{"id":50813,"date":"2022-08-19T00:00:00","date_gmt":"2022-08-19T07:00:00","guid":{"rendered":"https:\/\/griddb-linux-hte8hndjf8cka8ht.westus-01.azurewebsites.net\/%e6%9c%aa%e5%88%86%e9%a1%9e\/exploring-consumer-purchases-patterns-using-griddb-and-python\/"},"modified":"2025-11-14T07:55:34","modified_gmt":"2025-11-14T15:55:34","slug":"exploring-consumer-purchases-patterns-using-griddb-and-python","status":"publish","type":"post","link":"https:\/\/griddb.net\/ja\/%e6%9c%aa%e5%88%86%e9%a1%9e\/exploring-consumer-purchases-patterns-using-griddb-and-python\/","title":{"rendered":"GridDB\u3068Python\u3092\u7528\u3044\u305f\u6d88\u8cbb\u8005\u8cfc\u8cb7\u30d1\u30bf\u30fc\u30f3\u306e\u63a2\u7d22"},"content":{"rendered":"

\u6d88\u8cbb\u8005\u884c\u52d5\u3092\u7406\u89e3\u3059\u308b\u3053\u3068\u306f\u3001\u30de\u30fc\u30b1\u30c6\u30a3\u30f3\u30b0\u62c5\u5f53\u8005\u304c\u6d88\u8cbb\u8005\u306e\u8cfc\u8cb7\u610f\u6b32\u3092\u7406\u89e3\u3059\u308b\u4e0a\u3067\u91cd\u8981\u3067\u3059\u3002\u6d88\u8cbb\u8005\u306f\u305d\u308c\u305e\u308c\u72ec\u81ea\u306e\u8cfc\u8cb7\u30d1\u30bf\u30fc\u30f3\u3092\u6301\u3063\u3066\u3044\u307e\u3059\u3002\u8cfc\u8cb7\u30d1\u30bf\u30fc\u30f3\u3092\u8a8d\u8b58\u3057\u3001\u5206\u6790\u3057\u3001\u6e2c\u5b9a\u3059\u308b\u3053\u3068\u3067\u3001\u4f01\u696d\u306f\u30bf\u30fc\u30b2\u30c3\u30c8\u3068\u3059\u308b\u6d88\u8cbb\u8005\u3092\u3088\u308a\u3088\u304f\u7406\u89e3\u3057\u3001\u30ea\u30fc\u30c1\u3092\u62e1\u5927\u3067\u304d\u308b\u53ef\u80fd\u6027\u304c\u3042\u308b\u306e\u3067\u3059\u3002<\/p>\n

\u3053\u306e\u30d6\u30ed\u30b0\u3067\u7d39\u4ecb\u3057\u3066\u3044\u308b\u30bd\u30fc\u30b9\u30b3\u30fc\u30c9\u306e\u30ea\u30dd\u30b8\u30c8\u30ea\u306f\u3053\u3061\u3089\u3067\u3059\u3002 https:\/\/github.com\/griddbnet\/Blogs\/tree\/exploring_consumer_purchases<\/a><\/p>\n

\u3053\u306e\u30d6\u30ed\u30b0\u306e\u76ee\u7684\u306f\u3001Google Merchandise Store\u306e\u9867\u5ba2\u306e\u8cfc\u8cb7\u884c\u52d5\u3092GridDB\u306e\u529b\u3092\u4f7f\u3063\u3066\u5206\u6790\u3059\u308b\u3053\u3068\u3067\u3059\u3002\u5206\u6790\u7d50\u679c\u306f\u3001Google\u30a2\u30ca\u30ea\u30c6\u30a3\u30af\u30b9\u30c7\u30fc\u30bf\u306e\u4e0a\u306b\u30c7\u30fc\u30bf\u5206\u6790\u3092\u5229\u7528\u3059\u308b\u3053\u3068\u3092\u9078\u629e\u3057\u305f\u4f01\u696d\u306b\u3068\u3063\u3066\u3001\u3088\u308a\u5b9f\u884c\u53ef\u80fd\u306a\u696d\u52d9\u6539\u9769\u3084\u30de\u30fc\u30b1\u30c6\u30a3\u30f3\u30b0\u4e88\u7b97\u306e\u6709\u52b9\u6d3b\u7528\u306b\u3064\u306a\u304c\u308b\u304b\u3082\u3057\u308c\u307e\u305b\u3093\u3002<\/p>\n

