{"id":50818,"date":"2022-09-07T00:00:00","date_gmt":"2022-09-07T07:00:00","guid":{"rendered":"https:\/\/griddb-linux-hte8hndjf8cka8ht.westus-01.azurewebsites.net\/%e6%9c%aa%e5%88%86%e9%a1%9e\/sky-rocketing-prices-inflation-an-analysis-using-griddb-and-python\/"},"modified":"2025-11-14T07:55:38","modified_gmt":"2025-11-14T15:55:38","slug":"sky-rocketing-prices-inflation-an-analysis-using-griddb-and-python","status":"publish","type":"post","link":"https:\/\/griddb.net\/ja\/%e6%9c%aa%e5%88%86%e9%a1%9e\/sky-rocketing-prices-inflation-an-analysis-using-griddb-and-python\/","title":{"rendered":"\u7269\u4fa1\u3068\u30a4\u30f3\u30d5\u30ec\u306e\u9ad8\u9a30-GridDB\u30af\u30e9\u30a6\u30c9\u3068Python\u306b\u3088\u308b\u5206\u6790"},"content":{"rendered":"

\u6982\u8981<\/h2>\n

\u6700\u8fd1\u3067\u306f\u3001\u300c\u30a4\u30f3\u30d5\u30ec\u300d\u300c\u54c1\u4e0d\u8db3\u300d\u300c\u98df\u54c1\u4fa1\u683c\u300d\u300c\u30ac\u30bd\u30ea\u30f3\u4fa1\u683c\u300d\u306a\u3069\u306e\u691c\u7d22\u30ad\u30fc\u30ef\u30fc\u30c9\u306e\u4eba\u6c17\u304c100\uff5e140\uff05\u4e0a\u6607\u3057\u3066\u3044\u307e\u3059\uff08\u51fa\u5178\uff1aGoogle Trends\uff09\u3002 \u4e16\u754c\u4e2d\u306e\u4eba\u3005\u304c\u98df\u6599\u3084\u30ac\u30bd\u30ea\u30f3\u4fa1\u683c\u306e\u4e0a\u6607\u3092\u5fc3\u914d\u3057\u3066\u3044\u308b\u306e\u306f\u9593\u9055\u3044\u3042\u308a\u307e\u305b\u3093\u3002 \u98df\u6599\u3001\u30ac\u30bd\u30ea\u30f3\u3001\u5bb6\u5ead\u7528\u71c3\u6599\u306a\u3069\u306e\u7269\u4fa1\u306e\u9ad8\u9a30\u304c\u751f\u6d3b\u306e\u8cea\u3001\u7279\u306b\u5c11\u4eba\u6570\u4e16\u5e2f\u306b\u5927\u304d\u306a\u5f71\u97ff\u3092\u4e0e\u3048\u308b\u3053\u3068\u306f\u8a00\u3046\u307e\u3067\u3082\u3042\u308a\u307e\u305b\u3093\u3002\u3057\u305f\u304c\u3063\u3066\u3001\u751f\u5b58\u306e\u305f\u3081\u306e\u751f\u6d3b\u5fc5\u9700\u54c1\u306e\u4fa1\u683c\u52d5\u5411\u3092\u5206\u6790\u3057\u3001\u60c5\u5831\u3092\u5f97\u308b\u3053\u3068\u304c\u91cd\u8981\u3067\u3059\u3002\u3053\u306e\u91cd\u8981\u306a\u5206\u6790\u306bGridDB\u3068Python\u3092\u4e00\u7dd2\u306b\u4f7f\u3063\u3066\u307f\u307e\u3059\u3002<\/p>\n

jupyter\u306e\u30d5\u30a1\u30a4\u30eb\u3078\u306e\u30ea\u30f3\u30af\u306f\u3053\u3061\u3089\u3067\u3059\u3002
\nhttps:\/\/github.com\/griddbnet\/Blogs\/tree\/inflation_cpi<\/a><\/p>\n

\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u306b\u3064\u3044\u3066<\/h3>\n

\u624b\u5143\u306e\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u306f\u3001U.S. Bureau Labor of Statistics<\/a>\u304b\u3089\u53d6\u5f97\u3057\u305f\u6642\u7cfb\u5217\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u3067\u3059\u3002\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u306f\u3053\u3061\u3089<\/a>\u304b\u3089\u30c0\u30a6\u30f3\u30ed\u30fc\u30c9\u3067\u304d\u307e\u3059\u3002\u30c6\u30ad\u30b9\u30c8\u30d5\u30a1\u30a4\u30eb\u306f\u3001\u30a6\u30a7\u30d6\u30b5\u30a4\u30c8\u5185\u306e\u300cAverage Price Data\u300d\u30bb\u30af\u30b7\u30e7\u30f3\u304b\u3089\u30c0\u30a6\u30f3\u30ed\u30fc\u30c9\u3067\u304d\u307e\u3059\uff08\u4e0b\u56f3\u53c2\u7167\uff09\u3002<\/p>\n

\"\"<\/a><\/p>\n

\u4e0b\u306e\u753b\u50cf\u3067\u30cf\u30a4\u30e9\u30a4\u30c8\u3055\u308c\u3066\u3044\u308b\u30c6\u30ad\u30b9\u30c8\u30d5\u30a1\u30a4\u30eb\u304c\u89e3\u6790\u306b\u4f7f\u7528\u3055\u308c\u3066\u3044\u307e\u3059 \u3002<\/p>\n

\"\"<\/a><\/p>\n

GridDB\u3078\u306e\u30ed\u30fc\u30c9\u51e6\u7406\u3092\u8a08\u753b\u3059\u308b\uff0f\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u306e\u30b5\u30a4\u30ba\u3092\u628a\u63e1\u3059\u308b<\/h3>\n

\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u306e\u30b5\u30a4\u30ba\u3092\u628a\u63e1\u3059\u308b\u3053\u3068\u3067\u3001GridDB\u306b\u30c7\u30fc\u30bf\u3092\u30ed\u30fc\u30c9\u3059\u308b\u30d7\u30ed\u30bb\u30b9\u3092\u8a08\u753b\u3059\u308b\u3053\u3068\u304c\u3067\u304d\u308b\u306e\u3067\u3059\u3002 \u3053\u308c\u306f\u3001\u30d7\u30ed\u30b0\u30e9\u30e0\u304c\u52d5\u4f5c\u3057\u3066\u3044\u308b\u30b7\u30b9\u30c6\u30e0\uff08\u30af\u30e9\u30a4\u30a2\u30f3\u30c8\u30b7\u30b9\u30c6\u30e0\uff09\u306b\u5927\u304d\u306a\u8ca0\u62c5\u3092\u304b\u3051\u306a\u3044\u305f\u3081\u306b\u91cd\u8981\u306a\u3053\u3068\u3067\u3059\u3002 \u3053\u306e\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u306b\u306f40\u5e74\u4ee5\u4e0a\u306e\u30c7\u30fc\u30bf\u304c\u542b\u307e\u308c\u3066\u304a\u308a\u3001\u4fa1\u683c\u52d5\u5411\u306e\u5206\u6790\u3092\u884c\u3046\u306e\u306b\u9069\u3057\u3066\u3044\u307e\u3059\u3002<\/p>\n

\n
import os\nimport numpy as np\nimport pandas as pd\nimport nums_from_string as ns\nfrom IPython.core.display import Image, display\nimport seaborn as sns\nfrom matplotlib import pyplot as plt<\/code><\/pre>\n<\/div>\n
\u3053\u308c\u3089\u306e\u30c6\u30ad\u30b9\u30c8\u30d5\u30a1\u30a4\u30eb\u306f\u56fa\u5b9a\u5e45\u306e\u30d5\u30a1\u30a4\u30eb\u3067\u3042\u308b\u3053\u3068\u306b\u6ce8\u610f\u3057\u3066\u304f\u3060\u3055\u3044\u3002\u3057\u305f\u304c\u3063\u3066\u3001\u56fa\u5b9a\u5e45\u306e\u30d5\u30a1\u30a4\u30eb\u306fpandas\u306eread_fwf\u95a2\u6570\u3092\u4f7f\u3063\u3066\u30c0\u30a6\u30f3\u30ed\u30fc\u30c9\u3059\u308b\u5fc5\u8981\u304c\u3042\u308a\u307e\u3059\u3002<\/p>\n
