<!DOCTYPE html> <html> <head> <meta name="databricks-html-version" content="1"> <title>008_DiamondsPipeline_01ETLEDA - Databricks</title> <meta charset="utf-8"> <meta name="google" content="notranslate"> <meta http-equiv="Content-Language" content="en"> <meta http-equiv="Content-Type" content="text/html; charset=UTF8"> <link rel="stylesheet" href="https://fonts.googleapis.com/css?family=Source+Code+Pro:400,700"> <link rel="stylesheet" type="text/css" href="https://databricks-prod-cloudfront.cloud.databricks.com/static/201602081754420800-0c2673ac858e227cad536fdb45d140aeded238db/lib/css/bootstrap.min.css"> <link rel="stylesheet" type="text/css" href="https://databricks-prod-cloudfront.cloud.databricks.com/static/201602081754420800-0c2673ac858e227cad536fdb45d140aeded238db/lib/jquery-ui-bundle/jquery-ui.min.css"> <link rel="stylesheet" type="text/css" href="https://databricks-prod-cloudfront.cloud.databricks.com/static/201602081754420800-0c2673ac858e227cad536fdb45d140aeded238db/css/main.css"> <link rel="stylesheet" href="https://databricks-prod-cloudfront.cloud.databricks.com/static/201602081754420800-0c2673ac858e227cad536fdb45d140aeded238db/css/print.css" media="print"> <link rel="icon" type="image/png" href="https://databricks-prod-cloudfront.cloud.databricks.com/static/201602081754420800-0c2673ac858e227cad536fdb45d140aeded238db/img/favicon.ico"/> <script>window.settings = {"sparkDocsSearchGoogleCx":"004588677886978090460:_rj0wilqwdm","dbcForumURL":"http://forums.databricks.com/","dbfsS3Host":"https://databricks-prod-storage-sydney.s3.amazonaws.com","enableThirdPartyApplicationsUI":false,"enableClusterAcls":false,"notebookRevisionVisibilityHorizon":0,"enableTableHandler":true,"isAdmin":true,"enableLargeResultDownload":false,"nameAndEmail":"Raazesh Sainudiin (r.sainudiin@math.canterbury.ac.nz)","enablePresentationTimerConfig":true,"enableFullTextSearch":true,"enableElasticSparkUI":true,"clusters":true,"hideOffHeapCache":false,"applications":false,"useStaticGuide":false,"fileStoreBase":"FileStore","configurableSparkOptionsSpec":[{"keyPattern":"spark\\.kryo(\\.[^\\.]+)+","valuePattern":".*","keyPatternDisplay":"spark.kryo.*","valuePatternDisplay":"*","description":"Configuration options for Kryo serialization"},{"keyPattern":"spark\\.io\\.compression\\.codec","valuePattern":"(lzf|snappy|org\\.apache\\.spark\\.io\\.LZFCompressionCodec|org\\.apache\\.spark\\.io\\.SnappyCompressionCodec)","keyPatternDisplay":"spark.io.compression.codec","valuePatternDisplay":"snappy|lzf","description":"The codec used to compress internal data such as RDD partitions, broadcast variables and shuffle outputs."},{"keyPattern":"spark\\.serializer","valuePattern":"(org\\.apache\\.spark\\.serializer\\.JavaSerializer|org\\.apache\\.spark\\.serializer\\.KryoSerializer)","keyPatternDisplay":"spark.serializer","valuePatternDisplay":"org.apache.spark.serializer.JavaSerializer|org.apache.spark.serializer.KryoSerializer","description":"Class to use for serializing objects that will be sent over the network or need to be cached in serialized form."},{"keyPattern":"spark\\.rdd\\.compress","valuePattern":"(true|false)","keyPatternDisplay":"spark.rdd.compress","valuePatternDisplay":"true|false","description":"Whether to compress serialized RDD partitions (e.g. for StorageLevel.MEMORY_ONLY_SER). 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Sainudiin](https://nz.linkedin.com/in/raazesh-sainudiin-45955845) and [Sivanand Sivaram](https://www.linkedin.com/in/sivanand)\n\n*supported by* [](https://databricks.com/)\nand \n[ 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url](https://raw.githubusercontent.com/raazesh-sainudiin/scalable-data-science/master/db/week3/05_SparkSQLETLEDA/008_DiamondsPipeline_01ETLEDA.html) of this databricks notebook and its recorded Uji :\n\n[ 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ETL and EDA Part\n\nThis is the Spark SQL parts that are focussed on extract-transform-Load (ETL) and exploratory-data-analysis (EDA) parts of an end-to-end example of a Machine Learning (ML) workflow.\n\nThis is a scala*rific* break-down of the python*ic* [Diamonds ML Pipeline Workflow](/#workspace/databricks_guide/07 Spark MLlib/6 Example ML Workflow) in Databricks Guide.\n\n**We will see this example gain in the sequel**\n\nFor this example, we analyze the Diamonds dataset from the R Datasets hosted on DBC. \n\nLater on, we will use the [DecisionTree algorithm](http://spark.apache.org/docs/latest/ml-decision-tree.html) to predict the price of a diamond from its characteristics.\n\nHere is an outline of our pipeline:\n* **Step 1. *Load data*: Load data as DataFrame**\n* **Step 2. *Understand the data*: Compute statistics and create visualizations to get a better understanding of the data.**\n* Step 3. *Hold out data*: Split the data randomly into training and test sets. We will not look at the test data until *after* learning.\n* Step 4. On the training dataset:\n * *Extract features*: We will index categorical (String-valued) features so that DecisionTree can handle them.\n * *Learn a model*: Run DecisionTree to learn how to predict a diamond's price from a description of the diamond.\n * *Tune the model*: Tune the tree depth (complexity) using the training data. (This process is also called *model selection*.)\n* Step 5. *Evaluate the model*: Now look at the test dataset. Compare the initial model with the tuned model to see the benefit of tuning parameters.\n* Step 6. *Understand the model*: We will examine the learned model and results to gain further insight.\n\nIn this notebook, we will only cover **Step 1** and **Step 2.** above. The other Steps will be revisited in the sequel.\n","commandVersion":0,"state":"finished","results":null,"errorSummary":null,"error":null,"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":"","commandTitle":"","showCommandTitle":false,"hideCommandCode":false,"hideCommandResult":false,"iPythonMetadata":null,"nuid":"61420af2-c844-47a3-86d9-fadc239560b8"},{"version":"CommandV1","origId":50038,"guid":"e20e242d-39d8-4dd3-92ef-c2f853e1e3d0","subtype":"command","commandType":"auto","position":1.5,"command":"%md\n### Step 1. Load data as DataFrame\n\nThis section loads a dataset as a DataFrame and examines a few rows of it to understand the schema.\n\nFor more info, see the DB guide on [Accessing data](#workspace/databricks_guide/03 Accessing Data/0 Accessing Data).","commandVersion":0,"state":"finished","results":null,"errorSummary":null,"error":null,"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":"","commandTitle":"","showCommandTitle":false,"hideCommandCode":false,"hideCommandResult":false,"iPythonMetadata":null,"nuid":"3ff475e0-125b-4a8d-b2df-5d0d1cb59bd1"},{"version":"CommandV1","origId":50039,"guid":"c93cf194-2e7d-4f6e-901d-982e9f526aba","subtype":"command","commandType":"auto","position":1.75,"command":"// We'll use the Diamonds dataset from the R datasets hosted on DBC.\nval diamondsFilePath = \"dbfs:/databricks-datasets/Rdatasets/data-001/csv/ggplot2/diamonds.csv\"","commandVersion":0,"state":"finished","results":{"type":"html","data":"<div class=\"ansiout\">diamondsFilePath: String = dbfs:/databricks-datasets/Rdatasets/data-001/csv/ggplot2/diamonds.csv\n</div>","arguments":{},"addedWidgets":{},"removedWidgets":[]},"errorSummary":null,"error":null,"startTime":1.457582375439E12,"submitTime":1.457582339762E12,"finishTime":1.457582375477E12,"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":"r.sainudiin@math.canterbury.ac.nz","commandTitle":"","showCommandTitle":false,"hideCommandCode":false,"hideCommandResult":false,"iPythonMetadata":null,"nuid":"d7b0d929-de76-4f7e-a6de-323b388d8a89"},{"version":"CommandV1","origId":50043,"guid":"98a3e7ed-5524-4beb-832d-a2c11e4f4274","subtype":"command","commandType":"auto","position":1.8125,"command":"sc.textFile(diamondsFilePath).take(2) // looks like a csv file as it should","commandVersion":0,"state":"finished","results":{"type":"html","data":"<div class=\"ansiout\">res0: Array[String] = Array("","carat","cut","color","clarity","depth","table","price","x","y","z", "1",0.23,"Ideal","E","SI2",61.5,55,326,3.95,3.98,2.43)\n</div>","arguments":{},"addedWidgets":{},"removedWidgets":[]},"errorSummary":null,"error":null,"startTime":1.457582381008E12,"submitTime":1.457582345325E12,"finishTime":1.457582382163E12,"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":"r.sainudiin@math.canterbury.ac.nz","commandTitle":"","showCommandTitle":false,"hideCommandCode":false,"hideCommandResult":false,"iPythonMetadata":null,"nuid":"91d081e8-9342-40a9-a7bf-6ba68c37e379"},{"version":"CommandV1","origId":50042,"guid":"a5ddde68-bd30-4c64-8804-71bc2b78aa5a","subtype":"command","commandType":"auto","position":1.90625,"command":"val diamondsRawDF = sqlContext.read \n .format(\"com.databricks.spark.csv\") // use spark.csv package\n .option(\"header\", \"true\") // Use first line of all files as header\n .option(\"inferSchema\", \"true\") // Automatically infer data types\n //.option(\"delimiter\", \",\") // Specify the delimiter as comma or ',' DEFAULT\n .load(diamondsFilePath)","commandVersion":0,"state":"finished","results":{"type":"html","data":"<div class=\"ansiout\">diamondsRawDF: org.apache.spark.sql.DataFrame = [: int, carat: double, cut: string, color: string, clarity: string, depth: double, table: double, price: int, x: double, y: double, z: double]\n</div>","arguments":{},"addedWidgets":{},"removedWidgets":[]},"errorSummary":null,"error":null,"startTime":1.457582510162E12,"submitTime":1.45758247445E12,"finishTime":1.457582512154E12,"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":"r.sainudiin@math.canterbury.ac.nz","commandTitle":"","showCommandTitle":false,"hideCommandCode":false,"hideCommandResult":false,"iPythonMetadata":null,"nuid":"b4dd2b38-9413-4ded-8f9e-f7d5dd374ecd"},{"version":"CommandV1","origId":50041,"guid":"a989cd20-230b-4502-a19e-c8eaf7c96197","subtype":"command","commandType":"auto","position":1.9375,"command":"//There are 10 columns. We will try to predict the price of diamonds, treating the other 9 columns as features.\ndiamondsRawDF.printSchema()","commandVersion":0,"state":"finished","results":{"type":"html","data":"<div class=\"ansiout\">root\n |-- : integer (nullable = true)\n |-- carat: double (nullable = true)\n |-- cut: string (nullable = true)\n |-- color: string (nullable = true)\n |-- clarity: string (nullable = true)\n |-- depth: double (nullable = true)\n |-- table: double (nullable = true)\n |-- price: integer (nullable = true)\n |-- x: double (nullable = true)\n |-- y: double (nullable = true)\n |-- z: double (nullable = true)\n\n</div>","arguments":{},"addedWidgets":{},"removedWidgets":[]},"errorSummary":null,"error":null,"startTime":1.457582540969E12,"submitTime":1.457582505265E12,"finishTime":1.457582541006E12,"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":"r.sainudiin@math.canterbury.ac.nz","commandTitle":"","showCommandTitle":false,"hideCommandCode":false,"hideCommandResult":false,"iPythonMetadata":null,"nuid":"deaeabe2-7ce8-4b0d-9bb9-e4fa129ef178"},{"version":"CommandV1","origId":50588,"guid":"1ea75adc-9119-4b6b-95dd-59ee4ab8989b","subtype":"command","commandType":"auto","position":1.9453125,"command":"%md\n*Note:* `(nullable = true)` simply means if the value is allowed to be `null`.