Jupyter\u30d5\u30a1\u30a4\u30eb\uff08\u5168\u6587\uff09\u306f\u3053\u3061\u3089 <\/a><\/p>\n

\u30c1\u30e5\u30fc\u30c8\u30ea\u30a2\u30eb\u306e\u6982\u8981\u306f\u4ee5\u4e0b\u306e\u901a\u308a\u3067\u3059\u3002<\/p>\n

    \n
  1. \u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u6982\u8981<\/li>\n
  2. \u5fc5\u8981\u306a\u30e9\u30a4\u30d6\u30e9\u30ea\u306e\u30a4\u30f3\u30dd\u30fc\u30c8<\/li>\n
  3. \u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u306e\u8aad\u307f\u8fbc\u307f<\/li>\n
  4. \u30c7\u30fc\u30bf\u306e\u30af\u30ea\u30fc\u30cb\u30f3\u30b0\u3068\u524d\u51e6\u7406<\/li>\n
  5. \u30c7\u30fc\u30bf\u306e\u53ef\u8996\u5316\u306b\u3088\u308b\u5206\u6790<\/li>\n
  6. \u307e\u3068\u3081<\/li>\n<\/ol>\n

    \u524d\u63d0\u6761\u4ef6\u3068\u74b0\u5883\u8a2d\u5b9a<\/h2>\n

    \u3053\u306e\u30c1\u30e5\u30fc\u30c8\u30ea\u30a2\u30eb\u306f\u3001Windows \u30aa\u30da\u30ec\u30fc\u30c6\u30a3\u30f3\u30b0\u30b7\u30b9\u30c6\u30e0\u4e0a\u306e Anaconda Navigator (Python \u30d0\u30fc\u30b8\u30e7\u30f3 – 3.8.3) \u3067\u5b9f\u884c\u3055\u308c\u307e\u3059\u3002\u30c1\u30e5\u30fc\u30c8\u30ea\u30a2\u30eb\u3092\u7d9a\u3051\u308b\u524d\u306b\u3001\u4ee5\u4e0b\u306e\u30d1\u30c3\u30b1\u30fc\u30b8\u304c\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb\u3055\u308c\u3066\u3044\u308b\u5fc5\u8981\u304c\u3042\u308a\u307e\u3059\u3002<\/p>\n

      \n
    1. Pandas<\/li>\n
    2. NumPy<\/li>\n
    3. plotly<\/li>\n
    4. Matplotlib<\/li>\n
    5. Seaborn<\/li>\n
    6. griddb_python<\/li>\n
    7. scipy<\/li>\n
    8. json<\/li>\n
    9. squarify<\/li>\n
    10. random<\/li>\n<\/ol>\n

      \u3053\u308c\u3089\u306e\u30d1\u30c3\u30b1\u30fc\u30b8\u306f Conda \u306e\u4eee\u60f3\u74b0\u5883\u306b conda install package-name<\/code> \u3092\u4f7f\u3063\u3066\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb\u3059\u308b\u3053\u3068\u304c\u3067\u304d\u307e\u3059\u3002\u30bf\u30fc\u30df\u30ca\u30eb\u3084\u30b3\u30de\u30f3\u30c9\u30d7\u30ed\u30f3\u30d7\u30c8\u304b\u3089\u76f4\u63a5Python\u3092\u4f7f\u3063\u3066\u3044\u308b\u5834\u5408\u306f\u3001 pip install package-name<\/code> \u3067\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb\u3067\u304d\u307e\u3059\u3002<\/p>\n

