{"id":50753,"date":"2021-04-22T00:00:00","date_gmt":"2021-04-22T07:00:00","guid":{"rendered":"https:\/\/griddb-linux-hte8hndjf8cka8ht.westus-01.azurewebsites.net\/%e6%9c%aa%e5%88%86%e9%a1%9e\/using-pandas-dataframes-with-griddb\/"},"modified":"2025-11-14T07:54:46","modified_gmt":"2025-11-14T15:54:46","slug":"using-pandas-dataframes-with-griddb","status":"publish","type":"post","link":"https:\/\/griddb.net\/ja\/%e6%9c%aa%e5%88%86%e9%a1%9e\/using-pandas-dataframes-with-griddb\/","title":{"rendered":"GridDB\u4e0a\u3067Pandas\u306e\u30c7\u30fc\u30bf\u30d5\u30ec\u30fc\u30e0\u3092\u4f7f\u7528\u3059\u308b"},"content":{"rendered":"

\u306f\u3058\u3081\u306b<\/h2>\n

Pandas \u30c7\u30fc\u30bf\u30d5\u30ec\u30fc\u30e0\u306f\u3001\u305d\u306e\u30c9\u30ad\u30e5\u30e1\u30f3\u30c8\u3067\u5b9a\u7fa9\u3055\u308c\u3066\u3044\u308b\u3088\u3046\u306b\u3001\u300c2\u6b21\u5143\u306e\u3001\u30b5\u30a4\u30ba\u5909\u66f4\u53ef\u80fd\u306a\u3001\u6f5c\u5728\u7684\u306b\u7570\u7a2e\u306e\u8868\u5f62\u5f0f\u30c7\u30fc\u30bf\u300d\u3067\u3042\u308b\u30c7\u30fc\u30bf\u69cb\u9020\u3067\u3059\u3002\u30c7\u30fc\u30bf\u30d5\u30ec\u30fc\u30e0\u306f\u3001\u958b\u767a\u8005\u3084\u30c7\u30fc\u30bf\u30b5\u30a4\u30a8\u30f3\u30c6\u30a3\u30b9\u30c8\u304c\u9577\u3044\u30b3\u30fc\u30c9\u30d6\u30ed\u30c3\u30af\u3092\u66f8\u304f\u3053\u3068\u306a\u304f\u8fc5\u901f\u306b\u5b9f\u9a13\u3092\u884c\u3046\u3053\u3068\u3092\u53ef\u80fd\u306b\u3057\u3001\u30c7\u30fc\u30bf\u5206\u6790\u3092\u7d20\u65e9\u304f\u304b\u3064\u7c21\u5358\u306b\u884c\u3046\u3053\u3068\u304c\u3067\u304d\u307e\u3059\u3002<\/p>\n

\u3053\u306e\u30d6\u30ed\u30b0\u3067\u306f\u3001GridDB\u3068Pandas\u30c7\u30fc\u30bf\u30d5\u30ec\u30fc\u30e0\u9593\u306e\u57fa\u672c\u7684\u306a\u8aad\u307f\u66f8\u304d\u306e\u65b9\u6cd5\u3068\u3001GridDB\u958b\u767a\u8005\u304c\u3088\u304f\u4f7f\u3046\u5909\u63db\u65b9\u6cd5\u306b\u3064\u3044\u3066\u7d39\u4ecb\u3057\u307e\u3059\u3002<\/p>\n

\u30c7\u30fc\u30bf\u3092\u8aad\u307f\u8fbc\u3080<\/h2>\n

GridDB\u304b\u3089\u30c7\u30fc\u30bf\u30d5\u30ec\u30fc\u30e0\u306b\u30c7\u30fc\u30bf\u3092\u8aad\u307f\u8fbc\u3080\u306b\u306f\u3001\u901a\u5e38\u306e\u30af\u30a8\u30ea\u3092\u8a2d\u5b9a\u3057\u3001while rs.has_next()\u30eb\u30fc\u30d7\u306e\u4ee3\u308f\u308a\u306brs.fetch_rows()\u3092\u547c\u3073\u51fa\u3059\u3068\u3001\u4ee5\u4e0b\u306e\u4f8b\u306e\u3088\u3046\u306b\u3001\u7d50\u679c\u306e\u30c7\u30fc\u30bf\u30d5\u30ec\u30fc\u30e0\u304c\u8fd4\u3055\u308c\u307e\u3059\u3002<\/p>\n

\n
