{"id":50826,"date":"2022-11-04T00:00:00","date_gmt":"2022-11-04T07:00:00","guid":{"rendered":"https:\/\/griddb-linux-hte8hndjf8cka8ht.westus-01.azurewebsites.net\/%e6%9c%aa%e5%88%86%e9%a1%9e\/using-griddb-to-analyze-distances-of-asteroids-to-earth-5\/"},"modified":"2025-11-14T07:55:44","modified_gmt":"2025-11-14T15:55:44","slug":"using-griddb-to-analyze-distances-of-asteroids-to-earth-5","status":"publish","type":"post","link":"https:\/\/griddb.net\/ja\/%e6%9c%aa%e5%88%86%e9%a1%9e\/using-griddb-to-analyze-distances-of-asteroids-to-earth-5\/","title":{"rendered":"GridDB\u3092\u7528\u3044\u305f\u5c0f\u60d1\u661f\u306e\u5730\u7403\u3078\u306e\u8ddd\u96e2\u306e\u89e3\u6790"},"content":{"rendered":"<p>\u4eca\u56de\u306f\u3001\u5c0f\u60d1\u661f\u304c\u5730\u7403\u306b\u3068\u3063\u3066\u5371\u967a\u304b\u3069\u3046\u304b\u3001\u3064\u307e\u308a\u3001\u8ecc\u9053\u3092\u5916\u308c\u3066\u5730\u7403\u306b\u964d\u308a\u3001\u4f4f\u6c11\u306b\u5371\u5bb3\u3092\u52a0\u3048\u308b\u304b\u3069\u3046\u304b\u3092\u79d1\u5b66\u8005\u304c\u3069\u306e\u3088\u3046\u306b\u5224\u65ad\u3057\u3066\u3044\u308b\u304b\u3092GridDB\u3092\u4f7f\u3063\u3066\u89e3\u6790\u3057\u3066\u307f\u307e\u3059\u3002<\/p>\n<p>\u30bd\u30fc\u30b9\u30b3\u30fc\u30c9\u306e\u5168\u6587\u306f\u3053\u3061\u3089\u3067\u3054\u89a7\u3044\u305f\u3060\u3051\u307e\u3059\u3002<a href=\"https:\/\/github.com\/griddbnet\/Blogs\/tree\/asteroids_distance\">https:\/\/github.com\/griddbnet\/Blogs\/tree\/asteroids_distance<\/a><\/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\u30eaNo SQL\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\u3001GridDB\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>\u3053\u308c\u3089\u306f\u3001\u6211\u3005\u306e\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u306b\u5b58\u5728\u3059\u308b\u5217\u3067\u3059\u3002<\/p>\n<ol>\n<li>id : NASA\u306e\u79d1\u5b66\u8005\u304c\u3064\u3051\u305f\u5c0f\u60d1\u661f\u306eID\u3002<\/li>\n<li>new_name : NASA\u306e\u79d1\u5b66\u8005\u304c\u3064\u3051\u305f\u5c0f\u60d1\u661f\u306e\u540d\u524d\u3002<\/li>\n<li>est_diameter_min : \u5c0f\u60d1\u661f\u306e\u6700\u5c0f\u63a8\u5b9a\u76f4\u5f84\u3002<\/li>\n<li>est_diameter_max : \u5c0f\u60d1\u661f\u306e\u63a8\u5b9a\u6700\u5927\u76f4\u5f84\u3002<\/li>\n<li>relative_velocity : \u5c0f\u60d1\u661f\u306e\u5730\u7403\u306b\u5bfe\u3059\u308b\u76f8\u5bfe\u7684\u306a\u901f\u5ea6\u306e\u3053\u3068\u3002<\/li>\n<li>miss_distance : \u5c0f\u60d1\u661f\u306e\u5730\u7403\u304b\u3089\u306e\u8ddd\u96e2\u3002<\/li>\n<li>orbiting_body: \u5c0f\u60d1\u661f\u304c\u7279\u5b9a\u306e\u5929\u4f53\u306e\u5468\u308a\u3092\u516c\u8ee2\u3059\u308b\u3053\u3068\u3002<\/li>\n<li>sentry_object: \u5c0f\u60d1\u661f\u304c\u5b87\u5b99\u7a7a\u9593\u3067\u4ed6\u306e\u5929\u4f53\uff08\u4eba\u5de5\u885b\u661f\u306a\u3069\uff09\u306b\u885d\u7a81\u3057\u305f\u304b\u3069\u3046\u304b\u3002<\/li>\n<li>absolute_magnitude: \u8cea\u91cf\u6bd4\u306b\u3088\u3063\u3066\u5730\u7403\u306b\u885d\u7a81\u3059\u308b\u529b\u306e\u5927\u304d\u3055\u3002<\/li>\n<li>hazardous : \u5c0f\u60d1\u661f\u306f\u5371\u967a\u306a\u7bc4\u56f2\u306b\u3042\u308b\u306e\u304b\uff1f(\u6210\u679c\u5909\u6570)<\/li>\n<\/ol>\n<p>GridDB\u306b\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u3092\u30a2\u30c3\u30d7\u30ed\u30fc\u30c9\u3059\u308b\u305f\u3081\u306b\u3001<a href=\"https:\/\/www.kaggle.com\/datasets\/sameepvani\/nasa-nearest-earth-objects\">Kaggle \u3067\u516c\u958b\u3055\u308c\u3066\u3044\u308b\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8<\/a> \u304b\u3089\u53d6\u5f97\u3057\u305f\u30c7\u30fc\u30bf\u3092\u542b\u3080CSV\u30d5\u30a1\u30a4\u30eb\u3092\u8aad\u307f\u8fbc\u307f\u307e\u3059\u3002<\/p>\n<p>\u3053\u3053\u3067\u3001GridDB\u30b3\u30f3\u30c6\u30ca\u3092\u4f5c\u6210\u3057\u3066\u3001\u30c7\u30fc\u30bf\u30d9\u30fc\u30b9\u30b9\u30ad\u30fc\u30de\u3092GridDB\u306b\u6e21\u3057\u3001\u884c\u60c5\u5831\u3092\u633f\u5165\u3059\u308b\u524d\u306b\u3001\u30c7\u30fc\u30bf\u30d9\u30fc\u30b9\u306e\u30c7\u30b6\u30a4\u30f3\u3092\u751f\u6210\u3067\u304d\u308b\u3088\u3046\u306b\u3057\u307e\u3059\u3002\u6b21\u306b\u3001GridDB\u306b\u30c7\u30fc\u30bf\u3092\u633f\u5165\u3057\u307e\u3059\u3002\u3053\u308c\u3067\u3001\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u3092 GridDB \u30d7\u30e9\u30c3\u30c8\u30d5\u30a9\u30fc\u30e0\u306b\u30a8\u30af\u30b9\u30dd\u30fc\u30c8\u3059\u308b\u3053\u3068\u306b\u6210\u529f\u3057\u307e\u3057\u305f\u3002<\/p>\n<p>\u4e00\u65b9\u3001GridDB\u30d7\u30e9\u30c3\u30c8\u30d5\u30a9\u30fc\u30e0\u304b\u3089\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u3092\u53d6\u308a\u8fbc\u3080\u306b\u306f\u3001SQL\u306b\u4f3c\u305fGridDB\u306e\u554f\u3044\u5408\u308f\u305b\u8a00\u8a9e\u3067\u3042\u308bTQL\u3092\u4f7f\u7528\u3057\u307e\u3059\u3002\u30b3\u30f3\u30c6\u30ca\u3092\u4f5c\u6210\u3057\u3001\u305d\u306e\u4e2d\u306b\u53d6\u308a\u8fbc\u3093\u3060\u30c7\u30fc\u30bf\u3092\u683c\u7d0d\u3059\u308b\u3053\u3068\u306b\u306a\u308a\u307e\u3059\u3002\u6b21\u306b\u3001\u30ab\u30e9\u30e0\u60c5\u5831\u306e\u9806\u306b\u884c\u3092\u62bd\u51fa\u3057\u3001\u30c7\u30fc\u30bf\u30d5\u30ec\u30fc\u30e0\u306b\u4fdd\u5b58\u3057\u3066\u3001\u30c7\u30fc\u30bf\u306e\u53ef\u8996\u5316\u3084\u5206\u6790\u306b\u5229\u7528\u3059\u308b\u3053\u3068\u306b\u306a\u308a\u307e\u3059\u3002<\/p>\n<p>\u30c7\u30fc\u30bf\u306e\u53ef\u8996\u5316\u30fb\u89e3\u6790\u306b\u306f\u3001\u4ee5\u4e0b\u306eNodeJS\u7528\u30e9\u30a4\u30d6\u30e9\u30ea\u3092\u4f7f\u7528\u3059\u308b\u4e88\u5b9a\u3067\u3059\u3002<\/p>\n<ul>\n<li>DanfoJS &#8211; DataFrame\u3092\u6271\u3046\u305f\u3081\u306e\u3082\u306e\u3067\u3059\u3002<\/li>\n<\/ul>\n<div class=\"clipboard\">\n<pre><code