\n
household_prices = pd.read_fwf('ap.data.1.HouseholdFuels.txt')\ngasoline_prices = pd.read_fwf('ap.data.2.Gasoline.txt')\nfood_prices = pd.read_fwf('ap.data.3.Food.txt')<\/code><\/pre>\n<\/div>\n
\u6b21\u306b\u3001\u30c7\u30fc\u30bf\u30d5\u30ec\u30fc\u30e0\u304c\u4f7f\u7528\u3059\u308b\u30e1\u30e2\u30ea\u306b\u3064\u3044\u3066\u898b\u3066\u307f\u307e\u3057\u3087\u3046\u3002<\/p>\n
\n
print('Size of household_prices', household_prices.info(memory_usage='deep'))\nprint('Size of food_prices', food_prices.info(memory_usage='deep'))\nprint('Size of gasoline_prices', gasoline_prices.info(memory_usage='deep'))<\/code><\/pre>\n<\/div>\n
<class 'pandas.core.frame.DataFrame'>\nRangeIndex: 103139 entries, 0 to 103138\nData columns (total 5 columns):\n #   Column          Non-Null Count   Dtype  \n---  ------          --------------   -----  \n 0   series_id       103139 non-null  object \n 1   year            103139 non-null  int64  \n 2   period          103139 non-null  object \n 3   value           103139 non-null  object \n 4   footnote_codes  0 non-null       float64\ndtypes: float64(1), int64(1), object(3)\nmemory usage: 20.4 MB\nSize of household_prices None\n<class 'pandas.core.frame.DataFrame'>\nRangeIndex: 139986 entries, 0 to 139985\nData columns (total 5 columns):\n #   Column          Non-Null Count   Dtype  \n---  ------          --------------   -----  \n 0   series_id       139986 non-null  object \n 1   year            139986 non-null  int64  \n 2   period          139986 non-null  object \n 3   value           139986 non-null  object \n 4   footnote_codes  0 non-null       float64\ndtypes: float64(1), int64(1), object(3)\nmemory usage: 27.8 MB\nSize of food_prices None\n<class 'pandas.core.frame.DataFrame'>\nRangeIndex: 88754 entries, 0 to 88753\nData columns (total 5 columns):\n #   Column          Non-Null Count  Dtype  \n---  ------          --------------  -----  \n 0   series_id       88754 non-null  object \n 1   year            88754 non-null  int64  \n 2   period          88754 non-null  object \n 3   value           88754 non-null  float64\n 4   footnote_codes  0 non-null      float64\ndtypes: float64(2), int64(1), object(2)\nmemory usage: 13.0 MB\nSize of gasoline_prices None\n<\/code><\/pre>\n
\u4e0a\u56f3\u306e\u3088\u3046\u306b\u3001’house_prices’, ‘food_prices’, ‘gasoline_prices’ \u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u3092\u30e1\u30e2\u30ea\u306b\u30ed\u30fc\u30c9\u3057\u305f\u3068\u304d\u306e\u30b5\u30a4\u30ba\u306f\u305d\u308c\u305e\u308c 22.7MB, 28.7MB, 18.5MB \u3067\u3059\u3002 \u3053\u306e\u7d50\u679c\u3001\u5408\u8a0869.9MB\u306b\u306a\u308a\u307e\u3057\u305f\u3002\u3057\u305f\u304c\u3063\u3066\u3001GridDB\u306b\u30c7\u30fc\u30bf\u3092\u30ed\u30fc\u30c9\u3059\u308b\u969b\u306b\u306f\u3001\u52b9\u7387\u7684\u306a\u30ed\u30fc\u30c9\u6226\u7565\u304c\u5fc5\u8981\u306b\u306a\u308a\u307e\u3059\u3002<\/p>\n
\n
print(len(household_prices))\nprint(len(gasoline_prices))\nprint(len(food_prices))<\/code><\/pre>\n<\/div>\n
103139\n88754\n139986\n<\/code><\/pre>\n
\u4e0a\u8a18\u306f\u3001GridDB\u306b\u51e6\u7406\/\u30ed\u30fc\u30c9\u3055\u308c\u308b\u30ec\u30b3\u30fc\u30c9\u6570\u306e\u76ee\u5b89\u306b\u306a\u308a\u307e\u3059\u3002<\/p>\n
\u52b9\u7387\u7684\u306a\u30ed\u30fc\u30c9\u6226\u7565\u3068\u3057\u3066\u306f\u3001\u30c7\u30fc\u30bf\u30921000\u884c\u305a\u3064\u306b\u30c1\u30e3\u30f3\u30af\u3057\u3001\u4e00\u5ea6\u306b1\u30c1\u30e3\u30f3\u30af\u305a\u3064GridDB\u306b\u30ed\u30fc\u30c9\u3059\u308b\u3053\u3068\u3067\u3059\u3002\u3053\u306e\u5834\u5408\u3001GridDB \u306e API \u3092\u8907\u6570\u56de\u547c\u3073\u51fa\u3057\u3066\u30c7\u30fc\u30bf\u3092\u30ed\u30fc\u30c9\u3059\u308b\u3053\u3068\u306b\u306a\u308a\u307e\u3059\u3002 \u3057\u304b\u3057\u3001\u3053\u308c\u306b\u3088\u3063\u3066\u8aad\u307f\u8fbc\u307e\u308c\u308b\u30c7\u30fc\u30bf\u3092\u3088\u308a\u7d30\u304b\u304f\u5236\u5fa1\u3059\u308b\u3053\u3068\u304c\u3067\u304d\u308b\u3088\u3046\u306b\u306a\u308a\u307e\u3059\u3002\u307e\u305f\u3001\u30c7\u30fc\u30bf\u5168\u4f53\u3092\u4e00\u5ea6\u306b\u8aad\u307f\u8fbc\u3080\u3053\u3068\u3082\u3067\u304d\u307e\u3059\u304c\u3001\u305d\u306e\u5834\u5408\u3001\u591a\u304f\u306e\u30e1\u30e2\u30ea\u3068\u5e2f\u57df\u5e45\u3092\u4f7f\u7528\u3059\u308b\u3053\u3068\u306b\u306a\u308a\u307e\u3059\uff08\u3053\u306e\u5834\u5408\u3001HTTP\u63a5\u7d9a\u306e\u30bf\u30a4\u30e0\u30a2\u30a6\u30c8\u304c\u7de9\u3084\u304b\u306b\u8a2d\u5b9a\u3055\u308c\u3066\u3044\u308b\u3053\u3068\u3092\u78ba\u8a8d\u3057\u3066\u304f\u3060\u3055\u3044\uff09\u3002<\/p>\n
\u30c7\u30fc\u30bf\u30af\u30ea\u30fc\u30cb\u30f3\u30b0<\/h3>\n
GridDB\u306b\u30c7\u30fc\u30bf\u3092\u30ed\u30fc\u30c9\u3059\u308b\u524d\u306b\u3001\u30c7\u30fc\u30bf\u306e\u30af\u30ea\u30fc\u30cb\u30f3\u30b0\u3092\u884c\u3044\u307e\u3057\u3087\u3046\u3002
\n\u307e\u305a\u3001\u30c7\u30fc\u30bf\u306e\u7a2e\u985e\u3092\u4fdd\u5b58\u3059\u308b\u305f\u3081\u306e\u300cCategory\u300d\u30d5\u30a3\u30fc\u30eb\u30c9\u3092\u8ffd\u52a0\u3057\u307e\u3057\u3087\u3046\u3002\u3053\u3046\u3059\u308b\u3053\u3068\u3067\u3001\u5c06\u6765\u7684\u306b\u5fc5\u8981\u306a\u5834\u5408\u306b\u5099\u3048\u3066\u3001\u30ec\u30b3\u30fc\u30c9\u306e\u51fa\u6240\u3092\u7279\u5b9a\u3059\u308b\u3053\u3068\u304c\u3067\u304d\u307e\u3059\u3002<\/p>\n
