{"id":50823,"date":"2022-10-14T00:00:00","date_gmt":"2022-10-14T07:00:00","guid":{"rendered":"https:\/\/griddb-linux-hte8hndjf8cka8ht.westus-01.azurewebsites.net\/%e6%9c%aa%e5%88%86%e9%a1%9e\/using-griddb-to-analyze-the-methane-gas-emissions-globally\/"},"modified":"2025-11-14T07:55:42","modified_gmt":"2025-11-14T15:55:42","slug":"using-griddb-to-analyze-the-methane-gas-emissions-globally","status":"publish","type":"post","link":"https:\/\/griddb-linux-hte8hndjf8cka8ht.westus-01.azurewebsites.net\/ja\/%e6%9c%aa%e5%88%86%e9%a1%9e\/using-griddb-to-analyze-the-methane-gas-emissions-globally\/","title":{"rendered":"GridDB\u3092\u7528\u3044\u305f\u5168\u4e16\u754c\u306e\u30e1\u30bf\u30f3\u30ac\u30b9\u6392\u51fa\u91cf\u306e\u89e3\u6790"},"content":{"rendered":"<p>\u30e1\u30bf\u30f3\u306f\u7121\u8272\u30fb\u7121\u81ed\u306e\u6c17\u4f53\u3067\u3001\u81ea\u7136\u754c\u306b\u591a\u304f\u5b58\u5728\u3057\u3001\u4eba\u9593\u306e\u7279\u5b9a\u306e\u6d3b\u52d5\u306b\u3088\u3063\u3066\u751f\u6210\u3055\u308c\u308b\u3053\u3068\u3082\u3042\u308a\u307e\u3059\u3002\u30e1\u30bf\u30f3\u306f\u30d1\u30e9\u30d5\u30a3\u30f3\u7cfb\u70ad\u5316\u6c34\u7d20\u306e\u4e2d\u3067\u6700\u3082\u5358\u7d14\u306a\u7269\u8cea\u3067\u3042\u308a\u3001\u6e29\u5ba4\u52b9\u679c\u30ac\u30b9\u306e\u4e2d\u3067\u6700\u3082\u5f37\u529b\u306a\u30ac\u30b9\u306e\u4e00\u3064\u3067\u3001\u5316\u5b66\u5f0f\u306f<a href=\"https:\/\/www.britannica.com\/science\/methane\">CH4<\/a>\u3067\u3059\u3002<\/p>\n<p>\u30e1\u30bf\u30f3\u306f\u6e29\u5ba4\u52b9\u679c\u30ac\u30b9\u3067\u3042\u308b\u305f\u3081\u3001\u5730\u7403\u306e\u6c17\u6e29\u3084\u6c17\u5019\u306b\u5f71\u97ff\u3092\u4e0e\u3048\u307e\u3059\u3002\u30e1\u30bf\u30f3\u306e\u6392\u51fa\u6e90\u306f\u3001\u81ea\u7136\u8d77\u6e90\u3068\u4eba\u70ba\u8d77\u6e90\u306e2\u7a2e\u985e\u306b\u5206\u985e\u3055\u308c\u307e\u3059\u3002\u4eba\u70ba\u7684\u306a\u6392\u51fa\u6e90\u3068\u3057\u3066\u306f\u3001\u57cb\u7acb\u5730\u3001\u77f3\u6cb9\u30fb\u5929\u7136\u30ac\u30b9\u30b7\u30b9\u30c6\u30e0\u3001\u5de5\u696d\u30d7\u30ed\u30bb\u30b9\u3001\u77f3\u70ad\u63a1\u6398\u3001\u5b9a\u7f6e\u30fb\u79fb\u52d5\u71c3\u713c\u3001\u5ec3\u6c34\u51e6\u7406\u3001\u8fb2\u696d\u6d3b\u52d5\u306a\u3069\u304c\u6319\u3052\u3089\u308c\u307e\u3059\u3002\u81ea\u7136\u767a\u751f\u6e90\u3068\u3057\u3066\u306f\u3001\u6e7f\u5730\u306b\u304a\u3051\u308b\u690d\u7269\u4f53\u306e\u5206\u89e3\u3001\u5730\u4e0b\u57cb\u8535\u7269\u304b\u3089\u306e\u30ac\u30b9\u306e\u6d78\u900f\u3001\u5bb6\u755c\u306b\u3088\u308b\u98df\u7269\u306e\u6d88\u5316\u306a\u3069\u3001\u6709\u6a5f\u7269\u306e\u5206\u89e3\u3084\u8150\u6557\u304c\u6319\u3052\u3089\u308c\u307e\u3059\u3002<\/p>\n<p><a href=\"https:\/\/github.com\/griddbnet\/Blogs\/tree\/analyzing-methane-gas\">\u5168\u30bd\u30fc\u30b9\u30b3\u30fc\u30c9\u3068\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u306f\u3053\u3061\u3089<\/a><\/p>\n<p>\u4ee5\u4e0a\u3001\u30e1\u30bf\u30f3\u30ac\u30b9\u3068\u305d\u306e\u539f\u56e0\u306b\u3064\u3044\u3066\u7406\u89e3\u3057\u305f\u3068\u3053\u308d\u3067\u3001\u4eca\u5ea6\u306f\u89e3\u6790\u306e\u305f\u3081\u306b\u3001GridDB\u3092\u4f7f\u3063\u3066\u5927\u91cf\u306e\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