Sejong_DataIngestionExample.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "3d53a1c3",
   "metadata": {},
   "source": [
    "# Database Connection"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a88974e7",
   "metadata": {},
   "source": [
    "### InfluxDB Connection Token"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "4943ddf9",
   "metadata": {},
   "outputs": [],
   "source": [
    "DataServer={\n",
    "        \"url\":'http://192.168.193.246:8086/',\n",
    "        \"token\":\"nBDf7B-eRmdzN0tRf5Pqx1VE9TQKfnlkEfMai6p1c5C0ewl-8Bv8psoQ40P-Ox_rTBW1lKOsTQzCBaBHYtdz2g==\",\n",
    "        \"org\":\"clust\"      \n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "04341cef",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "4dacbb21",
   "metadata": {},
   "source": [
    "## InfluxDB client"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "964d04c0",
   "metadata": {},
   "outputs": [],
   "source": [
    "from data_influx import influx_Client_v2 as influx_Client\n",
    "db_client = influx_Client.influxClient(DataServer)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "875e0575",
   "metadata": {},
   "source": [
    "### Get Database List"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "1cfd021d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['air_indoor_어린이집',\n",
       " 'air_indoor_고등학교',\n",
       " 'air_indoor_중학교',\n",
       " 'air_indoor_유치원',\n",
       " 'air_indoor_요양원',\n",
       " 'air_indoor_체육시설',\n",
       " 'air_indoor_초등학교',\n",
       " 'air_indoor_아파트',\n",
       " 'air_indoor_경로당',\n",
       " 'air_indoor_도서관']"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "db_list = db_client.get_DBList()\n",
    "db_list"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2e2d6f88",
   "metadata": {},
   "source": [
    "### Get Measurement List"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "8b0128c4",
   "metadata": {},
   "outputs": [],
   "source": [
    "db_name = 'air_indoor_어린이집'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "4a6edc51",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['ICW0W2000635',\n",
       " 'ICW0W2000636',\n",
       " 'ICW0W2000637',\n",
       " 'ICW0W2000638',\n",
       " 'ICW0W2000639',\n",
       " 'ICW0W2000640',\n",
       " 'ICW0W2000641',\n",
       " 'ICW0W2000642',\n",
       " 'ICW0W2000643',\n",
       " 'ICW0W2000644',\n",
       " 'ICW0W2000645',\n",
       " 'ICW0W2000646',\n",
       " 'ICW0W2000760',\n",
       " 'ICW0W2001080',\n",
       " 'ICW0W2001081',\n",
       " 'ICW0W2001082',\n",
       " 'ICW0W2100043',\n",
       " 'ICW0W2100064',\n",
       " 'ICW0W2100146',\n",
       " 'ICW0W2100147',\n",
       " 'ICW0W2100148',\n",
       " 'ICW0W2100149',\n",
       " 'ICW0W2100150',\n",
       " 'ICW0W2100151',\n",
       " 'ICW0W2100152',\n",
       " 'ICW0W2100153']"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ms_list = db_client.measurement_list(db_name)\n",
    "ms_list"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "43711fc6",
   "metadata": {},
   "source": [
    "### Get Field List"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "86945474",
   "metadata": {},
   "outputs": [],
   "source": [
    "ms_name = 'ICW0W2100150'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "f04406fc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['in_voc',\n",
       " 'in_noise',\n",
       " 'in_pm10',\n",
       " 'in_humi',\n",
       " 'in_pm01',\n",
       " 'in_temp',\n",
       " 'in_co2',\n",
       " 'in_pm25']"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "field_list = db_client.get_fieldList(db_name, ms_name)\n",
    "field_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0a009d58",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "df2afe47",
   "metadata": {},
   "source": [
    "## Get Data Function"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c411de13",
   "metadata": {},
   "source": [
    "### Get All Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "dd89a5f7",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>in_co2</th>\n",
       "      <th>in_humi</th>\n",
       "      <th>in_noise</th>\n",
       "      <th>in_pm01</th>\n",
       "      <th>in_pm10</th>\n",
       "      <th>in_pm25</th>\n",
       "      <th>in_temp</th>\n",
       "      <th>in_voc</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>time</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2021-02-09 16:16:00+00:00</th>\n",
       "      <td>786.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>13.3</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-02-09 16:17:00+00:00</th>\n",