      GridDB\u306e\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb<\/h2>\n

      \u3053\u306e\u30c1\u30e5\u30fc\u30c8\u30ea\u30a2\u30eb\u3067\u306f\u3001\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u3092\u30ed\u30fc\u30c9\u3059\u308b\u969b\u306b\u3001GridDB \u3092\u4f7f\u7528\u3059\u308b\u65b9\u6cd5\u3068\u3001Pandas \u3092\u4f7f\u7528\u3059\u308b\u65b9\u6cd5\u306e 2 \u7a2e\u985e\u3092\u53d6\u308a\u4e0a\u3052\u307e\u3059\u3002Python\u3092\u4f7f\u7528\u3057\u3066GridDB\u306b\u30a2\u30af\u30bb\u30b9\u3059\u308b\u305f\u3081\u306b\u306f\u3001\u4ee5\u4e0b\u306e\u30d1\u30c3\u30b1\u30fc\u30b8\u3082\u4e88\u3081\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb\u3057\u3066\u304a\u304f\u5fc5\u8981\u304c\u3042\u308a\u307e\u3059\u3002<\/p>\n

        \n
      1. GridDB C\u30af\u30e9\u30a4\u30a2\u30f3\u30c8<\/a><\/li>\n
      2. SWIG (Simplified Wrapper and Interface Generator)<\/li>\n
      3. GridDB Python\u30af\u30e9\u30a4\u30a2\u30f3\u30c8<\/a><\/li>\n<\/ol>\n

        1. \u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u6982\u8981<\/h2>\n

        \u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u306b\u306fGStore\uff08Google swag\u30aa\u30f3\u30e9\u30a4\u30f3\u30b9\u30c8\u30a2\uff09\u3078\u306e\u8a2a\u554f\u306b\u95a2\u3059\u308b\u60c5\u5831\u304c\u542b\u307e\u308c\u3066\u304a\u308a\u3001\u5404\u884c\u306f\u4e00\u610f\u306e\u8a2a\u554f\u3067\u3042\u308a\u3001\u5404\u30e6\u30fc\u30b6\u30fc\u306f\u4e00\u610f\u306e\u300cfullVisitorId\u300d\u3092\u6301\u3063\u3066\u3044\u307e\u3059\u3002<\/p>\n

        1) fullVisitorId<\/code>– Google Merchandise Store\u306e\u5404\u30e6\u30fc\u30b6\u30fc\u306b\u4e00\u610f\u306a\u8b58\u5225\u5b50\u3002<\/p>\n

        2) channelGrouping<\/code> – \u30e6\u30fc\u30b6\u30fc\u304c\u30b9\u30c8\u30a2\u306b\u30a2\u30af\u30bb\u30b9\u3057\u305f\u7d4c\u8def\u3002<\/p>\n

        3) date<\/code> – \u30e6\u30fc\u30b6\u30fc\u304c\u30b9\u30c8\u30a2\u306b\u30a2\u30af\u30bb\u30b9\u3057\u305f\u65e5\u4ed8\u3002<\/p>\n

        4) device<\/code> – \u30b9\u30c8\u30a2\u306b\u30a2\u30af\u30bb\u30b9\u3059\u308b\u305f\u3081\u306b\u4f7f\u7528\u3055\u308c\u308b\u30c7\u30d0\u30a4\u30b9\u306e\u4ed5\u69d8\u3067\u3059\u3002<\/p>\n

        5) geoNetwork<\/code> – \u30e6\u30fc\u30b6\u30fc\u306e\u5730\u7406\u306b\u95a2\u3059\u308b\u60c5\u5831\u304c\u8a18\u8f09\u3055\u308c\u3066\u3044\u307e\u3059\u3002<\/p>\n

        6) socialEngagementType<\/code> – \u30a8\u30f3\u30b2\u30fc\u30b8\u30e1\u30f3\u30c8\u306e\u30bf\u30a4\u30d7\u3001\u300c\u30bd\u30fc\u30b7\u30e3\u30eb\u306b\u53c2\u52a0\u3057\u3066\u3044\u308b\u300d\u307e\u305f\u306f\u300c\u30bd\u30fc\u30b7\u30e3\u30eb\u306b\u53c2\u52a0\u3057\u3066\u3044\u306a\u3044\u300d\u306e\u3069\u3061\u3089\u304b\u3002<\/p>\n