import datetime\nimport griddb_python as griddb\nimport sys\nimport pandas as pd\nimport matplotlib.pyplot as plt\n\nfactory = griddb.StoreFactory.get_instance()\n\nargv = sys.argv\n\ngridstore = factory.get_store(\n    host=\"239.0.0.1\",\n    port=31999,\n    cluster_name=\"defaultCluster\",\n    username=\"admin\",\n    password=\"admin\"\n)\n\ncol = gridstore.get_container(\"precinct_108\")\n\nstart = datetime.datetime.utcnow().timestamp() \nquery = col.query(\"select *\")\nrs = query.fetch(False)\nrows = rs.fetch_rows()\nrows['CMPLNT_FR'] = pd.to_datetime(rows['CMPLNT_FR'])\nprint(rows)\n\n<\/code><\/pre>\n<\/div>\n
\u30c7\u30fc\u30bf\u3092\u66f8\u304d\u8fbc\u3080<\/h2>\n
\u30c7\u30fc\u30bf\u30d5\u30ec\u30fc\u30e0\u304b\u3089 GridDB \u3078\u306e\u30c7\u30fc\u30bf\u306e\u66f8\u304d\u8fbc\u307f\u3082\u3001\u7c21\u5358\u306b\u884c\u3046\u3053\u3068\u304c\u3067\u304d\u307e\u3059\u3002\u30c7\u30fc\u30bf\u30b9\u30ad\u30fc\u30de\u3092\u683c\u7d0d\u3057\u305fContainerInfo\u30aa\u30d6\u30b8\u30a7\u30af\u30c8\u3092\u4f5c\u6210\u3057\u3001\u30b3\u30f3\u30c6\u30ca\u3092\u958b\u304d\u3001\u30c7\u30fc\u30bf\u30d5\u30ec\u30fc\u30e0\u3092\u5f15\u6570\u3068\u3057\u3066put_rows()\u3092\u547c\u3073\u51fa\u3057\u307e\u3059\u3002\u6ce8\u610f\u70b9\u3068\u3057\u3066\u306f\u3001\u30b9\u30ad\u30fc\u30de\u306e\u5217\u9806\u3068\u30c7\u30fc\u30bf\u30d5\u30ec\u30fc\u30e0\u306e\u5217\u9806\u304c\u4e00\u81f4\u3057\u3066\u3044\u308b\u5fc5\u8981\u304c\u3042\u308a\u307e\u3059\u304c\u3001\u4ed5\u69d8\u66f8\u306e\u30ea\u30b9\u30c8\u306b\u6b63\u3057\u3044\u5217\u9806\u3092\u6307\u5b9a\u3057\u3066\u304a\u304f\u3068\u3001\u30c7\u30fc\u30bf\u30d5\u30ec\u30fc\u30e0\u306e\u5217\u9806\u3092\u7c21\u5358\u306b\u5909\u66f4\u3059\u308b\u3053\u3068\u304c\u3067\u304d\u307e\u3059\u3002<\/p>\n
\n
conInfo = griddb.ContainerInfo(\"PANDAS_AGGS\",\n                [[\"key\", griddb.Type.STRING], \n                [\"timestamp\", griddb.Type.TIMESTAMP],\n                [\"precinct\", griddb.Type.INTEGER],\n                [\"count\", griddb.Type.INTEGER]],\n            griddb.ContainerType.COLLECTION, True)\n\naggcn = gridstore.put_container(conInfo)\naggcn.put_rows(df[[\"key\", \"timestamp\",\"precinct\",\"count\"]])\n<\/code><\/pre>\n<\/div>\n
\u7c21\u5358\u306b\u5909\u63db\u3059\u308b<\/h2>\n
\u30c7\u30fc\u30bf\u30d5\u30ec\u30fc\u30e0\u3067\u5b9f\u884c\u3067\u304d\u308b\u7c21\u5358\u306a\u64cd\u4f5c\u306e\u4f8b\u306f\u305f\u304f\u3055\u3093\u3042\u308a\u307e\u3059\u304c\u3001\u3053\u3053\u3067\u306f\u305d\u306e\u3046\u3061\u306e\u3044\u304f\u3064\u304b\u3092\u7d39\u4ecb\u3057\u307e\u3059\u3002<\/p>\n