u3092\u8aad\u307f\u8fbc\u307f\u3001\u4fdd\u5b58\u3057\u3001\u30a2\u30af\u30bb\u30b9\u3059\u308b\u3053\u3068\u306b\u3057\u307e\u3057\u3087\u3046\u3002<\/p>\n<h2>GridDB\u3092\u4f7f\u3063\u305f\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u306e\u30a8\u30af\u30b9\u30dd\u30fc\u30c8\u3068\u30a4\u30f3\u30dd\u30fc\u30c8<\/h2>\n<p>GridDB\u306f\u3001\u9ad8\u3044\u30b9\u30b1\u30fc\u30e9\u30d3\u30ea\u30c6\u30a3\u3068\u6700\u9069\u5316\u3092\u5b9f\u73fe\u3057\u305f\u30a4\u30f3\u30e1\u30e2\u30eaNoSQL\u30c7\u30fc\u30bf\u30d9\u30fc\u30b9\u3067\u3001\u7279\u306b\u6642\u7cfb\u5217\u30c7\u30fc\u30bf\u30d9\u30fc\u30b9\u306b\u304a\u3044\u3066\u3001\u3088\u308a\u9ad8\u3044\u30d1\u30d5\u30a9\u30fc\u30de\u30f3\u30b9\u3068\u52b9\u7387\u6027\u3092\u5b9f\u73fe\u3059\u308b\u305f\u3081\u306e\u4e26\u5217\u51e6\u7406\u3092\u53ef\u80fd\u306b\u3057\u307e\u3059\u3002\u4eca\u56de\u306fGridDB\u306eNode.js\u30af\u30e9\u30a4\u30a2\u30f3\u30c8\u3092\u4f7f\u7528\u3057\u307e\u3059\u3002Node.js\u30af\u30e9\u30a4\u30a2\u30f3\u30c8\u3092\u4f7f\u3046\u3053\u3068\u3067GridDB\u3068Node.js\u3092\u63a5\u7d9a\u3057\u3001\u30ea\u30a2\u30eb\u30bf\u30a4\u30e0\u306b\u30c7\u30fc\u30bf\u3092\u30a4\u30f3\u30dd\u30fc\u30c8\u307e\u305f\u306f\u30a8\u30af\u30b9\u30dd\u30fc\u30c8\u3059\u308b\u3053\u3068\u304c\u3067\u304d\u307e\u3059\u3002<\/p>\n<p>\u4eca\u56de\u306e\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u306b\u306f\u3001\u4ee5\u4e0b\u306e\u30ab\u30e9\u30e0\u304c\u5b58\u5728\u3057\u307e\u3059\u3002<\/p>\n<ol>\n<li>Country : \u6392\u51fa\u8cac\u4efb\u3092\u8ca0\u3046\u3079\u304d\u56fd\u306e\u540d\u79f0\u3002<\/li>\n<li>Sector : \u6392\u51fa\u3092\u62c5\u5f53\u3057\u305f\u30a8\u30cd\u30eb\u30ae\u30fc\u90e8\u9580\u306e\u540d\u79f0\u3002<\/li>\n<li>Gas : \u30ac\u30b9\u306e\u540d\u79f0\u3002<\/li>\n<li>Unit : \u6392\u51fa\u91cf\u3092\u6e2c\u5b9a\u3059\u308b\u5358\u4f4d\u3002<\/li>\n<li>2018 : 2018\u5e74\u306e\u30ac\u30b9\u6392\u51fa\u91cf\u3002<\/li>\n<li>2017 : 2017\u5e74\u306e\u30ac\u30b9\u6392\u51fa\u91cf\u3002<\/li>\n<li>\n<p>2016 : 2016\u5e74\u306e\u30ac\u30b9\u6392\u51fa\u91cf\u3002<\/p>\n<p>.<\/p>\n<p>.<\/p>\n<p>.<\/p>\n<\/li>\n<li>\n<p>1990 : 1990\u5e74\u306e\u30ac\u30b9\u6392\u51fa\u91cf\u3002<\/p>\n<\/li>\n<\/ol>\n<h2>GridDB\u3078\u306e\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u306e\u66f8\u304d\u51fa\u3057<\/h2>\n<p>GridDB\u306b\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u3092\u30a2\u30c3\u30d7\u30ed\u30fc\u30c9\u3059\u308b\u306b\u306f\u3001\u3053\u306e<a href=\"https:\/\/www.kaggle.com\/datasets\/kkhandekar\/methane-emissions-across-the-world-19902018?select=methane_hist_emissions.csv\">Kaggle Dataset<\/a>\u304b\u3089\u53d6\u5f97\u3057\u305f\u30c7\u30fc\u30bf\u3092\u542b\u3080CSV\u30d5\u30a1\u30a4\u30eb\u3092\u8aad\u307f\u8fbc\u3080\u5fc5\u8981\u304c\u3042\u308a\u307e\u3059\u3002<\/p>\n<p>\u30c7\u30fc\u30bf\u306e\u53ef\u8996\u5316\u3068\u5206\u6790\u306b\u306f\u3001DataFrame\u3092\u6271\u3046\u305f\u3081\u306e\u30e9\u30a4\u30d6\u30e9\u30eaDanfo.js\u3092\u4f7f\u7528\u3057\u307e\u3059\u3002<\/p>\n<div class=\"clipboard\">\n<pre><code class=\"language-javascript\">var griddb = require('griddb_node');\n\nconst dfd = require(\"danfojs-node\")\nconst csv = require('csv-parser');\n\nconst fs = require('fs');\nvar lst = []\nvar lst2 = []\nvar i =0;\nfs.createReadStream('.