\n
household_prices['Category'] = 'Household Commodities'\ngasoline_prices['Category'] = 'Gasoline'\nfood_prices['Category'] = 'Food'<\/code><\/pre>\n<\/div>\n
\u305d\u308c\u3067\u306f\u3001\u4e0d\u8981\u306a\u5217\u3092\u524a\u9664\u3057\u3001\u671f\u9593\u30d5\u30a3\u30fc\u30eb\u30c9\u304b\u3089\u6708\u756a\u53f7\u3092\u62bd\u51fa\u3057\u3001\u5024\u30d5\u30a3\u30fc\u30eb\u30c9\u306e\u30c7\u30fc\u30bf\u30af\u30ea\u30fc\u30cb\u30f3\u30b0\u3092\u884c\u3063\u3066\u3059\u3079\u3066\u306e\u30c7\u30fc\u30bf\u304c\u6570\u5024\u3067\u3042\u308b\u3053\u3068\u3092\u78ba\u8a8d\u3057\u3001\u6700\u5f8c\u306b\u30c7\u30fc\u30bf\u30d5\u30ec\u30fc\u30e0\u5185\u306e\u5217\u3092\u4e26\u3079\u308b\u95a2\u6570\u3092\u5b9a\u7fa9\u3057\u3066\u307f\u307e\u3057\u3087\u3046\u3002 \u3053\u308c\u304c\u3067\u304d\u305f\u3089\u3001\u8aad\u307f\u8fbc\u3093\u30603\u3064\u306e\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\uff08dataframe\uff09\u306b\u5bfe\u3057\u3066\u3001\u3053\u306e\u95a2\u6570\u3092\u547c\u3073\u51fa\u3057\u307e\u3059\u3002<\/p>\n
\n
def data_cleaning (df):\n    df.drop('footnote_codes', axis=1, inplace=True) # Delete the column 'footnote_codes'\n    df['month'] = df['period'].str[-2:] #extract the month\n    # DataType conversions\n    df[\"month\"] = pd.to_numeric(df[\"month\"]) #convert to number\n    df[\"value\"] = df[\"value\"].replace(['-'],0) #Replace values that have a '-' to a 0\n    df[\"value\"] = pd.to_numeric(df[\"value\"]) #convert to number\n    cols = list(df.columns.values)\n    df = df[['series_id', 'year', 'period', 'value', 'month', 'Category']] #order the columns in the dataframe\n    return df<\/code><\/pre>\n<\/div>\n
\n
#Invoke the cleaning function on the 3 dataframes\nhousehold_prices = data_cleaning(household_prices)\ngasoline_prices = data_cleaning(gasoline_prices)\nfood_prices = data_cleaning(food_prices)<\/code><\/pre>\n<\/div>\n
GridDB\u306b\u304a\u3051\u308bContainer\u69cb\u9020\u306e\u4f5c\u6210<\/h3>\n
GridDB\u3067\u306e\u30b3\u30f3\u30c6\u30ca\u4f5c\u6210\u306b\u3064\u3044\u3066\u306f\u3001GridDB WebAPI<\/a>\u3092\u53c2\u7167\u3057\u3066\u304f\u3060\u3055\u3044\u3002<\/p>\n
\u3053\u308c\u304b\u3089\u3001GridDB\u306b\u30c7\u30fc\u30bf\u3092\u683c\u7d0d\u3059\u308b\u30b3\u30f3\u30c6\u30ca\u3092\u4f5c\u6210\u3059\u308b\u4f5c\u696d\u3092\u9032\u3081\u307e\u3059\u3002<\/p>\n
\n
import requests  # to make http calls\nimport http\nhttp.client.HTTPConnection.debuglevel = 1 #Tip - to enable detailed logging of http calls; this is not needed in case you don't detailed logging<\/code><\/pre>\n<\/div>\n
\n
#Construct an object to hold the request headers (ensure that you replace the XXX placeholder with the correct value that matches the credentials for your GridDB instance)\nheader_obj = {\"Authorization\":\"XXX\",\"Content-Type\":\"application\/json; charset=UTF-8\",\"User-Agent\":\"PostmanRuntime\/7.29.0\"}\n\n#Construct the base URL based on your GRIDDB cluster you'd like to connect to (ensure that you replace the placeholders in the URL below with the correct values that correspond to your GridDB instance)\nbase_url = 'https:\/\/[host]:[port]\/griddb\/v2\/[clustername]\/dbs\/[database_name]'<\/code><\/pre>\n<\/div>\n
\n
#Construct an object to hold the request body (i.e., the container that needs to be created)\ndata_obj = {\n    \"container_name\": \"Inflation_CPI_Analysis\",\n    \"container_type\": \"COLLECTION\",\n    \"rowkey\": False,\n    \"columns\": [\n    {\n    \"name\": \"series_id\",\n    \"type\": \"STRING\"\n    },\n    {\n    \"name\": \"year\",\n    \"type\": \"INTEGER\"\n    },\n    {\n    \"name\": \"period\",\n    \"type\": \"STRING\"\n    },\n    {\n    \"name\": \"value\",\n    \"type\": \"FLOAT\"\n    },\n    {\n    \"name\": \"month\",\n    \"type\": \"INTEGER\"\n    },\n    {\n    \"name\": \"category\",\n    \"type\": \"STRING\"\n    }        \n    ]\n}\n\n#Set up the GridDB WebAPI URL\nurl = base_url + '\/containers'\n\n#Invoke the GridDB WebAPI with the headers and the request body\nx = requests.post(url, json = data_obj, headers = header_obj)<\/code><\/pre>\n<\/div>\n
\u3053\u308c\u3067\u3001\u3059\u3079\u3066\u306e\u30c7\u30fc\u30bf\u3092\u683c\u7d0d\u3059\u308b\u30b3\u30f3\u30c6\u30ca\u304cGridDB\u306b\u4f5c\u6210\u3055\u308c\u307e\u3057\u305f\u3002\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u5185\u306b\u540c\u3058\u9805\u76ee\u304c\u8907\u6570\u56de\uff08\u6708\u3054\u3068\u3001\u5e74\u3054\u3068\uff09\u5b58\u5728\u3059\u308b\u305f\u3081\u3001series_id\u30d5\u30a3\u30fc\u30eb\u30c9\u304c\u4e00\u610f\u306b\u306a\u3089\u306a\u3044\u306e\u3067\u3001rowkey\u3092False\u306b\u8a2d\u5b9a\u3057\u3066\u3044\u308b\u3053\u3068\u306b\u6ce8\u610f\u3057\u3066\u304f\u3060\u3055\u3044\u3002<\/p>\n
GridDB\u3067\u30b3\u30f3\u30c6\u30ca\u306b\u30c7\u30fc\u30bf\u3092\u8ffd\u52a0\u3059\u308b\uff0f\u884c\u3092\u767b\u9332\u3059\u308b<\/h2>\n
\u30ed\u30fc\u30c7\u30a3\u30f3\u30b0\u306e\u6226\u7565\uff1a\u30c7\u30fc\u30bf\u30c1\u30e3\u30f3\u30af\u3092\u4f5c\u6210\u3059\u308b<\/h3>\n
