend.daemon.driver.DriverLocal.withAttributionContext(DriverLocal.scala:35)\n\tat com.databricks.logging.UsageLogging$class.withAttributionTags(UsageLogging.scala:178)\n\tat com.databricks.backend.daemon.driver.DriverLocal.withAttributionTags(DriverLocal.scala:35)\n\tat com.databricks.backend.daemon.driver.DriverLocal.execute(DriverLocal.scala:185)\n\tat com.databricks.backend.daemon.driver.DriverWrapper$$anonfun$tryExecutingCommand$2.apply(DriverWrapper.scala:589)\n\tat com.databricks.backend.daemon.driver.DriverWrapper$$anonfun$tryExecutingCommand$2.apply(DriverWrapper.scala:589)\n\tat scala.util.Try$.apply(Try.scala:161)\n\tat com.databricks.backend.daemon.driver.DriverWrapper.tryExecutingCommand(DriverWrapper.scala:584)\n\tat com.databricks.backend.daemon.driver.DriverWrapper.executeCommand(DriverWrapper.scala:488)\n\tat com.databricks.backend.daemon.driver.DriverWrapper.runInnerLoop(DriverWrapper.scala:391)\n\tat com.databricks.backend.daemon.driver.DriverWrapper.runInner(DriverWrapper.scala:348)\n\tat com.databricks.backend.daemon.driver.DriverWrapper.run(DriverWrapper.scala:215)\n\tat java.lang.Thread.run(Thread.java:745)\n\n\tat com.databricks.backend.daemon.driver.SQLDriverLocal.executeSql(SQLDriverLocal.scala:116)\n\tat com.databricks.backend.daemon.driver.SQLDriverLocal.repl(SQLDriverLocal.scala:128)\n\tat com.databricks.backend.daemon.driver.DriverLocal$$anonfun$execute$1.apply(DriverLocal.scala:196)\n\tat com.databricks.backend.daemon.driver.DriverLocal$$anonfun$execute$1.apply(DriverLocal.scala:185)\n\tat com.databricks.logging.UsageLogging$$anonfun$withAttributionContext$1.apply(UsageLogging.scala:145)\n\tat scala.util.DynamicVariable.withValue(DynamicVariable.scala:57)\n\tat com.databricks.logging.UsageLogging$class.withAttributionContext(UsageLogging.scala:140)\n\tat com.databricks.backend.daemon.driver.DriverLocal.withAttributionContext(DriverLocal.scala:35)\n\tat com.databricks.logging.UsageLogging$class.withAttributionTags(UsageLogging.scala:178)\n\tat com.databricks.backend.daemon.driver.DriverLocal.withAttributionTags(DriverLocal.scala:35)\n\tat com.databricks.backend.daemon.driver.DriverLocal.execute(DriverLocal.scala:185)\n\tat com.databricks.backend.daemon.driver.DriverWrapper$$anonfun$tryExecutingCommand$2.apply(DriverWrapper.scala:589)\n\tat com.databricks.backend.daemon.driver.DriverWrapper$$anonfun$tryExecutingCommand$2.apply(DriverWrapper.scala:589)\n\tat scala.util.Try$.apply(Try.scala:161)\n\tat com.databricks.backend.daemon.driver.DriverWrapper.tryExecutingCommand(DriverWrapper.scala:584)\n\tat com.databricks.backend.daemon.driver.DriverWrapper.executeCommand(DriverWrapper.scala:488)\n\tat com.databricks.backend.daemon.driver.DriverWrapper.runInnerLoop(DriverWrapper.scala:391)\n\tat com.databricks.backend.daemon.driver.DriverWrapper.runInner(DriverWrapper.scala:348)\n\tat com.databricks.backend.daemon.driver.DriverWrapper.run(DriverWrapper.scala:215)\n\tat java.lang.Thread.run(Thread.java:745)\n","workflows":[],"startTime":1.487149632081E12,"submitTime":1.487149632074E12,"finishTime":1.487149632138E12,"collapsed":false,"bindings":{},"inputWidgets":{},"displayType":"table","width":"801.4","height":"357.4","xColumns":null,"yColumns":null,"pivotColumns":null,"pivotAggregation":null,"customPlotOptions":{},"commentThread":[],"commentsVisible":false,"parentHierarchy":[],"diffInserts":[],"diffDeletes":[],"globalVars":{},"latestUser":"a user","commandTitle":"","showCommandTitle":false,"hideCommandCode":false,"hideCommandResult":false,"iPythonMetadata":null,"streamStates":{},"nuid":"3346cb02-4acf-4043-86ea-73a099801d84"},{"version":"CommandV1","origId":1704430907200400,"guid":"ea0c00f5-1cc9-42c3-89a0-4349464f32ec","subtype":"command","commandType":"auto","position":9.875,"command":"%sql\nDROP TABLE IF EXISTS prepped;\n\nCREATE TABLE prepped AS\nSELECT FIRST(zip) as zipped,\n FIRST(count) as count,\n SUM(single_returns) as sum_returns,\n SUM(joint_returns) as all_joint_returns,\n SUM(numdep) as all_numdep,\n SUM(total_income_amount) as income_amout,\n SUM(taxable_interest_amount) as tax_interest_amount,\n SUM(net_capital_gains) as capital_gains,\n SUM(biz_net_income) as business_net_income\nFROM joined\nGROUP BY zipcode\nORDER BY zipcode;","commandVersion":0,"state":"finished","results":{"type":"table","data":[],"arguments":{},"addedWidgets":{},"removedWidgets":[],"schema":[],"overflow":false,"aggData":[],"aggSchema":[],"aggOverflow":false,"aggSeriesLimitReached":false,"aggError":"","aggType":"","plotOptions":null,"isJsonSchema":true,"dbfsResultPath":null},"errorSummary":"Error in SQL statement: ParseException: \nmismatched input 'ORDER' expecting {'(', 'SELECT', 'FROM', 'ADD', 'DESC', 'WITH', 'VALUES', 'CREATE', 'TABLE', 'INSERT', 'DELETE', 'DESCRIBE', 'EXPLAIN', 'SHOW', 'USE', 'DROP', 'ALTER', 'MAP', 'SET', 'RESET', 'START', 'COMMIT', 'ROLLBACK', 'REDUCE', 'REFRESH', 'CLEAR', 'CACHE', 'UNCACHE', 'DFS', 'TRUNCATE', 'ANALYZE', 'LIST', 'REVOKE', 'GRANT', 'LOCK', 'UNLOCK', 'MSCK', 'EXPORT', 'IMPORT', 'LOAD'}(line 1, pos 0)\n\n== SQL ==\nORDER BY zip\n^^^\n","error":"com.databricks.backend.common.rpc.DatabricksExceptions$SQLExecutionException: org.apache.spark.sql.catalyst.parser.ParseException: \nmismatched input 'ORDER' expecting {'(', 'SELECT', 'FROM', 'ADD', 'DESC', 'WITH', 'VALUES', 'CREATE', 'TABLE', 'INSERT', 'DELETE', 'DESCRIBE', 'EXPLAIN', 'SHOW', 'USE', 'DROP', 'ALTER', 'MAP', 'SET', 'RESET', 'START', 'COMMIT', 'ROLLBACK', 'REDUCE', 'REFRESH', 'CLEAR', 'CACHE', 'UNCACHE', 'DFS', 'TRUNCATE', 'ANALYZE', 'LIST', 'REVOKE', 'GRANT', 'LOCK', 'UNLOCK', 'MSCK', 'EXPORT', 'IMPORT', 'LOAD'}(line 1, pos 0)\n\n== SQL ==\nORDER BY zip\n^^^\n\n\tat org.apache.spark.sql.catalyst.parser.ParseException.withCommand(ParseDriver.scala:196)\n\tat org.apache.spark.sql.catalyst.parser.AbstractSqlParser.parse(ParseDriver.scala:98)\n\tat org.apache.spark.sql.execution.SparkSqlParser.parse(SparkSqlParser.scala:45)\n\tat org.apache.spark.sql.catalyst.parser.AbstractSqlParser.parsePlan(ParseDriver.scala:52)\n\tat org.apache.spark.sql.SparkSession.sql(SparkSession.scala:593)\n\tat org.apache.spark.sql.SQLContext.sql(SQLContext.scala:699)\n\tat com.databricks.backend.daemon.driver.SQLDriverLocal$$anonfun$1.apply(SQLDriverLocal.scala:82)\n\tat com.databricks.backend.daemon.driver.SQLDriverLocal$$anonfun$1.apply(SQLDriverLocal.scala:28)\n\tat scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:244)\n\tat scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:244)\n\tat scala.collection.immutable.List.foreach(List.scala:318)\n\tat scala.collection.TraversableLike$class.map(TraversableLike.scala:244)\n\tat scala.collection.AbstractTraversable.map(Traversable.scala:105)\n\tat com.databricks.backend.daemon.driver.SQLDriverLocal.executeSql(SQLDriverLocal.scala:28)\n\tat com.databricks.backend.daemon.driver.SQLDriverLocal.repl(SQLDriverLocal.scala:128)\n\tat com.databricks.backend.daemon.driver.DriverLocal$$anonfun$execute$1.apply(DriverLocal.scala:196)\n\tat com.databricks.backend.daemon.driver.DriverLocal$$anonfun$execute$1.apply(DriverLocal.scala:185)\n\tat com.databricks.logging.UsageLogging$$anonfun$withAttributionContext$1.apply(UsageLogging.scala:145)\n\tat scala.util.DynamicVariable.withValue(DynamicVariable.scala:57)\n\tat com.databricks.logging.UsageLogging$class.withAttributionContext(UsageLogging.scala:140)\n\tat com.databricks.backend.daemon.driver.DriverLocal.withAttributionContext(DriverLocal.scala:35)\n\tat com.databricks.logging.UsageLogging$class.withAttributionTags(UsageLogging.scala:178)\n\tat com.databricks.backend.daemon.driver.DriverLocal.withAttributionTags(DriverLocal.scala:35)\n\tat com.databricks.backend.daemon.driver.DriverLocal.execute(DriverLocal.scala:185)\n\tat com.databricks.backend.daemon.driver.DriverWrapper$$anonfun$tryExecutingCommand$2.apply(DriverWrapper.scala:589)\n\tat com.databricks.backend.daemon.driver.DriverWrapper$$anonfun$tryExecutingCommand$2.apply(DriverWrapper.scala:589)\n\tat scala.util.Try$.apply(Try.scala:161)\n\tat com.databricks.backend.daemon.driver.DriverWrapper.tryExecutingCommand(DriverWrapper.scala:584)\n\tat com.databricks.backend.daemon.driver.DriverWrapper.executeCommand(DriverWrapper.scala:488)\n\tat com.databricks.backend.daemon.driver.DriverWrapper.runInnerLoop(DriverWrapper.scala:391)\n\tat com.databricks.backend.daemon.driver.DriverWrapper.runInner(DriverWrapper.scala:348)\n\tat com.databricks.backend.daemon.driver.DriverWrapper.run(DriverWrapper.scala:215)\n\tat java.lang.Thread.run(Thread.java:745)\n\n\tat com.databricks.backend.daemon.driver.SQLDriverLocal.executeSql(SQLDriverLocal.scala:116)\n\tat com.databricks.backend.daemon.driver.SQLDriverLocal.repl(SQLDriverLocal.scala:128)\n\tat com.databricks.backend.daemon.driver.DriverLocal$$anonfun$execute$1.apply(DriverLocal.scala:196)\n\tat com.databricks.backend.daemon.driver.DriverLocal$$anonfun$execute$1.apply(DriverLocal.scala:185)\n\tat com.databricks.logging.UsageLogging$$anonfun$withAttributionContext$1.apply(UsageLogging.scala:145)\n\tat scala.util.DynamicVariable.withValue(DynamicVariable.scala:57)\n\tat com.databricks.logging.UsageLogging$class.withAttributionContext(UsageLogging.scala:140)\n\tat com.databricks.backend.daemon.driver.DriverLocal.withAttributionContext(DriverLocal.scala:35)\n\tat com.databricks.logging.UsageLogging$class.withAttributionTags(UsageLogging.scala:178)\n\tat com.databricks.backend.daemon.driver.DriverLocal.withAttributionTags(DriverLocal.scala:35)\n\tat com.databricks.backend.daemon.driver.DriverLocal.execute(DriverLocal.scala:185)\n\tat com.databricks.backend.daemon.driver.DriverWrapper$$anonfun$tryExecutingCommand$2.apply(DriverWrapper.scala:589)\n\tat com.databricks.backend.daemon.driver.DriverWrapper$$anonfun$tryExecutingCommand$2.apply(DriverWrapper.scala:589)\n\tat scala.util.Try$.apply(Try.scala:161)\n\tat com.databricks.backend.daemon.driver.DriverWrapper.tryExecutingCommand(DriverWrapper.scala:584)\n\tat com.databricks.backend.daemon.driver.DriverWrapper.executeCommand(DriverWrapper.scala:488)\n\tat com.databricks.backend.daemon.driver.DriverWrapper.runInnerLoop(DriverWrapper.scala:391)\n\tat com.databricks.backend.daemon.driver.DriverWrapper.runInner(DriverWrapper.scala:348)\n\tat com.databricks.backend.daemon.driver.DriverWrapper.run(DriverWrapper.scala:215)\n\tat java.lang.Thread.run(Thread.java:745)\n","workflows":[],"startTime":1.487168137079E12,"submitTime":1.487168137066E12,"finishTime":1.48716815538E12,"collapsed":false,"bindings":{},"inputWidgets":{},"displayType":"table","width":"auto","height":"auto","xColumns":null,"yColumns":null,"pivotColumns":null,"pivotAggregation":null,"customPlotOptions":{},"commentThread":[],"commentsVisible":false,"parentHierarchy":[],"diffInserts":[],"diffDeletes":[],"globalVars":{},"latestUser":"a user","commandTitle":"","showCommandTitle":false,"hideCommandCode":false,"hideCommandResult":false,"iPythonMetadata":null,"streamStates":{},"nuid":"94b6ec6c-dff5-494f-9b74-1fc8097c3259"},{"version":"CommandV1","origId":3856078469921564,"guid":"b1adcef4-2489-4e57-b8d7-2c1bfbd89d39","subtype":"command","commandType":"auto","position":9.9375,"command":"%sql\nSELECT * FROM prepped LIMIT 