       "      <td>731.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>12.6</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-02-09 16:18:00+00:00</th>\n",
       "      <td>696.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>11.8</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-02-09 16:19:00+00:00</th>\n",
       "      <td>684.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.9</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-02-09 16:20:00+00:00</th>\n",
       "      <td>679.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>10.3</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-04-30 23:57:00+00:00</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20.5</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-04-30 23:58:00+00:00</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20.5</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-04-30 23:59:00+00:00</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20.5</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-05-01 00:00:00+00:00</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20.4</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-05-01 00:01:00+00:00</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20.4</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>485182 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                           in_co2  in_humi  in_noise  in_pm01  in_pm10  \\\n",
       "time                                                                     \n",
       "2021-02-09 16:16:00+00:00   786.0     13.0      54.0      9.0      9.0   \n",
       "2021-02-09 16:17:00+00:00   731.0     13.0      53.0     11.0     12.0   \n",
       "2021-02-09 16:18:00+00:00   696.0     13.0      54.0     11.0     12.0   \n",
       "2021-02-09 16:19:00+00:00   684.0     14.0      54.0      9.0     10.0   \n",
       "2021-02-09 16:20:00+00:00   679.0     15.0      55.0      9.0      9.0   \n",
       "...                           ...      ...       ...      ...      ...   \n",
       "2022-04-30 23:57:00+00:00     NaN      NaN       NaN      NaN      NaN   \n",
       "2022-04-30 23:58:00+00:00     NaN      NaN       NaN      NaN      NaN   \n",
       "2022-04-30 23:59:00+00:00     NaN      NaN       NaN      NaN      NaN   \n",
       "2022-05-01 00:00:00+00:00     NaN      NaN       NaN      NaN      NaN   \n",
       "2022-05-01 00:01:00+00:00     NaN      NaN       NaN      NaN      NaN   \n",
       "\n",
       "                           in_pm25  in_temp  in_voc  \n",
       "time                                                 \n",
       "2021-02-09 16:16:00+00:00      9.0     13.3     0.0  \n",
       "2021-02-09 16:17:00+00:00     11.0     12.6     0.0  \n",
       "2021-02-09 16:18:00+00:00     11.0     11.8     0.0  \n",
       "2021-02-09 16:19:00+00:00     10.0     10.9     0.0  \n",
       "2021-02-09 16:20:00+00:00      9.0     10.3     0.0  \n",
       "...                            ...      ...     ...  \n",
       "2022-04-30 23:57:00+00:00      NaN     20.5     NaN  \n",
       "2022-04-30 23:58:00+00:00      NaN     20.5     NaN  \n",
       "2022-04-30 23:59:00+00:00      NaN     20.5     NaN  \n",
       "2022-05-01 00:00:00+00:00      NaN     20.4     NaN  \n",
       "2022-05-01 00:01:00+00:00      NaN     20.4     NaN  \n",
       "\n",
       "[485182 rows x 8 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_data = db_client.get_data(db_name, ms_name)\n",
    "all_data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e1be8386",
   "metadata": {},
   "source": [
    "### Get First Time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "afbc0d33",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2021-02-09 16:16:00+0000', tz='tzutc()')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "first_time = db_client.get_first_time(db_name, ms_name)\n",
    "first_time"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d6ac2cce",
   "metadata": {},
   "source": [
    "### Get Last Time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "e026ba90",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2022-05-01 00:01:00+0000', tz='tzutc()')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "last_time = db_client.get_last_time(db_name, ms_name)\n",
    "last_time"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4f8184b8",
   "metadata": {},
   "source": [
    "### Get duration Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "87fde25b",
   "metadata": {},