\n\nLet us count the number of rows in ``diamondsDF``.","commandVersion":0,"state":"finished","results":null,"errorSummary":null,"error":null,"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":"","commandTitle":"","showCommandTitle":false,"hideCommandCode":false,"hideCommandResult":false,"iPythonMetadata":null,"nuid":"fa645c97-f794-4458-865e-8ef5a0154be3"},{"version":"CommandV1","origId":50590,"guid":"0335f3d6-726c-4afe-96f5-11692145e53c","subtype":"command","commandType":"auto","position":1.947265625,"command":"diamondsRawDF.count() // Ctrl+Enter","commandVersion":0,"state":"finished","results":{"type":"html","data":"<div class=\"ansiout\">res2: Long = 53940\n</div>","arguments":{},"addedWidgets":{},"removedWidgets":[]},"errorSummary":null,"error":null,"startTime":1.457582574327E12,"submitTime":1.457582538626E12,"finishTime":1.457582576264E12,"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":"r.sainudiin@math.canterbury.ac.nz","commandTitle":"","showCommandTitle":false,"hideCommandCode":false,"hideCommandResult":false,"iPythonMetadata":null,"nuid":"37e42d82-32d6-47c7-bde2-ed693ac88a89"},{"version":"CommandV1","origId":50589,"guid":"621142b7-0b2b-4451-8d47-7fbc681d812c","subtype":"command","commandType":"auto","position":1.94921875,"command":"%md\nSo there are 53940 records or rows in the DataFrame.\n\nUse the `show(n)` method to see the first `n` (default is 20) rows of the DataFrame, as folows:","commandVersion":0,"state":"finished","results":null,"errorSummary":null,"error":null,"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":"","commandTitle":"","showCommandTitle":false,"hideCommandCode":false,"hideCommandResult":false,"iPythonMetadata":null,"nuid":"970a529a-e53c-4f1b-a391-e88a5ed332da"},{"version":"CommandV1","origId":50591,"guid":"c942fcb4-1c62-4e98-9abe-60ae3489ca66","subtype":"command","commandType":"auto","position":1.951171875,"command":"diamondsRawDF.show()","commandVersion":0,"state":"finished","results":{"type":"html","data":"<div class=\"ansiout\">+---+-----+---------+-----+-------+-----+-----+-----+----+----+----+\n| |carat| cut|color|clarity|depth|table|price| x| y| z|\n+---+-----+---------+-----+-------+-----+-----+-----+----+----+----+\n| 1| 0.23| Ideal| E| SI2| 61.5| 55.0| 326|3.95|3.98|2.43|\n| 2| 0.21| Premium| E| SI1| 59.8| 61.0| 326|3.89|3.84|2.31|\n| 3| 0.23| Good| E| VS1| 56.9| 65.0| 327|4.05|4.07|2.31|\n| 4| 0.29| Premium| I| VS2| 62.4| 58.0| 334| 4.2|4.23|2.63|\n| 5| 0.31| Good| J| SI2| 63.3| 58.0| 335|4.34|4.35|2.75|\n| 6| 0.24|Very Good| J| VVS2| 62.8| 57.0| 336|3.94|3.96|2.48|\n| 7| 0.24|Very Good| I| VVS1| 62.3| 57.0| 336|3.95|3.98|2.47|\n| 8| 0.26|Very Good| H| SI1| 61.9| 55.0| 337|4.07|4.11|2.53|\n| 9| 0.22| Fair| E| VS2| 65.1| 61.0| 337|3.87|3.78|2.49|\n| 10| 0.23|Very Good| H| VS1| 59.4| 61.0| 338| 4.0|4.05|2.39|\n| 11| 0.3| Good| J| SI1| 64.0| 55.0| 339|4.25|4.28|2.73|\n| 12| 0.23| Ideal| J| VS1| 62.8| 56.0| 340|3.93| 3.9|2.46|\n| 13| 0.22| Premium| F| SI1| 60.4| 61.0| 342|3.88|3.84|2.33|\n| 14| 0.31| Ideal| J| SI2| 62.2| 54.0| 344|4.35|4.37|2.71|\n| 15| 0.2| Premium| E| SI2| 60.2| 62.0| 345|3.79|3.75|2.27|\n| 16| 0.32| Premium| E| I1| 60.9| 58.0| 345|4.38|4.42|2.68|\n| 17| 0.3| Ideal| I| SI2| 62.0| 54.0| 348|4.31|4.34|2.68|\n| 18| 0.3| Good| J| SI1| 63.4| 54.0| 351|4.23|4.29| 2.7|\n| 19| 0.3| Good| J| SI1| 63.8| 56.0| 351|4.23|4.26|2.71|\n| 20| 0.3|Very Good| J| SI1| 62.7| 59.0| 351|4.21|4.27|2.66|\n+---+-----+---------+-----+-------+-----+-----+-----+----+----+----+\nonly showing top 20 rows\n\n</div>","arguments":{},"addedWidgets":{},"removedWidgets":[]},"errorSummary":null,"error":null,"startTime":1.457582662737E12,"submitTime":1.457582627015E12,"finishTime":1.457582662962E12,"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":"r.sainudiin@math.canterbury.ac.nz","commandTitle":"","showCommandTitle":false,"hideCommandCode":false,"hideCommandResult":false,"iPythonMetadata":null,"nuid":"834974f1-4fc7-4c9f-b734-6f0d0c301e68"},{"version":"CommandV1","origId":50048,"guid":"6ee64435-fcc4-491c-8636-e8ebe83f9515","subtype":"command","commandType":"auto","position":1.953125,"command":"%md\nIf you notice the schema of `diamondsRawDF` you will see that the automatic schema inference of `SqlContext.read` method has cast the values in the column `price` as `integer`. \n\nTo cleanup:\n* let's recast the column `price` as `double` for downstream ML tasks later and \n* let's also get rid of the first column of row indices. ","commandVersion":0,"state":"finished","results":null,"errorSummary":null,"error":null,"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":"","commandTitle":"","showCommandTitle":false,"hideCommandCode":false,"hideCommandResult":false,"iPythonMetadata":null,"nuid":"8513d9cf-d18a-48d5-9a5a-e0b28a21bb71"},{"version":"CommandV1","origId":50045,"guid":"d95dd5e5-acf7-43d0-a1ef-895162e24eaf","subtype":"command","commandType":"auto","position":1.984375,"command":"import org.apache.spark.sql.types.DoubleType\n//we will convert price column from int to double later\nval diamondsDF = diamondsRawDF.select($\"carat\", $\"cut\", $\"color\", $\"clarity\", $\"depth\", $\"table\",$\"price\".cast(DoubleType).as(\"price\"), $\"x\", $\"y\", $\"z\")\ndiamondsDF.cache() // let's cache it for reuse\ndiamondsDF.printSchema // print schema","commandVersion":0,"state":"finished","results":{"type":"html","data":"<div class=\"ansiout\">root\n |-- carat: double (nullable = true)\n |-- cut: string (nullable = true)\n |-- color: string (nullable = true)\n |-- clarity: string (nullable = true)\n |-- depth: double (nullable = true)\n |-- table: double (nullable = true)\n |-- price: double (nullable = true)\n |-- x: double (nullable = true)\n |-- y: double (nullable = true)\n |-- z: double (nullable = true)\n\nimport org.apache.spark.sql.types.DoubleType\ndiamondsDF: org.apache.spark.sql.DataFrame = [carat: double, cut: string, color: string, clarity: string, depth: double, table: double, price: double, x: double, y: double, z: double]\n</div>","arguments":{},"addedWidgets":{},"removedWidgets":[]},"errorSummary":"<div class=\"ansiout\"><console>:37: error: not found: value DoubleType\n val diamondsDF = diamondsRawDF.select($"carat", $"cut", $"color", $"clarity", $"depth", $"table",$"price".cast(DoubleType).as("price"), $"x", $"y", $"z")\n ^\n</div>","error":null,"startTime":1.457582873031E12,"submitTime":1.457582837324E12,"finishTime":1.457582873102E12,"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":"r.sainudiin@math.canterbury.ac.nz","commandTitle":"","showCommandTitle":false,"hideCommandCode":false,"hideCommandResult":false,"iPythonMetadata":null,"nuid":"16003653-60f2-4080-8f08-b166c8c5197f"},{"version":"CommandV1","origId":50054,"guid":"93f18cd3-0d9b-4eed-8845-ca10e397f5a8","subtype":"command","commandType":"auto","position":1.9931640625,"command":"//View DataFrame\ndisplay(diamondsDF)","commandVersion":0,"state":"finished","results":{"type":"table","data":[[0.23,"Ideal","E","SI2",61.5,55.0,326.0,3.95,3.98,2.43],[0.21,"Premium","E","SI1",59.8,61.0,326.0,3.89,3.84,2.31],[0.23,"Good","E","VS1",56.9,65.0,327.0,4.05,4.07,2.31],[0.29,"Premium","I","VS2",62.4,58.0,334.0,4.2,4.23,2.63],[0.31,"Good","J","SI2",63.3,58.0,335.0,4.34,4.35,2.75],[0.24,"Very Good","J","VVS2",62.8,57.0,336.0,3.94,3.96,2.48],[0.24,"Very Good","I","VVS1",62.3,57.0,336.0,3.95,3.98,2.47],[0.26,"Very Good","H","SI1",61.9,55.0,337.0,4.07,4.11,2.53],[0.22,"Fair","E","VS2",65.1,61.0,337.0,3.87,3.78,2.49],[0.23,"Very 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Good","D","VVS2",60.6,57.0,2897.0,5.48,5.51,3.33],[0.76,"Premium","E","SI1",61.1,58.0,2897.0,5.91,5.85,3.59],[0.54,"Ideal","D","VVS2",61.4,52.0,2897.0,5.3,5.34,3.26],[0.72,"Ideal","E","SI1",62.5,55.0,2897.0,5.69,5.74,3.57],[0.72,"Good","F","VS1",59.4,61.0,2897.0,5.82,5.89,3.48],[0.74,"Premium","D","VS2",61.8,58.0,2897.0,5.81,5.77,3.58],[1.12,"Premium","J","SI2",60.6,59.0,2898.0,6.68,6.61,4.03]],"arguments":{},"addedWidgets":{},"removedWidgets":[],"schema":[{"name":"carat","type":"\"double\""},{"name":"cut","type":"\"string\""},{"name":"color","type":"\"string\""},{"name":"clarity","type":"\"string\""},{"name":"depth","type":"\"double\""},{"name":"table","type":"\"double\""},{"name":"price","type":"\"double\""},{"name":"x","type":"\"double\""},{"name":"y","type":"\"double\""},{"name":"z","type":"\"double\""}],"overflow":true,"aggData":[],"aggSchema":[],"aggOverflow":false,"aggSeriesLimitReached":false,"aggError":"","aggType":"","plotOptions":null,"isJsonSchema":true,"dbfsResultPath":null},"errorSummary":null,"error":null,"startTime":1.457582932569E12,"submitTime":1.457582896808E12,"finishTime":1.457582933669E12,"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":"r.sainudiin@math.canterbury.ac.nz","commandTitle":"","showCommandTitle":false,"hideCommandCode":false,"hideCommandResult":false,"iPythonMetadata":null,"nuid":"ca3cc670-7aea-4d24-8cdc-54655bec9c97"},{"version":"CommandV1","origId":50055,"guid":"7adb49b4-100f-4325-bd1a-1d9087ce53ae","subtype":"command","commandType":"auto","position":1.99658203125,"command":"%md\n### Step 2. Understand the data\n\nLet's examine the data to get a better understanding of what is there. We only examine a couple of features (columns), but it gives an idea of the type of exploration you might do to understand a new dataset.