{"id":50899,"date":"2024-05-16T00:00:00","date_gmt":"2024-05-16T07:00:00","guid":{"rendered":"https:\/\/griddb-linux-hte8hndjf8cka8ht.westus-01.azurewebsites.net\/%e6%9c%aa%e5%88%86%e9%a1%9e\/analyzing-world-population-data-in-python\/"},"modified":"2025-11-14T07:56:42","modified_gmt":"2025-11-14T15:56:42","slug":"analyzing-world-population-data-in-python","status":"publish","type":"post","link":"https:\/\/griddb.net\/ja\/%e6%9c%aa%e5%88%86%e9%a1%9e\/analyzing-world-population-data-in-python\/","title":{"rendered":"Python\u3067\u4e16\u754c\u306e\u4eba\u53e3\u30c7\u30fc\u30bf\u3092\u5206\u6790\u3059\u308b"},"content":{"rendered":"

\u3053\u306e\u8a18\u4e8b\u3067\u306f\u3001GridDB \u3092\u30c7\u30fc\u30bf\u30d9\u30fc\u30b9\u3068\u3057\u3066\u4f7f\u7528\u3057\u3001Python \u3067\u4e16\u754c\u306e\u4eba\u53e3\u30c7\u30fc\u30bf\u3092\u53ef\u8996\u5316\u3059\u308b\u65b9\u6cd5\u3092\u5b66\u3073\u307e\u3059\u3002GridDB Python \u30af\u30e9\u30a4\u30a2\u30f3\u30c8\u3092\u4f7f\u7528\u3057\u3066\u3001GridDB \u30c7\u30fc\u30bf\u30d9\u30fc\u30b9\u306b\u63a5\u7d9a\u3057\u3001\u4e16\u754c\u4eba\u53e3\u306e CSV \u30c7\u30fc\u30bf\u3092 GridDB \u306b\u633f\u5165\u3057\u3001\u305d\u306e\u30c7\u30fc\u30bf\u3092\u53d6\u5f97\u3057\u3001Python \u306e matplotlib\u3001pandas\u3001seaborn \u30e9\u30a4\u30d6\u30e9\u30ea\u3092\u4f7f\u7528\u3057\u3066\u69d8\u3005\u306a\u30d3\u30b8\u30e5\u30a2\u30e9\u30a4\u30bc\u30fc\u30b7\u30e7\u30f3\u3092\u30d7\u30ed\u30c3\u30c8\u3057\u307e\u3059\u3002<\/p>\n

**\u3053\u306e\u30d6\u30ed\u30b0\u306e\u30b3\u30fc\u30c9\u306fGridDB Blogs Github repo<\/a>\u306epopulation_analysis<\/code>\u3068\u3044\u3046\u30d6\u30e9\u30f3\u30c1\u306b\u3042\u308a\u307e\u3059\u3002<\/p>\n

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

Python\u30a2\u30d7\u30ea\u30b1\u30fc\u30b7\u30e7\u30f3\u3092GridDB\u306b\u63a5\u7d9a\u3059\u308b\u524d\u306b\u3001GridDB C\u30af\u30e9\u30a4\u30a2\u30f3\u30c8\u3068GridDB Python\u30af\u30e9\u30a4\u30a2\u30f3\u30c8\u3092\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb\u3059\u308b\u5fc5\u8981\u304c\u3042\u308a\u307e\u3059\u3002GridDB Python Package Index (Pypi)<\/a>\u306e\u8aac\u660e\u306b\u5f93\u3063\u3066\u3001\u3053\u308c\u3089\u306e\u30af\u30e9\u30a4\u30a2\u30f3\u30c8\u3092\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb\u3057\u3066\u304f\u3060\u3055\u3044\u3002<\/p>\n

\u4ee5\u4e0b\u306e\u30b9\u30af\u30ea\u30d7\u30c8\u306f\u3001\u3053\u306e\u30d6\u30ed\u30b0\u306e\u30b3\u30fc\u30c9\u3092\u5b9f\u884c\u3059\u308b\u305f\u3081\u306b\u5fc5\u8981\u306a\u30e9\u30a4\u30d6\u30e9\u30ea\u3092\u30a4\u30f3\u30dd\u30fc\u30c8\u3057\u307e\u3059\u3002<\/p>\n

\n
import griddb_python as griddb\nimport numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\nimport seaborn as sns\nsns.set(style=\"darkgrid\")<\/code><\/pre>\n<\/div>\n
\u4e16\u754c\u4eba\u53e3\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u306e\u30a4\u30f3\u30dd\u30fc\u30c8\u3068\u524d\u51e6\u7406<\/h2>\n
Kaggle\u306e\u4e16\u754c\u4eba\u53e3\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8<\/a>\u3092Python\u306e\u69d8\u3005\u306a\u53ef\u8996\u5316\u306e\u52a9\u3051\u3092\u501f\u308a\u3066\u5206\u6790\u3057\u307e\u3059\u3002\u3053\u306e\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u306f\u30011970\u5e74\u304b\u30892022\u5e74\u307e\u3067\u306e\u5404\u56fd\u306e\u4e16\u754c\u4eba\u53e3\u7d71\u8a08\u3092\u542b\u3080CSV\u30d5\u30a1\u30a4\u30eb\u3067\u69cb\u6210\u3055\u308c\u3066\u3044\u307e\u3059\u3002<\/p>\n
CSV\u30d5\u30a1\u30a4\u30eb\u3092\u30c0\u30a6\u30f3\u30ed\u30fc\u30c9\u3057\u3001\u4ee5\u4e0b\u306e\u30b9\u30af\u30ea\u30d7\u30c8\u3092\u5b9f\u884c\u3057\u3066\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u3092Pandas\u306e\u30c7\u30fc\u30bf\u30d5\u30ec\u30fc\u30e0\u306b\u30a4\u30f3\u30dd\u30fc\u30c8\u3057\u307e\u3059\u3002world_population.csv`\u30d5\u30a1\u30a4\u30eb\u306e\u5834\u6240\u306b\u3088\u3063\u3066\u3001\u30d1\u30b9\u3092\u66f4\u65b0\u3057\u3066\u304f\u3060\u3055\u3044\u3002<\/p>\n
\n
dataset = pd.read_csv(r\"\/mnt\/d\/Datasets\/world_population.csv\")\nprint(dataset.shape)\ndataset.head()<\/code><\/pre>\n<\/div>\n
Output:<\/strong><\/p>\n
\"\"<\/a><\/p>\n
