{"id":50821,"date":"2022-09-29T00:00:00","date_gmt":"2022-09-29T07:00:00","guid":{"rendered":"https:\/\/griddb-linux-hte8hndjf8cka8ht.westus-01.azurewebsites.net\/%e6%9c%aa%e5%88%86%e9%a1%9e\/analysing-global-climate-change-using-python-and-griddb\/"},"modified":"2025-11-14T07:55:41","modified_gmt":"2025-11-14T15:55:41","slug":"analysing-global-climate-change-using-python-and-griddb","status":"publish","type":"post","link":"https:\/\/griddb.net\/ja\/%e6%9c%aa%e5%88%86%e9%a1%9e\/analysing-global-climate-change-using-python-and-griddb\/","title":{"rendered":"Python\u3068GridDB\u3092\u7528\u3044\u305f\u5730\u7403\u6c17\u5019\u5909\u52d5\u306e\u89e3\u6790"},"content":{"rendered":"<p>\u6c17\u5019\u5909\u52d5\u306e\u5f71\u97ff\u3068\u305d\u308c\u306b\u4f34\u3046\u5730\u57df\u30fb\u5730\u65b9\u30b9\u30b1\u30fc\u30eb\u3067\u306e\u7570\u5e38\u6c17\u8c61\u3092\u7406\u89e3\u3059\u308b\u3053\u3068\u306f\u3001\u5b9f\u73fe\u53ef\u80fd\u306a\u9069\u5fdc\u7b56\u3092\u8a08\u753b\u30fb\u958b\u767a\u3059\u308b\u4e0a\u3067\u975e\u5e38\u306b\u91cd\u8981\u3067\u3059\u3002<\/p>\n<p>\u6c17\u5019\u5909\u52d5\u306f\u3001\u9593\u9055\u3044\u306a\u304f\u73fe\u4ee3\u306b\u304a\u3051\u308b\u4eba\u985e\u3078\u306e\u6700\u3082\u6df1\u523b\u306a\u8105\u5a01\u3067\u3059\u3002IPCC\u306b\u3088\u308c\u3070\u3001\u6c17\u5019\u5909\u52d5\u306b\u3088\u308b\u60aa\u5f71\u97ff\u3092\u7de9\u548c\u3059\u308b\u305f\u3081\u306b\u306f\u3001\u4e16\u754c\u306e\u5e73\u5747\u6c17\u6e29\u3092\u7523\u696d\u9769\u547d\u4ee5\u524d\u306e\u6c34\u6e96\u304b\u30891.5\u2103\u4ee5\u5185\u306b\u6291\u3048\u308b\u5fc5\u8981\u304c\u3042\u308b\u3068\u3055\u308c\u3066\u3044\u307e\u3059\u3002<\/p>\n<p>\u4eca\u56de\u306f\u3001Python\u3068GridDB\u306e\u529b\u3092\u4f7f\u3063\u3066\u3001\u6c17\u6e29\u5909\u52d5\u306e\u30de\u30c3\u30d7\u30c1\u30e3\u30fc\u30c8\u3084\u30a2\u30cb\u30e1\u30fc\u30b7\u30e7\u30f3\u3092\u4f5c\u6210\u3059\u308b\u65b9\u6cd5\u306b\u3064\u3044\u3066\u5206\u6790\u3057\u307e\u3059\u3002<\/p>\n<p>\u30d5\u30eb\u30bd\u30fc\u30b9\u30b3\u30fc\u30c9\u3068jupyter\u30d5\u30a1\u30a4\u30eb\u3078\u306e\u30ea\u30f3\u30af\u3067\u3059\u3002<br \/>\n<a href=\"https:\/\/github.com\/griddbnet\/Blogs\/tree\/analyzing-global-climate-change\">https:\/\/github.com\/griddbnet\/Blogs\/tree\/analyzing-global-climate-change<\/a><\/p>\n<p>\u30c1\u30e5\u30fc\u30c8\u30ea\u30a2\u30eb\u306e\u6982\u8981\u306f\u4ee5\u4e0b\u306e\u901a\u308a\u3067\u3059\u3002<\/p>\n<ol>\n<li>\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u6982\u8981<\/li>\n<li>\u5fc5\u8981\u306a\u30e9\u30a4\u30d6\u30e9\u30ea\u306e\u30a4\u30f3\u30dd\u30fc\u30c8<\/li>\n<li>\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u306e\u8aad\u307f\u8fbc\u307f<\/li>\n<li>\u30c7\u30fc\u30bf\u306e\u30af\u30ea\u30fc\u30cb\u30f3\u30b0\u3068\u524d\u51e6\u7406<\/li>\n<li>\u30c7\u30fc\u30bf\u306e\u53ef\u8996\u5316\u306b\u3088\u308b\u5206\u6790<\/li>\n<li>\u307e\u3068\u3081<\/li>\n<\/ol>\n<h2>\u524d\u63d0\u6761\u4ef6\u3068\u74b0\u5883\u8a2d\u5b9a<\/h2>\n<p>\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 &#8211; 3.8.5) \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<ol>\n<li>Pandas<\/li>\n<li>NumPy<\/li>\n<li>plotly<\/li>\n<li>Matplotlib<\/li>\n<li>Seaborn<\/li>\n<li>griddb_python<\/li>\n<\/ol>\n<p>\u3053\u308c\u3089\u306e\u30d1\u30c3\u30b1\u30fc\u30b8\u306f Conda \u306e\u4eee\u60f3\u74b0\u5883\u306b <code>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 <code>pip install package-name<\/code> \u3067\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb\u3067\u304d\u307e\u3059\u3002<\/p>\n<h3>GridDB\u306e\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb<\/h3>\n<p>\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<ol>\n<li><a href=\"https:\/\/github.com\/griddb\/c_client\">GridDB C\u30af\u30e9\u30a4\u30a2\u30f3\u30c8<\/a><\/li>\n<li>SWIG (Simplified Wrapper and Interface Generator)<\/li>\n<li><a href=\"https:\/\/github.com\/griddb\/python_client\">GridDB Python\u30af\u30e9\u30a4\u30a2\u30f3\u30c8<\/a><\/li>\n<\/ol>\n<h2>\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u6982\u8981<\/h2>\n<p>\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u306f\u30ed\u30fc\u30ec\u30f3\u30b9\u30fb\u30d0\u30fc\u30af\u30ec\u30fc\u56fd\u7acb\u7814\u7a76\u6240\u306b\u6240\u5c5e\u3059\u308b\u30d0\u30fc\u30af\u30ec\u30fc\u30fb\u30a2\u30fc\u30b9\u306b\u3088\u3063\u3066\u307e\u3068\u3081\u3089\u308c\u305f\u3082\u306e\u3067\u3001\u4e16\u754c\u4e2d\u306e\u6c17\u6e29\u30c7\u30fc\u30bf\u3092\u542b\u3093\u3067\u3044\u307e\u3059\u3002\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u306f\u6570\u500b\u306e\u30d5\u30a1\u30a4\u30eb\u306b\u5206\u304b\u308c\u3066\u3044\u307e\u3059\u304c\u3001\u5168\u4e16\u754c\u306e\u9678\u4e0a\u3068\u6d77\u9678\u306e\u6c17\u6e29 (GlobalTemperatures.csv) \u306b\u542b\u307e\u308c\u308b\u3044\u304f\u3064\u304b\u306e\u30c7\u30fc\u30bf\u306b\u3064\u3044\u3066\u7d39\u4ecb\u3057\u307e\u3059\u3002<\/p>\n<ul>\n<li>Date: \u9678\u4e0a\u5e73\u5747\u6c17\u6e29\u306f1750\u5e74\u304b\u3089\u3001\u9678\u4e0a\u6700\u9ad8\u30fb\u6700\u4f4e\u6c17\u6e29\u3068\u5168\u7403\u6d77\u6d0b\u30fb\u9678\u4e0a\u6c17\u6e29\u306f1850\u5e74\u304b\u3089\u958b\u59cb<\/li>\n<li>LandAverageTemperature: \u4e16\u754c\u5e73\u5747\u9678\u4e0a\u6c17\u6e29\uff08\u5358\u4f4d\uff1a\u6442\u6c0f\uff09<\/li>\n<\/ul>\n<p><a href=\"https:\/\/www.kaggle.com\/datasets\/berkeleyearth\/climate-change-earth-surface-temperature-data\">https:\/\/www.kaggle.com\/datasets\/berkeleyearth\/climate-change-earth-surface-temperature-data<\/a><\/p>\n<h2>\u5fc5\u8981\u306a\u30e9\u30a4\u30d6\u30e9\u30ea\u306e\u30a4\u30f3\u30dd\u30fc\u30c8<\/h2>\n<div class=\"clipboard\">\n<pre><code class=\"language-python\"># import griddb_python as griddb\nimport numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\nimport plotly\nimport plotly.graph_objs as go\nimport plotly.tools as tls\nimport plotly.express as px\nimport plotly.graph_objs as go\nimport seaborn as sns\nimport time\nimport warnings\nwarnings.filterwarnings('ignore')\n%matplotlib inline<\/code><\/pre>\n<\/div>\n<h2>\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u306e\u8aad\u307f\u8fbc\u307f<\/h2>\n<p>\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<h3>GridDB\u3092\u5229\u7528\u3059\u308b<\/h3>\n<p>\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\u3001\u9ad8\u3044\u62e1\u5f35\u6027\u3092\u6301\u3061\u3001\u9ad8\u6027\u80fd\u306e\u305f\u3081\u306b\u30c1\u30e5\u30fc\u30cb\u30f3\u30b0\u3055\u308c\u3001\u9ad8\u3044\u4fe1\u983c\u6027\u3092\u78ba\u4fdd\u3057\u305f\u6c4e\u7528\u6027\u306e\u9ad8\u3044\u30c7\u30fc\u30bf\u30b9\u30c8\u30a2\u306e\u63d0\u4f9b\u306b\u3042\u308a\u307e\u3059\u3002<\/p>\n<p>\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\u3001<a href=\"https:\/\/griddb.net\/ja\/blog\/using-pandas-dataframes-with-griddb\/\">GridDB\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<p>\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<div class=\"clipboard\">\n<pre><code class=\"language-python\">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(\"GlobalTemperatures\",\n                    [[\"dt\", griddb.Type.TIMESTAMP],[\"LandAverageTemperature\", griddb.Type.DOUBLE],\n                     [\"LandAverageTemperatureUncertainty\", griddb.Type.DOUBLE],\n                     [\"LandMaxTemperature\", griddb.Type.DOUBLE],\n                     [\"LandMaxTemperatureUncertainty\", griddb.Type.DOUBLE],\n                     [\"LandMinTemperature\", griddb.Type.DOUBLE],\n                     [\"LandMinTemperatureUncertainty\", griddb.Type.DOUBLE],\n                     [\"LandAndOceanAverageTemperature\", griddb.Type.DOUBLE],\n                     [\"LandAndOceanAverageTemperatureUncertainty\", griddb.Type.DOUBLE],\n                     , True)\n                     \n    cont = gridstore.put_container(info) \n    data = pd.read_csv(\"GlobalTemperatures.csv\")\n    #Add data\n    for i in range(len(data)):\n        ret = cont.put(data.iloc[i, :])\n        print(\"Data added successfully\")\n                     \n                     \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(\"GlobalLandTemperaturesByCity\",\n                    [[\"dt\", griddb.Type.TIMESTAMP],[\"AverageTemperature\", griddb.Type.DOUBLE],\n                     [\"AverageTemperatureUncertainty\", griddb.Type.DOUBLE],\n                     [\"City\", griddb.Type.STRING],[\"Country\", griddb.Type.STRING], \n                     [\"Latitude\", griddb.Type.STRING], [\"Longitude\", griddb.Type.STRING],True)\n                     \n    cont = gridstore.put_container(info) \n    data = pd.read_csv(\"GlobalLandTemperaturesByCity.csv\")\n    #Add data\n    for i in range(len(data)):\n        ret = cont.put(data.iloc[i, :])\n        print(\"Data added successfully\")\n                     \n                     \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(\"GlobalLandTemperaturesByState\",\n                    [[\"dt\", griddb.Type.TIMESTAMP],[\"AverageTemperature\", griddb.Type.DOUBLE],[\"AverageTemperatureUncertainty\", griddb.Type.DOUBLE],\n                     [\"State\", griddb.Type.STRING],[\"Country\", griddb.Type.STRING], True)\n    cont = gridstore.put_container(info) \n    data = pd.read_csv(\"GlobalLandTemperaturesByState.