\n\nFor more examples of using Databricks's visualization to explore a dataset, see the [Visualizations Notebook](#workspace/databricks_guide/04 Visualizations/0 Visualizations Overview).","commandVersion":0,"state":"finished","results":null,"errorSummary":null,"error":null,"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":"","commandTitle":"","showCommandTitle":false,"hideCommandCode":false,"hideCommandResult":false,"iPythonMetadata":null,"nuid":"346119d6-303e-4e69-a87e-648cdd99c370"},{"version":"CommandV1","origId":50057,"guid":"f922694b-e815-4192-947b-f52662b74650","subtype":"command","commandType":"auto","position":1.9974365234375,"command":"%md \nWe can see that we have a mix of \n* categorical features (`cut`, `color`, `clarity`) and \n* continuous features (`depth`, `x`, `y`, `z`). \n\n##### Let's first look at the categorical features.","commandVersion":0,"state":"finished","results":null,"errorSummary":null,"error":null,"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":"","commandTitle":"","showCommandTitle":false,"hideCommandCode":false,"hideCommandResult":false,"iPythonMetadata":null,"nuid":"a93b4f80-b478-41f1-971a-40a10bd4875c"},{"version":"CommandV1","origId":50592,"guid":"5c026ca7-e4ea-4684-ac38-dccd1b863650","subtype":"command","commandType":"auto","position":1.99786376953125,"command":"%md\nYou can also select one or more individual columns using so-called DataFrame API. \n\nLet us `select` the column `cut` from `diamondsDF` and create a new DataFrame called `cutsDF` and then display it as follows:","commandVersion":0,"state":"finished","results":null,"errorSummary":null,"error":null,"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":"","commandTitle":"","showCommandTitle":false,"hideCommandCode":false,"hideCommandResult":false,"iPythonMetadata":null,"nuid":"a8f204f9-bdd5-4abe-a4ac-519ca4a3208b"},{"version":"CommandV1","origId":50595,"guid":"03936eb9-34fe-4394-ad6f-74544212a58f","subtype":"command","commandType":"auto","position":1.9979705810546875,"command":"val cutsDF = diamondsDF.select(\"cut\") // Shift+Enter","commandVersion":0,"state":"finished","results":{"type":"html","data":"<div class=\"ansiout\">cutsDF: org.apache.spark.sql.DataFrame = [cut: string]\n</div>","arguments":{},"addedWidgets":{},"removedWidgets":[]},"errorSummary":null,"error":null,"startTime":1.457583037215E12,"submitTime":1.457583001462E12,"finishTime":1.457583037259E12,"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":"r.sainudiin@math.canterbury.ac.nz","commandTitle":"","showCommandTitle":false,"hideCommandCode":false,"hideCommandResult":false,"iPythonMetadata":null,"nuid":"e74562e3-cc00-4c21-9be7-3b26e3599926"},{"version":"CommandV1","origId":50593,"guid":"02f07cfe-7924-43bf-adf1-283ac2b6adaf","subtype":"command","commandType":"auto","position":1.998077392578125,"command":"display(cutsDF) // 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us use `distinct` to find the distinct types of `cut`s in the dataset. ","commandVersion":0,"state":"finished","results":null,"errorSummary":null,"error":null,"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":"","commandTitle":"","showCommandTitle":false,"hideCommandCode":false,"hideCommandResult":false,"iPythonMetadata":null,"nuid":"9be583cd-9de3-43b6-919a-66027c7060bc"},{"version":"CommandV1","origId":50056,"guid":"5f4c7104-128d-44b9-8805-2d84f609cd95","subtype":"command","commandType":"auto","position":1.99871826171875,"command":"// View distinct diamond cuts in dataset\nval cutsDistinctDF = diamondsDF.select(\"cut\").distinct()","commandVersion":0,"state":"finished","results":{"type":"html","data":"<div class=\"ansiout\">cutsDistinctDF: org.apache.spark.sql.DataFrame = [cut: string]\n</div>","arguments":{},"addedWidgets":{},"removedWidgets":[]},"errorSummary":"<div class=\"ansiout\"><console>:42: error: not found: value cuts\n display(cuts)\n ^\n<console>:46: error: not found: value cuts\nval $ires17 = cuts\n ^\n<console>:39: error: not found: value cuts\n cuts = diamondsDF.select("cut") //.distinct() //.collect()\n ^\n</div>","error":null,"startTime":1.457583081622E12,"submitTime":1.457583045459E12,"finishTime":1.457583081667E12,"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":"r.sainudiin@math.canterbury.ac.nz","commandTitle":"","showCommandTitle":false,"hideCommandCode":false,"hideCommandResult":false,"iPythonMetadata":null,"nuid":"216c05b4-835a-45be-8050-0766e10f84c4"},{"version":"CommandV1","origId":50596,"guid":"8e408a24-a880-4825-9d03-9aa64119cf5a","subtype":"command","commandType":"auto","position":1.998931884765625,"command":"cutsDistinctDF.show()","commandVersion":0,"state":"finished","results":{"type":"html","data":"<div class=\"ansiout\">+---------+\n| cut|\n+---------+\n| Ideal|\n| Fair|\n|Very Good|\n| Good|\n| Premium|\n+---------+\n\n</div>","arguments":{},"addedWidgets":{},"removedWidgets":[]},"errorSummary":"<div class=\"ansiout\"><console>:34: error: not found: value cutsDistinctDF\n cutsDistinctDF.show()\n ^\n</div>","error":null,"startTime":1.457583088104E12,"submitTime":1.457583052048E12,"finishTime":1.457583090126E12,"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":"r.sainudiin@math.canterbury.ac.nz","commandTitle":"","showCommandTitle":false,"hideCommandCode":false,"hideCommandResult":false,"iPythonMetadata":null,"nuid":"2fe285a9-56c4-41cf-aa67-eb713b95d1ff"},{"version":"CommandV1","origId":50598,"guid":"3aa97e83-f2f0-4a5f-8495-771ff126fe07","subtype":"command","commandType":"auto","position":1.9990386962890625,"command":"%md \nClearly, there are just 5 kinds of cuts.","commandVersion":0,"state":"finished","results":null,"errorSummary":null,"error":null,"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":"","commandTitle":"","showCommandTitle":false,"hideCommandCode":false,"hideCommandResult":false,"iPythonMetadata":null,"nuid":"2cd79a7e-f5bd-419d-b844-7505f6c68200"},{"version":"CommandV1","origId":50058,"guid":"dc049517-9f8f-4038-81a0-458e154ef657","subtype":"command","commandType":"auto","position":1.9991455078125,"command":"// View distinct diamond colors in dataset\nval colorsDistinctDF = diamondsDF.select(\"color\").distinct() //.collect()\ncolorsDistinctDF.show()","commandVersion":0,"state":"finished","results":{"type":"html","data":"<div class=\"ansiout\">+-----+\n|color|\n+-----+\n| D|\n| E|\n| F|\n| G|\n| H|\n| I|\n| J|\n+-----+\n\ncolorsDistinctDF: org.apache.spark.sql.DataFrame = [color: string]\n</div>","arguments":{},"addedWidgets":{},"removedWidgets":[]},"errorSummary":null,"error":null,"startTime":1.457583127117E12,"submitTime":1.457583091042E12,"finishTime":1.457583127738E12,"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":"r.sainudiin@math.canterbury.ac.nz","commandTitle":"","showCommandTitle":false,"hideCommandCode":false,"hideCommandResult":false,"iPythonMetadata":null,"nuid":"0719691c-5809-4374-9d2c-323c8f992c4e"},{"version":"CommandV1","origId":50059,"guid":"34189e4b-c221-4f85-856b-3003262292b2","subtype":"command","commandType":"auto","position":1.99957275390625,"command":"// View distinct diamond clarities in dataset\nval claritiesDistinctDF = diamondsDF.select(\"clarity\").distinct() // .collect()\nclaritiesDistinctDF.show()","commandVersion":0,"state":"finished","results":{"type":"html","data":"<div class=\"ansiout\">+-------+\n|clarity|\n+-------+\n| VS1|\n| VS2|\n| SI1|\n| SI2|\n| I1|\n| IF|\n| VVS1|\n| VVS2|\n+-------+\n\nclaritiesDistinctDF: org.apache.spark.sql.DataFrame = [clarity: 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For our dataset, the two plots can actually look very different due to the long-tailed distribution.\n\nWe will not examine the label distribution for now. It can be helpful to examine the label distribution, but it is best to do so only on the training set, not on the test set which we will hold out for evaluation. 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that columns of type string are not in the scatter plot!","commandVersion":0,"state":"finished","results":null,"errorSummary":null,"error":null,"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":"","commandTitle":"","showCommandTitle":false,"hideCommandCode":false,"hideCommandResult":false,"iPythonMetadata":null,"nuid":"469d3cc4-4255-4237-b192-1b29be0bbab6"},{"version":"CommandV1","origId":50582,"guid":"a2fe2647-4a37-4b63-a7e8-df958a79a780","subtype":"command","commandType":"auto","position":1.9999933242797852,"command":"diamondsDF.printSchema // Ctrl+Enter","commandVersion":0,"state":"finished","results":{"type":"html","data":"<div class=\"ansiout\">root\n |-- carat: double (nullable = true)\n |-- cut: string (nullable = true)\n |-- color: string (nullable = true)\n |-- clarity: string (nullable = true)\n |-- depth: double (nullable = true)\n |-- table: double (nullable = true)\n |-- price: integer (nullable = true)\n |-- x: double (nullable = true)\n |-- y: double (nullable = true)\n |-- z: double (nullable = true)\n\n</div>","arguments":{},"addedWidgets":{},"removedWidgets":[]},"errorSummary":null,"error":null,"startTime":1.457572225486E12,"submitTime":1.457572152791E12,"finishTime":1.457572225534E12,"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":"r.sainudiin@math.canterbury.ac.nz","commandTitle":"","showCommandTitle":false,"hideCommandCode":false,"hideCommandResult":false,"iPythonMetadata":null,"nuid":"b14cf204-dafc-4d32-819a-cfc4f731885e"},{"version":"CommandV1","origId":50601,"guid":"da543072-b12d-4ac3-a421-5989d333d951","subtype":"command","commandType":"auto","position":1.9999949932098389,"command":"%md\n### Let us run through some basic inteactive SQL queries next\n* HiveQL supports =, <, >, <=, >= and != operators. It also supports LIKE operator for fuzzy matching of Strings\n* Enclose Strings in single quotes\n* Multiple conditions can be combined using `and` and `or`\n* Enclose conditions in `()` for precedence\n* ...\n* ...\n\n**Why do I need to learn interactive SQL queries?**\n\nSuch queries in the widely known declarative SQL language can help us explore the data and thereby inform the modeling process!!!","commandVersion":0,"state":"finished","results":null,"errorSummary":null,"error":null,"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":"","commandTitle":"","showCommandTitle":false,"hideCommandCode":false,"hideCommandResult":false,"iPythonMetadata":null,"nuid":"1c17140d-8b2f-41ef-b7e8-ea31b3b55d61"},{"version":"CommandV1","origId":50600,"guid":"862bc215-2a19-4477-97ea-387dd1214792","subtype":"command","commandType":"auto","position":1.9999966621398926,"command":"%md\nUsing DataFrame API, we can apply a `filter` after `select` to transform the DataFrame `diamondsDF` to the new DataFrame `diamondsDColoredDF`.