\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u306f 234 \u884c\u300117 \u5217\u3067\u69cb\u6210\u3055\u308c\u3066\u3044\u307e\u3059\u3002\u30ab\u30e9\u30e0\u540d\u306b\u306f\u3044\u304f\u3064\u304b\u306e\u7279\u6b8a\u6587\u5b57\u304c\u542b\u307e\u308c\u3066\u3044\u307e\u3059\u304c\u3001GridDB \u30b3\u30f3\u30c6\u30ca\u3067\u306f\u30ab\u30e9\u30e0\u540d\u306b\u7279\u6b8a\u6587\u5b57\u3092\u542b\u3081\u308b\u3053\u3068\u304c\u3067\u304d\u306a\u3044\u305f\u3081\u3001\u3053\u308c\u3092\u524a\u9664\u3059\u308b\u5fc5\u8981\u304c\u3042\u308a\u307e\u3059\u3002<\/p>\n
\u4ee5\u4e0b\u306e\u30b9\u30af\u30ea\u30d7\u30c8\u306f\u3001\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u306e\u30ab\u30e9\u30e0\u306e\u7279\u6b8a\u6587\u5b57\u3092\u30a2\u30f3\u30c0\u30fc\u30b9\u30b3\u30a2\u306b\u7f6e\u304d\u63db\u3048\u3001\u30ab\u30e9\u30e0\u306e\u30c7\u30fc\u30bf\u578b\u3092\u8868\u793a\u3057\u307e\u3059\u3002<\/p>\n
\n
dataset.columns = dataset.columns.str.replace('[^a-zA-Z0-9]', '_', regex=True)\ndataset.dtypes<\/code><\/pre>\n<\/div>\n
Output:<\/strong><\/p>\n
\"\"<\/a><\/p>\n
\u5165\u529b\u30ab\u30e9\u30e0\u3092 GridDB \u6e96\u62e0\u306e\u30ab\u30e9\u30e0\u578b\u306b\u30de\u30c3\u30d4\u30f3\u30b0\u3059\u308b\u5fc5\u8981\u304c\u3042\u308b\u305f\u3081\u3001\u5165\u529b\u30ab\u30e9\u30e0\u306e\u578b\u3092\u78ba\u8a8d\u3057\u307e\u3059\u3002\u4f8b\u3048\u3070\u3001\u5f8c\u8ff0\u3059\u308b\u3088\u3046\u306b\u3001GridDB \u30b3\u30f3\u30c6\u30ca\u306b\u30c7\u30fc\u30bf\u3092\u633f\u5165\u3059\u308b\u524d\u306b\u3001object \u578b\u3092 string \u578b\u306b\u7f6e\u304d\u63db\u3048\u308b\u5fc5\u8981\u304c\u3042\u308a\u307e\u3059\u3002<\/p>\n
\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u306e\u524d\u51e6\u7406\u3092\u884c\u3044\u307e\u3057\u305f\u3002\u4ee5\u4e0b\u306e\u30b9\u30c6\u30c3\u30d7\u3067\u306f\u3001GridDB \u306b\u63a5\u7d9a\u3057\u3001CSV \u30c7\u30fc\u30bf\u3092\u633f\u5165\u3057\u307e\u3059\u3002<\/p>\n
**Pandas\u306e\u30c7\u30fc\u30bf\u30d5\u30ec\u30fc\u30e0\u3092\u4f7f\u7528\u3057\u3066\u3001Python\u3067\u76f4\u63a5\u30c7\u30fc\u30bf\u3092\u53ef\u8996\u5316\u3059\u308b\u3053\u3068\u304c\u3067\u304d\u307e\u3059\u3002\u3053\u306e\u30d6\u30ed\u30b0\u3067\u306f\u30c7\u30e2\u30f3\u30b9\u30c8\u30ec\u30fc\u30b7\u30e7\u30f3\u306e\u305f\u3081\u3001\u307e\u305a CSV \u30c7\u30fc\u30bf\u3092 GridDB \u30b3\u30f3\u30c6\u30ca\u306b\u633f\u5165\u3057\u3001\u633f\u5165\u3055\u308c\u305f\u30c7\u30fc\u30bf\u3092 Pandas dataframe \u306b\u53d6\u5f97\u3057\u3001GridDB \u304b\u3089\u53d6\u5f97\u3057\u305f\u30c7\u30fc\u30bf\u3092\u4f7f\u7528\u3057\u3066\u30d3\u30b8\u30e5\u30a2\u30e9\u30a4\u30bc\u30fc\u30b7\u30e7\u30f3\u3092\u30d7\u30ed\u30c3\u30c8\u3057\u307e\u3059\u3002<\/p>\n
GridDB \u3068\u306e\u63a5\u7d9a\u306e\u4f5c\u6210<\/h2>\n
\u63a5\u7d9a\u3092\u4f5c\u6210\u3059\u308b\u306b\u306f\u3001\u4ee5\u4e0b\u306e\u30b9\u30af\u30ea\u30d7\u30c8\u306e\u3088\u3046\u306b StoreFactory<\/code> \u30af\u30e9\u30b9\u306e\u30aa\u30d6\u30b8\u30a7\u30af\u30c8\u3092\u4f5c\u6210\u3059\u308b\u5fc5\u8981\u304c\u3042\u308a\u307e\u3059\u3002<\/p>\n
\u6b21\u306b\u3001\u30d5\u30a1\u30af\u30c8\u30ea\u30fc\u30aa\u30d6\u30b8\u30a7\u30af\u30c8\u306e get_store()<\/code> \u30e1\u30bd\u30c3\u30c9\u306b GridDB \u306e\u30db\u30b9\u30c8 URL\u3001\u30af\u30e9\u30b9\u30bf\u540d\u3001\u30e6\u30fc\u30b6\u540d\u3068\u30d1\u30b9\u30ef\u30fc\u30c9\u306e\u5024\u3092\u6e21\u3057\u307e\u3059\u3002\u3053\u308c\u3067\u5b8c\u4e86\u3067\u3059\u3002<\/p>\n
\u63a5\u7d9a\u304c\u78ba\u7acb\u3055\u308c\u3066\u3044\u308b\u3053\u3068\u3092\u78ba\u8a8d\u3059\u308b\u306b\u306f\u3001get_container()<\/code> \u30e1\u30bd\u30c3\u30c9\u3092\u4f7f\u7528\u3057\u3066\u4efb\u610f\u306e\u30b3\u30f3\u30c6\u30ca\u3092\u53d6\u5f97\u3057\u307e\u3059\u3002\u63a5\u7d9a\u306b\u554f\u984c\u304c\u3042\u308c\u3070\u3001\u4f8b\u5916\u304c\u767a\u751f\u3057\u307e\u3059\u3002<\/p>\n
\u4ee5\u4e0b\u306e\u30b9\u30af\u30ea\u30d7\u30c8\u3092\u5b9f\u884c\u3057\u3066\u304f\u3060\u3055\u3044\u3002\u63d0\u4f9b\u3055\u308c\u305f\u51fa\u529b\u304c\u8868\u793a\u3055\u308c\u308c\u3070\u3001\u63a5\u7d9a\u306f\u6210\u529f\u3057\u3066\u3044\u307e\u3059\u3002\u4f8b\u5916\u304c\u767a\u751f\u3057\u305f\u5834\u5408\u306f\u3001\u8a8d\u8a3c\u60c5\u5831\u3092\u78ba\u8a8d\u3057\u3001GridDB \u30b5\u30fc\u30d0\u304c\u8d77\u52d5\u3057\u3066\u3044\u308b\u3053\u3068\u3092\u78ba\u8a8d\u3057\u3001\u8a2d\u5b9a\u3092\u78ba\u8a8d\u3057\u3066\u304f\u3060\u3055\u3044\u3002<\/p>\n
