<!DOCTYPE html>
<html>
<head>
<meta name="databricks-html-version" content="1">
<title>012_1MSongsKMeans_Intro - 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). Can save substantial space at the cost of some extra CPU time."},{"keyPattern":"spark\\.speculation","valuePattern":"(true|false)","keyPatternDisplay":"spark.speculation","valuePatternDisplay":"true|false","description":"Whether to use speculation (recommended off for streaming)"},{"keyPattern":"spark\\.es(\\.[^\\.]+)+","valuePattern":".*","keyPatternDisplay":"spark.es.*","valuePatternDisplay":"*","description":"Configuration options for ElasticSearch"},{"keyPattern":"es(\\.([^\\.]+))+","valuePattern":".*","keyPatternDisplay":"es.*","valuePatternDisplay":"*","description":"Configuration options for ElasticSearch"},{"keyPattern":"spark\\.(storage|shuffle)\\.memoryFraction","valuePattern":"0?\\.0*([1-9])([0-9])*","keyPatternDisplay":"spark.(storage|shuffle).memoryFraction","valuePatternDisplay":"(0.0,1.0)","description":"Fraction of Java heap to use for Spark's shuffle or storage"},{"keyPattern":"spark\\.streaming\\.backpressure\\.enabled","valuePattern":"(true|false)","keyPatternDisplay":"spark.streaming.backpressure.enabled","valuePatternDisplay":"true|false","description":"Enables or disables Spark Streaming's internal backpressure mechanism (since 1.5). This enables the Spark Streaming to control the receiving rate based on the current batch scheduling delays and processing times so that the system receives only as fast as the system can process. Internally, this dynamically sets the maximum receiving rate of receivers. This rate is upper bounded by the values `spark.streaming.receiver.maxRate` and `spark.streaming.kafka.maxRatePerPartition` if they are set."},{"keyPattern":"spark\\.streaming\\.receiver\\.maxRate","valuePattern":"^([0-9]{1,})$","keyPatternDisplay":"spark.streaming.receiver.maxRate","valuePatternDisplay":"numeric","description":"Maximum rate (number of records per second) at which each receiver will receive data. Effectively, each stream will consume at most this number of records per second. Setting this configuration to 0 or a negative number will put no limit on the rate. See the deployment guide in the Spark Streaming programing guide for mode details."},{"keyPattern":"spark\\.streaming\\.kafka\\.maxRatePerPartition","valuePattern":"^([0-9]{1,})$","keyPatternDisplay":"spark.streaming.kafka.maxRatePerPartition","valuePatternDisplay":"numeric","description":"Maximum rate (number of records per second) at which data will be read from each Kafka partition when using the Kafka direct stream API introduced in Spark 1.3. See the Kafka Integration guide for more details."},{"keyPattern":"spark\\.streaming\\.kafka\\.maxRetries","valuePattern":"^([0-9]{1,})$","keyPatternDisplay":"spark.streaming.kafka.maxRetries","valuePatternDisplay":"numeric","description":"Maximum number of consecutive retries the driver will make in order to find the latest offsets on the leader of each partition (a default value of 1 means that the driver will make a maximum of 2 attempts). Only applies to the Kafka direct stream API introduced in Spark 1.3."},{"keyPattern":"spark\\.streaming\\.ui\\.retainedBatches","valuePattern":"^([0-9]{1,})$","keyPatternDisplay":"spark.streaming.ui.retainedBatches","valuePatternDisplay":"numeric","description":"How many batches the Spark Streaming UI and status APIs remember before garbage collecting."}],"enableReactNotebookComments":true,"enableResetPassword":true,"enableJobsSparkUpgrade":true,"sparkVersions":[{"key":"1.3.x-ubuntu15.10","displayName":"Spark 1.3.0","packageLabel":"spark-1.3-jenkins-ip-10-30-9-162-U0c2673ac85-Sa2ee4664b2-2016-02-09-02:05:59.455061","upgradable":true,"deprecated":false,"customerVisible":true},{"key":"1.4.x-ubuntu15.10","displayName":"Spark 1.4.1","packageLabel":"spark-1.4-jenkins-ip-10-30-9-162-U0c2673ac85-S33a1e4b9c6-2016-02-09-02:05:59.455061","upgradable":true,"deprecated":false,"customerVisible":true},{"key":"1.5.x-ubuntu15.10","displayName":"Spark 1.5.2","packageLabel":"spark-1.5-jenkins-ip-10-30-9-162-U0c2673ac85-S5917a1044d-2016-02-09-02:05:59.455061","upgradable":true,"deprecated":false,"customerVisible":true},{"key":"1.6.x-ubuntu15.10","displayName":"Spark 1.6.0","packageLabel":"spark-1.6-jenkins-ip-10-30-9-162-U0c2673ac85-Scabba801f3-2016-02-09-02:05:59.455061","upgradable":true,"deprecated":false,"customerVisible":true},{"key":"master","displayName":"Spark master (dev)","packageLabel":"","upgradable":true,"deprecated":false,"customerVisible":false}],"enableRestrictedClusterCreation":false,"enableFeedback":false,"defaultNumWorkers":8,"serverContinuationTimeoutMillis":10000,"driverStderrFilePrefix":"stderr","driverStdoutFilePrefix":"stdout","enableSparkDocsSearch":true,"prefetchSidebarNodes":true,"sparkHistoryServerEnabled":true,"sanitizeMarkdownHtml":true,"enableIPythonImportExport":true,"enableNotebookHistoryDiffing":true,"branch":"2.12.3","accountsLimit":-1,"enableNotebookGitBranching":true,"local":false,"displayDefaultContainerMemoryGB":6,"deploymentMode":"production","useSpotForWorkers":false,"enableUserInviteWorkflow":false,"enableStaticNotebooks":true,"dbcGuideURL":"#workspace/databricks_guide/00 Welcome to Databricks","enableCssTransitions":true,"pricingURL":"https://databricks.com/product/pricing","enableClusterAclsConfig":false,"orgId":0,"enableNotebookGitVersioning":true,"files":"files/","enableDriverLogsUI":true,"disableLegacyDashboards":false,"enableWorkspaceAclsConfig":true,"dropzoneMaxFileSize":4096,"enableNewDashboardViews":false,"driverLog4jFilePrefix":"log4j","enableMavenLibraries":true,"displayRowLimit":1000,"defaultSparkVersion":{"key":"1.5.x-ubuntu15.10","displayName":"Spark 1.5.2","packageLabel":"spark-1.5-jenkins-ip-10-30-9-162-U0c2673ac85-S5917a1044d-2016-02-09-02:05:59.455061","upgradable":true,"deprecated":false,"customerVisible":true},"clusterPublisherRootId":5,"enableLatestJobRunResultPermalink":true,"disallowAddingAdmins":false,"enableSparkConfUI":true,"enableOrgSwitcherUI":false,"clustersLimit":-1,"enableJdbcImport":true,"logfiles":"logfiles/","enableWebappSharding":false,"enableClusterDeltaUpdates":true,"csrfToken":"3dde4939-9fec-41a8-bc90-741d1aa44ca4","useFixedStaticNotebookVersionForDevelopment":false,"enableBasicReactDialogBoxes":true,"requireEmailUserName":true,"enableDashboardViews":false,"dbcFeedbackURL":"http://feedback.databricks.com/forums/263785-product-feedback","enableWorkspaceAclService":true,"someName":"Raazesh Sainudiin","enableWorkspaceAcls":true,"gitHash":"0c2673ac858e227cad536fdb45d140aeded238db","userFullname":"Raazesh Sainudiin","enableClusterCreatePage":false,"enableImportFromUrl":true,"enableMiniClusters":false,"enableWebSocketDeltaUpdates":true,"enableDebugUI":false,"showHiddenSparkVersions":false,"allowNonAdminUsers":true,"userId":100005,"dbcSupportURL":"","staticNotebookResourceUrl":"https://databricks-prod-cloudfront.cloud.databricks.com/static/201602081754420800-0c2673ac858e227cad536fdb45d140aeded238db/","enableSparkPackages":true,"enableHybridClusterType":false,"enableNotebookHistoryUI":true,"availableWorkspaces":[{"name":"Workspace 0","orgId":0}],"enableFolderHtmlExport":true,"enableSparkVersionsUI":true,"databricksGuideStaticUrl":"","enableHybridClusters":true,"notebookLoadingBackground":"#fff","enableNewJobRunDetailsPage":true,"enableDashboardExport":true,"user":"r.sainudiin@math.canterbury.ac.nz","enableServerAutoComplete":true,"enableStaticHtmlImport":true,"defaultMemoryPerContainerMB":6000,"enablePresenceUI":true,"tablesPublisherRootId":7,"enableNewInputWidgetUI":false,"accounts":true,"enableNewProgressReportUI":true,"defaultCoresPerContainer":4};</script>
<script>var __DATABRICKS_NOTEBOOK_MODEL = {"version":"NotebookV1","origId":64223,"name":"012_1MSongsKMeans_Intro","language":"scala","commands":[{"version":"CommandV1","origId":64466,"guid":"6c3ea9d0-9e2c-46f5-9d6e-3994520523b8","subtype":"command","commandType":"auto","position":0.25,"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":"49602bb6-7338-4d09-babf-21ee903c5383"},{"version":"CommandV1","origId":129723,"guid":"6e33cd40-02d9-4e81-aa57-a117b4b57d8d","subtype":"command","commandType":"auto","position":0.375,"command":"%md\nThe [html source url](https://raw.githubusercontent.com/raazesh-sainudiin/scalable-data-science/master/db/week4/07_UnsupervisedClusteringKMeans_1MSongs/012_1MSongsKMeans_Intro.html) of this databricks notebook and its recorded Uji ![Image of Uji, Dogen's Time-Being](https://raw.githubusercontent.com/raazesh-sainudiin/scalable-data-science/master/images/UjiTimeBeingDogen.png \"uji\"):\n\n[](https://www.youtube.com/v/_Lxtxmn0L-w?rel=0&autoplay=1&modestbranding=1&start=4697&end=4823)\n","commandVersion":0,"state":"error","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":"ea2b60d4-683f-47de-9683-c18de181d152"},{"version":"CommandV1","origId":64230,"guid":"fccd0056-c1de-44c9-bba6-ab04a745944b","subtype":"command","commandType":"auto","position":0.5,"command":"%md\n**SOURCE:** This is the scala version of the python notebook from the databricks Community Edition that has been added to this databricks shard at [Workspace -> scalable-data-science -> xtraResources -> dbCE -> MLlib -> unsupervised -> clustering -> k-means -> 1MSongsPy_ETLExploreModel](/#workspace/scalable-data-science/xtraResources/dbCE/MLlib/unsupervised/clustering/k-means/1MSongsPy_ETLExploreModel) as extra resources for this project-focussed course [Scalable Data Science](http://www.math.canterbury.ac.nz/~r.sainudiin/courses/ScalableDataScience/).","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":"a311140c-f63a-47e9-b5b3-1a890bc5bc9d"},{"version":"CommandV1","origId":64225,"guid":"8099443d-006e-44e4-ba66-1e647b028956","subtype":"command","commandType":"auto","position":1.0,"command":"%md\n#Data Science with Apache Spark and Scala in Databricks","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":"c9a78d7a-2003-43cc-808a-adf283f5ced9"},{"version":"CommandV1","origId":64226,"guid":"df6c75e9-12a0-4e68-999b-3594fd8e6597","subtype":"command","commandType":"auto","position":2.0,"command":"%md \nWhen you first hear a song, do you ever categorize it as slow or fast in your head? Is it even a valid categorization? If so, can one do it automatically? I have always wondered about that. That is why I got excited when I learned about the [Million Songs Dataset](million song dataset challenge). \n\nIn this tutorial we will walk through a practical example of a data science project with Apache Spark in Python. We are going to parse, explore and model a sample from the million songs dataset stored on distributed storage. This tutorial is organized into three sections:\n\n1. ETL: Parses raw texts and creates a cached table\n2. Explore: Explores different aspects of the songs table using graphs\n3. Model: Uses SparkML to cluster songs based on some of their attributes\n\n\n\n\nThe goal of this tutorial is to prepare you for real world data science projects. Make sure you go through the tutorial in the above order and use the exercises to make yourself familiar further with the API. Also make sure you run these notebooks on a **1.6.x** cluster.","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":"78fafc63-91bc-4f4c-b6d1-c65dda99a2ab"},{"version":"CommandV1","origId":64227,"guid":"a3b48c3d-8894-4292-85ad-dde7efa9a31d","subtype":"command","commandType":"auto","position":3.0,"command":"%md\n### 1. ETL\n\nThe first step of most data science projects is extracting, transforming and loading data into well formated tables. Our example starts with ETL as well. By following the ETL noteboook you can expect to learn about following Spark concepts:\n* RDD: Resilient Distributed Dataset\n* Reading and transforming RDDs\n* Schema in Spark\n* Spark DataFrame\n* Temp tables\n* Caching tables","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":"e4c3d6ec-881e-42eb-9004-e7a219d8637d"},{"version":"CommandV1","origId":64228,"guid":"548224b6-4b00-4a8c-8329-60bc865cf72c","subtype":"command","commandType":"auto","position":4.0,"command":"%md\n### 2. Explore\nExploratory analysis is a key step in any real data project. Data scientists use variety of tools to explore and visualize their data. In the second notebook of this tutorial we introduce several tools in Python and Databricks notebooks that can help you visually explore your large data. By reading this notebook and finishing its exercises you will become familiar with:\n* How to view the schema of a table\n* How to display ggplot and matplotlib figures in Notebooks\n* How to summarize and visualize different aspects of large datasets\n* How to sample and visualize large 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":"921efec4-7666-4dea-b6de-d667b0c098b4"},{"version":"CommandV1","origId":64229,"guid":"571aded5-bc66-47ac-8a7e-02a3379bfc1a","subtype":"command","commandType":"auto","position":5.0,"command":"%md\n### 3. Model\nThe end goal of many data scientists is producing useful models. These models are often used for prediction of new and upcoming events in production. In our third notebook we construct a simple K-means clustering model. In this notebook you will learn about:\n* Feature transformation\n* Fitting a model using SparkML API\n* Applying a model to data\n* Visualizing model results\n* Model tuning","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":"7018f83a-edf7-4378-b621-f5aac0d769d6"},{"version":"CommandV1","origId":64504,"guid":"58996288-ea07-41c6-877b-42ffd32dcfc7","subtype":"command","commandType":"auto","position":6.0,"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":"8365d67f-dc35-48e5-871d-c2dab6c48ce1"}],"dashboards":[],"guid":"e96fdb14-28d4-4467-9256-6fd3a95d3e5f","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>