100;","commandVersion":0,"state":"finished","results":{"type":"table","data":[[5921.0,1.0,270.0,280.0,280.0,26523.0,409.0,1186.0,831.0],[5924.0,1.0,130.0,160.0,150.0,15741.0,99.0,397.0,219.0],[5927.0,1.0,2350.0,1800.0,2440.0,380592.0,1745.0,18736.0,15182.0],[5933.0,2.0,1850.0,1860.0,2250.0,240366.0,1620.0,12217.0,6757.0],[5940.0,1.0,3430.0,1930.0,2940.0,240528.0,1746.0,5832.0,7230.0],[5941.0,1.0,320.0,180.0,250.0,19555.0,132.0,70.0,380.0],[5942.0,1.0,670.0,560.0,550.0,60746.0,636.0,5497.0,3598.0],[5944.0,1.0,410.0,410.0,400.0,42882.0,387.0,1171.0,1725.0],[5945.0,1.0,2040.0,1910.0,2030.0,212847.0,2905.0,11962.0,9567.0],[5947.0,1.0,690.0,610.0,770.0,76951.0,740.0,1737.0,2611.0],[5950.0,1.0,3140.0,2320.0,3410.0,293862.0,2900.0,8554.0,6226.0],[5952.0,2.0,390.0,320.0,310.0,30823.0,493.0,1676.0,841.0],[5960.0,1.0,7650.0,4920.0,6070.0,803197.0,7439.0,25383.0,30986.0],[5963.0,1.0,410.0,380.0,460.0,38798.0,133.0,570.0,61.0],[5970.0,1.0,7660.0,5500.0,7450.0,742593.0,7702.0,21090.0,16313.0],[5971.0,1.0,2010.0,1610.0,1830.0,186488.0,1435.0,4184.0,4416.0],[5971.0,1.0,4150.0,3830.0,5570.0,456329.0,2924.0,19331.0,15676.0],[5971.0,3.0,8750.0,5080.0,5430.0,1036618.0,17388.0,89500.0,46294.0],[5971.0,1.0,8180.0,5740.0,6890.0,895427.0,8820.0,55017.0,32183.0],[5972.0,1.0,1720.0,1610.0,1820.0,174775.0,1977.0,10914.0,5692.0],[5972.0,1.0,540.0,540.0,450.0,68461.0,974.0,5529.0,2344.0],[5974.0,1.0,900.0,1110.0,1430.0,127578.0,1453.0,7106.0,3754.0],[5974.0,1.0,340.0,380.0,340.0,36398.0,391.0,1836.0,1080.0],[5975.0,1.0,750.0,790.0,950.0,84717.0,563.0,2474.0,2451.0],[5975.0,1.0,210.0,210.0,240.0,25116.0,121.0,2035.0,973.0],[5975.0,1.0,630.0,800.0,800.0,73544.0,701.0,2050.0,1973.0],[5980.0,1.0,9080.0,3550.0,4830.0,552604.0,5039.0,20288.0,21330.0],[5980.0,2.0,1720.0,1550.0,1790.0,237871.0,2941.0,11501.0,8707.0],[5982.0,1.0,410.0,380.0,740.0,40354.0,242.0,610.0,1198.0],[510.0,1.0,1040.0,640.0,980.0,77500.0,475.0,1267.0,2933.0],[514.0,1.0,1070.0,960.0,1000.0,110102.0,866.0,3335.0,6406.0],[514.0,2.0,340.0,300.0,310.0,40892.0,736.0,2989.0,2885.0],[514.0,1.0,720.0,420.0,410.0,59867.0,621.0,1287.0,2948.0],[515.0,1.0,2070.0,1610.0,1930.0,192061.0,1207.0,3298.0,6044.0],[520.0,1.0,3810.0,2380.0,3450.0,310440.0,2482.0,10966.0,11132.0],[525.0,1.0,350.0,300.0,310.0,67471.0,1374.0,5405.0,2591.0],[525.0,1.0,1190.0,820.0,1000.0,169137.0,2613.0,10839.0,7576.0],[530.0,1.0,4050.0,2430.0,3170.0,369167.0,3232.0,22024.0,23663.0],[534.0,2.0,1220.0,910.0,970.0,133178.0,1813.0,11151.0,8710.0],[535.0,2.0,120.0,110.0,110.0,13775.0,103.0,65.0,567.0],[536.0,1.0,590.0,420.0,480.0,54277.0,406.0,1494.0,3781.0],[540.0,3.0,9010.0,2540.0,3700.0,729339.0,6714.0,50827.0,34561.0],[540.0,1.0,2580.0,930.0,1430.0,157330.0,589.0,3449.0,4615.0],[540.0,1.0,2890.0,1990.0,2470.0,302071.0,2266.0,9315.0,11000.0],[544.0,1.0,1590.0,1300.0,1610.0,170749.0,1346.0,7569.0,7933.0],[545.0,1.0,1060.0,970.0,1390.0,110713.0,1110.0,4888.0,2403.0],[545.0,1.0,5440.0,4400.0,5590.0,714747.0,4512.0,22177.0,21548.0],[545.0,1.0,510.0,500.0,500.0,72159.0,450.0,2235.0,2802.0],[546.0,1.0,1210.0,1120.0,1320.0,172477.0,1114.0,4405.0,7396.0],[546.0,1.0,770.0,570.0,730.0,75712.0,416.0,2156.0,2970.0],[546.0,1.0,1290.0,1410.0,1680.0,222622.0,1610.0,7191.0,8054.0],[547.0,1.0,540.0,500.0,720.0,45895.0,154.0,878.0,971.0],[547.0,1.0,1140.0,990.0,1160.0,187272.0,848.0,4863.0,7189.0],[547.0,1.0,3850.0,2760.0,4040.0,383927.0,2420.0,8886.0,11382.0],[548.0,1.0,2040.0,1750.0,2050.0,523854.0,6287.0,69239.0,20328.0],[548.0,2.0,490.0,400.0,370.0,72840.0,834.0,4314.0,4209.0],[549.0,1.0,1560.0,1280.0,1390.0,165131.0,1536.0,7116.0,5681.0],[549.0,2.0,420.0,410.0,470.0,63858.0,230.0,1141.0,2683.0],[4737.0,2.0,2560.0,2410.0,3660.0,233681.0,1387.0,8587.0,5790.0],[4737.0,1.0,9350.0,7150.0,12480.0,856444.0,6339.0,20962.0,25048.0],[4740.0,3.0,8960.0,6540.0,7820.0,1248030.0,11225.0,46569.0,44778.0],[4742.0,1.0,5280.0,5540.0,7330.0,561989.0,3470.0,7190.0,14855.0],[4742.0,1.0,1720.0,1910.0,2340.0,195838.0,1232.0,1940.0,4456.0],[4742.0,1.0,1500.0,1560.0,2140.0,170633.0,490.0,1369.0,3256.0],[4743.0,1.0,1030.0,860.0,1180.0,80624.0,649.0,822.0,1853.0],[4744.0,1.0,1750.0,1790.0,1830.0,202503.0,1283.0,7130.0,8512.0],[4745.0,1.0,830.0,1000.0,1430.0,91379.0,603.0,1322.0,1579.0],[4745.0,1.0,1290.0,1420.0,2090.0,124554.0,826.0,1833.0,4011.0],[4746.0,1.0,2300.0,2400.0,3090.0,221647.0,1719.0,3110.0,6854.0],[4754.0,1.0,2110.0,2000.0,3210.0,235944.0,2153.0,5114.0,5452.0],[4756.0,1.0,1180.0,1230.0,1530.0,128208.0,735.0,2038.0,1979.0],[4758.0,1.0,2460.0,2260.0,2950.0,239989.0,1727.0,4764.0,6733.0],[4759.0,1.0,5410.0,4430.0,6310.0,535915.0,3832.0,11448.0,19385.0],[4760.0,1.0,2760.0,3110.0,3700.0,334335.0,1931.0,5304.0,7157.0],[4762.0,1.0,2660.0,2940.0,3730.0,367754.0,2207.0,4210.0,7227.0],[4763.0,1.0,6580.0,8350.0,10980.0,1344534.0,8002.0,42024.0,47283.0],[4763.0,1.0,700.0,890.0,1050.0,101524.0,440.0,1223.0,2220.0],[4767.0,2.0,2760.0,2320.0,3410.0,270734.0,1644.0,2856.0,7413.0],[4771.0,1.0,4210.0,2540.0,4860.0,317513.0,1899.0,4506.0,7431.0],[4771.0,1.0,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class=\"ansiout\"><console>:2: error: ';' expected but integer literal found.\n SELECT * FROM prepped LIMIT 50;\n ^\n</div>","error":null,"workflows":[],"startTime":1.487150110313E12,"submitTime":1.487150110288E12,"finishTime":1.48715011146E12,"collapsed":false,"bindings":{},"inputWidgets":{},"displayType":"table","width":"auto","height":"auto","xColumns":null,"yColumns":null,"pivotColumns":null,"pivotAggregation":null,"customPlotOptions":{},"commentThread":[],"commentsVisible":false,"parentHierarchy":[],"diffInserts":[],"diffDeletes":[],"globalVars":{},"latestUser":"a user","commandTitle":"","showCommandTitle":false,"hideCommandCode":false,"hideCommandResult":false,"iPythonMetadata":null,"streamStates":{},"nuid":"69960d6a-124c-429a-a075-1a39d4e51ccc"},{"version":"CommandV1","origId":3207259124805093,"guid":"dba7e015-41f0-4399-b05c-dd72383ca097","subtype":"command","commandType":"auto","position":10.0,"command":"%sql\nDROP TABLE IF EXISTS finalPrep;\n\nCREATE TABLE finalPrep AS\nSELECT zipped as zipcode,\n SUM(count) as count,\n SUM(sum_returns) as sum_returns,\n SUM(all_joint_returns) as all_joint_returns,\n SUM(all_numdep) as all_numdep,\n SUM(income_amout) as income_amout,\n SUM(tax_interest_amount) as tax_interest_amount,\n SUM(capital_gains) as capital_gains,\n SUM(business_net_income) as business_net_income\nFROM prepped\nGROUP BY zipped;\n\nSELECT * FROM finalPrep LIMIT 100;","commandVersion":0,"state":"finished","results":{"type":"table","data":[[5947.0,1.0,690.0,610.0,770.0,76951.0,740.0,1737.0,2611.0],[9603.0,2.0,340.0,480.0,540.0,40005.0,439.0,571.0,1782.0],[748.0,1.0,3670.0,3440.0,4230.0,617725.0,3201.0,9210.0,20382.0],[5303.0,3.0,3180.0,3390.0,3670.0,442864.0,2465.0,10052.0,9412.0],[660.0,2.0,6080.0,2140.0,8350.0,408736.0,1784.0,3878.0,18307.0],[1382.0,4.0,6620.0,4960.0,6440.0,675004.0,5647.0,15570.0,21923.0],[8523.0,2.0,16380.0,17000.0,29250.0,2694093.0,15182.0,74247.0,88264.0],[8040.0,1.0,9440.0,7510.0,8420.0,1853847.0,19850.0,131972.0,62979.0],[9001.0,8.0,52560.0,23080.0,100520.0,3845636.0,28521.0,113103.0,241214.0],[1170.0,3.0,9070.0,4710.0,10880.0,1096977.0,8012.0,20831.0,27269.0],[774.0,1.0,7030.0,3560.0,9100.0,763412.0,8627.0,31564.0,37749.0],[2796.0,1.0,1160.0,990.0,2170.0,112431.0,735.0,1385.0,1960.0],[9340.0,2.0,11090.0,7290.0,9000.0,1446762.0,19664.0,66859.0,87468.0],[581.0,2.0,1890.0,1530.0,2110.0,185504.0,1253.0,4425.0,9473.0],[7861.0,2.0,18890.0,22330.0,37670.0,3536791.0,13296.0,65562.0,134533.0],[1590.0,3.0,6770.0,5320.0,6400.0,625608.0,6276.0,8088.0,20457.0],[3073.0,1.0,780.0,1120.0,1480.0,108655.0,363.0,595.0,3042.0],[3020.0,1.0,1940.0,1690.0,3770.0,187640.0,867.0,1034.0,5108.0],[4451.0,2.0,11660.0,7210.0,12020.0,967647.0,5885.0,5634.0,20798.0],[7212.0,1.0,570.0,710.0,980.0,66948.0,444.0,980.0,1898.0],[5315.0,1.0,8310.0,7930.0,8330.0,1300740.0,8959.0,31728.0,32986.0],[4524.0,3.0,8320.0,8430.0,12260.0,3123223.0,46789.0,258589.0,109263.0],[473.0,2.0,3700.0,3500.0,4820.0,358631.0,2763.0,5047.0,11327.0],[7749.0,2.0,19530.0,31450.0,56860.0,6359902.0,24323.0,177090.0,211869.0],[2840.0,1.0,5290.0,1710.0,5450.0,333137.0,1639.0,6050.0,11306.0],[8165.0,2.0,2110.0,2420.0,4890.0,282431.0,2391.0,4798.0,8938.0],[9678.0,3.0,27680.0,27560.0,41730.0,3588343.0,13927.0,50984.0,79065.0],[2635.0,2.0,2570.0,2540.0,3280.0,255530.0,1935.0,2764.0,5811.0],[3956.0,2.0,10310.0,9830.0,16800.0,1423847.0,8522.0,25080.0,57288.0],[7346.0,1.0,530.0,580.0,920.0,57457.0,177.0,980.0,1902.0],[9302.0,1.0,4550.0,3750.0,5030.0,729391.0,11177.0,54241.0,56630.0],[6642.0,1.0,280.0,320.0,370.0,36882.0,377.0,1512.0,447.0],[890.0,3.0,11930.0,5610.0,23570.0,1214916.0,7022.0,18210.0,38414.0],[2991.0,2.0,6840.0,7600.0,14070.0,1242749.0,14018.0,55435.0,52307.0],[8940.0,6.0,10770.0,10980.0,16370.0,1217950.0,6379.0,18372.0,22966.0],[3660.0,3.0,8970.0,4500.0,13330.0,800358.0,4658.0,9994.0,27011.0],[9582.0,1.0,8400.0,2860.0,8390.0,641484.0,6268.0,17232.0,30201.0],[7030.0,1.0,7580.0,7030.0,13120.0,1181298.0,9138.0,31259.0,40086.0],[6564.0,2.0,870.0,1270.0,1620.0,118947.0,600.0,1663.0,2579.0],[810.0,4.0,12350.0,7280.0,15620.0,1321332.0,6551.0,12200.0,36422.0],[3018.0,2.0,16760.0,17910.0,31060.0,2509392.0,9052.0,36189.0,68728.0],[9850.0,3.0,24210.0,20200.0,27610.0,3067319.0,26223.0,81081.0,97962.0],[9857.0,1.0,2180.0,2570.0,3700.0,280713.0,1706.0,4593.0,8437.0],[5319.0,2.0,3280.0,2350.0,3540.0,320331.0,2714.0,10519.0,7103.0],[5720.0,1.0,6160.0,5050.0,7120.0,750756.0,9020.0,30212.0,23428.0],[8565.0,2.0,4410.0,5960.0,8060.0,690227.0,3922.0,8986.0,11592.0],[2111.0,2.0,20930.0,14080.0,27690.0,3477837.0,19882.0,168023.0,83640.0],[494.0,2.0,800.0,670.0,720.0,76085.0,553.0,3015.0,4063.0],[9967.0,2.0,520.0,330.0,360.0,45391.0,529.0,1161.0,1624.0],[4690.0,1.0,7860.0,6440.0,10410.0,749381.0,4270.0,6977.0,16271.0],[1205.0,3.0,5390.0,4800.0,6300.0,952469.0,6406.0,25702.0,39633.0],[8241.0,2.0,380.0,360.0,430.0,39890.0,261.0,1163.0,1112.0],[2872.0,3.0,2590.0,2340.0,2700.0,270370.0,3820.0,12759.0,11445.0],[5752.0,1.0,220.0,240.0,290.0,24886.0,343.0,1778.0,727.0],[4881.0,3.0,5380.0,5380.0,6920.0,594128.0,3917.0,9013.0,13296.0],[4210.0,2.0,10350.0,6850.0,16870.0,757488.0,2795.0,10917.0,26806.0],[6462.0,1.0,1190.0,1080.0,1560.0,109955.0,1514.0,5469.0,5046.0],[2495.0,1.0,670.0,630.0,740.0,54461.0,441.0,515.0,2552.0],[4767.0,2.0,2760.0,2320.0,3410.0,270734.0,1644.0,2856.0,7413.0],[9595.0,2.0,3810.0,3700.0,3430.0,571455.0,8842.0,34749.0,48529.0],[5904.0,2.0,5500.0,4900.0,6100.0,613642.0,6171.0,21308.0,22643.0],[730.0,6.0,46860.0,24300.0,50570.0,5440929.0,19228.0,90944.0,177164.0],[2075.0,1.0,570.0,470.0,550.0,88631.0,370.0,1256.0,3039.0],[9310.0,7.0,22660.0,13500.0,23620.0,4320801.0,102211.0,625421.0,237797.0],[8331.0,2.0,3920.0,5340.0,11110.0,513824.0,6377.0,25258.0,17209.0],[7652.0,1.0,5930.0,7060.0,12330.0,659535.0,1807.0,7109.0,9372.0],[1907.0,1.0,4680.0,4310.0,4760.0,1577312.0,16246.0,146086.0,88652.0],[8508.0,1.0,5920.0,8460.0,12190.0,1328757.0,8728.0,38229.0,48470.0],[3678.0,2.0,640.0,260.0,1720.0,48479.0,56.0,337.0,662.0],[6547.0,1.0,1050.0,1380.0,2930.0,98386.0,71.0,321.0,-39.0],[6380.0,1.0,4280.0,3730.0,6760.0,500593.0,4826.0,25289.0,18900.0],[4432.0,1.0,3180.0,3100.0,4790.0,658645.0,3520.0,11483.0,19723.0],[7263.0,2.0,1860.0,1710.0,1260.0,165640.0,2265.0,8465.0,5865.0],[3265.0,1.0,2790.0,2630.0,3770.0,458201.0,4225.0,15386.0,11691.0],[125.0,1.0,600.0,470.0,510.0,82518.0,1037.0,5122.0,4865.0],[1453.0,2.0,7870.0,9310.0,11950.0,2437970.0,28367.0,167541.0,97946.0],[1912.0,11.0,48100.0,15030.0,57890.0,3618135.0,12039.0,49495.0,126497.0],[4654.0,1.0,870.0,950.0,1540.0,100278.0,781.0,1397.0,2599.0],[3580.0,2.0,10160.0,10090.0,11890.0,1947074.0,24821.0,78578.0,59124.0],[4250.0,2.0,2730.0,2580.0,4670.0,236969.0,1637.0,4012.0,9200.0],[5635.0,1.0,700.0,520.0,980.0,62851.0,323.0,760.0,945.0],[3909.0,1.0,1920.0,1710.0,4510.0,217130.0,1383.0,4493.0,12026.0],[4332.0,1.0,2420.0,2240.0,3620.0,252185.0,1411.0,4303.0,7326.0],[4880.0,4.0,5820.0,5280.0,8130.0,659819.0,5029.0,9325.0,17475.0],[9110.0,3.0,14320.0,8490.0,12490.0,2504286.0,24080.0,149999.0,96744.0],[5838.0,1.0,100.0,100.0,90.0,10747.0,88.0,571.0,277.0],[6690.0,1.0,1380.0,1250.0,1660.0,126857.0,1149.0,3603.0,4633.0],[9512.0,5.0,45530.0,42020.0,69070.0,1.1222232E7,69116.0,467520.0,453011.0],[7970.0,1.0,4490.0,3560.0,7540.0,559106.0,883.0,4965.0,19380.0],[4481.0,1.0,1390.0,1680.0,2350.0,169325.0,651.0,2034.0,5662.0],[1944.0,2.0,14010.0,11930.0,15760.0,2222134.0,17785.0,54631.0,73707.0],[2064.0,2.0,4420.0,3940.0,5850.0,750745.0,5067.0,10724.0,15425.0],[6553.0,1.0,4540.0,5580.0,7980.0,483702.0,4822.0,11961.0,20914.0],[9726.0,1.0,7080.0,4590.0,10590.0,522985.0,1748.0,4491.0,21210.0],[9548.0,1.0,5800.0,4670.0,9260.0,623921.0,9580.0,14523.0,45867.0],[6339.0,1.0,1850.0,1990.0,3060.0,230367.0,1409.0,4606.0,5698.0],[3045.0,1.0,6090.0,3450.0,8170.0,524714.0,3365.0,9778.0,11891.0],[9702.0,2.0,4770.0,4160.0,5850.0,539015.0,3501.0,12837.0,15501.0],[5640.0,1.0,6730.0,5420.0,7700.0,662956.0,7156.0,17708.0,17999.0],[8152.0,1.0,2770.0,3320.0,4750.0,390900.0,2079.0,6658.0,9960.0]],"arguments":{},"addedWidgets":{},"removedWidgets":[],"schema":[{"name":"zipcode","type":"\"integer\"","metadata":"{}"},{"name":"count","type":"\"long\"","metadata":"{}"},{"name":"sum_returns","type":"\"long\"","metadata":"{}"},{"name":"all_joint_returns","type":"\"long\"","metadata":"{}"},{"name":"all_numdep","type":"\"long\"","metadata":"{}"},{"name":"income_amout","type":"\"double\"","metadata":"{}"},{"name":"tax_interest_amount","type":"\"double\"","metadata":"{}"},{"name":"capital_gains","type":"\"double\"","metadata":"{}"},{"name":"business_net_income","type":"\"double\"","metadata":"{}"}],"overflow":false,"aggData":[],"aggSchema":[],"aggOverflow":false,"aggSeriesLimitReached":false,"aggError":"","aggType":"","plotOptions":null,"isJsonSchema":true,"dbfsResultPath":null},"errorSummary":"Error