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<pre><code>Data added successfully\n<\/code><\/pre>\n<p>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<div class=\"clipboard\">\n<pre><code class=\"language-python\">sql_statement1 = ('SELECT * FROM GlobalTemperatures.csv')\ndf1 = pd.read_sql_query(sql_statement1, cont)\n\nsql_statement2 = ('SELECT * FROM GlobalLandTemperaturesByCity.csv')\ndf2 = pd.read_sql_query(sql_statement2, cont)\n\nsql_statement3 = ('SELECT * FROM GlobalLandTemperaturesByState.csv')\ndf3 = pd.read_sql_query(sql_statement3, cont)<\/code><\/pre>\n<\/div>\n<p>\u5909\u6570 <code>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\u3002<code>credit_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\u30eb<a href=\"https:\/\/griddb.net\/ja\/blog\/using-pandas-dataframes-with-griddb\/\">GridDB\u3078\u306e\u8aad\u307f\u66f8\u304d<\/a>\u3092\u53c2\u7167\u3057\u3066\u304f\u3060\u3055\u3044\u3002<\/p>\n<p>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<h3>pandas\u306eread_csv\u3092\u4f7f\u7528\u3059\u308b<\/h3>\n<p>\u307e\u305f\u3001Pandas\u306e <code>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<div class=\"clipboard\">\n<pre><code class=\"language-python\">global_temp_country = pd.read_csv('GlobalLandTemperaturesByCity.csv')<\/code><\/pre>\n<\/div>\n<div class=\"clipboard\">\n<pre><code class=\"language-python\">global_temp_country.head()<\/code><\/pre>\n<\/div>\n<div style=\"overflow-x: scroll;overflow-y: hidden;\">\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }<\/p>\n<p>    .dataframe tbody tr th {\n        vertical-align: top;\n    }<\/p>\n<p>    .dataframe thead th {\n        text-align: right;\n    }\n  <\/style>\n<table border=\"1\" class=\"dataframe\">\n<thead>\n<tr style=\"text-align: right;\">\n<th>\n        <\/th>\n<th>\n          dt\n        <\/th>\n<th>\n          AverageTemperature\n        <\/th>\n<th>\n          AverageTemperatureUncertainty\n        <\/th>\n<th>\n          City\n        <\/th>\n<th>\n          Country\n        <\/th>\n<th>\n          Latitude\n        <\/th>\n<th>\n          Longitude\n        <\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<th>\n          0\n        <\/th>\n<td>\n          1743-11-01\n        <\/td>\n<td>\n          6.068\n        <\/td>\n<td>\n          1.737\n        <\/td>\n<td>\n          \u00c5rhus\n        <\/td>\n<td>\n          Denmark\n        <\/td>\n<td>\n          57.05N\n        <\/td>\n<td>\n          10.33E\n        <\/td>\n<\/tr>\n<tr>\n<th>\n          1\n        <\/th>\n<td>\n          1743-12-01\n        <\/td>\n<td>\n          NaN\n        <\/td>\n<td>\n          NaN\n        <\/td>\n<td>\n          \u00c5rhus\n        <\/td>\n<td>\n          Denmark\n        <\/td>\n<td>\n          57.05N\n        <\/td>\n<td>\n          10.33E\n        <\/td>\n<\/tr>\n<tr>\n<th>\n          2\n        <\/th>\n<td>\n          1744-01-01\n        <\/td>\n<td>\n          NaN\n        <\/td>\n<td>\n          NaN\n        <\/td>\n<td>\n          \u00c5rhus\n        <\/td>\n<td>\n          Denmark\n        <\/td>\n<td>\n          57.05N\n        <\/td>\n<td>\n          10.33E\n        <\/td>\n<\/tr>\n<tr>\n<th>\n          3\n        <\/th>\n<td>\n          1744-02-01\n        <\/td>\n<td>\n          NaN\n        <\/td>\n<td>\n          NaN\n        <\/td>\n<td>\n          \u00c5rhus\n        <\/td>\n<td>\n          Denmark\n        <\/td>\n<td>\n          57.05N\n        <\/td>\n<td>\n          10.33E\n        <\/td>\n<\/tr>\n<tr>\n<th>\n          4\n        <\/th>\n<td>\n          1744-03-01\n        <\/td>\n<td>\n          NaN\n        <\/td>\n<td>\n          NaN\n        <\/td>\n<td>\n          \u00c5rhus\n        <\/td>\n<td>\n          Denmark\n        <\/td>\n<td>\n          57.05N\n        <\/td>\n<td>\n          10.33E\n        <\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n<div class=\"clipboard\">\n<pre><code class=\"language-python\">global_temp=pd.read_csv('GlobalTemperatures.csv')<\/code><\/pre>\n<\/div>\n<div class=\"clipboard\">\n<pre><code class=\"language-python\">global_temp.head()<\/code><\/pre>\n<\/div>\n<div style=\"overflow-x: scroll;overflow-y: hidden;\">\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }<\/p>\n<p>    .dataframe tbody tr th {\n        vertical-align: top;\n    }<\/p>\n<p>    .dataframe thead th {\n        text-align: right;\n    }\n  <\/style>\n<table border=\"1\" class=\"dataframe\">\n<thead>\n<tr style=\"text-align: right;\">\n<th>\n        <\/th>\n<th>\n          dt\n        <\/th>\n<th>\n          LandAverageTemperature\n        <\/th>\n<th>\n          LandAverageTemperatureUncertainty\n        <\/th>\n<th>\n          LandMaxTemperature\n        <\/th>\n<th>\n          LandMaxTemperatureUncertainty\n        <\/th>\n<th>\n          LandMinTemperature\n        <\/th>\n<th>\n          LandMinTemperatureUncertainty\n        <\/th>\n<th>\n          LandAndOceanAverageTemperature\n        <\/th>\n<th>\n          LandAndOceanAverageTemperatureUncertainty\n        <\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<th>\n          0\n        <\/th>\n<td>\n          1750-01-01\n        <\/td>\n<td>\n          3.034\n        <\/td>\n<td>\n          3.574\n        <\/td>\n<td>\n          NaN\n        <\/td>\n<td>\n          NaN\n        <\/td>\n<td>\n          NaN\n        <\/td>\n<td>\n          NaN\n        <\/td>\n<td>\n          NaN\n        <\/td>\n<td>\n          NaN\n        <\/td>\n<\/tr>\n<tr>\n<th>\n          1\n        <\/th>\n<td>\n          1750-02-01\n        <\/td>\n<td>\n          3.083\n        <\/td>\n<td>\n          3.702\n        <\/td>\n<td>\n          NaN\n        <\/td>\n<td>\n          NaN\n        <\/td>\n<td>\n          NaN\n        <\/td>\n<td>\n          NaN\n        <\/td>\n<td>\n          NaN\n        <\/td>\n<td>\n          NaN\n        <\/td>\n<\/tr>\n<tr>\n<th>\n          2\n        <\/th>\n<td>\n          1750-03-01\n        <\/td>\n<td>\n          5.626\n        <\/td>\n<td>\n          3.076\n        <\/td>\n<td>\n          NaN\n        <\/td>\n<td>\n          NaN\n        <\/td>\n<td>\n          NaN\n        <\/td>\n<td>\n          NaN\n        <\/td>\n<td>\n          NaN\n        <\/td>\n<td>\n          NaN\n        <\/td>\n<\/tr>\n<tr>\n<th>\n          3\n        <\/th>\n<td>\n          1750-04-01\n        <\/td>\n<td>\n          8.490\n        <\/td>\n<td>\n          2.451\n        <\/td>\n<td>\n          NaN\n        <\/td>\n<td>\n          NaN\n        <\/td>\n<td>\n          NaN\n        <\/td>\n<td>\n          NaN\n        <\/td>\n<td>\n          NaN\n        <\/td>\n<td>\n          NaN\n        <\/td>\n<\/tr>\n<tr>\n<th>\n          4\n        <\/th>\n<td>\n          1750-05-01\n        <\/td>\n<td>\n          11.573\n        <\/td>\n<td>\n          2.072\n        <\/td>\n<td>\n          NaN\n        <\/td>\n<td>\n          NaN\n        <\/td>\n<td>\n          NaN\n        <\/td>\n<td>\n          NaN\n        <\/td>\n<td>\n          NaN\n        <\/td>\n<td>\n          NaN\n        <\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n<div class=\"clipboard\">\n<pre><code class=\"language-python\">GlobalTempState = pd.read_csv('GlobalLandTemperaturesByState.csv') <\/code><\/pre>\n<\/div>\n<div class=\"clipboard\">\n<pre><code class=\"language-python\">GlobalTempState.head()<\/code><\/pre>\n<\/div>\n<div style=\"overflow-x: scroll;overflow-y: hidden;\">\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }<\/p>\n<p>    .dataframe tbody tr th {\n        vertical-align: top;\n    }<\/p>\n<p>    .dataframe thead th {\n        text-align: right;\n    }\n  <\/style>\n<table border=\"1\" class=\"dataframe\">\n<thead>\n<tr style=\"text-align: right;\">\n<th>\n        <\/th>\n<th>\n          dt\n        <\/th>\n<th>\n          AverageTemperature\n        <\/th>\n<th>\n          AverageTemperatureUncertainty\n        <\/th>\n<th>\n          State\n        <\/th>\n<th>\n          Country\n        <\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<th>\n          0\n        <\/th>\n<td>\n          1855-05-01\n        <\/td>\n<td>\n          25.544\n        <\/td>\n<td>\n          1.171\n        <\/td>\n<td>\n          Acre\n        <\/td>\n<td>\n          Brazil\n        <\/td>\n<\/tr>\n<tr>\n<th>\n          1\n        <\/th>\n<td>\n          1855-06-01\n        <\/td>\n<td>\n          24.228\n        <\/td>\n<td>\n          1.103\n        <\/td>\n<td>\n          Acre\n        <\/td>\n<td>\n          Brazil\n        <\/td>\n<\/tr>\n<tr>\n<th>\n          2\n        <\/th>\n<td>\n          1855-07-01\n        <\/td>\n<td>\n          24.371\n        <\/td>\n<td>\n          1.044\n        <\/td>\n<td>\n          Acre\n        <\/td>\n<td>\n          Brazil\n        <\/td>\n<\/tr>\n<tr>\n<th>\n          3\n        <\/th>\n<td>\n          1855-08-01\n        <\/td>\n<td>\n          25.427\n        <\/td>\n<td>\n          1.073\n        <\/td>\n<td>\n          Acre\n        <\/td>\n<td>\n          Brazil\n        <\/td>\n<\/tr>\n<tr>\n<th>\n          4\n        <\/th>\n<td>\n          1855-09-01\n        <\/td>\n<td>\n          25.675\n        <\/td>\n<td>\n          1.014\n        <\/td>\n<td>\n          Acre\n        <\/td>\n<td>\n          Brazil\n        <\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n<h2>\u30c7\u30fc\u30bf\u30af\u30ea\u30fc\u30cb\u30f3\u30b0\u3068\u524d\u51e6\u7406<\/h2>\n<div