        7) totals<\/code> – \u3053\u306e\u30bb\u30af\u30b7\u30e7\u30f3\u306b\u306f\u3001\u30bb\u30c3\u30b7\u30e7\u30f3\u5168\u4f53\u306e\u96c6\u8a08\u5024\u304c\u542b\u307e\u308c\u307e\u3059\u3002<\/p>\n

        8) trafficSource<\/code> – \u3053\u306e\u30bb\u30af\u30b7\u30e7\u30f3\u306f\u3001\u30bb\u30c3\u30b7\u30e7\u30f3\u306e\u767a\u4fe1\u5143\u3067\u3042\u308b\u30c8\u30e9\u30d5\u30a3\u30c3\u30af\u30bd\u30fc\u30b9\u306b\u95a2\u3059\u308b\u60c5\u5831\u3092\u542b\u3093\u3067\u3044\u307e\u3059\u3002<\/p>\n

        9) visitId<\/code> – \u3053\u306e\u30bb\u30c3\u30b7\u30e7\u30f3\u306e\u8b58\u5225\u5b50\u3002\u3053\u308c\u306f\u3001\u901a\u5e38_utmb\u30af\u30c3\u30ad\u30fc\u3068\u3057\u3066\u4fdd\u5b58\u3055\u308c\u308b\u5024\u306e\u4e00\u90e8\u3067\u3059\u3002\u3053\u308c\u306f\u3001\u30e6\u30fc\u30b6\u30fc\u306b\u5bfe\u3057\u3066\u306e\u307f\u4e00\u610f\u3067\u3042\u308b\u3002\u5b8c\u5168\u306b\u4e00\u610f\u306aID\u306e\u305f\u3081\u306b\u306f\u3001fullVisitorId\u3068visitId\u3092\u7d44\u307f\u5408\u308f\u305b\u3066\u4f7f\u7528\u3059\u308b\u5fc5\u8981\u304c\u3042\u308a\u307e\u3059\u3002<\/p>\n

        10) visitNumber<\/code> – \u3053\u306e\u30e6\u30fc\u30b6\u30fc\u306e\u30bb\u30c3\u30b7\u30e7\u30f3\u756a\u53f7\u3067\u3059\u3002\u3053\u308c\u304c\u6700\u521d\u306e\u30bb\u30c3\u30b7\u30e7\u30f3\u3067\u3042\u308b\u5834\u5408\u3001\u3053\u308c\u306f1\u306b\u8a2d\u5b9a\u3055\u308c\u307e\u3059\u3002<\/p>\n

        11) visitStartTime<\/code> – \u30bf\u30a4\u30e0\u30b9\u30bf\u30f3\u30d7\uff08POSIX\u6642\u9593\u3068\u3057\u3066\u8868\u73fe\u3055\u308c\u308b\uff09\u3002<\/p>\n

        12) hits<\/code> – \u3053\u306e\u884c\u3068\u30cd\u30b9\u30c8\u3055\u308c\u305f\u30d5\u30a3\u30fc\u30eb\u30c9\u306f\u3001\u3042\u3089\u3086\u308b\u7a2e\u985e\u306e\u30d2\u30c3\u30c8\u306b\u3064\u3044\u3066\u5165\u529b\u3055\u308c\u307e\u3059\u3002\u3059\u3079\u3066\u306e\u30da\u30fc\u30b8\u8a2a\u554f\u306e\u8a18\u9332\u3092\u63d0\u4f9b\u3057\u307e\u3059\u3002<\/p>\n