\n
factory = griddb.StoreFactory.get_instance()\n\nDB_HOST = \"127.0.0.1:10001\"\nDB_CLUSTER = \"myCluster\"\nDB_USER = \"admin\"\nDB_PASS = \"admin\"\n\ntry:\n    gridstore = factory.get_store(\n        notification_member = DB_HOST,\n        cluster_name = DB_CLUSTER,\n        username = DB_USER,\n        password = DB_PASS\n    )\n\n    container1 = gridstore.get_container(\"container1\")\n    if container1 == None:\n        print(\"Container does not exist\")\n    print(\"Successfully connected to GridDB\")\n\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_location(i))\n        print(e.get_message(i))<\/code><\/pre>\n<\/div>\n
Output:<\/strong><\/p>\n
Container does not exist\nSuccessfully connected to GridDB\n<\/code><\/pre>\n
\u4e16\u754c\u306e\u4eba\u53e3\u30c7\u30fc\u30bf\u3092GridDB\u30b3\u30f3\u30c6\u30ca\u306b\u683c\u7d0d\u3059\u308b<\/h2>\n
GridDB\u30b3\u30f3\u30c6\u30ca\u306f\u3001\u30c6\u30fc\u30d6\u30eb\u306e\u30ab\u30e9\u30e0\u304c\u7279\u5b9a\u306e\u30c7\u30fc\u30bf\u5f62\u5f0f\u3067\u3042\u308b\u3053\u3068\u3092\u671f\u5f85\u3057\u307e\u3059\u3002\u8a73\u7d30\u306fGridDB\u306e\u30c7\u30fc\u30bf\u578b<\/a>\u3092\u53c2\u7167\u3057\u3066\u304f\u3060\u3055\u3044\u3002<\/p>\n
\u30b3\u30f3\u30c6\u30ca\u3092\u4f5c\u6210\u3059\u308b\u306b\u306f\u3001\u307e\u305a ContainerInfo<\/code> \u30af\u30e9\u30b9\u306e\u30aa\u30d6\u30b8\u30a7\u30af\u30c8\u3092\u4f5c\u6210\u3057\u3001\u30b3\u30f3\u30c6\u30ca\u540d\u3001\u30b3\u30f3\u30c6\u30ca\u30ab\u30e9\u30e0\u3001\u30b3\u30f3\u30c6\u30ca\u30bf\u30a4\u30d7\u3001\u304a\u3088\u3073\u30d6\u30fc\u30eb\u5024\u306e 4 \u3064\u306e\u30d1\u30e9\u30e1\u30fc\u30bf\u3092\u6e21\u3057\u307e\u3059\u3002<\/p>\n
\u30b3\u30f3\u30c6\u30ca\u30fb\u30ab\u30e9\u30e0\u306f\u30ea\u30b9\u30c8\u306e\u30ea\u30b9\u30c8\u3067\u306a\u3051\u308c\u3070\u306a\u3089\u305a\u3001\u5165\u308c\u5b50\u306b\u306a\u3063\u305f\u30ea\u30b9\u30c8\u306b\u306f\u305d\u308c\u305e\u308c\u30ab\u30e9\u30e0\u540d\u3068\u5bfe\u5fdc\u3059\u308b\u30ab\u30e9\u30e0\u306e\u578b\u304c\u542b\u307e\u308c\u307e\u3059\u3002\u4eca\u56de\u306e\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u306b\u306f\u6642\u7cfb\u5217\u30c7\u30fc\u30bf\u304c\u306a\u3044\u306e\u3067\u3001\u30b3\u30f3\u30c6\u30ca\u306e\u578b\u3092 Collection \u306b\u8a2d\u5b9a\u3057\u307e\u3059\u3002<\/p>\n
\u4ee5\u4e0b\u306e\u30b9\u30af\u30ea\u30d7\u30c8\u3067\u306f\u3001Pandas \u30c7\u30fc\u30bf\u30d5\u30ec\u30fc\u30e0\u306e\u30ab\u30e9\u30e0\u3092 GridDB \u306e\u30ab\u30e9\u30e0\u30bf\u30a4\u30d7\u306b\u30de\u30c3\u30d7\u3059\u308b map_pandas_dtype_to_griddb()<\/code> \u95a2\u6570\u3092\u5b9a\u7fa9\u3057\u3066\u3044\u307e\u3059\u3002\u3053\u306e\u30e1\u30bd\u30c3\u30c9\u306f\u5fc5\u8981\u306b\u5fdc\u3058\u3066\u5909\u66f4\u3059\u308b\u3053\u3068\u304c\u3067\u304d\u307e\u3059\u3002<\/p>\n
\u6b21\u306b\u3001\u30b3\u30f3\u30c6\u30ca\u60c5\u5831\u3092\u683c\u7d0d\u3059\u308b ContainerInfo<\/code> \u30af\u30e9\u30b9\u306e\u30aa\u30d6\u30b8\u30a7\u30af\u30c8\u3092\u4f5c\u6210\u3057\u307e\u3059\u3002<\/p>\n
\n
# see all GridDB data types: https:\/\/docs.griddb.net\/architecture\/data-model\/#data-type\n\ndef map_pandas_dtype_to_griddb(dtype):\n    if dtype == 'int64':\n        return griddb.Type.LONG\n    elif type == 'float64':\n        return griddb.Type.FLOAT\n    elif dtype == 'object':\n        return griddb.Type.STRING\n    # Add more column types if you want\n    else:\n        raise ValueError(f'Unsupported pandas type: {dtype}')\n\ncontainer_columns = []\nfor column_name, dtype in dataset.dtypes.items():\n    griddb_dtype = map_pandas_dtype_to_griddb(str(dtype))\n    container_columns.append([column_name, griddb_dtype])\n\ncontainer_info = griddb.ContainerInfo(\"PopulationStats\",\n                                      container_columns,\n                                      griddb.ContainerType.COLLECTION, True)<\/code><\/pre>\n<\/div>\n
\u6700\u5f8c\u306b\u3001\u30b3\u30f3\u30c6\u30ca\u30aa\u30d6\u30b8\u30a7\u30af\u30c8\u3092\u4f5c\u6210\u3059\u308b\u306b\u306f\u3001 put_container()<\/code> \u30e1\u30bd\u30c3\u30c9\u3092\u547c\u3073\u51fa\u3057\u3066\u3001\u5148\u307b\u3069\u4f5c\u6210\u3057\u305f container_info<\/code> \u30aa\u30d6\u30b8\u30a7\u30af\u30c8\u3092\u6e21\u3057\u307e\u3059\u3002<\/p>\n
