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import numpy as np \n import pandas as pd\n<\/code><\/pre>\n<\/div>\n\n- Python\u306e\u30e9\u30a4\u30d6\u30e9\u30ea\u306f\u3001\u30b0\u30e9\u30d5\u3084\u30d7\u30ed\u30c3\u30c8\u3092\u4f7f\u3063\u3066\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u3092\u63a2\u7d22\u3059\u308b\u305f\u3081\u306b\u4f7f\u308f\u308c\u307e\u3059\u3002<\/li>\n<\/ul>\n
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import seaborn as sns\n import matplotlib.pyplot as plt\n<\/code><\/pre>\n<\/div>\n\n- \u30ec\u30b3\u30e1\u30f3\u30c7\u30fc\u30b7\u30e7\u30f3\u30b7\u30b9\u30c6\u30e0\u30e2\u30c7\u30eb\u3092\u69cb\u7bc9\u3059\u308b\u305f\u3081\u306b\u4f7f\u7528\u3055\u308c\u308bPython\u30e9\u30a4\u30d6\u30e9\u30ea<\/li>\n<\/ul>\n
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from sklearn.preprocessing import StandardScaler\n from sklearn.neighbors import NearestNeighbors\n from sklearn.decomposition import PCA\n<\/code><\/pre>\n<\/div>\n\n- GridDB\u30af\u30e9\u30b9\u30bf\u306b\u63a5\u7d9a\u3059\u308b\u305f\u3081\u306ePython\u30e9\u30a4\u30d6\u30e9\u30ea<\/li>\n<\/ul>\n
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import griddb_python as griddb\n<\/code><\/pre>\n<\/div>\n\u5fc5\u8981\u306a\u30e9\u30a4\u30d6\u30e9\u30ea\u306e\u30a4\u30f3\u30dd\u30fc\u30c8\u306b\u6210\u529f\u3057\u305f\u3089\u3001\u307e\u305a \u30a4\u30f3\u30b0\u30e9\u30f3\u30c9\u30fb\u30d7\u30ec\u30df\u30a2\u30ea\u30fc\u30b0\u9078\u624b\u306e\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8<\/strong> \u3092\u8aad\u307f\u8fbc\u307f\u307e\u3059\u3002<\/p>\n\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u306e\u8aad\u307f\u8fbc\u307f<\/strong><\/h2>\nGridDB\u306f\u3001\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u3092\u4fdd\u5b58\u3059\u308b\u305f\u3081\u306b\u4f7f\u7528\u3059\u308b\u30c7\u30fc\u30bf\u30fb\u30b9\u30c8\u30ec\u30fc\u30b8\u30fb\u30e1\u30ab\u30cb\u30ba\u30e0\u3067\u3042\u308b\u305f\u3081\u3001\u6211\u3005\u306e\u30ec\u30b3\u30e1\u30f3\u30c7\u30fc\u30b7\u30e7\u30f3\u30b7\u30b9\u30c6\u30e0\u3092\u69cb\u7bc9\u3059\u308b\u4e0a\u3067\u91cd\u8981\u306a\u5f79\u5272\u3092\u679c\u305f\u3057\u307e\u3059\u3002\u30c7\u30fc\u30bf\u3092\u6b63\u5e38\u306b\u683c\u7d0d\u3059\u308b\u305f\u3081\u306b\u3001\u307e\u305a GridDB \u30b3\u30f3\u30c6\u30ca\u3092\u30ed\u30fc\u30c9\u3057\u307e\u3059\u3002\u3053\u308c\u306f\u3001\u5148\u307b\u3069\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb\u3057\u305f