        13) customDimensions<\/code> – \u3053\u306e\u30bb\u30af\u30b7\u30e7\u30f3\u306b\u306f\u3001\u30bb\u30c3\u30b7\u30e7\u30f3\u306b\u8a2d\u5b9a\u3055\u308c\u305f\u30e6\u30fc\u30b6\u30fb\u30ec\u30d9\u30eb\u307e\u305f\u306f\u30bb\u30c3\u30b7\u30e7\u30f3\u30fb\u30ec\u30d9\u30eb\u306e\u30ab\u30b9\u30bf\u30e0\u30fb\u30c7\u30a3\u30e1\u30f3\u30b7\u30e7\u30f3\u304c\u542b\u307e\u308c\u307e \u3059\u3002\u3053\u306e\u30d5\u30a3\u30fc\u30eb\u30c9\u306f\u7e70\u308a\u8fd4\u3057\u4f7f\u7528\u3055\u308c\u3001\u8a2d\u5b9a\u3055\u308c\u305f\u5404\u30c7\u30a3\u30e1\u30f3\u30b8\u30e7\u30f3\u306e\u30a8\u30f3\u30c8\u30ea\u304c\u3042\u308a\u307e\u3059\u3002<\/p>\n

        14) totals<\/code> – \u3053\u306e\u30ab\u30e9\u30e0\u306e\u30bb\u30c3\u30c8\u306b\u306f\u3001\u4e3b\u306b\u30cf\u30a4\u30ec\u30d9\u30eb\u306a\u96c6\u8a08\u30c7\u30fc\u30bf\u304c\u542b\u307e\u308c\u307e\u3059\u3002<\/p>\n

        https:\/\/www.kaggle.com\/competitions\/ga-customer-revenue-prediction\/data?select=train.csv<\/a><\/p>\n