   "outputs": [],
   "source": [
    "start_time = \"2021-01-01 00:00:00\"\n",
    "end_time = \"2021-02-28 23:59:59\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "3f1d8f2d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>in_co2</th>\n",
       "      <th>in_humi</th>\n",
       "      <th>in_noise</th>\n",
       "      <th>in_pm01</th>\n",
       "      <th>in_pm10</th>\n",
       "      <th>in_pm25</th>\n",
       "      <th>in_temp</th>\n",
       "      <th>in_voc</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>time</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2021-02-09 16:16:00+00:00</th>\n",
       "      <td>786.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>13.3</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-02-09 16:17:00+00:00</th>\n",
       "      <td>731.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>12.6</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-02-09 16:18:00+00:00</th>\n",
       "      <td>696.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>11.8</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-02-09 16:19:00+00:00</th>\n",
       "      <td>684.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.9</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-02-09 16:20:00+00:00</th>\n",
       "      <td>679.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>10.3</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-02-10 09:12:00+00:00</th>\n",
       "      <td>665.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>5.6</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-02-10 09:13:00+00:00</th>\n",
       "      <td>673.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>5.5</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-02-10 09:14:00+00:00</th>\n",
       "      <td>672.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>5.5</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-02-10 09:15:00+00:00</th>\n",
       "      <td>668.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>5.5</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-02-10 09:16:00+00:00</th>\n",
       "      <td>659.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>5.5</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1018 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                           in_co2  in_humi  in_noise  in_pm01  in_pm10  \\\n",
       "time                                                                     \n",
       "2021-02-09 16:16:00+00:00   786.0     13.0      54.0      9.0      9.0   \n",
       "2021-02-09 16:17:00+00:00   731.0     13.0      53.0     11.0     12.0   \n",
       "2021-02-09 16:18:00+00:00   696.0     13.0      54.0     11.0     12.0   \n",
       "2021-02-09 16:19:00+00:00   684.0     14.0      54.0      9.0     10.0   \n",
       "2021-02-09 16:20:00+00:00   679.0     15.0      55.0      9.0      9.0   \n",
       "...                           ...      ...       ...      ...      ...   \n",
       "2021-02-10 09:12:00+00:00   665.0     42.0      41.0     31.0     35.0   \n",
       "2021-02-10 09:13:00+00:00   673.0     43.0      41.0     31.0     36.0   \n",
       "2021-02-10 09:14:00+00:00   672.0     42.0      41.0     31.0     34.0   \n",
       "2021-02-10 09:15:00+00:00   668.0     42.0      41.0     32.0     35.0   \n",
       "2021-02-10 09:16:00+00:00   659.0     42.0      41.0     31.0     36.0   \n",
       "\n",
       "                           in_pm25  in_temp  in_voc  \n",
       "time                                                 \n",
       "2021-02-09 16:16:00+00:00      9.0     13.3     0.0  \n",
       "2021-02-09 16:17:00+00:00     11.0     12.6     0.0  \n",
       "2021-02-09 16:18:00+00:00     11.0     11.8     0.0  \n",
       "2021-02-09 16:19:00+00:00     10.0     10.9     0.0  \n",
       "2021-02-09 16:20:00+00:00      9.0     10.3     0.0  \n",
       "...                            ...      ...     ...  \n",
       "2021-02-10 09:12:00+00:00     32.0      5.6     0.0  \n",
       "2021-02-10 09:13:00+00:00     33.0      5.5     0.0  \n",
       "2021-02-10 09:14:00+00:00     32.0      5.5     0.0  \n",
       "2021-02-10 09:15:00+00:00     33.0      5.5     0.0  \n",
       "2021-02-10 09:16:00+00:00     33.0      5.5     0.0  \n",
       "\n",
       "[1018 rows x 8 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_by_time = db_client.get_data_by_time(start_time, end_time, db_name, ms_name)\n",
    "data_by_time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "291eaeda",
   "metadata": {},
   "outputs": [],