in SQL statement: ParseException: \nextraneous input 'display' expecting {'(', 'SELECT', 'FROM', 'ADD', 'DESC', 'WITH', 'VALUES', 'CREATE', 'TABLE', 'INSERT', 'DELETE', 'DESCRIBE', 'EXPLAIN', 'SHOW', 'USE', 'DROP', 'ALTER', 'MAP', 'SET', 'RESET', 'START', 'COMMIT', 'ROLLBACK', 'REDUCE', 'REFRESH', 'CLEAR', 'CACHE', 'UNCACHE', 'DFS', 'TRUNCATE', 'ANALYZE', 'LIST', 'REVOKE', 'GRANT', 'LOCK', 'UNLOCK', 'MSCK', 'EXPORT', 'IMPORT', 'LOAD'}(line 1, pos 0)\n\n== SQL ==\ndisplay(finalPrep.describe())\n^^^\n","error":"com.databricks.backend.common.rpc.DatabricksExceptions$SQLExecutionException: org.apache.spark.sql.catalyst.parser.ParseException: \nextraneous input 'display' expecting {'(', 'SELECT', 'FROM', 'ADD', 'DESC', 'WITH', 'VALUES', 'CREATE', 'TABLE', 'INSERT', 'DELETE', 'DESCRIBE', 'EXPLAIN', 'SHOW', 'USE', 'DROP', 'ALTER', 'MAP', 'SET', 'RESET', 'START', 'COMMIT', 'ROLLBACK', 'REDUCE', 'REFRESH', 'CLEAR', 'CACHE', 'UNCACHE', 'DFS', 'TRUNCATE', 'ANALYZE', 'LIST', 'REVOKE', 'GRANT', 'LOCK', 'UNLOCK', 'MSCK', 'EXPORT', 'IMPORT', 'LOAD'}(line 1, pos 0)\n\n== SQL ==\ndisplay(finalPrep.describe())\n^^^\n\n\tat org.apache.spark.sql.catalyst.parser.ParseException.withCommand(ParseDriver.scala:196)\n\tat org.apache.spark.sql.catalyst.parser.AbstractSqlParser.parse(ParseDriver.scala:98)\n\tat org.apache.spark.sql.execution.SparkSqlParser.parse(SparkSqlParser.scala:45)\n\tat org.apache.spark.sql.catalyst.parser.AbstractSqlParser.parsePlan(ParseDriver.scala:52)\n\tat org.apache.spark.sql.SparkSession.sql(SparkSession.scala:593)\n\tat org.apache.spark.sql.SQLContext.sql(SQLContext.scala:699)\n\tat com.databricks.backend.daemon.driver.SQLDriverLocal$$anonfun$1.apply(SQLDriverLocal.scala:82)\n\tat com.databricks.backend.daemon.driver.SQLDriverLocal$$anonfun$1.apply(SQLDriverLocal.scala:28)\n\tat scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:244)\n\tat scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:244)\n\tat scala.collection.immutable.List.foreach(List.scala:318)\n\tat scala.collection.TraversableLike$class.map(TraversableLike.scala:244)\n\tat scala.collection.AbstractTraversable.map(Traversable.scala:105)\n\tat com.databricks.backend.daemon.driver.SQLDriverLocal.executeSql(SQLDriverLocal.scala:28)\n\tat com.databricks.backend.daemon.driver.SQLDriverLocal.repl(SQLDriverLocal.scala:128)\n\tat com.databricks.backend.daemon.driver.DriverLocal$$anonfun$execute$1.apply(DriverLocal.scala:196)\n\tat com.databricks.backend.daemon.driver.DriverLocal$$anonfun$execute$1.apply(DriverLocal.scala:185)\n\tat com.databricks.logging.UsageLogging$$anonfun$withAttributionContext$1.apply(UsageLogging.scala:145)\n\tat scala.util.DynamicVariable.withValue(DynamicVariable.scala:57)\n\tat com.databricks.logging.UsageLogging$class.withAttributionContext(UsageLogging.scala:140)\n\tat com.databricks.backend.daemon.driver.DriverLocal.withAttributionContext(DriverLocal.scala:35)\n\tat com.databricks.logging.UsageLogging$class.withAttributionTags(UsageLogging.scala:178)\n\tat com.databricks.backend.daemon.driver.DriverLocal.withAttributionTags(DriverLocal.scala:35)\n\tat com.databricks.backend.daemon.driver.DriverLocal.execute(DriverLocal.scala:185)\n\tat com.databricks.backend.daemon.driver.DriverWrapper$$anonfun$tryExecutingCommand$2.apply(DriverWrapper.scala:589)\n\tat com.databricks.backend.daemon.driver.DriverWrapper$$anonfun$tryExecutingCommand$2.apply(DriverWrapper.scala:589)\n\tat scala.util.Try$.apply(Try.scala:161)\n\tat com.databricks.backend.daemon.driver.DriverWrapper.tryExecutingCommand(DriverWrapper.scala:584)\n\tat com.databricks.backend.daemon.driver.DriverWrapper.executeCommand(DriverWrapper.scala:488)\n\tat com.databricks.backend.daemon.driver.DriverWrapper.runInnerLoop(DriverWrapper.scala:391)\n\tat com.databricks.backend.daemon.driver.DriverWrapper.runInner(DriverWrapper.scala:348)\n\tat com.databricks.backend.daemon.driver.DriverWrapper.run(DriverWrapper.scala:215)\n\tat java.lang.Thread.run(Thread.java:745)\n\n\tat com.databricks.backend.daemon.driver.SQLDriverLocal.executeSql(SQLDriverLocal.scala:116)\n\tat com.databricks.backend.daemon.driver.SQLDriverLocal.repl(SQLDriverLocal.scala:128)\n\tat com.databricks.backend.daemon.driver.DriverLocal$$anonfun$execute$1.apply(DriverLocal.scala:196)\n\tat com.databricks.backend.daemon.driver.DriverLocal$$anonfun$execute$1.apply(DriverLocal.scala:185)\n\tat com.databricks.logging.UsageLogging$$anonfun$withAttributionContext$1.apply(UsageLogging.scala:145)\n\tat scala.util.DynamicVariable.withValue(DynamicVariable.scala:57)\n\tat com.databricks.logging.UsageLogging$class.withAttributionContext(UsageLogging.scala:140)\n\tat com.databricks.backend.daemon.driver.DriverLocal.withAttributionContext(DriverLocal.scala:35)\n\tat com.databricks.logging.UsageLogging$class.withAttributionTags(UsageLogging.scala:178)\n\tat com.databricks.backend.daemon.driver.DriverLocal.withAttributionTags(DriverLocal.scala:35)\n\tat com.databricks.backend.daemon.driver.DriverLocal.execute(DriverLocal.scala:185)\n\tat com.databricks.backend.daemon.driver.DriverWrapper$$anonfun$tryExecutingCommand$2.apply(DriverWrapper.scala:589)\n\tat com.databricks.backend.daemon.driver.DriverWrapper$$anonfun$tryExecutingCommand$2.apply(DriverWrapper.scala:589)\n\tat scala.util.Try$.apply(Try.scala:161)\n\tat com.databricks.backend.daemon.driver.DriverWrapper.tryExecutingCommand(DriverWrapper.scala:584)\n\tat com.databricks.backend.daemon.driver.DriverWrapper.executeCommand(DriverWrapper.scala:488)\n\tat com.databricks.backend.daemon.driver.DriverWrapper.runInnerLoop(DriverWrapper.scala:391)\n\tat com.databricks.backend.daemon.driver.DriverWrapper.runInner(DriverWrapper.scala:348)\n\tat com.databricks.backend.daemon.driver.DriverWrapper.run(DriverWrapper.scala:215)\n\tat java.lang.Thread.run(Thread.java:745)\n","workflows":[],"startTime":1.487168160683E12,"submitTime":1.487168160668E12,"finishTime":1.487168184089E12,"collapsed":false,"bindings":{},"inputWidgets":{},"displayType":"table","width":"auto","height":"auto","xColumns":null,"yColumns":null,"pivotColumns":null,"pivotAggregation":null,"customPlotOptions":{},"commentThread":[],"commentsVisible":false,"parentHierarchy":[],"diffInserts":[],"diffDeletes":[],"globalVars":{},"latestUser":"a user","commandTitle":"","showCommandTitle":false,"hideCommandCode":false,"hideCommandResult":false,"iPythonMetadata":null,"streamStates":{},"nuid":"dcd3ab0b-7bc1-4854-b134-829ccf1cdfaa"},{"version":"CommandV1","origId":2303935447651002,"guid":"33083764-28f5-457a-9f02-a036c46905b7","subtype":"command","commandType":"auto","position":10.5,"command":"val finalPrep = sqlContext.table(\"finalPrep\")","commandVersion":0,"state":"finished","results":{"type":"html","data":"<div class=\"ansiout\">finalPrep: org.apache.spark.sql.DataFrame = [zipcode: int, count: bigint ... 7 more fields]\n</div>","arguments":{},"addedWidgets":{},"removedWidgets":[]},"errorSummary":"<div class=\"ansiout\"><console>:44: error: value mkStrint is not a member of IndexedSeq[Any]\n joined.columns.deep.mkStrint(", ")\n ^\n</div>","error":null,"workflows":[],"startTime":1.487168435999E12,"submitTime":1.487168435988E12,"finishTime":1.487168436107E12,"collapsed":false,"bindings":{},"inputWidgets":{},"displayType":"table","width":"auto","height":"auto","xColumns":null,"yColumns":null,"pivotColumns":null,"pivotAggregation":null,"customPlotOptions":{},"commentThread":[],"commentsVisible":false,"parentHierarchy":[],"diffInserts":[],"diffDeletes":[],"globalVars":{},"latestUser":"a user","commandTitle":"","showCommandTitle":false,"hideCommandCode":false,"hideCommandResult":false,"iPythonMetadata":null,"streamStates":{},"nuid":"2de2f2ee-5807-44ff-beff-bc5238a08a29"},{"version":"CommandV1","origId":1704430907200392,"guid":"07d5be07-1cfe-4d82-ba0c-bf4ebde1ef31","subtype":"command","commandType":"auto","position":13.5,"command":"display(finalPrep.describe())","commandVersion":0,"state":"finished","results":{"type":"table","data":[["count","3460","3460","3460","3460","3460","3460","3460","3460","3460"],["mean","4744.146242774566","2.0679190751445087","7342.462427745665","5573.054913294798","9892.534682080925","1041323.4965317919","9667.44710982659","51515.29248554914","36190.0176300578"],["stddev","2822.2521101265834","1.638916625902973","10353.959052467151","5957.392773073175","13294.60838043966","1675751.2754670947","28528.205669694435","235525.90843179592","64273.34572201155"],["min","100","1","50","0","0","2151.0","0.0","-16.0","-1092.0"],["max","9983","18","143550","75480","207620","3.9620325E7","988415.0","6933043.0","1393990.0"]],"arguments":{},"addedWidgets":{},"removedWidgets":[],"schema":[{"name":"summary","type":"\"string\"","metadata":"{}"},{"name":"zipcode","type":"\"string\"","metadata":"{}"},{"name":"count","type":"\"string\"","metadata":"{}"},{"name":"sum_returns","type":"\"string\"","metadata":"{}"},{"name":"all_joint_returns","type":"\"string\"","metadata":"{}"},{"name":"all_numdep","type":"\"string\"","metadata":"{}"},{"name":"income_amout","type":"\"string\"","metadata":"{}"},{"name":"tax_interest_amount","type":"\"string\"","metadata":"{}"},{"name":"capital_gains","type":"\"string\"","metadata":"{}"},{"name":"business_net_income","type":"\"string\"","metadata":"{}"}],"overflow":false,"aggData":[],"aggSchema":[],"aggOverflow":false,"aggSeriesLimitReached":false,"aggError":"","aggType":"","plotOptions":null,"isJsonSchema":true,"dbfsResultPath":null},"errorSummary":"<div class=\"ansiout\">Unclosed block</div>","error":null,"workflows":[],"startTime":1.487168330941E12,"submitTime":1.48716833093E12,"finishTime":1.487168337128E12,"collapsed":false,"bindings":{},"inputWidgets":{},"displayType":"table","width":"auto","height":"auto","xColumns":null,"yColumns":null,"pivotColumns":null,"pivotAggregation":null,"customPlotOptions":{},"commentThread":[],"commentsVisible":false,"parentHierarchy":[],"diffInserts":[],"diffDeletes":[],"globalVars":{},"latestUser":"a