{"id":50746,"date":"2021-06-24T00:00:00","date_gmt":"2021-06-24T07:00:00","guid":{"rendered":"https:\/\/griddb-linux-hte8hndjf8cka8ht.westus-01.azurewebsites.net\/%e6%9c%aa%e5%88%86%e9%a1%9e\/time-series-analysis-with-griddb-and-python\/"},"modified":"2025-11-14T07:54:39","modified_gmt":"2025-11-14T15:54:39","slug":"time-series-analysis-with-griddb-and-python","status":"publish","type":"post","link":"https:\/\/www.griddb.net\/ja\/%e6%9c%aa%e5%88%86%e9%a1%9e\/time-series-analysis-with-griddb-and-python\/","title":{"rendered":"GridDB\u3068Python\u3092\u4f7f\u3063\u305f\u6642\u7cfb\u5217\u89e3\u6790"},"content":{"rendered":"

\u3053\u306e\u30c1\u30e5\u30fc\u30c8\u30ea\u30a2\u30eb\u3067\u306f\u3001Python\u3092\u4f7f\u3063\u3066GridDB\u306b\u683c\u7d0d\u3055\u308c\u305f\u6642\u7cfb\u5217\u30c7\u30fc\u30bf\u3092\u89e3\u6790\u3059\u308b\u65b9\u6cd5\u3092\u8aac\u660e\u3057\u307e\u3059\u3002\u30c1\u30e5\u30fc\u30c8\u30ea\u30a2\u30eb\u306e\u6982\u8981\u306f\u4ee5\u4e0b\u306e\u901a\u308a\u3067\u3059\u3002<\/p>\n

    \n
  1. SQL\u3068Pandas\u3092\u4f7f\u3063\u3066\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u3092\u8aad\u307f\u8fbc\u3080<\/li>\n
  2. NULL\u3084\u6b20\u640d\u5024\u306a\u3069\u306b\u5bfe\u5fdc\u3059\u308b\u305f\u3081\u306b\u30c7\u30fc\u30bf\u306e\u524d\u51e6\u7406\u3092\u884c\u3046<\/li>\n
  3. \u30c7\u30fc\u30bf\u306b\u5bfe\u3059\u308b\u5206\u985e\u3092\u69cb\u7bc9\u3059\u308b<\/li>\n<\/ol>\n

    \u524d\u63d0\u6761\u4ef6<\/h2>\n

    \u3053\u306e\u30c1\u30e5\u30fc\u30c8\u30ea\u30a2\u30eb\u3067\u306f\u3001GridDB\u3001Python3\u3001\u304a\u3088\u3073\u95a2\u9023\u3059\u308b\u30e9\u30a4\u30d6\u30e9\u30ea\u304c\u4e8b\u524d\u306b\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb\u3055\u308c\u3066\u3044\u308b\u3053\u3068\u3092\u524d\u63d0\u3068\u3057\u3066\u3044\u307e\u3059\u3002\u4ee5\u4e0b\u306e\u30d1\u30c3\u30b1\u30fc\u30b8\u304c\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb\u3055\u308c\u3066\u3044\u306a\u3044\u5834\u5408\u306f\u3001\u30c1\u30e5\u30fc\u30c8\u30ea\u30a2\u30eb\u3092\u9032\u3081\u308b\u524d\u306b\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb\u3057\u3066\u304f\u3060\u3055\u3044\u30021. GridDB<\/a> 2. Python 3<\/a> 3. GridDB Python Client<\/a> 4. NumPy<\/a> 5. Pandas<\/a> 6. Matplotlib<\/a> 7. Scikit-learn<\/a> 8. Lightgbm<\/a> 9. Seaborn<\/a><\/p>\n