\u5217\u306e\u5024\u3092\u8a2d\u5b9a\u3057\u307e\u3059\u3002<\/p>\n
\n
df['precinct'] = 108\n<\/code><\/pre>\n<\/div>\n
\u7570\u306a\u308b2\u3064\u306e\u5217\u3092\u7d44\u307f\u5408\u308f\u305b\u3066\u3001\u5404\u884c\u306e\u548c\u3084\u5dee\u3092\u6c42\u3081\u308b\u5834\u5408\u3082\u540c\u69d8\u3067\u3059\u3002<\/p>\n
\n
df['key'] = df['CMPLNT_FR'].dt.strftime('%Y-%m-%d') +\"_\"+ df['precinct'].astype('str')\n<\/code><\/pre>\n<\/div>\n
\u5217\u306e\u540d\u524d\u3092\u5909\u66f4\u3057\u307e\u3059\u3002<\/p>\n
\n
df = df.rename(columns={'CMPLNT_FR':'timestamp'})\n<\/code><\/pre>\n<\/div>\n
Binning, Bucketing<\/h2>\n
\u6642\u7cfb\u5217\u4e88\u6e2c\u306e\u30c1\u30e5\u30fc\u30c8\u30ea\u30a2\u30eb<\/a>\u3067\u306f\u3001\u500b\u3005\u306e\u82e6\u60c5\u3092\u6708\u3054\u3068\u306b\u5831\u544a\u3055\u308c\u305f\u82e6\u60c5\u306e\u6570\u306b\u5909\u63db\u3059\u308b\u5fc5\u8981\u304c\u3042\u308a\u307e\u3057\u305f\u3002\u3053\u308c\u306b\u306f\u8907\u6570\u306e\u30cd\u30b9\u30c8\u3057\u305ffor\u30eb\u30fc\u30d7\u304c\u5fc5\u8981\u3067\u3057\u305f\u3002Pandas \u30c7\u30fc\u30bf\u30d5\u30ec\u30fc\u30e0\u3092\u4f7f\u3048\u3070\u3001\u540c\u3058\u3053\u3068\u30921\u884c\u306e\u30b3\u30fc\u30c9\u3067\u5b9f\u73fe\u3059\u308b\u3053\u3068\u304c\u3067\u304d\u307e\u3059\u3002<\/p>\n
\n
monthly_df = rows.groupby([pd.Grouper(key='CMPLNT_FR',freq='M')]).size().reset_index(name='count')\n<\/code><\/pre>\n<\/div>\n
\u4e00\u898b\u3059\u308b\u3068\u8907\u96d1\u306a\u30b3\u30fc\u30c9\u306b\u898b\u3048\u307e\u3059\u304c\u3001\u884c\u306f\u5165\u529b\u5f15\u6570\u30ea\u30b9\u30c8\u3067\u5b9a\u7fa9\u3055\u308c\u305f\u30b0\u30eb\u30fc\u30d7\u306b\u5206\u5272\u3055\u308c\u307e\u3059\u3002\u3053\u308c\u3089\u306e\u30b0\u30eb\u30fc\u30d7\u306e\u305d\u308c\u305e\u308c\u306e\u30b5\u30a4\u30ba\u306f\u3001’count’\u5217\u306b\u5165\u308c\u308b\u3053\u3068\u304c\u3067\u304d\u307e\u3059\u3002<\/p>\n
\u307e\u305f\u3001\u66dc\u65e5\u3084\u6642\u9593\u3054\u3068\u306b\u30a4\u30d9\u30f3\u30c8\u306e\u767a\u751f\u72b6\u6cc1\u3092\u628a\u63e1\u3059\u308b\u3053\u3068\u3082\u6709\u52b9\u3067\u3059\u3002\u5b9f\u969b\u3001\u79c1\u306f\u65e5\u66dc\u65e5\u3084\u5348\u5f8c5\u6642\u306e\u30c7\u30fc\u30bf\u3060\u3051\u3092\u7c21\u5358\u306b\u7167\u4f1a\u3059\u308b\u305f\u3081\u306b\u3001\u69cb\u7bc9\u3057\u305f\u30b9\u30ad\u30fc\u30de\u306b dayofweek \u3068 hourofday \u30d5\u30a3\u30fc\u30eb\u30c9\u3092\u542b\u3081\u308b\u3053\u3068\u304c\u3088\u304f\u3042\u308a\u307e\u3059\u304c\u3001Pandas\u306e\u30c7\u30fc\u30bf\u30d5\u30ec\u30fc\u30e0\u3067\u306f\u3001\u3069\u3061\u3089\u3082\u4e00\u884c\u3067\u6e08\u307f\u307e\u3059\u3002<\/p>\n