user","commandTitle":"","showCommandTitle":false,"hideCommandCode":false,"hideCommandResult":false,"iPythonMetadata":null,"streamStates":{},"nuid":"8a49094e-66e6-4109-9525-c58a3227df85"},{"version":"CommandV1","origId":2303935447651007,"guid":"ec297901-9a4b-4680-9631-7740109ac46f","subtype":"command","commandType":"auto","position":15.0,"command":"val nonFeatureCols = Array(\"zipcode\", \"count\")\nval featureCols = finalPrep.columns.diff(nonFeatureCols)","commandVersion":0,"state":"finished","results":{"type":"html","data":"<div class=\"ansiout\">nonFeatureCols: Array[String] = Array(zipcode, count)\nfeatureCols: Array[String] = Array(sum_returns, all_joint_returns, all_numdep, income_amout, tax_interest_amount, capital_gains, business_net_income)\n</div>","arguments":{},"addedWidgets":{},"removedWidgets":[]},"errorSummary":"<div class=\"ansiout\"><console>:31: error: not found: value prepped\n val featureCols = prepped.columns.diff(nonFeatureCols)\n ^\n</div>","error":null,"workflows":[],"startTime":1.487168471394E12,"submitTime":1.487168471382E12,"finishTime":1.487168471528E12,"collapsed":false,"bindings":{},"inputWidgets":{},"displayType":"table","width":"auto","height":"auto","xColumns":null,"yColumns":null,"pivotColumns":null,"pivotAggregation":null,"customPlotOptions":{},"commentThread":[],"commentsVisible":false,"parentHierarchy":[],"diffInserts":[],"diffDeletes":[],"globalVars":{},"latestUser":"a user","commandTitle":"","showCommandTitle":false,"hideCommandCode":false,"hideCommandResult":false,"iPythonMetadata":null,"streamStates":{},"nuid":"99fef211-9876-4566-88cd-94538990ee45"},{"version":"CommandV1","origId":2303935447651011,"guid":"ddbf0b22-fc67-423c-a0e3-a1b2050ce458","subtype":"command","commandType":"auto","position":17.0,"command":"import org.apache.spark.ml.feature.VectorAssembler\n\nval assembler = new VectorAssembler()\n .setInputCols(featureCols)\n .setOutputCol(\"features\")\n\nval va_finalPrep = assembler.transform(finalPrep)","commandVersion":0,"state":"finished","results":{"type":"html","data":"<div class=\"ansiout\">import org.apache.spark.ml.feature.VectorAssembler\nassembler: org.apache.spark.ml.feature.VectorAssembler = vecAssembler_e36603cd98c8\nva_finalPrep: org.apache.spark.sql.DataFrame = [zipcode: int, count: bigint ... 8 more fields]\n</div>","arguments":{},"addedWidgets":{},"removedWidgets":[]},"errorSummary":"<div class=\"ansiout\"><console>:42: error: recursive value finalPrep needs type\n val finalPrep = assembler.transform(finalPrep)\n 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7651.0,33242.0],[6.0,3680.0,4600.0,6630.0,336029.0,2487.0,7355.0,12643.0],[1.0,7910.0,8790.0,11870.0,1140626.0,6129.0,22043.0,47457.0],[2.0,650.0,730.0,740.0,71559.0,1251.0,2658.0,1617.0],[2.0,6500.0,6770.0,13150.0,942049.0,4971.0,18414.0,22383.0],[3.0,16180.0,13970.0,18210.0,2133534.0,16739.0,87442.0,49373.0],[1.0,680.0,890.0,1300.0,68056.0,859.0,2413.0,2468.0],[2.0,890.0,910.0,1000.0,119202.0,1385.0,5295.0,5253.0],[1.0,740.0,780.0,1050.0,100329.0,484.0,2806.0,2204.0],[3.0,22190.0,6220.0,29680.0,1313124.0,2588.0,6370.0,60468.0],[1.0,5720.0,5760.0,7760.0,996198.0,8760.0,44381.0,37133.0],[3.0,11220.0,9930.0,13000.0,1801584.0,9871.0,34200.0,60405.0],[2.0,2890.0,2740.0,3520.0,450707.0,2290.0,7291.0,13033.0],[2.0,4580.0,2960.0,3660.0,441040.0,3286.0,24104.0,10299.0],[1.0,2440.0,1370.0,6390.0,243086.0,1154.0,5086.0,9436.0],[2.0,4980.0,5260.0,7730.0,506201.0,3498.0,8070.0,15879.0],[3.0,2960.0,4000.0,5170.0,357850.0,3460.0,14264.0,12331.0],[1.0,1900.0,1560.0,1990.0,236237.0,1127.0,2471.0,5664.0],[1.0,7920.0,3990.0,7160.0,1921179.0,67573.0,322619.0,85176.0],[2.0,11850.0,6910.0,12850.0,1393169.0,19582.0,48376.0,47528.0],[1.0,4720.0,3720.0,4550.0,970559.0,8232.0,67677.0,50798.0],[3.0,4930.0,3580.0,4360.0,660541.0,4635.0,28551.0,49404.0],[1.0,4330.0,3600.0,5350.0,458951.0,3898.0,9287.0,10265.0],[2.0,10270.0,13230.0,20020.0,3051342.0,13349.0,86100.0,95935.0],[2.0,12000.0,12580.0,33560.0,1341336.0,6171.0,14753.0,54692.0],[1.0,3240.0,3650.0,5120.0,875231.0,6137.0,28748.0,27891.0],[1.0,2890.0,3270.0,4540.0,429825.0,2588.0,3278.0,10490.0],[2.0,2270.0,1780.0,2190.0,541584.0,17631.0,69937.0,18082.0],[2.0,2520.0,2700.0,3580.0,380076.0,1676.0,6899.0,12312.0],[1.0,1710.0,1740.0,2090.0,201332.0,2001.0,3501.0,6285.0],[1.0,1930.0,2070.0,2920.0,227127.0,2155.0,3991.0,9528.0],[1.0,2210.0,2570.0,3960.0,227223.0,1717.0,4263.0,8303.0],[1.0,3550.0,4050.0,4670.0,575368.0,4854.0,13006.0,9183.0],[1.0,2010.0,1700.0,2980.0,242687.0,773.0,3819.0,3492.0],[1.0,720.0,650.0,880.0,74448.0,332.0,851.0,2486.0],[1.0,8830.0,6960.0,9950.0,1493219.0,20437.0,80837.0,38977.0],[1.0,230.0,260.0,280.0,30532.0,142.0,656.0,566.0],[1.0,3810.0,2380.0,3450.0,310440.0,2482.0,10966.0,11132.0],[2.0,15970.0,19500.0,39090.0,3321408.0,14175.0,103451.0,99045.0],[1.0,370.0,360.0,380.0,36880.0,193.0,569.0,1299.0],[5.0,26610.0,6810.0,25590.0,2489165.0,19086.0,185245.0,137970.0],[4.0,17850.0,18930.0,23960.0,2682177.0,30178.0,78205.0,119273.0],[1.0,2690.0,3540.0,4060.0,346452.0,2625.0,7656.0,19109.0],[2.0,8350.0,7780.0,10160.0,1854559.0,12010.0,66548.0,85030.0],[2.0,9520.0,4880.0,9120.0,856632.0,6284.0,12424.0,20907.0],[3.0,4070.0,2980.0,5450.0,364087.0,2698.0,6087.0,16968.0],[5.0,18050.0,16950.0,20460.0,2206452.0,20448.0,68462.0,70170.0],[3.0,18300.0,10650.0,21050.0,1649887.0,8205.0,23195.0,40596.0],[1.0,7220.0,5060.0,8020.0,724300.0,4588.0,14187.0,19500.0],[1.0,1370.0,1200.0,1850.0,115041.0,395.0,569.0,1877.0],[2.0,7540.0,3860.0,4190.0,1089030.0,13658.0,62757.0,54227.0],[1.0,2510.0,2850.0,4500.0,418889.0,1844.0,5171.0,8017.0],[3.0,2970.0,3510.0,5970.0,474029.0,2483.0,7402.0,10241.0],[1.0,3110.0,1930.0,7250.0,283214.0,1115.0,2924.0,10907.0],[3.0,13250.0,3950.0,13710.0,783250.0,3051.0,9123.0,26951.0],[1.0,1070.0,1110.0,1660.0,129799.0,663.0,1479.0,3037.0],[1.0,1540.0,1710.0,2180.0,189379.0,1615.0,4265.0,6257.0],[2.0,19880.0,11370.0,42670.0,2434685.0,17520.0,107877.0,121374.0],[2.0,2770.0,940.0,1290.0,517710.0,3546.0,23283.0,13803.0],[1.0,930.0,1050.0,1570.0,95966.0,730.0,1795.0,4014.0],[3.0,9540.0,8910.0,14630.0,1548465.0,10064.0,40680.0,43562.0],[1.0,10830.0,8210.0,11940.0,1285418.0,4766.0,14270.0,30235.0],[1.0,4840.0,3440.0,7470.0,631605.0,1920.0,3898.0,7217.0],[1.0,2120.0,2630.0,3450.0,206847.0,2152.0,4619.0,7959.0],[2.0,5120.0,5300.0,7470.0,1149373.0,7405.0,35852.0,45613.0],[5.0,21940.0,12140.0,32140.0,3652149.0,55542.0,277150.0,170382.0],[1.0,450.0,440.0,490.0,48253.0,117.0,211.0,993.0],[1.0,2790.0,3720.0,5200.0,1575888.0,24597.0,145291.0,58692.0],[1.0,2470.0,1030.0,3720.0,213696.0,905.0,1227.0,7440.0],[2.0,1220.0,1450.0,2430.0,124544.0,634.0,766.0,4770.0],[4.0,15210.0,10370.0,15410.0,1451027.0,11207.0,32467.0,35931.0],[2.0,7350.0,5410.0,8950.0,925160.0,10984.0,42970.0,28776.0],[2.0,2390.0,2410.0,2840.0,274073.0,5467.0,9053.0,9691.0],[2.0,7120.0,2970.0,4370.0,728409.0,9356.0,45847.0,41864.0],[1.0,380.0,460.0,610.0,46019.0,209.0,1111.0,949.0]],"arguments":{},"addedWidgets":{},"removedWidgets":[],"schema":[{"name":"count","type":"\"long\"","metadata":"{}"},{"name":"sum_returns","type":"\"long\"","metadata":"{}"},{"name":"all_joint_returns","type":"\"long\"","metadata":"{}"},{"name":"all_numdep","type":"\"long\"","metadata":"{}"},{"name":"income_amout","type":"\"double\"","metadata":"{}"},{"name":"tax_interest_amount","type":"\"double\"","metadata":"{}"},{"name":"capital_gains","type":"\"double\"","metadata":"{}"},{"name":"business_net_income","type":"\"double\"","metadata":"{}"}],"overflow":true,"aggData":[],"aggSchema":[],"aggOverflow":false,"aggSeriesLimitReached":false,"aggError":"","aggType":"","plotOptions":null,"isJsonSchema":true,"dbfsResultPath":null},"errorSummary":null,"error":null,"workflows":[],"startTime":1.487168625238E12,"submitTime":1.487168625208E12,"finishTime":1.487168632062E12,"collapsed":false,"bindings":{},"inputWidgets":{},"displayType":"scatterPlot","width":"797.4","height":"844.4","xColumns":null,"yColumns":null,"pivotColumns":null,"pivotAggregation":null,"customPlotOptions":{},"commentThread":[],"commentsVisible":false,"parentHierarchy":[],"diffInserts":[],"diffDeletes":[],"globalVars":{},"latestUser":"a user","commandTitle":"","showCommandTitle":false,"hideCommandCode":false,"hideCommandResult":false,"iPythonMetadata":null,"streamStates":{},"nuid":"3f70cdd3-45e4-4bed-9c06-2ee503f110ed"},{"version":"CommandV1","origId":2303935447651012,"guid":"d4d493f2-7d0c-4ed4-80ac-7608c326ca7e","subtype":"command","commandType":"auto","position":18.0,"command":"val Array(training, test) = va_finalPrep.randomSplit(Array(0.7, 0.3))\n\n// Going to cache the data to make sure things stay snappy!\ntraining.cache()\ntest.cache()\n\nprintln(training.count())\nprintln(test.count())","commandVersion":0,"state":"finished","results":{"type":"html","data":"<div class=\"ansiout\">2431\n1029\ntraining: org.apache.spark.sql.Dataset[org.apache.spark.sql.Row] = [zipcode: int, count: bigint ... 8 more fields]\ntest: org.apache.spark.sql.Dataset[org.apache.spark.sql.Row] = [zipcode: int, count: bigint ... 8 more fields]\n</div>","arguments":{},"addedWidgets":{},"removedWidgets":[]},"errorSummary":null,"error":null,"workflows":[],"startTime":1.487168822466E12,"submitTime":1.487168822432E12,"finishTime":1.487168834768E12,"collapsed":false,"bindings":{},"inputWidgets":{},"displayType":"table","width":"auto","height":"auto","xColumns":null,"yColumns":null,"pivotColumns":null,"pivotAggregation":null,"customPlotOptions":{},"commentThread":[],"commentsVisible":false,"parentHierarchy":[],"diffInserts":[],"diffDeletes":[],"globalVars":{},"latestUser":"a user","commandTitle":"","showCommandTitle":false,"hideCommandCode":false,"hideCommandResult":false,"iPythonMetadata":null,"streamStates":{},"nuid":"720f2b9f-e035-4d5b-b13d-b076cf393f8c"},{"version":"CommandV1","origId":3707028759293320,"guid":"1bd4922f-d689-42b9-853a-583e0c2374b2","subtype":"command","commandType":"auto","position":19.0,"command":"%md\n## 5. Simple Regression Model","commandVersion":0,"state":"error","results":null,"errorSummary":null,"error":null,"workflows":[],"startTime":0.0,"submitTime":0.0,"finishTime":0.0,"collapsed":false,"bindings":{},"inputWidgets":{},"displayType":"table","width":"auto","height":"auto","xColumns":null,"yColumns":null,"pivotColumns":null,"pivotAggregation":null,"customPlotOptions":{},"commentThread":[],"commentsVisible":false,"parentHierarchy":[],"diffInserts":[],"diffDeletes":[],"globalVars":{},"latestUser":"a user","commandTitle":"","showCommandTitle":false,"hideCommandCode":false,"hideCommandResult":false,"iPythonMetadata":null,"streamStates":{},"nuid":"91f8fce4-7e53-4715-bc36-d58d789c4263"},{"version":"CommandV1","origId":3707028759293321,"guid":"8dbaf944-7bd7-46cd-a7c3-598ca8d77ff5","subtype":"command","commandType":"auto","position":20.0,"command":"import org.apache.spark.ml.regression.LinearRegression\n\nval lrModel = new LinearRegression()\n .setLabelCol(\"count\")\n .setFeaturesCol(\"features\")\n .setElasticNetParam(0.5)\n\nprintln(\"Printing out the model Parameters:\")\nprintln(\"-\"*20)\nprintln(lrModel.explainParams)\nprintln(\"-\"*20)","commandVersion":0,"state":"finished","results":{"type":"html","data":"<div class=\"ansiout\">Printing out the model Parameters:\n--------------------\naggregationDepth: suggested depth for treeAggregate (>= 2) (default: 2)\nelasticNetParam: the ElasticNet mixing parameter, in range [0, 1]. For alpha = 0, the penalty is an L2 penalty. For alpha = 1, it is an L1 penalty (default: 0.0, current: 0.5)\nfeaturesCol: features column name (default: features, current: features)\nfitIntercept: whether to fit an intercept term (default: