{"id":50725,"date":"2021-01-14T00:00:00","date_gmt":"2021-01-14T08:00:00","guid":{"rendered":"https:\/\/griddb-linux-hte8hndjf8cka8ht.westus-01.azurewebsites.net\/%e6%9c%aa%e5%88%86%e9%a1%9e\/predicting-credit-card-attrition-using-python-and-griddb\/"},"modified":"2025-11-14T07:54:21","modified_gmt":"2025-11-14T15:54:21","slug":"predicting-credit-card-attrition-using-python-and-griddb","status":"publish","type":"post","link":"https:\/\/griddb-linux-hte8hndjf8cka8ht.westus-01.azurewebsites.net\/ja\/%e6%9c%aa%e5%88%86%e9%a1%9e\/predicting-credit-card-attrition-using-python-and-griddb\/","title":{"rendered":"Python\u3068GridDB\u3092\u7528\u3044\u305f\u30af\u30ec\u30b8\u30c3\u30c8\u30ab\u30fc\u30c9\u306e\u9000\u4f1a\u4e88\u6e2c"},"content":{"rendered":"<p>\u30c7\u30fc\u30bf\u5206\u6790\u306f\u3001\u30c7\u30fc\u30bf\u304b\u3089\u6709\u7528\u306a\u60c5\u5831\u3092\u62bd\u51fa\u3057\u3001\u610f\u601d\u6c7a\u5b9a\u30d7\u30ed\u30bb\u30b9\u306b\u5f79\u7acb\u3066\u308b\u3053\u3068\u3092\u76ee\u7684\u3068\u3057\u3066\u3044\u307e\u3059\u3002\u3057\u304b\u3057\u3001\u30e2\u30d0\u30a4\u30eb\u6a5f\u5668\u3084\u30bb\u30f3\u30b5\u30fc\u306a\u3069\u306e\u5916\u90e8\u30bd\u30fc\u30b9\u304b\u3089\u5f97\u3089\u308c\u308b\u751f\u30c7\u30fc\u30bf\u306b\u306f\u3001\u591a\u304f\u306e\u5916\u308c\u5024\u304c\u542b\u307e\u308c\u3066\u3044\u307e\u3059\u3002\u3055\u3089\u306b\u3001\u30c7\u30fc\u30bf\u306f\u9ad8\u6b21\u5143\u3067\u3042\u308b\u53ef\u80fd\u6027\u3082\u3042\u308a\u3001\u30c7\u30fc\u30bf\u306e\u8981\u7d04\u7d71\u8a08\u91cf\u3092\u89e3\u91c8\u3059\u308b\u306e\u306f\u96e3\u3057\u304f\u306a\u308a\u307e\u3059\u3002\u305d\u306e\u305f\u3081\u3001\u73fe\u5728\u3067\u306f\u3001\u751f\u30c7\u30fc\u30bf\u3092\u5165\u624b\u3057\u3066\u4eba\u9593\u304c\u89e3\u91c8\u3067\u304d\u308b\u7d50\u679c\u3092\u5f97\u308b\u307e\u3067\u306e\u30d7\u30ed\u30bb\u30b9\u3092\u7dcf\u79f0\u3057\u3066\u3001\u30c7\u30fc\u30bf\u5206\u6790\u3068\u547c\u3093\u3067\u3044\u307e\u3059\u3002\u3057\u305f\u304c\u3063\u3066\u3001\u30c7\u30fc\u30bf\u5206\u6790\u306f\u3001\u30c7\u30fc\u30bf\u304b\u3089\u610f\u5473\u306e\u3042\u308b\u60c5\u5831\u3092\u62bd\u51fa\u3067\u304d\u308b\u3088\u3046\u306b\u3001\u30c7\u30fc\u30bf\u306e\u30af\u30ea\u30fc\u30cb\u30f3\u30b0\u3001\u5909\u63db\u3001\u30e2\u30c7\u30ea\u30f3\u30b0\u3092\u884c\u3046\u3053\u3068\u3067\u69cb\u6210\u3055\u308c\u307e\u3059\u3002<\/p>\n<p><a href=\"#source-code\"> \u30bd\u30fc\u30b9\u30b3\u30fc\u30c9\u306f\u3053\u3061\u3089 <\/a><\/p>\n<p>\u512a\u308c\u305f\u30c7\u30fc\u30bf\u5206\u6790\u30b7\u30b9\u30c6\u30e0\u306e\u6700\u3082\u91cd\u8981\u306a\u524d\u63d0\u6761\u4ef6\u306f\u3001\u4fe1\u983c\u3067\u304d\u308b\u30c7\u30fc\u30bf\u30d9\u30fc\u30b9\u3067\u3059\u3002\u30c7\u30fc\u30bf\u30d9\u30fc\u30b9\u306f\u62e1\u5f35\u6027\u304c\u3042\u308a\u3001\u5927\u898f\u6a21\u306a\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u306b\u7c21\u5358\u306b\u30af\u30a8\u30ea\u3092\u5b9f\u884c\u3067\u304d\u308b\u5fc5\u8981\u304c\u3042\u308a\u307e\u3059\u3002\u3053\u306e\u3088\u3046\u306a\u6a5f\u80fd\u3092\u3059\u3079\u3066\u5099\u3048\u305f\u6700\u65b0\u306e\u30c7\u30fc\u30bf\u30d9\u30fc\u30b9\u306e\u3072\u3068\u3064\u304cGridDB\u3067\u3059\u3002GridDB\u306f\u9ad8\u6027\u80fd\u3067\u3001\u591a\u304f\u306e\u30d7\u30ed\u30b0\u30e9\u30df\u30f3\u30b0\u8a00\u8a9e\u3068\u7c21\u5358\u306b\u7d71\u5408\u3059\u308b\u3053\u3068\u304c\u3067\u304d\u307e\u3059\u3002\u3053\u306e\u8a18\u4e8b\u3067\u306f\u3001Python\u3068GridDB\u3092\u4f7f\u3063\u3066\u3044\u304f\u3064\u304b\u306e\u30c7\u30fc\u30bf\u3092\u5206\u6790\u3057\u3066\u307f\u307e\u3059\u3002\u5206\u6790\u306b\u306f\u69d8\u3005\u306a\u7a2e\u985e\u304c\u3042\u308a\u307e\u3059\u304c\u3001\u3053\u306e\u8a18\u4e8b\u3067\u306f\u30e9\u30f3\u30c0\u30e0\u30d5\u30a9\u30ec\u30b9\u30c8\u30e2\u30c7\u30eb\u306b\u7126\u70b9\u3092\u5f53\u3066\u307e\u3059\u3002<\/p>\n<h3 id=\"griddb-setup\">GridDB\u306e\u30bb\u30c3\u30c8\u30a2\u30c3\u30d7<\/h3>\n<p>\u3053\u306e<a href=\"https:\/\/www.youtube.com\/watch?v=yWCVfLoV9_0&amp;t=61s\">\u30d3\u30c7\u30aa<\/a>\u3067\u306f\u3001GridDB\u306ePython\u30af\u30e9\u30a4\u30a2\u30f3\u30c8\u306e\u30bb\u30c3\u30c8\u30a2\u30c3\u30d7\u30ac\u30a4\u30c9\u3092\u7d39\u4ecb\u3057\u3066\u3044\u307e\u3059\u3002<\/p>\n<h3 