\u30d6\u30ed\u30b0<\/a>\u306b\u8a73\u3057\u304f\u66f8\u304b\u308c\u3066\u3044\u307e\u3059\u3002<\/p>\n\u6642\u7cfb\u5217\u7bc4\u56f2\u306e\u30af\u30a8\u30ea<\/h3>\n
\u6642\u7cfb\u5217\u306e\u7bc4\u56f2\u306b\u95a2\u3059\u308b\u30af\u30a8\u30ea\u306f\u3001\u4ed6\u306e\u591a\u304f\u306e\u30af\u30a8\u30ea\u3068\u540c\u3058\u3088\u3046\u306b\u3001\u884c\u578b\u306e\u30bb\u30c3\u30c8\u3092\u8fd4\u3057\u307e\u3059\u3002\u3053\u306e\u95a2\u6570\u306f\u4ee5\u4e0b\u306e\u3088\u3046\u306a\u3082\u306e\u3067\u3059\u3002query_by_time_series_range(object start, object end, QueryOrder order=QueryOrder.ASCENDING)<\/code> \u3053\u308c\u306f\u3001\u958b\u59cb\u6642\u523b\u304b\u3089\u7d42\u4e86\u6642\u523b\u307e\u3067\u306e\u884c\u3092\u8fd4\u3057\u307e\u3059\u3002\u3064\u307e\u308a\u3001\u958b\u767a\u62c5\u5f53\u8005\u306f\u660e\u793a\u7684\u306a\u6642\u9593\u7bc4\u56f2\u3092\u53d6\u5f97\u3059\u308b\u7c21\u5358\u306a\u65b9\u6cd5\u3092\u5f97\u308b\u3053\u3068\u304c\u3067\u304d\u307e\u3059\u3002<\/p>\n\u5177\u4f53\u7684\u306a\u4f8b\u3092\u6319\u3052\u3066\u307f\u307e\u3057\u3087\u3046\u3002<\/p>\n
\n
rangeQuery = ts.query_by_time_series_range(time, addedTime, griddb.QueryOrder.ASCENDING)\nrangeRs = rangeQuery.fetch()\nwhile rangeRs.has_next():\n d = rangeRs.next()\n print(\"d: \", d)<\/code><\/pre>\n<\/div>\n\u7d50\u679c\u306f\u3001\u5358\u7d14\u306b\u7bc4\u56f2\u3067\u3059\u3002<\/p>\n
\n
d: [datetime.datetime(1999, 10, 1, 0, 0), 280203]\nd: [datetime.datetime(1999, 10, 1, 7, 0), 280203]\nd: [datetime.datetime(1999, 11, 1, 0, 0), 280471]\nd: [datetime.datetime(1999, 11, 1, 8, 0), 280471]\nd: [datetime.datetime(1999, 12, 1, 0, 0), 280716]\nd: [datetime.datetime(1999, 12, 1, 8, 0), 280716]\nd: [datetime.datetime(2000, 1, 1, 0, 0), 280976]\nd: [datetime.datetime(2000, 1, 1, 8, 0), 280976]\nd: [datetime.datetime(2000, 2, 1, 0, 0), 281190]\nd: [datetime.datetime(2000, 2, 1, 8, 0), 281190]\nd: [datetime.datetime(2000, 3, 1, 0, 0), 281409]\nd: [datetime.datetime(2000, 3, 1, 8, 0), 281409]\nd: [datetime.datetime(2000, 4, 1, 0, 0), 281653]\nd: [datetime.datetime(2000, 4, 1, 8, 0), 281653]\nd: [datetime.datetime(2000, 5, 1, 0, 0), 281877]\nd: [datetime.datetime(2000, 5, 1, 7, 0), 281877]\nd: [datetime.datetime(2000, 6, 1, 0, 0), 282126]\nd: [datetime.datetime(2000, 6, 1, 7, 0), 282126]\nd: [datetime.datetime(2000, 7, 1, 0, 0), 282385]\nd: [datetime.datetime(2000, 7, 1, 7, 0), 282385]\nd: [datetime.datetime(2000, 8, 1, 0, 0), 282653]\nd: [datetime.datetime(2000, 8, 1, 7, 0), 282653]\nd: [datetime.datetime(2000, 9, 1, 0, 0), 282932]\nd: [datetime.datetime(2000, 9, 1, 7, 0), 282932]<\/code><\/pre>\n<\/div>\n\u6642\u7cfb\u5217\u30b5\u30f3\u30d7\u30ea\u30f3\u30b0\u306b\u3088\u308b\u30af\u30a8\u30ea<\/h3>\n