true)\nlabelCol: label column name (default: label, current: count)\nmaxIter: maximum number of iterations (>= 0) (default: 100)\npredictionCol: prediction column name (default: prediction)\nregParam: regularization parameter (>= 0) (default: 0.0)\nsolver: the solver algorithm for optimization. If this is not set or empty, default value is 'auto' (default: auto)\nstandardization: whether to standardize the training features before fitting the model (default: true)\ntol: the convergence tolerance for iterative algorithms (>= 0) (default: 1.0E-6)\nweightCol: weight column name. If this is not set or empty, we treat all instance weights as 1.0 (undefined)\n--------------------\nimport org.apache.spark.ml.regression.LinearRegression\nlrModel: org.apache.spark.ml.regression.LinearRegression = linReg_1d6d6a4502c6\n</div>","arguments":{},"addedWidgets":{},"removedWidgets":[]},"errorSummary":null,"error":null,"workflows":[],"startTime":1.487168907313E12,"submitTime":1.487168907277E12,"finishTime":1.487168907498E12,"collapsed":false,"bindings":{},"inputWidgets":{},"displayType":"table","width":"auto","height":"auto","xColumns":null,"yColumns":null,"pivotColumns":null,"pivotAggregation":null,"customPlotOptions":{},"commentThread":[],"commentsVisible":false,"parentHierarchy":[],"diffInserts":[],"diffDeletes":[],"globalVars":{},"latestUser":"a user","commandTitle":"","showCommandTitle":false,"hideCommandCode":false,"hideCommandResult":false,"iPythonMetadata":null,"streamStates":{},"nuid":"93fd7c15-5f64-4fb3-8880-87ca1645b5e6"},{"version":"CommandV1","origId":3707028759293322,"guid":"2fad8560-1e9f-489d-aef7-3546c34d4e73","subtype":"command","commandType":"auto","position":21.0,"command":"val lrFitted = lrModel.fit(training)","commandVersion":0,"state":"finished","results":{"type":"html","data":"<div class=\"ansiout\">lrFitted: org.apache.spark.ml.regression.LinearRegressionModel = linReg_1d6d6a4502c6\n</div>","arguments":{},"addedWidgets":{},"removedWidgets":[]},"errorSummary":null,"error":null,"workflows":[],"startTime":1.487168927662E12,"submitTime":1.487168927628E12,"finishTime":1.487168928999E12,"collapsed":false,"bindings":{},"inputWidgets":{},"displayType":"table","width":"auto","height":"auto","xColumns":null,"yColumns":null,"pivotColumns":null,"pivotAggregation":null,"customPlotOptions":{},"commentThread":[],"commentsVisible":false,"parentHierarchy":[],"diffInserts":[],"diffDeletes":[],"globalVars":{},"latestUser":"a user","commandTitle":"","showCommandTitle":false,"hideCommandCode":false,"hideCommandResult":false,"iPythonMetadata":null,"streamStates":{},"nuid":"6159c838-483a-499f-b834-a91158526777"},{"version":"CommandV1","origId":3707028759293323,"guid":"3c12a20e-302a-4428-a148-f78498c9cecd","subtype":"command","commandType":"auto","position":22.0,"command":"val holdout = lrFitted\n .transform(test)\n .selectExpr(\"prediction as raw_prediction\", \n \"double(round(prediction)) as prediction\", \n \"count\", \n \"\"\"CASE double(round(prediction)) = count \n WHEN true then 1\n ELSE 0\nEND as equal\"\"\")\ndisplay(holdout)","commandVersion":0,"state":"finished","results":{"type":"table","data":[[1.3333197419617366,1.0,1.0,1.0],[1.420808017537211,1.0,2.0,0.0],[1.9850602141084643,2.0,2.0,1.0],[1.8591351905985873,2.0,1.0,0.0],[2.1426463822157933,2.0,1.0,0.0],[9.037711497831829,9.0,12.0,0.0],[4.205251774527863,4.0,4.0,1.0],[2.580036369025484,3.0,3.0,1.0],[2.2129698851879094,2.0,3.0,0.0],[2.696911000056155,3.0,2.0,0.0],[1.2710379229254591,1.0,4.0,0.0],[1.2104972766716797,1.0,1.0,1.0],[1.8441001977484741,2.0,3.0,0.0],[2.0765580534796864,2.0,1.0,0.0],[1.430446420178539,1.0,1.0,1.0],[2.0523953525933787,2.0,4.0,0.0],[12.964494027444003,13.0,11.0,0.0],[4.599342955550965,5.0,2.0,0.0],[2.0194986024477783,2.0,1.0,0.0],[1.9960627876138846,2.0,1.0,0.0],[1.8913212613041888,2.0,2.0,1.0],[1.2245753945246969,1.0,1.0,1.0],[1.9675859893816123,2.0,2.0,1.0],[1.510356287703298,2.0,1.0,0.0],[1.367470877494245,1.0,1.0,1.0],[1.3750494285548198,1.0,1.0,1.0],[1.3516488610895214,1.0,2.0,0.0],[2.0473546266947533,2.0,2.0,1.0],[1.6504285489477142,2.0,2.0,1.0],[1.3120927789204937,1.0,1.0,1.0],[2.0767647643141767,2.0,1.0,0.0],[1.6804263925261325,2.0,1.0,0.0],[1.6618491608778607,2.0,2.0,1.0],[1.2450224309882572,1.0,1.0,1.0],[1.6088107960688696,2.0,1.0,0.0],[1.2606112703354344,1.0,2.0,0.0],[1.3020479979597943,1.0,2.0,0.0],[2.768140090342465,3.0,2.0,0.0],[1.3042510478486147,1.0,1.0,1.0],[1.1840703354078916,1.0,1.0,1.0],[1.282436071912045,1.0,1.0,1.0],[1.3026721201369962,1.0,1.0,1.0],[1.3943616094143716,1.0,3.0,0.0],[1.2149054598605808,1.0,2.0,0.0],[2.1570329317022896,2.0,2.0,1.0],[2.058490858543235,2.0,1.0,0.0],[2.5724709499891243,3.0,3.0,1.0],[1.4039448447937757,1.0,2.0,0.0],[1.2464287051263443,1.0,1.0,1.0],[1.7747664439970796,2.0,2.0,1.0],[1.934886636439904,2.0,2.0,1.0],[1.818242817780208,2.0,1.0,0.0],[3.079152164550761,3.0,2.0,0.0],[1.3984441182861354,1.0,1.0,1.0],[1.7002528117257594,2.0,1.0,0.0],[2.229502156175881,2.0,1.0,0.0],[1.7755135357592209,2.0,2.0,1.0],[1.3780116138274372,1.0,2.0,0.0],[1.5313181083628935,2.0,1.0,0.0],[1.217657680419746,1.0,1.0,1.0],[1.2792190462420256,1.0,1.0,1.0],[2.5842972466294962,3.0,1.0,0.0],[1.4629446966913222,1.0,2.0,0.0],[1.3732543789169536,1.0,1.0,1.0],[1.3397624272305568,1.0,1.0,1.0],[1.3035488042750203,1.0,1.0,1.0],[1.2288086798762494,1.0,1.0,1.0],[2.455352775599578,2.0,2.0,1.0],[1.5521945423450147,2.0,2.0,1.0],[1.410189218157744,1.0,1.0,1.0],[1.6265186183769553,2.0,2.0,1.0],[2.353811801386552,2.0,3.0,0.0],[1.79100302686247,2.0,3.0,0.0],[2.806270285989313,3.0,3.0,1.0],[1.681923897427496,2.0,2.0,1.0],[1.6809846833928375,2.0,1.0,0.0],[2.9310850951192498,3.0,2.0,0.0],[1.2889072744751116,1.0,1.0,1.0],[1.830293213001462,2.0,1.0,0.0],[1.6737863721777169,2.0,1.0,0.0],[2.425047530822462,2.0,3.0,0.0],[2.0288705766124258,2.0,2.0,1.0],[3.859100789549578,4.0,3.0,0.0],[8.374118745227658,8.0,8.0,1.0],[1.960024553893975,2.0,3.0,0.0],[2.9623394277990553,3.0,3.0,1.0],[2.2866039452221187,2.0,3.0,0.0],[1.404168039227438,1.0,3.0,0.0],[4.704593431583453,5.0,5.0,1.0],[1.8536644174186132,2.0,2.0,1.0],[2.3084168558994422,2.0,1.0,0.0],[1.5853036168425507,2.0,2.0,1.0],[1.9446597768290521,2.0,4.0,0.0],[1.56215850122754,2.0,2.0,1.0],[1.1900791343883144,1.0,1.0,1.0],[1.4947487676326219,1.0,4.0,0.0],[1.423976702287013,1.0,2.0,0.0],[1.8336796600533867,2.0,1.0,0.0],[1.2620214172728055,1.0,1.0,1.0],[2.1489467584025026,2.0,1.0,0.0],[1.187180429352958,1.0,1.0,1.0],[1.225436001873212,1.0,1.0,1.0],[1.3055807412054665,1.0,2.0,0.0],[1.3036015968248198,1.0,2.0,0.0],[2.7242209672802913,3.0,3.0,1.0],[2.0990687395189083,2.0,3.0,0.0],[2.166874105517893,2.0,1.0,0.0],[1.6464227399923623,2.0,1.0,0.0],[1.478022213186973,1.0,1.0,1.0],[1.5311030908902552,2.0,3.0,0.0],[1.9058839707263018,2.0,2.0,1.0],[1.338493671869792,1.0,1.0,1.0],[1.2522694227666626,1.0,1.0,1.0],[1.4189953678691554,1.0,1.0,1.0],[1.3416566116637196,1.0,1.0,1.0],[1.299486802465018,1.0,1.0,1.0],[1.3573068654738545,1.0,1.0,1.0],[1.8908922247215474,2.0,3.0,0.0],[1.291526517484627,1.0,1.0,1.0],[1.2629587873031647,1.0,1.0,1.0],[1.4774132597203802,1.0,2.0,0.0],[1.30666332822268,1.0,1.0,1.0],[1.4065611364953705,1.0,1.0,1.0],[1.9868463304126782,2.0,1.0,0.0],[1.468599688245195,1.0,2.0,0.0],[1.1973939347072917,1.0,1.0,1.0],[1.4360471250420916,1.0,2.0,0.0],[1.341480686201774,1.0,2.0,0.0],[1.431887693137271,1.0,1.0,1.0],[1.2472999829016809,1.0,1.0,1.0],[2.253268312064953,2.0,2.0,1.0],[1.5081106524888743,2.0,1.0,0.0],[1.837323536415035,2.0,2.0,1.0],[2.0505700348521074,2.0,2.0,1.0],[1.3143059017507013,1.0,1.0,1.0],[1.2455774878473969,1.0,1.0,1.0],[1.268915580019291,1.0,1.0,1.0],[1.3580172657923921,1.0,2.0,0.0],[2.3098229586085717,2.0,2.0,1.0],[2.708501813353309,3.0,3.0,1.0],[2.3178636776684343,2.0,1.0,0.0],[2.1567788262011884,2.0,2.0,1.0],[1.3063318696193846,1.0,1.0,1.0],[1.9801530497317008,2.0,11.0,0.0],[2.9484152938866366,3.0,2.0,0.0],[1.3873060346470678,1.0,1.0,1.0],[2.376355763679444,2.0,1.0,0.0],[1.380351263767536,1.0,1.0,1.0],[1.4097929970694518,1.0,2.0,0.0],[2.056603341436456,2.0,1.0,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788034298679805,3.0,3.0],[2.957635528153956,3.0,2.0],[1.5861028879150705,2.0,1.0],[2.0056336138121416,2.0,1.0],[1.2564777355745718,1.0,1.0],[1.5205560596151426,2.0,1.0],[1.5917553684096477,2.0,1.0],[2.249961310958569,2.0,2.0],[1.3597789139791148,1.0,1.0],[1.5932683505626841,2.0,1.0],[1.2405538429140872,1.0,1.0],[1.7589506292891461,2.0,2.0],[1.2601250945573694,1.0,1.0],[2.213104293296458,2.0,3.0],[1.7499312894673347,2.0,2.0],[1.626146738371792,2.0,2.0],[2.5648757652944782,3.0,4.0],[1.2222403144509635,1.0,1.0],[2.340034621240021,2.0,1.0],[1.3800814759210658,1.0,2.0],[3.405819243751386,3.0,5.0],[1.2306942856056102,1.0,1.0],[6.092554487682092,6.0,6.0],[1.8446013567893376,2.0,3.0],[2.1985631490814677,2.0,5.0],[1.7760760320723956,2.0,1.0],[2.388337589529589,2.0,1.0],[1.688429004673409,2.0,1.0],[1.79575166736414,2.0,1.0],[1.5596867883414665,2.0,1.0],[1.898359413366776,2.0,2.0],[1.7628743668326141,2.0,3.0],[2.0392015235397714,2.0,1.0],[1.4858398508697725,1.0,2.0],[1.466372427708484,1.0,2.0],[1.2161773127043434,1.0,1.0],[1.2800113429863216,1.0,1.0],[2.7346122077048234,3.0,1.0],[1.4350470243741476,1.0,1.0],[2.406193473126997,2.0,1.0],[2.1592402179203094,2.0,2.0],[1.5572615834361874,2.0,2.0],[2.1568624387514053,2.0,1.0],[2.377343636157484,2.0,4.0],[2.172021308123701,2.0,3.0],[2.002248112980146,2.0,3.0],[2.075972859979123,2.0,3.0],[1.7871292125620277,2.0,2.0],[1.2138068647826485,1.0,1.0],[1.4597136659210554,1.0,1.0],[1.2938044860524516,1.0,2.0],[1.9667852715982115,2.0,3.0],[2.0693499210808453,2.0,2.0],[2.084913172158851,2.0,2.0],[1.9885232371364863,2.0,1.0],[2.5158027511859347,3.0,1.0],[3.25346586710918,3.0,4.0],[1.323302340595087,1.0,1.0],[1.8148328959212607,2.0,1.0],[1.3258274869523794,1.0,1.0],[4.646414783514295,5.0,6.0],[1.5005756650835829,2.0,2.0],[1.6374685204268296,2.0,1.0],[1.3718076156534318,1.0,2.0],[1.3948799011545017,1.0,1.0],[2.210885732628919,2.0,3.0],[1.1975294277481439,1.0,1.0],[3.6289667025797283,4.0,3.0],[2.067823075265578,2.0,3.0],[1.2345239604671987,1.0,1.0],[1.4586878176628129,1.0,1.0],[2.7509661137159926,3.0,2.0],[1.3271039749144231,1.0,1.0],[3.1175130229369628,3.0,5.0],[2.9453126717683262,3.0,7.0],[1.4506327104677696,1.0,1.0],[1.822623698367211,2.0,2.0],[2.5281661726905904,3.0,1.0],[1.7995337761270234,2.0,3.0],[1.3779338168551405,1.0,1.0],[2.0730528925767318,2.0,2.0],[1.7675006194719898,2.0,1.0],[1.5753065253153198,2.0,1.0],[1.9670099242242893,2.0,2.0],[2.606682342991494,3.0,4.0],[5.632112498540383,6.0,6.0],[1.4339934348158427,1.0,1.0],[2.30998893973483,2.0,3.0],[1.9793546471807704,2.0,3.0],[1.7634966730872765,2.0,2.0],[1.3874921006540715,1.0,1.0],[1.5691108870209916,2.0,1.0],[2.8498679621712655,3.0,3.0],[2.2922232994609546,2.0,2.0],[1.6895320462691559,2.0,1.0],[2.2639525790432113,2.0,4.0],[1.67073849477188,2.0,2.0],[3.1151461661970776,3.0,2.0],[1.9309318130776714,2.0,3.0],[1.2379688058776903,1.0,1.0],[1.7039928039490961,2.0,6.0],[1.4632116856339492,1.0,4.0],[2.097638353617313,2.0,3.0],[2.7492417525012494,3.0,2.0],[1.6682403227875808,2.0,1.0],[1.434862552860489,1.0,2.0],[1.2658535565981246,1.0,1.0],[1.4381984692962329,1.0,1.0],[10.44825516801075,10.0,10.0],[11.115231582161314,11.0,13.0],[1.879738002163202,2.0,1.0],[2.3421932937309755,2.0,5.0],[2.7140756355012314,3.0,1.0],[1.2168419793974936,1.0,1.0],[1.445600969846423,1.0,1.0],[2.123330974455804,2.0,2.0],[1.3001570551025639,1.0,1.0],[1.2281263860096887,1.0,1.0],[1.455135381925932,1.0,1.0],[1.4121027912343453,1.0,1.0],[1.3104534378910646,1.0,1.0],[1.2677808788433582,1.0,1.0],[1.2666796969166698,1.0,1.0],[1.738424476612974,2.0,2.0],[1.3115629557649287,1.0,1.0],[1.6472946553621957,2.0,1.0],[2.63907119177357,3.0,1.0],[3.09635726080061,3.0,3.0],[2.279422813748418,2.0,3.0],[3.3730670642886973,3.0,1.0],[1.5319468388209074,2.0,1.0],[2.5085441693758,3.0,2.0],[1.4725200765928907,1.0,2.0],[1.2965785494587392,1.0,1.0],[1.7716439108664983,2.0,2.0],[2.47090040727383,2.0,1.0],[1.2393677020091998,1.0,2.0],[2.0232030813198625,2.0,4.0],[1.8391225048036834,2.0,1.0],[1.9924330771425778,2.0,2.0],[2.247486650968658,2.0,3.0],[5.608948029313416,6.0,5.0],[1.722839551382668,2.0,1.0]],"arguments":{},"addedWidgets":{},"removedWidgets":[],"schema":[{"name":"raw_prediction","type":"\"double\"","metadata":"{}"},{"name":"prediction","type":"\"double\"","metadata":"{}"},{"name":"count","type":"\"long\"","metadata":"{}"}],"overflow":true,"aggData":[],"aggSchema":[],"aggOverflow":false,"aggSeriesLimitReached":false,"aggError":"","aggType":"","plotOptions":null,"isJsonSchema":true,"dbfsResultPath":null},"errorSummary":"<div