\n
weekly_df = rows.groupby(rows['CMPLNT_FR'].dt.day_name()).size().reset_index(name='count')\nhourly_df  = rows.groupby(rows['CMPLNT_FR'].dt.hour).size().reset_index(name='count')\n<\/code><\/pre>\n<\/div>\n
\u30d1\u30d5\u30a9\u30fc\u30de\u30f3\u30b9<\/h2>\n
\u3067\u306f\u3001\u5b9f\u969b\u306b\u3069\u3061\u3089\u304c\u901f\u3044\u306e\u3067\u3057\u3087\u3046\u304b\uff1fpyarray\u3084dataframes\u3078\u306e\u8aad\u307f\u66f8\u304d\u306f\uff1fBinning\u306b\u3064\u3044\u3066\u306f\u306f\u3069\u3046\u3067\u3057\u3087\u3046\u304b\uff1f<\/p>\n
\u5b9f\u306fPandas\u306e\u30c7\u30fc\u30bf\u30d5\u30ec\u30fc\u30e0\u306b\u306f\u6b20\u70b9\u304c\u3042\u308a\u307e\u3059\u3002\u305d\u308c\u306f\u901f\u5ea6\u304c\u9045\u3044\u3053\u3068\u3067\u3059\u3002<\/p>\n
10000\u884c\u306e\u66f8\u304d\u8fbc\u307f\u3068\u8aad\u307f\u8fbc\u307f\u3092\u884c\u3046\u7c21\u5358\u306a\u30d9\u30f3\u30c1\u30de\u30fc\u30af\u3092\u66f8\u3044\u3066\u307f\u307e\u3057\u305f\u304c\u3001\u30c7\u30fc\u30bf\u30d5\u30ec\u30fc\u30e0\u3092GridDB\u306b\u66f8\u304d\u8fbc\u3080\u3068\u3001\u540c\u3058\u30c7\u30fc\u30bf\u3092List of Lists\u3068\u3057\u3066 multiput\u3059\u308b\u3088\u308a\u3082\u7d0450%\u9045\u304f\u306a\u308a\u307e\u3059\u3002<\/p>\n
\n
blob = bytearray([65, 66, 67, 68, 69, 70, 71, 72, 73, 74])\nconInfo = griddb.ContainerInfo(\"col01\",\n    [[\"name\", griddb.Type.STRING],\n    [\"status\", griddb.Type.BOOL],\n    [\"count\", griddb.Type.LONG],\n    [\"lob\", griddb.Type.BLOB]],\n    griddb.ContainerType.COLLECTION, True)\n\ni=0\nrows=[]\nwhile i < 10000:\n    rows.append([str(uuid.uuid1()), False, random.randint(0, 1048576), blob])\n    i=i+1\n\ngridstore.drop_container(\"col01\")\nstart =  datetime.datetime.utcnow().timestamp()\ncol = gridstore.put_container(conInfo)\ncol.multi_put(rows)\nprint(\"multiput took \"+  str(datetime.datetime.utcnow().timestamp() - start) +\" seconds\")\n\n\ndf = pandas.DataFrame(rows, columns=[\"name\", \"status\", \"count\", \"lob\"])\n\ngridstore.drop_container(\"col01\")\nstart =  datetime.datetime.utcnow().timestamp()\ncol = gridstore.put_container(conInfo)\ncol.put_rows(df)\ncol.multi_put(rows)\nprint(\"putrows took \"+  str(datetime.datetime.utcnow().timestamp() - start) +\" seconds\")\n<\/code><\/pre>\n<\/div>\n