id=\"python-libraries\">Python\u30e9\u30a4\u30d6\u30e9\u30ea<\/h3>\n<p>\u5206\u6790\u306b\u306f<code>python 3.6<\/code>\u3068pandas\u3092\u4f7f\u7528\u3057\u307e\u3059\u3002<br \/>\n\u30e9\u30a4\u30d6\u30e9\u30ea\u306e\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb\u306b\u306f\u4ee5\u4e0b\u306e\u30b3\u30de\u30f3\u30c9\u3092\u4f7f\u7528\u3057\u307e\u3059\u3002<\/p>\n<div class=\"clipboard\">\n<pre><code class=\"lang-python\">\npip <span class=\"hljs-keyword\">install <\/span>pandas\npip <span class=\"hljs-keyword\">install <\/span><span class=\"hljs-keyword\">scikit-learn<\/span>\npip <span class=\"hljs-keyword\">install <\/span>plotly\npip <span class=\"hljs-keyword\">install <\/span>matplotlib\n<\/code><\/pre>\n<\/div>\n<p>\u5404\u30e9\u30a4\u30d6\u30e9\u30ea\u3092\u30a4\u30f3\u30dd\u30fc\u30c8\u3057\u307e\u3059\u3002<\/p>\n<div class=\"clipboard\">\n<pre><code class=\"lang-python\">\n<span class=\"hljs-keyword\">import<\/span> pandas <span class=\"hljs-keyword\">as<\/span> pd\n<span class=\"hljs-keyword\">import<\/span> matplotlib.pyplot <span class=\"hljs-keyword\">as<\/span> plt\n<span class=\"hljs-keyword\">import<\/span> plotly.graph_objs <span class=\"hljs-keyword\">as<\/span> go\n<span class=\"hljs-title\">from<\/span> plotly.subplots <span class=\"hljs-keyword\">import<\/span> make_subplots\n<span class=\"hljs-title\">from<\/span> sklearn.model_selection <span class=\"hljs-keyword\">import<\/span> train_test_split,cross_val_score\n<span class=\"hljs-title\">from<\/span> sklearn.ensemble <span class=\"hljs-keyword\">import<\/span> RandomForestClassifier\n<span class=\"hljs-title\">from<\/span> sklearn.metrics <span class=\"hljs-keyword\">import<\/span> f1_score <span class=\"hljs-keyword\">as<\/span> f1\n<\/code><\/pre>\n<\/div>\n<h3 id=\"data-collection\">\u30c7\u30fc\u30bf\u53ce\u96c6<\/h3>\n<p>GridDB\u306f\u3001\u30c7\u30fc\u30bf\u306b\u30a2\u30af\u30bb\u30b9\u3059\u308b\u305f\u3081\u306e\u512a\u308c\u305f\u30a4\u30f3\u30bf\u30fc\u30d5\u30a7\u30fc\u30b9\u3092\u63d0\u4f9b\u3057\u307e\u3059\u3002\u3053\u3061\u3089\u306e<a href=\"https:\/\/griddb.net\/ja\/blog\/using-griddbs-cpythonruby-apis\/\">GridDB Python\u30af\u30e9\u30a4\u30a2\u30f3\u30c8\u306b\u95a2\u3059\u308b\u30d6\u30ed\u30b0<\/a>\u3067\u306f\u3001GridDB\u30c7\u30fc\u30bf\u30d9\u30fc\u30b9\u3092\u30ea\u30f3\u30af\u3057\u3001\u3059\u3079\u3066\u306e\u30c7\u30fc\u30bf\u3092pandas\u306e\u30c7\u30fc\u30bf\u30d5\u30ec\u30fc\u30e0\u306b\u30d7\u30c3\u30b7\u30e5\u3059\u308b\u65b9\u6cd5\u3092\u8a73\u3057\u304f\u8aac\u660e\u3057\u3066\u3044\u307e\u3059\u3002<\/p>\n<p>\u4eca\u56de\u306e\u5206\u6790\u3067\u306f\u3001\u30af\u30ec\u30b8\u30c3\u30c8\u30ab\u30fc\u30c9\u306e\u30c7\u30fc\u30bf\u3092\u4f7f\u3063\u3066\u3001\u89e3\u7d04\u7387\u3092\u4e88\u6e2c\u3057\u307e\u3059\u3002\u30c7\u30fc\u30bf\u306f<a href=\"https:\/\/www.kaggle.com\/sakshigoyal7\/credit-card-customers\">\u3053\u3061\u3089<\/a>\u304b\u3089\u5165\u624b\u3067\u304d\u307e\u3059\u3002<\/p>\n<p>\u30b3\u30f3\u30c6\u30ca\u3092\u30a4\u30f3\u30b9\u30bf\u30f3\u30b9\u5316\u3057\u3001\u3059\u3079\u3066\u306e\u30c7\u30fc\u30bf\u3092pandas\u306e\u30c7\u30fc\u30bf\u30d5\u30ec\u30fc\u30e0\u306b\u683c\u7d0d\u3059\u308b\u3053\u3068\u3067\u3001GridDB\u3092\u30c7\u30fc\u30bf\u30d9\u30fc\u30b9\u3068\u3057\u3066\u8a2d\u5b9a\u3059\u308b\u3053\u3068\u304c\u3067\u304d\u307e\u3059\u3002<\/p>\n<div class=\"clipboard\">\n<pre><code class=\"lang-python\">\n<span class=\"hljs-built_in\">import<\/span> griddb_python as griddb\n<span class=\"hljs-comment\"># Initialize container<\/span>\n<span class=\"hljs-attr\">gridstore<\/span> = factory.get_store(<span class=\"hljs-attr\">host=<\/span> host, <span class=\"hljs-attr\">port=port,<\/span> \n            <span class=\"hljs-attr\">cluster_name=cluster_name,<\/span> <span class=\"hljs-attr\">username=uname,<\/span> \n            <span class=\"hljs-attr\">password=pwd)<\/span>\n\n<span class=\"hljs-attr\">conInfo<\/span> = griddb.ContainerInfo(<span class=\"hljs-string\">\"attrition\"<\/span>,\n                    [[<span