\n\nBelow, `$` is an alias for column. \n\nLet as select the columns named `carat`, `colour`, `price` where `color` value is equal to `D`.","commandVersion":0,"state":"finished","results":null,"errorSummary":null,"error":null,"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":"","commandTitle":"","showCommandTitle":false,"hideCommandCode":false,"hideCommandResult":false,"iPythonMetadata":null,"nuid":"de24a86f-57ee-4f96-beb2-dccf22fc56b9"},{"version":"CommandV1","origId":50603,"guid":"ebc3d45d-e3fd-496a-b781-4948fd75d727","subtype":"command","commandType":"auto","position":1.9999974966049194,"command":"val diamondsDColoredDF = diamondsDF.select($\"carat\", $\"color\", $\"price\").filter($\"color\" === \"D\") // Shift+Enter","commandVersion":0,"state":"finished","results":{"type":"html","data":"<div class=\"ansiout\">diamondsDColoredDF: org.apache.spark.sql.DataFrame = [carat: double, color: string, price: 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one could just write the SQL statement in scala to create a new DataFrame `diamondsDColoredDF_FromTable` from the table `diamonds` and display it, as follows:","commandVersion":0,"state":"finished","results":null,"errorSummary":null,"error":null,"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":"","commandTitle":"","showCommandTitle":false,"hideCommandCode":false,"hideCommandResult":false,"iPythonMetadata":null,"nuid":"67058c47-0f90-4c0b-9e4e-78e96eb1749e"},{"version":"CommandV1","origId":50612,"guid":"54111c51-900c-457f-be99-4b28ef97bf09","subtype":"command","commandType":"auto","position":1.9999998435378075,"command":"val diamondsDColoredDF_FromTable = 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Good","E","VS1",62.5,56.0,3063.0,5.64,5.68,3.54],[0.7,"Good","E","VS1",59.4,61.0,3063.0,5.79,5.83,3.45],[0.71,"Very Good","E","VS1",63.3,59.0,3064.0,5.64,5.68,3.58],[0.76,"Premium","E","VS2",61.7,62.0,3064.0,5.85,5.82,3.6],[0.7,"Ideal","F","VS2",61.4,56.0,3064.0,5.72,5.75,3.52],[0.7,"Ideal","F","VS2",61.6,55.0,3064.0,5.72,5.75,3.53],[0.72,"Very Good","E","VS2",63.0,58.0,3065.0,5.69,5.73,3.6],[0.7,"Ideal","G","VS1",61.5,56.0,3065.0,5.7,5.75,3.52],[0.77,"Ideal","I","VS1",61.4,56.0,3066.0,5.9,5.93,3.63],[0.71,"Ideal","F","VS1",62.0,57.0,3066.0,5.7,5.75,3.55],[0.71,"Ideal","F","VS1",62.1,57.0,3066.0,5.73,5.76,3.57],[0.73,"Very Good","E","VS2",63.1,55.0,3066.0,5.77,5.71,3.62],[0.7,"Very Good","E","VS1",63.4,60.0,3068.0,5.63,5.66,3.58],[0.7,"Ideal","E","VS2",62.6,56.0,3068.0,5.65,5.69,3.55],[0.85,"Very Good","I","VS2",60.0,57.0,3070.0,6.1,6.16,3.68],[0.82,"Ideal","I","VS1",61.6,56.0,3071.0,6.05,6.01,3.72],[0.71,"Good","G","VVS1",62.7,61.0,3072.0,5.64,5.68,3.55],[0.7,"Very Good","G","VVS1",63.1,56.0,3073.0,5.64,5.67,3.57],[0.7,"Ideal","G","VVS1",61.6,55.0,3073.0,5.72,5.75,3.53],[0.75,"Ideal","G","VS2",61.6,55.0,3073.0,5.86,5.89,3.62],[0.71,"Ideal","E","VS2",62.2,57.0,3073.0,5.69,5.73,3.55],[0.62,"Premium","E","VVS1",61.9,59.0,3073.0,5.62,5.5,3.44],[0.7,"Good","D","VS2",58.0,65.0,3073.0,5.81,5.73,3.39],[0.78,"Very Good","G","VS2",61.7,58.0,3074.0,5.87,5.92,3.64],[0.9,"Fair","I","VVS2",67.0,56.0,3074.0,5.91,5.83,3.93],[0.77,"Ideal","H","VS1",61.4,55.0,3074.0,5.89,5.93,3.63],[0.72,"Very Good","D","VS2",61.8,58.0,3075.0,5.73,5.76,3.55],[0.72,"Very Good","D","VS2",62.6,59.0,3075.0,5.69,5.72,3.57],[0.72,"Ideal","H","VVS1",62.2,57.0,3075.0,5.72,5.75,3.57],[0.76,"Ideal","I","VS2",61.7,56.0,3075.0,5.87,5.9,3.63],[0.73,"Ideal","E","VS2",62.7,56.0,3077.0,5.75,5.8,3.62],[0.71,"Fair","D","VS2",64.7,58.0,3077.0,5.61,5.58,3.62],[0.71,"Premium","D","VS2",60.3,62.0,3077.0,5.76,5.69,3.45],[0.72,"Premium","E","VS2",62.5,59.0,3078.0,5.7,5.73,3.57],[0.76,"Ideal","E","VS2",61.3,56.0,3079.0,5.79,5.83,3.56]],"arguments":{},"addedWidgets":{},"removedWidgets":[],"schema":[{"name":"carat","type":"\"double\""},{"name":"cut","type":"\"string\""},{"name":"color","type":"\"string\""},{"name":"clarity","type":"\"string\""},{"name":"depth","type":"\"double\""},{"name":"table","type":"\"double\""},{"name":"price","type":"\"integer\""},{"name":"x","type":"\"double\""},{"name":"y","type":"\"double\""},{"name":"z","type":"\"double\""}],"overflow":true,"aggData":[],"aggSchema":[],"aggOverflow":false,"aggSeriesLimitReached":false,"aggError":"","aggType":"","plotOptions":null,"isJsonSchema":true,"dbfsResultPath":null},"errorSummary":"org.apache.spark.sql.AnalysisException: Table not found: diamond; line 1 pos 14","error":"<div class=\"ansiout\">\tat org.apache.spark.sql.catalyst.analysis.package$AnalysisErrorAt.failAnalysis(package.scala:42)\n\tat org.apache.spark.sql.catalyst.analysis.Analyzer$ResolveRelations$.getTable(Analyzer.scala:306)\n\tat org.apache.spark.sql.catalyst.analysis.Analyzer$ResolveRelations$$anonfun$apply$9.applyOrElse(Analyzer.scala:315)\n\tat org.apache.spark.sql.catalyst.analysis.Analyzer$ResolveRelations$$anonfun$apply$9.applyOrElse(Analyzer.scala:310)\n\tat org.apache.spark.sql.catalyst.plans.logical.LogicalPlan$$anonfun$resolveOperators$1.apply(LogicalPlan.scala:57)\n\tat org.apache.spark.sql.catalyst.plans.logical.LogicalPlan$$anonfun$resolveOperators$1.apply(LogicalPlan.scala:57)\n\tat org.apache.spark.sql.catalyst.trees.CurrentOrigin$.withOrigin(TreeNode.scala:53)\n\tat org.apache.spark.sql.catalyst.plans.logical.LogicalPlan.resolveOperators(LogicalPlan.scala:56)\n\tat org.apache.spark.sql.catalyst.plans.logical.LogicalPlan$$anonfun$1.apply(LogicalPlan.scala:54)\n\tat org.apache.spark.sql.catalyst.plans.logical.LogicalPlan$$anonfun$1.apply(LogicalPlan.scala:54)\n\tat org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$4.apply(TreeNode.scala:265)\n\tat scala.collection.Iterator$$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.generic.Growable$class.$plus$plus$eq(Growable.scala:48)\n\tat scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:103)\n\tat scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:47)\n\tat scala.collection.TraversableOnce$class.to(TraversableOnce.scala:273)\n\tat scala.collection.AbstractIterator.to(Iterator.scala:1157)\n\tat scala.collection.TraversableOnce$class.toBuffer(TraversableOnce.scala:265)\n\tat scala.collection.AbstractIterator.toBuffer(Iterator.scala:1157)\n\tat scala.collection.TraversableOnce$class.toArray(TraversableOnce.scala:252)\n\tat scala.collection.AbstractIterator.toArray(Iterator.scala:1157)\n\tat org.apache.spark.sql.catalyst.trees.TreeNode.transformChildren(TreeNode.scala:305)\n\tat org.apache.spark.sql.catalyst.plans.logical.LogicalPlan.resolveOperators(LogicalPlan.scala:54)\n\tat org.apache.spark.sql.catalyst.plans.logical.LogicalPlan$$anonfun$1.apply(LogicalPlan.scala:54)\n\tat org.apache.spark.sql.catalyst.plans.logical.LogicalPlan$$anonfun$1.apply(LogicalPlan.scala:54)\n\tat org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$4.apply(TreeNode.scala:265)\n\tat scala.collection.Iterator$$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.generic.Growable$class.$plus$plus$eq(Growable.scala:48)\n\tat scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:103)\n\tat scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:47)\n\tat scala.collection.TraversableOnce$class.to(TraversableOnce.scala:273)\n\tat scala.collection.AbstractIterator.to(Iterator.scala:1157)\n\tat scala.collection.TraversableOnce$class.toBuffer(TraversableOnce.scala:265)\n\tat scala.collection.AbstractIterator.toBuffer(Iterator.scala:1157)\n\tat scala.collection.TraversableOnce$class.toArray(TraversableOnce.scala:252)\n\tat scala.collection.AbstractIterator.toArray(Iterator.scala:1157)\n\tat org.apache.spark.sql.catalyst.trees.TreeNode.transformChildren(TreeNode.scala:305)\n\tat org.apache.spark.sql.catalyst.plans.logical.LogicalPlan.resolveOperators(LogicalPlan.scala:54)\n\tat org.apache.spark.sql.catalyst.analysis.Analyzer$ResolveRelations$.apply(Analyzer.scala:310)\n\tat org.apache.spark.sql.catalyst.analysis.Analyzer$ResolveRelations$.apply(Analyzer.scala:300)\n\tat org.apache.spark.sql.catalyst.rules.RuleExecutor$$anonfun$execute$1$$anonfun$apply$1.apply(RuleExecutor.scala:83)\n\tat org.apache.spark.sql.catalyst.rules.RuleExecutor$$anonfun$execute$1$$anonfun$apply$1.apply(RuleExecutor.scala:80)\n\tat scala.collection.LinearSeqOptimized$class.foldLeft(LinearSeqOptimized.scala:111)\n\tat scala.collection.immutable.List.foldLeft(List.scala:84)\n\tat org.apache.spark.sql.catalyst.rules.RuleExecutor$$anonfun$execute$1.apply(RuleExecutor.scala:80)\n\tat org.apache.spark.sql.catalyst.rules.RuleExecutor$$anonfun$execute$1.apply(RuleExecutor.scala:72)\n\tat scala.collection.immutable.List.foreach(List.scala:318)\n\tat org.apache.spark.sql.catalyst.rules.RuleExecutor.execute(RuleExecutor.scala:72)\n\tat org.apache.spark.sql.execution.QueryExecution.analyzed$lzycompute(QueryExecution.scala:36)\n\tat org.apache.spark.sql.execution.QueryExecution.analyzed(QueryExecution.scala:36)\n\tat org.apache.spark.sql.execution.QueryExecution.assertAnalyzed(QueryExecution.scala:34)\n\tat org.apache.spark.sql.DataFrame.<init>(DataFrame.scala:133)\n\tat org.apache.spark.sql.DataFrame$.apply(DataFrame.scala:52)\n\tat org.apache.spark.sql.SQLContext.sql(SQLContext.scala:816)</div>","startTime":1.457584080868E12,"submitTime":1.457584045068E12,"finishTime":1.457584080978E12,"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":"r.sainudiin@math.canterbury.ac.nz","commandTitle":"","showCommandTitle":false,"hideCommandCode":false,"hideCommandResult":false,"iPythonMetadata":null,"nuid":"dc0cf118-0a4d-4c56-906a-a017df7d16c8"},{"version":"CommandV1","origId":50614,"guid":"e743cc70-998d-4938-8a23-d85d2602897b","subtype":"command","commandType":"auto","position":1.999999973922968,"command":"// Combining conditions\ndisplay(sqlContext.sql(\"select * from diamonds where clarity LIKE 'V%' and price > 10000\"))","commandVersion":0,"state":"finished","results":{"type":"table","data":[[1.7,"Ideal","J","VS2",60.5,58.0,10002.0,7.73,7.74,4.68],[1.03,"Ideal","E","VVS2",60.6,59.0,10003.0,6.5,6.53,3.95],[1.23,"Very Good","G","VVS2",60.6,55.0,10004.0,6.93,7.02,4.23],[1.25,"Ideal","F","VS2",61.6,55.0,10006.0,6.93,6.96,4.28],[1.21,"Very Good","F","VS1",62.3,58.0,10009.0,6.76,6.85,4.24],[1.51,"Premium","I","VS2",59.9,60.0,10010.0,7.42,7.36,4.43],[1.05,"Ideal","F","VVS2",60.5,55.0,10011.0,6.67,6.58,4.01],[1.6,"Ideal","J","VS1",62.0,53.0,10011.0,7.57,7.56,4.69],[1.35,"Premium","G","VS1",62.1,59.0,10012.0,7.06,7.02,4.37],[1.53,"Premium","I","VS2",62.0,58.0,10013.0,7.36,7.41,4.58],[1.13,"Ideal","F","VS1",60.9,57.0,10016.0,6.73,6.76,4.11],[1.21,"Premium","F","VS1",62.6,59.0,10018.0,6.81,6.76,4.25],[1.01,"Very Good","F","VVS1",62.9,57.0,10019.0,6.35,6.41,4.01],[1.04,"Ideal","E","VVS2",62.9,55.0,10019.0,6.47,6.51,4.08],[1.26,"Very Good","G","VVS2",60.9,56.0,10020.0,6.95,7.01,4.25],[1.5,"Very Good","H","VS2",60.9,59.0,10023.0,7.37,7.43,4.51],[1.12,"Premium","F","VVS2",62.4,59.0,10028.0,6.58,6.66,4.13],[1.27,"Premium","F","VS1",60.3,58.0,10028.0,7.06,7.04,4.25],[1.52,"Very Good","I","VS1",62.9,59.9,10032.0,7.27,7.31,4.59],[1.24,"Premium","F","VS1",62.5,58.0,10033.0,6.87,6.83,4.28],[1.23,"Very Good","F","VS1",62.0,59.0,10035.0,6.84,6.87,4.25],[1.5,"Good","G","VS1",63.6,57.0,10036.0,7.23,7.14,4.57],[1.22,"Ideal","G","VVS2",62.3,56.0,10038.0,6.81,6.84,4.25],[1.3,"Ideal","G","VS1",62.0,55.0,10038.0,6.98,7.02,4.34],[1.59,"Premium","I","VS2",60.2,60.0,10039.0,7.58,7.61,4.57],[1.83,"Premium","I","VS2",60.5,60.0,10043.0,7.93,7.86,4.78],[1.07,"Ideal","E","VVS2",61.4,56.0,10043.0,6.65,6.55,4.05],[1.51,"Very Good","H","VS1",61.5,54.0,10045.0,7.34,7.42,4.54],[1.08,"Ideal","F","VVS2",61.6,57.0,10046.0,6.57,6.6,4.06],[1.0,"Premium","D","VVS2",61.6,60.0,10046.0,6.41,6.36,3.93],[1.03,"Ideal","F","VVS2",61.1,57.0,10049.0,6.51,6.54,3.99],[1.52,"Very Good","I","VS2",62.3,58.0,10051.0,7.32,7.28,4.55],[1.08,"Ideal","F","VVS2",62.1,55.0,10052.0,6.57,6.6,4.09],[1.2,"Premium","G","VVS2",62.8,59.0,10053.0,6.72,6.65,4.2],[1.2,"Premium","E","VS1",60.7,57.0,10053.0,6.89,6.81,4.16],[1.2,"Premium","G","VVS2",61.2,58.0,10053.0,6.88,6.84,4.2],[1.71,"Premium","I","VS1",60.3,62.0,10055.0,7.76,7.7,4.66],[1.0,"Ideal","F","VVS1",62.3,53.0,10058.0,6.37,6.43,3.99],[1.07,"Ideal","F","VVS2",62.3,57.0,10061.0,6.56,6.58,4.09],[1.66,"Premium","J","VVS2",62.6,59.0,10062.0,7.58,7.54,4.73],[1.2,"Premium","F","VVS2",60.5,60.0,10064.0,6.98,6.87,4.19],[1.11,"Very