\u6700\u5f8c\u306b\u3001\u30b3\u30f3\u30c6\u30ca\u306b\u30ec\u30b3\u30fc\u30c9\u3092\u633f\u5165\u3059\u308b\u306b\u306f\u3001\u5165\u529b\u30c7\u30fc\u30bf\u30d5\u30ec\u30fc\u30e0\u306e\u884c\u3092\u7e70\u308a\u8fd4\u3057\u51e6\u7406\u3057\u3001\u5404\u884c\u3092 Python \u306e\u30ea\u30b9\u30c8\u306b\u5909\u63db\u3057\u3001\u305d\u306e\u30ea\u30b9\u30c8\u3092\u30b3\u30f3\u30c6\u30ca\u30aa\u30d6\u30b8\u30a7\u30af\u30c8\u306b\u633f\u5165\u3057\u307e\u3059\u3002<\/p>\n
\n
try:\n    cont = grid store.put_container(container_info)\n    for index, row in dataset.iterrows():\n        cont.put(row.tolist())\n    print(\"All rows have been successfully stored in the GridDB container.\")\n\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_location(i))\n        print(e.get_message(i))<\/code><\/pre>\n<\/div>\n
Output:<\/strong><\/p>\n
\u3059\u3079\u3066\u306e\u884c\u304c\u6b63\u5e38\u306b GridDB \u30b3\u30f3\u30c6\u30ca\u306b\u683c\u7d0d\u3055\u308c\u307e\u3057\u305f\u3002<\/code><\/p>\n
\u4e0a\u8a18\u306e\u30e1\u30c3\u30bb\u30fc\u30b8\u306e\u3088\u3046\u306b\u3001GridDB \u306b\u30c7\u30fc\u30bf\u304c\u6b63\u5e38\u306b\u633f\u5165\u3055\u308c\u307e\u3057\u305f\u3002<\/p>\n
\u6b21\u306b\u3001GridDB\u30b3\u30f3\u30c6\u30ca\u304b\u3089\u30c7\u30fc\u30bf\u3092\u8aad\u307f\u8fbc\u3080\u65b9\u6cd5\u3092\u5b66\u3073\u307e\u3059\u3002<\/p>\n
GridDB\u30b3\u30f3\u30c6\u30ca\u304b\u3089\u306e\u4e16\u754c\u4eba\u53e3\u30c7\u30fc\u30bf\u306e\u53d6\u5f97<\/h2>\n
\u3053\u306e\u30bb\u30af\u30b7\u30e7\u30f3\u3067\u306f\u3001GridDB\u30b3\u30f3\u30c6\u30ca\u304b\u3089\u4e16\u754c\u306e\u4eba\u53e3\u30c7\u30fc\u30bf\u3092\u53d6\u5f97\u3057\u3001\u6b21\u306e\u30bb\u30af\u30b7\u30e7\u30f3\u3067\u69d8\u3005\u306a\u30d3\u30b8\u30e5\u30a2\u30e9\u30a4\u30bc\u30fc\u30b7\u30e7\u30f3\u3092\u7528\u3044\u3066\u5206\u6790\u3057\u307e\u3059\u3002<\/p>\n
get_container()<\/code> \u30e1\u30bd\u30c3\u30c9\u306fGridDB\u304b\u3089\u30b3\u30f3\u30c6\u30ca\u30c7\u30fc\u30bf\u3092\u53d6\u5f97\u3057\u307e\u3059\u3002\u30b3\u30f3\u30c6\u30ca\u30aa\u30d6\u30b8\u30a7\u30af\u30c8\u306e query<\/code> \u30e1\u30bd\u30c3\u30c9\u3067\u306f\u3001\u3053\u306e\u30b3\u30f3\u30c6\u30ca\u306b\u5bfe\u3057\u3066 SQL \u30af\u30a8\u30ea\u3092\u5b9f\u884c\u3067\u304d\u307e\u3059\u3002<\/p>\n
\u4ee5\u4e0b\u306e\u30b9\u30af\u30ea\u30d7\u30c8\u3067\u306f\u3001fetch()<\/code> \u30e1\u30bd\u30c3\u30c9\u3092\u4f7f\u7528\u3057\u3066 PopulationStats<\/code> \u30b3\u30f3\u30c6\u30ca\u304b\u3089\u3059\u3079\u3066\u306e\u30c7\u30fc\u30bf\u3092\u9078\u629e\u3057\u307e\u3059\u3002\u6b21\u306b\u3001fetch_rows()<\/code> \u30e1\u30bd\u30c3\u30c9\u3092\u4f7f\u7528\u3057\u3066\u3059\u3079\u3066\u306e\u884c\u3092\u53d6\u5f97\u3057\u3001Pandas \u30c7\u30fc\u30bf\u30d5\u30ec\u30fc\u30e0\u3068\u3057\u3066\u30c7\u30fc\u30bf\u3092\u8fd4\u3057\u307e\u3059\u3002<\/p>\n
\n
population_container = gridstore.get_container(\"PopulationStats\")\nquery = population_container.query(\"select *\")\nrs = query.fetch()\npopulation_data = rs.fetch_rows()\n\nprint(population_data.shape)\npopulation_data.head()<\/code><\/pre>\n<\/div>\n
Output:<\/strong><\/p>\n
\"\"<\/a><\/p>\n
GridDB\u306e\u30c7\u30fc\u30bf\u3092\u4f7f\u3063\u3066Python\u3067\u4e16\u754c\u306e\u4eba\u53e3\u30c7\u30fc\u30bf\u3092\u8996\u899a\u5316\u3059\u308b<\/h2>\n
GridDB\u306b\u633f\u5165\u3057\u305f\u30c7\u30fc\u30bf\u3092Pandas\u306e\u30c7\u30fc\u30bf\u30d5\u30ec\u30fc\u30e0\u3068\u3057\u3066\u53d6\u308a\u51fa\u3059\u3053\u3068\u306b\u6210\u529f\u3057\u307e\u3057\u305f\u3002\u6b21\u306b\u3001\u3053\u306e\u30c7\u30fc\u30bf\u30d5\u30ec\u30fc\u30e0\u3092\u4f7f\u3063\u3066\u4e16\u754c\u4eba\u53e3\u306e\u69d8\u3005\u306a\u5074\u9762\u3092\u5206\u6790\u3059\u308b\u3053\u3068\u304c\u3067\u304d\u307e\u3059\u3002Pandas\u3001Matplotlib\u3001Seaborn\u306a\u3069\u306ePython\u30e9\u30a4\u30d6\u30e9\u30ea\u3092\u4f7f\u3063\u3066\u69d8\u3005\u306a\u53ef\u8996\u5316\u3092\u30d7\u30ed\u30c3\u30c8\u3059\u308b\u3053\u3068\u304c\u3067\u304d\u307e\u3059\u3002<\/p>\n
\u3044\u304f\u3064\u304b\u306e\u4f8b\u3092\u898b\u3066\u307f\u307e\u3057\u3087\u3046\u3002<\/p>\n
\u5927\u9678\u5225\u306e\u4e16\u754c\u4eba\u53e3\u306e\u5206\u6790<\/h3>\n