griddb_python<\/code> \u30e9\u30a4\u30d6\u30e9\u30ea\u3092\u4f7f\u3063\u3066\u884c\u3046\u3053\u3068\u304c\u3067\u304d\u307e\u3059\u3002\u6b21\u306b\u3001container.put<\/strong> \u3092\u4f7f\u3063\u3066\u30eb\u30fc\u30d7\u3092\u4f7f\u3063\u3066\u30c7\u30fc\u30bf\u3092\u633f\u5165\u3057\u307e\u3059\u3002\u3053\u308c\u304c\u7d42\u308f\u3063\u305f\u3089\u3001\u30c7\u30fc\u30bf\u3092\u30c7\u30fc\u30bf\u30d5\u30ec\u30fc\u30e0\u306b\u30ed\u30fc\u30c9\u3057\u3066\u3001\u30ec\u30b3\u30e1\u30f3\u30c7\u30fc\u30b7\u30e7\u30f3\u30b7\u30b9\u30c6\u30e0\u306e\u4f5c\u6210\u3092\u7d9a\u3051\u306a\u3051\u308c\u3070\u306a\u308a\u307e\u305b\u3093\u3002<\/p>\n\u3053\u306e\u30bb\u30af\u30b7\u30e7\u30f3\u3067\u8aac\u660e\u3059\u308b\u30b3\u30fc\u30c9\u306f\u6b21\u306e\u3088\u3046\u306b\u66f8\u304f\u3053\u3068\u304c\u3067\u304d\u307e\u3059\u3002<\/p>\n
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factory = griddb.StoreFactory.get_instance()\n\n# Provide the necessary arguments\ngridstore = factory.get_store(\n notification_member = '127.0.0.1:10001',\n cluster_name = 'myCluster',\n username = 'admin',\n password = 'admin'\n)\n\n# Define the container info\nconInfo = griddb.ContainerInfo(\n \"football_players\",\n [\n [\"name\", griddb.Type.STRING],\n [\"club\", griddb.Type.STRING],\n [\"age\", griddb.Type.DOUBLE],\n [\"position\", griddb.Type.STRING],\n [\"position_cat\", griddb.Type.DOUBLE],\n [\"market_value\", griddb.Type.DOUBLE],\n [\"page_views\", griddb.Type.DOUBLE],\n [\"fpl_value\", griddb.Type.DOUBLE],\n [\"fpl_sel\", griddb.Type.STRING],\n [\"fpl_points\", griddb.Type.DOUBLE],\n [\"region\", griddb.Type.DOUBLE],\n [\"nationality\", griddb.Type.STRING],\n [\"new_foreign\", griddb.Type.DOUBLE],\n [\"age_cat\", griddb.Type.DOUBLE],\n [\"club_id\", griddb.Type.DOUBLE],\n [\"big_club\", griddb.Type.DOUBLE],\n [\"new_signing\", griddb.Type.DOUBLE]\n ],\n griddb.ContainerType.COLLECTION, True\n)\n\n# Drop container if it exists\ngridstore.drop_container(conInfo.name)\n\n# Create a container\ncontainer = gridstore.put_container(conInfo)\n\n# Load the data\n\n# Put rows\nfor i in range(len(data)):\n row = data.iloc[i].tolist()\n try:\n container.put(row)\n except Exception as e:\n print(f\"Error on row {i}: {row}\")\n print(e)\n\ncont = gridstore.get_container(\"football_players\")\n\nif cont is None:\n print(\"Does not exist\")\n\nprint(\"connection successful\")\n\n# Define the exact columns you need\ncolumns = [\"*\"]\n\nselect_statement = \"SELECT \" + \", \".join(columns) + \" FROM football_players\"\n\n# Execute the query\nquery = container.query(select_statement)\nrs = query.fetch(False)\n\ndata = rs.fetch_rows()\n\nprint(data.head())<\/code><\/pre>\n<\/div>\n\u63a2\u7d22\u7684\u30c7\u30fc\u30bf\u5206\u6790<\/strong><\/h2>\n\u63a8\u9032\u30b7\u30b9\u30c6\u30e0\u3092\u69cb\u7bc9\u3059\u308b\u524d\u306b\u3001\u30c7\u30fc\u30bf\u306e\u77db\u76fe\u70b9\u3092\u898b\u3064\u3051\u3001\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u306e\u5168\u4f53\u7684\u306a\u8996\u899a\u5316\u3092\u53ef\u80fd\u306b\u3059\u308b\u63a2\u7d22\u7684\u30c7\u30fc\u30bf\u5206\u6790\u304b\u3089\u59cb\u3081\u306a\u3051\u308c\u3070\u306a\u308a\u307e\u305b\u3093\u3002\u307e\u305a\u3001\u5c5e\u6027\u306bNull\u5024\u304c\u306a\u3044\u304b\u30c1\u30a7\u30c3\u30af\u3059\u308b\u3053\u3068\u304b\u3089\u59cb\u3081\u307e\u3059\u3002\u3053\u308c\u306f\u4ee5\u4e0b\u306e\u30b3\u30fc\u30c9\u3067\u5b9f\u73fe\u3055\u308c\u307e\u3059\u3002<\/p>\n
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data.isnull().sum()<\/code><\/pre>\n<\/div>\n\u3053\u306e\u30bb\u30eb\u306f\u4ee5\u4e0b\u306e\u7d50\u679c\u3092\u51fa\u529b\u3057\u3001region<\/strong>\u5c5e\u6027\u306b1\u3064\u306e<\/strong>\u6b20\u640d\u5024\u304c\u3042\u308b\u3053\u3068\u3092\u793a\u3057\u307e\u3059\u3002<\/p>\n\n
name 0\nclub 0\nage 0\nposition 0\nposition_cat 0\nmarket_value 0\npage_views 0\nfpl_value 0\nfpl_sel 0\nfpl_points 0\nregion 1\nnationality 0\nnew_foreign 0\nage_cat 0\nclub_id 0\nbig_club 0\nnew_signing 0\ndtype: int64<\/code><\/pre>\n<\/div>\n\u6b20\u640d\u5024\u3092\u4e00\u6383\u3059\u308b\u306b\u306f\u3001\u7d44\u307f\u8fbc\u307f\u30e1\u30bd\u30c3\u30c9 dropna()<\/code> \u3092\u4f7f\u3046\u3053\u3068\u304c\u3067\u304d\u307e\u3059\u3002\u3053\u306e\u30e1\u30bd\u30c3\u30c9\u306f\u3001\u53e4\u3044\u30af\u30ea\u30fc\u30f3\u3067\u306a\u3044\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u306b\u4ee3\u308f\u3063\u3066\u3001\u65b0\u3057\u304f\u30af\u30ea\u30fc\u30f3\u5316\u3055\u308c\u305f\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u3092\u8fd4\u3057\u307e\u3059\u3002<\/p>\n\u3053\u308c\u306f\u4ee5\u4e0b\u306e\u30b3\u30fc\u30c9\u3067\u5b9f\u73fe\u3067\u304d\u307e\u3059\u3002<\/p>\n
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data = data.dropna()<\/code><\/pre>\n<\/div>\n\u3059\u3079\u3066\u306e\u6b20\u640d\u5024\u3092\u7f6e\u304d\u63db\u3048\u305f\u306e\u3067\u3001\u30c7\u30fc\u30bf\u306e\u53ef\u8996\u5316\u306b\u79fb\u308b\u3053\u3068\u304c\u3067\u304d\u307e\u3059\u3002\u30c7\u30fc\u30bf\u3092\u30b0\u30e9\u30d5\u5316\u3059\u308b\u305f\u3081\u306e\u6700\u521d\u306e\u30b9\u30c6\u30c3\u30d7\u306f\u3001corr()<\/code> \u30e1\u30bd\u30c3\u30c9\u3092\u4f7f\u7528\u3057\u3066\u3001\u4ed6\u306e\u9078\u624b\u3092\u4e88\u6e2c\u3059\u308b\u305f\u3081\u306b\u4f7f\u7528\u3067\u304d\u308b\u3055\u307e\u3056\u307e\u306a\u5c5e\u6027\u3092\u8868\u3059\u76f8\u95a2\u884c\u5217\u3092\u8a08\u7b97\u3059\u308b\u3053\u3068\u3067\u3059\u3002\u3053\u308c\u306f\u3001\u3069\u306e\u30c7\u30fc\u30bf\u30dd\u30a4\u30f3\u30c8\u304c\u30d7\u30ec\u30fc\u30e4\u30fc\u3092\u63a8\u85a6\u3059\u308b\u305f\u3081\u306b\u4f7f\u7528\u3067\u304d\u308b\u306