Good","F","VVS1",62.5,59.0,10069.0,6.59,6.63,4.13],[1.34,"Ideal","G","VS1",62.7,57.0,10070.0,7.1,7.04,4.43],[1.31,"Premium","G","VS1",61.5,59.0,10071.0,7.06,7.0,4.32],[1.31,"Ideal","G","VS1",62.2,56.0,10071.0,7.05,7.01,4.37],[1.31,"Ideal","G","VS1",61.5,57.0,10071.0,7.06,7.02,4.33],[1.53,"Very Good","H","VS1",59.5,63.0,10076.0,7.51,7.44,4.45],[1.26,"Premium","F","VS1",62.7,58.0,10076.0,6.93,6.86,4.32],[1.73,"Ideal","J","VS2",63.0,57.0,10076.0,7.64,7.6,4.8],[1.19,"Ideal","D","VS1",61.1,57.0,10079.0,6.84,6.87,4.19],[1.5,"Ideal","I","VS1",61.3,57.0,10080.0,7.35,7.32,4.5],[1.5,"Premium","I","VS1",62.7,59.0,10080.0,7.3,7.25,4.56],[1.5,"Ideal","H","VS1",61.3,55.0,10080.0,7.37,7.34,4.51],[1.21,"Premium","D","VS1",60.2,59.0,10083.0,6.89,6.86,4.14],[1.71,"Premium","H","VS2",59.2,61.0,10084.0,7.83,7.77,4.62],[1.82,"Very Good","J","VS1",62.2,56.0,10090.0,7.83,7.96,4.91],[1.51,"Very Good","H","VS2",61.9,57.0,10090.0,7.32,7.36,4.54],[1.3,"Ideal","F","VS2",62.2,56.0,10090.0,6.98,6.94,4.33],[1.3,"Premium","F","VS2",60.4,59.0,10090.0,7.12,7.06,4.28],[1.5,"Very Good","I","VVS2",63.3,58.0,10090.0,7.27,7.24,4.59],[1.57,"Ideal","I","VS2",61.5,56.0,10093.0,7.56,7.49,4.63],[1.07,"Ideal","F","VVS2",60.3,55.0,10093.0,6.65,6.68,4.02],[1.31,"Very Good","E","VS2",63.1,56.0,10094.0,6.95,6.9,4.37],[1.33,"Good","G","VS1",62.8,60.0,10096.0,6.87,6.92,4.33],[1.53,"Premium","I","VS1",61.2,59.0,10098.0,7.39,7.41,4.53],[1.61,"Ideal","I","VS2",62.5,57.0,10098.0,7.49,7.43,4.66],[1.31,"Ideal","G","VS1",61.9,56.0,10099.0,7.03,7.13,4.38],[1.22,"Ideal","F","VS1",62.3,57.0,10100.0,6.83,6.79,4.24],[1.07,"Ideal","E","VVS2",61.7,57.0,10104.0,6.55,6.61,4.06],[1.59,"Very Good","I","VS2",60.5,63.0,10106.0,7.52,7.45,4.53],[1.22,"Premium","G","VVS2",62.0,58.0,10111.0,6.9,6.85,4.26],[1.09,"Premium","E","VVS2",59.9,59.0,10111.0,6.73,6.7,4.02],[1.58,"Very Good","I","VS1",61.8,57.0,10112.0,7.5,7.56,4.64],[1.0,"Very Good","D","VVS2",61.7,58.0,10113.0,6.37,6.41,3.94],[1.23,"Ideal","G","VVS1",63.2,56.0,10113.0,6.78,6.83,4.3],[1.25,"Ideal","D","VS2",62.6,56.0,10114.0,6.87,6.84,4.29],[1.17,"Premium","D","VS1",61.7,59.0,10115.0,6.77,6.72,4.16],[1.28,"Ideal","G","VS1",62.1,57.0,10126.0,6.91,6.94,4.3],[1.43,"Ideal","H","VVS2",61.6,54.0,10129.0,7.25,7.29,4.48],[1.51,"Good","H","VS1",59.9,61.0,10129.0,7.34,7.39,4.41],[1.52,"Very Good","I","VS2",61.7,55.0,10130.0,7.39,7.32,4.54],[1.04,"Very Good","D","VVS2",60.8,58.0,10130.0,6.49,6.53,3.96],[1.07,"Ideal","E","VVS2",62.3,56.0,10133.0,6.51,6.61,4.09],[1.5,"Good","F","VS2",64.0,56.0,10134.0,7.18,7.13,4.64],[1.0,"Premium","E","VVS1",60.3,54.0,10134.0,6.59,6.47,3.94],[1.21,"Premium","E","VS1",60.3,58.0,10137.0,6.95,6.91,4.18],[1.24,"Ideal","F","VS1",61.5,54.0,10138.0,6.93,6.89,4.25],[1.24,"Ideal","F","VS1",60.9,54.0,10138.0,6.98,6.95,4.24],[1.11,"Very 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,"aggSeriesLimitReached":false,"aggError":"","aggType":"","plotOptions":null,"isJsonSchema":true,"dbfsResultPath":null},"errorSummary":"org.apache.spark.sql.AnalysisException: cannot resolve 'colour' given input columns depth, table, color, y, cut, price, x, carat, z, clarity; line 1 pos 49","error":"<div class=\"ansiout\">\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:60)\n\tat org.apache.spark.sql.catalyst.analysis.CheckAnalysis$$anonfun$checkAnalysis$1$$anonfun$apply$2.applyOrElse(CheckAnalysis.scala:57)\n\tat org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$transformUp$1.apply(TreeNode.scala:319)\n\tat org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$transformUp$1.apply(TreeNode.scala:319)\n\tat org.apache.spark.sql.catalyst.trees.CurrentOrigin$.withOrigin(TreeNode.scala:53)\n\tat org.apache.spark.sql.catalyst.trees.TreeNode.transformUp(TreeNode.scala:318)\n\tat org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$5.apply(TreeNode.scala:316)\n\tat org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$5.apply(TreeNode.scala:316)\n\tat org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$4.apply(TreeNode.scala:265)\n\tat scala.collection.Iterator$$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.generic.Growable$class.$plus$plus$eq(Growable.scala:48)\n\tat scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:103)\n\tat scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:47)\n\tat scala.collection.TraversableOnce$class.to(TraversableOnce.scala:273)\n\tat scala.collection.AbstractIterator.to(Iterator.scala:1157)\n\tat scala.collection.TraversableOnce$class.toBuffer(TraversableOnce.scala:265)\n\tat scala.collection.AbstractIterator.toBuffer(Iterator.scala:1157)\n\tat scala.collection.TraversableOnce$class.toArray(TraversableOnce.scala:252)\n\tat scala.collection.AbstractIterator.toArray(Iterator.scala:1157)\n\tat org.apache.spark.sql.catalyst.trees.TreeNode.transformChildren(TreeNode.scala:305)\n\tat org.apache.spark.sql.catalyst.trees.TreeNode.transformUp(TreeNode.scala:316)\n\tat org.apache.spark.sql.catalyst.plans.QueryPlan.transformExpressionUp$1(QueryPlan.scala:107)\n\tat org.apache.spark.sql.catalyst.plans.QueryPlan.org$apache$spark$sql$catalyst$plans$QueryPlan$$recursiveTransform$2(QueryPlan.scala:117)\n\tat org.apache.spark.sql.catalyst.plans.QueryPlan$$anonfun$2.apply(QueryPlan.scala:125)\n\tat scala.collection.Iterator$$anon$11.next(Iterator.scala:328)\n\tat scala.collection.Iterator$class.foreach(Iterator.scala:727)\n\tat scala.collection.AbstractIterator.foreach(Iterator.scala:1157)\n\tat scala.collection.generic.Growable$class.$plus$plus$eq(Growable.scala:48)\n\tat scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:103)\n\tat scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:47)\n\tat scala.collection.TraversableOnce$class.to(TraversableOnce.scala:273)\n\tat scala.collection.AbstractIterator.to(Iterator.scala:1157)\n\tat scala.collection.TraversableOnce$class.toBuffer(TraversableOnce.scala:265)\n\tat scala.collection.AbstractIterator.toBuffer(Iterator.scala:1157)\n\tat scala.collection.TraversableOnce$class.toArray(TraversableOnce.scala:252)\n\tat scala.collection.AbstractIterator.toArray(Iterator.scala:1157)\n\tat org.apache.spark.sql.catalyst.plans.QueryPlan.transformExpressionsUp(QueryPlan.scala:125)\n\tat org.apache.spark.sql.catalyst.analysis.CheckAnalysis$$anonfun$checkAnalysis$1.apply(CheckAnalysis.scala:57)\n\tat org.apache.spark.sql.catalyst.analysis.CheckAnalysis$$anonfun$checkAnalysis$1.apply(CheckAnalysis.scala:50)\n\tat org.apache.spark.sql.catalyst.trees.TreeNode.foreachUp(TreeNode.scala:105)\n\tat org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$foreachUp$1.apply(TreeNode.scala:104)\n\tat org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$foreachUp$1.apply(TreeNode.scala:104)\n\tat scala.collection.immutable.List.foreach(List.scala:318)\n\tat org.apache.spark.sql.catalyst.trees.TreeNode.foreachUp(TreeNode.scala:104)\n\tat org.apache.spark.sql.catalyst.analysis.CheckAnalysis$class.checkAnalysis(CheckAnalysis.scala:50)\n\tat org.apache.spark.sql.catalyst.analysis.Analyzer.checkAnalysis(Analyzer.scala:44)\n\tat org.apache.spark.sql.execution.QueryExecution.assertAnalyzed(QueryExecution.scala:34)\n\tat org.apache.spark.sql.DataFrame.<init>(DataFrame.scala:133)\n\tat org.apache.spark.sql.DataFrame$.apply(DataFrame.scala:52)\n\tat org.apache.spark.sql.SQLContext.sql(SQLContext.scala:816)</div>","startTime":1.457584208741E12,"submitTime":1.457584172939E12,"finishTime":1.45758420882E12,"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":"r.sainudiin@math.canterbury.ac.nz","commandTitle":"","showCommandTitle":false,"hideCommandCode":false,"hideCommandResult":false,"iPythonMetadata":null,"nuid":"e7971efd-ca38-40b3-89e7-5a1d6371fd3e"},{"version":"CommandV1","origId":50616,"guid":"4133af7a-01fd-4d9f-adec-31194952b287","subtype":"command","commandType":"auto","position":1.999999993480742,"command":"//renaming a field using as\ndisplay(sqlContext.sql(\"select carat as carrot, clarity, price from 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e,"aggError":"","aggType":"","plotOptions":null,"isJsonSchema":true,"dbfsResultPath":null},"errorSummary":"org.apache.spark.sql.AnalysisException: Table not found: diamond; line 1 pos 44","error":"<div class=\"ansiout\">\tat org.apache.spark.sql.catalyst.analysis.package$AnalysisErrorAt.failAnalysis(package.scala:42)\n\tat org.apache.spark.sql.catalyst.analysis.Analyzer$ResolveRelations$.getTable(Analyzer.scala:306)\n\tat org.apache.spark.sql.catalyst.analysis.Analyzer$ResolveRelations$$anonfun$apply$9.applyOrElse(Analyzer.scala:315)\n\tat org.apache.spark.sql.catalyst.analysis.Analyzer$ResolveRelations$$anonfun$apply$9.applyOrElse(Analyzer.scala:310)\n\tat org.apache.spark.sql.catalyst.plans.logical.LogicalPlan$$anonfun$resolveOperators$1.apply(LogicalPlan.scala:57)\n\tat org.apache.spark.sql.catalyst.plans.logical.LogicalPlan$$anonfun$resolveOperators$1.apply(LogicalPlan.scala:57)\n\tat org.apache.spark.sql.catalyst.trees.CurrentOrigin$.withOrigin(TreeNode.scala:53)\n\tat org.apache.spark.sql.catalyst.plans.logical.LogicalPlan.resolveOperators(LogicalPlan.scala:56)\n\tat org.apache.spark.sql.catalyst.plans.logical.LogicalPlan$$anonfun$1.apply(LogicalPlan.scala:54)\n\tat org.apache.spark.sql.catalyst.plans.logical.LogicalPlan$$anonfun$1.apply(LogicalPlan.scala:54)\n\tat org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$4.apply(TreeNode.scala:265)\n\tat scala.collection.Iterator$$anon$11.next(Iterator.scala:328)\n\tat scala.collection.Iterator$class.foreach(Iterator.scala:727)\n\tat scala.collection.AbstractIterator.foreach(Iterator.scala:1157)\n\tat scala.collection.generic.Growable$class.$plus$plus$eq(Growable.scala:48)\n\tat scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:103)\n\tat scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:47)\n\tat scala.collection.TraversableOnce$class.to(TraversableOnce.scala:273)\n\tat scala.collection.AbstractIterator.to(Iterator.scala:1157)\n\tat scala.collection.TraversableOnce$class.toBuffer(TraversableOnce.scala:265)\n\tat scala.collection.AbstractIterator.toBuffer(Iterator.scala:1157)\n\tat scala.collection.TraversableOnce$class.toArray(TraversableOnce.scala:252)\n\tat scala.collection.AbstractIterator.toArray(Iterator.scala:1157)\n\tat org.apache.spark.sql.catalyst.trees.TreeNode.transformChildren(TreeNode.scala:305)\n\tat org.apache.spark.sql.catalyst.plans.logical.LogicalPlan.resolveOperators(LogicalPlan.scala:54)\n\tat org.apache.spark.sql.catalyst.analysis.Analyzer$ResolveRelations$.apply(Analyzer.scala:310)\n\tat org.apache.spark.sql.catalyst.analysis.Analyzer$ResolveRelations$.apply(Analyzer.scala:300)\n\tat org.apache.spark.sql.catalyst.rules.RuleExecutor$$anonfun$execute$1$$anonfun$apply$1.apply(RuleExecutor.scala:83)\n\tat org.apache.spark.sql.catalyst.rules.RuleExecutor$$anonfun$execute$1$$anonfun$apply$1.apply(RuleExecutor.scala:80)\n\tat scala.collection.LinearSeqOptimized$class.foldLeft(LinearSeqOptimized.scala:111)\n\tat scala.collection.immutable.List.foldLeft(List.scala:84)\n\tat org.apache.spark.sql.catalyst.rules.RuleExecutor$$anonfun$execute$1.apply(RuleExecutor.scala:80)\n\tat org.apache.spark.sql.catalyst.rules.RuleExecutor$$anonfun$execute$1.apply(RuleExecutor.scala:72)\n\tat scala.collection.immutable.List.foreach(List.scala:318)\n\tat org.apache.spark.sql.catalyst.rules.RuleExecutor.execute(RuleExecutor.scala:72)\n\tat org.apache.spark.sql.execution.QueryExecution.analyzed$lzycompute(QueryExecution.scala:36)\n\tat org.apache.spark.sql.execution.QueryExecution.analyzed(QueryExecution.scala:36)\n\tat org.apache.spark.sql.execution.QueryExecution.assertAnalyzed(QueryExecution.scala:34)\n\tat org.apache.spark.sql.DataFrame.