\u30c7\u30fc\u30bf\u3092\u8aad\u307f\u8fd4\u3059\u3068\u3001\u7d50\u679c\u30bb\u30c3\u30c8\u3092pylist\u306b\u8ffd\u52a0\u3059\u308b\u3068\u3001\u30c7\u30fc\u30bf\u30d5\u30ec\u30fc\u30e0\u306b\u76f4\u63a5\u8aad\u307f\u8fbc\u3080\u3088\u308a\u3082\u7d0425\uff05\u901f\u304f\u306a\u308a\u307e\u3057\u305f\u3002\u30ea\u30b9\u30c8\u3092\u8aad\u307f\u8fbc\u3093\u3067\u304b\u3089\u30c7\u30fc\u30bf\u30d5\u30ec\u30fc\u30e0\u306b\u5909\u63db\u3059\u308b\u3068\u3001\u518d\u307350\uff05\u9045\u304f\u306a\u308a\u307e\u3057\u305f\u3002<\/p>\n
\n
col = gridstore.get_container(\"col01\")\n\nstart =  datetime.datetime.utcnow().timestamp()\nquery = col.query(\"select *\")\nrs = query.fetch(False)\nresult = []\nwhile rs.has_next():\n    result.append(rs.next())\nprint(\"pylist append took \"+  str(datetime.datetime.utcnow().timestamp() - start) +\" seconds\")\n\n\nstart =  datetime.datetime.utcnow().timestamp()\nquery = col.query(\"select *\")\nrs = query.fetch(False)\nresult = rs.fetch_rows()\nprint(\"fetch_rows took \"+  str(datetime.datetime.utcnow().timestamp() - start) +\" seconds\")\n\n<\/code><\/pre>\n<\/div>\n
Binning\u306b\u3064\u3044\u3066\u306f\u3001bin\u306e\u6570\u306b\u4f9d\u5b58\u3057\u307e\u3059\u3002\u4ee5\u4e0b\u306e\u30b3\u30fc\u30c9\u3067\u306f\u3001bin\u306e\u6570\u3084\u30b5\u30a4\u30ba\u3092\u8abf\u6574\u3059\u308b\u3053\u3068\u3067\u30d1\u30d5\u30a9\u30fc\u30de\u30f3\u30b9\u304c\u5909\u5316\u3057\u3066\u3044\u307e\u3059\u3002bin\u306e\u6570\u304c\u5c11\u306a\u3051\u308c\u3070\u30d1\u30d5\u30a9\u30fc\u30de\u30f3\u30b9\u304c\u826f\u3044\u3068\u3044\u3046\u3053\u3068\u306b\u306a\u308a\u307e\u3059\u304c\u3001\u4e00\u65b9\u3067\u30c7\u30fc\u30bf\u30d5\u30ec\u30fc\u30e0\u306e\u30d1\u30d5\u30a9\u30fc\u30de\u30f3\u30b9\u306f\u3001bin\u306e\u30b5\u30a4\u30ba\u3084\u6570\u3092\u8abf\u6574\u3057\u3066\u3082\u305d\u308c\u307b\u3069\u5909\u5316\u3057\u307e\u305b\u3093\u3067\u3057\u305f\u3002<\/p>\n
\n
tqls=[]\ndt =  datetime.datetime(2000, 1, 1, 0, 0, 0)\nwhile dt < datetime.datetime(2020, 1, 1, 0, 0):\n    start = int(dt.timestamp()*1000)\n    end = int((dt + relativedelta(months=+1)).timestamp()*1000)\n    query = \"select count(*) where CMPLNT_FR > TO_TIMESTAMP_MS(\"+str(start)+\")\"\n    query = query + \" AND CMPLNT_FR < TO_TIMESTAMP_MS(\"+str(end)+\")\"\n    tqls.append([dt, query])\n    dt = dt + relativedelta(months=+1);\n\ncol = gridstore.get_container(\"precinct_108\")\nstart = datetime.datetime.utcnow().timestamp() \n\nfor tql in tqls:\n    q = col.query(tql[1])\n    rs = q.fetch(False)\n    if rs.has_next():\n        data = rs.next()\n        count = data.get(griddb.Type.LONG)\n        print([str(tql[0].timestamp()), count ])\nprint(\"forloop \"+  str(datetime.datetime.utcnow().timestamp() - start) +\" seconds\")\n<\/code><\/pre>\n<\/div>\n