class=\"hljs-string\">\"CLIENTNUM\"<\/span>, griddb.Type.LONG],\n                    [<span class=\"hljs-string\">\"Gender\"<\/span>,griddb.Type.STRING],\n              .... <span class=\"hljs-comment\">#for all 23 variables      <\/span>\n                    griddb.ContainerType.COLLECTION, True)\n\n<span class=\"hljs-attr\">cont<\/span> = gridstore.put_container(conInfo)    \ncont.create_index(<span class=\"hljs-string\">\"CLIENTNUM\"<\/span>, griddb.IndexType.DEFAULT)\n<\/code><\/pre>\n<\/div>\n<p>\u4ee5\u4e0b\u306eSQL\u30af\u30a8\u30ea\u3092\u7528\u3044\u3066\u3001GridDB\u304b\u3089\u30c7\u30fc\u30bf\u3092\u53d6\u5f97\u3059\u308b\u3053\u3068\u304c\u3067\u304d\u307e\u3059\u3002<\/p>\n<div class=\"clipboard\">\n<pre><code class=\"lang-python\">\n<span class=\"hljs-keyword\">query<\/span> = cont.<span class=\"hljs-keyword\">query<\/span>(<span class=\"hljs-string\">\"select *\"<\/span>)\n<\/code><\/pre>\n<\/div>\n<p>\u30c7\u30fc\u30bf\u89e3\u6790\u306e\u30d1\u30a4\u30d7\u30e9\u30a4\u30f3\u306f\u3001\u4ee5\u4e0b\u306e\u30b9\u30c6\u30c3\u30d7\u3067\u69cb\u6210\u3055\u308c\u3066\u3044\u307e\u3059\u3002<\/p>\n<ol>\n<li>\n    <a href=\"#data-collection-and-exploration\"> <strong>\u30c7\u30fc\u30bf\u691c\u7d22<\/strong><\/a>: \u6211\u3005\u306f\u307e\u305a\u3001\u69d8\u3005\u306a\u5909\u6570\u306e\u8981\u7d04\u7d71\u8a08\u3092\u884c\u3044\u3001\u5f93\u5c5e\u5909\u6570\u3067\u3042\u308b\u751f\u5b58\u7387\u3068\u306e\u76f8\u95a2\u3092\u7406\u89e3\u3057\u3088\u3046\u3068\u3057\u307e\u3059\u3002\u307e\u305f\u3001\u5916\u308c\u5024\u304c\u3042\u308c\u3070\u305d\u308c\u3092\u9664\u53bb\u3059\u308b\u305f\u3081\u306b\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u306e\u30af\u30ea\u30fc\u30cb\u30f3\u30b0\u3092\u884c\u3044\u307e\u3059\u3002\n  <\/li>\n<li>\n    <a href=\"#feature-engineering\"> <strong>\u7279\u5fb4\u91cf\u30a8\u30f3\u30b8\u30cb\u30a2\u30ea\u30f3\u30b0<\/strong><\/a>: \u305d\u3057\u3066\u3001\u30e2\u30c7\u30ea\u30f3\u30b0\u306b\u4f7f\u7528\u3067\u304d\u308b\u7279\u5fb4\u91cf\u3092\u9078\u629e\u3057\u3066\u3044\u304d\u307e\u3059\u3002\u65e2\u5b58\u306e\u30c7\u30fc\u30bf\u3084\u30aa\u30fc\u30d7\u30f3\u30bd\u30fc\u30b9\u306e\u30ea\u30bd\u30fc\u30b9\u304b\u3089\u3001\u65b0\u3057\u3044\u7279\u5fb4\u91cf\u3092\u4f5c\u6210\u3059\u308b\u3053\u3068\u304c\u3067\u304d\u307e\u3059\u3002\n  <\/li>\n<li>\n     <a href=\"#data-modelling\"><strong>\u30e2\u30c7\u30ea\u30f3\u30b0<\/strong><\/a>: \u305d\u3057\u3066\u3001\u6a5f\u68b0\u5b66\u7fd2\u30e2\u30c7\u30eb\u3001\u3053\u3053\u3067\u306f\u30e9\u30f3\u30c0\u30e0\u30d5\u30a9\u30ec\u30b9\u30c8\u3092\u4f7f\u7528\u3057\u307e\u3059\u3002\u307e\u305a\u3001\u30c7\u30fc\u30bf\u3092\u30c6\u30b9\u30c8\u30bb\u30c3\u30c8\u3068\u30c8\u30ec\u30fc\u30cb\u30f3\u30b0\u30bb\u30c3\u30c8\u306b\u5206\u3051\u307e\u3059\u3002\u901a\u5e38\u306f\u3001\u5b66\u7fd2\u30c7\u30fc\u30bf\u3067\u30e2\u30c7\u30eb\u3092\u5b66\u7fd2\u3057\u3001\u30c6\u30b9\u30c8\u30bb\u30c3\u30c8\u3067\u8a55\u4fa1\u3057\u307e\u3059\u3002\u30e2\u30c7\u30eb\u306e\u30cf\u30a4\u30d1\u30fc\u30d1\u30e9\u30e1\u30fc\u30bf\u3092\u8abf\u6574\u3059\u308b\u305f\u3081\u306b\u3001\u691c\u8a3c\u30bb\u30c3\u30c8\u3092\u7528\u610f\u3057\u305f\u308a\u3001\u30af\u30ed\u30b9\u30d0\u30ea\u30c7\u30fc\u30b7\u30e7\u30f3\u3092\u884c\u3063\u305f\u308a\u3059\u308b\u3053\u3068\u3082\u3042\u308a\u307e\u3059\u3002\n  <\/li>\n<li>\n    <a href=\"#evaluation\"><strong>\u8a55\u4fa1<\/strong><\/a>: \u6700\u5f8c\u306b\u3001\u3053\u306e\u30e2\u30c7\u30eb\u3092\u4f7f\u3063\u3066\u4e88\u6e2c\u3092\u884c\u3044\u3001\u305d\u306e\u6027\u80fd\u3092\u5206\u6790\u3057\u307e\u3059\u3002\n  <\/li>\n<\/ol>\n<h3 id=\"data-collection-and-exploration\">\u30c7\u30fc\u30bf\u53ce\u96c6\u3068\u63a2\u67fb<\/h3>\n<p><code>pandas<\/code>\u3092\u4f7f\u3063\u3066\u30c7\u30fc\u30bf\u3092\u8aad\u307f\u8fbc\u307f\u307e\u3059\u3002\u6700\u5f8c\u306e2\u5217\u306f\u5225\u306e\u5206\u985e\u5668\u306e\u7d50\u679c\u306a\u306e\u3067\u524a\u9664\u3057\u307e\u3059\u3002<\/p>\n<div class=\"clipboard\">\n<pre><code class=\"lang-python\">\n<span class=\"hljs-class\"><span class=\"hljs-keyword\">data<\/span> = pd.read_csv('\/<span class=\"hljs-title\">kaggle<\/span>\/<span class=\"hljs-title\">input<\/span>\/<span class=\"hljs-title\">credit<\/span>-<span class=\"hljs-title\">card<\/span>-<span class=\"hljs-title\">customers<\/span>\/<span class=\"hljs-type\">BankChurners<\/span>.