\/Dataset\/methane_hist_emissions.csv')\n  .pipe(csv())\n  .on('data', (row) => {\n    lst.push(row);\n    console.log(lst);\n\n  })\n<\/code><\/pre>\n<\/div>\n<p>GridDB\u30b3\u30f3\u30c6\u30ca\u3092\u751f\u6210\u3057\u3001\u30c7\u30fc\u30bf\u3092\u633f\u5165\u3059\u308b\u305f\u3081\u306e\u30c7\u30fc\u30bf\u30d9\u30fc\u30b9\u30b9\u30ad\u30fc\u30de\u3092\u6e21\u3057\u307e\u3059\u3002<\/p>\n<div class=\"clipboard\">\n<pre><code class=\"language-javascript\">const conInfo = new griddb.ContainerInfo({\n    'name': \"methaneanalysis\",\n    'columnInfoList': [\n      [\"name\", griddb.Type.STRING],\n      [\"Country\", griddb.Type.STRING],\n        [\"Sector\", griddb.Type.STRING],\n        [\"Gas\", griddb.Type.STRING],\n        [\"Unit\", griddb.Type.STRING],\n        [\"2018\", griddb.Type.DOUBLE],\n        [\"2017\", griddb.Type.DOUBLE],\n        [\"2016\", griddb.Type.DOUBLE],\n        [\"2015\", griddb.Type.DOUBLE],\n        [\"2014\", griddb.Type.DOUBLE],\n        [\"2013\", griddb.Type.DOUBLE],\n        [\"2012\", griddb.Type.DOUBLE],\n        [\"2011\", griddb.Type.DOUBLE],\n        [\"2010\", griddb.Type.DOUBLE],\n        [\"2009\", griddb.Type.DOUBLE],\n        [\"2008\", griddb.Type.DOUBLE],\n        [\"2007\", griddb.Type.DOUBLE],\n        [\"2006\", griddb.Type.DOUBLE],\n        [\"2005\", griddb.Type.DOUBLE],\n        [\"2004\", griddb.Type.DOUBLE],\n        [\"2003\", griddb.Type.DOUBLE],\n        [\"2002\", griddb.Type.DOUBLE],\n        [\"2001\", griddb.Type.DOUBLE],\n        [\"2000\", griddb.Type.DOUBLE],\n        [\"1999\", griddb.Type.DOUBLE],\n        [\"1998\", griddb.Type.DOUBLE],\n        [\"1997\", griddb.Type.DOUBLE],\n        [\"1996\", griddb.Type.DOUBLE],\n        [\"1995\", griddb.Type.DOUBLE],\n        [\"1994\", griddb.Type.DOUBLE],\n        [\"1993\", griddb.Type.DOUBLE],\n        [\"1992\", griddb.Type.DOUBLE],\n        [\"1991\", griddb.Type.DOUBLE],\n        [\"1990\", griddb.Type.DOUBLE]\n    ],\n    'type': griddb.ContainerType.COLLECTION, 'rowKey': true\n});\n\n\/\/ Inserting data into the GridDB\nvar container;\n    var idx = 0;\n    \n    for(let i=0;i&lt;lst.length;i++){\n\n\n    store.putContainer(conInfo, false)\n        .then(cont => {\n            container = cont;\n            return container.createIndex({ 'columnName': 'name', 'indexType': griddb.IndexType.DEFAULT });\n        })\n        .then(() => {\n            idx++;\n            container.setAutoCommit(false);\n            return container.put([String(idx), lst[i]['Country'],lst[i][\"Sector\"],lst[i][\"Gas\"],lst[i][\"Unit\"],lst[i][\"2018\"],lst[i][\"2017\"],lst[i][\"2016\"],lst[i][\"2015\"],lst[i][\"2014\"],lst[i][\"2013\"],lst[i][\"2012\"],lst[i][\"2011\"],lst[i][\"2010\"],lst[i][\"2009\"],lst[i][\"2008\"],lst[i][\"2007\"],lst[i][\"2006\"],lst[i][\"2005\"],lst[i][\"2004\"],lst[i][\"2003\"],lst[i][\"2002\"],lst[i][\"2001\"],lst[i][\"2000\"],lst[i][\"1999\"],lst[i][\"1998\"],lst[i][\"1997\"],lst[i][\"1996\"],lst[i][\"1995\"],lst[i][\"1994\"],lst[i][\"1993\"],lst[i][\"1992\"],lst[i][\"1991\"],lst[i][\"1990\"]]);\n        })\n        .then(() => {\n            return container.commit();\n        })\n       \n        .catch(err => {\n            if (err.constructor.name == \"GSException\") {\n                for (var i = 0; i &lt; err.getErrorStackSize(); i++) {\n                    console.log(\"[\", i, \"]\");\n                    console.log(err.getErrorCode(i));\n                    console.log(err.getMessage(i));\n                }\n            } else {\n                console.log(err);\n            }\n        });\n    \n    }\n<\/code><\/pre>\n<\/div>\n<h2>GridDB\u304b\u3089\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u3092\u30a4\u30f3\u30dd\u30fc\u30c8\u3059\u308b<\/h2>\n<p>GridDB\u30d7\u30e9\u30c3\u30c8\u30d5\u30a9\u30fc\u30e0\u304b\u3089\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u3092\u53d6\u308a\u8fbc\u3080\u305f\u3081\u306b\u3001SQL\u306b\u4f3c\u305fGridDB\u306e\u30af\u30a8\u30ea\u8a00\u8a9e\u3067\u3042\u308bTQL\u3092\u4f7f\u7528\u3057\u307e\u3059\u3002\u307e\u305a\u3001\u30b3\u30f3\u30c6\u30ca\u3092\u4f5c\u6210\u3057\u3001\u53d6\u308a\u8fbc\u3093\u3060\u30c7\u30fc\u30bf\u3092\u4fdd\u5b58\u3057\u307e\u3059\u3002\u6b21\u306b\u3001\u30ab\u30e9\u30e0\u60c5\u5831\u306e\u9806\u306b\u884c\u3092\u62bd\u51fa\u3057\u3001\u30c7\u30fc\u30bf\u306e\u53ef\u8996\u5316\u3068\u5206\u6790\u306e\u305f\u3081\u306b\u30c7\u30fc\u30bf\u30d5\u30ec\u30fc\u30e0\u306b\u4fdd\u5b58\u3057\u307e\u3059\u3002<\/p>\n<div class=\"clipboard\">\n<pre><code class=\"language-javascript\"># Get the containers\nobtained_data = gridstore.get_container(\"methaneanalysis\")\n    \n# Fetch all rows - language_tag_container\nquery = obtained_data.query(\"select *\")\n\n# Creating Data Frame variable\nlet df = await dfd.readCSV(\".\/out.csv\")<\/code><\/pre>\n<\/div>\n<p>GridDB\u304b\u3089\u306e\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u306e\u30a4\u30f3\u30dd\u30fc\u30c8\u306b\u6210\u529f\u3057\u307e\u3057\u305f\u3002<\/p>\n<h2>\u30c7\u30fc\u30bf\u5206\u6790<\/h2>\n<p>\u30c7\u30fc\u30bf\u5206\u6790\u3092\u9032\u3081\u308b\u306b\u3042\u305f\u308a\u3001\u307e\u305a\u306f\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u306b\u3064\u3044\u3066\u78ba\u8a8d\u3057\u307e\u3059\u3002<br \/>\n\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u306e\u884c\u3068\u5217\u306e\u6570\u3092\u30c1\u30a7\u30c3\u30af\u3059\u308b\u3068\u30011738\u884c\u306833\u5217\u306e\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u3067\u3042\u308b\u3053\u3068\u304c\u5206\u304b\u308a\u307e\u3059\u3002<\/p>\n<div class=\"clipboard\">\n<pre><code class=\"language-javascript\">\nconsole.log(df.shape)\n\n\/\/  Output\n\/\/ [ 1738, 33 ]<\/code><\/pre>\n<\/div>\n<p>\u30ac\u30b9\u6b04\u306b\u306fCH4\u304c\u3001\u5358\u4f4d\u6b04\u306b\u306fMTCO2e\u304c\u5197\u9577\u306a\u5024\u3068\u3057\u3066\u542b\u307e\u308c\u3066\u3044\u307e\u3059\u3002\u305d\u306e\u7d50\u679c\u3001\u3053\u306e2\u3064\u306e\u5217\u306f\u5206\u6790\u304b\u3089\u9664\u5916\u3055\u308c\u3066\u3044\u307e\u3059\u3002\u30c7\u30fc\u30bf\u304c\u4f55\u3092\u8868\u3057\u3066\u3044\u308b\u304b\u3092\u77e5\u308b\u305f\u3081\u306b\u3001\u5217\u540d\u3068\u30c7\u30fc\u30bf\u578b\u3092\u898b\u3066\u307f\u307e\u3057\u3087\u3046\u3002<\/p>\n<div