        2. \u5fc5\u8981\u306a\u30e9\u30a4\u30d6\u30e9\u30ea\u306e\u30a4\u30f3\u30dd\u30fc\u30c8<\/h2>\n
        \n
        import griddb_python as griddb\n\nimport gc\nimport numpy as np\nimport pandas as pd\nfrom pandas.io.json import json_normalize\nfrom scipy.stats import norm\nimport json\nimport datetime\nimport random\n\nimport squarify\nimport seaborn as sns\nimport matplotlib.pyplot as plt\nfrom plotly.offline import init_notebook_mode, iplot\nimport plotly.graph_objs as go\nfrom plotly import tools\n\nimport warnings\nwarnings.filterwarnings('ignore')\n%matplotlib inline<\/code><\/pre>\n<\/div>\n
        3. \u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u306e\u8aad\u307f\u8fbc\u307f<\/h2>\n
        \u7d9a\u3051\u3066\u3001\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u3092\u30ce\u30fc\u30c8\u30d6\u30c3\u30af\u306b\u30ed\u30fc\u30c9\u3057\u3066\u307f\u307e\u3057\u3087\u3046\u3002<\/p>\n
        3.a GridDB\u3092\u5229\u7528\u3059\u308b<\/h3>\n
        \u6771\u829dGridDB\u2122\u306f\u3001IoT\u3084\u30d3\u30c3\u30b0\u30c7\u30fc\u30bf\u306b\u6700\u9069\u306a\u9ad8\u30b9\u30b1\u30fc\u30e9\u30d6\u30ebNoSQL\u30c7\u30fc\u30bf\u30d9\u30fc\u30b9\u3067\u3059\u3002GridDB\u306e\u7406\u5ff5\u306e\u6839\u5e79\u306f\u3001IoT\u306b\u6700\u9069\u5316\u3055\u308c\u305f\u6c4e\u7528\u6027\u306e\u9ad8\u3044\u30c7\u30fc\u30bf\u30b9\u30c8\u30a2\u306e\u63d0\u4f9b\u3001\u9ad8\u3044\u30b9\u30b1\u30fc\u30e9\u30d3\u30ea\u30c6\u30a3\u3001\u9ad8\u6027\u80fd\u306a\u30c1\u30e5\u30fc\u30cb\u30f3\u30b0\u3001\u9ad8\u3044\u4fe1\u983c\u6027\u306e\u78ba\u4fdd\u306b\u3042\u308a\u307e\u3059\u3002<\/p>\n
        \u5927\u91cf\u306e\u30c7\u30fc\u30bf\u3092\u4fdd\u5b58\u3059\u308b\u5834\u5408\u3001CSV\u30d5\u30a1\u30a4\u30eb\u3067\u306f\u9762\u5012\u306a\u3053\u3068\u304c\u3042\u308a\u307e\u3059\u3002GridDB\u306f\u3001\u30aa\u30fc\u30d7\u30f3\u30bd\u30fc\u30b9\u3067\u30b9\u30b1\u30fc\u30e9\u30d6\u30eb\u306a\u30c7\u30fc\u30bf\u30d9\u30fc\u30b9\u3068\u3057\u3066\u3001\u5b8c\u74a7\u306a\u4ee3\u66ff\u624b\u6bb5\u3068\u306a\u3063\u3066\u3044\u307e\u3059\u3002GridDB\u306f\u3001\u30b9\u30b1\u30fc\u30e9\u30d6\u30eb\u3067\u30a4\u30f3\u30e1\u30e2\u30ea\u306aNo SQL\u30c7\u30fc\u30bf\u30d9\u30fc\u30b9\u3067\u3001\u5927\u91cf\u306e\u30c7\u30fc\u30bf\u3092\u7c21\u5358\u306b\u4fdd\u5b58\u3059\u308b\u3053\u3068\u304c\u3067\u304d\u307e\u3059\u3002GridDB\u3092\u521d\u3081\u3066\u4f7f\u3046\u5834\u5408\u306f\u3001GridDB\u3078\u306e\u8aad\u307f\u66f8\u304d<\/a>\u306e\u30c1\u30e5\u30fc\u30c8\u30ea\u30a2\u30eb\u304c\u5f79\u306b\u7acb\u3061\u307e\u3059\u3002<\/p>\n
        \u3059\u3067\u306b\u30c7\u30fc\u30bf\u30d9\u30fc\u30b9\u306e\u30bb\u30c3\u30c8\u30a2\u30c3\u30d7\u304c\u5b8c\u4e86\u3057\u3066\u3044\u308b\u3068\u4eee\u5b9a\u3057\u3066\u3001\u4eca\u5ea6\u306f\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u3092\u30ed\u30fc\u30c9\u3059\u308b\u305f\u3081\u306eSQL\u30af\u30a8\u30ea\u3092python\u3067\u66f8\u3044\u3066\u307f\u307e\u3057\u3087\u3046\u3002<\/p>\n
        \n