class=\"clipboard\">\n<pre><code class=\"language-python\">global_temp_country.isna().sum()<\/code><\/pre>\n<\/div>\n<pre><code>dt                                    0\nAverageTemperature               364130\nAverageTemperatureUncertainty    364130\nCity                                  0\nCountry                               0\nLatitude                              0\nLongitude                             0\ndtype: int64\n<\/code><\/pre>\n<div class=\"clipboard\">\n<pre><code class=\"language-python\">global_temp_country.dropna(axis='index',how='any',subset=['AverageTemperature'],inplace=True)<\/code><\/pre>\n<\/div>\n<div class=\"clipboard\">\n<pre><code class=\"language-python\">def fetch_year(date):\n    return date.split('-')[0]\nglobal_temp['years']=global_temp['dt'].apply(fetch_year)\nglobal_temp.head()<\/code><\/pre>\n<\/div>\n<div style=\"overflow-x: scroll;overflow-y: hidden;\">\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }<\/p>\n<p>    .dataframe tbody tr th {\n        vertical-align: top;\n    }<\/p>\n<p>    .dataframe thead th {\n        text-align: right;\n    }\n  <\/style>\n<table border=\"1\" class=\"dataframe\">\n<thead>\n<tr style=\"text-align: right;\">\n<th>\n        <\/th>\n<th>\n          dt\n        <\/th>\n<th>\n          LandAverageTemperature\n        <\/th>\n<th>\n          LandAverageTemperatureUncertainty\n        <\/th>\n<th>\n          LandMaxTemperature\n        <\/th>\n<th>\n          LandMaxTemperatureUncertainty\n        <\/th>\n<th>\n          LandMinTemperature\n        <\/th>\n<th>\n          LandMinTemperatureUncertainty\n        <\/th>\n<th>\n          LandAndOceanAverageTemperature\n        <\/th>\n<th>\n          LandAndOceanAverageTemperatureUncertainty\n        <\/th>\n<th>\n          years\n        <\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<th>\n          0\n        <\/th>\n<td>\n          1750-01-01\n        <\/td>\n<td>\n          3.034\n        <\/td>\n<td>\n          3.574\n        <\/td>\n<td>\n          NaN\n        <\/td>\n<td>\n          NaN\n        <\/td>\n<td>\n          NaN\n        <\/td>\n<td>\n          NaN\n        <\/td>\n<td>\n          NaN\n        <\/td>\n<td>\n          NaN\n        <\/td>\n<td>\n          1750\n        <\/td>\n<\/tr>\n<tr>\n<th>\n          1\n        <\/th>\n<td>\n          1750-02-01\n        <\/td>\n<td>\n          3.083\n        <\/td>\n<td>\n          3.702\n        <\/td>\n<td>\n          NaN\n        <\/td>\n<td>\n          NaN\n        <\/td>\n<td>\n          NaN\n        <\/td>\n<td>\n          NaN\n        <\/td>\n<td>\n          NaN\n        <\/td>\n<td>\n          NaN\n        <\/td>\n<td>\n          1750\n        <\/td>\n<\/tr>\n<tr>\n<th>\n          2\n        <\/th>\n<td>\n          1750-03-01\n        <\/td>\n<td>\n          5.626\n        <\/td>\n<td>\n          3.076\n        <\/td>\n<td>\n          NaN\n        <\/td>\n<td>\n          NaN\n        <\/td>\n<td>\n          NaN\n        <\/td>\n<td>\n          NaN\n        <\/td>\n<td>\n          NaN\n        <\/td>\n<td>\n          NaN\n        <\/td>\n<td>\n          1750\n        <\/td>\n<\/tr>\n<tr>\n<th>\n          3\n        <\/th>\n<td>\n          1750-04-01\n        <\/td>\n<td>\n          8.490\n        <\/td>\n<td>\n          2.451\n        <\/td>\n<td>\n          NaN\n        <\/td>\n<td>\n          NaN\n        <\/td>\n<td>\n          NaN\n        <\/td>\n<td>\n          NaN\n        <\/td>\n<td>\n          NaN\n        <\/td>\n<td>\n          NaN\n        <\/td>\n<td>\n          1750\n        <\/td>\n<\/tr>\n<tr>\n<th>\n          4\n        <\/th>\n<td>\n          1750-05-01\n        <\/td>\n<td>\n          11.573\n        <\/td>\n<td>\n          2.072\n        <\/td>\n<td>\n          NaN\n        <\/td>\n<td>\n          NaN\n        <\/td>\n<td>\n          NaN\n        <\/td>\n<td>\n          NaN\n        <\/td>\n<td>\n          NaN\n        <\/td>\n<td>\n          NaN\n        <\/td>\n<td>\n          1750\n        <\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n<h2>\u30c7\u30fc\u30bf\u306e\u53ef\u8996\u5316\u306b\u3088\u308b\u5206\u6790<\/h2>\n<p>\u5404\u56fd\u306e\u5e73\u5747\u6c17\u6e29\u3092\u8a08\u7b97\u3057\u3066\u307f\u307e\u3057\u3087\u3046\u3002<\/p>\n<div class=\"clipboard\">\n<pre><code class=\"language-python\">avg_temp=global_temp_country.groupby(['Country'])['AverageTemperature'].mean().to_frame().reset_index()\navg_temp<\/code><\/pre>\n<\/div>\n<div style=\"overflow-x: scroll;overflow-y: hidden;\">\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }<\/p>\n<p>    .dataframe tbody tr