\u30a2\u30b8\u30a2\u304c\u4e16\u754c\u3067\u6700\u3082\u4eba\u53e3\u306e\u591a\u3044\u5927\u9678\u3067\u3042\u308b\u3053\u3068\u306f\u65e2\u306b\u3054\u5b58\u77e5\u3067\u3057\u3087\u3046\u3002\u3057\u304b\u3057\u3001\u30a2\u30b8\u30a2\u304c\u4e16\u754c\u306e\u4eba\u53e3\u306b\u5360\u3081\u308b\u5272\u5408\u3092\u3054\u5b58\u77e5\u3067\u3057\u3087\u3046\u304b\uff1f\u3053\u306e\u60c5\u5831\u306f\u3001\u5927\u9678\u5225\u306b\u3059\u3079\u3066\u306e\u56fd\u306e\u4eba\u53e3\u3092\u5408\u8a08\u3057\u3001\u5404\u5927\u9678\u306e\u4eba\u53e3\u306e\u5272\u5408\u3092\u8868\u793a\u3059\u308b\u5186\u30b0\u30e9\u30d5\u3092\u4f7f\u3063\u3066\u8996\u899a\u5316\u3067\u304d\u307e\u3059\u3002<\/p>\n
\u4e0b\u306e\u30b9\u30af\u30ea\u30d7\u30c8\u306f\u3001\u4e16\u754c\u306e\u4eba\u53e3\u3092\u5927\u9678\u5225\u306b\u5186\u30b0\u30e9\u30d5\u306b\u3057\u305f\u3082\u306e\u3067\u3059\u3002<\/p>\n
\n
population_by_continent = population_data.groupby('Continent')['2022_Population'].sum()\n\nplt.figure(figsize=(6,6))\nplt.pie(population_by_continent,\n        labels=population_by_continent.index,\n        autopct='%1.1f%%', startangle=140)\nplt.title('World Population by Continent in 2022')\nplt.show()<\/code><\/pre>\n<\/div>\n
Output:<\/strong><\/p>\n
\"\"<\/a><\/p>\n
\u5468\u77e5\u306e\u3088\u3046\u306b\u3001\u30a2\u30b8\u30a2\u5927\u9678\u306f\u4e16\u754c\u4eba\u53e3\u306e\u7d0459\uff05\u3092\u5360\u3081\u3001\u30a2\u30d5\u30ea\u30ab\u3001\u30e8\u30fc\u30ed\u30c3\u30d1\u304c\u305d\u308c\u306b\u7d9a\u304d\u307e\u3059\u3002<\/p>\n
\u4e16\u754c\u4eba\u53e3\u6bd4\u7387\u4e0a\u4f4d10\u30ab\u56fd\u306e\u53ef\u8996\u5316<\/h3>\n
\u3082\u3046\u4e00\u3064\u306e\u8208\u5473\u6df1\u3044\u5206\u6790\u306f\u3001\u4eba\u53e3\u4e0a\u4f4d10\u30ab\u56fd\u304c\u4e16\u754c\u4eba\u53e3\u306b\u3069\u308c\u3060\u3051\u8ca2\u732e\u3057\u3066\u3044\u308b\u304b\u3068\u3044\u3046\u3053\u3068\u3067\u3059\u3002\u3053\u308c\u3092\u884c\u3046\u306b\u306f\u3001\u56fd\u3084\u5730\u57df\u3068\u3001\u305d\u308c\u3089\u306b\u5bfe\u5fdc\u3059\u308b\u4e16\u754c\u4eba\u53e3\u306e\u5272\u5408\u3092\u8868\u793a\u3059\u308b\u68d2\u30b0\u30e9\u30d5\u3092\u4f5c\u6210\u3059\u308b\u3053\u3068\u304c\u3067\u304d\u307e\u3059\u3002<\/p>\n
\n
top_countries = population_data.sort_values('World_Population_Percentage', ascending=False).head(10)\n\nplt.figure(figsize=(10, 8))\nplt.barh(top_countries['Country_Territory'],\n         top_countries['World_Population_Percentage'],\n         color='skyblue')\n\nplt.xlabel('% of world population')\nplt.title('Top 10 Countries by World Population %')\nplt.gca().invert_yaxis()  # Ensure the largest value is at the top\nplt.show()<\/code><\/pre>\n<\/div>\n
Output:<\/strong><\/p>\n
\"\"<\/a><\/p>\n
\u4e0a\u8a18\u306e\u51fa\u529b\u306f\u3001\u4e2d\u56fd\u3068\u30a4\u30f3\u30c9\u304c\u305d\u308c\u305e\u308c\u4e16\u754c\u4eba\u53e3\u306e\u7d0417\uff05\u3092\u5360\u3081\u3066\u3044\u308b\u3053\u3068\u3092\u793a\u3057\u3066\u3044\u307e\u3059\u3002\u3064\u307e\u308a\u3001\u5730\u7403\u4e0a\u306e3\u4eba\u306b1\u4eba\u306f\u30a4\u30f3\u30c9\u4eba\u304b\u4e2d\u56fd\u4eba\u3068\u3044\u3046\u3053\u3068\u306b\u306a\u308a\u307e\u3059\u3002<\/p>\n
\u4eba\u53e3\u5bc6\u5ea6\u306e\u9ad8\u3044\u56fd\u30fb\u5730\u57df\u30c8\u30c3\u30d710\u306e\u5206\u6790<\/h3>\n
\u7dcf\u4eba\u53e3\u306b\u52a0\u3048\u3001\u3082\u3046\u3072\u3068\u3064\u8208\u5473\u6df1\u3044\u6307\u6a19\u3068\u3057\u3066\u4eba\u53e3\u5bc6\u5ea6\u304c\u3042\u308a\u307e\u3059\u3002<\/p>\n
\u3053\u308c\u306b\u3088\u3063\u3066\u3001\u3042\u308b\u56fd\u3084\u5730\u57df\u304c\u3069\u308c\u3060\u3051\u6df7\u96d1\u3057\u3066\u3044\u308b\u304b\u304c\u308f\u304b\u308a\u307e\u3059\u3002\u3053\u306e\u60c5\u5831\u3092\u5f97\u308b\u306b\u306f\u3001\u6b21\u306e\u30b9\u30af\u30ea\u30d7\u30c8\u306e\u3088\u3046\u306b\u3001\u4eba\u53e3\u5bc6\u5ea6\u306e\u9ad8\u3044\u56fd\u3092\u68d2\u30b0\u30e9\u30d5\u3067\u8868\u793a\u3057\u3066\u304f\u3060\u3055\u3044\u3002<\/p>\n
\n
top_density_countries = population_data.nlargest(10, 'Density__per_km__')\n\nplt.figure(figsize=(8, 6))\nsns.barplot(x='Country_Territory', y='Density__per_km__', data=top_density_countries)\nplt.xticks(rotation=45)\nplt.title('Top 10 Countries\/Territories by Population Density per Sq-km')\nplt.xlabel('Country')\nplt.ylabel('Density (per km\u00b2)')\nplt.show()<\/code><\/pre>\n<\/div>\n
Output:<\/strong><\/p>\n
\"\"<\/a><\/p>\n