    \u4ee5\u4e0b\u306e\u30c1\u30e5\u30fc\u30c8\u30ea\u30a2\u30eb\u306f\u3001Jupyter notebooks (Anaconda Navigator)<\/a>\u3067\u884c\u308f\u308c\u307e\u3059\u3002\u3053\u308c\u3089\u306e\u30d1\u30c3\u30b1\u30fc\u30b8\u306f\u3001conda install package_name<\/code>\u3092\u4f7f\u3063\u3066\u3001\u3042\u306a\u305f\u306e\u74b0\u5883\u306b\u76f4\u63a5\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb\u3059\u308b\u3053\u3068\u304c\u3067\u304d\u307e\u3059\u3002\u3042\u308b\u3044\u306f\u3001\u30b3\u30de\u30f3\u30c9\u30d7\u30ed\u30f3\u30d7\u30c8\u3001\u30bf\u30fc\u30df\u30ca\u30eb\u3067 pip install package_name<\/code> \u3068\u5165\u529b\u3057\u3066\u304f\u3060\u3055\u3044\u3002<\/p>\n

    \u5fc5\u8981\u306a\u30e9\u30a4\u30d6\u30e9\u30ea\u3092\u30a4\u30f3\u30dd\u30fc\u30c8\u3059\u308b<\/h2>\n

    \u5fc5\u8981\u306a\u30d1\u30c3\u30b1\u30fc\u30b8\u306e\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb\u304c\u5b8c\u4e86\u3057\u305f\u3089\u3001\u6b21\u306b\u4ee5\u4e0b\u306e\u30e9\u30a4\u30d6\u30e9\u30ea\u3092\u30a4\u30f3\u30dd\u30fc\u30c8\u3057\u3066\u304f\u3060\u3055\u3044\u3002<\/p>\n