<init>(DataFrame.scala:133)\n\tat org.apache.spark.sql.DataFrame$.apply(DataFrame.scala:52)\n\tat org.apache.spark.sql.SQLContext.sql(SQLContext.scala:816)</div>","startTime":1.457584234491E12,"submitTime":1.45758419871E12,"finishTime":1.45758423456E12,"collapsed":false,"bindings":{},"inputWidgets":{},"displayType":"table","width":"auto","height":"auto","xColumns":null,"yColumns":null,"pivotColumns":null,"pivotAggregation":null,"customPlotOptions":{},"commentThread":[],"commentsVisible":false,"parentHierarchy":[],"diffInserts":[],"diffDeletes":[],"globalVars":{},"latestUser":"r.sainudiin@math.canterbury.ac.nz","commandTitle":"","showCommandTitle":false,"hideCommandCode":false,"hideCommandResult":false,"iPythonMetadata":null,"nuid":"1e09dda4-76c3-401f-ba4d-b3dbd29b0fa8"},{"version":"CommandV1","origId":50617,"guid":"799058dc-4963-465d-8129-13416afeac65","subtype":"command","commandType":"auto","position":1.999999996740371,"command":"//sorting\ndisplay(sqlContext.sql(\"select carat, clarity, price from diamonds order by price 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Table not found: diamond; line 1 pos 34","error":"<div class=\"ansiout\">\tat org.apache.spark.sql.catalyst.analysis.package$AnalysisErrorAt.failAnalysis(package.scala:42)\n\tat org.apache.spark.sql.catalyst.analysis.Analyzer$ResolveRelations$.getTable(Analyzer.scala:306)\n\tat org.apache.spark.sql.catalyst.analysis.Analyzer$ResolveRelations$$anonfun$apply$9.applyOrElse(Analyzer.scala:315)\n\tat org.apache.spark.sql.catalyst.analysis.Analyzer$ResolveRelations$$anonfun$apply$9.applyOrElse(Analyzer.scala:310)\n\tat org.apache.spark.sql.catalyst.plans.logical.LogicalPlan$$anonfun$resolveOperators$1.apply(LogicalPlan.scala:57)\n\tat org.apache.spark.sql.catalyst.plans.logical.LogicalPlan$$anonfun$resolveOperators$1.apply(LogicalPlan.scala:57)\n\tat org.apache.spark.sql.catalyst.trees.CurrentOrigin$.withOrigin(TreeNode.scala:53)\n\tat org.apache.spark.sql.catalyst.plans.logical.LogicalPlan.resolveOperators(LogicalPlan.scala:56)\n\tat org.apache.spark.sql.catalyst.plans.logical.LogicalPlan$$anonfun$1.apply(LogicalPlan.scala:54)\n\tat org.apache.spark.sql.catalyst.plans.logical.LogicalPlan$$anonfun$1.apply(LogicalPlan.scala:54)\n\tat org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$4.apply(TreeNode.scala:265)\n\tat scala.collection.Iterator$$anon$11.next(Iterator.scala:328)\n\tat scala.collection.Iterator$class.foreach(Iterator.scala:727)\n\tat scala.collection.AbstractIterator.foreach(Iterator.scala:1157)\n\tat scala.collection.generic.Growable$class.$plus$plus$eq(Growable.scala:48)\n\tat scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:103)\n\tat scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:47)\n\tat scala.collection.TraversableOnce$class.to(TraversableOnce.scala:273)\n\tat scala.collection.AbstractIterator.to(Iterator.scala:1157)\n\tat scala.collection.TraversableOnce$class.toBuffer(TraversableOnce.scala:265)\n\tat scala.collection.AbstractIterator.toBuffer(Iterator.scala:1157)\n\tat scala.collection.TraversableOnce$class.toArray(TraversableOnce.scala:252)\n\tat scala.collection.AbstractIterator.toArray(Iterator.scala:1157)\n\tat org.apache.spark.sql.catalyst.trees.TreeNode.transformChildren(TreeNode.scala:305)\n\tat org.apache.spark.sql.catalyst.plans.logical.LogicalPlan.resolveOperators(LogicalPlan.scala:54)\n\tat org.apache.spark.sql.catalyst.plans.logical.LogicalPlan$$anonfun$1.apply(LogicalPlan.scala:54)\n\tat org.apache.spark.sql.catalyst.plans.logical.LogicalPlan$$anonfun$1.apply(LogicalPlan.scala:54)\n\tat org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$4.apply(TreeNode.scala:265)\n\tat scala.collection.Iterator$$anon$11.next(Iterator.scala:328)\n\tat scala.collection.Iterator$class.foreach(Iterator.scala:727)\n\tat scala.collection.AbstractIterator.foreach(Iterator.scala:1157)\n\tat scala.collection.generic.Growable$class.$plus$plus$eq(Growable.scala:48)\n\tat scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:103)\n\tat scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:47)\n\tat scala.collection.TraversableOnce$class.to(TraversableOnce.scala:273)\n\tat scala.collection.AbstractIterator.to(Iterator.scala:1157)\n\tat scala.collection.TraversableOnce$class.toBuffer(TraversableOnce.scala:265)\n\tat scala.collection.AbstractIterator.toBuffer(Iterator.scala:1157)\n\tat scala.collection.TraversableOnce$class.toArray(TraversableOnce.scala:252)\n\tat scala.collection.AbstractIterator.toArray(Iterator.scala:1157)\n\tat org.apache.spark.sql.catalyst.trees.TreeNode.transformChildren(TreeNode.scala:305)\n\tat org.apache.spark.sql.catalyst.plans.logical.LogicalPlan.resolveOperators(LogicalPlan.scala:54)\n\tat org.apache.spark.sql.catalyst.analysis.Analyzer$ResolveRelations$.apply(Analyzer.scala:310)\n\tat org.apache.spark.sql.catalyst.analysis.Analyzer$ResolveRelations$.apply(Analyzer.scala:300)\n\tat org.apache.spark.sql.catalyst.rules.RuleExecutor$$anonfun$execute$1$$anonfun$apply$1.apply(RuleExecutor.scala:83)\n\tat org.apache.spark.sql.catalyst.rules.RuleExecutor$$anonfun$execute$1$$anonfun$apply$1.apply(RuleExecutor.scala:80)\n\tat scala.collection.LinearSeqOptimized$class.foldLeft(LinearSeqOptimized.scala:111)\n\tat scala.collection.immutable.List.foldLeft(List.scala:84)\n\tat org.apache.spark.sql.catalyst.rules.RuleExecutor$$anonfun$execute$1.apply(RuleExecutor.scala:80)\n\tat org.apache.spark.sql.catalyst.rules.RuleExecutor$$anonfun$execute$1.apply(RuleExecutor.scala:72)\n\tat scala.collection.immutable.List.foreach(List.scala:318)\n\tat org.apache.spark.sql.catalyst.rules.RuleExecutor.execute(RuleExecutor.scala:72)\n\tat org.apache.spark.sql.execution.QueryExecution.analyzed$lzycompute(QueryExecution.scala:36)\n\tat org.apache.spark.sql.execution.QueryExecution.analyzed(QueryExecution.scala:36)\n\tat org.apache.spark.sql.execution.QueryExecution.assertAnalyzed(QueryExecution.scala:34)\n\tat org.apache.spark.sql.DataFrame.<init>(DataFrame.scala:133)\n\tat org.apache.spark.sql.DataFrame$.apply(DataFrame.scala:52)\n\tat org.apache.spark.sql.SQLContext.sql(SQLContext.scala:816)</div>","startTime":1.457584263552E12,"submitTime":1.457584226548E12,"finishTime":1.457584263651E12,"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":"r.sainudiin@math.canterbury.ac.nz","commandTitle":"","showCommandTitle":false,"hideCommandCode":false,"hideCommandResult":false,"iPythonMetadata":null,"nuid":"37ca8356-35d3-4741-9263-27d45fcdf34d"},{"version":"CommandV1","origId":50619,"guid":"61e774fb-2dab-4e85-a5a8-b4bf5115e754","subtype":"command","commandType":"auto","position":1.9999999975552782,"command":"diamondsDF.printSchema // since prince is integer in the DF turned into table we can rely on the descenting sort on integers","commandVersion":0,"state":"finished","results":{"type":"html","data":"<div class=\"ansiout\">root\n |-- carat: double (nullable = true)\n |-- cut: string (nullable = true)\n |-- color: string (nullable = true)\n |-- clarity: string (nullable = true)\n |-- depth: double (nullable = true)\n |-- table: double (nullable = true)\n |-- price: double (nullable = true)\n |-- x: double (nullable = true)\n |-- y: double (nullable = true)\n |-- z: double (nullable = true)\n\n</div>","arguments":{},"addedWidgets":{},"removedWidgets":[]},"errorSummary":"<div class=\"ansiout\"><console>:34: error: not found: value diamonds\n diamonds.printSchema()\n ^\n</div>","error":null,"startTime":1.457584312414E12,"submitTime":1.457584276407E12,"finishTime":1.457584312451E12,"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":"r.sainudiin@math.canterbury.ac.nz","commandTitle":"","showCommandTitle":false,"hideCommandCode":false,"hideCommandResult":false,"iPythonMetadata":null,"nuid":"7a06fb46-3abd-4069-b29b-25c0365a160e"},{"version":"CommandV1","origId":50620,"guid":"eee0a131-18f1-47be-9f2e-b56f72afbcf3","subtype":"command","commandType":"auto","position":1.9999999987776391,"command":"// sort by multiple fields\ndisplay(sqlContext.sql(\"select carat, clarity, price from diamonds order by carat asc, price 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ma":true,"dbfsResultPath":null},"errorSummary":"org.apache.spark.sql.AnalysisException: Table not found: diamond; line 1 pos 78","error":"<div class=\"ansiout\">\tat org.apache.spark.sql.catalyst.analysis.package$AnalysisErrorAt.failAnalysis(package.scala:42)\n\tat org.apache.spark.sql.catalyst.analysis.Analyzer$ResolveRelations$.getTable(Analyzer.scala:306)\n\tat org.apache.spark.sql.catalyst.analysis.Analyzer$ResolveRelations$$anonfun$apply$9.applyOrElse(Analyzer.scala:315)\n\tat org.apache.spark.sql.catalyst.analysis.Analyzer$ResolveRelations$$anonfun$apply$9.applyOrElse(Analyzer.scala:310)\n\tat org.apache.spark.sql.catalyst.plans.logical.LogicalPlan$$anonfun$resolveOperators$1.apply(LogicalPlan.scala:57)\n\tat org.apache.spark.sql.catalyst.plans.logical.LogicalPlan$$anonfun$resolveOperators$1.apply(LogicalPlan.scala:57)\n\tat org.apache.spark.sql.catalyst.trees.CurrentOrigin$.withOrigin(TreeNode.scala:53)\n\tat org.apache.spark.sql.catalyst.plans.logical.LogicalPlan.resolveOperators(LogicalPlan.scala:56)\n\tat org.apache.spark.sql.catalyst.plans.logical.LogicalPlan$$anonfun$1.apply(LogicalPlan.scala:54)\n\tat org.apache.spark.sql.catalyst.plans.logical.LogicalPlan$$anonfun$1.apply(LogicalPlan.scala:54)\n\tat org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$4.apply(TreeNode.scala:265)\n\tat scala.collection.Iterator$$anon$11.next(Iterator.scala:328)\n\tat scala.collection.Iterator$class.foreach(Iterator.scala:727)\n\tat scala.collection.AbstractIterator.foreach(Iterator.scala:1157)\n\tat scala.collection.generic.Growable$class.