diff --git a/notebook/2A94M5J1Z/note.json b/notebook/2A94M5J1Z/note.json index a37cf19a974..785ccea3cee 100644 --- a/notebook/2A94M5J1Z/note.json +++ b/notebook/2A94M5J1Z/note.json @@ -13,7 +13,7 @@ "groups": [], "scatter": {} }, - "editorHide": false + "editorHide": true }, "settings": { "params": {}, @@ -32,42 +32,9 @@ "status": "FINISHED", "progressUpdateIntervalMs": 500 }, - { - "title": "Prepare data", - "text": "import sys.process._\n//you will need \u0027wget\u0027 tool to download\n\"wget http://archive.ics.uci.edu/ml/machine-learning-databases/00222/bank.zip\" !\n\"mkdir data\" !\n\"unzip bank.zip -d data\" !\n\"rm bank.zip\" !", - "config": { - "colWidth": 12.0, - "graph": { - "mode": "table", - "height": 300.0, - "optionOpen": false, - "keys": [], - "values": [], - "groups": [], - "scatter": {} - }, - "title": true - }, - "settings": { - "params": {}, - "forms": {} - }, - "jobName": "paragraph_1417656535623_-196593192", - "id": "20141204-102855_1590713432", - "result": { - "code": "SUCCESS", - "type": "TEXT", - "msg": "import sys.process._\nwarning: there were 1 feature warning(s); re-run with -feature for details\nres1: Int \u003d 0\nwarning: there were 1 feature warning(s); re-run with -feature for details\nres2: Int \u003d 0\nwarning: there were 1 feature warning(s); re-run with -feature for details\nres3: Int \u003d 0\nwarning: there were 1 feature warning(s); re-run with -feature for details\nres4: Int \u003d 0\n" - }, - "dateCreated": "Dec 4, 2014 10:28:55 AM", - "dateStarted": "Apr 1, 2015 9:11:12 PM", - "dateFinished": "Apr 1, 2015 9:11:22 PM", - "status": "FINISHED", - "progressUpdateIntervalMs": 500 - }, { "title": "Load data into table", - "text": "import sys.process._\n// Zeppelin creates and injects sc (SparkContext) and sqlContext (HiveContext or SqlContext)\n// So you don\u0027t need create them manually\n\nval zeppelinHome \u003d (\"pwd\" !!).replace(\"\\n\", \"\")\nval bankText \u003d sc.textFile(s\"file://$zeppelinHome/data/bank-full.csv\")\n\ncase class Bank(age: Integer, job: String, marital: String, education: String, balance: Integer)\n\nval bank \u003d bankText.map(s \u003d\u003e s.split(\";\")).filter(s \u003d\u003e s(0) !\u003d \"\\\"age\\\"\").map(\n s \u003d\u003e Bank(s(0).toInt, \n s(1).replaceAll(\"\\\"\", \"\"),\n s(2).replaceAll(\"\\\"\", \"\"),\n s(3).replaceAll(\"\\\"\", \"\"),\n s(5).replaceAll(\"\\\"\", \"\").toInt\n )\n).toDF()\nbank.registerTempTable(\"bank\")\n\n", + "text": "import org.apache.commons.io.IOUtils\nimport java.net.URL\nimport java.nio.charset.Charset\n\n// Zeppelin creates and injects sc (SparkContext) and sqlContext (HiveContext or SqlContext)\n// So you don\u0027t need create them manually\n\n// load bank data\nval bankText \u003d sc.parallelize(\n IOUtils.toString(\n new URL(\"https://s3.amazonaws.com/apache-zeppelin/tutorial/bank/bank.csv\"),\n Charset.forName(\"utf8\")).split(\"\\n\"))\n\ncase class Bank(age: Integer, job: String, marital: String, education: String, balance: Integer)\n\nval bank \u003d bankText.map(s \u003d\u003e s.split(\";\")).filter(s \u003d\u003e s(0) !