class=\"clipboard\">\n<pre><code class=\"language-javascript\">\nconsole.log(df.columns)\n\n\/\/ Output\n\/\/ ['Country','Sector', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000', '1999', '1998','1997', '1996', '1995', '1994', '1993', '1992', '1991', '1990' ]<\/code><\/pre>\n<\/div>\n<div class=\"clipboard\">\n<pre><code class=\"language-javascript\">df.loc({columns:['Country',\n'Sector', \n'2018','2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', \n'2003', '2002', '2001', '2000', '1999', '1998','1997', '1996', '1995', '1994', '1993', '1992', '1991', '1990' ]}).ctypes.print()\n\n\/\/  Output\n\/\/ \u2554\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2557\n\/\/ \u2551 Country              \u2502 object  \u2551\n\/\/ \u255f\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2562\n\/\/ \u2551 Sector               \u2502 object  \u2551\n\/\/ \u255f\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2562\n\/\/ \u2551 2018                 \u2502 float64 \u2551\n\/\/ \u255f\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2562\n\/\/ \u2551 2017                 \u2502 float64 \u2551\n\/\/ \u255f\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2562\n\/\/ \u2551 2016                 \u2502 float64 \u2551\n\/\/ \u255f\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2562\n\/\/ \u2551 2015                 \u2502 float64 \u2551\n\/\/ \u255f\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2562\n\/\/ \u2551 2014                 \u2502 float64 \u2551\n\/\/ \u255f\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2562\n\/\/ \u2551 2013                 \u2502 float64 \u2551\n\/\/ \u255f\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2562\n\/\/ \u2551 2012                 \u2502 float64 \u2551\n\/\/ \u255f\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2562\n\/\/ \u2551 2011                 \u2502 float64 \u2551\n\/\/ \u255f\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2562\n\/\/ \u2551 2010                 \u2502 float64 \u2551\n\/\/ \u255f\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2562\n\/\/ \u2551 2009                 \u2502 float64 \u2551\n\/\/ \u255f\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2562\n\/\/ \u2551 2008                 \u2502 float64 \u2551\n\/\/ \u255f\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2562\n\/\/ \u2551 2007                 \u2502 float64 \u2551\n\/\/ \u255f\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2562\n\/\/ \u2551 2006                 \u2502 float64 \u2551\n\/\/ \u255f\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2562\n\/\/ \u2551 2005                 \u2502 float64 \u2551\n\/\/ \u255f\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2562\n\/\/ \u2551 2004                 \u2502 float64 \u2551\n\/\/ \u255f\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2562\n\/\/ \u2551 2003                 \u2502 float64 \u2551\n\/\/ \u255f\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2562\n\/\/ \u2551 2002                 \u2502 float64 \u2551\n\/\/ \u255f\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2562\n\/\/ \u2551 2001                 \u2502 float64 \u2551\n\/\/ \u255f\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2562\n\/\/ \u2551 2000                 \u2502 float64 \u2551\n\/\/ \u255f\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2562\n\/\/ \u2551 1999                 \u2502 float64 \u2551\n\/\/ \u255f\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2562\n\/\/ \u2551 1998                 \u2502 float64 \u2551\n\/\/ \u255f\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2562\n\/\/ \u2551 1997                 \u2502 float64 \u2551\n\/\/ \u255f\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2562\n\/\/ \u2551 1996                 \u2502 float64 \u2551\n\/\/ \u255f\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2562\n\/\/ \u2551 1995                 \u2502 float64 \u2551\n\/\/ \u255f\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2562\n\/\/ \u2551 1994                 \u2502 float64 \u2551\n\/\/ \u255f\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2562\n\/\/ \u2551 1993                 \u2502 float64 \u2551\n\/\/ \u255f\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2562\n\/\/ \u2551 1992                 \u2502 float64 \u2551\n\/\/ \u255f\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2562\n\/\/ \u2551 1991                 \u2502 float64 \u2551\n\/\/ \u255f\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2562\n\/\/ \u2551 1990                 \u2502 float64 \u2551\n\/\/ \u255a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u255d<\/code><\/pre>\n<\/div>\n<p>\u3053\u3053\u3067\u3001\u5f8c\u8ff0\u3059\u308b\u5217\u306e\u7d71\u8a08\u306e\u6982\u8981\u3092\u898b\u3066\u3001\u305d\u306e\u6700\u5c0f\u5024\u3001\u6700\u5927\u5024\u3001\u5e73\u5747\u5024\u3001\u6a19\u6e96\u504f\u5dee\u306a\u3069\u3092\u78ba\u8a8d\u3057\u307e\u3059\u3002<\/p>\n<div class=\"clipboard\">\n<pre><code class=\"language-javascript\">df.loc({columns:['2018', '2017', '2016', '2015', '2014', '2013']}).describe().round(2).print()\n\n\n\n\/\/ Output\n\/\/ \u2554\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2557\n\/\/ \u2551            \u2502 2018              \u2502 2017              \u2502 2016              \u2502 2015              \u2502 2014              \u2502 2013              \u2551\n\/\/ \u255f\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2562\n\/\/ \u2551 count      \u2502 1738              \u2502 1738              \u2502 1738              \u2502 1738              \u2502 1738              \u2502 1738              \u2551\n\/\/ \u255f\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2562\n\/\/ \u2551 mean       \u2502 17.20             \u2502 17.07             \u2502 16.98             \u2502 17.10             \u2502 16.94             \u2502 16.65             \u2551\n\/\/ \u255f\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2562\n\/\/ \u2551 std        \u2502 77.35             \u2502 77.15             \u2502 77.08             \u2502 77.09             \u2502 75.98             \u2502 74.64             \u2551\n\/\/ \u255f\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2562\n\/\/ \u2551 min        \u2502 0.00              \u2502 0.00              \u2502 