$plus$plus$eq(Growable.scala:48)\n\tat scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:103)\n\tat scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:47)\n\tat scala.collection.TraversableOnce$class.to(TraversableOnce.scala:273)\n\tat scala.collection.AbstractIterator.to(Iterator.scala:1157)\n\tat scala.collection.TraversableOnce$class.toBuffer(TraversableOnce.scala:265)\n\tat scala.collection.AbstractIterator.toBuffer(Iterator.scala:1157)\n\tat scala.collection.TraversableOnce$class.toArray(TraversableOnce.scala:252)\n\tat scala.collection.AbstractIterator.toArray(Iterator.scala:1157)\n\tat org.apache.spark.sql.catalyst.trees.TreeNode.transformChildren(TreeNode.scala:305)\n\tat org.apache.spark.sql.catalyst.plans.logical.LogicalPlan.resolveOperators(LogicalPlan.scala:54)\n\tat org.apache.spark.sql.catalyst.plans.logical.LogicalPlan$$anonfun$1.apply(LogicalPlan.scala:54)\n\tat org.apache.spark.sql.catalyst.plans.logical.LogicalPlan$$anonfun$1.apply(LogicalPlan.scala:54)\n\tat org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$4.apply(TreeNode.scala:265)\n\tat scala.collection.Iterator$$anon$11.next(Iterator.scala:328)\n\tat scala.collection.Iterator$class.foreach(Iterator.scala:727)\n\tat scala.collection.AbstractIterator.foreach(Iterator.scala:1157)\n\tat scala.collection.generic.Growable$class.$plus$plus$eq(Growable.scala:48)\n\tat scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:103)\n\tat scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:47)\n\tat scala.collection.TraversableOnce$class.to(TraversableOnce.scala:273)\n\tat scala.collection.AbstractIterator.to(Iterator.scala:1157)\n\tat scala.collection.TraversableOnce$class.toBuffer(TraversableOnce.scala:265)\n\tat scala.collection.AbstractIterator.toBuffer(Iterator.scala:1157)\n\tat scala.collection.TraversableOnce$class.toArray(TraversableOnce.scala:252)\n\tat scala.collection.AbstractIterator.toArray(Iterator.scala:1157)\n\tat org.apache.spark.sql.catalyst.trees.TreeNode.transformChildren(TreeNode.scala:305)\n\tat org.apache.spark.sql.catalyst.plans.logical.LogicalPlan.resolveOperators(LogicalPlan.scala:54)\n\tat org.apache.spark.sql.catalyst.analysis.Analyzer$ResolveRelations$.apply(Analyzer.scala:310)\n\tat org.apache.spark.sql.catalyst.analysis.Analyzer$ResolveRelations$.apply(Analyzer.scala:300)\n\tat org.apache.spark.sql.catalyst.rules.RuleExecutor$$anonfun$execute$1$$anonfun$apply$1.apply(RuleExecutor.scala:83)\n\tat org.apache.spark.sql.catalyst.rules.RuleExecutor$$anonfun$execute$1$$anonfun$apply$1.apply(RuleExecutor.scala:80)\n\tat scala.collection.LinearSeqOptimized$class.foldLeft(LinearSeqOptimized.scala:111)\n\tat scala.collection.immutable.List.foldLeft(List.scala:84)\n\tat org.apache.spark.sql.catalyst.rules.RuleExecutor$$anonfun$execute$1.apply(RuleExecutor.scala:80)\n\tat org.apache.spark.sql.catalyst.rules.RuleExecutor$$anonfun$execute$1.apply(RuleExecutor.scala:72)\n\tat scala.collection.immutable.List.foreach(List.scala:318)\n\tat org.apache.spark.sql.catalyst.rules.RuleExecutor.execute(RuleExecutor.scala:72)\n\tat org.apache.spark.sql.execution.QueryExecution.analyzed$lzycompute(QueryExecution.scala:36)\n\tat org.apache.spark.sql.execution.QueryExecution.analyzed(QueryExecution.scala:36)\n\tat org.apache.spark.sql.execution.QueryExecution.assertAnalyzed(QueryExecution.scala:34)\n\tat org.apache.spark.sql.DataFrame.<init>(DataFrame.scala:133)\n\tat org.apache.spark.sql.DataFrame$.apply(DataFrame.scala:52)\n\tat org.apache.spark.sql.SQLContext.sql(SQLContext.scala:816)</div>","startTime":1.457584356802E12,"submitTime":1.457584320986E12,"finishTime":1.457584356882E12,"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":"r.sainudiin@math.canterbury.ac.nz","commandTitle":"","showCommandTitle":false,"hideCommandCode":false,"hideCommandResult":false,"iPythonMetadata":null,"nuid":"f3a3fadc-6c68-4d67-94e4-01c481efb2f6"},{"version":"CommandV1","origId":50621,"guid":"e06d004f-08ff-479d-a5d7-5cf1ab6e3458","subtype":"command","commandType":"auto","position":1.9999999993888196,"command":"// use this to type cast strings into Int when the table is loaded with string-valued columns\n//display(sqlContext.sql(\"select cast(carat as Int) as carat, clarity, cast(price as Int) as price from diamond order by carat asc, price desc\"))","commandVersion":0,"state":"finished","results":null,"errorSummary":null,"error":null,"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":"","commandTitle":"","showCommandTitle":false,"hideCommandCode":false,"hideCommandResult":false,"iPythonMetadata":null,"nuid":"42a761da-1683-472d-ba69-3782191b9135"},{"version":"CommandV1","origId":50622,"guid":"868e129d-d99c-4651-a2ef-57d0bb59bf24","subtype":"command","commandType":"auto","position":1.9999999996944098,"command":"// sort by multiple fields and limit to first 5\ndisplay(sqlContext.sql(\"select carat, clarity, price from diamonds order by carat desc, price desc limit 5\"))","commandVersion":0,"state":"finished","results":{"type":"table","data":[[5.01,"I1",18018.0],[4.5,"I1",18531.0],[4.13,"I1",17329.0],[4.01,"I1",15223.0],[4.01,"I1",15223.0]],"arguments":{},"addedWidgets":{},"removedWidgets":[],"schema":[{"name":"carat","type":"\"double\""},{"name":"clarity","type":"\"string\""},{"name":"price","type":"\"integer\""}],"overflow":false,"aggData":[],"aggSchema":[],"aggOverflow":false,"aggSeriesLimitReached":false,"aggError":"","aggType":"","plotOptions":null,"isJsonSchema":true,"dbfsResultPath":null},"errorSummary":"org.apache.spark.sql.AnalysisException: Table not found: diamond; line 1 pos 34","error":"<div class=\"ansiout\">\tat org.apache.spark.sql.catalyst.analysis.package$AnalysisErrorAt.failAnalysis(package.scala:42)\n\tat org.apache.spark.sql.catalyst.analysis.Analyzer$ResolveRelations$.getTable(Analyzer.scala:306)\n\tat org.apache.spark.sql.catalyst.analysis.Analyzer$ResolveRelations$$anonfun$apply$9.applyOrElse(Analyzer.scala:315)\n\tat org.apache.spark.sql.catalyst.analysis.Analyzer$ResolveRelations$$anonfun$apply$9.applyOrElse(Analyzer.scala:310)\n\tat org.apache.spark.sql.catalyst.plans.logical.LogicalPlan$$anonfun$resolveOperators$1.apply(LogicalPlan.scala:57)\n\tat org.apache.spark.sql.catalyst.plans.logical.LogicalPlan$$anonfun$resolveOperators$1.apply(LogicalPlan.scala:57)\n\tat org.apache.spark.sql.catalyst.trees.CurrentOrigin$.withOrigin(TreeNode.scala:53)\n\tat org.apache.spark.sql.catalyst.plans.logical.LogicalPlan.resolveOperators(LogicalPlan.scala:56)\n\tat org.apache.spark.sql.catalyst.plans.logical.LogicalPlan$$anonfun$1.apply(LogicalPlan.scala:54)\n\tat org.apache.spark.sql.catalyst.plans.logical.LogicalPlan$$anonfun$1.apply(LogicalPlan.scala:54)\n\tat org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$4.apply(TreeNode.scala:265)\n\tat scala.collection.Iterator$$anon$11.next(Iterator.scala:328)\n\tat scala.collection.Iterator$class.foreach(Iterator.scala:727)\n\tat scala.collection.AbstractIterator.foreach(Iterator.scala:1157)\n\tat scala.collection.generic.Growable$class.$plus$plus$eq(Growable.scala:48)\n\tat scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:103)\n\tat scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:47)\n\tat scala.collection.TraversableOnce$class.to(TraversableOnce.scala:273)\n\tat scala.collection.AbstractIterator.to(Iterator.scala:1157)\n\tat scala.collection.TraversableOnce$class.toBuffer(TraversableOnce.scala:265)\n\tat scala.collection.AbstractIterator.toBuffer(Iterator.scala:1157)\n\tat scala.collection.TraversableOnce$class.toArray(TraversableOnce.scala:252)\n\tat scala.collection.AbstractIterator.toArray(Iterator.scala:1157)\n\tat org.apache.spark.sql.catalyst.trees.TreeNode.transformChildren(TreeNode.scala:305)\n\tat org.apache.spark.sql.catalyst.plans.logical.LogicalPlan.resolveOperators(LogicalPlan.scala:54)\n\tat org.apache.spark.sql.catalyst.plans.logical.LogicalPlan$$anonfun$1.apply(LogicalPlan.scala:54)\n\tat org.apache.spark.sql.catalyst.plans.logical.LogicalPlan$$anonfun$1.apply(LogicalPlan.scala:54)\n\tat org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$4.apply(TreeNode.scala:265)\n\tat scala.collection.Iterator$$anon$11.next(Iterator.scala:328)\n\tat scala.collection.Iterator$class.foreach(Iterator.scala:727)\n\tat scala.collection.AbstractIterator.foreach(Iterator.scala:1157)\n\tat scala.collection.generic.Growable$class.$plus$plus$eq(Growable.scala:48)\n\tat scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:103)\n\tat scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:47)\n\tat scala.collection.TraversableOnce$class.to(TraversableOnce.scala:273)\n\tat scala.collection.AbstractIterator.to(Iterator.scala:1157)\n\tat scala.collection.TraversableOnce$class.toBuffer(TraversableOnce.scala:265)\n\tat scala.collection.AbstractIterator.toBuffer(Iterator.scala:1157)\n\tat scala.collection.TraversableOnce$class.toArray(TraversableOnce.scala:252)\n\tat scala.collection.AbstractIterator.toArray(Iterator.scala:1157)\n\tat org.apache.spark.sql.catalyst.trees.TreeNode.transformChildren(TreeNode.scala:305)\n\tat org.apache.spark.sql.catalyst.plans.logical.LogicalPlan.resolveOperators(LogicalPlan.scala:54)\n\tat org.apache.spark.sql.catalyst.plans.logical.LogicalPlan$$anonfun$1.apply(LogicalPlan.scala:54)\n\tat org.apache.spark.sql.catalyst.plans.logical.LogicalPlan$$anonfun$1.apply(LogicalPlan.scala:54)\n\tat org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$4.apply(TreeNode.scala:265)\n\tat scala.collection.Iterator$$anon$11.next(Iterator.scala:328)\n\tat scala.collection.Iterator$class.foreach(Iterator.scala:727)\n\tat scala.collection.AbstractIterator.foreach(Iterator.scala:1157)\n\tat scala.collection.generic.Growable$class.$plus$plus$eq(Growable.scala:48)\n\tat scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:103)\n\tat scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:47)\n\tat scala.collection.TraversableOnce$class.to(TraversableOnce.scala:273)\n\tat scala.collection.AbstractIterator.to(Iterator.scala:1157)\n\tat scala.collection.TraversableOnce$class.toBuffer(TraversableOnce.scala:265)\n\tat scala.collection.AbstractIterator.toBuffer(Iterator.scala:1157)\n\tat scala.collection.TraversableOnce$class.toArray(TraversableOnce.scala:252)\n\tat scala.collection.AbstractIterator.toArray(Iterator.scala:1157)\n\tat org.apache.spark.sql.catalyst.trees.TreeNode.transformChildren(TreeNode.scala:305)\n\tat org.apache.spark.sql.catalyst.plans.logical.LogicalPlan.resolveOperators(LogicalPlan.scala:54)\n\tat org.apache.spark.sql.catalyst.analysis.Analyzer$ResolveRelations$.apply(Analyzer.scala:310)\n\tat org.apache.spark.sql.catalyst.analysis.Analyzer$ResolveRelations$.apply(Analyzer.scala:300)\n\tat org.apache.spark.sql.catalyst.rules.RuleExecutor$$anonfun$execute$1$$anonfun$apply$1.apply(RuleExecutor.scala:83)\n\tat org.apache.spark.sql.catalyst.rules.RuleExecutor$$anonfun$execute$1$$anonfun$apply$1.apply(RuleExecutor.scala:80)\n\tat scala.collection.LinearSeqOptimized$class.foldLeft(LinearSeqOptimized.scala:111)\n\tat scala.collection.immutable.List.foldLeft(List.scala:84)\n\tat org.apache.spark.sql.catalyst.rules.RuleExecutor$$anonfun$execute$1.apply(RuleExecutor.scala:80)\n\tat org.apache.spark.sql.catalyst.rules.RuleExecutor$$anonfun$execute$1.apply(RuleExecutor.scala:72)\n\tat scala.collection.immutable.List.foreach(List.scala:318)\n\tat org.apache.spark.sql.catalyst.rules.RuleExecutor.execute(RuleExecutor.scala:72)\n\tat org.apache.spark.sql.execution.QueryExecution.analyzed$lzycompute(QueryExecution.scala:36)\n\tat org.apache.spark.sql.execution.QueryExecution.analyzed(QueryExecution.scala:36)\n\tat org.apache.spark.sql.execution.QueryExecution.assertAnalyzed(QueryExecution.scala:34)\n\tat org.apache.spark.sql.DataFrame.