\u4ee5\u4e0b\u306e\u95a2\u6570\u306f\u3001\u30c7\u30fc\u30bf\u30d5\u30ec\u30fc\u30e0\u3092JSON\u30aa\u30d6\u30b8\u30a7\u30af\u30c8\u306b\u5909\u63db\u3057\u3001\u305d\u308c\u30921000\u884c\u305a\u3064\u306e\u8907\u6570\u306e\u30c1\u30e3\u30f3\u30af\u306b\u3057\u3066\u3044\u307e\u3059\u3002<\/p>\n
\n
#Setup the URL to be used to invoke the GridDB WebAPI to register rows in the container created previously\nurl = base_url + '\/containers\/Inflation_CPI_Analysis\/rows'\n\ndef create_chunks_to_load_to_GRIDDB(df):\n    list_of_dataframes = np.array_split(df, len(df)\/1000) #Returns a list of dataframes; here,# the chunk size is 1000\n    for i in list_of_dataframes:\n        df_json = i.to_json(orient='values')\n        request_body = df_json\n        x = requests.put(url, data=request_body, headers=header_obj) #Invoke the GridDB WebAPI using the request constructed\n        print(x.text)<\/code><\/pre>\n<\/div>\n
\u30c7\u30fc\u30bf\u3092\u30c1\u30e3\u30f3\u30af\u3057\u3066GridDB\u306b\u30ed\u30fc\u30c9\u3059\u308b\u95a2\u6570\u3092\u7528\u3044\u3066\u3001\u4eca\u3042\u308b3\u3064\u306e\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u3092\u30ed\u30fc\u30c9\u3057\u307e\u3059\u3002<\/p>\n
\n
create_chunks_to_load_to_GRIDDB(household_prices)<\/code><\/pre>\n<\/div>\n
\n
create_chunks_to_load_to_GRIDDB(food_prices)<\/code><\/pre>\n<\/div>\n
\n
create_chunks_to_load_to_GRIDDB(household_prices)<\/code><\/pre>\n<\/div>\n
\u30af\u30a4\u30c3\u30af\u30d0\u30ea\u30c7\u30fc\u30b7\u30e7\u30f3\uff06TQL\u5165\u9580<\/h2>\n
\u3055\u3066\u3001\u30c7\u30fc\u30bf\u306e\u8aad\u307f\u8fbc\u307f\u304c\u5b8c\u4e86\u3057\u305f\u306e\u3067\u3001\u5206\u6790\u3092\u9032\u3081\u308b\u3053\u3068\u306b\u3057\u307e\u3059\u3002\u4eca\u56de\u306e\u5206\u6790\u3067\u306f\u3001GridDB\u306eTQL\u3068\u547c\u3070\u308c\u308b\u30af\u30a8\u30ea\u8a00\u8a9e\u3092\u4f7f\u7528\u3057\u3066\u3044\u307e\u3059\u3002TQL\u306b\u3064\u3044\u3066\u3082\u3063\u3068\u77e5\u308a\u305f\u3044\u65b9\u306f\u3001\u3053\u3061\u3089\u306e\u30ea\u30bd\u30fc\u30b9<\/a>\u3092\u3054\u89a7\u304f\u3060\u3055\u3044\u3002\u307e\u305f\u3001TQL\u3092\u7d20\u65e9\u304f\u628a\u63e1\u3059\u308b\u305f\u3081\u306eYoutube\u30d3\u30c7\u30aa\u30c1\u30e5\u30fc\u30c8\u30ea\u30a2\u30eb\u3082\u3042\u308a\u307e\u3059\u3002 \u3053\u3061\u3089<\/a>\u3092\u30af\u30ea\u30c3\u30af\u3059\u308b\u3068\u30d3\u30c7\u30aa\u30c1\u30e5\u30fc\u30c8\u30ea\u30a2\u30eb\u3092\u898b\u308b\u3053\u3068\u304c\u3067\u304d\u307e\u3059\u3002 GridDB WebAPI \u30d6\u30ed\u30b0<\/a> \uff08\u300cFetching Data\u300d\u30bb\u30af\u30b7\u30e7\u30f3\u3092\u53c2\u7167\uff09\u306b\u5f93\u3044\u3001TQL\u6587\u306e\u30ea\u30af\u30a8\u30b9\u30c8\u306e\u57fa\u672c\u69cb\u9020\u3092\u5b66\u3073\u307e\u3059\u3002<\/p>\n
\u4ee5\u4e0b\u306eTQL\u30af\u30a8\u30ea\u306f\u3001GridDB\u306b\u633f\u5165\u3055\u308c\u305f\u30ec\u30b3\u30fc\u30c9\u6570\u304c\u3001\u30c7\u30fc\u30bf\u30ed\u30fc\u30c9\u306b\u4f7f\u7528\u3057\u305f3\u3064\u306e\u30c7\u30fc\u30bf\u30d5\u30ec\u30fc\u30e0\u306e\u30ec\u30b3\u30fc\u30c9\u306e\u5408\u8a08\u3068\u540c\u3058\u304b\u3069\u3046\u304b\u3092\u7c21\u5358\u306b\u30c1\u30a7\u30c3\u30af\u3059\u308b\u3082\u306e\u3067\u3059\u3002<\/p>\n
\n
#Setup the URL to be used to invoke the GridDB WebAPI to retrieve data from the container\nurl = base_url + '\/tql'\n\n#Construct the request body which has the TQL that is to be used to retrieve the data\n#Use the count function to get the number of records in the container \nrequest_body = '[{\"name\":\"Inflation_CPI_Analysis\", \"stmt\":\"SELECT count(*) \", \"columns\":[]}]' \n\n#Invoke the GridDB WebAPI\nx = requests.post(url, data=request_body, headers=header_obj)<\/code><\/pre>\n<\/div>\n
\n
print(x.text)<\/code><\/pre>\n<\/div>\n
[{\"columns\":[{\"name\":\"aggregationResult\",\"type\":\"DOUBLE\"}],\"results\":[[331879.0]]}]\n<\/code><\/pre>\n
\n
print(len(household_prices) + len(food_prices) + len(gasoline_prices))<\/code><\/pre>\n<\/div>\n
331879\n<\/code><\/pre>\n
 \u2705 – \u3053\u308c\u306f\u3001\u30c7\u30fc\u30bf\u30d9\u30fc\u30b9\u306b\u8aad\u307f\u8fbc\u307e\u308c\u305f\u3059\u3079\u3066\u306e\u30c7\u30fc\u30bf\u304c\u6b63\u5e38\u306b\u8aad\u307f\u8fbc\u307e\u308c\u305f\u3053\u3068\u3092\u78ba\u8a8d\u3059\u308b\u3082\u306e\u3067\u3059\u3002<\/justify><\/strong><\/p>\n
\u30de\u30c3\u30d4\u30f3\u30b0\u30c6\u30fc\u30d6\u30eb\u3092\u30c7\u30fc\u30bf\u30d5\u30ec\u30fc\u30e0\u3068\u3057\u3066\u8aad\u307f\u8fbc\u3080<\/h2>\n
\u4ee5\u4e0b\u306f\u3001\u30c0\u30a6\u30f3\u30ed\u30fc\u30c9\u3059\u308b\u30de\u30c3\u30d4\u30f3\u30b0\u30d5\u30a1\u30a4\u30eb\u3067\u3059\u3002<\/p>\n
\"\"<\/a><\/p>\n
\u3059\u3079\u3066\u306e\u30d5\u30a1\u30a4\u30eb\u306f\u30bf\u30d6\u533a\u5207\u308a\u30d5\u30a1\u30a4\u30eb\u3067\u3042\u308b\u3053\u3068\u306b\u6ce8\u610f\u3057\u3066\u304f\u3060\u3055\u3044\u3002\u3053\u308c\u3089\u306e\u30de\u30c3\u30d4\u30f3\u30b0\u30d5\u30a1\u30a4\u30eb\u306f\u3001\u5fc5\u8981\u306b\u5fdc\u3058\u3066\u30b3\u30fc\u30c9\u306e\u8aac\u660e\u3092\u53c2\u7167\u3059\u308b\u305f\u3081\u306b\u4f7f\u7528\u3059\u308b\u3053\u3068\u304c\u3067\u304d\u307e\u3059\u3002\u3053\u308c\u3089\u306fPython\u306b\u6b8b\u308a\u3001GridDB\u306b\u8aad\u307f\u8fbc\u307e\u308c\u308b\u3053\u3068\u306f\u3042\u308a\u307e\u305b\u3093\u3002<\/strong><\/p>\n