\u003d \"\\\"age\\\"\").map(\n s \u003d\u003e Bank(s(0).toInt, \n s(1).replaceAll(\"\\\"\", \"\"),\n s(2).replaceAll(\"\\\"\", \"\"),\n s(3).replaceAll(\"\\\"\", \"\"),\n s(5).replaceAll(\"\\\"\", \"\").toInt\n )\n).toDF()\nbank.registerTempTable(\"bank\")", "config": { "colWidth": 12.0, "graph": { @@ -90,11 +57,11 @@ "result": { "code": "SUCCESS", "type": "TEXT", - "msg": "import sys.process._\nsqlContext: org.apache.spark.sql.SQLContext \u003d org.apache.spark.sql.SQLContext@2c91e2d6\nwarning: there were 1 feature warning(s); re-run with -feature for details\nzeppelinHome: String \u003d /home/langley/lab/incubator-zeppelin\nbankText: org.apache.spark.rdd.RDD[String] \u003d /home/langley/lab/incubator-zeppelin/data/bank-full.csv MapPartitionsRDD[1] at textFile at \u003cconsole\u003e:31\ndefined class Bank\nbank: org.apache.spark.sql.DataFrame \u003d [age: int, job: string, marital: string, education: string, balance: int]\n" + "msg": "import org.apache.commons.io.IOUtils\nimport java.net.URL\nimport java.nio.charset.Charset\nbankText: org.apache.spark.rdd.RDD[String] \u003d ParallelCollectionRDD[32] at parallelize at \u003cconsole\u003e:65\ndefined class Bank\nbank: org.apache.spark.sql.DataFrame \u003d [age: int, job: string, marital: string, education: string, balance: int]\n" }, "dateCreated": "Feb 10, 2015 1:52:59 AM", - "dateStarted": "Apr 1, 2015 9:11:28 PM", - "dateFinished": "Apr 1, 2015 9:11:39 PM", + "dateStarted": "Jul 3, 2015 1:43:40 PM", + "dateFinished": "Jul 3, 2015 1:43:45 PM", "status": "FINISHED", "progressUpdateIntervalMs": 500 }, @@ -144,11 +111,11 @@ "result": { "code": "SUCCESS", "type": "TABLE", - "msg": "age\tvalue\n18\t12\n19\t35\n20\t50\n21\t79\n22\t129\n23\t202\n24\t302\n25\t527\n26\t805\n27\t909\n28\t1038\n29\t1185\n" + "msg": "age\tvalue\n19\t4\n20\t3\n21\t7\n22\t9\n23\t20\n24\t24\n25\t44\n26\t77\n27\t94\n28\t103\n29\t97\n" }, "dateCreated": "Feb 10, 2015 1:53:02 AM", - "dateStarted": "Apr 1, 2015 9:11:43 PM", - "dateFinished": "Apr 1, 2015 9:11:45 PM", + "dateStarted": "Jul 3, 2015 1:43:17 PM", + "dateFinished": "Jul 3, 2015 1:43:23 PM", "status": "FINISHED", "progressUpdateIntervalMs": 500 }, @@ -206,11 +173,11 @@ "result": { "code": "SUCCESS", "type": "TABLE", - "msg": "age\tvalue\n18\t12\n19\t35\n20\t50\n21\t79\n22\t129\n23\t202\n24\t302\n25\t527\n26\t805\n27\t909\n28\t1038\n29\t1185\n30\t1757\n31\t1996\n32\t2085\n33\t1972\n34\t1930\n" + "msg": "age\tvalue\n19\t4\n20\t3\n21\t7\n22\t9\n23\t20\n24\t24\n25\t44\n26\t77\n27\t94\n28\t103\n29\t97\n30\t150\n31\t199\n32\t224\n33\t186\n34\t231\n" }, "dateCreated": "Feb 12, 2015 2:54:04 PM", - "dateStarted": "Apr 1, 2015 9:12:03 PM", - "dateFinished": "Apr 1, 2015 9:12:03 PM", + "dateStarted": "Jul 3, 2015 1:43:28 PM", + "dateFinished": "Jul 3, 2015 1:43:29 PM", "status": "FINISHED", "progressUpdateIntervalMs": 500 }, @@ -279,11 +246,11 @@ "result": { "code": "SUCCESS", "type": "TABLE", - "msg": "age\tvalue\n18\t12\n19\t35\n20\t47\n21\t74\n22\t120\n23\t175\n24\t248\n25\t423\n26\t615\n27\t658\n28\t697\n29\t683\n30\t1012\n31\t1017\n32\t941\n33\t746\n34\t650\n35\t631\n36\t538\n37\t453\n38\t394\n39\t346\n40\t257\n41\t241\n42\t218\n43\t183\n44\t170\n45\t146\n46\t130\n47\t100\n48\t124\n49\t101\n50\t76\n51\t72\n52\t62\n53\t71\n54\t55\n55\t54\n56\t45\n57\t38\n58\t35\n59\t36\n60\t27\n61\t5\n63\t2\n66\t5\n67\t3\n68\t4\n69\t2\n70\t1\n71\t1\n72\t5\n73\t2\n77\t1\n83\t2\n86\t1\n" + "msg": "age\tvalue\n19\t4\n20\t3\n21\t7\n22\t9\n23\t17\n24\t13\n25\t33\n26\t56\n27\t64\n28\t78\n29\t56\n30\t92\n31\t86\n32\t105\n33\t61\n34\t75\n35\t46\n36\t50\n37\t43\n38\t44\n39\t30\n40\t25\n41\t19\n42\t23\n43\t21\n44\t20\n45\t15\n46\t14\n47\t12\n48\t12\n49\t11\n50\t8\n51\t6\n52\t9\n53\t4\n55\t3\n56\t3\n57\t2\n58\t7\n59\t2\n60\t5\n66\t2\n69\t1\n" }, "dateCreated": "Feb 