<init>(DataFrame.scala:133)\n\tat org.apache.spark.sql.DataFrame$.apply(DataFrame.scala:52)\n\tat org.apache.spark.sql.SQLContext.sql(SQLContext.scala:816)</div>","startTime":1.457584390115E12,"submitTime":1.457584354328E12,"finishTime":1.457584390192E12,"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":"r.sainudiin@math.canterbury.ac.nz","commandTitle":"","showCommandTitle":false,"hideCommandCode":false,"hideCommandResult":false,"iPythonMetadata":null,"nuid":"e8debf56-e212-429f-b4ed-0784aeec01db"},{"version":"CommandV1","origId":50623,"guid":"f6061085-357c-4526-8e8e-d93e680f6ecb","subtype":"command","commandType":"auto","position":1.999999999847205,"command":"//aggregate functions\ndisplay(sqlContext.sql(\"select avg(price) as avgprice from diamonds\"))","commandVersion":0,"state":"finished","results":{"type":"table","data":[[3932.799721913237]],"arguments":{},"addedWidgets":{},"removedWidgets":[],"schema":[{"name":"avgprice","type":"\"double\""}],"overflow":false,"aggData":[],"aggSchema":[],"aggOverflow":false,"aggSeriesLimitReached":false,"aggError":"","aggType":"","plotOptions":null,"isJsonSchema":true,"dbfsResultPath":null},"errorSummary":null,"error":null,"startTime":1.457584416214E12,"submitTime":1.457584380445E12,"finishTime":1.457584416344E12,"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":"r.sainudiin@math.canterbury.ac.nz","commandTitle":"","showCommandTitle":false,"hideCommandCode":false,"hideCommandResult":false,"iPythonMetadata":null,"nuid":"91aa04ea-0743-4221-8754-15e97c86b9e9"},{"version":"CommandV1","origId":50624,"guid":"441d8f39-3132-4c32-ac64-9c7fcb4793a4","subtype":"command","commandType":"auto","position":1.9999999999236024,"command":"//average operator seems to be doing an auto-type conversion from int to double\ndisplay(sqlContext.sql(\"select avg(cast(price as Double)) as avgprice from diamonds\"))","commandVersion":0,"state":"finished","results":{"type":"table","data":[[3932.799721913237]],"arguments":{},"addedWidgets":{},"removedWidgets":[],"schema":[{"name":"avgprice","type":"\"double\""}],"overflow":false,"aggData":[],"aggSchema":[],"aggOverflow":false,"aggSeriesLimitReached":false,"aggError":"","aggType":"","plotOptions":null,"isJsonSchema":true,"dbfsResultPath":null},"errorSummary":null,"error":null,"startTime":1.457576041711E12,"submitTime":1.457575968906E12,"finishTime":1.457576041829E12,"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":"r.sainudiin@math.canterbury.ac.nz","commandTitle":"","showCommandTitle":false,"hideCommandCode":false,"hideCommandResult":false,"iPythonMetadata":null,"nuid":"73b81707-5ab3-41ea-85e7-cb3e33f2f563"},{"version":"CommandV1","origId":50625,"guid":"d6945e8b-38db-479f-8498-76bd7eb049c6","subtype":"command","commandType":"auto","position":1.9999999999618012,"command":"//aggregate function and grouping\ndisplay(sqlContext.sql(\"select color, avg(price) as avgprice from diamonds group by color\"))","commandVersion":0,"state":"finished","results":{"type":"table","data":[["D",3169.9540959409596],["E",3076.7524752475247],["F",3724.886396981765],["G",3999.135671271697],["H",4486.669195568401],["I",5091.874953891553],["J",5323.81801994302]],"arguments":{},"addedWidgets":{},"removedWidgets":[],"schema":[{"name":"color","type":"\"string\""},{"name":"avgprice","type":"\"double\""}],"overflow":false,"aggData":[],"aggSchema":[],"aggOverflow":false,"aggSeriesLimitReached":false,"aggError":"","aggType":"","plotOptions":null,"isJsonSchema":true,"dbfsResultPath":null},"errorSummary":"org.apache.spark.sql.AnalysisException: cannot resolve 'colour' given input columns depth, table, color, y, cut, price, x, carat, z, clarity; line 1 pos 7","error":"<div class=\"ansiout\">\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$$anonfun$apply$2.applyOrElse(CheckAnalysis.scala:60)\n\tat org.apache.spark.sql.catalyst.analysis.CheckAnalysis$$anonfun$checkAnalysis$1$$anonfun$apply$2.applyOrElse(CheckAnalysis.scala:57)\n\tat org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$transformUp$1.apply(TreeNode.scala:319)\n\tat org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$transformUp$1.apply(TreeNode.scala:319)\n\tat org.apache.spark.sql.catalyst.trees.CurrentOrigin$.withOrigin(TreeNode.scala:53)\n\tat org.apache.spark.sql.catalyst.trees.TreeNode.transformUp(TreeNode.scala:318)\n\tat org.apache.spark.sql.catalyst.plans.QueryPlan.transformExpressionUp$1(QueryPlan.scala:107)\n\tat org.apache.spark.sql.catalyst.plans.QueryPlan.org$apache$spark$sql$catalyst$plans$QueryPlan$$recursiveTransform$2(QueryPlan.scala:117)\n\tat org.apache.spark.sql.catalyst.plans.QueryPlan$$anonfun$org$apache$spark$sql$catalyst$plans$QueryPlan$$recursiveTransform$2$1.apply(QueryPlan.scala:121)\n\tat scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:244)\n\tat scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:244)\n\tat scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)\n\tat scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:47)\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$$recursiveTransform$2(QueryPlan.scala:121)\n\tat org.apache.spark.sql.catalyst.plans.QueryPlan$$anonfun$2.apply(QueryPlan.scala:125)\n\tat scala.collection.Iterator$$anon$11.next(Iterator.scala:328)\n\tat scala.collection.Iterator$class.foreach(Iterator.scala:727)\n\tat scala.collection.AbstractIterator.foreach(Iterator.scala:1157)\n\tat scala.collection.generic.Growable$class.$plus$plus$eq(Growable.scala:48)\n\tat scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:103)\n\tat scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:47)\n\tat scala.collection.TraversableOnce$class.to(TraversableOnce.scala:273)\n\tat scala.collection.AbstractIterator.to(Iterator.scala:1157)\n\tat scala.collection.TraversableOnce$class.toBuffer(TraversableOnce.scala:265)\n\tat scala.collection.AbstractIterator.toBuffer(Iterator.scala:1157)\n\tat scala.collection.TraversableOnce$class.toArray(TraversableOnce.scala:252)\n\tat scala.collection.AbstractIterator.toArray(Iterator.scala:1157)\n\tat org.apache.spark.sql.catalyst.plans.QueryPlan.transformExpressionsUp(QueryPlan.scala:125)\n\tat org.apache.spark.sql.catalyst.analysis.CheckAnalysis$$anonfun$checkAnalysis$1.apply(CheckAnalysis.scala:57)\n\tat org.apache.spark.sql.catalyst.analysis.CheckAnalysis$$anonfun$checkAnalysis$1.apply(CheckAnalysis.scala:50)\n\tat org.apache.spark.sql.catalyst.trees.TreeNode.foreachUp(TreeNode.scala:105)\n\tat org.apache.spark.sql.catalyst.analysis.CheckAnalysis$class.checkAnalysis(CheckAnalysis.scala:50)\n\tat org.apache.spark.sql.catalyst.analysis.Analyzer.checkAnalysis(Analyzer.scala:44)\n\tat org.apache.spark.sql.execution.QueryExecution.assertAnalyzed(QueryExecution.scala:34)\n\tat org.apache.spark.sql.DataFrame.<init>(DataFrame.scala:133)\n\tat org.apache.spark.sql.DataFrame$.apply(DataFrame.scala:52)\n\tat org.apache.spark.sql.SQLContext.sql(SQLContext.scala:816)</div>","startTime":1.457576106477E12,"submitTime":1.457576033655E12,"finishTime":1.457576107192E12,"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":"r.sainudiin@math.canterbury.ac.nz","commandTitle":"","showCommandTitle":false,"hideCommandCode":false,"hideCommandResult":false,"iPythonMetadata":null,"nuid":"84b2a287-ba7f-4f89-9f3e-aef626b8ad66"},{"version":"CommandV1","origId":50627,"guid":"ecc1fed1-12aa-4ce1-a629-3f79e7d85938","subtype":"command","commandType":"auto","position":1.999999999971351,"command":"%md\n### Why do we need to know these interactive SQL queries?\nSuch queries can help us explore the data and thereby inform the modeling process!!!\n\nOf course, if you don't know SQL then don't worry, we will be doing these things in scala using DataFrames.\n\nFinally, those who are planning to take the Spark Develope Exams online, then you can't escape from SQL questions there...","commandVersion":0,"state":"finished","results":null,"errorSummary":null,"error":null,"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":"","commandTitle":"","showCommandTitle":false,"hideCommandCode":false,"hideCommandResult":false,"iPythonMetadata":null,"nuid":"db7cc06b-8b5d-48ad-802d-7d37131ac295"},{"version":"CommandV1","origId":50626,"guid":"08d333b7-b1bc-4d8e-8dc8-8fee22d82aef","subtype":"command","commandType":"auto","position":1.9999999999809006,"command":"%md\n#### Further Preparation\nFor more on SQL syntax, check the SQL tutorial on [W3Schools](http://www.w3schools.com/sql/default.asp) \nNote that [HiveQL](https://cwiki.apache.org/confluence/display/Hive/LanguageManual) supports only a subset of operations supported by SQL","commandVersion":0,"state":"finished","results":null,"errorSummary":null,"error":null,"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":"","commandTitle":"","showCommandTitle":false,"hideCommandCode":false,"hideCommandResult":false,"iPythonMetadata":null,"nuid":"f41c55ec-4c9f-48f3-b8be-90dd64d5f7f4"},{"version":"CommandV1","origId":49701,"guid":"2d03faa1-4b77-4129-9541-8246d149ed5c","subtype":"command","commandType":"auto","position":2.0,"command":"%md\n\n# [Scalable Data Science](http://www.math.canterbury.ac.nz/~r.sainudiin/courses/ScalableDataScience/)\n\n\n### prepared by [Raazesh Sainudiin](https://nz.linkedin.com/in/raazesh-sainudiin-45955845) and [Sivanand Sivaram](https://www.linkedin.com/in/sivanand)\n\n*supported by* [](https://databricks.com/)\nand \n[](https://www.awseducate.com/microsite/CommunitiesEngageHome)","commandVersion":0,"state":"finished","results":null,"errorSummary":null,"error":null,"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":"","commandTitle":"","showCommandTitle":false,"hideCommandCode":false,"hideCommandResult":false,"iPythonMetadata":null,"nuid":"b8c434bc-340a-4fd3-a4bd-bf24cf1990a4"}],"dashboards":[],"guid":"47a35e2e-3dbb-4737-a38b-3982a2c9f8d6","globalVars":{},"iPythonMetadata":null,"inputWidgets":{}};</script> <script src="https://databricks-prod-cloudfront.cloud.databricks.com/static/201602081754420800-0c2673ac858e227cad536fdb45d140aeded238db/js/notebook-main.js" onerror="window.mainJsLoadError = true;"></script> </head> <body> <script> if (window.mainJsLoadError) { var u = 'https://databricks-prod-cloudfront.cloud.databricks.com/static/201602081754420800-0c2673ac858e227cad536fdb45d140aeded238db/js/notebook-main.js'; var b = document.getElementsByTagName('body')[0]; var c = document.createElement('div'); c.innerHTML = ('<h1>Network Error</h1>' + '<p><b>Please check your network connection and try again.</b></p>' + '<p>Could not load a required resource: ' + u + '</p>'); c.style.margin = '30px'; c.style.padding = '20px 50px'; c.style.backgroundColor = '#f5f5f5'; c.style.borderRadius = '5px'; b.appendChild(c); } </script> </body> </html>