0.00              \u2502 0.00              \u2502 0.00              \u2502 0.00              \u2551\n\/\/ \u255f\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2562\n\/\/ \u2551 median     \u2502 0.82              \u2502 0.82              \u2502 0.83              \u2502 0.83              \u2502 0.84              \u2502 0.83              \u2551\n\/\/ \u255f\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2562\n\/\/ \u2551 max        \u2502 1238.95           \u2502 1239.28           \u2502 1242.43           \u2502 1237.80           \u2502 1206.51           \u2502 1178.21           \u2551\n\/\/ \u255f\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2562\n\/\/ \u2551 variance   \u2502 5983.40           \u2502 5951.64           \u2502 5942.20           \u2502 5942.84           \u2502 5773.28           \u2502 5571.78           \u2551\n\/\/ \u255a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u255d<\/code><\/pre>\n<\/div>\n<p>\u6b21\u306b\u3001\u68d2\u30b0\u30e9\u30d5\u3068\u5186\u30b0\u30e9\u30d5\u3092\u4f7f\u3063\u3066\u5206\u5e03\u3092\u53ef\u8996\u5316\u3057\u307e\u3059\u3002<\/p>\n<h3>\u68d2\u30b0\u30e9\u30d5<\/h3>\n<div class=\"clipboard\">\n<pre><code class=\"language-javascript\">## Distribution of Column Values\nconst { Plotly } = require('node-kernel');\nlet cols = df.columns\nfor(let i = 0; i &lt; cols.length; i++)\n{\n    let data = [{\n        x: cols[i],\n        y: df[cols[i]].values,\n        type: 'bar'}];\n    let layout = {\n        height: 400,\n        width: 700,\n        title: 'Global Methane Gas Emissions for the years (2018 - 1990)' +cols[i],\n        xaxis: {title: cols[i]}};\n    \/\/ There is no HTML element named `myDiv`, hence the plot is displayed below.\n    Plotly.newPlot('myDiv', data, layout);\n}\ndf.plot(\"plot_div\").bar()\n<\/code><\/pre>\n<\/div>\n<p><a href=\"https:\/\/griddb.net\/wp-content\/uploads\/2022\/08\/Barchart.png\"><img fetchpriority=\"high\" decoding=\"async\" src=\"https:\/\/griddb.net\/wp-content\/uploads\/2022\/08\/Barchart.png\" alt=\"\" width=\"479\" height=\"338\" class=\"aligncenter size-full wp-image-28707\" srcset=\"\/wp-content\/uploads\/2022\/08\/Barchart.png 479w, \/wp-content\/uploads\/2022\/08\/Barchart-300x212.png 300w\" sizes=\"(max-width: 479px) 100vw, 479px\" \/><\/a><\/p>\n<p>\u4e0a\u306e\u68d2\u30b0\u30e9\u30d5\u306f\u3001\u30e1\u30bf\u30f3\u30ac\u30b9\u306e\u6392\u51fa\u91cf\u304c\u7d4c\u5e74\u7684\u306b\u5897\u52a0\u3057\u30012018\u5e74\u306b\u6700\u3082\u9ad8\u3044\u30d4\u30fc\u30af\u3092\u8fce\u3048\u3066\u3044\u308b\u3053\u3068\u3092\u793a\u3057\u3066\u3044\u307e\u3059\u3002<\/p>\n<h3>\u5186\u30b0\u30e9\u30d5<\/h3>\n<p>\u3069\u306e\u56fd\u304c\u4e16\u754c\u6700\u5927\u306e\u30e1\u30bf\u30f3\u6392\u51fa\u56fd\u306a\u306e\u304b\u3001\u5186\u30b0\u30e9\u30d5\u3092\u30d7\u30ed\u30c3\u30c8\u3057\u30663\u30ab\u56fd\u306e2018\u5e74\u306e\u30e1\u30bf\u30f3\u6392\u51fa\u91cf\u3092\u691c\u8a3c\u3057\u3066\u307f\u307e\u3059\u3002\u4e2d\u56fd\u3001\u7c73\u56fd\u3001\u30ed\u30b7\u30a2\u306e3\u30ab\u56fd\u3067\u3059\u3002<\/p>\n<div class=\"clipboard\">\n<pre><code class=\"language-javascript\">const { Plotly } = require('node-kernel');\nValues: [sum_2018_US, sum_2018_China, sum_2018_Russia],\nName: [\"United States\", \"China\", \"Russia\"]\n\n df.plot(\"plot_div\").pie({ config: { values: \"Values\", labels: \"Name\" } });\n <\/code><\/pre>\n<\/div>\n<p><a href=\"https:\/\/griddb.net\/wp-content\/uploads\/2022\/08\/Piechart.png\"><img decoding=\"async\" src=\"https:\/\/griddb.net\/wp-content\/uploads\/2022\/08\/Piechart.png\" alt=\"\" width=\"373\" height=\"346\" class=\"aligncenter size-full wp-image-28708\" srcset=\"\/wp-content\/uploads\/2022\/08\/Piechart.png 373w, \/wp-content\/uploads\/2022\/08\/Piechart-300x278.png 300w\" sizes=\"(max-width: 373px) 100vw, 373px\" \/><\/a><\/p>\n<p>\u5186\u30b0\u30e9\u30d5\u306b\u3088\u308b\u3068\u30012018\u5e74\u306e\u30e1\u30bf\u30f3\u6392\u51fa\u91cf\u306f\u3001\u4e0a\u8a183\u30ab\u56fd\u306e\u4e2d\u3067\u4e2d\u56fd\u304c\u6700\u3082\u591a\u3044\u3067\u3059\u3002<\/p>\n<h2>\u7d50\u8ad6<\/h2>\n<p>\u30e1\u30bf\u30f3\u6392\u51fa\u91cf\u306e\u5897\u52a0\u306e\u4e3b\u306a\u539f\u56e0\u306f\u3001\u4eba\u70ba\u7684\u306a\u6d3b\u52d5\u306e\u5897\u52a0\u3067\u3059\u3002\u305d\u306e\u7d50\u679c\u3001\u5730\u7403\u6e29\u6696\u5316\u304c\u9032\u307f\u3001\u5730\u7403\u898f\u6a21\u3067\u6c17\u5019\u5909\u52d5\u304c\u8d77\u304d\u3066\u3044\u308b\u306e\u306f\u3054\u5b58\u3058\u306e\u3068\u304a\u308a\u3067\u3059\u3002\u3067\u3059\u304b\u3089\u3001\u3053\u306e\u307e\u307e\u3067\u306f\u3001\u79c1\u305f\u3061\u306f\u4f4f\u307f\u306b\u304f\u3044\u74b0\u5883\u306b\u306a\u3063\u3066\u3057\u307e\u3046\u306e\u3067\u3059\u3002<\/p>\n<p>\u6700\u5f8c\u306b\u3001\u4eca\u56de\u306e\u30c7\u30fc\u30bf\u5206\u6790\u306b\u306f\u3001\u30c7\u30fc\u30bf\u3078\u306e\u30a2\u30af\u30bb\u30b9\u304c\u65e9\u304f\u3001\u30c7\u30fc\u30bf\u306e\u8aad\u307f\u66f8\u304d\u3084\u4fdd\u5b58\u304c\u52b9\u7387\u7684\u306b\u884c\u3048\u308bGridDB\u3092\u4f7f\u7528\u3057\u307e\u3057\u305f\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"<p>\u30e1\u30bf\u30f3\u306f\u7121\u8272\u30fb\u7121\u81ed\u306e\u6c17\u4f53\u3067\u3001\u81ea\u7136\u754c\u306b\u591a\u304f\u5b58\u5728\u3057\u3001\u4eba\u9593\u306e\u7279\u5b9a\u306e\u6d3b\u52d5\u306b\u3088\u3063\u3066\u751f\u6210\u3055\u308c\u308b\u3053\u3068\u3082\u3042\u308a\u307e\u3059\u3002\u30e1\u30bf\u30f3\u306f\u30d1\u30e9\u30d5\u30a3\u30f3\u7cfb\u70ad\u5316\u6c34\u7d20\u306e\u4e2d\u3067\u6700\u3082\u5358\u7d14\u306a\u7269\u8cea\u3067\u3042\u308a\u3001\u6e29\u5ba4\u52b9\u679c\u30ac\u30b9\u306e\u4e2d\u3067\u6700\u3082\u5f37\u529b\u306a\u30ac\u30b9\u306e\u4e00\u3064\u3067\u3001\u5316\u5b66\u5f0f\u306fCH4\u3067\u3059\u3002 \u30e1 [&hellip;]<\/p>\n","protected":false},"author":41,"featured_media":49486,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[1005],"tags":[],"class_list":["post-50823","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-1005"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.1.1 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>GridDB\u3092\u7528\u3044\u305f\u5168\u4e16\u754c\u306e\u30e1\u30bf\u30f3\u30ac\u30b9\u6392\u51fa\u91cf\u306e\u89e3\u6790 | GridDB: Open Source Time Series Database for IoT<\/title>\n<meta name=\"description\" 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