class=\"language-javascript\">var griddb = require('griddb_node');\n\nconst dfd = require(\"danfojs-node\")\nvar fs     = require('fs');\n\nconst createCsvWriter = require('csv-writer').createObjectCsvWriter;\nconst csvWriter = createCsvWriter({\n  path: 'out.csv',\n  header: [\n    {id: \"id\", title:\"id\"}, \n    {id: \"new_name\", title:\"new_name\"}, \n    {id: \"est_diameter_min\", title:\"est_diameter_min\"}, \n    {id: \"est_diameter_max\", title:\"est_diameter_max\"}, \n    {id: \"relative_velocity\", title:\"relative_velocity\"}, \n    {id: \"miss_distance\", title:\"miss_distance\"}, \n    {id: \"orbiting_body\" , title:\"orbiting_body\"}, \n    {id: \"sentry_object\", title:\"sentry_object\"}, \n    {id: \"absolute_magnitude\", title:\"absolute_magnitude\"}\n  ]\n});\n\nconst factory = griddb.StoreFactory.getInstance();\nconst store = factory.getStore({\n    \"host\": '239.0.0.1',\n    \"port\": 31999,\n    \"clusterName\": \"defaultCluster\",\n    \"username\": \"admin\",\n    \"password\": \"admin\"\n});\n\n\/\/ For connecting to the GridDB Server we have to make containers and specify the schema.\nconst conInfo = new griddb.ContainerInfo({\n    'name': \"neoanalysis\",\n    'columnInfoList': [\n      [\"name\", griddb.Type.STRING],\n      [\"id\", griddb.Type.INTEGER],\n        [\"new_name\", griddb.Type.STRING],\n        [\"est_diameter_min\", griddb.Type.DOUBLE],\n        [\"est_diameter_max\", griddb.Type.DOUBLE],\n        [\"relative_velocity\", griddb.Type.DOUBLE],\n        [\"miss_distance\", griddb.Type.DOUBLE],\n        [\"absolute_magnitude\", griddb.Type.DOUBLE]\n    ],\n    'type': griddb.ContainerType.COLLECTION, 'rowKey': true\n});\n\n\n\/\/ \/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\n\n\nconst csv = require('csv-parser');\n\nconst fs = require('fs');\nvar lst = []\nvar lst2 = []\nvar i =0;\nfs.createReadStream('.\/Dataset\/neo.csv')\n  .pipe(csv())\n  .on('data', (row) => {\n    lst.push(row);\n    console.log(lst);\n\n  })\n  .on('end', () => {\n    var 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]['id'],lst[i][\"new_name\"],lst[i][\"est_diameter_min\"],lst[i][\"est_diameter_max\"],lst[i][\"relative_velocity\"],lst[i][\"miss_distance\"],lst[i][\"absolute_magnitude\"]]);\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    store.getContainer(\"neoanalysis\")\n    .then(ts => {\n        container = ts;\n      query = container.query(\"select *\")\n      return query.fetch();\n  })\n  .then(rs => {\n      while (rs.hasNext()) {\n\n\n          let rsNext = rs.next()\n\n          lst2.push(\n            \n            \n            {\n                'id': rsNext[1],\n                \"new_name\": rsNext[2],\n                \"est_diameter_min\": rsNext[3],\n                \"est_diameter_max\": rsNext[4],\n                \"relative_velocity\": rsNext[5],\n                \"miss_distance\": rsNext[6],\n                \"absolute_magnitude\": rsNext[7],\n            \n            }\n            \n          );\n          \n      }\n\n        csvWriter\n        .writeRecords(lst2)\n        .then(()=> console.log('The CSV file was written successfully'));\n\n      return \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>\u30c7\u30fc\u30bf\u5206\u6790<\/h2>\n<p>\u3053\u3053\u3067\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u306e\u30c1\u30a7\u30c3\u30af\u3068\u8aad\u307f\u8fbc\u307f\u3092\u884c\u3044\u3001\u30c7\u30fc\u30bf\u89e3\u6790\u3092\u884c\u3044\u307e\u3059\u3002<\/p>\n<p>orbiting_body\u3068sentry_object\u306f\u5217\u5168\u4f53\u3067\u5197\u9577\u306a\u5024\u3092\u6301\u3061\u3001\u89e3\u6790\u306b\u5f79\u7acb\u3064\u56fa\u6709\u306e\u5024\u304c\u306a\u3044\u305f\u3081\u3001\u4ee5\u4e0b\u306e\u3088\u3046\u306b\u3053\u306e2\u5217\u3092\u89e3\u6790\u304b\u3089\u7701\u304f\u3053\u3068\u306b\u3057\u307e\u3059\u3002<\/p>\n<p><a href=\"https:\/\/griddb.net\/ja\/wp-content\/uploads\/2022\/08\/Two_omitted_columns.png\"><img fetchpriority=\"high\" decoding=\"async\" src=\"https:\/\/griddb.net\/wp-content\/uploads\/2022\/08\/Two_omitted_columns.png\" alt=\"\" width=\"182\" height=\"783\" class=\"aligncenter size-full wp-image-28650\" srcset=\"\/wp-content\/uploads\/2022\/08\/Two_omitted_columns.png 182w, \/wp-content\/uploads\/2022\/08\/Two_omitted_columns-70x300.png 70w\" sizes=\"(max-width: 182px) 100vw, 182px\" \/><\/a><\/p>\n<p>\u4ee5\u4e0b\u306e\u3088\u3046\u306b\u3001csv\u30d5\u30a1\u30a4\u30eb\u3092DataFrame\u5909\u6570(df)\u306b\u30ed\u30fc\u30c9\u3059\u308b\u3053\u3068\u306b\u306a\u308a\u307e\u3059\u3002<\/p>\n<div class=\"clipboard\">\n<pre><code class=\"language-javascript\">let df = await dfd.readCSV(\".\/out.csv\")<\/code><\/pre>\n<\/div>\n<p>\u30c7\u30fc\u30bf\u5206\u6790\u3067\u306f\u3001\u307e\u305a\u3001\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u306e\u884c\u3068\u5217\u306e\u6570\u3092\u78ba\u8a8d\u3057\u307e\u3059\u3002<\/p>\n<p>\u884c\u6570\u306f90836\u3001\u5217\u6570\u306f8\u3067\u3059\u3002<\/p>\n<div class=\"clipboard\">\n<pre><code class=\"language-javascript\">console.log(df.shape)\n\n\/\/  Output\n\/\/ [ 90836, 8 ]<\/code><\/pre>\n<\/div>\n<p>\u3067\u306f\u3001\u4e0a\u8a18\u306e\u3088\u3046\u306b2\u3064\u306e\u5217\u3092\u7701\u7565\u3057\u305f\u5f8c\u306e\u5217\u306e\u540d\u524d\u3068\u3001\u30c7\u30fc\u30bf\u304c\u4f55\u3092\u8868\u3057\u3066\u3044\u308b\u304b\u3092\u77e5\u308b\u305f\u3081\u306e\u5217\u306e\u30c7\u30fc\u30bf\u578b\u3092\u898b\u3066\u307f\u307e\u3057\u3087\u3046\u3002<\/p>\n<div class=\"clipboard\">\n<pre><code class=\"language-javascript\">console.log(df.columns)\n\n\/\/ Output\n\/\/ ['id','new_name', 'est_diameter_min', 'est_diameter_max', 'relative_velocity', 'miss_distance', 'absolute_magnitude', 'hazardous']<\/code><\/pre>\n<\/div>\n<div class=\"clipboard\">\n<pre><code class=\"language-javascript\">df.loc({columns:['id',\n'new_name',\n'est_diameter_min',\n'est_diameter_max',\n'relative_velocity',\n'miss_distance','absolute_magnitude',\n'hazardous']}).