<span class=\"hljs-title\">csv'<\/span>)<\/span>\n<span class=\"hljs-class\"><span class=\"hljs-keyword\">data<\/span> = <span class=\"hljs-keyword\">data<\/span>[<span class=\"hljs-keyword\">data<\/span>.columns[:-2]]<\/span>\n<\/code><\/pre>\n<\/div>\n<p>\u307e\u305a\u3001\u3044\u304f\u3064\u304b\u306e\u5909\u6570\u306e\u8981\u7d04\u7d71\u8a08\u91cf\u3092\u4f5c\u6210\u3057\u307e\u3059\u3002\u7406\u60f3\u7684\u306b\u306f\u3059\u3079\u3066\u306e\u5909\u6570\u3092\u30c1\u30a7\u30c3\u30af\u3059\u308b\u3053\u3068\u3067\u3059\u304c\u3001\u7c21\u6f54\u306b\u3059\u308b\u305f\u3081\u306b\u3044\u304f\u3064\u304b\u306e\u91cd\u8981\u306a\u5909\u6570\u3092\u7d39\u4ecb\u3057\u307e\u3059\u3002<\/p>\n<h4 id=\"attrition_flag-\">Attrition_Flag:<\/h4>\n<div class=\"clipboard\">\n<pre><code class=\"lang-python\">\nattdata = data.groupby(['Attrition_Flag']).count()[[\"CLIENTNUM\"]].reset_index()\nattdata['percentage'] = attdata['CLIENTNUM']\/attdata['CLIENTNUM'].sum()\nattdata[attdata.Attrition_Flag  == \"Attrited Customer\"]\n<\/code><\/pre>\n<\/div>\n<table>\n<thead>\n<tr>\n<th style=\"text-align:left\">CLIENTNUM<\/th>\n<th style=\"text-align:center\">percentage<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td style=\"text-align:left\">1627<\/td>\n<td style=\"text-align:center\">0.16066<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p><\/p>\n<p>\u89e3\u7d04\u7387\u306f\u7d0416\uff05\u3067\u3059\u3002<\/p>\n<h4 id=\"demographic-variables\">\u4eba\u53e3\u7d71\u8a08\u5b66\u7684\u5909\u6570<\/h4>\n<h5 id=\"gender\">\u6027\u5225<\/h5>\n<div class=\"clipboard\">\n<pre><code class=\"lang-python\">\ngenderdata = data.groupby(['Gender']).count()[[\"CLIENTNUM\"]].reset_index()\ngenderdata['percentage'] = genderdata['CLIENTNUM']\/genderdata['CLIENTNUM'].sum()\ngenderdata[genderdata.Gender  == \"F\"]\n<\/code><\/pre>\n<\/div>\n<table>\n<thead>\n<tr>\n<th style=\"text-align:left\">CLIENTNUM<\/th>\n<th style=\"text-align:center\">percentage<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td style=\"text-align:left\">5358<\/td>\n<td style=\"text-align:center\">0.529081<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p><\/p>\n<p>\u5973\u6027\u304c52.9\uff05\u3044\u308b\u3053\u3068\u304c\u308f\u304b\u308a\u307e\u3059\u3002\u3057\u304b\u3057\u3001\u7537\u5973\u306e\u5dee\u306f\u305d\u308c\u307b\u3069\u5927\u304d\u304f\u3042\u308a\u307e\u305b\u3093\u3002<\/p>\n<h5 id=\"education\">\u6559\u80b2<\/h5>\n<div class=\"clipboard\">\n<pre><code class=\"lang-python\">\n<span class=\"hljs-class\"><span class=\"hljs-keyword\">data<\/span>.groupby(['<span class=\"hljs-type\">Education_Level<\/span>']).count()[[\"<span class=\"hljs-type\">CLIENTNUM<\/span>\"]].reset_index()<\/span>\n<\/code><\/pre>\n<\/div>\n<h5 id=\"education_level\">Education_Level<\/h5>\n<table>\n<thead>\n<tr>\n<th style=\"text-align:left\">Type<\/th>\n<th style=\"text-align:center\">Number<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td style=\"text-align:left\">College<\/td>\n<td style=\"text-align:center\">1013<\/td>\n<\/tr>\n<tr>\n<td style=\"text-align:left\">Doctorate<\/td>\n<td style=\"text-align:center\">451<\/td>\n<\/tr>\n<tr>\n<td style=\"text-align:left\">Graduate<\/td>\n<td style=\"text-align:center\">3128<\/td>\n<\/tr>\n<tr>\n<td style=\"text-align:left\">High School<\/td>\n<td style=\"text-align:center\">2013<\/td>\n<\/tr>\n<tr>\n<td style=\"text-align:left\">Post-Graduate<\/td>\n<td style=\"text-align:center\">516<\/td>\n<\/tr>\n<tr>\n<td style=\"text-align:left\">Uneducated<\/td>\n<td style=\"text-align:center\">1487<\/td>\n<\/tr>\n<tr>\n<td style=\"text-align:left\">Unknown<\/td>\n<td style=\"text-align:center\">1519<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>\u304a\u5ba2\u69d8\u306e\u7d047\u5272\u304c\u6559\u80b2\u3092\u53d7\u3051\u3066\u3044\u308b\u3053\u3068\u304c\u308f\u304b\u308a\u307e\u3059\u3002<\/p>\n<h5 id=\"income_category\">Income_Category<\/h5>\n<div class=\"clipboard\">\n<pre><code