\u305d\u306e\u7d50\u679c\u30011\u5e73\u65b9\u30ad\u30ed\u30e1\u30fc\u30c8\u30eb\u5f53\u305f\u308a\u306e\u4eba\u53e3\u5bc6\u5ea6\u304c\u6700\u3082\u9ad8\u3044\u306e\u306f\u30de\u30ab\u30aa\uff08\u4e2d\u56fd\u9818\uff09\u3067\u3001\u6b21\u3044\u3067\u30e2\u30ca\u30b3\u3001\u30b7\u30f3\u30ac\u30dd\u30fc\u30eb\u3068\u306a\u3063\u3066\u3044\u307e\u3059\u3002<\/p>\n
\u30e8\u30fc\u30ed\u30c3\u30d1\u3068\u30a2\u30b8\u30a2\u3067\u6700\u3082\u4eba\u53e3\u5bc6\u5ea6\u306e\u9ad8\u3044\u56fd\u30c8\u30c3\u30d710<\/h3>\n
\u3055\u3089\u306b\u6398\u308a\u4e0b\u3052\u3066\u3001\u30a2\u30b8\u30a2\u3068\u30e8\u30fc\u30ed\u30c3\u30d1\u306e2\u3064\u306e\u68d2\u30b0\u30e9\u30d5\u3092\u30d7\u30ed\u30c3\u30c8\u3057\u3001\u3053\u308c\u3089\u306e\u5927\u9678\u3067\u4eba\u53e3\u5bc6\u5ea6\u304c\u6700\u3082\u9ad8\u3044\u4e0a\u4f4d10\u30ab\u56fd\uff0f\u5730\u57df\u3092\u8868\u793a\u3057\u3066\u307f\u307e\u3057\u3087\u3046\u3002<\/p>\n
\n
europe = population_data[population_data['Continent'] == 'Europe'].sort_values('Density__per_km__', ascending=False).head(10)\nsouth_america = population_data[population_data['Continent'] == 'South America'].sort_values('Density__per_km__', ascending=False).head(10)\n\nfig, axes = plt.subplots(1, 2, figsize=(14, 6))\n\naxes[0].barh(europe['Country_Territory'], europe['Density__per_km__'], color='skyblue')\naxes[0].set_title('Top 10 Densely Populated Countries\/Territories in Europe')\naxes[0].invert_yaxis()\n\naxes[1].barh(south_america['Country_Territory'], south_america['Density__per_km__'], color='lightgreen')\naxes[1].set_title('Top 10 Densely Populated Countries\/Territories in South America')\naxes[1].invert_yaxis()  \n\nplt.tight_layout()\nplt.show()<\/code><\/pre>\n<\/div>\n
Output:<\/strong><\/p>\n
\"\"<\/a><\/p>\n
\u3054\u89a7\u306e\u901a\u308a\u3001\u30e2\u30ca\u30b3\u306f\u30e8\u30fc\u30ed\u30c3\u30d1\u3067\u6700\u3082\u4eba\u53e3\u5bc6\u5ea6\u306e\u9ad8\u3044\u9818\u571f\u3067\u3042\u308a\u3001\u30a8\u30af\u30a2\u30c9\u30eb\u306f\u5357\u7c73\u3067\u6700\u3082\u4eba\u53e3\u5bc6\u5ea6\u306e\u9ad8\u3044\u56fd\u3067\u3059\u3002<\/p>\n
\u4eba\u53e3\u4e0a\u4f4d10\u30ab\u56fd\u306e1970\u5e74\u304b\u30892022\u5e74\u307e\u3067\u306e\u4eba\u53e3\u5897\u52a0\u7387<\/h3>\n
\u4e16\u754c\u4eba\u53e3\u306e\u3082\u3046\u3072\u3068\u3064\u306e\u8208\u5473\u6df1\u3044\u5074\u9762\u306f\u3001\u4eba\u53e3\u5897\u52a0\u7387\u3067\u3059\u3002\u3053\u308c\u306f\u3001\u4eba\u53e3\u3092\u30b3\u30f3\u30c8\u30ed\u30fc\u30eb\u3059\u308b\u5fc5\u8981\u304c\u3042\u308b\u56fd\u3092\u7279\u5b9a\u3059\u308b\u306e\u306b\u5f79\u7acb\u3061\u307e\u3059\u3002<\/p>\n
\u4f8b\u3048\u3070\u3001\u6b21\u306e\u30b9\u30af\u30ea\u30d7\u30c8\u306f\u3001\u6298\u308c\u7dda\u30b0\u30e9\u30d5\u3092\u4f7f\u3063\u3066\u30011970\u5e74\u304b\u30892022\u5e74\u307e\u3067\u306e\u4eba\u53e3\u4e0a\u4f4d10\u30ab\u56fd\u306e\u4eba\u53e3\u5897\u52a0\u7387\u3092\u793a\u3057\u3066\u3044\u307e\u3059\u3002<\/p>\n
\n
top_countries = population_data.sort_values(by='World_Population_Percentage', ascending=False).head(10)\n\ndata = pd.melt(top_countries,\n               id_vars=['Country_Territory'],\n               value_vars=['1970_Population', '1980_Population', '1990_Population', '2000_Population', '2010_Population', '2015_Population', '2020_Population', '2022_Population'],\n               var_name='Year',\n               value_name='Population')\n\ndata['Year'] = data['Year'].str.extract('(d+)').astype(int)\n\n\nplt.figure(figsize=(9, 6))\nsns.line plot(x='Year',\n             y='Population',\n             hue='Country_Territory',\n             data=data, marker='o')\n\nplt.title('Population Growth from 1970 to 2022 for Top 10 Most Populous Countries')\nplt.legend(title='Country')\nplt.show()<\/code><\/pre>\n<\/div>\n
Output:<\/strong><\/p>\n
\"\"<\/a><\/p>\n
\u4e0a\u8a18\u306e\u30a2\u30a6\u30c8\u30d7\u30c3\u30c8\u304b\u3089\u3001\u30a4\u30f3\u30c9\u3068\u4e2d\u56fd\u306e\u4eba\u53e3\u5dee\u306f1970\u5e74\u306b\u306f\u7d042\u5104\u4eba\u3060\u3063\u305f\u304c\u30012022\u5e74\u672b\u306b\u306f\u307b\u3068\u3093\u3069\u7121\u8996\u3067\u304d\u308b\u307e\u3067\u306b\u7e2e\u307e\u3063\u3066\u3044\u308b\u3053\u3068\u304c\u308f\u304b\u308a\u307e\u3059\u3002<\/p>\n