\n
item_mapping = pd.read_csv('ap.item_mapping.txt',sep='t')\nperiod_mapping = pd.read_csv('ap.period_mapping.txt',sep='t')\nseasonal_mapping = pd.read_csv('ap.seasonal_mapping.txt',sep='t')\narea_mapping = pd.read_csv('ap.area_mapping.txt',sep='t')\nseries_mapping = pd.read_csv('ap.series_mapping.txt',sep='t')<\/code><\/pre>\n<\/div>\n
\u6642\u7cfb\u5217\u30c8\u30ec\u30f3\u30c9\u89e3\u6790<\/h2>\n
\u30b7\u30ca\u30ea\u30aa1\uff1a\u30a4\u30f3\u30d5\u30ec\u306f\u7c73\u4fa1\u306b\u3069\u306e\u3088\u3046\u306a\u5f71\u97ff\u3092\u4e0e\u3048\u305f\u304b\uff1f<\/h3>\n
\u30b7\u30ea\u30fc\u30baID APU0000701311 (Rice, white, long grain, precooked); APU0000701312 (Rice, white, long grain, uncooked) \u306e\u30c7\u30fc\u30bf\u3092\u53ce\u96c6\u3059\u308b\u305f\u3081\u306e\u30af\u30a8\u30ea\u3092\u8a18\u8ff0\u3057\u3066\u3044\u308b\u3068\u3053\u308d\u3067\u3059\u3002\u305d\u308c\u305e\u308c1\u30dd\u30f3\u30c9\u306e\u91cd\u3055\u3067\u3059\u3002\u5b63\u7bc0\u8abf\u6574\u306f\u3057\u3066\u3044\u307e\u305b\u3093\u3002<\/strong><\/p>\n
\n
#Setup the URL to be used to invoke the GridDB WebAPI to retrieve data from the container\nurl = base_url + '\/tql'\n\n#Construct the request body which has the TQL that is to be used to retrieve the data\n# Getting data for series_id APU0000701311 (Rice, white, long grain, precooked); APU0000701312 (Rice, white, long grain, uncooked)\nrequest_body = '[{\"name\":\"Inflation_CPI_Analysis\", \"stmt\":\"SELECT * WHERE series_id = 'APU0000701311' OR series_id = 'APU0000701312'\", \"columns\":[]}]' \n\n#Invoke the GridDB WebAPI\ndata_req1 = requests.post(url, data=request_body, headers=header_obj)\ndata_req1<\/code><\/pre>\n<\/div>\n
GridDB WebAPI \u547c\u3073\u51fa\u3057\u304b\u3089\u53d7\u3051\u53d6\u3063\u305f\u30ec\u30b9\u30dd\u30f3\u30b9\u3092\u51e6\u7406\u3057\u3001\u30ec\u30b9\u30dd\u30f3\u30b9\u306b\u542b\u307e\u308c\u308b\u30c7\u30fc\u30bf\u3092\u7528\u3044\u3066\u30c7\u30fc\u30bf\u30d5\u30ec\u30fc\u30e0\u3092\u69cb\u7bc9\u3057\u307e\u3059\u3002\u305d\u308c\u304c\u3067\u304d\u305f\u3089\u3001\u5404\u5e74\u306e\u5e73\u5747\u5024\u3092\u5f97\u308b\u305f\u3081\u306b\u3001\u5e74\u30d5\u30a3\u30fc\u30eb\u30c9\u3067\u30c7\u30fc\u30bf\u3092\u30b0\u30eb\u30fc\u30d7\u5316\u3057\u307e\u3059\u3002\u3053\u308c\u3092\u30d7\u30ed\u30c3\u30c8\u3059\u308b\u3068\u8996\u899a\u7684\u306b\u50be\u5411\u3092\u78ba\u8a8d\u3059\u308b\u3053\u3068\u304c\u3067\u304d\u307e\u3059\u3002<\/p>\n
\n
#Process the response received and construct a Pandas dataframe with the data from the response\nmyJson = data_req1.json()\nRice_trends = pd.DataFrame(myJson[0][\"results\"], columns=[myJson[0][\"columns\"][0][\"name\"], myJson[0][\"columns\"][1][\"name\"], myJson[0][\"columns\"][2][\"name\"], myJson[0][\"columns\"][3][\"name\"],myJson[0][\"columns\"][4][\"name\"],myJson[0][\"columns\"][5][\"name\"]])<\/code><\/pre>\n<\/div>\n
\n
#Group the data by year and get the mean for each year\nAvg_value_rice = pd.DataFrame(Rice_trends.groupby(['year'])['value'].mean())<\/code><\/pre>\n<\/div>\n
\n
#Construct a line plot with the data\nplt.rcParams[\"figure.figsize\"] = [12,12]\nplt.rcParams[\"figure.autolayout\"] = True\nax = sns.lineplot(x=\"year\", y=\"value\", data=Avg_value_rice)\nax.tick_params(rotation=45)\nax.set_title('Fig.1 Scenario 1 - Time Series Trends of Rice Prices', fontsize = 18)\nplt.xlabel('Year', fontsize=14)\nplt.ylabel('Average value ($ per pound)', fontsize=14)\nplt.show()<\/code><\/pre>\n<\/div>\n
\"\"<\/a><\/p>\n
\u30b7\u30ca\u30ea\u30aa1\u306b\u5bfe\u3059\u308b\u6d1e\u5bdf\uff1a\u30a4\u30f3\u30d5\u30ec\u306e\u7c73\u3078\u306e\u5f71\u97ff<\/h3>\n
Fig.1\u306e\u50be\u5411\u304b\u3089\u3001\u4ee5\u4e0b\u306e\u3088\u3046\u306a\u77e5\u898b\u304c\u5f97\u3089\u308c\u307e\u3057\u305f\u3002<\/p>\n
    \n
  1. 1980\u5e74\u306b\u7c73\u4fa1\u304c\u6025\u9a30\u3057\u305f\u5f8c\u30010.4\u5358\u4f4d\u3067\u4e0b\u843d\u3057\u3066\u3044\u307e\u3059\u3002<\/li>\n
  2. 1987\u5e74\u30682003\u5e74\u306f\u3001\u4ed6\u306e\u5e74\u306b\u6bd4\u3079\u3066\u5024\u304c\u4e0b\u304c\u3063\u3066\u3044\u308b\u3088\u3046\u3067\u3059\u3002<\/li>\n
  3. 1987\u5e74\u306f\u3001\u904e\u53bb\u6700\u4f4e\u306e\u5024\u3092\u8a18\u9332\u3057\u3066\u3044\u307e\u3059\u3002<\/li>\n
  4. 2020\u5e74\u4ee5\u964d\u3001\u7c73\u4fa1\u306f0.75\u53f0\u304b\u30890.9\u53f0\u3078\u3068\u6025\u9a30\u3057\u3066\u3044\u308b\u3053\u3068\u304c\u5206\u304b\u308a\u307e\u3059\u3002<\/li>\n<\/ol>\n
    \u30b7\u30ca\u30ea\u30aa2\uff1a\u30a4\u30f3\u30d5\u30ec\u306f\u9d8f\u8089\u4fa1\u683c\u306b\u3069\u306e\u3088\u3046\u306a\u5f71\u97ff\u3092\u4e0e\u3048\u305f\u304b\uff1f<\/h3>\n
    \u9d8f\u8089\u306f\u3001\u9d8f\u8089\u3068\u5375\u3092\u542b\u3080\u3059\u3079\u3066\u306e\u54c1\u76ee\u3092\u542b\u307f\u307e\u3059\u3002series_id APU0200FF1101 (chicken breast with bone); APU0300FF1101 (chicken breast boneless); APU0300706111 (Chicken whole); APU0300706211 (Chicken breast); APU0300706212 (Chicken legs); APU0300708111 (Eggs) \u306e\u30c7\u30fc\u30bf\u3092\u53ce\u96c6\u3059\u308b\u30af\u30a8\u30ea\u30fc\u3092\u8a18\u8ff0\u3059\u308b\u3053\u3068\u3067\u3059\u3002\u306a\u304a\u3001\u30c7\u30fc\u30bf\u901a\u308a\u3001\u3053\u308c\u3089\u306e\u9805\u76ee\u306f\u975e\u5b63\u7bc0\u8abf\u6574\u6e08\u307f\u3067\u3059\u3002\u4fa1\u683c\u306f1\u30dd\u30f3\u30c9(453.6gm)\u3042\u305f\u308a\u306e\u3082\u306e\u3067\u3059\u3002<\/strong><\/p>\n