e\u304b\u3001\u307e\u305f\u3001\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u306b\u793a\u3055\u308c\u305f\u5c5e\u6027\u306e\u89b3\u70b9\u304b\u3089\u3001\u3059\u3079\u3066\u306e\u30d7\u30ec\u30fc\u30e4\u30fc\u304c\u3069\u306e\u3088\u3046\u306b\u6e2c\u5b9a\u3055\u308c\u308b\u306e\u304b\u3092\u793a\u3059\u306e\u3067\u3001\u975e\u5e38\u306b\u4fbf\u5229\u3067\u3059\u3002<\/p>\n\u3053\u308c\u306f\u4ee5\u4e0b\u306e\u30b3\u30fc\u30c9\u3067\u5b9f\u73fe\u3067\u304d\u307e\u3059\u3002<\/p>\n
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sample = data.select_dtypes(include='number')\ncorr = sample.corr()\nmask = np.zeros_like(corr, dtype = np.bool_)\nmask[np.triu_indices_from(mask)] = True\nplt.figure(figsize=(10,10))\nsns.heatmap(corr, mask=mask)<\/code><\/pre>\n<\/div>\n\u3053\u306e\u30bb\u30af\u30b7\u30e7\u30f3\u3067\u8003\u3048\u305f\u30b0\u30e9\u30d5\u306f\u3001X<\/strong>\u8ef8\u3068Y<\/strong>\u8ef8\u306e\u5c5e\u6027\u3092\u53d6\u308a\u3001\u6240\u5b9a\u306e\u30b9\u30b1\u30fc\u30eb\u3067\u76f8\u95a2\u3092\u6e2c\u5b9a\u3059\u308b\u30d2\u30fc\u30c8\u30de\u30c3\u30d7\u3067\u3059\u3002\u30d2\u30fc\u30c8\u30de\u30c3\u30d7\u306f\u4ee5\u4e0b\u306e\u901a\u308a\u3067\u3059\u3002<\/p>\n![\"image\"](\"https:\/\/github.com\/SimoCs\/Soccer-Players-Recommendation-System\/assets\/32298957\/c863ddd6-7b06-47cd-b238-edd48aa10dcb\")
<\/p>\n
\u30ec\u30b3\u30e1\u30f3\u30c7\u30fc\u30b7\u30e7\u30f3\u30b7\u30b9\u30c6\u30e0<\/strong><\/h2>\n\u3053\u306e\u30bb\u30af\u30b7\u30e7\u30f3\u3067\u306f\u3001\u30ec\u30b3\u30e1\u30f3\u30c7\u30fc\u30b7\u30e7\u30f3\u30b7\u30b9\u30c6\u30e0\u306e\u30e2\u30c7\u30eb\u3092\u8a2d\u8a08\u3057\u3001\u6e96\u5099\u3057\u307e\u3059\u3002\u305d\u306e\u70ba\u306b\u3001\u307e\u305a\u6a19\u6e96\u7684\u306a\u30b9\u30b1\u30fc\u30e9\u30fc\u3092\u7528\u3044\u3066\u30c7\u30fc\u30bf\u30dd\u30a4\u30f3\u30c8\u3092\u6570\u5024\u306b\u5909\u63db\u3059\u308b\u3053\u3068\u304b\u3089\u59cb\u3081\u307e\u3059\u3002\u6b21\u306b\u3001\u30ec\u30b3\u30e1\u30f3\u30c7\u30fc\u30b7\u30e7\u30f3\u30b7\u30b9\u30c6\u30e0\u306e\u30a2\u30eb\u30b4\u30ea\u30ba\u30e0\u3068\u3057\u3066\u4f7f\u7528\u3059\u308b\u30cb\u30a2\u30ec\u30b9\u30c8\u30cd\u30a4\u30d0\u30fc\u30ba\u30e2\u30c7\u30eb\u3092\u5b9f\u884c\u3057\u307e\u3059\u3002\u3053\u306e\u30e2\u30c7\u30eb\u306f\u3001\u6211\u3005\u306e\u6570\u5024\u30c7\u30fc\u30bf\u3092\u5165\u529b\u3068\u3057\u3001\u5165\u529b\u306b\u8fd1\u3044\u7279\u5fb4\u3092\u6301\u3064\u6bd4\u8f03\u53ef\u80fd\u306a\u9078\u624b\u3092\u898b\u3064\u3051\u308b\u305f\u3081\u306b\u4f7f\u7528\u3055\u308c\u307e\u3059\u3002<\/p>\n
\u3053\u308c\u306f\u4ee5\u4e0b\u306e\u30b3\u30fc\u30c9\u3067\u5b9f\u73fe\u3067\u304d\u307e\u3059\u3002<\/p>\n