    \n
    import numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\nfrom sklearn.preprocessing import *\nfrom sklearn.model_selection import *\nfrom sklearn.metrics import *\nimport os\nfrom datetime import datetime\nimport time\nfrom lightgbm import LGBMRegressor\nimport seaborn as sns\nfrom sklearn import metrics<\/code><\/pre>\n<\/div>\n
    \n
    APP_PATH = os.getcwd()\nAPP_PATH<\/code><\/pre>\n<\/div>\n
    'C:\\Users\\SHRIPRIYA\\Desktop\\AW Group\\GridDB'\n<\/code><\/pre>\n
    \u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u3092\u8aad\u307f\u8fbc\u3080<\/h2>\n
    \u3053\u306e\u30c1\u30e5\u30fc\u30c8\u30ea\u30a2\u30eb\u3067\u4f7f\u7528\u3057\u3066\u3044\u308b\u6642\u7cfb\u5217\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u306f\u3001Kaggle<\/a>\u3067\u30aa\u30fc\u30d7\u30f3\u30bd\u30fc\u30b9\u5316\u3055\u308c\u3066\u3044\u307e\u3059\u3002zip\u30d5\u30a9\u30eb\u30c0\u306b\u306f\u3001\u30c8\u30ec\u30fc\u30cb\u30f3\u30b0\u7528\u3068\u30c6\u30b9\u30c8\u7528\u306e2\u3064\u306e\u30d5\u30a1\u30a4\u30eb\u304c\u5165\u3063\u3066\u3044\u307e\u3059\u3002\u3057\u304b\u3057\u3001\u30c6\u30b9\u30c8\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u306b\u306f\u30e9\u30d9\u30eb\u304c\u542b\u307e\u308c\u3066\u3044\u306a\u3044\u305f\u3081\u3001\u30e2\u30c7\u30eb\u306e\u6027\u80fd\u3092\u691c\u8a3c\u3059\u308b\u3053\u3068\u304c\u3067\u304d\u307e\u305b\u3093\u3002\u305d\u306e\u305f\u3081\u3001\u4eca\u56de\u306f\u30c8\u30ec\u30fc\u30cb\u30f3\u30b0\u7528\u306e\u30d5\u30a1\u30a4\u30eb\u3092\u5168\u4f53\u306e\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u3068\u3057\u3066\u4f7f\u7528\u3057\u3001\u5f8c\u306b\u30c8\u30ec\u30fc\u30cb\u30f3\u30b0\u7528\u3068\u30c6\u30b9\u30c8\u7528\u306b\u5206\u3051\u3066\u4f7f\u7528\u3057\u307e\u3059\u3002<\/p>\n
    \u5b66\u7fd2\u30d5\u30a1\u30a4\u30eb\u306f\u7d0448000\u884c\uff08\u307e\u305f\u306f\u30a4\u30f3\u30b9\u30bf\u30f3\u30b9\uff09\u3067\u30014\u3064\u306e\u5217\uff08\u307e\u305f\u306f\u5c5e\u6027\uff09\u3059\u306a\u308f\u3061ID, DateTime, Junction, and Vehicles<\/code>\u304c\u3042\u308a\u307e\u3059\u3002Vehicle<\/code>\u5217\u306f\u5f93\u5c5e\u5909\u6570\uff08\u307e\u305f\u306f\u5fdc\u7b54\u5909\u6570\uff09\u3067\u3001DateTime and Junction<\/code>\u306f\u72ec\u7acb\u5909\u6570\uff08\u307e\u305f\u306f\u8aac\u660e\u5909\u6570\uff09\u3067\u3059\u3002<\/p>\n
    SQL\u3092\u4f7f\u3046<\/h2>\n
    Python\u30b9\u30af\u30ea\u30d7\u30c8\u3084\u30b3\u30f3\u30bd\u30fc\u30eb\u3067\u4ee5\u4e0b\u306e\u3088\u3046\u306a\u6587\u3092\u5165\u529b\u3059\u308b\u3053\u3068\u3067\u3001GridDB<\/a> \u304b\u3089\u30c7\u30fc\u30bf\u3092\u53d6\u5f97\u3059\u308b\u3053\u3068\u304c\u3067\u304d\u307e\u3059\u3002GridDB\u306epython\u30af\u30e9\u30a4\u30a2\u30f3\u30c8<\/a>\u3092\u4f7f\u3046\u5229\u70b9\u306f\u3001\u7d50\u679c\u3068\u3057\u3066\u5f97\u3089\u308c\u308b\u30c7\u30fc\u30bf\u578b\u304cpandas\u306edataframe\u3067\u3042\u308b\u3053\u3068\u3067\u3059\u3002\u3053\u308c\u306b\u3088\u308a\u3001\u30c7\u30fc\u30bf\u64cd\u4f5c\u304c\u975e\u5e38\u306b\u7c21\u5358\u306b\u306a\u308a\u307e\u3059\u3002<\/p>\n
    statement = ('SELECT * FROM train_ml_iot')
    \ndataset = pd.read_sql_query(statement, cont)<\/code><\/p>\n
    \u51fa\u529b\u306f\u4ee5\u4e0b\u306e\u3088\u3046\u306b\u306a\u308a\u307e\u3059\u3002<\/p>\n
    \"\"<\/a><\/p>\n
    \u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u306e\u6982\u8981\u3092\u77e5\u308b<\/h2>\n
    \u3055\u3066\u3001\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u3092\u8aad\u307f\u8fbc\u3093\u3060\u3068\u3053\u308d\u3067\u3001\u3044\u3088\u3044\u3088\u305d\u306e\u4e2d\u8eab\u3092\u898b\u3066\u307f\u307e\u3057\u3087\u3046\u3002head<\/code>\u30b3\u30de\u30f3\u30c9\u3092\u4f7f\u3063\u3066\u3001\u6700\u521d\u306e5\u884c\u3092\u8868\u793a\u3059\u308b\u3053\u3068\u304c\u3067\u304d\u307e\u3059\u3002\u3082\u3063\u3068\u591a\u304f\u306e\u884c\u3092\u8868\u793a\u3057\u305f\u3044\u5834\u5408\u306f\u3001\u95a2\u6570\u306e\u5f15\u6570\u306b\u6570\u5b57\u3092\u5165\u529b\u3057\u307e\u3059\u3002\u4f8b\u3048\u3070\u3001dataset.head(15)<\/code>\u3068\u3059\u308b\u3068\u3001\u6700\u521d\u306e15\u884c\u3092\u8868\u793a\u3059\u308b\u3053\u3068\u304c\u3067\u304d\u307e\u3059\u3002\u4ed6\u306b\u3082\u3001tail<\/code>\u30b3\u30de\u30f3\u30c9\u3092\u4f7f\u3063\u3066\u6700\u5f8c\u306e5\u884c\u3092\u8868\u793a\u3059\u308b\u65b9\u6cd5\u3082\u3042\u308a\u307e\u3059\u3002<\/p>\n
    \n
    dataset.head()<\/code><\/pre>\n<\/div>\n
    \n