class=\"lang-python\">\n<span class=\"hljs-class\"><span class=\"hljs-keyword\">data<\/span>.groupby(['<span class=\"hljs-type\">Income_Category<\/span>']).count()[[\"<span class=\"hljs-type\">CLIENTNUM<\/span>\"]].reset_index()<\/span>\n<\/code><\/pre>\n<\/div>\n<table>\n<thead>\n<tr>\n<th style=\"text-align:left\">Income_Category<\/th>\n<th style=\"text-align:center\">Number<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td style=\"text-align:left\">$120K +<\/td>\n<td style=\"text-align:center\">727<\/td>\n<\/tr>\n<tr>\n<td style=\"text-align:left\">40k\u2212 60k<\/td>\n<td style=\"text-align:center\">1790<\/td>\n<\/tr>\n<tr>\n<td style=\"text-align:left\">60k\u2212 *80K<\/td>\n<td style=\"text-align:center\">1402<\/td>\n<\/tr>\n<tr>\n<td style=\"text-align:left\">Less than $40k<\/td>\n<td style=\"text-align:center\">3561<\/td>\n<\/tr>\n<tr>\n<td style=\"text-align:left\">Unknown<\/td>\n<td style=\"text-align:center\">1112<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>\u307b\u3068\u3093\u3069\u306e\u4eba\u304c4\u4e07\u30c9\u30eb\u4ee5\u4e0b\u306e\u53ce\u5165\u3067\u3042\u308b\u3053\u3068\u304c\u308f\u304b\u308a\u307e\u3059\u3002<\/p>\n<h4 id=\"bank-variables\">Bank variables<\/h4>\n<p>&#39;<strong>Months_on_book<\/strong>&#39;, &#39;<strong>Total_Relationship_Count<\/strong>&#39; (\u304a\u5ba2\u69d8\u304c\u304a\u6301\u3061\u306e\u88fd\u54c1\u306e\u5408\u8a08\u6570), &#39;<strong>Months_Inactive_12_mon<\/strong>&#39; (\u904e\u53bb12\u30f6\u6708\u9593\u306b\u6d3b\u52d5\u3057\u306a\u304b\u3063\u305f\u6708\u6570), \u305d\u3057\u3066 &#39;<strong>Credit_Limit<\/strong>&#39; (\u30af\u30ec\u30b8\u30c3\u30c8\u30ab\u30fc\u30c9\u306e\u3054\u5229\u7528\u9650\u5ea6\u984d) \u306e\u30d2\u30b9\u30c8\u30b0\u30e9\u30e0\u3092\u63cf\u3044\u3066\u307f\u307e\u3059\u3002<\/p>\n<div class=\"clipboard\">\n<pre><code class=\"lang-python\">\nfig = make_subplots(rows=<span class=\"hljs-number\">2<\/span>, cols=<span class=\"hljs-number\">2<\/span>)\ntr1=<span class=\"hljs-keyword\">go<\/span>.Histogram(<span class=\"hljs-keyword\">x<\/span>=data[<span class=\"hljs-string\">'Months_on_book'<\/span>],name=<span class=\"hljs-string\">'Months on book Box Plot'<\/span>)\ntr2=<span class=\"hljs-keyword\">go<\/span>.Histogram(<span class=\"hljs-keyword\">x<\/span>=data[<span class=\"hljs-string\">'Total_Relationship_Count'<\/span>],name=<span class=\"hljs-string\">'Total no. of products Histogram'<\/span>)\ntr3=<span class=\"hljs-keyword\">go<\/span>.Histogram(<span class=\"hljs-keyword\">x<\/span>=data[<span class=\"hljs-string\">'Months_Inactive_12_mon'<\/span>],name=<span class=\"hljs-string\">'number of months inactive Histogram'<\/span>)\ntr4=<span class=\"hljs-keyword\">go<\/span>.Histogram(<span class=\"hljs-keyword\">x<\/span>=data[<span class=\"hljs-string\">'Credit_Limit'<\/span>],name=<span class=\"hljs-string\">'Credit_Limit Histogram'<\/span>)\n\nfig.add_trace(tr1,row=<span class=\"hljs-number\">1<\/span>,<span class=\"hljs-keyword\">col<\/span>=<span class=\"hljs-number\">1<\/span>)\nfig.add_trace(tr2,row=<span class=\"hljs-number\">1<\/span>,<span class=\"hljs-keyword\">col<\/span>=<span class=\"hljs-number\">2<\/span>)\nfig.add_trace(tr3,row=<span class=\"hljs-number\">2<\/span>,<span class=\"hljs-keyword\">col<\/span>=<span class=\"hljs-number\">1<\/span>)\nfig.add_trace(tr4,row=<span class=\"hljs-number\">2<\/span>,<span class=\"hljs-keyword\">col<\/span>=<span class=\"hljs-number\">2<\/span>)\n\nfig.update_layout(height=<span class=\"hljs-number\">700<\/span>, width=<span class=\"hljs-number\">1200<\/span>, title_text=<span class=\"hljs-string\">\"Distribution of bank variables\"<\/span>)\nfig.show()\n<\/code><\/pre>\n<\/div>\n<p><img decoding=\"async\"\n    src=\"https:\/\/lh3.googleusercontent.com\/vqSOhxLVLzv8vgKtwqRAbplYd__UE7cZS2PaXjvgJxOsWjhs3xDMGdRKS4gRmxyYERrq-vVEPb2_sJ47a50qA2XE__UbrUw1H-IaXApUbA69ioxwP3LFP-_Cwsvuk3_PVlsSMXNU\"\n    