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scaled = StandardScaler()\nX = scaled.fit_transform(sample)\nrecommendations = NearestNeighbors(n_neighbors = 5, algorithm='kd_tree')\nrecommendations.fit(X)\nplayer_index = recommendations.kneighbors(X)[1]<\/code><\/pre>\n<\/div>\n\u30e2\u30c7\u30eb\u3092\u6e96\u5099\u3057\u305f\u306e\u3067\u3001\u4e0e\u3048\u3089\u308c\u305f\u540d\u524d\u306b\u57fa\u3065\u3044\u3066\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u4e2d\u306e\u9078\u624b\u306e\u30a4\u30f3\u30c7\u30c3\u30af\u30b9\u3092\u898b\u3064\u3051\u308b\u30e1\u30bd\u30c3\u30c9\u3092\u4f5c\u3089\u306a\u3051\u308c\u3070\u306a\u308a\u307e\u305b\u3093\u3002\u3053\u308c\u306f\u3001\u30ec\u30b3\u30e1\u30f3\u30c7\u30fc\u30b7\u30e7\u30f3\u30b7\u30b9\u30c6\u30e0\u306e\u7d50\u679c\u3092\u4f7f\u7528\u3057\u3066\u3001\u63a8\u85a6\u3055\u308c\u305f\u9078\u624b\u306e\u5c5e\u6027\u3092\u62bd\u51fa\u3059\u308b\u3053\u3068\u304c\u3067\u304d\u308b\u306e\u3067\u3001\u975e\u5e38\u306b\u4fbf\u5229\u3067\u3059\u3002<\/p>\n
\u3053\u308c\u306f\u4ee5\u4e0b\u306e\u30b3\u30fc\u30c9\u3067\u5b9f\u73fe\u3067\u304d\u307e\u3059\u3002<\/p>\n
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def find_index(x):\n return data[data['name']==x].index.tolist()[0]<\/code><\/pre>\n<\/div>\n\u6700\u5f8c\u306e\u30b9\u30c6\u30c3\u30d7\u306f\u3001\u9078\u624b\u306e\u60c5\u5831\u3092\u62bd\u51fa\u3059\u308b\u305f\u3081\u306e\u30e2\u30c7\u30eb\u3092\u4f7f\u3063\u305f\u30e1\u30bd\u30c3\u30c9\u3092\u4f5c\u6210\u3059\u308b\u3053\u3068\u3067\u3059\u3002\u8a00\u3044\u63db\u3048\u308c\u3070\u3001\u3059\u3079\u3066\u306e\u9078\u624b\u306b\u3064\u3044\u3066\u3001\u63a8\u5968\u3055\u308c\u308b\u9078\u624b\u3092\u8aac\u660e\u3059\u308b\u4e3b\u8981\u306a\u5024\u3092\u62bd\u51fa\u3057\u306a\u3051\u308c\u3070\u306a\u308a\u307e\u305b\u3093\u3002\u79c1\u305f\u3061\u306e\u4f8b\u3067\u306f\u3001\u4e3b\u306b\u9078\u624b\u306e\u540d\u524d\u3001\u5e02\u5834\u4fa1\u5024\u3001\u5e74\u9f62\u3001\u73fe\u5728\u306e\u30af\u30e9\u30d6\u306b\u7126\u70b9\u3092\u5f53\u3066\u3066\u3044\u307e\u3059\u3002<\/p>\n
\u3053\u308c\u306f\u4ee5\u4e0b\u306e\u30b3\u30fc\u30c9\u3067\u5b9f\u73fe\u3067\u304d\u307e\u3059\u3002<\/p>\n
\n
def recommendation_system(player):\n print(\"Here are four players who are similar to {}: \".format(player))\n index = find_index(player)\n \n for i in player_index[index][1:]:\n print(\"Player Name: {}nPlayer Market Value: \u20ac{}nPlayer Age: {}nPlayer Current Club: {}n\".format(\n data.iloc[i]['name'],\n data.iloc[i]['market_value'], \n data.iloc[i]['age'], \n data.iloc[i]['club']))<\/code><\/pre>\n<\/div>\n\u30e2\u30c7\u30eb\u8a55\u4fa1<\/h2>\n