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 id                   \u2502 int64   \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 new_name             \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 est_diameter_min     \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 est_diameter_max     \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 relative_velocity    \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 miss_distance        \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 absolute_magnitude   \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 hazardous            \u2502 bool    \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>GridDB \u30b3\u30f3\u30c6\u30ca\u304b\u3089\u30c7\u30fc\u30bf\u3078\u306e\u30a2\u30af\u30bb\u30b9\u306f\u4ee5\u4e0b\u306e\u3088\u3046\u306b\u884c\u3044\u307e\u3059\u3002<\/p>\n<div class=\"clipboard\">\n<pre><code class=\"language-javascript\">    # Get the containers\n    obtained_data = gridstore.get_container(\"redwinequality\")\n    \n    # Fetch all rows - language_tag_container\n    query = obtained_data.query(\"select *\")<\/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:['est_diameter_min','est_diameter_max','relative_velocity','miss_distance']}).describe().round(2).print()\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\u2557\n\/\/ \u2551            \u2502 est_diameter_min  \u2502 est_diameter_max  \u2502 relative_velocity \u2502 miss_distance     \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\u2562\n\/\/ \u2551 count      \u2502 90836             \u2502 90836             \u2502 90836             \u2502 9.1e+04           \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\u2562\n\/\/ \u2551 mean       \u2502 0.13              \u2502 0.28              \u2502 48066             \u2502 3.71e+07          \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\u2562\n\/\/ \u2551 std        \u2502 0.29              \u2502 0.67              \u2502 25293             \u2502 2.24e+07          \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\u2562\n\/\/ \u2551 min        \u2502 0.00061           \u2502 0.0014            \u2502 203               \u2502 6.74e+03          \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\u2562\n\/\/ \u2551 median     \u2502 0.05              \u2502 0.11              \u2502 44190             \u2502 3.78e+07          \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\u2562\n\/\/ \u2551 max        \u2502 