\u4ee5\u4e0a\u304c\u3001\u4e16\u754c\u4eba\u53e3\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u304b\u3089\u5f97\u3089\u308c\u305f\u8208\u5473\u6df1\u3044\u89b3\u5bdf\u7d50\u679c\u306e\u4e00\u90e8\u3067\u3059\u3002\u540c\u3058\u624b\u6cd5\u3092\u4f7f\u3063\u3066\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u3092\u3055\u3089\u306b\u5206\u6790\u3057\u3001\u8996\u899a\u5316\u3059\u308b\u3053\u3068\u3067\u3001\u3088\u308a\u30a8\u30ad\u30b5\u30a4\u30c6\u30a3\u30f3\u30b0\u306a\u60c5\u5831\u3092\u62bd\u51fa\u3059\u308b\u3053\u3068\u304c\u3067\u304d\u307e\u3059\u3002<\/p>\n
SQL UPDATE QUERY\u3092\u4f7f\u3063\u305fGridDB\u30c7\u30fc\u30bf\u306e\u66f4\u65b0<\/h2>\n
\u30c7\u30fc\u30bf\u306e\u633f\u5165\u3084\u53d6\u5f97\u306b\u52a0\u3048\u3066\u3001GridDB\u3067\u306f\u65e2\u5b58\u306e\u30c7\u30fc\u30bf\u3092\u7c21\u5358\u306b\u66f4\u65b0\u3059\u308b\u3053\u3068\u304c\u3067\u304d\u307e\u3059\u3002\u66f4\u65b0\u3059\u308b\u306b\u306f\u3001\u66f4\u65b0\u3057\u305f\u3044\u884c\u3068\u5217\u3092\u9078\u629e\u3057\u3001\u9078\u629e\u3057\u305f\u30bb\u30eb\u306b\u66f4\u65b0\u5024\u3092\u4ee3\u5165\u3057\u307e\u3059\u3002<\/p>\n
\u4f8b\u3048\u3070\u3001\u4ee5\u4e0b\u306e\u30b9\u30af\u30ea\u30d7\u30c8\u3092\u4f7f\u7528\u3057\u3066\u3001Country_Territory<\/code>\u30ab\u30e9\u30e0\u306b Afghanistan<\/code> \u304c\u542b\u307e\u308c\u3066\u3044\u308b\u3068\u304d\u306b Capital<\/code> \u30ab\u30e9\u30e0\u306e\u5024\u3092\u66f4\u65b0\u3059\u308b\u3053\u3068\u304c\u3067\u304d\u307e\u3059\u3002\u30b9\u30af\u30ea\u30d7\u30c8\u306f\u3001\u307e\u305a Country_Territory<\/code> \u30ab\u30e9\u30e0\u304c Afghanistan<\/code> \u3068\u7b49\u3057\u3044\u884c\u3092\u9078\u629e\u3057\u3001\u6b21\u306b Pandas \u30c7\u30fc\u30bf\u30d5\u30ec\u30fc\u30e0\u3092\u4f7f\u7528\u3057\u3066 Capital<\/code> \u30ab\u30e9\u30e0\u306e\u30a4\u30f3\u30c7\u30c3\u30af\u30b9\u3092\u9078\u629e\u3059\u308b\u3002\u6b21\u306b\u3001\u30b9\u30af\u30ea\u30d7\u30c8\u306f update()<\/code> \u30e1\u30bd\u30c3\u30c9\u3092\u4f7f\u3063\u3066\u30bb\u30eb\u306e\u5024\u3092\u66f4\u65b0\u3057\u307e\u3059\u3002\u6700\u5f8c\u306b\u3001\u30b3\u30f3\u30c6\u30ca\u306e commit()<\/code> \u95a2\u6570\u304c GridDB \u5185\u306e\u30b3\u30f3\u30c6\u30ca\u306b\u5909\u66f4\u3092\u30b3\u30df\u30c3\u30c8\u3057\u307e\u3059\u3002<\/p>\n
\n
population_container.set_auto_commit(False)\nquery = population_container.query(\"SELECT * WHERE Country_Territory = 'Afghanistan'\")\nrs = query.fetch(True)  \n\nif rs.has_next():\n    data = rs.next()\n    capital_index = population_data.columns.get_loc('Capital')\n\n    data[capital_index] = 'Kabool'\n    rs.update(data)\n\npopulation_container.commit()\nprint(\"Capital name updated successfully.\")\n\nquery = population_container.query(\"select *\")\nrs = query.fetch()\npopulation_data = rs.fetch_rows()\npopulation_data.head()<\/code><\/pre>\n<\/div>\n
Output:<\/strong><\/p>\n
\"\"<\/a><\/p>\n
SQL DELETE\u30af\u30a8\u30ea\u3092\u4f7f\u7528\u3057\u305fGridDB\u30c7\u30fc\u30bf\u306e\u524a\u9664<\/h2>\n
\u540c\u69d8\u306b\u3001GridDB \u30b3\u30f3\u30c6\u30ca\u304b\u3089\u884c\u3092\u524a\u9664\u3059\u308b\u306b\u306f\u3001fetch()<\/code> \u30af\u30a8\u30ea\u304c\u8fd4\u3059\u30aa\u30d6\u30b8\u30a7\u30af\u30c8\u306e remove()<\/code> \u30e1\u30bd\u30c3\u30c9\u3092\u4f7f\u7528\u3057\u307e\u3059\u3002\u4ee5\u4e0b\u306e\u30b9\u30af\u30ea\u30d7\u30c8\u3067\u306f\u3001Continent<\/code>\u30ab\u30e9\u30e0\u306b\u5024 Europe<\/code> \u304c\u542b\u307e\u308c\u308b\u884c\u3092\u3059\u3079\u3066\u524a\u9664\u3057\u3066\u3044\u307e\u3059\u3002<\/p>\n
\n
population_container.set_auto_commit(False)\n\nquery = population_container.query(\"SELECT * WHERE Continent = 'Europe'\")\n\nrs = query.fetch(True)\n\nwhile rs.has_next():\n    data = rs.next()\n    rs.remove()\n\n\npopulation_container.commit()\nprint(\"Records where Continent is 'Europe' have been successfully deleted.\")\n\nquery = population_container.query(\"select *\")\nrs = query.fetch()\npopulation_data = rs.fetch_rows()\npopulation_data.head()<\/code><\/pre>\n<\/div>\n
Output:<\/strong><\/p>\n
\"\"<\/a><\/p>\n
\u7d50\u8ad6<\/h2>\n
\u3053\u306e\u8a18\u4e8b\u3067\u306f\u3001GridDB\u3092\u30c7\u30fc\u30bf\u30d9\u30fc\u30b9\u3068\u3057\u3066\u3001Python\u3092\u4f7f\u3063\u3066\u4e16\u754c\u4eba\u53e3\u306e\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u3092\u5206\u6790\u3057\u307e\u3057\u305f\u3002\u3055\u3089\u306b\u3001Python\u3092GridDB\u30c7\u30fc\u30bf\u30d9\u30fc\u30b9\u306b\u63a5\u7d9a\u3059\u308b\u65b9\u6cd5\u3092\u5b66\u3073\u307e\u3057\u305f\u3002GridDB\u30b3\u30f3\u30c6\u30ca\u306bCSV\u30c7\u30fc\u30bf\u3092\u633f\u5165\u3057\u3001GridDB\u30b3\u30f3\u30c6\u30ca\u304b\u3089Pandas\u30c7\u30fc\u30bf\u30d5\u30ec\u30fc\u30e0\u306b\u30c7\u30fc\u30bf\u3092\u30d5\u30a7\u30c3\u30c1\u3059\u308b\u65b9\u6cd5\u3092\u5b66\u3073\u307e\u3057\u305f\u3002<\/p>\n