   "source": [
    "end_time = \"2021-03-10 00:00:00\"\n",
    "days=7"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "1cc7222d",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>in_co2</th>\n",
       "      <th>in_humi</th>\n",
       "      <th>in_noise</th>\n",
       "      <th>in_pm01</th>\n",
       "      <th>in_pm10</th>\n",
       "      <th>in_pm25</th>\n",
       "      <th>in_temp</th>\n",
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       "    <tr>\n",
       "      <th>time</th>\n",
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       "      <th>2021-04-29 13:14:00+00:00</th>\n",
       "      <td>1273.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>14.0</td>\n",
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       "      <th>2021-04-29 13:15:00+00:00</th>\n",
       "      <td>1289.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>22.7</td>\n",
       "      <td>10.0</td>\n",
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       "    <tr>\n",
       "      <th>2021-04-29 13:16:00+00:00</th>\n",
       "      <td>1305.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>22.9</td>\n",
       "      <td>35.0</td>\n",
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       "    <tr>\n",
       "      <th>2021-04-29 13:17:00+00:00</th>\n",
       "      <td>1317.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>22.9</td>\n",
       "      <td>41.0</td>\n",
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       "    <tr>\n",
       "      <th>2021-04-29 13:18:00+00:00</th>\n",
       "      <td>1314.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>22.8</td>\n",
       "      <td>36.0</td>\n",
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       "      <th>...</th>\n",
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       "    <tr>\n",
       "      <th>2022-04-30 23:57:00+00:00</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20.5</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-04-30 23:58:00+00:00</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20.5</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-04-30 23:59:00+00:00</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20.5</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-05-01 00:00:00+00:00</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20.4</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>2022-05-01 00:01:00+00:00</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20.4</td>\n",
       "      <td>NaN</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>484164 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                           in_co2  in_humi  in_noise  in_pm01  in_pm10  \\\n",
       "time                                                                     \n",
       "2021-04-29 13:14:00+00:00  1273.0     35.0      65.0     14.0     19.0   \n",
       "2021-04-29 13:15:00+00:00  1289.0     35.0      61.0     14.0     19.0   \n",
       "2021-04-29 13:16:00+00:00  1305.0     35.0      62.0     12.0     16.0   \n",
       "2021-04-29 13:17:00+00:00  1317.0     35.0      66.0     11.0     14.0   \n",
       "2021-04-29 13:18:00+00:00  1314.0     36.0      66.0     11.0     16.0   \n",
       "...                           ...      ...       ...      ...      ...   \n",
       "2022-04-30 23:57:00+00:00     NaN      NaN       NaN      NaN      NaN   \n",
       "2022-04-30 23:58:00+00:00     NaN      NaN       NaN      NaN      NaN   \n",
       "2022-04-30 23:59:00+00:00     NaN      NaN       NaN      NaN      NaN   \n",
       "2022-05-01 00:00:00+00:00     NaN      NaN       NaN      NaN      NaN   \n",
       "2022-05-01 00:01:00+00:00     NaN      NaN       NaN      NaN      NaN   \n",
       "\n",
       "                           in_pm25  in_temp  in_voc  \n",
       "time                                                 \n",
       "2021-04-29 13:14:00+00:00      9.0     22.4    21.0  \n",
       "2021-04-29 13:15:00+00:00      9.0     22.7    10.0  \n",
       "2021-04-29 13:16:00+00:00      7.0     22.9    35.0  \n",
       "2021-04-29 13:17:00+00:00      7.0     22.9    41.0  \n",
       "2021-04-29 13:18:00+00:00      7.0     22.8    36.0  \n",
       "...                            ...      ...     ...  \n",
       "2022-04-30 23:57:00+00:00      NaN     20.5     NaN  \n",
       "2022-04-30 23:58:00+00:00      NaN     20.5     NaN  \n",
       "2022-04-30 23:59:00+00:00      NaN     20.5     NaN  \n",
       "2022-05-01 00:00:00+00:00      NaN     20.4     NaN  \n",