alt=\"\"><\/p>\n<p>\u88fd\u54c1\u7dcf\u6570\u306e\u5206\u5e03\u306f\u307b\u307c\u4e00\u69d8\u3067\u3042\u308b\u3053\u3068\u304c\u308f\u304b\u308a\u307e\u3059\u306e\u3067\u3001\u7406\u60f3\u7684\u306b\u306f\u3053\u306e\u5909\u6570\u3092\u5206\u6790\u304b\u3089\u5916\u3059\u3053\u3068\u304c\u3067\u304d\u307e\u3059\u3002\u4ed6\u306e\u5909\u6570\u306f\u591a\u304f\u306e\u5909\u52d5\u3092\u793a\u3057\u3066\u3044\u308b\u306e\u3067\u3001\u305d\u308c\u3089\u3092\u7dad\u6301\u3059\u308b\u3053\u3068\u304c\u3067\u304d\u307e\u3059\u3002<\/p>\n<h4 id=\"card_category\">Card_Category<\/h4>\n<div class=\"clipboard\">\n<pre><code class=\"lang-python\">\n<span class=\"hljs-class\"><span class=\"hljs-keyword\">data<\/span>.groupby(['<span class=\"hljs-type\">Card_Category<\/span>']).count()[[\"<span class=\"hljs-type\">CLIENTNUM<\/span>\"]].reset_index()<\/span>\n<\/code><\/pre>\n<\/div>\n<table>\n<thead>\n<tr>\n<th style=\"text-align:left\">Card_Category<\/th>\n<th style=\"text-align:left\">Total<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td style=\"text-align:left\">Blue<\/td>\n<td style=\"text-align:left\">9496<\/td>\n<\/tr>\n<tr>\n<td style=\"text-align:left\">Gold<\/td>\n<td style=\"text-align:left\">116<\/td>\n<\/tr>\n<tr>\n<td style=\"text-align:left\">Platinum<\/td>\n<td style=\"text-align:left\">20<\/td>\n<\/tr>\n<tr>\n<td style=\"text-align:left\">Silver<\/td>\n<td style=\"text-align:left\">555<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>\u307b\u3068\u3093\u3069\u306e\u304a\u5ba2\u69d8\u304c<strong>Blue<\/strong>\u3092\u4f7f\u7528\u3057\u3066\u3044\u308b\u3053\u3068\u304c\u308f\u304b\u308a\u307e\u3059\u306e\u3067\u3001\u3053\u306e\u5909\u6570\u3082\u7121\u8996\u3057\u3066\u69cb\u3044\u307e\u305b\u3093\u3002<\/p>\n<h3 id=\"feature-engineering\">\u7279\u5fb4\u91cf\u30a8\u30f3\u30b8\u30cb\u30a2\u30ea\u30f3\u30b0<\/h3>\n<p>\u4eca\u56de\u306e\u5206\u6790\u3067\u306f\u3001<strong>\u9867\u5ba2\u756a\u53f7<\/strong>\u3092\u7121\u8996\u3059\u308b\u3053\u3068\u304c\u3067\u304d\u307e\u3059\u3002\u3057\u304b\u3057\u3001\u9280\u884c\u304c\u3088\u308a\u591a\u304f\u306e\u30c7\u30fc\u30bf\u30bd\u30fc\u30b9\u3092\u6301\u3063\u3066\u3044\u305f\u5834\u5408\u3001\u7570\u306a\u308b\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u3092\u7167\u5408\u3059\u308b\u305f\u3081\u306b<strong>\u9867\u5ba2\u756a\u53f7<\/strong>\u3092\u4f7f\u7528\u3059\u308b\u3053\u3068\u304c\u3067\u304d\u307e\u3059\u3002<\/p>\n<p>\u89e3\u7d04\u7387\u3092\u4e88\u6e2c\u3057\u305f\u3044\u306e\u3067\u3001<strong>\u89e3\u7d04\u7387\u30d5\u30e9\u30b0<\/strong>\u304c\u5f93\u5c5e\u5909\u6570\u3068\u306a\u308a\u307e\u3059\u3002\u3053\u308c\u30920,1\u306e\u5909\u6570\u3068\u3057\u3066\u30b3\u30fc\u30c7\u30a3\u30f3\u30b0\u3057\u307e\u3059\u3002<\/p>\n<p>\u6b21\u306b\u3001\u30c0\u30df\u30fc\u5909\u6570\u3092\u4f5c\u6210\u3057\u307e\u3059\u3002\u30c0\u30df\u30fc\u5909\u6570\u306f\u3001\u57fa\u672c\u7684\u306b1\u3064\u306e\u30ab\u30c6\u30b4\u30ea\u30fc\u306e\u5909\u6570\u3092\u30a8\u30f3\u30b3\u30fc\u30c9\u3057\u307e\u3059\u3002\u79c1\u305f\u3061\u306f\u30011\u3064\u306e\u30ab\u30c6\u30b4\u30ea\u30fc\u3092\u7701\u304d\u307e\u3059\u3002\u305d\u3046\u3057\u306a\u3044\u3068\u3001\u5171\u7dda\u6027\u306e\u554f\u984c\u306b\u76f4\u9762\u3059\u308b\u3053\u3068\u306b\u306a\u308a\u307e\u3059\u3002\u6211\u3005\u306f\u4eba\u53e3\u7d71\u8a08\u5b66\u7684\u5909\u6570\u304b\u3089\u4ee5\u4e0b\u306e\u30c0\u30df\u30fc\u5909\u6570\u3092\u4f5c\u6210\u3057\u307e\u3059\u3002\u3053\u308c\u306fHot-One Encoding\u3068\u3082\u547c\u3070\u308c\u307e\u3059\u3002<\/p>\n<ul>\n<li>Education_Levels<\/li>\n<li>Income<\/li>\n<li>Marital status<\/li>\n<\/ul>\n<div class=\"clipboard\">\n<pre><code class=\"lang-python\">\n<span class=\"hljs-keyword\">data<\/span>.Attrition_Flag = <span class=\"hljs-keyword\">data<\/span>.Attrition_Flag.replace({<span class=\"hljs-string\">'Attrited Customer'<\/span>:<span class=\"hljs-number\">1<\/span>,<span class=\"hljs-string\">'Existing Customer'<\/span>:<span class=\"hljs-number\">0<\/span>})\n<span class=\"hljs-keyword\">data<\/span>.Gender = <span class=\"hljs-keyword\">data<\/span>.Gender.replace({<span class=\"hljs-string\">'F'<\/span>:<span class=\"hljs-number\">1<\/span>,<span class=\"hljs-string\">'M'<\/span>:<span class=\"hljs-number\">0<\/span>})\n<span class=\"hljs-keyword\">data<\/span> = pd.concat([<span