GridDB\u306f\u975e\u5e38\u306b\u30b9\u30b1\u30fc\u30e9\u30d6\u30eb\u306a\u30aa\u30fc\u30d7\u30f3\u30bd\u30fc\u30b9\u30c7\u30fc\u30bf\u30d9\u30fc\u30b9\u3067\u3042\u308a\u3001NoSQL\u3084\u5f93\u6765\u306eSQL\u30af\u30a8\u30ea\u3092\u4f7f\u7528\u3057\u3066\u3001\u3042\u3089\u3086\u308b\u7a2e\u985e\u306e\u30c7\u30fc\u30bf\u3092\u52b9\u7387\u7684\u306b\u683c\u7d0d\u30fb\u53d6\u5f97\u3059\u308b\u3053\u3068\u304c\u3067\u304d\u307e\u3059\u3002GridDB\u30c7\u30fc\u30bf\u30d9\u30fc\u30b9\u306f\u3001IoT\u3084\u6642\u7cfb\u5217\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u306e\u7ba1\u7406\u306b\u975e\u5e38\u306b\u304a\u52e7\u3081\u3067\u3059\u3002<\/p>\n
\u3053\u306e\u30d6\u30ed\u30b0\u306eJupyter Notebook\u306fGithub\u306b\u3042\u308a\u307e\u3059<\/a>\u3002<\/p>\n
GridDB\u306b\u95a2\u3059\u308b\u8cea\u554f\u3084\u554f\u3044\u5408\u308f\u305b\u304c\u3042\u308c\u3070\u3001Stackoverflow\u306e\u6295\u7a3f\u3092\u4f5c\u6210\u3057\u307e\u3059<\/a>\u3002\u30a8\u30f3\u30b8\u30cb\u30a2\u304c\u8fc5\u901f\u306b\u5bfe\u5fdc\u3067\u304d\u308b\u3088\u3046\u306b\u3001griddb<\/code>\u30bf\u30b0\u3092\u5fd8\u308c\u305a\u306b\u4f7f\u7528\u3057\u3066\u304f\u3060\u3055\u3044\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"
\u3053\u306e\u8a18\u4e8b\u3067\u306f\u3001GridDB \u3092\u30c7\u30fc\u30bf\u30d9\u30fc\u30b9\u3068\u3057\u3066\u4f7f\u7528\u3057\u3001Python \u3067\u4e16\u754c\u306e\u4eba\u53e3\u30c7\u30fc\u30bf\u3092\u53ef\u8996\u5316\u3059\u308b\u65b9\u6cd5\u3092\u5b66\u3073\u307e\u3059\u3002GridDB Python \u30af\u30e9\u30a4\u30a2\u30f3\u30c8\u3092\u4f7f\u7528\u3057\u3066\u3001GridDB \u30c7\u30fc\u30bf\u30d9\u30fc\u30b9\u306b\u63a5\u7d9a\u3057\u3001\u4e16\u754c\u4eba\u53e3\u306e CS […]<\/p>\n","protected":false},"author":41,"featured_media":49681,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[1005],"tags":[],"class_list":["post-50899","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-1005"],"acf":[],"yoast_head":"\nPython\u3067\u4e16\u754c\u306e\u4eba\u53e3\u30c7\u30fc\u30bf\u3092\u5206\u6790\u3059\u308b | GridDB: Open Source Time Series Database for IoT<\/title>\n<meta name=\"description\" content=\"\u3053\u306e\u8a18\u4e8b\u3067\u306f\u3001GridDB \u3092\u30c7\u30fc\u30bf\u30d9\u30fc\u30b9\u3068\u3057\u3066\u4f7f\u7528\u3057\u3001Python \u3067\u4e16\u754c\u306e\u4eba\u53e3\u30c7\u30fc\u30bf\u3092\u53ef\u8996\u5316\u3059\u308b\u65b9\u6cd5\u3092\u5b66\u3073\u307e\u3059\u3002GridDB 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:\/\/griddb.net\/ja\/\u672a\u5206\u985e\/analyzing-world-population-data-in-python\/\" \/>\n<meta property=\"og:locale\" content=\"ja_JP\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Python\u3067\u4e16\u754c\u306e\u4eba\u53e3\u30c7\u30fc\u30bf\u3092\u5206\u6790\u3059\u308b | GridDB: Open Source Time Series Database for IoT\" \/>\n<meta property=\"og:description\" content=\"\u3053\u306e\u8a18\u4e8b\u3067\u306f\u3001GridDB \u3092\u30c7\u30fc\u30bf\u30d9\u30fc\u30b9\u3068\u3057\u3066\u4f7f\u7528\u3057\u3001Python \u3067\u4e16\u754c\u306e\u4eba\u53e3\u30c7\u30fc\u30bf\u3092\u53ef\u8996\u5316\u3059\u308b\u65b9\u6cd5\u3092\u5b66\u3073\u307e\u3059\u3002GridDB Python\" \/>\n<meta property=\"og:url\" content=\"https:\/\/griddb.net\/ja\/\u672a\u5206\u985e\/analyzing-world-population-data-in-python\/\" \/>\n<meta property=\"og:site_name\" content=\"GridDB: Open Source Time Series Database for IoT\" \/>\n<meta property=\"article:publisher\" content=\"https:\/\/www.facebook.com\/griddbcommunity\/\" \/>\n<meta property=\"article:published_time\" content=\"2024-05-16T07:00:00+00:00\" \/>\n<meta property=\"article:modified_time\" content=\"2025-11-14T15:56:42+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/griddb.net\/wp-content\/uploads\/2024\/05\/img8-line-plot-of-population-growth-of-top-10-countries.png\" \/>\n\t<meta property=\"og:image:width\" content=\"958\" \/>\n\t<meta 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