    \n
    #Setup the URL to be used to invoke the GridDB WebAPI to retrieve data from the container\nurl = base_url + '\/tql'\n\n#Construct the request body which has the TQL that is to be used to retrieve the data\n# Getting data for series_id APU0200FF1101 (chicken breast with bone); APU0300FF1101 (chicken breast boneless); APU0300706111 (Chicken whole); APU0300706211 (Chicken breast); APU0300706212 (Chicken legs); APU0300708111 (Eggs)\nrequest_body = '[{\"name\":\"Inflation_CPI_Analysis\", \"stmt\":\"SELECT * WHERE series_id = 'APU0200FF1101' OR series_id = 'APU0300FF1101' OR series_id = 'APU0300706111' OR series_id = 'APU0300706211' OR series_id = 'APU0300706212' OR series_id = 'APU0300708111' \", \"columns\":[]}]' \n\n#Invoke the GridDB WebAPI\ndata_req2 = requests.post(url, data=request_body, headers=header_obj)\ndata_req2<\/code><\/pre>\n<\/div>\n
    GridDB WebAPI \u547c\u3073\u51fa\u3057\u304b\u3089\u53d7\u3051\u53d6\u3063\u305f\u30ec\u30b9\u30dd\u30f3\u30b9\u3092\u51e6\u7406\u3057\u3001\u30ec\u30b9\u30dd\u30f3\u30b9\u306b\u542b\u307e\u308c\u308b\u30c7\u30fc\u30bf\u3092\u7528\u3044\u3066\u30c7\u30fc\u30bf\u30d5\u30ec\u30fc\u30e0\u3092\u69cb\u7bc9\u3057\u307e\u3059\u3002
    \n\u305d\u308c\u304c\u3067\u304d\u305f\u3089\u3001\u5404\u5e74\u306e\u5e73\u5747\u5024\u3092\u5f97\u308b\u305f\u3081\u306b\u3001\u5e74\u30d5\u30a3\u30fc\u30eb\u30c9\u3067\u30c7\u30fc\u30bf\u3092\u30b0\u30eb\u30fc\u30d7\u5316\u3057\u307e\u3059\u3002\u3053\u308c\u3092\u30d7\u30ed\u30c3\u30c8\u3059\u308b\u3068\u8996\u899a\u7684\u306b\u50be\u5411\u3092\u78ba\u8a8d\u3059\u308b\u3053\u3068\u304c\u3067\u304d\u307e\u3059\u3002<\/p>\n
    \n
    #Process the response received and construct a Pandas dataframe with the data from the response\nmyJson = data_req2.json()\nPoultry_trends = pd.DataFrame(myJson[0][\"results\"], columns=[myJson[0][\"columns\"][0][\"name\"], myJson[0][\"columns\"][1][\"name\"], myJson[0][\"columns\"][2][\"name\"], myJson[0][\"columns\"][3][\"name\"],myJson[0][\"columns\"][4][\"name\"],myJson[0][\"columns\"][5][\"name\"]])<\/code><\/pre>\n<\/div>\n
    \n
    #Group the data on a yearly basis and get the mean value for each year\nAvg_value_poultry = pd.DataFrame(Poultry_trends.groupby(['year'])['value'].mean())<\/code><\/pre>\n<\/div>\n
    \n
    #Construct a line plot with the data\nplt.rcParams[\"figure.figsize\"] = [12,12]\nplt.rcParams[\"figure.autolayout\"] = True\nax = sns.lineplot(x=\"year\", y=\"value\", data=Avg_value_poultry)\nax.tick_params(rotation=45)\nax.set_title('Fig.2 Scenario 2 - Time Series Trends of Poultry Prices',fontsize=18)\nplt.xlabel('Year', fontsize=14)\nplt.ylabel('Average value ($ per pound)', fontsize=14)\nplt.show()<\/code><\/pre>\n<\/div>\n
    \"\"<\/a><\/p>\n
    \u30b7\u30ca\u30ea\u30aa2\u306b\u5bfe\u3059\u308b\u6d1e\u5bdf\uff1a\u30a4\u30f3\u30d5\u30ec\u304c\u5bb6\u79bd\u985e\u306b\u4e0e\u3048\u308b\u5f71\u97ff\u306b\u3064\u3044\u3066<\/h3>\n
    \u3053\u3053\u306b\u306f\u3001\u975e\u5e38\u306b\u8208\u5473\u6df1\u3044\u50be\u5411\u304c\u3042\u308a\u307e\u3059\u3002Fig.2\u306e\u50be\u5411\u304b\u3089\u3001\u4ee5\u4e0b\u306e\u3088\u3046\u306a\u77e5\u898b\u304c\u5f97\u3089\u308c\u307e\u3057\u305f\u3002<\/p>\n
      \n
    1. \u9d8f\u8089\u306e\u5e73\u5747\u4fa1\u683c\u306f\u30011980\u5e74\u304b\u30892005\u5e74\u307e\u3067\u306f1\u30dd\u30f3\u30c9\u3042\u305f\u308a1.0\u301c2.25\u5186\u3067\u3057\u305f\u3002\u3057\u304b\u3057\u30012006\u5e74\u9803\u304b\u3089\u306f2.25\u306b\u4e0a\u6607\u3057\u3066\u3044\u307e\u3059\u3002<\/li>\n
    2. 2006\u5e74\u4ee5\u964d\u306f\u30012.25\u4ee5\u4e0a\u306b\u4e0a\u6607\u3057\u3066\u3044\u307e\u3059\u3002<\/li>\n<\/ol>\n
      \u30b7\u30ca\u30ea\u30aa3\uff1a\u30a4\u30f3\u30d5\u30ec\u306f\u4e73\u4fa1\u306b\u3069\u306e\u3088\u3046\u306a\u5f71\u97ff\u3092\u4e0e\u3048\u305f\u304b\uff1f<\/h3>\n
      APU0000709111 – Milk, fresh, whole, fortified; APU0000709211 – Milk, fresh, skim; APU0000709212 – Milk, fresh, low fat; APU0200709111 – Milk, fresh, whole, fortified; APU0200709211 – Milk, fresh, skim .\u304c\u8003\u616e\u3055\u308c\u307e\u3059\u3002\u306a\u304a\u30011.5\u30ac\u30ed\u30f3\u3067\u3001\u5b63\u7bc0\u8abf\u6574\u3055\u308c\u3066\u3044\u306a\u3044\u9805\u76ee\u306e\u307f\u3092\u8003\u616e\u3057\u307e\u3057\u305f\u3002<\/strong><\/p>\n
      \n
      #Setup the URL to be used to invoke the GridDB WebAPI to retrieve data from the container\nurl = base_url + '\/tql'\n\n#Construct the request body which has the TQL that is to be used to retrieve the data\n# Getting data for series_id APU0000709111 - Milk, fresh, whole, fortified; APU0000709211 - Milk, fresh, skim; APU0000709212 - Milk, fresh, low fat; APU0200709111 - Milk, fresh, whole, fortified; APU0200709211 - Milk, fresh, skim\nrequest_body = '[{\"name\":\"Inflation_CPI_Analysis\", \"stmt\":\"SELECT * WHERE series_id = 'APU0000709111' OR series_id = 'APU0000709211' OR series_id = 'APU0000709212' OR series_id = 'APU0200709111' OR series_id = 'APU0200709211' \", \"columns\":[]}]' \n\n#Invoke the GridDB WebAPI\ndata_req3 = requests.post(url, data=request_body, headers=header_obj)\ndata_req3<\/code><\/pre>\n<\/div>\n