\u3053\u306e\u6642\u70b9\u3067\u3001\u30e2\u30c7\u30eb\u3092\u8a55\u4fa1\u3059\u308b\u6e96\u5099\u304c\u6574\u3044\u307e\u3057\u305f\u3002\u3053\u306e\u4f8b\u3067\u306f\u3001\u9078\u624b\u540d\u3092\u5165\u529b\u3057\u3001\u5165\u529b\u8005\u306b\u5339\u6575\u3059\u308b4\u4eba\u306e<\/strong>\u30ad\u30fc\u30d7\u30ec\u30fc\u30e4\u30fc\u3092\u63a8\u85a6\u3059\u308b\u30ec\u30b3\u30e1\u30f3\u30c7\u30fc\u30b7\u30e7\u30f3\u30b7\u30b9\u30c6\u30e0\u3092\u5b9f\u884c\u3057\u307e\u3059\u3002<\/p>\n\u3053\u306e\u30b3\u30fc\u30c9\u306f\u6b21\u306e\u3088\u3046\u306b\u3057\u3066\u5b9f\u73fe\u3067\u304d\u307e\u3059\u3002<\/p>\n
\n
recommendation_system('Petr Cech')<\/code><\/pre>\n<\/div>\n\u6211\u3005\u306e\u30e2\u30fc\u30c9\u306e\u7d50\u679c\u306f\u4ee5\u4e0b\u306e\u901a\u308a\u3067\u3059\u3002<\/p>\n
\n
Here are four players who are similar to Petr Cech: \nPlayer Name: Willy Caballero\nPlayer Market Value: \u20ac1.5\nPlayer Age: 35\nPlayer Current Club: Chelsea\n\nPlayer Name: Nacho Monreal\nPlayer Market Value: \u20ac13.0\nPlayer Age: 31\nPlayer Current Club: Arsenal\n\nPlayer Name: Laurent Koscielny\nPlayer Market Value: \u20ac22.0\nPlayer Age: 31\nPlayer Current Club: Arsenal\n\nPlayer Name: Artur Boruc\nPlayer Market Value: \u20ac1.0\nPlayer Age: 37\nPlayer Current Club: Bournemouth<\/code><\/pre>\n<\/div>\n\u898b\u3066\u5206\u304b\u308b\u3088\u3046\u306b\u3001\u30ec\u30b3\u30e1\u30f3\u30c7\u30fc\u30b7\u30e7\u30f3\u30b7\u30b9\u30c6\u30e0\u306f\u5165\u529b\u9078\u624b\u3068\u975e\u5e38\u306b\u8fd1\u30445\u4eba\u306e\u9078\u624b\u3092\u9ad8\u3044\u7cbe\u5ea6\u3067\u4e88\u6e2c\u3059\u308b\u3053\u3068\u304c\u3067\u304d\u307e\u3057\u305f\u3002\u3053\u308c\u306f\u3001\u79c1\u305f\u3061\u306e\u30e2\u30c7\u30eb\u304c\u3059\u3050\u306b\u3067\u3082\u5c0e\u5165\u53ef\u80fd\u3067\u3042\u308a\u3001\u73fe\u5728\u306e\u9078\u624b\u306b\u57fa\u3065\u3044\u3066\u5c06\u6765\u306e\u624d\u80fd\u3092\u4e88\u6e2c\u3059\u308b\u305f\u3081\u306b\u4f7f\u7528\u3067\u304d\u308b\u3053\u3068\u3092\u8a3c\u660e\u3057\u3066\u3044\u307e\u3059\u3002<\/p>\n
\u7d50\u8ad6<\/strong><\/h2>\n\u30d3\u30b8\u30cd\u30b9\u306b\u304a\u3044\u3066\u30ec\u30b3\u30e1\u30f3\u30c7\u30fc\u30b7\u30e7\u30f3\u30b7\u30b9\u30c6\u30e0\u3092\u4f7f\u3063\u3066\u898b\u8fbc\u307f\u5ba2\u30ea\u30b9\u30c8\u3092\u4f5c\u6210\u3059\u308b\u3053\u3068\u306f\u3001\u7af6\u4e89\u4e0a\u306e\u512a\u4f4d\u6027\u306b\u306a\u308a\u307e\u3059\u3057\u3001\u3069\u306e\u30c1\u30fc\u30e0\u306e\u30de\u30cd\u30fc\u30b8\u30e3\u30fc\u3082\u8003\u616e\u3059\u3079\u304d\u3067\u3059\u3002\u3053\u306e\u8a18\u4e8b\u3067\u306f\u3001\u30a4\u30f3\u30b0\u30e9\u30f3\u30c9\u30fb\u30d7\u30ec\u30df\u30a2\u30ea\u30fc\u30b0\u306e\u9078\u624b\u3092\u4f7f\u3063\u305f\u30ec\u30b3\u30e1\u30f3\u30c7\u30fc\u30b7\u30e7\u30f3\u30e2\u30c7\u30eb\u3092\u30b9\u30c6\u30c3\