13, 2015 11:04:22 PM", - "dateStarted": "Apr 1, 2015 9:12:10 PM", - "dateFinished": "Apr 1, 2015 9:12:10 PM", + "dateStarted": "Jul 3, 2015 1:43:33 PM", + "dateFinished": "Jul 3, 2015 1:43:34 PM", "status": "FINISHED", "progressUpdateIntervalMs": 500 }, @@ -299,7 +266,8 @@ "values": [], "groups": [], "scatter": {} - } + }, + "editorHide": true }, "settings": { "params": {}, @@ -319,22 +287,55 @@ "progressUpdateIntervalMs": 500 }, { - "config": {}, + "text": "%md\n\nAbout bank data\n\n```\nCitation Request:\n This dataset is public available for research. The details are described in [Moro et al., 2011]. \n Please include this citation if you plan to use this database:\n\n [Moro et al., 2011] S. Moro, R. Laureano and P. Cortez. Using Data Mining for Bank Direct Marketing: An Application of the CRISP-DM Methodology. \n In P. Novais et al. (Eds.), Proceedings of the European Simulation and Modelling Conference - ESM\u00272011, pp. 117-121, Guimarães, Portugal, October, 2011. EUROSIS.\n\n Available at: [pdf] http://hdl.handle.net/1822/14838\n [bib] http://www3.dsi.uminho.pt/pcortez/bib/2011-esm-1.txt\n```", + "config": { + "colWidth": 12.0, + "graph": { + "mode": "table", + "height": 300.0, + "optionOpen": false, + "keys": [], + "values": [], + "groups": [], + "scatter": {} + }, + "editorHide": true + }, "settings": { "params": {}, "forms": {} }, "jobName": "paragraph_1427420818407_872443482", "id": "20150326-214658_12335843", + "result": { + "code": "SUCCESS", + "type": "HTML", + "msg": "\u003cp\u003eAbout bank data\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003eCitation Request:\n This dataset is public available for research. The details are described in [Moro et al., 2011]. \n Please include this citation if you plan to use this database:\n\n [Moro et al., 2011] S. Moro, R. Laureano and P. Cortez. Using Data Mining for Bank Direct Marketing: An Application of the CRISP-DM Methodology. \n In P. Novais et al. (Eds.), Proceedings of the European Simulation and Modelling Conference - ESM\u00272011, pp. 117-121, Guimarães, Portugal, October, 2011. EUROSIS.\n\n Available at: [pdf] http://hdl.handle.net/1822/14838\n [bib] http://www3.dsi.uminho.pt/pcortez/bib/2011-esm-1.txt\n\u003c/code\u003e\u003c/pre\u003e\n" + }, "dateCreated": "Mar 26, 2015 9:46:58 PM", + "dateStarted": "Jul 3, 2015 1:44:56 PM", + "dateFinished": "Jul 3, 2015 1:44:56 PM", + "status": "FINISHED", + "progressUpdateIntervalMs": 500 + }, + { + "config": {}, + "settings": { + "params": {}, + "forms": {} + }, + "jobName": "paragraph_1435955447812_-158639899", + "id": "20150703-133047_853701097", + "dateCreated": "Jul 3, 2015 1:30:47 PM", "status": "READY", "progressUpdateIntervalMs": 500 } ], "name": "Zeppelin Tutorial", "id": "2A94M5J1Z", + "angularObjects": {}, "config": { "looknfeel": "default" }, "info": {} -} +} \ No newline at end of file