class=\"ansiout\"><console>:44: error: not found: value equal\n display(holdout.drop(equal))\n ^\n</div>","error":null,"workflows":[],"startTime":1.487169289752E12,"submitTime":1.487169289741E12,"finishTime":1.48716928998E12,"collapsed":false,"bindings":{},"inputWidgets":{},"displayType":"scatterPlot","width":"723.4","height":"704.4","xColumns":null,"yColumns":null,"pivotColumns":null,"pivotAggregation":null,"customPlotOptions":{},"commentThread":[],"commentsVisible":false,"parentHierarchy":[],"diffInserts":[],"diffDeletes":[],"globalVars":{},"latestUser":"a user","commandTitle":"","showCommandTitle":false,"hideCommandCode":false,"hideCommandResult":false,"iPythonMetadata":null,"streamStates":{},"nuid":"45136a89-00f9-4fe0-b5c3-d8f96b73d2f3"},{"version":"CommandV1","origId":3707028759293327,"guid":"8e1a8313-dab8-4370-b4a8-8323e1d4c152","subtype":"command","commandType":"auto","position":26.0,"command":"import org.apache.spark.mllib.evaluation.RegressionMetrics\n\nval rm = new RegressionMetrics(\n holdout.select(\"prediction\", \"count\").rdd.map(x =>\n (x(0).asInstanceOf[Double], x(1).asInstanceOf[Double])))\n\nprintln(\"MSE: \" + rm.meanSquaredError)\nprintln(\"MAE: \" + rm.meanAbsoluteError)\nprintln(\"RMSE Squared: \" + rm.rootMeanSquaredError)\nprintln(\"R Squared: \" + rm.r2)\nprintln(\"Explained Variance: \" + rm.explainedVariance + \"\\n\")","commandVersion":0,"state":"error","results":{"type":"html","data":"<div class=\"ansiout\"></div>","arguments":{},"addedWidgets":{},"removedWidgets":[]},"errorSummary":"org.apache.spark.SparkException: Job aborted due to stage failure: Task 5 in stage 58.0 failed 1 times, most recent failure: Lost task 5.0 in stage 58.0 (TID 1791, localhost, executor driver): java.lang.ClassCastException: java.lang.Long cannot be cast to java.lang.Double","error":"<div class=\"ansiout\">\tat scala.runtime.BoxesRunTime.unboxToDouble(BoxesRunTime.java:119)\n\tat line2f8598f5590346a596d2f8c7e3b2f68c40.$read$$iwC$$iwC$$iwC$$iwC$$anonfun$1.apply(<console>:46)\n\tat line2f8598f5590346a596d2f8c7e3b2f68c40.$read$$iwC$$iwC$$iwC$$iwC$$anonfun$1.apply(<console>:45)\n\tat scala.collection.Iterator$$anon$11.next(Iterator.scala:328)\n\tat scala.collection.Iterator$$anon$11.next(Iterator.scala:328)\n\tat scala.collection.Iterator$class.foreach(Iterator.scala:727)\n\tat scala.collection.AbstractIterator.foreach(Iterator.scala:1157)\n\tat scala.collection.TraversableOnce$class.foldLeft(TraversableOnce.scala:144)\n\tat scala.collection.AbstractIterator.foldLeft(Iterator.scala:1157)\n\tat scala.collection.TraversableOnce$class.aggregate(TraversableOnce.scala:201)\n\tat scala.collection.AbstractIterator.aggregate(Iterator.scala:1157)\n\tat org.apache.spark.rdd.RDD$$anonfun$aggregate$1$$anonfun$22.apply(RDD.scala:1112)\n\tat org.apache.spark.rdd.RDD$$anonfun$aggregate$1$$anonfun$22.apply(RDD.scala:1112)\n\tat org.apache.spark.SparkContext$$anonfun$33.apply(SparkContext.scala:1980)\n\tat org.apache.spark.SparkContext$$anonfun$33.apply(SparkContext.scala:1980)\n\tat org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:87)\n\tat org.apache.spark.scheduler.Task.run(Task.scala:99)\n\tat org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:322)\n\tat java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142)\n\tat java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)\n\tat java.lang.Thread.run(Thread.java:745)\n\nDriver stacktrace:\n\tat org.apache.spark.scheduler.DAGScheduler.org$apache$spark$scheduler$DAGScheduler$$failJobAndIndependentStages(DAGScheduler.scala:1435)\n\tat org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1423)\n\tat org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1422)\n\tat scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)\n\tat scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:47)\n\tat org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:1422)\n\tat org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:802)\n\tat org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:802)\n\tat scala.Option.foreach(Option.scala:236)\n\tat org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:802)\n\tat org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.doOnReceive(DAGScheduler.scala:1650)\n\tat org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1605)\n\tat org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1594)\n\tat org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:48)\n\tat org.apache.spark.scheduler.DAGScheduler.runJob(DAGScheduler.scala:628)\n\tat org.apache.spark.SparkContext.runJob(SparkContext.scala:1918)\n\tat org.apache.spark.SparkContext.runJob(SparkContext.scala:1981)\n\tat org.apache.spark.rdd.RDD$$anonfun$aggregate$1.apply(RDD.scala:1114)\n\tat org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)\n\tat org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:112)\n\tat org.apache.spark.rdd.RDD.withScope(RDD.scala:362)\n\tat org.apache.spark.rdd.RDD.aggregate(RDD.scala:1107)\n\tat org.apache.spark.mllib.evaluation.RegressionMetrics.summary$lzycompute(RegressionMetrics.scala:57)\n\tat org.apache.spark.mllib.evaluation.RegressionMetrics.summary(RegressionMetrics.scala:54)\n\tat org.apache.spark.mllib.evaluation.RegressionMetrics.SSerr$lzycompute(RegressionMetrics.scala:65)\n\tat org.apache.spark.mllib.evaluation.RegressionMetrics.SSerr(RegressionMetrics.scala:65)\n\tat org.apache.spark.mllib.evaluation.RegressionMetrics.meanSquaredError(RegressionMetrics.scala:100)\nCaused by: java.lang.ClassCastException: java.lang.Long cannot be cast to java.lang.Double\n\tat scala.runtime.BoxesRunTime.unboxToDouble(BoxesRunTime.java:119)\n\tat Notebook$$anonfun$1.apply(<console>:46)\n\tat Notebook$$anonfun$1.apply(<console>:45)\n\tat scala.collection.Iterator$$anon$11.next(Iterator.scala:328)\n\tat scala.collection.Iterator$$anon$11.next(Iterator.scala:328)\n\tat scala.collection.Iterator$class.foreach(Iterator.scala:727)\n\tat scala.collection.AbstractIterator.foreach(Iterator.scala:1157)\n\tat scala.collection.TraversableOnce$class.foldLeft(TraversableOnce.scala:144)\n\tat scala.collection.AbstractIterator.foldLeft(Iterator.scala:1157)\n\tat scala.collection.TraversableOnce$class.aggregate(TraversableOnce.scala:201)\n\tat scala.collection.AbstractIterator.aggregate(Iterator.scala:1157)\n\tat org.apache.spark.rdd.RDD$$anonfun$aggregate$1$$anonfun$22.apply(RDD.scala:1112)\n\tat org.apache.spark.rdd.RDD$$anonfun$aggregate$1$$anonfun$22.apply(RDD.scala:1112)\n\tat org.apache.spark.SparkContext$$anonfun$33.apply(SparkContext.scala:1980)\n\tat org.apache.spark.SparkContext$$anonfun$33.apply(SparkContext.scala:1980)\n\tat org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:87)\n\tat org.apache.spark.scheduler.Task.run(Task.scala:99)\n\tat org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:322)\n\tat java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142)\n\tat java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)\n\tat java.lang.Thread.run(Thread.java:745)</div>","workflows":[],"startTime":1.487169392466E12,"submitTime":1.487169392466E12,"finishTime":1.487169393295E12,"collapsed":false,"bindings":{},"inputWidgets":{},"displayType":"table","width":"auto","height":"auto","xColumns":null,"yColumns":null,"pivotColumns":null,"pivotAggregation":null,"customPlotOptions":{},"commentThread":[],"commentsVisible":false,"parentHierarchy":[],"diffInserts":[],"diffDeletes":[],"globalVars":{},"latestUser":"a user","commandTitle":"","showCommandTitle":false,"hideCommandCode":false,"hideCommandResult":false,"iPythonMetadata":null,"streamStates":{},"nuid":"ef4b5834-8121-4c97-aa66-48b3b4807645"},{"version":"CommandV1","origId":3707028759293328,"guid":"09e2e8e3-8ff7-4d17-a0b4-842d0d296046","subtype":"command","commandType":"auto","position":27.0,"command":"%md\n## 7. Optimization","commandVersion":0,"state":"error","results":null,"errorSummary":null,"error":null,"workflows":[],"startTime":0.0,"submitTime":0.0,"finishTime":0.0,"collapsed":false,"bindings":{},"inputWidgets":{},"displayType":"table","width":"auto","height":"auto","xColumns":null,"yColumns":null,"pivotColumns":null,"pivotAggregation":null,"customPlotOptions":{},"commentThread":[],"commentsVisible":false,"parentHierarchy":[],"diffInserts":[],"diffDeletes":[],"globalVars":{},"latestUser":"a user","commandTitle":"","showCommandTitle":false,"hideCommandCode":false,"hideCommandResult":false,"iPythonMetadata":null,"streamStates":{},"nuid":"39770d45-eb73-4346-8cd7-f4850647cd8d"},{"version":"CommandV1","origId":3707028759293329,"guid":"fc0ab9f6-40ce-4eff-86ed-eba7f6e90deb","subtype":"command","commandType":"auto","position":28.0,"command":"import org.apache.spark.ml.regression.RandomForestRegressor\nimport org.apache.spark.ml.tuning.{ParamGridBuilder, CrossValidator}\nimport org.apache.spark.ml.evaluation.RegressionEvaluator\nimport org.apache.spark.ml.{Pipeline, PipelineStage}\n\nval rfModel = new RandomForestRegressor()\n .setLabelCol(\"count\")\n .setFeaturesCol(\"features\")\n .setFeatureSubsetStrategy(\"3\")\n\nval paramGrid = new ParamGridBuilder()\n .addGrid(rfModel.maxDepth, Array(7, 10))\n .addGrid(rfModel.numTrees, Array(40, 80))\n .build()\n\nval steps:Array[PipelineStage] = Array(rfModel)\n\nval pipeline = new Pipeline().setStages(steps)\n\nval cv = new CrossValidator()\n .setEstimator(pipeline)\n .setEstimatorParamMaps(paramGrid)\n .setEvaluator(new RegressionEvaluator().setLabelCol(\"count\"))\n\nval pipelineFitted = cv.fit(training)","commandVersion":0,"state":"finished","results":{"type":"html","data":"<div class=\"ansiout\">import org.apache.spark.ml.regression.RandomForestRegressor\nimport org.apache.spark.ml.tuning.{ParamGridBuilder, CrossValidator}\nimport org.apache.spark.ml.evaluation.RegressionEvaluator\nimport org.apache.spark.ml.