       "2022-05-01 00:01:00+00:00      NaN     20.4     NaN  \n",
       "\n",
       "[484164 rows x 8 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# duration : (end_time - days) ~ last_time\n",
    "data_by_days = db_client.get_data_by_days(end_time, days, db_name, ms_name)\n",
    "data_by_days"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "f7b29eeb",
   "metadata": {},
   "outputs": [],
   "source": [
    "number = 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "b8b9e777",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>2021-02-09 16:16:00+00:00</th>\n",
       "      <td>786.0</td>\n",
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       "      <th>2021-02-09 16:17:00+00:00</th>\n",
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       "      <td>53.0</td>\n",
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       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>2021-02-09 16:18:00+00:00</th>\n",
       "      <td>696.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>11.8</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           in_co2  in_humi  in_noise  in_pm01  in_pm10  \\\n",
       "time                                                                     \n",
       "2021-02-09 16:16:00+00:00   786.0     13.0      54.0      9.0      9.0   \n",
       "2021-02-09 16:17:00+00:00   731.0     13.0      53.0     11.0     12.0   \n",
       "2021-02-09 16:18:00+00:00   696.0     13.0      54.0     11.0     12.0   \n",
       "\n",
       "                           in_pm25  in_temp  in_voc  \n",
       "time                                                 \n",
       "2021-02-09 16:16:00+00:00      9.0     13.3     0.0  \n",
       "2021-02-09 16:17:00+00:00     11.0     12.6     0.0  \n",
       "2021-02-09 16:18:00+00:00     11.0     11.8     0.0  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# number 개수만큼 데이터 가져오기\n",
    "front_by_num = db_client.get_datafront_by_num(number, db_name, ms_name)\n",
    "front_by_num"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "6f888a3b",
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "                          in_co2 in_humi in_noise in_pm01 in_pm10 in_pm25  \\\n",
       "time                                                                        \n",
       "2022-04-30 23:59:00+00:00   None    None     None    None    None    None   \n",
       "2022-05-01 00:00:00+00:00   None    None     None    None    None    None   \n",
       "2022-05-01 00:01:00+00:00   None    None     None    None    None    None   \n",
       "\n",
       "                           in_temp in_voc  \n",
       "time                                       \n",
       "2022-04-30 23:59:00+00:00     20.5   None  \n",
       "2022-05-01 00:00:00+00:00     20.4   None  \n",
       "2022-05-01 00:01:00+00:00     20.4   None  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# number 개수만큼 마지막 데이터 가져오기\n",
    "end_by_num = db_client.get_dataend_by_num(number, db_name, ms_name)\n",
    "end_by_num"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "0168f750",
   "metadata": {},
   "outputs": [],
   "source": [
    "start_time = \"2021-01-01 00:00:00\"\n",
    "end_time = \"2021-02-01 00:00:00\"\n",
    "number = 10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "89291424",
   "metadata": {},
   "outputs": [
    {
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     "execution_count": 19,
     "metadata": {},
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   ],
   "source": [
    "# 설정한 기간 중, number 개수만큼 데이터 가져오기\n",
    "data_limit_by_time = db_client.get_data_limit_by_time(start_time, end_time, number, db_name, ms_name)\n",
    "data_limit_by_time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "a2e45f90",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "'0 days 00:01:00'"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
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   "source": [
    "# Data 주기\n",
    "data_freq = db_client.get_freq(db_name, ms_name)\n",
    "data_freq"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "d0f5351a",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "176300"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "# Data 개수\n",
    "data_count = db_client.get_data_count(db_name, ms_name)\n",
    "data_count"
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   "cell_type": "code",
   "execution_count": null,
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   "execution_count": null,
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