\u65b0\u3057\u3044\u95a2\u6570\u306e\u4e2d\u3067\u79c1\u304c\u500b\u4eba\u7684\u306b\u6700\u3082\u9762\u767d\u3044\u3068\u601d\u3046\u306e\u306f\u3001\u6700\u5f8c\u306e\u3082\u306e\u3067\u3059\u3002\u3053\u306e\u30b5\u30f3\u30d7\u30ea\u30f3\u30b0\u306f\u3001\u5404\u30dd\u30a4\u30f3\u30c8\u306e\u9593\u306b\u8a2d\u5b9a\u3055\u308c\u305f\u6642\u9593\u3092\u6301\u3064\u884c\u306e\u4e00\u69d8\u306a\u30b5\u30f3\u30d7\u30eb\u3092\u8fd4\u3057\u307e\u3059\u3002<\/p>\n
\u6642\u7cfb\u5217\u30b5\u30f3\u30d7\u30ea\u30f3\u30b0\u95a2\u6570\u3082\u884c\u306e\u96c6\u5408\u3092\u8fd4\u3057\u307e\u3059\u3002\u3053\u306e\u95a2\u6570\u306f\u3001\u3059\u3079\u3066\u306e\u95a2\u6570\u306e\u4e2d\u3067\u6700\u3082\u591a\u304f\u306e\u30d1\u30e9\u30e1\u30fc\u30bf\u3092\u53d6\u308a\u307e\u3059\u3002query_by_time_series_sampling(object start, object end, list[string] column_name_list, InterpolationMode mode, int interval, TimeUnit interval_unit)` \u3053\u308c\u3082\u958b\u59cb\u3068\u7d42\u4e86\u3092\u53d7\u3051\u53d6\u308a\u307e\u3059\u304c\u3001\u4eca\u56de\u306f\u5217\u540d\u306e\u30ea\u30b9\u30c8\u3092\u53d7\u3051\u53d6\u308a\u3001\u3055\u3089\u306b\u88dc\u9593\u30e2\u30fc\u30c9\u3068\u9593\u9694\u3001\u6642\u9593\u5358\u4f4d\u3092\u53d7\u3051\u53d6\u308a\u307e\u3059\u3002<\/p>\n
\u88dc\u9593\u30e2\u30fc\u30c9\u306f\u3001LINEAR_OR_PREVIOUS<\/code> \u307e\u305f\u306f EMPTY<\/code>\u306e\u3044\u305a\u308c\u304b\u3092\u9078\u629e\u3059\u308b\u3053\u3068\u304c\u3067\u304d\u307e\u3059\u3002\u30a4\u30f3\u30bf\u30fc\u30d0\u30eb\u306e\u5358\u4f4d\u306b\u306f\u3001\u4ee5\u4e0b\u306e\u9078\u629e\u80a2\u304c\u3042\u308a\u307e\u3059\u3002\u5e74\u3001\u6708\u3001\u65e5\u3001\u6642\u3001\u5206\u3001\u79d2\u3001\u30df\u30ea\u79d2<\/code> \u3000\u305f\u3060\u3057\u3001\u5e74\u3084\u6708\u306f\u5927\u304d\u3059\u304e\u3066\u9593\u9694\u3068\u3057\u3066\u8a8d\u3081\u3089\u308c\u306a\u3044\u306e\u3067\u3001\u57fa\u672c\u7684\u306b\u306f\u65e5\u4ee5\u4e0b\u306e\u9593\u9694\u3092\u9078\u629e\u3059\u308b\u3053\u3068\u306b\u306a\u308a\u307e\u3059\u3002<\/p>\n\u3053\u3053\u3067\u306f\u3001\u30d6\u30ed\u30b0\u306e\u305f\u3081\u306b\u884c\u3063\u305f\u5177\u4f53\u4f8b\u3092\u7d39\u4ecb\u3057\u307e\u3059\u3002<\/p>\n
\n
try: \n samplingQuery = ts.query_by_time_series_sampling(time, addedTime, [\"value\"], griddb.InterpolationMode.LINEAR_OR_PREVIOUS, 1, griddb.TimeUnit.DAY) # the columns need to be a list, hence the [ ]\n samplingRs = samplingQuery.fetch()\n while samplingRs.has_next(): \n d = samplingRs.next()\n print(\"sampling: \", d)\nexcept griddb.GSException as e:\n for i in range(e.get_error_stack_size()):\n print(\"[\", i, \"]\")\n print(e.get_error_code(i))\n print(e.get_message(i))<\/code><\/pre>\n<\/div>\naddedtime\u5909\u6570\u306f\u3001\u6700\u521d\u306e7\u5e74\u5f8c\u3067\u3059\u304c\u3001\u3053\u3053\u3067\u306f\u3001\u51fa\u529b\u3055\u308c\u305f\u30af\u30a8\u30ea\u306e\u307b\u3093\u306e\u4e00\u7aef\u3092\u7d39\u4ecb\u3057\u307e\u3059\u3002<\/p>\n
\n