u30d7\u30fb\u30d0\u30a4\u30fb\u30b9\u30c6\u30c3\u30d7\u3067\u4f5c\u6210\u3059\u308b\u30d7\u30ed\u30bb\u30b9\u3092\u53d6\u308a\u4e0a\u3052\u307e\u3057\u305f\u3002GridDB\u306f\u3001\u6211\u3005\u306e\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u306e\u30c7\u30fc\u30bf\u30d9\u30fc\u30b9\u30b9\u30c8\u30ec\u30fc\u30b8\u3068\u3057\u3066\u3001\u3053\u306e\u8a18\u4e8b\u3067\u5e83\u7bc4\u56f2\u306b\u4f7f\u7528\u3055\u308c\u307e\u3057\u305f\u3002\u3053\u306e\u30c7\u30fc\u30bf\u30d9\u30fc\u30b9\u306f\u3001\u5b66\u7fd2\u6e08\u307f\u30c7\u30fc\u30bf\u3092\u4fdd\u5b58\u3057\u3001\u30ec\u30b3\u30e1\u30f3\u30c7\u30fc\u30b7\u30e7\u30f3\u30b7\u30b9\u30c6\u30e0\u3092\u547c\u3073\u51fa\u3057\u3066\u3001\u4ef2\u9593\u306b\u57fa\u3065\u3044\u3066\u30b5\u30c3\u30ab\u30fc\u9078\u624b\u3092\u63a8\u85a6\u3059\u308b\u305f\u3081\u306b\u4f7f\u7528\u3055\u308c\u307e\u3059\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"
\u30b5\u30c3\u30ab\u30fc\u30d3\u30b8\u30cd\u30b9\u306f\u3001\u9ad8\u3044\u30d1\u30d5\u30a9\u30fc\u30de\u30f3\u30b9\u3092\u767a\u63ee\u3059\u308b\u30a2\u30b9\u30ea\u30fc\u30c8\u3001\u71b1\u72c2\u7684\u306a\u30d5\u30a1\u30f3\u3001\u305d\u3057\u3066\u5927\u304d\u306a\u30b9\u30dd\u30f3\u30b5\u30fc\u5951\u7d04\u3092\u7d44\u307f\u5408\u308f\u305b\u305f\u6570\u5341\u5104\u30c9\u30eb\u898f\u6a21\u306e\u7523\u696d\u3067\u3059\u3002\u4e16\u754c\u4e2d\u306e\u30c1\u30fc\u30e0\u30fb\u30aa\u30fc\u30ca\u30fc\u3084\u30de\u30cd\u30fc\u30b8\u30e3\u30fc\u306f\u3001\u81ea\u5206\u305f\u3061\u306e\u30c1\u30fc\u30e0\u306b\u52dd\u5229\u306e\u30b5\u30c3\u30ab\u30fc\u3092\u3082 […]<\/p>\n","protected":false},"author":41,"featured_media":49563,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[1005],"tags":[],"class_list":["post-50870","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-1005"],"acf":[],"yoast_head":"\n
\u6a5f\u68b0\u5b66\u7fd2\u3001Python\u3001GridDB\u3092\u7528\u3044\u305f\u30b5\u30c3\u30ab\u30fc\u9078\u624b\u306e\u30ec\u30b3\u30e1\u30f3\u30c7\u30fc\u30b7\u30e7\u30f3\u30b7\u30b9\u30c6\u30e0 | GridDB: Open Source Time Series Database for IoT<\/title>\n<meta name=\"description\" content=\"\u30b5\u30c3\u30ab\u30fc\u30d3\u30b8\u30cd\u30b9\u306f\u3001\u9ad8\u3044\u30d1\u30d5\u30a9\u30fc\u30de\u30f3\u30b9\u3092\u767a\u63ee\u3059\u308b\u30a2\u30b9\u30ea\u30fc\u30c8\u3001\u71b1\u72c2\u7684\u306a\u30d5\u30a1\u30f3\u3001\u305d\u3057\u3066\u5927\u304d\u306a\u30b9\u30dd\u30f3\u30b5\u30fc\u5951\u7d04\u3092\u7d44\u307f\u5408\u308f\u305b\u305f\u6570\u5341\u5104\u30c9\u30eb\u898f\u6a21\u306e\u7523\u696d\u3067\u3059\u3002\u4e16\u754c\u4e2d\u306e\u30c1\u30fc\u30e0\u30fb\u30aa\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" 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