class=\"hljs-keyword\">data<\/span>,pd.get_dummies(<span class=\"hljs-keyword\">data<\/span>[<span class=\"hljs-string\">'Education_Level'<\/span>]).drop(columns=[<span class=\"hljs-string\">'Unknown'<\/span>])],axis=<span class=\"hljs-number\">1<\/span>)\n<span class=\"hljs-keyword\">data<\/span> = pd.concat([<span class=\"hljs-keyword\">data<\/span>,pd.get_dummies(<span class=\"hljs-keyword\">data<\/span>[<span class=\"hljs-string\">'Income_Category'<\/span>]).drop(columns=[<span class=\"hljs-string\">'Unknown'<\/span>])],axis=<span class=\"hljs-number\">1<\/span>)\n<span class=\"hljs-keyword\">data<\/span> = pd.concat([<span class=\"hljs-keyword\">data<\/span>,pd.get_dummies(<span class=\"hljs-keyword\">data<\/span>[<span class=\"hljs-string\">'Marital_Status'<\/span>]).drop(columns=[<span class=\"hljs-string\">'Unknown'<\/span>])],axis=<span class=\"hljs-number\">1<\/span>)\n\n<span class=\"hljs-keyword\">data<\/span>.drop(columns = [<span class=\"hljs-string\">'Education_Level'<\/span>,<span class=\"hljs-string\">'Income_Category'<\/span>,<span class=\"hljs-string\">'Marital_Status'<\/span>,<span class=\"hljs-string\">'Card_Category'<\/span>,<span class=\"hljs-string\">'CLIENTNUM'<\/span>, <span class=\"hljs-string\">'Card_Category'<\/span>],inplace=True, errors = <span class=\"hljs-string\">\"ignore\"<\/span>)\n<\/code><\/pre>\n<\/div>\n<h3 id=\"data-modelling\">\u30c7\u30fc\u30bf\u30e2\u30c7\u30ea\u30f3\u30b0<\/h3>\n<p>\u6b21\u306b\u3001\u30c7\u30fc\u30bf\u3092\u30c6\u30b9\u30c8\u3068\u30c8\u30ec\u30fc\u30cb\u30f3\u30b0\u306b\u5206\u3051\u305f\u5f8c\u3001100\u672c\u306e\u6728\u3092\u4f7f\u3063\u3066\u30b7\u30f3\u30d7\u30eb\u306a\u30e9\u30f3\u30c0\u30e0\u30d5\u30a9\u30ec\u30b9\u30c8\u30e2\u30c7\u30eb\u3092\u5b9f\u884c\u3057\u307e\u3059\u3002<\/p>\n<div class=\"clipboard\">\n<pre><code class=\"lang-python\">\n<span class=\"hljs-attribute\">X_features<\/span> = [<span class=\"hljs-string\">'Customer_Age'<\/span>, <span class=\"hljs-string\">'Gender'<\/span>, <span class=\"hljs-string\">'Dependent_count'<\/span>,\n<span class=\"hljs-string\">'Months_on_book'<\/span>, <span class=\"hljs-string\">'Total_Relationship_Count'<\/span>, <span class=\"hljs-string\">'Months_Inactive_12_mon'<\/span>,\n<span class=\"hljs-string\">'Contcts_Count_12_mon'<\/span>, <span class=\"hljs-string\">'Credit_Limit'<\/span>, <span class=\"hljs-string\">'Total_Revolving_Bal'<\/span>,\n<span class=\"hljs-string\">'Avg_Open_To_Buy'<\/span>, <span class=\"hljs-string\">'Total_Amt_Chng_Q4_Q1'<\/span>, <span class=\"hljs-string\">'Total_Trans_Amt'<\/span>,\n<span class=\"hljs-string\">'Total_Trans_Ct'<\/span>, <span class=\"hljs-string\">'Total_Ct_Chng_Q4_Q1'<\/span>, <span class=\"hljs-string\">'Avg_Utilization_Ratio'<\/span>,\n<span class=\"hljs-string\">'College'<\/span>, <span class=\"hljs-string\">'Doctorate'<\/span>, <span class=\"hljs-string\">'Graduate'<\/span>, <span class=\"hljs-string\">'High School'<\/span>, <span class=\"hljs-string\">'Post-Graduate'<\/span>,\n<span class=\"hljs-string\">'Uneducated'<\/span>, <span class=\"hljs-string\">'<span class=\"hljs-variable\">$120<\/span>K +'<\/span>, <span class=\"hljs-string\">'<span class=\"hljs-variable\">$40<\/span>K - <span class=\"hljs-variable\">$60<\/span>K'<\/span>, <span class=\"hljs-string\">'<span class=\"hljs-variable\">$60<\/span>K - <span class=\"hljs-variable\">$80<\/span>K'<\/span>, <span class=\"hljs-string\">'<span class=\"hljs-variable\">$80<\/span>K - <span class=\"hljs-variable\">$120<\/span>K'<\/span>,\n<span class=\"hljs-string\">'Less than <span class=\"hljs-variable\">$40<\/span>K'<\/span>, <span class=\"hljs-string\">'Divorced'<\/span>, <span class=\"hljs-string\">'Married'<\/span>, <span class=\"hljs-string\">'Single'<\/span>]\n\n\nX = data[X_features]\ny = data[<span class=\"hljs-string\">'Attrition_Flag'<\/span>]\ntrain_x,test_x,train_y,test_y = train_test_split(X,y,random_state=<span class=\"hljs-number\">42<\/span>)\n\nrf = RandomForestClassifier(n_estimators = <span class=\"hljs-number\">100<\/span>, random_state = <span class=\"hljs-number\">42<\/span>)\nrf.fit(train_x,train_y)\n<\/code><\/pre>\n<\/div>\n<h3 id=\"evaluation\">\u8a55\u4fa1<\/h3>\n<p>\u4e88\u6e2c\u306eF1\u30b9\u30b3\u30a2\u3092\u6c42\u3081\u307e\u3059\u3002F1\u306f\u7cbe\u5ea6\u3068\u30ea\u30b3\u30fc\u30eb\u306e\u8abf\u548c\u7684\u5e73\u5747\u3068\u3057\u3066\u5b9a\u7fa9\u3055\u308c\u307e\u3059\u3002<\/p>\n<div class=\"clipboard\">\n<pre><code class=\"lang-python\">\nrf_prediction = rf_pipe.