\u30d7\u30ed\u30c3\u30c8\u3059\u308b<\/h2>\n
MatPlotLib\u3092\u4f7f\u3048\u3070\u3001\u30c7\u30fc\u30bf\u30d5\u30ec\u30fc\u30e0\u306e\u53ef\u8996\u5316\u3082\u7c21\u5358\u3067\u3059\u3002\u3053\u3053\u3067\u306f\u30012006\u5e74\u4ee5\u964d\u306e\u6708\u6b21\u96c6\u8a08\u306e\u5358\u7d14\u306a\u6298\u308c\u7dda\u30b0\u30e9\u30d5\u3092\u30d7\u30ed\u30c3\u30c8\u3057\u3066\u307f\u307e\u3057\u3087\u3046\u3002<\/p>\n
\n
monthly_df = monthly_df.set_index('timestamp')\nts = monthly_df['count']\nax = ts['2006':].plot()\nax.set_xlabel('Year')\nax.set_ylabel('Crime Complaints')\nax.set_xlabel('Year')\n<\/code><\/pre>\n<\/div>\n
\"\"<\/a><\/p>\n
\u540c\u69d8\u306b\u30011\u65e5\u306e\u4e2d\u3067\u72af\u7f6a\u304c\u5831\u544a\u3055\u308c\u305f\u6642\u9593\u5e2f\u306e\u5206\u5e03\u3092\u8868\u793a\u3059\u308b\u306e\u3082\u7c21\u5358\u3067\u3059\u3002<\/p>\n
\n
ax = hourly_df.plot.bar()\nax.set_xlabel('Hour Of Day')\nax.set_ylabel('Crime Complaints')\nax.get_legend().remove()\nplt.show()\n<\/code><\/pre>\n<\/div>\n
\"\"<\/a><\/p>\n
\u4e00\u9031\u9593\u306e\u3046\u3061\u306e\u5206\u5e03\u3092\u8868\u793a\u3059\u308b\u306b\u306f\u3001\u66dc\u65e5\u304c\u6bd4\u8f03\u9806\u306b\u306a\u3063\u3066\u3044\u306a\u3044\u305f\u3081\u5c11\u3057\u96e3\u3057\u3044\u306e\u3067\u3059\u304c\u3001\u6b63\u3057\u3044\u9806\u756a\u3067\u8f9e\u66f8\u3092\u4f5c\u6210\u3057\u3001\u305d\u306e\u8f9e\u66f8\u3067\u30a4\u30f3\u30c7\u30c3\u30af\u30b9\u3092\u8a2d\u5b9a\u3059\u308b\u3053\u3068\u3067\u554f\u984c\u306f\u89e3\u6c7a\u3057\u307e\u3059\u3002<\/p>\n
\n
order = ['Sunday', 'Monday', 'Tuesday', 'Wednesday','Thursday','Friday','Saturday']\nax = weekly_df.set_index('timestamp').loc[order].plot(kind='area')\nax.set_xlabel('Day Of Week')\nax.set_ylabel('Crime Complaints')\nplt.xticks(rotation=45)\nax.get_legend().remove()\nplt.show()\n<\/code><\/pre>\n<\/div>\n
\"\"<\/a><\/p>\n
\u307e\u3068\u3081<\/h2>\n
Pandas \u30c7\u30fc\u30bf\u30d5\u30ec\u30fc\u30e0\u306fGridDB\u306e\u5b8c\u74a7\u306a\u88dc\u5b8c\u7269\u3067\u3042\u308a\u3001\u958b\u767a\u8005\u306f\u30c7\u30fc\u30bf\u99c6\u52d5\u578b\u306e\u30a2\u30d7\u30ea\u30b1\u30fc\u30b7\u30e7\u30f3\u3092\u7d20\u65e9\u304f\u5c55\u958b\u3057\u305f\u308a\u3001mathplotlib\u3068\u7d44\u307f\u5408\u308f\u305b\u308b\u3053\u3068\u3067\u3001\u9762\u5012\u306a\u30c7\u30fc\u30bf\u5909\u63db\u3092\u884c\u3046\u3053\u3068\u306a\u304f\u30c7\u30fc\u30bf\u306b\u5bfe\u3059\u308b\u65b0\u3057\u3044\u6d1e\u5bdf\u3092\u5f97\u308b\u3053\u3068\u304c\u3067\u304d\u307e\u3059\u3002\u30c7\u30fc\u30bf\u30d5\u30ec\u30fc\u30e0\u306f\u3001\u5927\u898f\u6a21\u306a\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u3092\u5206\u6790\u3059\u308b\u305f\u3081\u306e\u3088\u308a\u67d4\u8edf\u3067\u30b7\u30f3\u30d7\u30eb\u306a\u65b9\u6cd5\u3092\u63d0\u4f9b\u3059\u308b\u305f\u3081\u3001\u30c7\u30fc\u30bf\u30d9\u30fc\u30b9\u306e\u89b3\u70b9\u304b\u3089\u898b\u305f\u751f\u306e\u8aad\u307f\u66f8\u304d\u306e\u901f\u5ea6\u3068\u5f15\u304d\u63db\u3048\u306b\u3001\u958b\u767a\u306e\u3057\u3084\u3059\u3055\u3068\u30b3\u30fc\u30c7\u30a3\u30f3\u30b0\u306e\u901f\u3055\u3067\u88dc\u3046\u3053\u3068\u304c\u3067\u304d\u307e\u3059\u3002<\/p>\n
\u3053\u306e\u30d6\u30ed\u30b0\u3067\u4f7f\u7528\u3057\u3066\u3044\u308bJupyter Notebook\u306f\u3001GridDB.net\u306eGitHub\u30da\u30fc\u30b8\u3053\u3061\u3089<\/a>\u3067\u516c\u958b\u3057\u3066\u3044\u307e\u3059\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"
\u306f\u3058\u3081\u306b Pandas \u30c7\u30fc\u30bf\u30d5\u30ec\u30fc\u30e0\u306f\u3001\u305d\u306e\u30c9\u30ad\u30e5\u30e1\u30f3\u30c8\u3067\u5b9a\u7fa9\u3055\u308c\u3066\u3044\u308b\u3088\u3046\u306b\u3001\u300c2\u6b21\u5143\u306e\u3001\u30b5\u30a4\u30ba\u5909\u66f4\u53ef\u80fd\u306a\u3001\u6f5c\u5728\u7684\u306b\u7570\u7a2e\u306e\u8868\u5f62\u5f0f\u30c7\u30fc\u30bf\u300d\u3067\u3042\u308b\u30c7\u30fc\u30bf\u69cb\u9020\u3067\u3059\u3002\u30c7\u30fc\u30bf\u30d5\u30ec\u30fc\u30e0\u306f\u3001\u958b\u767a\u8005\u3084\u30c7\u30fc\u30bf\u30b5\u30a4\u30a8\u30f3\u30c6\u30a3\u30b9\u30c8\u304c\u9577\u3044\u30b3\u30fc […]<\/p>\n","protected":false},"author":71,"featured_media":49227,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[1005],"tags":[],"class_list":["post-50753","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-1005"],"acf":[],"yoast_head":"\nGridDB\u4e0a\u3067Pandas\u306e\u30c7\u30fc\u30bf\u30d5\u30ec\u30fc\u30e0\u3092\u4f7f\u7528\u3059\u308b | GridDB: Open Source Time Series Database for IoT<\/title>\n<meta name=\"description\" content=\"\u306f\u3058\u3081\u306b Pandas\" \/>\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\/using-pandas-dataframes-with-griddb\/\" \/>\n<meta property=\"og:locale\" content=\"ja_JP\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" 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