sampling: [datetime.datetime(2006, 8, 1, 0, 0), 299263]\nsampling: [datetime.datetime(2006, 8, 2, 0, 0), 299269]\nsampling: [datetime.datetime(2006, 8, 3, 0, 0), 299279]\nsampling: [datetime.datetime(2006, 8, 4, 0, 0), 299288]\nsampling: [datetime.datetime(2006, 8, 5, 0, 0), 299298]\nsampling: [datetime.datetime(2006, 8, 6, 0, 0), 299307]\nsampling: [datetime.datetime(2006, 8, 7, 0, 0), 299317]\nsampling: [datetime.datetime(2006, 8, 8, 0, 0), 299326]\nsampling: [datetime.datetime(2006, 8, 9, 0, 0), 299336]\nsampling: [datetime.datetime(2006, 8, 10, 0, 0), 299345]\nsampling: [datetime.datetime(2006, 8, 11, 0, 0), 299354]\nsampling: [datetime.datetime(2006, 8, 12, 0, 0), 299364]\nsampling: [datetime.datetime(2006, 8, 13, 0, 0), 299373]\nsampling: [datetime.datetime(2006, 8, 14, 0, 0), 299383]\nsampling: [datetime.datetime(2006, 8, 15, 0, 0), 299392]\nsampling: [datetime.datetime(2006, 8, 16, 0, 0), 299402]\nsampling: [datetime.datetime(2006, 8, 17, 0, 0), 299411]\nsampling: [datetime.datetime(2006, 8, 18, 0, 0), 299421]\nsampling: [datetime.datetime(2006, 8, 19, 0, 0), 299430]\nsampling: [datetime.datetime(2006, 8, 20, 0, 0), 299440]\nsampling: [datetime.datetime(2006, 8, 21, 0, 0), 299449]\nsampling: [datetime.datetime(2006, 8, 22, 0, 0), 299459]\nsampling: [datetime.datetime(2006, 8, 23, 0, 0), 299468]\nsampling: [datetime.datetime(2006, 8, 24, 0, 0), 299478]\nsampling: [datetime.datetime(2006, 8, 25, 0, 0), 299487]\nsampling: [datetime.datetime(2006, 8, 26, 0, 0), 299497]\nsampling: [datetime.datetime(2006, 8, 27, 0, 0), 299506]\nsampling: [datetime.datetime(2006, 8, 28, 0, 0), 299516]\nsampling: [datetime.datetime(2006, 8, 29, 0, 0), 299525]\nsampling: [datetime.datetime(2006, 8, 30, 0, 0), 299535]\nsampling: [datetime.datetime(2006, 8, 31, 0, 0), 299544]\nsampling: [datetime.datetime(2006, 9, 1, 0, 0), 299554]\nsampling: [datetime.datetime(2006, 9, 2, 0, 0), 299560]\nsampling: [datetime.datetime(2006, 9, 3, 0, 0), 299570]\nsampling: [datetime.datetime(2006, 9, 4, 0, 0), 299579]\nsampling: [datetime.datetime(2006, 9, 5, 0, 0), 299589]\nsampling: [datetime.datetime(2006, 9, 6, 0, 0), 299598]\nsampling: [datetime.datetime(2006, 9, 7, 0, 0), 299607]\nsampling: [datetime.datetime(2006, 9, 8, 0, 0), 299617]\nsampling: [datetime.datetime(2006, 9, 9, 0, 0), 299626]\nsampling: [datetime.datetime(2006, 9, 10, 0, 0), 299636]<\/code><\/pre>\n<\/div>\n\u5143\u306e\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u3067\u306f\u3001\u6bce\u67081\u65e5\u306e\u6bcd\u6570\u3092\u5f97\u308b\u3053\u3068\u304c\u3067\u304d\u307e\u3059\u3002\u307e\u305f\u3001\u30b5\u30f3\u30d7\u30ea\u30f3\u30b0\u306b\u3088\u308a\u30011\u65e5\u5358\u4f4d\u3067\u6bcd\u96c6\u56e3\u306e\u5024\u3092\u63a8\u5b9a\u3059\u308b\u3053\u3068\u304c\u3067\u304d\u307e\u3059\u3002\u3053\u306e\u30c7\u30fc\u30bf\u304b\u3089\u3001kaggle\u304b\u3089\u5f97\u305f\u5024\u306f\u6b63\u3057\u304f\u3001\u305d\u306e\u65e5\u306b\u81f3\u308b\u307e\u3067\u306e\u6bcd\u96c6\u56e3\u306f\u59a5\u5f53\u3067\u3042\u308b\u3053\u3068\u304c\u308f\u304b\u308a\u307e\u3059\u3002\u4f8b\u3048\u3070\u30012006\u5e749\u67081\u65e5\u306e\u6bcd\u96c6\u56e3\u306e\u5024\u306f299554\u3067\u3001\u3053\u308c\u306fkaggle\u306e\u30c7\u30fc\u30bf\u3068\u4e00\u81f4\u3057\u3001\u305d\u306e\u524d\u65e5\u306e\u5024\u3082299544\u3067\u3059\u3002<\/p>\n