38                \u2502 85                \u2502 236990            \u2502 7.50e+07          \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\u2562\n\/\/ \u2551 variance   \u2502 8.91e-02          \u2502 4.45e-01          \u2502 6.40e+08          \u2502 4.99e+14          \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\u255d<\/code><\/pre>\n<\/div>\n<p>\u54c1\u8cea\u3068\u4ed6\u306e\u30ab\u30e9\u30e0\u306e\u6563\u5e03\u56f3\u3092\u30d7\u30ed\u30c3\u30c8\u3057\u307e\u3059\u3002<\/p>\n<div class=\"clipboard\">\n<pre><code class=\"language-javascript\">## Scatter Plot between miss_distance and relative_velocity\nlet cols = [...cols]\ncols.pop('miss_distance')\nfor(let i = 0; i &lt; cols.length; i++)\n{\n    let data = [{\n        x: df[cols[i]].values,\n        y: df['miss_distance'].values,\n        type: 'scatter',\n        mode: 'markers'}];\n    let layout = {\n        height: 400,\n        width: 700,\n        title: 'Missing Distance from Earth vs '+cols[i],\n        xaxis: {title: cols[i]},\n        yaxis: {title: 'Miss Distance'}};\n    \/\/ There is no HTML element named `myDiv`, hence the plot is displayed below.\n    Plotly.newPlot('myDiv', data, layout);    \n}<\/code><\/pre>\n<\/div>\n<p>2\u3064\u306e\u5217\u306e\u4f8b\u306b\u5bfe\u3059\u308b\u30d7\u30ed\u30c3\u30c8\u306f\u4ee5\u4e0b\u306e\u901a\u308a\u3067\u3059\u3002<\/p>\n<p><a href=\"https:\/\/griddb.net\/ja\/wp-content\/uploads\/2022\/08\/Scatterplot.jpg\"><img decoding=\"async\" src=\"https:\/\/griddb.net\/wp-content\/uploads\/2022\/08\/Scatterplot.jpg\" alt=\"\" width=\"409\" height=\"264\" class=\"aligncenter size-full wp-image-28652\" srcset=\"\/wp-content\/uploads\/2022\/08\/Scatterplot.jpg 409w, \/wp-content\/uploads\/2022\/08\/Scatterplot-300x194.jpg 300w\" sizes=\"(max-width: 409px) 100vw, 409px\" \/><\/a><\/p>\n<p>\u4e0a\u306e\u30d7\u30ed\u30c3\u30c8\u306f\u30012\u3064\u306e\u5909\u6570\u304c\u6b63\u306e\u7dda\u5f62\u95a2\u4fc2\u3092\u6301\u3064\u3053\u3068\u3092\u793a\u3057\u3066\u304a\u308a\u3001\u5730\u7403\u304b\u3089\u306e\u8ddd\u96e2\u304c\u96e2\u308c\u308b\u307b\u3069\u3001\u5c0f\u60d1\u661f\u306f\u5730\u7403\u306e\u91cd\u529b\u306e\u5f71\u97ff\u3092\u53d7\u3051\u306a\u3044\u306e\u3067\u3001\u76f8\u5bfe\u901f\u5ea6\u304c\u5927\u304d\u304f\u306a\u308b\u3053\u3068\u3092\u610f\u5473\u3057\u307e\u3059\u3002<\/p>\n<div class=\"clipboard\">\n<pre><code class=\"language-javascript\">## Correlation plot of the columns to see how these variables or factors related to each other\ncorrelogram(data)<\/code><\/pre>\n<\/div>\n<p>\u76f8\u95a2\u56f3\u306f\u4ee5\u4e0b\u306e\u901a\u308a\u3067\u3059\u3002<\/p>\n<p><a href=\"https:\/\/griddb.net\/ja\/wp-content\/uploads\/2022\/08\/Correlationplot.jpg\"><img decoding=\"async\" src=\"https:\/\/griddb.net\/wp-content\/uploads\/2022\/08\/Correlationplot.jpg\" alt=\"\" width=\"977\" height=\"501\" class=\"aligncenter