        factory = griddb.StoreFactory.get_instance()\n\nInitialize the GridDB container (enter your database credentials)\ntry:\n    gridstore = factory.get_store(host=host_name, port=your_port, \n            cluster_name=cluster_name, username=admin, \n            password=admin)\n\n    info = griddb.ContainerInfo(\"customer_data\",\n                    [[\"channelGrouping\", griddb.Type.STRING],[\"geoNetwork\", griddb.Type.STRING],[\"device\", griddb.Type.STRING],\n                     [\"date\", griddb.Type.TIMESTAMP],[\"sessionId\", griddb.Type.STRING],[\"socialEngagementType\", griddb.Type.STRING],\n                     [\"totals\", griddb.Type.STRING],[\"trafficSource\", griddb.Type.STRING],[\"visitStartTime\", griddb.Type.TIMESTAMP],\n                     [\"fullVisitorId\", griddb.Type.INTEGER],[\"visitNumber\", griddb.Type.INTEGER],[\"visitId\", griddb.Type.INTEGER],\n                    griddb.ContainerType.COLLECTION, True)\n    cont = gridstore.put_container(info) \n    data = pd.read_csv(\"False.csv\")\n    #Add data\n    for i in range(len(data)):\n        ret = cont.put(data.iloc[i, :])\n        print(\"Data added successfully\")<\/code><\/pre>\n<\/div>\n
        Data added successfully\n<\/code><\/pre>\n
        pandas\u30e9\u30a4\u30d6\u30e9\u30ea\u304c\u63d0\u4f9b\u3059\u308bread_sql_query\u95a2\u6570\u306f\u3001\u53d6\u5f97\u3057\u305f\u30c7\u30fc\u30bf\u3092pandas\u306e\u30c7\u30fc\u30bf\u30d5\u30ec\u30fc\u30e0\u306b\u5909\u63db\u3057\u3001\u30e6\u30fc\u30b6\u30fc\u304c\u4f5c\u696d\u3057\u3084\u3059\u3044\u3088\u3046\u306b\u3057\u307e\u3059\u3002<\/p>\n
        \n
        sql_statement = ('SELECT * FROM customer_data.csv')\ndf = pd.read_sql_query(sql_statement, cont)<\/code><\/pre>\n<\/div>\n
        \u5909\u6570 cont<\/code> \u306b\u306f\u3001\u30c7\u30fc\u30bf\u304c\u683c\u7d0d\u3055\u308c\u3066\u3044\u308b\u30b3\u30f3\u30c6\u30ca\u60c5\u5831\u304c\u683c\u7d0d\u3055\u308c\u3066\u3044\u308b\u3053\u3068\u306b\u6ce8\u610f\u3057\u3066\u304f\u3060\u3055\u3044\u3002credit_card_dataset<\/code> \u3092\u30b3\u30f3\u30c6\u30ca\u306e\u540d\u524d\u306b\u7f6e\u304d\u63db\u3048\u3066\u304f\u3060\u3055\u3044\u3002\u8a73\u7d30\u306f\u3001\u30c1\u30e5\u30fc\u30c8\u30ea\u30a2\u30ebGridDB\u3078\u306e\u8aad\u307f\u66f8\u304d<\/a>\u3092\u53c2\u7167\u3057\u3066\u304f\u3060\u3055\u3044\u3002<\/p>\n
        IoT\u3084\u30d3\u30c3\u30b0\u30c7\u30fc\u30bf\u306e\u30e6\u30fc\u30b9\u30b1\u30fc\u30b9\u306b\u95a2\u3057\u3066\u8a00\u3048\u3070\u3001GridDB\u306f\u30ea\u30ec\u30fc\u30b7\u30e7\u30ca\u30eb\u3084NoSQL\u306e\u9818\u57df\u306e\u4ed6\u306e\u30c7\u30fc\u30bf\u30d9\u30fc\u30b9\u306e\u4e2d\u3067\u660e\u3089\u304b\u306b\u969b\u7acb\u3063\u3066\u3044\u307e\u3059\u3002\u5168\u4f53\u3068\u3057\u3066\u3001GridDB\u306f\u9ad8\u53ef\u7528\u6027\u3068\u30c7\u30fc\u30bf\u4fdd\u6301\u3092\u5fc5\u8981\u3068\u3059\u308b\u30df\u30c3\u30b7\u30e7\u30f3\u30af\u30ea\u30c6\u30a3\u30ab\u30eb\u306a\u30a2\u30d7\u30ea\u30b1\u30fc\u30b7\u30e7\u30f3\u306e\u305f\u3081\u306b\u3001\u8907\u6570\u306e\u4fe1\u983c\u6027\u6a5f\u80fd\u3092\u63d0\u4f9b\u3057\u3066\u3044\u307e\u3059\u3002<\/p>\n
        3.b pandas\u306eread_csv\u3092\u4f7f\u7528\u3059\u308b<\/h3>\n
        \u307e\u305f\u3001Pandas\u306e read_csv<\/code> \u95a2\u6570\u3092\u4f7f\u7528\u3057\u3066\u30c7\u30fc\u30bf\u3092\u8aad\u307f\u8fbc\u3080\u3053\u3068\u3082\u3067\u304d\u307e\u3059\u3002\u3069\u3061\u3089\u306e\u65b9\u6cd5\u3092\u4f7f\u3063\u3066\u3082\u3001\u30c7\u30fc\u30bf\u306fpandas\u306edataframe\u306e\u5f62\u3067\u8aad\u307f\u8fbc\u307e\u308c\u308b\u306e\u3067\u3001\u4e0a\u8a18\u306e\u3069\u3061\u3089\u306e\u65b9\u6cd5\u3082\u540c\u3058\u51fa\u529b\u306b\u306a\u308a\u307e\u3059\u3002<\/p>\n
        \n
        df = pd.read_csv(\"customer_data.csv\")<\/code><\/pre>\n<\/div>\n
        \n
        df.head()<\/code><\/pre>\n<\/div>\n
        \n