      GridDB WebAPI \u547c\u3073\u51fa\u3057\u304b\u3089\u53d7\u3051\u53d6\u3063\u305f\u30ec\u30b9\u30dd\u30f3\u30b9\u3092\u51e6\u7406\u3057\u3001\u30ec\u30b9\u30dd\u30f3\u30b9\u306b\u542b\u307e\u308c\u308b\u30c7\u30fc\u30bf\u3092\u7528\u3044\u3066\u30c7\u30fc\u30bf\u30d5\u30ec\u30fc\u30e0\u3092\u69cb\u7bc9\u3057\u307e\u3059\u3002 \u305d\u308c\u304c\u3067\u304d\u305f\u3089\u3001\u5404\u5e74\u306e\u5e73\u5747\u5024\u3092\u5f97\u308b\u305f\u3081\u306b\u3001\u5e74\u30d5\u30a3\u30fc\u30eb\u30c9\u3067\u30c7\u30fc\u30bf\u3092\u30b0\u30eb\u30fc\u30d7\u5316\u3057\u307e\u3059\u3002\u3053\u308c\u3092\u30d7\u30ed\u30c3\u30c8\u3059\u308b\u3068\u8996\u899a\u7684\u306b\u50be\u5411\u3092\u78ba\u8a8d\u3059\u308b\u3053\u3068\u304c\u3067\u304d\u307e\u3059\u3002<\/p>\n
      \n
      #Process the response received and construct a Pandas dataframe with the data from the response\nmyJson = data_req3.json()\nMilk_trends = pd.DataFrame(myJson[0][\"results\"], columns=[myJson[0][\"columns\"][0][\"name\"], myJson[0][\"columns\"][1][\"name\"], myJson[0][\"columns\"][2][\"name\"], myJson[0][\"columns\"][3][\"name\"],myJson[0][\"columns\"][4][\"name\"],myJson[0][\"columns\"][5][\"name\"]])<\/code><\/pre>\n<\/div>\n
      \n
      #Group the data by rear and get the mean value for each year\nAvg_value_milk = Milk_trends.groupby('year', as_index=False)['value'].mean()<\/code><\/pre>\n<\/div>\n
      \n
      #Construct a line plot with the data; ensure that the ticks on the x axis are integer values\nfrom matplotlib.ticker import MaxNLocator\n\nplt.rcParams[\"figure.figsize\"] = [12,12]\nplt.rcParams[\"figure.autolayout\"] = True\n#year = (Milk_trends['year'])\nax = sns.lineplot(x=\"year\", y=\"value\", data=Avg_value_milk)\nax.tick_params(rotation=45)\nax.set_title('Fig.3 Scenario 3 - Time Series Trends of Milk Prices',fontsize=18)\nax.xaxis.set_major_locator(MaxNLocator(integer=True))\nplt.xlabel('Year', fontsize=14)\nplt.ylabel('Average value ($ per pound)', fontsize=14)\nplt.show()<\/code><\/pre>\n<\/div>\n
      \"\"<\/a><\/p>\n
      \u30b7\u30ca\u30ea\u30aa3\u306b\u5bfe\u3059\u308b\u6d1e\u5bdf\uff1a\u30a4\u30f3\u30d5\u30ec\u304c\u725b\u4e73\u306b\u4e0e\u3048\u308b\u5f71\u97ff\u306b\u3064\u3044\u3066<\/h3>\n
        \n
      1. \u725b\u4e73\u306e\u5e73\u5747\u4fa1\u683c\u306f\u30011990\u5e74\u6642\u70b9\u30671\u30dd\u30f3\u30c9\u3042\u305f\u308a\u7d041.5\u30c9\u30eb\u306b\u4e0a\u6607\u3057\u307e\u3057\u305f\u3002<\/li>\n
      2. 1980\u5e74\u304b\u30891986\u5e74\u306e\u9593\u3001\u725b\u4e73\u306e\u5e73\u5747\u4fa1\u683c\u306f1\u30c9\u30eb\u304b\u30891.1\u30c9\u30eb\u306e\u9593\u3067\u3057\u305f\u3002<\/li>\n
      3. 1990\u5e74\u4ee5\u964d\u3001\u5e73\u5747\u4fa1\u683c\u306f\u5e38\u306b1.4\u30c9\u30eb\u524d\u5f8c\u3067\u63a8\u79fb\u3057\u3066\u3044\u307e\u3059\u3002<\/li>\n<\/ol>\n
        \u30b7\u30ca\u30ea\u30aa4\uff1a2022\u5e74\u306e\u30a4\u30f3\u30d5\u30ec\u7387\u306f\u3069\u3046\u3060\u3063\u305f\u306e\u304b\uff1f<\/h3>\n
        \n
        #Setup the URL to be used to invoke the GridDB WebAPI to retrieve data from the container\nurl = base_url + '\/tql'\n\n#Construct the request body which has the TQL that is to be used to retrieve the data\n# Getting data for year 2022\nrequest_body = '[{\"name\":\"Inflation_CPI_Analysis\", \"stmt\":\"SELECT * WHERE year = 2022 \", \"columns\":[]}]' \n\n#Invoke the GridDB WebAPI\ndata_req4 = requests.post(url, data=request_body, headers=header_obj)\ndata_req4<\/code><\/pre>\n<\/div>\n
        \n
        #Process the response received and construct a Pandas dataframe with the data from the response\nmyJson = data_req4.json()\nyear_2022_trends = pd.DataFrame(myJson[0][\"results\"], columns=[myJson[0][\"columns\"][0][\"name\"], myJson[0][\"columns\"][1][\"name\"], myJson[0][\"columns\"][2][\"name\"], myJson[0][\"columns\"][3][\"name\"],myJson[0][\"columns\"][4][\"name\"],myJson[0][\"columns\"][5][\"name\"]])<\/code><\/pre>\n<\/div>\n
        \n
        #Construct a Seaborn lineplot with the data to display the trend lines for each category by month\nimport seaborn\nsns.lineplot('month', 'value', ci=None, hue='category', data=year_2022_trends).set(title='Fig.4 Scenario 4 - How was 2022 in terms of inflation?')\nplt.xlabel('Month (2022)', fontsize=14)\nplt.ylabel('Average value ($ per unit)', fontsize=14)\nplt.show()<\/code><\/pre>\n<\/div>\n
        \"\"<\/a><\/p>\n
        \u30b7\u30ca\u30ea\u30aa4\u306b\u5bfe\u3059\u308b\u6d1e\u5bdf\uff1a2022\u5e74\u3068\u30a4\u30f3\u30d5\u30ec\u7387<\/h3>\n
        2022\u5e74\u73fe\u5728\u30011.\u30ac\u30bd\u30ea\u30f3\u306e\u4fa1\u683c\u306f\u30a4\u30f3\u30d5\u30ec\u306e\u5f71\u97ff\u3092\u53d7\u3051\u3066\u3044\u308b\u3088\u3046\u3067\u3059\u30022. \u5bb6\u5ead\u7528\u54c1\u3084\u98df\u6599\u54c1\u306e\u4fa1\u683c\u306f\u30a4\u30f3\u30d5\u30ec\u306e\u5f71\u97ff\u3092\u3042\u307e\u308a\u53d7\u3051\u3066\u3044\u307e\u305b\u3093\u30023.2022\u5e742\u6708\u3088\u308a\u30ac\u30bd\u30ea\u30f3\u304c1\u500b\u3042\u305f\u308a0.5\u30c9\u30eb\u5024\u4e0a\u3052\u3055\u308c\u307e\u3057\u305f\u30024. 2022\u5e743\u6708\u73fe\u5728\u30ac\u30bd\u30ea\u30f3\u306e\u4fa1\u683c\u306f1\u500b\u3042\u305f\u308a1\u30c9\u30eb\u5024\u4e0a\u304c\u308a\u3057\u3066\u3044\u307e\u3059\u3002<\/p>\n