th {\n        vertical-align: top;\n    }<\/p>\n<p>    .dataframe thead th {\n        text-align: right;\n    }\n  <\/style>\n<table border=\"1\" class=\"dataframe\">\n<thead>\n<tr style=\"text-align: right;\">\n<th>\n        <\/th>\n<th>\n          Country\n        <\/th>\n<th>\n          AverageTemperature\n        <\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<th>\n          0\n        <\/th>\n<td>\n          Afghanistan\n        <\/td>\n<td>\n          13.816497\n        <\/td>\n<\/tr>\n<tr>\n<th>\n          1\n        <\/th>\n<td>\n          Albania\n        <\/td>\n<td>\n          15.525828\n        <\/td>\n<\/tr>\n<tr>\n<th>\n          2\n        <\/th>\n<td>\n          Algeria\n        <\/td>\n<td>\n          17.763206\n        <\/td>\n<\/tr>\n<tr>\n<th>\n          3\n        <\/th>\n<td>\n          Angola\n        <\/td>\n<td>\n          21.759716\n        <\/td>\n<\/tr>\n<tr>\n<th>\n          4\n        <\/th>\n<td>\n          Argentina\n        <\/td>\n<td>\n          16.999216\n        <\/td>\n<\/tr>\n<tr>\n<th>\n          &#8230;\n        <\/th>\n<td>\n          &#8230;\n        <\/td>\n<td>\n          &#8230;\n        <\/td>\n<\/tr>\n<tr>\n<th>\n          154\n        <\/th>\n<td>\n          Venezuela\n        <\/td>\n<td>\n          25.482422\n        <\/td>\n<\/tr>\n<tr>\n<th>\n          155\n        <\/th>\n<td>\n          Vietnam\n        <\/td>\n<td>\n          24.846825\n        <\/td>\n<\/tr>\n<tr>\n<th>\n          156\n        <\/th>\n<td>\n          Yemen\n        <\/td>\n<td>\n          25.768408\n        <\/td>\n<\/tr>\n<tr>\n<th>\n          157\n        <\/th>\n<td>\n          Zambia\n        <\/td>\n<td>\n          20.937623\n        <\/td>\n<\/tr>\n<tr>\n<th>\n          158\n        <\/th>\n<td>\n          Zimbabwe\n        <\/td>\n<td>\n          19.822971\n        <\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>\n    159 rows \u00d7 2 columns\n  <\/p>\n<\/div>\n<div class=\"clipboard\">\n<pre><code class=\"language-python\">fig=px.choropleth(avg_temp,locations='Country',locationmode='country names',color='AverageTemperature')\nfig.update_layout(title='Choropleth map of average temperature')\nfig.show()<\/code><\/pre>\n<\/div>\n<p><a href=\"https:\/\/griddb.net\/wp-content\/uploads\/2022\/08\/Screenshot_16.png\"><img fetchpriority=\"high\" decoding=\"async\" src=\"https:\/\/griddb.net\/wp-content\/uploads\/2022\/08\/Screenshot_16.png\" alt=\"\" width=\"813\" height=\"542\" class=\"aligncenter size-full wp-image-28666\" srcset=\"\/wp-content\/uploads\/2022\/08\/Screenshot_16.png 813w, \/wp-content\/uploads\/2022\/08\/Screenshot_16-300x200.png 300w, \/wp-content\/uploads\/2022\/08\/Screenshot_16-768x512.png 768w, \/wp-content\/uploads\/2022\/08\/Screenshot_16-600x400.png 600w\" sizes=\"(max-width: 813px) 100vw, 813px\" \/><\/a><\/p>\n<div class=\"clipboard\">\n<pre><code class=\"language-python\"># The average temperature and Horizontal Bar sort by countries\n\nsns.barplot(x=avg_temp.sort_values(by='AverageTemperature',ascending=False)['AverageTemperature'][0:20],y=avg_temp.sort_values(by='AverageTemperature',ascending=False)['Country'][0:20])<\/code><\/pre>\n<\/div>\n<pre><code>&lt;AxesSubplot:xlabel='AverageTemperature', ylabel='Country'&gt;\n<\/code><\/pre>\n<p><a href=\"https:\/\/griddb.net\/wp-content\/uploads\/2022\/08\/output_33_1.png\"><img decoding=\"async\" src=\"https:\/\/griddb.net\/wp-content\/uploads\/2022\/08\/output_33_1.png\" alt=\"\" width=\"477\" height=\"262\" class=\"aligncenter size-full wp-image-28660\" srcset=\"\/wp-content\/uploads\/2022\/08\/output_33_1.png 477w, \/wp-content\/uploads\/2022\/08\/output_33_1-300x165.png 300w\" sizes=\"(max-width: 477px) 100vw, 477px\" \/><\/a><\/p>\n<h3>\u5730\u7403\u6e29\u6696\u5316\u306f\u9032\u3093\u3067\u3044\u308b\u306e\u3067\u3057\u3087\u3046\u304b\uff1f<\/h3>\n<div class=\"clipboard\">\n<pre><code class=\"language-python\">data=global_temp.groupby('years').agg({'LandAverageTemperature':'mean','LandAverageTemperatureUncertainty':'mean'}).reset_index()\ndata.head()<\/code><\/pre>\n<\/div>\n<div style=\"overflow-x: scroll;overflow-y: hidden;\">\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }<\/p>\n<p>    .dataframe tbody tr th {\n        vertical-align: top;\n    }<\/p>\n<p>    .dataframe thead th {\n        text-align: right;\n    }\n  <\/style>\n<table border=\"1\" class=\"dataframe\">\n<thead>\n<tr style=\"text-align: right;\">\n<th>\n        <\/th>\n<th>\n          years\n        <\/th>\n<th>\n          LandAverageTemperature\n        <\/th>\n<th>\n          LandAverageTemperatureUncertainty\n        <\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<th>\n          0\n        <\/th>\n<td>\n          1750\n        <\/td>\n<td>\n          8.719364\n        <\/td>\n<td>\n          2.637818\n        <\/td>\n<\/tr>\n<tr>\n<th>\n          1\n        <\/th>\n<td>\n          1751\n        <\/td>\n<td>\n          7.976143\n        <\/td>\n<td>\n          2.781143\n        <\/td>\n<\/tr>\n<tr>\n<th>\n          2\n        <\/th>\n<td>\n          1752\n        <\/td>\n<td>\n          5.779833\n        <\/td>\n<td>\n          2.977000\n        <\/td>\n<\/tr>\n<tr>\n<th>\n          3\n        <\/th>\n<td>\n          1753\n        <\/td>\n<td>\n          8.388083\n        <\/td>\n<td>\n          3.176000\n        <\/td>\n<\/tr>\n<tr>\n<th>\n          4\n        <\/th>\n<td>\n          1754\n        <\/td>\n<td>\n          8.469333\n        <\/td>\n<td>\n          3.494250\n        <\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n<div class=\"clipboard\">\n<pre><code class=\"language-python\">data['Uncertainty top']=data['LandAverageTemperature']+data['LandAverageTemperatureUncertainty']\ndata['Uncertainty bottom']=data['LandAverageTemperature']-data['LandAverageTemperatureUncertainty']<\/code><\/pre>\n<\/div>\n<div class=\"clipboard\">\n<pre><code class=\"language-python\">fig=px.line(data,x='years',y=['LandAverageTemperature',\n       'Uncertainty top', 'Uncertainty bottom'],title='Average Land Tmeperature in World')\nfig.show()<\/code><\/pre>\n<\/div>\n<p><a href=\"https:\/\/griddb.net\/wp-content\/uploads\/2022\/08\/Screenshot_17.png\"><img decoding=\"async\" src=\"https:\/\/griddb.net\/wp-content\/uploads\/2022\/08\/Screenshot_17.png\" alt=\"\" width=\"813\" height=\"539\" class=\"aligncenter size-full wp-image-28667\" srcset=\"\/wp-content\/uploads\/2022\/08\/Screenshot_17.png 813w, \/wp-content\/uploads\/2022\/08\/Screenshot_17-300x199.png 300w, \/wp-content\/uploads\/2022\/08\/Screenshot_17-768x509.png 768w, \/wp-content\/uploads\/2022\/08\/Screenshot_17-600x398.png 600w\" sizes=\"(max-width: 813px) 100vw, 813px\" \/><\/a><\/p>\n<p>\u3053\u306e\u30b0\u30e9\u30d5\u306f\u3001\u73fe\u5728\u3001\u5730\u7403\u304c\u6e29\u6696\u5316\u3057\u3066\u3044\u308b\u3053\u3068\u3092\u793a\u3057\u3066\u3044\u307e\u3059\u3002\u5730\u8868\u306e\u5e73\u5747\u6c17\u6e29\u306f\u3001\u904e\u53bb3\u4e16\u7d00\u3067\u6700\u3082\u9ad8\u3044\u30ec\u30d9\u30eb\u306b\u9054\u3057\u3066\u3044\u307e\u3059\u3002\u3053\u305330\u5e74\u3067\u6700\u3082\u901f\u3044\u30b9\u30d4\u30fc\u30c9\u3067\u6c17\u6e29\u304c\u4e0a\u6607\u3057\u3066\u3044\u308b\u306e\u3067\u3059\u3002\u65e9\u304f\u4eba\u985e\u304c\u30a8\u30b3\u30ed\u30b8\u30fc\u306a\u30a8\u30cd\u30eb\u30ae\u30fc\u6e90\u306b\u5b8c\u5168\u79fb\u884c\u3057\u3066\u3001CO2\u3092\u6e1b\u3089\u3057\u3066\u307b\u3057\u3044\u3082\u306e\u3067\u3059\u3002\u3053\u306e\u307e\u307e\u3067\u306f\u5927\u5909\u306a\u3053\u3068\u306b\u306a\u308a\u307e\u3059\u3002\u3053\u306e\u30b0\u30e9\u30d5\u306b\u306f\u4fe1\u983c\u533a\u9593\u3082\u3042\u308a\u3001\u8fd1\u5e74\u3001\u6c17\u6e29\u306e\u6e2c\u5b9a\u304c\u6b63\u78ba\u306b\u306a\u3063\u3066\u304d\u3066\u3044\u308b\u3053\u3068\u304c\u308f\u304b\u308a\u307e\u3059\u3002<\/p>\n<h3>\u5404\u5b63\u7bc0\u306e\u5e73\u5747\u6c17\u6e29<\/h3>\n<div class=\"clipboard\">\n<pre><code class=\"language-python\">global_temp = global_temp[['dt', 'LandAverageTemperature']]\n\nglobal_temp['dt'] = pd.to_datetime(global_temp['dt'])\nglobal_temp['year'] = global_temp['dt'].map(lambda x: x.year)\nglobal_temp['month'] = global_temp['dt'].map(lambda x: x.month)\n\ndef get_season(month):\n    if month >= 3 and month &lt;= 5:\n        return 'spring'\n    elif month >= 6 and month &lt;= 8:\n        return 'summer'\n    elif month >= 9 and month &lt;= 11:\n        return 'autumn'\n    else:\n        return 'winter'\n    \nmin_year = global_temp['year'].min()\nmax_year = global_temp['year'].max()\nyears = range(min_year, max_year + 1)\n\nglobal_temp['season'] = global_temp['month'].apply(get_season)\n\nspring_temps = []\nsummer_temps = []\nautumn_temps = []\nwinter_temps = []\n\nfor year in years:\n    curr_years_data = global_temp[global_temp['year'] == year]\n    spring_temps.append(curr_years_data[curr_years_data['season'] == 'spring']['LandAverageTemperature'].mean())\n    summer_temps.append(curr_years_data[curr_years_data['season'] == 'summer']['LandAverageTemperature'].mean())\n    autumn_temps.append(curr_years_data[curr_years_data['season'] == 'autumn']['LandAverageTemperature'].mean())\n    winter_temps.append(curr_years_data[curr_years_data['season'] == 'winter']['LandAverageTemperature'].mean())<\/code><\/pre>\n<\/div>\n<div