\u307e\u3068\u3081<\/h2>\n
\u3053\u306e\u30d6\u30ed\u30b0\u3067\u306f\u3001Docker \u3092\u4f7f\u3063\u3066\u3001\u3042\u308b\u3044\u306f Dockerfile \u306e\u30b9\u30c6\u30c3\u30d7\u30d0\u30a4\u30b9\u30c6\u30c3\u30d7\u306e\u6307\u793a\u306b\u5f93\u3063\u3066\u3001\u65b0\u3057\u3044 Python \u30af\u30e9\u30a4\u30a2\u30f3\u30c8\u3092\u69cb\u7bc9\u3059\u308b\u65b9\u6cd5\u3092\u7d39\u4ecb\u3057\u307e\u3057\u305f\u3002\u307e\u305f\u3001csv<\/code>\u30d5\u30a1\u30a4\u30eb\u304b\u3089\u306e\u975e\u5e38\u306b\u30b7\u30f3\u30d7\u30eb\u306a\u30c7\u30fc\u30bf\u30a4\u30f3\u30b8\u30a7\u30b9\u30c8\u3082\uff08java\u7d4c\u7531\u3067\uff09\u5b9f\u6f14\u3057\u307e\u3057\u305f\u3002\u305d\u3057\u3066\u6700\u5f8c\u306b\u3001\u65b0\u3057\u3044\u6642\u7cfb\u5217\u95a2\u6570\u304c\u6642\u7cfb\u5217\u5206\u6790\u306b\u3069\u308c\u3060\u3051\u6709\u7528\u3067\u3042\u308b\u304b\u3092\u793a\u3057\u307e\u3057\u305f\u3002<\/p>\n\u3053\u306e\u30d6\u30ed\u30b0\u306e\u5168\u30b3\u30fc\u30c9\u306f\u3053\u3061\u3089\u304b\u3089\u30c0\u30a6\u30f3\u30ed\u30fc\u30c9<\/a>\u3067\u304d\u307e\u3059\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"\u306f\u3058\u3081\u306b GridDB Python\u30af\u30e9\u30a4\u30a2\u30f3\u30c8\u306e\u65b0\u30d0\u30fc\u30b8\u30e7\u30f3\u304c\u30ea\u30ea\u30fc\u30b9\u3055\u308c\u3001\u3044\u304f\u3064\u304b\u306e\u65b0\u3057\u3044\u6642\u7cfb\u5217\u95a2\u6570\u304c\u8ffd\u52a0\u3055\u308c\u307e\u3057\u305f\u3002\u3053\u308c\u3089\u306e\u95a2\u6570\u306f Python \u30af\u30e9\u30a4\u30a2\u30f3\u30c8\u306e\u65b0\u6a5f\u80fd\u3067\u3059\u304c\u3001\u3053\u306e\u30ea\u30ea\u30fc\u30b9\u4ee5\u524d\u304b\u3089\u3001GridDB \u306e\u30cd […]<\/p>\n","protected":false},"author":1,"featured_media":50126,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[1005],"tags":[],"class_list":["post-50829","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-1005"],"acf":[],"yoast_head":"\n
GridDB Python\u30af\u30e9\u30a4\u30a2\u30f3\u30c8\u306b\u65b0\u3057\u3044\u6642\u7cfb\u5217\u95a2\u6570\u3092\u8ffd\u52a0\u3059\u308b | GridDB: Open Source Time Series Database for IoT<\/title>\n<meta name=\"description\" content=\"\u306f\u3058\u3081\u306b GridDB Python\u30af\u30e9\u30a4\u30a2\u30f3\u30c8\u306e\u65b0\u30d0\u30fc\u30b8\u30e7\u30f3\u304c\u30ea\u30ea\u30fc\u30b9\u3055\u308c\u3001\u3044\u304f\u3064\u304b\u306e\u65b0\u3057\u3044\u6642\u7cfb\u5217\u95a2\u6570\u304c\u8ffd\u52a0\u3055\u308c\u307e\u3057\u305f\u3002\u3053\u308c\u3089\u306e\u95a2\u6570\u306f Python\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.griddb.net\/ja\/\u672a\u5206\u985e\/griddb-python-client-adds-new-time-series-functions\/\" \/>\n<meta property=\"og:locale\" content=\"ja_JP\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"GridDB 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