        \u30b7\u30ca\u30ea\u30aa5\uff1a\u904e\u53bb20\u5e74\u9593\u3001\u30ac\u30bd\u30ea\u30f3\u306e\u4fa1\u683c\u306b\u5b63\u7bc0\u6027\u306f\u3042\u3063\u305f\u306e\u304b\uff1f<\/h3>\n
        \n
        #Setup the URL to be used to invoke the GridDB WebAPI to retrieve data from the container\nurl = base_url + '\/tql'\n\n#Construct the request body which has the TQL that is to be used to retrieve the data\n# Getting data for the 'Gasoline' category for the last 20 years\nrequest_body = '[{\"name\":\"Inflation_CPI_Analysis\", \"stmt\":\"SELECT * WHERE year >= 2001 and year <= 2022 and category = 'Gasoline'\", \"columns\":[]}]' \n\n#Invoke the GridDB WebAPI\ndata_req5 = requests.post(url, data=request_body, headers=header_obj)\ndata_req5<\/code><\/pre>\n<\/div>\n
        \n
        #Process the response received and construct a Pandas dataframe with the data from the response\nmyJson = data_req5.json()\ngasoline_data_20_years = pd.DataFrame(myJson[0][\"results\"], columns=[myJson[0][\"columns\"][0][\"name\"], myJson[0][\"columns\"][1][\"name\"], myJson[0][\"columns\"][2][\"name\"], myJson[0][\"columns\"][3][\"name\"],myJson[0][\"columns\"][4][\"name\"],myJson[0][\"columns\"][5][\"name\"]])<\/code><\/pre>\n<\/div>\n
        \n
        #Construct a boxplot of the year wise trends for the last 20 years\nfig, ax = plt.subplots(nrows=1, ncols=1, figsize=(15, 6))\nsns.boxplot(gasoline_data_20_years['year'], gasoline_data_20_years['value'], ax=ax)\nax.set_title('Fig.5 Scenario 5 - Year-wise trends', fontsize = 16, loc='center')\nax.set_xlabel('Year', fontsize = 12)\nax.set_ylabel('Value ($ per unit)', fontsize = 12)\nplt.xticks(rotation=45)<\/code><\/pre>\n<\/div>\n
        \"\"<\/a><\/p>\n
        \u30b7\u30ca\u30ea\u30aa5\u306b\u5bfe\u3059\u308b\u6d1e\u5bdf\uff1a\u904e\u53bb20\u5e74\u9593\u306e\u30a4\u30f3\u30d5\u30ec\u7387<\/h3>\n
          \n
        1. \u30ac\u30bd\u30ea\u30f3\u4fa1\u683c\u306e\u30a4\u30f3\u30d5\u30ec\u7387\u306b\u5b63\u7bc0\u6027\u306f\u898b\u3089\u308c\u307e\u305b\u3093\u3002<\/li>\n
        2. \u3057\u304b\u3057\u30012022\u5e74\u306b\u306f\u3001\u30a4\u30f3\u30d5\u30ec\u304c\u904e\u53bb\u6700\u9ad8\u6c34\u6e96\u306b\u306a\u308b\u3053\u3068\u306f\u660e\u3089\u304b\u3067\u3059\u3002<\/li>\n
        3. \u30d7\u30ed\u30c3\u30c8\u304b\u3089\u5206\u304b\u308b\u3088\u3046\u306b\u30012008\u5e74\u306f1\u5e74\u306e\u5927\u534a\u3092\u9ad8\u5024\u3067\u63a8\u79fb\u3057\u3066\u3044\u307e\u3057\u305f\u3002\u3057\u304b\u3057\u3001\u5e74\u672b\u306b\u304b\u3051\u3066\u4fa1\u683c\u304c\u6025\u6fc0\u306b\u4f4e\u4e0b\u3057\u3066\u3044\u307e\u3059\u3002\u3053\u306e\u3053\u3068\u306f\u30012008\u5e74\u306e\u30dc\u30c3\u30af\u30b9\u30d7\u30ed\u30c3\u30c8\u306e\u7570\u5e38\u5024\u304b\u3089\u3082\u660e\u3089\u304b\u3067\u3059\u3002<\/li>\n
        4. 2019\u5e74\u304b\u30892021\u5e74\u306e\u9593\u306b\u3001\u30ac\u30bd\u30ea\u30f3\u4fa1\u683c\u306e\u6025\u6fc0\u306a\u4e0a\u6607\u304c\u4f55\u5ea6\u304b\u3042\u3063\u305f\u3053\u3068\u304c\u3001\u4e0a\u3072\u3052\u3092\u8d8a\u3048\u308b\u5916\u308c\u5024\u3067\u89b3\u5bdf\u3055\u308c\u307e\u3059\u3002\u30ac\u30bd\u30ea\u30f3\u4fa1\u683c\u306e\u6025\u6fc0\u306a\u4e0a\u6607\u306f\u3001\u4e0a\u3072\u3052\u306e\u5916\u5074\u306b\u3042\u308b\u7570\u5e38\u5024\u3067\u89b3\u5bdf\u3055\u308c\u308b\u3088\u3046\u306b\u3001\u4f55\u5ea6\u3082\u3042\u308a\u307e\u3057\u305f\u3002<\/li>\n<\/ol>\n
          \u307e\u3068\u3081<\/h2>\n
          \u4e0a\u8a18\u306e\u30d6\u30ed\u30b0\u8a18\u4e8b\u3067\u306f\u3001GridDB\u3092\u4f7f\u3063\u3066\u5927\u91cf\u306e\u30c7\u30fc\u30bf\u3092\u4fdd\u5b58\u3057\u3001GridDB\u306eTQL\u30af\u30a8\u30ea\u6a5f\u80fd\u3092\u4f7f\u3063\u3066\u52b9\u7387\u7684\u306b\u30c7\u30fc\u30bf\u3092\u53d6\u5f97\u3059\u308b\u65b9\u6cd5\u3092\u7d39\u4ecb\u3057\u307e\u3057\u305f\u3002\u30c7\u30fc\u30bf\u306e\u5927\u304d\u3055\u306b\u3082\u304b\u304b\u308f\u3089\u305a\u3001\u30c7\u30fc\u30bf\u306e\u8aad\u307f\u8fbc\u307f\u306f\u304b\u306a\u308a\u9ad8\u901f\uff08\u79d2\u5358\u4f4d\uff09\u3067\u3057\u305f\u3002\u307e\u305f\u3001\u30af\u30a8\u30ea\u6642\u9593\u3082\u304b\u306a\u308a\u77ed\u304f\u3001\u6570\u79d2\u3067\u7d50\u679c\u3092\u53d6\u5f97\u3059\u308b\u3053\u3068\u304c\u3067\u304d\u307e\u3057\u305f\u3002\u307e\u305f\u3001GridDB\u306b\u30b3\u30f3\u30c6\u30ca\u3092\u4f5c\u6210\u3057\u3001GridDB\u306e\u30b3\u30f3\u30c6\u30ca\u306b\u5bfe\u3057\u3066\u30af\u30a8\u30ea\u3092\u5b9f\u884c\u3059\u308b\u3053\u3068\u304c\u7c21\u5358\u306b\u3067\u304d\u308b\u3053\u3068\u3092\u5b9f\u8a3c\u3067\u304d\u307e\u3057\u305f\u3002<\/p>\n
          \u4eca\u306a\u3089 GridDB \u30af\u30e9\u30a6\u30c9\u3092\u7121\u6599\u3067\u8a66\u3059\u3053\u3068\u304c\u3067\u304d\u307e\u3059\uff01<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"
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