{Pipeline, PipelineStage}\nrfModel: org.apache.spark.ml.regression.RandomForestRegressor = rfr_34c791b8a98c\nparamGrid: Array[org.apache.spark.ml.param.ParamMap] = \nArray({\n\trfr_34c791b8a98c-maxDepth: 7,\n\trfr_34c791b8a98c-numTrees: 40\n}, {\n\trfr_34c791b8a98c-maxDepth: 7,\n\trfr_34c791b8a98c-numTrees: 80\n}, {\n\trfr_34c791b8a98c-maxDepth: 10,\n\trfr_34c791b8a98c-numTrees: 40\n}, {\n\trfr_34c791b8a98c-maxDepth: 10,\n\trfr_34c791b8a98c-numTrees: 80\n})\nsteps: Array[org.apache.spark.ml.PipelineStage] = Array(rfr_34c791b8a98c)\npipeline: org.apache.spark.ml.Pipeline = pipeline_d5988b81ab1d\ncv: org.apache.spark.ml.tuning.CrossValidator = cv_d37702fff62e\npipelineFitted: org.apache.spark.ml.tuning.CrossValidatorModel = cv_d37702fff62e\n</div>","arguments":{},"addedWidgets":{},"removedWidgets":[]},"errorSummary":null,"error":null,"workflows":[],"startTime":1.487177674913E12,"submitTime":1.4871776749E12,"finishTime":1.487177745472E12,"collapsed":false,"bindings":{},"inputWidgets":{},"displayType":"table","width":"auto","height":"auto","xColumns":null,"yColumns":null,"pivotColumns":null,"pivotAggregation":null,"customPlotOptions":{},"commentThread":[],"commentsVisible":false,"parentHierarchy":[],"diffInserts":[],"diffDeletes":[],"globalVars":{},"latestUser":"a user","commandTitle":"","showCommandTitle":false,"hideCommandCode":false,"hideCommandResult":false,"iPythonMetadata":null,"streamStates":{},"nuid":"ba4ad64d-7dc5-4e0c-915c-0fc25c2d6d10"},{"version":"CommandV1","origId":3707028759293330,"guid":"e179199a-e065-4855-8f2e-43d32961e895","subtype":"command","commandType":"auto","position":29.0,"command":"println(\"The Best Parameters:\\n--------------------\")\nprintln(pipelineFitted.bestModel.asInstanceOf[org.apache.spark.ml.PipelineModel].stages(0))\npipelineFitted\n .bestModel.asInstanceOf[org.apache.spark.ml.PipelineModel]\n .stages(0)\n .extractParamMap","commandVersion":0,"state":"finished","results":{"type":"html","data":"<div class=\"ansiout\">The Best Parameters:\n--------------------\nRandomForestRegressionModel (uid=rfr_9364a08c57f7) with 40 trees\nres50: org.apache.spark.ml.param.ParamMap = \n{\n\trfr_9364a08c57f7-cacheNodeIds: false,\n\trfr_9364a08c57f7-checkpointInterval: 10,\n\trfr_9364a08c57f7-featureSubsetStrategy: 3,\n\trfr_9364a08c57f7-featuresCol: features,\n\trfr_9364a08c57f7-impurity: variance,\n\trfr_9364a08c57f7-labelCol: count,\n\trfr_9364a08c57f7-maxBins: 32,\n\trfr_9364a08c57f7-maxDepth: 7,\n\trfr_9364a08c57f7-maxMemoryInMB: 256,\n\trfr_9364a08c57f7-minInfoGain: 0.0,\n\trfr_9364a08c57f7-minInstancesPerNode: 1,\n\trfr_9364a08c57f7-numTrees: 40,\n\trfr_9364a08c57f7-predictionCol: prediction,\n\trfr_9364a08c57f7-seed: 235498149,\n\trfr_9364a08c57f7-subsamplingRate: 1.0\n}\n</div>","arguments":{},"addedWidgets":{},"removedWidgets":[]},"errorSummary":null,"error":null,"workflows":[],"startTime":1.487177753396E12,"submitTime":1.487177753383E12,"finishTime":1.487177753477E12,"collapsed":false,"bindings":{},"inputWidgets":{},"displayType":"table","width":"auto","height":"auto","xColumns":null,"yColumns":null,"pivotColumns":null,"pivotAggregation":null,"customPlotOptions":{},"commentThread":[],"commentsVisible":false,"parentHierarchy":[],"diffInserts":[],"diffDeletes":[],"globalVars":{},"latestUser":"a user","commandTitle":"","showCommandTitle":false,"hideCommandCode":false,"hideCommandResult":false,"iPythonMetadata":null,"streamStates":{},"nuid":"079fbd5c-c94d-4da0-8141-dee5dc386ce3"},{"version":"CommandV1","origId":3707028759293331,"guid":"35b8c855-7016-4582-a1f0-682d9a80695a","subtype":"command","commandType":"auto","position":30.0,"command":"println(pipelineFitted.avgMetrics)\npipelineFitted.getEstimatorParamMaps.foreach(println)\npipelineFitted.avgMetrics.deep.mkString(\", \")","commandVersion":0,"state":"finished","results":{"type":"html","data":"<div class=\"ansiout\">[D@379ec158\n{\n\trfr_34c791b8a98c-maxDepth: 7,\n\trfr_34c791b8a98c-numTrees: 40\n}\n{\n\trfr_34c791b8a98c-maxDepth: 7,\n\trfr_34c791b8a98c-numTrees: 80\n}\n{\n\trfr_34c791b8a98c-maxDepth: 10,\n\trfr_34c791b8a98c-numTrees: 40\n}\n{\n\trfr_34c791b8a98c-maxDepth: 10,\n\trfr_34c791b8a98c-numTrees: 80\n}\nres51: String = 1.1682121853867309, 1.1774837085557652, 1.172469387505176, 1.1825235873361428\n</div>","arguments":{},"addedWidgets":{},"removedWidgets":[]},"errorSummary":null,"error":null,"workflows":[],"startTime":1.48717775844E12,"submitTime":1.487177758428E12,"finishTime":1.4871777586E12,"collapsed":false,"bindings":{},"inputWidgets":{},"displayType":"table","width":"auto","height":"auto","xColumns":null,"yColumns":null,"pivotColumns":null,"pivotAggregation":null,"customPlotOptions":{},"commentThread":[],"commentsVisible":false,"parentHierarchy":[],"diffInserts":[],"diffDeletes":[],"globalVars":{},"latestUser":"a user","commandTitle":"","showCommandTitle":false,"hideCommandCode":false,"hideCommandResult":false,"iPythonMetadata":null,"streamStates":{},"nuid":"56afe328-a2d9-47dd-90ce-fc2000fe4f03"},{"version":"CommandV1","origId":3707028759293332,"guid":"eafa19d8-ea83-41a8-9f86-6db0e6a910c4","subtype":"command","commandType":"auto","position":31.0,"command":"val holdout2 = pipelineFitted.bestModel\n .transform(test)\n .selectExpr(\"prediction as raw_prediction\", \n \"double(round(prediction)) as prediction\", \n \"double(count) as count\", \n \"\"\"CASE double(round(prediction)) = count \n WHEN true then 1\n ELSE 0\nEND as equal\"\"\")\ndisplay(holdout2)","commandVersion":0,"state":"finished","results":{"type":"table","data":[[1.365053287175341,1.0,1.0,1.0],[1.4302537283147074,1.0,2.0,0.0],[1.7417823422734031,2.0,2.0,1.0],[1.946327901473201,2.0,1.0,0.0],[2.02912907053866,2.0,1.0,0.0],[5.755180005293068,6.0,12.0,0.0],[3.8688234163603865,4.0,4.0,1.0],[2.5957210343735464,3.0,3.0,1.0],[1.6877240743563973,2.0,3.0,0.0],[2.2291833337728826,2.0,2.0,1.0],[1.174400044107004,1.0,4.0,0.0],[1.0971246707793871,1.0,1.0,1.0],[1.6876980307334128,2.0,3.0,0.0],[2.002201754110026,2.0,1.0,0.0],[1.2695555464410453,1.0,1.0,1.0],[2.0325656846122473,2.0,4.0,0.0],[5.522347333970066,6.0,11.0,0.0],[5.062336385609578,5.0,2.0,0.0],[1.9919676387516905,2.0,1.0,0.0],[1.8213220305333333,2.0,1.0,0.0],[1.7469548260182648,2.0,2.0,1.0],[1.0649437306861271,1.0,1.0,1.0],[2.336219956891724,2.0,2.0,1.0],[1.6905438353356241,2.0,1.0,0.0],[1.2327083760353523,1.0,1.0,1.0],[1.3042991068017924,1.0,1.0,1.0],[1.3110770066006412,1.0,2.0,0.0],[1.9612187199182647,2.0,2.0,1.0],[1.7596255062499782,2.0,2.0,1.0],[1.3187368501375722,1.0,1.0,1.0],[1.656985153334686,2.0,1.0,0.0],[1.5354070555631019,2.0,1.0,0.0],[1.7593179694160277,2.0,2.0,1.0],[1.1293323189545685,1.0,1.0,1.0],[1.7147427229814913,2.0,1.0,0.0],[1.2196154005189035,1.0,2.0,0.0],[1.2777944113055362,1.0,2.0,0.0],[3.511484335312152,4.0,2.0,0.0],[1.308382535600988,1.0,1.0,1.0],[1.04060541299772,1.0,1.0,1.0],[1.1532382815553062,1.0,1.0,1.0],[1.3935359197889268,1.0,1.0,1.0],[1.2984320678386678,1.0,3.0,0.0],[1.04060541299772,1.0,2.0,0.0],[3.1174396229021903,3.0,2.0,0.0],[1.8957133826463537,2.0,1.0,0.0],[2.493269137978363,2.0,3.0,0.0],[1.6731985338006736,2.0,2.0,1.0],[1.2639059234863796,1.0,1.0,1.0],[1.8082363732175697,2.0,2.0,1.0],[2.0918875781347097,2.0,2.0,1.0],[1.8029926955755187,2.0,1.0,0.0],[3.2567951489482416,3.0,2.0,0.0],[1.302245835359624,1.0,1.0,1.0],[1.7921917025689793,2.0,1.0,0.0],[1.831254765722159,2.0,1.0,0.0],[1.7739835436152585,2.0,2.0,1.0],[1.6466749296124181,2.0,2.0,1.0],[1.5074315332514996,2.0,1.0,0.0],[1.146334210690654,1.0,1.0,1.0],[1.1649224805345209,1.0,1.0,1.0],[2.589186087695038,3.0,1.0,0.0],[1.4967139047738773,1.0,2.0,0.0],[1.384260175999116,1.0,1.0,1.0],[1.3564617813551851,1.0,1.0,1.0],[1.3014724609671267,1.0,1.0,1.0],[1.181786531418656,1.0,1.0,1.0],[1.9056734205608628,2.0,2.0,1.0],[1.49263625616551,1.0,2.0,0.0],[1.3193350170134126,1.0,1.0,1.0],[1.4778751091513942,1.0,2.0,0.0],[2.117916699185641,2.0,3.0,0.0],[2.404272181675087,2.0,3.0,0.0],[2.561195250258284,3.0,3.0,1.0],[1.5545418020870083,2.0,2.0,1.0],[1.7419933518564317,2.0,1.0,0.0],[3.2387191281529035,3.0,2.0,0.0],[1.2989969526304992,1.0,1.0,1.0],[1.7638726941916822,2.0,1.0,0.0],[1.7659597590512395,2.0,1.0,0.0],[2.26255196701548,2.0,3.0,0.0],[1.6139370551679826,2.0,2.0,1.0],[4.0610133149820005,4.0,3.0,0.0],[4.825174923806889,5.0,8.0,0.0],[1.8277683159871683,2.0,3.0,0.0],[2.953986435856117,3.0,3.0,1.0],[2.4124799831997983,2.0,3.0,0.0],[1.3579587264961537,1.0,3.0,0.0],[3.5378057080099774,4.0,5.0,0.0],[1.795843203592509,2.0,2.0,1.0],[2.184723984339236,2.0,1.0,0.0],[1.7464154315073348,2.0,2.0,1.0],[1.8508034049784257,2.0,4.0,0.0],[1.6797197908847832,2.0,2.0,1.0],[1.04060541299772,1.0,1.0,1.0],[1.4475504263402281,1.0,4.0,0.0],[1.3498428438192889,1.0,2.0,0.0],[1.7660405494255678,2.0,1.0,0.0],[1.1490716319320704,1.0,1.0,1.0],[2.004035812936611,2.0,1.0,0.0],[1.0292211105122067,1.0,1.0,1.0],[1.1134740062910775,1.0,1.0,1.0],[1.3192872056266771,1.0,2.0,0.0],[1.2996078985191895,1.0,2.0,0.0],[2.911990024125476,3.0,3.0,1.0],[2.0633215272223255,2.0,3.0,0.0],[1.9580817772845218,2.0,1.0,0.0],[1.714374880146622,2.0,1.0,0.0],[1.4718373737777541,1.0,1.0,1.0],[1.4277108121366158,1.0,3.0,0.0],[1.847231117582328,2.0,2.0,1.0],[1.4752246708743484,1.0,1.0,1.0],[1.3108218655333579,1.0,1.0,1.0],[1.3862612560423107,1.0,1.0,1.0],[1.2997545401657862,1.0,1.0,1.0],[1.2912082141590364,1.0,1.0,1.0],[1.2872314374829563,1.0,1.0,1.0],[1.8442957180331099,2.0,3.0,0.0],[1.2751012129560328,1.0,1.0,1.0],[1.1800802267672696,1.0,1.0,1.0],[1.4357938478234273,1.0,2.0,0.0],[1.3030409038215682,1.0,1.0,1.0],[1.3732409541938606,1.0,1.0,1.0],[2.0337155238269586,2.0,1.0,0.0],[1.741941179786228,2.0,2.0,1.0],[1.1036292618402792,1.0,1.0,1.0],[1.7629627795967042,2.0,2.0,1.0],[1.2300234957848795,1.0,2.0,0.0],[1.5025453883887914,2.0,1.0,0.0],[1.1855538928641873,1.0,1.0,1.0],[2.2539948889637964,2.0,2.0,1.0],[1.7219285523589278,2.0,1.0,0.0],[1.8180810560138123,2.0,2.0,1.0],[2.0645122080677583,2.0,2.0,1.0],[1.3235000073291971,1.0,1.0,1.0],[1.1404428437405514,1.0,1.0,1.0],[1.1530174919055924,1.0,1.0,1.0],[1.3115690416635062,1.0,2.0,0.0],[2.408777794863694,2.0,2.0,1.0],[2.841621140883089,3.0,3.0,1.0],[2.2097321313398606,2.0,1.0,0.0],[4.66817860418938,5.0,2.0,0.0],[1.601525568310819,2.0,1.0,0.0],[1.9863535442275786,2.0,11.0,0.0],[3.1449673273579464,3.0,2.0,0.0],[1.731882287398351,2.0,1.0,0.0],[2.195638345292523,2.0,1.0,0.0],[1.6336571179395658,2.0,1.0,0.0],[1.518315211181751,2.0,2.0,1.0],[1.8321207052357942,2.0,1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0,0.0],[1.4718419934166682,1.0,1.0,1.0],[3.9435418675091767,4.0,6.0,0.0],[1.4077308095038865,1.0,2.0,0.0],[1.7385317186056333,2.0,1.0,0.0],[1.3155960237688524,1.0,2.0,0.0],[1.3866221257162339,1.0,1.0,1.0],[1.9143333621892098,2.0,3.0,0.0],[1.04060541299772,1.0,1.0,1.0],[3.997932970193733,4.0,3.0,0.0],[1.9634446753704133,2.0,3.0,0.0],[1.1845235906163747,1.0,1.0,1.0],[1.8043757354059462,2.0,1.0,0.0],[3.6562968886504676,4.0,2.0,0.0],[1.319843958825313,1.0,1.0,1.0],[3.4061568182145443,3.0,5.0,0.0],[3.052844651466392,3.0,7.0,0.0],[1.4270969494176349,1.0,1.0,1.0],[2.049338541187214,2.0,2.0,1.0],[2.441120756129846,2.0,1.0,0.0],[1.887048528555661,2.0,3.0,0.0],[1.2890308724014408,1.0,1.0,1.0],[2.201226268402255,2.0,2.0,1.0],[1.8147736505410659,2.0,1.0,0.0],[1.4739991991947181,1.0,1.0,1.0],[1.55186600961026,2.0,2.0,1.0],[3.8427291118636075,4.0,4.0,1.0],[6.853725338164168,7.0,6.0,0.0],[1.83751514949141,2.0,1.0,0.0],[2.215205233071398,2.0,3.0,0.0],[1.8251758076633802,2.0,3.0,0.0],[2.2945083237232735,2.0,2.0,1.0],[1.3509061949184125,1.0,1.0,1.0],[1.6334657439925209,2.0,1.0,0.0],[2.479342723173197,2.0,3.0,0.0],[1.4650659157466857,1.0,2.0,0.0],[1.521465582956521,2.0,1.0,0.0],[2.079000151822816,2.0,4.0,0.0],[1.7855565488290939,2.0,2.0,1.0],[2.7450798332686555,3.0,2.0,0.0],[1.6004617444062863,2.0,3.0,0.0],[1.1180730917809913,1.0,1.0,1.0],[1.7710189507873715,2.0,6.0,0.0],[1.6126455255349914,2.0,4.0,0.0],[1.814020790130148,2.0,3.0,0.0],[2.2169796769606336,2.0,2.0,1.0],[1.884159834587276,2.0,1.0,0.0],[1.7241922851827414,2.0,2.0,1.0],[1.6931178497311354,2.0,1.0,0.0],[1.5752680649719366,2.0,1.0,0.0],[7.586226435746148,8.0,10.0,0.0],[5.3018360036702425,5.0,13.0,0.0],[2.235076564820816,2.0,1.0,0.0],[2.1239911626363708,2.0,5.0,0.0],[2.507346550641521,3.0,1.0,0.0],[1.0586504228703473,1.0,1.0,1.0],[1.5540462538502593,2.0,1.0,0.0],[2.0770731377598093,2.0,2.0,1.0],[1.2705864393531612,1.0,1.0,1.0],[1.2536441675033683,1.0,1.0,1.0],[1.5984480304979098,2.0,1.0,0.0],[1.374799016228323,1.0,1.0,1.0],[1.3296826721310464,1.0,1.0,1.0],[1.1537109439367428,1.0,1.0,1.0],[1.1592208123577954,1.0,1.0,1.0],[1.7469696241124644,2.0,2.0,1.0],[1.384106019182908,1.0,1.0,1.0],[1.7578681690318656,2.0,1.0,0.0],[2.0228012835015936,2.0,1.0,0.0],[2.9909159654998065,3.0,3.0,1.0],[1.6627397487583238,2.0,3.0,0.0],[2.77830579802258,3.0,1.0,0.0],[1.514361557558799,2.0,1.0,0.0],[1.872840572251779,2.0,2.0,1.0],[1.4189844809797847,1.0,2.0,0.0],[1.3747623545011165,1.0,1.0,1.0],[1.7699039172649058,2.0,2.0,1.0],[1.9757039248331512,2.0,1.0,0.0],[1.1551367169627809,1.0,2.0,0.0],[2.934747749660287,3.0,4.0,0.0],[1.7535764626259311,2.0,1.0,0.0],[1.9351117224900403,2.0,2.0,1.0],[2.201666927416664,2.0,3.0,0.0],[6.428082868066551,6.0,5.0,0.0],[1.725446192450016,2.0,1.0,0.0]],"arguments":{},"addedWidgets":{},"removedWidgets":[],"schema":[{"name":"raw_prediction","type":"\"double\"","metadata":"{}"},{"name":"prediction","type":"\"double\"","metadata":"{}"},{"name":"count","type":"\"double\"","metadata":"{}"},{"name":"equal","type":"\"integer\"","metadata":"{}"}],"overflow":true,"aggData":[],"aggSchema":[],"aggOverflow":false,"aggSeriesLimitReached":false,"aggError":"","aggType":"","plotOptions":null,"isJsonSchema":true,"dbfsResultPath":null},"errorSummary":null,"error":null,"workflows":[],"startTime":1.487177762873E12,"submitTime":1.487177762849E12,"finishTime":1.48717776851E12,"collapsed":false,"bindings":{},"inputWidgets":{},"displayType":"table","width":"auto","height":"auto","xColumns":null,"yColumns":null,"pivotColumns":null,"pivotAggregation":null,"customPlotOptions":{},"commentThread":[],"commentsVisible":false,"parentHierarchy":[],"diffInserts":[],"diffDeletes":[],"globalVars":{},"latestUser":"a