class=\"clipboard\">\n<pre><code class=\"language-python\">sns.set(style=\"whitegrid\")\nsns.set_color_codes(\"pastel\")\nf, ax = plt.subplots(figsize=(10, 6))\n\nplt.plot(years, summer_temps, label='Summers average temperature', color='orange')\nplt.plot(years, autumn_temps, label='Autumns average temperature', color='r')\nplt.plot(years, spring_temps, label='Springs average temperature', color='g')\nplt.plot(years, winter_temps, label='Winters average temperature', color='b')\n\nplt.xlim(min_year, max_year)\n\nax.set_ylabel('Average temperature')\nax.set_xlabel('Year')\nax.set_title('Average temperature in each season')\nlegend = plt.legend(loc='center left', bbox_to_anchor=(1, 0.5), frameon=True, borderpad=1, borderaxespad=1)<\/code><\/pre>\n<\/div>\n<p><a href=\"https:\/\/griddb.net\/wp-content\/uploads\/2022\/08\/output_41_0.png\"><img loading=\"lazy\" decoding=\"async\" src=\"https:\/\/griddb.net\/wp-content\/uploads\/2022\/08\/output_41_0.png\" alt=\"\" width=\"834\" height=\"388\" class=\"aligncenter size-full wp-image-28661\" srcset=\"\/wp-content\/uploads\/2022\/08\/output_41_0.png 834w, \/wp-content\/uploads\/2022\/08\/output_41_0-300x140.png 300w, \/wp-content\/uploads\/2022\/08\/output_41_0-768x357.png 768w, \/wp-content\/uploads\/2022\/08\/output_41_0-600x279.png 600w\" sizes=\"(max-width: 834px) 100vw, 834px\" \/><\/a><\/p>\n<div class=\"clipboard\">\n<pre><code class=\"language-python\"># Statewise scenario of average temperature.\ncountry_state_temp = GlobalTempState.groupby(by = ['Country','State']).mean().reset_index().sort_values('AverageTemperature',ascending=False).reset_index()\ncountry_state_temp\ncountry_state_temp[\"world\"] = \"world\" \nfig = px.treemap(country_state_temp.head(200), path=['world', 'Country','State'], values='AverageTemperature',\n                  color='State',color_continuous_scale='RdGr')\nfig.show()<\/code><\/pre>\n<\/div>\n<p><a href=\"https:\/\/griddb.net\/wp-content\/uploads\/2022\/08\/Screenshot_18.png\"><img loading=\"lazy\" decoding=\"async\" src=\"https:\/\/griddb.net\/wp-content\/uploads\/2022\/08\/Screenshot_18.png\" alt=\"\" width=\"818\" height=\"538\" class=\"aligncenter size-full wp-image-28668\" srcset=\"\/wp-content\/uploads\/2022\/08\/Screenshot_18.png 818w, \/wp-content\/uploads\/2022\/08\/Screenshot_18-300x197.png 300w, \/wp-content\/uploads\/2022\/08\/Screenshot_18-768x505.png 768w, \/wp-content\/uploads\/2022\/08\/Screenshot_18-600x395.png 600w\" sizes=\"(max-width: 818px) 100vw, 818px\" \/><\/a><\/p>\n<h2>\u7d50\u8ad6<\/h2>\n<p>\u3053\u306e\u30c1\u30e5\u30fc\u30c8\u30ea\u30a2\u30eb\u3067\u306f\u3001Python\u3068GridDB\u3092\u4f7f\u7528\u3057\u3066\u4e16\u754c\u306e\u6c17\u5019\u3092\u5206\u6790\u3057\u307e\u3057\u305f\u3002\u30c7\u30fc\u30bf\u3092\u30a4\u30f3\u30dd\u30fc\u30c8\u3059\u308b\u65b9\u6cd5\u3068\u3057\u3066\u3001(1) GridDB \u3068 (2) Pandas \u306e2\u3064\u306e\u65b9\u6cd5\u3092\u691c\u8a0e\u3057\u307e\u3057\u305f\u3002GridDB\u306f\u3001\u30aa\u30fc\u30d7\u30f3\u30bd\u30fc\u30b9\u3067\u62e1\u5f35\u6027\u304c\u9ad8\u3044\u305f\u3081\u3001\u5927\u898f\u6a21\u306a\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u306e\u5834\u5408\u3001\u30ce\u30fc\u30c8\u30d6\u30c3\u30af\u306b\u30c7\u30fc\u30bf\u3092\u30a4\u30f3\u30dd\u30fc\u30c8\u3059\u308b\u305f\u3081\u306e\u512a\u308c\u305f\u4ee3\u66ff\u624b\u6bb5\u3092\u63d0\u4f9b\u3057\u307e\u3059\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"<p>\u6c17\u5019\u5909\u52d5\u306e\u5f71\u97ff\u3068\u305d\u308c\u306b\u4f34\u3046\u5730\u57df\u30fb\u5730\u65b9\u30b9\u30b1\u30fc\u30eb\u3067\u306e\u7570\u5e38\u6c17\u8c61\u3092\u7406\u89e3\u3059\u308b\u3053\u3068\u306f\u3001\u5b9f\u73fe\u53ef\u80fd\u306a\u9069\u5fdc\u7b56\u3092\u8a08\u753b\u30fb\u958b\u767a\u3059\u308b\u4e0a\u3067\u975e\u5e38\u306b\u91cd\u8981\u3067\u3059\u3002 \u6c17\u5019\u5909\u52d5\u306f\u3001\u9593\u9055\u3044\u306a\u304f\u73fe\u4ee3\u306b\u304a\u3051\u308b\u4eba\u985e\u3078\u306e\u6700\u3082\u6df1\u523b\u306a\u8105\u5a01\u3067\u3059\u3002IPCC\u306b\u3088\u308c\u3070\u3001\u6c17\u5019\u5909\u52d5\u306b\u3088 [&hellip;]<\/p>\n","protected":false},"author":41,"featured_media":49352,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[1005],"tags":[],"class_list":["post-50821","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-1005"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.1.1 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Python\u3068GridDB\u3092\u7528\u3044\u305f\u5730\u7403\u6c17\u5019\u5909\u52d5\u306e\u89e3\u6790 | GridDB: Open Source Time Series Database for IoT<\/title>\n<meta name=\"description\" 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