<span class=\"hljs-keyword\">predict<\/span>(test_x)\n<span class=\"hljs-keyword\">print<\/span>('F1 <span class=\"hljs-keyword\">Score<\/span> of Random Forest Model <span class=\"hljs-keyword\">On<\/span> <span class=\"hljs-keyword\">Test<\/span> <span class=\"hljs-keyword\">Set<\/span> {}'.<span class=\"hljs-keyword\">format<\/span>(f1(rf_prediction,test_y)))\n<\/code><\/pre>\n<\/div>\n<p>\u307e\u305f\u3001\u30e9\u30f3\u30c0\u30e0\u30d5\u30a9\u30ec\u30b9\u30c8\u30e2\u30c7\u30eb\u304b\u3089\u306f\u3001\u76f8\u5bfe\u7684\u306a\u5909\u6570\u306e\u91cd\u8981\u6027\u3092\u5f97\u308b\u3053\u3068\u304c\u3067\u304d\u307e\u3059\u3002<\/p>\n<div class=\"clipboard\">\n<pre><code class=\"lang-python\">\nimportances = rf.feature_importances_\n<span class=\"hljs-built_in\">indices<\/span> = <span class=\"hljs-built_in\">np<\/span>.argsort(importances)\nplt.<span class=\"hljs-built_in\">title<\/span>('Feature Importances')\nplt.barh(<span class=\"hljs-built_in\">range<\/span>(len(<span class=\"hljs-built_in\">indices<\/span>)), importances[<span class=\"hljs-built_in\">indices<\/span>], <span class=\"hljs-built_in\">color<\/span>='b', align='<span class=\"hljs-built_in\">center<\/span>')\nplt.yticks(<span class=\"hljs-built_in\">range<\/span>(len(<span class=\"hljs-built_in\">indices<\/span>)), [X_features[i] <span class=\"hljs-keyword\">for<\/span> i <span class=\"hljs-keyword\">in<\/span> <span class=\"hljs-built_in\">indices<\/span>])\nplt.<span class=\"hljs-built_in\">xlabel<\/span>('Relative Importance')\nplt.<span class=\"hljs-built_in\">show<\/span>()\n<\/code><\/pre>\n<\/div>\n<p><img decoding=\"async\"\n    src=\"https:\/\/lh5.googleusercontent.com\/qxvRYnlyPxkqzcxyz6ncyqUE9M1haCPAS9o84JIovW7EMrISruZVNGXlFmHyWKiY_pZKbp7t_9YANX_1CqmALpJARcJckERHgtQMajM7qFP-0M1SGnAzlF5cbNLxBn28t_Ss5kKO\"\n    alt=\"\"><\/p>\n<p>\u30c8\u30e9\u30f3\u30b6\u30af\u30b7\u30e7\u30f3\u306e\u5408\u8a08\u91d1\u984d\u304c\u6700\u3082\u91cd\u8981\u306a\u5909\u6570\u3067\u3042\u308b\u3053\u3068\u304c\u308f\u304b\u308a\u307e\u3059\u3002<\/p>\n<h3 id=\"conclusion\">\u307e\u3068\u3081<\/h3>\n<p>\u3053\u306e\u8a18\u4e8b\u3067\u306f\u3001python\u3068GridDB\u3092\u4f7f\u3063\u305f\u30c7\u30fc\u30bf\u5206\u6790\u3068\u4e88\u6e2c\u30e2\u30c7\u30ea\u30f3\u30b0\u306e\u57fa\u790e\u3092\u5b66\u3073\u307e\u3057\u305f\u3002<\/p>\n<h3 id=\"source-code\"> \u30bd\u30fc\u30b9\u30b3\u30fc\u30c9 <\/h3>\n<p><a href=\"https:\/\/griddb.net\/ja\/download\/19325\/\"> <span class=\"download-button\"> \u5168\u30bd\u30fc\u30b9\u30b3\u30fc\u30c9\u306e\u30c0\u30a6\u30f3\u30ed\u30fc\u30c9\u306f\u3053\u3061\u3089\u304b\u3089 <\/span><\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>\u30c7\u30fc\u30bf\u5206\u6790\u306f\u3001\u30c7\u30fc\u30bf\u304b\u3089\u6709\u7528\u306a\u60c5\u5831\u3092\u62bd\u51fa\u3057\u3001\u610f\u601d\u6c7a\u5b9a\u30d7\u30ed\u30bb\u30b9\u306b\u5f79\u7acb\u3066\u308b\u3053\u3068\u3092\u76ee\u7684\u3068\u3057\u3066\u3044\u307e\u3059\u3002\u3057\u304b\u3057\u3001\u30e2\u30d0\u30a4\u30eb\u6a5f\u5668\u3084\u30bb\u30f3\u30b5\u30fc\u306a\u3069\u306e\u5916\u90e8\u30bd\u30fc\u30b9\u304b\u3089\u5f97\u3089\u308c\u308b\u751f\u30c7\u30fc\u30bf\u306b\u306f\u3001\u591a\u304f\u306e\u5916\u308c\u5024\u304c\u542b\u307e\u308c\u3066\u3044\u307e\u3059\u3002\u3055\u3089\u306b\u3001\u30c7\u30fc\u30bf\u306f\u9ad8\u6b21\u5143\u3067 [&hellip;]<\/p>\n","protected":false},"author":41,"featured_media":49188,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[1005],"tags":[],"class_list":["post-50725","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>Python\u3068GridDB\u3092\u7528\u3044\u305f\u30af\u30ec\u30b8\u30c3\u30c8\u30ab\u30fc\u30c9\u306e\u9000\u4f1a\u4e88\u6e2c | GridDB: Open Source Time Series Database for IoT<\/title>\n<meta name=\"description\" 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