user","commandTitle":"","showCommandTitle":false,"hideCommandCode":false,"hideCommandResult":false,"iPythonMetadata":null,"streamStates":{},"nuid":"e8d9b5d3-1d0d-4448-ae17-82befe71ca0e"},{"version":"CommandV1","origId":3707028759293333,"guid":"05208a11-c884-48a5-a321-5d3e145a8e21","subtype":"command","commandType":"auto","position":32.0,"command":"import org.apache.spark.mllib.evaluation.RegressionMetrics\n\nval rm2 = new RegressionMetrics(\n holdout2.select(\"prediction\", \"count\").rdd.map(x =>\n (x(0).asInstanceOf[Double], x(1).asInstanceOf[Double])))\n\nprintln(\"MSE: \" + rm2.meanSquaredError)\nprintln(\"MAE: \" + rm2.meanAbsoluteError)\nprintln(\"RMSE Squared: \" + rm2.rootMeanSquaredError)\nprintln(\"R Squared: \" + rm2.r2)\nprintln(\"Explained Variance: \" + rm2.explainedVariance + \"\\n\")","commandVersion":0,"state":"finished","results":{"type":"html","data":"<div class=\"ansiout\">MSE: 1.5869776482021378\nMAE: 0.7900874635568513\nRMSE Squared: 1.2597530107930435\nR Squared: 0.426710632258417\nExplained Variance: 1.418942976329777\n\nimport org.apache.spark.mllib.evaluation.RegressionMetrics\nrm2: org.apache.spark.mllib.evaluation.RegressionMetrics = org.apache.spark.mllib.evaluation.RegressionMetrics@261ddcfc\n</div>","arguments":{},"addedWidgets":{},"removedWidgets":[]},"errorSummary":"org.apache.spark.SparkException: Job aborted due to stage failure: Task 1 in stage 625.0 failed 1 times, most recent failure: Lost task 1.0 in stage 625.0 (TID 5885, localhost, executor driver): java.lang.ClassCastException: java.lang.Long cannot be cast to java.lang.Double","error":"<div class=\"ansiout\">\tat scala.runtime.BoxesRunTime.unboxToDouble(BoxesRunTime.java:119)\n\tat line2f8598f5590346a596d2f8c7e3b2f68c54.$read$$iwC$$iwC$$iwC$$iwC$$iwC$$anonfun$1.apply(<console>:63)\n\tat line2f8598f5590346a596d2f8c7e3b2f68c54.$read$$iwC$$iwC$$iwC$$iwC$$iwC$$anonfun$1.apply(<console>:62)\n\tat scala.collection.Iterator$$anon$11.next(Iterator.scala:328)\n\tat scala.collection.Iterator$$anon$11.next(Iterator.scala:328)\n\tat scala.collection.Iterator$class.foreach(Iterator.scala:727)\n\tat scala.collection.AbstractIterator.foreach(Iterator.scala:1157)\n\tat scala.collection.TraversableOnce$class.foldLeft(TraversableOnce.scala:144)\n\tat scala.collection.AbstractIterator.foldLeft(Iterator.scala:1157)\n\tat scala.collection.TraversableOnce$class.aggregate(TraversableOnce.scala:201)\n\tat scala.collection.AbstractIterator.aggregate(Iterator.scala:1157)\n\tat org.apache.spark.rdd.RDD$$anonfun$aggregate$1$$anonfun$22.apply(RDD.scala:1112)\n\tat org.apache.spark.rdd.RDD$$anonfun$aggregate$1$$anonfun$22.apply(RDD.scala:1112)\n\tat org.apache.spark.SparkContext$$anonfun$33.apply(SparkContext.scala:1980)\n\tat org.apache.spark.SparkContext$$anonfun$33.apply(SparkContext.scala:1980)\n\tat org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:87)\n\tat org.apache.spark.scheduler.Task.run(Task.scala:99)\n\tat org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:322)\n\tat java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142)\n\tat java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)\n\tat java.lang.Thread.run(Thread.java:745)\n\nDriver stacktrace:\n\tat org.apache.spark.scheduler.DAGScheduler.org$apache$spark$scheduler$DAGScheduler$$failJobAndIndependentStages(DAGScheduler.scala:1435)\n\tat org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1423)\n\tat org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1422)\n\tat scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)\n\tat scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:47)\n\tat org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:1422)\n\tat org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:802)\n\tat org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:802)\n\tat scala.Option.foreach(Option.scala:236)\n\tat org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:802)\n\tat org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.doOnReceive(DAGScheduler.scala:1650)\n\tat org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1605)\n\tat org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1594)\n\tat org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:48)\n\tat org.apache.spark.scheduler.DAGScheduler.runJob(DAGScheduler.scala:628)\n\tat org.apache.spark.SparkContext.runJob(SparkContext.scala:1918)\n\tat org.apache.spark.SparkContext.runJob(SparkContext.scala:1981)\n\tat org.apache.spark.rdd.RDD$$anonfun$aggregate$1.apply(RDD.scala:1114)\n\tat org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)\n\tat org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:112)\n\tat org.apache.spark.rdd.RDD.withScope(RDD.scala:362)\n\tat org.apache.spark.rdd.RDD.aggregate(RDD.scala:1107)\n\tat org.apache.spark.mllib.evaluation.RegressionMetrics.summary$lzycompute(RegressionMetrics.scala:57)\n\tat org.apache.spark.mllib.evaluation.RegressionMetrics.summary(RegressionMetrics.scala:54)\n\tat org.apache.spark.mllib.evaluation.RegressionMetrics.SSerr$lzycompute(RegressionMetrics.scala:65)\n\tat org.apache.spark.mllib.evaluation.RegressionMetrics.SSerr(RegressionMetrics.scala:65)\n\tat org.apache.spark.mllib.evaluation.RegressionMetrics.meanSquaredError(RegressionMetrics.scala:100)\nCaused by: java.lang.ClassCastException: java.lang.Long cannot be cast to java.lang.Double\n\tat scala.runtime.BoxesRunTime.unboxToDouble(BoxesRunTime.java:119)\n\tat Notebook$$anonfun$1.apply(<console>:63)\n\tat Notebook$$anonfun$1.apply(<console>:62)\n\tat scala.collection.Iterator$$anon$11.next(Iterator.scala:328)\n\tat scala.collection.Iterator$$anon$11.next(Iterator.scala:328)\n\tat scala.collection.Iterator$class.foreach(Iterator.scala:727)\n\tat scala.collection.AbstractIterator.foreach(Iterator.scala:1157)\n\tat scala.collection.TraversableOnce$class.foldLeft(TraversableOnce.scala:144)\n\tat scala.collection.AbstractIterator.foldLeft(Iterator.scala:1157)\n\tat scala.collection.TraversableOnce$class.aggregate(TraversableOnce.scala:201)\n\tat scala.collection.AbstractIterator.aggregate(Iterator.scala:1157)\n\tat org.apache.spark.rdd.RDD$$anonfun$aggregate$1$$anonfun$22.apply(RDD.scala:1112)\n\tat org.apache.spark.rdd.RDD$$anonfun$aggregate$1$$anonfun$22.apply(RDD.scala:1112)\n\tat org.apache.spark.SparkContext$$anonfun$33.apply(SparkContext.scala:1980)\n\tat org.apache.spark.SparkContext$$anonfun$33.apply(SparkContext.scala:1980)\n\tat org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:87)\n\tat org.apache.spark.scheduler.Task.run(Task.scala:99)\n\tat org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:322)\n\tat java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142)\n\tat java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)\n\tat java.lang.Thread.run(Thread.java:745)</div>","workflows":[],"startTime":1.487177774258E12,"submitTime":1.487177774245E12,"finishTime":1.487177774438E12,"collapsed":false,"bindings":{},"inputWidgets":{},"displayType":"table","width":"auto","height":"auto","xColumns":null,"yColumns":null,"pivotColumns":null,"pivotAggregation":null,"customPlotOptions":{},"commentThread":[],"commentsVisible":false,"parentHierarchy":[],"diffInserts":[],"diffDeletes":[],"globalVars":{},"latestUser":"a user","commandTitle":"","showCommandTitle":false,"hideCommandCode":false,"hideCommandResult":false,"iPythonMetadata":null,"streamStates":{},"nuid":"dcacf0bd-661c-4d4a-b96d-ea9e0a62d328"},{"version":"CommandV1","origId":3707028759293334,"guid":"dfcb87cf-976e-473e-af15-06f3a9e15106","subtype":"command","commandType":"auto","position":33.0,"command":"display(holdout2.selectExpr(\"sum(equal)/sum(1)\"))","commandVersion":0,"state":"finished","results":{"type":"table","data":[[0.4305150631681244]],"arguments":{},"addedWidgets":{},"removedWidgets":[],"schema":[{"name":"(sum(equal) / sum(1))","type":"\"double\"","metadata":"{}"}],"overflow":false,"aggData":[],"aggSchema":[],"aggOverflow":false,"aggSeriesLimitReached":false,"aggError":"","aggType":"","plotOptions":null,"isJsonSchema":true,"dbfsResultPath":null},"errorSummary":null,"error":null,"workflows":[],"startTime":1.487177784207E12,"submitTime":1.487177784193E12,"finishTime":1.487177784379E12,"collapsed":false,"bindings":{},"inputWidgets":{},"displayType":"table","width":"auto","height":"auto","xColumns":null,"yColumns":null,"pivotColumns":null,"pivotAggregation":null,"customPlotOptions":{},"commentThread":[],"commentsVisible":false,"parentHierarchy":[],"diffInserts":[],"diffDeletes":[],"globalVars":{},"latestUser":"a user","commandTitle":"","showCommandTitle":false,"hideCommandCode":false,"hideCommandResult":false,"iPythonMetadata":null,"streamStates":{},"nuid":"607414b5-b651-4db7-9318-010a1a4ae48b"},{"version":"CommandV1","origId":3707028759293335,"guid":"8383215d-3a81-4fa9-bb5c-18ae7f54b1e2","subtype":"command","commandType":"auto","position":34.0,"command":"%md\n## 8. Conclusion\n\nObtimized Results using crossvalidated randomforest are not good comparing to single linearregression model. We can try to build crossvalidated regression with multiple hiper-parameters.","commandVersion":0,"state":"error","results":null,"errorSummary":"<div class=\"ansiout\"><console>:2: error: ';' expected but double literal found.\n ## 8. Conclusion\n ^\n</div>","error":null,"workflows":[],"startTime":1.487178176501E12,"submitTime":1.487178176501E12,"finishTime":1.487178176538E12,"collapsed":false,"bindings":{},"inputWidgets":{},"displayType":"table","width":"auto","height":"auto","xColumns":null,"yColumns":null,"pivotColumns":null,"pivotAggregation":null,"customPlotOptions":{},"commentThread":[],"commentsVisible":false,"parentHierarchy":[],"diffInserts":[],"diffDeletes":[],"globalVars":{},"latestUser":"a user","commandTitle":"","showCommandTitle":false,"hideCommandCode":false,"hideCommandResult":false,"iPythonMetadata":null,"streamStates":{},"nuid":"1775862f-d998-4f9a-acd9-030ee5df9d00"}],"dashboards":[],"guid":"20bed6bd-bdfa-48ee-a0f7-bce65cff39b3","globalVars":{},"iPythonMetadata":null,"inputWidgets":{}};</script>
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