size-full wp-image-28651\" srcset=\"\/wp-content\/uploads\/2022\/08\/Correlationplot.jpg 977w, \/wp-content\/uploads\/2022\/08\/Correlationplot-300x154.jpg 300w, \/wp-content\/uploads\/2022\/08\/Correlationplot-768x394.jpg 768w, \/wp-content\/uploads\/2022\/08\/Correlationplot-600x308.jpg 600w\" sizes=\"(max-width: 977px) 100vw, 977px\" \/><\/a><\/p>\n<p>\u4e0a\u306e\u76f8\u95a2\u56f3\u306f\u3001\u5404\u5909\u6570\u306e\u5024\u3092\u793a\u3057\u3066\u304a\u308a\u3001\u5024\u304c\u9ad8\u3044\u307b\u3069\u4e21\u8005\u306e\u95a2\u4fc2\u306f\u5bc6\u63a5\u3067\u3059\u3002\u3057\u305f\u304c\u3063\u3066\u3001\u5c0f\u60d1\u661f\u306e\u8ddd\u96e2\u3068\u76f8\u5bfe\u901f\u5ea6\u304c\u6700\u3082\u9ad8\u3044\u5024\uff080.33\uff09\u3092\u793a\u3057\u3001\u3053\u306e2\u3064\u304c\u5c0f\u60d1\u661f\u306e\u5730\u7403\u3078\u306e\u885d\u7a81\u70b9\u3092\u6c7a\u5b9a\u3059\u308b\u4e0a\u3067\u6700\u3082\u91cd\u8981\u306a\u5909\u6570\u3067\u3042\u308b\u3053\u3068\u304c\u5206\u304b\u308a\u307e\u3059\u3002\u305f\u3060\u3057\u3001\u540c\u3058\u5909\u6570\u540c\u58eb\u306f\u5f53\u7136\u306a\u304c\u3089\u6700\u3082\u5f37\u3044\u76f8\u95a2\u3092\u6301\u3064\u306e\u3067\u3001\u76f8\u95a2\u56f3\u3067\u306f\u4e0a\u306e\u5bfe\u89d2\u7dda\u306f\u7121\u8996\u3057\u3066\u3044\u307e\u3059\u3002<\/p>\n<h2>\u7d50\u8ad6<\/h2>\n<p>NASA\u306e\u79d1\u5b66\u8005\u306f\u3001\u5c0f\u60d1\u661f\u304c\u5730\u7403\u306b\u885d\u7a81\u3059\u308b\u304b\u3069\u3046\u304b\u3092\u5224\u65ad\u3059\u308b\u969b\u306b\u3001\u3055\u307e\u3056\u307e\u306a\u5909\u6570\u3092\u8003\u616e\u3057\u307e\u3059\u3002\u305d\u3057\u3066\u3001\u3082\u3057\u885d\u7a81\u3057\u305f\u5834\u5408\u306b\u306f\u3001\u3067\u304d\u308b\u3060\u3051\u591a\u304f\u306e\u8cb4\u91cd\u306a\u4eba\u547d\u3092\u6551\u3046\u305f\u3081\u306b\u3001\u6b63\u78ba\u306a\u5ea7\u6a19\u3092\u77e5\u308b\u5fc5\u8981\u304c\u3042\u308a\u307e\u3059\u3002<\/p>\n<p>\u6700\u5f8c\u306b\u3001\u4eca\u56de\u306e\u30c7\u30fc\u30bf\u5206\u6790\u306b\u306f\u3001\u30c7\u30fc\u30bf\u306e\u8aad\u307f\u51fa\u3057\u3001\u66f8\u304d\u8fbc\u307f\u3001\u4fdd\u5b58\u304c\u5bb9\u6613\u306b\u884c\u3048\u308bGridDB\u3092\u4f7f\u7528\u3057\u307e\u3057\u305f\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"<p>\u4eca\u56de\u306f\u3001\u5c0f\u60d1\u661f\u304c\u5730\u7403\u306b\u3068\u3063\u3066\u5371\u967a\u304b\u3069\u3046\u304b\u3001\u3064\u307e\u308a\u3001\u8ecc\u9053\u3092\u5916\u308c\u3066\u5730\u7403\u306b\u964d\u308a\u3001\u4f4f\u6c11\u306b\u5371\u5bb3\u3092\u52a0\u3048\u308b\u304b\u3069\u3046\u304b\u3092\u79d1\u5b66\u8005\u304c\u3069\u306e\u3088\u3046\u306b\u5224\u65ad\u3057\u3066\u3044\u308b\u304b\u3092GridDB\u3092\u4f7f\u3063\u3066\u89e3\u6790\u3057\u3066\u307f\u307e\u3059\u3002 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